Impact of Annual Temperature and Rainfall Anomalies on Maize Yields in Machakos County: An Analysis from 1993 To 2023

Purpose: To investigate the impact of Annual temperature and Rainfall Anomalies on Maize Yields in Machakos County from 1993 to 2023.

Methodology: The study utilized qualitative and quantitative data, collected through structured questionnaires as primary data and a secondary time series data template for secondary data. The target population included households, agricultural officers, and administrative officers in Matungulu East, Kaani/Kaewa, Mwala/Makutano, Ikombe, and Kangundo East wards. The sampled administrative wards contained 36,976 maize farming households selected through purposive sampling. The population was determined using Yamane's formula, resulting in a sample size of 395 maize farmers (households). Maize farming households were identified using cluster random sampling.

Results:  The study found a statistically significant negative effect of temperature variability on maize yields (β=-0.054, p=0.000). This implies that a one-unit increase in temperature variability is associated with a 0.054 tones per hectare decrease in maize yields, highlighting the sensitivity of maize to temperature extremes. Conversely, rainfall variability showed a negative but statistically insignificant effect on maize yields (β=-0.020, p=0.946). This suggests that other factors, possibly adaptation strategies, may mitigate the impact of inconsistent rainfall on maize yields.

Unique contribution to theory, policy and practice: The study underscores the differential impacts of temperature and rainfall variability on maize yields, emphasizing the need for targeted adaptation strategies to manage temperature fluctuations. Recommendations include promoting heat-resistant maize varieties, improving irrigation infrastructure, and enhancing water management practices. These insights contribute to agricultural planning and policy-making, aiming to enhance the resilience of farming households in Machakos County to climate variability.

Maize Yield (In Tonnes Output (kgs), Land size(acres).Temperature variability, Rainfall variability

1. Bosire, E. N., Gitau, W., Karanja, F., & Ouma, G. (2019). Analysis of Climate Variability, Trends, and Expected Implication on Crop Production in a Semi-Arid Environment of Machakos County, Kenya. Kenya. Adv. Agric. Sci, 7, 99-115. [Google Scholar] [Crossref]

2. Government of Kenya. (2018). National Climate Change Action Plan 2018–2022 Volume II: Adaptation Technical Analysis Report" [Google Scholar] [Crossref]

3. Mendelsohn, R. (2014). The impact of climate change on agriculture in Asia. Journal of Integrative Agriculture, 13(4), 660-665. [Google Scholar] [Crossref]

4. Jegede, A. O. (2018). Africa versus Climate Change Loss and Damages: Exploring AU Regional Channels for Influencing National Policy. Journal of African Foreign Affairs, 5(2), 207–226. [Google Scholar] [Crossref]

5. Zhang, Z., Shan, L., & Li, Y. (2018). Prolonged dry periods between rainfall events shorten the growth period of the resurrection plant Reaumuria soongorica. Ecology and Evolution, 8(2), 920-927. [Google Scholar] [Crossref]

6. Machakos County. (2015). County Integrated Development Plan. Available at: https://machakosassembly.go.ke/notices/machakos-county-cidp-plan-2023-2027/ [Google Scholar] [Crossref]

7. Maitah, M., Malec, K., & Maitah, K. (2021). Influence of Precipitation and Temperature on Maize Production in the Czech Republic from 2002 to 2019. Scientific Reports, 11(1), 1-11. [Google Scholar] [Crossref]

8. Mwanzia, M. S. (2020). Analysis of Farmers’ Adaptation Strategies to Climate Variability in Kyawango Location, Machakos County, Kenya (Doctoral dissertation, University of Nairobi). [Google Scholar] [Crossref]

9. Sagero, P. O., Shisanya, C. A., & Makokha, G. L. (2018). Rainfall Variability over Kenya. Journal of Environmental and Agricultural Sciences (JEAS). Sagero et al, 14, 1-15. [Google Scholar] [Crossref]

10. Aggarwal, P. K. (2003). Impact of climate change on Indian agriculture. Journal of Plant Biology-new Delhi, 30(2), 189-198. [Google Scholar] [Crossref]

11. Kilavi, M., MacLeod, D., Ambani, M., Robbins, J., Dankers, R., Graham, R., ... & Todd, M. C. (2018). Extreme rainfall and flooding over central Kenya including Nairobi city during the long-rains season 2018: causes, predictability, and potential for early warning and actions. Atmosphere, 9(12), 472. [Google Scholar] [Crossref]

12. Velesi, D. S. (2018). Factors Affecting Maize Yield in Machakos County. https://sdgs.un.org/goals [Google Scholar] [Crossref]

13. Esayas, B., Simane, B., Teferi, E., Ongoma, V., & Tefera, N. (2019). Climate variability and farmers’ perception in Southern Ethiopia. Advances in Meteorology, 2019(1), 7341465. [Google Scholar] [Crossref]

14. Mohamed, E. S. E. (2022). Climate change, agricultural production and food security in Sudan. Journal of Economics and Research, 3(1), 1-19. [Google Scholar] [Crossref]

15. Pielke, R. (2018). Opening up the climate policy envelope. Issues in Science and Technology, 34(4), 30-36. [Google Scholar] [Crossref]

16. Douglas, P. H. (1976). The Cobb-Douglas production function once again: its history, its testing, and some new empirical values. Journal of political economy, 84(5), 903-915. [Google Scholar] [Crossref]

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