Optimizing Nitrogen Management for Soft Red Winter Wheat Yield, Grain Protein, and Grain Quality Using Precision Agriculture and Remote Sensing Techniques.

protein variability;delayed harvest;grain quality;winter wheat;remote sensing

Farrer, Dianne ; Dr. P. Randall Weisz, Committee Chair,Farrer, Dianne ; Dr. P. Randall Weisz ; Committee Chair

University:North Carolina State University

关键词: protein variability;    delayed harvest;    grain quality;    winter wheat;    remote sensing;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/5800/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

The purpose of this research was to improve nitrogen (N) management for soft red winter wheat (Triticum aestivum L.) in North Carolina with three areas of focus: delayed harvest effects on grain quality, explaining grain protein variability caused by management practices, and developing N recommendations at growth stage (GS) 30 using aerial color infrared (CIR) photography. Delayed harvest significantly reduced grain yield and test weight in the majority of trials. Yield reductions were attributed to dry, warm environments, possibly due to shattering. Test weight reductions were attributed to the negative effects of wetting and drying cycles. Of the 20 quality parameters investigated, flour falling number, clear flour, and farinograph breakdown times were significantly reduced due to delayed harvest, while grain deoxynivalenol (DON) levels increased with a delayed harvest. Environment contributed to grain protein variability (23%), though the majority of that variability was attributed to N management (52%). It was found that as grain protein levels increased at higher N rates and with the majority of N applied at GS 30, the overall grain protein variability increased. The recommendations to reduce grain protein variability are; to reduce the range in N fertilizer rates used, to avoid over application of N beyond what is required to optimize yields, and to apply spring N at GS 25. Relationships between derived agronomic optimum N rates and three spectral bands and 39 indexes were weak, but after separating the data into two biomass classes (low < 1000 kg ha-1 and high > 1000 kg ha-1), the relationships of optimum N rates with a relative Red and Green bands (relative to a high N-status reference plot) had the best (quadratic) relationships (R2 = 0.80 and 0.81, respectively) for the high biomass class. These results indicate that agronomic optimum N rates at GS 30 can be estimated using aerial CIR photographs if areas of low and high biomass can be determined.