Low grain protein in hard red winter (HRW) wheat (Triticum aestivum L.) is a serious challenge for rainfed wheat growers, particularly in years with elevated grain yield. Proper nitrogen (N) management with adequate N rate and application timing is critical for optimizing grain yield and protein content. This 2‐yr experiment evaluated the effects of different N rates and application timings (fall, spring, and split) on grain yield and protein of two HRW wheat cultivars. Field studies were conducted at four different sites across Nebraska under rainfed conditions in 2018/2019 (Year 1) and 2019/2020 (Year 2). A split‐plot randomized complete block design with wheat cultivars as the whole plots and factorial combinations of six N rates and three application timings as the subplots was used in four replications. Grain yield was associated positively and grain protein negatively with the water supply to demand ratio (WS/WD) in the season. Freeman cultivar yielded better in a year with higher WS/WD and a newly developed cultivar, Ruth, yielded better in a lower WS/WD year. Nitrogen fertilization significantly increased grain yield in the site‐years with moderately higher WS/WD. There was an increase in grain protein with increasing N rates at all site‐years. Spring and split‐applied N resulted in better grain yield than fall application in the site‐year when there was a risk of N loss. This experiment suggested that an effective N management strategy for winter wheat should account for and be adaptable to weather variability to optimize grain yield and protein content.

Besides a proper agronomic management followed by Nepalese farmers, wheat (Triticum aestivum L.) production has been severely affected by changing climate. There are many interventions, including climate-smart practices, to cope with this situation and possibly enhance crop and soil productivity. Field experiments were set up in a randomized complete block design with six treatments (TRT) with four replications in three locations (LOC) during wheat-growing seasons in Nepal from 2014 to 2016. Treatments included (i) Controlled Practice (CP), (ii) Improved Low (IL), (iii) Improved High (IH), (iv) Climate Smart Agriculture Low (CSAL), (v) Climate Smart Agriculture Medium (CSAM), and (vi) Climate Smart Agriculture High (CSAH), whereas those LOC were Banke, Rupandehi and Morang districts. There was a significant main effect of TRT and LOC on grain yield and a significant interactionn effect of TRT × LOC on biomass yield in 2014–2015. About 55.5% additional grain yield was produced from CSAM treatment compared to CP in 2014–2015. Among locations, grain yield was the highest in Banke (3772.35 kg ha⁻¹) followed by Rupandehi (2504.47 kg ha⁻¹) and Morang districts (2504.47 kg ha⁻¹). In 2015–2016, there was a significant interaction effect of TRT × LOC on grain and biomass yields. The highest grain yield was produced from CSAH treatment in Banke district in 2015–2016. Overall, grain yield and other parameters showed a better response with either of the climate-smart interventions (mostly CSAH or CSAM) despite variability in geography, climate, and other environmental factors indicating the potential of climate-smart practices to improve wheat production in southern plains of Nepal.

Proso millet (Panicum miliaceum L.), one of the major cultivated millets, serves as a complement to major cereal crops due to its drought tolerance and low input demands. Timing of heading is one of the key agronomic traits associated with its adaptation to a target environment and a major focus in breeding. Conventionally, heading percentage of a plot (genotype) was rated visually by breeders in field. Despite many successful studies reported in automatic head detections in other small grain species, little progress had been made to estimate heading percentage especially when multiple tillers exist. This study aimed to develop a method for automatic proso millet panicle detection and, more importantly, heading percentage estimation using regular red‐green‐blue images collected by an unmanned aerial vehicle. Aerial images of two dates were collected at heading stage in 2020 in Scottsbluff, NE. Faster regions with convolutional neural network models were trained to detect and count proso millet panicles in each plot. Then, using a sigmoid model, the number of detected panicles was converted to heading percentage without having the information of stand count and the number of tillers. Overall, the system achieved the highest coefficient of determination of 0.728 for proso millet heading percentage estimation, and an accuracy of 92.4% in determining whether a plot reached a certain threshold of heading (50% in this study). The methods developed in this study on heading percentage estimation can directly aid in decision making in proso millet breeding and can be ultimately incorporated into an automated proso millet high‐throughput phenotyping pipeline.

Sugar beet (Beta vulgaris L.) accounts for 55% of the total sugar production in the United States. Optimizing fertilizer nitrogen (N) management is pivotal for its economical and sustainable production and is challenging. Three‐year field experiments (2020–2022) were conducted in western Nebraska to evaluate the effects of fertilizer N rates on beet root yield, sugar concentration, sugar loss to molasses (SLM), estimated recoverable sugar (ERS), and nitrogen use efficiency (NUE). Treatments included 0%, 50%, 80%, 100%, and 125% of recommended N based on the current University of Nebraska‐Lincoln recommendation. Fertilizer application increased the root yield, ERS, and SLM but decreased sugar concentration in most cases compared to the control treatment. Beet NUE decreased with increasing total available N. Linear‐plateau regression models fitted to root yield and ERS response curves showed that the agronomic optimum N rates (AONRs) were 179 and 166 kg N ha⁻¹ for root yield of 68.86 Mg ha⁻¹ and ERS of 11.95 Mg ha⁻¹, respectively. The findings showed that the root yield‐based model required 35% less N rate than the current UNL beet N algorithm, and the ERS‐based model required 13 kg N ha⁻¹ less N rate than the root yield‐based model. Because of the trade‐off effect of total available N on root yield and quality, the ERS‐based N recommendation can be a potential strategy to optimize N management for economic and environmentally sustainable sugar beet production.

A proper understanding of variability of soil chemical properties over an area is important for identifying the soil nutrients related production constraints. The suggestions for remedial measures and execution of appropriate nutrient management strategies are also based on the good knowledge of variation in soil nutrients. A case study was undertaken with an objective of assessing spatial variability of selected soil chemical properties of agricultural land. Soil samples from the surface (0 to 15 cm) were collected from farmers’ field in the central part of Kavrepalanchowk district of Nepal in February 2018. Soil samples were analyzed for soil pH, total organic carbon (TOC), total nitrogen (N), phosphorous (P), and potassium (K). Descriptive statistics revealed that the soil in the study area was slightly acidic and contained the low status of TOC, P, and K, while the N content was medium. Coefficient of variation (CV) indicated that soil pH was the least variable (CV= 9.37% with values ranging from 5.3 to 7.7) among the investigated soil samples while N was the most variable (CV= 98.81% with content values ranging from 0.03% to 0.67%). Other selected properties (TOC, P, and K) were found to be highly variable (CV= 49.94%, 94.89%, and 57.53% respectively). These variations in soil chemical properties were mostly related to the different cropping systems and soil management practices, including nutrient management carried out in the study area. Owing to the higher variability of nutrients, the results suggested to take into account various soil and nutrient management practices for sustained soil fertility and enhanced productivity.