Climate Yield Models for Some Arable Crops in Ondo State

Omotayo, F. S., Lasisi, M. O and Ogundare S. A
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Omotayo, F. S., Lasisi, M. O: Department of Agricultural and Bio-Environmental Engineering, The Federal Polytechnic, Ado- Ekiti.
Ogundare S. A: Department of Agricultural and Bio-Environmental Engineering, The Federal Polytechnic, Ado- Ekiti.

International Journal of Research and Innovation in Social Science, 2024, vol. 8, issue 2, 2123-2132

Abstract: This study looks at the connection between easily observed climate variables and crop production. Selected arable crops’ yield statistics spanning ten years were analyzed, including melon, rice, cassava, yam, and maize. Using multiple linear regression models calibrated for each of the selected crops, the crop yield was the dependent variable in SPSS statistical package version 22.0. The meteorological data, including mean temperature (oC), rainfall (mm), evaporation (mm/yr), and relative humidity (%), were correlated with the crop yield. Standard error of estimates (SE), coefficient of determination (R2), and correlation coefficient (R) are the goodness-of-fit parameters that are used to validate the models. The results demonstrated that the R-value for maize is 0.83 with a SE of 4.4 tons/ha, and the R-value for cassava is 0.85 with a SE of 10.2 tons/ha. R-values for yam and melon are 0.77 and 44.5 tons/ha, respectively; for rice and cocoyam, the values were 0.64 and 16.6 tons/ha, respectively. Melon has an R-value of 0.69 and 18.6 tons/ha. Factors such as crop management methods, diseases, nutrients, and other factors that are challenging to account for in the current statistical modeling approach could be the cause of the difference in the goodness-of-fit. There is a great need for efforts to produce trustworthy data for agricultural productivity forecasting and planning in the study region as well as all over the nation.

Date: 2024
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