China Agricultural University

Mingsheng Fan

Yanan Hao

Ying Huang

Yaojun Wang

Fenglu Niu

A rice nitrogen of panicle fertilizer accurate recommendation method based on crop model and remote sensing coupling includes the following steps: S. constructing a basic database; S. agricultural parameter inversion modeling based on the remote sensing vegetation index; S. rice nitrogen nutrition diagnosis based on the nitrogen nutrition index NNI; and S. accurately recommending panicle fertilizer nitrogen based on a crop growth model and remote sensing nitrogen nutrition diagnosis. According to the method, the rice growth trend and nitrogen nutrition estimation in the key growth period is achieved through a remote sensing inversion technology, a multi-source remote sensing inversion spatial dataset based on the rice growth trend is taken as a combination point coupling crop growth model to simulate the rice yield in real time, the rice nitrogen nutrition condition and the nitrogen demand are further combined, and the plots-level nitrogen of panicle fertilizer accurate recommendation is finally achieved.

Method for recommending rice panicle fertilizer nitrogen based on crop model and remote sensing coupling

A soil-climate intelligent type determination method for rice target yield and nitrogen fertilizer amount includes steps: construct a basic database; obtain historical weather data from the region where the rice planting area is to be determined over the past few years, and obtain real-time GFS weather data of the region in a predicted year; obtain 100 m×100 m grid sampling point soil data and a rice field vector layer of rice planting area to be determined; obtain at least three years of nitrogen fertilizer gradient test data of the rice planting area to be determined, rice variety information and management data of each farmer in each year; obtain accurate weather data of the prediction year; prepare soil data of 100 m×100 m plot-scale; localize genetic parameters of the rice growth model DSSAT; and run the localized DSSAT model to predict the plot-scale target yield and recommend the nitrogen application amount.

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Beijing, China

Mingsheng Fan