Precision Agriculture Technology Adoption for Cotton Production

Transcription

1 Precson Agrculture Technology Adopton for Cotton Producton Paxton, Kenneth W.; Mshra, Ashok K.; Chntawar, Sachn; Larson, James A.; Roberts, Roland K.; Englsh, Burton C.; Lambert, Dayton M.; Marra, Mchele C.; Larkn, Sherry L.; Reeves, Jeanne M.; Martn, Steven W. Correspondence to: Ashok K. Mshra Assocate Professor Department of Agrcultural Economcs and Agrbusness Lousana State Unversty AgCenter 211 Ag. Admn. Bldg. Baton Rouge, LA Tel: Fax: E-mal: Selected Paper prepared for presentaton at the Southern Agrcultural Economcs Assocaton Annual Meetng, Orlando, FL, February 6-9, 2010 Copyrght 2010 by Paxton et al.,. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes.

2 Precson Agrculture Technology Adopton for Cotton Producton Kenneth W. Paxton, Professor Lousana State Unversty AgCenter Baton Rouge, LA Phone: E-mal: Sachn Chntawar Graduate Research Assstant Lousana State Unversty AgCenter Baton Rouge, LA Phone: E-mal: Roland K. Roberts, Professor The Unversty of Tennessee 308B Morgan Hall, 2621 Morgan Crcle Knoxvlle, TN Phone: E-mal: Dayton M. Lambert, Assstant Professor The Unversty of Tennessee 321C Morgan Hall, 2621 Morgan Crcle Knoxvlle, TN Phone: Emal: Sherry L. Larkn, Assocate Professor Unversty of Florda, P.O. Box Ganesvlle, FL Phone: (352) Emal: Ashok K. Mshra, Assocate Professor Lousana State Unversty AgCenter Baton Rouge, LA Phone: E-mal: James A. Larson, Assocate Professor The Unversty of Tennessee 308G Morgan Hall, 2621 Morgan Crcle Knoxvlle, TN Phone: Emal: Burton C. Englsh, Professor The Unversty of Tennessee 308C Morgan Hall, 2621 Morgan Crcle Knoxvlle, TN Phone: Mchele C. Marra, Professor, North Carolna State Unversty Box 8109 Ralegh, NC Phone: Emal: Jeanne M. Reeves, Drector, Agrcultural Research, Cotton Incorporated 6399 Weston Parkway Cary, NC Phone: E-mal: Steven W. Martn, Assocate Professor and Extenson Economst Delta Research and Extenson Center Msssspp State Unversty Stonevlle, MS Phone: E-mal:

3 Precson Agrculture Technology Adopton for Cotton Producton Abstract Many studes on the adopton of precson technologes have generally used logt models to explan the adopton behavor of ndvduals. Ths study nvestgates factors affectng the number of specfc types of precson agrculture technologes adopted by cotton farmers. Partcular attenton s gven to the nfluence of spatal yeld varablty on the number of precson farmng technologes adopted, usng a Count data estmaton procedure and farm-level data. Results ndcate that farmers wth more wthn-feld yeld varablty adopted a larger number of precson agrculture technologes. Younger and better educated producers and the number of precson agrculture technologes were sgnfcantly correlated. Fnally, farmers usng computers for management decsons also adopted a larger number of precson agrculture technologes. Keywords: precson technologes, Posson, Negatve Bnomal, count-data method, GIS, educaton, cotton

4 Precson Agrculture Technology Adopton for Cotton Producton Introducton Precson agrculture (PA) or precson farmng (PF) generally refers to a system that assesses wthn-feld varablty n both sol and crops. Informaton gathered n these assessments s then used to develop ste specfc management practces that optmze crop producton. A wde varety of technologes are used n collectng ste specfc data and deployng the ste specfc management practces. Some of these technologes have been commercally avalable snce the late 1980s and ncludes yeld montorng/mappng, varable rate applcaton, and a host of other spatal management technologes. The adopton of precson agrculture technologes s somewhat dfferent from many other technologes ntroduced n agrcultural producton. A major dfference s the fact that precson agrculture technologes consst of a complex set of technologes, each wth a specfc purpose (Lowenberg-DeBoer 1998, Khanna, Epouhe, and Hornbaker 1999, Khanna 2001). Therefore, farmers may adopt one or more technologes and evaluate those before adoptng addtonal technologes (Byerlee and de Polanco 1986; Leathers and Smale 1991). The most recent studes have examned the adopton of several specfc technologes (Daberkow et al. 2002; Daberkow and McBrde 2000; Fountas et al. 2003; Grffn et al. 2004). The adopton of PA technology n cotton producton has been somewhat dfferent than n gran crops, because cotton yeld montors were not avalable untl the late 1990s whle yeld montors for combnes were ntroduced n the late 1980s (Grffn et al., 2004). The unavalablty of yeld montors nfluenced cotton producers to use grd sol samplng or other sol mappng technques as an entry pont for adoptng precson agrculture technology (Walton et al., 2008).

5 Snce the ntroducton of the cotton yeld montor, several studes have examned the adopton of precson agrculture technologes n cotton producton (Roberts et al. 2004; Banerjee et al 2008; Larson et al. 2008; Walton et al. 2008). Most of these studes estmate the lkelhood of adoptng utlzng a logt model. Ths study s unque n determnng the nfluence of varous farm, operator, and locaton attrbutes on the number of precson farmng technologes adopted by farmers. Partcular attenton s gven to the role of spatal yeld varablty. The technologes evaluated nclude yeld mappng, varable rate applcaton, yeld montorng, grd samplng, and others. Because precson agrculture conssts of a set of technologes that may be adopted sequentally, one must go beyond the smple bnomal logt to understand past growth and to predct future growth n adopton. Ths nformaton s crtcal to (1) the development of educatonal programs addressng precson agrculture, and (2) antcpaton of future demand by cotton producers, crop consultants, dealershps, and equpment manufacturers. Lterature Revew Precson agrculture (PA) s an approach to re-organze the total system of agrculture producton towards one that uses fewer nputs, s more effcent, and s sustanable. The early lterature provdes broad agreement that proftablty and/or nput cost reducton from new nnovaton or technology adopton plays a key role n the extent and rate of technology adopton (Feder et al. 1985; Rogers 1995). In 1997, Whelan et al. concluded that the desre to respond to producton varablty on a fne-scale has become the goal of precson agrculture. Swnton and Lowenberg-DeBorer (1998) conclude that because precson farmng practces are ste-specfc, ther proftablty potental s also ste-specfc. In a follow-up study, Lowenberg-DeBorer (1999) showed that ste-specfc farmng, to whch most of PA technologes s geared, could reduce

6 whole-feld yeld varablty. Fnally, Zhang et al. (2002), whle assessng the role of precson agrculture throughout the world, concluded that the success of precson agrculture technologes wll have to be measured by economc and envronmental gans. It has long been recognzed that the advancement of the PA approach depends on the emergence and convergence of several technologes (Shbusawa 1998), ncludng geographc nformaton systems (GIS), Global Postonng System (GPS), n-feld remote sensng, automatc controls, mnaturzed computer components, moble computng, and telecommuncatons (Gbbons 2000). Erckson and Lowenberg-DeBorer (2000) conclude that yeld montors, GPS recevers, and GIS mappng are useful to mantan precse records of the locaton, planted acres, and yeld of crops. In 2002, Cox revewed developments n nformaton technology that are contrbutng to global mprovements n crop and lvestock producton. In a case study of sx leadng early adopters of precson agrculture technologes, Batte and Arnholt (2003) pont out that precson farmng has the potental to help farmers mprove nput allocaton decsons. The specfc role of GIS and GPS n precson farmng was explored by Nemeny et al. (2003) and they concluded that GIS maps created by complex computng backgrounds are essental n makng effectve agrotechnologcal decsons. Whle both the potental for PA to mprove sustanablty (fscal and envronmental) and the need for contnual advancements n a sute of technology are crtcal factors to the ultmate success of ths farmng approach, the behavor of ndvdual farmers n adoptng new technologes s also of paramount mportance. To that end, Roberts et al. (2000) found that the proftablty of precson farmng as assessed by cotton farmers wth varyng degrees of adoptng a sute of technologes depends mmensely on the degree of spatal varablty of sol attrbutes and yeld response. In the case of precson agrculture technologes, record keepng

7 and documentaton functons nherent n PA systems may help farmers ncrease yelds and hence profts. In studyng adopton of PA technologes n the U.S., Daberkow and McBrde (2003) noted that farm sze, human captal, rsk preference, off-farm labor supply, locaton, and tenure are some of the factors that affect adopton. Wth respect to human captal n partcular, Daberkow and McBrde (2003) also noted that human captal could take the form of famlarty wth related technologes. The authors show that farmers who kept computerzed fnancal records are more lkely to be assocated wth PA technologes. In our study we advance the lterature related to PA by focusng on spatal yeld varablty and how that farm characterstc relates to the number of PA technologes adopted. The focus on explanng the number of PA technologes s unque to the adopton lterature and s deally suted to the case study, whch uses a sample of cotton farmers n the Southern U.S. Ths s because the producton of cotton can employ a suffcent number of technologes to support the emprcal analyss. Emprcal Approach In some cases, such as number of patents (Cncera, 1997), vsts to doctors (Cameron and Trved 2009), and number of foregn domestc nvestment frms (Gopnath and Vasavada, 1999) the count s the varable of ultmate nterest. In other cases, such as medcal expendtures (Cameron and Trved, 2009) and the varable of ultmate nterest s the contnuous varable. In our case, the data are the count of the number of precson technologes adopted by each cotton farmer. Cameron and Trved (2006) pont out that n such cases count data models are approprate. To analyze the effects of varous farm, operator, and regonal characterstcs on the number of precson technologes (such as yeld montors wth GPS, yeld montors wthout GPS, sol samplng grd, sol samplng zone, aeral photos, satellte mages, sol survey maps, and handheld

8 GPS/PDAs), we use the method employed n patent lterature (e.g., Hausman et al., (1984); Cameron and Trved (1986) ; Cncera,(1997)). In our study, the number of precson technologes adopted by a cotton farmer s a functon of a set of ndependent varables ( X ): ln( λ ) = α + β X (1) 0 where λ s the number of precson technologes adopted by farm operator. Data on the number of precson technologes used consttute a nonnegatve, nteger-valued, random varable. Several authors (e.g., Hausman et al.; Cameron and Trved; Cncera) have presented and dscussed count data models as an alternatve method to the classcal lnear model. 1 In the count data models, the prmary varables of nterest are event counts. We consder the Posson and the negatve bnomal dstrbutons, whch are wthn the lnear exponental famly, for analyzng the number of precson technologes used by farm operators. We wll brefly descrbe the Posson and negatve bnomal models below. Posson Model Let Y be the observed event count (number of precson technologes used) for the th farm operator. The Y are assumed to be ndependent and have a Posson dstrbuton. The parameters β depend on a set of explanatory varables ( X ), whch are the factors affectng the number of precson technologes used by a farm operator. where ( ) λ ( β ) E Y X = = exp X, =1... N, (2) λ s the ntensty-of-rate parameter when referrng to the Poson dstrbuton as [ ] The probablty densty functon for the Posson model s: p λ. 1 See Wnkelmann and Zmmermann for a recent overvew of count data models.

9 Y e Pr ( ) ( ) λ λ Y = y = f Y =, Y = 0,1,2..., Y! (3) The frst two moments of p[ λ ] are EY [ ] = λ and V[ Y] = λ ; the Posson specfcaton assumes equal mean and varance. Overdsperson has a qualtatvely smlar consequence to falure of the homoscedastcty assumpton n the lnear regresson model. For lnear models wth E [ Y X] = Xβ, the estmated coeffcents β are nterpreted as the effect of a one unt change n regressors on the condtonal mean. The Negatve Bnomal Model A drawback to the Posson specfcaton s the assumpton of equal mean and varance of Y, a testable hypothess. In the negatve bnomal model, whch s more flexble than the Posson, λ s assumed to follow a gamma dstrbuton wth parameters ( γ, δ ), where γ = ( X β) exp and δ s common across frms. The gamma dstrbuton for λ s ntegrated by parts to obtan a negatve bnomal dstrbuton wth parameters (, ) 1 Pr Y e λ f λ dλ λ Y ( ) = ( ) Y 0 ( ) ( ) ( ) γ δ. Specfcally, Γ γ + λ δ = 1 Γ γ Γ λ + 1 δ + 1 γ ( + δ ) λ. (4) The above framework suggests that the number of precson technologes used by a cotton producer s expressed as a functon of varous farm, operator, household, and regonal characterstcs. Specfcally, λ = exp( β X ) where X s a set of explanatory varables such as age and educaton of the operator, farmng experence, farm sze, yeld ndex, and state dummes. A subsequent queston then arses as to whch model (Posson or negatve bnomal) s more

10 approprate. Cameron and Trved (2009) proposed a number of tests for the over- or under dsperson n the Posson regresson model. They test the underlyng assumpton of meanvarance equalty, where the null hypothess, ( ) * hypothess, H ( Y ) μ α g( μ ) H : Var 1 Y = μs compared wth the alternatve : Var 1 = +. The functon g(.) s a specfed functon that maps from R + to R +. Tests for overdsperson or underdsperson are tests of whether α = 0. 2 We use a smlar test n our study. The margnal effect of a change n an ndependent varable on the condtonal mean of the dependent varable was calculated usng the STAT software. Cameron and Trved (2009, pp ) provde a detaled explanaton and nterpretaton of margnal effects of the Posson and negatve bnomal models. Specfcally, Cameron and Trved (2009) pont out that the margnal effect of the th varable (ME )= E( y x) * β. The choce of attrbutes assocated wth the number of precson technologes used s guded by human captal theory, farm and producton characterstcs, and other adopton models. Nelson and Phelps (1980), Khald (1979), and Woznak (1989) use educaton as a measure of human captal to reflect the ablty to nnovate (ether technology or nsurance). In addton, other factors affectng the adopton of precson farmng technologes are drven by the lterature (Feder et al., 1985; Rogers, 1995; Deberkow and McBrde, 2003). In our model, we use fnancal, locaton, and the physcal attrbutes of the farm frm that may also nfluence proftablty and, ultmately the adopton of precson agrculture technologes (Deberkow and McBrde, 2003). 2 Tests for over dsperson and under dsperson are mportant Falure has consequences smlar to those of heteroskedastcty n Lnear Regresson Model Cameron and Trved (1990).

11 Data Data for ths analyss was obtaned from a survey of cotton producers n the Southeastern part of the Unted States (Alabama, Arkansas, Florda, Georga, Lousana, Msssspp, Mssour, North Carolna, South Carolna, Tennessee, and Vrgna). The survey utlzed a questonnare to obtan nformaton about producer atttudes toward and use of precson agrculture technologes. Followng Dllman s (1978) general mal survey procedures, the questonnare, a postage-pad return envelope, and a cover letter were sent to each producer. A remnder post card was sent one week after the ntal malng. Three weeks later a second malng was sent to those not respondng to the orgnal malng and remnder. The malng lst of potental cotton producers for the crop year was obtaned from the Cotton Board n Memphs, Tennessee (Skorupa, 2004). The survey was maled n January and February of Of the 12,245 questonnares maled, 18 were returned undelverable, 184 respondents were no longer cotton producers, and 1,215 respondents provded useable nformaton for a response rate of 10 percent. Fgure 1 provdes nformaton of the dstrbuton of the number of precson technologes adopted by cotton farmers n About 39 percent of farmers reported usng one or more precson technologes; addtonally about 9 percent of cotton farmers have used 3 or more precson technologes. Table 1 provdes defntons and summary statstcs for the varables used n emprcal model. The average cotton farmer n the Southern Unted States s 49 years of age and has 14 years of schoolng. An average cotton farmer has about 26 years of farmng experence and receves 73 percent of household ncome from farmng. The modal cotton precson farmer used one precson technology (Fgure 1) whle average precson technology use was 0.85 (Table 1). Addtonally, 54 percent of cotton farmers thought precson technologes would be proftable n

12 the near future. About 18 percent of the farms were located n Georga or North Carolna compared to 13 and 12 percent n Msssspp and Alabama. Arkansas was used as the benchmark state n the regresson. Results Frst the choce of Posson and negatve bnomal model was tested and results ndcated that the null hypothess of equal mean and varance was rejected. The test statstcs (overdsperson) was sgnfcant at the 1 percent level (Table 2, last row). Therefore, Table 2 only presents the parameter estmates from the negatve bnomal model and ther margnal effects. The estmated model fts reasonably well as ndcated by the 70-pecent correlaton between observed and predcted values (Table 2). Results suggest that an addtonal year of age (OP_AGE) s assocated wth 2 percent fewer precson technologes adopted by farmers (Table 2, 3 rd column) 3. Ths fndng s consstent the adopton lterature (Feder et al., 1985; Daberkow and McBrde, 2003) and wth the hypothess that older farmers are less lkely to adopt new technologes because of a lower expected payoff from a shortened plannng horzon over whch the benefts can accumulate. Results suggest that educatonal attanment (OP_EDUC) postvely nfluences the number of precson technologes adopted (Table 2). One addtonal year of schoolng s assocated wth approxmately an 8 percent ncrease n the number of precson technologes adopted. A plausble explanaton s that many educated farmers are young and are often hypotheszed to be more wllng to nnovate and adopt new technologes that reduce tme spent 3 Cameron and Trved (2009) show that another way of nterpretng the margnal effect s to obtan exponentated coeffcents ( e β ), thus one addtonal year n age s assocated wth number of PA technologes decreasng by The Exponentated coeffcent apples to any Maxmum Lkelhood estmaton (see Cameron and Trved, 2009, page: ).

13 farmng (Mshra et al., 2002). In partcular, Mshra et al (2002) pont out that many young farmers are more educated and often have off-farm jobs. Our results are also consstent wth the fndngs of Daberkow and McBrde (2003) who nvestgated the mpact of educaton, n addton to other factors, on PA technology adopton. Mshra, El-Osta, and Johnson (1999) concluded that cash gran farms who kept computerzed fnancal records were more lkely to be successful. In a smlar ven, computer use for fnancal record keepng may be an ndcator of preferences toward usng nformaton technology tools for farm management. The margnal effect of COMPFARM 4 ndcates that farmers who use computers for farm management ncrease the number of PA technologes by 43 percent. The 2005 Southern cotton survey quered farmers on farm plannng. In partcular, farmers were asked f they planned to expand the sze of ther operaton or acqure addtonal assets to generate addtonal ncome (FARMPLAN), and 72 percent responded postvely. Cotton farmers who planned to expand ther operatons decreased the number of precson technologes adopted by 21 percent. A possble explanaton s that farmers plannng to expand ther operatons may use ther resources (partcularly ncome and labor) to purchase addtonal land rather than nvestng t n an addtonal PA technology. Future expectaton of ncreased profts through precson technologes (FUTURE_ADOPT) has a postve mpact on the number of precson technologes adopted by cotton farmers. The margnal effect for ths varable suggests that farmers who thought precson technologes would be proftable n the future ncreased the number of precson technologes adopted by 42 percent. 4 Potental endogenety of ths varable was test usng the Hausman test. Based on the statstcs the null hypothess of endogenety was rejected.

14 As the share of farm ncome n total household ncome (F_INCOME) ncreases, the number of precson technologes adopted by farmers ncreases by only 0.2 percent. Ths result s consstent wth the tradeoff between on-farm and off-farm labor requrements. A lower percentage of household ncome earned from farmng mples more household labor s employed off the farm, and less household labor s avalable to evaluate and mplement new technologes. An mportant fndng s that spatal yeld varablty 5 (LN_SPYVAR) has a postve mpact on the number of PA technologes adopted by cotton farmers. The margnal effect ndcates that a 1 percent ncrease n spatal yeld varablty s assocated wth 7 percent ncrease n the number of precson technologes adopted by cotton farmers n the South. Fnally, locaton of the farm has an mportant role n the number of precson technologes adopted by cotton farmers. Cotton farmers n Msssspp and Mssour are lkely to use a hgher number of PA technologes when compared to farmers n the benchmark state of Arkansas (Table 2), whle cotton farmers n Florda are lkely to use fewer precson technologes compared to farmers n Arkansas. Conclusons Ths study examned the effects of varous farm, operator, and regonal characterstcs on the number of precson agrculture technologes adopted by cotton farmers n the southeast. A negatve bnomal count model was used to analyze data collected through a 2005 survey of cotton producers n the southeast Unted States. Ths study contrbutes to the lterature n two ways. Frst, ths study uses count data estmaton procedure to examne the mpact of varous factors on the number of precson agrculture technologes adopted by cotton farm operators. 5 We use Larson and Roberts (2004) method to calculate spatal varablty. The log of spatal yeld varablty s used to scale down the varable.

15 Second, t ncorporates a measure of wthn-feld yeld varablty as a factor nfluencng the number of technologes adopted. Results from ths study ndcate that the number of precson agrculture technologes employed by producers s postvely correlated wth the educatonal level of the producer and negatvely correlated wth the age of the operator. These results suggest that younger, better educated producers adopt a larger number of precson agrculture technologes. Farmers usng computers for management decsons also adopted a larger number of precson agrculture technologes. These results suggest that targetng these groups for educatonal programs would ncrease the probabltes of success for those programs. Results of ths analyss demonstrated that farmers wth more wthn-feld yeld varablty adopted a larger number of precson agrculture technologes. Wthn-feld yeld varablty has long been thought of as the prmary drver of precson agrculture adopton. Results of ths study confrm ths long held belef. Overall, the results obtaned here help dentfy groups of cotton producers that are more lkely to be responsve to precson agrculture technology educatonal programs. These results also dentfy those groups where educatonal programs may be used to expand precson agrculture technology adopton.

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18 Mshra,A. K., El-Osta, H., Johnson, J. D. Factors contrbutng to earnngs success of cash gran farms. J. Agrc. Appled Econ. 31(1999): Nemeny, M., P.A. Mesterhaz, Zs. Pecze, and Zs. Stepan. The Role of GIS and GPS n Precson Farmng. Computers & Electroncs n Agrculture 40(2003), Roberts, R.K., B.C. Englsh, J.A. Larson, R.L. Cochran, W.R. Goodman, S.L. Larkn, M.C. Marra, S.W. Martn, W.D. Shurley, and J.M. Reeves. Adopton of Ste-Specfc Informaton and Varable-Rate Technologes n Cotton Precson Farmng. J. Agr. and Appled Econ. 36(2004): Roberts, R.K., Englsh, B.C., Mahajanashett, S.B. Evaluatng the returns to varable rate ntrogen applcaton. Journal of Agrcultural and Appled Economcs 32 (1) 2000, Rogers, E.M. Dffuson of Innovatons. 4 th edton, Free Press, New York, Shbusawa, S. Precson Farmng and Terra-mechancs. Ffth ISTVS Asa-Pacfc Regonal Conference n Korea, October 20-22, Skorupa, B. Cotton Board, 871 Rdgeway Loop, Ste Memphs, TN, Swnton, S.M., Lowenberg-DeBoer, J., Evaluatng the proftablty of ste-specfc farmng. Journalof Producton Agrculture 11 (4) Walton, J.C., Lambert, D.M., Roberts, R.K., Larson, J. A., Englsh, B.C., Larkn, S.L., Martn, S.W., Marra, M.C., Paxton, K.W., and Reeves, J.M. Adopton and Abandonment of Precson Sol Samplng n Cotton Producton. J. Agr. and Resource Econ. 33(2008): Whelan, B.M., A.B. McBratney, B.C. Boydell. The Impact of Precson Agrculture. Proceedngs of the ABARE Outlook Conference, The Future of Croppng n NW NSW, Moore, UK July 1997, p. 5. Wnkelmann, R., and K. F. Zmmermann. Recent Development n Count Data Modellng: Theory and Applcaton. Journal of Economc Surveys, 9(1995):1-24. Zhang, N., Wang, M. and Wang, N. Precson agrculture a worldwde revew. Computers & Electroncs n Agrculture 36 (2002),

19 Fgure 1: Dstrbuton of number of precson technologes by Cotton Farmers n the Southern Unted States

20 Table 1: Defnton of varables and summary statstcs Varable Defnton Means (Std. dev) NUMTECH Number of precson technology adopted 0.85 (1.204) OP_AGE Age of farm operator (years) (11.275) F_EXPERIENCE Farmng experence (years) (11.443) OP_EDUC Formal educaton of farm operator (years) (2.196) COMPFARM =1 f farmer uses computer for farm management 0.58 (0.492) SHARE_RENTED Percentage of rented acres n total operated acres (33.772) FARMPLAN =1 f the farm operator s plannng to expand sze of the operaton or acqure assets to generate addtonal ncome 0.72 (0.446) FUTURE_ADOPT =1 f the farm operator thnks t would be proftable to use precson technologes n the future 0.54 (0.498) F_INCOME Percentage of farm ncome n total household ncome (27.814) LN_SP_YVAR Log Spatal yeld varablty (1.132) S_ALABAMA Dummy varable, =1 f state s Alabama 0.12 (0.321) S_NR_CAROLINA Dummy varable, =1 f state s North Carolna 0.18 (0.383) S_FLORIDA Dummy varable, =1 f state s Florda 0.02 (0.133) S_GEORGIA Dummy varable, =1 f state s Georga 0.18 (0.381) S_MISSISSIPPI Dummy varable, =1 f state s Msssspp 0.13 (0.339) S_LOUISIANA Dummy varable, =1 f state s Lousana 0.07 (0.258) S_SO_CAROLINA Dummy varable, =1 f state s South Carolna 0.06 (0.238) S_MISSOURI Dummy varable, =1 f state s Mssour 0.03 (0.181) S_TENNESSEE Dummy varable, =1 f state s Tennessee 0.09 (0.280) S_VIRGINA Dummy varable, =1 f state s Vrgna 0.03 (0.171) Sample 892 Source: 2005 Southern Precson Farmng Survey

21 Margnal effect 2 Table 2: Parameter estmates of factors affectng number of precson farmng tools by cotton farmers n the Southern U.S. Varable Negatve Bnomal Model Parameter Estmates 1 Intercept *** -- (0.685) OP_AGE ** ** (0.008) OP_EDUC 0.093*** 0.080*** (0.023) F_EXPERIENCE (0.008) COMPFARM 0.553*** 0.425*** (0.108) SHARE_RENTED (0.001) FARMPLAN ** ** (0.110) FUTURE_ADOPT 0.530*** 0.416*** (0.103) F_INCOME 0.002** 0.002** (0.001) LN_SPYVAR 0.078** 0.070** (0.037) S_ALABAMA (0.026) S_NR_CAROLINA (0.196) S_FLORIDA ** (0.513) S_GEORGIA (0.199) S_MISSISSIPPI 0.509*** 0.521*** (0.190) S_LOUISIANA (0.217) S_SO_CAROLINA (0.234) S_MISSOURI 0.472* 0.507** (0.269) S_TENNESSEE (0.220) S_VIRGINA (0.309) Wald ch Square *** Correlaton between observed and predcted Log-lkelhood Overdsperson test 33.20*** 1 Numbers n parentheses are standard errors. Sgnfcance at the 10%, 5%, and 1% are ndcated by sngle, double and trple astersks, respectvely. 2 The margnal s calculated on the sample mean. Usng STATA one can obtan the effect on the condtonal mean of y of a change n one of the regressors, say x j