Wheat Trials Research Articles

Isocycloseram, a novel isoxazoline insecticide seed treatment for protection of wheat and barley and mortality of wireworms, Limonius californicus (Coleoptera: Elateridae).

Populations of various economic species of wireworms are increasing in the key cereal crop production areas of Canada and the United States. To address this problem, seed treatments are under development that both provide crop protection and significantly reduce populations equivalent in effectiveness to the formerly used but now deregistered organochlorine lindane. Herein, we evaluated isocycloseram (PLINAZOLIN technology), the first isoxazoline (GABA-gated Chloride Channel Allosteric Modulator) agricultural insecticide, as a seed treatment for the protection of cereal crops from the sugarbeet wireworm, Limonius californicus (Mannerheim). In wheat and barley field trials conducted over 4 years under extreme wireworm pressure, isocycloseram applied as a seed treatment at 5.0-7.5 g AI/100 kg seed was as effective as or more effective than the current industry standard thiamethoxam at 20.0 g AI/100 kg seed in protecting crop stand and yield. Isocycloseram also reduced neonate wireworms (produced from eggs during the growing season) and resident wireworms (in the field at the time of planting) to levels expected from the formerly used seed treatment lindane.

Comparison of additive main effect–multiplicative interaction model and factor analytic model for genotypes ordination from multi‐environment trials

AbstractAn additive main effects and multiplicative interaction (AMMI) model is used to explore the genotype × environment interaction (GEI) in complete multi‐environmental trials. This model orders genotypes (G) according to their performance across environments (E) on a vectorial plane generated by the first two axes of a principal component analysis (AMMI‐biplot). Alternatively, interaction terms can be regarded as random effects, which can be predicted from linear mixed models using a factor analytic (FA) covariance structure for the GEI terms. Here, an FA‐biplot was obtained by plotting the G and E scores derived from the FA mixed model with complete and incomplete data. The aim of this work was to compare AMMI‐biplot with FA‐biplot for balanced data and then show the impact of the imbalance on the FA‐biplot. The G ordinations were assessed in four scenarios generated using datasets of 3 consecutive years obtained from comparative wheat trials conducted under a complete random block design in different environments across the Argentine network of cultivar assessment. For each scenario, G with the lowest performance in the third year were deleted, one by one, from all sites to generate a scenario with missing G. Although we used different statistical procedures to obtain AMMI‐biplot and FA‐biplot, they showed the same interaction pattern in the case of up to 50% of G dropped from all E in the last year of the multiyear trials. We conclude that the FA‐biplot yields a robust G ordination even when with incomplete datasets.

Environmental effects and their impact on yield in adjacent experimental plots of high-stem and short-stem wheat varieties

BackgroundIn regional wheat trials, when short-stem wheat varieties and high-stem wheat varieties are planted adjacent to each other in small plots, changes in their marginal plot environment can lead to bias in yield evaluation. Currently, there is no relevant research revealing the degree of their mutual influence.ResultsIn a regional wheat experiment, when high-stem wheat varieties and short-stem wheat varieties were planted adjacent to one another, there was no significant change in soil temperature or humidity in the high-stem wheat variety experimental plot from November to May compared to the control plot, while the soil humidity in the short-stem wheat variety experimental plot was greater than that in the control plot. In May, the soil temperature of the short-stem wheat varieties in the experimental plot was lower than that in the control plot. Illumination of the wheat canopy in the high-stem wheat variety experimental plot had a significant positive effect in April and May, while illumination of the wheat canopy in the short-stem wheat variety experimental plot had a negative effect. The chlorophyll fluorescence parameters of flag leaves in the high-stem wheat variety experimental plots showed an overall increasing trend, while the chlorophyll fluorescence parameters of flag leaves in the experimental plots of short-stem wheat varieties showed a decreasing trend. The analysis of the economic yield, biological yield, and yield factors in each experimental plot revealed that the marginal effects of the economic yield and 1000-grain weight were particularly significant and manifested as positive effects in the high-stem wheat variety experimental plot and as negative effects in the short-stem wheat variety experimental plot. The economic yield of the high-stem wheat variety experimental plot was significantly greater than that of the control plot, the economic yield of the short-stem wheat variety experimental plot was significantly lower than that of the control plot, and the economic yield of the high-stem experimental plot was significantly greater than that of the short-stem experimental plot. When the yield of the control plot of the high-stem wheat varieties was compared to that of the control plot of the short-stem wheat varieties, the yield of the control plot of the short-stem wheat varieties was significantly greater than that of the control plot of the high-stem wheat varieties.ConclusionsBased on these findings, it is concluded that plots with high-stem and short-stem wheat varieties are adjacent in regional wheat trials, the plots of high-stem wheat varieties are subject to marginal positive effects, resulting in a significant increase in economic yield; the plots of short-stem wheat varieties are subject to marginal negative effects, resulting in a decrease in economic yield. This study reveals the mutual influence mechanism of environment and yield with adjacent planting of high-stem and short-stem wheat varieties in regional wheat trials, providing a useful reference and guidance for optimizing the layout of regional wheat trials.

Effects of post-flowering drought and supplemental irrigation on grain yield and agro-phenological traits in durum wheat

Water deficit and unbalanced distribution rainfall in the Mediterranean rainfed regions are major problems threatening agricultural sustainability, especially durum wheat (Triticum turgidum L. var. durum) production. This study aimed to investigate the effects of drought and supplemental irrigation in post-flowering stage on traits performance of durum wheat trials and to identify traits that significantly contribute to increase the grain yield of durum wheat under different water regimes and recommend new genotypes adapted to climate change. An eight-year field study was conducted from 2014 to 2022, consisting of 36 field experiments, 18 under rainfed and 18 under supplemental irrigation (two times irrigation in flowering and grain-filling stages) conditions through six experimental series of elite durum wheat yield trials each with same genotypes. In each experiment, grain yield, 1000-kernel weight (TKW), plant height (PLH), days to heading (DHE) and maturity (DMA) were recorded. Drought stress tolerance index (STI) and mean productivity (MP) were applied to differentiate high-performing and drought tolerant durum wheat genotypes in each experimental series. The combined analysis of variance for traits studied of each experimental series indicated that the year, water stress treatment, genotype and their interaction effects were significant. For grain yield, year was the main source of variation and had the highest impact on genotypes performance. The results indicated that the supplemental irrigation, depending on genetic materials, significantly increased grain yield (18.7%-45.8%), TKW (8.2%-12.9%), and PLH (1.1%-8.2%) compared with the rainfed condition across years. Supplemental irrigation exhibited for no significant effect on the increase of the maturity time (0.2%-2.2%) of genotypes tested compared with the rainfed condition. The results confirmed the systematic effects of post-flowering drought on yield, grain weight and plant stature of durum wheat. A positive and significant trend (R2=0.62; P<0.01) was observed between yield production in rainfed condition and cumulative rainfall, showing that in 62% of the cases higher rainfall resulted in higher performance. TKW and PLH significantly and positively correlated with mean yield in the both rainfed and irrigated conditions, suggesting the importance of these two traits in breeding programs to assist in developing high yielding genotypes under different water regime conditions. Evaluation of genotypes in terms of mean yield and stability performance using GGE biplot, resulted in identification of superior lines that outperformed the check cultivars. The STI and MP were desirable selection criteria for high yielding and drought tolerant genotypes, thus high yielding genotypes that are tolerant to drought would be expected to have good yield stability in stressful environments. The results highlight the significant effects of drought stress after flowering upon grain yield, and the significant contribution of 1000-kernel weight and plant height to increase grain yield under different water regimes, that could be utilized to aid breeding of high-performing and drought tolerant durum wheat genotypes under the future climate conditions.

Unravelling the Complexities of Genotype-Soil-Management Interaction for Precision Agriculture

The knowledge of interactions among crop genotypes, soil types, and crop management is essential for precision agriculture. This paper explores these interactions through the analysis of 27 years of winter wheat trials, with 276 unique varieties tested across seven distinct soil types and more than 8000 plots. The study investigates how different winter wheat crop varieties respond to varying soil types and preceding crops. The findings revealed a significant interaction between variety, soil type, and preceding crop. With only a few exceptions, the highest-yielding varieties were predominantly the most recently developed. The ranking of the varieties exhibited inconsistency across the various soil types, implying that a variety yields differently when cultivated in different soil types. Furthermore, the influence of preceding crops on yield varied with soil type. This suggests that taking field-specific soil variation and the preceding crop into account during variety selection may improve the yield potential. Furthermore, the study highlights consistent yield increases due to advancements in breeding programs, with yearly increases ranging from 0.05 to 0.1 t/ha per year across all soil types. Integration of insights from genetics, soil attributes, and management practices demonstrates how farmers can increase productivity.

Multi-year belowground data of minirhizotron facilities in Selhausen

The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil–plant continuum and the root images to develop and compare image analysis methods.

Layered-Strip Fertilization Improves Nitrogen Use Efficiency by Enhancing Absorption and Suppressing Loss of Urea Nitrogen

Appropriate deep application of fertilizer is the key basis for improving nitrogen use efficiency (NUE). However, the effects of different deep application methods and fertilizer types on nutrient migration, NUE and biomass in wheat season are unclear. Therefore, in this study, a barrel planting test with multilayer fertilization (15N labeled urea (U) and coated urea (CU)) was conducted in a long-term positioning trial of winter wheat in the North China Plain (NCP). We quantified the migration of fertilizer N (Ndff) in soil–plant–atmosphere and its effects on wheat biomass and NUE based on surface (Usur, CUsur), layered-strip (Ustr, CUstr) and layered-mix fertilization (Umix, CUmix) of U and CU. Compared with surface fertilization, the concentration of mineral N in root zone (0–40 cm) was increased by Ustr and Umix (8.6–50.3%), and the concentration of ammonium N was decreased by CUstr and CUmix (49.6–76.0%), but there was no change in the nitrate N. The biomass and total N absorption of wheat tissues (straw and root) were increased by 12.3–38.9% under Ustr and CUstr. Meanwhile, the distribution of Ndff in the 0–10 cm soil was decreased under Ustr and CUstr, but it was increased in the 10–30 cm soil, thereby promoting the absorption of Ndff in wheat tissues by 12.3–28.7%. The rates of absorption and loss of Ndff were the highest (57.6–58.5%) and the lowest (4.5%) under Ustr and CUstr, respectively, compared with other treatments. Consequently, layered-strip fertilization optimized the migration and utilization of Ndff within the soil–plant–atmosphere system. This approach equalized distribution, enhanced absorption and minimized losses of Ndff, resulting in an increase in NUE by 9.6–16.7%. Under the same treatment, CU was more suitable for crop nutrient requirements than U, which was more conducive to the improvement of NUE. Our findings will provide a scientific basis for the precise directional fertilization of winter wheat in the NCP.

Plasma Treated Cattle Slurry Moderately Increases Cereal Yields

Plasma treatment offers an approach to enhance the nitrogen (N) content of livestock slurry and biogas digestate, thereby increasing the efficacy of organic fertilizers. This innovative method is used to produce nitrogen-enriched organic fertilizer (NEO) containing a double concentration of plant-available N. Over three years, we conducted a comprehensive study in 14 spring wheat and barley field trials in Norway. The primary objective was to assess and compare the cereal grain yield achieved by applying NEO to other conventional fertilizers. The NEO utilized in our research was derived from the unit developed by the Norwegian company N2 Applied. The results indicated that 120 kg N ha−1 in NEO yielded in the same range of cereal grains as 95 kg N ha−1 in mineral fertilizer. Moreover, the combination of untreated slurry and 55 kg N ha−1 in mineral fertilizer Opti-NS yielded the same as 120 kg N ha−1 in NEO. Surprisingly a combination of 12 kg N ha−1 in mineral fertilizer at sowing day and 108 kg N ha−1 in NEO at the three-leaf stage led to a higher yield in spring wheat than 120 kg N ha−1 NEO spread at sowing day in two out of three experimental years. Moreover, applying NEO directly to plants has shown no visible signs of harm. Lastly, filtering the slurry resulted in higher cereal grain yields than the untreated slurry. In conclusion, despite possessing the same N content, utilizing NEO yielded a 15–20% lower cereal grain yield than mineral fertilizer. Nonetheless, 20–30% more yield than the native amount of cattle slurry it derived. However, we have observed an unexplained loss of approximately 17% of the nitrogen in NEO, which does not translate into increased grain yield or nitrogen productivity.

Ohio grain crop response to sulfur fertilization

AbstractContinuous declines in atmospheric sulfur (S) deposition along with increased S removal rates with crop harvest has the potential to lead to S deficiency in Ohio field crops. As a result, S fertilization has become more common over the past decade. However, the extent of S deficiency is unknown, as inherent soil properties and management practices influence S availability and uptake. We conducted 96 replicated trials, from 2013 to 2021 to (1) examine the response of corn (Zea mays L.), soybean [Glycine max (L.) Merr], and wheat (Triticum aestivum L.) to S application on a wide range of Ohio soils and differing management practices and (2) determine the ability of Mehlich‐3 extractable S in soil, leaf S, and grain S concentrations to predict grain yield response to S fertilizer. Our results showed limited grain yield increases to S fertilization with an overall response rate of 7.3% (4 of 50 corn trials, 3 of 34 soybean trials, and 0 of 12 wheat trials). Sulfur fertilization increased leaf and grain S concentrations by 19.4% and 12% in corn, by 22.2% and 7.7% in soybean, and by 41.7% and 0% in wheat, respectively. These increases in leaf or grain S concentrations were not directly related to yield responses. Diagnostic tools of Mehlich‐3 soil S, leaf S, and grain S concentrations failed to predict yield response to S. We conclude that S deficiency is not widespread in Ohio soils and that optimizing grain crop production does not currently require S fertilization.

Virulence of Puccinia triticina and Puccinia tritici-duri on durum wheat in southern Spain, from 2020 to 2022

Leaf rust is a major wheat disease in southern Spain, where durum wheat is an important crop. Until the 2019/2020 season, this disease was effectively managed, as the most widely planted cultivars in southern Spain had effective resistance genes. A problem arose in the spring of 2020, when every farm field and durum wheat trial examined displayed leaf rust symptoms. Leaves had few but large uredinial pustules, different from those of the normal leaf rust caused by Puccinia triticina, and telia developed rapidly after only a few days. The symptoms clearly fitted with P. tritici-duri, another wheat leaf rust species already reported in the western Mediterranean Basin. This species is not new in southern Spain but has never been observed at such high severity on almost every durum wheat cultivar grown in that region. Leaf rust severity was assessed in durum wheat field trials in the 2020, 2021 and 2022 growing seasons in four provinces of southern Spain. During 2020 and 2021, 13 single pustule isolates of leaf rust were also collected from different cultivars of durum wheat. Inoculation of the isolates on a differential host set showed that four different races were present, two being of P. tritici-duri. Only cultivar Calero showed consistent resistance to the races of P. tritici-duri employed in this study.

Performance of Nitrogen Fertilization and Nitrification Inhibitors in the Irrigated Wheat Fields

Effective nitrogen (N) management practices are critical to sustain crop production and minimize nitrate (NO3−) leaching loss from irrigated fields in the Columbia Basin (U.S.), but studies on the applied practices are limited. Therefore, from 2014 to 2016, two separate field studies were conducted in sandy loam soils in the region to evaluate the performance of various N fertilizers in spring and winter wheat. The treatments consisted of two nitrification inhibitors (NIs) (Instinct® II and Agrotain® Ultra) in combination with two N fertilizers (urea and urea ammonium nitrate [UAN]) under two application methods (single vs. split-application) and two rates (100% vs. 85% of growers’ standard). The results from these field trials demonstrated that N fertilizer treatments did not affect wheat grain yield (GY) and grain protein (GP). In the spring wheat trial, higher NH4⁺-N content but lower NO3−-N content was observed in the UAN treatments (0–30 cm). However, the application of NIs had no considerable effect on soil N content. In the winter wheat trial, the split N application generally reduced NO3−-N and total mineral nitrogen (TMN) content, especially at 30–60 cm, in comparison to a single application. The use of Instinct® II tended to reduce NO3−-N and TMN contents, while Agrotain® Ultra was not effective in inhibiting nitrification. Our findings suggest that more studies on the effectiveness of NIs and N applications would enable growers to optimize N use efficiency and crop production in the region.

Improving the efficiency of multi-location field trials with complete and incomplete relationship information

The increasingly cost-efficient availability of ‘omics’ data has led to the development of a rich framework for predicting the performance of non-phenotyped selection candidates in recent years. The improvement of phenotypic analyses by using pedigree and/or genomic relationship data has however received much less attention, albeit it has shown large potential for increasing the efficiency of early generation yield trials in some breeding programs. The aim of this study was accordingly to assess the possibility to enhance phenotypic analyses of multi-location field trials with complete relationship information as well as when merely incomplete pedigree and/or genomic relationship information is available for a set of selection candidates. For his purpose, four winter bread wheat trial series conducted in Eastern and Western Europe were used to determine the experimental efficiency and accuracy of different resource allocations with a varying degree of relationship information. The results showed that modelling relationship between the selection candidates in the analyses of multi-location trial series was up to 20% more efficient than employing routine analyses, where genotypes are assumed to be unrelated. The observed decrease in efficiency and accuracy when reducing the testing capacities was furthermore less pronounced when modelling relationship information, even in cases when merely partial pedigree and/or genomic information was available for the phenotypic analyses. Exploiting complete and incomplete relationship information in both preliminary yield trials and multi-location trial series has thus large potential to optimize resource allocations and increase the selection gain in programs that make use of various predictive breeding methods.

UAV-Based Estimation of Grain Yield for Plant Breeding: Applied Strategies for Optimizing the Use of Sensors, Vegetation Indices, Growth Stages, and Machine Learning Algorithms

Non-destructive in-season grain yield (GY) prediction would strongly facilitate the selection process in plant breeding but remains challenging for phenologically and morphologically diverse germplasm, notably under high-yielding conditions. In recent years, the application of drones (UAV) for spectral sensing has been established, but data acquisition and data processing have to be further improved with respect to efficiency and reliability. Therefore, this study evaluates the selection of measurement dates, sensors, and spectral parameters, as well as machine learning algorithms. Multispectral and RGB data were collected during all major growth stages in winter wheat trials and tested for GY prediction using six machine-learning algorithms. Trials were conducted in 2020 and 2021 in two locations in the southeast and eastern areas of Germany. In most cases, the milk ripeness stage was the most reliable growth stage for GY prediction from individual measurement dates, but the maximum prediction accuracies differed substantially between drought-affected trials in 2020 (R2 = 0.81 and R2 = 0.68 in both locations, respectively), and the wetter, pathogen-affected conditions in 2021 (R2 = 0.30 and R2 = 0.29). The combination of data from multiple dates improved the prediction (maximum R2 = 0.85, 0.81, 0.61, and 0.44 in the four-year*location combinations, respectively). Among the spectral parameters under investigation, the best RGB-based indices achieved similar predictions as the best multispectral indices, while the differences between algorithms were comparably small. However, support vector machine, together with random forest and gradient boosting machine, performed better than partial least squares, ridge, and multiple linear regression. The results indicate useful GY predictions in sparser canopies, whereas further improvements are required in dense canopies with counteracting effects of pathogens. Efforts for multiple measurements were more rewarding than enhanced spectral information (multispectral versus RGB).

Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data.

Linking high-throughput environmental data (enviromics) to genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (G × E). This study developed a data-driven approach based on Environment-Phenotype Association (EPA) aimed at recycling important G × E information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes and (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. These results were then used as a dimensionality reduction strategy, integrating historical data to feed multi-environment GP models, which led to the development of four new G × E kernels considering genomics, enviromics, and EPA outcomes. The wheat trial data used included 36 locations, 8 years, and three target populations of environments (TPEs) in India. Four prediction scenarios and six kernel models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. Nevertheless, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the G × E kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of G × E prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because G × E is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.

Are all ultra‐processed foods created equal?

Are all ultra‐processed foods created equal?

Long-term experimental data and crop modelling to inform the ecological intensification of irrigated wheat production in South Africa

Wheat (Triticum aestivum L.) production is threatened by climate change and the decline in agricultural land available due to urbanisation, farmers switching to other crops, and environmental degradation. The aim of this study was to investigate the ecological intensification (EI) of irrigated wheat production in South Africa. Data from the long-term wheat trial at the University of Pretoria and an intensive growth analysis conducted in 2019 were used to calibrate and evaluate the APSIM model. Following adequate model performance, improved management scenarios including crop rotation, manure application, optimised inorganic nitrogen (N) fertiliser application rate, objectively scheduled irrigation, and a combination of all these improved management practices together, were tested. The adoption of all management practices simultaneously was shown to be the best practice, achieving an increase of 18% in yield and reducing deep drainage and N leaching by 31%. Whereas measured data indicated a decrease in soil organic matter (SOM) from 1.20% in 1950 to 0.58% in 2019, adopting EI measures could reduce the loss of SOM to only 0.68%. Farmers are encouraged to adopt one or more of these EI management practices as their site-specific situations allow.

Phenotyping winter wheat for early ground cover

The relationship between the early ground cover and the grain yield in winter wheat is not yet fully understood. In a winter wheat breeding programme, selection for early ground cover is traditionally made using visual scoring. Although visual scoring is preferred as a phenotypic screening tool by wheat breeders, its output may not be reliable, as it requires experience. A smartphone camera-based digital image technique can be recommended as a feasible, reliable, repeatable, affordable, and fast selection tool for early ground cover in wheat as an alternative to visual scoring. For this purpose, two wheat trials were conducted in the 2017–2018 and 2019–2020 seasons. In both seasons, 215 wheat genotypes in total, together with three checks from spring wheat, were tested under rain-fed conditions in the spring wheat zone in Turkey. All the tested wheat genotypes were grouped into spring, facultative, and winter growth habit using visual scoring. Simultaneously, photos were taken from each plot with a smartphone camera, and the early ground cover (%) was estimated using the smartphone camera-based digital image technique. The relationships between grain yield, visual scoring, and early ground cover could so be estimated. In both seasons, significant negative correlation between grain yield and visual scoring (r = −0.679** and r = −0.704**, respectively) and significant positive correlation between the grain yield and the early ground cover (r = 0.745** and r = 0.747**, respectively) were observed. The correlation between visual scoring and early ground cover were negative (r = −0.862** and r = −0.926**, respectively). The broad sense heritability estimates in both seasons were 0.51 and 0.85, respectively, for early ground cover, 0.91 and 0.94 for visual scoring, and 0.86 and 0.69 for grain yield. In this study, we revealed that testing winter wheat genotypes in the spring wheat zone rather than in the winter wheat zone could be a more effective way to unveil the positive relationship between the early ground cover and the grain yield. We have shown that the smartphone-based digital image technique is a useful selection tool for early ground cover in winter wheat.

Registration of ‘Hardy 2519’ wheat

Abstract‘Hardy 2519’ (Reg. no. CV‐1189, PI 692613), a hard red winter (HRW) wheat (Triticum aestivum L.) cultivar, was derived from the cross VA06HRW‐108 × ‘Vision 30’ (PI 661153) using a modified bulk breeding method. Hardy 2519 was tested as VA14HRW‐25 in replicated yield trials in Virginia (2015–2018) and in the USDA‐ARS Uniform Bread Wheat Trials (2016–2018) and released by the Virginia Agricultural Experiment Station in 2019. Hardy 2519 is a widely adapted, high‐yielding HRW wheat with 2*, 5+10 gluten subunits. Hardy 2519 is awned, semidwarf (Rht 2 gene) with medium spike emergence, resistant to leaf rust, and moderately resistant to powdery mildew in the mid‐Atlantic region. In the Virginia Bread Wheat Elite Trials from 2018 to 2020, Hardy 2519 produced a mean yield of 5,393 kg ha–1, which was similar to that of the HRW wheat cultivar ‘Vision 45’ (PI 667642) at 5,267 kg ha–1. Hardy 2519 has acceptable milling and baking quality on the basis of comparisons with the HRW wheat check cultivar ‘Jagger’.

Genetics, not environment, contributed to winter wheat yield gains in Ontario, Canada.

Changes in entries' market classes and genetic improvements within classes-not environmental changes-enhanced yields over thirty-one years of wheat trials. Correlations between yields and ancestries drove genomic prediction accuracies. Increasing crop yields is important for enhancing farmers' livelihoods, meeting market demands, and reducing the environmental impact of agriculture. We analyzed the yield trends of Ontario winter wheat variety trials between 1988 and 2018. Over this period, wheat yields steadily increased by 38kgha-1yr-1, or 0.68%yr-1 relative to the mean. While fungicide treatment of trials contributed a one-time 670kgha-1 yield increase, yields were otherwise unaffected by long-term changes in agronomic practice, climate, or other non-genetic factors. Genetic improvement entirely accounted for yield improvement. Market class changes over the 31year span accounted for some yield improvement. More importantly, genetic improvement occurred within each market class. Entry yield estimates calculated from genomic prediction models strongly correlated with field estimated yields with a mean r of 0.68. Genomic prediction accuracies were high because yields differed across genetically distinct subpopulations. Despite environmental changes, genetic improvement will likely increase Ontario winter wheat yields into the future.

Community Perceptions of Zinc Biofortified Flour during an Intervention Study in Pakistan.

Zinc-biofortified flour may be a cost-effective approach to improve zinc status of populations in low-resource settings. The success of biofortification programmes is subject to acceptability and uptake by consumers. This study explored community leaders’ and community members’ (n = 72) experiences and attitudes towards the flour provided during a cluster randomised controlled trial of zinc biofortified wheat in rural Pakistan (BiZiFED2). Focus group discussions (n = 12) were conducted and thematic analysis applied using an inductive, semantic, contextualist approach. Five themes were identified: (1) Contribution to food security; (2) Better sensory and baking properties than local flour; (3) Perceived health benefits; (4) Willingness to pay for the flour; and (5) Importance of trusted promoters/suppliers. Although the participants were blind to whether they had received control or biofortified flour, referred to collectively as “study flour”, the results indicated that the study flour performed well in terms of its taste and bread making qualities, with no adverse reports from participants in either arm of the BIZIFED2 RCT. Participants suggested that they would buy the biofortified wheat if this was available at a fair price due to perceived health benefits, reporting positive sensory characteristics and cooking attributes when compared to the flour available in the local markets. Overall, there was a positive reception of the programme and flour among the participants, and members of the community hoped for its continuation and expansion.