Assessing Farmers’ Knowledge of Weather Forecast Information in Crop Production: Evidence from Rural Bangladesh

Published on 15 March 2026 | AgroEnvironmental Sustainability
Vol. 4 No. 1 (2026): March Issue | DOI: 10.59983/s2026040102
Open Access
Download PDF
Check for updates via Crossmark
AgroEnvironmental Sustainability
Sharmin Akter , Swarnasree Sarker , Mohammed Nasir Uddin , Solaiman Saad , Mohammad Maruf Hasan , Saifur Rahman , Md. Rayhan Sojib

Abstract

This study assessed farmers’ knowledge of forecast information in crop production, explored the relationship between knowledge and selected socioeconomic characteristics, and identified key constraints limiting its effective use. Eighty farmers from two villages were chosen at random in Raiganj upazila, Sirajganj district, Bangladesh, and interviewed using a structured questionnaire. Farmers’ knowledge was measured across six cognitive levels following Bloom’s taxonomy, and data analysis was done through Pearson’s correlation and multiple linear regression. The results indicate that most farmers (78.8%) possessed a moderate level of knowledge, with 20.0\% demonstrating a high level of understanding. Among the eleven characteristics examined, age, education, farming experience, farm size, and credit received were positively correlated with knowledge, whereas media contact with extension and sources of forecast information showed negative relationships. Regression analysis indicated that education level, annual income, and forecast information sources accounted for 35.3% of the variance in knowledge. Over half (55%) of the farmers faced high constraints in using forecast information, while 45% faced them at a moderate level. The most critical barriers included lack of awareness of forecast information sources, poor access to forecast services for crop production, and insufficient advisory support from agricultural extension agents. These findings underscore the importance of strengthening farmers’ capacity to understand and apply forecast information by improving access and providing targeted training programs and extension services. Filling such these gaps can enable farmers to make well-informed decisions about their production, reduce risks associated with climatic variability, and ultimately improve agricultural productivity and rural livelihoods in Bangladesh.

Keywords

forecast information farmers’ knowledge crop production extension services

References

  1. Alam, G. M. M., Alam, K., Mushtaq, S., Sarker, M. N. I., & Hossain, M. (2020). Hazards, food insecurity and human displacement in rural riverine Bangladesh: Implications for policy. International Journal of Disaster Risk Reduction, 43, 101364. https://doi.org/10.1016/j.ijdrr.2019.101364 [Google Scholar]
  2. Anderson, L.W., Krathwohl, D.A. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman, New York. [Google Scholar]
  3. Antwi-Agyei, P., Dougill, A. J., & Abaidoo, R. C. (2021). Opportunities and barriers for using climate information for building resilient agricultural systems in Sudan savannah agro-ecological zone of north-eastern Ghana. Climate Services, 22, 100226. https://doi.org/10.1016/j.cliser.2021.100226 [Google Scholar]
  4. Archie, K. M., Dilling, L., Milford, J. B., & Pampel, F. C. (2014). Unpacking the ‘information barrier’: Comparing perspectives on information as a barrier to climate change adaptation in the interior mountain West. Journal of Environmental Management, 133, 397–410. https://doi.org/10.1016/j.jenvman.2013.12.015 [Google Scholar]
  5. Bacci, M., Ousman Baoua, Y., & Tarchiani, V. (2020). Agrometeorological Forecast for Smallholder Farmers: A Powerful Tool for Weather-Informed Crops Management in the Sahel. Sustainability, 12(8), 3246. https://doi.org/10.3390/su12083246 [Google Scholar]
  6. Bloom, B. S. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. Longmans, New York. [Google Scholar]
  7. Bruno Soares, M., Daly, M., & Dessai, S. (2018). Assessing the value of seasonal climate forecasts for decision‐making. WIREs Climate Change, 9(4). https://doi.org/10.1002/wcc.523 [Google Scholar]
  8. Chisadza, B., Mushunje, A., Nhundu, K., & Phiri, E. E. (2020). Opportunities and challenges for seasonal climate forecasts to more effectively assist smallholder farming decisions. South African Journal of Science, 116(1/2). https://doi.org/10.17159/sajs.2020/4649 [Google Scholar]
  9. Chisama, B. F. (2016). Farmers’ use of mobile phone technology for agricultural information services in Lilongwe District, Malawi. Master’s thesis, Purdue University, USA. [Google Scholar]
  10. Christel, I., Hemment, D., Bojovic, D., Cucchietti, F., Calvo, L., Stefaner, M., & Buontempo, C. (2018). Introducing design in the development of effective climate services. Climate Services, 9, 111–121. https://doi.org/10.1016/j.cliser.2017.06.002 [Google Scholar]
  11. Cliffe, N., Stone, R., Coutts, J., Reardon-Smith, K., & Mushtaq, S. (2016). Developing the capacity of farmers to understand and apply seasonal climate forecasts through collaborative learning processes. The Journal of Agricultural Education and Extension, 22(4), 311–325. https://doi.org/10.1080/1389224x.2016.1154473 [Google Scholar]
  12. Cocks, J. (2021). How resilient farmers thrive in the face of adversity. Kellogg Rural Leaders Programme Course 43. [Google Scholar]
  13. Curtis, S., Fair, A., Wistow, J., Val, D. V., & Oven, K. (2017). Impact of extreme weather events and climate change for health and social care systems. Environmental Health, 16(S1). https://doi.org/10.1186/s12940-017-0324-3 [Google Scholar]
  14. Das, M. K., Islam, M. M., & Billah, M. M. (2019). Farmers’ Knowledge, Attitude and Practice (KAP) Regarding Nitrogen Fortified Organic Manure in Crop Production. Asian Journal of Agricultural Extension, Economics & Sociology, 1–12. https://doi.org/10.9734/ajaees/2019/v33i330176 [Google Scholar]
  15. Department of Agricultural Extension (DAE). (2018). Baseline survey report to assess user satisfaction of agro-meteorological advisories and products under the agro-meteorological information systems development project. DAE, Dhaka. [Google Scholar]
  16. Diouf, N. S., Ouedraogo, I., Zougmoré, R. B., Ouedraogo, M., Partey, S. T., & Gumucio, T. (2019). Factors influencing gendered access to climate information services for farming in Senegal. Gender, Technology and Development, 23(2), 93–110. https://doi.org/10.1080/09718524.2019.1649790 [Google Scholar]
  17. Ebhuoma, E. E. (2022). Factors Undermining the Use of Seasonal Climate Forecasts Among Farmers in South Africa and Zimbabwe: Implications for the 1st and 2nd Sustainable Development Goals. Frontiers in Sustainable Food Systems, 6. https://doi.org/10.3389/fsufs.2022.761195 [Google Scholar]
  18. Fabregas, R., Harigaya, T., Kremer, M., & Ramrattan, R. (2022). Digital Agricultural Extension for Development. In Introduction to Development Engineering (pp. 187–219). Springer International Publishing. https://doi.org/10.1007/978-3-030-86065-3_8 [Google Scholar]
  19. Farouque, M. G., Mukta, M. Z. N., Mithun, M. N. A. S., Hasan, M. M., Rahman, M. A., & Fuyuki, K. (2025). Investigating Eco-Friendly Practices in Vegetable Cultivation: A Comprehensive Analysis of Knowledge and Use among Farmers in Narsingdi, Bangladesh. AgroEnvironmental Sustainability, 3(3), 236–245. https://doi.org/10.59983/s2025030306 [Google Scholar]
  20. Gbangou, T., Sarku, R., Slobbe, E. V., Ludwig, F., Kranjac-Berisavljevic, G., & Paparrizos, S. (2020). Coproducing Weather Forecast Information with and for Smallholder Farmers in Ghana: Evaluation and Design Principles. Atmosphere, 11(9), 902. https://doi.org/10.3390/atmos11090902 [Google Scholar]
  21. Ghosh, M.K., Hasan, S.S., Haque, E. (2020). Knowledge of farmers to sustainable agriculture practices: A case study in southwestern region in Bangladesh. Scholars Journal of Agriculture and Veterinary Sciences, 7(1): 5–12, https://doi.org/10.36347/sjavs.2020.v07i01.002. [Google Scholar]
  22. Guido, Z., Zimmer, A., Lopus, S., Hannah, C., Gower, D., Waldman, K., Krell, N., Sheffield, J., Caylor, K., & Evans, T. (2020). Farmer forecasts: Impacts of seasonal rainfall expectations on agricultural decision-making in Sub-Saharan Africa. Climate Risk Management, 30, 100247. https://doi.org/10.1016/j.crm.2020.100247 [Google Scholar]
  23. Hu, Q., Zillig, L. M. P., Lynne, G. D., Tomkins, A. J., Waltman, W. J., Hayes, M. J., Hubbard, K. G., Artikov, I., Hoffman, S. J., & Wilhite, D. A. (2006). Understanding Farmers’ Forecast Use from Their Beliefs, Values, Social Norms, and Perceived Obstacles*. Journal of Applied Meteorology and Climatology, 45(9), 1190–1201. https://doi.org/10.1175/jam2414.1 [Google Scholar]
  24. Ife-Adediran, O. O., & Aboyewa, O. B. (2021). Climate Change Resistant Energy Sources for Global Adaptation. In African Handbook of Climate Change Adaptation (pp. 1955–1966). Springer International Publishing. https://doi.org/10.1007/978-3-030-45106-6_106 [Google Scholar]
  25. Intergovernmental Panel on Climate Change (IPCC). (2014). Climate change 2014: Impacts, adaptation and vulnerability. Fifth assessment report. Cambridge University Press. [Google Scholar]
  26. Intergovernmental Panel on Climate Change (IPCC). (2021). Summary for policymakers. In: Climate change 2021: The physical science basis. Cambridge University Press. [Google Scholar]
  27. Jones, J., Hansen, J., Royce, F., & Messina, C. (2000). Potential benefits of climate forecasting to agriculture. Agriculture, Ecosystems & Environment, 82(1-3), 169–184. https://doi.org/10.1016/s0167-8809(00)00225-5 [Google Scholar]
  28. Kabir, M. H., Biswas, S., Rahman, M. S., Islam, M. S., & Tan, M. L. (2022). Determinants of vegetable growers’ knowledge and willingness to adopt botanical pesticides. International Journal of Pest Management, 70(4), 1029–1038. https://doi.org/10.1080/09670874.2022.2066733 [Google Scholar]
  29. Kamal, M., & Bablu, T. A. (2023). Mobile applications empowering smallholder farmers: A review of the impact on agricultural development. International Journal of Social Analysis, 8, 36–50. [Google Scholar]
  30. Kumar, U. (2021). Bridging science and society: Tailor-made hydroclimatic information services with farmers in Lower Bengal Delta, Bangladesh. PhD thesis, Wageningen University, Netherlands. [Google Scholar]
  31. Kumar, U., Werners, S., Roy, S., Ashraf, S., Hoang, L. P., Kumar Datta, D., & Ludwig, F. (2020). Role of Information in Farmers’ Response to Weather and Water Related Stresses in the Lower Bengal Delta, Bangladesh. Sustainability, 12(16), 6598. https://doi.org/10.3390/su12166598 [Google Scholar]
  32. Kumar, U., Werners, S. E., Paparrizos, S., Datta, D. K., & Ludwig, F. (2021). Co-producing climate information services with smallholder farmers in the Lower Bengal Delta: How forecast visualization and communication support farmers’ decision-making. Climate Risk Management, 33, 100346. https://doi.org/10.1016/j.crm.2021.100346 [Google Scholar]
  33. Kundu, S., Kabir, M. E., Morgan, E. A., Davey, P., & Hossain, M. (2020). Building Coastal Agricultural Resilience in Bangladesh: A Systematic Review of Progress, Gaps and Implications. Climate, 8(9), 98. https://doi.org/10.3390/cli8090098 [Google Scholar]
  34. Mapiye, O., Makombe, G., Molotsi, A., Dzama, K., & Mapiye, C. (2021). Information and communication technologies (ICTs): The potential for enhancing the dissemination of agricultural information and services to smallholder farmers in sub-Saharan Africa. Information Development, 39(3), 638–658. https://doi.org/10.1177/02666669211064847 [Google Scholar]
  35. Mitheu, F., Petty, C., Tarnavsky, E., Stephens, E., Ciampi, L., Butsatsa, J., & Cornforth, R. (2022). Identifying the Barriers and Opportunities in the Provision and Use of Weather and Climate Information for Flood Risk Preparedness: The Case of Katakwi District, Uganda. Frontiers in Climate, 4. https://doi.org/10.3389/fclim.2022.908662 [Google Scholar]
  36. Muema, E., Mburu, J., Coulibaly, J., & Mutune, J. (2018). Determinants of access and utilisation of seasonal climate information services among smallholder farmers in Makueni County, Kenya. Heliyon, 4(11), e00889. https://doi.org/10.1016/j.heliyon.2018.e00889 [Google Scholar]
  37. Norton, G. W., & Alwang, J. (2020). Changes in Agricultural Extension and Implications for Farmer Adoption of New Practices. Applied Economic Perspectives and Policy, 42(1), 8–20. https://doi.org/10.1002/aepp.13008 [Google Scholar]
  38. Nyadzi, E., Nyamekye, A. B., Werners, S. E., Biesbroek, R. G., Dewulf, A., Slobbe, E. V., Long, H. P., Termeer, C. J., & Ludwig, F. (2018). Diagnosing the potential of hydro-climatic information services to support rice farming in northern Ghana. NJAS: Wageningen Journal of Life Sciences, 86-87(1), 51–63. https://doi.org/10.1016/j.njas.2018.07.002 [Google Scholar]
  39. Ouedraogo, I., Diouf, N. S., Ablouka, G., Zougmoré, R. B., & Whitbread, A. (2021). Utility and Triggers in Uptake of Agricultural Weather and Climate Information Services in Senegal, West Africa. Atmosphere, 12(11), 1515. https://doi.org/10.3390/atmos12111515 [Google Scholar]
  40. Paparrizos, S., Smolenaars, W., Gbangou, T., Slobbe, E., & Ludwig, F. (2020). Verification of Weather and Seasonal Forecast Information Concerning the Peri-Urban Farmers’ Needs in the Lower Ganges Delta in Bangladesh. Atmosphere, 11(10), 1041. https://doi.org/10.3390/atmos11101041 [Google Scholar]
  41. Podder, S. K., Rahman, S. M. A., Hoque, M. J., Mithun, M. N. A. S., & Billah, M. M. (2022). Exploring attitudes towards yield gap minimization of rice: A case from Mymensingh district, Bangladesh. Socio Economic Policy Studies, 2(1), 14–20. https://doi.org/10.26480/seps.01.2022.14.20 [Google Scholar]
  42. Rahman, M. M., Ghosh, T., Salehin, M., Ghosh, A., Haque, A., & Hossain, M. A. (2020). Ganges-Brahmaputra-Meghna Delta, Bangladesh and India: A transnational mega-delta. In: Deltas in the Anthropocene. Springer, pp. 23–51, https://doi.org/10.1007/978-3-030-23517-8 [Google Scholar]
  43. Roy, P. (2017). Knowledge on food and nutrition of rural women involved in BRAC program in Mymensingh district. MS thesis, Department of Agricultural Extension Education, Bangladesh Agricultural University, Mymensingh. [Google Scholar]
  44. Roy, S., & Alam, A. (2020). Impacts of climatic disasters in the coastal area of Bangladesh: ‘Climate service’ a way forward. In: Leal Filho, W., Jacob, D. (eds.), Handbook of Climate Services. Springer, pp. 311–325, https://doi.org/10.1007/978-3-030-36875-3_16 [Google Scholar]
  45. Sarku, R., Addi, F., & Attoh, E. M. (2025). New Information and Communication Technologies for climate services: Evidence from farmers in Ada East District, Ghana. Climate Services, 37, 100535. https://doi.org/10.1016/j.cliser.2024.100535 [Google Scholar]
  46. Shrestha, N. (2020). Detecting Multicollinearity in Regression Analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39–42. https://doi.org/10.12691/ajams-8-2-1 [Google Scholar]
  47. K. K. Singh, Kripan Ghosh, S. C. Bhan, Priyanka Singh, Lata Vishnoi, R. Balasubramanian, S. D. Attri, Sheshakumar Goroshi, & R. Singh (2023). Decision support system for digitally climate informed services to farmers in India. Journal of Agrometeorology, 25(2), 205–214. https://doi.org/10.54386/jam.v25i2.2094 [Google Scholar]
  48. Tarchiani, V., Coulibaly, H., Baki, G., Sia, C., Burrone, S., Nikiema, P. M., Migraine, J., & Camacho, J. (2021). Access, Uptake, Use and Impacts of Agrometeorological Services in Sahelian Rural Areas: The Case of Burkina Faso. Agronomy, 11(12), 2431. https://doi.org/10.3390/agronomy11122431 [Google Scholar]
  49. Templeton, S. R., Shane Perkins, M., Aldridge, H. D., Bridges, W. C., & Lassiter, B. R. (2013). Usefulness and uses of climate forecasts for agricultural extension in South Carolina, USA. Regional Environmental Change, 14(2), 645–655. https://doi.org/10.1007/s10113-013-0522-7 [Google Scholar]
  50. Adekunle Stephen Toromade, , Deborah Aanuoluwa Soyombo, , Eseoghene Kupa, , & Tochukwu Ignatius Ijomah, (2024). Reviewing the impact of climate change on global food security: Challenges and solutions. International Journal of Applied Research in Social Sciences, 6(7), 1403–1416. https://doi.org/10.51594/ijarss.v6i7.1300 [Google Scholar]
  51. Uddin, M. N., Das, A. K., Sarker, M. A., Roy, D., Mithun, M. N. A. S., Rahman, S., & Uddin, M. S. (2024). Problems and Its Related Factors Affecting the Hatchery Owners in Producing Fish Seeds in Rural Bangladesh. Agricultural Research, 14(2), 363–374. https://doi.org/10.1007/s40003-024-00770-2 [Google Scholar]
  52. Uddin, M. N., Kabir, K. H., Roy, D., Hasan, M. T., Sarker, M. A., & Dunn, E. S. (2021). Understanding the constraints and its related factors in tilapia (Oreochromis sp.) fish culture at farm level: A case from Bangladesh. Aquaculture, 530, 735927. https://doi.org/10.1016/j.aquaculture.2020.735927 [Google Scholar]
  53. Vedeld, T., Hofstad, H., Mathur, M., Büker, P., & Stordal, F. (2020). Reaching out? Governing weather and climate services (WCS) for farmers. Environmental Science & Policy, 104, 208–216. https://doi.org/10.1016/j.envsci.2019.11.010 [Google Scholar]
Article Statistics

Usage statistics will be available soon.

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Akter, S., Sarker, S., Uddin, M. N., Saad, S., Hasan, M. M., Rahman, S., & Sojib, M. R. (2026). Assessing Farmers’ Knowledge of Weather Forecast Information in Crop Production: Evidence from Rural Bangladesh. AgroEnvironmental Sustainability, 4(1), 8–18. https://doi.org/10.59983/s2026040102

Search author on: PubMed | Google Scholar

Similar Articles

1-10 of 65

You may also start an advanced similarity search for this article.