ASSESSMENT OF RAINFALL PATTERN AND HOUSEHOLD FOOD SECURITY IN OLUYOLE AND IDDO LOCAL GOVERNMENT AREAS OF OYO STATE, NIGERIA
Keywords:
Rainfall Pattern, Household Food Security, Ibadan, Nigeria, and Climatic ConditionsAbstract
Food security and appropriate nutrition are essential for making progress towards sustainable development. It has been stated that about two (2) billion people worldwide, particularly those living in rural and semi-urban regions, face moderate to severe food insecurity, and hunger rates have risen in recent years after falling steadily for decades. the FAO ascribed much of this growth to climate change. There is a lack of empirical evidence on the relationship between climatic conditions and food security in Ibadan metropolis; a city with mix of rural, semi-urban areas; therefore, an assessment of the Rainfall Pattern (RP) and Household Food Security (HFS) was conducted in Oluyole and Iddo Local Government Areas (LGAs) of Oyo state, Nigeria. Climatic data RP for the research locations were collected from the International Institute of Tropical Agriculture's (IITA) Ibadan Meteorological Station between 2018 and 2019. The sample included 265 families from 37 enumeration areas, and was intended to be represntative of both Oluyole and Iddo LGAs. Multi-stage cluster sampling was employed, and the EAs represented the entire LGA, which were randomly picked using a GPS offset of 0-10km. Follow-ups were undertaken in 2020-21, and 2022-2023, with an attrition rate of approximately 3% per cycle. Sets with fixed effects Regression models were used to investigate the relationships between rainy season precipitation and two household food security indicators: the Food Consumption Score (FCS) and the Reduced Coping Strategies Index (rCSI). Data were analysed using descriptive and inferential statistics with alpha=0.05. In 2018-19,292-21, and 2022-2023, rainy season precipitation averaged (1375.60mm, 1398.34mm, and 1420.18mm, respectively). Each home has an average family size of five, and 56.3% of respondents are involved in farming and related activities. The primary source of income of chosen households was derived from staple crop (Maize) farming, with farm sizes ranging from 1-6ha. Maize yield was 15% higher on average, resulting in net returns ranging from N2,900 to N4,795 per hectare. There was a significant link between the FCS (2.78) and rCSI* (-0.014), indicating that rainfall pattern affected food security of households. Households in both Oluyole and Iddo LGAs were particularly susceptible to changes in rainfall patterns. Interventions, such as increasing access to extension services, expanding agricultural index insurance, and implementing programs to mitigate climate-induced food security, may promote climate change resilience in Oyo state's Oluyole and Iddo local government areas.
References
Acevedo M., Pixley K., Zinyengere N., Meng S., Tufan H., Cichy K., Bizikova L., Isaacs K., Ghezzi-Kopel K., & Porciello J. (2020). A scoping review of adoption of climate-resilient crops by small-scale producers in low- and middle-income countries. Nat. Plants 6, 1231–1241. 10.1038/s41477-020-00783-z
Agnolucci, P., Rapti C., Alexander, P., De Lipsis V., Holland R.A., Eigenbrod, F., & Ekins, P. (2020). Impacts of rising temperatures and farm management practices on global yields of 18 crops. Nat. Food 1, 562–571. 10.1038/s43016-020-00148- Arce CE, Caballero J, 2015. Nigeria Agricultural Sector Risk Assessment.
Arslan, A., Belotti, F., & Lipper, L. (2017). Smallholder productivity and weather shocks: Adoption and impact of widely promoted agricultural practices in Nigeria. Food Policy 69, 68–81. 10.1016/j.foodpol.2017.03.005
Asfaw, S., Scognamillo, A., Caprera, G.D., Sitko, N., & Ignaciuk, A. (2019). Heterogeneous impact of livelihood diversification on household welfare: Cross-country evidence from Sub-Saharan Africa. World Dev. 117, 278–295. 10.1016/j.worlddev.2019.01.017
Aylward, C., Biscaye, P., Harris, K., LaFayette, M., True, Z., Anderson, C.L., & Reynolds, T. (2015). Maize Yield Trends and Agricultural Policy in East Africa (EPAR Technical Report No. 310). University of Washington.
Baffes, J., Kshirsagar, V., & Mitchell, D. (2019). What Drives Local Food Prices? Evidence from the Nigerian Maize Market. World Bank Econ. Rev 33, 160–184.
Bahru, B.A., Bosch, C., Birner, R., & Zeller, M. (2019). Drought and child undernutrition in Ethiopia: A longitudinal path analysis. PLoS ONE 14. 10.1371/journal.pone.0217821.
Bai, Y., Naumova, E.N., & Masters, W.A. (2020). Seasonality of diet costs reveals food system performance in Africa. Sci. Adv 6, eabc2162. 10.1126/sciadv.abc2162 Barrett CB, 2010. Measuring Food Insecurity. Science 327, 825–828. 10.1126/science.1182768.
Barrios, S., Ouattara, B., & Strobl, E.(2008). The impact of climatic change on agricultural production: is it different for Africa? Food Policy 33, 287–298.
Beck, H.E., Zimmermann, N.E., McVicar, T.R., Vergopolan, N., Berg, A., & Wood, E.F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214. 10.1038/sdata.2018.214.
Bertelli, O., (2019). Boucher, S.R., Carter, M.R., Flatnes, J.E., Lybbert, T.J., Malacarne, J.G., Marenya, P., & Paul, L.A. (2021). Bundling Stress Tolerant Seeds and Insurance for More Resilient and Productive Small-scale Agriculture (Working Paper No. 29234), Working Paper Series. National Bureau of Economic Research. 10.3386/w29234.
Brander, M., Bernauer, T., & Huss, M. (2021). Improved on-farm storage reduces seasonal food insecurity of smallholder farmer households – Evidence from a randomized control trial in Nigeria. Food Policy, Food Loss and Waste: Evidence for effective policies 98, 101891. 10.1016/j.foodpol.2020.101891.
Carletto, C., Zezza, A., & Banerjee, R. (2013). Towards better measurement of household food security: Harmonizing indicators and the role of household surveys. Glob. Food Secur 10.1016/j.gfs.2012.11.006.
Carpena, F. (2019). How do droughts impact household food consumption and nutritional intake? A study of rural India. World Dev. 122, 349–369. 10.1016/j.worlddev.2019.06.005.
Conway, D., Mittal, N., van Garderen, E.A., Pardoe, J., Todd, M., Vincent, K., & Washington, R. (2017). Country Climate Brief: Future climate projections for Nigeria. Future Climate for Africa (FCFA).
Cooper, M., Brown, M.E., Azzarri, C., & Meinzen-Dick, R. (2019). Hunger, nutrition, and precipitation: evidence from Ghana and Bangladesh. Popul. Environ 41, 151–208. 10.1007/s11111-019-00323-8.
Dinku, T., Funk, C., Peterson, P., Maidment, R., Tadesse, T., Gadain, H., & Ceccato, P. (2018). Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Q. J. R. Meteorol. Soc 144, 292–312. 10.1002/qj.3244.
Evans School Policy Analysis and Research Group, 2019. Agricultural Development Data Curation [WWW Document]. URL https://evans.uw.edu/policy-impact/epar/agricultural-development-data-curation.
Fanzo, J., Davis, C., McLaren, R., & Choufani, J.(2018). The effect of climate change across food systems: Implications for nutrition outcomes. Glob. Food Security 18, 12–19. 10.1016/J.GFS.2018.06.001 FAO, 2016. Country Profile - United Republic of Nigeria. Rome, Italy.
FAO, IFAD, UNICEF, WFP, & WHO (2020). The State of Food Security and Nutrition in the World 2020. Transforming food systems for affordable healthy diets. FAO, Rome. 10.4060/ca9692en
FAO, IFAD, UNICEF, WFP, & WHO (2019). The State of Food Security and Nutrition in the World 2019. Safeguarding against economic slowdowns and downturns. Rome.
FAO, IFAD, UNICEF, WFP, & WHO (2018). The State of Food Security and Nutrition in the World: Building Climate Resilience for Food Security and Nutrition. Rome.
FEWS NET (2013). Nigeria - Seasonal Calendar [WWW Document]. URL https://fews.net/east-africa/Nigeria/seasonal-calendar/december-2013.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066. 10.1038/sdata.2015.66.
Funk, C., Peterson, P., Peterson, S., Shukla, S., Davenport, F., Michaelsen, J., Knapp, K.R., Landsfeld, M., Husak, G., Harrison, L., Rowland, J., Budde, M., Meiburg, A., Dinku, T., Pedreros, D., & Mata, N. (2019). A high-resolution 1983–2016 TMAX climate data record based on infrared temperatures and stations by the climate hazard center. J. Clim 32, 5639–5658. 10.1175/JCLI-D-18-0698.1.
Gebrechorkos, S.H., Hülsmann, S., & Bernhofer, C. (2019). Changes in temperature and precipitation extremes in Ethiopia, Kenya, and Nigeria. Int. J. Climatol 39, 18–30. 10.1002/joc.5777.
GuimarãesNobre, G., Davenport, F., Bischiniotis, K., Veldkamp, T., Jongman, B., Funk, C.C., Husak, G., Ward, P.J., & Aerts, J.C.J.H. (2019). Financing agricultural drought risk through ex-ante cash transfers. Sci. Total Environ 653, 523–535. 10.1016/j.scitotenv.2018.10.406.
Hadley, C., & Crooks, D.L. (2012). Coping and the biosocial consequences of food insecurity in the 21st century. Am. J. Phys. Anthropol 149, 72–94. 10.1002/ajpa.22161.
Headey, D., & Ecker, O. (2013). Rethinking the measurement of food security: From first principles to best practice. Food Security. 10.1007/s12571-013-0253-0.
Hoddinott, J., & Kinsey, B., (2001). Child Growth in the Time of Drought. Oxf. Bull. Econ. Stat 63, 409–436. 10.1111/1468-0084.t01-1-00227
Iizumi, T., & Ramankutty, N. (2016). Changes in yield variability of major crops for 1981–2010 explained by climate change. Environ. Res. Lett 11, 034003. 10.1088/1748-9326/11/3/034003.
Jones, A.D., Ngure, F.M., Pelto, G., & Young, S.L. (2013). What Are We Assessing When We Measure Food Security? A Compendium and Review of Current Metrics. Adv. Nutr 4, 481–505. 10.3945/an.113.004119.
Kolenikov, S., & Angeles, G. (2009). Socioeconomic status measurement with discrete proxy variables: Is principal component analysis a reliable answer? Rev. Income Wealth 55, 128–165. 10.1111/j.1475-4991.2008.00309.x.
Laudien, R., Schauberger, B., Makowski, D., & Gornott, C. (2020). Robustly forecasting maize yields in Nigeria based on climatic predictors. Sci. Rep 10, 19650. 10.1038/s41598-020-76315-8
Lesk, C., Rowhani, P., & Ramankutty, N. (2016). Influence of extreme weather disasters on global crop production. Nature 529, 84–87. 10.1038/nature16467.
Letta, M., Montalbano, P., & Tol, R.S.J. (2018). Temperature shocks, short-term growth and poverty thresholds: Evidence from rural Nigeria. World Dev. 112, 13–32. 10.1016/j.worlddev.2018.07.013.
Linden, A. (2019). BMI: Stata module to compute Body Mass Index. Stat. Software. Compon.
Lobell, D.B., Bänziger, M., Magorokosho, C., & Vivek, B.(2011). Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat. Clim. Change 1, 42–45. 10.1038/nclimate1043.
Masters, W.A., Bai, Y., Herforth A., Sarpong, D.B., Mishili, F., Kinabo, J, & Coates, J.C. (2018). Measuring the Affordability of Nutritious Diets in Africa: Price Indexes for Diet Diversity and the Cost of Nutrient Adequacy. Am. J. Agric. Econ 100, 1285–1301. 10.1093/ajae/aay059 [PMC free article] [PubMed].
Maxwell, D., & Caldwell, R. (2008). The Coping Strategies Index Field Methods Manual Second Edition. Washington, DC.
Maxwell, D, Vaitla, B, & Coates, J. (2014). How do indicators of household food insecurity measure up? An empirical comparison from Ethiopia. Food Policy 47, 107–116. 10.1016/j.foodpol.2014.04.003.
Michler, J.D., Josephson, A., Kilic, T., & Murray, S. (2021). Estimating the Impact of Weather on Agriculture (Working Paper). World Bank, Washington, DC. 10.1596/1813-9450-9867.
Mueller, V, & Gray, C. (2018). Heat and adult health in China. Popul. Environ 40, 1–26. 10.1007/s11111-018-0294-6
Mulmi, P., Block, S.A., Shively, G.E, & Masters, W.A, (2016). Climatic conditions and child height: Sex-specific vulnerability and the protective effects of sanitation and food markets in Nepal. Econ. Hum. Biol 23, 63–75. 10.1016/j.ehb.2016.07.002
National Bureau of Statistics (2009). Nigeria National Panel Survey 2008-2009 (Round 1).
Parkes, B., Higginbottom, T.P., Hufkens, K., Ceballos, F., Kramer B., & Foster T. (2019). Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change. Environ. Res. Lett 14, 124089. 10.1088/1748-9326/ab5ebb
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Kolawole FARINLOYE
This work is licensed under a Creative Commons Attribution 4.0 International License.
