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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Sustainable agriculture aims to use biological resources to improve crop quality and productivity. This approach promotes alternatives, such as replacing synthetic pesticides with biological ones and substituting mineral fertilizers with organic fertilizers. Field trials were conducted using two different factors: fertilizer treatments (ammonium nitrate and pig manure digestate) and plant protection treatments (pesticides, Artemisia dubia Wall biomass mulch, and strips). After harvesting the winter wheat, the productivity and quality (weight of 1000 grains, protein, gluten, starch, sedimentation of grains) were evaluated. The two-year studies showed that pig manure digestate positively affected winter wheat grain quality. Mugwort biomass outperformed other plant protection options in three key grain quality indicators (protein, gluten, and sedimentation). Furthermore, in 2023, the highest grain yield of 5798 ± 125 kg ha−1 was observed in the pesticides and pig manure digestate treatment. The quick impact and mode of action of vegetation pesticides were more easily felt over the two years of study, leading to the highest yield of wheat grains compared to other plant management measures. This study shows that mugwort biomass can positively influence wheat grain quality, a significant milestone in utilizing nonfood crops as alternatives for agricultural productivity. [ABSTRACT FROM AUTHOR]

The accurate estimation of nitrogen content in crop plants is the basis of precise nitrogen fertilizer management. Unmanned aerial vehicle (UAV) imaging technology has been widely used to rapidly estimate the nitrogen in crop plants, but the accuracy will still be affected by the variety, the growth stage, and other factors. We aimed to (1) analyze the correlation between the plant nitrogen content of winter wheat and spectral, texture, and structural information; (2) compare the accuracy of nitrogen estimation at single versus multiple growth stages; (3) assess the consistency of UAV multispectral images in estimating nitrogen content across different wheat varieties; (4) identify the best model for estimating plant nitrogen content (PNC) by comparing five machine learning algorithms. The results indicated that for the estimation of PNC across all varieties and growth stages, the random forest regression (RFR) model performed best among the five models, obtaining R2, RMSE, MAE, and MAPE values of 0.90, 0.10%, 0.08, and 0.06%, respectively. Additionally, the RFR estimation model achieved commendable accuracy in estimating PNC in three different varieties, with R2 values of 0.91, 0.93, and 0.72. For the dataset of the single growth stage, Gaussian process regression (GPR) performed best among the five regression models, with R2 values ranging from 0.66 to 0.81. Due to the varying nitrogen sensitivities, the accuracy of UAV multispectral nitrogen estimation was also different among the three varieties. Among the three varieties, the estimation accuracy of SL02-1 PNC was the worst. This study is helpful for the rapid diagnosis of crop nitrogen nutrition through UAV multispectral imaging technology. [ABSTRACT FROM AUTHOR]

Quantifying the vertical distribution of leaf chlorophyll content (LCC) is integral for a comprehensive understanding of the physiological status and function of winter wheat crops, having significant implications for crop management and yield optimization. In this study, we investigated the vertical LCC trait of winter wheat during two consecutive field growth seasons using proximal multispectral imaging measurements to evaluate vertical variations of LCC within winter wheat canopies. The results revealed the non-uniform vertical LCC distribution varied across the entire growth season. The effects of nitrogen fertilization rate on LCC among vertical layers increased gradually from upper to lower layers of canopy. To enhance LCC prediction accuracy, this study proposes a deep transfer learning network model for leaf trait estimation (LeafTNet). It integrates the advantages of physical radiative transfer simulations with deep neural network through transfer learning. The results demonstrate that the LeafTNet achieved remarkable predictive performance and strong robustness. Furthermore, the proposed method exhibits superior estimation accuracy compared to empirical statistical method and traditional machine learning method. This study highlights the performance of LeafTNet in accurately and efficiently quantifying LCC from proximal multispectral data, which provide technical support for the estimation of the vertical distribution of leaf traits and improve crop management. [ABSTRACT FROM AUTHOR]

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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]

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]

Winter wheat constitutes approximately 20% of China's total cereal production. However, calculations of total production based on multiplying the planted area by the yield have tended to produce overestimates. In this study, we generated sample points from existing winter wheat maps and obtained samples for different years using a temporal migration method. Random forest classifiers were then constructed using optimized features extracted from spectral and phenological characteristics and elevation information. Maps of the harvested and planted areas of winter wheat in Chinese eight provinces from 2018 to 2022 were then produced. The resulting maps of the harvested areas achieved an overall accuracy of 95.06% verified by the sample points, and the correlation coefficient between the CROPGRIDS dataset is about 0.77. The harvested area was found to be about 13% smaller than the planted area, which can primarily be attributed to meteorological hazards. This study represents the first attempt to map the winter wheat harvested area at 10–m resolution in China, and it should improve the accuracy of yield estimation. [ABSTRACT FROM AUTHOR]

In China, the exigency for precise wheat grain protein content (GPC) data rises with growing food consumption demands and global market competition. However, due to the lack of extensive, prolonged high-resolution benchmark data, previous GPC studies have primarily focused on experimental fields, small geographic units, and limited temporal scopes. Additionally, the diverse geographical terrain in China exacerbates the challenges of large-scale GPC estimation. To address this challenge and the data gap, the first 500-meter spatial resolution, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500) was created by integrating multi-source data from ERA5 and MODIS. The results demonstrate that the GPC estimation model based on hierarchical linear model significantly outperformed other conventional models. The validation dataset exhibited an R2 of 0.45 and an RMSE of 0.96%. In cross-validation, the RMSE values ranged from 0.90% in Gansu to 1.32% in Anhui. For leave-one-year-out cross-validation, the RMSE values ranged from 0.77% to 1.11%. CNWheatGPC-500 offers valuable insights for enhancing wheat production, quality control, and agricultural decision-making. [ABSTRACT FROM AUTHOR]

Societal Impact Statement Summary Winter wheat is one of the most important crops in the world. Microplastics, as an emerging pollutant, are widespread in agricultural soils due to various modern agricultural practices and can have adverse impacts on agricultural soils and plant growth. Herein, we investigated the effects of 10 types of microplastics on the properties of three agricultural soil types and the growth of winter wheat. This study contributes insights toward the conservation of agricultural soils and potential wheat yield responses to microplastic. Understanding the mechanisms that underpin the differences in responses to this pollutant class is of great importance for management recommendations. Microplastics (MPs) (size < 5 mm) are increasingly recognized as anthropogenic contaminants that severely affect terrestrial ecosystems. These particles are always detected as a mixture of various polymer types and shapes. However, we have limited knowledge of the effect of combined MPs on plant–soil systems. To address this, we selected 10 types of MP, applied to three soil types singly and in combination along an increasing gradient of 1, 2, 5, 8, and 10 MP types at a content of 0.4% (w/v). After 8 weeks of pre‐incubation, winter wheat (TOBAK) was grown in each pot for another 8 weeks. Shoot and root biomass, soil aggregation, and carbon and nitrogen content were measured. The effects of the same MP on both soil and plant properties were drastically different (in size and effect direction) in the different soil types. However, no clear patterns were observed along an increasing number of microplastic types, suggesting that knowing the number of microplastic types in a sample, at equity of overall concentration, does not help predict effects. In contrast, our findings reveal the complex effects of multiple MPs on the soil–plant system and highlight that soil properties need to be taken into consideration when studying MP effects on terrestrial systems. [ABSTRACT FROM AUTHOR]

Garlic constitutes a significant small-scale agricultural commodity in China. A key factor influencing garlic prices is the planted area, which can be accurately and efficiently determined using remote sensing technology. However, the spectral characteristics of garlic and winter wheat are easily confused, and the widespread intercropping of these crops in the study area exacerbates this issue, leading to significant challenges in remote sensing image analysis. Additionally, remote sensing data are often affected by weather conditions, spatial resolution, and revisit frequency, which can result in delayed and inaccurate area extraction. In this study, historical data were utilized to restore Sentinel-2 remote sensing images, aimed at mitigating cloud and rain interference. Feature combinations were devised, incorporating two vegetation indices into a comprehensive time series, along with Sentinel-1 synthetic aperture radar (SAR) time series and other temporal datasets. Multiple classification combinations were employed to extract garlic within the study area, and the accuracy of the classification results was systematically analyzed. First, we used passive satellite imagery to extract winter crops (garlic, winter wheat, and others) with high accuracy. Second, we identified garlic by applying various combinations of time series features derived from both active and passive remote sensing data. Third, we evaluated the classification outcomes of various feature combinations to generate an optimal garlic cultivation distribution map for each region. Fourth, we developed a garlic fragmentation index to assess the impact of landscape fragmentation on garlic extraction accuracy. The findings reveal that: (1) Better results in garlic extraction can be achieved using active–passive time series remote sensing. The performance of the classification model can be further enhanced by incorporating short-wave infrared bands or spliced time series data into the classification features. (2) Examination of garlic cultivation fragmentation using the garlic fragmentation index aids in elucidating variations in accuracy across the study area's six counties. (3) Comparative analysis with validation samples demonstrated superior garlic extraction outcomes from the six primary garlic-producing counties of the North China Plain in 2021, achieving an overall precision exceeding 90%. This study offers a practical exploration of target crop identification using multi-source remote sensing data in mixed cropping areas. The methodology presented here demonstrates the potential for efficient, cost-effective, and accurate garlic classification, which is crucial for improving garlic production management and optimizing agricultural practices. Moreover, this approach holds promise for broader applications, such as nationwide garlic mapping. [ABSTRACT FROM AUTHOR]

This document is an introduction to a special issue of the journal "Plants" on the topic of "The Application of Spectral Techniques in Agriculture and Forestry." The special issue includes twelve research papers that explore various applications of spectral analysis in agricultural and forestry environments. The papers cover topics such as crop health monitoring, disease detection, forest parameter estimation, soil quality assessment, water stress analysis, and nutrient management. The studies highlight the use of advanced spectral indices, remote sensing techniques, and machine learning algorithms to monitor plant physiological parameters and improve resource management in agricultural ecosystems. The research presented in this special issue demonstrates the broad applicability of spectral technologies in different geographical regions and crops, emphasizing the efforts of scientists from multiple countries to promote precision agriculture and forestry practices. [Extracted from the article]

Phytohormones are vital for developmental processes, from organ initiation to senescence, and are key regulators of growth, development, and photosynthesis. In natural environments, plants often experience high light (HL) intensities coupled with elevated temperatures, which pose significant threats to agricultural production. However, the response of phytohormone-related genes to long-term HL exposure remains unclear. Here, we examined the expression levels of genes involved in the biosynthesis of ten phytohormones, including gibberellins, cytokinins, salicylic acid, jasmonic acid, abscisic acid, brassinosteroids, indole-3-acetic acid, strigolactones, nitric oxide, and ethylene, in two winter wheat cultivars, Xiaoyan 54 (XY54, HL tolerant) and Jing 411 (J411, HL sensitive), when transferred from low light to HL for 2–8 days. Under HL, most genes were markedly inhibited, while a few, such as TaGA2ox, TaAAO3, TaLOG1, and TaPAL2, were induced in both varieties. Interestingly, TaGA2ox2 and TaAAO3 expression positively correlated with sugar content but negatively with chlorophyll content and TaAGP expression. In addition, we observed that both varieties experienced a sharp decline in chlorophyll content and photosynthesis performance after prolonged HL exposure, with J411 showing significantly more sensitivity than XY54. Hierarchical clustering analysis classified the phytohormone genes into the following three groups: Group 1 included six genes highly expressed in J411; Group 2 contained 25 genes drastically suppressed by HL in both varieties; and Group 3 contained three genes highly expressed in XY54. Notably, abscisic acid (ABA), and jasmonic acid (JA) biosynthesis genes and their content were significantly higher, while gibberellins (GA) content was lower in XY54 than J411. Together, these results suggest that the differential expression and content of GA, ABA, and JA play crucial roles in the contrasting responses of tolerant and sensitive wheat cultivars to leaf senescence induced by long-term HL. This study enhances our understanding of the mechanisms underlying HL tolerance in wheat and can guide the development of more resilient wheat varieties. [ABSTRACT FROM AUTHOR]

Water management is critical for wheat production under extreme drought conditions, and the mechanisms by which root dynamics and soil water utilization affect wheat yield are uncertain. This study was conducted in 2023–2024 under a mesophilic semi-arid climate with a two-factor partitioned experimental design, aiming to assess the response of different irrigation amounts in winter wheat crops on root growth and development, soil water utilization, and yields in different soil horizons. The results showed that variety and irrigation volume had significant effects on the spatial and temporal distribution of root and yield components, with irrigation volume having the greatest effect on yield. Compared with CK, deficit-regulated irrigation significantly promoted root penetration to deeper layers and delayed root senescence. DRWD, RLD, RSA, and RV decreased gradually with increasing soil depth, and the peaks of RLD, RSA, and RV appeared at the tassel to flowering stage, respectively; and under deficit-regulated irrigation, the contribution of the A2W4 treatment to stable yield was greater. Therefore, A2W4 is an effective water-saving irrigation method to improve grain yield and water-use efficiency under deficit-regulated irrigation. [ABSTRACT FROM AUTHOR]

In order to optimize the allocation of water resources in the Yellow River Basin and the irrigation regime for winter wheat crops in southeast Shanxi Province areas, based on the meteorological data from 1960 to 2019 in Yangcheng County, the annual changes of water requirement and rainfall at different growth stages of winter wheat were analyzed and calculated. In the typical irrigation year (25%, 50%, 75% and 95%), the coupling analysis of the water requirement and rainfall was carried out, and the irrigation water consumption of the winter wheat was calculated. The results show that in the past 60 years, the climate tendency rate of winter wheat water requirement increases with the change of 0.593 mm/(10a), and the annual average water requirement is 586.09 mm in southeast Shanxi Province areas. The climatic tendency rate of rainfall increases by 9.827 mm/(10 a), and the annual average value of rainfall is 213.66 mm. The climatic tendency rate of water requirement of winter wheat is the highest at jointing-heading stage. Under four typical irrigation years (25%, 50%, 75% and 95%), the coupling degree of rainfall and water demand in the whole growth period of winter wheat is 0.43, 0.33, 0.28 and 0.18 respectively. Natural rainfall cannot meet the water requirement of winter wheat growth. The amount of supplementary irrigation water is 261.74 mm, 427.45 mm, 554.34 mm and 674.66 mm respectively. Except typical irrigation years of 25%, the amount of supplementary irrigation water is the largest at heading to filling period. The supplementary irrigation water should be 116.87 mm, 142.55 mm and 171.54 mm respectively. [ABSTRACT FROM AUTHOR]

Background: Fusarium head blight (FHB), caused by Fusarium graminearum, is a major disease of wheat in North America. FHB infection causes fusarium damaged kernels (FDKs), accumulation of deoxynivalenol (DON) in the grain, and a reduction in quality and grain yield. Inheritance of FHB resistance is complex and involves multiple genes. The objective of this research was to identify QTL associated with native FHB and DON resistance in a 'D8006W'/'Superior', soft white winter wheat population. Results: Phenotyping was conducted in replicated FHB field disease nurseries across multiple environments and included assessments of morphological and FHB related traits. Parental lines had moderate FHB resistance, however, the population showed transgressive segregation. A 1913.2 cM linkage map for the population was developed with SNP markers from the wheat 90 K Infinium iSelect SNP array. QTL analysis detected major FHB resistance QTL on chromosomes 2D, 4B, 5A, and 7A across multiple environments, with resistance from both parents. Trait specific unique QTL were detected on chromosomes 1A (visual traits), 5D (FDK), 6B (FDK and DON), and 7D (DON). The plant height and days to anthesis QTL on chromosome 2D coincided with Ppd-D1 and were linked with FHB traits. The plant height QTL on chromosome 4B was also linked with FHB traits; however, the Rht-B1 locus did not segregate in the population. Conclusions: This study identified several QTL, including on chromosome 2D linked with Ppd-D1, for FHB resistance in a native winter wheat germplasm. [ABSTRACT FROM AUTHOR]

Crop statistics are crucial for developing a demand-based export and import strategy to ensure a country's sustainable food security. Remote sensing efficiently generates essential crop statistics, while ground-based supplementary sensor data offers sufficient information for crop delineation. This study explored the multispectral satellite imagery using in-situ ground-based hyperspectral reflectance phenology information as training data to delineate wheat from other competitive winter crops in Northwestern Bangladesh as a case study. Wheat spectral signatures were primarily obtained through a hand-held Spectroradiometer at various phenological stages, aligned with Sentinel-2 data availability. Five vegetation indices (VIs), namely Normalized Difference Vegetation Index (NDVI), Red-edge NDVI (RENDVI), Enhanced Vegetation Index (EVI), Greenness Chromatic Coordinate (GCC) and Soil-Adjusted Vegetation Index (SAVI), were derived from Spectroradiometer-data across six wheat growth stages: seedling, tillering, booting, flowering, grain development, and maturity. Maximum and minimum threshold values for the VIs at those six growth stages were determined from regression analysis of the values collected from Spectroradiometer and Sentinel-2. A rule-based classification technique was then used to categorize Sentinel-2 for wheat crop delineation based on those threshold values. The results revealed that maps based on NDVI, EVI, and SAVI showed overall accuracies of 83.33%, 85.18%, and 81.48%, respectively. These accuracies were found to be statistically acceptable (p < 0.05) outcomes. A positive agreement was observed when comparing the remotely sensed area at the union (4th tier administrative level) with the officially reported data of Bangladesh. This innovative method has the potential to be extended for developing phenology and area delineation for other major crops locally and globally. Highlights: A phenology-based algorithm was applied for delineating winter wheat. The algorithm is developed by combining Sentinel-2 images and Spectroradiometer data. Vegetative indices of wheat exhibited a strong relationship between two sensors' data. Accuracy assessment and validation of the produced maps demonstrated significant results. The method has the potential to be extended to area mapping for other crops locally and globally. [ABSTRACT FROM AUTHOR]

Introduction: To examine the impacts of varied water and nitroge combinations on wheat yield and quality under drip irrigation in the Huang-Huai-Hai area, a field experiment was conducted over two growing seasons of winter wheat from 2019 to 2021. Methods: Traditional irrigation and fertilization methods served as the control (CK), with two nitrogen application rates set: N1 (180 kg/ha) and N2 (210 kg/ha). The irrigation schedules were differentiated by growth stages: jointing, anthesis (S2); jointing, anthesis, and filling (S3); and jointing, booting, anthesis, and filling (S4), at soil depths of 0-10 cm (M1) and 0-20 cm (M2). Results: Results indicated that compared toCK, the 3 and 4 times irrigation treatments comprehensively improved grain yield (GY) by 8.0% and 13.6% respectively, increased the average plant partial factor productivity of nitrogen fertilizer (PFPN) and irrigation use efficiency (IUE) by 57.5% and 38.2%, and 62.2% and 35.8%, respectively. The gluten content (GC) of 3 irrigationswas 1.6% higher than CK, and othermetrics such as dough tenacity (DT), softness (ST), water absorption (WAS), and gluten hardness (GH) also showed improvements. Furthermore, the contents of amylose, amylopectin, and total starch under 3 irrigations significantly increased by 9.4%, 11.4%, and 9.8%, respectively, with higher than 4 irrigations. The crude protein content and soluble sugar content in 3 irrigations rose by 6.5% and 9.8% respectively over two years. These irrigation treatments also optimized gelatinization characteristics of grains, such as breakdown viscosity (BDV), consistency peak viscosity (CPV), consistency setback viscosity (CSV), pasting temperature (PeT), and pasting time (PaT). Discussion: The study demonstrated that appropriate drip irrigation can effectively synchronize water and nitrogen supply during critical growth stages in winter wheat, ensuring robust late-stage development and efficient transfer of photosynthetic products into the grains, thus enhancing grain mass and yield. This also led to improved utilization of water and fertilizer and enhanced the nutritional and processing quality of the grain. However, excessive irrigation did not further improve grain quality. In conclusion, given the goals of saving water and fertilizer, achieving excellent yield, and ensuring high quality, the N1S3M1 treatment is recommended as an effective production management strategy in the Huang-Huai Hai area; N1S3M2 could be considered in years of water scarcity. [ABSTRACT FROM AUTHOR]

Background: Inulin and inulin-derived fructooligosaccharides (FOS) are well-known prebiotics for use in companion animals and livestock. The mechanisms by which FOS contribute to health has not been fully established. Further, the fine chemistry of fructan structures from diverse sources, such as graminan-type fructans found in cereal crops, has not been fully elucidated. New methods to study fructan structure and microbial responses to these complex carbohydrates will be key for evaluating the prebiotic potency of cereal fructans found in cattle feeds. As the rumen microbiome composition is closely associated with their metabolic traits, such as feed utilization and waste production, prebiotics and probiotics represent promising additives to shift the microbial community toward a more productive state. Results: Within this study, inulin, levan, and graminan-type fructans from winter wheat, spring wheat, and barley were used to assess the capacity of rumen-derived Bifidobacterium boum, Bifidobacterium merycicum, and Lactobacillus vitulinus to metabolize diverse fructans. Graminan-type fructans were purified and structurally characterized from the stems and kernels of each plant. All three bacterial species grew on FOS, inulin, and cereal crop fructans in pure cultures. L. vitulinus was the only species that could metabolize levan, albeit its growth was delayed. Fluorescently labelled polysaccharides (FLAPS) were used to demonstrate interactions with Gram-positive bacteria and confirm fructan metabolism at the single-cell level; these results were in agreement with the individual growth profiles of each species. The prebiotic potential of inulin was further investigated within naïve rumen microbial communities, where increased relative abundance of Bifidobacterium and Lactobacillus species occurred in a dose-dependent and temporal-related manner. This was supported by in situ analysis of rumen microbiota from cattle fed inulin. FLAPS probe derived from inulin and fluorescent in situ hybridization using taxon-specific probes confirmed that inulin interacts with Bifidobacteria and Lactobacilli at the single-cell level. Conclusion: This research revealed that rumen-derived Bifidobacteria and Lactobacilli vary in their metabolism of structurally diverse fructans, and that inulin has limited prebiotic potential in the rumen. This knowledge establishes new methods for evaluating the prebiotic potential of fructans from diverse plant sources as prebiotic candidates for use in ruminants and other animals. [ABSTRACT FROM AUTHOR]

A new winter wheat (Triticum aestivum L.) cultivar “Joongmo2015” was developed by the NICS (National Institute of Crop Science), RDA (Rural Development Administration) in 2019. Its heading date was April 20 and its maturity date was June 1, which was similar to Keumkang. “Joongmo2015” had a longer culm length (80 cm), similar spike length (7.8 cm) and spikes per m2 (804), lower 1,000-grain weight (43.0 g) than “Keumkang” (78 cm, 7.8 cm, 804 g, 46.3 g, respectively). “Joongmo2015” was showed stronger to winter hardiness than “Keumkang”, and susceptible to fusarium head blight and powdery mildew. The average grain yield in the advanced yield trial (AYT) was 4.97 MT/ha, which were 26% more than “Keumkang” and in the regional yield trial (RYT) was 5.75 MT/ha in upland and 5.27 MT/ha in paddy field, which were 16% and 18% higher than those of “Keumkang” (4.95 MT/ha and 4.46 MT/ha, respectively). “Joongmo2015” showed lower protein content (11.7%), SDS-sedimentation volume (42.8 ml), gluten content (9.0%) and flour lightness(90.76) than “Keumkang” (13.6%, 61.8 ml, 11.4% and 91.50, respectively). “Joongmo2015” showed higher lightness (83.10) of noodle dough sheet than “Keumkang” (82.48). “Joongmo2015” exhibited higher hardness (3.92N) and similar springiness and cohesiveness of cooked noodles (0.94 and 0.60) compared to “Keumkang” (3.65N, 0.93, and 0.59, respectively). High molecular weight gluten subunits (HMW-GS) composition are Glu-D1d (5+10), granule-bound starch synthase (GBSS) composition are Wx-A1a, Wx-B1a, Wx-D1a and composition of puroindolines are Pina-D1a, Pinb-D1a (Registration No. 9790). [ABSTRACT FROM AUTHOR]

The application of organic manure is an effective way to develop sustainable agriculture. However, the application of organic manure may be associated with a potential risk of heavy metal pollution for soil and crops. In this study, the effects of organic cow manure (T1) (as base fertilizer), organic pig manure (T2) (as base fertilizer) and chemical fertilizer (T3) on winter wheat grain yields, grain quality, heavy metal concentrations and heavy metal bioconcentration factors (BCFs) in a soil–wheat system were studied from November 2021 to June 2023. The results showed that the winter wheat grain yields in the T1 and T2 treatments were lower than those in the T3 treatment by 2.57–38.0% and 10.5–25%, respectively. There were no significant differences in quality indexes of winter wheat grain among different fertilizer treatments. The concentrations of heavy metals in topsoil and winter wheat were 0.12–76.11 μg/g and 0.01–43.25 μg/g, respectively. The BCFs of heavy mental in the soil–wheat grain system was 0–2.92. In general, there were no significant differences in heavy metals' concentrations in topsoil and wheat grain among different fertilizer treatments. In summary, compared with chemical fertilizer, the short-term application of organic manures had no significant effect on heavy metals concentrations in topsoil and wheat. [ABSTRACT FROM AUTHOR]

Wheat grain productivity is different from year to year because growing environments are highly seasonally variable as a result of climate change. This study analyzed the variation in the weather conditions in the 2010–2023 growing seasons and evaluated the crop developmental phase, yield-related components, and the correlations of the variables in the southern plain of South Korea, measuring agronomic traits, including the above-ground dry weight, young-panicle length, spike number per m2, number of grains per spike, thousand-grain weight, and grain yield. The number of days in the heading and ripening phase showed less differences than the other growth phases. The thousand-grain weight showed low variations over the fourteen years observed, unlike the number of grains per spike, the marketable grain yield, and the straw yield, with comparatively high variations. The grain yield was negatively correlated with the average air temperature during the winter dormancy phase (R = −0.687, p = 0.007) and precipitation (R = −0.726, p = 0.003), but showed positive associations with the number of days in the winter dormancy phase (R = 0.597, p = 0.024) and the number of grains per spike (R = 0.809, p = 0.000). In conclusion, longer winter dormancy and a longer tillering phase delay young-panicle development but increase the number of spikes and the number of grains per spike, resulting in a higher wheat grain yield in Southern Korean weather conditions. [ABSTRACT FROM AUTHOR]

More than one-third of the global population relies on wheat as a staple food. To ultimately reduce inorganic nitrogen (N) usage through applying an organic fertilizer under drip irrigation and evaluate its effect on the yield, quality, and N utilization efficiency of winter wheat (variety Jimai 22) under various irrigation systems, an experiment was established and conducted in Yanghe Town, Jiaozhou City, from October 2020 to June 2022. The trial was designed with seven treatments, including a control (CK), to achieve a 25% total nitrogen reduction in all treatments except for CK. These treatments included drip irrigation with urea as CK, one-time application of urea through drip irrigation (FU1), one-time application of organic water-soluble fertilizer through furrow irrigation (FO1), one-time application of organic water-soluble fertilizer through drip irrigation (DO1), two-time application of organic water-soluble fertilizer through drip irrigation (DO2), one-time application of urea through drip irrigation (DU1), and two-time application of urea through drip irrigation (DU2). The results indicated that the application of a reduced N fertilizer plus an organic fertilizer significantly improved the dry matter accumulation (DMA) and the efficiency of N absorption and thus increased the grain yield. The DO2 treatment significantly exhibited a 15.5% and 16.9% increase in the DMA and the grain DMA in post-anthesis, respectively, compared to those of CK in the season of 2020–2021. Overall, the apparent nitrogen use efficiency with the drip irrigation topdressing treatments (DO1, DO2, DU1, DU2) increased significantly over two years in comparison with the urea fertilization through traditional furrow irrigation (CK), while the DO2 and DU2 treatments improved most significantly in the N use efficiency and N agronomic efficiency. Therefore, a reduced use of the inorganic N fertilizer with some organic fertilizers significantly increased the weight of thousand-grains and the yield of winter wheat, especially in the DO2 treatment, with an 11.7 t/ha and 10.9 t/ha increase, respectively, in both growing seasons of two years, while the DO2 treatment also improved the extensibility of wheat flour dough from grains harvested in both rainy (2020–2021) and less rainy (2021–2022) growing seasons. Therefore, we strongly recommend that two-time application of an organic water-soluble fertilizer through drip irrigation be the option to reduce the use of inorganic N fertilizers and increase the yield and quality of winter wheat under the conditions of this experiment. [ABSTRACT FROM AUTHOR]

The dry–hot–windy climate frequently occurs during the grain-filling stage of winter wheat on the North China Plain (NCP) and thus negatively influences wheat yield. Sprinkler irrigation can improve field temperature and humidity and can be used to mitigate dry–hot–windy climate disasters. A two-season field experiment was carried out on the NCP to test how sprinkler irrigation influences the microclimate, canopy temperature and photosynthetic traits, as well as the grain-filling process and final grain yield, when spraying 1.5–2 mm of water on dry–hot–windy days. Field experiments revealed that, compared with the no-spraying treatment, spraying with 2 mm of water each time caused the air and canopy temperatures to decrease by 2.3–7.6 °C and 4.3–9.9 °C, respectively, during and just after spraying stopped, and the temperatures returned to their previous levels approximately one hour after spraying. The air humidity increased by up to 10% during and after spraying. The photosynthesis and transpiration rates and the stomatal conductivity after spraying increased by 34–235%, 15–55% and 24–79%, respectively. The linear relationships between photosynthesis rates and transpiration rates with respect to stomatal conductivity suggest that increases in both photosynthesis and transpiration rates are the main contributors to the increase in stomatal conductivity, which is due mainly to the improved canopy temperature and humidity conditions caused by spraying practices. The grain-filling process was improved by spraying, which ultimately increased the unit grain mass by approximately 5%. One spraying event on a dry–hot–windy day influenced the field microclimate and canopy photosynthetic traits for 90 min (30 min in spraying time + 60 min after spraying). When the intensity of the dry–hot–windy climate is strong, two spraying events can be applied. Spraying 2–2.5 mm of water each time was sufficient when the leaf area index was 4–5 during the grain-filling stage of winter wheat. Spray events can have a slight effect on grain yield when a dry–hot–windy climate occurs within the last five days before harvest. [ABSTRACT FROM AUTHOR]

Fertilization significantly affects the growth and development of wheat. However, the precise mechanisms underlying gene regulation during flowering in response to fertilization deficiency remain elusive. In this study, fertilization (F) and non-fertilization (CK)) treatments were set up to reveal examine the effect of fertilization on the photosynthetic capacity of winter wheat during the flowering period through physiological, biochemical, and transcriptome analyses. Upon analyzing analysing their yield, leaf photosynthetic system exchange parameters during flowering, antioxidant enzyme activity, and endogenous hormone parameters, we found that the F treatment resulted in higher net photosynthetic rates during flowering periods than the CK treatment. The superoxide dismutase (SOD) (83.92%), peroxidase (POD) (150.75%), and catalase (CAT) (22.74%) activities of leaves in treated with F during the flowering period were notably elevated compared to those of CK-treated leaves. Abscisic acid (ABA) (1.86%) and gibberellin acid (GA3) (33.69%) levels were reduced, whereas Auxin auxin (IAA) (98.27%) content was increasedwas increased under F treatment compared to those the results under the CK treatment. The chlorophyll a (32.53%), chlorophyll b (56%), total chlorophyll (37.96%), and carotenoid contents (29.80%) under F treatment were also increased compared to CK., exceeded exceeding those obtained under the CK treatment. Furthermore, transcriptional differences between the F and CK conditions were analyzed, and key genes were screened and validated by using q-PCR. Transcriptome analysis identified 2281 differentially expressed genes (DEGs), with enriched pathways related to photosynthesis and light harvesting. DEGs were subjected to cluster simulation, which revealed that 53 DEGS, both up- and down-regulated, responded to the F treatment. qRT-PCR-based validation confirmed the differential expression of genes associated with carbohydrate transport and metabolism, lipid transport, and signal transduction. This study revealed distinctive transcriptional patterns and crucial gene regulation networks in wheat during flowering under fertilization, providing transcriptomic guidance for the precise regulation of wheat breeding. [ABSTRACT FROM AUTHOR]