Reflectance-based vegetation indices can be valuable for assessing crop nitrogen (N) status and predicting grain yield. While proximal sensors have been widely studied in agriculture, there is increasing interest in utilizing aerial sensors. Given that few studies have compared aerial and proximal sensors, the objective of this study was to quantitatively compare the sensitivity of aerially sensed Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge Index (NDRE) and proximally sensed NDVI for assessing total N uptake at panicle initiation (PI-N_UP) and predicting grain yield in rice. Nitrogen response trials were established over a 3-year period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. At PI, a multispectral unmanned aircraft system (UAS) was used to measure NDVI_UAS and NDRE_UAS (average ground sampling distance: 3.7 cm pixel⁻¹), and a proximal GreenSeeker (GS) sensor was used to record NDVI_GS. To enable direct comparisons across the different indices on an equivalent numeric scale, each index was normalized by calculating the Sufficiency-Index (SI) relative to a non-N-limiting plot. Kernel density distributions indicated that NDVI_UAS had a narrower range of values that were poorly differentiated compared to NDVI_GS and NDRE_UAS. The critical PI-N_UP where yields did not increase with higher PI-N_UP averaged 109 kg N ha⁻¹ (±4 kg N ha⁻¹). The relationship between SI and PI-N_UP for the NDVI_UAS saturated lower than this critical PI-N_UP (96 kg N ha⁻¹), whereas NDVI_GS and NDRE_UAS saturated at 111 and 130 kg N ha⁻¹, respectively. This indicates that NDVI_UAS was less suitable for making N management decisions at this crop stage than NDVI_GS and NDRE_UAS. Linear mixed effects models were developed to evaluate how well each SI measured at PI was able to predict grain yield. The NDVI_UAS was least sensitive to variation in yields as reflected by having the highest slope (2.4 Mg ha⁻¹ per 0.1 SI). In contrast, the slopes for NDVI_GS and NDRE_UAS were 0.9 and 1.1 Mg ha⁻¹ per 0.1 SI, respectively, indicating greater sensitivity to yields. Altogether, these results indicate that the ability of vegetation indices to inform crop management decisions depends on the index and the measurement platform used. Both NDVI_GS and NDRE_UAS produced measurements sensitive enough to inform N fertilizer management in this system, whereas NDVI_UAS was more limited.

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.

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 < 0.05) and 19% (p < 0.05) under average and N-limited conditions, respectively. However, wheat was 3% (p < 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.

Agronomic cropping systems in many regions face growing economic and management challenges as well as new regulations designed to address negative environmental and social externalities. At the same time, public support for agricultural education and extension is decreasing. Hence, new approaches are necessary to understand the most pressing on‐farm issues and help prioritize critical needs. With a diversity of agronomic crops and new regulations for water and nitrogen, California is an important case study for other regions. The objective of this study was to identify major grower and industry concerns, management challenges, and motivations in making management decisions. In 2020, 483 growers, consultants, and allied industry of agronomic crop production responded to an online survey. The crops most widely grown by respondents included rice (Oryza sativa L.), alfalfa (Medicago sativa L.), wheat (Triticum aestivum L.), and corn (Zea mays L.). Four out of the five top concerns were related to water. Weed control and irrigation/water management were primary management challenges, though differences occurred by crop and region. The highest priorities considered in grower management decision‐making were water, profitability, and land stewardship. Crop rotation benefits were a primary reason for growing agronomic crops, with profitability and tradition ranking closely behind. This study highlights opportunities to guide research and extension efforts based on critical needs identified by growers and industry, while also informing larger policy and institutional decisions regarding new programs and funding to address key issues in agronomic crop production.

Weeds are the major biotic constraint to rice production. Field observations have suggested that certain fertilizer regimes could enhance infestations of particular weed species emerging with rice. The study objective was to determine the effect of surface-applied calcium phosphate on weed growth in flooded California rice systems. In field and pot studies, triple superphosphate (TSP) applied to the soil surface increased weed emergence. Surface-applied TSP increased the number of sedge and broadleaf weeds, including smallflower umbrella sedge, blue-flowered ducksalad, redstem, ricefield bulrush, waterhyssop, and California arrowhead. A laboratory study measured germination of smallflower umbrella sedge and ricefield bulrush in response to the application of phosphorus (P) and calcium (Ca), which comprise 20 and 15% of TSP, respectively. Calcium stimulated smallflower umbrella sedge germination and had no effect on ricefield bulrush germination. Phosphorus did not stimulate either smallflower umbrella sedge or ricefield bulrush germination. Results indicate that surface applications of calcium phosphate increase the growth of certain weed species and that Ca may stimulate germination of smallflower umbrella sedge. By incorporating preplant applications of calcium phosphate into the soil profile, growers can reduce weed pressure from certain species. Alternatively, surface applications of calcium phosphate may be useful to stimulate weed emergence in stale-seedbed management.