Purpose: Smallholder farmers' agriculture in developing countries particularly in low and lower-middle-income countries is known for poor production and productivity levels which has been related to the inadequate use of improved agricultural inputs and marketing systems, and in this case, contract farming. Therefore, this study aimed to investigate the various factors which affect contract farming among smallholder farmers in developing countries. However, trying to investigate those contract farming determinants by a single analytical method leads it to have limited findings, narrowed generalizability and difficulty in investigating major determinants that determine contract farming success. Therefore, a joint approach using factor analysis and meta-analysis can give a more comprehensive understanding of contract farming factors. Methodology: The data was gathered through a Systematic Literature Review, finding a total of 3007 studies from SCOPUS, PubMed, PubAg, and EMBASE. The PRISMA method was applied to, and finally, 35 peer-reviewed articles in English between January 1990 to September 2023 were selected. Findings: The meta-analysis showed interesting insights into factors influencing contract farming participation. Education and household size exhibited a negative association, suggesting that farmers with higher education levels and larger families may be less likely to participate in contract farming arrangements. This could be due to a preference for independent decision-making or the need for family labor in alternative income-generating activities. On the other hand, farmers with larger landholdings are more inclined to participate. To encourage broader participation, policymakers, and program designers could consider targeted outreach and support services for these specific demographics. Originality and value: Our new approach, joining meta-analysis and factor analysis, sheds innovative light on contract farming determinants. While the expected determinants like farm and household size, along with education, remained significant, our analysis showed unexpected nuances. Age of the household head emerged as potentially favoring younger, less experienced farmers. Moreover, access to extension services played an important role, while large household size might have a more complex influence depending on age composition. This comprehensive approach offers valuable insights for targeted outreach programs and collaboration with extension services to optimize contract farming adoption and success in developing countries.
| Published in | American Journal of Agriculture and Forestry (Volume 12, Issue 5) |
| DOI | 10.11648/j.ajaf.20241205.15 |
| Page(s) | 356-365 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Contract Farming Participation, Factor Analysis, Low and Lower-middle-income Countries, Meta-regression Analysis, Smallholder Farmers
| [1] | D. K. Behera, “Farmer’s participation in contract farming in India: A study of Bihar,” 2019. |
| [2] | G. Ton, W. Vellema, S. Desiere, S. Weituschat, and M. D’Haese, “Contract farming for improving smallholder incomes: What can we learn from effectiveness studies?” World Development, vol. 104. Elsevier Ltd, pp. 46–64, Apr. 01, 2018. |
| [3] | A. K. Mishra, A. Kumar, P. K. Joshi, and A. D’Souza, “Production risks, risk preference, and contract farming: Impact on food security in India,” Applied Economic Perspectives and Policy, vol. 40, no. 3, pp. 353–378, 2018, |
| [4] |
S. B. Jarrell and T. D. Stanley, “Meta-Regression Analysis: A Quantitative Method of Literature Surveys,” 1989. [Online]. Available:
onlinelibrary.wiley.com/Doi/10.1111/J.1467-6419.1989.tb00064.x/Abstract |
| [5] | K. Thorlund et al., “Evolution of heterogeneity (I2) estimates and their 95% confidence intervals in large meta-analyses,” PLoS ONE, vol. 7, no. 7, Jul. 2012, |
| [6] | H. M. Vesterinen et al., “Meta-analysis of data from animal studies: A practical guide,” Journal of Neuroscience Methods, vol. 221. Elsevier, pp. 92–102, Jan. 15, 2014. |
| [7] | H. A. Ba, Y. de Mey, S. Thoron, and M. Demont, “Inclusiveness of contract farming along the vertical coordination continuum: Evidence from the Vietnamese rice sector,” Land Use Policy, vol. 87, Sep. 2019, |
| [8] |
I. I. Kutawa, “Empirical evidence on contract farming in northern Nigeria: Case study of tomato production,” Asian Journal of Agriculture and Rural Development, vol. 6, no. 12, pp. 240–253, 2016,
https://doi.org/10.18488/journal.1005/2016.6.12/1005.12.240.253 |
| [9] | J. Mushunje and A. Musitini, “Factors influencing smallholder farmer participation in tobacco contract farming arrangements in Mount Darwin district of Zimbabwe,” International Journal of Development and Sustainability, vol. 7, no. 12, pp. 2986–3001, 2018, [Online]. Available: |
| [10] | A. Nuzulia, gewandte Chemie International Edition, 6(11), 951–952., vol. 7, no. 1, pp. 5–24, 1967. |
| [11] | C. Dubbert, “Participation in contract farming and farm performance: Insights from cashew farmers in Ghana,” Agricultural Economics (United Kingdom), vol. 50, no. 6, pp. 749–763, 2019, |
| [12] | Y. I. Hussaini, “Determinants of Participation in Contract Farming,” vol. 20, no. 4, pp. 147–160, 2022, |
| [13] | P. Maganga Nsimbila, “Determinants of Contract Farming Adoption and its Impact on Productivity of Smallholder Cotton Producers in Tanzania,” International Journal of Social and Administrative Sciences, vol. 6, no. 2, pp. 55–69, 2021, |
| [14] | E. G. Johnny, J. Kabubo-mariara, R. Mulwa, and G. M. Ruigu, “African Journal of Economic Review, Volume VII, Issue 2, July 2019,” African Journal of Economic Review, vol. VII, no. 2, pp. 91–112, 2019. |
| [15] | R. Mosahab et al., Increase sales and efficiency with weather marketing vol. 4, no. 3, pp. 410–419, 2011. |
| [16] | Rokhani et al., “Assessing determinants of farmer’s participation in sugarcane contract farming in Indonesia,” Agraris, vol. 6, no. 1, pp. 12–23, 2020, |
| [17] | J. K. Bidzakin, S. C. Fialor, D. Awunyo-Vitor, and I. Yahaya, “Contract farming and rice production efficiency in Ghana,” Journal of Agribusiness in Developing and Emerging Economies, vol. 10, no. 3, pp. 269–284, 2020, |
| [18] | G. A. Akuriba and D. F. Tangonyire, “Effects of nucleus-farmer out-grower schemes on profitability among smallholder farmers: Empirical evidence from Northern Ghana,” Cogent Food and Agriculture, vol. 6, no. 1, 2020, |
| [19] | I. Mounirou, “Does participation in contracts affect agricultural income? Empirical evidence from parboiled rice farmers in central Benin,” Cogent Food and Agriculture, vol. 6, no. 1, 2020, |
| [20] | A. Bezabeh, F. Beyene, J. Haji, and T. Lemma, “Impact of contract farming on an income of smallholder malt barley farmers in Arsi and West Arsi zones of Oromia region, Ethiopia,” Cogent Food and Agriculture, vol. 6, no. 1, 2020, |
| [21] | A. T. Nguyen, “Does contract farming enhance the income of farmers? Evidence in tea production of Vietnam,” Journal of Agricultural Sciences - Sri Lanka, vol. 15, no. 1, pp. 37–49, 2020, |
| [22] | M. P. Loquias, L. N. Digal, S. G. Placencia, I. J. T. Astronomo, M. L. G. Orbeta, and C. Q. Balgos, “Factors Affecting Participation in Contract Farming of Smallholder Cavendish Banana Farmers in the Philippines,” Agricultural Research, vol. 11, no. 1, pp. 146–154, 2022, |
| [23] | Saroj, K. R. Paltasingh, and P. K. Jena, “Does contract farming enhance farm efficiency? A case of wheat growers of Haryana, India,” Heliyon, vol. 9, no. 4, Apr. 2023, |
| [24] | J. K. Bidzakin, S. C. Fialor, D. Awunyo-Vitor, and I. Yahaya, “Impact of contract farming on rice farm performance: Endogenous switching regression,” Cogent Economics and Finance, vol. 7, 2019. |
| [25] | A. Kumar, D. Roy, P. K. Joshi, G. Tripathi, and R. P. Adhikari, “Impact of contract farming of paddy seed on smallholder farm profits: evidence from Nepal,” Agricultural Economics Research Review, vol. 32, no. 1, p. 25, 2019, |
| [26] | A. M. and M. H. R. Islam, “This document is discoverable and free to researchers across the globe due to the work of AgEcon Search” AgEcon Search, p. 18, 2015. |
| [27] | M. E. Marwa and J. Manda, “Do youth farmers benefit from participating in contract farming? Evidence from French beans youth farmers in Arusha, Tanzania,” Agrekon, vol. 61, no. 4, pp. 379–398, 2022, |
| [28] | V. Hoang and V. Nguyen, “Determinants of small farmers’ participation in contract farming in developing countries: A study in Vietnam,” Agribusiness, vol. 39, no. 3, pp. 836–853, Jul. 2023, |
| [29] | I. B. Lambrecht and C. Ragasa, “Do development projects crowd out private sector activities? Evidence from contract farming participation in Northern Ghana,” Food Policy, vol. 74, no. November 2017, pp. 9–22, 2018, |
| [30] | F. E. Mmbando, E. Z. Wale, and L. J. S. Baiyegunhi, “Determinants of smallholder farmers’ participation in maize and pigeonpea markets in Tanzania,” Agrekon, vol. 54, no. 1, pp. 96–119, Jan. 2015, |
| [31] | B. B. Swain et al., “Effects of Contract Farming on Small-Holder Soybean Farmers’ Income in the Eastern Corridor of the Northern Region, Ghana, Online,” Sustainability (Switzerland), vol. 7, no. 3, pp. 24–43, Apr. 2021, |
| [32] | A. Dagnew, D. Goshu, L. Zemedu, and M. Sileshi, “Determinants of Participation in Contract Farming Among Malt Marley Farmers in North-Western Ethiopia,” American Journal of Theoretical and Applied Business, vol. 9, no. 2, pp. 44–56, 2023, |
| [33] | O. A. Olounlade et al., “Impact of participation in contract farming on smallholder farmers’ income and food security in rural Benin: PSM and LATE parameter combined,” Sustainability (Switzerland), vol. 12, 2020. |
| [34] | M. F. Bellemare, “Examining the determinants and effects of Contract Farming on Farm Income in the Northern Region of Ghana,” World Development, vol. 40, no. 7, pp. 1418–1434, 2012, [Online]. Available: |
| [35] | I. H. Yusuf, G. S. Umar, and W. M. Jamiu, “Impact of a Contract Farming Scheme on Income, Food Security, and Nutrition among Maize Farmers in North Western, Nigeria,” Journal of Nutrition and Food Security, vol. 6, pp. 101–106, 2021. |
| [36] | A. M. San, “Participation Decision and Impact of Contract Farming System on Rice Farms in Myanmar.” [Online]. Available: |
| [37] | P. Winters, P. Simmons, and I. Patrick, “Evaluation of a hybrid seed contract between smallholders and a multinational company in East Java, Indonesia,” Journal of Development Studies, vol. 41, no. 1, pp. 62–89, 2005, |
| [38] | Akumu, W. Odongo, and B. Mugonola, “Determinants of contract farming for smallholder sunflower producers in northern Uganda,” African Crop Science Journal, Vol. 28, No. 4, pp. 585 – 594, 2020, |
APA Style
Abreham, G., Ayalew, Z., Chanie, E., Muchie, M., Sete, Y. (2024). Contract Farming in Low and Lower-Middle-Income Countries: Using Meta-Analysis and Factor Analysis. American Journal of Agriculture and Forestry, 12(5), 356-365. https://doi.org/10.11648/j.ajaf.20241205.15
ACS Style
Abreham, G.; Ayalew, Z.; Chanie, E.; Muchie, M.; Sete, Y. Contract Farming in Low and Lower-Middle-Income Countries: Using Meta-Analysis and Factor Analysis. Am. J. Agric. For. 2024, 12(5), 356-365. doi: 10.11648/j.ajaf.20241205.15
AMA Style
Abreham G, Ayalew Z, Chanie E, Muchie M, Sete Y. Contract Farming in Low and Lower-Middle-Income Countries: Using Meta-Analysis and Factor Analysis. Am J Agric For. 2024;12(5):356-365. doi: 10.11648/j.ajaf.20241205.15
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Download@article{10.11648/j.ajaf.20241205.15,
author = {Getahun Abreham and Zemen Ayalew and Essa Chanie and Mammo Muchie and Yordanos Sete},
title = {Contract Farming in Low and Lower-Middle-Income Countries: Using Meta-Analysis and Factor Analysis
},
journal = {American Journal of Agriculture and Forestry},
volume = {12},
number = {5},
pages = {356-365},
doi = {10.11648/j.ajaf.20241205.15},
url = {https://doi.org/10.11648/j.ajaf.20241205.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajaf.20241205.15},
abstract = {Purpose: Smallholder farmers' agriculture in developing countries particularly in low and lower-middle-income countries is known for poor production and productivity levels which has been related to the inadequate use of improved agricultural inputs and marketing systems, and in this case, contract farming. Therefore, this study aimed to investigate the various factors which affect contract farming among smallholder farmers in developing countries. However, trying to investigate those contract farming determinants by a single analytical method leads it to have limited findings, narrowed generalizability and difficulty in investigating major determinants that determine contract farming success. Therefore, a joint approach using factor analysis and meta-analysis can give a more comprehensive understanding of contract farming factors. Methodology: The data was gathered through a Systematic Literature Review, finding a total of 3007 studies from SCOPUS, PubMed, PubAg, and EMBASE. The PRISMA method was applied to, and finally, 35 peer-reviewed articles in English between January 1990 to September 2023 were selected. Findings: The meta-analysis showed interesting insights into factors influencing contract farming participation. Education and household size exhibited a negative association, suggesting that farmers with higher education levels and larger families may be less likely to participate in contract farming arrangements. This could be due to a preference for independent decision-making or the need for family labor in alternative income-generating activities. On the other hand, farmers with larger landholdings are more inclined to participate. To encourage broader participation, policymakers, and program designers could consider targeted outreach and support services for these specific demographics. Originality and value: Our new approach, joining meta-analysis and factor analysis, sheds innovative light on contract farming determinants. While the expected determinants like farm and household size, along with education, remained significant, our analysis showed unexpected nuances. Age of the household head emerged as potentially favoring younger, less experienced farmers. Moreover, access to extension services played an important role, while large household size might have a more complex influence depending on age composition. This comprehensive approach offers valuable insights for targeted outreach programs and collaboration with extension services to optimize contract farming adoption and success in developing countries.
},
year = {2024}
}
TY - JOUR T1 - Contract Farming in Low and Lower-Middle-Income Countries: Using Meta-Analysis and Factor Analysis AU - Getahun Abreham AU - Zemen Ayalew AU - Essa Chanie AU - Mammo Muchie AU - Yordanos Sete Y1 - 2024/10/29 PY - 2024 N1 - https://doi.org/10.11648/j.ajaf.20241205.15 DO - 10.11648/j.ajaf.20241205.15 T2 - American Journal of Agriculture and Forestry JF - American Journal of Agriculture and Forestry JO - American Journal of Agriculture and Forestry SP - 356 EP - 365 PB - Science Publishing Group SN - 2330-8591 UR - https://doi.org/10.11648/j.ajaf.20241205.15 AB - Purpose: Smallholder farmers' agriculture in developing countries particularly in low and lower-middle-income countries is known for poor production and productivity levels which has been related to the inadequate use of improved agricultural inputs and marketing systems, and in this case, contract farming. Therefore, this study aimed to investigate the various factors which affect contract farming among smallholder farmers in developing countries. However, trying to investigate those contract farming determinants by a single analytical method leads it to have limited findings, narrowed generalizability and difficulty in investigating major determinants that determine contract farming success. Therefore, a joint approach using factor analysis and meta-analysis can give a more comprehensive understanding of contract farming factors. Methodology: The data was gathered through a Systematic Literature Review, finding a total of 3007 studies from SCOPUS, PubMed, PubAg, and EMBASE. The PRISMA method was applied to, and finally, 35 peer-reviewed articles in English between January 1990 to September 2023 were selected. Findings: The meta-analysis showed interesting insights into factors influencing contract farming participation. Education and household size exhibited a negative association, suggesting that farmers with higher education levels and larger families may be less likely to participate in contract farming arrangements. This could be due to a preference for independent decision-making or the need for family labor in alternative income-generating activities. On the other hand, farmers with larger landholdings are more inclined to participate. To encourage broader participation, policymakers, and program designers could consider targeted outreach and support services for these specific demographics. Originality and value: Our new approach, joining meta-analysis and factor analysis, sheds innovative light on contract farming determinants. While the expected determinants like farm and household size, along with education, remained significant, our analysis showed unexpected nuances. Age of the household head emerged as potentially favoring younger, less experienced farmers. Moreover, access to extension services played an important role, while large household size might have a more complex influence depending on age composition. This comprehensive approach offers valuable insights for targeted outreach programs and collaboration with extension services to optimize contract farming adoption and success in developing countries. VL - 12 IS - 5 ER -
Department of Agricultural Economics, University of Gondar, Gondar, Ethiopia
Department of Agricultural Economics, Bahir-Dar University, Bahir-Dar, Ethiopia
UNICEF Innocenti - Global Office of Research and Foresight, Florence, Italy
Department of Science and Technology, Tshwane University of Technology, Pretoria, South Africa
Department of Agricultural Economics, University of Gondar, Gondar, Ethiopia
