Determinants of access to trainings on post – harvest loss management among maize farmers in Uganda: a binary logistic regression approach

References

• Abass, A. B., Ndunguru, G., Mamiro, P., Alenkhe, B., Mlingi, N., & Bekunda, M. (2014). Post-harvest food losses in a maize-based farming system of semi-arid Savannah area of Tanzania. Journal of Stored Products Research, 57, 49–17. https://doi.org/10.1016/j.jspr.2013.12.004
   Web of Science ®Google Scholar
• Abdallah, A.-H., & Abdul-Rahaman, A. (2019). Determinants of access to agricultural extension services: Evidence from smallholder rural women in Northern Ghana. Asian Journal of Agricultural Extension, Economics & Sociology, 9(3), 1–8. https://doi.org/10.9734/AJAEES/2016/23478
   Google Scholar
• Adeyanju, D., Mburu, J., Mignouna, D., & Akomolafe, K. J. (2021). Determinants of youth participation in agricultural training programs: The case of Fadama program in Nigeria. International Journal of Training Research, 19(2), 142–166. https://doi.org/10.1080/14480220.2021.1905685
   Web of Science ®Google Scholar
• Affognon, H., Mutungi, C., Sanginga, P., & Borgemeister, C. (2015). Unpacking postharvest losses in sub-Saharan Africa: A meta-analysis. World Development, 66, 49–68. https://doi.org/10.1016/j.worlddev.2014.08.002
   Web of Science ®Google Scholar
• Ahmed, Y., Ebrahim, S., & Ahmed, M. (2022). Determinants of solar technology adoption in rural households: The case of Belesa districts, Amhara region of Ethiopia Determinants of solar technology adoption in rural households: The case of Belesa districts, Amhara region of Ethiopia. Cogent Economics & Finance, 10(1). https://doi.org/10.1080/23322039.2022.2087644
   Google Scholar
• Ainab, A. A. (2019). Determinants of access to extension service among cashew nut farmers in Kilifi County – Kenya. International Journals of Academics and Research, 1(2), 177–184. https://doi.org/10.32898/ihssj.02/1.4article14
   Google Scholar
• Akudugu, M. A., Guo, E., & Dadzie, S. K. (2012). Adoption of Modern Agricultural Production Technologies by farm households in Ghana: What factors influence their decisions? Journal of Biology, Agriculture and Healthcare, 2(3), 1–13. https://www.researchgate.net/profile/Samuel_Dadzie/publication/235751741_Adoption_of_modern_agricultural_production_technologies_by_farm_households_in_Ghana_What_factors_influence_their_decisions/links/00463533b1249ebdf1000000.pdf
   Google Scholar
• Alemu, A. (2021). Determinants of participation in farmers training centre based extension training in Ethiopia. Journal of Agricultural Extension, 25(2), 86–95. https://doi.org/10.4314/jae.v25i2.8
   Web of Science ®Google Scholar
• Amanullah, A., Nawi, N., Kamarulzaman, N., & Shamsudin, M. (2020). Factors influencing post-harvest losses of apples among growers in Paktia, Afghanistan. Food Research, 4(6), 2313–2321. https://doi.org/10.26656/fr.2017.4(6).302
   Google Scholar
• Arah, I. K., Amaglo, H., Kumah, E. K., & Ofori, H. (2015). Preharvest and postharvest factors affecting the quality and shelf life of harvested tomatoes: A mini review. International Journal of Agronomy, 2015. https://doi.org/10.1155/2015/478041
   Google Scholar
• Aryal, J. P., Sapkota, T. B., Rahut, D. B., Krupnik, T. J., Shahrin, S., Jat, M. L., & Stirling, C. M. (2020). Major climate risks and adaptation strategies of smallholder farmers in coastal Bangladesh. Environmental Management, 66(1), 105–120. https://doi.org/10.1007/s00267-020-01291-8
   PubMed Web of Science ®Google Scholar
• Asante, B. O., Afari-Sefa, V., & Sarpong, D. B. (2011). Determinants of small scale farmers’ decision to join farmer based organizations in Ghana. African Journal of Agricultural Research, 6(10), 2273–2279. https://doi.org/10.5897/AJAR10.979
   Web of Science ®Google Scholar
• Assogba, P. N., Haroll Kokoye, S. E., Yegbemey, R. N., Djenontin, J. A., Tassou, Z., Pardoe, J., & Yabi, J. A. (2017). Determinants of credit access by smallholder farmers in North-East Benin. Journal of Development and Agricultural Economics, 9(8), 210–216. https://doi.org/10.5897/JDAE2017.0814
   Google Scholar
• Atsbeha, A. T., & Gebre, G. G. (2021). Factors affecting women access to agricultural extension services: Evidence from poultry producer women’s in Northwestern Tigray, Ethiopia. Cogent Social Sciences, 7(1). https://doi.org/10.1080/23311886.2021.1975413
   Google Scholar
• Atube, F., Malinga, G. M., Nyeko, M., Okello, D. M., Alarakol, S. P., & Okello-Uma, I. (2021). Determinants of smallholder farmers’ adaptation strategies to the effects of climate change: Evidence from northern Uganda. Agriculture and Food Security, 10(1), 1–14. https://doi.org/10.1186/s40066-020-00279-1
   Google Scholar
• Awili, M. A., White, P., & Kimotho, S. (2016). Factors that influence effective communication of agricultural information among farmers – The case of farmers in South West Kisumu Ward, Kisumu County. Journal of Developing Country Studies, 1(1), 1–20. https://www.iprjb.org/journals/index.php/JDCS/article/view/21
   Google Scholar
• Badstue, L., Petesch, P., Farnworth, C. R., Roeven, L., & Hailemariam, M. (2020). Women farmers and agricultural innovation: Marital status and normative expectations in rural Ethiopia. Sustainability (Switzerland), 12(23), 1–22. https://doi.org/10.3390/su12239847
   PubMed Web of Science ®Google Scholar
• Baird, S., Maonga, B. B., Assa, M. M., Haraman, E. M. K., Mildred, B., Jackline, B.-W., Pearce, J., Kumar, G. D. S., Popat, M. N., Finlayson, J. D., Lawes, R. A., Metcalf, T., Robertson, M. J., Ferris, D., Ewing, M. A., Moser, C. M., Barrett, C. B., Heilman, P., Malone, R. W., & Division, N. (2003). Assessing factors affecting adoption of agricultural technologies: The case of Integrated Pest Management (IPM) in Kumi District, Eastern Uganda. Journal of Sustainable Development in Africa, 22(3), 146. https://hdl.handle.net/10919/36266
   Google Scholar
• Banik, N., Koesoemadinata, A., Wagner, C., Inyang, C., & Bui, H. (2013). The impact of group based training approaches on crop yield, household income and adoption of pest management practices in the smallholder horticultural subsector of Kenya. Journal of Sustainable Development in Africa, 15(1), 117–140. https://doi.org/10.1190/segam2013-0137.1
   Google Scholar
• Bekele, D. (2021). Role of postharvest management for food security: A review. Advances in Crop Science and Technology, 9(7), 1–6. https://www.omicsonline.org/open-access/role-of-postharvest-management-for-food-security-a-review-116715.html%0Ahttps://www.omicsonline.org/peer-reviewed/role-of-postharvest-management-for-food-security-a-review-116715.html
   Google Scholar
• Bilaliib Udimal, T., Jincai, Z., Mensah, O. S., & Caesar, A. E. (2017). Factors influencing the agricultural technology adoption: The case of improved rice varieties (Nerica) in the Northern Region, Ghana. Journal of Economics and Sustainable Development, 8(8), 1700–2222. www.iiste.org
   Google Scholar
• Bonabana-Wabbi, J., Hezron, M., Onesmus, S., Julian, K., & Basil, M. (2016). Adoption of integrated soil fertility management by groundnut farmers in Eastern Uganda. Journal of Development and Agricultural Economics, 8(4), 86–94. https://doi.org/10.5897/JDAE2014-0627
   Google Scholar
• Chune, M. D., Beatrice, A., Obrine, A. B., & Kwesiga, M. (2022). Measuring technical efficiency and its determinants among sweet potatoes farmers in Western Uganda. African Journal of Agricultural Research, 18(2), 117–126. https://doi.org/10.5897/AJAR2021.15878
   Google Scholar
• Debebe, S. (2022). Post ‑ harvest losses of crops and its determinants in Ethiopia : Tobit model analysis. Agriculture & Food Security, 1–8. https://doi.org/10.1186/s40066-022-00357-6
   Google Scholar
• Dzanku, F. M., & Osei, R. D. (2018). Impact of pre – And post-harvest training reminders on crop losses and food poverty in Mali. 30th International Conference of Agricultural Economists, 0–28.
   Google Scholar
• Endalew, B., & Yenewa, W. (2021). Determinants of farm households ’ participation in fish production in Southwest Ethiopia. Cogent Food & Agriculture. https://doi.org/10.1080/23311932.2020.1728107
   Google Scholar
• Eticha, D. (2021). Analysis of stallholder farmers’ participation in agricultural extension services in Yayo and Hurumu Districts of Oromia, South-West Ethiopia. Journal of Economics and Sustainable Development, 12(21), 10–21. https://doi.org/10.7176/jesd/12-21-02
   Google Scholar
• Fahad, S., & Wang, J. (2018). Farmers’ risk perception, vulnerability, and adaptation to climate change in rural Pakistan. Land Use Policy, 79(April), 301–309. https://doi.org/10.1016/j.landusepol.2018.08.018
   Google Scholar
• FAO. (2002). The role of agriculture in the development of least-developed countries and their integration into the world economy. Fao, 7. http://www.fao.org/3/a-y3997e.pdf
   Google Scholar
• FAO. (2018). AGRISurvey programme in Uganda key findings of the annual agricultural survey 2018. Food and Agriculture Organization of the United Nations.
   Google Scholar
• Greene, W. H., & Hensher, D. A. (2010). Ordered choices and heterogeneity in attribute processing. Journal of Transport Economics and Policy, 44(3), 331–364. https://www.jstor.org/stable/25801404
   Google Scholar
• Ismail, I. J., & Changalima, I. A. (2019). Postharvest losses in maize : Determinants and effects on profitability of processing agribusiness enterprises in Tanzania department of business administration and management department of business administration and management. East African Journal of Social and Applied Sciences (EAJ-SAS), 1(2), 203–211. https://www.mocu.ac.tz
   Google Scholar
• Jaza, F. A. J., Tsafack, P. P., & Kamajou, F. (2018). Logit model of analysing the factors affecting the adoption of goat raising activity by farmers in the non-pastoral centre region of Cameroon. Tropicultura, 36(1), 54–62. https://doi.org/10.25518/2295-8010.976
   Google Scholar
• Jegede, O. O. (2020). Node between firm’s knowledge-intensive activities and their propensity to innovate: Insights from Nigeria’s mining industry. African Journal of Science, Technology, Innovation and Development, 12(7), 873–881. https://doi.org/10.1080/20421338.2020.1746043
   Web of Science ®Google Scholar
• Jose, A., Philip, M., Prasanna, L. T., & Manjula, M. (2020). Comparison of probit and logistic regression models in the analysis of dichotomous outcomes. Current Research in Biostatistics, 10(1), 1–19. https://doi.org/10.3844/amjbsp.2020.1.19
   Google Scholar
• Kemboi, E., Muendo, K., & Kiprotich, C. (2020). Crop diversification analysis amongst smallholder farmers in Kenya(empirical evidence from Kamariny ward, Elgeyo Marakwet County). Cogent Food and Agriculture, 6(1), 1834669. https://doi.org/10.1080/23311932.2020.1834669
   Web of Science ®Google Scholar
• Kumar, S., & Underhill, S. (2019). Smallholder farmer perceptions of postharvest loss and its determinants in Fijian tomato value chains. Horticulturae, 5(4), 1–14. https://doi.org/10.3390/horticulturae5040074
   Web of Science ®Google Scholar
• Kurdyś-Kujawska, A., Strzelecka, A., & Zawadzka, D. (2021). The impact of crop diversification on the economic efficiency of small farms in Poland. Agriculture (Switzerland), 11(3). https://doi.org/10.3390/agriculture11030250
   Google Scholar
• MAAIF. (2016). The republic of Uganda, agriculture sector strategic plan 2015/2016-2019/2020. 1–100. http://npa.go.ug/wp-content/uploads/2016/08/ASSP-Final-Draft.pdf
   Google Scholar
• MAAIF. (2017). The Republic of Uganda Ministry of Agriculture, Animal Industries and Fisheries: Performance Review Report. 150. https://www.agriculture.go.ug/
   Google Scholar
• MAAIF. (2020). The Republic of Uganda Ministry of Agriculture, Animal Industry and Fisheries,Draft Annual Report. 1– 150(October), 150.
   Google Scholar
• Manzoor, A., Tanwir, F., Akhtar, S., & Shahbaz, B. (2020). An assessment of knowledge level and training gap of the farmers regarding post-harvest losses of wheat in Punjab, Pakistan. Journal of Pure and Applied Agriculture, 5(1), 2617–8680. http://jpaa.aiou.edu.pk/
   Google Scholar
• Martey, E., Etwire, P. M., Wiredu, A. N., & Dogbe, W. (2014). Factors influencing willingness to participate in multi-stakeholder platform by smallholder farmers in Northern Ghana: Implication for research and development. Agricultural and Food Economics, 2(1), 1–15. https://doi.org/10.1186/s40100-014-0011-4
   Google Scholar
• Mbachu, H. I., Nduka, E. C., & Nja, M. E. (2012). Designing a Pseudo R-Squared Goodness-of-Fit Measure in Generalized Linear Models. Journal of Mathematics Research, 4(2). https://doi.org/10.5539/jmr.v4n2p148
   Google Scholar
• Midamba, D. C. (2022). Determinants of maize production income in Western Uganda. East African Journal of Agriculture and Biotechnology, 5(1), 1–13. https://doi.org/10.37284/eajab.5.1.532
   Google Scholar
• Midamba, D. C., Obrine, A. B., Kwesiga, M., Beatrice, A., & Kizito, O. (2022). Drivers of access to credit among smallholder farmers in Uganda: Application of binary logistic model. East African Journal of Business and Economics, 5(1), 154–163. https://doi.org/10.37284/eajbe.5.1.622
   Google Scholar
• Mohamed, K. S., & Temu, A. E. (2008). Access to credit and its effect on the adoption of agricultural technologies: The case of Zanzibar. Savings and Development, 32(SUPPL), 45–89. https://doi.org/10.2307/41410533
   Google Scholar
• Mthombeni, D. L., Antwi, M. A., & Oduniyi, O. S. (2022). Factors influencing access to agro-processing training for small-scale crop farmers in Gauteng province of South Africa. Agriculture and Food Security, 11(1), 1–7. https://doi.org/10.1186/s40066-022-00370-9
   Google Scholar
• Mugwe, J., Mugendi, D., Mucheru-Muna, M., Merckx, R., Chianu, J., & Vanlauwe, B. (2009). Determinants of the decision to adopt integrated soil fertility management practices by smallholder farmers in the central highlands of Kenya. Experimental Agriculture, 45(1), 61–75. https://doi.org/10.1017/S0014479708007072
   Web of Science ®Google Scholar
• Mwaura, F. M., & Adong, A. (2016). Determinants of households’ land allocation for crop production in Uganda. Journal of Sustainable Development, 9(5), 229. https://doi.org/10.5539/jsd.v9n5p229
   Google Scholar
• Nahayo, A., Omondi, M. O., Zhang, X. H., LI, L. Q., PAN, G. X., & Joseph, S. (2017). Factors influencing farmers’ participation in crop intensification program in Rwanda. Journal of Integrative Agriculture, 16(6), 1406–1416. https://doi.org/10.1016/S2095-3119(16)61555-1
   Web of Science ®Google Scholar
• Nakano, Y., Tsusaka, T. W., Aida, T., & Pede, V. O. (2018). Is farmer-to-farmer extension effective? The impact of training on technology adoption and rice farming productivity in Tanzania. World Development, 105, 336–351. https://doi.org/10.1016/j.worlddev.2017.12.013
   Web of Science ®Google Scholar
• Ngeywo, J., Basweti, E., & Shitandi, A. (2015). Influence of Gender, Age, Marital Status and Farm Size on Coffee Production: A Case of Kisii County, Kenya. Asian Journal of Agricultural Extension, Economics & Sociology, 5(3), 117–125. https://doi.org/10.9734/ajaees/2015/15702
   Google Scholar
• Njenga, M. W., Mugwe, J. N., Mogaka, H., Nyabuga, G., Kiboi, M., Ngetich, F., Mucheru-Muna, M., Sijali, I., & Mugendi, D. (2021). Communication factors influencing adoption of soil and water conservation technologies in the dry zones of Tharaka-Nithi County, Kenya. Heliyon, 7(10), e08236. https://doi.org/10.1016/j.heliyon.2021.e08236
   PubMed Web of Science ®Google Scholar
• Odendo, M., Obare, G., & Salasya, B. (2009). Factors responsible for differences in uptake of integrated soil fertility management practices amongst smallholders in western Kenya. African Journal of Agricultural Research, 4(11), 1303–1311. https://www.academicjournals.org/AJAR
   Web of Science ®Google Scholar
• Ojo, C. O., Bila, Y., & Iheanacho, A. C. (2012). Agricultural resource access and the influence of socioeconomic characteristics among rural women in borno state, Nigeria. Agris On-Line Papers in Economics and Informatics, 4(2), 27–34. https://doi.org/10.22004/ag.econ.131360
   Google Scholar
• Okeyo, S. O., Ndirangu, S. N., Isaboke, H. N., Njeru, L. K., & Omenda, J. A. (2020). Analysis of the determinants of farmer participation in sorghum farming among small-scale farmers in Siaya County, Kenya. Scientific African, 10. https://doi.org/10.1016/j.sciaf.2020.e00559
   Google Scholar
• Ouko, K. O., Robert, O. G., Alworah, G. O., Zephaniah, M., & Sharon, V. O. (2020). Effects of covid-19 pandemic on food security and household livelihoods in Kenya. Review of Agricultural and Applied Economics, 23(2), 72–80. https://doi.org/10.22004/ag.econ.308403
   Google Scholar
• Pratiwi, A., & Suzuki, A. (2017). Effects of farmers’ social networks on knowledge acquisition: Lessons from agricultural training in rural Indonesia. Journal of Economic Structures, 6(1). https://doi.org/10.1186/s40008-017-0069-8
   Google Scholar
• Saqib, S. E., Kuwornu, J. K. M., Panezia, S., & Ali, U. (2018). Factors determining subsistence farmers’ access to agricultural credit in flood-prone areas of Pakistan. Kasetsart Journal of Social Sciences, 39(2), 262–268. https://doi.org/10.1016/j.kjss.2017.06.001
   Google Scholar
• Shee, A., Mayanja, S., Simba, E., Stathers, T., Bechoff, A., & Bennett, B. (2019). Determinants of postharvest losses along smallholder producers maize and Sweet potato value chains: An ordered Probit analysis. Food Security, 11(5), 1101–1120. https://doi.org/10.1007/s12571-019-00949-4
   Web of Science ®Google Scholar
• Shively, G., & Hao, J. (2012). A review of agriculture, food security and human nutrition issues in Uganda. University Department of Agricultural …, September, 1–42. http://ageconsearch.umn.edu/bitstream/135134/2/12-3Shively.Hao.pdf
   Google Scholar
• Sugri, I., Abubakari, M., Owusu, R. K., & Bidzakin, J. K. (2021). Postharvest losses and mitigating technologies: Evidence from Upper East Region of Ghana. Sustainable Futures, 3(December 2020), 100048. https://doi.org/10.1016/j.sftr.2021.100048
   Google Scholar
• Tolno, E., Kobayashi, H., Ichizen, M., Esham, M., & Balde, B. S. (2015). Economic analysis of the role of farmer organizations in enhancing smallholder potato farmers’ income in Middle Guinea. Journal of Agricultural Science, 7(3), 123–137. https://doi.org/10.5539/jas.v7n3p123
   Google Scholar
• UBOS. (2017). Area specific profiles Nwoya District. The National Population and Housing Census 2014
   Google Scholar
• UBOS. (2018a). Annual agricultural survey 2018 statistical release (p. 4).
   Google Scholar
• UBOS. (2018b). Uganda bureau of statistics. The 2018 statistical report. 345.
   Google Scholar
• Vu, H., Van, Ho, H., & Le, Q. H. (2020). Impact of farmers’ associations on household income: Evidence from tea farms in Vietnam. Economies, 8(4), 92. https://doi.org/10.3390/economies8040092
   Web of Science ®Google Scholar
• Weber, N. C. (2020). Field and post-harvest factors affecting the quality and shelf life of soft fruits. Modern Concepts & Developments in Agronomy, 6(2), 631–634. https://doi.org/10.31031/mcda.2020.06.000634
   Google Scholar
• Yahaya, S., & Mardiyya, A. (2019). Review of post-harvest losses of fruits and vegetables. Biomedical Journal of Scientific & Technical Research, 13(4), 10192–10200. https://doi.org/10.26717/bjstr.2019.13.002448
   Google Scholar
• Yang, Q., Zhu, Y., & Wang, F. (2021). Exploring mediating factors between agricultural training and farmers ’ adoption of drip fertigation system : Evidence from banana farmers in China. Water (Switzerland), 1–18. https://doi.org/10.3390/w13101364
   Google Scholar
• Yeshiwas, Y., & Tadele, E. (2021). An and their handling investigation into major causes for postharvest losses of horticultural crops practice in Debre Markos, North-Western Ethiopia. Advances in Agriculture, 2021. https://doi.org/10.1155/2021/1985303
   Google Scholar
• Zangeneh, M., Akram, A., Nielsen, P., & Keyhani, A. (2015). Developing location indicators for agricultural service center: A Delphi–TOPSIS–FAHP approach. Production and Manufacturing Research, 3(1), 124–148. https://doi.org/10.1080/21693277.2015.1013582
   Web of Science ®Google Scholar