Abstract:
Maize (Zea mays L.) productivity in Kenya has witnessed a decline attributed
to the effects of climate change and biophysical constraints. The assessment of
agronomic practices across agroecological zones (AEZs) is limited by inadequate
data quality, hindering a precise evaluation of maize yield on a large scale. In
this study, we employed the DSSAT-CERES-Maize crop model (where CERES is
Crop Environment Resource Synthesis and DSSAT is Decision Support System for
Agrotechnology Transfer) to investigate the impacts of different agronomic practices on maize yield across different AEZs in two counties of Kenya. The model
was calibrated and evaluated with observed grain yield, biomass, leaf area index,
phenology, and soil water content from 2-year experiments. Remote sensing (RS)
images derived from the Sentinel-2 satellite were integrated to delineate maize areas, and the resulting information was merged with DSSAT-CERES-Maize yield simulations. This facilitated a comprehensive quantification of various agronomic measures
at pixel scales. Evaluation of agronomic measures revealed that sowing dates and
cultivar types significantly influenced maize yield across the AEZs. Notably, AEZ
II and AEZ III exhibited elevated yields when implementing combined practices of
early sowing and cultivar H614. The impacts of optimal management practices varied across the AEZs, resulting in yield increases of 81, 115, and 202 kg ha−1 in
AEZ I, AEZ II, and AEZ III, respectively. This study underscores the potential of the
CERES-Maize model and high-resolution RS data in estimating production at larger
scales. Furthermore, this integrated approach holds promise for supporting agricultural decision-making and designing optimal strategies to enhance productivity while
accounting for site-specific conditions.
