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Using ENSO conditions to optimize rice yield for Nepal’s Terai

Prakash K. Jha*, Panos Athanasiadis, Silvio Gualdi, Antonio Trabucco, Valentina Mereu, Vakhtang Shelia, Gerrit Hoogenboom

*Corresponding author:

ABSTRACT: The direct application of forecasts from Seasonal Prediction Systems (SPSs) in agriculture is limited by their skill, and SPSs are more skilled at El Niño-Southern Oscillation (ENSO) prediction than precipitation prediction. An alternative to the direct application of forecasts from SPSs could be to link the forecast of ENSO conditions with dynamic crop models to evaluate alternate crop management options prior to the start of the actual planting. Although potential benefits of this approach have been tested in many areas of the world, so far limited evidence exists regarding its application in Nepal’s Terai region. The overall goal of this study was to determine the potential relation between ENSO and summer monsoon precipitation over Nepal’s Terai and SPSs’ skill. This analysis included disentangling the relative contribution of precipitation to the interannual rice yield variability from other factors using a cropping system model, namely, the Crop Environment Resource Synthesis-Rice (CSM-CERES-Rice). The crop model was also employed to explore options for increasing rice yield and minimizing risk by adjusting crop management. This study found that precipitation was the main variable that affects the interannual variability in rice yield, that SPSs are good at predicting ENSO, and that the ENSO signal can be used to predict the seasonal precipitation anomaly in the study area in all years except the ENSO neutral years. The prior knowledge on seasonal precipitation anomaly can then be used to optimize rice yield using a crop model, and to ultimately assist farmers with decision making.