A leaf reflectance-based crop yield modeling in Northwest Ethiopia

Abstract

Crop yield prediction provides information to policymakers in the agricultural production system. This study used leaf reflectance from a spectroradiometer to model grain yield (GY) and aboveground biomass yield (ABY) of maize (Zea mays L.) at Aba Gerima catchment, Ethiopia. A FieldSpec IV (350–2,500 nm wavelengths) spectroradiometer was used to estimate the spectral reflectance of crop leaves during the grain-filling phase. The spectral vegetation indices, such as enhanced vegetation index (EVI), normalized difference VI (NDVI), green NDVI (GNDVI), soil adjusted VI, red NDVI, and simple ratio were deduced from the spectral reflectance. We used regression analyses to identify and predict GY and ABY at the catchment level. The coefficient of determination (R2), the root mean square error (RMSE), and relative importance (RI) were used for evaluating model performance. The findings revealed that the best-fitting curve was obtained between GY and NDVI (R2 = 0.70; RMSE = 0.065; P < 0.0001; RI = 0.19), followed by EVI (R2 = 0.65; RMSE = 0.024; RI = 0.61; P < 0.0001). While the best-fitting curve was obtained between ABY and GNDVI (R2 = 0.71; RI = 0.24; P < 0.0001), followed by NDVI (R2 = 0.77; RI = 0.17; P < 0.0001). The highest GY (7.18 ton/ha) and ABY (18.71 ton/ha) of maize were recorded at a soil bunded plot on a gentle slope. Combined spectral indices were also employed to predict GY with R2 (0.83) and RMSE (0.24) and ABY with R2 (0.78) and RMSE (0.12). Thus, the maize’s GY and ABY can be predicted with acceptable accuracy using spectral reflectance indices derived from spectroradiometer in an area like the Aba Gerima catchment. An estimation model of crop yields could help policy-makers in identifying yield-limiting factors and achieve decisive actions to get better crop yields and food security for Ethiopia.