Can Farmers Receive Their Expected Seasonal Tomato Price in Ghana? A Probit Regression Analysis

Transcription

1 Sustanable Agrculture Research; Vol. 3, No. 2; 2014 ISSN X E-ISSN Publshed by Canadan Center of Scence and Educaton Can Farmers Receve Ther Expected Seasonal Tomato Prce n Ghana? A Probt Regresson Analyss Caleb Attoh 1, Edward Martey 2, G.T. M. Kwadzo 3, Prnce M. Etwre 2 & Alexander N. Wredu 2,4 1 Audt Servce, Cape-Coast, Ghana 2 CSIR-Savanna Agrcultural Research Insttute, Ghana 3 Department of Agrcultural Economcs and Agrbusness, Unversty of Ghana, Legon, Ghana 4 Rural Development Theory and Polcy, Insttute of Agrcultural Economcs and Socal Scences n Tropcs and Subtropcs, Unversty of Hohenhem, Stuttgart, Germany Correspondence: Edward Martey, P.O. Box TL 52, Tamale, Ghana. Tel: E-mal: eddemartey@gmal.com Receved: October 25, 2013 Accepted: February 7, 2014 Onlne Publshed: February 10, 2014 do: /sar.v3n2p16 URL: Abstract Tomato prce s an mportant ndcator for farmers to contnue producng the crop n Ghana. There are sometmes alleged reports that farmers tend to commt sucde when they are unable to meet ther expected tomato prce and thereby unable to recover ther cost of producton. However, data avalable ndcate that the domestc producton of fresh tomato s on the declne. The paper therefore, assesses the factors that affect whether or not farmers can receve ther expected tomato prce. A multstaged sample survey of 215 farmers across three regons was subjected to the bnomal probt model. Results ndcate that for farmers to receve ther expected prce, they have to adapt to produce the crop n the drer seasons, where tomato shortage can be observed. Farmer educaton and experence are also mportant factors that are lkely to help farmers receve ther expected tomato prce. The studytherefore recommends the promoton of strateges that are cost reducng especally n the dry season and also to mprove access to crtcal producton technques. Keywords: seasonal, expected prce, tomato, cultvaton, bnomal probt model, Ghana 1. Introducton 1.1 Background and Problem Statement The Ghanaan agrcultural sector plays an mportant role n economc development. It contrbutes sgnfcantly to food securty, poverty reducton and GDP growth n the country. The sector s the largest sngle employer of labour n the Ghanaan economy and s domnated by many rural smallholder farms and agrcultural enterprses (Aryeetey & Nyanteng, 2006; Kolavall et al., 2011). The crops sub-sector s a major component n the agrcultural sector (MoFA, 2010). Ths sub-sector ncludes tomato producton whch s one of the essental farmng and mport actvtes n the country. Tomato (Lycoperscon spp.) s an essental vegetable, a vtal ngredent n the det of Ghanaans and t s easy to grow. Tomato s used n many dshes n Ghana n ts raw or processed form. It grows well n the tropcs, whch also makes t hghly pershable. When harvested rpped, they tend to rot wthn a few days and create problems for farmers and marketers n tmes of seasonal glut when they are unable to sell mmedately. A useful substtute of ths pershable frut s n ts preserved form as puree or paste. Processng appears to be the man remedy to the perennal gluts n the tomato producng sector (Robnson & Kolavall, 2010). Tomato producton as an agrcultural busness has great potental for growth and employment generaton (Robnson & Kolavall, 2010). Although tomato requres a relatvely cool, dry clmate for hgh yeld and qualty, t can adapt to a wde range of clmatc condtons from temperate to hot and humd tropcal. The optmum temperature for most varetes les between 21 ºC and 24 ºC. The plants can survve a range of temperature, but the plant tssues are damaged below 10 ºC and above 38 ºC (Naka et al., 2005). The possbltes of ncreasng ts domestc producton wll not only nvolve transformng some nsttutons such as the land tenure systems, nput and credt but also the prcng polces and strateges farmers adopt (Donkoh et al., 2013). The producton of tomato s characterzed by seasonal patterns and prces vary substantally even wthn a week (Ihle & 16

2 Sustanable Agrculture Research Vol. 3, No. 2; 2014 Amkuzuno, 2010). Its seasonalty creates perods of abundance and scarcty reflectng n market prces. Tomato producton n Ghana follows the dfference n ranfall patterns and access to water. Dfferent regons therefore, partcpate n tomato producton at dfferent tmes of the year. The use of washed seeds s stll done by Ghanaan farmers who produce wthout targetng any buyer and delver to the open market (Clottey et al., 2009). Ths stuaton ncreases transacton cost and exposes farmers to hgh market rsk. Fresh tomato prces are affected by seasonalty than by qualty. Wth the dfferent varetes of tomato produced n Ghana and that from Burkna Faso, tomato qualty deterorates as a result of napproprate postharvest practces, such as lack of approprate storage, cookng facltes and unsutable commercal contaners (Horna et al., 2007). All these can possbly affect the tomato prce receved by farmers. However, fgures ndcate that there s a declnng trend n domestc producton of fresh tomato (FAOSTAT, 2011; Attoh, 2011). On the other hand, tomato prce s an mportant ndcator for farmers to contnue producng by nfluencng ther profts. The man objectve of ths research therefore, s to dentfy factors that nfluence the seasonal expected tomato prce negotated and receved by farmers. 2. Method 2.1 Study Area The research uses three (3) man tomato producng dstrcts n three (3) regons of Ghana namely Brong Ahafo, Upper East and Ashant Regons. The three dstrcts are descrbed below: Wench Dstrct: Ths dstrct, now a muncpalty can be found n the western part of the Brong Ahafo Regon. It s located northwest to Sunyan, the regonal captal. Major economc actvtes n the muncpalty are agrculture and forestry. Most farmers acqure land through nhertance or work on famly lands. The major crops grown are maze, tomatoes, yam, plantan and cassava. Hred labour s the major source of labour (ghanadstrcts.com, 2011). Communtes nvolved n tomato producton n the dstrct are Nkonsa, Awsa and Asubnja. Offnso North Dstrct: The dstrct can be found n the North-Western part of the Ashant regon bordered by the Brong Ahafo Regon n the north and west. The dstrct experences sem-equatoral conventonal clmate. Sols are sutable for the cultvaton of crops such as cassava, yam, maze and vegetables (for example tomatoes). Agrculture s the domnant economc actvty n the dstrct (ghanadstrcts.com, 2011). Communtes nvolved n tomato producton n the dstrct ncludes Akomadan and Afrancho. Kasena Nankana East Dstrct: Ths dstrct falls wthn the savannah vegetaton belt and ranfall s modest wth only one rany season. Ths allow for the cultvaton of legumes, cereals and vegetable for own consumpton and for the market only n a season wthout the use of rrgaton (ghanadstrcts.com, 2011). Communtes nvolved n tomato producton n the dstrct ncludes Tono, Doba, Mngu, Pungu Bavugena and Nyangua. 2.2 Samplng Technque and Data Collecton The study used a multstage samplng technque to select the farmers. It started wth a purposve selecton of three regons namely Upper East, Brong Ahafo and Ashant Regons. These regons are well known as areas for commercal tomato producton n Ghana. The next stage nvolved a purposve selecton of one dstrct from each regon namely Kasena Nankana East Dstrct n the Upper East Regon, Wench Dstrct n the Brong Ahafo Regon and Offnso North Dstrct n the Ashant Regon wth the ad of the offcals at the Mnstry of Food and Agrculture (MoFA). Fnally, farmers were selected randomly from the lst of tomato producng households n each of the dentfed dstrcts. In summary, 159 farmers (60 respondents from Akomadan, 52 from Kasena Nankana East and 47 from Wench) were sampled and ntervewed. Data for the research was collected based on the number of tmes each farmer was engaged n producng tomato durng the 2010/2011 producton year (.e. seasons of producton). The questons were then repeated for the number of seasons dentfed for each farmer. There were three seasons categorzed, representng seasons n whch farmers produced tomato. Prmary data captured nclude soco-economc, demographc and nsttutonal factors. Secondary data source was also used to augment the prmary data. In all, 215 data ponts on seasonal producton bass were collected and used for the analyss n the research. 2.3 Method of Analyss Bnary Probt Model Analyss Tomato harvestng can span for a few weeks before t s over. Therefore, farmers ether sold ther harvest mmedately or wated for a fewdays when they thnk the prce mght be better. For a farmer to negotate and 17

3 Sustanable Agrculture Research Vol. 3, No. 2; 2014 choose to receve any prce for the 52 kg crate of tomato or not can be vewed as a dscrete choce. Dscrete choce models lke probt and logt model can therefore, be used for ths analyss. However, the probt model specfcaton has an advantage over the logt model specfcaton for small samples (less than 1000) (Johannes et al., 2010). The present study therefore, used a Bnomal Probt Model to examne determnants of whether or not farmers can receve ther expected tomato prce. It s assumed that whether farmers receve ther expected prce or not s closely related to the way farmers use ther techncal nformaton to negotate for a better tomato prce. They decde on how much to sell knowng that the harvest season wll span a few days (more than one day). The decson maker (farmer) s assumed to maxmze expected utty (expected proft) by negotatng a possbly hgher prce subject to some constrants. The structure of the model s therefore, represented as: Y 1 f( X, ) e 0 and Y 0 f( X, ) e 0 (1) Y X e (2) ' 0 Where Y s the dscrete acceptance varable, s the vector of parameters, X s the vector of explanatory varables, s 0 s the constant term and e s the error term. Farmers were asked to provde ther expected prce range for ther tomato for the dfferent season dentfed. Where the weghted average prce per season receved by the farmer for ther 52 kg crate of tomato for a partcular season falls wthn the expected prce range the farmer mentoned, the farmer s then assumed to have negotated hs expected prce for the partcular season. The dependent varable s such that a farmer who receves hs expected prce n each season n the 2010/2011 tomato producton year s gven a value of one and f not, a value of zero. The model estmates gve the drectonal effect whles the margnal effect of changes n an explanatory varable on whether or not the farmer can receve hs expected tomato prce s gven below (Fufa & Hassan, 2006): Y E[ ] X X X ' ( ) Where s the standard normal densty functon. 2.4 Descrpton of Explanatory Varables The ndependent explanatory varables used n the model of study nclude farmer educaton and experence, whether the farmer has off-farm ncome, use of seeds form formal sources, access to credt, beng a member of a farmer assocaton, farm sze for tomato cultvaton and tomato prce. Output prce serves as an ncentve for farmers to decde the marketng channel to use (Martey et al., 2012). The amount receved by a farmer s an ndcaton of ther negotatng ablty. The weghted average prce of tomato s therefore, used as a proxy for the negotatng ablty of farmers. If farmers thnk they can negotate a good and hgher prce, then they wll expect a hgher prce for ther produce. Farmer experence s hypotheszed to nfluence farmer expectaton of tomato prce. Older and more experenced farmers have the advantage of dealng wth most buyers and are able to use the nformaton for ther beneft. They have better contacts possbly at lower cost. Farmer experence helps them gan acquantance wth exstng systems and nformaton (Fufa & Hassan, 2006) whch they are expected to use effcently n order to gan ther expected commodty prce. Also, farmer educaton s expected to nfluence farmer expectaton of the commodty prce. Hghly educated farmers have a negotatve urge and can better artculate ther poston for a hgher tomato prce. Farmer educaton mproves the allocatve ablty of decson makers by enablng them thnk crtcally and use nformaton source effcently (Fufa & Hassan, 2006; Johannes et al., 2010). A farmer wth off-farm ncome s hypotheszed to affect farmer expectaton of commodty prce. It would reduce ther percepton of rsk (Shapro & Brorsen, 1988; Chebl et al., 2009) aganst a lower commodty prce. The expectaton therefore, of such a farmer could be towards a lower commodty prce. However, the nature of a Ghanaan farmer s to expect a good and hgher commodty prce n order to acheve ther expected proft level. A farmer who uses seeds from formal sources s expected to nfluence hs expected commodty prce. These seeds are expected to be the mproved varetes that have market value and therefore farmers have a postve expectaton of commodty prce for usng such seeds. Havng access to credt s an mportant ndcaton for a farmer to expect a hgher commodty prce, n order to recover the cost of usng credt. Access to credt s (3) 18

4 Sustanable Agrculture Research Vol. 3, No. 2; 2014 therefore hypotheszed to postvely nfluence farmer s expectaton of tomato prce. Beng a member of a farmer assocaton s postvely assocated wth farmer s expectaton of commodty prce. Ths s because these farmers have access to nformaton and tranng derved from collectve nvestments (Chebl et al., 2009) to beneft them when negotatng the commodty prce. Fnally, farm sze s expected to gve an ndcaton of a farmer s expectaton of tomato prce. If a farmer expects a hgher tomato prce, he or she could ncrease farm land to tomato to take advantage of hgher profts. On the order hand, f a farmer expects a lower tomato prce, he or she cultvates a small farm land to tomato and a bgger farmland to other crops whch he or she expects a hgher commodty prce. Two nteracton terms were also used. Seasonalty (used as a dummy) s nteracted wth use of any type of rrgaton technology and farmer experence was nteracted wth the weghted average prce of tomato. 3. Results and Dscusson 3.1 Descrptve Results Seasonalty s characterzed nto three, season one, season two and season three whch represents harvestng perods between Aprl to July, August to November and December to March respectvely. Season one and season two can be observed as the major and mnor rany season for Akomadan and Wench. Season two can be observed as the modest sngle rany season for Kasena Nankana East but season three s the dry season for all locatons. Relatvely, from the sample, the man tomato producng season for Akomadan, Wench and Kasena Nankana East are season one, season two and season three respectvely. These recorded a hgher percentage of cultvaton by tomato farmers n these seasons durng the 2010/2011 producton year. Ths s shown n appendx two. Farmers consder prcng as a very mportant part of the producton system. Ths depends on ther ablty to negotate mostly wth market women from across the country. Most farmers sell ther produce on ther farms. Ths apples n all dstrcts, however, only Kasena Nankana East Dstrct had a few farmers sendng some of ther produce to sell n a nearby market. Tomato output prce, overall, vared across locatons. The average prce farmers receved for the sample s Ghana ceds per 52 kg crate of tomato. Kasena Nankana East Dstrct farmers receved the hghest average prce of Ghana ceds per 52 kg crate of tomato (an average of Ghana ceds n season two and Ghana ceds for season three). Ths s followed by Akomadan farmers who receved an average prce of Ghana ceds per 52 kg crate of tomato (an average of Ghana ceds n season one, Ghana ceds n season two and Ghana ceds for season three). Wench farmers then followed wth an average prce of 41.5 Ghana ceds per 52 kg crate of tomato (an average of Ghana ceds n season one, Ghana ceds n season two and Ghana ceds for season three). Tomato prces are hghest n season three comparable wth a hgher majorty of farmers recevng hgher prces as shown by ther smaller standard devaton. Ths s represented n appendx 3. Out of the 215 responses or data ponts generated n the sample, percent are from Wench Dstrct n the Brong Ahafo Regon (45.71 percent for season one, percent for season two and 5.71 percent for season three), percent are from Akomadan n the Ashant Regon (51.09 percent for season one, percent for season two and percent for season three) and percent are from Kasena Nankana East n the Upper East Regon (none for season one, 1.89 percent for season two and percent for season three). 3.2 Probt Estmates for the Expected Tomato Prce Receved The bnomal probt model was used to estmate coeffcents of the determnants of whether or not farmers can receve ther expected tomato prce n Ghana. The model estmates are shown n appendx 4. The Stata 11 software was used to estmate parameters and ther margnal effect. The model has explanatory power wth a Pseudo R squared of whch ndcate that 50 percent of the varaton n the dependent varable s explaned by the ndependent varables. It also has a wld ch squared value of sgnfcant at a one percent confdence level. The margnal effects estmated are presented n Table 1. The emprcal results of the Bnomal Probt Model show that three (3) out of the nne (9) used ndependent varables were sgnfcant. From the results, the nteracton term of seasonalty and use of any rrgaton technology s sgnfcant n the model. Ths means that as the seasons wears on and ranfall decreased towards the drer seasons, a farmer who uses any rrgaton technology has a hgher lkelhood of recevng hs/her expected tomato prce he/she negotated for. Ths could be due to tomato shortages experenced durng the dry season n Ghana whch s expected to generally ncrease the prce of tomato. As season progressed toward the drer season, a farmer wth any rrgaton scheme s lkely to ncrease hs probablty of recevng hs/her expected tomato prce by 83 percent. Also the nteracton term of farmer experence and weghted average prce of tomato s sgnfcant whch means that experenced farmers who can bargan for a good and hgher prce (have a hgher negotatng ablty) for ther 52 kg crate of tomato have a hgher lkelhood of recevng ther expected tomato prce they negotate for. Ths 19

5 Sustanable Agrculture Research Vol. 3, No. 2; 2014 could be due to the fact that experenced farmers gan acquantance wth new nformaton whch they use effcently that helps them negotate better (Fufa & Hassan, 2006). An experenced farmer who can negotate a hgher tomato prce s lkely to ncrease hs probablty of recevng hs expected tomato prce by 0.21 percent. Farmer educaton s also sgnfcant whch means that educated farmers have a hgher lkelhood of recevng ther expected tomato prce they negotate for. Ths s probably because they become aware of addtonal sources of nformaton and are more effcent at evaluatng, nterpretaton and negotaton (Fufa & Hassan, 2006; Johannes et al., 2010). An addtonal year of educaton s lkely to ncrease the probablty of a farmer to receve hs / her expected tomato prce by 9.32 percent. Table 1. Margnal effect of the bnomal probt model Varable Margnal Effect Seasonalty*Access to any rrgaton technology ** Farmer educaton ** Farmer experence*weghted seasonal average tomato prce of a crate *** Source: Regresson Estmaton from Survey Data (2011) ***p < 0.01, **p < 0.05 and *p < Concluson The bnomal probt model shows that, n Ghana, farmers can receve ther expected tomato prce. As seasons progressed towards the drer season, a farmer who uses any rrgaton technology s lkely to ncrease hs chances of recevng hs expected prce. Also, experenced farmers who have cultvated tomato for a longer perod of tme and can negotate a hgher prce for ther tomato also have a hgher lkelhood of ncreasng ther chance of recevng ther expected hgher prce. Farmer educaton s also lkely to nfluence the lkelhood and chance for a farmer to gan a hgher prce for ther 52 kg crate of tomato. Fundamental to mprovng farmers chances of recevng ther expected prce depends on strateges that help farmers reduce ther cost of rrgaton whch could nclude cost sharng arrangements or the buldng of dams for collectve use especally n the drer areas and seasons. It s also mportant to use strateges that encourage more farmers to grow tomatoes n the drer seasons whch can ncrease ncome and thereby the welfare of these farmers. Farmers can be helped wth credt to gan farm land close to already exstng dams and also for agrcultural development organsaton to develop seeds that do well n drer envronments and to educate farmer on possble and approprate storage facltes. Acknowledgements The author acknowledge the many tomato farmers who cultvate and produce to meet the needs of the country and especally to those who took a long tme to st and answer questons that were repeated on seasonal bass. The revewers of ths paper are also apprecated for ther valuable comments. Reference Aryeetey, E., & Nyanteng, V. (2006). Agrcultural Market Access In Ghana. ISSER Dscusson Paper No. 30, ISSER, Unversty of Ghana, Legon. Attoh, C. (2011). Assessng the Compettveness and Factors Affectng Farmers Seasonal Tomato Producton Decsons n Ghana. An unpublshed MPhl Thess, Department of Agrcultural Economcs and Agrbusness, Unversty of Ghana, Legon. Chebl, A., Nasr, H., & Zabet, L. (2009). Factors Affectng Farmers Wllngness to Adopt Salt-Tolerant Forage crops n South Eastern Tunsa. AfJare, 3(1), Clottey, V. A., Karbo, N., & Gyas, K. O. (2009). The Tomato Industry n Northern Ghana: Producton Constrants and Strateges to Improve Compettveness. Afrcan Journal of Food, Agrculture Nutrton and Development, 9(6), Donkoh, S. A., Tachega, M., & Amowne, N. (2013). Estmatng Techncal Effcency of Tomato Producton n Northern Ghana. Amercan Journal of Expermental Agrculture, 3(1), FAOSTAT. (2011). accessed on 15th February, Fufa, B., & Hassan, R. M. (2006). Determnants of fertlzer use on maze n Eastern Ethopa: A weghted 20

6 Sustanable Agrculture Research Vol. 3, No. 2; 2014 endogenous samplng analyss of the extent and Intensty of adopton. Agrekon, 45(1), Ghanadstrcts.com. (2011). ghanadstrcts.com, accessed on 8th Aprl, Horna, D., Tmpo, S., Al-Hassan, M. R., Smale, M., & Falck-Zepeda, J. (2007). Vegetable producton and pestcde use n Ghana: World GM Varetes have an Impact at the Farm Level? (pp ). AAAE Conference Proceedngs. Ihle, R., & Amkuzuno, J. (2010). Assessng Seasonal Asymmetrc Prce transmsson n Ghanaan Tomato markets wth the Johansen Estmaton Method. Georg-August-Unverstat Gottngen, Dscusson papers No. 53. Johannes, T. A., Vab, M. B., & Malaa, D. K. (2010). Adopton of maze and cassava Producton Technologes n the Forest-Savannah zone of Cameroon: Implcatons for Poverty Reducton. World Appled Scences Journal, 11(2), Kolavall, S., Robnson, E., Dao, X., Alpuerto, V., Folledo, R., Slavova, M., & Asante, F. (2011). Economc Transformaton n Ghana. A paper presented at the IFPRI-Unversty of Ghana Conference (pp. 1-5). Understandng Economc Transformaton n Sub-Saharan Afrca. Martey, E., Annn, K., Wredu, A. N., & Attoh, C. (2012). Does Access to Market Informaton Determne the Choce of Marketng Channel among Small holder Yam Farmers n the Brong Ahafo Regon of Ghana? A Multnomal Logt Regresson Analyss. Journal of Economcs and Sustanable Development, 3(12), Mnstry of Food and Agrculture. (2010). Agrculture n Ghana, Facts and Fgures (2009), Statstcal, Research Informaton Drectorate (SRID), Mnstry of Food and Agrculture, MoFA, Accra. Naka, S., van Ldt de Jeude, J., de Goffau, M., Hlm, M., & van Dam, B. (2005). Cultvaton of Tomato. Producton, Processng and Markettng (pp. 1-10). Agrodok 17, Dffraf, Wagenngen, Netherlands. Robnson, J. Z. E., & Kolavall, S. L. (2010). The Case of Tomato n Ghana: Insttutonal Support. Ghana Strategy Support Program (GSSP) Workng Paper No. 22. IFPRI, Accra. Shapro, B. I., & Brorsen, B. W. (1988). Factors Affectng Farmers Hedgng Decsons. North Central Journal of Agrcultural Economcs, 10(2),

7 Sustanable Agrculture Research Vol. 3, No. 2; 2014 Appendces Appendx 1. Explanatory varables for the bnomal probt model No Varable Measurement Expected Sgn 1 Locaton of Farmer 0 f Kasena Nankana East, 1 f Wench and 2 f Akomadan Dstrcts +/- 2 Seasonalty 0 f season one, 1 f season two and 2 f season three +/- 3 Farmer educaton Number of years of formal educaton + 4 Farmer experence Number of years n tomato farmng + 5 Off farm ncome source Dummy: 1=Agrc and any off-farm ncome actvty, 0=only Agrc +/- 6 Source of Seed Dummy: 1=from formal sources lke Agrc + Shop/NGOs, 0=prepared by farmer/frends 7 Farm land for tomato cultvaton Hectare + 8 Weghted seasonal average prce Ghana ceds + of tomato 9 Farmer assocaton Dummy: 1=member of any farmer assocaton, + 0=otherwse 10 Access to credt Dummy: 1=f receved credt, 0=otherwse + 11 Irrgaton Dummy: 1=f used any rrgaton system, 0=otherwse + Interacton terms 12 Seasonalty and Access to any rrgaton technology 13 Farmer experence and Weghted average prce Seasonalty*Access to any rrgaton technology +/- Farmer experence *Seasonal Weghted average prce + Appendx 2. Locaton of farmers and seasonalty of tomato producton n the three dstrcts Locaton of Farmer (percentage) Seasonaly(percentage) Wench (N=70) Akomadan (N=92) K. Nankana East (N=53) Season one(n=79) Season two(n=64) Season three(n=72)

8 Sustanable Agrculture Research Vol. 3, No. 2; 2014 Apendx 3. Summary statstcs of weghted average prce across locatons and seasons Seasons Season 1 Season 2 Season 3 Total Wench Mean Mnmum Maxmum SD Akomadan Mean Mnmum Maxmum SD K. Nankana Mean Mnmum Maxmum Source: Author s computaton from feld data. SD Appendx 4. Bnomal probt model results for the expected tomato prce receved Varable Coeffcent Standard Error Farmer Locaton Seasonalty*Access to any rrgaton technology ** Farmer educaton ** Off farm ncome source Source of Seed Farm sze for tomato cultvaton Farmer assocaton Access to credt Farmer experence*weghted average prce *** Source: Regresson Estmaton from Author s Tomato Farmer Survey Data (2011) ***p < 0.01, **p < 0.05 and *p < 0.10 Copyrghts Copyrght for ths artcle s retaned by the author(s), wth frst publcaton rghts granted to the journal. Ths s an open-access artcle dstrbuted under the terms and condtons of the Creatve Commons Attrbuton lcense ( 23