Crop Sensor Based Non-destructive Estimation of Nitrogen Nutritional Status, Yield, and Grain Protein Content in Wheat

Marta Aranguren, Ander Castellón and Ana Aizpurua
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Marta Aranguren: NEIKER-Basque Institute for Agricultural Research and Development, Department of Plant Production and Protection, Berreaga 1, 48160 Derio, Biscay, Spain
Ander Castellón: NEIKER-Basque Institute for Agricultural Research and Development, Department of Plant Production and Protection, Berreaga 1, 48160 Derio, Biscay, Spain
Ana Aizpurua: NEIKER-Basque Institute for Agricultural Research and Development, Department of Plant Production and Protection, Berreaga 1, 48160 Derio, Biscay, Spain

Agriculture, 2020, vol. 10, issue 5, 1-22

Abstract: Minimum NNI (Nitrogen Nutrition Index) values have been developed for each key growing stage of wheat ( Triticum aestivum ) to achieve high grain yields and grain protein content (GPC). However, the determination of NNI is time-consuming. This study aimed to (i) determine if the NNI can be predicted using the proximal sensing tools RapidScan CS-45 (NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge)) and Yara N-Tester TM and if a single model for several growing stages could be used to predict the NNI (or if growing stage-specific models would be necessary); (ii) to determine if yield and GPC can be predicted using both tools; and (iii) to determine if the predictions are improved using normalized values rather than absolute values. Field trials were established for three consecutive growing seasons where different N fertilization doses were applied. The tools were applied during stem elongation, leaf-flag emergence, and mid-flowering. In the same stages, the plant biomass was sampled, N was analyzed, and the NNI was calculated. The NDVI was able to estimate the NNI with a single model for all growing stages ( R 2 = 0.70). RapidScan indexes were able to predict the yield at leaf-flag emergence with normalized values ( R 2 = 0.70–0.76). The sensors were not able to predict GPC. Data normalization improved the model for yield but not for NNI prediction.

Keywords: Triticum aestivum; RapidScan CS-45; Yara N-Tester TM; NNI; precision agriculture; remote sensing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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