Nitrogen and water are two key factors for wheat production due to their major roles in plant growth and development, photosynthesis, yield, and grain protein content. Plant uptake of water and N is fundamentally interactive. Our objectives were: (a) to analyze the effects of different irrigation (IR) and N rates on spring wheat (Triticum aestivum L.) yield and grain protein, and spectral indices (relative greenness [SPAD] and Normalized Difference Vegetation Index [NDVI]), and (b) to identify the optimum IR and N requirements for wheat grain production in semi‐arid conditions of Montana and Idaho. This article details the results from field experiments conducted at three locations for two growing seasons (6 site‐years). Relative greenness measured by SPAD chlorophyll meter was used to assess plant N status, whereas NDVI was used for both plant N status and estimation of wheat yield. Both SPAD and NDVI values increased as N and IR application rates increased. The SPAD and NDVI values explained 80 and 84% of the variation in wheat yield, respectively. We found that IR at 75% of evapotranspiration (ET) throughout the growing season is adequate to optimize wheat yield and grain protein. Nitrogen rate was not correlated with wheat yield at any of the site‐years. Based on this study's results, approximately 150 kg N ha⁻¹ (total, soil residual N plus N added as fertilizer) may be sufficient to optimize yield and grain protein content of irrigated spring wheat in semi‐arid cropping systems.

Nitrogen (N) is the most limiting nutrient in cereal production, yet its use efficiency remains very low at only 35%. Nutrient use efficiency (NUE) is crucial for increasing crop yield and quality while reducing fertilizer inputs and minimizing environmental damage. Optimum N rates that maximize yield without reducing NUE have been found to vary from location to location. This field study assessed the effect of N rates on the yield and quality of spring wheat (Triticum aestivum L.) at five locations in southern Idaho in 2015–2017. Nitrogen was applied as urea (46–0–0) immediately after planting at five rates: 0, 84, 168, 252, and 336 kg ha^–1. Nitrogen application improved grain quality (increased protein) even when no increase in yield was noted. Nitrogen use efficiency and N uptake were affected by N rate at only 2 and 4 of 14 site‐years, respectively. These observations highlight the challenging task of pinpointing the appropriate N rates for optimizing wheat yield, grain protein, N uptake and NUE; and the importance of adjusting N rates based on location, year, and prevalent environmental conditions.

Accurate sensor-based prediction of crop yield and grain quality in-season would enable growers to adjust nitrogen (N) fertilizer management for optimized production. This study assessed the feasibility (and compared the accuracy) of wheat (Triticum aestivum L.) yield, grain N uptake, and protein content prediction with in-season crop spectral reflectance measurements (Normalized Difference Vegetative Index, NDVI) obtained with a handheld GreenSeeker (GS) sensor and an Unmanned Aerial Vehicle (UAV)-mounted sensor. A strong positive correlation was observed between GS NDVI and UAV NDVI at Feekes 5 (R² = 0.78) and Feekes 10 (R² = 0.70). At Feekes 5, GS NDVI and UAV NDVI explained 42% and 43% of wheat yield, respectively. The correlation was weaker at Feekes 10 (R² of 0.34 and 0.25 for GS NDVI and UAV NDVI, respectively). The accuracy of wheat grain N uptake prediction was comparable to that of yield: the R² values for GS NDVI and UAV NDVI were 0.53 and 0.37 at Feekes 5 and 0.13 and 0.20 at Feekes 10. We found that neither GS NDVI nor UAV NDVI in-season data were useful in prediction of wheat grain protein content. In conclusion, wheat yield and grain N uptake can be estimated at Feekes 5 using either handheld or aerial based NDVI with comparable accuracy.

The accuracy of the traditional soil and plant‐based techniques for assessing sugarbeet demand for nitrogen (N) and yield prediction is generally low. Refining N and irrigation water management is a key to maximizing return for sugarbeet (Beta vulgaris L.) growers from agronomic, economic, and environmental perspective. The use of Normalized Difference Vegetative Index (NDVI) in combination with the unmanned aerial vehicle (UAV)‐based data collection for in‐season estimation of sugarbeet root yield and sugar concentration has potential for precision N management. Sugarbeet field trials were conducted in Idaho in 2019 and 2020 to assess (1) effects of water and N fertilizer rates on yield and estimated recoverable sugar (ERS) and (2) feasibility of predicting root yield and ERS using UAV NDVI. At the lowest N rate, application of water at 100% level resulted in greater yield, compared to 50%, in both years. At higher N rates, 50% level produced higher yields. At each N level, application of water at 100% level resulted in lower ERS, compared to 50%. The UAV NDVI was strongly correlated with root yield and ERS. The relationship between UAV NDVI and root yield and ERS was stronger in July (60 days after planting) compared to June (40 days after planting). Estimating the yield and ERS potential in late June/early July and topdressing the crop before the end of July may help to improve N use efficiency while optimizing sugarbeet production.

Increasing the efficiency of spring wheat (Triticum aestivum L.) production is vitally important to Montana growers’ bottom line. In semiarid regions of the Northern Great Plains, agronomic practices must aim at maximum capture and utilization efficiency of plant growth resources. Appropriate plant population and N management are important for optimizing wheat yield and quality by enhancing N uptake and improving nitrogen use efficiency (NUE). Field experiments were conducted in 2014 at two locations in northcentral Montana. The aim of this study was to assess the effect of four N levels (0, 28, 84, and 140 kg N ha⁻¹) in dryland no‐till hard red spring wheat seeded at two seeding rate levels (67 and 135 kg ha⁻¹ pure live seed [PLS]). Nitrogen rate significantly improved wheat grain yield, grain protein content, protein yield, and N uptake at both locations, and NUE at one of two locations. The 135 kg ha⁻¹ did not have an advantage over the 67 kg ha⁻¹ PLS seeding rate, resulting in comparable wheat grain parameters, N uptake, and NUE. Wheat yield and quality was highest at 140 kg N ha⁻¹; higher N rate (especially split‐applied) may have improved wheat production (especially grain protein content) further.

Precision agriculture provides efficient means of obtaining real‐time data to guide nitrogen (N) management based on predicted crop profitability. This study was conducted to assess the efficacy of using in‐season measurements (plant height, biomass weight, biomass N, soil plant analysis development [SPAD], GreenSeeker [GS] normalized difference vegetative index [NDVI], and unmanned aerial vehicle [UAV] NDVI) at Feekes 5 (tillering) and Feekes 10 (anthesis) to estimate wheat (Triticum aestivum L.) yield and protein. The secondary aim was to determine whether the accuracy of yield and protein prediction varies by wheat class and cultivar. Six cultivars—hard red spring (HRS) wheat ‘Jefferson’ and ‘SY Basalt’, hard white spring (HWS) wheat ‘Dayn’ and ‘UI Platinum’, and soft white spring (SWS) wheat ‘Seahawk’ and ‘UI Stone’—were planted at two locations in Idaho in 2018–2020. Plots were arranged in a randomized complete block design with four replications with each cultivar evaluated at seven N rates (0, 50, 100, 150, 200, 250, and 300 kg N ha^–1). The determination of the Pearson correlation coefficients revealed that all parameters were linearly correlated with yield except for SPAD at Feekes 5 and biomass weight at Feekes 10. Although estimation of in‐season grain protein remains a challenge, NDVI was strongly correlated with yield especially at Feekes 5. The accuracy of yield prediction was similar for all wheat classes. Comparable accuracy of yield estimation was achieved with GS NDVI and UAV NDVI. Both hand‐held and aerial‐based spectral measurements could be used to prescribe N rates to be applied during tiller formation when wheat yield can be optimized.