RESUMO
Introduction: The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current large-scale rice production process. Methods: WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice. Results: The research results indicate that the training set R2 and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R2 and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme. Discussion: This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization.
RESUMO
BACKGROUND: Rice growers are interested in new technologies that can reduce input costs while maintaining high field yields and grain quality. The bed-and-furrow (BF) water management system benefits farmers through decreased water usage, labor, and fuel as compared to standard flood management. Fertilizer inputs can be reduced by producing rice in rotation with soybeans, a nitrogen-fixing crop, and with the use of slow-release fertilizers that reduce nitrogen volatilization and run-off. However, the influence of these cultural management practices on rice physicochemical properties is unknown. Our objective was to evaluate the influence of nitrogen fertilizer source, water management system, and crop rotation on rice grain quality. RESULTS: Grain protein concentration was lower in a continuous rice production system than in a rice-soybean rotation. Neither amylose content nor gelatinization temperature was altered by fertilizer source, crop rotation, or water management. BF water management decreased peak and breakdown viscosities relative to a flooded system. Peak and final paste viscosities were decreased by all fertilizer sources, whereas, crop rotation had no influence on the Rapid Visco Analyser profile. CONCLUSION: Sustainable production systems that decrease water use and utilize crop rotations and slow-release fertilizers have no major impact on rice physicochemical properties.