Returns to fertilizer use: does it pay enough? Some new evidence from Sub Saharan Africa

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1 TSE 669 July 2016 Returns to fertlzer use: does t pay enough? Some new evdence from Sub Saharan Afrca Estelle Koussoubé and Célne Nauges

2 Returns to fertlzer use: does t pay enough? Some new evdence from Sub-Saharan Afrca Estelle Koussoubé PSL, Unversté Pars Dauphne, LEDa, DIAL UMR 225 IRD, UMR DIAL, France Célne Nauges Toulouse School of Economcs, INRA, Unversty of Toulouse Captole, France Abstract The low level of modern nputs adopton by Afrcan farmers s consdered to be a major mpedment to food securty and poverty reducton n Sub-Saharan Afrca. The government of Burkna Faso, followng the example of a number of other countres n the regon, launched a subsdy program n 2008 to encourage farmers uptake of chemcal fertlzers and foster cereal producton. Ths artcle explores the mportance of fertlzer proftablty n explanng the relatve, apparent low use of chemcal fertlzers by farmers n Burkna Faso. Usng largescale plot data, we estmate maze yeld response to ntrogen to be 19 kg/ha on average and to vary wth sol characterstcs. Proftablty, whch we measure through the calculaton of a margnal value cost rato, s estmated at 1.4 on those plots whch receved fertlzers, wth sgnfcant varatons across regons. For those plots on whch fertlzers were not appled, we predct that fertlzers should have been proftable n most cases under the current level of subsdzed fertlzer prces. These fndngs suggest that the low uptake of chemcal fertlzers mght have been drven by factors other than proftablty, ncludng nsuffcent supply of subsdzed fertlzers to farmers n need. Our results also call for ncreasng the avalablty of credt to farmers n order to encourage adopton of chemcal fertlzers. Fnally, our results also show that not takng nto account the endogenety of ntrogen use n the yeld equaton may produce based estmates of the maze yeld response to ntrogen. Keywords: Burkna Faso; fertlzers; maze yeld; subsdzaton program; technology adopton.

3 1. Introducton The seemngly low use of modern nputs (ncludng well-known technologes such as chemcal fertlzers) by Afrcan farmers s consdered to be a major mpedment to food securty and poverty reducton n Sub-Saharan Afrca (SSA) (Morrs et al., 2007; Dzanku et al., 2015). Average cereal yelds and average ntensty of modern nputs are stagnant n contrast to what has been observed n most developng regons (World Bank, 2007). In response to ths, n recent years several governments n SSA (wth the assstance of nternatonal donors) have ntroduced nput subsdy programs to foster the use of modern nputs and ncrease agrcultural productvty (Drulhe and Barrero-Hurlé, 2012; Jayne and Rashd, 2013). The dscusson about these polces s stll ongong, wth recent artcles focusng prmarly on the crowdng out of the commercal fertlzer sector, dverson of nputs programs, and other factors related to the desgn of these programs (e.g. Pan and Chrstaensen, 2012; Takeshma and Nkonya, 2014; Jayne et al., 2013). The results from these subsdy programs are mxed and ther performance s found to vary dependng on the country and the characterstcs of the nterventon (for a comprehensve overvew, see Jayne and Rashd, 2013). Ths calls for further rgorous ex-post analyses assessng the effectveness of these polces. Our artcle contrbutes to ths lterature by studyng the proftablty of chemcal fertlzers usng large-scale plot data from Burkna Faso. As far as we know, no such estmates have been produced so far for ths partcular country. The government of Burkna Faso, followng the example of a number of other countres n SSA, launched a subsdy program n 2008 to encourage farmers uptake of chemcal fertlzers and foster cereal producton. The nterventon was unversal but targeted at specfc crops (maze and rce n partcular). The program cost was estmated at 9.2 bllon CFA

4 francs 1 n 2008, about 8% of total agrcultural spendng (MAFAP, 2013). The subsequent decrease n the market prce of fertlzers was estmated to be n the range 20-40% dependng on the source (Wanzala-Mlobela et al., 2013; Sr, 2013). Although the ntensty of fertlzer use has ncreased snce the frst year of the program mplementaton, t has remaned low. In 2008, the average fertlzer use ntensty (calculated as the rato of total fertlzer use to total arable land n the country) was around 9.5 kg/ha. Ths level s slghtly below the average level of fertlzer use for SSA (14.7 kg/ha), and well below the targeted level of 50 kg/ha that should have been reached by 2015, accordng to the 2006 Abuja Declaraton. 2 Fertlzer use ntensty has ncreased slghtly snce 2008, reachng an estmated 11 kg/ha n A number of factors explanng the low use of modern nputs by Afrcan farmers have been dentfed n the lterature: low proftablty (Duflo et al., 2008; Sur, 2011); credt and labor constrants (Croppenstedt et al., 2003; Moser and Barrett, 2006; Lambrecht et al., 2014); rsks (Dercon and Chrstaensen, 2011; Gné and Yang, 2009); transacton and transportaton costs (Zerfu and Larson, 2010; Mnten et al., 2013); and lmted knowledge of the technology and lack of access to extenson servces (Conley and Udry, 2010; Krshnan and Patnam, 2014). In ths artcle, we provde new evdence on the role of proftablty (or lack of proftablty) n explanng the low adopton of chemcal fertlzers n SSA. Usng data from more than 7,800 maze plots cultvated n Burkna Faso, we estmate maze yeld response to ntrogen applcaton. Our modellng framework allows the endogenety of ntrogen levels n the maze yeld equaton to be taken nto account, along wth data censorng ssues (n our sample, fertlzer was appled to only about one-thrd of the plots). One of the man dffcultes wth ex-post analyses of technology adopton s to solate the mpact of the technology from the nfluence of farmer- and plot-specfc characterstcs on yeld (Barrett et 1 1 USD 460 CFA francs (average exchange rate n 2008). 2 Accordng to the World Bank, average fertlzer ntensty s 146 kg/ha n South Asa and 107 kg/ha n Latn Amerca (World Development Indcators accessed 2 Aprl 2016).

5 al., 2004). Our plot data are cross-sectonal and refer to the year 2008 only, but about 70% of households n our sample owned more than one plot. Ths data specfcty allows us to partally control for unobserved household characterstcs by employng the method used by Mundlak (1978) and Chamberlan (1980), and to estmate a fxed-effects model on the subsample of households whch cultvated more than one plot. The latter, whch allows for household-unobserved heterogenety to be solated, s used as a test of robustness for the fndngs of our preferred model estmated on the entre set of plots. We are unable to control for plot-specfc unobserved characterstcs but household-unobserved heterogenety should partally control for dfferences n sol qualty across farms. 3 Our estmates of maze yeld response and the proftablty of fertlzers are n lne wth estmates for other countres n SSA. We also show that not controllng for the endogenety of fertlzer use n the yeld equaton leads to based estmates of maze yeld response to ntrogen. The rest of the artcle s organzed as follows. The next secton brefly revews the related emprcal lterature. Secton 3 provdes some background nformaton about crop producton and modern nput use n Burkna Faso. Secton 4 descrbes the data set used n ths artcle. Secton 5 presents the emprcal model used to estmate the maze yeld response to ntrogen applcaton. Secton 6 presents the estmaton results, followed by a more detaled analyss of margnal returns and proftablty of fertlzers n Secton 7. Secton 8 concludes. 3 We expect the varaton n sol qualty across plots belongng to the same household to be lower than the varaton n sol qualty across farmers.

6 2. Lterature revew The proftablty of fertlzer use depends on both the techncal response to fertlzers,.e. the unts of output produced from one unt of nutrent, and the relatonshp between output prce and fertlzer prce (Yanggen et al., 1998). The lterature on the proftablty of fertlzer use n SSA focuses especally on maze, gven the mportance of ths crop and the relatvely hgh ntensty of fertlzer use on maze plots n ths regon (Morrs et al., 2007; Smale et al., 2013). A number of estmates of maze yeld response rate to fertlzers have been reported n both the agronomc and economc lterature, usng data from both experment statons and farm surveys. Earler studes summarzed n Hesey and Mwang (1996) and Yanggen et al. (1998), prmarly conducted by agronomsts, reported maze response rates for several SSA countres, varyng from a low of 5 kg of gran per kg of nutrent (usually ntrogen) to a hgh of 25 kg of gran or more. These estmates are found to be n lne wth those publshed recently n economc journals and based on farm surveys: 4 response rates of around 8-12 kg of maze per 1 kg ntrogen were reported for Zamba based on a natonally representatve survey (Burke, 2012), Malaw (Rcker-Glbert et al., 2013), and Northwestern Ethopa (Mnten et al., 2013). Hgher-range estmates (15-25 kg of gran) were obtaned n Kenya (Marenya and Barrett, 2009; Matsumoto and Yamano, 2011; Sheahan et al., 2013), Zamba (Xu et al., 2009), and Ghana (Chapoto and Ragasa, 2013). Response rates however are hghly varable, even wthn small regons, dependng on factors such as weather, plantng date, ntrogen applcaton method, and sol type. 5 The role of sol type n condtonng response rate to fertlzers has been the focus of extensve agronomc 4 Fndngs from the recent economc lterature are summarzed n Jayne and Rashd (2013). 5 In the context of Ghana for example, Henao et al. (1992) fnd maze response rate to vary from 7.6 kg per kg of fertlzer nutrent (ntrogen, phosphorus and potassum) n the nteror savanna zone to 18.6 kg n the Volta regon located n the sem-decduous forest.

7 research (e.g. Chanu et al., 2012) snce declnng sol fertlty due to nutrent losses s a major problem n SSA (Henao and Baanante, 2006). A common measure of fertlzer proftablty s the so-called margnal value cost rato (MVCR). The numerator of the MVCR s the revenue generated by the applcaton of an extra klogram of ntrogen. It s calculated as the margnal maze yeld response to ntrogen multpled by the prce of maze. The denomnator of the MVCR s the cost of a klogram of ntrogen. Estmates of MVCRs for maze plots n SSA are summarzed n Jayne and Rashd (2013). The estmated MVCRs are found to vary between 1 and 1.75 n two studes conducted n Kenya (Marenya and Barrett, 2009a; and Matsumoto and Yamano, 2011) and to be n the range n Uganda (Matsumoto and Yamano, 2011). Also usng data from Kenya, Sheahan et al. (2013) report an average (nstead of margnal) value cost rato varyng from 1.3 to 3.7, dependng on the regon. In theory, an MVCR greater than 1, whch ndcates that ntrogen s margnally proftable, should nduce farmers to use chemcal fertlzers. However, t s common to consder MVCRs above 1.5 or even 2 as proftable, n order to account for unobserved transacton costs and the potental rsk premum f farmers are rsk averse (Jayne and Rashd, 2013; Sheahan et al., 2013). Recent research has shed lght on the mportance of non-market factors n explanng the proftablty of fertlzer use n SSA. Usng cross-sectonal survey data from Western Kenya, Marenya and Barrett (2009a, 2009b) show that sol qualty (measured n terms of carbon content) condtons the margnal productvty of fertlzer (maze yeld response). Ths result has mportant polcy mplcatons: snce poor farmers farm less fertle sols on average, they may beneft less from nterventons that am at ncreasng fertlzer use. These fndngs are consstent wth analyses from large-scale panel data from Kenya (Sheahan et al., 2013) and other East and Southern Afrcan countres (e.g. Xu et al., 2009). Sheahan et al. (2013) use panel data for the perod to estmate the proftablty of fertlzer use on maze

8 plots n Kenya. Ther fndngs suggest that an ncrease n average fertlzer applcaton rates wll only lead to an ncrease n maze productvty f attenton s gven to the use of complementary nputs and heterogenety n sol condtons. They also fnd that current fertlzer applcaton rates are proftable for many maze growers across the country. However, estmates of the optmal levels of fertlzer use suggest that an ncrease n fertlzer use (to the level recommended by the government) may not be proftable for most farmers. Ths latter fndng s supported by a study from feld experments n Kenya by Duflo et al. (2008). These authors fnd that the mean annualzed rate of return to fertlzers s 69.5% when fertlzers are used n approprate quanttes but that other quanttes of fertlzers (n partcular the level recommended by the government) are not proftable for the average farmer. These fndngs challenge the common vews that explan n part the renewed emphass on nput subsdes n SSA: () an ncrease n fertlzer use wll be proftable under current condtons; () low fertlzer use s due manly to poor nfrastructure and market mperfectons. Recent analyses of exstng programs have rased concerns about the effcacy of nput subsdes programs whle suggestng ways of mprovements (Banful, 2011; Pan and Chrstaensen, 2012; Holden and Ludunka, 2014). However, the renewed emphass on nput subsdes has also called for more analyses of fertlzer proftablty that take nto account the heterogeneous constrants and opportuntes faced by Afrcan farmers (Jayne and Rashd, 2013). Whle the body of lterature on the costs and benefts of modern nputs use n SSA s growng rapdly, the evdence has come prncpally from a few countres, and even from a few regons wthn these countres. These fndngs may be less nformatve for other countres n SSA where farmers face dfferent producton condtons and constrants.

9 3. Agrcultural producton and modern nputs use n Burkna Faso Burkna Faso s a West Afrcan country wth a populaton of about 17 mllon. The Gross Natonal Income was estmated at 700 USD per capta n 2014, below the SSA average (1,657 USD per capta). 6 In 2009, the contrbuton of agrculture to the Gross Domestc Product was estmated at 35% (MAFAP, 2013). The sector s domnated by smallholders: n 2008, 72% of the farms were smaller than fve hectares (MAFAP, 2013). Agrculture s prncpally ran-fed and domnated by staple food crops (maze, mllet, and sorghum) and cash crops (cotton and rce prmarly). Cereal producton represents about 77% of the total cultvated area for the perod (MAFAP, 2013). Cotton s the prncpal cash crop grown by farmers and accounts for a substantal part of the country s exports. Maze s the thrd cereal crop grown n Burkna Faso, accountng for about 17% of the country s total cereal producton. Maze producton has been expandng over recent years producton multpled by a factor of 3.7 between 2000 and Maze s grown by around 80% of rural households and s prmarly consumed natonally (MAFAP, 2013). Currently, average cereal yelds n Burkna Faso are 1,209 kg/ha, below the average for SSA (1,427 kg/ha). 7 Ths may be partly explaned by the relatvely low use of chemcal fertlzers and mproved seeds: n our data (whch are representatve of the whole country), only 13% of plots cultvated n 2008 receved chemcal fertlzers and only 5% of plots were planted wth mproved seed varetes. 8 Snce the food crss, the government of Burkna Faso has ntervened to ncrease fertlzer uptake and cereal yeld. These nterventons, targeted at specfc crops nstead of 6 Source: World Development Indcators (World Bank), avalable at accessed 2 Aprl For comparson, the average cereal yeld s 2,835 kg/ha n Latn Amerca and the Carbbean and 2,376 kg/ha n South Asa. Source: World Development Indcators (World Bank), avalable at accessed 2 Aprl The man types of chemcal fertlzers used by farmers are NPK (Ntrogen, Phosphorus and Potassum fertlzer) and urea. About 19% of plots receved organc fertlzers, ncludng anmal manure, household waste and compost.

10 categores of farmers as n Malaw and Kenya for nstance, consst prmarly of the dstrbuton of mproved seeds and chemcal fertlzer subsdes. Rce and maze are the man cereal crops targeted under Burkna Faso s nputs subsdy programs (see Drulhe and Barrero-Hurlé, 2012; Sr, 2013). Fertlzer subsdes accounted for about 60% of the total value of nput subsdes for cereal crops. NPK and urea are the two types of fertlzers targeted by the program. Over the perod , subsdzed fertlzers dstrbuted by the government for cereal crop producton amounted to 52,460 metrc tons (an average of 11% of fertlzer mports), and nduced a decrease n the average market prce of fertlzer by 20 to 40% n 2008 (Wanzala-Mlobela et al., 2013; Sr, 2013). Under Burkna Faso s fertlzer subsdy program, fertlzer needs for farmers growng maze and rce are estmated by each Provncal Drectorate of Agrculture (DPA) based on the area expected to be planted and recommended fertlzer applcaton rates. Provnce-level fertlzer needs are then aggregated at the regonal level by the Regonal Drectorate of Agrculture (DRA) before beng transmtted to the Mnstry of Agrculture. The Mnstry of Agrculture mports the fertlzers and dstrbutes them to each regon accordng to the regonal expressed needs. Provncal offces are then responsble for the transport of the amount of fertlzers they requre from the DRA warehouses to DPA warehouses. Farmers (or farmers organzatons) acqure the subsdzed fertlzers from the DPA and are responsble for the transport of the fertlzers to ther farms. However, as noted by Wanzala-Mlobela et al. (2013) and Sr (2013), the program encounters mportant ssues related to fertlzer dstrbuton. In addton to delays n delvery and dscrepances between the expressed needs and the amounts of fertlzer dstrbuted n the regons or provnces, farmers have to bear the transportaton costs from the DRA warehouses to DPA warehouses (see Wanzala-Mlobela et al., 2013, for further detals on Burkna Faso s nput subsdes program).

11 4. Data and descrptve statstcs The data used n ths artcle come from Phase II of Burkna Faso s Agrcultural Census (RGA ). The RGA was a large scale, natonally representatve survey conducted by the Mnstry of Agrculture of Burkna Faso (Drectorate of Agrcultural Statstcs DPSA) between 2006 and The prmary objectve of the RGA was to provde statstcs on the agrcultural sector (crop producton, lvestock development, arborculture, and fshery). Data collected durng Phase II nclude detaled household and plot level data on agrcultural nputs and outputs, lvestock, agrcultural equpment, ncome, access to credt, etc. The sample selecton for Phase II was made usng a two-stage stratfed samplng method. Vllages were selected n the frst stage wth probabltes proportonal to sze. Agrcultural households were then randomly selected n the second stage. Addtonal stratfcaton n each stage was ntroduced n the samplng desgn usng the data collected n Phase I (2006). 9 For nstance, n each provnce, agrcultural households were splt between two strata: smallholder farmers and large-scale farmers (for more detals on the samplng desgn, see DPSA, 2007). In total, 7,500 households were selected n 1,311 vllages. Household characterstcs and plot level crop producton nformaton were collected between June and December 2008 for a subset of 6,795 households n 1,283 vllages. We have detaled nformaton on the 63,407 plots cultvated by these households. The sample covers the 13 admnstratve regons and 45 provnces of the country. For the purposes of ths study, the analytcal sample conssts of 7,845 maze plots belongng to 4,481 households. 10 Around 70% of the surveyed households grew maze on more than one 9 Phase I of the RGA conssted of the lstng of all agrcultural households n the country, and data collecton on actvtes undertaken by the households, as well as on lvestock and equpment owned. 10 Plots for whch yeld (measured n kg/ha) was declared to be 0 were removed from the database and yeld values below the frst percentle and above the 99 th percentle were elmnated. Extremely low values are lkely to reflect neglected or abandoned plots whle extremely hgh values are lkely to be due to measurement error. We also deleted observatons for whch ntrogen applcaton (n kg/ha) was above the 99 th percentle.

12 plot. The average maze yeld n our sample was 1,300 kg/ha, varyng v fromm a low of 112 to a hgh of 3,960 kg/ha (medan 1,200 kg/ha). Chemcal fertlzers were appled on 35% of the plots and the average applcaton rate was 32 kg of ntrogen (N) per hectare (the medan s 26 kg of N), varyng from almost 0 to 198 kg/ha. 11 The latter s nn the range of applcaton rates recordedd for maze plots n Kenya (Sheahan et al., 2013) and Zamba (Burke, 2012); for f other estmates on SSA, see Jayne and Rashd (2013, Table 3). The average maze yeld on plots recevng chemcal fertlzers was 1,590 kg/ha whle the averagee yeld was 1,145 kg/ha when noo chemcal fertlzers are appledd (the dfference s statstcally sgnfcant at the 1% level) ). The dfference n yelds y between plots wth and wthout t chemcal fertlzers ss further llustrated n Fgure 1, whch w features non-parametrc (kernel) estmates of the densty of yeldss for the two types of plots. Fgure 1: Non-parametrc (kernel) estmaton of the densty of maze m yeldss (7,845 plots) 11 The quantty of ntrogen appled s of the form NPK (14% ntrogen) and urea (46% ntrogen).

13 lnear and concave relatonshp between Usng local polynomal regresson on plots whch receved chemcal fertlzers, we fnd a non- ntrogen applcaton rate and maze yeld (Fgure 2). The graph suggests a postve effect of ntrogen on yeld up to a threshold (around kg/ha) but detrmental effectss on yeld wth applcaton rates above a ths threshold and large negatvee effects when more than 150 kg of N per ha were appled on the plot. Fgure 2: Local polynomal regresson (wth 95% confdence nterval) of maze yeld on ntrogenn applcaton rates (2,746 plots) We next compare average plot and household characterstcs for plotss wth and wthout chemcal fertlzers (Table 1). Plots on whch chemcal fertlzers were appled were larger n sze (0.91 ha versus 0.26 ha for those plots whch dd not receve any fertlzers) and more often pure maze plots (88% versus 76% %). Only 52% of the plots p whch receved fertlzers were planted wth maze the year before whle ths proporton s 81% for plots whchh dd not

14 receve any fertlzers. Plots on whch fertlzers were appled were more often borrowed or rented than the other plots (15% versus 10%), 12 and external pad labor was more lkely to be used on plots whch receved fertlzers. Table 1. Comparson of mean characterstcs of plots wth and wthout fertlzers No fertlzer used on plots Fertlzers used on plots Test of mean equalty a Plot-level characterstcs Maze yeld (kg/ha) 1,145 1,590 *** Plot sze (ha) *** Plot s located n the plans (0/1) n.s. Pure maze on plot (no ntercrop) (0/1) *** Maze grown on plot year before (0/1) *** Plot s borrowed or rented (0/1) *** Use of external pad labor (0/1) *** Household-level characterstcs Head of household s male (0/1) *** Age of household head (0/1) *** Household head s llterate (0/1) *** Prce of fertlzer (CFA franc/kg) b *** Access to credt over last 3 years (0/1) *** Less than one hour to market (0/1) ** Total lvestock unts *** Household owns a plough (0/1) *** Household owns small materal (0/1) c *** Poor sol qualty (0/1) d *** Drought or floodng event (0/1) *** Number of observatons 5,099 2,746 Notes: a *, **, *** ndcate that the means of the two groups are statstcally dfferent at the 10, 5, and 1% level, respectvely; n.s. s not sgnfcant. b Medan prce n the dstrct. c Takes the value 1 f the household owns a hopper, a seeder, a harrow, or a hoe, and 0 otherwse. d Takes the value 1 f the household head beleves that the sol of hs/her parcel s of low qualty, and 0 otherwse. The adopton of chemcal fertlzers was more lkely for household heads who were lterate, had better access to credt, and owned more lvestock unts, as well as a plough and some 12 The dummy varable descrbng plot status takes the value 1 f the plot was ether borrowed or rented, and the value 0 f the plot belongs to the household (ether because t was purchased, receved as a gft, or nherted).

15 small agrcultural machnery. Households who appled fertlzers on ther plot benefted from lower fertlzer prces on average (the prce shown n the table s the average of the medan prce of fertlzers n the dstrct). Interestngly, the proporton of household heads who perceved the qualty of ther sol to be poor was hgher for plots that dd not receve any fertlzers. 13 Ths may reflect the fact that poor households whch were often more lkely to work on lower qualty plots faced more constrants regardng access to fertlzers, ether through dffcult access to credt or dffcult access (n terms of tme, dstance, and avalablty of products) to nput markets. It may also be an ndcaton that fertlzers are less proftable on lower qualty sols (Marenya and Barrett, 2009a). Fnally, those plots on whch fertlzers were not appled were more affected by catastrophc clmatc events such as droughts and floods. Maze yelds and the use of fertlzers vary sgnfcantly across the 13 regons (Table 2). Average yeld vares by a factor of three: from a low of 655 kg/ha n Sahel to a hgh of 1,877 kg/ha n Hauts Bassns. The proporton of plots on whch fertlzers were appled also vares sgnfcantly. In Sahel, where agrculture s less developed, 14 fertlzers were very rarely used (1% of the plots) whle they were appled on 75% of the plots n Hauts Bassns. Average ntrogen applcaton was n the range kg/ha for most regons. Regonal dfferences may be explaned by dfferences n envronmental condtons (weather, alttude, type of sol etc.) as well as by dfferences n terms of nfrastructure, dstance to markets and nput supplers, access to extenson servces and other nformaton sources. For nstance, both maze yelds 13 The farmers were asked about ther constrants on agrcultural producton, whch nclude a shortage of land, the lack of techncal knowledge, the poor qualty of sols, clmatc shocks and pest attacks. The ndcator of sol qualty s the household head s subjectve evaluaton of the qualty of the sol: a bnary ndcator equal to one f the head evaluates the sol as poor. 14 The Sahel regon, whch s stuated n the extreme north of Burkna Faso, s relatvely less suted to agrcultural producton. Nonetheless, lvestock rasng and agrculture reman the prncpal actvtes of the households who lve n ths part of the country.

16 and fertlzer use were partcularly hgh n cotton producng regons such as Hauts Bassns, Cascades and Boucle du Mouhoun where access to fertlzer and extenson servces s easer. 15 Table 2. Average maze yeld and fertlzer use by regon Regon Obs. Maze yeld Fertlzers Ntrogen use a used on plots (kg/ha) (kg/ha) Boucle du Mouhoun 932 1,338 58% 37.1 Cascades 441 1,704 68% 26.6 Centre 174 1,030 21% 21.7 Centre Est 821 1,398 27% 33.1 Centre Nord 460 1,072 11% 14.5 Centre Ouest 593 1,396 47% 41.0 Centre Sud 640 1,222 29% 29.7 Est 906 1,425 17% 24.9 Hauts Bassns 851 1,877 75% 36.8 Nord 268 1,022 30% 17.2 Plateau Central 600 1,087 11% 23.0 Sahel % 9.4 Sud Ouest 1, % 25.0 Note: a Average calculated over the plots whch receved some fertlzer. 5. Estmaton methodology Our purpose s to estmate a maze yeld functon and the yeld response to ntrogen applcaton. The local polynomal regresson dscussed above suggests a non-lnear relatonshp between ntrogen applcaton and maze yeld, whch calls for the use of a quadratc functonal form (.e., wth the square of ntrogen applcaton as one of the rghthand-sde varables). There s also evdence n the lterature that the margnal productvty of ntrogen may vary dependng on plot characterstcs such as sol qualty (Marenya and Barrett, 2009a), so our yeld functon also ncludes nteractons between ntrogen applcaton and plot characterstcs. 15 The dverson of cotton fertlzer to maze felds s a well-known phenomenon n Burkna Faso cotton producng areas (e.g. Schwartz, 2008).

17 Yeld also depends drectly on plot and household characterstcs. Regardng a plot, we have nformaton on ts sze, whether t was a pure maze plot or not, whether maze was grown on the plot the year before, whether the parcel was borrowed or rented, whether some external laborers were hred to work on the plot, and whether the plot had been affected by some catastrophc clmatc events over whch the farmer had no control (flood or drought). To control for households heterogenety, we use the number of lvestock unts (as a proxy for the use of norganc fertlzers such as manure), the household s equpment (plough, small machnery), gender, age, and lteracy of the household head, plus hs/her percepton of the sol qualty. Fnally we nclude regonal dummes to control for varyng clmatc condtons and sol qualty across the country. Regonal dummes mght also capture dfferences n access to nformaton and extenson servces across regons. The general form of the yeld functon s the followng: p, p, p, p, p, y f N, x, w; β, (1) where y p, represents maze yeld (n kg/ha) on plot p belongng to household ; N p, s ntrogen applcaton rate (kg/ha); x p, and w are vectors of observable covarates at the plot (p) and household () level respectvely; β s the vector of unknown parameters; p, and represent plot- and household-specfc unobservables; and p, s an dosyncractc error term wth assumed mean zero. Snce our data are n a cross-sectonal form, panel data technques are not avalable to control for unobserved plot-specfc effects p, (.e., there s no way to separate p, from, p ) and endogenety bas mght arse f some observable varables were correlated wth the unobserved plot-specfc effects. Unobservable plot characterstcs, such as sol qualty, plot

18 exposure or slope, are unobserved by the econometrcan but known to the farmer. So we mght expect these characterstcs to have a drect nfluence on yeld but also to nfluence the farmer s use of ntrogen. To control for the possble endogenety of ntrogen applcaton n the yeld equaton, we estmate an equaton featurng ntrogen use ( N p, ) as the dependent varable smultaneously wth the yeld functon: y f N, x, w ; β N N N N N Np, g xp,, w ; β p, y y y y y p, p, p, p,. (2) We add superscrpts y and N to dfferentate the vectors of observable characterstcs, unknown parameters, and error terms between the two equatons. We have removed the unobservable plot characterstcs p, from the equatons n the system snce they are not separable from the dosyncratc error term. The vector of exogenous varables n the ntrogen equaton ncludes: plot sze; plot locaton (whether n the plans or not); cultvaton practces on the plot (whether ths s a pure maze plot, and whether maze was grown the year before); plot status (borrowed or rented parcel); adverse clmatc events (drought or flood) that affected the plot; the medan prce of fertlzers n the dstrct; 16 total lvestock unts; the household s dstance to market; gender, age, and lteracy of household s head and hs/her percepton of sol qualty; and regonal dummes. Regonal dummes n ths model are used to control for possble nfrastructure and nput supply constrants snce we do not have any precse nformaton on the dstance between the farm and the fertlzer supplers. The prce of fertlzers and the dstance to market (a dummy varable takng the value 1 f the market s less than one hour away, and zero f not) play the role of dentfyng nstruments n the ntrogen equaton. Tests on the valdty of these nstruments are performed and dscussed n the next secton. 16 The dstrct s another admnstratve unt, at a lower level than the provnce.

19 It mght also be the case that some unobservable household characterstcs, are correlated wth both the dependent varables and observable plot characterstcs. In the yeld equaton for example, an experenced and well-nformed plot manager mght have developed cultvaton practces (ntercroppng, crop rotaton) that have a drect nfluence on yelds but also on hs/her choce of ntrogen applcaton. To control for possble correlatons between unobservable household characterstcs and some of the plot-specfc observables x p,, we follow the approach of Mundlak (1978) and Chamberlan (1980). We specfy a lnear relatonshp for the dependence between the unobserved household specfc effects and the exogenous covarates as follows: x a (3) where x s the vector of household-specfc mean plot characterstcs (calculated over the plots owned by each household). We assume that x allows us to control for the correlaton between household unobserved specfc effects and plot observable characterstcs and that, condtonal on x, the remanng error a can be assumed to be ndependent of x p,. Mean plot characterstcs wll be nformatve only for households whch own more than one plot (about 70% of the surveyed households). The same approach can be appled to both equatons n the system. 17 Omttng the constant whch s embedded wth the constant of the orgnal models, the system s wrtten as follows: y f N, x, w ; β N x a N N N N N N N N p, g xp,, w ; β x a p, y y y y y Y y p, p, p, p,. (4) 17 We also nclude the household-specfc mean ntrogen applcaton ( N ) n the yeld equaton.

20 Fnally, to account for the fact that a large number of plots dd not receve any chemcal fertlzers, we follow Shonkwler and Yen (1999). Ths mples estmatng an augmented verson of system (4): y y y y y Y y, x, w ; β N x a N N N z g, ; p,α x w β x zp,α yp, f Np, p, p, N a N N N N p, p, p,, (5) where. and. are the probablty densty functon and cumulatve dstrbuton functon of a unvarate standard normal, respectvely; z p, represents the vector of exogenous varables n a probt model descrbng the decson whether or not to use chemcal fertlzers on plot p; and α s the correspondng vector of unknown parameters (for more detals on the procedure, see Shonkwler and Yen, 1999). 18 We nclude n z observable characterstcs of the plot (sze, whether or not ths s a pure maze plot, whether the parcel has been borrowed or rented), household characterstcs (gender, age, and lteracy of the household s head, sol qualty percepton, number of lvestock unts, dstance to market, and fertlzer prce), and regonal dummes. We use as the excluded nstrument the household s access to credt (whch s measured by a dummy varable takng the value 1 f the household had access to credt over the prevous three years, and zero f not) snce ths has been dentfed as one of the mportant factors explanng the low uptake of modern nput use n SSA. The estmaton of system (5) nvolves two steps. Frst, we estmate the probablty that some chemcal fertlzer s appled on each plot (a bnary ndcator) usng maxmum lkelhood. 19 Then we use the estmated parameters to compute z p, α ˆ and p, ˆ z α. In the second 18 N N N N The term z g w x α x, ; β p, p, z α corresponds to the uncondtonal mean of ntrogen p, use per hectare. 19 More precsely we estmate a random-effects probt model and nclude household-specfc mean plot characterstcs to control for unobserved household specfc effects.

21 stage, the system n whch z p, α ˆ and z p, α ˆ are used n place of z p, α and z p, α s estmated usng three-stage least squares (3SLS). Because of the two-stage estmaton, we bootstrap standard errors n the second stage. For comparson purposes, we estmate the system of equatons descrbed n (5) controllng for farmer fxed effects. Ths mples losng around one-thrd of the orgnal observatons snce only households who grew maze on multple plots can be consdered. 20 The fxed-effect approach allows us to account for household-unobserved specfc factors such as personal sklls and knowledge, hstory of fertlzer use that may mpact ntrogen use as well as maze yeld. Ths system s run as a test of robustness for the 3SLS estmates obtaned usng the full sample of plots (see Marenya and Barrett, 2009a, for use of a smlar approach). Fnally, we estmate the yeld functon (frst equaton n system (5)) usng Ordnary Least Squares (OLS). If the OLS estmates dffer sgnfcantly from the 3SLS estmates, then ths wll be evdence of endogenety of the quantty of ntrogen used. 6. Estmaton results Maxmum-lkelhood estmaton results of the frst-stage probt model show that access to credt, market condtons (the prce of fertlzers) and dstance to market are mportant drvers of the decson to use fertlzers on a plot. 21 Access to credt, whch s used as an excluded nstrument, s hghly sgnfcant and was found to ncrease the probablty to use fertlzers. 22 Lower fertlzer prces and a shorter dstance to the market (less than one hour walkng tme) encourage fertlzers adopton. Fertlzers were also more lkely to be used on larger plots and 20 We do not consder here the possble selecton ssue of farmers holdng multple plots. 21 Estmaton results of the frst-stage probt model are not shown here but are avalable on request. 22 Access to credt s assumed to nfluence only the decson to use fertlzers but not the quantty of fertlzers used. The correlaton between access to credt and per hectare ntrogen use s ndeed low (less than 0.1).

22 on pure maze plots. Households characterstcs do matter n the adopton of chemcal fertlzers: we fnd that young and lterate male heads were more lkely to apply ntrogen on ther maze plots. 23 Fnally, regonal dummes are sgnfcant, whch may reflect dfferences n the avalablty of subsdzed fertlzers across regons. Estmaton results for the maze yeld equaton are shown n Table 3. We report estmates obtaned usng 3SLS on the full sample of plots (7,845 observatons), 3SLS wth farmer fxed effects (3SLS-FE) on a sub-sample of plots (5,499 observatons), and OLS (7,845 observatons). We frst comment on our preferred model, whch s the system of equatons estmated on the full sample usng 3SLS. Our fndngs confrm that yeld s a non-lnear (concave) functon of ntrogen applcaton and that the margnal productvty of ntrogen s lower on plots of lower qualty (as measured by farmer s percepton) as well as on plots whch were planted wth maze the year before. We dscuss the maze response rate to ntrogen applcaton n greater detal n the next secton. Cultvaton practces have a drect mpact on yeld: n our sample, growng pure maze plots and growng maze on a plot whch was planted wth maze the year before are found to ncrease yeld on average by 80 kg/ha and 114 kg/ha, respectvely. Maze yeld on a borrowed or rented parcel s sgnfcantly lower, by 133 kg/ha on average, possbly because such parcels are of lower qualty or because plot managers put less effort on these plots compared to the plots that they own. As expected, catastrophc clmatc events such as floods and droughts have a sgnfcant mpact on yeld, leadng to an average loss of about 435 kg/ha. 23 We only consder the characterstcs (gender, age, and lteracy) of the household head and not those of the plot manager for two man reasons: 96% of the household heads and 93% of the plot managers are male n our sample and there s a hgh collnearty between characterstcs of household heads and plot managers. Also, usng characterstcs of plot managers nstead of those of household heads dd not mprove the overall model qualty and entaled a loss of about 10% of the observatons because nformaton was mssng for a number of plot managers.

23 Yeld s found to be sgnfcantly larger (+100kg/ha) on larger plots. Ths result contradcts a number of prevous studes whch found evdence of an nverse relatonshp between sze and productvty (for a revew of earler studes, see Holden and Fsher, 2013). The total number of lvestock unts has a postve effect on yeld, whch may reflect the household s use of manure as a substtute or complement to chemcal fertlzers. Yeld s also sgnfcantly hgher on plots belongng to households who own small agrcultural machnery; the margnal effect s estmated at 48 kg/ha. The household head s soco-demographcs (gender, age, lteracy level) are not found to be sgnfcant n ths model. 24 The varable measurng the household head s percepton of land qualty has the expected negatve sgn but s not sgnfcant, whch may reflect an mperfect assessment of sol qualty by the survey respondents. Fnally, regonal dummes reflect dfferences n yelds across regons, wth the hghest average yelds recorded n Hauts Bassns, Est, and Cascades (reference regon: Sud Ouest), and the lowest yeld recorded n Sahel. The estmated coeffcent of the man varable of nterest, ntrogen per hectare, s sgnfcantly hgher when endogenety of ntrogen use s taken nto account. In the two models featurng a system of equatons (3SLS and 3SLS-FE), the estmated (drect) effect of ntrogen on maze yeld s estmated at around 23 kg/ha, whle the estmated effect s 7 kg/ha when the maze yeld equaton s estmated by OLS. Ths s an ndcaton that ntrogen use may be endogenous n the yeld equaton and that not controllng for ts endogenety may lead to based estmates of the maze yeld response to ntrogen. Even f the 3SLS and 3SLS-FE 24 Plot manager characterstcs, when used nstead of household head characterstcs, were not found sgnfcant. Ths fndng should not be nterpreted as evdence for smlar yelds on men and women s plots because of very few plots managed by women n our sample. An earler study n Burkna Faso found hgher yelds on men s plots than on smlar women s plots smultaneously planted wth the same crop wthn the same household (Udry et al., 1995).

24 estmates are not drectly comparable because they were obtaned usng dfferent samples, they are found to be statstcally equal n most cases, whch ncreases the confdence n our preferred model (3SLS). Estmates of maze yeld responses obtaned usng the three models are dscussed n the next secton. Under- and over-dentfcaton tests were performed n a two-stage least squares settng and confrmed the valdty of the nstruments for the quantty of fertlzers used: the prce of fertlzers and the dstance to the market. 25 The Cragg-Donald Wald F Statstc (101.51) also confrmed that our nstruments passed the weak dentfcaton test. Estmaton results for the second equaton of the system (featurng per hectare ntrogen use as the dependent varable) are shown n the Appendx (Table A1). We only report the 3SLS estmates obtaned on the full sample of plots snce the 3SLS-FE estmates are not statstcally dfferent for most varables. The quantty of ntrogen appled on a plot s found to be lower on larger plots and on plots that were planted wth maze the year before. Plots that belonged to households whch perceved ther land as low qualty receved less ntrogen on average (almost sgnfcant at the 10% level). Ths may be explaned by the cost of ntrogen outweghng the expected beneft from ntrogen applcaton. As dscussed above, the margnal productvty of ntrogen s sgnfcantly lower on low qualty sols and usng ntrogen on these sols may smply not be proftable (see Marenya and Barrett, 2009a, for related dscussons on data from Kenya). Male heads were also found to apply more ntrogen than female heads. 25 The null hypothess of the under-dentfcaton test s that the nstruments are not vald n the sense that they are not correlated wth the endogenous regressor. The null hypothess of the over-dentfcaton test s that the nstruments are vald,.e., uncorrelated wth the error term and correctly excluded from the estmated equaton. We reject the null of under-dentfcaton (p-value = 0.000) and do not reject the null of over-dentfcaton (pvalue = 0.30).

25 Table 3. Maze yeld equaton (3SLS, 3SLS-FE, and OLS estmates) 3SLS 3SLS-FE OLS Maze yeld (kg/ha) Coef. a Std.Err. b Coef. a Std.Err. b Coef. a Std.Err. Constant *** *** Plot characterstcs Ntrogen applcaton (kg/ha) *** ** *** Plot sze (ha) *** *** *** Plot located n the plans Pure maze crop *** *** *** Maze grown year before *** ** Hred labor ** *** ** Borrowed or rented parcel ** ** ** Drought or flood *** *** *** Household characterstcs Total lvestock unts 1.603* *** Household has a plough ** Household owns small machnery * *** Household head s a male Age of household head Household head s llterate Low sol qualty (percepton) ** Regonal dummes Boucle du Mouhoun ** *** Cascades *** *** Centre Centre Est *** *** Centre Nord *** *** Centre Ouest *** *** Centre Sud *** *** Est *** *** Hauts Bassns *** *** Nord Plateau Central * Sahel Sud Ouest Interactons Ntrogen x Ntrogen *** ** *** Ntro. x Low qualty sol *** * Ntro. x Maze year before ** Mean plot characterstcs (Mean) Ntrogen applcaton ** * (Mean) Plot sze ** (Mean) Plot located n plans (Mean) Pure maze crop *** *** (Mean) Maze year before (Mean) Hred labor

26 (Mean) Borrowed or rented plot *** *** (Mean) Drought or flood ** * Number of observatons 7,845 5,499 7,845 Note: a *, **, *** ndcates sgnfcance at the 10, 5, and 1% levels, respectvely. b Bootstrapped standard errors (1,000 replcatons). The bootstrapped standard errors have been clustered by household to account for wthnhousehold correlaton of errors across plots. The yeld equaton ncludes varables (apart from ntrogen applcaton) that are choce varables for the farmer and may hence be endogenous. These nclude a dummy varable descrbng whether some external laborers had been hred to work on the plot, plot status (.e., whether the plot s borrowed or rented), and plot sze. Because of the dffculty to fnd approprate nstruments, we tested the robustness of the 3SLS estmates by re-estmatng the system of equatons after excludng each of these varables, one after the other. The results are qualtatvely the same and the estmated maze yeld response s not statstcally dfferent from the 3SLS coeffcent reported n Table 3 (23.2). 26 Fnally, about 8% of the plots were planted wth mproved maze seeds. Ths varable was not used snce t was not found sgnfcant n any of the models. As a robustness check, we re-estmated the system of equatons on the subsample of plots that were not planted wth mproved seeds (7,206 observatons). Results are smlar to those shown n Table 3 (the coeffcent of the ntrogen applcaton rate s estmated at 20.9). 26 The estmated coeffcent for ntrogen applcaton rate n the yeld equaton s 24.3 when the varable measurng hred labor s removed, 23.9 when plot status s removed, and 29.0 when plot sze s excluded.

27 7. Margnal return and the proftablty of fertlzers Table 4 shows the maze yeld response to ntrogen applcaton calculated from the estmated coeffcents of the three models (3SLS, 3SLS-FE, and OLS) at the mean of the sample, along wth 95% confdence ntervals (calculated usng the bootstrapped standard errors n the case of the 3SLS and 3SLS-FE models). Table 4. Estmated maze yeld response (kg/ha) at the sample mean Model Mean 95% CI Lower bound 95% CI Upper bound System of equatons 3SLS System of equatons 3SLS-FE Sngle yeld equaton OLS The maze yeld response s estmated at 19 kg/ha n our preferred model (3SLS), varyng between 11 and 27 kg/ha wth a 95% level of confdence. Ths s n the range of prevous estmates reported n the lterature for other countres n SSA (Jayne and Rashd, 2013). The estmated yeld response obtaned usng the OLS model s sgnfcantly based downwards due to the lack of control for endogenety of ntrogen use. The maze yeld response calculated from the 3SLS-FE model s 22 kg/ha, closer to the 3SLS estmate, but less precse due to the lower number of observatons. The mpact of sol condtons on (margnal) maze yeld response to ntrogen applcaton s llustrated n Table 5. Usng the 3SLS estmates, we derve the quantty of ntrogen that maxmzes maze yeld for dfferent sol condtons, n partcular sol qualty and the type of crop grown the year before. Maze yeld response s calculated based on the average use of ntrogen under dfferent sol condtons. The quantty of ntrogen that maxmzes maze yeld s estmated at 78 kg/ha and the maze yeld response at 14.2 kg/ha when the sol s of low qualty and maze was grown on the plot the year before. Wth a hgh qualty sol and when

28 the plot was not planted wth maze the prevous year, the ntrogen applcaton that maxmzes maze yeld s estmated at 106 kg/ha and maze yeld response s 19.4 kg/ha. Table 5. Ntrogen applcaton that maxmzes maze yeld under dfferent sol condtons (95% confdence nterval s shown n brackets) Low qualty sol Maze grown year before Optmal N applcaton (kg/ha) Maze yeld response (kg/ha) yes yes 78 [65;90] 14.2 [7.6;20.9] yes no 90 [74;107] 16.2 [8.5;24.0] no yes 93 [84;103] 16.9 [9.5;24.2] no no 106 [92;120] 19.4 [10.7;28.1] We next assess the proftablty of fertlzer use on the plots that receved ntrogen (2,746 observatons), by comparng the expected ncrease n revenue nduced by the use of an extra klogram of ntrogen wth ts cost (Table 6). In order to lower the rsk of measurement error and possble endogenety of household-specfc prces, we use medan prces n each of the 13 regons, for both maze and ntrogen. Ntrogen prces 27 were derved from the reported quantty and value of fertlzer purchased by farmers. As sources of fertlzer nclude publc sources, non-governmental organzatons and farmers organzatons, these reported fertlzer prces may not fully reflect the market prce of fertlzer. The average prce of ntrogen was 1,355 CFA franc/kg (approxmately 3 USD/kg) n our sample, wth varatons across regons (from 1,255 CFA franc/kg n Boucle du Mouhoun to 1,888 CFA franc/kg n Centre Nord and 3,494 CFA franc/kg n Sahel). These values seem realstc n comparson to prces reported n a recent World Bank report (Sr, 2013): wthout any subsdes, the prce of ntrogen n 27 The prce of 1 kg of ntrogen n urea s the prce per tonne of urea dvded by 460 as urea contans 46% ntrogen. The prce of 1 kg of ntrogen n NPK s the prce per tonne of NPK dvded by 140 as the NPK fertlzers used n the study area contan 14% ntrogen.