Small-grain crop growers need to match their crops' nitrogen (N) needs with fertilizer applications. This can be challenging because small grains are grown under diverse conditions and their growing season interacts with unpredictable precipitation. Resulting conditions can lead to nitrate-N leaching and runoff losses. More widespread and accurate soil N testing could help growers improve N fertilizer use efficiency, reduce the risk of N loss, and fulfill regulatory requirements. Soil samples from across California small-grain growing regions were tested with a soil nitrate quick test as well as standard laboratory procedures. The quick test is inexpensive and easy to use, and it provides rapid results. A correction factor was developed to convert the quick test values to lab and fertilizer equivalents. The correction factor is based on site-specific soil bulk density and the extracting solution used. An interactive webtool was developed that integrates this information for users. The quick tests provide accurate, real-time estimates of soil nitrate-N in the field to help improve fertilizer use efficiency and reduce N losses.

Malting barley (Hordeum vulgare) requires precise nitrogen (N) fertilizer management to achieve a narrow range of grain protein content (∼9-10.5%) while maintaining yields, but practical tools to accomplish this are lacking. This study hypothesized that canopy reflectance (Normalized Difference Vegetation Index (NDVI)) measured at tillering (Feekes 2-3) and expressed as a sufficiency index (SI), can estimate the likelihood of a site-specific response to in-season N fertilizer in malting barley. Canopy reflectance was measured from plots at tillering with a GreenSeeker and unmanned aerial vehicle (UAV) borne multispectral cameras in trials across heterogeneous California agroecosystems. Field experiments included a range of N fertilizer application rates (0-168 kg N ha −1) and timings (pre-plant, tillering, or evenly split), and resulted in a range of crop N sufficiency/deficiency. NDVI-based SI measurements were categorized into one of three quantitative categories (low, medium, and high) without additional experimental context using Gaussian mixture modeling. Despite that 85% of variation in protein yield was due to site-year, the reflectance-based categories indicated whether N fertilizer applied in-season would increase protein yield (p < .01). Nitrogen application at tillering increased yield and protein for plots in the "low" and "medium" SI categories (45 and 4% for yield and 16 and 12% for protein, respectively) (p < .05), while "high" SI plots had neither yield (p = .23) nor protein (p = .26) increases. Importantly, the broader agronomic conditions of a site primarily determined whether response to in-season N manifested as increased yield or protein.

The objective of this research was to determine if canopy reflectance measured by an Unmanned Aerial Vehicle (UAV) equipped with a 5-band multi-spectral camera can differentiate between water and nitrogen (N) deficiency in irrigated maize. Crop reflectance was used to generate a Normalized Difference Red Edge (NDRE), Green Leaf Index (GLI), and a Blue Reflectance Index (BRI). These indices were then used in combination to categorize N and water stressed experimental units into a Combined Index (CI) indicating water-stressed, N-stressed, or non-stressed crops. The CI generated at blister (R2) successfully identified 90% of experimental treatments to the correct group but only identified 60% of treatments when generated at the 14th leaf stage (V14). The CI methodology was subsequently applied to two independent site-years where only N deficiency gradients were imposed. The CI was not successful at separating treatments at the validation sites, incorrectly identifying water stress where there was none. Among individual indices investigated, NDRE had the strongest relationship to grain yields (r2 = 0.62, p < 0.001) but a weaker linear relationship compared to the CI (r2 = 0.68, p < 0.005) where deficit irrigation treatments were imposed. At sites where irrigation was sufficient to meet crop water demand, NDRE (r2 = 0.63, p < 0.05) had a stronger relationship to grain yield compared to the CI (r2 = 0.41, p = 0.31). This study found that, under narrow cropping system circumstances, N and irrigation-induced differences in maize productivity can be differentiated in-season by a combination of reflectance indices, but that NDRE alone provides superior information under broader contexts.

Malting barley productivity and grain quality are of critical importance to the malting and brewing industry. In this study, we analyzed two experiments: a multi-environment variety trial and a nitrogen management trial. In the first experiment, we analyzed 12 malting barley genotypes across eight locations in California and three years (2017-18, 2018-19 and 2020-21). The effects of genotype (G), location (L), year (Y) and their interactions were assessed on grain yield (kg ha-1), grain protein content (GPC; %), individual-grain weight, grain size (plump and thin; %), onset gelatinization temperature (GT), peak GT, offset GT, difference between onset and peak GT and difference between peak and offset GT. L, Y and their interaction explained the largest variance for all traits except peak GT and difference between onset and peak GT, for which G explained the largest variance. The 2020-21 samples formed partially distinct clusters in principal component analysis, mainly discriminated by high percentage of thin grains and high onset GT. In the second experiment, we analyzed a dataset with two genotypes across three locations (with varying nitrogen fertilizer levels) from the 2016-17 season to assess the effect of added nitrogen on the same traits. Added nitrogen at tillering explained 18% of variance in the difference between onset and peak GT, and 5% of the variance in GPC, but was minimal for all other traits, with the largest variance explained by location and genotype. These findings illustrate the key roles of G, L and Y in determining malting barley productivity and quality.

Wheat (Triticum aestivum L.) is a major global commodity and the primary source for baked products in agri-food supply chains. Consumers are increasingly demanding more nutritious food products with less environmental degradation, particularly related to water and fertilizer nitrogen (N) inputs. While triticale (× Triticosecale) is often referenced as having superior abiotic stress tolerance compared to wheat, few studies have compared crop productivity and resource use efficiencies under a range of N-and water-limited conditions. Because previous work has shown that blending wheat with triticale in a 40:60 ratio can yield acceptable and more nutritious baked products, we tested the hypothesis that increasing the use of triticale grain in the baking supply chain would reduce the environmental footprint for water and N fertilizer use. Using a dataset comprised of 37 site-years encompassing normal and stress-induced environments in California, we assessed yield, yield stability, and the efficiency of water and fertilizer N use for 67 and 17 commercial varieties of wheat and triticale, respectively. By identifying environments that favor one crop type over the other, we then quantified the sustainability implications of producing a mixed triticale-wheat flour at the regional scale. Results indicate that triticale outyielded wheat by 11% (p &lt; 0.05) and 19% (p &lt; 0.05) under average and N-limited conditions, respectively. However, wheat was 3% (p &lt; 0.05) more productive in water-limited environments. Overall, triticale had greater yield stability and produced more grain per unit of water and N fertilizer inputs, especially in high-yielding environments. We estimate these differences could translate to regional N fertilizer savings (up to 555 Mg N or 166 CO2-eq kg ha−1) in a 40:60 blending scenario when wheat is sourced from water-limited and low-yielding fields and triticale from N-limited and high-yielding areas. Results suggest that optimizing the agronomic and environmental benefits of triticale would increase the overall resource use efficiency and sustainability of the agri-food system, although such a transition would require fundamental changes to the current system spanning producers, processors, and consumers.