Abstract/Overview
Rainfall variability has led to detrimental influence on food crop production in different parts of the world. Many countries experience cases of reduced crop production thus lowering food security. Kenya being an agricultural country, has been affected by variation of rainfall leading to reduced food production. Nyando Sub-County has experienced incidences of rainfall variability which has affected crops that are rain-fed. Despite the fact that studies have been conducted on the effect of rainfall variability on food crop production, there was pending need to provide detailed information on how rain had affected maize, beans, and African nightshade. The data used in this study was for the past 10 years (2013 -2022) because it is within this period that the study area received fluctuating rainfall in terms of magnitude, duration and timing which affected production of maize, beans and African nightshade. The crops are the staples within the study area but their production was perceived to decline over the same period. Therefore, the purpose of the study was to assess the effect of rainfall variability on selected food crops production. The specific objectives of this study were: to examine the effect of duration of rainfall on maize, beans and African nightshade production; to establish the effect of magnitude of rainfall on maize, beans and African nightshade production; to assess the effect of timing of rainfall on maize, beans and African nightshade production in Nyando Sub County. A Quasi-longitudinal research design was adopted. The study was conducted in five wards in the sub-county namely; Ahero, Awasi, Kobura, East Kano and Kabonyo. A sample size of 384 household heads was selected using Fischer’s formula from a target population of 24,866 households. The household heads’ selection was done through simple random sampling for Questionnaire administration. Primary data collection methods were Observation, Photography, Key informant interview and Focus Group Discussions. Literature from KMD and Sub County and County Agricultural offices provided secondary data. Qualitative data was analyzed through themes. Quantitative data was analyzed using descriptive statistics such as means, percentages and standard deviation. Simple regression analysis was conducted to determine the effect of rainfall duration, magnitude and timing on yields of maize, beans and African nightshade. The regression model was found linear and significant; Rainfall duration and maize yield was [F (383) =25.63, P < .001, R2 = .65], Beans yield [F (383) =20.42, P < .001, R2 = .47], and African nightshade Yield [F (383) =19.41, P < .001, R2 = .38]. This is because both beans and the African nightshade are cover crops which are susceptible to floods. Rainfall magnitude and maize yield showed [F (383) =11.45, P < .001, R2 =.44], Beans yield [F (383) =16.08, P < .001, R2 = .37], and African nightshade Yield [F (383) =8.73, P < .001, R2 = .34]. This was so because the mean rainfall volume was not enough for maximum maize yield. The reduction in both beans and nightshade yields was possibly due to extreme fluctuations in rainfall volumes during short rains seasons. Rainfall timing and maize yield [F (383) =13.68, P < .001, R2 =.44], beans yield [F (383) =21.24, P < .001, R2= .38], and African Nightshade Yield [F (383) =14.45, P < .059, R2 =34]. Poor timing affected maize yields in short rains timing. Similarly, the depreciation in both beans and African nightshade yields was possibly due to rainfall unpredictability which is common during short rains. However, correct rainfall timing resulted in the increase in the African nightshade yields. The findings were fundamental to the farmers as they advised on the importance of timing of rainfall enable them prepare adequately for onset of long and short rains to realize best crop yields. The findings showed that rainfall variability affected the production of the three crops hence the need to minimize absolute reliance on rain-fed farming, adopt smart farming and use hybrid seeds that mature faster. Meteorological data interpretations should be availed to farmers for timely planting