An analysis of factors affecting the adoption of economic and productive data recording methods of Chilean farmers

Transcription

1 Cen. Inv. Agr. 37(2): research paper An analyss of factors affectng the adopton of economc and productve data recordng methods of Chlean farmers Alejandra Engler, 1 and Roger Toledo 2 1 Departamento de Economía Agrara, Facultad de Cencas Agraras, Unversdad de Talca, Caslla 747 Talca, Chle. 2 Departamento de Economía Agrara, Insttuto de Investgacones Agropecuaras INIA Qulamapu, Caslla 426 Chllán, Chle. Abstract A. Engler, and R. Toledo An analyss of factors affectng the adopton of economc and productve data recordng methods of Chlean farmers. Cen. Inv. Agr. 37(2): Integraton of the Chlean domestc economy nto nternatonal markets has created the need to ncorporate more technology, nformaton, and management tools and to generate better entrepreneural sklls at the farm level. These changes requre the development of strategc capabltes and farmer changes n atttude. The goal for farmers s to be more prepared for the decson-makng process and to have adequate evaluaton and control systems to face the complexty of the farm busness. The lterature suggests that usng management tools postvely correlates wth profts and concludes that management sklls are postvely related to farmers well-beng. Survey nformaton on 211 farmers from central and southern Chle was used to estmate a probt model where the dependent varable was record-keepng by farmers. The results show that the farmers educatonal level, age, membershp a Technologcal Transfer Group, land leasng, and the farmers own percepton of ther averson to rsk are statstcally sgnfcant varables n the model. The model goodness of ft s 0.41, and the model has good predctve power for groups of farmers. Key words: Record-keepng, management sklls, probablty models. Introducton Past decades, the Chlean agrcultural sector has evolved nto a more specalzed busness by ncreasng product and export value. In the last decade alone, the agrcultural PIB ncreased 1.3 tmes, whle the export value grew 2.7 tmes (ODEPA, 2009). The development of the sector has allowed the country to set the short-run objectve of becomng a member of the top ten Receved July 23, Accepted August 21, Correspondng author: mengler@utalca.cl agr-food exporters. However, ths goal demands developng a more compettve and nclusve agrculture along wth the agr-food supply (Barrera, 2005). To meet ths challenge, large and small farmers wll need to ncorporate more technology, nformaton, and management tools and to develop better entrepreneural sklls, all of whch mply developng strategc capabltes and a change of atttude (McElwee et al., 2006). Some of the requred sklls can be acheved by developng effectve management strateges, makng nformed decsons, and havng better evaluaton and control systems for the farm

2 102 cenca e nvestgacón agrara busness. Controllng economc and productve results of the agrcultural actvty leads to effcent busness management. El-Osta et al. (2007) even conclude that management sklls are postvely related to the well-beng of farmers. Moreover, there are studes that reveal that computer use on the farm s postvely correlated to proftablty, ndcatng that the use of nformaton n decson makng ncreases busness performance (Gelb et al. 2001). Nuthall (2004) concludes that even though he could not fnd a relatonshp between computer use and proftablty, computer use does allow farmers to methodologcally collect, enter, and nterpret data, all of whch has a synergstc effect on mental decsons. The prmary use of computers on the farm s record-keepng. Accordng to Batte (2005), 89.1% of a sample of farmers n Oho, USA ndcated that ther prmary use of computers was for keepng fnancal and economc records, and 76.7% thnk keepng records s one of the most mportant applcatons. Governmental and prvate nsttutons n Chle have been generatng mechansms to help farmers keep and analyze economc and productve nformaton. As a result of these ntatves, several Management Centers (MCs) have been created throughout the country snce the late 1990s to mplement economc record-keepng to support the decson-makng process of ther assocated farmers. Each MC was created to group farmers of the same geographc area, havng the same or dfferent agrcultural actvtes. The farmer s vsted by a professonal of the MC staff once a month to collect economc nformaton and to gve hm/her feedback on the monthly expense and ncome statements prepared by the MC. Each MC holds an annual workshop to analyze the fnal economc results of each crop. The dynamcs of the MCs have allowed farmers to () know the results of each of ther productve actvtes, () mprove effcency n aspects where they had hgher costs compared to the group, and () ncrease profts on the whole. MC managers generally report that farmers have ncreased profts snce they have been partcpatng n the MC (Managers of Cer Los Lagos and CEAgroChle, personal communcaton). After a decade, only a small porton of the potental farmer populaton s made up of MC clents or assocates. The need today s to develop and ncrease farmers management abltes, and a relevant concern s the capacty farmers have to process nformaton for decson makng. Keepng economc and fnancal records are the frst steps n promotng modern management tools n the farmng busness. Record-keepng s essental n buldng up management sklls and makng nformed decsons. The objectve of ths paper s twofold: frst, to dentfy how popular the practce of keepng dgtal economc and/or productve records s n the Chlean agrcultural sector, and second, to determne whch factors favor a hgher probablty of keepng such records. Understandng of these factors wll permt polcy makers to create ncentves to mprove farmers management abltes and wll allow MCs to focus ther marketng strategy on farmers who are more wllng to adopt advanced management practces. To address these objectves, we admnstered a comprehensve survey to a sample of farmers from central and southern Chle. The survey collected current management and recordkeepng practces utlzed by farmers, as well as farm and farmer characterstcs. A probt model was estmated to assess the probablty of mplementng a record-keepng system. Probablty models have been extensvely used n adopton lterature (technology, qualty measures, standards, and management tools) and are, therefore, a sutable model for our purposes (Ampansah, 1995; Hoag et al., 1999; Batte et al., 2005). Materals and methods Descrpton of sample and data collecton Factors that nfluence the use of computers on the farm or the use of data n the decson-

3 VOLUME 37 Nº2 MAY - AUGUST makng process have been analyzed n prevous lterature (Nuthall, 2004; Batte, 2005; Just et al., 2003). The man conclusons have been the relevance of both demographc varables and agrcultural system factors. Educaton, busness sze, off-farm ncome, nvestments, and regonal locaton have been found to be partcularly sgnfcant (Batte, 2005; Just et al., 2003). Byma and Tauer (2007) found that, along wth hgher operator educaton and larger farm sze, other factors contrbutng to ncreased farm operaton effcency were extended partcpaton n a farm management program and younger farmer age. Usng these results, our study was based on statstcal analyses of a survey of a random sample of 400 farmers from central and southern Chle, ncludng the Bo-Bo, La Araucanía, Los Lagos, and Los Ríos Regons (In 2008, the Los Lagos and Los Ros Regons started operatng as separate admnstratve areas. When the survey was conducted, both corresponded to the Los Lagos Regon; therefore, the Los Lagos Regon ncludes the two new regons n ths artcle). In-person surveys were conducted between May and August From the orgnal sample, only 358 were correctly completed and used n the study. The Bo-Bo, La Araucanía, and Los Lagos Regons turned out 136, 112, and 110 surveys, respectvely. For the study at hand, only farmers who keep dgtal records and farmers who do not keep any records were consdered. By usng ths selecton crteron, the sample was reduced to 211 observatons, n whch 159 farmers declared keepng dgtal records, and 52 dd not keep any records. The remanng 147 farmers who declared keepng records n notebooks were left out n order to generate a strong dfferentaton between farmers who use and do not use records n ther management. The crteron was selected usng the argument that the computer allows farmers to methodologcally collect, enter, and nterpret data (Nuthall, 2004). Furthermore, onfarm nformaton wll provde the farmer wth a decson-makng tool to evaluate and control hs/her busness (Amponsah, 1995). Agrculture s consdered a tradtonal occupaton n the area studed, although farmng systems vary throughout the zone. The most mportant actvtes are cereals, dary, and cattle farmng. The three regons accounted for approxmately 33% of the natonal agrcultural producton n 2008, ncludng 92% of mlk, 78% of wheat, and 57% of bovne meat n 2007 (ODEPA, 2009). Estmaton procedure The dependent varable was constructed as a bnary varable and assgned the value 1 for farmers who kept productve and/or economc records dgtal form and 0 for those farmers who dd not have records of any knd. Based on the approach of ratonal choce theory (Cha, 2001) n whch ndvduals are assumed to be purposve and ntentonal and n whch decsons and actons are shaped by ratonal preferences and constrants, we argue that keepng records for decson-makng s a choce related to farmer and farm system characterstcs. Followng ths approach, a farmer s lkely to adopt records f the utlty of adoptng, U, s greater than the utlty of not 1 adoptng, U, as shown n equaton 1: 0 (1) U U 1 0 = β Z 1 + ε 1 = β Z0 + ε0 where Z s a vector of farmer and farm system characterstcs assocated wth choosng 1 to keep records, Z 0 are characterstcs assocated wth not keepng them, β s a vector of parameters to be estmated, and ε 1 and ε0 are random error terms assumed to follow a normal dstrbuton. Therefore, the probablty of adopton can be expressed as P U > U ) = P( β Z β Z > ε ε ) o r ( ( y > 0) = P( ε > α X ), where P y states the dfferences n utlty, ε the dfference n error terms, and α X s the systematc component of the equaton that ncludes all farmerand farm-system characterstcs referred to as the ndex functon (Greene, 2003). The above-mentoned equaton s known as the la- tent regresson model where the varable y s not observable and can be lnked to y = 1 when y > 0. For P y = 1) = F( α X ), F s the (

4 104 cenca e nvestgacón agrara normal cumulatve dstrbuton functon that depends on α and X parameters and varables. Parameters of the model were estmat- ed by maxmum lkelhood estmators wth NLogt 4.0. Results and dscusson Data descrpton The dependent varable, dgtal record-keepng as explaned n the methodology, was a bnary varable whose value was 1 when the surveyed farmer kept productve and/or economc dgtal records and 0 when he/she dd not. Table 1 provdes a detaled explanaton of the ndependent varables used n the model. They were dvded nto the followng two groups: () farmer characterstcs that ncluded age (age), educatonal level (edu), own percepton of rsk-averson (relav), membershp n a Management Center (MC) and Technologcal Transfer Group (TTG); and () farm system characterstcs ncludng sze (sze), ncome sources (nc), and leased land (lease). A summary of the varable statstcs s presented n Table 2. It can be observed from the descrptve statstcs that 25% of the sample dd not keep records of any knd, meanng that 75% kept dgtal records of hs/ her actvtes. Was a dfference between the two groups regardng ther partcpaton n MCs and TTGs. In both cases, the proporton of farmers who partcpated n these ntatves was hgher n the dgtal record-keepers. Ths was expected because farmers assocated wth an MC are more nterested n usng nformaton and mprovng management sklls, or they at least consder management as a relevant busness factor. Technologcal Transfer Groups, whch were created to ncrease opportuntes for sharng knowledge and technology between farmers, were also expected to nfluence recordkeepng snce management tools are one of the topcs dscussed and analyzed n the group meetngs along wth technologcal topcs. The age and educaton level varables also exhbted dfferences between groups. The group Table 1. Descrpton of ndependent varables. Code Descrpton Type of varable lease TTG MC 1 = Farm uses leased land. 0 = otherwse 1 = Belongs to a Technologcal Transfer Group. 0 = otherwse 1 = Belongs to a Management Center 0 = otherwse Bnary varable Bnary varable Bnary varable Age Age of the producer Contnuous, measured n years Edu Categorcal varable that ndcates the level of educaton from 1 to 11, where 1 means no educaton and 11 means a graduate degree. Dscrete Relav Inc Categorcal varable that measures the farmers percepton of hs/her rskaverson. The categores are: 1 = the farmer s wllng to take more rsks than the mean only on a few occasons, 2 = the farmer s wllng to take more rsks than the mean on some occasons, and 3 = s generally wllng to take more rsks. Categorcal varable that ndcates the proporton of the total ncome that s derved from agrcultural actvtes: 1 = low proporton, 2 = close to 50%, 3 = hgh proporton, 4 = 100%. Dscrete Dscrete Sze Total hectares dedcated to the busness. Contnuous, measured n hectares

5 VOLUME 37 Nº2 MAY - AUGUST Table 2. Characterstcs of farmers, farm systems, and groups wth and wthout dgtal record-keepng. Varable Descrpton No record-keepng Dgtal record-keepng Number of farmers 52 25% % Farmer characterstcs Age Mean age 56 years 47 years Edu Category mean of educatonal level from 1 to 11 (hgher category mples hgher educatonal level). 5 8 Relav Percentage of farmers n a rsk-averson category. 1.: Wll rarely take more rsk than the mean: 54% 2. Wll take more rsk than the mean on some occasons: 31% 3. Wll generally take more rsk than the mean : 15% 1. Wll take more rsk than the mean: 35% 2. Wll take more rsk than the mean on some occasons: 41% 3. Wll generally take more rsk than the mean: 24% TTG MC Percentage of farmers partcpatng n a Technologcal Transfer Group. Percentage of farmers partcpatng n a Management Center. 12% 40% 8% 45% Farm system characterstcs Sze Inc Lease Mean sze of the productve system Percentage of farmers n each ncome source category. Percentage of farmers who lease land ha 533 ha 1. Low proporton derved from agrculture: 13% 2. Close to 50% derved from agrculture: 13% 3. Hgh proporton derved from agrculture: 23% 4. All ncome derved from agrculture: 50% 1. Low proporton derved from agrculture: 14% 2. Close to 50% derved from agrculture: 9% 3. Hgh proporton derved from agrculture: 36% 4. All ncome derved from agrculture: 41% 29% 50% of record-keepers tended to be younger and to have acheved a hgher educatonal level. In ths group, the mean educatonal category was 8 (techncal certfcaton from a professonal nsttute), whle n the non-record-keeper group, the mean category was 5 (hgh school completon). These results were consstent wth Batte s (2005) fndngs analyzng computer use by farmers n Oho, USA. When analyzng dfferences n the producton between groups, there was an evdent dfference n sze. Record-keepers tended to have larger farms wth a mean of 533 hectares, and non-record-keepers hectares. The ncome source dstrbuton was smlar n both groups, wth approxmately 75% of farmers reportng that ether most or all of ther ncome comes from agrcultural actvtes. However, n the non-record-keeper group, the percentage of farmers whose ncome was exclusvely derved from agrculture was hgher. On the other hand, the percentage of farmers who leased land was hgher n the record-keeper

6 106 cenca e nvestgacón agrara group. We argue that producers who lease land need to control better the results of each actvty n order to evaluate the convenence of future leasng. Fnally, another varable consdered n the study was the farmers own percepton of ther averson to rsk. The queston asked how often they were wllng to take more rsk compared to other farmers. Twenty-four percent of the record-keepers reported generally takng more rsks than other farmers, whereas only 15% of the non-record-keeper group made ths statement. Thus, on average, dgtal record-keepers declared themselves less rsk-averse. The descrptve statstcs reveal that there were sgnfcant dfferences between the two groups of farmers. The tendency showed record-keepers as younger, more educated, less rsk-averse, and more lkely to be leasng land. The probablty model n the next secton answers the queston of how relevant each of these factors was n determnng the use of records. Estmaton results Estmaton results are shown n Table 3. The coeffcents reported correspond to maxmum lkelhood estmators whch ndcate the greatest probablty of obtanng the observed value. The coeffcent sgns determne the drecton of the effect of the varables on the probablty that the farmer wll keep records. The model was estmated correctng for heteroscedastcty usng a varance functon for MC and nc. The dstrbuton of the varance functon was not sgnfcant for the other varables ncluded n the model. The model performance can be rated as slghtly strong because t presents an Estrella crteron value of goodness of ft of Estrella measures goodness of ft of bnary dependent varable regressons by usng a transformaton of the log-lkelhood functon. The measure s comparable to R 2 n terms of ts range and the underlyng relatonshp wth the test statstcs, and t s therefore a sutable measure for the estmated model. The nterpretaton of the nteror pont can be assmlated to the same range used for R 2, where a 0.25 value or lower s consdered weak, 0.5 to 0.74 strong, and 0.75 or hgher as very strong (Estrella, 1998). Moreover, by analyzng the predctve success of the model, t can be observed that t correctly predcted a dgtal record-keeper 92% of the tme, and a non record-keeper 50% of the tme, meanng the model had a hgh predctve power. The varables n the model that were sgnfcant ncluded educatonal level, age, membershp n a TTG, the producer s relatve averson to rsk, and f the farm system had any leased land. Prevous studes dentfed age and educatonal level as relevant factors when predctng the use of nformaton n decson makng and computer use (Batte, 2005: Amponsah, 1995). Accordng to our results, both varables were hghly sgnfcant wth less than 1% probablty of rejecton. As expected, when educatonal level ncreased, the probablty of keepng records was hgher. For the age varable, the coeffcent ndcates that older farmers were less lkely to adopt dgtal records, a fact consstent wth the feld observatons. In general, new generatons of farmers are more conscous of runnng the farm as a busness and of ncorporatng management tools n ther decsons. An nterestng feature of the model was that belongng to a TTG also nfluenced the sgnfcance factor, and these farmers were more lkely to keep dgtal records. In ths case, TTG usually deal wth technologcal ssues because ths s ther prmary objectve, but many of them have been ncorporatng the use of management tools as a technologcal topc n ther regular meetngs, a fact whch explans our results. On the contrary, belongng to an MC s not a sgnfcant varable. We expected that MCs would have motvated farmers to mprove management abltes and make them more wllng to adopt management tools. However, MCs keep records for ther members and so t may be that these farmers do not feel the need to adopt a record-keepng system. The estmaton result showed that are neutral n the probablty of keepng dgtal records, whch could be explaned by the two hypotheses offsettng each other. As seen n the descrptve statstcs, there

7 VOLUME 37 Nº2 MAY - AUGUST Table 3. Estmaton results of the probt model and goodness of ft measurements. Varable Coeffcent Standard error P-value Probablty Index functon Constant Lease TTG MC Age Edu Relav Inc Sze Varance functon MC <0.001 Inc <0.001 Log-lkelhood Restrcted log-lkelhood Estrella =1-(L/L0) -2L0/n 0.41 McFadden R Number of observatons a Legend: p<0.1;p<0.05; p<0.01. McFadden R2 s calculated R 1 (ln ˆ / ˆ McF = Lu l n Lr ) as, where Lˆ u s the unrestrcted log-lkelhood functon and Lˆ s the restrcted log-lkelhood functon. Count R2 s based on the table of observed and r predcted outcomes and s calculated as 2 R Count = 1/ N n where n represents the correct predctons for outcome j. jj j jj was a hgher partcpaton n MCs among the dgtal record-keeper group. Nevertheless, from the aforementoned results, t can be nferred that the possblty of sharng the experence of usng management tools, such as n the TTG, was an effectve way to ncrease management ablty among producers. The lease varable, whch ndcated whether the farmer had leased land and had to pay annual rent, was also sgnfcant and postve, meanng that producers who needed to pay rent were more lkely to adopt dgtal records of ther busness performance. Ths result s understandable n that farmers who lease land to ncrease ther busness were constantly evaluatng the convenence of rentng or not. Sze of the farm was expected to be relevant n record-keepng. Larger farms manage more resources and for ths reason t was expected that they would need more management tools to support decsons. Batte (2005) found that farm sze measured n terms of total sales was sgnfcant for computer use as a management tool. Contrary to our hypothess, sze was not sgnfcant. Ths result revealed that small and large farmers have the same potental to ncrease ther management ablty, and the relevant varable n ths case would be educatonal level and age, as already shown. Usng the same arguments, we would have expected that as long as the agrcultural actvtes represented a hgher porton of the total ncome of the farmer, the probablty of keepng records would ncrease, but the nc varable was not sgnfcant. Fnally, a varable was ncluded n the model to capture the mpact of the farmers percepton of ther own averson to rsk. The hypothess was that as farmers declared themselves more rsk-averse, they would be more lkely to adopt records. The varable was scaled n three levels where 1 represented more averson and 3 less averson. The results show that the varable was negatvely sgnfcant, thus our hypothess.

8 108 cenca e nvestgacón agrara Concluson The present study has provded strong evdence that the adopton of management tools, represented n ths case by dgtal economc and productve nformaton records, was affected by farmer and producton system characterstcs. Results were consstent wth the theory of farmers as ratonal because producers who lease land and beleve they are more rskaverse show a hgher probablty of keepng nformaton to make decsons. On the other hand, sze was not sgnfcant n the model and was not a relevant varable n the decson to keep nformaton. The possblty of sharng experence through TTGs ncreased the probablty of keepng dgtal records. Fnally, younger farmers wth hgher levels of educaton were more wllng to adopt dgtal records. Gven these results, t can be concluded that an effectve way to ncorporate management tools throughout Chlean agrculture s by drectng establshed groups of farmers to nto the use of record keepng tool. What s more, younger farmers should be targeted, snce they present a hgher rate of adopton of computer tools. Resumen A. Engler y R. Toledo Análss de los factores que afectan la adopcón de métodos regstros productvos y económcos entre productores chlenos. Cen. Inv. Agr. 37(2): La ntegracón de la economía chlena a los mercados nternaconales ha creado la necesdad de ncorporar más tecnología, nformacón, nuevas herramentas de gestón y generar nuevas habldades empresarales a nvel de predo, lo cual mplca el desarrollo de capacdades estratégcas y un cambo en la acttud por parte de los agrcultores. El objetvo para el agrcultor es estar mejor preparados para la toma de decsones y contar con sstemas de evaluacón y control adecuados a la complejdad del negoco agrícola. La lteratura sugere que el uso de herramentas de gestón tene una correlacón postva con los benefcos, lo que permte conclur que la capacdad de gestón ncrementa las posbldades de buenos resultados. A partr de una encuesta a 211 agrcultores de la zona centro y centro sur de Chle se estmó un modelo probt, donde la varable dependente corresponde a productores que mantenen regstros dgtales. Los resultados muestran que el nvel educaconal del productor, edad, pertenenca a un Grupo de Transferenca Tecnológca, arrendo de terra y la percepcón personal respecto a aversón al resgo son varables estadístcamente sgnfcatvas. La bondad de ajuste del modelo es de 0,41, y presenta un buen nvel de predccón, para agrcultores con dferente método de regstro. Palabras clave: Regstro de datos, capacdad de gestón, modelos probablístco. References Amponsah, W Computer adopton and use of nformaton servces by North Carolna commercal farmers. Journal of Agrcultural and Appled Economcs 27: Barrera, A La nueva nsttuconaldad públca almentara. In: Barrera, A., V. Venegas, T. Tomc, and H. Rojas (eds). Economía del crecmento y Nueva Agrcultura. LOM Edcones Ltda. Santago, Chle. 325 pp. Batte, M Changng computer use n agrculture: evdence of Oho. Computers and Electroncs n Agrculture 47: Byma, J., and L. Tauer Explorng the role of manageral ablty n determnng frm effcency. Paper presented at the Annual Meetng of the

9 VOLUME 37 Nº2 MAY - AUGUST Amercan Agrcultural Economc Assocaton. July 30, 31. Portland, Oregon, USA. Cha, S.K Choosng an Identty: A general model of preferences and belef formaton. The Unversty of Mchgan Press. Ann Arber. 336 pp. El Osta, H., A. Mshra and M. Morehart Determnants of economc well-beng among U.S. farmers operators households. Agrcultural Economcs 36: Estrella, A A new measure of ft for equatons wth dchotomous dependent varable. Journal of Busness and Economc Statstcs 16: Gelb, E.M., C. Parker, P. Wagner, and K. Rooskoph Why s the ICT adopton rate by farmers stll so low? In: Summary of EFITA 2001 Conference Questonnares and Symposum. Proceedngs of the European Conference of Informaton Technology n Agrculture. Greene, W.H Econometrc Analyss. 5th ed. Prentce Hall. Upper Saddle Rver, New Jersey, USA pp. Hoag, D., J. Ascough, and M. Fraser Farm computer adopton n the Great Plans. Journal of Agrculture and Appled Economcs 31: Just, R., S. Wolf, and D. Zlberman Prncples of rsk management servces relatons n agrculture. Agrcultural Systems 75: McElwee, G., A. Anderson and K. Vesala The strategc famer: a cheese producer wth cold feet? Journal of Busness Strategy 27: Nuthall, P Case studes of the nteractons of farm proftablty and the use of a farm computer. Computers and Electroncs n Agrculture 42: ODEPA Statstcal Databases. Avalable onlne at: Webste accessed March, 2009.

10