Modelling Agri-Food Policy Impact at Farm-household Level in Developing Countries (FSSIM-DEV)

Transcription

1 Modellng Agr-Food Polcy Impact at Farm-ouseold Level n Developng Countres (FSSIM-DEV) Applcaton to Serra Leone Autors: Kamel Louc, Sergo Gomez y Paloma, Hatem Beloucette, Tomas Allen, Jacques Fabre, María Blanco Fonseca, Roza Cenoune, Szvetlana Acs and Gullermo Flcman Edtors: Kamel Louc and Sergo Gomez y Paloma 2013 Report EUR xxxxx EN 1

2 European Commsson Jont Researc Centre Insttute for Xxxxxxxx Contact nformaton Forename Surname Address: Jont Researc Centre, Va Enrco Ferm 2749, TP xxx, Ispra (VA), Italy E-mal: Tel.: +xx xxxx xx xxxx Fax: +xx xxxx xx xxxx ttp://xxxx.rc.ec.europa.eu/ ttp:// Ts publcaton s a Reference Report by te Jont Researc Centre of te European Commsson. Legal Notce Neter te European Commsson nor any person actng on bealf of te Commsson s responsble for te use wc mgt be made of ts publcaton. Europe Drect s a servce to elp you fnd answers to your questons about te European Unon Freepone number (*): (*) Certan moble telepone operators do not allow access to numbers or tese calls may be blled. A great deal of addtonal nformaton on te European Unon s avalable on te Internet. It can be accessed troug te Europa server ttp://europa.eu/. JRCxxxxx EUR xxxxx EN ISBN xxx-xx-xx-xxxxx-x (pdf) ISBN xxx-xx-xx-xxxxx-x (prnt) ISSN xxxx-xxxx (prnt) ISSN xxxx-xxxx (onlne) do:xx.xxxx/xxxxx Luxembourg: Publcatons Offce of te European Unon, 20xx European Unon, 20xx Reproducton s autorsed provded te source s acnowledged. Prnted n Xxxxxx 2

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4 Acnowledgements Ts study s based on deas developed at te Sustanable Agrculture (SUSTAG) acton of JRC IPTS AGRILIFE Unt. It as been carred out by a networ of researcers form dfferent nsttutons: Medterranean Agronomc Insttute of Montpeller (Gullermo Flcman, Hatem Beloucette, and Roza Cenoune), Unversty of Perpgnan Va Domta (Tomas Allen), Tecncal Unversty of Madrd (María Blanco Fonseca), Dataé (Jacques Fabre) and JRC- IPTS (Sergo Gomez y Paloma, Kamel Louc and Szvetlana Acs). Te autors would le to gratefully acnowledge te contrbuton of Alpa Lao (Nala Unversty, Freetown Serra Leone) wo coordnated te collecton of prmary data and of local experts ntervewed for ts proect, wtout wc ts study would not ave been possble: Mr. Jesse Olu Jon and Mr. Andrea RC Conte from Natonal Federaton of Farmers of Serra Leone; Mr. B.J. Bangura, Mr. Josep S. Bangur,, Mr. J.A Jallo, Mr. F.S. Kanu and Mr. Moamed Aouba Serff from Mnstry of Agrculture, Forestry and Food Securty; Mr. Moamed T. Lau from Nala Unversty; and Mr. Nazr A. Momood from Serra Leone Agrcultural Researc Insttute. Specal tan goes to Jacques Delncé, Head of Agrculture and Lfe Scences n te Economy Unt, for s contnung support and addtonal nput to te producton of ts report. 4

5 Table of contents Executve summary Introducton Te Bo-Economc Farm Houseold Model: FSSIM-Dev Introducton FSSIM, a generc bo-economc farm model FSSIM specfcaton and components Bref overvew on man FSSIM modules FSSIM-Dev: bacground and ams Crops module: mprovement of te resource constrant Modellng seasonalty Modellng labour slls Calbraton module: mprovement and extenson Te rs approac Te Postve Matematcal Programmng (PMP) approac Concluson Investment and perennal actvtes module Revew of nvestment modellng approaces Modellng te mx of annual and perennal crops Module for perennal actvtes Concluson Farm-ouseold module: non-separablty of consumpton and producton decsons Bref lterature revew on farm ouseold modellng Modellng farm ouseold decsons n FSSIM-Dev Concluson Trend and polcy modules Outloo parameters for buldng baselne scenaro Polcy parameters Transton from farm to aggregated levels Adustment of exstng modules to facltate aggregaton Aggregaton module Calbratng aggregate farm ouseold models

6 2.10. Concluson FSSIM-Dev Components & Tecncal Implementaton Introducton FSSIM-Dev Electronc Survey Global data Regonal data Farm data FSSIM-Dev Database Database Module Integraton Code Module FSSIM-Dev Grapcal User Interface Concluson Applcaton of FSSIM-Dev n a Serra Leone case study for assessng rce support polcy Introducton Selecton of current agrcultural actvtes Classfcaton by ecosystem Cluster analyss Farm ouseold typology Farm economc sze Farm specalzaton Descrpton of farm ouseold types Concluson Defnton and mplementaton of te smulated scenaros Baselne scenaro Polcy scenaros Results and dscusson Polcy scenaro 1 (PS1) Polcy scenaro 2 (PS2) Concluson Concluson and recommendatons References Appendces... 5 Appendx 1. Indexes, parameters, varables and equatons n FSSIM-Dev

7 Appendx 2. Food Securty Indcators... 5 Appendx 3. Tecncal and economc coeffcents of rce croppng systems... 5 Appendx 4. Man FSSIM-Dev results for dfferent farm ouseold types

8 Lst of Tables Table 1. Data requrement for perennal crops... 5 Table 2. Rce croppng calendar by ecosystem... 5 Table 3. Average and standard devaton of rce yeld by ecosystem... 5 Table 4. Set of selected rce croppng systems... 5 Table 5. Labour requrement for rce croppng systems... 5 Table 6. Man caracterstcs of te rce_small farm ouseold type... 5 Table 7. Man caracterstcs of te rce_veg_small farm ouseold type... 5 Table 8. Man caracterstcs of te rce_per_small farm ouseold type... 5 Table 9. Man caracterstcs of te rce_med farm ouseold type... 5 Table 10. Man caracterstcs of te rce_per_med farm ouseold type... 5 Table 11. Man caracterstcs of te rce_mx_med farm ouseold type... 5 Table 12. Man caracterstcs of te rce_bg farm ouseold type... 5 Table 13. Man caracterstcs of te rce_mx_bg farm ouseold type... 5 Table 14. Man caracterstcs of te rce_per_bg farm ouseold type... 5 Table 15. Lst of ntervewed local experts and staeolders... 5 Table 16. Costs and yeld of current and alternatve rce actvtes... 5 Table 17. Rce yeld and costs after applcaton of N fertlser... 5

9 Lst of Fgures Fgure 1. FSSIM and ts modules... 5 Fgure 2. FSSIM-Dev and ts modules... 5 Fgure 3. Calbraton by Postve Matematcal Programmng... 5 Fgure 4. Non-separable producton and consumpton decsons... 5 Fgure 5. Te man menu of FSSIM-Dev electronc survey... 5 Fgure 6. Crop lst form... 5 Fgure 7. Arable and perennal crops forms... 5 Fgure 8. Agro-envronmental zones form... 5 Fgure 9. Producton tecnques & Producton Systems forms... 5 Fgure 10. Inputs form... 5 Fgure 11. Externaltes form... 5 Fgure 12. Unts form... 5 Fgure 13. Country and Regon forms... 5 Fgure 14. Input unt costs form... 5 Fgure 15. Crops form... 5 Fgure 16. Rotatons form... 5 Fgure 17. Labour requrement form... 5 Fgure 18. Inputs form... 5 Fgure 19. Outputs form... 5 Fgure 20. Crop yeld form... 5 Fgure 21. Farm types form... 5 Fgure 22. Farm data form... 5 Fgure 23. Ln between database and GAMS fles... 5 Fgure 24. Scenaro descrpton n FSSIM-Dev GUI... 5 Fgure 25. Model setup n FSSIM-Dev GUI: lvestoc module... 5 Fgure 26. Model setup n FSSIM-Dev GUI: perennal module... 5 Fgure 27. Model calbraton n FSSIM-Dev GUI... 5 Fgure 28. Reference run n FSSIM-Dev GUI... 5 Fgure 29. Smulaton run n FSSIM-Dev GUI... 5 Fgure 30. Dsplay results n FSSIM-Dev GUI... 5 Fgure 31. Dsplay results usng GDXVIEWER... 5 Fgure 32. Farm typology... 5 Fgure 33. Effect of sowng date on rce yeld... 5 Fgure 34. Effect of N fertlzer and seed varetes on rce yeld... 5 Fgure 35. Soco-economc mpacts of polcy scenaro 1 (PS1) on dfferent farm ouseold types... 5 Fgure 36. Impacts of polcy scenaro 1 (PS1) on rce actvtes' area... 5

10 Fgure 37. Cange n producton and consumpton of rce under polcy scenaro 1 (PS1)... 5 Fgure 38. Farm ouseold ncome cange under smulated polcy scenaros (PS1 & PS2)... 5 Fgure 39. Crop pattern cange under polcy scenaro 2 (PS2)... 5 Fgure 40. Cange n producton and consumpton of rce under smulated polcy scenaros (PS1 & PS2)... 5 Fgure 41. Percent mprovement of poverty gap under smulated polcy scenaros (PS1 & PS2)

11 Lst of acronyms ABM AGRISP AIDS APES AROPA BECRA CAPRI -FT CIHEAM DA DM DMF EMP EU FAAS FAMOS FARMIS FSSIM FSSIM-AM FSSIM-Dev FSSIM-MP GAMS GUI IAMM ICM INRA LDC LP MAD MAFFS ME MIP MP NAFFSL OLS PAD PDI SD PIGLOG PMP SCP SEAMLESS SLARI USD Agent based model Agrcultural Regonal Integrated Smulaton Pacage Almost Ideal Demand System Agrcultural Producton and Externaltes Smulator Agrculture, Recomposton de l'offre et Poltque Agrcole Bo-Economc analyss of clmate cange mpact and adaptaton of Cotton and Rce based Agrcultural producton systems n Mal and Burna Faso Common Agrcultural Polcy Ratonalsed Impact Modelng-Farm Types Centre nternatonal de Hautes Etudes Agronomques Médterranéennes Dressed anmal Database Module Data Management Faclty Econometrc-matematcal programmng European Unon Federaton Accounts Allocaton Forest and Agrcultural Optmsaton Model Farm Modellng Informaton System Farmng System Smulator (SEAMLESS verson) Farmng System Smulator - Agrcultural management module Farmng System Smulator for developng countres Farmng System Smulator - Matematcal programmng model General Algebrac Modellng System Grapcal User Interface Insttut Agronomque Médterranéen de Montpeller Integraton Code Module Insttut Natonal de Recerce Agronomque Less Developed Country Lnear Programmng Mean Absolute Devaton Mnstry of Agrculture, Forestry and Food Securty Maxmum Entropy Mxed nteger programmng Matematcal programmng Natonal Federaton of Farmers of Serra Leone Ordnary Least Squares Percent Absolute Devaton Proten Dgestble n te Intestne Statstcs Dvson Prce Independent Generalzed Log-Lnearty Postve matematcal programmng Smallolder Commercalzaton Program System for Envronmental and Agrculture Modellng, Lnng Europe Scence and Socety Serra Leone Agrcultural Researc Insttute Unted States Dollar

12 Executve summary Ts report descrbes te generc template of a farm-ouseold model for use n te context of developng countres n order to gan nowledge on food securty and rural poverty allevaton under dfferent economc condtons and agr-food polcy optons. Ts model, called FSSIM-Dev (Farmng System Smulator for Developng Countres), s an extenson of te FSSIM model developed wtn te SEAMLESS proect. FSSIM s a boeconomc farm model for ex-ante mpact assement of agrcultural and envronmental polces on farm performances across Europe. It conssts of an agrcultural management module (FSSIM-AM) and a matematcal programmng module (FSSIM-MP). FSSIM-AM ams to dentfy current and alternatve actvtes and to quantfy ter nput output coeffcents (bot yelds and envronmental effects). FSSIM-MP sees to descrbe te farmer s beavour gven a set of bopyscal, soco-economc and polcy constrants and to predct s/er reactons under new tecnologes, polcy and maret canges. FSSIM-Dev s a farm ouseold model to be appled for famly (.e. mostly small-medum farms) or peasant agrculture were, n general, farm ouseold producton, consumpton and labour allocaton decsons are non-separable due to maret mperfectons. Contrary to most well-nown ouseold models wc are econometrc based, FSSIM-Dev s a non-lnear optmzaton model wc reles on bot te general ouseold's utlty framewor and te farm's producton tecncal constrants, n a non-separable regme. FSSIM-Dev s referred to as a statc Postve Matematcal Programmng (PMP) wc optmse at farm ouseold level, wt te opportuntes to smulate te excange of producton factors among farmouseolds. FSSIM-Dev s desgned to capture fve ey features of developng countres or/and rural areas: () non-separablty of producton and consumpton decsons; () nteracton among farm-ouseolds for maret factors; () eterogenety of farm ouseolds wt respect to ter bot consumpton basets (demand sde) and resource endowments (supply sde); (v) nter-lnage between transacton costs and maret partcpaton decsons; and (v) te seasonalty of farmng actvtes and resource use. From te tecncal pont of vew, FSSIM- Dev as several strengts: () easly applcable for ndvdual (.e. real) or representatve farms (.e. typcal or average farms); () smootly ntegrate results from bo-pyscal models needed to assess te envronmental effects of te producton process; and () suffcently generc and modular to be re-usable, adaptable and easly extendable to aceve dfferent modellng goals. Model use s llustrated n ts report wt an analyss of te combned effects of rce support polcy, namely fertlser subsdy polcy, and mproved rce croppng managements (practces) on te lvelood of representatve farm ouseolds n Serra Leone. Results sow tat, frst, te mprovement of rce croppng managements s a ey factor to boost sgnfcantly farm ouseold ncome n te studed regon. Second, te amount of N fertlzer requred for, manly, upland rce appears too g and costly and could not be appled by farm ouseolds wtout polcy support (.e. subsdes). Trd, bot te smulated rce polcy and te mproved crop managements would ncrease farm productvty and boost ouseold ncome but tey are not suffcent to fgt poverty snce most of te farm ouseold types would contnue to lve below te extreme poverty lne of 1 USD-equvalent per day.

13 1. Introducton Food securty 1 as become one of te most mportant tems on today's nternatonal poltcal agenda and a serous ssue for governments around te world. Guaranteeng sustanable and equtable food n te context of clmate cange, prce volatlty and global fnancal crss s, n fact, a callengng tas. Even f food avalablty as grown sgnfcantly and consstently over tme, bot globally and n developng countres, access to food s stll lmted partcularly n many low ncome economes. Accordng to te 2008 World Development Report (World Ban, 2008), tree-quarters of te world's poor lve n rural areas and most of tem are farmng. Altoug tere are food securty callenges across te world, maor progress s yet to be made n Afrca and Sout Asa s rural areas were most of te populaton s extremely poor (.e. wt less ten 1 USD-equvalent per day at ter dsposal) and dependent on small oldngs. To reduce rural poverty and mprove food securty bot te natonal governments and nternatonal communty ave developed several polces and programs. Te European Unon (EU) as made a sgnfcant contrbuton n ts sense snce 2005 troug te STABEX (Stablsaton of Export Earnngs) funds (an nstrument of te 8t EDF European Development Fund). Tese supports fall wtn a common polcy framewor for te EU and ts Member States n te fgt aganst world unger and malnutrton 2. Ts s motvated by ts poston as world s man donor and one of te most mportant commercal partners of developng countres. Tese support polces ave taen dfferent forms suc as: () ncreasng agrcultural productvty troug te support of agrcultural nputs (manly mproved seeds and fertlzers), tranng and mecanzaton; () facltatng te use of agrcultural nowledge and tecnologes; () mprovng nfrastructure (rural roads, storage facltes, processng ); (v) facltatng access to credt marets; etc. Impact assessments of suc supports upon te food securty of farm ouseolds are owever scarce and not always founded on sold scence-based metods. Most studes ave focused on te food securty ssue at te natonal level wc may mas te food securty stuaton at te ouseold level. In fact, a country may be food secure at natonal level wle large numbers of ts ouseolds are food nsecure. For a better understandng of farm ouseold food securty status, t s preferable to use metods and tools worng at mcro level, capable of provdng detaled results on a farm ouseold scale and of capturng eterogenety across ouseolds. Wtn ts context, te man am of ts report s to present a decson mang tool for exante and ex-post assessment of suc EU supports and to gan nowledge on food securty and rural poverty allevaton n tese countres under dfferent polcy optons. Ts tool conssts of a generc farm ouseold model, called FSSIM-Dev (Farm System Smulator for Developng Countres), wc s able to capture ey features of developng countres agrculture, to provde detaled dsaggregaton regardng commodtes and tecnology coces and to smootly ntegrates results from bo-pyscal models needed to mprove nowledge on land degradaton, land resources tenure and use. Te term 'generc' ere refers to ts potental capacty to be re-usable, adaptable and easly extendable to aceve dfferent modellng goals. Model use s llustrated n ts report wt an analyss of te mcro-economc mpacts of bot rce support polcy and nnovatve tecnology/management on te lvelood of farm 1 More detals on food securty concepts are gven n Appendx 2. 2 European Commsson (2010): Communcaton from te Commsson to te Councl and te European Parlament. An EU polcy framewor to assst developng countres n addressng food securty callenges. SEC(2010)379, COM (2010)127 fnal, , Brussels. Avalable from: ttp://ec.europa.eu/development/center/repostory/comm_pdf_com_2010_0127_en.pdf 13

14 ouseolds n Serra Leone. Te am s to gan nowledge on farmers' lvelood strateges and to assess te combned effects of nput (fertlser) subsdy polcy and mproved rce croppng managements, usng a set of FSSIM-Dev ndcators suc as land use, supply and demand of basc food commodtes, labour use, farm ouseold ncome and poverty gap. Poverty gap s measured ere by te percent devaton between te extreme poverty lne of 1 USD-equvalent per person per day and te farm ouseold ncome per ouseold unt. Te report s structured as follows: secton 2 presents te template of te developed FSSIM- Dev model. Secton 3 descrbes te components and tecncal mplementatons of FSSIM- Dev,.e. te electronc survey, te database and te Grapcal User Interface. In secton 4, te applcaton of te model to te case of Serra Leone wt relevant polcy analyss s dealt wt n more detal. In secton 5, we conclude on te relevance of ts type of modellng framewor and stress te added value of our results n comparson wt oter studes. 14

15 2. Te Bo-Economc Farm Houseold Model: FSSIM-Dev 2.1. Introducton Over te last decade, development and use of farm-level models for polcy analyss as become one of te maor actvtes of agrcultural economsts. Ts growng nterest can be attrbuted to several reasons among tem () te ncreasng demand for tools and metods for mpact assessment at very dsaggregated level; () te better understandng of farm-level decson mang; () te g eterogenety among farms n term of bot polcy representaton (.e. polces are more and more farm specfc) and polcy mpacts (.e. polcy affects farms and regons dfferently); and last but not least; (v) tey can be easly andled wt standard computer pacages, ncludng spreadseets and more sopstcated pacages suc as GAMS (General Algebrac Modellng System). Fve approaces are often used for buldng a farm level model: matematcal programmng (MP) (ncludng lnear programmng (LP), non-lnear programmng (NLP), mxed nteger programmng (MIP) and postve matematcal programmng (PMP)), econometrc approac, smulaton approac, agent based model (ABM) and an advanced tecnque termed by Buysse et al., (2007b) as econometrc-matematcal programmng (EMP). Te last approac as not been fully appled yet to polcy analyss, manly because of data avalablty and numercal solvng problems (a frst test was done recently by Jansson and Hecele, 2011 and by Henry de Fraan et al., 2011). Agent based models are appled for case studes but not at a larger geograpcal scale due to ter g data requrement. Te coce of one of te fve approaces depends often on data avalablty, model specfcaton and researc scope. Our focus n ts study s farm models based on matematcal programmng (MP) approac, wc conssts to optmze (maxmze or mnmze) one or several obectves under a set of constrants. Farm programmng models play a strong role n agrcultural polcy analyss, partcularly were relable tme-seres data are absent, or sfts n maret nsttutons, or constrants ave canged substantally over tme. Te lterature revew reveals a wde range of farm programmng models wc nvestgate dfferent questons at varous locatons. Tey can be grouped nto two broad categores: () farm supply models seeng to descrbe te decson mang process of one (ndvdual or representatve) farmer consdered as a pure producer or entrepreneur ; and () farm ouseold model, wc combnes consumer and producer models nto a sngle model (.e., te consumer s also te producer), amng to represent ouseold beavour. Te former are used manly n developed countres wle te latter are appled n rural area or/and developng countres were producton and consumpton decsons are lned. Wtn eac category, models can be classfed nto emprcal vs. mecanstc, normatve vs. postve, statc vs. dynamc, determnstc vs. stocastc, dscrete vs. contnuous, etc. (Janssen and van Ittersum, 2007). As an example of recently publsed farm supply programmng models, we can cte tose used for te followng purposes: () to assess te mpact of EU Common Agrcultural Polcy, e.g. FARMIS (Offermann et al., 2005); Onate et al., 2006; Resgo and Gomez-Lmon, 2006; Semaan et al., 2007; Vagg et al., 2010; FSSIM (Louc et al., 2010); AGRISP (Arfn and Donat, 2011); CAPRI-FT (Goct and Brtz, 2011); () to andle landscape and resource conservaton problems, e.g. Bamère et al., 2011; Sculer and Kacele, 2003; 15

16 FAMOS (Scönart et al., 2011); () to antcpate farms responses to clmate cange (Duer et al., 2007; AROPA (De Cara and Jayet, 2011), etc. For farm ouseold programmng models, numerous models, mostly appled n Afrca and Sout-Amerca, are descrbed n te lterature. Te more recent ones are used for smulatng te effects of: publc goods polces (Sanfo and Gerard, 2012), new energy plants (Bartoln and Vagg, 2012), HIV/AIDS (Gll, 2010), new tecnologes (Laborte et al., 2009), alternatve deforestaton soluton (Dolsca et al., 2008), new rce croppng systems (Yrdoe et al., 2006), ncreasng farm sze and mecanzaton (Van den Berg et al., 2006) and better access to off-farm ncome (Holden et al., 2004). However, most of te exstng farm models are developed for specfc purposes and locatons and are not easly adaptable and reusable. A farm model enables to set up assessments for a wde range of farm types dfferng n () resource endowments (land, labour, equpment avalablty); () producton ntensty (.e. output per ectare); () specalsaton (arable, lvestoc, mxed); (v) bopyscal condtons (sol, weater); (v) farm management (organc, conventonal, ntegrated); and (v) producton orentaton (maret, self-consumpton), are scarce. Ts seems to be due, on te one and, to te dversty of stuatons and polcy scemes and, on te oter and, to te need of a consderable collaboratve effort between scentsts, programmers and software engneers to provde a user-frendly, easly-accessble modellng system, a goal tat s not easy to reac. Te only publsed farm models tat can be consdered as partally generc and potentally extendable and adaptable for dfferent contexts and condtons n te EU and elsewere are: FSSIM (Farmng System Smulator), a generc bo-economc farm model developed wtn te SEAMLESS proect (Van Ittersum et al., 2008) and EU-FARMIS (Farm Group Model for German Agrculture), a comparatve-statc farm group model developed by BMELV (Osterburg et al., 2001, Offermann et al., 2005). Neverteless, bot of tem are farm supply models and tey need adustments and mprovements to correctly represent farm ouseold beavour n developng countres or/and rural areas. In ts study, we opted for usng FSSIM as a startng pont, wt te am to develop, based on ts model, a generc farm ouseold model tat enables ex-ante assessment of agr-food and rural polces on te lvelood of farms/farm ouseolds n developng countres. Ts model s called FSSIM-Dev by reference to FSSIM and to developng countres (Dev). In order to aceve tese obectves, te followng mprovements and developments are made n te FSSIM model: Improvement of te exstng FSSIM-MP modules n bot metodologcal and tecncal aspects (crops, calbraton, trend and polcy modules); Development of new modules to furter amelorate te capablty of te FSSIM-MP model to nvestgate farm-ouseold systems n developng countres (ouseold and perennal modules); Development of an up-scalng module for aggregatng results at regonal/vllage level, wc enables to capture nteracton among farm ouseolds; Development of a more generc polcy module tat allows parameterzaton of a wder range of polcy nstruments; Development of a user frendly nterface to facltate te development and use of te model by scentsts, model developers and regulatory users; 16

17 Development of an electronc survey for te collecton of te requred nformaton to ensure model re-usablty; Development of a stragtforward procedure to ntegrate te outputs of bopyscal models (yeld and envronmental externaltes) as nputs n te FSSIM-Dev database. Improvement and extenson of exstng database n order to nclude nformaton requred by te new/modfed modules; Ts secton gves a detaled descrpton of te extended FSSIM model verson (.e. FSSIM- Dev). It s structured as follows. In secton 2.1, an overvew of te SEAMLESS FSSIM model s provded. In secton 2.2, te bacground and te ams of FSSIM-Dev s brefly reported. In sectons 2.3 and 2.4, te metodologes for calbratng te model and for modellng perennal actvtes are presented. In secton 2.5, te ouseold module used to represent bot supply and ouseold decsons s set out. In sectons 2.6, te polcy and trend modules are descrbed. In te last secton, te aggregaton module used to upscale results from farm to ger (regonal/natonal) levels s exposed FSSIM, a generc bo-economc farm model FSSIM specfcaton and components FSSIM s a bo-economc farm model developed wtn te SEAMLESS proect, to assess te mpact of agrcultural and envronmental polces on te performance of farms and on ndcators of sustanablty (Louc et al., 2010a; Janssen et al., 2010; Louc et al., 2010b). It conssts of a data module for agrcultural management (FSSIM-AM) and a matematcal programmng model (FSSIM-MP). FSSIM-AM ams to dentfy current and alternatve actvtes and to quantfy ter nput and output coeffcents (bot yelds and envronmental effects) usng te bopyscal feld model APES (Agrcultural Producton and Externaltes Smulator) and oter data sources. Once tese actvtes ave been generated, FSSIM-MP cooses tose tat best ft te farmer s beavour, gven te set of resources, te tecnologcal and poltcal constrants, and forecasts farmer responses to new tecnologes, as well as to polcy and maret canges (Louc et al., 2010a). Te prncpal outputs generated from FSSIM for a specfc polcy are forecasts on land use, producton, nput use, farm ncome and envronmental externaltes (e.g. ntrogen surplus, ntrate leacng, pestcde use, etc.). Tese outputs can be used drectly or translated nto ndcators to provde measures of te mpact of polces (Fgure 1). 17

18 FSSIM-AM DATA BASE ON FARM RESOURCES DATA BASE ON SOIL, CLIMATE... DATA BASE ON CROPS, ANIMALS, PRODUCTION TECHNIQUES, AGRONOMIC RULES... Land Water Labour Macner y FSSIM-MP... AGRICULTURAL ACTIVITY GENERATOR LIVESTOCK MODULE CROPS MODULE PMP MODULE PRODUCTION ENTERPRISE GENERATOR PRODUCTION TECHNIQUE GENERATOR PERENNIAL MODULE COMMON MODULE PREMIUM MODULE SET OF CURRENT AND ALTERNATIVE AGRICULTURAL ACTIVITIES Producton enterprse wt specfed producton tecnque (level, type... of nput) TREND MODULE POLICY MODULE RISK MODULE Bopyscal model "APES" Yeld, externaltes DATA ON AGRICULTURAL POLICIES AND SOCIO- ECONOMC ENVIRONMENT TECHNICAL COEFFICIENT GENERATOR INPUT/OUTPUT COEFFICIENTS Costs, prces, premums, labour and macne need Maxmze: Farm expected utlty Subect to: agronomc, tecncal, economc, nsttutonal, feedng... constrants OUTPUTS - Farm ncome - Postve/negatve externaltes - Agrcultural actvty levels... Fgure 1. FSSIM and ts modules FSSIM was desgned suffcently generc and wt a transparent syntaxes n order to be appled to many dfferent farmng systems across Europe and elsewere. It as a modular setup to be re-usable, adaptable and easly extendable to aceve dfferent modellng goals. It ncludes a set of modules, namely crops, perennal, premum, Postve Matematcal Programmng (PMP), rs, trend and polcy modules. Tese modules are solved smultaneously; tey are lned ndrectly by an ntegratve module named te common module nvolvng te obectve functon and te common constrants. Tans to ts modularty, FSSIM-MP provdes te ablty to swtc on/off modules (and ter correspondng constrants) followng te needs of te smulaton, to select one or several calbraton approaces between dfferent optons (rs, standard PMP, Röm and Dabbert s PMP approac and Kanellopoulos et al. PMP approac) and to control te flow of data between te database and software tools. FSSIM-MP can be run wt smple or detaled survey data (.e. accordng to te level of detal of te avalable data). Addtonally, t can read nput data stored n any database (e.g. MS ACCESS DB), Excel or GAMS-Include fles, provded tat tey are structured n te requred format. FSSIM can be appled to ndvdual (.e. real) or representatve farms (.e. typcal or average farm) as well as to natural (terrtoral) or admnstratve regon by consderng te selected regon as a large farm (.e. f te eterogenety among farms nsde te regon s nsgnfcant) or by aggregatng te results of ndvdual or representatve farms (.e. f te nterdependences between farms are mnors). It can be used for two purposes: () to allow detaled regonal mpact assessment of polcy decsons, maret cange and tecnologcal nnovatons on farmng practces and sustanablty of te dfferent farmng systems; () to facltate te 18

19 ln of mcro and macro levels n ntegrated way troug te estmaton of supply-response functons tat can be ntegrated n a partal equlbrum maret model. Te matematcal programmng module of FSSIM (FSSIM-MP) s a constrant optmzaton model wc maxmzes an obectve functon at gven prces and subsdes subect to a set of resource and polcy constrants. It conssts of a non-lnear programmng model, wc maxmzes te farm s utlty defned as te expected ncome mnus rs, accordng to te Mean-Standard devaton metod (Hazell and Norton, 1986). FSSIM s referred to as a postve matematcal programmng (Howtt, 1995a) model wc ntegrates a large number of crop and anmal actvtes. Te man specfcatons of FSSIM are (Louc et al., 2010b): 1 A statc programmng model wc optmzes an obectve functon for one perod (.e. one year) over wc decsons are taen. Ts mples tat t does not explctly tae account of tme. Neverteless, to ncorporate some temporal effects, agrcultural actvtes are based on crop rotatons 3 and dressed anmal 4 rater tan ndvdual crops and anmals. 2 A postve model n te sense tat ts emprcal applcatons explot te observed beavour of economc agents to reproduce te observed producton stuaton as precsely as possble; 3 An actvty based model wat means tat one product can be produced by dfferent actvtes 5, and eac actvty can produce several products. Ts maes sutable te ntegrated assessment of new polces wc are lned to actvty and not to product. Ts s te case of sol conservaton polces n te USA, were all farm subsdes depend on te use of specfc agrcultural practces. In Europe, te Ntrate Drectve s also an example of a polcy targetng producton processes/actvtes, not products. Ts approac maes possble to tae nto account postve and negatve ontness n outputs (.e. ont producton) 4 A prmal based model were tecnology s explctly represented n order to smulate te swtc between producton tecnques as well as between producton systems; 5 A dscrete based model to ntegrate easly te engneerng producton functons generated from bopyscal models and to account postve and negatve ontness n outputs (.e. ont producton) assocated wt te producton process. Tese specfcatons enable FSSIM to explore te mpacts of polcy canges and tecnologcal nnovaton not only on te relatonsp between maret and nonmaret goods, but also on te producton process. 3 Crop rotaton s te practce of growng a seres of dssmlar types of crops n te same area n sequental seasons for varous benefts suc as to avod te buld-up of patogens and pests tat often occurs wen one speces s contnuously cropped. 4 Te concept of dressed anmal represents an adult anmal and young stoc tang nto account te replacement rate. 5 An arable actvty corresponds to a crop rotaton grown under specfc sol and clmate condtons and under well-defned management descrbng maor feld operatons n detal. 19

20 6 A template based model: FSSIM uses a model template for all te applcatons,.e. te equatons and varables used n FSSIM are te same but te set of parameters depend on farm data. Te general matematcal formulaton of FSSIM s presented below: Max U x 0 s.t Ax b = Z-φσ (1) Were U s te utlty functon to maxmse, z s te expected ncome, x s te nx1 vector of te smulated levels of te agrcultural actvtes, φ s te rs averson coeffcent, σ s te standard devaton of ncome due to prce and yeld varaton, A s a (n m) matrx of tecncal coeffcents, and b s a n 1 vector of avalable resources and upper bounds to te polcy constrants. Te farm s expected ncome s defned as total revenues ncludng sales from agrcultural products and compensaton payments (subsdes) mnus total varable costs from crop and anmal producton. Total varable costs nclude accounted lnear costs for fertlzers, rrgaton water, crop protecton, seeds and plant materal, anmal feed and cost of red labour as well as unaccounted cost due to management and macnery capacty reflected by te quadratc term of te cost functon. Usng matematcal notaton, te non-lnear ncome functon can be presented as follow: Z a a = p' s + p ' s + ( sb a) x -d'x xQx' (2) Were Z s te expected ncome, p s te (n 1) vector of te expected product prces, s s te (n 1) vector of smulated sold products, p a s te (n 1) vector of addtonal prce tat te farmer gets wen sellng wtn quota, s a s te (n 1) vector of smulated sold products wtn quota, x s te (n 1) vector of te smulated levels of te agrcultural actvtes, sb s te (n 1) vector of subsdes, a s te (n 1) vector of accountng cost, d s te (n 1) vector of te lnear part of te actvtes mplct cost functon and Q s te (n n) matrx of te quadratc part of te actvtes mplct cost functon. Te accountng costs nclude costs for fertlzers, crop protecton and seeds as well as plant materal and cost of red labour. Q and d are estmated usng a varant of te Postve Matematcal Programmng approac wc guarantees exact reproducton of actvty levels observed n te base year. In prncple, any non-lnear convex cost functon wt te requred propertes can reproduce te base year soluton. For smplcty and lacng strong arguments for oter type of functons, a quadratc cost functon s usually employed. Te standard devaton of ncome (σ) s calculated accordng to te followng formulaton: σ = ( Z Z) Ν 2 1/ 2 (3) 20

21 Z: expected ncome Z : ncome over states of nature (). Z s calculated usng te same equaton appled for calculatng te expected ncome Z (.e. equaton (4)). Te unque dfference s tat te average producer prce (p) and te average yeld (y) are replaced, respectvely, by te producer prce (p ) and te yeld (y ) over state of nature (). p and y are vectors of ndependent random numbers normally dstrbuted (.e. tey are calculated usng a normal dstrbuton functon based on te average and te standard devaton of prce and yeld). Due to data mssng, we assumed tat tere s no dependence between yeld and prce varaton (.e. no covarance). Ts assumpton could be mproved f more data are avalable. N s te number of states of nature FSSIM as been appled for dfferent clmate zones and sol types and to a range of dfferent farm types wt dfferent specalzatons, ntenstes and szes. In most applcatons FSSIM as been used to assess te effects of polcy canges (Louc et al., 2010a; Kanellopoulos et al., 2009; Mouratadou et al., 2010) and to assess te mpact of tecnologcal nnovatons (Beloucette et al., 2011). In te varous applcatons, dfferent data sources, level of detal and model confguratons ave been used. Te model s avalable for applcatons to oter condtons and researc ssues, and t s avalable to be furter tested and to be extended wt new components, ndcators or lnages to oter models Bref overvew on man FSSIM modules FSSIM conssts of 9 modules. Te core module s te Common module tat connects all te oter modules,.e. Crops module, Lvestoc module, Perennal module, PMP module, Premum module, Rs module, Polcy module and Trend module. Te tree most mportant modules of FSSIM developed for Europe are sortly descrbed ere: Crops, Lvestoc and Common modules. Te explanaton concernng te oter modules can be found n Louc et al., (2010a) and Janssen et al., (2010) Crops module In FSSIM, crop actvtes are defned as crop rotatons 6 grown under specfc sol and clmate condtons and under well-defned management descrbng maor feld operatons n detal. It s assumed tat n eac year, all crops of a gven rotaton are grown on equal sares of te land. Te concept of crop rotatons allows accountng for temporal nteractons between crops. Te agrcultural management of arable actvtes descrbes operatons assocated wt fertlzaton, sol preparaton, sowng, arvestng, rrgaton and pest management of crops and results n dfferent nputs and outputs. To quantfy te amount of nputs and outputs (e.g. costs, labour requrements, nput of agrocemcals, yelds, externaltes) assocated to eac crop actvty, a smple survey s used completed by data generated from te agrcultural management component (FSSIM-AM) and te bopyscal model. Te selecton of optmal crop actvtes s subected to a set of tecncal, agronomc, nsttutonal constrants. Among tese constrants wc are mplemented n te crops module, te farm resource (manly land and water) constrants used to matc te avalable 6 Agrcultural actvtes can be based on ndvdual crops (.e. mono-crop rotatons) f data on crop rotatons are not avalablty. 21

22 resources tat can be used n a producton operaton and te possble uses made of t by te dfferent actvtes. Two land constrants are retaned - total and rrgable lands - and eac land constrant s specfed by sol type (or agr-envronmental zone). Te same generc rule s appled n all of tese constrants: for eac farm resource (f), te sum of te resource requrements of te selected crop actvtes n eac farm cannot exceed ntal avalable resource (B). A, f x, B f (4) were: f ndexes producton factors (.e. farm resources) ndexes agrcultural actvtes A s a matrx of producton factor requrements (.e. resource use coeffcents) B s a vector of ntal avalable factors (.e. resources avalablty) x s a vector of agrcultural actvty levels (.e. x s a decsonal varable) In addton to tese constrants, te crops module ncludes two equatons for computng expected crop ncome (wtout premums) and crop ncome over states of nature. Te expected crop ncome s defned as total revenues from sellng crop products mnus accountng costs for fertlzers, rrgaton water, crop protecton, seeds and plant materal. Te ncome over state of nature s calculated n te same way as expected ncome but nstead of usng te average prce and te average yeld we use te prce and te yeld over state of nature Lvestoc module Tree dfferent anmal actvtes are modelled n FSSIM,.e. dary, beef, and small rumnants (seep and goats). To represent lvestoc actvty te concept of dressed anmal (DA) s used wc represents a productve anmal and a sare of young anmals. Tat s, all te anmal categores of te same famly are regrouped togeter under a dressed anmal component, assumng a fxed erd sze. Several dressed anmals can be consdered, dependng on te lvestoc actvtes undertaen (e.g. dary, beef) and producton ntensty level (lower, medum or g), and tang nto account te ln between ntensty level and replacement and fertlty rates. In te case of dary actvty, one dressed anmal may comprse a productve cow, a bull and ter off-sprngs. A replacement rate s based on te actual ml producton per cow and sets te sare of young anmals n a dary actvty.e. calves and efers. For example, a typcal dary actvty n Flevoland (Te Neterland) may consst of 60.5% cows, 17.5% efers, 20.8% calves and 1.2% bulls. Increasng te actvty level by 1 unt wll cause an ncrease n te number of all anmals so tat te sare of anmals n te actvty remans constant. Beef actvtes are modelled n a smlar way. Two dstnct metods of rasng anmals for beef producton are avalable.e. a sucler system comprsng a cow and ts offsprng, and a fattenng system, wc merely fattens purcased young anmals tll te moment of sellng. Te small rumnant actvtes for meat and ml producton are modelled n a way smlar to dary and beef actvtes. Te ml and meat producton s used to determne an approprate replacement rate and te feed requrements of dfferent anmals (Torne et al., 2009). 22

23 Te modellng of lvestoc actvtes as requred mposng some constrants wc are mplemented n te lvestoc module. Te frst constrant specfes tat te feed requrements of te erd n terms of fbre, energy and proten ave to be covered by rougage produced on farm (fres, ay or slage), purcased rougage (ay or slage), concentrates produced onfarm or purcased concentrates. Feed crops le grass and fodder maze are grown eter n a rotaton wt oter crops or as mono-crop actvtes. Te quanttes of on-farm produced and purcased feed depend manly on prces of crop product (ncludng feed) and nput prces. J u v + pf v x A J J, nut sf,nut sf, nut nut, sf (5) were nut: ndexes nutrent term, suc as energy (UF) and proten (PDI) sf: ndexes te set of purcased supplement feed. V: nutrent value of te feed produced for on-farm use (grass, fodder and crop products) as well as of te purcased feed expressed n term of proten and energy per t DM. pf: quantty of purcased supplement feed (t DM). u s a vector of on-farm used producton (t DM) A: feed requrement per lvestoc actvty (.e. dressed anmal, ntensty level, and producton system) expressed n term of energy and proten. Ts requrement s calculated tang nto account requrements for mantenance, ml producton, growt, gestaton perod, and grazng/movng. X: level of te selected lvestoc actvty (n ead) Tree oter feed restrctons are mplemented: () fll unt dstrbuted sould be lower or equal to ntae capacty; () te sare of concentrates n anmal dets expressed n energy term s bounded to a maxmum; () an upper bound for feed avalablty from grazng s also mposed. Te last constrant lmts te anmal populaton to te lvestoc buldng capacty wc depends on te ntal farm buldng avalablty and te nvestment n new buldng. Te lvestoc buldng enlargement depends on farm nvestment capacty and on anmal requrement for buldng: x. A B + N ' Buld ', ' Buld' ' Buld ' (6) X: level of te selected anmal actvtes (n ead) A: anmal requrement for buldng (m2/ead) B: ntal buldng avalablty (m2) N: nvestment n new buldng (m2) As n te crops module, two equatons for computng expected lvestoc ncome (wtout premums) and lvestoc ncome over states of nature are ncluded n te lvestoc module. 23

24 Common module Te common module nvolves te FSSIM obectve functon as well as a set of constrants lnng between dfferent modules. Te obectve functon s based on te expected ncomes (wtout premums) from arable crops, perennal crops and anmal actvtes computed on crops, perennal and lvestoc modules, respectvely, plus te amount of premums computed n te premum module, mnus te PMP terms and rs component calculated n PMP and rs modules, respectvely. A part from te rs equaton used to compute te standard devaton of ncome, tree constrants are developed n te common module: labour, equpment and quota constrants. Te frst two constrants, wc are very smlar to land and water constrants developed n te crops module, specfy tat te sum of requrement for selected crop and lvestoc actvtes n labour and equpment, expressed n our, sould be less tan te amount of ntal labour and equpment avalable n te farm. Te quota restrcton states tat for all quoted products (Quota prd,l 0), te sales wtn quota cannot exceed te quota level. a q l Quota, l, (7) ndexes agrcultural (crop or anmal) products l ndexes quota types (e.g. for sugar t s A and B) q a s a vector of sold producton wtn quota Quota s a vector of quota level 2.3. FSSIM-Dev: bacground and ams Despte ts strong relevance n bot conceptual and tecncal terms (e.g. generc and modular setup, explct representaton of tecnology), te FSSIM model presents some lmtatons for representng farm-ouseold beavour n developng countres or/and rural areas. Te frst lmtaton s tat snce t s based on farm producton teory, an approac wc recognzes tat producton, labour allocaton and consumpton decsons are separable, t s not sutable for rural areas were tese decsons are nterdependent (.e. non-separable). Te exstence of suc non-separablty ndcates, n fact, te presence of maret mperfectons or falures tat may ave mportant polcy mplcaton. In suc case, a farm ouseold approac mgt be necessary dependng on weter te good for wc maret fals s mportant n producton (Sng et al., 1986). Te send lmtaton s tat land marets, possbltes for off-farm labour, and structural canges are exogenously defned. Te model ndependently smulates te beavour of eac farm, so t does not endogenously capture te nteracton among farms for maret factors, wc are very mportant n rural areas. Te trd lmtaton s related to te smulaton of perennal actvtes. In FSSIM, perennal actvtes are ntroduced n an extremely smplfed way, as constant surfaces wt an annual cost. Ts means tat te age structure of plantatons as well as te dfferent costs and economc returns assocated to te perod of development of specfc speces can not be taen nto account wt te FSSIM model. 24

25 Te fourt lmtaton s ts ncapablty to represent farmer beavour wt respect to producton actvtes tat are not observed durng te reference perod, commonly referred to as a self-selecton problem. Te last lmtaton s related to te trend and polcy modules. Ts part of te FSSIM model as been developed and specfed for European context and cannot drectly be used for local agr-food polces n developng countres. To overcome tese lmtatons, a sgnfcant number of mprovements and extensons were made n FSSIM-Dev suc as te development of ouseold, perennal and up-scalng modules, te mprovement of te calbraton module wt sound metods, and te extenson of te trend and polcy modules (Fgure 2). Moreover, tang nto account te ambton of a generc tool, all te modfcatons are ntegrated n a manner tat respects exstng model arctecture and allows te swtcng on/off modules accordng to data avalablty and applcaton type. Ts means tat te resultng model s not a specalzed model, applcable to specfc condton but, rater, a model wc can be appled to any relevant farm-ouseold systems n developng countres. Furter n te next sectons te newly developed and mproved modules of FSSIM-Dev, mentoned above, are descrbed n detal, namely: crops module, perennal module, ouseold module, up-scalng module, calbraton module, trend and polcy modules. FSSIM-AM DATA BASE ON FARM RESOURCES DATA BASE ON SOIL, CLIMATE... DATA BASE ON CROPS, ANIMALS, PRODUCTION TECHNIQUES, AGRONOMIC RULES... Land Water Labour Macner y FSSIM-MP... AGRICULTURAL ACTIVITY GENERATOR LIVESTOCK MODULE CROPS MODULE PMP MODULE PRODUCTION ENTERPRISE GENERATOR PRODUCTION TECHNIQUE GENERATOR PERENNIAL MODULE COMMON MODULE PREMIUM MODULE SET OF CURRENT AND ALTERNATIVE AGRICULTURAL ACTIVITIES Producton enterprse wt specfed producton tecnque (level, type... of nput) TREND MODULE HOUSEHOLD MODULE POLICY MODULE RISK MODULE AGGREGATION MODULE Bopyscal model "APES" Yeld, externaltes DATA ON AGRICULTURAL POLICIES AND SOCIO- ECONOMC ENVIRONMENT TECHNICAL COEFFICIENT GENERATOR INPUT/OUTPUT COEFFICIENTS Costs, prces, premums, labour and macne need Maxmze: wegted sum of farm ouseold utlty Subect to: resource, agronomc, feedng, consumpton, cas, prce bands, maret clearng... constrants/condtons OUTPUTS (ndvdual & aggregated levels) - Farm/ouseold ncome - Supply & demand of goods and factors - Postve/negatve externaltes - Agrcultural actvty levels Module newly developed Module mproved and extended Module slgtly mproved 25

26 Fgure 2. FSSIM-Dev and ts modules 2.4. Crops module: mprovement of te resource constrant Te man mprovements of te FSSIM crop module are related to te extenson of resource constrant for modellng te seasonalty of farmng actvtes and resource use as well of te labour slls Modellng seasonalty Most of croppng actvtes are seasonal and are confned to perods of te year wen temperature and ranfall are conducve to plan growt. Wtn croppng seasons, cultural operatons are also performed n set sequences, and most of tese ave to be performed wtn a relatvely sort perod of tme (e.g. sowng s usually performed n one or two days wtn a feld so tat te crop wll grow and rpen eventually). Tese caracterstcs of farmng lead to dstnct seasonal patterns n resource use and avalable supples of producton factors suc as labour and equpments (Hazell and Norton, 1986). Unfortunately n FSSIM, te seasonalty of croppng actvtes and resource use are not explctly consdered. In fact, resources are constranng only at annual level, wc means tat f a resource s lmtng n certan seasons wtn te year, t cannot be captured wt ts nd of model. To tae ts nto account, we ave adopted a generc formulaton n wc te resource requrement (.e. demand) and avalablty (.e. supply) can be defned eter by year or by season wtn te year accordng to context and data avalablty. Ts s performed usng two new parameters dr and da wc represent, respectvely, te dstrbuton over te seasons of annual resource requrement and annual resource avalablty. Accordng to ts new specfcaton, te resource constrant becomes te followng: A, f dr,f, se x B da f f,se (8) f ndexes producton factors (land, labour, water and equpments) ndexes agrcultural actvtes se ndexes seasons (te number and te lengt of seasons wll be decded by user accordng to regon specfcaton and data avalablty. A season could be for example one mont). A, f s a (m x n) matrx of annual producton factor requrements (.e. demand) for eac crop/anmal wtn agrcultural actvty (we assume a common matrx for entre farms wtn te regon) B f s (n 1) vector of annual producton factor avalablty dr,f,se represents dstrbuton over seasons of annual producton factor requrements (.e. demand) da f,se represents dstrbuton over seasons of annual producton factor avalablty (.e. supply) If only one season was retaned t means tat te resource constrant s defned at annual level and te coeffcents dr and da are equal to one (dr = 1; da = 1) and f t s more tan one season te constrant s worng at seasonal level and te coeffcents dr and da are less tan one (dr <1; da < 1). 26

27 Modellng labour slls Dfferences n labour slls are also commonly recognzed n farm models because not all worers are equally capable of performng some tass. For example, only some of te worers may ave tractor drvng slls. Anoter example arses n parts of Afrca and Asa were tradton requres tat some tass must be performed by female (e.g. plantng paddy). In te same lne, cldren may be able to do some specfc tass suc as brd scarng from te crops or to elp n undertang basc feld operatons. In order to ncorporate labour slls n te FSSIM-Dev model we opt for a generc formulaton based on two new parameters: te frst one represents te dstrbuton of seasonal labour requrement over tas (.e. plantng, scarng brds ) and over labour types (men, women and cldren) and te second one represents te dstrbuton of seasonal labour avalablty over labour type. Accordng to ts new specfcaton, te labour resource constrant (.e. f = labour) becomes te followng:, ta A, f dr,f, se dl f, se, la, ta x B da f f,se dl f, se, la (9) la ndexes labour types (men, women, cldren) ta ndexes tas types (.e. plantng, scarng brds ) f ndexes producton factors ndexes agrcultural actvtes se ndexes seasons A, f f s a (m x n) matrx of annual producton factor requrements (.e. demand) for eac crop/anmal wtn agrcultural actvty B s (n 1) vector of annual producton factor avalablty dr,f,se represents dstrbuton over seasons of annual producton factor requrements (.e. demand) da f,se represents dstrbuton over seasons of annual producton factor avalablty (.e. supply) dl f,se,m,ta represents dstrbuton of seasonal labour requrement over tas and labour types da f,se,m represents dstrbuton of seasonal labour avalablty over labour type 2.5. Calbraton module: mprovement and extenson FSSIM s used to represent farmer decson mang assumng tat producton, labour allocaton and consumpton decsons are separable. To reproduce ts decson two calbraton metods were appled: te rs or/and te Postve Matematcal Programmng (PMP) approac. Te frst approac calbrates te model approxmately and te latter calbrates t exactly explotng te observed farmer s beavour. Tree PMP approaces, wc can be selected by user accordng to data avalablty, are mplemented n FSSIM: () 27

28 te standard PMP approac (Howtt, 1995a); () te Röm and Dabbert s PMP approac (2003); and () te Kanellopoulos et al. s PMP approac (2009). In FSSIM-Dev, te calbraton process s strongly mproved by usng a generc procedure to swtc between dfferent PMP approaces (standard, Röm and Dabbert, Kanellopoulos) as well as between PMP varants (standard, average cost, almost-lnear, ) but also by mplementng more robust PMP metods (Maxmum Entropy crteron) based on estmaton procedure, wc are very useful wen multple data ponts are avalable. Te oter mprovement s tat tese PMP approaces are not only used to calbrate te farm supply decson under fxed farm lmtng resources, as t as been done wtn te FSSIM, but to calbrate smultaneously te supply and consumpton decsons of te farm ouseold under fxed and varable farm lmtng resources (.e. at farm ouseold and aggregated levels). Te followng secton descrbes tese entre PMP approaces usng stylzed matematcal notaton. For an easer understandng, tese approaces are performed for ndvdual farm model and assumng fxed farm resources Te rs approac Most of te rs metods assume tat a non-correspondence between smulated and observed results means one of tese two factors: () omsson of some mportant element of te cost structure, suc as specalzed management slls n growng g-value vegetable; () nadequate specfcaton of te crops rsness and farmer rs averson (Hazell and Norton, 1986). Snce t s dffcult to estmate adequately ts last factor wtout rc data set, te more common used way s to parameterze te model for dfferent values of rs averson and ten to coose te value of te parameter tat gves ft between te model s predcted crop allocaton and te observed values (.e. te rs averson coeffcent s used to partally calbrate te model). Te dfference between bot values s assessed statstcally by usng te Percent Absolute Devaton 7 (PAD). Te man weaness of te rs approac s tat t cannot calbrate te model exactly, but rater acceptng a resdual devaton between smulaton and realty. Anoter problem tat appears n ts case relates to defnng ow credble te model s and determnng te level of confdence tat can be placed on model predctons. Tere s no consensus on te statstc measure to be used n evaluatng te ft of predctons, but n most cases smple measures, suc as te mean absolute devaton (MAD) or te percentage absolute devaton (PAD), are used. In addton, clearly and obectvely defned tresold values of te dfferent measures tat determne acceptance or reecton of a model do not seem to be avalable n te lterature. In FSSIM, we ave adopted Hazell and Norton s suggeston wc sows tat a Percent Absolute Devaton (PAD) for producton and acreage below 10% s good, equal to 5% s exceptonal and more tan 15% ndcates tat te model may need mprovement before t can be used. 7 Percent absolute devaton: PAD (%) n Xˆ X = 1 = n Xˆ = Were Xˆ s te observed value of te varable and X s te smulated value (te model predcton). Te best calbraton s reaced wen PAD s close to 0. 28

29 Te Postve Matematcal Programmng (PMP) approac Remnder on PMP Te PMP approac was developed to calbrate lnear programmng models of agrcultural supply and to overcome ter common problems: overspecalzaton and umpy beavour. Te basc dea bend PMP s tat, a dvergence between model s predcton and observed farmer beavour means tat bot tecncal constrants and cost (or yeld) specfcaton were not taen nto account, and so tey ad to be estmated usng addtonal nformaton from observed actvty levels and ncluded n te obectve functon troug a nonlnear cost (or/and producton) functon (Howtt, 1995a). Te man advantages of te PMP specfcaton are not only te automatc and exact calbraton of optmzaton models, but also te smootness of te model responses to polcy canges and te possblty to mae use of very few data to model agrcultural polces (Röm and Dabbert, 2003). Te orgnal PMP approac developed by Howtt (1995a) nvolves tree steps: calbraton, estmaton and smulaton. Calbraton: conssts of wrtng an LP model as usual but addng to te set of lmtng resource constrants a set of calbraton constrants tat bnd te actvtes to te observed levels of te base year perod. Te sole purpose of ts pase s to obtan an accurate and consstent measure of te vector of dual values assocated wt te calbraton constrants, but as ponted out by Hecele and Wolff (2003) ts pase can be ntegrated n te estmaton pase by means of Lagrangean multplers (Howtt, 1995a). Hecele and Brtz (2005) nterpret ts vector as capturng any type of model mss-specfcaton, data errors, wrong or lacng representaton of rs beavour, unobserved producton costs, mssng tecnology nformaton, aggregaton bas, etc. Max Z = s.t Ax B x 0 [] ρ x x (1 + ε) p' y' x - c' x [] λ (10) Were Z s te obectve functon value, x and c are (n x 1) vectors of non-negatve actvty levels, and accountng costs per unt of actvty, respectvely, p and y are (1 x 1) vectors of expected output prces and yeld per actvty, respectvely. A represents an (m x n) matrx of coeffcents n resource constrants, B and ρ are (m x 1) vectors of resource avalablty and ter correspondng sadow prces. Te (n x 1) x o non-negatve vector of observed actvty levels, ε s an (n x 1) vector of small postve numbers for preventng lnear dependency between te structural and te calbraton constrants, and λ s an (n x 1) vector of duals assocated wt te calbraton constrants. 8 For a revew of PMP models see Henry de Fraan et al. (2007), Pars (2011) and Hecele et al. (2012). 29

30 Estmaton: conssts of employng te dual values λ delvered by te frst pase to specfy addtonal non-lnear terms n te obectve functon wc allows reproducng te observed actvty levels wtout calbraton constrants. Tese terms mostly refer to ncreasng margnal cost or/and a decreasng margnal yelds (Howtt, 1995a). Tat s, bot yeld and cost canges are probably present; owever, data on yeld varablty are more easly obtaned by an emprcal modeller tan cost varaton. A frequent case consders calbratng te parameters of a varable cost functon C(x), suc tat te varable margnal cost MC of te actvtes s equal to te sum of te nown cost c and te nonspecfed margnal cost λ. In case of a quadratc functon form 9, te followng condton for calbraton s mpled: C = d' x + 0.5x' Qx v C ( x ) o MC = = d + Q x = c + λ (11) x Were MC s te vector of margnal costs, d s an (n x 1) vector of parameters of te cost functon and Q s an (n x n) symmetrc, postve (sem-) matrx, c s te vector of observed varable costs per unt of actvty, λ are dual varables assocated wt te calbraton constrants and x o s te vector of observed actvty levels. To solve te undermned system (11) wt N equatons and [N+(N+1)/2] parameters, te lterature suggests many solutons wc nclude smple ad oc procedures wt some parameters set a pror (Howtt, 1995a; Röm and Dabbert, 2003), te use of supply elastctes (Helmng et al., 2001), te drect dervaton of te unnown parameters from te Kun-Tucer condtons (Judez et al., 2001), and te employment of maxmum entropy crteron (Pars and Howtt, 1998; Hecele and Brtz, 2000; Pars and Arfn, 2000). All tese PMP metods would exactly calbrate te ntal model as long as equatons (11) are verfed, but lead to dfferent smulaton responses to external canges. Tat s, any margnal cost curve passng troug te pont M would be able to calbrate te model (Fgure 3), meanng tat tere an nfnte number of parameter values for d and Q wc satsfy te specfcaton condtons (11). 9 Oter functonal forms are possble. Te generalzed Leontef and te wegted-entropy varable cost functon (Pars and Howtt, 1998) and te constant elastcty of substtuton (CES) producton functon (Howtt, 1995b) n addton to te constant elastcty of transformaton producton functon (Grandorge et al., 2001) ave also been used. 30

31 Costs ( ) MC PMP p d c M λ MC LP C LP MC LP = c C PMP MC PMP = c' x = d' x + 0.5x' Qx = d + Qx 0 MC PMP ( x 0 ) = d + Qx = c + λ 0 x Area (a) Fgure 3. Calbraton by Postve Matematcal Programmng Te dfferences between te most nown PMP approaces are summarsed n te followng secton. Tey can be dvded n two groups: PMP based on a sngle observaton and PMP based on multple observatons. PMP metods based on a sngle observaton descrbed below are te followng: te standard PMP approac, te Röm and Dabbert s PMP approac, te Kanellopoulos et al. s PMP approac and te Helmng et al. s PMP approac; wle, PMP metods based on multple observatons are te Maxmum Entropy- PMP approaces proposed by Hecele and Brtz (2000) and Pars and Arfn (2000). Except te Helmng et al. s PMP approac, all te oter approaces are mplemented n FSSIM-Dev to be selected by user accordng to data avalablty. Smulaton: n ts last step, te calbraton constrants of te frst stage are removed and te estmated non-lnear terms (cost (producton) functon) n stage two are added to te PL obectve functon n order to calbrate te model exactly to te observed stuaton. Te obtaned PMP model s ready for smulaton. Max Z = x 0 s.t Ax B [] ρ p' y'x - d'x - 0.5x'Qx (12) PMP approaces based on a sngle observaton As explaned above, te number of avalable observatons s usually not enoug to allow estmaton of te undermned system (11) troug tradtonal econometrc procedure. In fact, ust one observaton s often avalable and, n many cases, no exogenous addtonal data tat can be moblsed. Due to ts data restrcton, most of te exstng PMP approaces ave assumed tat te symmetrc matrx Q s dagonal (.e. all off-dagonal elements of Q are set to zero) and calculated te remanng parameters usng ad oc assumpton. Te mplct assumpton tat off-dagonal elements are zero means tat cross-actvty relatonsps are gnored. Among tese approaces, tree partcular ones ave been retaned and mplemented 31

32 n te FSSIM-Dev: te standard PMP approac, te Röm and Dabbert s PMP approac and te Kanellopoulos et al. s PMP approac. Wtn eac approac, user can coose between varous PMP varants based on dfferent wegts of te lnear and te non-lnear cost functons. A detaled descrpton of tese approaces s gven below. Anoter approac based on sngle observaton and use exogenous nformaton on supply elastctes was proposed by Helmng et al. (2001). Ts approac s also descrbed below, owever s not mplemented n FSSIM-Dev as t can be easly substtuted by te Maxmum Entropy-PMP approac presented furter. Te standard PMP approac by Howtt (1995a) Te standard PMP approac s te orgnal one developed by Howtt n In ts approac, te ll-problem (11) s solved by equatng d to c (d = c ) and settng te dagonal elements of Q matrx as: Q = λ x 0 From te standard approac, a set of PMP varants ave been proposed n te recent years suc as: Te average cost PMP varant: n ts varant te proposed soluton equates te accountng cost vector c to te average cost of te quadratc cost functon, wc produces: d = c λ and Q = 2λ x 0 Te almost-lnear PMP varant: ts varant assumes a very small value for te non-lnear terms Q. It conssts of retrevng a large sare of te dual value λ from te non-lnear term and addng t to te lnear term d suc as: d = c λ and Q = 0.02λ x 0 In te same lne, Hecele (1997) suggests to set te lnear cost term d to zero (d = 0) and calculatng te dagonal elements Q as follow: Q = ( c + λ ) x 0. Te common formula for all tese ad oc PMP varants can be represented as follow: d = c + λ -α λ and Q = α λ Were α s an (n x 1) vector of parameters tat determnes te wegts of te lnear and te non-lnear costs of actvtes. Te larger te value of α, te less senstve te model becomes to prce canges. Inversely, a lower value of α mples a small ncrease n margnal costs and te model beave almost as a lnear programmng. A larger value of α can result n a negatve ntercept of te margnal cost functon. Ts common formula was mplemented n FSSIM-Dev n order to facltate te swtc x 0 (13) 32

33 between te dfferent ad oc PMP varants by smply cangng te value of α. Wle beng an appealng metod for calbraton, te orgnal PMP as sown sortcomngs n model calbraton tat, n turn, motvated furter developments. One of tese sortcomngs dscussed at several occasons n te lterature s te unequal treatment of te margnal and preferable actvtes (.e. te problems of zero-margnal product (cost) for one of te calbratng constrants) (Pars and Howtt, 2001). Because te dfferental margnal costs of te margnal actvtes captured by te dual vector λ are zero, te margnal costs of supplyng tese actvtes are ndependent of ter levels wle tose of supplyng te preferable actvtes are not under te average cost approac of calbraton. For tese margnal actvtes, calbrated margnal costs are equal to average costs and margnal profts are equal to average profts. Te second sortcomng s te mssng representaton of economc beavours wt regard to actvtes of farms wose ntal observed supply level s zero durng te reference perod (.e. self-selecton problem). Te trd standard PMP sortcomng ponted out by Röm and Dabbert (2003) s te ncluson of greater compettveness among close compettve actvtes wose requrements for lmtng resources are more smlar tan wt oter actvtes. Due to tese lmtatons and oters, a number of Postve Matematcal Programmng (PMP) metods ave been developed n te recent years. Some of tem are descrbed below. Te Röm and Dabbert's PMP approac In te orgnal PMP approac, te parameters of te cost functon for eac crop are recovered separately from eac oter. In ts way, te same crop grown under dfferent varants (e.g. dfferent agro-managements) are consdered as two separate crops. Consequently, n te smulaton pase substtuton among tese crops s lower tan expected. Röm and Dabbert (2003) propose a dfferent modellng approac to tae nto account te ger elastcty of substtuton between dfferent varants tan between dfferent crops. For example, a reducton of payments for an agr-envronmental measure (e.g. cover crop after weat) wll probably lead to a declne of adopton of ts management measure. Te land under ts management s more lely to be allocated to te same crop (e.g. conventonally produced weat) tan to a dfferent crop (e.g. corn). Suc an adustment s facltated by ncludng n te frst step of PMP a set of addtonal calbraton constrants wc restrcts te level of eac crop and varant to ts observed level. Let s denotes te set of crops and v te set of varants, te frst PMP step accordng to ts approac can be wrtten as follow: 33

34 Max Z = x 0 s.t x v v x v v a x v 0 v x v v x v b 0 v (1+ ε 2 p ) v y [ ρ ] [ λ' ] v v (1+ ε 1 x ) v - c [ λ ] v x v (14) Were Z s te obectve functon value, x v and c v are (n x 1) vectors of non-negatve actvty (.e. combnaton of crop and varant v) levels, and accountng costs per unt of actvty, respectvely, p v, and y v are (1 x 1) vectors of expected output prces and yeld per actvty, respectvely. A v represents an (m x n) matrx of coeffcents n resource constrants, b and ρ are (m x 1) vectors of resource avalablty and ter correspondng sadow prces. Te (n x 1) x v o non-negatve vector of observed actvty levels, and ε 1 and ε 2 are (n x 1) vectors of small postve numbers (ε 1 < ε 2 ) and λ and λ are (n x 1) vectors of duals assocated wt calbraton constrants. Te frst calbraton constrant s related to crop specfed by varant type and te second one s related to crop. Te generated dual values λ and λ from (7) are ten used to estmate te parameters of te cost functon wc satsfy te followng condtons. c v + λ + λ' v = d v + Q' v x 0 v + Q v As sown n equaton (8), Röm and Dabbert (2003) dvde te slope of te cost functon of eac crop nto two parts, one for crop and oter for varant. As n te PMP standard approac, multple sets of cost functon parameters can satsfy te margnalty condtons and one of tese sets suggested by autors would be te followng: d Q v Q' vv = c = λ' = λ v v v x 0 v x 0 v x 0 v (15) (16) A more generc formula can be represented as follow: d Q' Q v vv = c v = α' λ' = α λ + λ' v v v + λ -α' x 0 v x 0 v λ' v -α λ (17) Were α and α are (n x 1) vectors of parameters tat determnes te wegts of te lnear and te non-lnear costs. Te fnal non-lnear model accordng to ts approac can be wrtten n te followng way: 34

35 Max Z = s.t x v v a 0 v x v v b p v y v -d v x v 0. 5Q' v x 2 v 0. 5Q x 2 v (18) Ts approac seems te more approprate for calbratng FSSIM because bot are based on actvty (.e. combnaton of crop and varant) rater tan crops (.e. products). However, te mplementaton of ts approac requres data on observed level per actvty wc are often unavalable. Te PMP approaces usng exogenous nformaton In order to andle te problems of zero-margnal cost for margnal actvtes and avod arbtrary parameter specfcatons, some PMP approaces ave proposed te use of exogenous nformaton on supply responses or on sadow prces of resources. Among tese approaces we can cte: A frst soluton proposed by Helmng et al. n 2001 wc consst of usng exogenous own-prce supply elastctes for dervng te parameters of te quadratc cost functon accordng to te followng procedure: Margnal revenue = margnal varable cost + margnal opportunty cost r = c + d + Q x + m ρ x r 1 r x (c + d + Q x + ε = = = r x ε x r x ρm Assumng : 0 x 0 1 x x Q = r ε x = Q = Q 0 ε r r d = c + λ Q x m a m ρ m A m ) x r (19) Te man lmtaton of ts approac s tat t assumes an nsenstve margnal opportunty cost to land cange (.e. myopc approac), wc can leads sometmes to sgnfcant dfferences between estmated and used elastctes. However, te dea of usng supply elastctes to calbrate model parameters was rapdly ncreased and most of applcatons today rely on exogenous nformaton on supply elastctes or on sadow prces of resources (Hecele et al., 2012). Ts approac s not mplemented n FSSIM-Dev as f data on supply elastctes exst t would be better to apply te Maxmum Entropy-PMP approac presented furter by usng tese elastctes as pror. A second approac proposed by Kanellopoulos et al. (2010) and mplemented n FSSIM-Dev s based on te use of te land rental values to estmate te non-lnear cost term of margnal actvty. Ts s aceved by () addng te costs of rented land n 35

36 te obectve functon; () replacng te resource constrant of te avalable land wt a flexble constrant were land s decson varable; and () ncludng a second set of calbraton constrants (more detals are gven n Kanellopoulos et al., 2010). Max Z = s.t Ax b x l x x x 0 [] ρ 0 x (1+ ε) 0 x (1 ε) p' y'x - c' x - gl land [ λ1] [ λ2] (20) Were, g denotes te average gross margn and l te rented land n a. Te frst calbraton constrant s related to actvtes tat result n gross margns ger tan te average gross margn and te second one s related to actvtes tat result n gross margns lower tan te average gross margn (ε). As n te PMP standard approac, te dual values λ1 and λ2 are used to estmate te lnear and te non-lnear PMP terms of te cost functon accordng to te followng formalsm. d Q = c + ( λ1 + λ2 ) -α λ1 = α λ1 + λ2 x 0 + λ2 (21) Anoter ad oc approac was dscussed on Röm and Dabbert (2003) but rarely used conssts of retrevng some sare of one lmtng resource dual value ρ and addng t to te calbraton dual vector λ to obtan a modfed calbraton dual vector for bot margnal and preferable actvtes. Dscusson on PMP wt a sngle observaton All te PMP approaces presented above use a sngle observaton and go wtout any type of estmaton by settng all off-dagonal elements of Q to zero and calculatng te remanng parameters usng some ad oc assumptons or exogenous nformaton. Tese specfcatons became, neverteless, strongly dsputable over te last years. Te use of ust one observaton generates models tat mgt not be robust and leads to an arbtrary specfcaton of te obectve functon s parameters. As remnded by Hecele and Brtz (2005), wen very lttle nformaton s avalable, te recovered parameters mgt be nconsstent and te reproducton of supply responses mgt be unrealstc. Moreover, te assumpton of dagonal Q s also unrealstc. Ts assumpton mples tat tere s no substtuton or complementarty cost effects between producton actvtes carred out n te same regon or farm. However, te practce of rotatons n crop producton, for example, ndcates tat farmers are well aware of te nterdependences among crops and use tem to stablze or ncrease profts. 36

37 To overcome tese problems; Pars and Howtt (1998) suggest usng multple observatons and more robust estmaton metods suc as Maxmum Entropy. Multple observatons mean ere several years of data (tme seres) or several farm data pooled togeter. However, PMP wt large farm-level datasets but wtout any estmaton procedure wll only mae use of a sngle data pont and mposes consderable structure on te tecnology as emboded n te cost functon (Henry de Fraan, 2005). Inversely, PMP wt sngle data and wt estmaton procedure wll leads to same results as standard PMP approac PMP approaces based on multple observaton In order to reduce te arbtrary beavour of te model and to estmate more relable cost functons coverng all te parameters, several economsts ave suggested usng large number of observatons wt an estmaton procedure. A lterature revew sows te exstng of a set of PMP approaces wc use estmaton procedure and cross-sectonal data for te specfcaton of non-lnear cost functons. Foremost among tese approaces are: () te Maxmum entropy (ME)-PMP approac proposed by Pars and Howtt (1998) and extended by Hecele and Brtz (2000); and () te Maxmum entropy (ME)-PMP approac suggested by Pars and Arfn (2000). Bot approaces are PMP based because tey use te frst step were te sadow values are derved from a lnear programmng model. Tese two approaces are mplemented n FSSIM-Dev and can be used wen consderng several years of data or wen te data on several farms can be pooled togeter. Te Maxmum Entropy-PMP approac by Hecele and Brtz (2000) To recover all te parameters of te cost functon and to capture te possble nteractons among te varous actvtes, Pars and Howtt (1998) suggested usng te Maxmum Entropy (ME) crteron 10. ME approac as been manly used to overcome two emprcal problems tat amper tradtonal econometrcs for parameter estmaton: mult-collnearty and ll-posed problems (.e. wen te number of parameters to estmate s greater tan te number of observatons). Ts approac allows emprcal specfcaton and estmaton of underdetermned models as well as ncluson of pror nowledge n a tecncally stragtforward way, mang estmates potentally more effcent (Jansson, 2007). Moreover, t as te potental of ncorporatng more tan one observaton on actvty levels nto te specfcaton of te parameters. Te prncpal lmtaton of Maxmum Entropy s te defnton of support ponts for te unnown parameters (.e. d and Q). Pars and Howtt (1998) ave demonstrated ow to recover cost functons from very lmted data sets usng a ME estmator. Tey re-parameterze te Q matrx based on LDL (Colesy) decomposton to ensure approprate curvature propertes of te estmated cost functons. Ts estmator n combnaton wt PMP enables to calbrate a quadratc varable cost functon accommodatng complementartes and compettveness among actvtes stll based on a sngle observaton but usng a pror nformaton on support bounds. However, accordng to Hecele and Brtz (2000), usng maxmum entropy wt only one observaton s not an 10 Te defnton of entropy as nformaton measure s due to Sannon (1948) and after Janes (1957) ntroduces te maxmum entropy prncple n order to obtan probablty dstrbuton tat s consstent wt te avalable nformaton (cted by Golan et al. 1996). 37

38 mprovement over te standard PMP approac. Tat s, wtout any addtonal nformaton suc as a full matrx of supply elastctes, te beavoural smulaton of te resultng calbrated model would be stll arbtrary because eavly domnated by te support ponts for te unnown parameters. Tey propose a ME extenson wc maes t possble to use more tan one cross sectonal framewor and to defne pror drectly on Q (and not on te elements of a LDL decomposton of Q as proposed by Pars and Howtt 1998). Tey obtan a greater successful ex-post valdaton tan usng te standard "sngle observaton" maxmum entropy approac. Ts extenson was used to calbrate te cost functons of te regonal actvty supples of te Common Agrcultural Polcy Regonal Impact (CAPRI) modellng system (Hecele and Brtz, 2001). Te adaptaton of ts approac to farm model suc as FSSIM-Dev can be descrbed as follow: Te margnal cost of te f-t farm can be represented by te followng equaton: MC 0 0 = d f + Qf x f = λ f + cf x 0 (22) f > Were Q f represents a (n n) matrx of te quadratc part of te actvtes mplct cost functon n farm f and t s equal to: g ' Q f = ( cp f ) S f BS f (23) were cp f stands for a farm crop proftablty ndex defned as te relaton between te cp ( ' / ) /( ' / ) farm and average regonal revenue per ectare f = p y f L f p y f L f were p denotes te (n 1) vector of prce, y represents te (n 1) vector of crop yeld n farm f, and L s te total arable land n farm f. Te parameter g s te exponent of crop proftablty ndex to be estmated, S f consttutes te (n n) dagonal scalng matrces for eac farm f, and t s gven 0 by S f,, = 1/ x f,, and fnally B s a (n n) parameter matrx related to Q f. Te matrx B- common across farms nsde te same regon s estmated as to descrbe te dfferences n margnal costs dependng on te dfferences n levels (Hecele and Brtz, 2000). To stress te effect of scalng mecansm, Hecele and Brtz (2000) gve an example for two farms wt dentcal total area but dfferent sares of crop land. Accordng to te example, assume tat tere s 10 a ncrease n te acreage of a crop. If te total acreage of ts crop n farm one s 1 a, and 100 ectares n farm two pror to te cange of te acreage, ten 10 ectare ncrease n te acreage of ts crop would mply 1000 percent relatve ncrease for te frst farm but only 10 percent for te second farm. Hence, te scalng of B matrx assures te same margnal cost ncreases n bot farms for te same percentage ncrease n crop acreage. Usng ts scalng mecansm t s possble to tae nto account ts dfference n te calculaton of margnal costs dependng on te dfferences n crop acreage for dfferent farms. Te general formulaton of te correspondng ME problem s as follows: max H ( pd, pb, pg) = f pg pd pb ln pg f ln pd ln pb f f f (24) 38

39 Subect to: Data-consstency constrants λ f d b f g 0 + c = d + ( cp ) s b s x f,,, x f = pd f f zd f f f, = pb zb, g = pg zg Addng-up or normalzaton constrants pb pd f = 1, = 1 f, pg = 1 Curvature restrctons b = b, f f 0 > 0 l l = b l b = l l 2 Non-negatvty condtons l 0 ; pb l, and > 0; pd f 0 ; pg 0 were and ndex crop actvtes, d f s a (n 1) vector of te lnear part of te actvtes mplct cost functon n farm f, Q f represents a (n n) matrx of te quadratc part of te actvtes mplct cost functon n farm f, S f consttutes te (n n) dagonal scalng matrces for eac farm f, and fnally B s a (n n) parameter matrx related to Q f. zd, zb and zg are te support ponts for te unnown parameters d, te exponent of crop proftablty ndex g and te matrx B, respectvely. Te tree equatons (18.8; 18.9; 18.10) are nown as curvature restrctons and tey result from a classc Colesy decomposton of te form B = L.L. Tey are ncluded n order to guarantee tat a postve (sem) defnte matrx B and consequently postve (sem) defnte matrces Q wll be recovered. A volated curvature property mgt result n a specfcaton of te obectve functon tat does not calbrate to te base year, snce n ts case only frst order but not second order condtons for a maxmum are satsfed at te observed actvty levels (Hecele and Brtz, 2000). 39

40 To defne te number of support ponts for te unnown parameters d and Q, ter centre (.e. a pror expectaton), ter bounds and ter spacng, Hecele and Brtz (2000) ave taen te followng assumpton: for te lnear terms d, 5 support ponts (.e., K=5) are also cosen, centred around te observed costs and ranged between ± 90 tmes te regonal average n revenue per ectare: zd = c + ( 90, 30, 0, + 30, + 90 ) p'y / f f L f f p denotes te (n 1) vector of prce, y represents te (n 1) vector of crop yeld n farm f, and L s te total arable land n farm f. for te B matrx, te support values are suggested to be defned as follows: zb Were zbs amc = zbs amc ( ,., 0, , ) = = ( 2, 2/ 3, 0, + 2/ 3, + 2 ) = (MC + MC ) 1 2 MC represents te land wegted average of margnal cost for actvty across farms. As explaned above, ts approac allows estmatng a full matrx for all te observed actvtes n eac farm type, but not for te unobserved (not produced) actvtes: ts s te self-selecton problem mentoned prevously. Ts leads to two furter problems. Frst, te cost functon must accommodate true zeroes. Second, t s necessary for smulaton tat te parameters of te cost functon are estmated for all te outputs for all te farms n te sample. Te Maxmum Entropy-PMP approac by Pars and Arfn (2000) Pars and Arfn (2000) ave proposed an extended PMP approac wc s very useful for calbratng farm model based on cross-sectonal data. Tey use Maxmum Entropy (t sould be consdered tat Ordnary Least Squares (OLS) metod can also be used for suc estmaton) for estmatng an overall cost functon assocated wt a wole Tecncal Economc Orentaton (fronter cost); eac farm n te sample terefore beng caracterzed by te same cost functon and a u errors vector able to reflect ts dstance from te cost fronter. Ts means tat te sample farms are assumed to operate under a common tecnology. Te data-consstency constrants of ts ME-PMP model can be specfed as follows: λ + c = Qx λf + c f = Qx f + u f x > λf + c f Qx f + u f x = 0 Q = LL' 40

41 u f f = 0 Were ( λ + c ) s te vector of margnal cost for te entre sample n eac regon, u s te error term representng devatons from te cost fronter, x 0 s te observed producton levels, and Q f represents te (n n) symmetrc and postve matrx wc s common across farms nsde te same regon. Te two last equatons respectvely requre () te necessary condtons for te Colesy factorzaton; and () te sum of te u errors s equal to zero. Wt ts approac te total varable cost for te f-t farm s stated as c ( x f ) = u' f x f + 0.5x' f Qx f, wle te correspondng cost functon for te entre sample n eac regon s c ( x) = 0.5x' Qx. Te estmated fronter cost functon wll allows overcomng te self-selecton problem (.e. unobserved actvtes) durng te smulaton step. Dscusson on PMP wt multple data ponts Te use of multple cross-sectonal observatons n combnaton wt PMP and an estmaton procedure consttutes one of te successful PMP extensons. In fact, tese approaces allow overcomng most of te well nown orgnal PMP problems suc as te unequal treatment of te margnal and preferable actvtes, te self-selecton problem, te under-determned system, te lac of cross-actvty relatonsps. However, Hecele and Wolff (2003) ave explaned tat PMP s not well suted to te estmaton of programmng models tat use multple cross-sectonal or cronologcal observatons. Tey sow tat te sadow prces of resource constrants derved from te lnear model s expected to be dfferent from te one mpled by te non-lnear model wc s assumed to represent farmer beavour. Tey argue tat te second stage of te standard PMP uses tese apparently wrong values at te observed actvty levels troug enforcement of te margnal cost equatons, tereby mplctly mposng based values for te estmaton of te margnal cost as well. To avod nconsstency between steps 1 and 3, tey suggest to sp te frst step altogeter and employ drectly te optmalty condtons of te desred programmng model to estmate smultaneously sadow prces and parameters wtout usng dual values on calbraton constrants. Ter examples deal wt te estmaton of te parameters of varous optmsaton models tat (1) ncorporate a quadratc cost functon and only one constrant on land avalablty, (2) allocate varable and fxed nputs to producton actvtes represented by actvty-specfc producton functons or (3) allocate fxed nputs to producton actvtes represented by actvty-specfc proft functons (Henry de Fraan, 2005). As stated by ter autors, ts alternatve approac to PMP as some teoretcal advantage over te orgnal PMP for te estmaton of programmng models. It also as some emprcal advantage over standard econometrc procedures for te estmaton of more complex models caracterzed by more flexble functonal forms and more constrants as well as te ncorporaton of addtonal constrants relevant for smulaton purpose. Despte ts attractveness, ts approac as been rarely appled to polcy analyss, manly because of data avalablty and numercal solvng problems. Te only applcaton at farm level was done at te best of our nowledge by Buysse et al. (2007a) to analyse te reform of te Common Maret Organzaton n te EU s sugar sector. Tey use a sample of 117 Belgan sugar beet farms across 9 years to estmate parameters of a cost functon quadratc n actvty 41

42 levels by employng GME on te frst order condtons of te farm programmng models. Jansson and Hecele (2011) ave developed recently a larger applcaton but worng at regonal and not at farm level. Tey estmate te parameters of 217 regonal programmng models wt 23 crop producton actvtes for te EU usng Bayesan gest posteror densty estmator. However, n tese two applcatons and n order to render te estmaton exercse feasble, autors assume tat constrants are bndng for all observatons Concluson Ts secton descrbed te set of calbraton approaces tat are mplemented n FSSIM-Dev to be selected by user accordng to data avalablty and modeller convcton on rs/pmp approaces. Some of tese approaces are complementary and oters are rater substtutable. All te mplemented PMP approaces and varants guarantee exact calbraton of supply decson at farm and aggregated levels tang nto account te trade of factors among farms. Neverteless, dfferent approac can produce dfferent results wen tey are used to predct te future beavour of te farmer. To assst user on selectng te sutable approac for s/er specfc context, we suggest, frst, to cec te avalable dataset (sngle or multple data; exsts or not pror nformaton; observed data s specfed by products or by agrcultural actvtes...) and, ten, to select te correspondng one accordng to te descrpton gven above. In te case wen dfferent approaces can be appled to te same data set, te only soluton to select te more effcent one wll be troug an ex-post valdaton or a senstvty analyss. Along ts same lne, a senstvty analyss procedure was mplemented wtn FSSIM-Dev to be used for comparng model beavour under dfferent calbraton approaces. In ts senstvty analyss, we run some smulatons based on 10 % ncreases of sngle product prces and we calculate te percentage cange n supply related to ts prce cange. Te estmated pont elastctes can, ten, be compared wt comparable estmatons from lterature Investment and perennal actvtes module Ts secton presents te modellng approac for perennal actvtes and nvestments n te FSSIM-Dev model. Te approac bulds on te general teory of agent beavour, namely tat farmers adust to a rsy and cangng envronment mang sequental decsons at several tmescales: a) Intra-seasonal decsons on nput use; b) Yearly decsons on cropland allocaton to annual crops; and c) Long-run decsons on perennal crops, macnery and oter nvestments. Modellng farmers beavour requres tang nto account te nterdependences between producton and nvestment decsons on one and and te dynamc structure of te producton process on te oter and. Ideally, tese nvestment and dynamc decsons sould be modelled dynamcally. Neverteless, snce FSSIM-Dev s a comparatve statc model, we ave cosen to frame te dynamc decson ssues nto a statc modellng framewor. Te only dfference compared to an approac usng a dynamc model performng nter-temporal optmsaton s tat te results do not sow te pat of development n tme, but only te ntal and te fnal stuaton. However, gven te g level of uncertanty assocated to eac of te steps represented n dynamc models, te complexty of a model performng ntertemporal optmsaton s rarely ustfed n practcal terms. 42

43 Some dynamc features can be easly ncorporated n a statc framewor. Ts s te case wen adustment costs are relatvely low. Assumng tat farmers can yearly adust producton decsons wtout ncurrng addtonal maor costs, tese decsons can be modelled as essentally statc decsons. Followng ts reasonng, a steady state approac as been adopted for modellng crop rotatons and lvestoc actvtes n FSSIM. On te contrary, n te case of frut producton, adustment of land-use decsons nvolves rater g costs. Perennal producton s a dynamc process, caractersed by sgnfcant establsment costs, long gestaton perods and nterrelated producton and nvestment decsons. Moreover, nvestment n tree-crops entals g sun costs, as te resale prce of te plantaton s close to zero. Because of te g sun costs assocated to tree-crop nvestments, current decsons are largely nfluenced by past decsons and ave effects on future ones. Gven te relatvely long lfetme of perennal crops, adoptng a steady state approac wll gve te response of perennal producers to canges n ncentves n te longrun. As a result, dfferent approaces may be used to model tree-crop actvtes dependng on te orzon of te analyss: In te sort term, we assume a constant area for perennal crops, tat s, no land competton between annual and perennal crops are depcted. In te long term, we adopt a steady state approac, allowng for adustments n te area allocated to perennal actvtes and, terefore, modellng te competton for land between annual and perennal crops. In te medum term, an nnovatve modellng approac s used to tae nto consderaton sun cost and adustment cost effects of nvestment decsons. Ts modellng framewor provdes te md-term response of perennal producers to canges n ncentves. Bascally, we mae a dstncton between te exstng stoc of perennal crops and new plantatons n terms of nput-output coeffcents. Hereafter a bref revew of te economc lterature on nvestment modellng s gven, wt a focus on farmer nvestment beavour and tree-crop nvestments. Next, te suggested approaces to model perennal actvtes n te sort, medum and long run are presented. Fnally, te perennal module as t s ntegrated n FSSIM-Dev s descrbed Revew of nvestment modellng approaces Modellng farmer nvestment beavour Contrbutons to modellng nvestment beavour are modest compared to tose n oter areas of farm beavour. Te underlyng prncple of te optmal nter-temporal nvestment teory s te adustment cost ypotess wc s based on te premse tat, n te sort run, decson maers ncur costs n adustng to canges n economc condtons (Arrow, 1982). Te adustment problem faced by farmers s often attrbuted to asset fxty n agrculture, wc s defned n terms of te dvergence between acquston prce and salvage value of durable assets (Hsu and Cang, 1990). A number of wors recognze te mportance of accountng for adustment costs n modellng nvestment decsons (Lucas, 1967; Epsten, 1981). Apart from asset fxty and adustment costs, te lterature on nvestment beavour as focused on related topcs, suc as rs and uncertanty, tecncal cange and mperfect credt marets. 43

44 Investment beavour under rreversblty and uncertanty as receved muc attenton n recent economc lterature (Dxt and Pndyc, 1994; Abel and Eberly, 1994). As stated by Dxt and Pndyc (1994), a decson to nvest made now wll mply canges n te set of future potental decsons. Tey ntroduce nto te analyss te opton to postpone decsons untl more nformaton s avalable. Ts nvestment teory focuses n te value of watng for more nformaton and te resultng delay n nvestments, wc can be especally mportant for rreversble decsons. Irreversblty s often ted up wt te concept of sun costs, wc by defnton are non recoverable. Irreversblty occurs wen tecnology for replacng an nvestment does not ext (tecncal rreversblty) or wen reversblty s tecncally feasble but at a so g cost tat becomes economcally non-optmal (economc rreversblty). In te case of perennal producton, economc rreversblty appens because restoraton costs are very g (reversng land to te orgnal pre-plantng stuaton n order to allow for canges n land use s very costly). Oter approaces ave also been used to analyse nvestment decsons under rreversblty. Abel and Eberly (1994) ntroduce an augmented adustment-cost functon tat ncludes tradtonal convex adustment costs, as well as te possblty of sun costs. Cavas (1994) examne producton and nvestment decsons under sun costs and temporal uncertanty. Te model ntegrates producton and nvestment decsons wt entry-ext decsons. Most of above mentoned studes use econometrc approaces. Regardng matematcal programmng approaces, regonal and sector models often neglect nvestment decsons. Most regonal and sector models adopt a statc representaton of output supply, adoptng steady-state specfcatons for lvestoc, perennal crops and oter nvestment decsons. One of te few exceptons s te wor from Alg et al. (1998), wo buld a dynamc sector model lnng te agrcultural and forestry sectors n te Unted States. At te farm level, owever, many dynamc optmsaton models ave been bult. Dynamc optmsaton tecnques are often used to analyse farm nvestment decsons suc as macnery replacement, lvestoc actvtes or nvestment on rrgaton tecnology (Abalu, 1975; Kennedy, 1986; Hu et al., 1992) Tree-crop modellng n te economc lterature Economc models addressng decsons on te mx of annual and perennal crops are scarce n te economc lterature. Most studes on tree-crop modellng deal only wt perennal crops. Te producton of perennal crops nvolves tme dmensons not smlarly found n annual crops. Among te specfctes of perennal crop producton, we glgt: 1) a long bologcal lag between plantng and frst bearng; and 2) an uneven dstrbuton of yelds over te lfe of te tree, usually ncreasng untl reacng maturaton, rater stll durng te full producton perod and decreasng afterwards. Most researc on perennal crop supply s related to ndvdual perennal crops. Early studes eter rely on tme seres data to estmate beavoural functons (Frenc and Mattews, 1971) or on lnear programmng tecnques (Dean and De Benedcts, 1964). Startng from te wor of Frenc and Matews (1971), a number of papers analyse te supply response of perennal crops tang nto account plantng and removal relatonsps (Frenc et 44

45 al., 1985; Ayama and Trved, 1987; Hartley et al., 1987). Most of tese studes focus on te nfluence of maret condtons and prce antcpatons on perennal crop supply response but fal to consder te nvestment nature of perennal producton decsons. However, tree plantng decsons are nerently dynamc processes. Terefore, tey are better vewed from an nvestment perspectve. Unle barley or oter annual crops, tree-crops requre sgnfcant establsment costs and ave long gestaton perods. Wle farmers can replace annual crops costly from one year to te oter, removng and replacng tree-crops wt oter crops requre a sgnfcant amount of resources and effort. In sort, tree-crops are farly rreversble nvestments. Ts fact as mportant mplcatons for farm cropland allocaton, snce t reduces farm flexblty to react to cangng tecnologcal, economc or nsttutonal condtons. Several autors adopt te Dxt and Pndyc framewor for assessng nvestment decsons n perennal crops (Prce and Wetzsten, 1999). Te real opton value can be dentfed eter by dynamc programmng or contngent clam analyss. Most applcatons of ts approac use te contngent clam analyss because t does not requre te nowledge of rs and tme preferences of producers. Prce and Wetzsten (1999) use an rreversble sun-cost nvestment model for analysng optmal entry and ext tresolds for peac producton. Wle some dynamc optmsaton models deal wt perennal crops, tey usually do not model land allocaton mecansms between annual and perennal crops. For nstance, Teague and Lee (1988) developed a lnear programmng model for analysng perennal crop decsons under dfferent captal constrants. Attempts to model te mx of annual and perennal crops are very rare n te economc lterature. At te farm level, we can menton te two-stage stocastc model developed by Marques et al. (2005). Tey consder a frst stage for perennal decsons and a second stage for annual decsons. However, n ts model, only long-run perennal decsons are modelled and ten, te model gves te optmal long-run soluton. In summary, a number of studes analyse n great detal perennal producton decsons, usng eter econometrc or optmsaton tecnques, but ntegrated models tat tae nto account te nterdependence between perennal and oter land-use optons are stll lacng Modellng te mx of annual and perennal crops Te perennal module n FSSIM-Dev We wll use a stylzed matematcal notaton to llustrate te approac selected to model perennal actvtes n FSSIM-Dev. Assume tat we want to model te mx of annual and perennal actvtes n a ont matematcal programmng framewor. Denote te set of annual actvtes and te set of perennal actvtes, te matematcal programmng model can be formulated: 45

46 Max s. t. Z = X a r X + + X X + a X 0 ; X 0 l r X X b (obectve functon) (land (oter constrant) constrants) (non - negatvty condtons) were X X r r l b a a area allocated to annual crop area allocated to perennal crop net return assocated to annual crop net return assocated to perennal crop total land avalablty avalablty of resource nput requrements assocated to annual crop nput requrements assocated to perennal crop Te maor factors affectng te desred level of producton of eac commodty are expected proftablty of ts commodty and expected proftablty of alternatve land uses. Gven tat some farm resources are lmted, decsons on annual and permanent crops are made ontly. Te modellng approac wll depend on te tme frame of te analyss Sort-term modellng of tree-crop actvtes In te sort-run, land allocated to perennal actvtes would be fxed to te exstng capacty n te base year: X = X 0 Gven te age structure of te exstng plantaton, costs and revenues assocated to eac perennal actvty can be calculated. In te sort-run, gven tat te tree-crop area s fxed, te supply response of perennals s qute small. However, farmers usually adust crop producton dependng on economc and nsttutonal condtons. If we want to tae nto account te possblty of adustng decsons on permanent crops, we need to consder not only sort-term decsons but also long-run decsons Long-term modellng of tree-crop actvtes In te long-run, decsons on te mx of annual and perennal crops can be smulated n a mult perod framewor. In tat case, understandng plantng and replantng decsons over tme s mportant to model te long-run response of perennal producers. In a mult perod framewor, decsons on new plantng and replantng are modelled n an explct way. Te model gves te optmal adustment pat troug tme and tere s no need to assume tat te stoc of trees wll ever attan equlbrum. Neverteless, under some specfc condtons, a steady-state soluton could be reaced after some tme. Once te steady-state soluton as been reaced, te optmal cropland allocaton 46

47 does not cange over tme and ten te replacement rate remans constant. As a result, n te long-run, land allocated to perennal actvtes would be fxed to te steady-state stuaton: X = X s Assumng tat te steady-state soluton could be reaced n a reasonable tme frame, te perennal actvtes can be accommodated n a statc framewor n a smlar way to annual actvtes, by consderng annualzed costs and revenues. Tree-crops do not yeld a unform stream of output over ter economc lfetme, nor do tey ave unform nput requrements. Terefore, proftablty of tree-crops sould be measured troug te net present value. Denotng n te economc lfespan of te t tree-crop, t te age of te tree-crop, d te dscount factor, R,t te revenue assocated to te t tree-crop wt age t and C,t te assocated cost, te net present value s gven by: NPV n = t = 1 d t 1 ( R, t C, t ) Te net present value gves an ndcaton about te proftablty of te tree-crop actvty troug ts lfetme. In order to compare actvtes wt unequal lfespan, te equvalent annual value (EAV) sould be used: EAV = NPV n t = 1 d t 1 = ρ NPV ρ 1 = n 1 d 1 d n wen wen d = 1 0 < d < 1 Te equvalent annual value can be used for bot annual and perennal actvtes. In ts way, we can model tree-crop actvtes and annual actvtes n a ont framewor. Te resultng model wll gve te long-run response. Te steady-state soluton gves te optmal stoc of productve trees, tang nto account tat for eac ectare of productve trees tere wll be a percentage of young non-bearng trees necessary to mantan te productve stoc troug tme. However, gven te extent of te gestaton perod and te long lfespan of most perennal crops, reacng te steady-state would tae a rater long tme. As a result, for most modellng purposes, te steady-state soluton mgt be unrealstc Medum-term modellng of tree-crop actvtes In te case we are nterested n modellng tree-crop actvtes n te medum term, prevous approaces mgt not be satsfactory: sort-run approaces do not allow for any flexblty n tree-crop decsons and long-run approaces mgt be too unrealstc. Loong for an ntermedate soluton, we suggest modfyng te steady-state approac to allow tang nto consderaton possble effects lned to te rreversble nature of nvestment decsons. Tree-crop plantng decsons are typcal long-run decsons, but farmers can adust prevous nvestment decsons on a yearly bass. Terefore, te dea s to consder te potental adustment decsons tat farmers can coose for perennal actvtes at any gven moment of tme. Assumng ntal steady-state equlbrum, tese decsons can be summed up n te followng four adustment strateges: 47

48 Mantenance of te exstng plantaton: te ntal steady-state stuaton s ept by replacng old trees (replacement rate fxed to te ntal steady-state rate). Declne of te plantaton area: exstng trees are ept untl te end of te lfespan but old trees are not replaced (zero replacement rate), so plantaton area wll gradually decrease troug tme. Removal of te plantaton area: some trees are removed before te end of ter economc lfetme n order to allocate land to oter actvtes. Hence plantaton area wll abruptly decrease. Growt of te plantaton area: plantng new trees wll ncrease te area allocated to perennal crops (a steady-state stuaton s assumed for new plantngs). Te farmer can coose a combnaton of tese four strateges, tat s, e/se can for nstance decde to replace old trees n a sare of te ntal plantaton area and not to replace n te rest. As a result, bot te total land allocated to te perennal crop and te age structure of te plantaton can cange over tme. Ts modellng framewor would enable to tae nto consderaton sun cost and adustment cost effects of nvestment decsons, allowng for more flexblty n tree-crop related decsons and provdng a md-term response of perennal producers to canges n ncentves. One way to ncorporate tese adustment optons n a statc model would be to defne a mxed perennal actvty" from tese four adustment strateges (actvtes n te perennal module). Ts mxed perennal actvty" (X ) defned (by te model) as a perennal crop wt a gven age structure can enter te model n te same way as for annual actvtes. Denotng rr te replacement rate and δ te deprecaton rate, and n te economc lfespan of te t tree-crop, t follows tat te steady-state rate wll be: rr s = δ = 1 n L beng te tme lag between te base year and te baselne n te model, te followng addtonal equatons are needed to account for tese four adustment strateges: Equaton (1) X = x 0 + X g X r Equaton (2) X m + X d + X r = x 0 Equaton (3) X r d = X L δ were X x 0 X m X d X r X g total area allocated to tree-crop exstng plantaton area n te base year area of te plantaton mantenance actvty area of te plantaton declne actvty area of te plantaton removal actvty area of te plantaton growt actvty 48

49 Equaton (1) ponts out tat te area allocated to perennal crops can be mantaned (constant replacement rate), can decrease because old trees are not replaced (replacement rate equals zero), or can ncrease because new trees are planted (reducng te area allocated to annual crops). As for equaton (2), t specfes te relatonsp between ntal area and area ept n te baselne. Equaton (3) depcts tree removal wen old trees are not replaced. Input-output coeffcents for eac perennal actvty (plantaton mantenance, plantaton declne, plantaton growt and plantaton removal) can be determned tang nto account te correspondng age structure of te actvty. For nstance, denotng by a t te requrement of nput for tree-crop of age t, te nput requrements for te "mantenance actvty" (a m ) wll be: a m, 1 = n n t= 1 a m,, t Gven tat te age structure of te "mxed perennal actvty" can cange over tme, te resultng nput-output coeffcents wll depend upon te adustment decsons taen. Te equvalent annual value (EAV) can be formulated as te dfference between te equvalent annual revenue (EAR) and te equvalent annual cost (EAC). Accountng for te equvalent annual revenue (EAR) and te equvalent annual cost (EAC) for eac perennal actvty (plantaton mantenance, plantaton declne, plantaton growt and plantaton removal) also requres tang nto consderaton te correspondng age structure of te actvty. Cost and revenue calculaton for plantaton mantenance (superscrpt m) EAC m = n m t 1 m m m ρ d C, t ; EAR = ρ t = 1 n t = 1 d t 1 R m, t Smlarly, for plantaton declne (superscrpt d) n 1 n d d t 1 d EAC = ρ d C, t ; 1 d d EAR = ρ t= 1 t= 1 d t 1 R d, t Plantaton growt (superscrpt g) EAC g = n g t 1 g g g ρ d C, t ; EAR = ρ t = 1 t= 1 n d t 1 R g, t Plantaton removal (superscrpt r) r r EAC = C, t 49

50 Te equvalent annual factors can be expressed: m g 1 ρ = ρ = ; n ρ t 1 d t= 1 t= 1 1 d = n 1 d t 1 Ts modellng framewor enables mang a dstncton between exstng tree stoc and new plantatons. In a ypotetcal stuaton were new economc condtons do not favour frut producton, farmers could decde to eep exstng trees untl te end of ter economc lfe wtout replacng tem afterwards. In a frst moment, mantenance of te exstng plantaton and new plantaton are treated n a smlar way; tat s, assumng a steady-state stuaton. As a result, nput-output coeffcents wll be te same for bot actvtes. Neverteless, we can also ncorporate rsng costs of adustment related to new plantngs Module for perennal actvtes Integratng annual and perennal crops n FSSIM-Dev Bascally, FSSIM-Dev s a statc farm model. However, t can ncorporate some dynamc features, suc as crop rotatons or lvestoc actvtes. In order to ntegrate nter-temporal effects, agrcultural actvtes are defned under crop rotatons and dressed anmal nstead of ndvdual crops and anmals. Even toug decsons on crop rotatons can be vewed as mult-year decsons, t seems reasonable to assume tat farmers can swtc from one rotaton to anoter wtout g adustment costs. Unle crop rotatons, cangng decsons on perennal actvtes from year to year mples g adustment costs. Decsons on perennal actvtes are better vewed as long-run nvestment decsons nvolvng sun costs and terefore requre a dfferent modellng approac. Te obectve of te perennals component s to provde a modellng framewor to represent perennal actvtes and ter lnages wt annual crops. Lacng nformaton on current age structure of perennal crops, we wll assume a steadystate stuaton n te base year. Tat means, for eac ectare of a perennal crop wose lfespan s n years, we consder tat tere are 1/n ectares of trees planted n te current perod, 1/n ectares of one-year old trees, 1/n ectares of two-year old trees, and so on. Faced to canges n te tecnologcal, economc or nsttutonal settng, te farmer wll decde eter to mantan te ntal plantaton or to cange te area allocated to te perennal crop. In order to model tree-crop decsons n a statc framewor, we dstngus four adustment strateges: Mantenance of te exstng plantaton: te ntal steady-state stuaton s ept by replacng old trees. Te replacement rate wll terefore be equal to te pyscal deprecaton rate (1/n, beng n te lfespan of te plantaton). 50

51 Declne of te plantaton area: exstng trees are ept untl te end of ter economc lfetme but old trees are not replaced. In ts case, te replacement rate wll be zero and, terefore, te plantaton area wll decrease by te pyscal deprecaton rate eac year. Removal of te plantaton area: some trees are removed before te end of ter economc lfetme n order to allocate land to oter actvtes. Hence plantaton area wll decrease. Growt of te plantaton area: plantng new trees wll ncrease te area allocated to te perennal crop. As te farmer can coose a combnaton of tese four strateges, all possble tree-croppng decsons can be modelled by means of tese four adustment strateges. Eac "mxed perennal actvty" s ten bult as a combnaton of tese four actvtes. Once tese mxed perennal actvtes ave been defned, tey can enter te FSSIM-Dev model n te same way as for annual actvtes. Te perennal module wll conssts of defnng tese mxed perennal actvtes for eac perennal crop as well as assessng te assocated nput-output coeffcents Defnton of perennal actvtes Croppng actvtes are defned as a combnaton of crop rotaton (r), agr-envronmental zone (s), producton tecnque (t) and producton system (sys). A dstncton s made between current actvtes, or actvtes currently practced n te regon under consderaton, and alternatve actvtes, or actvtes unobserved n te baseyear but tat mgt be practced n te future. For perennal crops, actvtes are defned as mono-crop rotatons. For smplcty, we wll denote an annual actvty as a crop rotaton () n an agr-envronmental zone (s). Smlarly, a perennal actvty wll be defned as a perennal crop wt a gven age structure () n an agr-envronmental zone (s). Two alternatve ways to andle perennal actvtes can be suggested: Opton 1. For eac perennal crop, we defne four perennal actvtes (steady-state plantaton, non-replaced plantaton, new plantaton and plantaton removal). Opton 2. For eac perennal crop, we defne a "mxed perennal actvty", bult upon te four potental strateges. Once te mxed perennal actvtes ave been defned, tey can be ncorporated n te FSSIM-Dev model n te same way as for annual actvtes. Wtn bot optons, some prevous calculatons are needed n order to obtan te nput-output coeffcents for eac perennal actvty. Opton 1 mgt seem preferable because te adustment strateges reman vsble and te nput-output coeffcents assocated to eac actvty reman exogenous. However, gven tat we defne four nterrelated actvtes for eac perennal crop, we wll need to specfy te lnages between tese actvtes by means of addtonal equatons. In any case, bot optons are equvalent so te coce between tem wll be based on computatonal ease and ntegraton wtn te FSSIM-Dev framewor. Regardless of te opton fnally cosen, te ntegraton of perennal actvtes wll affect some equatons n te FSSIM-Dev model, manly te equatons on cropland allocaton, wc are defned by agr-envronmental zone. Bascally, we need addtonal equatons to defne te 51

52 lnages between te assumed potental strateges and between perennal and annual crop actvtes. Beng X,s and X,s te land allocated to te annual actvty and te perennal actvty n agr-envronmental zone s, and denotng Tland s te total avalable land n agr-envronmental zone s, te land constrant can be formulated: X,, s + X s Tlands (25) Te defnton of te mxed perennal actvtes X,s depends on te tmeframe of te analyss. In te sort-run, we wll assume a fxed area for perennal actvtes: Sort-run X = x (26) 0, s, s In te long-run, te area of tree-crops can be adusted and steady-state equlbrum could be reaced: Long-run m X s X, s, = (27) In te medum-run, te area of tree-crops wll depend bot on te ntal plantaton level and on te strategc decsons taen. Assumng a constant deprecaton rate δ for eac perennal actvty (1/n ), and denotng x 0 s te observed level of te perennal actvty n te base year per sol type (n a), and l te tme lag between te base year and te baselne n te model, te followng equatons must old: X d, s (1 δ ) l + X m, s = x 0, s X r, s m d g r X, s = X, s + X, s + X, s X, s X, s 0 g r = x, s + X, s X, s (28) X m, s + X d, s + X r, s = x 0, s X r, s d = X L δ, s were X,s x 0,s X m,s X d,s X r,s total area allocated to tree-crop n agr-envronmental zone s exstng plantaton area n te baseyear area of te plantaton mantenance actvty area of te plantaton declne actvty area of te plantaton removal actvty 52

53 X g,s area of te plantaton growt actvty In ts way, a dfference s made between exstng plantatons and new ones. Wen decdng about new plantngs, te farmer wll compare total proftablty of tese actvty (ncludng establsment costs) wt tose of oter land-use optons. On te contrary, for exstng plantatons, te farmer wll contnue producton as long as full-producton proftablty (excludng establsment costs) wll be greater for ts actvty tan for oter optons Input-output coeffcents for perennal actvtes As we ave ust seen, perennal actvtes n FSSIM-Dev are defned as mono-crop rotatons. For eac sample regon, data for current perennal actvtes come from two man data sources: local-expert surveys and oter studes. For annual croppng actvtes, data collecton and estmaton of nput-output coeffcents are based on averaged yearly values over a reference perod. On te contrary, n te case of perennal actvtes, nput-output coeffcents depend on te age of te tree-crop and terefore average values for te plantaton wll depend on ts age structure. Data on observed plantaton area n te base year s easly avalable but wtout tang nto account te age-dependence of tese varables. As nformaton on te age structure of treecrops s not provded, we wll assume an ntal steady-state stuaton. We can llustrate ts wt an example. Imagne tat we try to represent a peac-tree plantaton wose lfespan s 15 years. A steady-state stuaton mples 1/15 sare of one-year old trees, 1/15 sare of two-year old trees and so on. Crop management data as well as economc data come from te feld surveys. Input-output coeffcents for perennal crops are age-specfc. However, gvng te dffculty to obtan detaled management and economc data troug te lfetme of te plantaton, a decson as been made to smplfy data collecton by dstngusng tree age-classes for eac perennal crop. Beng t te age of te tree-crop, p te number of years needed to reac maturty and n te economc lfetme of te perennal crop, we dfferentate tree age-classes te, tg and tp: (20) establsment perod: from plantng to frst bearng (te :{1,...,g-1}) (21) growt perod or maturaton perod: from frst bearng to full producton (tg :{g,...,p- 1}) (22) productve perod: full producton (tp :{p,...d}) (23) declne perod: declnng producton (td :{d,...n}) Input-output coeffcents are assumed constant nsde eac age-class. Table 1 sows man survey data for eac perennal crop. Apart from revenue and cost data, nput requrements for eac age-class and nput category (fertlsers, pestcdes, water, labour, etc.) are defned. Bascally, nput-output data for perennal crops do not dffer from tose of annual crops, apart from te fact tat tey are dfferentated by age-class and tat we need to consder establsment cost and removal costs. Table 1. Data requrement for perennal crops 53

54 Plantaton perod Growt perod Productve perod Perod lengt te :{1,...,g-1} tg :{g,...,p-1} tp :{p,...n} Average cost ( /a) CE,s CG,s CP,s Average revenue ( /a) Removal cost ( /a) RG,s RP,s CR s Input use (unts/a) IE,,s IG,,s IP,,s From tese prmary data, we compute te equvalent annual cost (EAC) and te equvalent annual revenue (EAR) for eac perennal actvty. As for nput requrements, we compute te equvalent annual nput use (EI), tang nto account te coeffcents of presence of trees of eac age-class. Actually, some calculatons wll be needed n order to turn te prmary age-class dependent data nto age dependent data or perennal actvty dependent data. One opton wll be to drectly compute te parameters assocated to eac perennal actvty (steady-state plantaton, non-replaced plantaton, new plantng and plantaton removal). Tat s, we wll compute te perennal actvty data from te prmary age-class dependent data. In te peac-tree example above, assumng tat te peac-tree starts bearng frut n te second year after plantng, reaces maturty n te fft year and as a lfespan of 15 years, we can defne four peac actvtes (steady-state plantaton, non-replaced plantaton, new plantaton and plantaton removal). Te frst one (steady-state plantaton) mples 1/15 sare of non-bearng trees, 4/15 sare of young trees and 10/15 sare of mature trees. Input-output coeffcents for ts peac actvty wll be calculated accordngly. For nstance, te equvalent annual cost wll be 1/15 sare of establsment costs (CE), 4/15 sare of growt costs (CG) and 10/15 sare of full producton costs (CP). A bref descrpton of te needed calculatons follows. Coeffcents calculaton for plantaton mantenance (superscrpt m) g = 1 p n m m t 1 t 1 t 1 EAC, s ρ d CE, s d CG, s d CP, t = 1 t = g t = p p 1 n m m t 1 t 1 EAR, s = ρ d RG, s + d RP, t = g t = p [( g 1) IE + ( p g ) IE + ( n p ) IE ] 1 EAI, m,, s =,, s,, s 1 n s s, s Coeffcents calculaton for plantaton declne (superscrpt d) 54

55 55 + = = = n p t s t p g t s t d d s CP d CG d EAC, 1 1, 1, ρ + = = = n p t s t p g t s t d d s RP d RG d EAR, 1 1, 1, ρ ( ) ( ) [ ] s s d s IG p n IG g p g n EAI,,,,,, = Coeffcents calculaton for plantaton growt (superscrpt g) + + = = = = n p t s t p g t s t g t s t g g s CP d CG d CE d EAC, 1 1, 1 1 1, 1, ρ + = = = n p t s t p g t s t g g s RP d RG d EAR, 1 1, 1, ρ ( ) ( ) ( ) [ ] s s s g s IE p n IE g p IE g n EAI,,,,,,, = Coeffcents calculaton for plantaton removal (superscrpt r) s r s CR EAC,, = An alternatve opton wll be to turn age-class data nto age dependent data. In ts case, te age structure of te perennal actvty can be made explct. And accountng for equvalent annual costs and revenues for te alternatve strateges wll be stragtforward. Input requrements for eac sub-actvty wll be te sum, across all ages, of te percentage area of a gven age multpled by te nput requrement assocated wt ts age. Contnung wt te peac-tree example, we frst defne te nput-output coeffcents for eac age of te perennal crop, wc s qute stragtforward, and ten we use te standard formulaton to compute te equvalent annual cost, revenue and nput use parameters Concluson Ts secton presented te tree adopted approaces for modellng perennal actvtes n te FSSIM-Dev model. Te selecton of a modellng approac depends on te tme frame of te analyss: In te sort term modellng approac, we assume a constant area for perennal crops, tat s, no land competton between annual and perennal crops are depcted.

56 In te long term modellng approac, we adopt a steady state approac, allowng for adustments n te area allocated to perennal actvtes and, terefore, modellng te competton for land between annual and perennal crops. In te medum term modellng, an nnovatve modellng approac s used to tae nto consderaton sun cost and adustment cost effects of nvestment decsons. Ts modellng framewor provdes te md-term response of perennal producers to canges n ncentves. Bascally, we mae a dstncton between te exstng stoc of perennal crops and new plantatons n terms of nput-output coeffcents. 56

57 2.7. Farm-ouseold module: non-separablty of consumpton and producton decsons Farm ouseold models are a sample of mcro researc on less-developed country (LDC) rural economes. Tey are mostly appled for famly or peasant agrculture (.e. small farms) were producton and consumpton decsons are lned due to maret mperfecton. Te fundamental dfference between a farm ouseold model and a pure consumer model s tat, n te latter, te ouseold budget s generally assumed to be fxed, wereas n ouseoldfarm models t s endogenous and depends on producton decsons tat contrbute to ncome troug farm profts (Taylor and Adelman, 2003). Te prmary motvaton for constructng ouseold models s to analyze te mpact of producton and consumpton decsons on varables of nterest, ncludng farm ouseold welfare, maret excange, ouseold resource use and sustanablty ssues (Sng et al., 1986). Tese models can also provde a promsng perspectve for understandng te mpacts of exogenous factors suc as tecnologcal nnovaton and polcy canges on farm ouseold beavour and rural areas (Taylor and Adelman, 2003). Te followng secton gves a sort overvew on farm ouseold models, ter specfctes and ter man assumptons and revews te well-nown ones. Ts revew wll facltate te selecton of te sutable approac to mplement n FSSIM-Dev for modellng ouseold decsons Bref lterature revew on farm ouseold modellng Wy non-separablty? Tradtonally, economsts model ndvdual decsons dependng on wc pont of vew te queston s addressed; one strand of te economc lterature dscusses te ssues related to consumpton beavours wen producton decsons are consdered n a dfferent settng and worers' allocaton of tme between on farm labour, off farm labour and lesure n anoter one. Ts assumpton of separablty between producton and consumpton decsons facltates analyss, but entals several mportant restrctons. Te mplcatons of assumng separablty are certanly less damageable n developed countres. However, ts separaton s often less clear-cut for agrcultural ouseolds n developng countres were ouseolds may be te locus for bot consumpton and producton. Houseold farms are a fundamental productve unt caracterzng most developng economes. Te dual role of many ouseold n developng countres as producer and consumer rases te queston of te nterdependence between producton, labour allocaton and consumpton decsons. Frst, agrculture s stll caracterzed by auto-consumpton, partly because local marets may fal to satsfy demand or to provde outlets for producton. Second, te opportunty cost of employng members of te famly on te farm may be very low gven te scarcty of land. Houseold agrcultural actvtes can terefore be bot a source of ceap food and an opportunty to allocate labour between famly members. Farm-ouseold models were frst ntroduced to explan emprcal puzzles, suc as decreases n mareted surplus after ncreases n prce of staple for Japanese rural ouseolds (Kuroda and Yotopoulos, 1978). As farm profts are a part of famly ncome, ncreases n prces can ave a postve effect on revenue and a drect negatve mpact on consumer's utlty. Demand 57

58 depends on prces and ncome as usual, but prces now ave an added effect on ncome va profts. Ts proft effect reduces te usual negatve relatonsp between prce and quantty demanded. Farm-ouseold models are also useful for understandng ouseold beavour wen marets are mperfect. Producton and consumpton decsons are lned because te decdng entty s bot a producer and a consumer. However, remans te queston f consumpton affects producton n return. As long as marets are perfect for all goods, ncludng labour, ouseolds are ndfferent between consumng own-produced and maret-purcased goods and allocate ndfferently producton between consumpton and maret sales. In oter words, consumpton decsons do not affect producton decsons and producton s ndependent of ouseold preferences and ncome. Te man queston to answer s to determne weter or not tere are maret falures. If tere are maret falures, a ouseold approac mgt be necessary dependng on weter te good for wc maret fals s mportant n producton. In presence of transacton costs, sellng prce dffers from buyng prce. Transactons costs generate tus a prce band. If te wdt of te prce band s large and te ouseolds margnal cost curve crosses ts demand curve wtn te band, ten te ouseold does not partcpate n te maret. A maret exsts but some ouseolds won't enter because te benefts from excangng are less tan te costs ncurred. A maret fals wen te cost of a transacton troug maret excange creates dsutlty greater tan te utlty gan tat t produces, suc tat no maret transacton occurs (De Janvry et al., 1991). It as to be noted tat maret falure s ouseold specfc and not commodty specfc. Tests for separablty rely tus essentally on testng weter or not ouseold caracterstcs n consumpton sgnfcantly affect producton decsons. Varables on ouseold sze, suc as te number of adults and cldren (Vas et al., 2004), or educaton bacground are usually consdered as ouseold caracterstcs n consumpton. Wen one or more maret s mssng ten recursveness breas down and non-separablty regardng producton and consumpton decsons as to be assumed; consumpton varables determne producton. Farm-Houseold models provde explanaton of ouseold beavour n response to exogenous socs n mssng marets scenaros. Some ouseolds wll beave as net-buyers, oters net-sellers and oters autarc. A man ssue n farm ouseold modellng s ow to determne te ouseold-specfc sadow prce. Emprcal evdence largely supports te ypotess tat farm ouseold producton and consumpton decsons are "non-separable". Benamn (1992) suggests testng for separablty by controllng weter demograpc varables affect farm producton decsons. He founds tat, for agrcultural ouseolds n rural Java, te separaton ypotess cannot be reected. However, followng a smlar approac, Lopez (1986) found tat te ypotess of ndependence must be reected on Canadan data. Bowlus and Scular (2003) sow on Cnese data tat labour demand s a functon of ouseold sze and composton. Carter and Yao (2002) demonstrate tat ouseolds wose land endowment les wtn two crtcal values, wll not enter te land rental maret, tus exbtng non-separablty (Lovo, 2011) Teoretcal framewor and quanttatve metods A lterature revew reveals te exstence of tree alternatve economc teores regardng farm ouseold beavour. Eac teory assumes tat ouseolds ave an obectve functon to maxmze, wt a set of constrants. Te frst s te proft-maxmzng teory, wc as been crtczed on te grounds tat t overloos te aspect of consumpton n ouseold 58

59 decson-mang processes (Scultz, 1964). Te second, te utlty-maxmzng teory, ncorporates bot te producton and consumpton goals of ouseolds (Cayanov, 1966; Sng et al., 1986; Sadoulet and De Janvry, 1995). Fnally, te rs averson teory states tat te obectve functon of ouseolds s to secure te survval of te ouseold by avodng rs (Roumasset, 1976; Morduc, 1993; Mas-Colell et al., 1995) 11. Utlty-maxmzng teory s te most used wen ouseold consumpton and producton decsons are nterdependent suc as n rural areas. Accordng to ts teory, farm ouseolds are assumed to maxmze te utlty derved from consumpton of all avalable commodtes (.e. ome-produced goods, maret-purcased goods, and lesure), subect to full ncome constrants. Formally, te basc farm ouseold model can be presented as follows (Sadoulet and De Janvry, 1995): Max U (, c c ; z ) st.. a a l l l p c + w c = π + w El f( q, x, l) = 0 c l s + l = l E l (30) wt v x l π = p v p x wl (31) were U s a consumer's utlty functon defned over a vector of commodtes consumed c and lesure c l, dependng on ouseold 's caracterstcs z. f(q,x,l) represents te ouseold's producton tecnology, relatng farm outputs q to te amount of nputs x and labour l. l s and B l are te ouseold's labour supply and te ntal endowment of labour. π s te proft functon, wt p v, p a and p x are te vectors of sellng prces, buyng prces and nput prces, respectvely. v are te output quanttes sold on te maret and w l te wage rate. If perfect marets are assumed, te ouseold model s separable. Despte exbtng a sngle decson-mang process, te optmsaton program can be solved recursvely: frst, te producer's proft maxmzaton problem, ten, te consumer's utlty maxmzaton problem, under te constrant of a gven optmzed level of proft. As mentoned prevously, te ey asset of farm-ouseold models s tat tey account for te proft effect. However, f te maret fals for a ouseold, separablty does not old any more and te ouseold's producton and consumpton decsons must be solved smultaneously (Sng et al., 1986). Te soluton to te ouseold model yelds a set of equatons for outputs, nput and consumpton demands. In case of maret falures, sadow prces must be computed for nontradable goods. Followng Kuper (2004), we retan tree approaces to translate te teoretcal framewor to a quanttatve ouseold model: reduced-form equaton, a system of structural equatons and a Matematcal Programmng model. A reduced-form equaton approac 11 For a revew of tese tree teores see Mendola (2007). 59

60 To address specfc researc questons, t may not be necessary to estmate a complete ouseold model. For te varable of nterest, exogenous varables need to be dentfed and te resultng equaton estmated econometrcally. Reduced-form equatons can be derved from te frst-order condtons of te ouseold maxmzaton program, descrbng ow endogenous varables relate to an exogenous varable of nterest. It ten suffce to postulate general condtons on te functonal form (Paolsso et al., 2002; Smale et al., 2001; Woldenanna and Osam, 2001). One of te man advantages of ts approac s tat t doesn't requre specfcaton of te utlty and/or producton functons. However, reducedform models do not detect nternal adustments wtn te ouseold. A system of structural equatons approac If more tan one endogenous varable s of nterest, te wole system of structural equatons of te ouseold model need to be estmated (Kuper, 2004). Frst-order condtons are ten derved from te ouseold maxmsaton problem to determne a system of equatons tat descrbe ouseold s producton and consumpton decsons. If decsons are assumed to be separable, te model may be estmated recursvely. Oterwse, te model sould be solved smultaneously, usng numercal tecnques wc may nvolve proceedng to econometrc estmatons (De Janvry et al., 1991). Non-separablty mples ndeed smultaneously coosng, on te producer's sde, te allocaton of labour and oter nputs to producton, on te worer's sde, te allocaton of ncome from farm profts and labour sales, and, on te consumer's sde, te allocaton of te budget between commodtes and servces. Estmatng structural equatons of te ouseold model requres selectng functonal forms for te demand and supply functons. Tese may be derved from utlty and producton functons, or may be postulated (Kuper, 2004). Estmaton of te structural equatons can be complcated because of te endogenety of some varables and because of ndetermnate sgns of coeffcents mang t dffcult to dstngus te true nference from spurous correlaton (Kruseman, 2000). In addton, data necessary for estmaton purposes may be dffcult to gater. Matematcal programmng approac Prevous autors ave derved systems of structural and reduced-form equatons from te frst order condtons (De Janvry et al., 1991). However, estmaton of a ouseold model n reduced-form or as a system of equatons may not be possble. Frst, te structure of te ouseold model may be too complex to derve a lmted number of equatons. Ts apples n partcular n te case of non-separable ouseold models, wen t cannot realstcally be assumed ndependence between utlty and proft maxmzaton. Second, Second, econometrc estmaton may be ndered by unobservable varables. Suc cases can occur, for example, wen ouseolds produce commodtes for ome use only and not for maret sale. Trd, econometrc estmaton sets requrements n terms of te number of observatons, tme orzon and varaton n varables (Kuper, 2004). A trd approac relyng on Matematcal Programmng as also been used n te last years (Omamo, 1998; Kruseman, 2000). Te optmsaton approac follows te general framewor of an obectve functon maxmsed under constrants and solved te program troug optmsaton algortms. Restng on numercal tecnques, t does not requre dervng frst order condtons. However, a functonal form to te obectve utlty functon needs to be 60

61 specfed. Econometrcally estmated parameters may be entered n te model to reduce data requrements. A lterature revew sows an ncreased number of farm ouseold programmng models beng used to address a multtude of questons. McGregor et al. (2001) revewed studes usng tese nds of model up to Maor contrbuton s te Malan Farm Houseold Model developed by Ruben and Ruven (2001) to assess farmers responses to agraran polces. It conssts of a non-lnear dynamc recursve optmzaton model worng at ouseold level and smulatng bologcal processes by mang use of meta-modellng prncple. Oumu et al. (2000) developed te Gnc Farm Houseold Model. It s a dynamc non-lnear and multobectve programmng model wc optmses a wegted utlty functon were tree goals are ncorporated: cas ncome, lesure and basc food producton. Barber and Bergeron (1999) bult a recursve and dynamc lnear programmng model wc maxmzes full ncome as a proxy for utlty were mnmum consumpton requrements are mposed. Te farm ouseolds wt dfferent resource endowments are ten aggregated to te regonal level to assess te supply response and te potental prce effects as tey nteract wt demand. Seperd and Soule (1998) developed a dynamc smulaton model tat ncorporates ouseold needs, constrants and fnancal flows. An nterestng aspect of ter wor s tat tey consder ouseolds wt dfferent resource endowments and n dfferent envronmental contexts. Holden et al. (2004) tae truly advantage of te ouseold decson-mang structure of programmng models to ntegrate economc optmzaton n producton and consumpton wt envronmental feedbacs n a non-separable regme. Ts ouseold model maxmzes a welfare functon measured as te dscounted utlty of a certanty equvalent full ncome. Ts full ncome s specfed as a functon of an expected ncome based on te probablty of expected prces and expected outputs n drougt years and years wtout drougt. Dolsca et al. (2008) use a smlar model to evaluate te role of varous polcy nstruments on large-ncome farm ouseolds and low-ncome farm ouseolds welfare and forest conservaton n Hat. Laborte et al. (2009) use a farm ouseold model to evaluate te potental attractveness to Plppne farmers of tree nnovatve producton tecnologes. In ts optmzaton model, t was agan assumed tat utlty could be replaced by dscretonary ncome. Lewse, oter farm ouseold models were developed for dfferent countres to nvestgate varous questons. Among tese countres/models one could menton: Zamba (Holden, 1993); Kenyan (Wataa et al., 2006); Gana (Yrdoe et al., 2006); Etopa (Babulo et al., 2008), Plppne (Waler et al., 2009). Te problem s tat most of tese programmng models are, on te one and, unable to exactly reproduce te observed beavour and terefore to be sutable for polcy analyss, and, on te oter and, tey are used for a specfc purpose and locaton and terefore cannot be easly adaptable and extendable to oters contexts and condtons. Moreover, n most of tese models ouseold consumpton s modelled troug a mnmum consumpton constrant wc s unable to capture ouseold consumpton decson correctly. Te model presented n ts report reles on ts approac and attempts to overcome te above ssues usng te Postve Matematcal Programmng approac (Howtt, 1995a) and a consumpton functon parameterzed troug Generalzed Maxmum Entropy estmaton. Anoter model's novelty s tat, because of non-separablty assumpton, te prce at wc te ouseold values a commodty s generated by te model (.e. endogenous) dependng on ouseold tradng status. 61

62 Modellng farm ouseold decsons n FSSIM-Dev Te am of ts secton s to present te ouseold modellng approac to mplement n FSSIM-Dev for tang nto account farm ouseold supply and consumpton decsons. Based on matematcal programmng, ts approac reles on bot te general ouseold's utlty framewor and te farm's producton tecncal constrants, n a non-separable regme. However, before descrbng our approac, we dscuss brefly some ssues related to te specfcaton of ouseold utlty functon Dscusson on ouseold utlty functon In Economcs, utlty refers to te perceved value of a good or servce by a consumer. It s a measure of te relatve satsfacton from consumpton of goods and servces. Ts concept of utlty s essentally mportant for ranng te dfferent consumpton bundles. If consumer s beavour satsfed axoms of completeness and transtvty, consumer can consstently ran all basets of commodtes n order of preference. Wt ts measure, one may spea meanngfully of ncreasng or decreasng utlty, and tereby explan economc beavour n terms of attempts to ncrease one's utlty. Ts nd of utlty s referred to as ordnal utlty, as opposed to cardnal utlty wc treats te magntude of utlty dfferences as a sgnfcant quantty. Ordnal utlty s te core assumpton towards people s preferences and utlty functons and s suffcent to model consumer beavour. Tus, gven a certan preference orderng, we can assume a utlty functon to descrbe ts ranng. Utlty functon s te matematcal expresson tat sows te relatonsp between utlty values and every possble baset of goods. One of te maor dffcultes nvolved n usng revealed preference approaces s te specfcaton of functonal forms. Te most common utlty functons found n te lterature are: Negatve Exponental utlty functon x ux ( ) = e α, α > 0 (32) wt x, te consumpton bundle and α, te coeffcent of (absolute) rs averson. Logartmc utlty functon: ux ( ) = logx (33) Power utlty functon: α x u( x) =, γ > 1 γ (34) wt γ and α, constant terms. Te power utlty functon s one of te most flexble forms. Iso-elastc utlty functon (or CRRA or CES ): 62

63 1 ρ x ux ( ) =, ρ > 1 1 ρ (35) wt ρ, te coeffcent of (relatve) rs averson. Many efforts ave been made to model functonal forms wc satsfy teoretcal condtons and large sectons of Demand Teory ave been dedcated to dervng demand functons drectly from maxmzng a utlty functon. Tree of tem ave receved consderable attenton because of ter relatve emprcal expedency. Tey are te Lnear Expendture System (LES) developed by Stone (1954), te Almost Ideal Demand System (AIDS) developed by Deaton and Muellbauer (1980), and te combnaton of tese two systems nto a Generalzed Almost Ideal Demand System (GAIDS) proposed by Bollno (1990). Oter complete demand systems found n te lterature but not as wdely used are te Rotterdam model of Tel (1976) and Barten (1969) and te translog model of Crstensen, Jorgenson, and Lau (1975) (cted by Sadoulet and De Janvry, 1995). In FSSIM-Dev we opted for te Lnear Expendture System (LES) as t s te easer n term of parametersaton and calbraton. However, ts system can be easly substtuted by a mnmum consumpton constrant f data on ncome and prce elastctes are mssng or f te number of observaton s lmted. In suc case te user as ust to provde data on mnmum energy requrement per adult and te nutrent (energy) value per consumed goods Modellng farm ouseold consumpton decson A lnear expendture system (LES) was used to descrbe te ouseold's consumpton beavour. In ts system, te set of demand functons s expressed n expendture form and assumed to be lnear n prces and ncomes as follows: c p = β, ( Y γ, ' p, ' ) + γ, p, = (36),, ' 0 < β, < 1 β, = 1 γ, < c, (37) were p s te (n 1) vector of prces of goods, c s te (n 1) vector of consumed quantty of goods, Y s te farm ouseold full ncome (farm ouseold expected ncome (R) plus te value of te ouseold's land endowment), γ s te uncompressble consumpton (nterpreted as mnmum subsstence or commtted quanttes below wc consumpton cannot fall) and β s te margnal budget sare ( pc R). γ, ' p, ' s te subsstence expendture and te ' term ( Y γ p ) s generally nterpreted as "uncommtted" or "supernumerary" ' =, ', ' 63

64 64 ncome wc s spent n fxed proportons β between te commodtes (Sadoulet and De Janvry, 1995). Te Generalzed Maxmum Entropy (GME) metod was used to estmate γ and β parameters for te sampled farm ouseolds, usng nformaton on ncome elastctes and te Frsc parameter 12 from lterature (Seale et al., 2003). As stated by Golan et al (1996), te GME metod permts te consstent and effcent estmaton of a demand system wt nonnegatvty constrants and a large number of goods wtout mposng restrctons on te error process. It also allows ncluson of pror nowledge n a tecncally stragtforward way, mang estmates potentally more effcent (Jansson, 2007). Te applcaton of te GME estmator to te lnear expendture system s based on te soluton of te followng problem: = '',, ",, ",, ',, ',, ',,,,,,,, " ln " ' ln ' ln '') ',, ( max w w w w w w w w w H (38) Subect to: Data-consstency constrants, ) (,,, ',,,,, p p Y p c + + = μ γ γ β,,,,, = Z w β, ' ' ' ', ',,, = Z w γ Z w, " ", '', '' ",,, = μ Addng-up or normalzaton constrants, 1,, w =, 1 ' ' ',, w =, 1 " '' ",, w = Accountng restrcton 12 Te Frsc parameter s a rato of te total expendture and te dscretonary (supernumerary) ncome. For furter detals see Dervs et al. (1982).

65 β, = 1 γ, < c,, μ, = 0 Non-negatvty condtons β, > 0 ; γ, > 0; w,, 0; w',, ' 0 ; w",, '' 0 were μ s te error term wc s specfc to eac good and farm ouseold, K, K' and K" are te numbers of support ponts assocated to bot te unnown parameters and te error vector, Z, Z' and Z" are te values of support ponts set exogenously, and w, w' and w" are ter unnown probabltes, respectvely. Te prncple of Generalzed Maxmum Entropy conssts of selectng te values of γ, β and μ wose dstrbutons w, w and w" maxmze te functon H n (8), subect to te dataconsstency constrant (8.1), te addng-up or normalzaton constrants (8.5-7) wc ensures tat probabltes approprately sum to one, te accountng constrant (8.8-10) and te nonnegatvty condton (8.11). Te accountng restrcton (8.8) s mposed n order to ensure tat total ouseold expendture and total ouseold ncome at farm ouseold level are equal. Ts procedure ensures tat all (agrcultural and non-agrcultural) goods are taen nto account smultaneously. Ts s aceved by ntroducng an addtonal category of goods m "maret goods", as suggested by Broos et al. (2011), wt correspondng expendture equals to te dfference between ouseold ncome and ouseold agrcultural consumpton. p, mc, m = Y γ, ' ' m p, ' (39) To mae te GME estmaton operatonal, we need to defne te support ponts for te unnown parameters as well as for te error vector. As ponted out by several studes, te coce of support ponts n te context of GME s an mportant ssue, as t can strongly affect model outcomes (Golan et al., 1996). To defne te number of support ponts, ter bounds, spacng, and te mpled pror expectaton, we ave made te followng assumpton: for te β parameters of agrcultural food products (rce, cassava, groundnuts, sweet potatoes, palm ol and fruts), 11 support ponts (.e., K=11) are cosen, centred around te ncome elastcty tmes te average budget sare at dstrct/regon level as follows: Z were za avs = za η avs = [ 0.01,0.3,0.6,0.9,1.2,1.5,1.8,2.1,2.4,2.7,3 ] p c = Y m (40) 65

66 were η s te ncome elastcty and avs s te average budget sare devoted by ouseolds to good (average across sample ouseolds wtn eac dstrct/regon). We assume tat all farm ouseolds ave te same ncome elastcty for eac good and te same Frsc parameter (Seale et al., 2003). for te β parameter of te addtonal good maret goods, 11 support ponts are also cosen bounded between zero and one, and equally spaced wt a dstance of 0.1 (.e., we assume a pror expectaton of 0.5 because ts category ncorporates te expendture for all non-agrcultural goods wc can easly account for up to ffty per cent of te total ouseold ncome). Z [ 0.01, 01,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,. ] m = 1 = for te γ parameter, 5 support ponts are cosen (.e. K = 5), rangng between ± 100% tmes te average uncompressble consumpton across farm ouseolds for eac good. It s gven by: (41) Z ' were zb γ = = zb γ = [ 0,0.5,1,1.5,2 ] Y p avs η avs + ϕ (42) were γ s te average uncompressble consumpton across sample ouseolds wtn eac dstrct/regon, η s te ncome elastcty, ϕ s te Frsc parameter and avs s te average budget sare across (Savard, 2003). For te error term μ we use te common assumpton were tree support ponts (.e., K = 3) are symmetrcally defned around zero and bounded by te so-called tree-sgma rule (Puelsem, 1994). Z '' = were [-3σ ˆ, 0, + 3σˆ ] σˆ : sample standard devaton of consumpton and expendture n good (43) Transformaton of te FSSIM obectve functon In order to tae nto account bot farm ouseold producton and consumpton decsons, n a non-separable regme, te FSSIM utlty functon was transformed from a famer's expected utlty functon wc focus manly on agrcultural (farm) ncome to a ouseold's expected utlty functon based on ncomes from all economc actvtes of a famly lvng n te same ouseold. Ts new expected utlty functon n FSSIM-Dev s defned as a farm ouseold ncome (.e. full ncome) mnus ts standard devaton due to rs averse towards prce and yeld varaton: 66

67 U = R φ σ (44) were U s te expected farm ouseold utlty functon, denotes farm ouseolds, R s te farm ouseold expected ncome, φ s te rs averson coeffcent and σ s te standard devaton of agrculture ncome due to prce and yeld varaton. Farm ouseold ncome R s defned as te ncome earned from all economc actvtes of a famly lvng n te same ouseold and s composed of tree components: agrcultural ncome z, ncome from mareted factors of producton (non-farm wages, rent of land and equpment) and off-agrcultural/farm ncomes. Te off-farm ncomes are exogenously defned and can orgnate from dfferent sources suc as non-farm salares, petty tradng, self employed craftsmansp, pensons, transfer, donatons, etc. Te farm ouseold s ncome R s defned as follows: R = z + s, tf p, tf b, p, + exnc tf (45) were Z s te agrcultural ncome, s te (n 1) vector of sold quanttes of goods or tradable factors tf (land, labour and equpment), p s te (n 1) vector of prces of goods or tradable factors tf, c s s te (n 1) vector of self-consumed quanttes of goods, b s te (n 1) vector of bougt quanttes of goods or rented-n tradable factors and exnc s te exogenous off-farm ncomes for ouseolds. Agrcultural (farm) ncome z s defned as te value tat farm-ouseolds ave earned by sellng or consumng ter own agrcultural products (.e. self-consumpton). It s calculated accordng to te followng formulaton: z c f = (d, (s, + c s, Q,' )p x,, )x +, sb tf, x ( b,,tf + λ' a,tf, x, ) p,tf (46) were x s te (n 1) vector of te smulated levels of te agrcultural actvtes, sb s te (n 1) vector of producton subsdes, a s te (n 1) vector of accountng cost, fc s te fxed cost, λ ' s te (m 1) vector of mplct unt costs of tradable factors (.e. te dfference between te maret prce and te effectve unt cost of a tradable factor), d s te (n 1) vector of te lnear part of te actvtes mplct cost functon and Q s a (n x n) symmetrc, postve (sem-) matrx of te actvtes mplct cost functon. Q, d and λ ' are estmated usng Postve Matematcal Programmng approac. Agrcultural commodty prces (.e. maret prces) are exogenously fxed for ouseolds partcpatng n marets. We assume tat tose farm ouseolds are prce taers on commodty marets. However, te prce at wc te ouseold values a commodty wll be generated by te model dependng on ouseold tradng status (net buyer, net seller or selfsuffcent) wc n turn s related to transacton costs. Ts means tat te commodty prces 67

68 are endogenously determned by te model and not exogenously gven as t was te case n FSSIM Modellng maret partcpaton decson Te decson for a farm ouseold to partcpate n te maret depends ardly on transacton costs. Transacton costs are any costs tat an agent ncurs n order to perform a maret transacton. Tey are caused by, for example, long dstances to marets, g transportaton costs, poor nfrastructure, non-compettve maret structures, and ncomplete nformaton. A buyer facng transacton costs perceves te effectve prce of commodtes e wants to buy as ger tan te maret prce. Smlarly, a seller facng transacton costs perceves te effectve sale prce as lower tan te maret prce (Broos et al., 2011). Due to tese costs, producton and consumpton decsons become non-separable 13 and te ouseold may coose to lve n partal or total autary (Hennng and Hennngsen, 2007). As long as marets are perfect for all goods, farm-ouseolds are ndfferent between consumng own-produced and maretpurcased goods and allocate ndfferently producton between consumpton and maret sales. However, f tere are maret falures, a ouseold approac mgt be necessary dependng on weter te good for wc maret fals s mportant n ouseold producton (Sng et al., 1986). As ts stuaton s very common n many low ncome economes, FSSIM-Dev was desgned to tae transacton costs nto account and to endogenously capture maret partcpaton decsons. Ts s aceved usng te concept of prce band, based on maret prce (p m ) and multplcatve transacton costs (t). Fgure 4 llustrates ts stuaton. Prce FH Supply (net buyer) FH Supply (self-suffcent) Purcase prce = p m x t b Purcased Exogenous maret prce = p m Buyer s transacton costs Seller s transacton costs Sold FH Supply (net seller) Sale prce = p m x t s FH Demand Quantty Fgure 4. Non-separable producton and consumpton decsons Source: adapted from Sadoulet and De Janvry (1995) As sown n Fgure 4, te wedge between buyng prce and sellng prce generated by transacton costs leads to dscontnuous beavour n wc te ouseold could be a net 13 Non-separablty can result from oter factors as well, suc as maret power, rs, and mperfect substtutablty between ome goods and purcased goods (see Roe and Graam-Tomas, 1986; Sadoulet and De Janvry, 1995). 68

69 buyer, a net seller, or self-suffcent. Te prce at wc te ouseold () values a commodty wll tus generated by te model dependng on te ouseold tradng status. If te ouseold's margnal cost (supply) curve crosses ts demand curve above te prce m b band, ten te ouseold s a net buyer and ts buyng prce wll be ( p, = p t, ) ger tan maret prce ( t b, 1) If te ouseold's margnal cost (supply) curve crosses ts demand curve below te prce m s band, ten te ouseold s a net seller and ts sellng prce wll be ( p, = p t, ) lower tan maret prce ( t s, 1) If te ouseold's margnal cost (supply) curve crosses ts demand curve wtn te prce band, ten te ouseold does not partcpate n te maret (self-suffcent) and ts nternal sadow prce wll be used to values te consumed quanttes of goods. Te sadow prce s determned by te ntersecton of ouseold supply and demand and vares among ouseolds accordng to ter resource endowment and soco-economc condtons (.e. ouseolds use ter own nternal sadow prce f and only f tey do not partcpate n te maret). m s m b p t, p p t, For modellng maret partcpaton decson n FSSIM-Dev tree blocs of equatons are ncluded: Te frst bloc for settng an upper and a lower bounds for commodty prces p p, m s t, p t p m b,, (47) Te second bloc, so-called complementary slacness condtons, s used to guarantee tat a farm ouseold use ts own nternal sadow prce f and only f t does not partcpate n te maret for goods. s b,, ( p ( p,, p t p m s, m b t, ) = 0 ) = 0 (48) Te trd bloc to ensure tat a farm ouseold can be eter a buyer or a seller but not bot (ouseolds can also be self-suffcent,.e. neter buyng nor sellng goods). s b 0 (49),, = were s s te (n 1) vector of sold quanttes of goods, b s te (n 1) vector of bougt quanttes of goods, p m s te (n 1) vector of maret prces of goods, p s te (n 1) vector of farm ouseold prces of goods and t b et t s are (n 1) vectors of multplcatve buyer and seller transacton costs, respectvely. Havng transacton costs n te model requres tat we explctly express a cas constrant for ouseolds. Te followng cas constrant, mposed n FSSIM-Dev, states tat te total value 69

70 of nputs, goods and tradable factors tat a ouseold can purcase s constraned by ts total cas ncome from te maret sales of goods and tradable factors plus producton subsdes and (exogenous) off-farm ncomes f tey exsts. + exnc + s, a p,, x, + b s,, tf p p,, tf + + sb ' ( b + λ, tf, x,, tf ) p, tf (50) were s te (n 1) vector of sold quanttes of goods or tradable factors tf (land, labour and equpment), p s te (n 1) vector of prces of goods or tradable factors tf, c s s te (n 1) vector of self-consumed quanttes of goods, b s te (n 1) vector of bougt quanttes of goods or rented-n tradable factors, sb s te (n 1) vector of producton subsdes, a s te (n 1) vector of accountng costs and exnc s te exogenous off-farm ncomes for ouseolds. To ensure commodty balance at ouseold level () after te ncluson of ts consumpton functon, a new constrant was embedded n te FSSIM-Dev constrant system. Ts constrant mples tat te sum of producton and maret demand for eac good must be equal to consumpton plus maret sales. q, b, = s, + c, + (51) were q s te (n 1) vector of produced quanttes of goods, b s te (n 1) vector of bougt quanttes, s s te (n 1) vector of sold quanttes and c s te (n 1) vector of consumed quanttes. Accordng to ts new specfcaton, te general matematcal formulaton of te FSSIM-Dev, at farm ouseold level, can be presented as follows: Max V = U H = 1 = R w U φ σ (1) S.t. Resource constrants A x B + b s Lnear expendture system (LES) Prce bands & complementary slacness condtons Maret clearng condtons Cas constrant were V s te value of te obectve functon, denotes representatve farm ouseold and w ts wegt wtn te vllage, regon or regon, U s te farm ouseold utlty, R s te farm ouseold expected ncome, φ s te rs averson coeffcent and σ s te standard devaton of farm agrcultural ncome due to prce and yeld varaton, p s a (n 1) vector of prces of goods and x s te (n 1) vector of te smulated levels of te agrcultural actvtes. A s a (n m) matrx of tecncal coeffcents, s s a (m 1) vector of rented-out tradable factors, b s (2) 70

71 a (m 1) vector of rented-n tradable factors and B s a (m 1) vector of ntal avalable resources and upper bounds to polcy and cas constrants Concluson Ts secton presented te man structure of te FSSIM-Dev ouseold module. Te man efforts were focused on developng a generc, computable modellng of farm-ouseold consumpton decsons, and easly mplementable wtn te FSSIM-Dev framewor. Apart ts consstency wt teoretcal lterature, ts ouseold formulaton requres lttle nformaton on ouseolds' consumpton beavours. Elastctes of demand and Frsc parameter suffce to parameterze te model. Furtermore, ts approac accounts for te exstence of a prce-band wc offers gudance for analysng te effects of transacton costs on ouseold's producton and consumpton responses Trend and polcy modules Outloo parameters for buldng baselne scenaro FSSIM-Dev structure offers te possblty of buldng a specfc baselne scenaro to use as reference for te nterpretaton and analyss of dfferent polcy scenaros. Te baselne scenaro (also nown as 'reference' or 'bencmar' or 'non-nterventon' scenaros) s nterpreted as a proecton n tme coverng te most probable future development n term of tecnologcal and maret canges. In some case, te baselne may be a smple proecton of te current stuaton assumng no canges (te expresson Busness as Usual scenaro s generally used to specfy ts nd of baselne) and n oter cases, te baselne may cange drastcally. Te prncpal outloo parameters predefned n te FSSIM-Dev trend module tat can be used to buld te baselne scenaro are te followng: nflaton rate, prce proecton, yeld growt and farm structure cange. Tese parameters are accessble from FSSIM-Dev GUI (see secton 3.7) and can be manpulated easly by te user wtout gong troug te GAMS code Inflaton Regardng nflaton te user as to precse troug te GUI te nflaton rate and te model nflates automatcally all monetary values (.e. all nput out puts prces as well as premums and PMP terms) usng te followng nflaton coeffcent: ( 1+ nf/ ) ( Ybl_Yby) Inflaton = 100 (52) Ybl s te year n wc baselne was performed Yby s te year n wc base year was performed nf represents te nflaton rate (n %) Inflaton represents te nflaton coeffcent 71

72 Prce proecton and yeld growt FSSIM-Dev offers te possblty of varyng maret prces and yelds between base year and baselne n order to tae nto account tecnologcal nnovaton and maret canges. Ts can be performed troug te GUI usng data from oters sources/studes or by smply assumng a certan percent cange. Te baselne default value of maret prce (.e. prce proecton) and yeld canges (.e. yeld growt) are equal to zero and cangng tem for example to +10 and +20 wll ncrease prce and yeld by +10 and +20 percent, respectvely. Note tat negatve percent (.e. -10 and -20) are also possble. p ( + Δp /100) bl by = p 1 (53) by p s a vector of average maret prces used n te base year bl p s a vector of average maret prces used n te baselne scenaro Δp s a vector representng te percentage cange of average maret prces between base year and baselne. Ts vector, expressed n percent, s gven by users troug FSSIM-Dev GUI. Y ( 1+ Δy /100) = Y (54) bl by,, by Y, s a vector of average yeld of eac crop product wtn agrcultural actvty used n te base year bl Y, s a vector of average yeld of eac crop product wtn agrcultural actvty used n te baselne scenaro Δy s a vector representng te percentage cange of average crop yeld between base year and baselne. Ts vector, expressed n percent, s related to crop and not to actvty and t s gven by users troug FSSIM-Dev GUI Farm structure cange Users can also assume tat farm structure could cange between base year and baselne due to maret and polcy canges or because of new resource avalablty. In suc case, t s necessary to cange farm resource endowments especally, land, labour and equpments. Ts can be aceved by varyng eter te wegt of eac representatve farms or te Rgt-Hand Sde (RHS) coeffcents of resource constrants (.e. ncreases or decreases farm sze, avalable rrgable land, avalable labour, avalable water ) usng te structure cange parameters ( Δr f ) stored on te FSSIM-Dev database and accessble from te FSSIM-Dev GUI. Te baselne default values for tese parameters are zero and are mplemented as follow: B ( + Δr /100) by, f B, f 1, f = (55) by B, s a vector of RHS coeffcents of resource constrants used n te base year f 72

73 B, f s a vector of RHS coeffcents of resource constrants used n te baselne scenaro Δrf s a vector representng te percentage cange of RHS coeffcents between base year and polcy scenaros. Ts vector, expressed n percent, s gven by users troug FSSIM-Dev GUI Polcy parameters In addton to representng farmer beavour n te base year and forecastng s/er reacton under baselne scenaro, FSSIM-Dev structure provdes te possbltes of smulatng te mpact of maret cange, tecnologcal nnovaton and water prcng polces. Ts maes t possble tans to a set of polcy parameters ncluded n te FSSIM-Dev GUI and wc can be easly andled by users Maret prce cange Te cange n maret prces can be performed troug te GUI usng exogenous assumpton or data from oters sources/studes. Ts would concern eter output () or nput () prces. In case of mssng data on dsaggregated account costs per nput categores, user can smulate te mpact of varyng total account costs. p ( + Δp /100) bl = p 1 (56) bl p s a vector of output maret prces used n te baselne p s a vector of output maret prces used n te polcy scenaro Δp s a vector representng te percentage cange of output prces between baselne and polcy scenaros. Ts vector, expressed n percent, s gven by users troug FSSIM-Dev GUI. p ( + Δp /100) bl = p 1 (57) bl p s a vector of nput maret prces used n te baselne p s a vector of nput maret prces used n te polcy scenaro Δp s a vector representng te percentage cange of average nput prces between baselne and polcy scenaros. Ts vector, expressed n percent, s gven by users troug FSSIM-Dev GUI Transacton costs cange Te user can also cange, troug te FSSIM-Dev GUI, te value of transacton costs expressed as te dfference between te farm ouseold sellng/buyng prces and te maret prces. In te absence of transacton costs (.e. transacton costs equal to zero), te multplcatve buyer and seller transacton costs (t) are equal to one and te farm ouseold prces are equal to maret prces (.e. separable regme). In suc stuaton, te farm ouseold model s collapsed to a farm supply model worng wt exogenous prces and an addtonal 73

74 constrant of consumpton. In contrary, n te presence of transacton costs (.e. transacton costs are postves), farm ouseold s prces are dfferent to maret prces and producton and consumpton decsons are non-separable and te ouseold may coose to lve n partal or total autary. p p, m s t, p t p m b,, (58) were p m s te (n 1) vector of maret prces of goods, p s te (n 1) vector of farm ouseold prces of goods and t b et t s are (n 1) vectors of multplcatve buyer and seller transacton costs, respectvely Tecnologcal cange FSSIM-Dev provdes te possblty of smulatng te mpact of alternatve actvtes f tey are explctly defned and ncluded n te lst of agrcultural actvtes. Tese alternatve actvtes could be new crops, new varety, new crop rotatons, new management practces Ts opton exsts already wtn te FSSIM, but wat s new ere s tat tey can be easly swtced on/off from FSSIM-Dev GUI Water tarff polces Water resource plays a central role n te sustanablty of agrcultural systems of many economes, bot developng and developed. Because of ts scarcty and neffcency use, t as been te focus of many nterventon polces wc see multple obectves, ncludng ncome transfer, food producton, envronmental sustanablty, and resource conservaton. Tese polces are based on a pletora of polcy nstruments, namely water prcng, quotas, water rgt assgnments Because water s a common problem of many economes, te template of FSSIM-Dev was desgned to allow te smulaton of dfferent water prcng polces and to elp polcy maers and researc n assessng ter mpacts. Among te set of water tarff tat can be tested troug te FSSIM-Dev GUI are: o Fxed tarffs (ndependent to water consumpton) o Volumetrc tarffs proportonal to water consumpton (lnear tarffs) o Volumetrc tarffs ncrease wt water consumpton (ncreasng-bloc-rate tarffs) o Bnomal tarffs were a volumetrc tarff s combned wt a fxed tarff Tese water tarff polces are andled n FSSIM-Dev usng tree equatons: o Te frst equaton expresses tat te sum of water requrement for rrgated actvtes cannot exceed te water avalablty. were ( A,," water" x ) B," water" (59) A,, water s a vector of water requrement for eac agrcultural actvty 74

75 B, water s a vector of water avalablty o Te second equaton s related to te upper bound of eac water bloc and expresses tat te consumed water n eac bloc cannot exceed te water avalablty per bloc. Qw, wb Aw, wbb," rland" (60) were wb ndexes water blocs (3 blocs are ncluded: w1, w2 and w3) Qw,wb s a vector of consumed water per bloc Aw,wb s a vector of avalable water per bloc and per ectare of rrgable land B, rland s a vector of avalable rrgable land o Te last equaton s used to compute water costs to be ncluded n te obectve functon water _ costs = fp* B," " + Qw, * vp rland wb wb wb (61) were fp s a vector of fxed water prce per actare of rrgable land vp wb s te bloc-rate water prce per unt (.e. m3). QW,wb s a vector of consumed water per bloc B, rland s a vector of avalable rrgable land 2.9. Transton from farm to aggregated levels Ts secton presents te requred adustments n te exstng modules as well as te set of new varables and equatons to develop for facltatng te transton of mpact analyss from farm to vllage/regonal and natonal levels. Tese adustments and new equatons mae t easy, on te one and, te nter-lnage of all ndvdual farm ouseold models nto one aggregate model and, on te oter and, te modellng of nteracton among farm ouseolds for maret factors (land, labour and equpments) wc are very common n developng countres. Te development of ts part of te model as started wtn te BECRA proect 14, owever only from tecncal pont of vew. Most of te conceptual ssues lned to aggregaton process are nvestgated wtn ts study. Among ts, we can recall te aggregaton bas, suc as unrealstc product specalzaton or/and excessve resources excange, wc may arse f farms are not properly aggregated. In addton to tradtonal metods for reducng aggregaton bas, lely groupng farms wt smlar caracterstcs (Bucwell and Hazell, 1975), assumng multple crop producton actvtes (Heady and Srvastava, 1975) and usng rotatons nstead of sngle crops (Heady and Srvastava, 1975) wc are already taen nto account n FSSIM, a calbraton procedure based on PMP (Pars 14 BECRA: Bo-Economc analyss of clmate cange mpact and adaptaton of Cotton and Rce based Agrcultural producton systems n Mal and Burna Faso (2010, CE). 75

76 et al., 2011) as been mplemented n order to estmate transacton costs and elmnate (or at least reduce) aggregaton errors Adustment of exstng modules to facltate aggregaton Smultaneous optmsaton of farm level models It conssts of ncludng tree new dmensons (.e. ndex postons) on data and varable structure n all te exstng modules n order to mae t easy te smultaneous solve of several farm models reproducng te beavour of dfferent farm types. Tese dmensons are (Ms,Re,Ft), were Ms ndexes Member state (or country or regon), Re ndexes regon (or zone or sub-regon or vllage) and Ft ndexes farm ouseold type. Tans to ts tecncal specfcaton, t wll be possble to smulate te nteractons among farms for factor marets (land, labour and captal) as well as for common resources (e.g. common pasture), wc are very mportant n developng countres. It permts as well te aggregaton of results from farm ouseold to vllage, regonal or natonal levels. Wt ts new specfcaton, te FSSIM-Dev obectve functon, mplemented n te common module, becomes te maxmzaton of te wegted sum of representatve farm ouseolds utlty. Utlty s defned as farm ouseold expected ncome mnus ts standard devaton due to rs averse towards prce and yeld varaton. Te general matematcal formulaton of te model s now te followng: Max V wt U = w U = R φ σ (62) were V s te value of te obectve functon, denotes representatve farm ouseolds and w ter wegt wtn te vllage/regon, R s te farm ouseold expected ncome, φ s te rs averson coeffcent and σ s te standard devaton of agrculture ncome due to prce and yeld varaton. In addton to cangng te model's obectve functon, a set of adustments ave to be ncluded wtn te aggregaton process for reflectng competton among farms for factor marets and common resources. Ts mples tat a set of new equatons as to be ncluded to balance te demand and supply for goods and tradable factors at te aggregated (regonal, dstrct or vllage) level. Ts s expressed by te clearng condtons explaned below Modellng te possble excange of tradable factors among farm ouseolds Land, labour and equpment (.e. captal) are assumed to be tradable factors and can be excanged among farms wtn te same regon (or vllage). For modellng te possble excange of tradable factors among farm ouseolds, a new varable was ncluded n te rgt Rgt-Hand Sde (RHS) of resource constrants. Tat s to say tat for eac farm ouseold () and for eac tradable factor (tf), te requred quantty of factor sould be less or equal to 76

77 ntal avalable quantty plus quantty rented-n mnus quantty rented-out (.e. traded quanttes) 15. A,, tf x, B, tf + s, tf b, tf (63) were ndexes farm ouseold, ndexes agrcultural actvtes, tf ndexes tradable factors (land, labour and captal), A s a (m x n) matrx of nput coeffcents (.e. nput use of factor tf nto actvty ), B s (n 1) vector of ntal resources endowment and s and b are (n 1) vectors of rented-out and rented-n tradable factors, respectvely. Te second adustment s by addng nto te FSSIM-Dev obectve functon te cost/beneft from tradng producton factors. Tradefactors _ outcome = w ( s, tf b, tf ) p, tf tf (64) were s and b are (n 1) vectors of rented-out and rented-n tradable factors (tf) n eac farm ouseold () and p s a (n 1) vector of tradable factor prces Aggregaton module Ts module ncludes two maret clearng condtons worng at regonal level: te frst one for tradable factors and te second one for agrcultural goods Regonal balance for tradable factors Ts balance equaton mples tat, for eac regon and for eac tradable factor (tf), agrcultural demand for factor plus te export of factor from oter sectors sould be equal to agrcultural supply for factor plus te mport of factor from oter sectors, assumng a close regonal maret for tradable factors. Ts means tat any surplus of tradable factors can be exported outsde te agrcultural sector at maret prces. Conversely, any excess demand for tradable factors can be satsfed by mportng from te oters sectors wtn te same regon (e.g. excange of land and equpment among sectors are not allowed). Regonal tradable factors balance s, tf + M tf = wb, tf w + E tf (65) ndexes representatve farm ouseold tf ndexes tradable factors w s te wegt of representatve farm ouseolds wtn te regon s s te quanttes of tradable factors rented-out b s te quanttes of tradable factors rented-n 15 To facltate understandng, we swtc-off from te resource constrant te parameters used to capture seasonalty and labor slls. 77

78 M s te mported quanttes of tradable factors from oter sectors wtn te regon E s te exported quanttes of tradable factors to oter sectors wtn te regon Regonal balance for commodty Smlar to te prevous one, ts balance equaton expresses tat for eac tradng regon and for eac agrcultural good (), te demand and te supply for goods sould be n equlbrum at regonal level, assumng a close regonal maret for goods. Regonal tradable factors balance s, + M = wb, w + E (66) ndexes representatve farm ouseold ndexes goods w s te wegt of representatve farm ouseolds wtn te regon s s te sold quanttes of goods b s te bougt quanttes of goods M s te mported quanttes of goods from oter sectors wtn te regon E s te exported quanttes of goods to oter sectors wtn te regon Te man queston arsng from ts step s ow to ensure tat te model reflects correctly te excanged producton factors between farms and ow to capture transacton costs f tey exst. Te followng secton present a new calbraton procedure developed n Pars et al (2011) wc can be used for answerng suc nd of questons Calbratng aggregate farm ouseold models Aggregaton s an mportant ssue wen modellng te agrcultural sector usng matematcal programmng. Serous error, suc as unrealstc product specalsaton and excessve resource excange, may arse f farms are not properly aggregated (Onal and McCarl, 1991). Apart from te fact tat matematcal programmng models suffer from over-specalzaton of te optmal soluton, te man cause of ts dscrepancy often orgnates n te transacton costs lned to te trade of maret factors across farms. In general, ts pece of crucal nformaton s measured wt a degree of mprecson. Ts secton presents a calbraton procedure wc explots all avalable nformaton to mae aggregate model generate solutons tat perfectly reproduce te supply and demand of goods at ndvdual level as well as te trade flows for a gven base year. Based on PMP approac ts procedure attempts () to calbrate te model and guarantees exact reproducton of observed producton and consumpton levels of te base year; and () to estmate te effectve margnal cost of tradable factors. As explaned n te prevous secton, PMP applcaton s performed troug tree steps: n te frst step a set of calbraton constrants s added. Ts set of constrants bnds producton (q) and rented-n (b) tradable factors to ter observed levels (q 0 and b 0 ) at te base year perod. Te matematcal formulaton of te PMP frst step s te followng: 78

79 Max V = s.t. A, f q b x, f,, tf q b x 0 w B 0, 0, tf, f (s, + c b,tf p tf + b ( 1+ ε), f ( 1+ ε' ) s,,tf )p b s, f [ ρ, f ] [ λ, ] ' [ λ ], tf, +, sb p,, x, fc a φ σ, x, (67) were and are vectors of small postve numbers for preventng lnear dependency between te resource and te calbraton constrants. Te dual values λ of te frst calbraton constrants represent te mplct (.e. unobserved) costs related to producton process (Howtt, 1995a). Te dual value λ ' of te second calbraton constrant, wc may be eter postve or negatve, can be nterpreted as te mplct unt costs (.e. unt transacton costs) lned to excange of tradable factors (for furter detals on ts procedure see Pars et al., 2011). Te same calbraton procedure could be appled for te oter tradable factors (land, captal) f data on observed traded quantty s avalable. In te second step te dual values λ s employed to estmate te parameters d and Q of te non-lnear mplct costs (te procedure for estmatng tese parameters are explaned n te secton dealng wt calbraton module). In te trd and last step, te calbraton constrants of te frst stage are removed and te estmated non-lnear mplct cost functons as well as te mplct labour costs are embedded nto te obectve functon. Te model s now calbrated and can be used for assessng farm ouseold's response to parameter varatons of nterest from a polcy vewpont. Te fnal matematcal structure of te FSSIM-Dev s presented n Appendx Concluson Ts secton presented a detaled descrpton of te template of te FSSIM-Dev model to be used for ex-ante assessment of agr-food and rural polces on te lvelood of farmouseolds n developng countres. In addton to te set of mprovements ncluded wtn te exstng FSSIM modules suc as crops, calbraton, trend and polcy modules, ts verson nvolves tree new modules wc are ndspensable for representng farm-ouseold beavours n developng countres: ouseold, perennal and aggregaton modules. Moreover, FSSIM-Dev nvolves advanced tecncal aspects wc can ncrease model reusablty and applcatons for dfferent case studes and polcy scenaros. Te next secton descrbes te components of FSSIM-Dev newly developed under te current proect framewor, suc as te electronc survey, te database and te Grapcal User Interface of te FSSIM-Dev. After a detaled tecncal descrpton of tese components, te frst applcaton of te FSSIM-Dev model s llustrated va an example n Serra Leone to exante assess te mpacts of new croppng managements n te lvelood of a set of representatve farm ouseolds. 79

80 3. FSSIM-Dev Components & Tecncal Implementaton 3.1. Introducton FSSIM-Dev, descrbed n te prevous secton, s developed usng GAMS (General Algebrac Modellng System) software wc s a g-level modellng system for matematcal programmng and optmzaton tecnque. It s solved usng a well suted solver for non-lnear programmng model, namely CONOPT. An Electronc survey was used to enter, vsualze and manage data n te FSSIM-Dev Database. A frendly User Interface was developed on te Wndev (PC-Soft ) programmng envronment to extract data from te Database nto text fles readable by GAMS. Ts nterface s also used for settng/defnng polcy scenaros, selectng calbraton metods, te swtcng on/off modules & constrants, runnng polcy scenaros and vsualzng model results. Ts secton gves a detaled descrpton of tese tree FSSIM-Dev components: Electronc Survey, Database and Grapcal User Interface. Ts could serve as a manual for users of FSSIM-Dev FSSIM-Dev Electronc Survey Te FSSIM-Dev Electronc survey s a User Interface developed n MS Vsual Basc (VBA) to enter, vsualze and manage data n te FSSIM-Dev Database. In order to launc te electronc survey, double-clc on te FSSIM-Dev Electronc survey.mdb fle sould be made, te MS-Access wll open, load te fle and te man menu (Fgure 5) wll appear. Ts man menu allows access to te FSSIM-Dev data wc can be grouped nto 3 specfc categores: global data, regonal data and farm data. In te frst category, te user defnes (or selects from exstng lst) te set of crops, producton tecnque and agr-envronmental zone lned to s/er applcaton. In te second category, after defnng te set of agrcultural actvtes (rotaton, tecnque, agr-envronmental zone and system) at regonal level, e/se flls te data on nput output coeffcents lned to tese actvtes. In te last category, te user flls farm data suc as resources endowment and calbraton data. Fgure 5. Te man menu of FSSIM-Dev electronc survey 80

81 Global data Te global data ncludes all te nformaton tat can be used to defne te set of agrcultural actvtes, suc as te lsts of crops, agr-envronmental zones (or sol type), producton tecnque (agro-management) and producton system. It ncludes as well te set of envronmental ndcators tat can be computed by te model and ter unts. Tey are called Global because most of tem are not regon or farm specfc Defnton of crops Ts button gves access to te lst of crops (Fgure 6) tat already contans te man crops as defned by te FAO. Te lst can be sorted n alpabetc order or by crop famles. By clcng on te Add new button, te user can defne a new crop. Selectng a crop n te lst and clcng te Delete button allows te user to delete a crop n te lst. Fgure 6. Crop lst form By double-clcng on te name of a crop, te crop form opens (Fgure 7a) n wc te crop data are defned: Crop name: te full name of te crop GAMS code: te code used n te GAMS model to manpulate te crop Perennal (Y/N): te user as to specfy f te crop s perennal or not. If yes, e/se as to defne te duraton (n years) of te dfferent stages along te crop lfe: Establsment, Growt, Producton, Declne (Fgure 7b). 81

82 FADN code: ts combo box contans te lst of crops retaned n te European FADN database. Te FADN crops lst s more aggregated tan FSSIM crops lst and n some cases due to data mssng, model calbraton could be based on FADN lst rater tan on FSSIM lst. In suc case, te user must create a relatonal mappng between te two lsts of crops. Products, prces and prces varaton: eac crop can produce dfferent products (for example, rce can produce gran and straw). Te combnaton set of crops-products are cosen from a lst and ten for eac combnaton crop-product, te average prce per unt and te prce varaton (te prce varaton s usually te standard devaton of te prces across several years) ave to be defned. Famles: eac crop can be assocated wt one or more crop famles/groups. (a) Arable crop Fgure 7. Arable and perennal crops forms (b) Perennal crop Defnton of agro-envronmental zones Ts button gves access to te predefned lst of agro-envronmental zones. By clcng on te Add new button, te user can defne a new zone n te Agro-envronmental zones form (Fgure 8). In suc case, t as ust to gve a name and a GAMS code to ts new zone, to specfy te sol and clmate assocated to ts zone and f necessary a sort descrpton. Te buttons gve access to te sol and clmates lsts, troug wc sols and clmates can be defned n te same way. 82

83 Fgure 8. Agro-envronmental zones form Defnton of producton tecnques and producton systems Tese two buttons wor n te same way and are used to defne te producton tecnques and te producton systems (Fgure 9). Two producton systems are already predefned and must not be deleted,.e. current and alternatve systems. Te frst one s lned to current agrcultural actvtes and te second one to alternatve actvtes. (a) Producton tecnques Fgure 9. Producton tecnques & Producton Systems forms (b) Producton systems Specfcaton of Input categores In ts screen, te user defnes te dfferent categores of nputs tat wll later be assocated wt eac crop wtn te agrcultural actvtes (rotatons). For eac nput category te followng nformaton are needed: 83

84 Input name. Unt: a combo box gves access to te unts tat are defned by te user (see 2.7, below). Gams code: a sort code tat refers to te nput n te GAMS model. Labour (Y/N): ceced f te nput conssts of labour (dfferent from nputs le fertlzers, seeds, water etc.). For tese nputs, te unt s Day. Perod of croppng (f labour): a combo box gves access to a lst of perods n te cultural cycle of te crop,.e. Sol preparaton, seedng, Crop mantenance and Harvest and post-arvest. If necessary, te user can assocate eac labour nput wt one of tese perods. Manpower: agan for labour nputs, te user can assocate a nd of manpower needed to accompls tat nd of wor. From te combo box, te user can coose Men, Women, Cldren, Men/Women or All. Fgure 10. Inputs form Defnton of envronmental outputs Ts form allows defnng te set of envronmental externaltes (.e. envronmental outputs) to be computed by te model (Fgure 11). Only te name, te unt and te GAMS code of te envronmental outputs are ncluded n ts form. Name Unt Gams code Fgure 11. Externaltes form 84

85 Defnton of Unt In ts form, te unts of dfferent nputs and outputs can be defned. Fgure 12. Unts form Regonal data Defnton of te study Regon/Country Frst, te user wll coose n te combo boxes te State (.e. Country) and te assocated regons n wc te survey taes place. If necessary, a new country (Fgure 13a) or/and new regons (Fgure 13b) can be defned. Eac country as to be defned by ts full name and GAMS code. By clcng on te Done button, te sub-form wll be closed and te data saved. A new regon s defned n te same way, but t as to be assocated to a specfc country. Clcng te Done button wll agan close te sub-form and save te data. Te Parameter button allows te user to enter te maret prces of labour and water at regonal scale (Fgure 14). 85

86 (a) Defnng Country (b) Defnng Regon for te selected Country Fgure 13. Country and Regon forms Fgure 14. Input unt costs form Wen a Country and a regon ave been selected, te user wll ten select, from a predefned lst, te set of crops tat can be cultvated n ts regon and defne as well te set of crop rotatons and farm types exstng n ts regon Selecton of relevant crops Te set of crops tat can be grown n te selected regon are cosen from te lst of all te crops (Fgure 15) usng one of tese two buttons: > or >>. Te < button s used to remove a crop; te << button to remove all crops from te selected crops lst. 86

87 Fgure 15. Crops form Defnton of crop rotatons and agrcultural actvtes Frst, te user as to defne te set of agrcultural actvtes one by one by specfyng for eac of tem ts crop rotaton and te agro-envronmental zone n wc t s grown (Fgure 16). Ten, for eac crop wtn te rotaton, t as to specfy ts order n te rotaton, ts producton tecnque usng te Tecnque combo-box, and ts growt stage f t as a perennal crop (Establsment, Growt, Producton or Declne). Eac tme a crop s added/deleted to/from te rotaton, te rotaton descrpton s modfed to reflect te successon of crops n te rotaton. If needed, te Duplcate button wll copy all nformaton (ncludng nputs/outputs) n a new lne n te same rotaton. Te buttons on te bottom-left of te form ( ) are used to navgate between te set of agrcultural actvtes tat ave been defned for te selected regon and create a new actvty. 87

88 Fgure 16. Rotatons form Quantfcaton of Input Output coeffcents IO coeffcents are defned n FSSIM-Dev at regonal level and tey are not farm specfc. To fll te data on labour requrement, nput use, yeld and envronmental externaltes assocated to eac crop wtn agrcultural actvty (Fgure 17 to 20), te user as to clc on te button Inputs/Outputs to open a wndow wt te followng Tables. Labour requrement: In ts screen te labour requrement for eac crop wtn te rotaton ave to be defned. Te user as to coose frst te dfferent nput categores (plantng, seedng, arvestng, etc.) from te combo box and ten to set te unt cost and te quantty of labour (number of days requred to execute te wor) requred by crop and by nput category as well as te type of labour (men, women and cldren) tat can do te wor. Fgure 17. Labour requrement form 88

89 Input use: For all nput categores, te user as to select from te combo box te type of nputs (seed, fertlser, water etc.) and to set te unt cost, te used quantty and, f necessary, a number of applcatons of ts nput for eac crop wtn te rotaton (for example n te case of water). Fgure 18. Inputs form Envronmental outputs: Te defnton of outputs s made n te same way as nput data. Fgure 19. Outputs form Crop yeld: To defne te yeld of one crop, frst te user as to coose from a combo box te set of products tat can be assocated to suc crop and ten to specfy, for eac crop product, te average yeld and te yeld varablty defned by unt surface (usually 1 a). 89

90 Fgure 20. Crop yeld form Farm data Defnton of farm types In ts screen, te user defnes te set of farm types representatve of relevant farmng systems on te study regon. For eac type, te user gves a name and a sort descrpton (Fgure 21). Fgure 21. Farm types form Settng Farm Data Ts form (Fgure 22) wll allow te user to specfy all te caracterstcs of eac farm type manly n terms of farms endowments (e.g. land, labour avalablty) and observed actvty levels. Note tat tese farms can be real ones or vrtual farm types resultng of a typology. Note also tat te farm s automatcally lned to te selected regon. Te nformaton to be added to te model s te followng: o Survey number: t can be a number or a sort name tat wll be used as a GAMS code. 90

91 o Farm type: n case of real farms, tey can be assocated wt a farm type as defned prevously. o Number: ts number ndcates ow many farms n te regon belongng to tat type of farms. o Famly composton: te number of men, women and cldren n te famly. o Actve male adults, actve female adults, actve cldren: te number of men, women and cldren avalable n eac mont for worng n and off-farm. o Water avalablty: te montly water avalable for rrgaton. o Observed actvty levels: ere te user coose from te combo box te set of agrcultural actvtes (rotatons) tat are grown n te base year (te year n wc te survey was performed) and to set ter respectve areas. o Self-consumpton: for eac crop produced durng te base year a lne s automatcally added to set te percentage of producton used for self-consumpton. o Irrgable land, Grazng land: for eac farm type, te user gves te sare of rrgable/grazng land n total area and ten te correspondng area s calculated automatcally. Fgure 22. Farm data form 3.3. FSSIM-Dev Database Databases and data ntegraton procedures are ncreasngly mportant n matematcal programmng modellng applcatons, especally n te case of generc models wc ave been desgned to assess numerous scenaros. Te manual ntroducton of data eter drectly n te model or n text fles s partcularly error prone, dffcult, and user ostle, and terefore 91

92 nfeasble for large models or multple scenaro smulatons. FSSIM-Dev as been developed as a generc model amng at ncreased model re-usablty and applcatons of dfferent case studes and scenaros. For reacng ts am, a model-specfc Data Management Faclty (DMF) was created. Te DMF serves two purposes: t s used as a database for storng, manpulatng and nterfacng te FSSIM-Dev data (database module DM), and as a tool for retrevng te data from te DM and transformng tem nto text fles readable by GAMS (ntegraton code module - ICM). A number of approaces are avalable regardng data management and data ntegraton nto economc modellng. Typcally used data management approaces for matematcal programmng models wrtten n GAMS nvolve te use of MS Excel, MS Access, and My SQL. My SQL s a well-establsed and free of carge database management system, but t s less wde-spread compared to MS Access or MS Excel. MS Excel s a wdely used tool, lacng owever te data management propertes of a specalsed database product, suc as ) easer and faster data control, ) possblty of usng structured procedures for database populaton, ) mantenance of data ntegrty, and v) possblty of lnng te database tool to oter external databases and/or models. MS Access s a user-frendly easly accessble database management system, offerng te above MS Excel capabltes. Addtonally, te use of MS Access, as opposed to MS Excel, s advantageous for te retreval of te data and ter wrtng nto text fles. Te loadng of fles nto GAMS s sgnfcantly faster wen usng MS Access, compared to usng MS Excel. Te system s also more generc and re-usable, snce one as to specfy te range of cells to be mported. Ts means tat for eac applcaton te range of data to be mported would ave to be re-specfed, dependng on te number of records of eac nput fle. Tus, MS Access as been used for te development of te DMF Database Module Te Database module was structured accordng to FSSIM-Dev GAMS requrements. Te nputs requred by FSSIM-Dev can be dstngused nto data concernng te defnton and specfcaton of te agrcultural system, and nto data descrbng caracterstcs of ts system. Tese data correspond to set elements or parameter values wtn te model. Tere s a clear analogy between te two types of data and wat s specfed as a set or a parameter n te model. Te sets usually act as te data on system defnton, wle te parameters assocated to te sets act as te data on te descrpton of caracterstcs of ts system. In a model wrtten n GAMS, sets are defned as te basc buldng blocs of a GAMS model, correspondng exactly to te ndces n te algebrac representatons of models (Rosental, 2008). Sets can be one-dmensonal or mult-dmensonal. Te parameters are te core data of a model and tey can be scalars or dmensonal parameters. A scalar s a parameter of zero dmensonalty, tus tere are no assocated sets and tere s exactly one number assocated wt t (bd). Te dmensonal parameters are assocated to one or more sets of te model. For te desgn of te DM, specal attenton was gven so tat te actual relatonsps between sets and parameters tat exst n FSSIM-Dev are also used for establsng te relatonsps between te dfferent DM felds. Te buldng blocs of any database are te database tables. Eac table s caractersed by ) a table name: a unque dentfer for te relaton defned n te table; ) a prmary ey: a unque dentfer assgned to one of te table attrbutes, enforcng tat no row wll be duplcated; and ) columns wt ter eadngs and value types: eac column represents an attrbute wc sould be related to te prmary ey and t s assgned a specfc value type e.g. strng, 92

93 numercal, boolean, etc. Te table names sgnal te contents of te table n terms of type of data and specfc relaton. Specfcally, te name conssts of a prefx, wc dentfes te data type, and a root wc descrbes te relaton contaned n te table. Te referental ntegrty constrants follow te conceptual and tecncal lns of te FSSIM-Dev nput data for te establsment of one-to-many relatonsps, as mplctly establsed by te dmensons of te set and parameter domans n te model. In order to facltate database populaton, a number of SQL append queres ave been developed. Tese queres combne nformaton tat ave already been entered n oter tables and mport tese combnatons on te dependant table, so tat te user does not need to manually re-ntroduce t. Te names of te queres are dentcal to te names of te tables to be populated n order to easly dentfy to wc table tey correspond Integraton Code Module Based on te use of Vsual Basc for Applcatons, te Integraton Code Module (ICM) s a tool tat retreves data from te MS Access database nto text fles readable by GAMS. ICM s based on te specfcaton of te source database, te SQL query for eac data feld to be extracted, and te data destnaton fles. Te advantage of te VB applcaton s tat t allows replcatng exactly te data text fles used n FSSIM-Dev. Moreover, t allows eepng te same structure as FSSIM components (Fgure 23). Te executon of ts module s andled troug te FSSIM-Dev Grapcal User Interface (FSSIM-Dev GUI) presented below. FSSIM-AM Database FSSIM-MP Include fle (.nc) GAMS fle ( _data.gms) GAMS fle ( _model.gms) Fgure 23. Ln between database and GAMS fles 3.4. FSSIM-Dev Grapcal User Interface FSSIM-Dev as been coupled to a Grapcal User Interface (GUI) to elp modellers to evaluate model performance and polcy maers to smulate polcy scenaros. Ts GUI was targeted at users wo would le to apply FSSIM-Dev wtout avng a deep nowledge of GAMS programmng languages. It nvolves a set of screens wc provdes dfferent functonaltes gong from scenaro descrpton to dsplay results. Scenaro descrpton: troug ts screen (Fgure 24), te user may buld up a proect tat 93

94 specfes an assessment exercse. A proect s caracterzed by te defnton of te problem t tres to solve or study, and t ncorporates at least one experment confguraton, tat s, te confguraton of te models to be executed durng te analyss. An experment, n turn, s assocated wt a specfc set of modules and constrants, a sngle calbraton metod and s parameterzed by te specfcaton of a context and a polcy opton. Troug a sngle proect, several alternatves can be nvestgated and compared. Based on te results of te computaton, te calculated model outputs become avalable and can be vsualzed under dfferent formats accessble from te dsplay results screen Fgure 24. Scenaro descrpton n FSSIM-Dev GUI Model setup: from ts screen (Fgure 25) te user can select te set of modules to be ncluded n te smulaton run and ter correspondng constrants. Certan modules and constrants suc as te crops and aggregaton modules are selected by default and cannot be swtc off. Fgure 25. Model setup n FSSIM-Dev GUI: lvestoc module In te perennal module, te user can coose between tree dfferent modellng approaces: sort, medum and long-term approaces (Fgure 26). In te sort term, we assume a constant area for perennal crops, tat s, no land 94

95 competton between annual and perennal crops are depcted. In te medum term, an nnovatve modellng approac s used to tae nto consderaton sun cost and adustment cost effects of nvestment decsons. Ts modellng framewor provdes te md-term response of perennal producers to canges n ncentves. Bascally, we mae a dstncton between te exstng stoc of perennal crops and new plantatons n terms of nput-output coeffcents. In te long term, we adopt a steady state approac, allowng for adustments n te area allocated to perennal actvtes and, terefore, modellng te competton for land between annual and perennal crops. Fgure 26. Model setup n FSSIM-Dev GUI: perennal module Model calbraton: ts screen (Fgure 27) allows selectng te metod to use for calbratng te model durng te base year. It provdes several functonaltes suc as te: swtcng on/off rs (.e. swtcng between proft and utlty maxmzaton problems) coosng between dfferent metods for settng rs averson coeffcents [0-2]. swtcng on/off calbraton wt PMP approac coosng between dfferent PMP approaces (Standard PMP approac by Howtt; Röm and Dabbert PMP approac; Kanellopoulos et al. PMP approac; Maxmum Entropy-PMP approac by Hecele and Brtz; and Maxmum Entropy-PMP approac by Pars and Arfn) as well as between dverse PMP varants based on dfferent wegts of te lnear and te non-lnear terms of te cost functons (.e. standard, average cost and almost-lnear). Settng te transacton costs as te percentage cange between seller/buyer and maret prces. 95

96 Fgure 27. Model calbraton n FSSIM-Dev GUI Reference run: troug ts screen (Fgure 28), te user buld te baselne (.e. reference run) scenaro to be used as reference for comparng smulated polcy scenaros. It ncludes te set of parameters tat can be canged between baseyear and baselne suc as te nflaton rate, te ntal farm resources endowment (land, labour, captal and water avalabltes), te maret commodty prces and te average transacton costs between farm ouseold and maret prces. Fgure 28. Reference run n FSSIM-Dev GUI - Run smulaton: troug ts screen (Fgure 29) t s possble to smulate a set of polcy 96

97 scenaros related to te cange n maret commodty prces, te cange n accountng costs, te swtc on/off of alternatve (new) actvtes and te mplementaton of water prcng polces (more detal about tese polces are gven n te secton descrbng te polcy module). Ts screen nvolves also te requred button for runnng te model, namely run. By clcng ts button, te nput data requred by FSSIM-Dev are generated from te Database nto text fles and, ten, GAMS program uses ts nput fles to run te so-called FSSIM.experment.gms fle wc control all te FSSIM-Dev GAMS fles. 97

98 Fgure 29. Smulaton run n FSSIM-Dev GUI - Dsplay results: ts screen (Fgure 30) allows vsualzng FSSIM-Dev results stored under Excel, Gdx and Lst fles. Tese results are reported at bot farm and aggregated levels. Fgure 30. Dsplay results n FSSIM-Dev GUI Gdx fles are dsplayed wt a GDXVIEWER tool dstrbuted freely wt te GAMS software to vew and convert data contaned n GDX fles. After loadng a GDX fle n GDXVIEWER te content of te fle s dsplayed n lst vew (Fgure 31). Te left-and sde of te wndow sows te ndex of te GDX fle organzed n a tree structure. Wen clcng on an dentfer, te rgt-and-sde wll dsplay te actual data for te dentfer. Wen te rgt mouse button s clced on an dentfer a pop-up menu s presented tat allows exportng an dentfer to a number of target formats. GDXVIEWER as a bult-n faclty to qucly plot data. It ncludes LINE, BAR and PIE carts, examples are sown below. Te plots can be made troug te menu Fle Plot. 98

99 Fgure 31. Dsplay results usng GDXVIEWER 3.5. Concluson Ts secton gves a detaled tecncal descrpton of te FSSIM-Dev components, especally ter specfcaton, structure and nter-lnages. It sows te transparency, te flexblty and te usefulness of tese components for enterng, vsualsng and managng data as well as for settng/defnng polcy scenaros, selectng calbraton metods, swtcng on/off modules & constrants, runnng polcy scenaros and vsualzng results. Tans to tese specfcatons, FSSIM-Dev could be used to smulate and to assess te effects of dfferent polcy optons on te performances and caracterstcs of dfferent farmng (and farm ouseold) systems. 99

100 4. Applcaton of FSSIM-Dev n a Serra Leone case study for assessng rce support polcy 4.1. Introducton In order to llustrate te applcablty of te newly developed FSSIM-Dev model, t s appled to a representatve sample of farm ouseolds belongng to te Nortern Regon of Serra Leone, more precsely on te Bombal regon, n order to assess te combned effects of nput (fertlser) subsdy polcy and mproved rce croppng managements. Serra Leone s a West Afrcan country, bordered by Gunea n te Norteast, Lbera n te Souteast, and te Atlantc Ocean to te Soutwest. It s dvded nto four Regons (Nortern, Soutern, Eastern and Western) wt a total surface of m² and a populaton estmated at 6.7 mllon n 2008 (World Ban, 2009). In addton to a favourable envronment for tropcal agrculture wt abundant ran ( mm per annum) and g bodversty, te country as rc marne resources and mnerals ncludng damonds, gold, rutle and ron ore (Jallo, 2006). In terms of economc prospects, Serra Leone s one of te poorest countres n te world. Its gross domestc product (GDP) per capta was estmated to be only slgtly more tan 300 US dollars (USD) n Te agrcultural sector plays a very mportant role n te economy. It contrbuted around 46% to te GDP n 2008 and employed 75% of te populaton (MAFFS, 2009). Te agrcultural sector s made up of crops, lvestoc, forestry and fseres sub-sectors. Arable land represents 74% of te total area of te country (around 5.365,000 a). However, due to several reasons among tem te nadequate tools and lmted access to marets and nputs only 36% of ts area s under cultvaton. Te average farm ouseold cultvates 1.56 ectares. 56% of te farms ave less or equal to 1 a and te maorty of oldngs range from 0.5 to 2 cropped ectares wle eepng potental arable land under fallow (FAO, 2005). In most of cases, farmers manage dfferent plots, of wc 60-80% are located n te upland, and 20-40% are n te lowlands (Inland Valley Swamps (IVS), Bollands, Mangrove Swamps and Rveran Grasslands see n Annex XX te descrpton of te West Afrcan ecosystems). Rce yeld n te upland s owever generally lower tan n te lowlands (MAFFS, 2009). Te Nortern and Eastern regons are consdered te most productve regons n te country due to te larger agrcultural areas under cultvaton. Most of te land under cultvaton s dedcated to rce wc represents te staple food of te populaton and s grown by over 95% of te farmers. It s commonly cultvated under mxed croppng wt cassava, maze, mllet, groundnuts and sweet potatoes n varyng proportons (MAFFS, 2009). However, due to very low productvty, most of te farms are for subsstence or sem-subsstence purposes, and te produced outputs are manly for famly consumpton. To contrbute to te mprovement of food securty and te economc development of Serra Leone's agrcultural sector, bot natonal governments and te nternatonal communty Te European Unon as made a donor snce 2005 troug te Stablsaton of Export Earnngs (STABEX) funds te 8 t European Development Fund. Te man goal of ts nterventon s te mprovement of rce producton and te reabltaton of cocoa and coffee plantatons to aceve ts food securty goals and mprove te agrcultural export sector of te country. Most of te support provded between 2007 and 2009 by tese proects (wc reaced a value of 4,378,000 EUR) focused on ncreasng yelds troug a set of measures and 100

101 ave developed dfferent agr-food and rural polces. Te promoton of domestc rce producton s a ey element n ts respect, snce t drectly contrbutes to mprovement of food securty, stmulates economc growt and ncreases rural ncome. Serra Leone s agrcultural development polcy as been focused on te acevement of rce self-suffcency among oter obectves, wc as been already supported by governmental and donor organzatons wt lmted success (JICA, 2009). Te current polcy related to te rce producton s descrbed n te Natonal Rce Development Strategy (NRDS), were a framewor s lad down amng at sgnfcant ncrease of rce producton. One of te ways to aceve ts obectve s to ensure an ncrease n te sustanable productvty and producton of rce n Serra Leone (JICA, 2009). Te am of ts case study s to assess troug te FSSIM-Dev model te combned effects of nput (fertlser) subsdy polcy and mproved rce croppng managements on te lvelood of farm ouseolds n te poorest regon (Bombal) of Serra Leone. In order to aceve ts obectve te followng steps are taen: 1 Identfcaton and analyss of te current croppng and farmng systems: ts step sees to dentfy te set of representatve farm ouseolds n te regon as well as ter man croppng systems. It s aceved usng two dfferent face-to-face ntervews: ) n te frst one, more tan 190 smallolders ave been ntervewed n summer 2009, n order to collect data on ter resources as well as on ter soco-economc condtons, and ) n te second one, a set of regonal experts as been ntervewed durng December 2011 n order to collect agronomc data for maor crops n te regons (.e. expermental data, statstcal data, etc.). Te Prncpal Component Analyss was used to elaborate te farm typology and defne te most representatve farmng systems. 2 Identfcaton of alternatve rce producton systems: performed usng expert nowledge (ncludng bblograpy analyss), ts step sees to dentfy a set of rce actvtes wt ger yelds (tan te exstng ones) usng better management practces (N fertlzaton, adustng sowng date ). Te man dea from ts step s to avod te use of complex models (e.g. CropSyst, APES ), wc are developed to be used for plot analyss wt a large quantty of observed data. Expert nowledge s also used to determne te relatonsps between agrcultural practces, crop yelds and envronmental effects n a context of g sol qualty varablty. 3 Defnton and mplementaton of te smulated scenaros: n ts step two polcy scenaros were mplemented, based on a lterature revew and nteracton wt smallolders and polcymaer, and ter results are presented and dscussed n comparson to a reference scenaro (.e. baselne). Ts secton descrbes n detals tese tree steps. It starts wt te dentfcaton of maor agrcultural actvtes n te studed area, ten, t sows te results of te farm ouseold typology, and fnally, t provdes a bref descrpton of te smulated scenaros and dscusses ter results n comparson to te reference scenaro. tecncal assstance wc manly dealt wt factors amperng or/and ncreasng () smallolder agrcultural productvty, () current and potental farm ncome and, more generally, rural poverty. 101

102 4.2. Selecton of current agrcultural actvtes In order to dentfy te man current actvtes n te targeted regon (crop rotatons and crop practces: fertlzaton, rrgaton, seedng, etc.) te survey, carred out durng 2009 n te Bombal regon was analysed. Te survey tself and te detals about te collected data are descrbed n te Gomez y Paloma et al., (2012) report. One of te targets of ts survey was to dentfy at regonal scale te current cultvated crops and rotatons n te studed area. Te prncpal crops n te Bombal regon are rce, vegetables, palm ol, sorgum, maze and vegetables crops. Combned wt te results of surveys on management nformaton, tese crops were defned as te current agrcultural actvtes n ts regon. Concretely, te metod used to dentfy te man current actvtes n te Bombal regon s dvded n two steps: ) frst te man actvtes by ecosystem (Upland, IVS and Bolland) are dentfed, ten, ) te varables on yeld and on te total requested labours for eac actvty are used to cluster te man actvtes by usng te Prncpal Component Analyss (PCA). Te palm ol actvty s classfed only based on te yeld nformaton. Table 2 presents te calendar of te rce croppng systems. Table 2. Rce croppng calendar by ecosystem Upland rce IVS rce Bolland rce Brusng/Fellng/Clearng February- Aprl - - Brusng and Moundng - Aprl Aprl Plowng and seedng Md-Jun - / Puddlng - July July Transplantng - July July Weedng Md-July - - Harvestng November- December November- December November- December Classfcaton by ecosystem Te frst classfcaton s realzed based on te tree ecosystems: upland, IVS and Bolland. By analysng te surveyed data, we can sow tat te upland rce s te domnant actvty grown by 117 farms (42%), followed by te IVS rce actvty cultvated by 115 farms (41%) and ten te Bolland actvty observed only n 49 farms (17%) (Table 3). Te Bolland sol s often eavy clay, mang t very ard to wor, especally durng te dry season for sol tllage and weedng. By loong to rce producton, te gest yeld s observed for te IVS actvty (0.38 t/a), followed by te Bolland (0.31 t/a) and ten te upland rce (0.28 t/a). For yeld varablty, te IVS system presents te gest varablty (standard devaton = 0.2 t/a) by comparson to te rce grown on Bolland or upland. Ts could be explaned by te problem of water management under te IVS system. In fact, n suc a context, farmers ave not te requested equpments to manage adequately te ranfall water dstrbuton wtn te rce feld. Table 3. Average and standard devaton of rce yeld by ecosystem 102

103 Numbers Percentage (%) Average yeld (t/a) Standard devaton (t/a) Upland rce ,28 0,11 Ivs rce ,38 0,20 Bolland 17 rce 49 0,31 0,15 Total Cluster analyss For eac ecosystem, a cluster analyss was undertaen by consderng te total farm labour and yeld crtera. For eac type of rce and for eac crteron (labour, yeld) two classes are dentfed: low and g yeld and low and g labour. Rce classes are created automatcally by usng te PCA analyss. Ts analyss was performed usng TANAGRA (Raotomalala, 2005), wc elped to create for eac crtera (yeld, labour) two statstcally dfferent classes. Tus, 12 rce patterns (croppng systems) were dentfed as sown n Table 4. Overall, te rce croppng systems producng te low yelds are caracterzed by a g yeld varablty compared to te rce croppng systems wt a g yelds. Ts varablty s almost te same for te tree types of rce (around 30%). However, for te gest yeld, te upland rce sows te lowest yeld varablty, followed by te IVS and ten te Bolland. By combnng te ecosystems and te total labour classfcaton, four rce croppng systems are dentfed for eac ecosystem (Table 4). For eac rce croppng system a type of sol and a percentage of organc matter are also assocated based on expert nowledge and bblograpy analyss (Sato et al., 2010). Table 4. Set of selected rce croppng systems Ecosystem Classes Croppng systems Yeld (t/a) Upland IVS Low yeld low labour Low yeld g labour Hg yeld low labour Hg yeld g labour Low yeld low labour Low yeld g labour Standard devaton Labour (day/a ) Type of sol Organc matter (%) CS1 0,21 0, Ultsol 0,60 CS2 0,23 0, Oxsol 0,60 CS3 0,40 0, Ultsol 0,90 CS4 0,44 0, Oxsol 0,90 CS5 0,27 0, Sandyclay CS6 0,33 0, Sandyclay 0,90 0,90 103

104 Hg yeld low labour Hg yeld g labour Low yeld low labour CS7 0,71 0, Sandyclay 2,10 CS8 0,50 0, Sandyclay 2,10 Bolland CS9 0,19 0,08 85 Sandyclay 0,60 Low yeld g labour Hg yeld low labour CS10 0,20 0, Sandyclay 0,60 CS11 0,35 0, Sandyclay 0,90 Hg yeld g labour CS12 0,47 0, Sandyclay 0,90 Te results obtaned from ts classfcaton were presented and valdated by te local experts n one day meetng eld n te Mnstry of Agrculture n Freetown (December, 2011). Te lst and te functon of te expert pool are presented n Table 15. Overall, te detaled analyss of te tree domnant rce croppng systems (upland, IVS and Bolland) sowed tat: 1- Upland rce: for te same yeld, te actual labour requrement could be twce as muc from one farm to anoter. Ts statement s te same for all te tass from sowng to arvestng. Ts result could be explaned manly by te crop cycle duraton and te sol type and qualty. Generally, te performance sould be for a relatvely flat land (wt less tan 10% slope) and sort crop cycle (2 to 3 monts). Were relatvely flat land s not avalable, land wt steep slope (>30%) sould be contour-bunded to reduce excessve run off and eroson. Ts operaton s very tme consumng (Rodes, 2005). Table 5 sows tat te sol preparaton (brusng, plowng and seedng) and te weedng practces are very labour consumng. 2- IVS rce: smlarly to te upland rce, n te IVS system te sol preparaton and te transplantaton are te two tass tat requested te gest labours (Table 5). 3- Bolland rce: ere also te sol preparaton (plowng and seedng) and te weedng events are very labour consumng (Table 5). In te Bolland ecosystem, te brusng operaton s less tme consumng tan n te upland because ts type of ecosystem (Bolland) s not sutable for te development of srubs as for upland ecosystems. 104

105 Table 5. Labour requrement for rce croppng systems

106 4.3. Farm ouseold typology In order to gve a representatve pcture of te current farmng systems n te selected regon and to capture eterogenety among farms ouseolds, a typology was performed usng te data collected n 191 farms troug a face-to-face ntervew. Tese farms are manly arable and cultvate partcularly rce, vegetables (suc as groundnuts, manoc and beans), cassava and orcards (manly palm ol). A segmentaton analyss followed by a clusterng analyss s undertaen for selectng te more relevant farm ouseold types n te studed regon. Te segmentaton analyss was based on two structure crtera (farm sze and specalzaton). Two steps are, tus, taen n order to caracterse eac farm: ) frst, calculatng te total standard producton, wc expresses te economc sze of farms and, ) second, determnng te sare of te man crop n te total producton, n order to defne te farm specalzaton. Based on tese crtera, farms were dvded nto dfferent clusters usng te clusterng analyss. For representng te farm ouseold type two approaces are generally used: te average or te typcal farm ouseold. Te average farm ouseold could be defned as a vrtual (not observed n realty) farm ouseold wc s derved by averagng data from farm ouseolds tat are grouped n te same farm ouseold type. A typcal farm ouseold s an exstng (observed) farm ouseold wt representatve, for a certan farm ouseold type, propertes and caracterstcs. Dfferent approaces could be used wen tryng to dentfy a representatve typcal farm ouseold (e.g. selectng te farm ouseold tat s close to te average farm ouseold or te one wt te medan proft). In ts study, te average farm ouseold was selected to represent all farm ouseolds tat belong to te same farm ouseold type. By smulatng te beavour of average farm ouseolds, we ensure tat all mportant crop products tat are produced by farm ouseolds wll be part of te smulated producton plan. Ts s very mportant for up-scalng results from farm to regonal level. However, smulatng te average farm ouseold as also mportant drawbacs. Frst, an average farm ouseold does not exst, and consequently, an average crop/actvty pattern also does not exst. Second, te crop/actvty pattern of te average farm ouseold s muc more dversfed tan te one of ndvdual farms (for furter detals on advantages/dsadvantages of eac approac see Louc et al. 2010) Farm economc sze Te total standard producton s used to determne te economc sze of farms. It s expressed va te economc unt dmenson" (EUD) (RICA, 2010),.e. te farm ncome of eac farm. Te farm ncome as been calculated for eac farm ouseold n te survey. Te total farm ncome s calculated as follows: Farm ncome = Prce (rce) * Producton (rce) labour costs Were: Prce (rce): s te average rce prce n te Bombal regon calculated from te survey. Te average prce ncludes te tree rce types (Upland, IVS and Bolland) and t s equals to Leones/ton.

107 Producton (rce): s te average rce producton by farm type calculated from te survey. Te rce producton at farm level s calculated as te sum of producton of te tree rce types. Labour costs: ts ncludes only te cost of red labour. Te unt costs of red labour are dfferent accordng to labour category: men (6000 Leones/day), women (3600 Leones/day) and cldren (400 Leones/day). Tose costs are establsed by te experts of te Mnstry of Agrcultural durng te feld vst to Serra Leone (December, 2011). Regardng rce seeds, most of small ouseolds, as n Bombal, self produce ter own seed,.e. seeds for sowng n a gven year are stoed from te arvest of te prevous year. Oter farmers get, for free, ter rce seeds at te Mnstry of Agrculture or NGOs. For ts reason, we consder n ts applcaton, te cost of seeds as zero. Te same formula and assumptons are used for calculatng te crop ncome of oter crops (suc as palm ol, vegetables). All calculaton detals are presented n Appendx 3. Te total standard producton s te total farm ncome expressed n term of equvalent rce. Te total standard ncome s ten defned as te number of ectare of rce tat ensures te same total ncome. More specfcally, n order to calculate te rce equvalence of te farm ncome for eac surveyed farms te followng four steps are taen: 1- Calculatng te total rce producton for all te farms. 2- Calculatng te average rce producton (Prodaverage rce). 3- Assocatng te equvalent EUD unts for te average rce producton. In ts applcaton, 1 EUD rce s equal to Leones (te ncome of 1 tonne of rce). 4- Determnng te farm EUD by dvdng te total farm ncome by te rce equvalent EUD. By usng te total standard producton (.e. producton standard (PS)), a Prncpal Component Analyss as been undertaen. As a consequence, tree classes are obtaned: 1- Farms wt low ncome standard. Tere are 69 farms, representng 36% of te total farms surveyed n te Bombal regon. Tose farms are caracterzed by a low EUD, comprses between 0.2 and 0.88 EUD ( and Leones). 2- Farms wt medum ncome standard. Tey are represented by 84 farms (44% of te total farms n Bombal). Tey are caracterzed by an EUD tat comprsed between 0.9 and 2.23 ( and Leones). 3- Farms wt g ncome standard. Tere are 38 farms, representng 20% of te total farms surveyed n te Bombal regon. Ter EUD exceeds 2.27 (more tan Leones) Farm specalzaton A second farm typology s undertaen wt te focus on farm specalzaton. For ts, te sare of producton of te man crop n te total farm ncome s determned. Four types of 107

108 crop specalzatons are dentfed: rce farms, mxed farms (rce and vegetable), rce and perennal crops farms, and dversfed farms (rce, vegetables and perennal). Based on tese four specalzatons and te producton standards (PS) 9 farm ouseold types can be dentfed. Te specfcaton of tese 9 farm ouseold types s llustrated n Fgure 32. Fgure 32. Farm typology - In te small farms (wt low producton standard), 59 farms produce exclusvely rce, 3 are mxed farms (rce and vegetables) and 7 are cultvatng rce and perennal crops (manly palm ol). - For te medum farms (wt medum producton standard), 69 farms are producng manly rce, 11 farms are cultvatng rce and perennal crops and only 4 farms cultvatng rce, perennal and vegetables (dversfed farms). - For te bg farms (wt g producton standard), most of te farms are producng manly rce (59 farms), followed by te rce and perennal farms (10 farms) and ten 3 farms are dentfed as dversfed farms (rce, perennal and vegetables) Descrpton of farm ouseold types For ts study, 9 farm ouseold types are retaned (Fgure 32) wc represent more tan 190 surveyed farms n te regon (see secton 4.3.2). Te secton provdes a detaled descrpton of tese farm ouseold types. Rce_small: t s a small farm wt rce as a man producton. Te area of ts farm type s 0.92 a n wc te rce represents 84% of te total area followed by te perennal crops (12%) and vegetables (4%). Te tree types of rce are cultvated n ts farm type. Te perennal crops are represented only by palm ol. Manoc, groundnuts and beans represented te man vegetable crops. Te rce ensures 96% of te total farm producton estmated at Leones (0.78 UDE). Te rce actvty consumes 94% of te total farm labour (Table 6). Table 6. Man caracterstcs of te rce_small farm ouseold type Caracterstc Sze Producton Labour Unt a % Leones % Days % 108

109 Total 0, Perennal crop 0, Palm ol (establsment) 0, Rce 0, Upland1 0, Upland 2 0, Upland 3 0, Upland 4 0, IVS 1 0, IVS3 0, IVS4 0, Bolland 1 0, Bolland 2 0, Bolland3 0, Bolland 4 0, Vegetable 0, Cassava 0, Groundnuts 0, Beans 0, Rce_veg_small: t s a mxed farm type cultvatng manly rce and vegetables. Te average area of ts farm type s about 1.08 a n wc te vegetable crops occupy 50% of te total area. Te tree types of rce are cultvated: upland rce wt low yeld and low labour, IVS rce wt g yeld and g labour, and Bollandrce wt low yeld and g labour. Despte te mportant area of vegetable crops (50% of te total farm area), tey produce only 29% of te total farm producton. In fact, rce ensures 71% of te total producton and consumes 56% of te total farm labour (Table 7). Table 7. Man caracterstcs of te rce_veg_small farm ouseold type Caracterstc Sze Producton Labour Unt a % Leones % Days % Total 1, Rce 0, Upland 1 0, IVS 3 0, Bolland 2 0,

110 Vegetable 0, Cassava 0, Groundnuts 0, % Rce_per_small: t s a small mxed farm cultvatng manly rce and palm ol. Te average area of ts farm type s about 1.56 a dvded almost equally between rce (41% of te total farm area) and palm ol (57% of te total farm area). Te orange trees represent only 2% of te total area. Te tree types of rce are cultvated as descrbed n Table 8. Te rce remans te man crop n terms of producton (76% of te total farm producton) and labour use (72% of te total farm labour use) (Table 8). Table 8. Man caracterstcs of te rce_per_small farm ouseold type Caracterstc Sze Producton Labour Unt a % Leones % Days % Total 1, Perennal crop 0, Palm ol/ establsment 0, Palm ol /full producton 0, Orange 0, Rce 0, Upland 4 0, IVS 4 0, Bolland 2 0, Rce_med: t s a medum farm type cultvatng manly rce. Te average area of ts farm type s about 2.08 a represented manly by rce (75%), palm ol (22%) and vegetables (3%). Two types of palm ol are cultvated n ts farm type: palm ol at establsed and full producton stages, 12 types of rce (upland, Bollandand IVS combned wt low yeld and labour and g labour and yeld) and vegetables represented by groundnuts and Manoc. Te rce remans te man crop n terms of producton (94% of te total farm producton) and labour use (89% of te total farm labour use) (Table 9). Table 9. Man caracterstcs of te rce_med farm ouseold type Caracterstc Sze Producton Labour Unt a % Leones % Days % Total 2, Perennal crop 0, palm ol /establsment 0, palm ol/ début producton 0,

111 Rce 1, Upland 1 0, Upland 2 0, Upland 3 0, Upland 4 0, IVS 1 0, IVS 2 0, IVS 3 0, IVS 4 0, Bolland 1 0, Bolland 2 0, Bolland 3 0, Bolland 4 0, Vegetable 0, Cassava 0, Groundnuts 0, Rce_per_med: t s a medum farm type cultvatng rce and palm ol. Te average area of ts farm type s about 2.76 a represented exclusvely by rce (45%) and palm ol (55%). Almost all te types of rce are cultvated and contrbute to 70% of te total farm producton and consume almost 72% of te total farm labour use (Table 10). Table 10. Man caracterstcs of te rce_per_med farm ouseold type Caracterstc Sze Producton Labour Unt a % Leones % Days % Total 2, Perennal crop 1, Palm ol /establsment 0, Palm ol /début producton 0, Palm ol /full producton 0, Orange 0, Rce 1, Upland 1 0, Upland 2 0, Upland 4 0, IVS 1 0,

112 IVS 4 0, Bolland 1 0, Bolland 4 0, Rce_mx_med: t s a medum farm type cultvatng rce, palm ol and vegetables. Te average area of ts farm type s about 2.83 a. Tose crops occuped 48%, 23% and 29% respectvely for palm ol, rce and vegetables. In ts farm te palm ol s manly n te establsment stage and te IVS rce and te Bolland rce are te only types of cultvated rce. Te rce ensures almost te alf of te total farm producton and consumes 39% of te total farm labour use (Table 11). Table 11. Man caracterstcs of te rce_mx_med farm ouseold type Caracterstc Sze Producton Labour Unt a % Leones % Days % Total 2, Perennal crop 1, Palm ol /establsment 1, Palm ol /début producton 0, Rce 0, IVS 4 0, Bolland 2 0, Vegetable 0, Cassava 0, Beans 0, Rce_bg: t s a bg farm type cultvatng manly rce. Te average area of ts farm type s about 3.56 a represented manly by rce (73%) and palm ol (27%). Te palm ol s manly n te establsment stage. All te types of rce are cultvated except for te upland rce wt low yeld and low labour. Te total farm producton s manly produced by rce (95%) wc uses almost 88% of te total farm labour (Table 12). Table 12. Man caracterstcs of te rce_bg farm ouseold type Caracterstc Sze Producton Labour Unt a % Leones % Days % Total 3, Perennal crop 0, Palm ol /establsment 0, Palm ol /début producton 0, Rce 2,

113 Upland 2 0, Upland 3 0, Upland 4 0, IVS 1 0, IVS 2 0, IVS 3 0, IVS 4 0, Bolland 1 0, Bolland 2 0, Bolland 3 0, Bolland 4 0, Rce_mx_bg: t s a bg farm type cultvatng rce, palm ol and vegetables. Te average area of ts farm type s around 3.57 a. Tree types of crops are observed: palm ol (45%), rce (34%) and vegetables (21%). Only two types of rce are cultvated (IVS and Bolland). Rce and palm ol ensure respectvely 38% and 44% of te total farm producton (Table 13). Table 13. Man caracterstcs of te rce_mx_bg farm ouseold type Caracterstc Sze Producton Labour Unt a % Leones % Days % Total 3, Perennal crop 1, Palm ol /establsment 0, Palm ol /début producton 0, Palm ol /full producton 0, Orange 0, Rce 1, IVS 4 0, Bolland 2 0, Vegetable 0, Cassava 0, Beans 0, Rce_per_bg: bg farm type cultvatng manly rce and palm ol. Te average area of ts farm type s about 4.84 a. Tose crops occuped 50 and 48 for palm ol and rce, respectvely. Te rest s cultvated wt vegetables. Te man caracterstc of ts farm type s tat palm ol s defned as beng under dfferent stages of development: establsment, 113

114 growt and full producton. Te rce ensures almost te 70 of te total farm producton (Table 14). Table 14. Man caracterstcs of te rce_per_bg farm ouseold type Caracterstc Sze Producton Labour Unt a Leones Days Total 4, Perennal crop 2, Palm ol /establsment 1, Palm ol /début producton 0, Palm ol /full producton 0, Rce 2, Upland 2 0, Upland 3 0, IVS 1 0, IVS 2 0, IVS 4 0, Bolland 1 0, Bolland 3 0, Bolland 4 0, Vegetable crop 0, Cassava 0, Concluson Te Prncpal Component Analyss was used to elaborate te farm typology and defne te most representatve farmng systems n te Serra Leone Bombal regon. Apart from te bopyscal endowments, two soco-economc crtera are used to capture farm eterogenety: te economc farm sze and specalzaton. Overall, 9 types of farm ouseold are selected. Eac farm ouseold type represents a gven number of real farms. All farm ouseold types produce rce wt dfferent sare accordng to te ecosystem and te maorty of tem cultvate te tree types of rce (Upland, IVS and Bolland) but wt a large dversty n term of croppng systems. We observed also tat for te same croppng system labour requrement can be very dfferent among farm ouseold types leadng to a g varablty of labour productvty and farm proftablty Defnton and mplementaton of te smulated scenaros In ts secton, we descrbe te two selected polcy scenaros (Polcy scenaros 1 and 2) by comparson to busness as usual scenaro (baselne scenaro). Tese scenaros are desgned to assess te combned effects of nput (fertlser) subsdy polcy and mproved practces (.e. better management) on rce productvty and farm ouseold vablty n te Serra Leone's 114

115 nortern regon. Te man dea s to analyse te nteractons between agro-ecologcal condtons, mproved tecnology and polcy measure at feld scale and ten to assess ter overall soco-economc mpacts at farm and regonal scales Baselne scenaro Te base year nformaton for wc te model was calbrated stems from te year 2009, te year n wc te survey was carred out. To defne a sutable tme orzon for farmng systems scenaros analyss, te temporal scale may be cosen from a wde range accordng to te nature of te external drvng forces and te ntensty of te perturbng socs. In several studes, for te expected socs at te medum-term (as opposed to partcular ntermttent socs), a perod of at least years s recommended, wen studyng te ssue of external drvng forces (Ross et al., 2008). However, t s clear tat te greater te tme orzon, te greater s te uncertanty of te maret beavour. Te volatlty of te product prces and possble agrcultural polces tat could be appled are te man reasons for suc uncertanty. Tus, te year 2020 (tme orzon of 11 years) s taen as te tme orzon for runnng smulatons (.e. te baselne scenaro). Apart from nflaton, all te parameters are assumed to reman uncanged up to Te oter modellng assumptons are: Due to te lac of relable nformaton, no exogenous assumptons n terms of tecnologcal and maret canges are adopted between te base year and te baselne; Te excanges of labour between farms are allowed, wle te excanges of land and equpment are not; Only current producton actvtes are consdered n te baselne Polcy scenaros Procedure for dentfcaton of polcy scenaros In ts study te dentfcaton of polcy scenaros was done troug consultaton wt local experts, staeolders and agents of te agrcultural extenson servce (Table 15). Te dentfcaton of polcy scenaros was accomplsed n two 2 steps: 1- Presentaton of te study's obectve: a frst document descrbng te study area and te obectve of te study was presented to all experts (Mnstry of Agrcultural, Free Town). Ts presentaton gave also a summary of te modellng approac to use n ts polcy analyss. Table 15. Lst of ntervewed local experts and staeolders Name Poston Insttuton Emal adress Alpa Lao Professor/Reasercer Nala Unversty alpalao@yaoo.co.u> Jesse Olu Jon Presdent NAFFSL NaFFSL2009@yaoo.fr Andrea RC Dretor NAFFSL/ FAAS Claudacenter@yaoo.fr Conte Josep S. Bangur Asst drector MAFFS/SD able@yaoo.fr B.J. Bangura Drector MAFFS Bbangusa01@yaoo.fr J.A Jallo Asst. Drector extenson MAFFS aallo@yaoo.com 115

116 Nazr. A Momood Moamed A Serff R.O.SLARI SLARI nazrnade@yaoo.com Assstant drector MAFFS/PEMSD moameduba@yaoo.com Foday S.Kanu PME specalst MAFFS/SCP Fodaymot/cay@yaoo.fr Moamed T.Lau Scool of agrculture Nala Unversty drmtlala@yaoo.fr 2- Identfcaton of polcy scenaros: as a next step a meetng of alf day wt all experts was eld n December, 2011 at te Mnstry of Agrcultural. Te am of te meetng was to dentfy te man bopyscal and soco-economc constrants/problems of rce producton n te Bombal regon and a lst of potental solutons to overcome tese constrants. To aceve ts obectve, experts were ased to answer te followng two man questons: Wat are te man bopyscal, agro-envronmental (sols, senstvty to pests and dseases, senstvty to excess and defct of water etc.) and tecncal (sowng, arvestng, etc.) problems faced by farmers to cultvate rce? Wat are te man tecnologcal nnovatons tat could be appled to mprove rce producton? From te meetng dscusson and a large bblograpy analyss, we conclude tat even f Serra Leone s naturally endowed wt suffcent land, water, uman resources and favourable clmatc condtons, crop productvty remans very low and ts mprovement seems to be a bg callenge due to several barrers suc as: () te low-qualty of seeds; () te defcent access and use of fertlser (less tan 4g/a); () te lmted use of mproved plantng materals and producton metods (FAO, 2005), especally for cocoa and coffee (low denstes, age of te orcards, use of old cultvars, lac of mantenance, nadequate cultvaton metods, etc.), () te lac use of mecansaton; (v) te lmted access to agrcultural fnancal servces and to mcro-credts facltes; and last but not least (v) te fallow land constrant; usually after a crop cycle of 2-3 years, te land s often left, for a long dle/fallow perod n order to regenerate organc matter, sol structure and nutrents stoc... Currently, te dle/fallow duraton (bar sol) as progressvely been sortened from an average of 20 years n te 1960s to a mere 4-7 years currently (MAFFS, 2009). Te nadequacy or neffectveness of government support programmes and te slow adopton of mproved tecnologes by farmers ave also contrbuted to te poor crop productvty n Serra Leone (MAFFS, 2009). After a large dscusson wt local experts and an extensve lterature revew, two scenaros based on better rce managements are selected: () te adustment of sowng date and amount of seeds; and () te applcaton of subsdzed mneral fertlzer. Te am of ts scenaro analyss s to answer te followng questons: ow many farmers could adopt tese new croppng systems, tang nto account te current large croppng dversty and te local agroecologcal condtons and wat are ter lely mpacts on rce productvty and farm proftablty? Polcy scenaro 1 (PS1): Baselne & Adustment of sowng date and amount of seeds One of te ways for mprovng rce producton n Serra-Leone would be te selecton of approprate sowng date enables to ncrease rce producton and to tae nto account te arrval date of te monsoon. In fact, te standard devaton of average yeld due to te varaton n te sowng date s estmated to be between 17 and 68 (RARC, 2010). Te Upland 116

117 and te Bollandrce are more senstve to ts varaton ten te IVS rce. Fgure 33 sows ts varaton for te upland rce accordng to tree varetes (Ro 16, Nerca 1 and Nerca 4) and tree sowng dates. Form ts Fgure 33 t appears clearly tat: - Te optmal date for te tree varetes s te 16 t of June. A delay or an antcpaton of te sowng date by 15 days due to te arrval date of te monsoon or te not avalablty of labours could reduce yeld by Te delay of te sowng date reduces more te fnal yeld ten te early sowng date. Te observed dfference n term of yeld between te two dates could reac 11. In fact, te delay of te sowng date maes te rce more senstve to drougt n te reproductve pase. - Te more productve varety (Ro 16) s te most senstve cultvar to te sowng date. Fgure 33. Effect of sowng date on rce yeld Source: RARC, 2010 In ts scenaro, we consder tat te farmer s able to adust te plantng dates of rce n te dfferent ecosystems (Upland, Bolland and IVS) as well as te seedng amount. Te yeld and te costs of tese mproved rce actvtes (by adustng seedlng rates and sowng dates) are estmated based on nteractons wt te ntervewed local experts durng te meetng eld n Agrcultural Mnsteral n December Te estmated costs and yelds are reported n Table 16 and are related manly to te local varety, RoK16, wc s te more used one n te Bombal regon. Te yelds expressed by experts n Table 16 (alternatve yeld) are, owever, lower tan tose n Fgure 33 wc are well fertlzed. Table 16. Costs and yeld of current and alternatve rce actvtes Rotaton Sol* Tecnque up_rce_m x-fall up_rce_m x-fall Current costs (Leones/a) Alternatve costs (Leones /a) Current yeld (t/a) Alternatve yeld (t/a) Upland 1 T1_UP Upland 1 T2_UP

118 up_rce_m x-fall Upland 2 T1_UP up_rce_m x-fall Upland 2 T2_UP vs_rce IVS1 T1_IVS vs_rce IVS1 T2_IVS vs_rce IVS2 T1_IVS vs_rce IVS2 T2_IVS bol_rce Bolland 1 T1_BOLI bol_rce Bolland 1 T2_BOLI bol_rce Bolland 2 T1_BOLI bol_rce Bolland 2 T2_BOLI Source: Farm survey 2009 & expert nowledge * upland 1: low sol fertlty, upland 2: g sol fertlty; T1: low labour use, T2 g labour use Polcy scenaro 2 (PS2): Polcy scenaro 1 & applcaton of ntrogen fertlzer Te development of rce cultvars tat are adapted to fertlzaton s also one of proposed solutons to promote rce producton n Serra Leone. In fact, te current tradtonal varetes wc are stll grown n an extensve way are nown to be gly weed compettve, resstant to local botc and abotc stresses and adapted to low fertlzaton. However, t as a low yeld potental due to poor resstance to lodgng and gran satterng. In fact tose cultvars (te tradtonal ones) are generally tall and low-yeldng wt few tllers and pancles. In ts context, te N and P fertlzaton s seen as mportant and crucal alternatve to mprove te current rce croppng systems. Fgure 34 gves an overvew on te effects of N fertlzer on tree rce varetes. Ts Fgure sows also tat te tmng of applcaton of N fertlzer can strongly affect te rce yeld. 118

119 Source: RARC, 2010 Fgure 34. Effect of N fertlzer and seed varetes on rce yeld In ts scenaro, we attempt to assess te effects of ntrogen fertlzer on rce producton and on wole-farm proftablty. We assume tat te farmer wll receve wtn te STABEX program a fully subsdsed fertlser for producng rce. Te amount of subsdes wll be dstrbuted drectly to farmer dependng on te appled quantty of N fertlzer wc, n turn, vares accordng to te ecosystem. Table 17 reports te estmated yeld and costs of te alternatve rce actvty (.e. rce wt fertlser) based on expert nowledge as mentoned n prevous secton. Te rce yelds expressed n Table 17 concerns te tradtonal varety (.e. Ro16) cultvated n te Bombal regon. Table 17. Rce yeld and costs after applcaton of N fertlser Rotaton Sol Tecnque Yeld (T/a) Costs (Leones/a) up_rce_mx-fall Upland 1 T1_UP up_rce_mx-fall Upland 1 T2_UP up_rce_mx-fall Upland 2 T1_UP up_rce_mx-fall Upland 2 T2_UP vs_rce IVS1 T1_IVS vs_rce IVS1 T2_IVS vs_rce IVS2 T1_IVS vs_rce IVS2 T2_IVS bol_rce Bolland 1 T1_BOLI bol_rce Bolland 1 T2_BOLI bol_rce Bolland 2 T1_BOLI bol_rce Bolland 2 T2_BOLI Source: expert nowledge 119

120 4.5. Results and dscusson Te obectve of ts secton s to analyze and dscuss te results of te smulated scenaros (baselne vs. PS 1 and PS 2) usng a set of structural and economc ndcators computed at te ndvdual (.e. farm ouseold) and regonal levels: land use, croppng pattern, supply and demand of rce and farm ouseold ncome. In order to ease te nterpretaton of results and ter comparson across scenaros, most mpacts were measured as percentage canges to te baselne (.e. reference run). Te results n absolute terms are reported n Appendx Polcy scenaro 1 (PS1) Fgure 35 presents te varaton of farm ouseold ncome and labour use between baselne and polcy scenaro PS1 n te nne farm ouseold types as well as n te average regonal farm. As expected, te adustment of rce sowng date and of seed amount (.e. polcy scenaro 1) would boost ouseold ncome of most arable farms and mae farmers feel more secure. Te maorty of te farm ouseold types would be postvely affected and ter farm ouseold ncome would rse, n dfferent degrees, accordng to agro-clmate, resource endowment and soco-economc condtons. Te bggest percentage ncrease would occur n specalsed rce farms suc as rce_small, rce_med and rce_bg farm ouseold types, followed by mxed farms combnng rce and vegetable or rce and perennal crops. Te lowest ncrease s observed for farms domnated by perennal crops (palm ol) suc as rce_per_bg farm type. Te average percentage ncrease at regonal level would be also sgnfcant at around 23% (.e. average regonal farm) and t s manly drven by te g sare of specalsed rce farms wtn te studed regon. Te mplementaton of ts polcy scenaro would also nduce an ncrease of labour use n most of te farm ouseold types roundng te 4% explaned by te g labour requrement of alternatve rce practces, n comparson to current ones. 35% 30% 25% Farm ouseold ncome Labour use % cange to baselne 20% 15% 10% 5% 0% Rce_small Rce_veg_small Rce_per_small Rce_med Rce_per_med Rce_mx_med Rce_bg Farm ouseold types Rce_per_bg Rce_mx_bg Average Reg. Farm Fgure 35. Soco-economc mpacts of polcy scenaro 1 (PS1) on dfferent farm ouseold types 120

121 To understand tose soco-economc results, a deep analyss of te cange n land use, crop allocaton, producton and consumpton of man actvtes provoed by ts polcy scenaro s requred. Te frst outcome from ts deep analyss s tat te mplementaton of te polcy scenaro PS1 does not affect at all te current crop pattern. In fact, n all farm ouseold types, te model selects te same crop allocaton as for te baselne; but t substtutes te current rce practces by te alternatve ones wc are more effcent n terms of seedlng rates and sowng dates. Te only cases were ts substtuton would not appen are for te farm ouseold types "rce_mx_med" and "rce_per_bg" because of ter g labour costs. An example of suc results s sown n Fgure 36 for two farm ouseold types. Ts means tat, on te one and, te ncrease n rce productvty provoed by te adustment of sowng date and amount of seeds s not suffcent to boost rce actvty and encourage farmers to ncrease ts area n detrment to oters crops and, on te oter and, te ncrease n farm ouseold ncome reported above s explaned more by te ncreases n rce productvty per unt area n all ecosystems (.e. sol type) rater tan by te rse n rce area. A part from te agro-clmatc constrants (.e. crop rotaton and sol type), one of te reasons of lower compettveness of new rce actvtes compared to oter crops s ts ger labour requrement as revealed n Fgure 35. 2,5 Baselne SP1 6 Baselne SP Area (a) 1,5 1 Area (a) 3 2 0,5 1 0 CURR CURR CURR CURR CURR ALTE CURR ALTE T2_BOLI1 T2_BOLI2 T2_IVS1 T1_IVS2 T2_UP1 T2_UP1S T2_UP2 T2_UP2S bol1 bol2 vs1 vs2 up1 up2 bol_rce vs_rce up_rce_mx Rce 0 CURR CURR CURR CURR CURR CURR CURR ALTE CURR ALTE T_cass T_perennal T2_BOLI1 T2_BOLI2 T2_IVS1 T1_IVS2 T2_UP1 T2_UP1S T2_UP2 T2_UP2S vs1 up1 bol1 bol2 vs1 vs2 up1 up2.. bol_rce vs_rce up_rce_mx Cassava OP Rce Crops (a) Farm ouseold type: Rce_small (b) Farm ouseold type: Rce_bg Fgure 36. Impacts of polcy scenaro 1 (PS1) on rce actvtes' area Te mpacts of ts polcy scenaro on te farm ouseold's producton and consumpton of rce are reported n Fgure 37. As expected, te full substtuton of current rce practces by te alternatve ones would lead an ncrease of rce producton n most farm ouseold types wt dfferent levels. Te least ncrease would occur, naturally, n farm ouseold types wt low rce land sare. Te bggest ncrease would tae place n farm ouseold types wt g ntal sare of Upland rce because rce yeld n te Upland ecosystem s more senstve to te varaton of sowng date, n comparson to Bolland and IVS. Ts mples tat, te ncrease n farm ouseold ncome s postvely correlated to te land sare of upland rce. In fact, currently te rce yeld n upland s lower compared to IVS and Bolland, consequently, an adustng n te seedlng rate and te sowng date could ave a sgnfcant postve effect on yeld. Neverteless, because of te ntal low productvty and te fallow rotaton constrant, te expanson of rce n ts ecosystem remans lmted. Te average percentage ncrease of 121

122 rce producton at regonal level would exceed te 20% (.e. average regonal farm) explaned by te relatvely g ncrease of rce producton n specalsed rce farms. Regardng consumpton, te smulated polcy scenaro PS1 would lead to an ncrease n te consumpton of rce n most of te farm ouseold types rangng from 5% to 20$ (except for two farm ouseold types were te mpact s mnm), wt an average regonal level exceedng te 13%. Ts ncrease s explaned by te rse n bot rce producton and ouseold ncome. Neverteless, snce rce s a necessty good ts demand ncreases less tan proportonally to te rse n ncome. Moreover, gven te large dfference between producton and consumpton levels, most of te addtonal rce quanttes (surplus) generated by ts polcy scenaro would be sold n te maret wc would decrease food prces for urban ouseolds. 35% % cange to baselne 30% 25% 20% 15% 10% 5% 0% Rce producton Rce consumpton Rce_small Rce_veg_small Rce_per_small Rce_med Rce_per_med Rce_mx_med Rce_bg Farm ousold types Rce_per_bg Rce_mx_bg Average Reg. Farm Fgure 37. Cange n producton and consumpton of rce under polcy scenaro 1 (PS1) Polcy scenaro 2 (PS2) Te combned effects of te approprate sowng date and amount of seeds as well as of te applcaton of subsdzed fertlzer (.e. PS2) on all te computed ndcators (.e. farm ouseold ncome, crop pattern, producton and consumpton of rce ) would be more pronounced tan n te PS1 scenaro. As expected, farm ouseold ncome wll ncrease n all farm ouseold types n comparson to te baselne and PS1 (Fgure 38). Te gest percentage ncrease would arse n farm types producng manly rce suc as Rce_small and Rce_med, reacng te 96% and 102%, respectvely. In absolute term, te man ncrease s observed n bg rce farm type, namely Rce_bg (for furter detal see Table 5.3 n Appendx 4). Te average farm ouseold ncome at regonal level (.e. average regonal farm) would also be postvely affected and ts percentage ncrease, n comparson to baselne, would expand from 23%, n te PS1, up to 72% under te SP2 scenaro. However, contrarly to te SP1 scenaro, te ncrease n farm ncome n SP2 would concern all farm ouseold types and, for te maorty of tem, t s drven by te rse n bot rce productvty and rce area. 122

123 120% 100% % cange to baselne 80% 60% 40% 20% PS1 PS2 0% Rce_small Rce_veg_small Rce_per_small Rce_med Rce_per_med Rce_mx_med Rce_bg Farm ouseold types Rce_per_bg Rce_mx_bg Average Reg. Farm Fgure 38. Farm ouseold ncome cange under smulated polcy scenaros (PS1 & PS2) As sown n Fgure 39, te mplementaton of te polcy scenaro PS2 would nduce a slgt cange n crop pattern for certan farm ouseold types and a full substtuton of current rce practces by te alternatve ones (wt fertlser) for te maorty of tem. Te cange n crop pattern s manfested by a slgt ncrease n rce area to te detrment of groundnuts, cassava and, to a lesser extent, beans. However, ts ncrease remans very small, except for te Rce_per_bg farm type were te rase would reac te 30%. Te expanson of rce land s nbted manly by te agro-clmatc condtons (.e. sol types and crop rotaton constrant), te gly labour costs of mproved rce actvtes and te consumpton requrement. In fact, vegetable crops are very useful for self-consumpton n most of te farm ouseolds. 40% 20% % cange to baselne 0% -20% -40% -60% Rce_small Rce_med Rce_per_bg Rce_mx_bg Average Reg. Farm -80% -100% Rce Beans Cassava Groundnuts Farm ousold types Fgure 39. Crop pattern cange under polcy scenaro 2 (PS2) In addton, all te selected rce actvtes are fertlsed; except for rce actvtes n IVS ecosystem were te sol s very fertle and te fertlzaton wll not mprove sgnfcantly te rce yeld. Ts means tat te mproved rce actvtes wt approprate sowng date, amount 123

124 of seeds and fertlser are more effcent compared to te current ones. However, ts effcency s strongly dependent on subsdy for fertlser because, accordng to our smulaton, wtout subsdes no fertlsed rce actvtes would be selected. Ts means tat te amounts of N fertlzer requred for, manly, upland rce appear too g and costly and could not be appled by farm ouseolds wtout polcy support (.e. subsdes). As expected, te expanson of rce producton would be more sgnfcant under te SP2 scenaro and t would exceed te 40% for most of te farm ouseold types, reacng te 80% for some of tem (Fgure 40). Ts expanson s explaned by te rse n productvty n Upland and by te ncrease n rce area n IVS. Ts s to say tat one of te man ways of enancng farm vablty n Serra Leone could be by mprovng rce productvty n Upland; especally because te vast maorty of arable lands are located n ts ecosystem. As for te prevous scenaro, consumpton of rce would expand under te SP2 scenaro followng te rse n bot rce producton and ouseold ncome. Its percentage ncrease would be dfferent across farm ouseold types rangng between 8% and 58%, wt an average regonal level at around 42%. As we sad before, gven te large dfference between producton and consumpton levels generated by ts scenaro and because of te low storage capacty of most farm ouseolds, te sold quantty would ncrease and ts can ave a postve mpact on food prces for urban ouseolds. However, ts nd of mpacts can not be captured by our model. % cange to baselne 90% 80% 70% 60% 50% 40% 30% 20% Rce producton_ps1 Rce consumpton_ps1 Rce producton_ps2 Rce consumpton_ps2 10% 0% Rce_small Rce_veg_small Rce_per_small Rce_med Rce_per_med Rce_mx_med Farm ousold types Rce_bg Rce_per_bg Rce_mx_bg Average Reg. Farm Fgure 40. Cange n producton and consumpton of rce under smulated polcy scenaros (PS1 & PS2) Regardng te poverty assessment, bot scenaros would ave very lttle mpact on poverty reducton n te studed regon. As sown n Fgure 41, te mprovement of poverty gap under bot scenaros would be small n most of te farm ouseold types, less tan 10%. Ts means tat even f all farms would mprove ter ncome and reduce ter poverty gaps, most of tem would contnue to stay under te extreme poverty lne of 1 USD-equvalent per day. 124

125 % cange to baselne 10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0% PS1 PS2 Rce_small Rce_veg_small Rce_per_small Rce_med Rce_per_med Rce_mx_med Rce_bg Farm ouseold types Rce_per_bg Rce_mx_bg Average Reg. Farm Fgure 41. Percent mprovement of poverty gap under smulated polcy scenaros (PS1 & PS2) 4.6. Concluson In ts secton, te FSSIM-Dev model was used to assess te combned effects of nput (fertlser) subsdy polcy and mproved rce croppng managements on te lvelood of representatve farm ouseolds n te Bombal regon of Serra Leone. Te man fndng of ts applcaton s tat: () te mprovement of rce croppng management/practces s a ey factor to boost sgnfcantly farm ouseold ncome n Bombal regon; () te amounts of N fertlzer requred for, manly, upland rce appear too g and costly and could not be appled by farm ouseolds wtout polcy support (.e. subsdes); and () te rce polcy and te mproved managements would ncrease farm productvty and boost ouseold ncome but tey are not suffcent to fgt poverty snce most of te farm ouseold types would contnue to lve below te extreme poverty lne of 1 USD-equvalent per day. Ts applcaton provde also quanttatve evdence of te maor role of agronomc and economc constrants, frequently rased n te lterature to explan te poor rce productvty n Serra Leone (Sato et al., 2012; Jallo et al., 2012). From te metodologcal vewpont, ts applcaton glgts te relevance of ts nd of models for mang a fner polcy analyss and for tang nto account ey features of low ncome economes. In fact, FSSIM-Dev model can be used for testng eter very smple scenaros or more complex scenaros combnng agronomc and economc drvers. In addton, ts nd of approac facltates te collaboraton and te excange of nowledge between scentsts, staeolders and polcymaers suc s te case n ts study. However, le any model, t suffers from some lmtatons. Te frst s te arbtrary coce of te lnear expendture system. More flexble functonal forms suc as te Translog functon may be preferable. Te second lmtaton s te lac of crtcal assessment of te model's beavour/performance n te smulaton pase due to unavalablty of data (e.g. a second dataset or prce elastcty). 125

126 5. Concluson and recommendatons In ts report, a farm ouseold model, called FSSIM-Dev, as been presented as a generc tool to be used n te context of developng countres to gan nowledge on farm ouseolds' lvelood strateges and to assess ter responses to polcy and tecnologcal canges. In order to llustrate te applcablty of ts model, t was appled to a representatve sample of farm ouseolds belongng to te Nortern regon of Serra Leone (Bombal). Te am was to assess te combned effects of rce support polcy, namely fertlzer subsdy polcy, and mproved rce croppng managements (practces). Te man fndngs of ts applcaton n terms of polcy mpact are: () te mprovement of rce croppng management s a ey factor to boost sgnfcantly farm ouseold ncome n Bombal regon; () te amounts of N fertlzer requred for, manly, upland rce appear too g and costly and could not be appled by farm ouseolds wtout polcy support (.e. subsdes); and () bot te rce polcy and te mproved managements would ncrease farm productvty and boost ouseold ncome but tey are not suffcent to fgt poverty snce most of te farm ouseold types would contnue to lve below te extreme poverty lne of 1 USD-equvalent per day. Moreover, t appears clearly tat te most effort to ncrease rce producton n Serra Leone's nort regon sould be done on te upland rce wc as a very low productvty. Off course, oter tecnologcal nnovatons, suc as mprovng transplantng rce n IVS ecosystems, could be also tested. Alternatvely, t may be advsable to concentrate rce producton n Lowland (IVS and Bolland) and use Upland for tree crop producton and oter annual crops tat cannot tolerate water loggng condtons. Tere s presently enoug Lowland area to produce suffcent rce for local consumpton and even export. Ts alternatve wll sgnfcantly reduce te Upland area needed to grow rce and tereby ncrease te area tat can grow nto forest. Tese fndngs ave to be consdered, owever, wt some cauton due to model assumptons and lmtatons. Te Serra Leone applcaton opens up many opportuntes to extend te analyss for testng new croppng systems or/and new tecnology n oter regons or/and under dfferent socoeconomc condtons. For example, by nteracton wt local experts from te mnstry of agrculture two new scenaros of nterest as emerged and wc could be tested n furter model applcaton: () te ntroducton of macnery for sol tllage and weedng; and () te use of new seed varetes resstant to maor dseases. From metodologcal prospects, ts applcaton glgts te relevance of FSSIM-Dev for mang a fner polcy analyss and for tang nto account te man caracterstcs of developng countres. To te best of our nowledge, te model presented ere s one of te few farm ouseold programmng models wc attempts to reproduce farm ouseold producton and consumpton decsons n a separable regme and to endogenze ouseold prces. Ts model provdes also te possblty of tang nto account farm eterogenety and of smulatng endogenously te swtcng between dfferent forms of agrcultural management for a gven polcy scenaro. Furtermore, a number of ey metodologcal coces, drven by te obectve of te study and data avalablty (e.g. smulatng average farms nstead of ndvdual real ones, swtcng-off te rs module, etc), was made for te Serra Leone case study and wc can be easly adusted for future model applcatons f t s needed.

127 From ts statement, te man queston wll be to now f te FSSIM-Dev model can be easly extended to oter Afrca regons and wat are te precautons to be consdered for suc purpose? Altoug FSSIM-Dev was desgned suffcently generc and easly adaptable, answerng ts queston s not so easy and depends strongly on te target of te study. Globally, tree stuatons, wt dfferent degree of modellng can complexty, can be notfed: - Use of FSSIM-Dev as an analytc tool to analyse te effects of polcy optons on te beavour of representatve farmng systems suc as te case n ts study. In ts case great efforts are requred before te farm modellng: ) to collect detaled nformaton from lteratures, farm ouseolds and experts to caracterze te current agrcultural dverstes, to defne te farm ouseold types and to evaluate te model performance and, ) to apply a croppng system models (or an alternatve approac suc expert nowledge) for smulatng, at feld level, te effects of bopyscal (sol salnty, ranfall, fertlty, toxcty) and crop practces (mx crop, rrgaton, ranfed, sol tllage) on crop yelds (and maybe envronmental externaltes). For suc purpose, te re-usablty of FSSIM-Dev for oter Afrcan regons s possble but t s costly n term of data collecton (.e. model parametersaton) and model adustment and calbraton. - Use te FSSIM-Dev as a tool to elp polcy maer to tae strategc decsons. In tat stuaton, te number of farm ouseolds could be reduced and data collecton wll be less costly tan n te frst opton. However, te model results wll be also less accurate because only global specfcaton wll be explctly modelled. For suc purpose, te FSSIM-Dev can be easly mplemented wtout addtonal development. However, model results ave to be consdered wt more cauton and can be used only n a relatve way for comparng scenaros. - Use te FSSIM-Dev as a model-asssted partcpatory approac to elp local actors for mang sared decsons. FSSIM-Dev could be easly used for suc purpose because actors are generally nterested by () te modellng of small numbers of real farms; and () by te relatve cange of beavour n comparson to a reference stuaton, wc facltate model applcaton. No need n suc case to spend muc tme and effort on model calbraton and valdaton as well as on up-scalng results at aggregated levels. In addton, data access and data collecton wll be easer and less costly. 127

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139 7. Appendces Appendx 1. Indexes, parameters, varables and equatons n FSSIM-Dev Indexes & f,' f tf Descrpton Farm ouseolds Agrcultural (crop and lvestoc) actvtes Goods and factors Goods Factors (land, labour, water and captal) Tradable factors (land, labour and captal) Parameters w φ Descrpton Representaton (wegt) of farm ouseolds wtn te vllage/regon Rs averson coeffcent A, f, Input coeffcents (.e. nput use of factor f nto actvty ) B, Intal resources endowment f y,, Economc output coeffcent (.e. yeld of actvty ) a, Accountng costs d, & Q Implct cost functon s parameters estmated wt PMP-ME approac,' ' λ Implct margnal costs of tradable factors revealed troug PMP approac,tf β, & γ Houseold expendture functon s parameters, sb, m p f Subsdes Maret prces of goods and tradable factors t, t Multplcatve transacton costs of goods (buyer, seller) b f s f fc exnc Hc K Ρ Fxed costs Exogenous off-farm ncomes for ouseolds Houseold composton (adult equvalent) Number of state of nature Poverty lne

140 Varables V U R Y Descrpton Wegted sum of representatve farm ouseolds utlty Farm ouseold utlty Farm ouseold (expected) ncome Farm ouseold full ncome Rn, Farm ouseold ncome over state of nature (.e. random full ncome) σ π x, Standard devaton of farm ouseold ncome Agrcultural (expected) ncome Agrcultural actvty levels (.e. land use and anmal number) q, Produced quanttes of goods s, Sold quanttes of goods / rented-out tradable factors f b, Bougt quanttes of goods / rented-n tradable factors f c, Consumed quanttes of goods c, Self-consumed quanttes of goods s p, Prces of goods and tradable factors faced by ouseolds f M f & E Imported and exported quanttes of goods and tradable factors f N Equatons Descrpton (1) Max V = w U Model obectve functon (2) U [ φ σ ] (3) R R = Farm ouseold utlty functon = + s, tf p, tf b, p, + tf π exnc Farm ouseold ncome (4) Y = R + B f b Farm ouseold full,, f ncome f = land (5) π = ( d ( s,, + c + 0.5Q s, ) p x,, ', ) x +, sb tf, x ( b,, tf, ' + λ ) p tf a x, tf, fc Agrcultural ncome 140

141 1/ 2 (6) 2 (, ) Standard devaton of Rn R farm ouseold ncome σ = due to prce and yeld K varatons (7) A,, f x, B +, f s, f b Resource constrants at, f farm ouseold level (land, labour, water, captal ) (8) q, + b, = s, + c Quantty balance for, goods at farm ouseold level s (9) q, = y,, x, = s, + c Produced goods at farm, ouseold level (10) p p, m s t, p t p m b,, Prce bands for goods (11) s b,, ( p ( p,, p t p m s, m b t, ) = 0 ) = 0 Complementary slacness condtons (12) s, b, = 0 Houseolds buy or sell goods, not bot (13) + exnc + s, a p,, x +, tf b s,, tf p p,, tf + + tf sb ' ( b + λ, tf, x,, tf ) p, tf Cas constrant (14) c p = β Y γ p + γ,,,, ', ',, ' = p Farm ouseold expendture functon (15) w s + M = wb + E Quantty balance of,, goods at aggregated level (regon/vllage) (16) w s tf + M tf = wb tf + E Quantty balance of,, tf tradable factors at aggregated level (regon/vllage) (17) ( P R / Hc ) PG = sup 0, * 100 P Poverty Gap Lst of outputs generated by FSSIM-Dev at farm and aggregated (vllage/regonal) levels Type Output Unt 141

142 Economc Input use Poverty Gap % Farm ouseold ncome Farm ncome Farm ouseold ncome per ouseold unt Farm ncome per wor unt Farm ncome per a Gross producton Total subsdses Total costs Land sadow prce Produced quanttes of goods Bougt quanttes of goods Consumed quanttes of goods Self-consumed quanttes of goods Total ntrogen use Water use Ntrogen use Pestcde use Labour use Labour rented-n and rented-out Energy use of rrgaton Energy use of tllage Energy use of mneral ntrogen Energy use of anmal food Energy use of anmal ousng Total energy use for crops Total energy use for lvestoc Total energy use Ntrogen use per forage area Use of organc ntrogen Use of mneral ntrogen fertlzer Natonal currency Natonal currency Natonal currency/hunt Natonal currency/wunt Natonal currency /a Natonal currency Natonal currency Natonal currency Natonal currency Tons Tons Tons Tons Kg N/a mm/a Kg N/a g/a Hours/a Hours/a toe/a toe/a toe/a toe/a toe/a toe/a toe/a toe/a Kg N/a Kg N/a Kg N/a 142

143 Envronment (.e. postve and negatve externaltes) Structural Sol eroson Water dranage Ntrate volatlzaton Sol Fertlty rate Sol Fertlty gan Sol organc matter Pestcde volatlzaton Pestcde runoff Pestcde leacng Runoff Average energy effcency for crops Average energy effcency for lvestoc Eroson pea Runoff Pea Average farm ntrogen surplus Farm gate N surplus Farm gate N effcency Crop dversty Land use Crop area (per crop) Crop actvty level Anmal number (per anmal type) Anmal actvty level Stocng rate (lvestoc densty) Stocng rate (lvestoc densty) on te total forage area Stocng rate (lvestoc densty) on te total grassland area T/a mm/a Kg NH3-N/a Ha Ha g/a g/a g/a mm/a toe/tdm toe/tdm T/a mm/a Kg N/a Kg N Ha Ha Ha Ha Head Head LU/a LU/a LU/a 143

144 Appendx 2. Food Securty Indcators 1. Introducton Food uncertantes are at te top of te agendas of Afrcan regonal organsatons and te nternatonal communty n general. Accordng to te FAO (1996), food securty s defned as a stuaton tat exsts wen all people, at all tmes, ave pyscal, socal and economc access to suffcent, safe and nutrtous food tat meets ter detary needs and food preferences for an actve and ealty lfe. Food securty s a maor callenge for developng countres, especally n Afrca. Cronc food nsecurty and unger are wdespread n sub-saaran Afrca (Boon, 2007). Accordng to USAID, n August 2011, famne expanded n Somala wle te food securty emergency deterorated n te rest of te eastern Horn of Afrca. About 3.7 mllon people n Somala, 3.7 mllon people n Kenya, 4.8 mllon people n Etopa, and 0.16 mllon people n Dbout are n need of assstance (USAID, 2011, p.1). All Afrcan agrcultural and food securty polces stress te demograpc callenges faced. Between 1950 and 2010, West Afrca s populaton ncreased fvefold and wll double agan between 2011 and Urban growt rates are stll more spectacular: twentyfold ncrease between 1950 and 2010 and an expected fourfold ncrease n urban populaton between 2011 and 2050 (OECD, 2011). Ts growng global populaton and sftng food demands put stran on natural resources, alled to te mpact of clmate cange and prce volatlty. Ts paper wll brefly trace food securty global trends n Afrca and ntroduce te concept of food securty and ts four dmensons: avalablty, accessblty, utlsaton/qualty and stablty. Man measurement ndcators assessng eac of tese aspects wll be presented. Te measurement of relatve levels of food securty s not a stragtforward matter. 2. Food crses and food securty n Afrca Snce te late 1970 s, te FAO Global Informaton and Early Warnng System (GIEWS) as been montorng all countres of te world to detect factors leadng to mpendng crses and gve an early warnng to affected countres and te nternatonal communty. Te retrospectve analyss of ts nformaton sows tat te number of countres struc by an acute food securty crss (rater tan te number of people affected) as ncreased snce te early 1980 s, natural causes (slow or sudden onset dsasters) beng relatvely more prevalent durng te frst decade, natural and man-made crses (lned to conflct and nsecurty, but also to economc socs) evolvng more closely from ten on. Generally speang, Afrca as been te regon wt te gest number of countres n crss, and frequency of occurrence, by country (FAO, 2011). Te number of countres n food securty crss at any gven tme reaced a record g recently owng to te combned effects of te food prce crss, and te 2009 global fnancal crss. Te World food prce crss Te World food prce crss saw very sarp ncreases n maze and weat prces, of 54 and 125 respectvely, over a perod of a few monts. Ts crss ad been buldng up for some years, canges n ncomes and consumpton patterns n populous countres of Asa 144

145 gradually drvng down te global cereal stoc-to-utlzaton rato, and rsng energy prces. It was precptated by severe producton sortfalls n some of te world s maor producng countres, and polcy-drven ncreased relance on bofuels n oters. Subsequently, between October 2007 and Aprl 2008, world maret prces for rce trpled, gong from $335/ton to over $1000/ton, an unprecedented ncrease tat was probably te most serous soc to world food securty n te prevous 25 years, snce rce s te most mportant source of calores for te world poor (Dawe and Slayton, 2010). Sub-Saaran Afrca taes up nearly one-trd of te world s rce exports, and West Afrca, wc ncludes countres wt some of te contnent s gest rates of per capta rce consumpton (Gunea-Bssau: 86 g/cap/year, Senegal: 74 g, Côte d Ivore: 64 g), s also te largest sub-contnental mporter, accountng for about alf of sub-saaran Afrca rce mports. In response to te ncrease n mported cereal prces n , mportng countres reduced or suspended mport dutes on cereals. Some, le Mal and Burna Faso put a ban on exports of domestc and mported cereals but wt very lmted effectveness. Oter countres ave also announced or ave taen steps n order to reduce ter dependency on mports. In addton, a number of blateral agreements ave been negotated to secure rce supples over several years (Senegal wt Inda for sx years, Ngera wt Taland, etc.) (Gago and Dennng, 2010). West Afrcan crses West Afrca as been confronted wt bot natural dsasters and uman-nduced crses (eter economc socs or conflct related) wt a sgnfcant mpact on poverty and food nsecurty. Bot natural dsasters and sgnfcant uman-nduced (or antropogenc) crses are wellnown to set countres bac along te development pat, for nstance, and most countres of te regon ave been affected by bot. However, dstngusng between natural and manmade factors s not always a smple tas, and tere ave been many cases, suc as n complex emergences, were te root factors of food nsecurty ave wored n combnaton wt one anoter. Human nduced crses, for example, can ntertwne soco-economc stress and conflcts n ntrcate ways: socal nequaltes can lead to conflcts, wle conflcts destroy assets and can leave a country mpoversed for many years. Lebanon and Iraq are ready examples, but one can also tn of Lbera, Serra Leone or Côte d Ivore as well. Protracted crss stuatons, on te oter and, are caracterzed by recurrent natural dsasters and/or conflct, te longevty of food crses, a breadown n lvelood systems and an nsuffcent capacty to react to or brea out of, crses 17. Most West-Afrcan countres ave made progress n reducng under-noursment and ter vulnerablty to food nsecurty: undernoursment rates fell over te last 30 years n egt countres, were stable n four, and ncreased n 6. Regon-wde, undernoursment rates n West Afrca reman muc lower tan n East, Central and Soutern Afrca 18. Snce te late 1970 s West Afrcan countres ave also fgured less often tan oter countres of te contnent on te FAO/GIEWS lst of countres n crss requrng external assstance as we sow n te secton on crses. Stll, undernoursment rates reman clearly ger tan n most oter parts of te world, and a very recent global assessment of food nsecurty rs 19 puts See te FAO/State of Food Insecurty n te World See te FAO/State of Food Insecurty n te World Maplecroft, n collaboraton wt WFP,

146 almost all West-Afrca countres n te g rs category; Gana beng assessed as a medum rs and Lbera an extreme one. 3. How to measure food securty Te concept of food securty Te food securty concept as sgnfcantly evolved over tme n parallel wt te development of te offcal poltcal tougt (Clay, 2002). Food securty as become a maor concern for nternatonal nsttutons snce te global food crses of , followng maor epsodes of drougt and food sortages n te Sael (Hedues et al., 2004). Te term of food securty appeared at te 1974 World Food Summt and was manly defned n regards to food supply, Avalablty at all tmes of adequate world supples of basc food-stuffs ( ), to sustan a steady expanson of food consumpton ( ) and to offset fluctuatons n producton and prces (UN, 1975). Ts prmary approac was substantally a matter of food avalablty wc s determned by food producton level, stocs and trade. Towards a mult-dmensonal understandng of food securty From te md-1980s, concerns wt food securty ave evolved, especally after Sen's wors on uman development (1981). Attenton to food securty as sfted from natonal food supply assessment to ts allocaton at te ouseold and ndvdual levels, and focused on te mecansms carred out by te populatons to gan access to food. In 1983, te FAO analyses focused on food accessblty condtons wc are recognzed as a ey determnant of food securty. Tus, t comes to amng at ensurng tat, at any tme, any ndvdual as a pyscal and economcal access to te food supples wc e/se needs. In 1989, te World Food Program defned food securty as wen every person as, at all tmes, pyscal and economc access to meet ter basc food needs. A natonal food securty strategy cannot be contemplated wtout guaranteeng food securty at te level of te ome. In te same ven, Franenberg (1992) acnowledged tat Te vablty of te ouseold as a productve and reproductve unt (not) treatened by food sortage. In ts regard, te analyses concernng te ntra-ouseold dstrbuton of consumpton glgted te vulnerablty of some populatons (women, cldren and elderly people). Tus, te fgt for food securty as sfted from te ouseold level to te ndvdual level (Padlla, 1997). At te ndvdual level, te approac to food securty n quanttatve terms as sfted to a concept of mcro-nutrent ntae qualty n order to aceve a balanced and nutrtous det. Snce te md-1990's, attenton as been drawn on te correlaton between ealt, ygene, food qualty, santaton practces and on te safety and nutrtonal qualty of food. Staatz (1990) states tat food securty conssts of Te ablty ( ) to assure, on a long term bass, tat te food system provdes te total populaton access to a tmely, relable and nutrtonally adequate supply of food. Te Rome Declaraton on World Food Securty: Te Rome Declaraton on World Food Securty (1996) gves te followng defnton, wc s now wdely acnowledged: Food securty, at te ndvdual, ouseold, natonal, regonal and global levels [s aceved] wen all people, at all tmes, ave pyscal and economc access to suffcent, safe and nutrtous food to meet ter detary needs and food preferences for an actve and ealty lfe. Te man determnants of food securty tus fall nto tree 146

147 broad categores: avalablty, accessblty and utlzaton; some measure of stablty and predctablty n eac of tese tree categores also beng regarded as crtcal. Te stablty and predctablty of governance systems are crtcal factors, but natural or antropogenc (uman-nduced) crses can also affect food securty n varous ways. Tese nclude loss of lfe and assets, conflct-related nsecurty, or transmsson of external economc socs troug te ncome and expendture sdes of ouseold budgets, and tus food consumpton and nutrton. Avalablty Te concept of avalablty refers to te pyscal exstence of food weter t comes from self-producton or marets. Food avalablty s assessed complng food balance seets. It conssts of te dfference between, on te one and, producton, trade balance (mports exports) and stoc varatons, and, on te oter and, any oter uses wc s not amed at uman consumpton (seed, anmal feed, waste, etc.). It terefore apples to food supply at a regonal and natonal level (Rely et al., 1995). However, n te absence of food consumpton survey data, ts avalablty s often consdered smlar to energy ntaes. Even toug te FAO tself warns tat ts food balance seets ndcate uman consumpton only from a supply perspectve, tey are commonly used to defne country profles and consumpton trends. Ts data are especally used to assess te nutrton polces flagsp ndcators suc as te DES Detary Energy Supply. Man ndcators: 1 Te DES (Detary Energy Supply) estmates food avalable for uman consumpton expressed n localores per person per day (cal/person/day). At a natonal level, t s calculated as te food remanng for uman use after deducton of all non-food consumpton (exports, anmal feed, ndustral use, seed and waste). 2 Food group contrbuton to te total energy avalablty. Te food groups taen nto account are: grans, roots and tubers, ols, fats and anmal products. Oter plant orgn products (starces, groundnuts, oleagnous seeds, sweeteners, vegetables and condments) are not ncluded. 3 Nutrent contrbuton to te total energy avalablty. It analyses te det composton accordng to te contrbuton of energy provdng nutrents (suc as carbo-ydrates, protens and fats) to te total energy avalablty amed at uman consumpton. 4 Te FAO Cronc Hunger ndex s based on te prevalence of under-noursment, correspondng to te percentage of a country s populaton wt a level of detary energy consumpton (DEC) lower tan te detary energy requrements (DER). Ts approac reles on probablstc models of te ont dstrbutons of DEC and DER, n turn dependng on country level food balance seets and varous types of ouseoldlevel studes (e.g. food consumpton, det dversty or nutrton surveys, lvng standards measurement surveys, etc.). Te prevalence of under-noursment s an ndcator of cronc unger capturng te evoluton of fundamental elements drvng long term nutrtonal status. It does not reflect suc sort-term penomena as seasonal food sortages or te mpact of temporary food prce ncreases, nor does t tae nto account mecansms used by ouseolds to cope wt temporary food crses (Gennar, 2011). Smlarly, te IFPRI Global Hunger Index also uses te prevalence of undernoursment and combnes t wt te prevalence of underwegt n cldren 147

148 under fve years and te under-fve mortalty rate, all tree factors beng gven equal wegts. In terms of trends and relatve ranngs, t provdes smlar results to te FAO ndex. Te DES s te most mportant ndcator mentoned n te nutrent profles by country establsed by te FAO n partcular. Smt (1998) as proven tat ts ndcator s a good reflecton of food avalablty on a natonal scale but t does not adequately measure te ouseold access to food wc s te ey to food securty. He tus argued for ouseold surveys wc are useful tools to complement te DES analyses n so far as ter results are sometmes notably dfferent. However, snce Dowler and Seo (1985), some researces ave demonstrated te unrelablty of supply estmates as proxy ndcators of consumpton and queston ter current usage for food polcy desgn and management. Accessblty Accessblty s ensured wen every ouseold, and every ndvdual wtn tese ouseolds, ave suffcent resources to get approprate food for a nutrtous det (Rely et al., 1995). It depends on ouseold resource level and on prces. Note owever tat ouseolds can ave an adequate access wtout beng self-suffcent n food producton. Te most mportant tng s te ouseold ablty to generate suffcent ncome wc, n conuncton wt selfproducton, can be used to meet ter food needs. Man ndcators: As a frst approxmaton of accessblty, varous measures of ncome can be used suc as te average daly ncome per person. On te oter and, te Food and Nutrton Tecncal Assstance (FANTA) proect and Food Ad Management 20 (FAM) focused on te ouseold ncome. More accurate poverty ndcators ave been ntroduced, for example te eadcount poverty ndex set up by te World Ban. Natonal poverty rate or eadcount ndex s te percentage of te populaton lvng below te natonal poverty lne. HFIAS (Houseold Food Insecurty Access Scale), a composte ndcator developed by te FANTA proect (wt FAO collaboraton), provdes nformaton on food nsecurty at te ouseold level wtn trty days after survey. Te nne questons assess te ouseold accessblty to food, ncludng anxety about procurng food, and quantty and qualty of dets (varety/dversty s assessed). Tere s anoter smplfed ndcator, te Houseold Hunger Scale (HSS), wc s derved from HFIAS. Te HFIAS ndcator as been tested by Becquet et al. (2010) n Ouagadougou (Burna Faso) to approxmate te adequacy of urban ouseolds dets. In concluson, HFIAS performed well n approxmatng adequacy of urban ouseolds dets 21. Tey are nformatve ndcators about urban food nsecurty, promsng for evaluaton and montorng but not for ouseold targetng gven ter nsuffcent predctve power USAID Te ndvdual detary dversty score (IDDS) was also assessed and found postvely correlated wt te mean adequacy rato, used n bot case as te bencmar for det adequacy. See sub-secton

149 Utlsaton and qualty Te Hunger ndex, mentoned above, s somewat lmted. In partcular, t s based on te assumpton tat energy defcences as opposed to nutrent defcences - are te ey ndcator of unger. On te contrary, qualty refers to food n tself and n partcular to ts nutrtonal ntaes. Wen enoug food s avalable and accessble, ouseolds ave to mae decson concernng te purcase, preservaton, preparaton, consumpton and ntra-ouseold dstrbuton. Even toug te overall access to food may be measured as suffcent, some ndvduals mgt suffer from nutrtonal defcency wen ntra-ouseold dstrbuton s uneven. Te same apples f te composton of foods consumed s not balanced. Man ndctors: 1. Malnutrton defnes an abnormal pysologcal condton caused by an unbalanced, excessve or nadequate consumpton of macro-nutrents (carbo-ydrates, protens, fats) and mcro-nutrents. Ts condton ncludes all te devatons from an adequate nutrton, suc as undernoursment (or proten, carbo-ydrate, fat, and/or vtamn and mneral defcences), overeatng (or excessve consumpton of some food components suc as saturated fats, added sugars, combned wt low pyscal actvty), and specfc defcences (or excess) of essental nutrents,.e. vtamns and mnerals. 2. MAR (Mean Adequacy Rato) measures a standard measure of average adequacy to recommended nutrtonal ntaes (Madden et al., 1976). It s te mean raton of ntaes to recommended ntaes for selected nutrents. 3. Te Detary Dversty, HDDS (Houseold) and IDDS (Indvdual), scores (FANTA). Tey ln te adequate nutrent ntaes (coverage of basc food needs n terms of macro and mcro-nutrents) wt a vared and balanced det, two of te most mportant elements of food qualty. 4. Composte ndcators: Te DQI-I (Det Qualty Index Internatonal), defned by Km et al. (2003) focuses on four maor aspects of a g-qualty det,.e. varety, adequacy, moderaton and overall balance. Te HEI (Healty Eatng Index), developed by Kennedy (1995), s based on a 10-component system of fve food groups, four nutrents, and a measure of varety n food ntae. Eac of te 10 components as a score rangng from 0 to 10, so te total possble ndex score s 100. Te MAR s a well recognzed and proven ndcator. It was tested by Torem et al. (2004) n a study n a Malan urban area not only as an ndcator but also as a valdaton tool for two oter ndcators focusng on te varety (Food Varety Score) and dversty of food (Detary Dversty Score). Stablty Stablty deals wt te queston of food vulnerablty and reslence. Tese concepts combne exposure to rs and assessment of ts mpacts (vulnerablty), and te ndvdual or communty capacty to andle t more or less effcently and recover (reslence) dependng on 149

150 ter productve, uman or socal captal (Bebbngton, 1999). More precsely, te concept of reslence allows an accurate reflecton of te strateges carred out by te ouseolds wen confronted wt exogenous socs. Te mcro-economc analyss as used te reslence analyss framewor to study te ouseold vulnerablty, especally concernng food. Vulnerablty s defned as te treat of beng affected by poverty. Concernng food, vulnerablty could be defned as te probablty for an ndvdual or a group to fnd ter food securty eopardzed by an unexpected clmatc or economc event (Droy and Rasolofo, 2004, p.2). Vulnerablty to food nsecurty s caused by te presence of factors tat place people at rs of becomng food nsecure or malnoursed ncludng tose factors tat affect ter ablty to cope. It can also be defned as te rato between Rs and Copng Capacty, rs tself often beng expressed as te expected value of a armful event (.e. te probablty of t appenng multpled by te loss mputable to te event). A populaton group wt low exposure to rs and g copng capacty s deemed to ave low vulnerablty. Conversely, a group wt g exposure to rs and low copng capacty s consdered gly vulnerable. Several estmators ave been proposed tat determne te ouseolds' reslence and vulnerablty to crses factors. Man ndcators: Te reslence tool was frst ploted n Palestne n 2007 by te FAO n cooperaton wt te World Food Programme (WFP) and te Palestnan Bureau of Statstcs. Te Global Informaton and Early Warnng System (GIEWS) of FAO recently developed a composte ndex measurng te relatve vulnerablty of a country to natural and antropogenc socs (Troubat, 2011). It reflects bot (a) socal copng capacty, as strongly nfluenced by te relatve level of educaton, ealt and (b) macro-economc copng capacty, reflected troug a country s economc performance and capacty to moblze resources. It was desgned to detect sort term canges n a country s senstvty to te rs of food nsecurty on te bass of long term structural ndcators and of nformaton tat can be frequently updated, suc as consumer prces. Te ndex ranges n values from 0 to 1, wt ger values correspondng to te most vulnerable countres. Te FAO/GIEWS Vulnerablty Index tus captures tree components of vulnerablty: Te degree of exposure to potental socs, te relatve severty of socs and copng ablty (socal and macroeconomc). A food prces' fluctuaton ndex as sometmes been ntroduced by te FAO. As prces came out as one of te most mportant accessblty crtera, ter propensty to fluctuate accordng to regons as been seen as an element partcpatng n te concept of stablty. A reslence tool as been developed by te FAO (Alnov et al., 2009) and as prevously been studed concernng te Palestnan case n Ts composte ndcator was successfully tested n fve Palestnan sub-regons and resulted n sgnfcant dfferences. Ts reslence tool uses avalable data from many natonal surveys of lvng standards. It can tus be used n several countres and allows an analyss of te ouseolds' reslence to socs. Concluson 150

151 Te man determnants of food securty nteract wt, and can offset, eac oter to a consderable extent. Relatvely ample and steady domestc food avalablty can elp offset lmted access to nternatonal marets, wle relatvely g ncomes, even wen obtaned troug remttances, can compensate for low domestc producton. By te same toen, ample supples and g ncomes only translate nto g levels of food securty under condtons of proper nutrton and food utlzaton by te body. Ts mples good cld care and feedng practces, ygene, good drnng water and santaton, etc. In te end, sgnfcant and sustanable gans n te food securty status of a populaton come from approprate ncreases wdely dstrbuted wtn te populaton n te varables underpnnng all tree determnant categores (avalablty, access, utlzaton). Some balance must be mantaned between tese tree broad determnants 22. Acnowledgements Te autor would le to acnowledge valuable contrbutons of Cyrlle Arfeulle and Benot Dupus on ts capter. Addtonal references Clay, E., (2002). Food Securty: Concepts and Measurement, Paper for FAO Expert Consultaton on Trade and Food Securty: Conceptualsng te Lnages Rome, July Publsed as Capter 2 of Trade Reforms and Food Securty: conceptualsng te lnages. Rome: FAO, Droy, I., Rasolofo, P. (2004). Les approces de la vulnérablté almentare dans le sud de Madagascar. Documents de traval 105, Groupe d'econome du Développement de l'unversté Montesqueu Bordeaux IV. FAO, (1996). Rome Declaraton on World Food Securty and World Food Summt Plan of Acton. World Food Summt November Rome, Italy. Gennar, P., (2011). Measurng Food Insecurty and Assessng te Sustanablty of Global Food Systems. Te FAO Cronc Hunger Index. Natonal Academy of Scences, Wasngton, D.C. Kennedy, E. T., Ols, J., Carlson, S., Flemng, K., (1995). Te Healty Eatng Index: Desgn and Applcatons. Journal of te Amercan Detetc Assocaton 95, Km S., Hanes P.S., Sega-Rz, A.M., Popn, B.M. (2003). Te Det Qualty Index- Internatonal (DQI-I) provdes an effectve tool for cross-natonal comparson of det qualty as llustrated by Cna and te Unted States. Journal of Nutrton 133, Madden, J.P., Goodman, S.J., Gutre H.A., (1976). Valdty of te 24- recall. Analyss of data obtaned from elderly subects. Journal of te Amercan Detetc Assocaton 68, Smt, L., (1998). Can FAO's measure of cronc undernoursment be strengtened? Food Polcy 23(5), Te defnton of oter relevant concepts, suc as malnutrton, stuntng, wastng, etc. are presented n te Appendx 1secton at te outset of ts report. 151

152 Torem, L.E., Ouattara, F., Darra, M.M., Tam, F.D., Barmo, I., Hatløy, A., Osaug, A., (2004). Nutrent adequacy and detary dversty n rural Mal: assocaton and determnants. European Journal of Clncal Nutrton 58(4), Troubat, N., (2011).Te Early Warnng Index for External Assstance Requrement for Food (EWEAR). Global Informaton and Early Warnng System, FAO. Rome, Italy. Unted Natons, (1975). Report of te World Food Conference. Rome 5-16 November New Yor, USA. 152

153 Appendx 3. Tecncal and economc coeffcents of rce croppng systems 1- Upland rce croppng system (CS1) wt low yeld (0.21t/a) and low labour (174 day/a). Hred labour (Man days) Famly Labour (Man days) CS1 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,21 Producton of mnor crop (cassava) (Leones/a) Producton of rce (Leones/a) Farm ncome wt famly labour (Leones/a) Farm ncome wtout famly labour (Leones/a) Upland rce croppng system (CS2) wt low yeld (0.23t/a) and g labour (319 day/a). Hred labour (Man days) Famly Labour (Man days) CS2 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,23 Producton of mnor crop (cassava) (Leones/a) Producton of rce (Leones/a) Farm ncome wt famly labour (Leones/a) Farm ncome wtout famly labour (Leones/a)

154 3- Upland rce croppng system (CS3) wt g yeld (0.39t/a) and low labour (163 day/a). Hred labour (Man days) Famly Labour (Man days) CS3 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,39 Producton of mnor crop (cassava) (Leones/a) Producton of rce (Leones/a) Farm ncome wt famly labour (Leones/a) Farm ncome wtout famly labour (Leones/a) Upland rce croppng system (CS4) wt g yeld (0.44t/a) and g labour (268 day/a). Hred labour (Man days) Famly Labour (Man days) CS4 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,44 Producton of mnor crop (cassava) (Leones/a) Producton of rce (Leones/a) Farm ncome wt famly labour (Leones/a) Farm ncome wtout famly labour (Leones/a)

155 5- IVS rce croppng system (CS5) wt low yeld (0.28t/a) and low labour (158 day/a) Hred labour (Man days) Famly Labour (Man days) CS5 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,28 Producton of rce (Leones/a) Farm ncome wt famly labour (Leones/a) Farm ncome wtout famly labour (Leones/a) IVS rce croppng system (CS6) wt low yeld (0.33t/a) and g labour (248 day/a) Hred labour (Man days) Famly Labour (Man days) CS6 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,33 Producton of rce (Leones/a) Farm ncome wt famly labour (Leones/a) Farm ncome wt famly labour (Leones/a)

156 7- IVS rce croppng system (CS7) wt g yeld (0.71t/a) and low labour (178 day/a) Hred labour (Man days) Famly Labour (Man days) CS7 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,71 Producton of rce (Leones/a) Farm ncome wt famly labour (Leones/a) Farm ncome wtout famly labour (Leones/a) IVS rce croppng system (CS8) wt g yeld (0.52 t/a) and g labour (334 day/a) Hred labour (Man days) Famly Labour (Man days) CS8 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,52 Producton of rce (Leones/a) Farm ncome wt famly labour (Leones/a) Farm ncome wtout famly labour (Leones/a)

157 9- Bolland rce croppng system (CS9) wt low yeld (0.19 t/a) and low labour (85 day/a) Hred labour (Man days) Famly Labour (Man days) CS9 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,19 Producton of rce (Leones/a) Farm ncome wt famly labour (Leones/a) Farm ncome wtout famly labour (Leones/a) Bolland rce croppng system (CS10) wt low yeld (0.20 t/a) and g labour (225 day/a) Hred labour (Man days) Famly Labour (Man days) CS10 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,20 Producton of rce (Leones/a) ,302 Farm ncome wt famly labour (Leones/a) Farm ncome wtout famly labour (Leones/a)

158 11- Bolland rce croppng system (CS11) wt g yeld (0.36 t/a) and low labour (145 day/a) Hred labour (Man days) Famly Labour (Man days) CS11 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,36 Producton of rce (Leones/a) Farm ncome wt famly labour (Leones/a) Farm ncome wtout famly labour (Leones/a) Bolland rce croppng system (CS12) wt g yeld (0.47 t/a) and g labour (217 day/a) Hred labour (Man days) Famly Labour (Man days) CS12 Male Female Total Male Female Cldren Total Total 1. Brusng/Fellng/Clearng Brusng and Moundng Plowng and seedng Harrowng Plantng of mnor crops Frst brd scarng Puddlng Transplantng Weedng Fencng Second brd scarng Harvestng Tresng/Wnnowng Dryng Total cost Rce prce (Leones/a) Rce yeld (t/a) 0,48 Producton of rce (Leones/a) ,509 Farm ncome wt famly labour (Leones/a) Farm ncome wtout famly labour (Leones/a)

159 Table A5.1. Baselne vs. Polcy scenaro 1 (PS1) results Appendx 4. Man FSSIM-Dev results for dfferent farm ouseold types Rce_small Rce_veg_small Rce_per_small Rce_med Baselne PS1 Baselne PS1 Baselne Baselne PS1 Baselne Farmer utlty (Leones) Farm ncome (Leones) Income per HHUnt (Leones/HHU) Poverty Gap (%) Income per WUnt (Leones/WU) Income per a (Leones/a) Total outputs (Leones) Accountng costs (Leones) Oter costs & ncome (Leones) Total land use (a) Total labour use (Days) Land sadow prce (Leones) Source: FSSIM-Dev results 159

160 Table A5.2. Baselne vs. Polcy scenaro 1 (PS1) results (follow-up) Rce_mx_med Rce_bg Rce_per_bg Rce_mx_bg Baselne PS1 Baselne PS1 Baselne PS1 Baselne PS1 Farmer utlty (Leones) Farm ncome (Leones) Income per HHUnt (Leones/HHU) Poverty Gap (%) Income per WUnt (Leones/WU) Income per a (Leones/a) Total outputs (Leones) Accountng costs (Leones) Oter costs & ncome (Leones) Total land use (a) Total labour use (Days) Land sadow prce (Leones) Source: FSSIM-Dev results 160

161 Table A5.3. Baselne vs. Polcy scenaro 2 (PS2) results Rce_small Rce_veg_small Rce_per_small Rce_med Rce_per_med Baselne PS2 Baselne PS2 Baselne PS2 Baselne PS2 Baselne PS2 Farmer utlty (Leones) Farm ncome (Leones) Income per HHUnt (Leones/HHU) Poverty Gap (%) Income per WUnt (Leones/WU) Income per a (Leones/a) Total outputs (Leones) Accountng costs (Leones) Oter costs & ncome (Leones) Total land use (a) Total labour use (Days) Land sadow prce (Leones) Source: FSSIM-Dev results 161

162 Table A5.4. Baselne vs. Polcy scenaro 2 (PS2) results (follow-up) Rce_mx_med Rce_bg Rce_per_bg Rce_mx_bg Baselne PS2 Baselne PS2 Baselne PS2 Baselne PS2 Farmer utlty (Leones) Farm ncome (Leones) Income per HHUnt (Leones/HHU) Poverty Gap (%) Income per WUnt (Leones/WU) Income per a (Leones/a) Total outputs (Leones) Accountng costs (Leones) Oter costs & ncome (Leones) Total land use (a) Total labour use (Days) Land sadow prce (Leones) Source: FSSIM-Dev results 162

163 European Commsson EUR xxxxx Jont Researc Centre Insttute for Xxxxxxxx Ttle: Man Ttle of te Report Autor(s): Forename Surname, Forename Surname, Forename Surname Luxembourg: Publcatons Offce of te European Unon 20xx xx pp x 29.7 cm EUR Scentfc and Tecncal Researc seres ISSN xxxx-xxxx (prnt), ISSN xxxx-xxxx (onlne) ISBN xxx-xx-xx-xxxxx-x (pdf) ISBN xxx-xx-xx-xxxxx-x (prnt) do:xx.xxxx/xxxxx Abstract Bustruptur, optasperumet magnat etur? Qua pres aborberum et psam cus nae adsc aut facant omna ex endtur repert, sus. Ulparupt omntate od od ut res magnm fuga. Ut offcl nto. Itats qus autats torepts vd que non esto doluptae pro et maonsed ut aut aut mn ne offcd tunt volorem unt maxmet pltatur altae con remqua nonsed et qus apelat. Sequbuscs aborerum que re, serrovt ea vent, sus mo temperbus aborum necullo reprae. Cpsam arum fugt verum quunt amens molore eum re, qu omnsn ctemquo dcmposa de pre voluptato. Itaquassum qu optatur repedt aescs aut arupt scdunt offc tem quam lquos alqubus. Modtatur? Tem aut que ods dolorumquas dolest, conesed mao estum n res dolut ex est rem faca qu veltae ctasts excerb usctae ex eosamus antur, ut us eroro. Itaqus eos aut maxmus, quaepudam men tem res eat occumque nullaut vel molluptur? Qua num nam faccab ntur alt as ut dolupta temqua acerbustus re optatur, um accaerc psapcats nma cume sapersp edps. 163

164 z LB-NA-xxxxx-EN-N As te Commsson s n-ouse scence servce, te Jont Researc Centre s msson s to provde EU polces wt ndependent, evdence-based scentfc and tecncal support trougout te wole polcy cycle. Worng n close cooperaton wt polcy Drectorates-General, te JRC addresses ey socetal callenges wle stmulatng nnovaton troug developng new standards, metods and tools, and sarng and transferrng ts now-ow to te Member States and nternatonal communty. Key polcy areas nclude: envronment and clmate cange; energy and transport; agrculture and food securty; ealt and consumer protecton; nformaton socety and dgtal agenda; safety and securty ncludng nuclear; all supported troug a cross-cuttng and mult-dscplnary approac.