Maize yield forecast using GIS and remote sensing in Kaffa Zone, South West Ethiopia

Abstract

Background: Ethiopian policy makers, government planners, and farmers all demand up-to-date information on maize yield and production. The Kaffa Zone is the country's most important maize-producing region. The Central Statistical Agency's manual gathering of field data and data processing for crop predictions takes a long time to complete before official conclusions are issued. In various investigations, satellite remote sensing data has been shown to be an accurate predictor of maize yield. With station data from 2008 to 2017, the goal of this study was to develop a maize yield forecast model in the Kaffa Zone using time series data from the Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index, actual evapotranspiration, potential evapotranspiration, and Climate Hazards Group Infrared Precipitation. The indicators' correctness in describing the production was checked using official grain yield data from Ethiopia's Central Statistical Office. Crop masking was applied on cropland, and agro ecological zones suited for the crop of interest were used to change the crop. Throughout the long wet season, correlation studies were utilized to investigate correlations between crop productivity, spectral indices, and agro climatic factors for the maize harvest. There were indicators established that demonstrated a strong relationship between maize yield and other factors. Results: The Normalized Difference Vegetation Index Average and Climatic Hazards Group Infrared Precipitation with station data rainfall exhibit substantial associations with maize productivity, with correlations of 84 percent and 89 percent, respectively. To put it another way, these variables have a significant beneficial impact on maize yield. The derived spectro-agro meteorological yield model (r2 = 0.89, RMSE = 1.54qha−1, and 16.7% coefficient of variation) matched the Central Statistical Agency's expected Zone level yields satisfactorily. Conclusion: As a result, remote sensing and geographic information system-based maize yield forecasts improved data quality and timeliness while also distinguishing yield production levels/areas and simplifying decision-making for decision-makers, demonstrating the clear potential of spectro-agro meteorological factors for maize yield forecasting, particularly in Ethiopia.