Rice yield prediction using Bayesian analysis on rainfed lands in the Sumbing-Sindoro Toposequence, Indonesia

Since rainfed rice fields typically lack nutrients, frequently experience drought, and require more fund to support farming operations, the production results become erratic and unpredictable. This research aims to construct location-specific rice yield predictions in the rainfed rice fields among the Sumbing-Sindoro Toposequence, Central Java, using a Bayesian method. This study is a survey with an exploratory descriptive methodology based on data from both field and laboratory research. Prediction model analysis using the Bayesian Neural Network (BNN) method on 12geographical units, sampling spots were selected with intention. The following variables were measured: soil (pH level, Organic-C, Total-N, Available-P, Available-K, soil types, elevation, slope) and climate (rainfall, evapotranspiration). According to the statistical analysis used, the BNN model’s performance has the highest accuracy, with an RMSE value of 0.448 t/ha, which compares to the MLR and SR models, indicating the lowest error deviation. To obtain the ideal parameter sampling design, parameter distribution is directly and simultaneously optimised using an optimisation technique based on Pareto optimality. The top 7 data sets (slope, available-P, evapotranspiration, soil type, rainfall, organic-C, and pH) yielded the highest accuracy based on the test results for the three-parameter groups. The coefficient of determination has the highest value, 0.855, while the RMSE test for the model using the top 7 data set has the lowest error value at 0.354 t/ha and 18.71%, respectively. By developing location-specific rice yield predictions using a Bayesian method, farmers and agricultural practitioners can benefit from more accurate and reliable estimates of crop productivity