Maize yield prediction and condition monitoring at the sub-county scale in Kenya: synthesis of remote sensing information and crop modeling.

Agricultural production assessments are crucial for formulating strategies for closing yield gaps and enhancing production efficiencies. While in situ crop yield measurements can provide valuable and accurate information, such approaches are costly and lack scalability for large-scale assessments. Therefore, crop modeling and remote sensing (RS) technologies are essential for assessing crop conditions and predicting yields at larger scales. In this study, we combined RS and a crop growth model to assess phenology, evapotranspiration (ET), and yield dynamics at grid and sub-county scales in Kenya. We synthesized RS information from the Food and Agriculture Organization (FAO) Water Productivity Open-access portal (WaPOR) to retrieve sowing date information for driving the model simulations. The findings showed that grid-scale management information and progressive crop growth could be accurately derived, reducing the model output uncertainties. Performance assessment of the modeled phenology yielded satisfactory accuracies at the sub-county scale during two representative seasons. The agreement between the simulated ET and yield was improved with the combined RS-crop model approach relative to the crop model only, demonstrating the value of additional large-scale RS information. The proposed approach supports crop yield estimation in data-scarce environments and provides valuable insights for agricultural resource management enabling countermeasures, especially when shortages are perceived in advance, thus enhancing agricultural production.

Trend analysis of rainfall from 1981-2022 over Zambia.

There is insufficient paucity of information on trends in long-term monthly and decadal rainfall in Zambia. This study assessed the monthly and decadal trends in rainfall over the agro-ecological regions (AERs) and Zambia from 1981 to 2022. The Mann-Kendall test statistic was used at 5 % significant level to compute trends in rainfall at monthly and decadal time step on CHIRPS v2 at 0.05° resolution. R/RStudio Sen's slope estimator was used to give the magnitude of the observed trends. The monthly rainfall time series trend over Zambia ranges from -0.04 to 0.03. The decadal trend analysis of rainfall at annual and monthly time step exhibits a decreasing/increasing trend with Sen's slope between -49.27 and 71.26 mm. Decadal trend at annual time step in AERIII, AERIIa, AERIIa and AERI exhibits a Sen's slope of -44.11 to 62.48 mm, -15.29 to 41.58 mm, -6.08 and 71.26 mm, and 2.20-64.86 mm, respectively. The decadal trend at monthly time step in AERIII, AERIIa, AERIIa and AERI exhibits a Sen's slope of -132.08 to -3.15 mm, -123.39 to -8.57 mm, -73.08 to -15.17 mm, and -80.02 to -5.21 mm, respectively. Decrease in rainfall is expected to affect agriculture, energy, water resources, sanitation and socio-economic aspects. Rainfall pattern shows spatio-temporal variability over Zambia. The results provide valuable input into the National Adaptation Plan and also useful for strategic planning purposes in water resources management under a changing climate. It is evident that spatio-temporal time steps utilized in this study provides new insights of rainfall trends at seasonal, monthly, and annual and decadal time steps.