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Cover crops and nitrogen (N) fertilization may affect soil mineral N, N uptake, and N productivity (grain yield per unit plant available N) in wheat (Triticum aestivum L.). We evaluated the effect of summer cover crops and N fertilization rates on soil mineral N and winter wheat grain yield, N uptake, and N productivity in the Loess Plateau of China. We measured cover crop aboveground biomass N, soil mineral N (0-20 cm), and winter wheat yield, N uptake, and N productivity for four seasons from 2017-2018 to 2020-2021. Cover crops were soybean (Glycine max L., SB), sudangrass (Sorghum sudanense [Piper] Stapf, SG], soybean and sudangrass mixture (SS), and no cover crop (CK) and N fertilization rates were 0, 60, and 120 kg N ha−1 applied to wheat. The SB increased cover crop biomass N and soil mineral N by 15 to 65%, but SS increased winter wheat yield, protein concentration, and N uptake by 25 to 55% compared to SG and CK at most N fertilization rates and years. Nitrogen fertilization rate had a variable effect on cover crop and wheat parameters. Wheat N productivity was 55 to 85% greater with CK than cover crops, but decreased with increased N fertilization rates. The N fertilizer equivalence of cover crops for wheat yield and N uptake ranged from 31 kg N ha−1 for SG to 150 kg N ha−1 for SS. Overall, SS with 60 kg N ha−1 can sustain winter wheat N uptake and N productivity compared to SG or CK with or without N fertilization. [ABSTRACT FROM AUTHOR]

This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance observations and partial least squares regression (PLSR) to predict foliar concentrations of macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), micronutrients (boron, copper, iron, manganese, zinc, molybdenum, aluminum, and sodium), and moisture content in winter wheat. A total of 360 fresh wheat leaf samples were collected from a wheat breeding population over two growing seasons. These leaf samples were used to collect VSWIR reflectance observations across a spectral range spanning 350 to 2,500 nm. These samples were then processed for nutrient composition to allow for the examination of the ability of reflectance to accurately model diverse chemical components in wheat foliage. Models for each nutrient were developed using a rigorous cross-validation methodology in conjunction with three distinct component selection methods to explore the trade-offs between model complexity and performance in the final models. We examined absolute minimum predicted residual error sum of squares (PRESS), backward iteration over PRESS, and Van der Voet's randomized t -test as component selection methods. In addition to contrasting component selection methods for each leaf trait, the importance of spectral regions through variable importance in projection scores was also examined. In general, the backward iteration method provided strong model performance while reducing model complexity relative to the other selection methods, yielding R 2 [relative percent difference (RPD), root mean squared error (RMSE)] values in the validation dataset of 0.84 (2.45, 6.91), 0.75 (1.97, 18.67), 0.78 (2.13, 16.49), 0.66 (1.71, 17.13), 0.68 (1.75, 14.51), 0.66 (1.72, 12.29), and 0.84 (2.46, 2.20) for nitrogen, calcium, magnesium, sulfur, iron, zinc, and moisture content on a wet basis, respectively. These model results demonstrate that VSWIR reflectance in combination with modern statistical modeling techniques provides a powerful high throughput method for the quantification of a wide range of foliar nutrient contents in wheat crops. This work has the potential to advance rapid, precise, and nondestructive field assessments of nutrient contents and deficiencies for precision agricultural management and to advance breeding program assessments. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Aims Methods Results Conclusion The direct effect of winter cover crops (WCCs) or living mulches (LMs) on a first vegetable crop has already been investigated. However, little is known about the effect on growth and yield of a second cash crop in the rotation.The aim of the study was to assess the carry‐over effect of legumes grown as WCC or LM on winter wheat as a second crop after cabbage, measured in yield and nitrogen release.Two field trials were carried out in Germany between 2019 and 2022. In the WCC trial, rye, rye with vetch, vetch, pea, and faba bean were used as WCC and compared to bare soil. The WCC biomass was incorporated before cabbage planting in late spring. For the LM trial, perennial ryegrass or white clover was used as LM during cabbage cultivation and compared to bare soil. The LM biomass was incorporated with the cabbage residues and compared to an early incorporation of LM biomass before cabbage planting. In both trials, winter wheat was sown in the fall as the second following main crop in the rotation.Leguminous WCC species had significant higher wheat yield compared to non‐legumes but not compared to the control without WCC. Late incorporation of LM biomass resulted in increased wheat yield at 10.1–10.4 Mg ha−1 compared to an early incorporation before cabbage planting at 9.35 Mg ha−1. Net N releases show that for WCC, the main effect of legume nitrogen fixation is achieved in the first crop cabbage immediately after incorporation of WCC biomass. In the case of leguminous LM, the effects of legume nitrogen fixation are of much higher relevance in the second main crop, winter wheat, due to LM biomass incorporation after cabbage cultivation.Therefore, we suggest to consider not only the direct but also the carry‐over effects of leguminous cover cropping in vegetable crop rotations. [ABSTRACT FROM AUTHOR]

ABSTRACT Septoria nodorum blotch (SNB) is a fungal disease of wheat caused by the necrotrophic fungus Parastagonospora nodorum (Berk.), considered as one of the most devastating fungal diseases affecting winter wheat (Triticum aestivum L.). The complex inheritance of resistance to SNB poses significant challenges to breeding programmes. Improving selection precision and identifying novel resistance QTLs are crucial for enhancing SNB resistance. This study investigated strategies for crop genetic improvement, including genome‐wide association mapping (GWAS) and genomic prediction (GP), within a practical breeding programme. A population of 1500 winter wheat breeding lines was phenotyped for SNB resistance over 5 years across 19 geographical locations under natural infection conditions. Despite highly unbalanced breeder's data, medium to high heritabilities for SNB resistance were achieved. GWAS identified 11 significant marker‐trait associations for SNB resistance across Chromosomes 2A, 2B, 4B, 4D, 5D and 7B. GP fivefold cross‐validation analysis revealed a predictive ability of 0.52 for SNB resistance. The resistant wheat genotypes and SNP markers identified in this study will be valuable assets for future breeding efforts to enhance SNB resistance in wheat. [ABSTRACT FROM AUTHOR]

Wheat yellow (stripe) rust, caused by the fungus Puccinia striiformis f.sp. tritici (Pst), is a devastating disease of wheat worldwide. The success of Pst is largely due to the pathogens ability to rapidly overcome host resistance, generating new races that are easily dispersed between territories through wind-borne transmission of Pst urediniospores. Thus, first signs of entry of new Pst races into a region is usually captured by changes in disease severity. To examine any alterations of winter wheat variety response to Pst infection in Serbia, we analyzed yield and Pst disease severity in field trials conducted in 2014, 2021, and 2023. We specifically focused on analyzing Pst disease severity at growth stages related to yield. Associations between qualitative variables (variety, year) and quantitative variables (yield in untreated plots, yield loss, and disease index (DI) of Pst infection) were analyzed using Principal Component Analysis with mixed data. A General Linear Model was used to investigate the most influential factors on yield, yield loss, and Pst infection. The results indicated that yellow rust disease severity increased over the past decade, suggesting a potential recent change in the Pst population in Serbia. Comparative population genetic analysis of Pst samples from the 2023 wheat season and those collected in Serbia in 2014 confirmed a potential change in the Pst population. In addition, we found that yield losses across wheat varieties varied independently of Pst infection levels, indicating that wheat varieties differ in their ability to overcome damage caused by high levels of Pst infection. Given that the level of pathogen pressure triggering susceptibility reactions is cultivar-specific, our study highlights the need for a deeper focus on the mechanisms underlying these differences. Expanding our understanding of the interactions between pathogens, plant defense responses, and the ability of cultivars to mitigate yield losses will better equip us to predict and prevent potential yield losses in commercial wheat varieties due to yellow rust in the future. [ABSTRACT FROM AUTHOR]

Introduction: The production of high-quality food for the growing world population on the one hand and the reduction of chemical-synthetic pesticides on the other hand represents a major challenge for agriculture worldwide. The effectiveness of a combination of microbial and non-microbial biostimulants (BSs) with various nitrogen (N) forms in pathogen defense is discussed as a promising, but still poorly understood bio-based alternative for crop protection. Methods: For this reason, nitrate and stabilized ammonium fertilizer both combined with a consortium of Pseudomonas brassicacearum , Bacillus amyloliquefaciens , and Trichoderma harzianum as soil treatment or with a mixture of seaweed extract (Ascophyllum nodosum) together with chitosan-amended micronutrient fertilizer as foliar spray application were compared under controlled greenhouse conditions. Furthermore, a combination of microbial and different non-microbial BSs (seaweed extracts + chitosan) and micronutrients with nitrate or with stabilized ammonium fertilizer was tested under field conditions to improve nutrient availability, promote plant growth, and suppress Zymoseptoria tritici (Zt) in winter wheat. Results and discussion: While plant-protective effects against Zt by the microbial consortium application could be observed particularly under ammonium fertilization, the application of seaweed extract–chitosan mixture expressed plant defense against Zt more strongly under nitrate fertilization. In the field trial, the combination of microbial consortium with the seaweed extract–chitosan mixture together with micronutrients zinc (Zn) and manganese (Mn) showed positive effects against Zt under ammonium fertilization, associated with increased levels of defense metabolites. Furthermore, the additional input of Zn and copper (Cu) from the chitosan application improved the micronutrient status by minimizing the risk of Zn and Cu deficiency under controlled and field conditions. The use of BSs and the inoculation of Zt did not show any effects on plant growth and yield neither under controlled greenhouse conditions nor in the field. Summarized, microbial and non-microbial BSs separately applied or even combined together as one treatment did not influence plant growth or yield but made a positive contribution to an N form-dependent promotion of pathogen defense. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB) is a devastating disease of wheat, causing yield losses, reduced grain quality, and mycotoxin contamination. Breeding can mitigate the severity of FHB epidemics, especially with genomics-assisted methods. The mechanisms underlying resistance to FHB in wheat have been extensively studied, including phenological traits and genome-wide markers associated with FHB severity. Here, we aimed to improve genomic prediction for FHB resistance across breeding programs by incorporating FHB-correlated traits and FHB-associated loci as model covariates. We combined phenotypic data on FHB severity, anthesis date, and plant height with genome-wide marker data from five Central European winter wheat breeding programs for genome-wide association studies (GWAS) and genomic prediction. Within all populations, FHB was correlated with anthesis date and/or plant height, and a marker linked to the semi-dwarfing locus Rht-D1 was detected with GWAS for FHB. Including the Rht-D1 marker, anthesis date, and/or plant height as covariates in genomic prediction modeling improved prediction accuracy not only within populations but also in cross-population scenarios. [ABSTRACT FROM AUTHOR]

Subsampling a reduced number of accessions from ex situ genebank collections, known as core collections, is a widely applied method for the investigation of stored genetic diversity and for an exploitation by breeding and research. Optimizing core collections for genome-wide association studies could potentially maximize opportunities to discover relevant and rare variation. In the present study, eight strategies to sample core collections were implemented separately for two traits, namely susceptibility to yellow rust and stem lodging, on about 6,300 accessions of winter wheat (Triticum aestivum L.). Each strategy maximized different parameters or emphasized another aspect of the collection; the strategies relied on genomic data, phenotypic data or a combination thereof. The resulting trait-customized core collections of eight different sizes, covering the range between 100 and 800 accession samples, were analyzed based on characteristics such as population stratification, number of duplicate genotypes and genetic diversity. Furthermore, the statistical power for an association study was investigated as a key criterion for comparisons. While sampling extreme phenotypes boosts the power especially for smaller core collections of up to 500 accession samples, maximization of genetic diversity within the core collection minimizes population stratification and avoids the accumulation of less informative duplicate genotypes when increasing the size of a core collection. Advantages and limitations of different strategies to create trait-customized core collections are discussed for different scenarios of the availability of resources and data. [ABSTRACT FROM AUTHOR]

The integration of remote sensing technology and machine learning algorithms represents a new research direction for the rapid and large-scale detection of water stress in modern agricultural crops. However, in solving practical agricultural problems, single machine learning algorithms cannot fully explore the potential information within the data, lacking stability and accuracy. Stacking ensemble learning (SEL) can combine the advantages of multiple single machine learning algorithms to construct more stable predictive models. In this study, threshold values of stomatal conductance (gs) under different soil water stress indices (SWSIs) were proposed to assist managers in irrigation scheduling. In the present study, six irrigation treatments were established for winter wheat to simulate various soil moisture supply conditions. During the critical growth stages, gs was measured and the SWSI was calculated. A spectral camera mounted on an unmanned aerial vehicle (UAV) captured reflectance images in five bands, from which vegetation indices and texture information were extracted. The results indicated that gs at different growth stages of winter wheat was sensitive to soil moisture supply conditions. The correlation between the gs value and SWSI value was high (R2 > 0.79). Therefore, the gs value threshold can reflect the current soil water stress level. Compared with individual machine learning models, the SEL model exhibited higher prediction accuracy, with R2 increasing by 6.67–17.14%. Using a reserved test set, the SEL model demonstrated excellent performance in various evaluation metrics across different growth stages (R2: 0.69–0.87, RMSE: 0.04–0.08 mol m−2 s−1; NRMSE: 12.3–23.6%, MAE: 0.03–0.06 mol m−2 s−1) and exhibited excellent stability and accuracy. This research can play a significant role in achieving large-scale monitoring of crop growth status through UAV, enabling the real-time capture of winter wheat water deficit changes, and providing technical support for precision irrigation. [ABSTRACT FROM AUTHOR]

The development of multi-cropping systems is hampered by the seasonal shortage problem of growing two or three crops within a year. Exploring strategies to alleviate phenological constraints in multi-cropping systems is crucial for increasing grain production. Using a county-level panel dataset with detailed crop progress information from China, this study investigates whether mechanized winter wheat harvest can alleviate the phenological constraints of a shorter growing season for subsequent summer corn in a wheat-corn double cropping system. The results show that mechanized winter wheat harvest considerably extends the length of the summer corn growing season. This spillover effect of mechanized winter wheat harvest is particularly evident in plains and hilly areas with larger farm sizes. Further analysis of the mechanism indicates that mechanized winter wheat harvest affects the length of the summer corn growing season by advancing the planting date and delaying the maturity date. These results underscore the importance of advancing agricultural mechanization to enhance food security under limited land resources. [ABSTRACT FROM AUTHOR]

Promoting arthropods in agricultural landscapes can contribute substantially to stop their decline and enhance pest control. Higher soil moisture and the presence of field margins can increase the abundance of arthropods in agricultural landscapes and influence their distribution within crop fields. However, little is known about the influence of soil moisture and distance from field margins on the overwintering of arthropods in arable fields. We investigated the influence of soil moisture and distance from a field margin on the numbers of arthropods, ground beetles and spiders emerging from soil in winter wheat fields. We established transects in winter wheat fields away from two different types of field margins: (i) around small standing water bodies (kettle holes) to capture a wide range of soil moisture values and (ii) other semi-natural landscape elements. At three distances (1 m, 20 m, 50 m), we sampled arthropods with emergence traps and measured soil moisture between March and June. We found that soil moisture had a positive effect on the emergence numbers of arthropods in general and ground beetles and spiders in particular. Distance from field margins generally had negative effects on the emergence numbers of ground beetles, but positive effects on the emergence numbers of spiders. Emergence numbers and soil moisture content did not differ significantly between the two types of field margins. The high emergence numbers inside the fields indicate that arable fields are important overwintering habitats for beneficial arthropods. Proper management of arable soils to promote soil water holding capacity and soil moisture content may have the added benefit of promoting the production of beneficial natural enemies from local soils. [ABSTRACT FROM AUTHOR]

The effectiveness of frequent compost application in improving soil health is well‐documented. Less is known on the long‐term effects of infrequent compost application to semiarid soils. Compost made of dairy manure and straw bedding was applied once in a dryland organic hard red winter wheat (Triticum aestivum L. emend. Thell.)–fallow system at 50 Mg ha−1 dry wt. in 1994 in a randomized complete block design with three replicates. Twenty‐eight years later, yields in composted plots (1.4 Mg ha−1) remained higher (p < 0.1) than in control plots (0.79 Mg ha−1). Plant‐available P, acid phosphatase activity (ACP), and total N were higher in composted plots by 143%, 37%, and 29%. Soil organic carbon (SOC) and dehydrogenase enzyme activity were greater by 25% and 20% with compost compared to the control, as were aggregate stability determined using SLAKES method, autoclave‐extractable protein, and CO2‐96 h by 143%, 22%, and 16%. Soil extractable K and Zn also increased with compost application. The interaction of ACP and estimated evapotranspiration (ET) emerged as a pivotal factor in explaining the variation in yield. These findings suggest that growers may see some yield improvements from periodic compost applications to dryland organic winter wheat–fallow systems. This strategy could help rebuild SOC and partially counter the challenges of low and variable precipitation. Core Ideas: Twenty‐six years after compost application, soil health indicators were over 140% higher than without compost.Potential acid phosphatase activity strongly predicted yield variation based on path analysis.A large compost application provided lasting benefits to soil health and improved yield. [ABSTRACT FROM AUTHOR]

Precision management (PM) aims to reduce inputs while increasing land productivity and economical return and enhancing cropping system resiliency to climate change. This study evaluated how climate (precipitation) and management influenced yields and soil nutrients in a dryland agricultural system. We compared an "aspirational" (ASP) system (no‐till, 4‐year rotation of winter wheat [Triticum aestivum L.], corn [Zea mays L.], proso millet [Panicum miliaceum L.], and fallow/flex) to a traditional "business‐as‐usual" (BAU) system (reduced tillage, 2‐year rotation of winter wheat and fallow: W–F). Phases of each rotation were included yearly throughout the study period (2018–2022) with three replications. The ASP system incorporated PM by dividing each ASP field into three zones (high‐, medium‐, and low‐PM) according to prior yield and topography. Nitrogen was applied at high, medium, or low application rates within those zones. Under favorable precipitation, wheat, corn, and millet yields responded to PM treatments, with yields increasing proportional to N addition. Years with low in‐season precipitation had a significant reduction in wheat and corn yields (2020 and 2022) and complete millet yield failures (2020 and 2021). Low soil organic matter accumulation (0.1%–0.5%) and a reduction in soil macro‐ and micronutrient status suggested that soil nutrient additions are needed to prevent soil‐nutrient degradation. The ASP treatment added a third crop every 4 years and did so without significantly decreasing wheat yield following fallow. The ASP management shows promise as an alternative to BAU in the Great Plains dryland production region. Core Ideas: Precision management could provide resilient cropping, maximize resources, and increase returns in dryland cropping.Dryland crop yields increased in response to in‐season precipitation and timing.Nitrogen responses were amplified/more notable in years with higher in‐season precipitation.Low soil organic matter accumulation and crop nutrient removal showed a potential to reduce soil nutrients status.Crop diversification and management could improve yields and mitigate climate variability of the Plains region. [ABSTRACT FROM AUTHOR]

Diversified crop rotations with no‐till management are considered fundamental to sustainable agroecosystems; however, associated uncertainty in economic returns may hinder farmers' decisions to adopt these practices. The objectives of this study were to compare economic performance (gross revenue, net revenue, and production cost) among various low‐input diversified versus conventional crop rotations. The experiment was initiated in the fall of 2000 with winter wheat (Triticum aestivum L.) planting and planting of remaining crops in the spring of 2001 near Brookings, SD. Economic analysis was performed from data collected during the fifth complete 4‐year crop rotational cycle (2017–2020) of six crop rotations: (1) 4‐year corn (Zea mays L.)‐soybean [Glycine max L. (Merr.)]‐spring wheat‐sunflower (Helianthus annus L.) (CSSwSf), (2) 4 year corn‐soybean‐spring wheat‐pea (Pisum sativum L.) (CSSwP), (3) 4‐year corn‐pea‐winter wheat‐soybean (CPWwS), (4) 4‐year corn‐oat (Avena sativa L.)‐winter wheat‐soybean (COWwS), (5) 2‐year corn‐soybean (CS), and (6) continuous corn (CCC, treatment initiated in 2017). Corn yield in CSSwP rotation, where corn followed peas, was higher (p < 0.05) by 20%, 25%, 45%, and 89%, compared to CPWwS, CSSwSf, CS, and CCC rotations, respectively. Similarly, soybean yield following winter wheat was significantly higher by 16%–38% in COWwS and 13%–38% in CPWwS compared to CSSwP, CSSwSf, and CS. Overall, diversified crop rotations improved both corn and soybean yield and net revenue compared to 2‐year CS and monoculture CCC rotations. Moreover, 4‐year diversified systems, specifically COWwS, CPWwS, and CSSwP, demonstrated economic resilience by maintaining stable production costs. Core Ideas: Corn yield improved in diversified cropping systems, particularly when corn followed a leguminous crop (peas).Incorporation of leguminous crops such as pea and soybean in rotations reduced production expenses.Soybean yield increased when planted after winter wheat in a rotation.Monoculture, non‐diversified systems showed high production costs, reduced yields, and lower economic returns. [ABSTRACT FROM AUTHOR]

It is a consensus that Earth's climate has been warming. The impact of global warming is asymmetric, that is, there is more substantial warming in the daily minimum surface air temperature and lower warming in the maximum surface air temperature. Previous studies have reported diurnal temperature differences greatly affecting winter wheat yield. However, only a few studies have investigated the impact of global warming on the growth and yield of winter wheat, yet the influence of night warming on quality has not been deeply evaluated. In this study, two wheat cultivars were used as materials: Jimai 44 (JM44) with strong gluten and Jimai 22 (JM22) with medium gluten, to explore the effects of high nighttime temperatures (HNTs) on the growth, yield, and quality of wheat. The results show that HNTs significantly shortened seedling emergence and anthesis periods in both cultivars compared with ambient temperatures (ATs). In addition, HNTs increased the respiration rate at anthesis and grain-filling stages, impeding wheat pollination and grain maturity. HNTs also accelerated leaf senescence and increased the number of sterile spikelets and plant height, but decreased the effective tiller number, the number of spikes per unit area, and grains per spike. As a result, the grain yield of JM22 and JM44 was decreased by 24.6% and 21.2%, respectively. Moreover, HNTs negatively influenced the flour quality of the two wheat cultivars. The current findings provide new insights into the effects of HNTs on the growth, development, yield, and quality of different wheat genotypes during the whole growth period. [ABSTRACT FROM AUTHOR]

Purpose: The organic farming sector is growing, and environmental aspects are a matter of discussion next to the quantity and quality of organic products. The life cycle assessment (LCA) method is an efficient solution to fill these gaps. Methods: A three-year field experiment was conducted to evaluate the response of winter wheat varieties and their mixtures in grain yield and quality. Four single cropping of winter wheat cultivars and eight mixed cropping systems sown with mixed or alternate rows of two cultivars were compared. The LCA method via to cradle-to-farm gate approach was employed. The SimaPro 9.6.0.1 Analyst software, and integrated method of the software ReCiPe 2016 Midpoint and Endpoint, Hierarchical (H) V1.1 / World (2010) H and primarily the Cut-off System Model approach was applied, considering one kg of grain protein yield in dry matter as the functional unit. Results: The results were expressed in impact categories of global warming, GW (kg CO2 eq); terrestrial acidification, TA (kg SO2 eq); freshwater eutrophication, FE (kg P eq); marine eutrophication, ME (kg N eq); terrestrial ecotoxicity, TE (kg 1,4-DCB); freshwater ecotoxicity, FET (kg 1,4-DCB); human non-carcinogenic toxicity, HNCT (kg 1,4-DCB); land use, LU (m2a crop eq), and water consumption, WC (m3). The variants with the lowest environmental impact levels tend to be those where Vanessa was implemented. The hotspots were detected on the level of direct field emissions for the impact category of GW, FE, or ME, then on the level of manure management for the impact category of TA, or on the level of agrotechnology in the impact category of TE and FET. Conclusions: The results of this research may be used as an example of an environmental design application based on hotspot detection in organic agriculture from the LCA perspective. The study results can also be implemented into the cropping system related to organic farming on arable land. [ABSTRACT FROM AUTHOR]

Winter wheat (Triticum aestivum L.) is a crucial crop that guarantees food supply in the North China Plain (NCP). As the frequency of extreme cold events increases, it is necessary to explore the freezing resistance of different wheat varieties in order to clarify planting boundaries and help with risk assessment. In this study, 2-year controlled experiments were conducted to explore the effect of freezing temperatures (Tair) and freezing durations on three winterness types. A set of indexes were used to characterize the subfreezing stress on wheat tiller, leaf, and final yield. Logistical regressions were used to quantify the temperature threshold for 10%, 30%, and 50% of freezing injury. The results showed that the lower temperature threshold of tiller (LT) varied from −9.6 to −15.9°C, −10.7 to −19.1°C and −11.4 to −21.2°C for LT10, LT30, and LT50, respectively. The difference between LT and yield loss (YL) indexes reduced with decreased winterness types and was −0.1 to 3.4°C, −0.7 to 2.1°C, and 0.3 to 0.9°C higher compared with YL thresholds for winterness, semi-winterness, and weak-winterness types, respectively. The average minimum soil temperature was 7.5, 4.8, and 4.2°C higher than Tair for 1-, 2-, and 3-day treatment, respectively. Soil effective negative accumulated temperature hours (TSEh) ranged from 6.9 to 12.0, 48.4 to 6.9, and 84.7 to 106.9°C·h for 10%, 30%, and 50% tiller mortality, respectively. Freezing treatment with Tair < −12, −9, and −8°C obviously decreased leaf Fv/Fm for the three varieties and Fv/Fm declined obviously after 5 days of recovery under field conditions. Our results provided multiple indexes for quantifying subfreezing damage in practical wheat production and could shed light on future risk assessment. [ABSTRACT FROM AUTHOR]

The article "Field efficacy of urease inhibitors for mitigation of ammonia emissions in agricultural field settings: A systematic review" explores the effectiveness of urease inhibitors in reducing ammonia emissions in various agricultural field settings. The study reviews multiple research studies from different countries that have tested the impact of urease inhibitors on different soil types and crops using various measuring methods. The findings suggest that nitrogen fertilizers with urease inhibitors can help reduce ammonia and nitrous oxide losses while maintaining crop yield in temperate grassland environments. [Extracted from the article]

BACKGROUND RESULTS CONCLUSION Sorghum (Sorghum bicolor L. Moench) is a cereal crop known for its biological nitrification inhibition (BNI) capacity, a plant‐mediated activity limiting nitrification pathway. The use of BNI‐producing plants represents an environmentally friendly and cost‐effective approach to reduce nitrogen (N) losses, such as nitrate (NO3−) leaching and nitrous oxide (N2O) gas emissions. The present study aimed to test the effectiveness of different S. bicolor cultivars in rotation to retain ammonium (NH4+) in soils and promote N availability for the subsequent wheat crop. A two‐year field rotation was established with four sorghum cultivars followed by winter wheat (Triticum aestivum L.). Urea alone or combined with the urease inhibitor N‐(n‐butyl) thiophosphoric triamide was applied to promote a NH4+‐based fertilization regimes.AddingN‐(n‐butyl) thiophosphoric triamide maintained higher soil NH4+ content and reduced ammonia‐oxidizing bacteria population during sorghum cultivation. However, the benefits of the inhibitor on sorghum growth were cultivar‐dependent. Notably, the further reduction in ammonia‐oxidizing bacteria abundance for sorghum Voyenn and the increased soil NH4+ content for Vilomene suggested a BNI potential for these cultivars. Importantly, the Vilomene precedent enhanced wheat yield for both fertilization regimes.Overall, the present study confirms that sorghum is a suitable catch crop and emphasizes the importance of selecting the proper sorghum cultivar to maximize the yield of the target wheat crop, at the same time as minimizing N losses. Furthermore, developing combined strategies with selected sorghum cultivars and the application of urease inhibitors enables to enhance sorghum productivity as forage, achieving added value to the rotation. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Wheat grain yields, quality, and nitrous oxide (N2O) emissions were controlled through the type and application rate of nitrogen (N) fertilizer. Here, we investigated the optimal management of N fertilization by examining the combined effects of organic and N fertilizers on wheat yields, quality, and N2O emissions. Field trials under six treatments were located on campus farms at Anhui Science and Technology University, including farmer's common practice (270 kg N ha-1, N270), 2/3 reduction in N270 (90 kg N ha-1, N90), organic fertilizer with equal N270 (OF270), 2/3 reduction in OF270 (OF90), 4/5 reduction OF270 + 1/5 reduction N270 (20% OF270 + 80% N270), and 4/ 5 reduction OF90 + 1/5 reduction N90 (20% OF90 + 80% N90) were applied to winter wheat. The plots were arranged in a randomized complete block experimental design. The N2O emissions were quantified under different fertilization measures in the peak wheat growing season during sowing, jointing, and grain filling stages, respectively. Compared with N270 and N90 treatments, N2O emissions were significantly decreased by 18.6% and 27.2%, respectively, under 20% OF270 + 80% N270% and 20% OF90 + 80% N90 (p < 0.05). Further, N2O emissions in N270 were increased by 50.8% relative to N90. Wheat yields increased significantly under 20% OF90 + 80% N90 by 27.6% (N270) and 16.4% (N90), and were considerably enhanced under 20% OF270 + 80% N270 by 40.6% (N270) and 12.7% (N90) in contrast to OF270 and N270 (p < 0.05). Compared with N90, the content of wet gluten, protein and starch under 20% OF90 + 80% N90 treatment significantly increased by 7.7%, 13.8% and 7.9%, and enhanced by 7.6%, 4.8%, 8.0% relative to OF90, respectively (p < 0.05). The starch content increased significantly by 2.0%, whereas the settlement value decreased considerably by 2.9% under 20% OF270 + 80% N270 (p < 0.05), and there was no notable difference in the wet gluten and protein contents (p > 0.05). Our findings indicated that organic fertilizer mixed with N fertilizer can effectively reduce N2O emissions, increase both the grain yields and quality in wheat field compared with N fertilizer alone. [ABSTRACT FROM AUTHOR]

Medium Resolution Spectral Imager-II (MERSI-II) on FengYun 3D (FY-3D) meteorological satellite is a visible and infrared spectral imaging instrument comparable with Moderate Resolution Imaging Spectroradiometer (MODIS), which provides ample opportunities for regional crop mapping. However, current research may have overlooked the potential of combining FY-3D MERSI-II data with deep neural network (DNN) algorithms for winter wheat identification. This research utilizes a DNN method to train a nonlinear model with the synthetic normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) at a 250 m resolution from FY-3D MERSI-II as the eigenvalues, enabling to accurately identify spatial distribution of winter wheat in China’s main production regions and filling the gap in the application of FY-3D MERSI-II data. The results indicate that the winter wheat identification with the combination of NDVI and EVI achieved an accuracy of 95.32%, with a Kappa coefficient of 0.87 and a determination coefficient (R2) of 0.73, improving the overall accuracy by 1.27% and 1.07% compared to models trained solely using NDVI or EVI, respectively. In terms of spatial distribution, the overlap rate with the winter wheat dataset of China from MODIS at a 500 m resolution exceeded 95%, with an area bias of 2.67%. This study indicates that the medium resolution FY-3D MERSI-II remote sensing dataset combined with the DNN algorithm can accurately identify crop information over large areas. [ABSTRACT FROM AUTHOR]

Groundwater recharge can greatly contribute to the crop growth in farmland for the sustainable development of regional production in modern agriculture. It is crucial to precisely quantify the rational utilization of groundwater sources. Among them, groundwater recharge can be broadly classified into empirical and mechanistic models in recent years. However, some limitations are still remained in practical application, due to the complexity and highly nonlinear of groundwater recharge. In this study, a series of experiments were conducted on the winter wheat under different groundwater levels and irrigation treatments using 24 clustered large-scale lysimeters. The AquaCrop model was calibrated and validated using the soil moisture content, canopy coverage, biomass, and grain yield of winter wheat as evaluation indicators. The calibrated AquaCrop model was then utilized to simulate the growth and development of winter wheat, as well as the soil water dynamics under varying irrigation treatments and groundwater depths. An estimation model was proposed for the groundwater recharge in the period of winter wheat growth. Thereby, the critical groundwater levels were determined for the different growth stages, and the threshold depth of water table suitable for winter wheat growth. Results showed that: 1) The AquaCrop model was performed best on the growth and development of winter wheat under different irrigation treatments and groundwater levels. Simultaneously, the better performance was achieved in the statistical parameters between the simulated and measured values of soil water content, canopy coverage, biomass, and grain yield during the growth period, with the determination coefficient (R²)≥0.8, the relative error (RE) and the normalized root mean square error (NRMSE) are both＜16%. 2) The validated AquaCrop model was employed to simulate the growth of winter wheat under 52 scenarios of groundwater depth and irrigation treatments. Subsequently, the nonlinear regressions of soil water storage - groundwater recharge intensity and groundwater depth - soil water storage were fitted during the growth period of winter wheat. The groundwater recharge intensity was calculated to compare with the measured values for verification. The statistical parameters fell well within the acceptable error range of the model, such as R²≥0.873, RE≤9.22%, RMSE≤0.316 mm/d and NRMSE≤11.89%. 3) The critical tables of groundwater were calculated in different growth stages of winter wheat under various irrigation treatments. The critical tables of groundwater were 3.28, 3.36 and 3.54m, respectively, for the winter wheat during the seedling, frozen and reviving-jointing stages under irrigation. Furthermore, the critical table of groundwater was 3.38 m under the sufficient irrigation treatment in the whole growth period. There was the various behavior in the different growth periods of winter wheat under the insufficient irrigation treatments. In general, the critical table of groundwater decreased with the increasing irrigation amount. The optimal threshold water table was ranged from 2.0 to 2.5 m for the winter wheat, according to the relationship between grain yields and groundwater levels under 52 simulated scenarios. Therefore, the yield and water use efficiency (WUE) of winter wheat were simultaneously reached their optimal levels under the insufficient irrigation treatment (at reviving and filling stages), which were 8.848 t/hm² and 2.17 to 2.43 kg/m³, respectively. The findings can also provide the technical support for the accurate assessments of groundwater contribution for the precision irrigation and decision-making management. making management. [ABSTRACT FROM AUTHOR]

This work is devoted to studying the features of the generation of metal nanoparticles (NPs) by underwater discharges and their use in the technology of growing grain crops. The purpose of the study is to use the latest nano-containing preparations based on NPs of biofunctional metals, which will allow the introduction of modern technologies and technological processes for environmentally friendly crop production while simultaneously increasing the quality indicators of the final wheat harvest. To obtain NPs, metals of the biogenic group were chosen, the content of which in known agrochemical preparations is at least 0.01%. Metal NPs were obtained in discharge chambers filled with deionized water. As a result of underwater electric spark discharges, metal colloids were obtained as a preparative form of nanomaterials for further use and processing of wheat crops. To carry out the certification of NPs by the methods of electron microscopy (SEM and TEM) and X-ray structural analysis, the metal phase of the colloidal solutions was separated from the dispersion medium by drying. We determined the composition, structure, and average size of the target NPs. A complex preparation was studied, the composition of which included metal NPs, in particular Fe − 1800 ppm; Cu − 400 ppm; Zn − 1000 ppm; Mn − 800 ppm. Testing of the drug was carried out in field conditions during the cultivation of winter wheat at various stages of organogenesis. The application rate of the drug was 1–1.5 L/ha, accordingly, 1.0–1.5 L of the drug was diluted in 250 L of water. Foliar treatment of wheat plants was carried out. The analysis of grain quality indicators showed a 30% increase in the total protein content and a more than two-fold increase in gluten, which indicates the extraordinary prospects of using the obtained preparations. [ABSTRACT FROM AUTHOR]

Spatial variability and uncertainty associated with soil volumetric moisture content (SVMC) is crucial in moisture prediction accuracy, this paper sets out to address this point of SVMC by developing data-driven model. Grid samples of SVMC covered approximately a 3-ha field during the jointing growth stage of winter wheat, and SVMC were measured by Time Domain Reflectometry (TDR), located in North China Plain, China. Bayesian inference was performed to explore spatial heterogeneity, robustness, transparency, interpretability and uncertainty related to SVMC using python-based PyMC3 combined with Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation (INLA-SPDE) model. The results showed that the prediction surface of SVMC, the lower and upper limits of 95% credible intervals quantified uncertainty associated with SVMC, cauchy prior of the flexibility and adaptability to obtain state-of-the-art predictive performance is more robust than gaussian prior for SVMC prediction, the transparency and interpretability of SVMC prediction model were revealed by MCMC (Markov-Chain Monte-Carlo) trace plots, KDE (Kernel density estimates), and rank plots. The uncertainty associated with SVMC can explicitly be described using the highest-posterior density interval, the prediction lower and upper limits. [ABSTRACT FROM AUTHOR]

The conclusions of the European Food Safety Authority (EFSA) following the peer review of the initial risk assessments carried out by the competent authorities of the rapporteur Member State Sweden for the pesticide active substance pirimicarb are reported. The context of the peer review was that required by Commission Implementing Regulation (EU) No 844/2012, as amended by Commission Implementing Regulation (EU) No 2018/1659. The conclusions were reached on the basis of the evaluation of the representative use(s) of pirimicarb as an insecticide on winter and spring wheat and sugar beet via foliar spray application and on ornamental pot plants in permanent greenhouses. The reliable end points, appropriate for use in regulatory risk assessment are presented. Missing information identified as being required by the regulatory framework is listed. Concerns are identified. [ABSTRACT FROM AUTHOR]

We selected 46 core winter–spring wheat cultivars from China's main wheat-producing areas as experimental materials to clarify the differences in the physiological parameters of their cold resistance and provide a theoretical basis and high-quality germplasm for cold resistance breeding. We investigated differences in their cold resistance under field conditions for two consecutive years, and determined the physiological parameters of the cold resistance, yield, and quality indicators of different winter–spring wheat cultivars. Our results showed that the cold resistance of winter wheat cultivars was higher than that of spring wheat cultivars. The chlorophyll (Chl), soluble sugar (SS), soluble protein (SP), and free proline (Pro) contents of different winter–spring wheat cultivars were positively correlated with cold resistance, and malondialdehyde (MDA) content was negatively correlated with cold resistance. The five physiological parameters can be used as physiological indicators for the breeding of cold-resistant cultivars. The cold resistance, yield, and quality indicators of different spring and winter wheat cultivars were comprehensively evaluated by using the average membership value and comprehensive score. It was found that the average membership value and comprehensive score of winter wheat cultivars were higher than those of spring wheat cultivars. Through classification using the K-means method, the cold-resistant, high-yield, and high-quality cultivars were screened out, namely, Jimai23 (JM23), Jimai44 (JM44), Shannong57 (SN57), and Jinmai 919 (JM919). [ABSTRACT FROM AUTHOR]

The mass of nutrients accumulated in the vegetative parts of winter wheat (WW) in the period from the beginning of booting to the full flowering stage (Critical Cereal Window, CCW) allows for the reliable prediction of the grain yield (GY) and its components, and the grain protein content (GPC) and its yield. This hypothesis was verified in a one-factor field experiment carried out in the 2013/2014, 2014/2015, and 2015/2016 growing seasons. The field experiment included seven nitrogen-fertilized variants: 0, 40, 80, 120, 160, 200, and 240 kg N ha−1. The N, P, K, Ca, Mg, Fe, Mn, Zn, and Cu content in wheat vegetative parts (leaves, stems) was determined in two growth stages: (i) beginning of booting (BBCH 40) and (ii) full flowering (BBCH 65). We examined the response of eight WW traits (ear biomass at BBCH 65, EAB; grain yield, GY; grain protein content, GPC; grain protein yield, GPY; canopy ear density, CED; number of grains per ear, GE; number of grains per m−2—canopy grain density, CGD; and thousand grain weight, TGW) to the amount of a given nutrient accumulated in the given vegetative part of WW before flowering. The average GY was very high and ranged from 7.2 t ha−1 in 2016 to 11.3 t ha−1 in 2015. The mass of ears in the full flowering stage was highest in 2016, a year with the lowest GY. The highest N mass in leaves was also recorded in 2016. Only the biomass of the stems at the BBCH 65 stage was the highest in 2015, the year with the highest yield. Despite this variability, 99% of GY variability was explained by the interaction of CGD and TGW. Based on the analyses performed, it can be concluded that in the case of large yields of winter wheat, GE is a critical yield component that determines the CGD, and in consequence the GY. The leaf nutrient mass at the BBCH 40 stage was a reliable predictor of the GPC (R2 = 0.93), GPY (0.92), GE (0.84), and CED (0.76). The prediction of the GY (0.89), CGD (0.90), and TGW (0.89) was most reliable based on the leaf nutrient mass at the BBCH 65 stage. The best EAB prediction was obtained based on the mass of nutrients in WW stems at the BBCH 65 stage. The magnesium accumulated in WW parts turned out to be, with the exception of TGW, a key predictor of the examined traits. In the case of the TGW, the main predictor was Ca. The effect of Mg on the tested WW traits most often occurred in cooperation with other nutrients. Its presence in the developed stepwise regression models varied depending on the plant part and the WW trait. The most common nutrients accompanying Mg were micronutrients, while Zn, Fe, Mn, and Ca were the most common macronutrients accompanying Mg. Despite the apparently small impact of N, its yield-forming role was indirect. Excessive N accumulation in leaves in relation to its mass in stems, which appeared in the full flowering phase, positively impacted the EAB and GPC, but negatively affected the GE. Increasing the LE/ST ratio for both Mg and Ca resulted in a better formation of the yield components, which, consequently, led to a higher yield. This study clearly showed that nutritional control of WW during the CCW should focus on nutrients controlling N action. [ABSTRACT FROM AUTHOR]

As one of the major global food crops, the monitoring and management of the winter wheat planting area is of great significance for agricultural production and food security worldwide. Today, the development of high-resolution remote sensing imaging technology has provided rich sources of data for extracting the visual planting information of winter wheat. However, the existing research mostly focuses on extracting the planting plots that have a simple terrain structure. In the face of diverse terrain features combining mountainous areas, plains, and saline alkali land, as well as small-scale but complex planting structures, the extraction of planting plots through remote sensing imaging is subjected to great challenges in terms of recognition accuracy and model complexity. In this paper, we propose a modified Segformer model for extracting winter wheat planting plots with complex structures in rural areas based on the 0.8 m high-resolution multispectral data obtained from the Gaofen-2 satellite, which significantly improves the extraction accuracy and efficiency under complex conditions. In the encoder and decoder of this method, new modules were developed for the purpose of optimizing the feature extraction and fusion process. Specifically, the improvement measures of the proposed method include: (1) The MixFFN module in the original Segformer model is replaced with the Multi-Scale Feature Fusion Fully-connected Network (MSF-FFN) module, which enhances the model's representation ability in handling complex terrain features through multi-scale feature extraction and position embedding convolution; furthermore, the DropPath mechanism is introduced to reduce the possibility of overfitting while improving the model's generalization ability. (2) In the decoder part, after fusing features at four different scales, a CoordAttention module is added, which can precisely locate important regions with enhanced features in the images by utilizing the coordinate attention mechanism, therefore further improving the model's extraction accuracy. (3) The model's input data are strengthened by incorporating multispectral indices, which are also conducive to the improvement of the overall extraction accuracy. The experimental results show that the accuracy rate of the modified Segformer model in extracting winter wheat planting plots is significantly increased compared to traditional segmentation models, with the mean Intersection over Union (mIOU) and mean Pixel Accuracy (mPA) reaching 89.88% and 94.67%, respectively (an increase of 1.93 and 1.23 percentage points, respectively, compared to the baseline model). Meanwhile, the parameter count and computational complexity are significantly reduced compared to other similar models. Furthermore, when multispectral indices are input into the model, the mIOU and mPA reach 90.97% and 95.16%, respectively (an increase of 3.02 and 1.72 percentage points, respectively, compared to the baseline model). [ABSTRACT FROM AUTHOR]

Novel recycled fertilizers could help close environmental nutrient cycles in the circular economy. To better understand their performance and residual value, commercially available biobased nitrogen (N) and phosphorus (P) fertilizers (BBFs) were tested in a two-year crop cycle of winter wheat and ryegrass. The N fertilizer replacement value of N-BBFs ranged from 47 to 80% in the main crop. Not all BBFs led to a similarly high N concentration as the mineral reference in the wheat straw. However, full and early fertilization with incorporation could make the fertilizing effect of N-BBFs more reliable. The P fertilizer replacement value ranged between 105 and 161% for the crop cycle. We assume that the N contained in biobased phosphorus fertilizers can be seen as unproblematic for losses during winter and can serve as a starter fertilizer already present in the soil for the succeeding crop in spring. In general, biobased P fertilizers had a higher residual value than biobased N fertilizers. However, these residual values were comparable to those of mineral fertilizer references. While P-BBFs proved to be a sustainable and reliable nutrient source for a crop cycle, the N-BBFs used as the main crop fertilizer were found to be more prone to environmental influences. [ABSTRACT FROM AUTHOR]

Breeding high-photosynthetic-efficiency wheat varieties is a crucial link in safeguarding national food security. Traditional identification methods necessitate laborious on-site observation and measurement, consuming time and effort. Leveraging unmanned aerial vehicle (UAV) remote sensing technology to forecast photosynthetic indices opens up the potential for swiftly discerning high-photosynthetic-efficiency wheat varieties. The objective of this research is to develop a multi-stage predictive model encompassing nine photosynthetic indicators at the field scale for wheat breeding. These indices include soil and plant analyzer development (SPAD), leaf area index (LAI), net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO2 concentration (Ci), stomatal conductance (Gsw), photochemical quantum efficiency (PhiPS2), PSII reaction center excitation energy capture efficiency (Fv'/Fm'), and photochemical quenching coefficient (qP). The ultimate goal is to differentiate high-photosynthetic-efficiency wheat varieties through model-based predictions. This research gathered red, green, and blue spectrum (RGB) and multispectral (MS) images of eleven wheat varieties at the stages of jointing, heading, flowering, and filling. Vegetation indices (VIs) and texture features (TFs) were extracted as input variables. Three machine learning regression models (Support Vector Machine Regression (SVR), Random Forest (RF), and BP Neural Network (BPNN)) were employed to construct predictive models for nine photosynthetic indices across multiple growth stages. Furthermore, the research conducted principal component analysis (PCA) and membership function analysis on the predicted values of the optimal models for each indicator, established a comprehensive evaluation index for high photosynthetic efficiency, and employed cluster analysis to screen the test materials. The cluster analysis categorized the eleven varieties into three groups, with SH06144 and Yannong 188 demonstrating higher photosynthetic efficiency. The moderately efficient group comprises Liangxing 19, SH05604, SH06085, Chaomai 777, SH05292, Jimai 22, and Guigu 820, totaling seven varieties. Xinmai 916 and Jinong 114 fall into the category of lower photosynthetic efficiency, aligning closely with the results of the clustering analysis based on actual measurements. The findings suggest that employing UAV-based multi-source remote sensing technology to identify wheat varieties with high photosynthetic efficiency is feasible. The study results provide a theoretical basis for winter wheat phenotypic monitoring at the breeding field scale using UAV-based multi-source remote sensing, offering valuable insights for the advancement of smart breeding practices for high-photosynthetic-efficiency wheat varieties. [ABSTRACT FROM AUTHOR]

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R2) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations. [ABSTRACT FROM AUTHOR]

Mapping and monitoring crops are the most complex and difficult tasks for experts processing and analyzing remote sensing (RS) images. Classifying crops using RS images is the most expensive task, and it requires intensive labor, especially in the sample collection phase. Fieldwork requires periodic visits to collect data about the crop's physiochemical characteristics and separating them using the known conventional machine learning algorithms and remote sensing images. As the problem becomes more complex because of the diversity of crop types and the increase in area size, sample collection becomes more complex and unreliable. To avoid these problems, a new segmentation model was created that does not require sample collection or high-resolution images and can successfully distinguish wheat from other crops. Moreover, UNet is a well-known Convolutional Neural Network (CNN), and the semantic method was adjusted to become more powerful, faster, and use fewer resources. The new model was named Fast-UNet and was used to improve the segmentation of wheat crops. Fast-UNet was compared to UNet and Google's newly developed semantic segmentation model, DeepLabV3+. The new model was faster than the compared models, and it had the highest average accuracy compared to UNet and DeepLabV3+, with values of 93.45, 93.05, and 92.56 respectively. Finally, new datasets of time series NDVI images and ground truth data were created. These datasets, and the newly developed model, were made available publicly on the Web. [ABSTRACT FROM AUTHOR]

Surface ozone (O3) pollution has adverse impacts on the yield of winter wheat. The North China Plain (NCP), one of the globally significant primary regions for winter wheat production, has been frequently plagued by severe O3 pollution in recent years. In this study, the effects of O3 pollution on winter wheat yield and economic impact were evaluated in the NCP during the 2015–2018 seasons using the regional atmospheric chemical transport model (WRF-Chem), O3 metrics including the phytotoxic surface O3 dose above 12 nmol m−2 s−1 (POD12), and the accumulated daytime O3 above 40 ppb (AOT40). Results showed that the modeled O3, exposure-based AOT40, and flux-based POD12 increased during the winter wheat growing season from 2015 to 2018. The annual average daytime O3, exposure-based AOT40, and flux-based POD12 were 44 ppb, 5.32 ppm h, and 1.78 mmol m−2, respectively. During 2015–2018, winter wheat relative production loss averaged 10.9% with AOT40 and 14.6% with POD12. This resulted in an average annual production loss of 12.4 million metric tons, valued at approximately USD 4.5 billion. This study enhances our understanding of the spatial sensitivity of winter wheat to O3 impacts, and suggests that controlling O3 pollution during the key growth stages of winter wheat or improving its O3 tolerance will enhance food security. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB), caused by the Fusarium graminearum species complex, causes significant losses in grain yield and quality of wheat (Triticum aestivum) by inducing floret sterility. Grains become contaminated with mycotoxins, especially deoxynivalenol (DON), making them unsuitable for consumption. To clarify the impact of wheat cultivar resistance, infection level, and climate after anthesis on the efficacy of a fungicide for the control of FHB, we treated two moderately susceptible cultivars and 11 susceptible cultivars with fungicide (48% phenamacril + tebuconazole) at anthesis over two years. FHB incidence (INC), disease severity index (DSI), Fusarium-damaged kernels, DON contamination, thousand-kernel weight, and yield were evaluated under artificially inoculated and naturally infected field trials in 2018 and 2021. The results of multi-factor variance analysis show that the control efficacy with respect to INC and DSI is affected by cultivar, fungicide, infection level, and climatic conditions including the average daily temperature, average daily relative humidity, and total rainfall from anthesis to 21 days after anthesis (p < 0.01). Notably, cultivar resistance (deviance = 13.34, 9.55, and 11.22) is more important than fungicide (deviance = 5.77, 6.66, and 6.69) to control the efficacy of INC, DSI, and DON. The results also suggest that infection level appears to be more important than cultivars and fungicide to control the efficacy of INC, and more important than fungicide to control the efficacy of DSI. Total rainfall is more important than other climatic factors. Our results reveal that fungicide is more effective in moderately susceptible cultivars ('Zhengmai 9023' and 'Xinong 979', 89.5%~98.9%) and some susceptible cultivars than in other susceptible cultivars ('Zhengmai 7698' and 'Zhoumai 27', 51.9%~67.2%). Thus, integrating cultivar resistance with fungicide application can be an effective strategy for the management of FHB and DON in winter wheat in the Huang-huai-hai Plain of China. [ABSTRACT FROM AUTHOR]

Timely and accurate prediction of winter wheat yields, which is crucial for optimizing production management, maintaining supply–demand balance, and ensuring food security, depends on interactions among numerous factors, such as climate, surface characteristics, and soil quality. Despite the extensive application of deep learning models in this field, few studies have analyzed the effect of the large-scale geospatial characteristics of neighboring regions on crop yields. Therefore, we present an attention-based spatio-temporal Graph Neural Network (ASTGNN) model coupled with geospatial characteristics and multi-source data for improved accuracy of winter wheat yield estimation. The datasets used in this study included multiple types of remote sensing, meteorological, soil, crop yield, and planting area data for Anhui, China, from 2005 to 2020. The results showed that multi-source data led to higher prediction performance than single-source data, and enabled accurate prediction of winter wheat yields three months prior to harvest. Furthermore, the ASTGNN model provided better prediction performance than two traditional crop yield prediction models (R2 = 0.70, RMSE = 0.21 t/ha, MAE = 0.17 t/ha). Therefore, ASTGNN enhances the accuracy of crop yield prediction by incorporating geospatial characteristics. This research has implications for improving agricultural production management, promoting the development of digital agriculture, and addressing climate change in agriculture. [ABSTRACT FROM AUTHOR]

The cultivation or 'tillage' system is one of the most important elements of agrotechnology. It affects the condition of the soil, significantly modifying its physical, chemical, and biological properties, and the condition of plants, starting from ensuring appropriate conditions for sowing and plant growth, through influencing the efficiency of photosynthesis and ultimately, the yield. It also affects air transmission and the natural environment by influencing greenhouse gas (GHG) emissions potentially. Ultimately, the cultivation system also has an impact on the farmer, providing the opportunity to reduce production costs. The described experiment was established in 1998 at the Brody Agricultural Experimental Station belonging to the University of Life Sciences in Poznań (Poland) on a soil classified as an Albic Luvisol, while the described measurements were carried out in the 2022/2023 season, i.e., 24 years after the establishment of the experiment. Two cultivation methods were compared: Conventional Tillage (CT) and No Tillage (NT). Additionally, the influence of two factors was examined: nitrogen (N) fertilization (0 N—no fertilization, and 130 N–130 kg N∙ha−1) and the growth phase of the winter wheat plants (BBCH: 32, 65 and 75). The growth phase of the plants was assessed according to the method of the Bundesanstalt, Bundessortenamt and CHemische Industrie (BBCH). We present the results of soil properties, soil respiration, wheat plants chlorophyll fluorescence, and grain yield. In our experiment, due to low rainfall, NT cultivation turned out to be beneficial, as it was a key factor influencing the soil properties, including soil organic carbon (SOC) content and soil moisture, and, consequently, creating favorable conditions for plant nutrition and efficiency of photosynthesis. We found a positive effect of NT cultivation on chlorophyll fluorescence, but this did not translate into a greater yield in NT cultivation. However, the decrease in yield due to NT compared to CT was only 5% in fertilized plots, while the average decrease in grain yield resulting from the lack of fertilization was 46%. We demonstrated the influence of soil moisture as well as the growth phase and fertilization on carbon dioxide (CO2) emissions from the soil. We can clearly confirm that the tillage system affected all the parameters discussed in the work. [ABSTRACT FROM AUTHOR]

The ecological assessment of the contents of Cr, Pb, Cd, Hg, and As in arable soils and crops grown in the central chernozemic region of Russia was carried out by the example of Belgorod oblast. It was found that average bulk contents of Cr, Pb, As, Cd, and Hg in leached arable chernozem were 19.8, 10.0, 4.15, 0.22, and 0.021 mg/kg; in typical arable chernozems, 20.0, 10.3, 4.18, 0.23, and 0.022 mg/kg; and in ordinary chernozems 20.9, 11.2, 5.48, 0.35, and 0.023 mg/kg, respectively. The average contents of mobile forms of Pb, Cr, and Cd were 0.52, 0.14, and 0.06 mg/kg in leached arable chernozems; 0.46, 0.13, and 0.05 mg/ka in typical arable chernozems; and 0.55, 0.13, and 0.04 mg/kg in ordinary chernozems, respectively. These elements did not exceed the established maximum permissible concentrations (MPCs) and tentative permissible concentrations (TPCs) for the studied soils. Organic fertilizers were found to be the main source of heavy metals in the arable soils of Belgorod oblast. Average concentrations of Cr, Pb, As, Cd, and Hg in cattle manure were 0.90, 0.78, 0.277, 0.060, and 0.0084 mg/kg, respectively. Among studied crops, sunflower seeds had the highest concentrations of Cd, Pb, and Cr, while winter wheat grains had the highest concentrations of Hg and As. Soybeans contained the lowest average contents of Pb, Hg, and As, and the corn grain had the lowest average contents of Cr, Cd, and As. In the grains of winter wheat, corn, soybeans, and sunflower seeds, the contents of Pb, Cd, Hg, and As did not exceed MPCs established for products intended for food purposes, and the concentration of Cr did not exceed the MPC established for feed. [ABSTRACT FROM AUTHOR]

For precision agriculture, agricultural development planning, and regional food security, accurate crop mapping is essential. The GF-6 satellite offers novel approaches for crop identification. As Jinzhou, Hebei province, China, comprises a mix of grain and fruit trees, this research has selected the area for a case study. First, spectral, index, and texture features are combined in various classification feature schemes. Second, the random forest algorithm is employed to ascertain the significance of these features. Optimal features are then chosen to accurately identify winter wheat, summer maize, and pear orchards. Concurrently, land cover for wheat and maize seasons is mapped. The results demonstrate that: (1) multisource features considerably enhanced the accuracy of crop classification based on GF-6 WFV data. Among these, index features have the greatest contribution, followed by spectral features, while the contribution of texture features, is almost negligible. (2) The classification effect utilizing random forest feature optimization is superior, with an overall accuracy of 94% and a kappa coefficient of 0.91. The extracted land area for each scheme exhibits an accuracy of over 70% when compared to the statistical area. (3) The critical time phases for extracting winter wheat, summer maize, and pear orchards are identified as, in sequence, the leaf-out and jointing stage, the tasselling stage, the leaf-fall stage, fruit development, and the first flowering/full flowering stages. Based on GF-6 WFV data, these research findings will offer a technical reference for the identification of grain crops and the extraction of pear orchards. [ABSTRACT FROM AUTHOR]

Freezing damage in spring is one of the serious agricultural meteorological disasters for winter wheat production in the Huang-Huai-Hai plain. In order to effectively identify freezing damages of winter wheat in spring, this paper used a deep learning long short-term memory model LSTM to identify the occurrences of freezing damages of winter wheat in spring in Henan province, which was an important winter wheat planting area in China. The spatiotemporal distributions and decadal changes of freezing damages of winter wheat in spring based on the multi-source meteorological, crop, and disaster data from 99 meteorological stations from 1981 to 2020 were explored. The results indicated that: (1) the optimized LSTM model effectively identified freezing damages of winter wheat in spring. The daily minimum temperature simulated by LSTM model from 2017 to 2020 showed an average absolute percentage error (MAPE) of 8.73% and a goodness of fit (R2 ) with 0.90 compared to the actual minimum temperature. Based on the disaster data and freezing damage index for winter wheat in spring, the actual disaster situations from 2017 to 2020 were found to be consistent with the simulated results by the optimized LSTM model. (2) From 1981 to 2020, the harm of mild freezing damage to winter wheat in spring in Henan province had gradually weakened, and the high frequency area of mild freezing damage moved from the Northeast to the East. The overall distribution of mild freezing damage of winter wheat in spring in Henan province between 1981 and 2020 was higher in the northeastern Henan, and lower in the southwestern and southeastern Henan, with an average frequency of 0-1.75 times·10y-1 during the past 40 years. The frequency of mild freezing damage of winter wheat in spring decreased per decade, gradually decreasing from 0.843 times per decade to 0.157 times per decade. The high frequency area of mild freezing damage of winter wheat in spring moved from the Northeast to the East. (3) From 1981 to 2020, the frequency of severe freezing damage of winter wheat in spring in Henan province showed a trend of first increasing and then decreasing. The high frequency area of severe freezing damage of winter wheat in spring moved from the Northeast to the East. The overall distribution of severe freezing damage during the 40a period was higher in the East than in the West, and higher in the North than in the South. The average frequency of severe freezing damage in the 40a period was 0-2.75times·10y-1 . The frequency of severe freezing damage of winter wheat in spring increased from 0.508 times per decade to 0.857 times per decade and then decreased to 0.289 times per decade. The high frequency area of severe freezing damage moved from the Northeast to the East. The overall trend of severe freezing damage of winter wheat in spring in Henan Province was decreasing, but the frequency of extreme weather events under climate warming was increasing. Strengthening the researches on the impacts of major agricultural meteorological disasters on agricultural production in various regions in China under climate change was still one of the future research focuses. Various accuracy evaluation indicators of the deep learning LSTM model for identifying freezing damage of winter wheat in spring proposed in this study were improved, and the simulation results of freezing damage by LSTM model were basically consistent with the actual occurrences. Our results can provide ideas and methods for large-scale freezing damage identification of winter wheat under global climate change, and also have certain reference values for other agricultural meteorological disaster identification. [ABSTRACT FROM AUTHOR]

Catch crops are intermediate crops sown between two main crop cycles. Their adoption into the cropping system has increased considerably in the last years due to its numerous benefits, in particular its potential in carbon fixation and preventing nitrogen leaching during winter. The growth period of catch crops in Germany is often marked by dense cloud cover, which limits land surface monitoring through optical remote sensing. In such conditions, synthetic aperture radar (SAR) emerges as a viable option. Despite the known advantages of SAR, the understanding of temporal behavior of radar parameters in relation to catch crops remains largely unexplored. Hence, in this study, we exploited the dense time series of Sentinel-1 data within the Copernicus Space Component to study the temporal characteristics of catch crops over a test site in the center of Germany. Radar parameters such as VV, VH, VH/VV backscatter, dpRVI (dual-pol Radar Vegetation Index) and VV coherence were extracted, and temporal profiles were interpreted for catch crops and preceding main crops along with in situ, temperature, and precipitation data. Additionally, we examined the temporal profiles of winter main crops (winter oilseed rape and winter cereals), that are grown parallel to the catch crop growing cycle. Based on the analyzed temporal patterns, we defined 22 descriptive features from VV, VH, VH/VV and dpRVI, which are specific to catch crop identification. Then, we conducted a Kruskal–Wallis test on the extracted parameters, both crop-wise and group-wise, to assess the significance of statistical differences among different catch crop groups. Our results reveal that there exists a unique temporal pattern for catch crops compared to main crops, and each of these extracted parameters possess a different sensitivity to catch crops. Parameters VV and VH are sensitive to phenological stages and crop structure. On the other hand, VH/VV and dpRVI were found to be highly sensitive to crop biomass. Coherence can be used to detect the sowing and harvest events. The preceding main crop analysis reveals that winter wheat and winter barley are the two dominant main crops grown before catch crops. Moreover, winter main crops (winter oilseed rape, winter cereals) cultivated during the catch crop cycle can be distinguished by exploiting the observed sowing window differences. The extracted descriptive features provide information about sowing, harvest, vigor, biomass, and early/late die-off nature specific to catch crop types. In the Kruskal–Wallis test, the observed high H-statistic and low p-value in several predictors indicates significant variability at 0.001 level. Furthermore, Dunn's post hoc test among catch crop group pairs highlights the substantial differences between cold-sensitive and legume groups (p < 0.001). [ABSTRACT FROM AUTHOR]

Inadequate nitrogen (N)-fertilisation practices, that fail to consider seasonally variable weather conditions and their impacts on crop yield potential and N-requirements, cause reduced crop N-use efficiency. As a result, both the ecological and economic sustainability of crop production systems are put at risk. The aim of this study was to develop a season-specific crop forecasting approach that allows for a targeted application of N in winter wheat while maintaining farm revenue compared to empirical N-fertilisation practices. The crop forecasts of this study were generated using the process-based crop model SSM in combination with state-of-the-art seasonal ensemble weather forecasts (SEAS5) for the case study region of Eastern Austria. Results from three winter wheat on-farm experiments showed a significant reduction in applied N when implementing a crop forecast-based N-application approach (-43.33 kgN ha-1, -23.42%) compared to empirical N-application approaches, without compromising revenue from high-quality grain sales. The benefit of this reduced N-application approach was quantified through the economic return to applied N (ERAN). While maintaining revenue, the lower amounts of applied N led to significant benefits of + 30.22% (+ 2.20 € kgN-1) in ERAN. [ABSTRACT FROM AUTHOR]

A significant basis for winter wheat production in China is the North China Plain (NCP). However, winter wheat production is severely hampered by water shortages in this area. Transpiration co‐occurs with photosynthesis, affecting crop water productivity (CWP). The purpose of this experiment is to use Root Zone Water Quality Model (RZWQM2) to study actual transpiration (AT) and evaporation (AE) under different irrigation schedules and then determine its impact on grain yield and CWP. In the 2019–2022 winter wheat growing seasons, four experimental treatments were set up: no irrigation during growth period (I0), irrigation at jointing stage (I1), irrigation at jointing and anthesis stage (I2) and irrigation at jointing, anthesis and filling stage (I3), and the RZWQM2 model was calibrated and verified in this experiment. A higher yield (7840.90 kg/ha for an average of 3 years) and the highest CWP can be obtained in I2 treatment (increased by 12.72%, 5.98% and 4.28% for an average of 3 years, respectively, compared to the other three treatments). The model has a good simulation effect on soil water dynamic change and plant physiological performance of the four treatments; the model showed that irrigation increased the simulated AE and AT; however, reduced AE/actual evapotranspiration. For the whole growth period, AT in I3, I2, I1 and I0 was 351.70, 317.30, 271.50 and 223.70 mm, respectively. Especially in the late growth stage of winter wheat, the AT in I3 was 65.20 mm for an average of 3 years, which was significantly higher than I2, I1 and I0 by 31.60, 13.50 and 10.00 mm, respectively. Thus, I3 increased AT at the late growth stage of winter wheat and resulted in an increase in grain yield; however, it did not significantly increase CWP. This study demonstrated that irrigation at winter wheat jointing and anthesis stages can improve the CWP to achieve the goal of stable grain yield and water saving. [ABSTRACT FROM AUTHOR]

The increase in crop yield can be primarily attributed to the combined effect of genetic advancements, as well as increased contributions from nitrogen (N) and water. The accumulation of dry matter plays a crucial role in determining grain yield in winter wheat. The current study aimed to better understand the source‐sink dynamics, analyse the dry matter transport (DMT) before and after anthesis and calculate its ratio to grain yield. In this experiment, eight main cultivars of winter wheat that have been widely cultivated in Shaanxi Province since the 1940s were selected as planting material. Field examinations were conducted using three levels of both irrigation and N. The yield‐related parameters, dry matter accumulation, dry matter distribution, DMT and DMT efficiency were measured. The results showed that irrigation prolonged the time of dry matter accumulation, and the application of N fertiliser increased the rate of dry matter accumulation. The analysis of the dynamic characteristic parameters of dry matter in winter wheat showed that irrigation and N had a significant impact on rate of dry matter accumulation. The proportion of stems, leaves and spikes of new varieties increased significantly (p < 0.05), which increased by 56.67%–69.7%, 13.26%–18.07% and 15.78%–28.26%, respectively, with the varietal improvement. The DMT efficiency increased with varietal improvement and also with increased irrigation and N application. The response of irrigation treatment to DMT and dry matter was more significant. In addition, the irrigation treatment had a higher effect on the DMT efficiency of vegetative organs after anthesis. The logistic equation analysis revealed that water stress accelerated the fulfilment of maximum daily temperature (Tmax), while both the maximum growth rate (Gmax) and dry matter weight (Wmax) decreased as water stress increased. There was a positive linear relationship between dry matter accumulation after anthesis and grain yield. The structural equation model showed that varieties, N application and irrigation had significant positive effects on DMT, post‐anthesis dry matter and grain yield, while irrigation had significant negative effects on DMT efficiency. The accumulation of dry matter in winter wheat after anthesis showed a significant increase with the succession of varieties. The combination of moderate deficit irrigation and fertilisation improved transportation and dry matter accumulation after anthesis in winter wheat, thereby enhancing its production capacity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this research paper analysis of the dynamics of the moisture content of the soil layer of chernozem and the accumulation of productive moisture reserves in its thickness was carried out, based on a set of data from 1947 to 2022. The change in the moisture regime within the periodically leaching water regime of the central part of the Left Bank Forest- Steppe of Ukraine has been analyzed. The analysis was performed under the crops: winter wheat, spring barley, corn for grain and silage, sugar beet, sunflower in crop rotations of various types to a depth of 0-100 cm to 0-300 cm. The purpose of the research was to establish the features of the formation of reserves of productive moisture in the chernozem thickness in a seasonal dimension upon the dynamics of climatic parameters against the background of their intracentury climatic variability for the conditions of the left bank part of the central Forest-Steppe of Ukraine. The generally accepted research methods were used: field, laboratory, mathematical, statistical and comparative calculation. Based on the conducted research, the following results were obtained. In order to form a non-flushing humidification regime, it is necessary to accumulate 117-135 mm in the thickness of 0-100 cm, which will correspond to a reserve in the thickness of 0-200 cm - 117-167 mm. For periodic flushing water regime 160-165 mm of moisture should be accumulated in the 0-100 cm thickness, which will provide a moisture reserve of 300 mm in the 0-200 cm thickness. The flushing water regime is formed with a moisture reserve in the thickness of 0-100 cm in the amount of 175 mm, which provides a moisture reserve in the thicknesses of 0-150 cm and 0-200 cm - 250 mm and 315 mm, which corresponds to the reserve of NV-VRK, and the thickness of 0-300 cm is saturated with moisture in an amount of more than 400 mm (HB-VK). Calculation of the paired correlation coefficients showed that an inverse strong correlation (R > -0.70) was found between the amount of precipitation for November - March and the supply of moisture to the meter layer of soil (R > -0.70), and the consumption of moisture from the soil for April - July correlated with the precipitation for November - March at the level of direct strong correlation (R ≥ 0.70). The relationship with the moisture consumption from the moisture potential for April - August was at the level of an inverse strong correlation (R < 0.70), i.e. the higher the amount of precipitation during the autumn-winter-spring period (cold period), the less moisture is recorded in a meter-thick layer, the greater its consumption in April-July from the soil and the smaller consumption of moisture potential in April-August. The zone of the central Forest-Steppe is characterized by a periodically leaching water regime, when an average of 165 mm of moisture accumulates in a meter-thick chernozem, and therefore gravitates towards the values of the moisture reserve in the non-flushing water regime, which exceeds its value by 25 mm, which is a stable trend of aridization of soil conditions in spring period. The conditions of moistening chernozems, formed in a long-term time interval under the present climatic conditions of the central part of the Left Bank Forest- Steppe, cannot be shifted under the influence of agrotechnical influences beyond the parameters of the non-leaching or periodically leaching water conditions in the agrocenosis. There is manifested the process of the self-regulation stability of the Forest-Steppe zone against active aridization and the "attack" of the Steppe on the Forest-Steppe. [ABSTRACT FROM AUTHOR]

Winter plants rely on vernalization, a crucial process for adapting to cold conditions and ensuring successful reproduction. However, understanding the role of histone modifications in guiding the vernalization process in winter wheat remains limited. In this study, we investigated the transcriptome and chromatin dynamics in the shoot apex throughout the life cycle of winter wheat in the field. Two core histone modifications, H3K27me3 and H3K36me3, exhibited opposite patterns on the key vernalization gene VERNALIZATION1 (VRN1), correlating with its induction during cold exposure. Moreover, the H3K36me3 level remained high at VRN1 after cold exposure, which may maintain its active state. Mutations in FERTILIZATION-INDEPENDENT ENDOSPERM (TaFIE) and SET DOMAIN GROUP 8/EARLY FLOWERING IN SHORT DAYS (TaSDG8/TaEFS), components of the writer complex for H3K27me3 and H3K36me3, respectively, affected flowering time. Intriguingly, VRN1 lost its high expression after the cold exposure memory in the absence of H3K36me3. During embryo development, VRN1 was silenced with the removal of active histone modifications in both winter and spring wheat, with selective restoration of H3K27me3 in winter wheat. The mutant of Tafie-cr-87, a component of H3K27me3 "writer" complex, did not influence the silence of VRN1 during embryo development, but rather attenuated the cold exposure requirement of winter wheat. Integrating gene expression with H3K27me3 and H3K36me3 patterns identified potential regulators of flowering. This study unveils distinct roles of H3K27me3 and H3K36me3 in controlling vernalization response, maintenance, and resetting in winter wheat. [ABSTRACT FROM AUTHOR]

Optimal irrigation water depth is a crucial parameter in irrigation engineering, often referred to as root zone depth. It is typically assumed to lie between 1 and 1.5 m below the ground surface, depending on the crop and soil types as well as the practitioner's skill and experience. This approach can lead to inefficient irrigation scheduling. Coupling Richards' equation with the Soil Conservation Service Curve Number (SCS-CN) concept and using the three-phase diagram of soil column widely used in geotechnical engineering, this paper suggests an analytical expression for optimal irrigation water depth providing the maximum storage capacity of a soil depending on its hydraulic/storage properties. The results for winter wheat crop in different hydrologic soil groups show that the use of the proposed concept can lead to savings of 71.79% and 57.69% of irrigation water in sandy soils (HSG-A) compared to that used in traditional irrigation considering lump-sum 1.5 m and 1 m optimal irrigation water depths, respectively. In the case of silty loam soils (HSG-C), these savings can assume 52.42% and 28.62%, respectively. The proposed relation can also be of great help in volumetric assessment of field capacity, moisture content, maximum water storage capacity (of different agricultural soils), and avoiding the issue of waterlogging that may arise from over-irrigation and thus is useful in efficient irrigation scheduling as well as in sustainable agricultural water management. [ABSTRACT FROM AUTHOR]

The seed priming method like hydropriming rejuvenate the seed and fulfil the need of seed germination and establishment. In cereals like wheat which is the staple food of global population often encounter stress during seedling establishment stage. To avoid this, the present experiment aims to explore the effect of different pre-sowing techniques on germination, growth characteristics, and yield and quality traits of wheat variety (HD-2967). The field trial was laid in Randomised block design during Rabi season 2022-2023 under three replications at Agricultural research Farm of Graphic Era Hill University, Dehradun and Lab studies was conducted in Agronomy lab, Department of Agronomy, School of Agricultural Sciences, Graphic Era Hill University, Dehradun. The seeds of wheat variety HD 2967 were primed with seven different treatments including varied time and different concentrations of GA3 and organopriming. The results of investigation revealed that hydropriming for 12h significantly improved the growth and yield characteristics. All pre-sowing techniques enhances the seeds quality and yield as it increases the enzyme activity such as amylase, protease and lipase that have great role in development and growth of an embryo. So, hydropriming can be the cheapest source of priming for a farmer which is easy to perform. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Leaf rust (Puccinia triticina) is a common disease that causes significant yield losses in wheat. The most frequently used methods to control leaf rust are the application of fungicides and the cultivation of resistant genotypes. However, high genetic diversity and associated adaptability of pathogen populations hamper achieving durable resistance in wheat. Emerging alternatives, such as microbial priming, may represent an effective measure to stimulate plant defense mechanisms and could serve as a means of controlling a broad range of pathogens. In this study, 175 wheat genotypes were inoculated with two bacterial strains: Ensifer meliloti strain expR+ch (producing N-acyl homoserine lactone (AHL)) or transformed E. meliloti carrying the lactonase gene attM (control). In total, 21 genotypes indicated higher resistance upon bacterial AHL priming. Subsequently, the phenotypic data of 175 genotypes combined with 9917 single-nucleotide polymorphisms (SNPs) in a genome-wide association study to identify quantitative trait loci (QTLs) and associated markers for relative infection under attM and expR+ch conditions and priming efficiency using the Genome Association and Prediction Integrated Tool (GAPIT). In total, 15 QTLs for relative infection under both conditions and priming efficiency were identified on chromosomes 1A, 1B, 2A, 3A, 3B, 3D, 6A, and 6B, which may represent targets for wheat breeding for priming and leaf-rust resistance. [ABSTRACT FROM AUTHOR]