Rice yield prediction using Bayesian analysis on rainfed lands in the Sumbing-Sindoro Toposequence, Indonesia
نویسندگان
چکیده
Since rainfed rice fields typically lack nutrients, frequently experience drought, and require more fund to support farming operations, the production results become erratic unpredictable. This research aims construct location-specific yield predictions in among Sumbing-Sindoro Toposequence, Central Java, using a Bayesian method. study is survey with an exploratory descriptive methodology based on data from both field laboratory research. Prediction model analysis Neural Network (BNN) method 12geographical units, sampling spots were selected intention. The following variables measured: soil (pH level, Organic-C, Total-N, Available-P, Available-K, types, elevation, slope) climate (rainfall, evapotranspiration). According statistical used, BNN model’s performance has highest accuracy, RMSE value of 0.448 t/ha, which compares MLR SR models, indicating lowest error deviation. To obtain ideal parameter design, distribution directly simultaneously optimised optimisation technique Pareto optimality. top 7 sets (slope, available-P, evapotranspiration, type, rainfall, organic-C, pH) yielded accuracy test for three-parameter groups. coefficient determination value, 0.855, while set at 0.354 t/ha 18.71%, respectively. By developing method, farmers agricultural practitioners can benefit accurate reliable estimates crop productivity
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ژورنال
عنوان ژورنال: ??????? ?????????
سال: 2023
ISSN: ['2312-1572', '2313-0482']
DOI: https://doi.org/10.48077/scihor7.2023.149