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.

Keywords: protein variability, delayed harvest, grain quality, winter wheat, remote sensing.

A site-specific N management approach has the potential to manage in-field variability and increase production and economic efficiencies by optimizing the nitrogen (N) inputs. Field studies were conducted to investigate the grain yield and protein responses of hard red winter wheat (Triticum aestivum L.) to several N management strategies across variable landscapes. Nine N treatments consisted of various combinations of N rates, sources and timings were applied at specific stages of crop development. Delta yield, delta protein and net returns were calculated to determine the spatial response to N across the field. Those parameters for each treatment varied spatially across the field. Normalized difference vegetation index and leaf area index could not explain the spatial response to N accurately. Overall, grain yield and protein responses to N strategies were highly dependent on the spatial position in each field; however, predicting the responses in time for deploying N management strategies were only weakly associated with canopy sensor data or soil characteristics.

Sensor technology has become more important in precision agriculture, by real time sensing for site specific management to monitor crops during the season especially nitrogen (N). In Kansas N available in the soils can vary year to year or over a course of a year. The objective of this study was to compare current available passive (PS) and active optical sensor technologies (AOS) performance in regards to sky conditions effects and derive the NDVI (normalized difference vegetation index) relationship to wheat yield, as well as evaluate KSU optical sensor-based N recommendations against KSU soil test N recommendation system and sUAS (small unmanned aircraft systems) based recommendation algorithms with the PS and AOS platforms. Each year (2015-2016 & 2016-2017) five field trails across Kansas were conducted during the winter wheat crop year in cooperation with county ag agents, farmers, and KSU Agronomy Experiment Fields. Treatments consisted of N response curve, 1st and 2nd generation KSU N recommendation algorithms, sUAS based recommendation algorithms, and KSU soil test based N recommendations applied in the spring using N rates ranging from 0 to 140 kg ha−1. Results indicate the Holland Scientific Rapid Scan and MicaSense RedEdge NDVI data was strongly correlated and generated strong relationships with grain yield at 0.60 and 0.57 R2 respectively. DJI X3 lacks an NIR band producing uncalibrated false NDVI and no relationship to grain yield at 0.03 R2. Calibrated NDVI from both sensors are effective for assessing yield potential and could be utilized for developing N recommendation algorithms. However, sensor based treatments preformed equal to higher yields compared the KSU soil test recommendations, as well as reduced the amount of fertilizer applied compared to the soil test recommendation. The intensive management algorithm was the most effective in determining appropriate N recommendations across locations. This allows farmers to take advantage of potential N mineralization that can occur in the spring. Further research is needed considering on setting the NUE (nitrogen use efficiency) in KSU N rec. algorithms for effects of management practice, weather, and grain protein for continued refinement.

Growers require pre-harvest information about grain protein to optimize nitrogen (N) fertilizer inputs and grain harvest. The aim of this dissertation was to predict final grain protein of dryland wheat based on mid-seasonal remote sensing data. Grain protein predictions have relied on weather, cultivar and crop N status information. The latter has been remotely sensed by means of spectral indices. These indices generally employ narrow wavebands (40 nm) that are sensitive to chlorophyll a and b content and leaf area index (LAI) both of which usually co-vary with variations in crop N status. However, remote sensing crop N status is complicated by N-independent variations in LAI and soil background reflectance. Chapter 1 shows that N-independent variations in LAI confound remote predictions of crop N status that are based on single indices, but have only a minor effect if combined indices are used. The new combined index derived from the ratio of Modified Chlorophyll Absorption Ratio Index (MCARI) and the second Modified Triangular Vegetation Index (MTVI2) has the lowest sensitivity to variations in LAI (r2 = 0.01) and the highest sensitivity to crop N status (r2 = 0.54). Chapter 2 evaluates the sensitivity of spectral indices to variation in soil reflectance. The results indicate that spectral indices are affected by soil background reflectance when LAI