Abstract
Rainfed farming systems that are prevalent in sub-Saharan Africa are prone to climate change. Most studies have only estimated the impacts of climate change on agricultural productivity at a regional or national level. This overlooks localized effects at the subnational level, especially within defined agro-ecological regions. Using 30 years (1981–2011) of crop yield and weather data in Zambia, we apply the Just and Pope framework to determine how rainfall and temperature affect yield and yield variability of beans and maize at the national and agro-ecological region levels. At the national level, we find significant negative effect of a rise in temperature on bean and maize yield, while rainfall increases have a positive effect on bean and maize yields. These results differ by agro-ecological region. Rainfall has a positive and significant effect on maize yield in the low rainfall regions I and II, but it has a negative effect on maize yield in the high-rainfall region III and no significant effect on bean yield. Temperature has varied effects by regions and crops. Predicted impacts using HadGEM-ES2 global circulation model show that major yield decreases (25% for maize and 34% for beans) by 2050 will be in region II and will be driven mainly by temperature increase offsetting the positive gains from rainfall increase. The model predominantly overpredicts bean yield and under predicts maize yields. These results call for agro-ecological region-specific adaptation strategies and inventive agricultural policy interventions which are more robust to climate change.
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Notes
Primarily, there are three agro-ecological regions but AER II is divided into two, (a) and (b), to further capture the different soil types.
“Representative Concentration Pathway (RCP) 4.5 is a scenario of long-term, global emissions of greenhouse gases, short-lived species, and land-use-land cover which stabilizes radiative forcing at 4.5 Watts per meter squared (approximately 650 ppm CO2-equivalent) in the year 2100 without ever exceeding that value.” (Thomson et al. 2011 p. 1).
HadGEM2-ES GCM is the Hadley Centre Global Environmental Model 2-Earth System (HadGEM2-ES) developed by the UK Met Office. It is one of the models used for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and the associated cycle of the fifth phase of the CMIP5. For a detailed description, see (Bellouin et al. 2011).
References
Agnolucci, P., & De Lipsis, V. (2019). Long-run trend in agricultural yield and climatic factors in Europe. Climatic Change. https://doi.org/10.1007/s10584-019-02622-3.
Alexandrov, V. A., & Hoogenboom, G. (2000). The impact of climate variability and change on crop yield in Bulgaria. Agricultural and Forest Meteorology, 104(4), 315–327. https://doi.org/10.1016/S0168-1923(00)00166-0.
Bannayan, M., Lotfabadi, S. S., Sanjani, S., Mohamadian, A., & Aghaalikhani, M. (2011). Effects of precipitation and temperature on crop production variability in northeast Iran. International Journal of Biometeorology, 55(3), 387–401. https://doi.org/10.1007/s00484-010-0348-7.
Barnwal, P., & Kotani, K. (2013). Climatic impacts across agricultural crop yield distributions: An application of quantile regression on rice crops in Andhra Pradesh, India. Ecological Economics, 87, 95–109. https://doi.org/10.1016/j.ecolecon.2012.11.024.
Bellouin, N., Rae, J., Jones, A., Johnson, C., Haywood, J., & Boucher, O. (2011). Aerosol forcing in the Climate Model Intercomparison Project (CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate. Journal of Geophysical Research, 116(D20), D20206. https://doi.org/10.1029/2011JD016074.
Bisht, P., Kumar, P., Yadav, M., Rawat, J. S., Sharma, M. P., & Hooda, R. S. (2013). Spatial dynamics for relative contribution of cropping pattern analysis on environment by integrating remote sensing and GIS. International Journal of Plant Production, 8(1), 1–17. https://doi.org/10.22069/ijpp.2014.1369.
Blanc, E. (2012). The Impact of Climate Change on Crop Yields in Sub-Saharan Africa. American Journal of Climate Change, 01(01), 1–13. https://doi.org/10.4236/ajcc.2012.11001.
Burke, W. J., Jayne, T. S., & Chapoto, A. (2010). Factors Contributing to Zambia’s 2010 Maize Bumper Harvest. Food Security Collaborative Policy Brief No. 97034. Michigan State University, Department of Agricultural, Food, and Resource Economics.
Cabas, J., Weersink, A., & Olale, E. (2010). Crop yield response to economic, site and climatic variables. Climatic Change, 101(3), 599–616. https://doi.org/10.1007/s10584-009-9754-4.
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Developmenthttps://doi.org/10.5194/gmd-7-1247-2014.
Chen, C. C., McCarl, B. A., & Schimmelpfennig, D. E. (2004). Yield variability as influenced by climate: A statistical investigation. Climatic Change, 66(1–2), 239–261. https://doi.org/10.1023/B:CLIM.0000043159.33816.e5.
Connolly-Boutin, L., & Smit, B. (2016). Climate change, food security, and livelihoods in sub-Saharan Africa. Regional Environmental Change, 16(2), 385–399. https://doi.org/10.1007/s10113-015-0761-x.
Crane-Droesch, A. (2018). Machine learning methods for crop yield prediction and climate change impact assessment in agriculture. Environmental Research Letters, 13(11), 114003. https://doi.org/10.1088/1748-9326/aae159.
Deressa, T. T. (2007). Measuring The Economic Impact of Climate Change on Ethiopian Agriculture : Ricardian Approach (No. 4342; Policy Research Workin Paper, Issue September).
Deressa, T. T., Hassan, R. M., Ringler, C., Alemu, T., & Yesuf, M. (2009). Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environmental Change, 19(2), 248–255. https://doi.org/10.1016/j.gloenvcha.2009.01.002.
Exenberger, A., Pondorfer, A., & Wolters, M. H. (2014). Estimating the impact of climate change on agricultural production: Accounting for technology heterogeneity across countries. Kiel Working Papers.
Fonta, W. M., Kedir, A. M., Bossa, A. Y., Greenough, K. M., Sylla, B. M., & Ayuk, E. T. (2018). A Ricardian valuation of the impact of climate change on Nigerian cocoa production: Insight for adaptation policy. International Journal of Climate Change Strategies and Management, 10(5), 689–710. https://doi.org/10.1108/IJCCSM-05-2016-0074.
Fufa, B., & Hassan, R. M. (2003). Stochastic maize production technology and production risk analysis in Dadar district, east Ethiopia. Agrekon, 42(2), 116–128. https://doi.org/10.1080/03031853.2003.9523615.
Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., et al. (2015). Shock waves: Managing the impacts of climate change on poverty. The World Bank. https://doi.org/10.1596/978-1-4648-0673-5.
Hamududu, B. H., & Ngoma, H. (2019). Impacts of climate change on water resources availability in Zambia: implications for irrigation development. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-019-00320-9.
Harris, J., Chisanga, B., Drimie, S., & Kennedy, G. (2019). Nutrition transition in Zambia: Changing food supply, food prices, household consumption, diet and nutrition outcomes. Food Security, 11(2), 371–387. https://doi.org/10.1007/s12571-019-00903-4.
Isik, M., & Devadoss, S. (2006). An analysis of the impact of climate change on crop yields and yield variability. Applied Economics. https://doi.org/10.1080/00036840500193682.
Islam, S., Cenacchi, N., Sulser, T. B., Gbegbelegbe, S., Hareau, G., Kleinwechter, U., et al. (2016). Structural approaches to modeling the impact of climate change and adaptation technologies on crop yields and food security. Global Food Security, 10, 63–70. https://doi.org/10.1016/j.gfs.2016.08.003.
Jain, S. (2007). An empirical economic assessment of impacts of climate change on agriculture in Zambia. The World Bank. https://doi.org/10.1596/1813-9450-4291.
Jayne, T. S., Govereh, J., Chilonda, P., Mason, N., & Chapoto, A. (2007). Trends in Agricultural and Rural Development Indicators in Zambia ReSAKSS Working Paper No.2 (No. 2; ReSAKSS Working Paper). www.sa.resakss.org.
Just, R. E., & Pope, R. D. (1978). Stochastic specification of production functions and economic implications. Journal of Econometrics, 7(1), 67–86. https://doi.org/10.1016/0304-4076(78)90006-4.
Kabubo-mariara, J., & Karanja, F. K. (2007). The economic impact of climate change on Kenyan crop agriculture : A Ricardian approach. Global and Planetary Change, 57, 319–330. https://doi.org/10.1016/j.gloplacha.2007.01.002.
Kotir, J. H. (2011). Climate change and variability in Sub-Saharan Africa: A review of current and future trends and impacts on agriculture and food security. Environment, Development and Sustainability, 13(3), 587–605. https://doi.org/10.1007/s10668-010-9278-0.
Libanda, B., & Ngonga, C. (2018). Projection of frequency and intensity of extreme precipitation in Zambia: a CMIP5 study. Climate Research, 76(1), 59–72. https://doi.org/10.3354/cr01528.
Libanda, B., Zheng, M., & Ngonga, C. (2019). Spatial and temporal patterns of drought in Zambia. Journal of Arid Land, 11(2), 180–191. https://doi.org/10.1007/s40333-019-0053-2.
Lin, Y., Liu, A., Ma, E., & Zhang, F. (2013). Impacts of Future Climate Changes on Shifting Patterns of the Agro-Ecological Zones in China. Advances in Meteorology, 2013, 163248. https://doi.org/10.1155/2013/163248.
Martin, E., Perine, C., Lee, V., & Ratcliffe, J. (2018). Decentralized governance and climate change adaptation: Working locally to address community resilience priorities. Climate Change Management. https://doi.org/10.1007/978-3-319-72874-2_1.
Mulenga, B. P., Wineman, A., & Sitko, N. J. (2017). Climate Trends and Farmers’ Perceptions of Climate Change in Zambia. Environmental Management, 59(2), 291–306. https://doi.org/10.1007/s00267-016-0780-5.
Mulungu, K., & Tembo, G. (2015). Effects of weather variability on crop abandonment. Sustainability (Switzerland). https://doi.org/10.3390/su7032858.
Mulungu, K., & Ng’ombe, J. . (2019). Climate change impacts on sustainable maize production in Sub-Saharan Africa: A Review. In Maize - Production and Use: IntechOpen. https://doi.org/10.5772/intechopen.90033.
Ng’ombe, J. N. . (2017). Technical efficiency of smallholder maize production in Zambia: a stochastic meta-frontier approach. Agrekon, 56(4), 347–365. https://doi.org/10.1080/03031853.2017.1409127.
Onofri, L., Bianchin, F., & Boatto, V. (2019). How to assess future agricultural performance under climate change? A case-study on the Veneto region. Agricultural and Food Economics, 7(1), 16. https://doi.org/10.1186/s40100-019-0131-y.
Onyekuru, N. J. A., & Marchant, R. (2016). Assessing the economic impact of climate change on forest resource use in Nigeria: A Ricardian approach. Agricultural and Forest Meteorology, 220, 10–20. https://doi.org/10.1016/j.agrformet.2016.01.001.
Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W., Christ, R., et al. (2014). Climate change 2014 synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. Geneva: IPCC.
Saha, A., Havenner, A., & Talpaz, H. (1997). Stochastic production function estimation: Small sample properties of ML versus FGLS. Applied Economics, 29(4), 459–469. https://doi.org/10.1080/000368497326958.
Sarker, M. A. R., Alam, K., & Gow, J. (2019). Performance of rain-fed Aman rice yield in Bangladesh in the presence of climate change. Renewable Agriculture and Food Systems, 34(4), 304–312. https://doi.org/10.1017/S1742170517000473.
Schlenker, W., & Lobell, D. B. (2010). Robust negative impacts of climate change on African agriculture. Environmental Research Letters, 5(1), 014010. https://doi.org/10.1088/1748-9326/5/1/014010.
Schreurs, M. A. (2008). From the Bottom Up: Local and Subnational Climate Change Politics. The Journal of Environment & Development, 17(4), 343–355. https://doi.org/10.1177/1070496508326432.
Serdeczny, O., Adams, S., Baarsch, F., Coumou, D., Robinson, A., Hare, W., et al. (2017). Climate change impacts in Sub-Saharan Africa: from physical changes to their social repercussions. Regional Environmental Change, 17(6), 1585–1600. https://doi.org/10.1007/s10113-015-0910-2.
Shi, W., & Tao, F. (2014). Vulnerability of African maize yield to climate change and variability during 1961–2010. Food Security, 6(4), 471–481. https://doi.org/10.1007/s12571-014-0370-4.
Sichilima, T., Mapemba, L., & Tembo, G. (2016). Drivers of dry common beans trade in Lusaka, Zambia: A trader’s perspective. Sustainable Agriculture Research. https://doi.org/10.22004/ag.econ.234990.
Singh, K., Kumar, P., & Singh, B. K. (2013). An associative relational impact of water quality on crop yield: A comprehensive index analysis using LISS-III Sensor. IEEE Sensors Journal, 13(12), 4912–4917. https://doi.org/10.1109/JSEN.2013.2276760.
Sultan, B., Defrance, D., & Iizumi, T. (2019). Evidence of crop production losses in West Africa due to historical global warming in two crop models. Scientific Reports, 9(1), 1–15. https://doi.org/10.1038/s41598-019-49167-0.
Sultan, B., & Gaetani, M. (2016). Agriculture in West Africa in the twenty-first century: Climate change and impacts scenarios and potential for adaptation. Frontiers in Plant Science., 7, 1262.
Thomson, A. M., Calvin, K. V., Smith, S. J., Kyle, G. P., Volke, A., Patel, P., et al. (2011). RCP45: A pathway for stabilization of radiative forcing by 2100. Climatic Change, 109(1), 77–94. https://doi.org/10.1007/s10584-011-0151-4.
Thurlow, J., Zhu, T., & Diao, X. (2009). The impact of climate variability and change on economic growth and poverty in Zambia: In IFPRI discussion papers (No. 890; IFPRI Discussion Papers). International Food Policy Research Institute (IFPRI).
Thurlow, J., Zhu, T., & Diao, X. (2012). Current climate variability and future climate change: Estimated growth and poverty impacts for Zambia. Review of Development Economics, 16(3), 394–411. https://doi.org/10.1111/j.1467-9361.2012.00670.x.
Traore, B., Corbeels, M., van Wijk, M. T., Rufino, M. C., & Giller, K. E. (2013). Effects of climate variability and climate change on crop production in southern Mali. European Journal of Agronomy, 49, 115–125. https://doi.org/10.1016/j.eja.2013.04.004.
Willmott, C., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/cr030079.
Wineman, A., & Crawford, E. W. (2017). Climate change and crop choice in Zambia: A mathematical programming approach. NJAS - Wageningen Journal of Life Sciences, 81, 19–31. https://doi.org/10.1016/J.NJAS.2017.02.002.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). Cambridge: MIT Press.
Zampieri, M., Ceglar, A., Dentener, F., & Toreti, A. (2017). Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environmental Research Letters, 12(6), 64008. https://doi.org/10.1088/1748-9326/aa723b.
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Mulungu, K., Tembo, G., Bett, H. et al. Climate change and crop yields in Zambia: historical effects and future projections. Environ Dev Sustain 23, 11859–11880 (2021). https://doi.org/10.1007/s10668-020-01146-6
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DOI: https://doi.org/10.1007/s10668-020-01146-6