ABSTRACT. KOTSIRI, SOFIA. Three Essays on the Economics of Precision Agriculture Technologies. (Under the direction of Roderick M. Rejesus.

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1 ABSTRACT KOTSIRI, SOFIA. Three Essays on the Economcs of Precson Agrculture Technologes. (Under the drecton of Roderck M. Rejesus.) Ths study conssts of three essays focused on economc ssues related to cotton precson farmng (PF) technologes n the Southeastern regon of the Unted States. Precson farmng technologes are typcally delneated as ether ste specfc nformaton gatherng (SSIG) technologes or as varable rate technologes (VRT). The frst essay nvestgates the effect of farmers perceved spatal yeld varablty on precson technology adopton. We extend the theoretcal model developed by Isk and Khanna (2002) and fnd emprcal evdence that farmers who perceve ther yelds as varable wll more lkely adopt at least one of the precson farmng components. In addton, farmers who use computer n ther farm management, beleve that the technology wll be proftable and mportant n the future, acqure precson farmng nformaton through unversty publcatons, as well as younger ones who plan on beng n the ndustry for more years, wll more lkely adopt at least one of the technology's components. The second essay explores factors that dfferentate envronmentally motvated farmers (.e., those who rank envronmental benefts hgher than proft, based on a Lkert style rankng) from farmers who make decsons based solely on fnancal crtera. To acheve ths objectve, we apply a proportonal odds model that accounts for the ordered nature of the dependent varable. Our results ndcate that partcpaton n agrcultural easement programs, perceved mportance of precson farmng (PF) n the future, as well as the perceved mprovement n envronmental qualty followng the precson technologes

2 use, all postvely nfluence the decson to adopt for envronmental reasons. In contrast, educatonal attanment and use of Unversty Publcatons to acqure nformaton about precson agrculture have a postve mpact on adopton based on proft motves. A second ssue that we study s examnng the factors that affect farmers perceptons about perceved mprovements n envronmental qualty followng the precson technology adopton. The thrd essay examnes the factors affectng the duraton of duraton of use of sol samplng and yeld montor technologes. A duraton analyss that accounts for selectvty bas s used to nvestgate the mpact of dfferent varables on the speed of abandonment. The estmated Webull model for sol samplng suggests that farm sze s the most mportant determnant n usng sol samplng. Farmers wth larger farm szes tend to use sol samplng for a shorter perod of tme. On the other hand more experenced farmers wth longer plannng horzons, whose ncome comes manly from farmng sources, wll lkely use sol samplng longer. Regardng yeld montors, more experenced cotton farmers, those who beleve that PF wll be mportant n the future, those that have hgher levels of educatonal attanment, and farmers whose ncome s not solely dependent on farmng, wll more lkely utlze the technology longer.

3 Copyrght 2013 by Sofa Kotsr All Rghts Reserved

4 Three Essays on the Economcs of Precson Agrculture Technologes by Sofa Kotsr A dssertaton submtted to the Graduate Faculty of North Carolna State Unversty n partal fulfllment of the requrements for the Degree of Doctor of Phlosophy Economcs Ralegh, North Carolna 2013 APPROVED BY: Roderck M. Rejesus Commttee Char Mchele C. Marra Kelly Zerng Ncholas E. Pggott

5 DEDICATION To my husband Ioanns, wthout whom ths thess wouldn t have started and, needless to say, been completed to my son Andreas, wthout whom ths thess would have been completed a year earler and to my parents Andreas and Lana, wthout whom there would have been no me.

6 BIOGRAPHY Sofa Kotsr was born n 1984 n Patras, Greece. She was the frst chld of Andreas Kotsrs and Lana Terz. In 2001 she was named a scholar of the Natonal Scholarshp Foundaton of Greece for her admsson rankng n the Unversty of Patras (1 st among 150 students). She receved her undergraduate and masters degrees n Economcs from the Unversty of Patras n 2005 and 2007, respectvely. Along wth her academc studes, she worked for the Natonal Telecommuncatons Organzaton of Greece (OTE). In August of 2007 she came to the Unted States to pursue her PhD n Economcs at North Carolna State Unversty. Her doctorate was funded by scholarshps from the Foundaton of George and Vctora Karelas, and from A. Mentzelopoulos bequest. As a graduate student, she taught undergraduate courses n Economcs, worked for the Graduate School, and had nternshps at the North Carolna Solar Center (NCSC) and the Insttute for Emergng Issues (IEI).

7 ACKNOWLEDGMENTS There are no adequate words to express my grattude to my advsor Rod Rejesus. He has been a role model for me wth hs professonalsm, hard work, nsghts when nothng seemed to work, mmeasurable support, flexblty and patence especally durng these very challengng months of my lfe. Thank you for the gudance, the dedcaton and for belevng n me. Thank you Kelly Zerng for the drectons you gave me durng the frst steps of my research, for the nce collaboraton we had n the past summers and for your fnancal support. I do really apprecate these. Thank you Mchele Marra and Nck Pggott for your valuable feedback and suggestons on how to make my paper stronger. Thank you Gudo van der Hoeven for your support and your valuable feedback regardng the applcable aspect of my research. Thank you Bobby Puryear and Tamah Morant for trustng me to teach my own class and for the departmental support. Thank you Melssa Bostrom, Barb Honeycutt and the Graduate School s Preparng Future Leaders (PFL) team for teachng me the mportance of transferable sklls and professonal development. Thank you Shay Fatal for the opportunty you gave me to work for the North Carolna Solar Center (NCSC) and for the Insttute for Emergng Issues (IEI). I acknowledge the George and Vctora Karelas Foundaton and the Unversty of Patras (A.Mentzelopoulos grantor) for the fnancal support durng the frst years of my studes. Thank you Aleen Laptan because you were the reason I approached my advsor. Thank you Sant Sanglestsawa for beng a good frend, and for helpng me durng my v

8 qualfers preparaton, although you were sck. I wll always remember how supportve you have been. Thank you Dmtr for the long skype conversatons, for all your medcal advce and for beng a great brother. The very deepest thanks go to my parents. Mom and dad, you are the reason I am wrtng these words today. You are the reason behnd every choce n my lfe. I wll always strve to make you proud. Last, I am ndebted for lfe to the sunshne of my lfe, my husband Yanns, who encouraged me to pursue a PhD. Thank you for makng me a better person and for gvng me hope and strength for the future. Thank you for beng a great father for our son lttle fellow: you are my lfe; you are my everythng v

9 TABLE OF CONTENTS LIST OF TABLES... x LIST OF FIGURES... x CHAPTER INTRODUCTION... 1 CHAPTER FARMERS PERCEPTIONS ABOUT SPATIAL YIELD VARIABILITY AND PRECISION TECHNOLOGY ADOPTION Introducton Model Conceptual Framework Comparatve Statcs: Technology Adopton and Perceved Spatal Varaton Estmaton Two Step Approach: Multnomal Logt/Probt Endogenety of Spatal Yeld Varablty Perceptons Three Step Approach: Heckman Correcton Model Data Varable Constructon & Emprcal Specfcaton Dependent Varables Explanatory Varables Instruments for Spatal Yeld Varablty Perceptons Results & Dscusson v

10 Multnomal Logt/Probt Estmaton Heckman Correcton Model Conclusons References CHAPTER PRECISION FARMING TECHNOLOGIES, ENVIRONMENTALLY MOTIVATED FARMERS, AND PERCEIVED IMPROVEMENTS IN ENVIRONMENTAL QUALITY Introducton Emprcal Approaches Emprcal Approach for Objectve 1: Adopton due to Envronmental Reasons Conceptual Framework: Adopton due to Envronmental Reasons Estmaton Methods: Proportonal Odds Model POM (Ordered Logt) Robustness Checks Emprcal Specfcaton: Adopton due to Envronmental Reasons Emprcal Approach for Objectve 2: Perceved Envronmental Improvements Conceptual Framework: Perceved Envronmental Improvements Estmaton Method: Perceved Envronmental Improvements Emprcal Specfcaton: Perceved Envronmental Improvements Survey and Data Descrpton Descrpton of Mult-Year Surveys Data Descrpton for Objectve 1: Adopton due to Envronmental Reasons Data Descrpton for Objectve 2: Perceved Envronmental Improvements v

11 3.4 Results Objectve 1 Results: Adopton due to Envronmental Reasons Results of the Proportonal Odds Model (Ordered Logt) Robustness Check Results: Rare Events Logt and Multnomal Logt Objectve 2 Results: Perceved Envronmental Improvements Results of the Pooled Logstc Regresson Robustness Check Results: Separate Logt Models Concluson References CHAPTER WHAT MAKES CERTAIN PRECISION TECHNOLOGIES MORE DURABLE THAN OTHERS Introducton Conceptual/Emprcal Framework Utlty Theory Duraton Analyss Data, Estmaton Procedures, and Emprcal Specfcaton Data Descrpton Estmaton Procedures Emprcal Specfcaton Results and Dscusson Nonparametrc Analyss: Kaplan-Meer Curves v

12 4.4.2 Parametrc Duraton Analyss Conclusons References CHAPTER CONCLUSIONS APPENDIX APPENDIX x

13 LIST OF TABLES Table 2.1 Summary of dependent and ndependent varables used n the Multnomal Logt Model & Heckman Model Table 2.2 OLS regresson of SYCV Table 2.3 Weghted Multnomal Logt and Probt Parameter Estmates (N=770) Table 2.4 Weghted Margnal Effects* from the Multnomal Logt Model (N=770) Table 2.5 Weghted Margnal Effects* from the Multnomal Probt Model (N=770) Table 2.6 Weghted 1 st Step Probt Model of SSIG Adopton Table 2.7 Weghted * 2 nd Step Probt Model of VRT Adopton Table 3.1 Summary Statstcs of the Varables used n Objectve 1 (Adopton due to Envronmental Reasons) Table 3.2 Summary Statstcs of the Varables used n Objectve 2 (Perceved Improvements n Envronmental Qualty) Table 3.3 Comparson of Varable Statstcs of the 3 Groups of Farmers Table 3.4 Margnal Effects of the Covarates usng Ordered Logt Table 3.5 Margnal Effects of the Covarates for each Outcome usng Ordnal Logstc Regresson Models Table 3.6 Margnal Effects of the Covarates of Each Outcome usng Multnomal Logt Model Table 3.7 Parameter Estmates of the Covarates usng Rare Events Logt & Bnary Logt.. 95 Table 3.8 Logt Parameter Estmates of the Covarates usng a Pooled Regresson (N=738) 96 Table 3.9 Coeffcent estmates of the covarates for each outcome usng Bnary Logt x

14 Table 3.10 Average margnal effects of each outcome usng Bnary Logt Models Table 4.1 Descrptve Statstcs of the Varables Table 4.2 Summary Statstcs for the two categores of farmers (Abandon vs Contnue) Table 4.3 Akake Informaton Crteron for dfferent dstrbutons Table 4.4 Webull Model Estmates for SAMPLING (N=977) Table 4.5 Webull Model Estmates for MONITORS (N=919) x

15 LIST OF FIGURES Fgure 2.1: Varable Rate Technology (VRT) Constructon (2.1A and 2.1B) Fgure 2.2: Ste Specfc Informaton Gatherng Technologes (SSIG) Constructon Fgure 2.3: Perceved Spatal Yeld Varablty (SYCV) Constructon Fgure 3.1: Percentage of Responses for the Reasons to Adopt Precson Technology Fgure 4.1: Number of farmers usng dfferent SSIG technologes Fgure 4.2A: Length of use of each SSIG Technology Fgure 4.2B: Abandonment of each SSIG Technology Fgure 4.3: Kaplan-Meer survval curve of MONITORS duraton Fgure 4.4: Kaplan-Meer survval curve of SAMPLING duraton Fgure 4.5: Kaplan-Meer survval curve of PHOTOS duraton Fgure 4.6: Kaplan-Meer survval curve of MAPS duraton x

16 CHAPTER 1 INTRODUCTION Precson agrculture conssts of ste specfc nformaton gatherng technologes (SSIG), such as yeld montors, sol samplng, aeral magery etc., as well as varable rate nput applcaton technology (VRT). These technologes allow farmers to dentfy the spatally heterogeneous parts of ther felds, and apply ther nputs at a varable rate based on locatonspecfc needs rather than unformly based on average condtons. In partcular, SSIG equpment uses nformaton about sol, clmate factors, landscape and through software they desgn a dgtal map that depcts the varaton n dfferent parts of the feld. The farmer can afterwards collect and analyze these spatal data and mplement the approprate management response based on the nformaton. The use of VRT requres the adopton of at least one SSIG technology. As a consequence, farmers can mnmze the over or under applcaton of ther nputs, whch mples potental ncrease n profts - gven the hgh prce of fertlzers, they now use nputs more effcently - and mprovement n envronmental qualty, due to less chemcal runoff. Ste specfc technology has been utlzed n the producton of major commodty crops (.e., wheat, soybeans, corn, peanuts), but n our study we focus only on cotton producton n the Southeastern regon of the Unted States. The frst essay examnes the effect of perceved spatal yeld varablty on precson technology adopton. Usng a two step multnomal probt/logt model, and a sequental Heckman correcton model, we fnd that farmers who beleve that ther wthn-feld spatal yeld varablty s hgh are the ones more lkely to utlze precson farmng nformaton 1

17 technologes and/or apply ther nputs at a varable rate. On the other hand, farmers who perceve ther yelds as more spatally homogeneous would tend not to adopt any of the precson technologes bundle. The multnomal probt model accounts for decsons made at one tme, snce currently many producers hre VRT servce provders to do the grd sol samplng, data analyss, and nput recommendatons. On the other hand, the two step Heckman correcton model consders the possblty of evaluatng the effectveness of SSIG frst before adoptng VRT at a second tme. The mportance of our study les n the fact that t examnes the effect of perceptons about a varable that affects proftablty of the technology and not perceptons about attrbutes of technology as t s the case wth most technology adopton studes. Also, n realty what really matters s the farmers pror percepton about wthn-feld spatal varablty rather than the actual spatal varablty. My second essay has two objectves. The frst examnes what dfferentates envronmentally motvated farmers (.e., those who rank envronmental benefts hgher than proft, based on a Lkert style rankng) from farmers who make decsons based solely on proft maxmzaton crtera. Knowng the characterstcs of precson technology users that adopt for envronmental reasons and are more n tune wth the envronmental benefts of the technology would allow for more targeted educatonal and nformaton dssemnaton programs that focus on the envronmental advantages of the technology. A proportonal odds model (POM) s proposed to estmate the factors affectng the degree of socal responsblty on the technology adopton. The margnal effects ndcate that the partcpaton n agrcultural easement programs, the perceved mportance of precson farmng (PF) n the future, as well as the perceved mprovement n envronmental qualty followng the 2

18 precson technologes use, all postvely nfluence the decson to adopt for envronmental reasons. In contrast, educatonal attanment and use of Unversty Publcatons to acqure nformaton about precson agrculture have a postve mpact on adopton based on proft motves. These results suggest that there may be a need for further techncal advce and nformaton from Extenson focusng on envronmental benefts of precson agrculture. The second objectve explores the characterstcs of producers who observed these mprovements followng precson technology adopton. Based on logstc regressons, the probablty that a farmer observed any mprovements n envronmental qualty (through the adopton of technology) was hgher f the farmer used manure n hs/her felds, was less dependent on ncome comng only from farmng sources, and perceved that PF wll be proftable n the future. The thrd essay dentfes factors that determne the duraton of use of sol samplng and yeld montors. Prevous studes have focused on the factors affectng the abandonment of the sol samplng technology, but none have nvestgated factors affectng the duraton of use of ste-specfc nformaton gatherng technologes. Ths analyss wll gve some nsghts as to what factors make the adopton of the specfc technologes more sustanable. We can only observe the actual length of use for those who adopted and abandoned the technology pror to the survey date. For the farmers who have not dscontnued ts use at the tme of the survey, we have the problem of censorng, because they mght qut n the future, but ths s not an observable acton. For these ndvduals, the statstcal process s to rght censor the duraton at the end of the observaton perod. In ths case, we assume that the farmer wll eventually abandon the technology. We estmate a sample selecton Webull model 3

19 (Boehmke et al., 2006), and compare t wth the smple parametrc (Webull) and semparametrc (Cox) specfcatons of the hazard functon n ths analyss. The estmated Webull model for sol samplng suggests that cotton producers wth larger farms tend to use sol samplng technologes for a shorter perod of tme. On the other hand, farmers wth longer plannng horzons, and more experence wll more lkely use sol samplng for a longer duraton. We also fnd that the length of use of yeld montors tend to be longer for farmers who beleve that precson farmng wll be mportant n the future, are more educated and more experenced, and whose ncome comes manly from farmng. 4

20 CHAPTER 2 FARMERS PERCEPTIONS ABOUT SPATIAL YIELD VARIABILITY AND PRECISION TECHNOLOGY ADOPTION 2.1 Introducton Large agrcultural felds consst of numerous stes (or sub-locatons) that typcally dffer from one another wth respect to the factors that affect crop yelds (.e., dfferent sol characterstcs for dfferent locatons). Varable rate technologes (VRT) am to take advantage of the heterogenety wthn felds by allowng farmers to vary nput applcatons dependng on locaton-specfc needs. By contrast, conventonal farm management practces apply nputs at a sngle rate unformly across the entre feld (typcally based on ts average condtons). If the responsveness of yelds to nput vares substantally across a feld, ths average unform applcaton strategy (URT) can result n over-applcaton of nputs on some parts of the feld and under-applcaton on other parts. Thus, varable rate applcaton can potentally mprove effcency of nput use (Torbet et al., 2007) and may lead to ncreased proftablty and hgher envronmental benefts, especally n felds that are spatally heterogeneous. Moreover, t gves greater flexblty to farmers n choosng employees, snce the electronc montorng (gudance systems) does most of the steerng and the detaled work (Grffn et al., 2004). A prerequste for successful mplementaton of VRT s the use of ste-specfc nformaton gatherng (SSIG) technologes that enable one to determne the degree of spatal 5

21 heterogenety n felds. These SSIG technologes range from yeld montors to grd sol samplng and provde nformaton about the varaton n the physcal and chemcal propertes of the sol across a feld. Usng spatally-referenced data from these ste-specfc technologes (e.g., nutrent content, sol qualty, ste-specfc yelds) allows one to apply varyng nput rates to match the wthn feld varablty. Hence, varable rate technology (VRT) adopton necessarly requres the adopton of at least one SSIG technology. However, the use of VRT n cotton producton has hstorcally been low relatvely to other major crops. Reasons could be the hgh fxed cost of nvestment, software ncompatblty (Grffn et al., 2004), and possbly lack of nformaton and suffcent networks that would dffuse the feasblty and benefts of precson technology. Prevous studes based on farm survey data and dscrete choce modelng technques have nvestgated factors nfluencng the adopton of varable rate technologes (Fernandez- Cornejo, Daberkow, and McBrde (2001); Khanna, Epouhe, and Hornbaker (1999); Khanna (2001); Roberts et al. (2004)). All of these studes amed to determne farm (or farmer) characterstcs (e.g., farm sze, age, educaton, etc.) that sgnfcantly nfluence the decson to use VRT. Khanna (2001) and Roberts et al. (2004) assessed the mpact of these farm characterstcs wthn a framework that allows for sequental adopton of SSIG and VRT. Note that none of these studes specfcally explored the role of farmers perceptons about wthn-feld varablty n the decson to adopt the so-called VRT bundle (at least one SSIG technology along wth VRT). The objectve of ths paper s to determne whether farmers perceptons about ther spatal yeld varablty sgnfcantly nfluence the decson to adopt precson farmng 6

22 technology. Prevous lterature has shown that the proftablty of VRT crtcally depends on the degree of spatal heterogenety n farmers felds (Roberts, Englsh, and Mahajanashett, 2000; Isk and Khanna, 2002). Hgher yeld varablty typcally results n hgher economc returns from varable rate applcatons, under the assumpton of certanty and rsk neutralty. But n realty what really matters s the farmers pror percepton about wthn-feld spatal varablty rather than the actual spatal varablty. For example, a farmer who has not used any SSIG technology may beleve that the spatal varablty of the feld s low (.e., beleves that the feld s more spatally homogenous) based solely on pror experence of farmng (See Rejesus et al., 2010 for evdence of ths behavor). Hence, ths partcular farmer may decde not to utlze the VRT bundle because he beleves that the potental economc returns from ths nvestment may not be worth t due to the perceved lack of spatal heterogenety (even f the feld s, n realty, spatally heterogeneous). Examnng whether spatal yeld varablty percepton affects VRT adopton behavor s consstent wth recent lterature that advocates the use of subjectve perceptons n emprcal models explanng economc behavor (Nyarko and Schotter, 2002; Mansk, 2004; Bellemare, 2009). As Delavande, Gne, and McKenze (2009) have shown, there are a number of studes n the agrcultural economcs lterature that demonstrate how subjectve perceptons nfluence decson-makng n agrculture. For example, Hll (2007) found that subjectve expectatons about future coffee prces nfluence the allocaton of labor used n coffee producton. Gne, Townsend and Vckery (2008) reveal that farmers perceptons about the start of the monsoon season affect ther plantng decsons even after controllng for a wde-range of farmer characterstcs. The role of perceptons has also been examned n a 7

23 number of technology adopton studes as well (Gould, et al., 1989; Adesna and Znnah, 1993; Adesna and Badu-Forson, 1995; Sall et al., 2000; Abad Ghadm, Panell, and Burton, 2005). Adran et al. (2005) looks nto three types of perceptons regardng precson technology adopton: the perceved usefulness, perceved net benefts and perceved ease of use. But note that most of these technology adopton studes nvestgate the nfluence of perceptons about the attrbutes of the technology tself and not the effects of perceptons about a spatally explct factor that determnes the proftablty of the technology (.e., feld heterogenety). Most recent studes (Larson and Roberts, 2004, Marra and Rejesus, 2010) studed how the use of SSIG technologes and specfcally yeld montors nfluence the perceved wthn feld spatal yeld varaton. To the best of our knowledge, no study has yet nvestgated the mpact of perceptons about a spatally explct varable n the adopton of agrcultural technologes and ths paper contrbutes to the lterature n ths regard. 2.2 Model Conceptual Framework We consder a constant returns producton functon Y f ( x j, z ), where x s the appled nput (e.g., fertlzer) per acre and z s the level of sol attrbute (e.g., nutrent content) per acre at ste (Isk and Khanna, 2002). Under a unform rate of nput applcaton, we would expect that z z (.e., from the producer s perspectve, sol attrbute s fxed at the perceved average based on nformaton from pror experence farmng). However, we assume that producers who choose to use only SSIG technologes recognze varablty n z across the 8

24 dfferent stes and there s some uncertanty wth the nformaton from SSIG (.e., nformaton from SSIG s not perfect). In ths case, our producton functon s represented as U Y f ( x, z ) and the stuaton n whch the farmer does not use any SSIG technologes s represented as Y f ( x U, z). Let S be a bnary ndcator of whether a farmer has utlzed at least one SSIG technology, and V be a bnary ndcator that reflects the varable rate nput applcaton after the evaluaton of the nformaton obtaned from the SSIG technologes. Both SSIG and VRT adopton can be observed, so we can dstngush between the followng scenaros: Scenaro 1: S=0 and V=0 [No SSIG use and hence no VRT adopton] Scenaro 2: S=1 and V=0 [SSIG use but no postve evaluaton, hence no VRT adopton] Scenaro 3: S=1 and V=1 [SSIG use and postve evaluaton, hence VRT adopton] The farmer s problem s to choose the optmal nput level x after s/he has decded on whether s/he wll use the conventonal practces (scenaro 1), use at least one SSIG technology but apply nputs unformly (scenaro 2), or purchase the whole package (scenaro 3). Thus, the farmer wll maxmze the followng expected utlty: max E{ U} E{(1 S) EU S(1 V ) EU ( SV ) EU }, (1) x, x where EU n (n=1, 2, and 3) are the expected utltes under each of the three scenaros. Incorporatng the partal adopton of precson technology (scenaro 2) n equaton (1) and buldng on the specfcaton used n Isk and Khanna (2002; p. 252), equaton (1) can be rewrtten as: 9

25 U U S V V (2) E U E S W0 A S V W0 A K SV W0 A K x, x max { } {(1 )( ) (1 )( ) ( )( )} or as U S (3) max E{ U} E{ W0 A SK SV ( A K)}, x, x where W 0 reflects the ntal wealth, costs) usng conventonal unform rate technology (URT), U s the quas-rent (revenue mnus fertlzer/nput V s the quas-rent under VRT, wth V U, A denotes the fxed land such that for N homogeneous stes of sze A, t holds that N A A, and S K, V K are the fxed cost for SSIG and VRT practces, respectvely, wth K s K V, and V s K K K. The utlty-maxmzng adopton decson s obtaned through a two-stage decson process. The farmer frst obtans the utlty-maxmzng level of nput use for each of the three adopton scenaros above. Assumng an nternal soluton, the frst-order condton (FOC) to determne nput use under scenaro 1 s: U U (4) [ W( x(, ) )] 0 U E U Pf x z w, x where z s the average value of the sol attrbute. Equaton (4) reduces to the proft- U maxmzng condton: Pf ( x, z) w. On the other hand, the optmal nput levels for x scenaro 2 (SSIG only), and scenaro 3 (VRT adopton) are determned usng the FOC: U j (5) [ W ( x(, ) )] 0 j E U Pf x z z w, x where j represent scenaros 2 and 3 ( j = U, V), and s the sol attrbute uncertanty that arses from the mperfect nformaton provded by the SSIG technology error, whch s 10

26 assumed to vary proportonally wth the level of the sol attrbute. The second-order condton functon. U 2 0 j2 H x s satsfed under rsk averson and a quas-concave producton Comparatve Statcs: Technology Adopton and Perceved Spatal Varaton Gven the optmal nput levels from the FOCs n (4) and (5), the farmer decdes to adopt SSIG technologes only (scenaro 2) f the followng condtons hold: U S U (6) EU[ W0 A K ] EU[ W0 A ], and U S V V (7) EU[ W0 A K ] EU[ W0 A K ]. Smlarly, the farmer wll decde to purchase the VRT bundle (scenaro 3) only f the followng condtons hold: V V U (8) EU[ W0 A K ] EU[ W0 A ] V V U S (9) EU[ W0 A K ] EU[ W0 A K ]. It s necessary to add more structure to the decson rules (.e., add more detaled specfcatons) n equaton (6) to (7) to ascertan the effect of perceved spatal varaton on the precson technology adopton decson. Frst, we assume an exponental utlty functon U e W UWW where the Arrow-Pratt measure of rsk averson s defned as: the varablty of returns assocated wth precson technology scenaros 2 (.e., SSIG UW. Second, 11

27 2 2 adopton only) and 3 (.e., SSIG and VRT adopton) are represented by U and V, respectvely. Gven these addtonal assumptons and usng a second-order Taylor seres approxmaton of expected utlty, a rsk-averse decson-maker would choose scenaro 2 (SSIG only) f the followng condtons hold: (10) (11) U W A K U W A U W A K, and U S U U S 2 [ 0 ] [ 0 ] W[ 0 ] U 0 U W A K U W A K U W A K. U S V V U S 2 [ 0 ] [ 0 ] W[ 0 ] U 0 Smlarly, a rsk-averse producer would choose scenaro 3 (SSIG and VRT) f the followng condtons hold: (12) (13) U W A K U W A U W A K, and V V U V V 2 [ 0 ] [ 0 ] W[ 0 ] V 0 U W A K U W A K U W A K. V V U S V V 2 [ 0 ] [ 0 ] W[ 0 ] V 0 The frst two terms n the above equatons ndcate that ncentves to adopt precson technologes, ether SSIG only (scenaro 2) or the VRT bundle (scenaro 3), ncreases as the expected utlty followng adopton ncreases, whereas the thrd term shows that ncentves to adopt decrease as the varablty of precson technology returns 2 2 ( U, U) and the degree of rsk averson ( ) ncrease. Explctly utlzng the exponental utlty specfcaton and to be more consstent wth the decson rules n the earler equatons (6) to (9), the rsk-averse farmer wll choose scenaro 2 (SSIG only) f: U S U ( W0 A K ) ( W0 A ) (14) E[ e ] E[ e ] 0, and U S V V ( W0 A K ) ( W0 A K ) (15) E[ e ] E[ e ] 0. 12

28 Smlarly, the rsk-averse farmer wll choose scenaro 3 (SSIG and VRT) f: V V U ( W0 A K ) ( W0 A ) (16) E[ e ] E[ e ] 0, and ( W ) ( ) (17) [ 0 A K W0 A E e ] E[ e ] 0. V V Hence, gven the same monotonc utlty functons for all scenaros, t s clear from (15) to (17) that rsk averse farmers wll adopt precson technologes (scenaros 2 or 3) f the quasrent dfferental between the adopton and non-adopton of precson technologes s postve. Ths quas-rent dfferental usng the assumpton above s derved n detal n Appendx A and can be expressed as: U (18) P ( f ) F [ ( ) ( )]. 2 f A (1 F ) xx x z fxz A 2 N The quas-rent dfferental expresson n (18) can then be used to evaluate the effect of spatal yeld varablty perceptons on ncentves to adopt precson technology because of the 2 presence of the z varable on the rght-hand sde of the equaton. Ths represents the wthn feld spatal varablty of the sol attrbute. But note that ths s not perfectly known and, as argued n the ntroducton, the perceved wthn feld spatal yeld varablty s a typcally used proxy n order to actually make ex ante adopton decsons. Hence, we can represent the perceved wthn feld yeld varablty as 2 ˆ z. Usng equaton (18) and a moment generatng functon approach (See Yassour et al. 1981; Isk and Khanna, 2002), t can be shown that the necessary condton for the farmer to choose scenaro 2 (SSIG only) s: 13

29 (19) AP ( f ) F [ ˆ ( ) ( )] 0, 2 f A (1 F ) 2 xx x S 2 z fxz A N K 2 U and the necessary condton for the farmer to choose scenaro 3 (SSIG and VRT) s: (20) AP ( f ) F [ ˆ ( ) ( )] 0. 2 f A (1 F ) 2 xx x V 2 z fxz A N K 2 V Gven the postve sgn of condtons (19) and (20), precson technology adopton occurs f the quas-rent dfferental s greater than the fxed cost plus the rsk premum ( 2 2 V ). Therefore, ncentves for adopton of precson technology adopton ncrease wth ncreases n expected returns from the technologes due to hgher perceved wthn feld spatal yeld varablty. In other words, the theoretcal decson rules n (18) and (19) suggest that a hgher perceved wthn feld spatal yeld varablty ncreases returns to adopton of precson technologes and, consequently, ncreases ncentves to adopt these precson technologes. The postve relatonshp between perceved wthn feld spatal varablty and adopton ncentves shown n ths theoretcal model s what we want to emprcally test below. 2.3 Estmaton The technology adopton decson s demonstrated as a dscrete choce, whch takes the followng values: (21) * * * * 1 f U PF U 1 PF and U 2 PF U 1 PF3 * * * * Y 2 f U PF U and. 2 PF U 1 PF U 2 PF 3 * * * * 3 f U PF U and 3 PF U 1 PF U 3 PF2 14

30 We approached ths problem both from the perspectve of: (1) a smultaneous decson (Multnomal Logt/Probt Model), where farmers decde smultaneously whether they wll utlze SSIG technologes and/or VRT, and (2) a sequental adopton decson (three-step Heckman Correcton Model), where cotton producers frst decde on the SSIG technology they wll use, and then on the approprate management response,.e., VRT or URT mplementaton. The motvaton behnd the Multnomal Logt/Probt was that nowadays many farmers hre VRT servce provders to do all the work,.e., grd sol samplng, data analyss, nput recommendaton and varable rate nput applcaton (Surjandar and Batte, 2003). Thus a farmer does not necessarly need to adopt SSIG technology frst and then on second tme decde on the nput applcaton. Wth ths approach we also account for the fact that farmers may adopt specfc components of the technology (e.g., SSIG only) rather than the whole bundle. Gven that we have survey data wth a relatvely low response rate (see data descrpton n Secton 2.3 below) and possble over-representaton of larger farms (>500 acres), we mplemented a weghted estmaton based on the 2002 Ag. Census. More specfcally, we estmated post stratfed survey weghts n a weghted multnomal logt/probt regresson. Ths would adjust for potental under or over representaton of survey respondents wthn strata. Farmers were classfed nto h=1 to 72 strata dependng on farm locaton (by state) and cotton acreage (12 states 6 acreage classes [1 99, , , , , or acres]). Weghts were then estmated usng a rankng procedure suggested by Brackstone and Rao (1976). 15

31 2.3.1 Two Step Approach: Multnomal Logt/Probt From the above model, the emprcal equatons to be estmated are specfed as follows: (22) Y1 1 Z 1 (23) Y2 2 Z 2 (24) Y 3 Z. 3 3 We frst estmate a multnomal logt (MNL) model where the dependent varable (.e., the precson farmng technology choce Y ) s dscrete and takes the values of 1, 2, and 3, respectvely. MNL s an extenson of the bnary logstc regresson and allows for more than two dscrete outcomes for the dependent varable. Producers n our specfcaton are consdered consumers of agrcultural technologes that are choosng among these alternatves. The probabltes assocated wth the choces n (22)-(24) take the form: (25) Prob( Y j) P, j. Thus, the log lkelhood for a multnomal logt model wth ndependent observatons becomes: (26) Log L Yj log P, j. Snce we cannot dentfy separate βs for all of the choces, we set the coeffcents for one of the outcomes (.e., the reference or base alternatve) equal to one (Jones, 2000). Hence, the probablty of farmer choosng alternatve j s gven by: j 16

32 exp( x j) (27) P, j P( Y j), j 2,3, 3 1 exp( x ) and the choce probablty for the base s: 1 (28) P, j P( Y 1), 3 1 exp( x ) j 1 where x s the vector of ndependent varables assocated wth farmer, and of parameters assocated wth the alternatve j. j 1 j j j s the vector In our case, the non adopton of both SSIG and VRT (scenaro 1) may be treated as the baselne category. The multnomal logt model, whch accounts for the smultanety of choces, would then dentfy the probablty of usng SSIG and applyng nputs at a unform rate (relatve to the non adopton alternatve) and the probablty of usng SSIG and applyng nputs at a varable rate (relatve to the non adopton choce). The estmated parameters of a multnomal logt tend to be dffcult to nterpret drectly as compared to a bvarate choce model. To capture the effect of the explanatory varables on the farm management decsons, we examne the dervatve of the probabltes wth respect to the explanatory varables. These dervatves are defned as (Greene 1990): (29) Prob( Y j) 3 Pj [ jk Prob( Y ) ] j 1 j jk, j=1, 2, 3; k=1,, K. xk The above relatonshp demonstrates the margnal effect of xk on the probablty of adoptng one of alternatves 1, 2, and 3. 17

33 We calculate both the average margnal effects AME (.e., calculatng the margnal effects for each observaton and then takng the average of t), as well as the margnal effects at the average, MEA (.e., margnal effects calculated at the means of each ndependent varable). Studes have shown though, that evaluatng the dervatves at ther sample means leads to based predctons (Akay and Tsakas, 2008), and, thus, AME may be preferred over MEA, whch s why we only report AME n the tables. The MNL method s computatonally smpler than other mult-choce regresson approaches (e.g., multnomal probt), but t reles on the restrctve assumpton of Independence of Irrelevant Alternatves (IIA). Ths property states that the probablty of choosng among two alternatves s not affected by the presence of addtonal alternatves,.e., the error terms of the choce equatons are ndependent and homoscedastc (Greene, 2003). If the IIA assumpton s volated, then the MNL approach may not be approprate and other mult-choce models that do not rely on ths assumpton would be preferred, such as the multnomal probt (MNP) model. The MNP model s a natural alternatve to the MNL approach n that t relaxes the IIA assumpton nherent n the MNL model, and allows a more flexble pattern of error correlaton (Cameron and Trved, 2005). The structural equatons of the MNP model are (Greene, 2003): (30) U, j xj, j, j, where j = 1, 2, 3 and ε j ~N(0,Σ) Therefore, the probablty assocated wth alternatve j equals: ' (31) P Prob( Y j) Prob{ ( x x ) } for all k, j, k, j, j, k 18

34 The computaton s tme consumng (for dmensonalty hgher than 2), because the dstrbuton of ε s such that (30) does not have a closed form soluton. Moreover, Σ has to be postve defnte. To facltate the estmaton of MNP, one often makes the assumpton that the alternatve errors are ndependent standard normal so that Σ=I. In ths case, (30) reduces to a one dmensonal ntegral, that can be approxmated by quadrature methods (Cameron and Trved, 2005) 1. In ths study, we estmate both the MNL and MNP models. But we also perform a Hausman test (Hausman and McFadden, 1984) to determne whether the IIA assumpton s volated n our data (and n essence determne whether MNL or MNP s more approprate). The basc dea of the Hausman test s to estmate the model wth all of the alternatves and then to re-estmate t, droppng one of the alternatves. If IIA holds, then omttng alternatves n the estmaton should not change the parameter estmates systematcally. Statstcally sgnfcant dfferences n the parameter estmates suggest that the IIA assumpton s volated and the results from the MNP may be preferred Endogenety of Spatal Yeld Varablty Perceptons The explanatory varable of man nterest n ths study s a measure of farmer s spatal yeld varablty perceptons (denoted as SYCV and s part of the vector x ). We want to determne whether spatal varablty perceptons affect the precson technology choce. A potental problem wth ths varable s endogenety, snce t s lkely that there are unobserved 1 Snce we use only 3 choces, dentfcaton should not be a problem, and restrctons on standard devatons and correlatons are not requred (Greene, 2003). 19

35 varables (e.g., unobserved management ablty, motvaton) that nfluence both the spatal varablty perceptons and the choce of precson technologes to adopt. Hence, we utlze an nstrumental varable (IV) approach to control for ths endogenety, where the frst stage s estmatng the equaton below by ordnary least squares (OLS) regresson: (32) SYCV αw 1 e, where error term. actual W s a vector of control covarates (that nclude nstrumental varables) and e s an The predcted value of SYCV n (32) s then utlzed n the MNL/MNP nstead of the SYCV value. The use of an IV approach n the estmaton requres good nstruments n W that are correlated wth SYCV, but lkely uncorrelated wth the unobservables that affect precson farmng adopton decsons (emboded n the error term). Wthout any strong nstruments, the nferences from our estmaton must be nterpreted wth cauton. The use of the predcted values of SYCV n the MNL/MNP necesstates the use of bootstrapped standard errors, snce the conventonal standard errors would be ncorrect. We followed a bootstrappng technque based on replcate weghts for complex survey data. We resample wth replacement n stratum h, snce problems arse ether when the unts are sampled wthout replacement or when the number of sampled clusters per stratum s small (Shao, 1996). 20

36 Three Step Approach: Heckman Correcton Model An alternatve approach to estmate the effect of spatal yeld varablty perceptons on the adopton of varable rate technologes s wth a bnary choce model (.e., probt or logt): * (33) VRT, n 1SYCV, n 2 X, n n, n=0, 1, * where VRT, 1 f VRT (.e., producer adopted the VRT bundle) and VRT, 0 n n, 0 otherwse. As above, SYCVn, s a measure of the wthn-feld spatal varablty perceptons, X s a vector of observable covarates, 1 s the effect of spatal varablty perceptons to be estmated, β 2 s vector of parameters to be estmated, and n s an error term, where ~ N(0, ). We can vew the decson model n (33) as part of a sequental decson n process where the frst step s a decson to adopt SSIG technology and the second step s a decson to adopt VRT technology (gven that one adopts SSIG). Ths s plausble because the adopton of VRT technologes necessarly requres adopton of a SSIG technology. Only SSIG adopters have the choce of applyng the VR technology. Therefore, wthn a sequental decson process, one can estmate (33) usng only the subsample of SSIG technology adopters. A concern wth the approach of ncludng only the SSIG subsample s non-random sample selecton (.e., farmers may self-select nto the SSIG adopter pool). To account for ths selecton ssue, one can use Heckman s (1979) procedure of appendng an nverse mlls rato to equaton (33). A SSIG adopton equaton as specfed below can frst be estmated usng a bnary choce model (.e., probt or logt): n 21

37 (34) where SSIG, 1 f m SSIG * m, 0 γz v, m=0, 1, *, m, m m SSIG (.e., producer adopted SSIG technologes) and SSIGm, 0 otherwse, m, Z s a vector of observable covarates, γ s a vector of parameters to be estmated, and vm s an error term. Note that estmatng (34) requres the full sample of cotton producers that ncludes both the adopters of SSIG and the non-adopters of SSIG. From the parameter estmates n (34), the nverse mlls rato for the sub-sample SSIG, 1 s computed as follows: m (35) ( ˆ ˆ Z γ) ( ) m, Z ˆ m, γ, where and are standard normal probablty densty functon (pdf) and cumulatve densty functon (cdf), respectvely. The nverse mlls rato n (35) s then appended to equaton (33) as an extra explanatory varable to account for potental non-random sample selecton. The performance of the Heckman model depends on the collnearty between the nverse Mlls rato and the explanatory varables n the outcome equaton (33) (Jones, 2001). The fact that we dd not exclude any of the regressors used n frst stage regresson, from the outcome equaton, along wth the hgh degree of non-response, mght ncrease the rsk of hgh collnearty 2. As wth the MNL model above, endogenety of the spatal varablty perceptons varable s stll a problem n the estmaton of equaton (33). An nstrumental varable 2 The VIFs (Varance Inflaton Factors) dagnostcs are all below 1.6 for all ndependent varables, whch suggests that none of them could be consdered as a lnear combnaton of other regressors. Hence, multcollnearty s not a problem for our specfcaton 22

38 approach usng predcted values of SYCV s also used n the three-step Heckman correcton model here. 2.4 Data Data for ths study were collected from a survey of cotton producers n 12 states: Alabama, Arkansas, Florda, Georga, Lousana, Msssspp, Mssour, North Carolna, South Carolna, Tennessee, Texas and Vrgna. Ths survey was developed to query cotton producers about ther atttudes toward and use of precson farmng technologes (.e., SSIG and VRT). Followng Dllman s (1978) general mal survey procedures, the questonnare, a postage-pad return envelope, and a cover letter explanng the purpose of the survey were sent to each producer. The ntal malng of the questonnare was on February 20, 2009, and a remnder post card was sent two weeks later on March 5, A follow-up malng to producers not respondng to prevous nqures was conducted three weeks later on March 27, The second malng ncluded a letter ndcatng the mportance of the survey, the questonnare, and a postage pad return envelope. A malng lst of 14,089 potental cotton producers for the marketng year was furnshed by the Cotton Board n Memphs, Tennessee. Among responses receved, 1981 were counted as vald, and thus used n our study. Our survey conssted of three man sectons: 1. precson agrculture technology (.e., sources of nformaton about technology, ways of applyng nputs, expectatons, etc.), 2. farm and producton data (.e., farm locaton, acres of owned and/or rented land, yelds per acre 23

39 etc.), and 3. socoeconomc characterstcs (.e., age, experence wth farmng, educaton level, ncome etc). Only 35% of the vald responses ndcated use of at least one SSIG technologes (some producers made use of more than one technology), and around 19% appled ther nputs at a varable rate. The most popular SSIG technologes were grd and zone sol samplng, followed by yeld montors wth GPS. The most used varable rate management applcatons were for fertlty or lme, then followed by growth regulators. Less than half of respondents are hgh school graduates and almost 25% has a bachelor s degree. Most of the farmers ncome ranges from $50,000 to $99,000 annually, whereas as 10% of cotton producers n our survey have ncome above $500, Varable Constructon & Emprcal Specfcaton Dependent Varables Based on the estmaton strateges descrbed above, we construct the necessary dependent and ndependent varables usng responses from the survey questonnare. To construct the dependent VRT n, varable, we utlzed the survey queston that asked farmers to ndcate the acres on whch fve nformaton gatherng technologes (.e., yeld montorng wth GPS, aeral satellte, handheld GPS unts, green seeker and electrcal conductvty) were used n order to make the varable rate decson (.e., dranage, lme, seedng, growth regulator, fungcde, herbcde, rrgaton etc). Moreover, VRT applcaton ncludes map-based and sensor-based methods. Thus, we checked whether the farmers who reported number of acres on whch they appled VRT, also ndcated how they generate ther nput applcaton map. 24

40 (See Fgure 1, questons 17, 26 and 28). Any producer who provded an answer for these questons 3 s a VRT adopter ( VRT, 1). For the MNL/MNP approach, ths varable concdes wth scenaro 3 (.e., Y =3: SSIG and VRT adopton). n To construct the Ste-Specfc Informaton Gatherng Technology (SSIG) varable, we used the answers of farmers who checked one of the followng n queston 16 of the survey questonnare: cotton yeld montors, grd samplng, sol maps, satellte magery, aeral photography or COTMAN technologes (See Fgure 2). Ths sample conssts of all SSIG adopters (that also adopted ether URT or VRT approach). For the MNL/MNP model, we used the same queston to depct SSIG users, but we excluded the farmers who utlze VRT (.e., Y =2: SSIG and URT adopton). The remanng producers that dd not adopt both SSIG and VRT correspond to the frst (non-adopter) category n the MNL/MNP approach (.e. Y =1: no adopton of SSIG and VRT). The SYCV varable, on the other hand, s calculated based on the answers about the least productve, average productve and most productve sectons of the farmer s feld (See Fgure 3). We frst utlze the spatal varablty formula used n Larson and Roberts (2004) to calculate the perceved Spatal Yeld Varablty (SYVAR) varable: (36) SYVAR Y Y Y Y Y Y *( LOW AVG ) 0.33*( MID AVG ) 0.33*( HIGH AVG ), where Y LOW s the estmate for the yeld of the least productve porton of feld, Y AVG s the estmated average yeld for the typcal feld, Y HIGH s the estmated yeld for the most 3 There was a small fracton of farmers who answered don t know. We ncluded them n the adopters category n the sense that they mght have not been aware of the exact number of acres where they utlzed VR practces. 25

41 productve porton, and Y MID =3* Y AVG - Y LOW Y HIGH. We, then, used the SYVAR and Y AVG, n order to create the coeffcent of spatal yeld varablty (SYCV ) statstc based on the followng formula: (37) SYCV SYVAR Y avg 0.5 *100, where 0.5 SYVAR s the standard devaton of spatal yeld varablty estmated usng (36) Explanatory Varables From our revew of the exstng lterature, we dentfed the potental explanatory varables affectng precson farmng adopton decsons and created proxy varables when needed (based on the avalablty of data). The same set of explanatory varables s used n the MNL/MNP model and the Heckman model 4. Farmer and farm characterstcs, that are hypotheszed to affect technology decsons (based on the lterature) were educaton, age, use of computer n farm management, experence n farmng, percentage of taxable ncome from farmng, perceptons about future mportance and proftablty of precson farmng, manure applcaton, nformaton about precson agrculture through unversty publcatons, average yelds, plan of farmng, partcpaton n agrcultural easement programs, and locaton dummes. 4 In addton, we utlzed the same set of explanatory varables n both stages of the 2-step Heckman approach (wth the only dfference that the VRT probt regresson ncludes the regon dummes). In practce, t s very dffcult to fnd plausble dentfcaton restrctons, n whch case the Heckman model s estmated wth the same set of regressors n each equaton. Then, dentfcaton reles on the non-lnearty of nverse mlls rato (Jones 2001, Cameron and Trved, 2005). 26

42 We would expect that producers wth a bachelors or a graduate degree are most lkely to adopt precson technology (EDUC), because of the human captal and the techncal sklls that they have acqured through ther educaton. Younger farmers (AGE) are expected to be more famlar wth the new technologes, thus more lkely to adopt precson farmng. Nonetheless, they are also less experenced, whch mples that they mght not be aware of ther feld varablty, thus not eager to adopt new technologes. Hence, the sgn cannot be determned a pror. The sgn ndetermnacy s smlar regardng the years of farmng (EXPERIENCE). The use of computer (COMPUTER) s hypotheszed to have a postve effect on precson farmng, snce ths means that farmers may be more comfortable wth new technologes and ths varable can also be consdered as a proxy for nnovatveness (Surjandar and Batte, 2003). To capture the effect of ncome n technology adopton, we used a proxy varable that accounted for the percentage of the 2007 taxable household ncome comng only from farmng (INCOME), whch s dfferent from categores of pretax total household ncome from both farm and nonfarm sources (as used n Banerjee et al., 2008). We would expect that the hgher ths percentage, the hgher the probablty of adoptng precson technology, n the sense that farmers who make a lvng mostly by farmng wll probably nvest more on practces mprovng ther yelds and proftablty. Smlarly, nformaton resultng from unversty publcatons (INFO) wll lkely affect farmers n favor of precson technology. Last, farmers who partcpated n agrcultural easement programs (AG EASE) are more concerned about the envronment, thus more lkely to adopt envronmentally-frendly practces, such as precson technologes. 27

43 Moreover, we ncorporated farmers perceptons about the potental proftablty and mportance of precson farmng n the near future (.e., 5 years from the tme the survey was conducted). We would expect that farmers who perceve that precson farmng wll be mportant (IMPORTANCE), as well as those who beleve that ts use wll be more proftable n 5 years (PROFIT), are more lkely to adopt SSIG and VRT technologes. We added varables for manure use (MANURE), past average yelds (YIELDS),, farmer s future plan about farmng (PLAN), as well as 12 state dummes (AL, AR, FL, GA, LA, MS, MO, NC, SC, TN, TX and VA) that capture the effect of farm locaton on VRT adopton 5. We would expect that hgher average cotton lnt yelds n the past (YIELDS) may mply postve effect on the probablty of adoptng VRT. The effect of manure on VRT s expected to be negatve. Farmers who use manure mght less lkely adopt VRT, snce manure s not as responsve to ste-specfc nformaton technologes compared to norganc fertlzers (Khanna, 2001). On the other hand, the more years somebody plans to work at hs farm (PLAN), the hgher the probablty of utlzng precson technologes. Regardng the varable of nterest ( SYCV ˆ ), we expect that farmers who perceve ther yelds as more varable, would be more lkely to utlze nformaton gatherng technologes, n order to better see ther true wthn feld varablty. In addton, there s a hgher lkelhood that farmers who have hgher spatal yeld varablty perceptons wll also apply ther nputs at a varable rate. Farmers, who perceve ther yelds as more homogeneous than what they are n realty, wll more lkely not utlze the technology. 5 We omtted the TX dummy durng the estmaton. 28

44 2.5.3 Instruments for Spatal Yeld Varablty Perceptons For the vector W, we ncluded two nstrumental varables -- the 10-year county average yelds and the sum of dryland and rrgated cotton acres (own and rented). We used the 2007 acres as a reference and the 2008 acreage f there s no nformaton based on the 2007 acres. The 10-year county average (PINDEX) may be a good nstrument snce t s publcly avalable nformaton that gves a benchmark to ndvdual producers as to where ther feld may stand n comparson to the county (NASS, USDA). Hence, we post that t nfluences perceptons about spatal varablty but s not correlated wth farm-level unobservable varables. The expected sgn of PINDEX s ambguous and depends on how farmers see ther felds n hgh or low yeldng areas. Regardng the total acreage (FARM SIZE), prevous studes (Isk and Khana, 2002) have ndcated a postve relatonshp between the farm-sze and the spatal wthn feld yeld varablty. Therefore, we would expect that producers who operate large farms would beleve that ther yelds are more varable. 2.6 Results & Dscusson Multnomal Logt/Probt Estmaton The frst stage perceved spatal yeld varablty regresson ndcate sgnfcant coeffcents and sensble sgns. Farmers n hgh yeldng areas perceve that ther yelds are more homogenous, whereas farmers wth large acreage beleve that ther yelds are more varable (Table 2). 29

45 In Table 3, we present the parameter estmates from both the MNL and MNP models. As noted above, we use the non-adopton category (alternatve 1) as the reference pont or the base category for the second-stage estmaton of the multnomal logt/probt model. Results of the Hausman test to determne the approprateness of IIA ndcate that the null hypothess of IIA valdty s rejected (.e., dfferences n coeffcents are systematc). The chsquared statstc when omttng alternatve 2: ((SSIG & URT) and comparng t wth the full model wth all alternatves s (p-value<0.001). On the other hand, the ch-squared statstc when omttng alternatve 3: ((SSIG & VRT) and comparng t wth the full model wth all alternatves s 5.28 (p-value=0.0053). These results suggest that IIA may be volated and the MNP model may be preferred n ths case. The parameter estmates from the MNL or MNP model are smply the values that maxmze the lkelhood functon and typcally do not have drect nterpretaton. Hence, margnal effects need to be calculated to properly assess the effects of the explanatory varables on precson technology choce. Based on Table 5, the condtonal probablty of adoptng at least one SSIG technology wth unform rate nput applcatons (.e. choosng alternatve 2) was sgnfcantly related wth the predcted spatal yeld varablty perceptons ( SYCV ˆ ), the educatonal level of the producer (EDUC), the nformaton receved from unversty publcaton about precson technology (INFO), the expectatons about the potental proftablty of precson farmng n 5 years (PROFIT), the perceptons about ts future mportance (IMPORTANCE), and the years somebody plans on beng nto farmng (PLAN). Producers, who perceve that ther yelds are more varable ( SYCV ˆ ), wll more lkely adopt SSIG technologes (by 0.01%). More educated farmers (EDUC), producers who beleve that nformaton gatherng 30

46 technologes wll be mportant n fve years (IMPORTANCE), and those who plan on farmng more years (PLAN), wll more lkely utlze these technologes (by 0.2, 13.6, and 1.6 percentage ponts, respectvely). Smlarly survey partcpants who acqured nformaton about precson agrculture through unversty publcatons have 4.3% hgher probablty of adoptng SSIG technologes than the other two alternatves, all else held constant. On the other hand, the perceptons about future proftablty (PROFIT) seem to nversely affect the use of nformaton gatherng technologes and the unform rate nput applcaton. Farmers who beleve that the new technology wll be less proftable n the future are lkely to use SSIG technologes but apply ther nputs at a unform rate (11.2%). Although, these farmers may access ther true yeld varablty through SSIG, they are reluctant to purchase VRT f they consder precson farmng potentally non proftable. Among the regonal dummes, AL, AR, LA, MS, NC, SC, TN, and VA all postvely affect the probablty that farmers, whose felds are located n one of the above states, wll adopt SSIG technologes compared to a farm n TX, whch s consdered as the benchmark category due to the hgh number of farms. Wth respect to farmers choosng the thrd alternatve (.e., adopton of at least one SSIG technology and then applcaton of VRT), we found that hgher perceved spatal yeld varablty ( SYCV ˆ ) ncreases the probablty of adopton by 0.007%. Farmers wth more years of formal educaton (EDUC), who use computer (COMPUTER), unversty publcatons to access precson farmng nformaton (INFO), have less experence n farmng (EXPERIENCE), as well as those who beleve that precson agrculture wll be proftable n the future (PROFIT) wll more lkely use the VRT bundle (by 1.3%, 8.3%, 10.5%, 0.2% and 31

47 13.9% 6, respectvely). The postve and sgnfcant coeffcent of the PROFIT varable s ndcatve of the mportance of the proft maxmzng decson n farmer s behavor. The probablty of usng a new technology s hgher for those who beleve that ths technology would brng profts n the near future (.e., 5 years from now). Last, all state dummes except from Florda (FL) have a postve mpact on the probablty of VRT adopton, all held constant. Our margnal effects are consstent wth Roberts et al. (2004) and Khanna (2001) for the majorty of the explanatory varables. However, we should not gnore the fact that our varables are constructed usng survey questons, thus dfferences n the sgns mght result from the dfferences between the data and the varable constructon Heckman Correcton Model The estmaton n the frst stage of the Heckman approach ncludes both the farmers who used URT as well as the VRT adopters (Table 6). Second stage results are presented n Table 7. The effect of perceved spatal yeld varablty ( SYCV ˆ ) on the probablty of adoptng VRT (condtonal on the adopton of SSIG) s postve and ts AME s statstcally sgnfcant at the 1% level. It suggests that farmers, who tend to the thnk that ther wthn-feld spatal yeld varablty s hgher, are the ones more lkely to adopt VRT (0.007%). Lkewse, those who reported hgher expectatons for future proftablty (PROFIT) wll more lkely adopt the VRT bundle (14% ncrease n probablty of adopton). The p-value of the nverse mlls rato 6 The nterpretaton of margnal effects s as follows: a 1 unt ncrease n log(income) whch represents a 100% ncrease n INCOME, wll ncrease the probablty of adopton P[Y=3] by That s a 100% ncrease n INCOME ncreases the probablty by 0.1 percentage ponts. 32

48 (IMR) ndcates that sample selecton s not a potental problem n our model specfcaton. The rest of our explanatory varables are statstcally nsgnfcant. 2.7 Conclusons Applyng econometrc models (MNL/MNP and Heckman) that account for smultaneous and sequental adopton, we examne the role of perceved spatal yeld varablty as t relates to the precson farmng adopton decson. Hgher lkelhood of adoptng precson technologes s assocated wth spatal yeld varablty perceptons, age, nformaton, plans about farmng, use of computer, and perceptons about expected proftablty and mportance of precson technologes. Our results suggest that farmers who perceve ther yelds as more spatally varable are the ones most lkely to adopt ste-specfc technologes, and/or apply ther nputs at a varable rate. Those who vew ther yelds as less heterogeneous (.e., lower spatal yeld varablty) wll probably not utlze any of the ste specfc technologes. Ths s consstent wth the theoretcal nsght n Isk and Khanna (2002) who found that hgher spatal varablty ncrease the ncentves to adopt precson technologes. Our estmates are robust under the two regresson model specfcatons nvestgated. Furthermore, by gvng dfferent weghts to farms of dfferent szes, we allevate the problem of over-representaton that may come from larger cotton farms (.e., non-representatve sample due to a low response rate). Our results have mportant mplcatons for polcy makers, agrbusness frms and technology supplers. Snce the perceved yeld varablty of SSIG adopters leads to hgher 33

49 condtonal probablty of VRT adopton, VRT dealers have ncentves to offer dscounted or free ntal spatal maps of farmer felds so that producers can better see ther true wthn feld varablty and potentally adopt the SSIG/VRT technology. The mpact of perceptons on precson farmng makes the role of mass meda, extenson, and socal networks more crtcal n provdng nformaton and tranng about the benefts of precson agrculture. More nformed producers wll lkely have more accurate perceptons about ther spatal yeld varablty. Economc characterstcs, rsk factors, and past outcomes play a systematc role n the formaton of expectatons (Delavande et al., 2009). If we neglect to account for the factors affectng the subjectve belefs, then estmated perceptons may be naccurate and the decson to adopt precson technologes may be affected. The use of subjectve perceptons n our study expands the range of demographc varables that have been used throughout the adopton lterature. However, there are current lmtatons that need to be further addressed. Frst, our regressons were based on farmers estmates about ther spatal yeld varablty. Asde from the lmtaton of usng nstruments that we had to deal wth, the spatal varablty estmates provded by farmers also do not necessarly reflect accurate predctons. Studes have showed that, when people are asked about ther expectatons (.e., average feld productvty), only 23% s closest to the true mean, whereas the majorty gves the medan and/or mode (Delavande et al., 2009). Furthermore, we do not have estmates about nput prces and we dd not ncorporate VRT equpment costs that could nfluence farmers economc ncentves by choosng VRT versus URT. Another lmtaton s the cross sectonal nature of our dataset. The nput applcaton decsons affected by perceved yeld varablty cannot be extended over a longer tme 34

50 horzon, n order to compare perceptons of the ndvdual farmer, before and after the adopton. Future research could nvolve more extensve data from prevous surveys (2001 and 2005). The respondents mght be dfferent but we can possbly stll shed more lght on the effect of farmers perceptons on technology adopton over ths longer nne-year perod. Moreover, we could nclude the thrd and fourth moments (.e., skewness and kurtoss of the reported yelds for dfferent proportons of the feld) and see ther mpact on technology adopton. Another study could also measure the ntensty of perceved yeld varablty. For example, farmers were asked to ndcate by how much more/less were ther average yelds more/less varable (.e., by 5%, 10%, 20%, 30%, 40%, 50% or above) after usng yeld montors. Ths nformaton was not examned n the current study because of the small number of respondents who answered ths queston. In addton, we could nvestgate how the evaluaton of dfferent sources of nformaton about precson technology affect the perceptons about yeld varablty (e.g., do farmers who receve nformaton from farm dealers report hgher yeld estmates than those who rely more on unversty extenson or crop consultants). 35

51 Table 2.1 Summary of dependent and ndependent varables used n the Multnomal Logt Model & Heckman Model Varable Name Descrpton Obs Mean SYCV Perceved Spatal Yeld Varablty (lbs. lnt/acre) PINDEX Sol productvty ndex usng 10-year county yelds as a proxy (US Dept. of Agrculture, Natonal Agrcultural Statstcs Servce, 2010) FARM SIZE Total acreage of dry land (sum of rented and owned acres) for the 2007 crop season SSIG (for MNL/MNP) Farmer used at least one ste-specfc nformaton gatherng technology (yes=2; no=0) but not VRT SSIG (for Heckman) Farmer used at least one ste-specfc nformaton gatherng technology and/or VRT (yes=1; no=0) VRT Farmer appled her nputs at a varable rate (yes=1; no=0) EDUC Years of Formal Educaton Excludng Kndergarten AGE Age of the farm operator (as of the 2009 survey year) EXPERIENCE Number of Years farmng IMPORTANCE Farmer perceved that precson farmng would be mportant n fve years from now (yes=1; no=0) PROFIT Farmer perceved that precson farmng would be proftable to use n the future (yes=1; no=0) INCOME Percentage of 2007 taxable household ncome comng only from farmng sources COMPUTER Farmer uses computer for farm management (yes=1; no=0) YIELDS Estmate of average yeld per acre for 2007 crop season MANURE Farmer appled manure on hs/her felds (yes=1; no=0) INFO Farmer used Unversty publcatons to obtan precson farmng nformaton (yes=1; no=0) PLAN Years to plan farmng n the future AG EASE Farmer partcpated n agrcultural easement programs (yes=1; no=0)

52 Table 2.1 contnued Locaton Dummes AL Farm located n Alabama (yes=1; no=0) AR Farm located n Arkansas (yes=1; no=0) FL Farm located n Florda (yes=1; no=0) GA Farm located n Georga (yes=1; no=0) LA Farm located n Lousana (yes=1; no=0) MS Farm located n Msssspp (yes=1; no=0) MO Farm located n Mssour (yes=1; no=0) NC Farm located n North Carolna (yes=1; no=0) SC Farm located n South Carolna (yes=1; no=0) TN Farm located n Tennessee (yes=1; no=0) TX Farm located n Texas (yes=1; no=0) VA Farm located n Vrgna (yes=1; no=0)

53 Table 2.2 OLS regresson of SYCV Varable Coeffcent (Std.err) P-value PINDEX * (0.052) FARMSIZE ** (0.088) INTERCEPT *** (333.9) <0.001 No. of Obs. 750 F(2, 747) = 4.61 *, **, *** denote sgnfcance levels at a 10%, 5% and 1% respectvely 38

54 Table 2.3 Weghted Multnomal Logt and Probt Parameter Estmates (N=770) Multnomal Logt Multnomal Probt Varable Coeffcent P-value Coeffcent P-value Scenaro 2: SSIG-URT Adopton (Y =2) INTERCEPT ** (1.316) ** (1.234) SYCV ˆ (0.0007) (0.0006) EDUC * (0.035) * (0.023) AGE (0.051) (0.035) YIELDS (0.0003) (0.0002) INFO *** (0.128) < *** (0.099) IMPORTANCE ** (0.486) ** (0.314) PROFIT ** (0.110) ** (0.082) INCOME (0.002) (0.002) COMPUTER ** (0.107) ** (0.076) EXPERIENCE (0.034) (0.026) MANURE (0.120) (0.101)

55 Table 2.3 contnued PLAN ** (0.048) ** (0.039) AG EASE (0.134) (0.090) Scenaro 3: SSIG-VRT Adopton (Y =3) INTERCEPT ** (2.029) ** (2.121) SYCV ˆ ** (0.0002) ** (0.0002) EDUC (0.079) (0.064) AGE (0.016) (0.010) YIELDS (0.0002) (0.0001) INFO *** (0.118) < *** (0.082) <0.001 IMPORTANCE ** (0.196) ** (0.134) PROFIT ** (0.223) *** (0.142) INCOME (0.009) (0.006) COMPUTER * (0.379) ** (0.248)

56 Table 2.3 contnued EXPERIENCE (0.014) (0.009) MANURE (0.207) (0.169) PLAN (0.080) (0.053) AG EASE (0.174) (0.140)

57 Table 2.4 Weghted Margnal Effects* from the Multnomal Logt Model (N=770) Scenaro 1=No Adopton (Y =1) Scenaro 2=SSIG-URT Adopton (Y =2) Scenaro 3=SSIG-VRT Adopton (Y =3) Varable Margnal Effect P-value Margnal Effect P-value Margnal Effect P-value SYCV ˆ ** (<0.001) * (<0.001) * (<0.001) EDUC ** (0.007) ** (0.001) ** (0.006) AGE (0.004) (0.005) (0.002) YIELDS (<0.001) e-06 (<0.001) (<0.001) INFO *** (0.019) < ** (0.017) *** (0.005) <0.001 IMPORTANCE *** (0.038) < ** (0.052) (0.024) PROFIT ** (0.010) *** (0.017) < *** (0.018) <0.001 INCOME (0.0007) (0.0002) (0.0008) COMPUTER *** (0.018) < (0.020) ** (0.032) EXPERIENCE (0.003) (0.003) * (0.001) MANURE * (0.011) (0.020) (0.024) PLAN (0.010) *** (0.004) < (0.006)

58 Table 2.4 contnued AL *** (0.023) < *** (0.018) *** (0.030) <0.001 AR *** (0.053) < ** (0.043) ** (0.077) FL (0.053) (0.098) (0.105) GA *** (0.024) < (0.053) *** (0.035) <0.001 LA *** (0.021) < *** (0.018) < *** (0.018) <0.001 MS *** (0.086) < *** (0.025) < ** (0.070) MO * (0.066) (0.042) *** (0.027) <0.001 NC *** (0.040) < *** (0.018) < *** (0.040) <0.001 SC *** (0.047) < *** (0.025) < *** (0.048) <0.001 TN *** (0.067) < *** (0.033) < *** (0.050) <0.001 VA *** (0.069) < ** (0.091) *** (0.036) <0.001 * Note: Reported Values are the Average Margnal Effects (AME) and the P-Values based on Bootstrapped Robust Standard Errors 43

59 Table 2.5 Weghted Margnal Effects* from the Multnomal Probt Model (N=770) Scenaro 1=No Adopton (Y =1) Scenaro 2=SSIG-URT Adopton (Y =2) Scenaro 3=SSIG-VRT Adopton (Y =3) Varable Margnal Effect P-value Margnal Effect P-value Margnal Effect P-value SYCV ˆ ** (<0.001) * (<0.001) (<0.001) <0.001 EDUC ** (0.007) ** (0.001) * (0.007) AGE (0.004) (0.005) (0.001) YIELDS (<0.001) e-06 (<0.001) (<0.001) INFO *** (0.019) < ** (0.017) *** (0.004) <0.001 IMPORTANCE *** (0.029) < ** (0.045) (0.026) PROFIT ** (0.011) *** (0.018) < *** (0.016) <0.001 INCOME (0.0008) (0.0001) (0.0008) COMPUTER *** (0.018) < (0.016) ** (0.027) EXPERIENCE (0.003) (0.003) ** (0.001) MANURE (0.013) (0.023) (0.027) PLAN (0.010) *** (0.004) (0.005)

60 Table 2.5 contnued AL *** (0.023) < *** (0.014) < *** (0.028) <0.001 AR *** (0.065) < ** (0.043) * (0.083) FL (0.053) (0.066) (0.089) GA *** (0.025) < (0.050) *** (0.033) <0.001 LA *** (0.023) < *** (0.026) < *** (0.027) <0.001 MS ** (0.090) *** (0.028) < ** (0.071) MO * (0.067) (0.039) ** (0.030) NC *** (0.037) < *** (0.020) < *** (0.039) <0.001 SC *** (0.062) < *** (0.018) < *** (0.056) TN *** (0.062) < *** (0.030) < *** (0.048) <0.001 VA *** (0.071) < ** (0.091) *** (0.033) <0.001 * Note: Reported Values are the Average Margnal Effects (AME) and the P-Values based on Bootstrapped Robust Standard Errors 45

61 Table 2.6 Weghted 1 st Step Probt Model of SSIG Adopton Varable Coeffcent P-value Margnal Effect P-value INTERCEPT ** (0.665) EDUC ** (0.028) ** (0.008) AGE ** (0.009) ** (0.002) YIELDS (<0.001) ( ) INFO *** (0.129) < *** (0.035) <0.001 IMPORTANCE ** (0.198) ** (0.059) PROFIT (0.144) (0.043) INCOME (0.002) (0.0007) COMPUTER ** (0.133) ** (0.039) EXPERIENCE (0.008) (0.002) MANURE (0.182) (0.055) PLAN (0.040) (0.012) Wald ch2(12) = Pseudo R-squared = N=

62 Table 2.7 Weghted * 2 nd Step Probt Model of VRT Adopton Varable Coeffcent P-value Margnal Effect P-value INTERCEPT (4.256) SYCV ˆ ** (0.0001) ** (<0.001) IMR (4.729) (0.963) EDUC (0.083) (0.015) AGE (0.020) (0.004) YIELDS (<0.001) (<0.001) INFO (0.647) (0.123) IMPORTANCE (0.487) (0.097) PROFIT ** (0.199) *** (0.031) <0.001 INCOME (0.007) (0.001) COMPUTER (0.406) (0.074) EXPERIENCE (0.012) (0.002) MANURE (0.115) (0.023) PLAN (0.027) (0.005) AG EASE (0.069) (0.014) AL *** (0.186) *** (0.029) <0.001 AR ** (0.323) ** (0.081) FL * (0.320) * (0.076) GA *** (0.068) < *** (0.035) <0.001 LA ** (0.185) *** (0.021) <0.001 MS ** (0.267) ** (0.069)

63 Table 2.7 contnued MO ** (0.104) *** (0.029) <0.001 NC *** (0.112) < *** (0.039) <0.001 SC *** (0.180) *** (0.049) <0.001 TN ** (0.204) *** (0.060) VA ** (0.215) *** (0.060) N= 727 * Note: Reported Values are the Average Margnal Effects (AME) and the P-Values based on Bootstrapped Robust Standard Errors 48

64 Fgure 2.1A Fgure 2.1B Fgure 2.1: Varable Rate Technology (VRT) Constructon (2.1A and 2.1B) 49

65 Fgure 2.2: Ste Specfc Informaton Gatherng Technologes (SSIG) Constructon Fgure 2.3: Perceved Spatal Yeld Varablty (SYCV) Constructon 50

66 References Abad, Ghadm A., Panell, D.J., and Burton M.P., 2005, Rsk, uncertanty, and learnng n adopton of a crop nnovaton, Agrcultural Economcs, Volume 33, Issue 1, p.p. 1-9 Adran, A., Norwood, S., Mask, P., 2005, Producers Perceptons and Atttudes towards Precson Agrculture Technologes, Computers and Electroncs n Agrculture, Akay, A. and Tsakas, E., 2008, Asymptotc Bas Reducton for a condtonal margnal effects estmator n sample selecton models, Appled Economcs, 40, Adesna, A., and Badu-Forson, J., 1995, Farmers perceptons and adopton of new agrcultural technology: Evdence from analyss n Burkna Faso and Gunea, West Afrca. Agrcultural Economcs, Volume 13, Issue 1 Banerjee, S.B., Martn, S.W., Roberts, R.K., Larkn, L.S., Larson, J.A., Paxton, W.K., Englsh, B.C., Marra, M.C., Reeves, M.J., 2008, A Bnary Logt Estmaton of Factors Affectng Adopton of GPS Gudance Systems by Cotton Producers, Journal of Agrcultural and Appled Economcs, 40, 1: Bellemare, M.F., 2009, When Percepton s Realty: Subjectve Expectatons and Contractng, Amercan Journal of Agrcultural Economcs, 91: Brackstone, G.J. and Rao, J.N.K. 1976, Survey Methodology: Rankng Rato Estmators. A Journal Produced by Statstcal Servces Feld, Statstcs Canada. 2(1):p Cameron, C.A., Trved, P.K., 2005, Mcroeconometrcs: Methods and Applcatons, Cambrdge Unversty Press 51

67 Delavande, A., Gne, X., and McKenze, D., 2010, Measurng Subjectve Expectatons n Developng Countres: A Crtcal Revew and New Evdence, Journal of Development Economcs Englsh, B.C., Mahajanashett, S.B., Roberts, R.K., 2001, Assessng Spatal Break-even Varablty n Felds wth Two or More Management Zones, Journal of Agrcultural and Appled Economcs; 33, 3, Fernandez-Cornejo, J., Daberkow, S.G., and McBrde, W.D., 2001, Decomposng the Sze Effect on the Adopton of Innovatons: Agrobotechnology and Precson Farmng. AgBoForum 4: Gne, X., Townsend R., and Vckery, J., 2008, Ratonal Expectatons? Evdence from Plantng Decsons n Sem-Ard Inda, Workng Paper No.166, Bureau for Research and Economc Analyss of Development (BREAD) Gould, B.W., Saupe, W.E., & Klemme, R.M., 1989, Conservaton tllage: The Role of farm and operator characterstcs and the percepton of sol eroson, Land Economcs, 65, Greene, W.H., 2003, Econometrc Analyss, 5 th Edton, prentce Hall, NJ USA Grffn, T.W., Lowenberg-DeBoer, J., Lambert, D.M., Peone, J., Payne, T., Daberkow S.G., 2004, Precson Farmng: Adopton, Proftablty, and Makng Better Use of Data, Ste Specfc Management Center (SSMC) Purdue Unversty and USDA-ERS Guterrez, R., 2008, Analyzng Survey Data usng Stata 10, StataCorp LP, Summer NASUG, Chcago 52

68 Hausman, J. and Mc.Fadden, D., 1984, Specfcaton Tests for the Multnomal Logt Model, Econometrca 52: Hll, Ruth Vargas, 2006, Coffee Prce Rsk n the Market: Exporter, Producer and Trader Data from Uganda, Mmeo. IFPRI Isk, M., Khanna, M., 2002, Uncertanty and Spatal Varablty: Incentves for Varable Rate Technology Adopton n Agrculture, Rsk Decson and Polcy, vol.7 pp Jones, A. 2006, Appled Econometrcs for Health Economsts, 2 nd Edton Offce of Health Economcs (OHE) Just, D., Wolf, S.A., Wu, S., Zlberman, 2002, D. Consumpton of Economc Informaton n Agrculture, Amercan Journal of Agrcultural Economcs, 84(1), Khanna, M., 2001, Sequental Adopton of Ste Specfc Technologes and ts Implcatons for Ntrogen Productvty: A double selectvty model, Amercan Journal of Agrcultural Economcs, 83, Khanna, M., Epouhe, O.F., and Hornbaker, R. 1999, Ste-Specfc Crop Management: Adopton Patterns and Incentves. Rev. Agr. Econ., 21: Larkn, S.L., Perruso, L., Marra, M.C., Roberts, R.K., Englsh, B.C., Larson, J.A., Cochran, R.L., Martn, S., W., 2005, Factors Affectng Perceved Improvements n Envronmental Qualty from Precson Farmng, Journal of Agrcultural and Appled Economcs, 37, 3, Larson, J.A and Roberts, R.K, 2004, Farmers Perceptons about Spatal Yeld Varablty as Influenced by Precson Farmng Informaton Gatherng Technologes, Selected 53

69 Paper presented at annual meetng of the Southern Agrcultural Economcs Assocaton, Tulsa OK, February Mansk, C., 2004, Measurng Expectatons, Econometrca, Vol.72 No.5 ( ), September Marra, M.C., Rejesus, R.M., Roberts, R.K., Englsh, B.C., Larson, J.A., Larkn, S.L., Martn, S., Estmatng the Demand and Wllngness to Pay for Cotton Yeld Montors Precson Agrc., DOI /s z Mooney, D.F., Roberts, R.K., Englsh, B.C., Lambert, D.M., Larson, J.A., Velanda, M., Larkn, S.L., Marra, M.C., Martn, S.W., Mshra, A., Paxton, K.W., Rejesus, R., Segarra, E., Wang, C. and Reeves, J.M., 2010, Precson Farmng by Cotton Producers n Twelve Southern States: Results from the 2009 Southern Cotton Precson Farmng Survey, Research Seres Dept. of Agrcultural and Resource Economcs, The Unversty of Tennessee Nyarko, Y., and Schotter, A., 2002, An Expermental Study of Belef Learnng Usng Elcted Belefs, Econometrca, Volume 70 Issue 3, p.p Rejesus, R.M., Marra, M.C., Roberts, R.K., Englsh, B.C., Larson, A.J., Paxton, W.K., 2010, Changes n Producers Perceptons of Wthn-Feld Yeld Varablty Followng Adopton of Cotton Yeld Montor, Selected Paper prepared for presentaton at the Agrcultural and Appled Economcs Assocaton 2010 AAEA, Denver Colorado Roberts, R.K., Englsh, B.C., Larson, J.A., Cochran, R.L., Goodman, R.W., Larkn, S.,L., Marra, M.C., Martn, S.W., Shurley, W.D., Reeves, J.M., 2004, Adopton of Ste 54

70 Specfc Informaton and Varable Rate Technologes n Cotton Precson Farmng, Journal of Agrcultural and Appled Economc., 36,1: Sall, S., Norman, D., and Featherstone, A.M., 2000, Quanttatve assessment of mproved rce varety adopton: the farmer s perspectve, Agrcultural Systems, Volume 66, Issue 2, p.p Shao, J., 1996, Resamplng methods n sample surveys ', Statstcs 27, Surjandar, I., and Batte, M., 2003, Adopton of Varable Rate Technology, Makara, Teknolog, Vol 7, No.3 Torbet, J.C., Roberts, R.K., Larson, J.A., Englsh, B.C., 2007, Perceved mportance of precson farmng technologes n mprovng phosphorus and potassum effcency n cotton producton, Precson Agrculture 8:

71 CHAPTER 3 PRECISION FARMING TECHNOLOGIES, ENVIRONMENTALLY MOTIVATED FARMERS, AND PERCEIVED IMPROVEMENTS IN ENVIRONMENTAL QUALITY 3.1 Introducton Ths essay s part of the precson farmng lterature that focuses on non-fnancal factors affectng farmers decsons about precson technology adopton. Farmers who adopt precson agrculture technologes typcally expect that they can decrease the use of fertlzers and chemcals, thereby mprovng profts and envronmental qualty due to the lower lkelhood of fertlzer/chemcal runoff (Auernhammer, 2001). The concept of envronmental qualty s mult-dmensonal and t s lnked to a number of nterrelated factors,.e., sol management practces, sol type, topography, organc matter content, crop, weather effects, and pror management (Hatfeld, 2000). Although the reducton of nput use through precson farmng (PF) can logcally translate nto potental mprovements n envronmental qualty, these postve envronmental externaltes from precson farmng technologes have not yet been fully understood over a longer duraton of tme. Regardng farmers atttudes towards the envronment, the lterature shows that ther envronmental nterests are not clear and they seem to affect the speed of entry rather than the probablty of adopton (Wynn et al., 2001). Farmers may realze the mportance of envronmental benefts, but they mght not be wllng to adopt new practces wth large fxed 56

72 cost for equpment and uncertan profts, that would potentally rsk the soco-economc vablty of the farm enterprse (Naper and Brown, 1993). For example, Van Kooten et al. (1990) found that farmers are unwllng to sacrfce as lttle as 5% reductons n net returns n favor of mproved sol qualty, although some sol qualty mprovement due to precson farmng may contrbute to an addtonal 7.2% n revenue (Swnton and Lowerberg-DeBoer, 1998). Other studes found that a regulaton to adopt envronmentally-frendly practces s more effectve than educaton n nducng adopton (Bosch et al., 1995). However, there have also been studes that demonstrate farmers wllngness to be envronmentally motvated and attenuate an amount of ther profts n order to meet ther socal goals. A large percentage (80%) of farmers n a survey conducted n rural areas of Msssspp agreed that precson technology can be used to acheve a cleaner envronment and that they would be wllng to pay n order to protect the envronment for human health reasons (Hte et al., 2002). Another study found that farmers were wllng to forgo hgher yelds by reducng nput use n order to avod the rsk of a moderate envronmental damage (Lohr et al., 1999). A farmer may, however, adopt envronmentally-frendly technologes n order to decrease the possblty of future envronmental regulatons mposed by the government rather than to be envronmentally motvated (Mudalge and Weersnk, 2004). To consder the jont role of fnancal vablty and envronmental responsblty, Morrs and Potter (1995) classfed farmers n the followng four groups: ) actve partcpants who voluntary adopt agr-envronmental measures (AEM) for both envronmental protecton as well as fnancal reasons (Wlson and Hart (2001), ) passve adopters, who practce AEM mostly for fnancal reasons, ) condtonal non adopters who 57

73 would partcpate only under certan crcumstances (e.g., f there s payment/subsdy for adoptng), and v) resstant non adopters who are aganst the adopton of agr-envronmental measures. In a smlar framework, Lynne and Casey (1998) added the assumpton of other nterest n addton to the prmary self nterest (.e., proft maxmzng). The challenge to the scentfc communty at large would be to better understand the dfferent types of farmers lsted above and provde fnancally rewardng technologes/practces that also promote sustanable envronmentally-frendly farmng. Ths study conssts of two objectves related to the envronmental aspects of precson agrculture technologes. Frst, we nvestgate the characterstcs of farmers adoptng precson technology due to factors other than the proft motvatons (.e., envronmental goals). Second, we examne varables ncludng the relatve mportance of varous reasons for adoptng precson farmng that are correlated wth perceved mprovements n envronmental qualty followng adopton of precson farmng technology. Whether or not the farmer characterstcs are found to be correlated wth adopton motves s mportant as prevous studes of precson farmng adopton decsons are assumed solely based on the proft motve followng mcroeconomc theory. Our frst objectve extends the work of Pandt et al. (2011) by lookng more carefully at factors nfluencng the adopton of precson technologes based prmarly on envronmental motves. 7 What are the characterstcs of farmers who adopt precson 7 Pandt et al. (2011) more generally nvestgated the dfferent factors that affect the three dfferent motves for adoptng precson agrculture proft, envronmental reasons, and beng at the forefront of technology usng a smultaneous equatons framework. Our study s more focused n the sense that we examne those ndvduals who rank potental envronment benefts hgher than potental proft advantage as ther man reason for adoptng precson technologes (.e., an envronmentally motvated farmer). The Pandt et al. (2011) study does not make 58

74 technologes manly for ts envronmental benefts? We dstngush between the proft maxmzng farmers (.e., those who rank profts strctly hgher than the envronmental benefts) and the more envronmentally motvated farmers (.e., those who prortze potental envronmental mprovements over profts as the reason for adoptng the technology). The defnton of an envronmentally motvated farmer was based on a Lkert-style rankng of the mportance of three reasons for adoptng precson technologes: (1) proft, (2) envronmental benefts, and (3) beng at the forefront of technology. For our frst objectve, we analyze farm and/or farmer characterstcs assocated wth those who explctly state that they adopt precson technologes mostly for ther envronmental benefts rather than for the potentally hgher profts. Our second objectve s to determne what factors affect any perceved mprovements n envronmental qualty that precson cotton farmers experence durng the eght year perod where surveys were conducted (.e., we wll use data from three cross secton surveys n 2001, 2005 and 2009; see data dscusson below). Larkn et al. (2005) dentfed factors affectng perceved mprovement n envronmental qualty due to precson technologes usng the 2001 survey, and found that about 36% of precson technology adopters n 2001 perceved some envronmental qualty mprovement after the adopton. Our study extends Larkn et al. s (2005) work and provdes more evdence on whether relatonshps found n 2001 are consstent over tme, and/or whether dfferent factors have become more domnant wth the gradual dffuson of precson technology over tme. Note that we do not have a ths more specfc delneaton n ther analyss (.e., they used the actual reported rankng of each motve as the dependent varable regardless of whether the envronmental motve s ranked hgher or lower than the proft motve). 59

75 quantfable measure of envronmental qualty,.e., actual measures of mprovements n sol propertes. We only observe farmers reported perceptons of whether they experenced mprovement n envronmental qualty followng the adopton of precson farmng (PF). Havng knowledge of farmers characterstcs who adopt precson technologes for envronmental reasons and/or who perceve envronmental benefts from these technologes would help dentfy where to ntate educatonal and regulatory efforts desgned to ncrease the use of envronmentally-frendly producton practces lke precson farmng. Knowng the characterstcs of precson technology users that adopt for envronmental reasons and are more n tune wth the envronmental benefts of the technology would allow for more targeted educatonal and nformaton dssemnaton programs that focus on the envronmental advantages of the technology. Agrbusness provders and extenson educators would be able to know whch type of precson farmers are more nformed about the envronmental contrbutons of precson technologes and the consequent nterventons needed to ncrease awareness. Results from ths study would also be useful n developng more targeted educatonal programs promotng green producton practces, such as organc farmng or ntegrated pest management (IPM). Knowler et al. (2007) tred to synthesze the factors affectng adopton of envronmentally-frendly conservaton practces comng from 31 studes, and they found that there are no unversally sgnfcant ndependent varables. Larkn et al. (2005) and Pandt et al. (2011) also demonstrated that there are several farmer characterstcs (e.g., farm sze, yeld levels, farmer age, and experence) that systematcally nfluence envronmental motves for adopton and perceved envronmental mprovements from precson farmng based on 60

76 sngle cross-secton data. We buld on these exstng studes to further explore whether these relatonshps hold over tme (.e., especally for the factors nfluencng perceved envronmental mprovements) and whether there are other mportant elements that affect envronmental motves/perceved envronmental mprovements. 3.2 Emprcal Approaches Emprcal Approach for Objectve 1: Adopton due to Envronmental Reasons Conceptual Framework: Adopton due to Envronmental Reasons Technology adopton s usually modeled as a choce between two alternatves: the tradtonal technology and the new one (.e., n our case, the precson technology). Farmers choose the alternatve that maxmzes ther expected utlty (Fernandez et al., 2004). A farmer s lkely to adopt precson technologes f the utlty of adoptng, U,PF s larger than the utlty of not adoptng, U,NO, that s U * = U,PF - U,NO >0. Snce the actual utltes are not observable, we defne U*,j = V,j +ε j, where V s the systematc component of U related to the expected utlty of adoptng (j=pf), and of not adoptng (j=no), and defne a random dsturbance (ε) that accounts for errors n percepton and measurement, as well as unobserved attrbutes and preferences (Payne et al., 2003). The potental envronmental benefts (EB) and proft benefts (PB) of precson technologes are two man factors that determne the utlty derved from adopton and these two varables are typcally ncluded n V (.e., V, j f ( EB, PB) ). Assume there exsts a latent 61

77 varable or ndex (Y * ) that measures the degree of mportance of EB relatve to PB n determnng the utlty derved from adopton of precson technologes. Hence, hgher values of Y * ndcate that the relatve weght gven to EB s more than to PB, and the expected utlty derved from precson technologes s determned more by envronmental reasons rather than potental proft mprovements. Lower values of Y * mply the reverse (.e., more weght to PB than EB). There s also a value of Y * where the relatve weghts of EB and PB n determnng utlty are equal (.e., ndfference between EB and PB). Gven the exstence of Y * for each farmer, we are nterested n determnng the factors and/or characterstcs that affect Y * such that: (1) Y * = X 'β + ε, * where Y s the unobserved latent varable (as defned above) that depends lnearly on X, β are parameters to be estmated, and ε s the standard normal dstrbuted random error * (Greene, 1997). The problem wth the specfcaton n (1) s that Y s unobserved. However, n the precson agrculture survey for 2009, farmer respondents were asked to rank the mportance (.e., 1 to 5 scale, 5 beng very mportant) of the followng reasons for adoptng precson farmng technologes: envronmental benefts, profts, and beng at the forefront of agrcultural technology. If the rankng for envronmental benefts s strctly lower than the * proft motve, then we can assume that the unobserved Y s below some mnmum threshold μ 1. If the rankng of envronmental benefts as a reason for adoptng precson technologes s * strctly hgher than the proft motve, then we can know that the unobserved Y s above a maxmum threshold μ 2. Lastly, f the rankng of envronmental benefts as a reason for 62

78 * adoptng precson technologes s equal to the proft motve, then the unobserved Y s n between the mnmum (μ 1 ) and maxmum (μ 2 ) thresholds. Wth the observed rankng structure above, one can represent the unobserved ndex that represent the mportance of envronmental benefts as follows: (2) Y = 1 f Y * <μ 1 Y = 2 f μ 1 <Y * < μ 2 Y = 3 f μ 2 <Y *. Gven the ordnal nature of the observed varable n (2), a proportonal odds model (or what s more commonly known as an ordered logt model) can be used to emprcally examne the factors that nfluence envronmental motves as a reason for adoptng precson technologes Estmaton Methods: Proportonal Odds Model POM (Ordered Logt) The proportonal-odds (or cumulatve) logt model s a common model for an ordnal response varable based on the assumpton that the slope of coeffcents does not vary over dfferent alternatves except after passng the cut-off ponts (McCullagh and Nelder, 1989, Peterson and Harrell, 1990). The structure of the ordnal dependent varable n (2) ndcates that we can categorze the farmer respondents as follows: proft orented f Y = 1, ndfferent f Y = 2, and envronmentally motvated f Y = 3. In order to estmate (1) gven the ordnal dependent varable n (2), some of the threshold values need to be fxed, thus the lowest value s set at mnus nfnty μ 1 =-, and the hghest value s set at plus nfnty μ 2 =+. Then, (3) * ' Pr[ Y j] Pr[ j 1 Y j ] Pr[ j 1 X j ] 63

79 ' ' Pr[ j 1 X j X ] F X F X, ' ' ( j ) ( j 1 ) where F s the cdf of ε. 2005): (4) The margnal effects n the probabltes are computed as (Cameron and Trved, Pr[ Y j] ' ' { F '( j 1 X ) F '( j X j )}, X where F' denotes the dervatve of F. For the ordered logt model, ε s logstcally dstrbuted z wth Fz () e z. Assumng a lnear utlty functon and choce probabltes that depend (1 e ) only on observed ndvdual-specfc characterstcs (Judge et al., 1985), the proportonalodds model s defned as: (5) Pr[ ] log [Pr( )] log Y j t Y j e j X, Pr[ Y j ] where the odds rato Pr[ Y ] j Pr[ Y j] denotes the rato of the probablty of adoptng PF to the probablty of not adoptng PF, condtonal on the vector X of explanatory varables. In ths study, we have specfed 3 ordnal choces (j=1, 2, and 3). Thus the cumulatve logt model can be represented wth 2 ntercepts (μ 1 and μ 2 ), nstead of one ntercept, as would be the case for the bnary choce model Robustness Checks The POM descrbed above s very restrctve because t assumes that all varables meet the proportonal odds/parallel lnes assumpton (Wllams, 2006). Ths mples that all 64

80 coeffcents (except the ntercepts) would be the same except for samplng varablty (Wllams, 2006). To deal wth ths problem, the followng solutons have been suggested: a) mplement a less parsmonous non-ordnal alternatve, such as multnomal logt, b) apply a generalzed ordered logt model, that relaxes the parallel lnes assumpton for all varables, or c) try a more flexble approach: the partal proportonal odds model, that relaxes the constrant of proportonal odds only for those varables where t s volated (see Robustness Checks secton below). Followng the Peterson and Harrell (1990), we assume a gamma parameterzaton of partal proportonal odds model wth a logt functon, shown as: (6) exp[ j ( X j T j )] P( Y j) g( X j ), 1 exp[ ( X T )] j j j where T s a q 1 vector, q m, contanng the values of degree of socal responsblty on the subset of m explanatory varables for whch the proportonal odds assumpton does not hold, and γ j s a q 1 vector of regresson coeffcents assocated only wth the jth cumulatve logt, and representng the devatons from the proportonalty. Another way to estmate the model s va a dchotomous choce method, where the farmer s ether more lkely to adopt based on proft, or more lkely to adopt based on envronmental crtera (.e., we do not consder the ndfferent scenaro). Due to the very small number of envronmentally motvated farmers (only 3%), a standard logstc regresson can underestmate the probablty of rare events. Thus, we address ths ssue by followng a rare events logt model as presented n Kng and Zeng s (2000) study. We defne an ndcator varable Y that takes on the value of one f the farmer values envronmental benefts hgher 65

81 than proft, or zero f the farmer values proft hgher than envronmental benefts. The unobserved varable Y * s dstrbuted accordng to a logstc densty wth mean m, such that (7) PY * ( ) * ( Y m) e * ( Y m) 2 (1 e ), and for the observed varable Y, the model becomes: (8) 1 Pr( Y 1 ) Pr( Y 0 ) Logstc( Y m ) dy, * * * 0 1 e x where parameters are calculated usng maxmum lkelhood, assumng ndependence over the observatons. The rare events logt usually yelds small estmates of Pr( Y 1 x ), unless the model has a very good explanatory power. A multnomal logt model s also utlzed as a robustness check to explore the varous factors affectng a producer s decson to adopt PF for envronmental reasons. For an ndvdual we assume a random utlty model V * j = X 'β j + u j assocated wth the followng alternatves: j=1, f the farmer s proft-orented, j=2 f he/she values proft and envronmental benefts equally and j=3 f the farmer s envronmentally conscous. Agan, X ' reflects the set of observed characterstcs, β the vector of parameters to be estmated and u j the stochastc error term. Assumng that the dsturbances of the dfferent combnatons are ndependent and dentcally dstrbuted the probablty of choosng alternatve j s specfed as (Greene 1997): (9) From equaton (9) we can derve P j k ' exp( X j ), j=0,, k (k=2) exp( X ) l 1 ' l 66

82 Pj ' (10) exp( X ( k )), k j, P k whch holds for every subset of elgble combnatons, ncludng k and j. To ensure dentfcaton, β j s set to zero for one of the categores, and the coeffcents are then nterpreted wth respect to ths base category (Cameron and Trved, 2009). Agan, maxmum lkelhood procedure s appled to estmate the parameters of the model Emprcal Specfcaton: Adopton due to Envronmental Reasons To emprcally estmate equaton (1) and determne the characterstcs of farmers who adopt PF for envronmental reasons, we need to specfy the explanatory varables to be ncluded n vector X. Based on the precson farmng lterature (Banerjee et al. 2004, Roberts et al. 2008, Pandt et al. 2011), we frst nclude soco-demographc varables and farm characterstcs as possble factors that nfluence the decson to adopt PF for envronmental reasons. Socoeconomc varables ncluded n the specfcaton are: age (AGE), years of farmng experence (EXPERIEN), and years of educaton (EDUC). Farm characterstcs n the specfcaton are: farm sze (ACRES), prevous years yeld (YIELDS), and percentage of total household ncome from farmng (INCOME). See Table 1 for detaled varable defntons. Second, we nclude varables assocated wth dfferent management practces that a farmer utlzes n hs/her operaton. Farm management varables ncluded n the specfcaton are: use of extenson publcatons (PUBLICAT), use of computers (COMPUTER), use of agrcultural easements (AG EASE), varable rate nput applcaton (VRT), use of manure as fertlzer (MANURE), and number of years n ther farm plannng horzon (PLAN). 67

83 Lastly, farmer perceptons about varous aspects of precson farmng are also ncluded as covarates n the specfcaton. Percepton varables ncluded n the specfcaton are: perceved mportance of precson farmng n the future (IMPORTA), and whether they beleve precson farmng wll be proftable n the future (PROFIT). Note that state dummy varables were also ncluded n the specfcaton to account for regonal varatons Emprcal Approach for Objectve 2: Perceved Envronmental Improvements Conceptual Framework: Perceved Envronmental Improvements Based on our conceptual framework n the prevous secton, we show that the systematc component of a strctly concave utlty of the representatve producer s a functon of the potental envronmental (EB) and proft (PB) benefts of precson technologes. Snce the precse effects of varables X on EB and PB are unknown, the expected utlty functon s: (11) E U[ EB( X; ), PB( X; )] ( X; ) (1 ) E PB( X; ),, where β s a constant assgnng the producer s relatve weght on potental envronmental and proft benefts of precson farmng ( (0,1 ]). If 1then the adopton decson depends entrely on potental envronmental benefts. For values of β less than 1, the proft benefts followng the precson technologes also enter the adopton decson (Amacher and Feather, 1997). The term X s a vector contanng farm and farmer characterstcs (see Emprcal Specfcaton dscusson below). The ndependent random parameters ε 1 and ε 2 reflect the 8 The state dummy varables ncluded n the specfcaton are: AL, AR, FL, GA, LA, MS, MO, NC, SC, TN, VA; wth TX as the omtted state to assure dentfcaton n the regresson. 68

84 uncertanty assocated wth SSIG technologes and VRT adopton on envronmental (EB) and proft (PB) benefts. In ths framework, the true EB s typcally not perfectly observable such that perceptons about EB (e.g., EB * ) are the real drvers of farmer choce rather than the true EB and PB. Suppose the producer have adopted at least one precson technology (.e., one of the SSIG technologes and/or VRT), then the producer can utlze these technologes to update hs/her belefs about EB *. Ths mples that we can defne EB* as: (12a) EB* f ( VRT, X ), where VRT s a dummy varable ndcatng whether or not the producer adopted VRT, and X s a vector of farm/farmer characterstcs. Equaton (12a) can then be re-wrtten n lnear form such that: (12b) EB * = β X + ε = 1,..., n, where X s a vector that ncludes all the ndependent varables that are hypotheszed to nfluence EB *. Expected changes n EB are assumed to be an ncreasng functon of SSIG and VRT use: ( VRT, X; 1) (13) E 0 1 VRT 69

85 Estmaton Method: Perceved Envronmental Improvements Note that perceved envronmental benefts n (12a) are not observable (.e., t s a latent varable). What s observable from the survey s the farmers answer to the queston Have you experenced any mprovements n envronmental qualty through the use of precson technologes? (.e., the ENVIRON varable). Hence, the varable EB s then defned as a dummy varable that takes on the value one f the answer s yes or zero f the answer s no or do not know. Emprcally, ths dependent varable s lnked to the latent varable as follows: (14) EB = 1 f EB * > 0 EB = 0 otherwse Equatons (12) and (14) represent a dchotomous qualtatve response model. The observable dependent ndcator varable Y s 1 wth probablty p, and 0 wth probablty 1 p. Observed values of EB reflect a bnomal process wth probabltes that change from observaton to observaton from changes n X. Thus, equatons (12) and (14) descrbe the probablty that a farmer observes envronmental benefts after adoptng precson farmng,.e.: (15) Prob(EB = 1) = Prob(u > - β X ) = p = 1 F (- β X ), where F s the cumulatve dstrbuton functon for the random error u, that s assumed to be ndependently and dentcally dstrbuted logstcally such that: (16) 1 F( X ). 1 exp( X ) 70

86 Thus, (17) exp( X ) 1 F( X ). 1 exp( X ) One advantage to usng ths dstrbuton s that F can be represented n a closed-form expresson that can be used to evaluate the lkelhood functon by substtuton: (18) L F( X ) [1 F( X Y 0 Y 1 The unknown parameters can be estmated usng maxmum lkelhood to acheve consstent and unbased estmates. We appled a pooled logstc regresson for the three cross secton surveys, controllng for the year the observaton was taken (year fxed effects). We then compare estmaton results from the pooled regresson wth results from three separate bnary logt models, one for each year. )] Emprcal Specfcaton: Perceved Envronmental Improvements To emprcally mplement the estmaton procedure descrbed n the prevous secton and estmate the parameters n (12b), the varables n X need to be specfed. Smlar to the emprcal specfcaton for objectve 1 (See secton ), we frst nclude socodemographc varables and farm characterstcs as possble determnants of whether or not farmers perceve envronmental mprovements from precson technology use. Based on prevous lterature (Larkn et al. 2005, Roberts and Larson, 2004 and Hte et al 2002), the followng soco-demographc varables and farm characterstcs varable are ncluded n the specfcaton: age (AGE), years of farmng experence (EXPERIEN), whether or not the 71

87 farmer attended college (COLLEGE) 9, farm sze (ACRES), prevous years yeld (YIELDS), and percentage of total household ncome from farmng (INCOME). We also ncorporate the followng farm management practces n the specfcaton: use of computers (COMPUTER) and use of manure as fertlzer (MANURE). The varables related to extenson publcatons (PUBLICAT), use of agrcultural easements (AG EASE), and number of years n ther farm plannng horzon (PLAN) are not ncluded n the pooled regresson, but were ncluded n the 2009 run of the logt model snce data for these varables are only avalable for the 2009 crop year. Moreover, as explaned n Larkn et al. (2005) and shown n (12a), the use of VRT would also lkely nfluence whether a farmer perceve envronmental mprovements from precson farmng and a varable representng ths (VRT) s also ncluded n the specfcaton. Consstent wth Larkn et al. (2005), farmer perceptons about varous aspects of precson farmng are also ncluded as covarates n the specfcaton. Percepton varables ncluded n the specfcaton are: perceved mportance of precson farmng n the future (IMPORTA) and whether they beleve precson farmng wll be proftable n the future (PROFIT). Note that state dummy varables were also ncluded n the specfcaton to account for regonal dfferences For ths objectve, the varable COLLEGE nstead of the EDUC s used, so that our result can be easly compared to the earler study by Larkn et al. (2005). 10 The state dummy varables ncluded n the specfcaton for the pooled regresson are: AL, FL, GA, MS, NC; wth TN as the omtted state to assure dentfcaton n the regresson. These are the states that are consstently n the data set from 2001 to For the separate logt regressons, we nclude state dummes for all the states n the data for each year and omttng the followng: TX for 2009, FL for 2005, and TN for

88 3.3 Survey and Data Descrpton Descrpton of Mult-Year Surveys We use survey data from three questonnares sent to farmers n the Southeastern regon of the U.S. n 2001, 2005 and The sample from the 2001 survey ncluded farmers from Alabama, Florda, Msssspp, North Carolna and Tennessee. They were asked questons about precson farmng technologes for each of seven prmary crops (.e., cotton, tobacco, peanuts, soybeans, corn, wheat and rce). Followng Dllman s general mal survey procedures, the questonnare, a postage-pad return envelope, and a cover letter explanng the purpose of the survey were maled on January 16, 2001, and a remnder postcard was sent on January 23, Of the 6,423 questonnares sent, 1,131 were returned (19% response rate). For the 2005 survey, the States of Arkansas, Georga, Lousana, Msssspp, Mssour, South Carolna and Vrgna were added to the states ncluded n the 2001 survey. Ths ncreased the number of states n the study to eleven. The frst 12,243 questonnares were sent on January 28, 2005 and remnders followed on February 4, 2005 and February 23, 2005, respectvely. The ndvduals who provded usable responses were 1,215 (10% response rate). For the 2009 survey, a total of 1,692 surveys were returned out of 14,089 questonnares that were maled n February 20, 2009, along wth the remnder post card on March 5, Texas was added to the sample of states ncluded n the pror 2005 survey, thereby ncreasng the number of states to twelve. The response rate mproved to 12.5% for ths survey year (Mooney et al., 2010). 73

89 3.3.2 Data Descrpton for Objectve 1: Adopton due to Envronmental Reasons To systematcally explore the characterstcs of farmers who adopt precson technologes manly for envronmental reasons (objectve 1), our analyss only utlzes the 2009 survey, n order to make comparable nferences wth Pandt et al. (2011), who utlzed the same data 11. Of the 665 farmers who ranked the three reasons to adopt precson agrculture (.e., proft, envronmental benefts, and beng at the forefront of technology) n 2009, 62.5% of them ranked profts strctly hgher than any other reason for adopton (See Fgure 1). About 34.1% of farmers valued envronmental benefts equally wth proft, and only 3.3% ranked the envronmental motves strctly hgher than proft. These were consdered to be the envronmentally motvated farmers n our sample. Regardng the 2005 survey, 57.9% valued profts hgher than envronment, 39% were ndfferent, and agan 3% were envronmentally motvated. Last, for the 2001 survey, the proft-orented were 57.3%, the ndfferent between profts and envronmental benefts conssted of 38.8% of the respondents, and smlarly only 3.8% were the envronmentally conscous. Table 3 summarzes the varous characterstcs that dstngush the more envronmentally motvated farmers from the other groups and how these have changed over tme on average. Envronmentally conscous farmers had relatvely smaller farms, 11 Moreover, 2001 and 2005 surveys dd not have questons that are dentcal to the 2009 questons,.e., they both ncluded the extra choce of adopt n order not to be left behnd n addton to the other three reasons for adopton ncluded n 2009 (proft, envronmental benefts and beng at the forefront of technology). Thus omttng the responses of that queston n order to create an dentcal varable mght create addtonal bas. Nevertheless, we estmated the model usng prevous years data but the margnal effects of all varables were not statstcally sgnfcant. 74

90 partcpated more n agrcultural easement programs, they had more experence n farmng and they were older. Farmers who adopted PF manly for envronmental reasons tend to use computer n ther farm management less, and a hgher percentage of ther ncome comes from agrcultural sources. Ther average yelds were slghtly lower than the other groups, but they all had hgher expectatons regardng the future mportance of precson agrculture. Producers who ranked proft hgher than the other reasons, n contrast, were younger, used Unversty publcatons to obtan nformaton about precson farmng, had larger farms, and more years of formal educaton Data Descrpton for Objectve 2: Perceved Envronmental Improvements To more carefully analyze the factors nfluencng perceved envronmental mprovements (objectve 2), we utlze all three surveys n 2001, 2005, and Use of mult-year survey data allows us to buld on the work of Larkn et al. (2005) to determne whether the factors that nfluenced perceved envronmental mprovements n 2001 hold through tme. All survey questonnares asked farmers who adopted at least one precson farmng technology whether they had experenced any mprovement n envronmental qualty n ther felds. Of the 1668 farmers who responded to ths queston, 26.4% of them (442 farmers) repled that they had observed envronmental mprovements, and the rest ether dd not perceve any mprovement or dd not know whether they dd. Table 2 shows the descrptve statstcs (.e., mean and standard devaton) of each varable used to acheve objectve 2. 75

91 3.4 Results Objectve 1 Results: Adopton due to Envronmental Reasons Results of the Proportonal Odds Model (Ordered Logt) To determne the characterstcs of farmers who adopt PF for envronmental reasons, we estmate the proportonal odds and the partal proportonal odds model, both of whch are nested n the non-proportonal (generalzed ordered logstc) model. The lkelhood rato test of proportonalty of odds across response categores s statstcally nsgnfcant (χ 2 (25) = wth Prob> χ 2 = ), ndcatng that the parallel regresson assumpton has not been volated. The statstcs under the gamma parameterzaton suggest that the partal proportonal odds model (PPOM) may not be approprate to use (χ 2 (28) = 9.76 wth Prob> χ 2 = ). Moreover, the Wald tests ndcate that all varables satsfy the proportonalty tests (.e., varables whose effects sgnfcantly dffer across equatons).gven the results of the Wald tests, the proportonal odds model (POM) or the cumulatve logt model may be the best alternatve to use. To further test the specfcaton of the model, we conducted a RESET test. The RESET test ndcates a non sgnfcant χ 2 statstc for specfcaton error (chsquared of 0.06 wth a p-value of ) and suggests that the POM model provdes a good ft wth low specfcaton error. The average margnal effects along wth ther delta standard errors n parentheses are presented n Table 4. The parameter estmates cannot reveal the effect of changes n explanatory varables on the dependent varable, holdng other factors constant. Thus, we calculated the margnal effects of the ndependent varables on the probablty of reportng 76

92 envronment as the most mportant reason to adopt precson technologes. The hgh number of dscrete, and partcularly bnary varables, rased an ssue of multcollnearty. Multcollnearty dagnostcs ndcated a mean VIF (Varance Inflaton Factor) of 1.35 and Tolerance levels between 0.74 and The only correlaton coeffcents that dd not follow the condton ndces were AGE and EXPERIEN, both of whch were statstcally nsgnfcant n our estmaton analyss. Of the statstcally sgnfcant margnal effects, expected mportance of precson agrculture 5 years from now (IMPORTAN), and farmers that have agrcultural easements (AG EASE), are more lkely to make ther PF adopton decsons based mostly on envronmental reasons. Farmers, who partcpate n agrcultural easement programs, may be less concerned about losses that would rsk ther farm s economc vablty, thus value envronmental motvatons hgher. In contrast, the use of unversty publcatons (PUBLICAT) as a means to obtan PF nformaton, as well as the use of computer n farm management (COMPUTER) both negatvely affect the probablty that a farmer would adopt precson farmng technologes for ts potental to mprove envronmental outcomes. Interestngly, more educated farmers (EDUC) are less lkely to adopt for envronmental reasons. We would expect that college degree respondents are more aware of the potental envronmental benefts of precson technologes and would more lkely adopt for ths reason. Farmers who expect that precson technologes wll be mportant fve years from now (IMPORTA) and those who use agrcultural easements (AG EASE) are also the ones 12 A commonly gven rule of thumb s that VIFs of 10 or hgher (or equvalently, tolerances of.010 or less) may be reason for concern (Ender, P., UCLA) 77

93 more lkely to adopt PF for envronmental reasons based on the margnal effects n Table 4. The postve effect of IMPORTA suggests that the mportance of PF n the future may be lnked to envronmental outcomes. The observed postve AG EASE s reasonable snce farmers that have agrcultural easements are typcally more nclned to protect the envronment, and consequently adopt envronmentally-frendly practces. The negatve sgn of the varable sgnfyng use of unversty publcatons s somewhat unexpected. A pror we expect ths varable to ncrease the lkelhood of adoptng PF for envronmental reasons because these types of publcatons often emphasze the potental postve envronmental benefts of the technology (.e., mnmzaton of over or under applcaton of chemcal nputs based on locaton-specfc condtons). Nevertheless, the negatve relatonshp may have resulted from the way we constructed the varable PUBLICAT. There were a substantal number of farmers who answered do not know n the queston of whether they used Unversty publcatons n order to obtan nformaton about precson farmng. These respondents were not dropped from the model but were ncorporated to the no respondents (.e., no use of publcatons), takng on the value of zero Robustness Check Results: Rare Events Logt and Multnomal Logt Robustness checks usng the rare events logt and multnomal logt approaches provde results that are farly consstent wth the POM results above. In the rare events logt, along wth PUBLICAT, the varable COMPUTER had a statstcally sgnfcant negatve effect on the lkelhood of a farmer adoptng PF for envronmental reasons, whle the AG EASE 78

94 varable stll exhbted a statstcally sgnfcant postve effect (Table 5). The negatve parameter estmate for the COMPUTER s somewhat expected gven the results n Pandt et al. (2011) that computer use s more lkely to be assocated wth the proft motve rather than the envronmental goals for adoptng precson technologes. Farmers who use computers for farm management purposes are typcally the ones who only adopt new technologes f these contrbute postvely to profts. In addton, a shorter plannng horzon s more lkely to be assocated wth envronmentally conscous farmers. For the multnomal logt estmates, presented n Table 6, COMPUTER, PUBLICAT, EDUC and AG EASE are stll statstcally sgnfcant and follow the same sgn as n the POM approach. These robustness check results suggest that these varables consstently have a strong statstcally sgnfcant effect on the lkelhood of adoptng PF for envronmental reasons, regardless of the estmaton approach Objectve 2 Results: Perceved Envronmental Improvements Results of the Pooled Logstc Regresson The results of the pooled estmaton to determne the characterstcs of farmers who perceve envronmental mprovements from PF are reported n Table 8. Based on the margnal effect calculatons, we fnd that the varables INCOME and MANURE are consstently sgnfcant across all three perods, whereas ADOPT_2 (Adopton due to envronmental reasons) and VRT are postve and statstcally sgnfcant for the 2005 and 2009 perods. Ths suggests that farmers, who appled ther nputs at a varable rate (VRT), dd not rely on organc fertlzers (MANURE), adopted PF mostly due to ts envronmental potental benefts 79

95 (ADOPT_2), and whose ncome came manly from farmng sources (INCOME), were more lkely to perceve envronmental mprovements followng the use of precson technologes. On the contrary, 2001 ndvduals who used manure as a farm management practce (MANURE) and whose ncome dd not come manly from farmng sources (INCOME) were more lkely to observe mprovements n envronmental qualty after the PF adopton. The mpact of VRT n the latest studes reveals a more envronmentally conscous profle of farmers Robustness Check Results: Separate Logt Models As a robustness check, we estmated three separate logt models for the three dfferent datasets. Note that poolng three separate years of cross secton data s typcally favored over separate regressons for each year because one can get a larger sample sze. By poolng the ndependent cross sectons, one may be able to get more precse estmates and test statstcs wll have more power (Wooldrdge, 2006, p. 449). In ths study, however, the practcal shortcomng of poolng the data s that some varables that can be observed n later surveys may not have been avalable n older surveys. For example, the varable AG EASE was only avalable n the 2009 data and t s not avalable n the 2005 and 2001 data (.e. the queston about agrcultural easement use was not asked n the earler surveys). Hence, ths varable cannot be ncluded n the pooled regresson snce t s mssng n the earler years. Runnng separate regresson for each year, where the varables AG EASE, PUBLICAT and PLAN are ncluded n 2009, would then be nformatve n ths case. Calculated margnal effects based on separate logt regressons for 2001, 2005, 2009 are presented n Table

96 As per the 2009 survey, the PF adopton due to envronmental reasons (ADOPT_2), the use of unversty publcatons to obtan nformaton about precson agrculture (PUBLICAT), the nput applcaton at a varable rate (VRT), farmers perceptons about future PF proftablty (PROFIT), and the plannng horzon (PLAN) all postvely affected the probablty of reportng envronmental qualty mprovements from the use of PF. Lkewse, the varables ADOPT_2, VRT, and PROFIT also postvely nfluence perceved mprovements n envronmental qualty n the 2005 survey. On the contrary, ADOPT_2 and VRT had negatve sgns but were statstcally nsgnfcant n the 2001 survey. Farmers, whose ncomes were less dependent on farmng (INCOME), used computer n ther farm management (COMPUTER) and appled manure on ther felds (MANURE), were more lkely to observe mprovements n envronmental qualty. The dfferences between the three separate yearly runs may result from the fact that we could not use the exact set of explanatory varables for 2009 and older surveys (only 2001 and 2005 models have dentcal regressors). Note that the 2001 logt estmaton n our study s not a replcaton of the logt estmaton n Larkn s paper (2005), snce we used a dfferent set of explanatory varables (e.g., ACRES n our model ncludes only cotton producton, whereas Larkn s study accounts for other crops such as wheat, peanuts, soybeans, tobacco, corn, and rce), n order to acheve comparable results among the three surveys n ths study. To reveal dfferences across tme, frst, we found that the use of VRT was not a statstcally sgnfcant factor n perceved mprovement n envronmental qualty n 2001, but t postvely and sgnfcantly affected perceved envronmental mprovements n 2005 and The use of unversty publcatons (PUBLICAT), and the farmng horzon (PLAN) were 81

97 also crtcal n postvely nfluencng the perceptons, but we only had avalable data for 2009, so we could not compare across the three surveys. Farmers who used computer n ther farm management (COMPUTER) were more lkely to observe mprovements n 2001, but not n 2009 and The perceptons for future proftablty of precson technologes (PROFIT) postvely affect the perceptons about envronmental mprovement n all years. Ths was the only varable whose mpact was statstcally sgnfcant and of the same drecton for all years. Our estmaton ndcates mnmum regonal dfferences, thus the results of locaton dummes are not reported, but are avalable upon request. 3.5 Concluson Our study provdes further understandng about two key envronmental aspects of precson technology adopton: adopton drven by envronmental motvatons and perceved mprovements n envronmental qualty followng adopton of precson agrculture. An advantage of ths study s that farmers were asked about reasons drvng real adopton that had already occurred, contrary to most studes whch focus on factors affectng farmers expected adopton. Explorng the fnancal and soco-economc factors affectng farmers technology adopton decsons and ther perceptons towards the technology and envronment, can help polcy makers desgn schemes that would mprove adopton rates of precson agrculture and the effectveness of polces amed at envronmental awareness. Frst, we examned characterstcs of producers who adopt precson farmng prmarly for envronmental reasons. Cotton farmers from the Southeastern regon were 82

98 asked about the mportance of potental proft and envronmental benefts of the technology n ther adopton decsons. Based on the rankng, we constructed a varable that measured the degree of farmers envronmental responsblty (.e., how they value envronmental benefts compared to proft maxmzaton), and we then related these to farm characterstcs through varous regresson methods. In partcular, a proportonal odds model (POM) was utlzed to estmate the factors affectng the role of envronmental responsblty on the technology adopton. Our analyss showed that personal and structural factors play an mportant role n the adopton of precson technologes for envronmental reasons. The estmated margnal effects ndcate that the partcpaton n agrcultural easement programs, the perceved mportance of PF n the future, as well as the perceved mprovement n envronmental qualty followng the PF use, all postvely nfluence the decson to adopt for envronmental reasons. On the other hand, educatonal attanment only had a postve mpact on adopton based on proft motves, although educated farmers are better nformed not only about technologes tself, but also about the detrmental effects of unsustanable practces (Ervn and Ervn, 1982). Smlarly, farmers who used Unversty Publcatons to acqure nformaton about precson agrculture are more lkely adopt based on proft maxmzng crtera. These results suggest that there may be a need for further techncal advce and nformaton from Extenson focusng on envronmental benefts of precson agrculture. Regardng the mportance of perceptons on decson makng, farmers perceptons can be shaped from regonal polcy makers or other networks towards envronmental awareness, thus knowng whether they play a sgnfcant role n adopton can nfluence the effectveness of nformal nformaton as well as socal networks (Defrancesco et al, 2006). Moreover, 83

99 researchers can drect ths nformaton to the socal channels that would make farmers more aware of the envronmental benefts of PF. The second envronmental ssue examned n ths study s to determne whether producers dd perceve envronmental qualty mprovements followng the adopton of precson technologes, and to explore the characterstcs of producers who observed these mprovements. Understandng the factors shapng these perceptons can provde nsghts about the decson makng process that affect the practce of precson agrculture. Based on logstc regressons, the probablty that a farmer observed any mprovements n envronmental qualty (through the adopton of technology) was hgher f the farmer used manure n hs/her felds, was less dependent on ncome comng only from farmng sources, and perceved that PF wll be proftable n the future. These elements were found to be statstcally sgnfcant for separate cross-secton data collected over an eght year perod. For later surveys, the nput applcaton at a varable rate and envronmentally motvated behavor both postvely affect the perceved mprovements n envronmental benefts. It would have been nterestng to know the level of perceptons (.e., whether the farmer observed lttle or sgnfcant envronmental qualty mprovement) as n the case of preferences wth respect to adopton (objectve 1). An nterestng follow-up study would be to nvestgate whether the length of use of nformaton gatherng technologes affects farmers perceptons about mprovement n envronmental qualty. An mplcaton for the polcymakers s that whle the vast majorty of cotton farmers n the Southeastern U.S. regon are strongly motvated by profts (as would be expected), there are stll envronmentally-mnded cotton farmers who practce precson farmng. For 84

100 future work, t may be nterestng to explore the underlyng motvaton for adoptng precson technologes based on envronmental reasons. Are these farmers truly altrustc such that they want to adopt precson technologes purely for envronmental reasons and therefore provdng postve externaltes to socety? Or are there stll long-term, prvate motves drvng these decsons such as the desre to bequeath a hgh qualty (.e., non-degraded) and envronmentally sustanable farm to future generatons (.e., ther hers). Are the farmers who adopted for envronmental reasons dong ths to avod future regulatons? Explorng these ssues n the future may requre a more dynamc framework. Future research may also explore the role of socal captal n farmers level of envronmental conscousness n addton to the human captal and farm physcal characterstcs. One can nvestgate whether farmers wth more socal captal (.e., socal networkng comng from farm dealers, crop consultants, other farmers, news/meda, etc.) and better communty organzaton are more wllng to adopt based on envronmental crtera. Extendng our study to account for knowledge of marketng and prcng methods (.e., whether the farmer used conventonal prces, ncludng future prces or cost of producton as a source of prcng nformaton) would help also help further understand precson farmng producers who are motvated by envronmental goals. 85

101 Table 3.1 Summary Statstcs of the Varables used n Objectve 1 (Adopton due to Envronmental Reasons) Varables Descrpton Mean Std. Dev. Mn Max ADOPT Farmer adopted PF because he ranks envronment hgher than proft (yes=1; no=0) ADOPT_2 Farmer adopted PF because he ranks profts hgher than envronment (y=1), ranks envronment equal to proft (y=2), and ranks envronment hgher than proft (y=3) ACRES Total acreage of dry land (sum of rented and owned acres) for the 2007 crop season YIELDS Estmate of average cotton lnt yeld per acre for 2007 crop season EDUC Number of Years of Formal Educaton excludng kndergarten AGE Age of the farm operator (as of the 2009 survey year) EXPERIEN Number of Years farmng IMPORTA Farmer perceved that precson farmng would be mportant n fve years from now (yes=1; no=0) PROFIT Farmer perceved that PF would be proftable to use n the future (yes=1; no=0) INCOME Percentage (%) of 2007 taxable household ncome comng only from farmng sources COMPUTER Farmer uses computer for farm management (yes=1; no=0) MANURE Farmer appled manure on hs/her felds (yes=1; no=0) PUBLICAT Farmer used Unversty publcatons to obtan PF nformaton (yes=1; no=0) AG EASE The farm currently has agrcultural easement (yes=1; no or don t know=0) VRT Farmer appled hs nputs at a varable rate (yes=1; no=0) PLAN Years to plan farmng n the future

102 Table 3.2 Summary Statstcs of the Varables used n Objectve 2 (Perceved Improvements n Envronmental Qualty) Varables Descrpton Mean Std. Dev. Mn Max ADOPT Farmer adopted PF because he ranks envronment hgher than proft (yes=1; no=0) ADOPT_2 Farmer adopted PF because he ranks profts hgher than envronment (y=1), ranks envronment equal to proft (y=2), and ranks envronment hgher than proft (y=3) ENVIRON Farmer perceved mprovement n envronmental qualty through the PF use (yes=1; no=0) ACRES Total acreage of dry land (sum of rented and owned acres) for the 2007 crop season YIELDS Estmate of average cotton lnt yeld per acre for 2007 crop season COLLEGE Farmer attended College AGE Age of the farm operator (as of each survey year) EXPERIEN Number of Years farmng (yes=1; no=0) IMPORTA PROFIT Farmer perceved that precson farmng would be mportant n fve years from now (yes=1; no=0) Farmer perceved that precson farmng would be proftable to use n the future (yes=1; no=0) INCOME Percentage (%) of each year s taxable household ncome comng only from farmng sources COMPUTER Farmer uses computer for farm management (yes=1; no=0) MANURE Farmer appled manure on hs/her felds (yes=1; no=0) VRT Farmer appled hs nputs at a varable rate (yes=1; no=0)

103 Table 3.3 Comparson of Varable Statstcs of the 3 Groups of Farmers Proft Orented (Y=1) Indfferent (Y=2) Envron Motvated (Y=3) Varables Mean Mean Mean Mean Mean Mean Mean Mean Mean FARM SIZE YIELDS PUBLICAT N/A N/A N/A N/A N/A N/A COMPUTER IMPORTAN VRT PROFIT INCOME AG_EASE N/A N/A N/A N/A N/A N/A PLAN N/A N/A N/A N/A N/A N/A MANURE EXPERIEN AGE COLLEGE

104 Table 3.4 Margnal Effects of the Covarates usng Ordered Logt Average Margnal Effects Varables (St.E) Proft Orented (Y=1) Indfferent (Y=2) Envron Motvated (Y=3) ACRES 1.26e e ( ) ( ) (0.0006) YIELDS -9.98e e e-06 ( ) ( ) (5.83e-06) PUBLICAT (0.045) COMPUTER (0.049) VRT (0.046) IMPORTAN ** (0.115) PROFIT (0.057) INCOME (0.0008) AG_EASE ** (0.069) (0.037) (0.041) (0.038) ** (0.095) (0.047) (0.0006) ** (0.057) (0.008) (0.009) (0.008) ** (0.023) (0.010) (0.0001) ** (0.014) 89

105 PLAN Table 3.4 contnued (0.013) MANURE (0.049) EXPERIEN (0.003) AGE (0.003) EDUC ** (0.009) Note: *** p<0.01, ** p<0.05, * p<0.1 (0.011) (0.040) (0.002) (0.002) ** (0.007) (0.002) (0.008) (0.0006) (0.0006) ** (0.001) 90

106 Varables ACRES Table 3.5 Margnal Effects of the Covarates for each Outcome usng Ordnal Logstc Regresson Models NPOM (Generalzed Ordered Logt) PPOM (Gamma) Proft Orented (Y=1) 2.77e-06 Indfferent (Y=2) -2.31e-06 Envron Motvated (Y=3) -4.63e-07 Proft Orented (Y=1) 2.77e-06 Indfferent (Y=2) -2.31e-06 Envron Motvated (Y=3) -4.63e-07 ( ) ( ) (3.97e-06) ( ) ( ) (3.97e-06) YIELDS e e-06 ( ) ( ) (5.51e-06) ( ) ( ) (5.51e-06) PUBLICAT (0.045) (0.038) (0.007) (0.045) (0.038) (0.007) COMPUTER ** ** (0.050) (0.047) (0.019) (0.050) (0.047) (0.019) VRT ** (0.047) (0.045) (0.018) (0.047) (0.045) (0.018) IMPORTAN ** ** ** ** ** ** (0.116) (0.096) (0.021) (0.116) (0.096) (0.021) PROFIT (0.057) (0.048) (0.009) (0.057) (0.048) (0.009) 91

107 Table 3.5 contnued INCOME (0.0008) (0.0007) (0.0001) (0.0008) (0.0007) (0.0001) AG_EASE ** ** ** ** ** ** (0.070) (0.058) (0.012) (0.070) (0.058) (0.012) PLAN (0.013) (0.011) (0.002) (0.013) (0.011) (0.002) MANURE (0.049) (0.041) (0.008) (0.049) (0.041) (0.008) EXPERIEN (0.003) (0.002) (0.0005) (0.003) (0.002) (0.0005) AGE (0.003) (0.002) (0.0006) (0.003) (0.002) (0.0006) EDUC ** ** ** ** ** ** (0.009) (0.008) (0.001) (0.009) (0.008) (0.001) 92

108 Table 3.6 Margnal Effects of the Covarates of Each Outcome usng Multnomal Logt Model Proft Orented (Y=1) Indfferent (Y=2) Envron Motvated (Y=3) Average Margnal Effects Average Margnal Effects Average Margnal Effects Varables (St.E) (St.E) (St.E) ACRES 8.95e-07 (.00002) (0.003) -3.13e-06 ( ) YIELDS ( ) ( ) -5.76e-07 ( ) PUBLICAT (0.046) (0.045) * (0.020) COMPUTER (0.051) (0.050) ** (0.019) IMPORTAN (34.07) (25.48) (59.55) PROFIT (0.058) (0.058) (0.021) INCOME (0.0008) (0.0008) (0.0004) AG_EASE ** (0.072) (0.071) ** (0.025) PLAN (0.014) (0.014) * (0.005) 93

109 Table 3.6 contnued VRT (0.047) (0.046) * (0.020) MANURE (0.050) (0.050) (0.020) EXPERIEN (0.003) (0.003) (0.001) AGE (0.003) (0.003) (0.001) EDUC ** (0.009) ** (0.009) (0.004) Note: *** p<0.01, ** p<0.05, * p<0.1 94

110 Table 3.7 Parameter Estmates of the Covarates usng Rare Events Logt & Bnary Logt Varables Rare Events Logt (N=491) Bnary Logt (N=491) CONSTANT * (2.528) * (3.092) ACRES (0.0003) (0.0004) YIELDS -3.02e-06 (0.0004) (0.0004) PUBLICAT * (0.487) ** (0.622) COMPUTER ** (0.491) ** (0.599) PROFIT (0.518) (0.687) IMPORTAN N/A N/A VRT ** (0.489) (0.632) EXPERIEN (0.048) (0.047) INCOME (0.009) (0.012) AG_EASE ** (0.618) ** (0.773) PLAN * (0.136) * (0.163) MANURE (0.650) (0.668) AGE (0.045) (0.052) EDUC (0.125) (0.123) 95

111 Table 3.8 Logt Parameter Estmates of the Covarates usng a Pooled Regresson (N=738) Pseudo R 2 = LR ch2(52) = Varables Prob > ch2 = Log lkelhood = Coeffcents (St.E) Average Margnal Effects CONSTANT (2.875) --- (St.E) ADOPT_ (0.562) (0.102) VRT (0.775) (0.140) ACRES (0.001) (0.0002) YIELDS (0.001) (0.0002) COMPUTER (0.732) (0.132) IMPORTAN (1.239) (0.226) PROFIT ** (1.531) ** (0.276) INCOME ** (0.012) ** (0.002) MANURE ** (0.775) ** (0.140) EXPERIEN (0.055) (0.010) AGE (0.058) (0.010) COLLEGE (0.796) (0.144) 96

112 Table 3.8 contnued 2005 Interacton Effects Year (3.301) (0.602) ADOPT_2_ ** (0.660) ** (0.119) VRT_ ** (0.865) ** (0.156) ACRES_ (0.001) (0.0002) YIELDS_ (0.001) (0.0002) COMPUTER_ * (0.854) * (0.154) IMPORTAN_ (1.643) (0.299) PROFIT_ (1.621) (0.294) INCOME_ ** (0.013) ** (0.002) MANURE_ * (0.895) * (0.161) EXPERIEN_ (0.064) (0.011) AGE_ (0.066) (0.012) COLLEGE_ (0.904) (0.164) 97

113 Table 3.8 contnued 2009 Interacton Effects Year (3.183) (0.580) ADOPT_2_ * (0.598) * (0.108) VRT_ ** (0.810) ** (0.146) ACRES_ (0.001) (0.0002) YIELDS_ (0.001) (0.0002) COMPUTER_ (0.782) (0.142) IMPORTAN_ (1.648) (0.300) PROFIT_ (1.575) (0.286) INCOME_ ** (0.012) ** (0.002) MANURE_ ** (0.818) ** (0.147) EXPERIEN_ (0.058) (0.010) AGE_ (0.062) (0.011) COLLEGE_ (0.839) (0.152) Note: *** p<0.01, ** p<0.05, * p<0.1 98

114 Table 3.9 Coeffcent estmates of the covarates for each outcome usng Bnary Logt 2009 (N=440) 2005 (N=202) 2001 (N=85) LR ch2(26) = Pseudo R 2 = LR ch2(17) = Pseudo R 2 = LR ch2(17) = Pseudo R 2 = Varables Coeffcents (St.E) Coeffcents (St.E) Coeffcents (St.E) CONSTANT *** (1.474) ** (1.611) (2.875) VRT *** (0.273) ** (0.379) (0.775) ADOPT_ *** (0.215) *** (0.350) (0.562) ACRES (0.0001) (0.0001) (0.001) YIELDS (0.0002) (0.0003) (0.001) PUBLICAT ** (0.254) N/A N/A COMPUTER (0.297) (0.440) (0.732) IMPORTAN (1.104) (1.066) (1.239) PROFIT ** (0.381) ** (0.532) **( 1.531) INCOME (0.004) (0.006) ** (0.012) AG_EASE (0.402) N/A N/A PLAN ** (0.084) N/A N/A MANURE (0.279) (0.453) ** (0.775) EXPERIEN (0.020) (0.028) (0.055) AGE (0.021) (0.027) (0.058) COLLEGE (0.280) (0.416) (0.796) Note: *** p<0.01, ** p<0.05, * p<0.1 99

115 Table 3.10 Average margnal effects of each outcome usng Bnary Logt Models 2009 (N=440) 2005 (N= 202) 2001 (N=85) Varables LR ch2(26) = Pseudo R 2 = Average Margnal Effects (St.E) LR ch2(17) = Pseudo R 2 = Average Margnal Effects (St.E) LR ch2(17) = Pseudo R 2 = Average Margnal Effects (St.E) VRT *** (0.045) ** (0.068) (0.120) ADOPT_ *** (0.033) *** (0.053) (0.090) ACRES ( ) ( ) (0.0001) YIELDS ( ) ( ) (0.0001) PUBLICAT ** (0.043) N/A N/A COMPUTER (0.051) (0.082) * (0.111) IMPORTAN (0.190) (0.199) (0.199) PROFIT ** (0.064) ** (0.095) ** (0.222) INCOME (0.0008) (0.001) *** (0.001) AG_EASE (0.069) N/A N/A PLAN ** (0.014) N/A N/A MANURE (0.048) (0.085) ** (0.112) EXPERIEN 9.81e-06 (0.003) (0.005) (0.008) AGE (0.003) (0.005) (0.009) COLLEGE (0.048) (0.077) (0.124) Note: *** p<0.01, ** p<0.05, * p<

116 70 Proft, Proft, 57.9 Proft, Indfferent, 34.1 Indfferent, 39 Indfferent, Envronment, 3.3 Envronment, 3 Envronment, Indfferent Proft Envronment Fgure 3.1: Percentage of Responses for the Reasons to Adopt Precson Technology 101

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123 CHAPTER 4 WHAT MAKES CERTAIN PRECISION TECHNOLOGIES MORE DURABLE THAN OTHERS 4.1 Introducton In ths chapter, we want to nvestgate the factors affectng the duraton of ste specfc nformaton gatherng (SSIG) technology use. The frst applcaton of duraton analyss n socal scences was Lancaster s study (1972) that explaned the duraton of unemployment before workers enter the job market. Recently, there have been studes n the agrcultural economcs lterature that utlzed duraton analyses to study the length of tme before adoptng a technology, or the speed of adopton (Burton et al., 2003, Dad et al., 2004, Odendo et al., 2010). Our paper tres to explan the length of tme before precson technologes are abandoned (.e. speed of abandonment). In the context of the above duraton models, the entrance or start tme n our study s the date when precson technology adopton frst took place, whereas the ext date s the tme the farmer dscontnued the use of the specfc technology. To the best of our knowledge, ths ssue has not been nvestgated before and we contrbute to the lterature n ths regard. Prevous cross sectonal studes have focused on the adopton and abandonment decsons wthn a statc framework (.e., at a certan pont n tme) usng bnary choce models such as logt and probt (Roberts et al., 2004, Khanna, 2001, Banerjee et al., 2008). Walton et al. (2008) dentfed the factors affectng the abandonment of precson sol 108

124 samplng, but no study has yet examned what specfc factors affect the amount of tme precson technologes are utlzed (.e., ther duraton of use over tme). Our study wll try to fll ths gap n the lterature about the dynamcs of technology adopton, and estmate the effect of tme-varyng and tme-ndependent varables on duraton (Dad et al., 2004, Odendo et al., 2010). The mportance of the duraton analyss les n the fact that t s not a one-tme event such as the adopton or the abandonment choce. Farmers decson on whether they wll contnue usng the technology requres a comparson between the pror expected net benefts before the adopton, and the observed net benefts followng the adopton (Walton et al., 2008). Moreover, the use of tme-dependent varables helps explan whether and when a farmer adopts a technology, thereby accountng for the growth of adopton over tme. Ths has been gnored n cross sectonal studes that estmate adopton and abandonment decsons separately (D Emden et al., 2006). In our case, where adopton of SSIG technologes requres accumulaton of nformaton, techncal sklls, and physcal captal, varous economc and socal varables may not be adequate to capture the abandonment choce at the tme of abandonment. It would be nterestng to see whether varables such as educaton, ncome, age etc. that affect the SSIG technology adopton also nfluence the tme taken to abandonment. Bult on dchotomous choce tools, the duraton methods combne both ndvdual adopton decsons as well as the cumulatve aspect of nnovaton dffuson (Feder et al., 1985). Gven the cost of technology dffuson and farmers costs n learnng the new technologes, t s useful to know whch future SSIG technologes are more lkely to be dscontnued (.e., abandoned faster) and what type of farmers dscontnue dfferent types of 109

125 SSIG technologes frst. Velanda et al. (2010) found that 75% of farmers used combned nformaton about precson farmng from Extenson and prvate sources (e.g., consultants, farm dealershps, meda and other farmers). Thus, from an extenson programmng perspectve, knowng the characterstcs of farmers who gve up earler on essentally effectve SSIG technologes would help agents and specalsts target educatonal modules that can help mprove technology retenton rates, as well as provde better nformaton about precson technologes. 4.2 Conceptual/Emprcal Framework Utlty Theory In ths study, we focus on farmers who at some pont n the past (t 0 ) have already adopted SSIG technologes and decde at some future perod (t) whether or not to abandon ts use. At tme t 0, gven that the farmer adopted SSIG, we can post that the expected utlty from adoptng SSIG technologes at tme t 0 (U s0 ) s greater than the expected utlty from not adoptng SSIG (U n0 ),.e. U 0 * = U s0 - U n0 > The utlty from not adoptng SSIG (U n0 ) can be nterpreted as the opportunty cost of SSIG adopton (.e., the foregone utlty from usng the next best alternatve technology). After adopton n tme t 0, the farmers then have a seres of one-perod decsons as to the best technology to use n subsequent perods. The farmer 13 By choosng to adopt SSIG, the producer self-selects nto a non-random sample of famers that n subsequent perods wll have to decde whether to contnue adoptng SSIG or dscontnue ts use. Ths characterstc of the sample suggests that the estmaton approaches should account for selecton bas (see p. 10 n the estmaton procedures secton). 110

126 abandons the SSIG technology at tme t f the expected utlty from SSIG (U st ) becomes less than the expected utlty from not usng SSIG (U nt ),.e., U st < U nt or U t * = U st - U nt < 0. The utlty from adoptng SSIG technology s assumed to be drven by monetary benefts (.e. drect monetary or proft benefts) and non-monetary benefts (.e., envronmental or cotton qualty benefts) such that Ust M st Est, where M st s monetary benefts and E st s non-monetary benefts. Further assume that M st s a functon of observable (possbly tme-varyng) varables (X) that represent farmer/farm characterstcs, and unobservable varables (ε) that affect the monetary benefts from SSIG, such that M X (where α s a parameter). The non-monetary benefts from SSIG ( E st ) s st st st not drectly and perfectly measured such that Est Est est where E st s the observable or perceved non-monetary beneft from the perspectve of the producer and est s an error term that represents the uncertan or unobserved factors affectng the non-monetary benefts of SSIG. The utlty from not adoptng SSIG (or the opportunty cost of SSIG) s also not perfectly observable such thatunt Unt st, where U nt s the perceved utlty derved from an alternatve technology and st represents uncertanty n the perceved beneft from not adoptng SSIG. The term st can also be consdered as unobserved mprovements n alternatve technologes that evolve through tme. Based on the defntons above, we can represent the decson to abandon SSIG technology as follows: (1) U* X E U u 0, st st st st 111

127 where ust st est st. Equaton (1) mples that observed farm/farmer characterstcs, perceved non-monetary benefts of SSIG, and perceved opportunty costs of SSIG determnes whether to contnue the use of SSIG t years after adopton. In partcular, hgher perceved non-monetary benefts would make t more lkely to contnue the use of SSIG, whle hgher perceved opportunty cost reduces the probablty that an SSIG adopter would contnue the use of the technology. More unobserved mprovements n the alternatve technology would also lead to lower probablty of contnued SSIG use. Equaton (1) then serves as a good pont of departure for the emprcal specfcaton n ths study. Gven the constant perod-by-perod revew of the expected utlty from SSIG, the queston of abandonment probablty n each perod essentally becomes a queston of tme to abandonment. Note that the latent varable U* n each perod s not observable, but our data allow us to observe the perod from adopton untl the data was collected (2009) and then whether or not SSIG was abandoned at ths pont n tme (2009). Hence, duraton tme n the context of our study s the perod from SSIG adopton untl abandonment or, n the case of farmers who have not abandoned at the tme of data collecton, the perod from adopton through when the data was collected (2009). 14 We can consder ths perod of use as our dependent varable n the emprcal specfcaton, and varables that represent the other terms n (1) as possble ndependent varables (.e., can then be tested. X st, E st, U st ) where ts hypotheszed effects 14 In the latter case, the farmers have not yet abandoned SSIG at the tme of data collecton (2009). The tme of abandonment s unknown (although t may occur n the future) and the data s effectvely rght-censored. Emprcal approaches for ths type of duraton data should take ths n to account (see p. 10 n the estmaton procedures secton). 112

128 4.2.2 Duraton Analyss In lght of the expected utlty framework descrbed above (and the avalable data on rghtcensored data on abandonment perod), duraton analyss (or duraton models) s an approprate emprcal framework to examne factors affectng the perod of use for SSIG technologes (.e., from tme of adopton to ther abandonment). In our duraton model, we defne the hazard functon h(t) as the probablty that a farmer wll abandon the SSIG technology at tme t, condtonal on hm/her beng a SSIG user up to that pont n tme (t). The hazard functon can then be expressed as follows: (2) f() t ht (), St () where f() t s the probablty densty functon of abandonng the technology at tme t, and St () s the survval functon,.e., the probablty that someone wll use SSIG technologes up to at least tme t. In estmatng duraton models, the densty functon f() t s used for uncensored observatons, for whch we observe ther actual tme of abandonment, and the survval functon St () s used for censored observatons, for whch we only observe that SSIG technologes have been used at least up to tme t. The hazard functon can be thought of as the contnuous tme verson of a sequence of condtonal probabltes (n ths case, condtonal probabltes of abandonment) (Burton et al., 2003). The cumulatve dstrbutons, e.g., the survvor and hazard functons, are equvalent ways of expressng the dstrbuton of the perod untl abandonment. Note that for an ndvdual farmer, 1 - St () s gves the probablty that an ndvdual wll have abandoned 113

129 SSIG by tme t, but f one consders a populaton of SSIG adopters, t wll also represent the expected abandonment rate through that populaton, that s, the share of the populaton that has abandoned the technology. To analyze explanatory varables that affect the tme from SSIG adopton untl abandonment, the hazard functon needs to be specfed to allow observable varables to affect the hazard functon. The most common way of expressng such a hazard functon s as follows: (3) h( t, X,, ) h0 ( t, ) g( X, ), where h (, ) 0 t denotes the baselne hazard whch s ndependent of X (.e., t represents the hazard for the ndvdual under standard condtons, say X = 0), X s a vector of observable varables, β s a vector of parameters assocated wth the observable varables, θ s a vector of parameters assocated wth the baselne hazard, and the functon gx (, ) acts multplcatvely on the baselne hazard. The most wdely-used and convenent specfcaton of the functon gx (, ) s: (4) g( X, ) exp( x ), whch ensures non-negatvty of gx (, ) wthout mposng restrctons on β. Ths also makes the functon easer to estmate because t becomes lnear n logarthms. Once gx (, ) s specfed, the baselne hazard functon h (, ) 0 t also needs to be specfed so that the hazard functon n (3) follows a partcular parametrc shape (.e., a parametrc approach). One of the more popular functonal forms used for the baselne hazard s h ( t, p ) = 0 pt p 1 where p s the shape parameter to be estmated from the data. Ths specfcaton of the 114

130 baselne hazard and the specfcaton gx (, ) n (3), results n the Webull model/dstrbuton for the hazard functon, as follows: (5) p 1 h( t, X,, ) pt exp( X ). If p > 1 there s ncreasng duraton dependence, whle p < 1 means there s decreasng duraton dependence. In the context of ths study, p > 1 suggests that the lkelhood of a farmer abandonng SSIG wll ncrease the longer he has used SSIG (or alternatvely t becomes less lkely that the farmer wll contnue to use SSIG f he has already used t for a long tme). The reverse holds for p < 1. When p = 1, the hazard functon would then be consdered the exponental model/dstrbuton: (6) h( t, X,, ) exp( X ). Hence, the exponental form s nested wthn the Webull model for the hazard functon. Other commonly used parametrc functonal forms to specfy the hazard functon are lognormal, loglogstc, and Gompertz model. 15 In emprcal applcatons, a best-fttng dstrbuton based on the data s chosen usng tests or crtera (.e. Akake Informaton Crtera, below n estmaton procedures secton). 16 The fully parametrc specfcaton of the hazard functon n (5) and (6) that allows for explanatory varables to affect the hazard rate s commonly called a proportonal hazard (PH) 15 The detaled specfcatons for these dstrbutons are not dscussed here n detal n the sprt of concseness. But the nterested reader can refer to Cleves et al (2008) for the specfc detals on each functonal form. 16 Note that when a partcular parametrc dstrbuton s used to specfy the hazard functon ths s called the parametrc approach. The success of the parametrc approach greatly depends on how correctly we specfed the dstrbuton based on the data. A sem-parametrc approach, called the Cox proportonal hazard model, could also be an alternatve where the baselne hazard s left unspecfed. The beneft of ths sem-parametrc approach s that no assumptons are made about the shape of the hazard functon. However, the sem-parametrc approach s less effcent than the parametrc approach snce the Cox proportonal hazard model gnores what happens to the explanatory varables when no falures occur (or abandonments n our case). Also, to the best of our knowledge, there has been no approach yet that controls for selecton ssues for the Cox proportonal hazard model. 115

131 model. In the PH model, a postve coeffcent assocated wth the explanatory varables means that an ncrease n the explanatory varable ncreases the hazard rate (.e., n our case, ncreases the probablty of abandonment or decreases the lkelhood of contnued SSIG use). An alternatve to the PH model s the Accelerated Falure Tme (AFT) model. In an AFT specfcaton, the Webull hazard functon can specfed as follows: (7) p 1 h( t, X,, ) pt exp( px ), and the exponental specfcaton (when p =1) can be specfed as: (8) h( t, X,, ) exp( X ). 17 In the AFT model, a postve coeffcent assocated wth an explanatory varable suggests that the watng tme for falure s ncreased. In the context of our study, a postve β coeffcent n the AFT model ndcates that an ncrease n the explanatory varable wll ncrease the watng tme untl abandonment s observed (.e., or alternatvely ncrease the lkelhood of contnung to use SSIG). In the present study, a PH specfcaton s used and nterpretatons of the duraton model coeffcents are based on ths specfcaton. 4.3 Data, Estmaton Procedures, and Emprcal Specfcaton Data Descrpton Our data for ths study were collected from a 2009 survey sent to cotton farmers n 12 Southeastern states: Alabama, Arkansas, Florda, Georga, Lousana, Msssspp, Mssour, North Carolna, South Carolna, Tennessee, Texas and Vrgna. Ths survey was developed 17 The nterested reader s agan referred to Cleves et al. (2008) for the detaled functonal forms of the other dstrbutons (e.g., lognormal and loglogstc) n an AFT specfcaton. 116

132 to query cotton producers about ther atttudes toward and use of precson farmng technologes (.e., SSIG and VRT). Followng Dllman s (1978) general mal survey procedures, the questonnare, a postage-pad return envelope, and a cover letter explanng the purpose of the survey were sent to each producer. The ntal malng of the questonnare was on February 20, 2009, and a remnder post card was sent two weeks later on March 5, A follow-up malng to producers not respondng to prevous nqures was conducted three weeks later on March 27, The second malng ncluded a letter ndcatng the mportance of the survey, the questonnare, and a postage pad return envelope. A malng lst of 14,089 potental cotton producers for the marketng year was furnshed by the Cotton Board n Memphs, Tennessee. Among responses receved, 1981 were counted as vald, and thus used n our study. We only have responses regardng the duraton of use of ste specfc nformaton gatherng (SSIG) technologes, but not for the varable rate nput practces. The survey conveys nformaton about a) the physcal characterstcs of the farm (e.g., locaton, acres); b) demographcs (e.g., ncome, educaton, experence); c) nput management decsons (e.g., fertlzers, rrgaton, dranage); d) sources of nformaton (dealers, unversty extenson); and e) farmers perceptons about envronmental and cotton qualty ssues. An adopter of at least one SSIG technology s dentfed as everyone who assesses hs wthn feld yeld varablty ether wth yeld montors, sol samplng, maps, aeral photography, or combnatons of the above technologes. In ths context, the duraton of nterest s the length of tme t takes a farmer to dscontnue a SSIG technology. Among the SSIG technologes, 117

133 sol samplng and maps are beng used for a longer perod (see Table 2), possbly because farmers had suffcent tme to evaluate benefts and costs (Walton et al., 2008). For the varable of nterest -- the expected duraton -- we utlzed questons 19 and 20 from the survey (See Fgure 4.2). We only consdered data for whch the length of use dd not exceed 30 years, snce precson agrculture was ntroduced n the early 80s Estmaton Procedures Duraton models allow correctons for censorng and duraton dependence. Censorng and partcularly rght censorng s a problem of ncomplete observatons for the farmers who have not experenced the event of nterest by the end of the observaton perod (.e., abandonment n our case). We can only observe the actual tme of SSIG use (or duraton of use) for those who abandoned SSIG technologes pror to or durng the 2009 crop year, whch s the year for whch we have avalable survey data (see footnote 14). Thus, the survval tme s not completely determned, because there are farmers who stll use the technology but may abandon n the future. For these ndvduals, the statstcal process s to rght censor the duraton at the end of the observaton perod (Burton et al, 2003). In ths case, we assume that the probablty of remanng SSIG users s greater than the censored value (.e., the tme of survey). Standard models of duraton data are bult on the assumpton that everyone wll eventually fal, or n our case abandon (Jones, 2006). As for the ssue of duraton dependence, t occurs when the rsk of a farmer abandonng the technology depends on how long s/he has been n the non-abandonment stage, a case where the standard 118

134 logt/probt models fal to account (Botelho et al., 2008, Beck et al., 1998). Agan, duraton analyss s more approprate n ths case. Sample selecton s an addtonal problem we may encounter, when dealng wth the survey data we have. Sample selecton problems occur when observatons selected are not ndependent of the outcome varable, causng based nferences (Berk, 1983). In our case, selecton problem arses because our full sample ncludes farmers who adopted n the past, and not the entre populaton of cotton producers at the moment of abandonment. Ths s a self-selected sample n tself, because farmers made ths choce based on ther awareness for precson technologes and other factors (See footnote 1). Moreover, producers may not be aware of all the technology aspects and those aware are not lkely to represent a random sample of populaton (McBrde and Daberkow, 2003). Thus, factors that affect duraton of SSIG technologes mght also affect the farmer s decson to abandon SSIG technologes. To address ths problem, prevous lterature has typcally employed dummy varables of sol type, weather, clmate, avalablty of nformaton, etc. to represent locaton factors that affect adopton of precson agrculture (Khanna, 2001, Daberkow and McBrde, 2003). Gven the potental ssues wth regards to rght-censorng, duraton dependence, and selecton problems, a duraton model that accounts for sample selecton s the estmaton procedure of choce n ths study. To more formally present ths estmaton approach, we follow Boehmke et al. (2006), where we frst defne Y as a contnuous varable 18 that follows densty f(y ). The outcome depends on a set of covarates, by lettng y =exp(-x β)ε. The selecton process leads us to observe Y only for the observatons for whch the bnary 18 Our data only provde nteger values, although duraton n years could be contnuous. However other studes (Boehmke et al., 2006, Dad et al., 2004, Martns et al., 2010) have essentally used nteger values as well. 119

135 censorng varable c takes on the value of 1 (abandon) and d s an ndcator of rghtcensorng. Thus, the probablty of uncensored observatons s Pr(Y =y, c =1), whereas for censored s Pr(c =0). Combnng the two probabltes yelds (9) Pr( Y, c) n 1 (Pr( c 1 Y y ) f ( y )) c 1 (Pr( c 0)) c 0 To calculate the above probabltes, Gumbel (1960) developed a bvarate exponental model wth the followng jont and cumulatve densty functons: (10) f ( x, y) exp( x y)(1 a(2exp( x) 1)(2exp( y) 1)) F( x, y) (1 exp( x))(1 exp( y))(1 aexp( x y)) The model can be wrtten as a functon of systematc and stochastc components as c * =exp(w,γ)u, where c =0 f c * 1or c =1 f c * 1 Thus the probablty that an observaton s censored becomes Pr(exp( w ) 1) and u F( 1 exp( w )) (1 exp( exp( w )). Usng the bvarate exponental dstrbuton above, the condtonal cumulatve densty s: (11) F ( x Y y) 1 exp( x)(1 a(2exp( y) 1)(exp( x) 1)) By substtutng c * =exp(w,γ)u and y =exp(-x β)ε and defnng λ 1 =exp(-w,γ) and λ 2 =exp(x β), the cdf n (11) can be rewrtten as: (12) F u, y ) 1 exp( u )(1 a(2exp( y ) 1)(exp( u ) 1)) ( The jont probablty that an observaton selects n and has duraton greater than the rght censorng pont, y 0 s calculated as: (13) Pr( y y, u 0 exp( y 1 exp( w )) 1 F( y )[1 (1 exp( y 2 ) F(1 ) F(1, y ))(1 exp( ))] 1 0, )

136 Combnng uncensored observatons wth an observed falure tme, uncensored observatons that are rght censored, and censored observatons yelds the exponental lkelhood functon: (14) ln(,,, p X, W, Y, c, d) c (1 d ln(1 (1 2exp( y ))(1 exp( )))] c d [ y n 1 1 ln(1 (1 exp( y )[ y ln( ) ))(1 exp( )))] (1 c )[ln(1 exp( ))] where d s an ndcator for whether an observed duraton s rght-censored or not. The Webull lkelhood wth rght censorng s derved by repeatng the same steps as wth the exponental above, and substtutng ths nto the bvarate exponental cdf: p p (15) F( x, y) (1 exp( x ))(1 exp( y))(1 exp( x y)) To get the followng: (16) ln(,,, p X, W, Y, c, d) c (1 d ln( p)ln( ) ( p 1)ln( Y ) ( Y) 2 ln(1 (1 exp( Y ) 2 (1 c )[ln(1 exp( ))] 1 p 2 n 1 2 )(1 exp( )))] )[ ln(1 (2 exp( Y ) p 1 ] c d [ ( Y ) p 2 p 1 ) 1) exp( ) 1)) where β, γ, α are parameters to be estmated; p s the Webull dstrbuton shape parameter, when the error term ε follows an exponental dstrbuton; X s the vector of explanatory varables; W s the Webull dstrbuton, Y s the duraton of SSIG technologes use; c s a censorng bnary varable, and exp( w ) and exp( ) , 121

137 4.3.3 Emprcal Specfcaton We assume that the condtonal probablty of duraton, gven the tme nvarant regressors X, the tme varyng regressors Z (t ), and errors θ s: (17) f ( t ) Z ( t ) b X ( t ) c, where t denotes the duraton of a partcular ste-specfc nformaton gatherng technology s for an ndvdual farmer. Last, we assume that X and Z (t ) are exogenously determned and observable, whle θ s unobserved. In (17), perceptons about non-monetary benefts (.e., n the utlty theory dscusson above) and opportunty costs of SSIG (.e., theory dscusson) are ncluded as part of the vector of tme-nvarant regressors. U st n the utlty Due to lack of a suffcent number of observatons for all ndvdual technologes, we grouped them n 4 categores as follows: MONITORS nclude the yeld montors wth GPS and yeld montors wthout GPS, SAMPLING conssts of grd sol samplng and zone sol samplng, PHOTOS reflect aeral photos and satellte mages, and MAPS account for sol survey maps, COTMAN plant mappng, and dgtzed mappng (See Fgure 4.1). Ths groupng would account for cases of farmers who may have abandoned yeld montors wthout GPS, by addng a GPS to ther yeld montor 19. Therefore, ths study consders the perod of use of the aforementoned groups of technologes as the dependent varable of nterest n our models. For our duraton analyss, we specfcally focus on the duraton of use of SAMPLING because t has been the most wdely adopted precson farmng technology, for whch farmers E st 19 The survey does not provde nformaton on whether farmers, who dscontnued a specfc type of technology, later adopted another precson technology. 122

138 had suffcent tme to evaluate ts benefts and costs (Walton et al., 2008). Along wth varable rate P and K applcaton, sol testng has been the most offered servce snce the ntroducton of precson agrculture technologes. Then we compare our results wth the duraton of use of MONITORS, whch s the most recent SSIG technology that became commercally vable n Fertlzer management through the sol samplng method s recommended when there s no hstory of lvestock or manure nfluence on the feld, and when yeld maps or other sources of spatal nformaton show consstency from one layer to another. On the other hand, grd samplng s preferred when felds wth dfferent crop hstores have been merged nto one, or lvestock, heavy manure applcaton and rrgaton have taken place (Ferguson Hergert, undated). We decded on the explanatory varables X and Z (t ) to be ncluded n the specfcaton based on the utlty theory presented above (See Secton 4.2.1) and also on prevous lterature (Walton et al., 2008, Roberts et al., 2004, Velanda et al., 2010). The utlty theory dscusses the mportance of observable characterstcs that nfluence the perodby-perod decson to whether or not abandon the use of SSIG technology. Hence, we nclude farmer and farm characterstcs that could potentally nfluence the perod of use of SSIG technologes. Wth regards to the farmer characterstcs ncluded n our emprcal model, we frst consder human captal varables such as age, years of experence and educaton level. These factors have prevously been consdered as mportant determnants n the theory of technology adopton (Khanna et al., 2001, Feder et al., 1985). Younger farmers have both longer lfe and plannng horzons (PLAN) n whch they can make changes, and are lkely to 123

139 nvest on precson technologes over a longer perod of tme. On the contrary, there are farmers who nvest more on mnmzng the tme spent on farmng rather than maxmze ther farm s profts (Nehrng, Fernandez-Cornejo, and Banker, 2002). Hence the expected sgn of PLAN coeffcent s ndetermnate. Increased farmng experence may ndcate better sklls on how to manage the feld, thus the sgn of EXPERIEN could be expected as negatve (longer duraton). Experenced farmers may be more reluctant to nvest n new technologes, but those who have already adopted wll more lkely use t for a longer perod of tme. Regardng the years of formal educaton (EDUC), we would expect the more educated farmers to abandon SSIG technologes slower, due to ther human captal that recognzes the benefts of precson farmng. Moreover, hgher levels of educaton ndcate ncreased analytcal ablty of managng the massve amount of data generated by precson agrculture (Batte, Jones, and Schntkey 1990). Hence, the expected sgn of the varable EDUC would be negatve, as well. From a dfferent aspect, more educated farmers would be more lkely to experment and contnuously adopt mproved technologes. Hence, they tend to abandon older technologes faster n order to adopt newer technologes or advanced versons of the same technology (e.g., ncorporate GPS n the same SSIG technology). Furthermore, snce most of these technologes are nterrelated wth computer systems, farmers who use computer or handheld devces n ther farm management (COMPUTER) wll lkewse preserve the precson farmng for a longer perod of tme, thus we would expect negatve correlaton wth the hazard rate. The effect of percentage of farm ncome (INCOME) s dffcult to be determned a pror. Farmers who are more dependent on farmng may be more wllng to nvest ther 124

140 fnancal resources on precson technologes, thus abandon the technologes at a slower rate. In a dfferent perspectve, a large percentage of ncome comng from farmng may ndcate a more rsk-averse behavor. Hence, a farmer may abandon the technology faster f t does not yeld the expected benefts. Indcator varables that represent whether farmers partcpate n agrcultural easement programs (AG_EASE) and whether farmers have access to unversty publcatons and extenson (PUBLICAT) are also ncluded n the emprcal specfcaton as part of the group of farmer characterstcs that can potentally affect duraton of SSIG use. Exposure to unversty publcatons would lkely lead farmers to adopt as well as use technologes for a longer perod (Hall et al., 2003, Velanda et al., 2010). Those farmers who partcpate n easement programs wll also more lkely acknowledge the envronmental benefts of precson farmng apart from the potentally ncreased proftablty from adopton. Hence, t s reasonable to expect that they would be more eager to rely on these technologes for a longer perod of tme. Farmer perceptons about the spatal yeld varablty of ther felds, perceptons about the envronmental and qualty benefts of SSIG, and opnons about the future proftablty/mportance of precson technologes are addtonal farmer characterstcs ncluded n the emprcal specfcaton. Perceptons about spatal yeld varablty (SYVAR) may be another factor affectng duraton of SSIG use. If farmers realze or perceve lack of yeld varablty n ther felds, they wll more lkely dscontnue the use of SSIG technology more rapdly. In order to calculate the perceved Spatal Yeld Varablty (SYVAR) varable, we utlzed the spatal varablty formula used n Larson and Roberts (2004): 125

141 (18) SYVAR Y Y Y Y Y Y ( LOW AVG ) 0.33 ( MID AVG ) 0.33 ( HIGH AVG ), where Y LOW s the estmate for the yeld of the least productve porton of feld, Y AVG s the estmated average yeld for the typcal feld, Y HIGH s the estmated yeld for the most productve porton, and Y MID =3* Y AVG - Y LOW Y HIGH. We, then, used the SYVAR and Y AVG, n order to create the coeffcent of spatal yeld varablty (SYCV ) statstc based on the followng formula: (19) SYCV 0.5 SYVAR 100, Y avg where 0.5 SYVAR s the standard devaton of spatal yeld varablty estmated usng (18). Prevous studes (Walton et al., 2008, Roberts et al., 2003) have ponted out the mportance of perceptons about precson farmng on technology adopton and technology abandonment. Hence, varables that represent these perceptons are ncluded n the emprcal specfcaton. Farmers, who perceve that precson agrculture wll be proftable (PROFIT) and mportant (IMPORTA) n the future wll more lkely utlze technologes for a longer tme, thus abandon at a slower rate. Therefore, the sgn of PROFIT and IMPORTA would be expected to be negatve under the expectaton of future payoffs (Naper et al., 2000, Roberts et al., 2004). Note that the PROFIT dummy varable can also be nterpreted as a crude proxy for the opportunty costs of SSIG (.e., U st n the utlty theory dscusson) because f one vews precson farmng to be proftable n the future t suggests that he/she vews the opportunty costs of precson farmng to be low (.e., the expected utlty or benefts from usng alternatve technologes are perceved to be low). However, cauton must be taken n nterpretng the PROFIT varable ths way because t refers to the proftablty of precson 126

142 technologes n general (not just SSIG n partcular), and also t asks about future proftablty rather than the proftablty percepton durng the t perods when the producer decdes whether or not to abandon the technology. As explaned n the utlty theory secton, non-monetary benefts may also play a role n the decson to whether or not to contnue use of SSIG technologes. Burton et al. (2003) also found that farmers atttude towards the envronment affects the adopton decsons, and lkewse the length of use. Hence, dummy varables that reflect producer opnons about the potental envronmental and qualty benefts of SSIG are ncluded n the emprcal specfcaton. Producers who perceved mprovements n cotton qualty (QUALITY) and envronmental benefts (ENVIRON) through the use of PF wll more lkely retan the technology for a longer term. Farm characterstcs ncluded n our specfcaton are farm sze and average cotton yelds (n the past crop year). Large farm sze (ACRES) s assocated wth easer nvestment n new technologes as well as economes of scale. Thus, we expect hgher adopton rates n ths case (Larkn et al., 2005). Ths mples that a farmer wll less lkely abandon the technology fast. Lkewse, farmers whose average yelds are hgh (YIELDS) wll more lkely adopt (Banerjee et al., 2008) and utlze the technology for a longer perod of tme. Farmers who apply manure n ther felds are relatvely more concerned about water polluton that would be caused by the excessve use of chemcal fertlzers. Thus they would be more wllng to adopt precson technologes that are envronmentally frendly (Torbett et al., 2007). Hence, we would expect MANURE to have a postve nfluence both on adopton as well as duraton. 127

143 To evaluate the degree of correlaton between the covarates, we checked for collnearty dagnostcs. The values of the Varance Inflaton Factors (VIF) are all very small (below 4), so the ncluson of the above varables n our estmaton does not seem to be worrsome n terms of multcollnearty. Table 4.1 contans a descrpton of the varables along wth ther hypotheszed sgns and the descrptve statstcs of the sample. Note that the hypotheszed sgns n the table reflect the mpact on SSIG duraton and not the hazard rate (whch, n ths case, would be the opposte). 4.4 Results and Dscusson Nonparametrc Analyss: Kaplan-Meer Curves Before presentng the results of our duraton model estmaton, t s mportant to dscuss some results that gve a broad overvew about the abandonment behavor of farmers n our sample. Asde from the summary statstcs that provde the average abandonment perods of farmers n the survey (See Table 2), the non-parametrc Kaplan-Meer curve would be another method to show general trends n the abandonment behavor of producers n the sample. The nonparametrc Kaplan-Meer method (1958), the lfe table method, and the Nelson-Aalen method (1978), do not make assumptons about the dstrbuton of falure tme and how covarates shft the survval experence (Stevenson, 2009). Moreover, they assume that censorng s ndependent of survval tme. In ths study, we use the Kaplan-Meer (1958) estmator snce t s the most often used non-parametrc method n prevous duraton analyss 128

144 studes. The Kaplan-Meer approach non-parametrcally estmates the probablty of survvng past tme t. The Kaplan-Meer estmates of the survval functons for the fve dfferent groups of SSIG technologes are depcted n Fgures 3 through 6, respectvely. The horzontal axs reflects the number of years that passed from the date the farmer started usng a partcular technology to the tme of abandonment. The tme nterval goes from 1 to 16 years for yeld montors and from 1 to 30 years for sol samplng, whch s the longest duraton perod taken nto consderaton (we excluded observatons for whch farmers reported sol samplng use greater than 30 years). Note that the startng year s 1 nstead of 0, snce the sample contans only SSIG adopters. The vertcal axs represents the estmated probablty of SSIG contnuaton for a hypothetcal cohort (and not the actual probablty of survval). All technologes slopes except from the yeld montors are relatvely flat, ndcatng that the speed of abandonment was not rapd. People seem to abandon yeld montors faster compared to sol samplng. The frst drop n the montors survval functon s observed at year 5, and abandonment becomes even more frequent after ths. On the other hand, 25% wll abandon samplng technologes n 20 years, whereas 25% wll qut montors n less than 10 years of use. Ths mples that farmers seem to be pleased wth samplng, and montors users ether abandoned precson farmng completely or swtched to a dfferent technology. A smlar stuaton to sol samplng holds for the other two SSIG technologes -- PHOTOS and MAPS -- whch also tend to be more lkely abandoned after 20 years of use. 129

145 4.4.2 Parametrc Duraton Analyss Tables 3 and 4 present the results of the estmated Webull model wth sample selecton. We focus on SAMPLING and MONITORS only. The dependent varable s the length of use of each SSIG technology, and s measured n years. It should be noted that each equaton has been estmated ndependently. Postve values of the parameters have a postve mpact on the hazard, thus the tme untl falure (n our case SSIG abandonment) s shorter. We frst needed to determne the most approprate parametrc functonal form for the baselne hazard functon. The Akake Informaton Crteron (AIC) s utlzed n ths study to fnd the functonal form that best fts our data. The dstrbutons taken nto consderaton were the Webull, the Exponental, the lognormal, the loglogstc, and the Gompertz (see Table 3). For MONITORS, the AIC yelds the smallest value for the Webull dstrbuton. Thus, the Webull functonal form s preferred. For SAMPLING, the AICs for Webull and the other dstrbutons almost concded. For consstency, we stll proceeded wth the Webull model for the SAMPLING duraton model because the dvergence n the AICs above was small. Thus, n ths study we only focus on the estmates of Webull model wth sample selecton 20. Results of the Wald test ndcate that the null hypothess that all slope coeffcents equal to zero can be rejected at less than a 0.01 sgnfcance level. The next step s to address the selecton problems nherent n our abandonment data. As suggested n Secton 4.3.2, prevous studes typcally use locatonal factors that affect 20 The Cox sem-parametrc model does not facltate control of selectvty ssues. Thus t was not used n our analyss, snce we beleve that sample selecton s a problem n our case. The results from Cox regressons are consstent wth Webull regressons wthout selecton (see tables 3 and 4), meanng that the bas arsng from unobserved heterogenety or msspecfed baselne hazard may not be sgnfcant. 130

146 technology adopton to control for selecton ssues n a frst-stage probt model. Hence, we control for the: a) July humdty (HUMIDITY), snce n cotton, heavy humdty could spol producton and reduce qualty, and b) July temperature (TEMPERA) (Walton et al., 2008), snce hgh temperature s a prmary controller for rapd cotton growth. However, excessve temperatures have an adverse effect on cotton development. These varables were collected from the USDA/ERS webste (2004 ERS County Typology), and were consdered to be exogenous and beyond farmers control. Parameter estmates of the selecton equatons are also presented n Tables 3 and 4 (labeled Selecton ). Most of the coeffcents n the selecton equatons n both the SAMPLING and MONITORS duraton models are statstcally sgnfcant (except for temperature n the MONITORS equaton). Cotton farmers facng less favorable envronmental condtons (.e., lower temperatures and/or hgher humdty) wll more lkely adopt precson farmng technologes. Hgher temperatures and low humdty would make for deal cotton growng condtons, thus farmers n low temperature and hgh humdty countes have more ncentves to use technologes that would beneft them. On the bass of the log-lkelhood statstc, the Webull model wth sample selecton provdes a better ft to the data for both sol samplng and yeld montors. Ths mples that sample selecton represents unobserved characterstcs that nfluence the condtonal probablty of the length of use and the probablty of beng n the sample, whch are not taken nto account n the explanatory varables (Boehmke et al. 2006). By accountng for selecton n sol samplng the constant term ncreases from n the ndependent Webull model to n the Webull wth selecton. Lkewse for yeld montors, t ncreases from 131

147 n the ndependent Webull model to n the Webull model wth selecton. Thus, the Webull model wth sample selecton ndcate a larger hazard rate and hence shorter tmes untl abandonment for both SSIG technologes. The postve duraton dependence (p= 1.371) for sol samplng mples that the rsk of dscontnung sol samplng ncreases over tme at a decreasng rate, 1<p<2. Lkewse, the postve duraton dependence for yeld montors (p= 1.952) mples that the rsk of dscontnung yeld montors ncreases over tme at decreasng rate 1<p<2. These results are consstent wth the non-parametrc analyss (Kaplan- Meer curves) that ndcated faster rate of falure (.e., abandonment) for yeld montors compared to sol samplng. Parameters wth dfferent sgns among the two models ndcate possble endogenety for the respectve varables. In the sol samplng model (SAMPLING), the varable ACRES s statstcally sgnfcant and postvely affects the hazard of SSIG technology s length of use (Table 3). A large farm sze may postvely affect technology adopton, but we have no evdence that suggests that t lengthens the use of sol samplng. A possble explanaton for the estmated sgn of the farm sze varable could be that larger farmers may not have the manageral tme to conduct precson sol samplng, thus have a hgher probablty of abandonment (Walton et al., 2008). Other statstcally sgnfcant varables n the SAMPLING model are EXPERIEN and PLAN. Both had a negatve mpact on the proportonal hazard (.e., less lkely to abandon). Farmers wth longer plannng horzons tend to utlze the samplng technology longer. For technologes requrng long-term nvestments, longer plannng horzons are usually related wth younger farmers who have a longer tme frame n whch to gan benefts (Lapar, 1999). 132

148 Smlarly, farmers wth more experence wll more lkely use the technology for a longer perod. Ths could be nterpreted that often experenced farmers report that they plan on farmng for a long tme n the future. In Table 4, the IMPORTA, EDUC, EXPERIEN and INCOME varables have a negatve mpact on the yeld montors hazard (MONITORS). Ths means that farmers who beleve that precson farmng wll be mportant n the future, are more experenced, have attaned a hgher level of educaton, and farmers whose ncome comes manly from agrculture wll most lkely use the yeld montors for a longer perod of tme. More educated farmers possess the human captal to recognze the benefts of precson farmng, and thus wll more lkely use t for a longer perod. Smlarly to sol samplng, the years of farmng experence EXPERIEN have a negatve effect on hazard, whch mples that more experenced farmers wll more lkely use yeld montors for a longer perod of tme. We would expect that farmers who are more dependent on farm ncome wll more lkely use sol samplng for a longer perod. We attrbute ths fndng to farmers wth ncome manly from farmng sources beng more lkely to be more rsk averse, thus contnue usng a technology for whch they made a sgnfcant nvestment. In Secton 4.3.3, we post that PROFIT can be consdered a crude measure of farmer percepton about the opportunty cost of SSIG. Farmers that thnk precson technologes would be proftable n the future are the ones who beleve that the opportunty costs of SSIG would be low (and hence contnue adoptng SSIG). But ths does not seem to be the case here. As we cautoned n Secton 4.3.3, t could be that the PROFIT varable n ths case s a very crude proxy such that t does not capture the opportunty cost of SSIG per se snce t 133

149 asks about the proftablty of precson technologes n general (not just SSIG) and also pertans to future proftablty rather than the t perods where one needs to decde whether or not to abandon the technology. However, PROFIT does not seem to sgnfcantly nfluence the duraton of nether sol samplng nor yeld montors. Lkewse, perceved mprovements n envronmental and cotton qualty do not seem to sgnfcantly affect the length of ether sol samplng or yeld montors. Ths s consstent wth the profle of farmers n our sample. Our survey respondents conssted of only 3.3% of farmers who valued envronmental benefts more than proft, whereas 62.5% of farmers n the sample were more proft orented and the rest ranked envronment and proft equally. Our regressons (from Chapter 3 of ths dssertaton) ndcates that farmers who perceved that precson farmng would not be mportant n the future, acqured nformaton about precson technologes from Unversty publcatons, and were more educated, would more lkely adopt based on monetary reasons. On the contrary, farmers who partcpated n agrcultural easement programs, expected PF to be mportant n the future and experenced mprovements n envronmental qualty, would more lkely be envronmentally motvated, that s adopt PF mostly because of ts potental envronmental benefts. 134

150 4.5 Conclusons In ths study, we examned the factors determnng the length of use of selected cotton precson farmng technologes. A duraton analyss that accounts for selectvty bas s used to nvestgate the mpact of dfferent varables on the speed of abandonment of precson technologes for cotton farmers n the Southeastern US. We focus on sol samplng, whch s consdered the most wdely used SSIG technology, and we compared our fndngs wth yeld montors, whch are the most recently ntroduced precson technologes. The estmated Webull model for sol samplng suggests that farm sze, experence, and farm plannng are mportant determnants n the duraton of sol samplng usage. Cotton producers wth larger farms tend to use sol samplng technologes for a shorter perod of tme. On the other hand, farmers wth longer plannng horzons, and more experence wll more lkely use sol samplng for a longer duraton. We also fnd that the length of use of yeld montors tend to be longer for farmers who beleve that precson farmng wll be mportant n the future, are more educated and more experenced, and whose ncome comes manly from farmng. Our results could provde nsghts about desgnng polces that would ncrease the duraton of use of these technologes (or slow down ther abandonment). Regardng the yeld montors, when formulatng educatonal programs, t may be more benefcal to target less experenced farmers, those wth lower educatonal attanment, those do not beleve that PF wll be mportant n the future, and farmers whose ncome s not manly dependent on farmng. Farmers wth these characterstcs are the ones less lkely to use SSIG technologes and targetng these producers may encourage them to contnue use of these technologes. In 135

151 addton, further study of the behavor of these producers may be worthwhle n the future to further understand why they abandon SSIG technologes faster. Although ths study provdes some nferences about the factors nfluencng how long cotton producers use precson technologes, there are stll several nterestng topcs that can be pursued n the future. An extenson of ths study would be to explore potental substtuton effects among dfferent SSIG technologes,.e., study whether farmers who used a specfc technology n the survey conducted n 2005, stll contnue ts use n the survey conducted n 2009 or qut and/or swtched to another SSIG technology. Unfortunately, we do not have adequate nformaton from our sample snce the 2005 survey respondents do not necessarly concde wth the 2009 respondents. On the other hand, we observed complementartes wthn technologes, and ths justfed the groupng of technologes we made n ths study (e.g., use of grd along wth zone sol samplng). Another ssue to be nvestgated s whether there has been a change n start-up costs of the technologes that nduced farmers to dscontnue specfc technologes faster (e.g., whether specfc precson technologes have advanced such that start-up costs are low, or whether certan technologes were promoted more). Last, there s always the effect of obsolescence rsk, as t s the case wth all technologes. The constant creaton and promoton of software advancements may create ncompatblty ssues wth older precson hardware, whch has not been mproved dramatcally snce the ntroducton of precson farmng. Hence the farmer may end up buyng hardware that wll be obsolete sooner because of software ncompatbltes, and even f he wants to use that technology, the newer software may no longer be supported by the hardware company from whch he purchased t. A lmtaton of our study s that we cannot 136

152 account for jont usage of technologes, for example a smultaneous applcaton of montors along wth aeral photos. Moreover, our results are based on a relatvely small sample of cotton farmers located n a specfc geographc regon. For technologes that farmers abandon faster, Extenson can focus ther tranng programs that would ncrease awareness about the potental envronmental and fnancal benefts of precson farmng. Lkewse, other outlets (e.g., dealers, crop consultants, meda, etc.) could make nformaton about precson technologes more avalable, snce hgher accessblty may reflect slower abandonment rate. At ths pont, we have no evdence whether the dstance from the markets affects speed of abandonment decsons. 137

153 Table 4.1 Descrptve Statstcs of the Varables Varables Descrpton Hypotheszed Sgn Mean Std. Dev Adopton Duraton HUMIDITY Mean Hours of Humdty n July, / TEMPERA Mean Temperature for July, / MONITORS Number of years farmer used yeld montors (wth and/or w/o GPS) SAMPLING Number of years farmer used sol samplng (grd and/or zone) ACRES Total acreage of dry land (sum of rented and owned acres) for the 2007 crop season YIELDS Estmate of average cotton lnt yeld per acre for 2007 crop season EDUC Number of Years of Formal Educaton excludng kndergarten AGE Age of the farm operator (as of the 2009 survey year) +/ EXPERIEN Number of Years farmng SYVAR Perceved Spatal Yeld Varablty (lbs. lnt/acre) + IMPORTA Farmer perceved that precson farmng would be mportant n fve years from now (yes=1; no=0) PROFIT Farmer perceved that precson farmng would be proftable to use n the future (yes=1; no=0) INCOME Percentage (%) of 2007 taxable household ncome comng only from farmng sources +/ COMPUTER Farmer uses computer for farm management (yes=1; no=0) MANURE Farmer appled manure on hs/her felds (yes=1; no=0) PUBLICAT Farmer used Unversty publcatons to obtan precson farmng nformaton (yes=1; no=0) AG EASE The farm currently has agrcultural easement (yes=1; no or don t know=0)

154 Table 4.1 contnued QUALITY Farmer experenced mprovement n cotton qualty (yes=1; no or don t know=0) PLAN Years to plan farmng n the future +/ ENVIRON Farmer perceved mprovement n envronmental qualty through the PF use (yes=1; no=0)

155 Table 4.2 Summary Statstcs for the two categores of farmers (Abandon vs Contnue) Varables Obs Mean (years) Mn (years) Max (years) Std. Devaton MONITORS Abandon Contnue SAMPLING Abandon Contnue PHOTOS Abandon Contnue MAPS Abandon Contnue Table 4.3 Akake Informaton Crteron for dfferent dstrbutons Dstrbuton AIC Samplng AIC Montors Exponental Webull (w/o selecton) Loglogstc Lognormal Gompertz

156 Table 4.4 Webull Model Estmates for SAMPLING (N=977) Samplng Varables β (Robust S.E) β (Robust S.E) β (S.E) Selecton INTERCEPT * (2.028) HUMIDITY *** (0.024) TEMPERA ** (0.024) Webull (wth Selecton) Naïve Duraton Models [1] Webull [2] Cox INTERCEPT (1.343) *** (3.400) --- EDUC (0.059) ** (0.167) ** (0.159) EXPERIEN ** (0.010) (0.050) (0.051) PUBLICAT (0.278) (0.720) (0.712) SYVAR (0.007) (0.007) (0.006) COMPUTER (0.254) (1.064) (0.944) IMPORTAN (0.525) *** (1.580) *** (2.413) PROFIT (0.299) (0.827) (0.797) INCOME (0.004) (0.018) (0.015) AG_EASE (0.361) (1.653) (1.434) 141

157 Table 4.4 contnued PLAN * (0.095) (0.306) (0.306) MANURE (0.263) *** (0.746) *** (0.687) ACRES ** (0.0001) (0.0006) (0.0006) YIELDS (0.0003) (0.001) (0.001) QUALITY (0.304) (1.291) (1.073) ENVIRON (0.254) (1.662) (1.340) rho (error correlaton) (0.069) ln_p *** (0.061) (0.272) --- p (duraton dependence) *** (0.084) (0.394) --- N U (Uncensored) Log Lkelhood Wald *** *** *** Note: *** p<0.01, ** p<0.05, * p<

158 Table 4.5 Webull Model Estmates for MONITORS (N=919) Montors Varables β (Robust S.E) β (Robust S.E) β (Robust S.E) Selecton INTERCEPT (2.524) HUMIDITY *** (0.002) TEMPERA (0.031) Webull (wth Selecton) Naïve Duraton Models [1] Webull [2] Cox INTERCEPT ( 2.786) (11.197) --- EDUC * (0.127) ** (0.206) * (0.196) EXPERIEN ** (0.031) * (0.132) (0.109) PUBLICAT (0.812) (2.195) (2.369) SYVAR (0.019) COMPUTER (0.604) (2.296) (1.342) IMPORTAN ** (1.301) (9.643) (omtted) PROFIT (0.684) (3.398) (2.657) INCOME ** (0.010) * (0.047) (0.059) AG_EASE (1.493) (2.757) (0.868) PLAN (0.267) N/A N/A 143

159 Table 4.5 contnued MANURE (0.674) (1.894) (2.134) ACRES (0.0002) (0.0003) (0.0002) YIELDS (0.0006) (0.003) (0.002) QUALITY (0.926) (4.136) (3.366) ENVIRON (0.675) (5.089) (3.732) rho (Error Correlaton) (0.124) ln_p *** (0.104) (0.708) --- p (Duraton Dependence) *** (0.204) (2.256) --- N U (Uncensored) Log Lkelhood Wald *** *** *** Note: *** p<0.01, ** p<0.05, * p<

160 Yeld Montors-Wth GPS 250 Yeld Montors-No GPS Grd Sol Samplng 200 Zone Sol Samplng Aeral Photos Satellte Images Sol survey maps 50 0 Montors Samplng Photos MAPS COTMAN Plant Mappng Dgtzed Mappng Fgure 4.1: Number of farmers usng dfferent SSIG technologes 145

161 Fgure 4.2A: Length of use of each SSIG Technology Fgure 4.2B: Abandonment of each SSIG Technology 146

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