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-TesterTM 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 (R2 = 0.70). RapidScan indexes were able to predict the yield at leaf-flag emergence with normalized values (R2 = 0.70–0.76). The sensors were not able to predict GPC. Data normalization improved the model for yield but not for NNI prediction.

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