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Seeding rate recommendations for wheat (Triticum aestivum L.) are often 150–450 seeds m−2. However, we hypothesize that wheat grown with high resource availability (i.e., fertility and moisture) can maximize yield under considerably lower rates. Our objectives were to explore winter wheat response to low populations under high resource availability using yield‐contest fields as a case study. A factorial experiment evaluated four wheat varieties (i.e., Joe, WB‐Grainfield, Langin, and LCS Revere) exposed to five seeding rates (50, 100, 150, 200, and 250 seeds m−2) during five seasons in commercial wheat fields managed by yield‐contest winning producers near Leoti, KS. Fields were silt‐loam soils with high available water‐holding capacity, long‐term history of manure application with non‐limiting fertility, and adopted a sorghum [Sorghum bicolor (L.) Moench]–fallow–wheat rotation. Plant density ranged from 26 to 341 plants m−2 and yield ranged from 3.2 to 6.8 Mg ha−1. Water use efficiency of 19.5 kg ha−1 mm−1 suggested no management limitations to yield. Quadratic models portrayed the grain yield–plant density relation well, with 95% of the maximum yield reached at 68 to 91 plants m−2 for crops sown at the optimum date (4 out of 5 years), and at 312 plants m−2 for a late‐sown crop. Greater fall temperature accumulation reduced the relative yield between the maximum and minimum seeding rates. There was no variety × plant density interaction and Langin was the most consistent yielding variety across environments. Optimum plant density for winter wheat in environments with high resource availability may be considerably lower than current recommendations. Core Ideas: Winter wheat response to plant density depends on resource availability.Under high resource (fertility and moisture), winter wheat response to density relied on fall temperature.Note that 68–91 plants m−2 reached 95% maximum yield in four out of five seasons under optimum sowing time.Note that 312 plants m−2 reached 95% maximum yield in one out of five seasons with late sowing.Current seeding rate recommendations could be lowered for intensively managed fields. [ABSTRACT FROM AUTHOR]

Light interception (LI) measurements are essential for modeling crop biomass and grain yield. Canopy LI is often measured with linear quantum sensors, but the need for unobstructed sunlight near solar noon can make field measurements impractical. We aimed to evaluate the use of the fraction of green canopy cover (FGCC) as a surrogate for LI in winter wheat (Triticum aestivum L.) canopies. A linear quantum sensor and a camera were used to collect 930 observations during the 2021 growing season across three locations in Kansas. Images were analyzed with the Canopeo application. The quantum sensor was also used to estimate the LI and leaf area index (LAI) in different canopy strata. Growing season shoot dry biomass was modeled with LI values from images and the quantum sensor. The LI estimated by the quantum sensor and images resulted in a strong positive linear correlation (r = 0.97). During the vegetative stages, LI values from images were generally greater than the LI values from the quantum sensor. During the reproductive stages, LI values from images were generally lower than those obtained by the quantum sensor, which was partly attributed to the better discrimination of FGCC for senescing vegetation. Using images to measure LI was easier and faster in prostrate canopies, and less sensitive to soil microrelief and changing sky conditions. The quantum sensor was useful for quantifying LI and LAI nondestructively in different canopy layers. Final growing season shoot dry biomass determined by either method was within 5% of the observed biomass. Core Ideas: Winter wheat light interception (LI) was estimated by a linear quantum sensor and fraction of green canopy cover.LI estimated by a quantum sensor and images resulted in a positive linear correlation.Determining LI from images was easier & not prone to measurement error in prostrate canopies. [ABSTRACT FROM AUTHOR]

BACKGROUND: Integrated wheat management strategies can affect grain yield and flour end‐use properties. However, the effect of integrated management and its interaction with environmental factors on the phenolic acid profiles of wheat has not been reported. The phenolic acid profile has become another parameter for the evaluation of wheat quality due to its potential health benefits. RESULTS: Year × location × management and year × management × genotype interactions were significant for the total phenolic content (TPC) of wheat samples. The year × location × management × genotype interaction was significant for the concentration of trans‐ferulic acid and several other phenolic acids. Field management practices with no fungicide application (e.g., farmer's practice, enhanced fertility) may lead to increased accumulation of phenolic compounds, especially for WB4458, which is more susceptible to fungi infection. However, this effect was also related to growing year and location. Higher soil nitrogen content at sowing also seems to affect the TPC and phenolic acid concentration positively. CONCLUSION: Wheat phenolic acid profiles are affected by genotype, field management, environment, and their interactions. Intensified field management, in particular, may lead to decreased concentration of wheat phytochemicals. The level of naturally occurring nitrogen in the soil may also affect the accumulation of wheat phytochemicals. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Resistance to wheat spike blast (WSB), caused by the Magnaporthe oryzae triticum pathotype (MoT), has relied upon a single major source: the 2NvS translocation introgressed from the wild relative Aegilops ventricosa Tausch. However, this resistance is partial and recently partially overcome by newer MoT races. To characterize potential novel loci conferring resistance to WSB, we conducted a genome‐wide association study (GWAS) using a diverse panel of 384 wheat genotypes phenotyped under three controlled‐environment conditions using MoT isolates T‐25 (301 genotypes), B‐71 (87 genotypes), and 008 (49 genotypes). Genotyping‐by‐sequencing identified 13,175 single nucleotide polymorphisms (SNPs) after filtering. Principal components analysis (PCA) identified two clusters based on the presence or absence of the 2NvS translocation, and the first three PCAs explained 13% of the genetic variation. Three individual analyses were performed (full [all genotypes combined], 2NvS genotypes only, and non‐2NvS genotypes only) using a linear mixed model and a threshold of significance of false discovery rate at 5%. Association analysis detected 25 significant SNPs for the full GWAS with isolate T‐25, in which 21 were mapped on chromosome 2A in the same physical position as the 2NvS translocation. Highly significant linkage disequilibrium among these SNPs suggested they might tag the same quantitative trait locus (QTL). No significant associations were identified with isolates B‐71 and 008, likely due to the small sample size. A QTL pyramiding analysis showed that the combination of multiple QTL was not statistically different from the individual effect of the 2A QTL. Further validation of these genomic regions can aid breeding for broad spectrum and durable WSB resistance. [ABSTRACT FROM AUTHOR]

Ammonia (NH3) volatilization from broadcast urea may lead to significant N losses in winter wheat. We aimed to: (a) quantify N losses through NH3 volatilization from fields fertilized with urea and urea amended with a urease inhibitor (NBPT) under cold weather months (February–April), and (b) investigate the impact of N losses through NH3 volatilization on the winter wheat production. We employed the integrated horizontal flux (IHF) method with passive NH3 samplers to quantify NH3 volatilization at five sites in Kansas. Urea and urea + NBPT were broadcast at a rate of 60 kg N ha–1 over circular plots. We assessed the impact of NH3 losses on wheat at three sites employing different rates of urea and urea + NBPT. NH3 losses volatilization varied from 0.3 to 29.6% of total N applied. The largest N losses (>23% of applied N) occurred when urea was broadcasted to moist soils followed by a dry period. Amending urea with NBPT reduced NH3 volatilization losses by more than 20% on the campaigns with the largest N losses (>23%). However, our results showed no significant differences for wheat yield, N‐recovery and agronomic efficiency between urea and urea + NBPT treatments likely due to the reduced NH3 volatilization (<17%) where the impact on winter wheat was measured. Our results suggest that winter wheat farmers should carefully evaluate the soil surface moisture conditions before broadcasting urea to avoid potential NH3 volatilization losses even under cold conditions (average soil temperature ranging from 2.5 to 7.7 °C). [ABSTRACT FROM AUTHOR]