Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models: A Review

被引:4
|
作者
Silva, Luis [1 ,2 ,3 ,4 ]
Conceicao, Luis Alcino [3 ,4 ]
Lidon, Fernando Cebola [1 ,2 ]
Macas, Benvindo [2 ,5 ]
机构
[1] Univ Nova Lisboa, Fac Ciencias & Tecnol, Earth Sci Dept, Campus Capar, P-2829516 Caparica, Portugal
[2] Univ Nova Lisboa, Fac Ciencias & Tecnol, Geobiotec Res Ctr, Campus Capar, P-2829516 Caparica, Portugal
[3] Inst Politecn Portalegre, P-7300110 Portalegre, Portugal
[4] Inst Politecn Portalegre, VALORIZA Ctr Invest Valorizacao Recursos Endogenos, P-7300110 Portalegre, Portugal
[5] IP, Inst Nacl Invest Agr & Vet, Estr Gil Vaz,Ap 6, P-7350901 Elvas, Portugal
来源
AGRICULTURE-BASEL | 2023年 / 13卷 / 04期
关键词
conservative agriculture; crop nutrition; nitrogen crop sensor; machine learning; decision support systems; LEAF-AREA INDEX; VEGETATION INDEXES; WINTER-WHEAT; SPECTRAL REFLECTANCE; CHLOROPHYLL CONTENT; DILUTION CURVE; USE EFFICIENCY; CLIMATE-CHANGE; GRAIN-YIELD; SENSOR;
D O I
10.3390/agriculture13040835
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Nitrogen use efficiency (NUE) is a central issue to address regarding the nitrogen (N) uptake by crops, and can be improved by applying the correct dose of fertilizers at specific points in the fields according to the plants status. The N nutrition index (NNI) was developed to diagnose plant N status. However, its determination requires destructive, time-consuming measurements of plant N content (PNC) and plant dry matter (PDM). To overcome logistical and economic problems, it is necessary to assesses crop NNI rapidly and non-destructively. According to the literature which we reviewed, it, as well as PNC and PDM, can be estimated using vegetation indices obtained from remote sensing. While sensory techniques are useful for measuring PNC, crop growth models estimate crop N requirements. Research has indicated that the accuracy of the estimate is increased through the integration of remote sensing data to periodically update the model, considering the spatial variability in the plot. However, this combination of data presents some difficulties. On one hand, at the level of remote sensing is the identification of the most appropriate sensor for each situation, and on the other hand, at the level of crop growth models is the estimation of the needs of crops in the interest stages of growth. The methods used to couple remote sensing data with the needs of crops estimated by crop growth models must be very well calibrated, especially for the crop parameters and for the environment around this crop. Therefore, this paper reviews currently available information from Google Scholar and ScienceDirect to identify studies relevant to crops N nutrition status, to assess crop NNI through non-destructive methods, and to integrate the remote sensing data on crop models from which the cited articles were selected. Finally, we discuss further research on PNC determination via remote sensing and algorithms to help farmers with field application. Although some knowledge about this determination is still necessary, we can define three guidelines to aid in choosing a correct platform.
引用
收藏
页数:23
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