Abstract:
The adverse impacts of climate change on food security and agriculture are especially severe in Sub-Saharan Africa. Monitoring and analyzing crop yield accurately is crucial for food security and enhancing farming practices. Remote sensing technology, particularly the Normalized Difference Vegetation Index (NDVI), offers valuable insights into vegetation health, which correlates closely with crop yield. This study examines Nyagatare District in Eastern Rwanda, focusing on the spatial and temporal variations of NDVI and maize greenness, and their relationship with crop yield.
Landsat images from 2016, 2019, and 2022 were utilized to calculate NDVI values for agricultural Seasons A and B, using the red and near-infrared (NIR) bands. A linear regression model was applied to evaluate the correlation between NDVI values and maize crop yield, using the R-squared value as a measure. Additionally, household surveys and interviews with maize farmers provided supplementary data.
The study found that in 2016, maize yield was largely within the Medium to Very High NDVI range during Season A, but in the Low to Medium range during Season B. By 2019, NDVI values suggested the potential for above-average yields, with Season A values ranging from 0.13 to 0.57 and Season B from 0.11 to 0.51. In 2022, the NDVI values were mostly in the Medium to Very High range, indicating areas with high crop yields. The positive correlation between NDVI levels, maize greenness, and crop yield was strong, with an R-squared value of 0.871 for both seasons. The linear regression model showed a moderately strong relationship, with an R-squared value of 0.7812, supporting NDVI's effectiveness as a predictor of agricultural productivity.
These results underscore the potential of remote sensing technology for predicting crop yield and optimizing crop management, contributing to improved agricultural productivity and sustainability in Nyagatare District. Policymakers can use these insights to enhance maize crop monitoring systems and implement targeted interventions such as fertilization programs, irrigation management, and early warning systems for drought or pest outbreaks. Integrating NDVI-based monitoring into agricultural policies could lead to better resource allocation, timely farmer support, and sustainable practices that secure food availability
