Effects of rainfall variability and integrated soil fertility management on maize productivity in Embu county, Kenya
Kisaka, Masika Oscar
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Drier parts of Embu County endure high atmospheric heat, prolonged dry spells, declining soil fertility and erratic rainfall. Local soil and climatic variability contributes to large variations among replications of experimental treatments and inconsistence in research results leading to poor comparability of results within and without different agro-ecological zones (AEZs) hence need for site specific scenario analyses through modelling. Thus, this study sought to; i) evaluate seasonal rainfall variability, ii) rainfall effects on maize productivity and iii) its interactive effects with selected fertility management (FM) technologies on maize yield, soil water content, nutrient use efficiency (NUE) and water use efficiency (WUE) in Embu County. Long-term rainfall dailies were sourced from the Kenya Meteorological Department (KMD) while agro-phenological data were acquired from experimental trials (based on randomized complete block design) on FM and WUE at a research station in Machang’a in Mbeere Sub-County. Rainfall dailies’ homogeneity and frequency were analysed using RAINBOW-software. Rainfall trends and seasonal variability were based on Cumulative Departure Index (CDI) and Rainfall Anomaly Index (RAI) and variation (CV). Effects of rainfall variability on maize yield utilized correlation and regression analyses, and Coefficient of Correlation (rho). The Agricultural Production Systems-Simulator (APSIM model) was used to quantify the interactive effects of selected FM and mulch on maize yield and soil properties. APSIM calibration and validation was based on goodness-of-fit between observed and simulated parameters derived from residual-errors statistics; root mean square error (RMSE), square of the correlation coefficient (R2), and model efficiency (EF). Rainfall homogeneity was accepted at 99% probabilities. Analyses showed 90% chance of below cropping-threshold rainfall exceeding 213.5 mm (Machang’a) and 258.1mm (Embu) during SRs for one year return-period. Rainfall variability was found to be high in seasonal amounts (CV=0.56 and 0.38) and in number of rainy-days (CV=0.88 and 0.27) at Machang’a and Embu, respectively. Monthly rainfall variability was found to be equally high even during April and November (CV=0.42 & 0.48 and 0.76 & 0.43) with high probabilities (0.40 and 0.67) of droughts exceeding 15 days in Embu and Machang’a, respectively. Effects of seasonal rainfall variability (CV=53%) on maize productivity were high during the flowering (CV=0.49) and cob formation (CV=0.59) stages. Rainfall influenced more the productive phase (53%) than the vegetative phase (47%). Rainfall influence on maize yield variability (CV=0.91) was found to reduce and stabilize (to CV=0.71) under sub-soiling. APSIM simulations adequately predicted observed maize crop-growth (Leaf Area Index; LAI, Grain yield, and biomass). Grain prediction was good (R2=0.67 and EF>0.9) but biomass was slightly under-predicted (R2=67 and EF=0.87). The model adequately reproduced the average trend of maize grain yield response to N inputs from manure, Mucuna pruriens, Lantana camara and fertilizer. Long-term (13-year) simulations showed that moderate and low cost application of N (40kg N ha−1 from combined manure and mineral fertilizer) improved both long-term average and the minimum guaranteed grain yield (2.5 Mg ha-1) and thus recommended for smallholder farmers especially in dry areas principally during SRs. These findings should be considered in conditions where P is added proportionally to N (P/N in the range of 20 to 30%). Further studies on the interaction of P and N rates/sources, their effects on yield and soil properties under other WUE technologies are recommended.