Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil

Large-scale assessment of crop yields plays a fundamental role for agricultural planning and to achieve food security goals. In this study, we evaluated the robustness of data-driven models for estimating soybean yields at 120\ua0days after sow (DAS) in the main producing regions in Brazil; and evaluated the reliability of the “best” data-driven model as a tool for early prediction of soybean yields for an independent year. Our methodology explicitly describes a general approach for wrapping up publicly available databases and build data-driven models (multiple linear regression—MLR; random forests—RF; and support vector machines—SVM) to predict yields at large scales using gridded data of weather and soil information. We filtered out counties with missing or suspicious yield records, resulting on a crop yield database containing 3450 records (23\ua0years 7 150 “high-quality” counties). RF and SVM had similar results for calibration and validation steps, whereas MLR showed the poorest performance. Our analysis revealed a potential use of data-driven models for predict soybean yields at large scales in Brazil with around one month before harvest (i.e. 90 DAS). Using a well-trained RF model for predicting crop yield during a specific year at 90 DAS, the RMSE ranged from 303.9 to 1055.7\ua0kg\ua0ha–1 representing a relative error (rRMSE) between 9.2 and 41.5%. Although we showed up robust data-driven models for yield prediction at large scales in Brazil, there are still a room for improving its accuracy. The inclusion of explanatory variables related to crop (e.g. growing degree-days, flowering dates), environment (e.g. remotely-sensed vegetation indices, number of dry and heat days during the cycle) and outputs from process-based crop simulation models (e.g. biomass, leaf area index and plant phenology), are potential strategies to improve model accuracy

Comparação entre métodos de amostragem do solo para recomendação de calagem e adubação do cafeeiro conilon Comparison between soil sampling methods for conilon coffee liming and fertilization recommendation

O objetivo deste trabalho foi comparar a metodologia convencional de amostragem de solo com a análise espacial para a recomendação de calagem e adubação de nitrogênio, fósforo e potássio em solo cultivado com café conilon. O experimento foi realizado nas safras de 2004/2005 e 2005/2006, em área de 1,0 ha, com as amostras retiradas na profundidade de 0-0,20 m. No método convencional, coletaram-se 15 subamostras em caminhamento ziguezague, constituindo uma amostra composta, e, no método espacial, construiu-se uma malha amostral de 109 pontos georreferenciados. Com os resultados das análises, foi calculada a necessidade de calagem e adubação em função do teor do elemento no solo e da produtividade esperada das plantas. Os dados foram analisados por meio da estatística clássica (descritiva e exploratória) e pela análise espacial, utilizando técnicas de geoestatística (modelagem da estrutura de variabilidade espacial e realização de inferências) e geoprocessamento (álgebra de mapas). Com exceção da recomendação de fósforo em 2005 e potássio em 2006, todas as demais recomendações apresentaram dependência espacial. A análise dos dados pelo método espacial possibilitou identificar zonas de déficit ou excesso de calagem e de adubação na área, que não poderiam ser definidas com o método convencional de amostragem (ziguezague).<br>This study aimed to evaluate the soil sampling conventional methodology with spatial analysis for liming and fertilization with nitrogen, phosphorus and potassium on soil cultivated with conilon coffee. The trial was carried out during the 2004/2005 - 2005/2006 harvests in a 1.0 ha area with samples collected at 0-0.20 m depth. Fifteen sub samples in zigzag were collected for the conventional method forming a compound sample; as for the spatial method, 109 georeferenced points formed a sample grid. After the analysis results, the liming and fertilization needs were calculated based on the function of the element content in the soil and on the plant expected yield. Data were analyzed by both the classical statistics (descriptive and exploratory) and spatial analysis, using geostatistics techniques (modeling of the spatial variability structure and inferences) and geoprocessing (map algebra). Except for phosphorus in 2005 and potassium in 2006, every other recommendation showed spatial dependence. Data analysis by the spatial method provided the identification of zones with deficient or excessive liming and fertilization which could not be defined by the conventional sampling method (zigzag)