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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]

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]

The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO2 stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO2 stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses. [ABSTRACT FROM AUTHOR]

Accurate yield prediction is essential for global food security and effective agricultural management. Traditional empirical statistical models and crop models face significant limitations, including high computational demands and dependency on high-resolution soil and daily weather data, that restrict their scalability across different temporal and spatial scales. Moreover, the lack of sufficient observational data further hinders the broad application of these methods. In this study, building on the SCYM method, we propose an integrated framework that combines crop models and machine learning techniques to optimize crop yield modeling methods and the selection of vegetation indices. We evaluated three commonly used vegetation indices and three widely applied ML techniques. Additionally, we assessed the impact of combining meteorological and phenological variables on yield estimation accuracy. The results indicated that the green chlorophyll vegetation index (GCVI) outperformed the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in linear models, achieving an R2 of 0.31 and an RMSE of 396 kg/ha. Non-linear ML methods, particularly LightGBM, demonstrated superior performance, with an R2 of 0.42 and RMSE of 365 kg/ha for GCVI. The combination of GCVI with meteorological and phenological data provided the best results, with an R2 of 0.60 and an RMSE of 295 kg/ha. Our proposed framework significantly enhances the accuracy and efficiency of winter wheat yield estimation, supporting more effective agricultural management and policymaking. [ABSTRACT FROM AUTHOR]

Reliable and accurate crop yield prediction at the field scale is critical for meeting the global demand for reliable food sources. In this study, we tested the viability of VENμS satellite data as an alternative to other popular and publicly available multispectral satellite data to predict winter wheat yield and produce a yield prediction map for a field located in southwestern Ontario, Canada, in 2020. Random forest (RF) and support vector regression (SVR) were the two machine learning techniques employed. Our results indicate that machine learning models paired with vegetation indices (VIs) derived from VENμS imagery can accurately predict winter wheat yield 1~2 months prior to harvest, with the most accurate predictions achieved during the early fruit development stage. While both machine learning approaches were viable, SVR produced the most accurate prediction with an R2 of 0.86 and an RMSE of 0.3925 t/ha using data collected from tillering to the early fruit development stage. NDRE-1, NDRE-2, and REP from various growth stages were ranked among the top seven variables in terms of importance for the prediction. These findings provide valuable insights into using high-resolution satellites as tools for non-destructive yield potential analysis. [ABSTRACT FROM AUTHOR]

A two-year field experiment was conducted at Lovely Professional University, Phagwara during the Rabi season of 2018–2019 and 2019-20 to study the effect of crop establishment methods and sowing schedule on the growth and yield of wheat under the rice-wheat cropping system. The experiment comprised different rice residue management-based wheat establishment methods, sowing dates, and varying nitrogen levels. The results confirmed that growth, yield and yield attributes were all influenced by different crop establishment methods, sowing dates and level of nitrogen application. The results showed that maximum plant height, number of tillers per plant, number of spikes per plant, spike length, number of grains per spike and grain yield were observed under 20th November sowing (timely sowing) and with 100% RDN (recommended dose of nitrogen). Moreover, the maximum number of spikes per plant, spike length and grain yield were significantly highest for the residue incorporation method of wheat establishment. The optimum time of sowing also improved the grain yield of wheat. Among the different crop establishment methods, the best attainment of available nitrogen status in soil was found in residue incorporation treatment. [ABSTRACT FROM AUTHOR]

Agricultural intensification and expansion are regarded as main drivers of biodiversity loss. This conclusion is mainly based on observed declines of local diversity (α-diversity), while effects on community composition homogenization (decrease of β-diversity) at a larger spatial scale are less well understood. Carabid beetles and spiders represent two widespread guilds and are important predators of pest species. Here we surveyed carabid beetles and spiders in 66 winter wheat fields in four northwestern European countries (Germany, the Netherlands, Sweden and UK) and analyzed how their community composition was related to geographic distance (separation distance between any pairwise fields) and three environmental variables: crop yield (proxy for land-use intensity), percentage cropland (proxy for landscape complexity) and soil organic carbon content (proxy for local soil conditions). We further analyzed whether the relationship between carabid beetle and spider community composition and geographic distance was influenced by environmental variables. We found that, 55 % and 75 % of all observed carabid and spider individuals, respectively, belonged to species that occurred in all four countries. However, individuals of species that were unique to a particular country only accounted for 3 % of all collected individuals for both taxa. Furthermore, we found a negative relationship between distance and similarity of spider communities but not for carabid beetle communities. None of the environmental variables were related to similarity of carabid beetle and spider communities, nor moderated the effects of distance. Our study indicates that across a great part of the European continent, arthropod communities (especially carabid beetles) in agricultural landscapes are composed of very similar species that are robust to current variations in environment and land-use. [ABSTRACT FROM AUTHOR]

A multiplicity of substantial disruptions in the physiological systems of the wheat crop (Triticum aestivum L.) are the result of a potassium deficiency.The foliar application of potassium improves the quality of wheat and the production of grains for end-use purposes. This study was carried out during the growing seasons of 2021-22 to investigate the response of winter wheat cultivars to foliar application of potassium (0, 1, and 2 g/L), at two growth stages (green up and heading) on the grain yield (t/ha), protein content (%), specific weight and plant vegetative indicators (chlorophyll content, flag leaf area and plant height). Foliar applications of potassium fertilizer were implemented at concentrations of (0, 1 and 2 g/L). The treatments were applied at both the tillering and flowering stages. Potassium treatments, wheat cultivars, and their interactions significantly differed in most measured parameters. Application of 2g/L of K increases grain yield, protein content, and plant height than 0, 1 g/L by 5.51 t/ha, 10.52% and 95.60 cm, respectively. Foliar application of nano-potassium timed at both tillering and flowering stages would be beneficial in improving the yield and its component. Furthermore, the various trait responses of cultivars to K application can be attractive to wheat breeders for selection. [ABSTRACT FROM AUTHOR]

Zinc (Zn) fertilization of hard red and soft white winter wheat (Triticum aestivum L.) is uncommon in the low precipitation zone of the inland Pacific Northwest. It is uncommon because there have been no indications of deficiency. Soil test data, however, show Zn levels have been declining over time. We conducted a four‐site‐year experiment to evaluate effects of fertilization on early‐season tissue Zn concentration (TZC), dry matter accumulation (DMA), Zn uptake (ZNU), the grain test weight (TWT), protein content (PRO), and yield (YLD) of two regionally adapted cultivars. Planting occurred late in September or early in October. Zinc fertilizer, placed with the seed while planting, was applied at rates of 0 and 5 kg Zn ha−1. Application of 5 kg Zn ha−1 increased TZC and ZNU at one of two sites. There were corresponding trends of increased DMA and improved YLD. Response to fertilization occurred on a relatively shallow soil that had a diethylenetriamine‐pentaacidic acid–extractable Zn test level of 0.3 mg kg−1. Core Ideas: Zinc fertilization increased tissue Zn concentration and Zn uptake in winter wheat (Triticum aestivum L.) at one of two experimental sites.There were corresponding treatment‐induced trends of increased dry matter accumulation and improved grain yield.Response to fertilization occurred on a relatively shallow soil that had a diethylenetriamine‐pentaacidic acid‐extractable soil test Zn level of 0.3 mg kg−1.Response to fertilization may have been due, at least in part, to the near‐surface position of carbonates and alkalinity. [ABSTRACT FROM AUTHOR]

The optimized sowing method and appropriate seed rate can improve wheat N use efficiency. However, the interactive effect of the sowing method and seed rate on N use efficiency, particularly N uptake and root length density, are unclear. A field experiment was conducted for two growing seasons in southern Shanxi province, China, using a split-plot design with the sowing method as the main plot (wide-belt sowing, WBS, and conventional narrow-drill sowing, NDS) and seed rate as the sub-plot (100–700 m−2). Our results showed that WBS had a significant and positive effect on N use efficiency (yield per unit of available N from the fertilizer and soil, by 4.7–15.4%), and the relatively higher seed rates (>300 or 400 m−2) enlarged the effects. The N use efficiency increases under WBS were mainly attributed to the increases in N uptake before anthesis, resulting from the promoted nodal roots per plant and per unit area, and root length density in the top layer(s). WBS promoted N translocation and the N harvest index, resulting in equivalent grain protein concentration and processing quality compared to NDS. Thus, adopting higher seed rates (>300 m−2) combined with WBS is recommended for achieving greater N efficiencies while maintaining the grain protein concentration and processing quality of winter wheat. [ABSTRACT FROM AUTHOR]

Long-term excessive use of fertilizers and intensive cultivation not only decreases soil organic carbon (SOC) and productivity, but also increases greenhouse gas emissions, which is detrimental to sustainable agricultural development. The purpose of this paper is to identify organic amendments suitable for winter wheat growth in the North China Plain by studying the effects of organic amendments on the economic benefits, carbon emissions, and carbon sequestration for winter wheat fields and to provide a theoretical basis for the wide application of organic amendments in agricultural fields. The two nitrogen rates were N0 (0 kg ha−1) and N240 (240 kg ha−1), and the four organic amendments were straw, manure, mushroom residue (M R), and biochar. The results showed that, compared to N0, N240 significantly increased the yield by 244.1–318.4% and the organic carbon storage by 16.7–30.5%, respectively, but increased the carbon emissions by 29.3–45.5%. In addition, soil carbon stocks increased with all three types of organic amendments compared to the straw amendment, with the biochar treatment being the largest, increasing carbon storage by 13.3–33.6%. In terms of yield and economic benefits, compared to the straw amendment, the manure and biochar amendments increased winter wheat yields by 0.0–1.5% and 4.0–13.3%, respectively, and M R slightly decreased wheat yield; only the economic benefit of the M R amendment was greater than that of the straw amendment, with an increase in economic benefit of 1.3% and 8.2% in the 2021–2022 and 2022–2023 seasons, respectively. Furthermore, according to the net ecosystem productivity (NEP), N0 was the source of CO2, while N240 was a sink of CO2. The TOPSIS results showed that N240 with a mushroom residue amendment could be recommended for increasing soil carbon stocks and economic benefits for winter wheat in the NCP and similar regions. Low-cost M R can increase farmer motivation and improve soil organic carbon, making a big step forward in the spread of organic materials on farmland. [ABSTRACT FROM AUTHOR]

Crop coefficient (Kc), the ratio of crop evapotranspiration (ETc) to reference evapotranspiration (ETo), is used to schedule an efficient irrigation regime. This research was conducted to investigate variations in ETc and growth-stage-specific Kc in flood-irrigated winter wheat as a forage crop from 2021 to 2023 in the Lower Rio Grande Valley of southern New Mexico, USA, and evaluate the performances of two temperature-based ETo estimation methods of Hargreaves–Samani and Blaney–Criddle with the widely used Penman–Monteith method. The results indicated that the total ETc over the whole growth stage for flood-irrigated winter wheat was 556.4 mm on a two-year average, while the average deep percolation (DP) was 2.93 cm and 2.77 cm, accounting for 28.8% and 27.2% of applied irrigation water in the 2021–2022 and 2022–2023 growing seasons, respectively. The ETo over the growing season, computed using Penman–Monteith, Hargreaves–Samani, and Blaney–Criddle equations, were 867.0 mm, 1015.0 mm, and 856.2 mm in 2021–2022, and 785.6 mm, 947.0 mm, and 800.1 mm in 2022–2023, respectively. The result of global sensitivity analysis showed that the mean temperature is the main driving factor for estimated ETo based on Blaney–Criddle and Hargreaves–Samani methods, but the sensitivity percentage for Blaney–Criddle was 76.9%, which was much higher than that of 48.9% for Hargreaves–Samani, given that Blaney–Criddle method is less accurate in ETo estimation for this area, especially during the hottest season from May to August. In contrast, wind speed and maximum temperature were the main driving factors for the Penman–Monteith method, with sensitivity percentages of 70.9% and 21.9%, respectively. The two-year average crop coefficient (Kc) values at the initial, mid, and late growth stage were 0.54, 1.1, and 0.54 based on Penman–Monteith, 0.51, 1.0 and 0.46 based on Blaney–Criddle, and 0.52, 1.2 and 0.56 based on Hargreaves–Samani. The results showed that the Hargreaves–Samani equation serves as an alternative tool to predict ETo when fewer meteorological variables are available. The calculated local growth-stage-specific Kc can help improve irrigation water management in this region. [ABSTRACT FROM AUTHOR]

Farmers in Kazakhstan face unreliable water resources. This includes water scarcity in the summer, high fluctuations in precipitation levels, and an increase in extreme weather events such as snow, rain, floods, and droughts. Wheat production is regulated and subsidized by the Kazakh government to strengthen food security. The proper monitoring of crop production is vital to government agencies, as well as insurance and banking structures. These organizations offer subsidies through different levels support. Some farmers already use farmland soil monitoring combined with adaptive combinations of different crops. These include winter–spring plowing crop programs. Winter wheat crops are generally more adaptive and may survive summer droughts. Kazakhstan is a large country with large plots of farmland, which are complicated to monitor. Therefore, it would be reasonable to adapt more efficient technologies and methodologies, such as remote sensing. This research work presents a method for identifying winter wheat crops in the foothills of South Kazakhstan by employing multi-temporal Sentinel-2 data. Here, the researchers adapted and applied a Plowed Land Index, derived from the Brightness Index. The methodology encompasses satellite data processing, the computation of Plowed Land Index values for the swift recognition of plowed fields and the demarcation of winter wheat crop sowing regions, along with a comparative analysis of the acquired data with ground surveys. [ABSTRACT FROM AUTHOR]

Croplands have been the focus of substantial investigation due to their considerable potential for sequestering carbon. Understanding the potential for soil organic carbon (SOC) sequestration and necessary management strategies will be enabled with accurate process-based models. Accurately representing crop growth and agricultural practices will be critical for realistic SOC modeling. The MEMS 2 model incorporates a current understanding of SOC formation and stabilization, measurable SOC pools, and deep SOC dynamics and is seen as a highly promising tool to inform management intervention for SOC sequestration. Thus far, MEMS 2 has been developed to represent grasslands. In this study, we further developed MEMS 2 to model annual grain crops and common agricultural practices, such as irrigation, fertilization, harvesting, and tillage. Using four Ameriflux sites, we demonstrated an accurate simulation of crop growth and development. Model performance was strong for simulating aboveground biomass (index of agreement [d] range of 0.89–0.98) and green leaf area index (d from 0.90 to 0.96) across corn, soybean, and winter wheat. Good agreement with observations was also achieved for net ecosystem CO2 exchange (d from 0.90 to 0.96), evapotranspiration (d from 0.91 to 0.94), and soil temperature (d of 0.96), while discrepancy with the available soil water content data remain (d from 0.14 to 0.81 at four depths to 100 cm). While we will continue model testing and improvement, MEMS 2 (version 2.14) has now demonstrated its ability to effectively simulate the growth of common grain crops and practices [ABSTRACT FROM AUTHOR]

With an increase in the frequency of hot-dry-windy weather and reported significant increase in winter wheat (Triticum aestivum L.) in-season average temperatures, it is important to understand how soil management affects yield stability. This study quantified the combined effects of tillage and fertilizer-N on winter wheat yield stability in a wheat–sorghum [Sorghum bicolor (L.) Moench]–fallow crop rotation. The research was conducted from 1975 to 2022 and the experimental design was a randomized complete block with a split-split-plot arrangement. Crop phases were the main plots, tillage (conventional tillage [CT], reduced tillage [RT], and no-tillage [NT]) in the sub-plot, and N application rates (0, 22, 45, and 67 kg N ha−1 or 0, 45, 90, and 134 kg N ha−1) in the sub-sub-plot. Results showed winter wheat yield under NT increased by 0.8 Mg ha−1, compared with about 1.1 Mg ha−1 for CT and RT, when environmental average increased by 1 Mg ha−1 for N rates ≤90 kg ha−1. Yields with CT and RT increased by 0.8 Mg ha−1, compared with about 1.1 Mg ha−1 rate for NT, when environmental average increased by 1 Mg ha−1 for N >90 kg ha−1. In 30%–52% of the time, CT wheat yield was greater than NT, but yield advantage with CT decreased as fertilizer-N rate increases. Yield stability of winter wheat varied by tillage and fertilizer rate. We concluded that NT wheat required greater N rates to reach the same yield potential as CT and RT, and long-term CT or RT wheat at 45 kg N ha−1 was most stable [ABSTRACT FROM AUTHOR]

Low-temperature flooding and ice encasement (LTFIE) cause variable survival of winter wheat (Triticum aestivum L.) in Ontario, which limits the adoption of wheat into crop rotations by growers. The development of novel cultivars capable of withstanding LTFIE is a promising avenue for improvement, but the methods used to assess the survival of winter wheat under LTFIE are restricted. This study developed updated methods to determine the survival of wheat cultivars under LTFIE using controlled environments and, to our knowledge, is the first method since the 1980s to use Canadian eastern soft red winter wheat (CESRW) to conduct cold tolerance studies. Chamber-acclimated plants of AC Carberry (spring wheat control), Branson (CESRW), CM614 (CESRW), and Norstar (hardy Canadian western red winter control) cultivars were used to estimate the days (LD50) and temperature (LT50) to reach 50% mortality under ice and without ice treatments. Norstar had the longest LD50 at 33 days, Branson and CM614 had similar LD50 of 18 and 20 days, and AC Carberry did not reach an LD50 as it died early in both treatments. The LT50 of each cultivar was different; Norstar had the lowest LT50 (−13.6 °C day 0 and −13.2 °C day 7), and AC Carberry had the highest LT50 (−6.6 °C day 0 and −2.7 °C day 7). The detailed methods developed in this study were more reliable compared to older methods based on the more accurate reported LD50 and LT50 of the cultivars, therefore, these methods can be used to screen winter cereals for LTFIE in the future. [ABSTRACT FROM AUTHOR]

BACKGROUND: The continuous cultivation of rice–wheat in the same field is a key element of double‐cropping systems in the Indo‐Gangetic plains. Yields of such cropping systems are increasingly challenged as climate change drives increases in temperature, terminal stress and uneven rainfall, delaying rice harvesting and subsequently delaying sowing of wheat. In this paper, we evaluate the optimum sowing dates to achieve high grain yield and quality of wheat cultivars in northwest India. Three cultivars of wheat, HD‐2967, HD‐3086 and PBW‐723, were sown on three different dates at the research farm of ICAR‐IARI, New Delhi, to generate different weather conditions at different phenological stages. Different biophysical attributes, photosynthetic rate, stomatal conductance and transpiration rate, were measured at different phenological stages. Yield and grain quality parameters such as protein, starch, amylopectin, amylose and gluten were measured in different cultivars sown on different dates. RESULTS: Biophysical parameters were found to be higher in timely sown crops followed by late‐sown and very late‐sown crops. Further, the different sowing dates had a significant (P < 0.05) impact on the grain quality parameters such as protein, starch, amylopectin, amylose and gluten content. Percentage increases in the value of starch and amylose content under timely sown were ~7% and 11.6%, ~5% and 8.4%, compared to the very late‐sown treatment. In contrast, protein and amylopectin contents were found to increase by ~9.7% and 7.5%, ~13.8% and 16.6% under very late‐sown treatment. CONCLUSION: High‐temperature stress during the grain‐filling periods significantly decreased the grain yield. Reduction in the grain yield was associated with a reduction in starch and amylose content in the grains. The protein content in the grains is less affected by terminal heat stress. Cultivar HD‐3086 had higher growth, yield as well as quality parameters, compared to HD‐2967 and PBW‐723 in all treatments, hence could be adopted by farmers in northwest India. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The water absorption capacity (WAC) of hard wheat (Triticum aestivum L.) flour affects end‐use quality characteristics, including loaf volume, bread yield, and shelf life. However, improving WAC through phenotypic selection is challenging. Phenotyping for WAC is time consuming and, as such, is often limited to evaluation in the latter stages of the breeding process, resulting in the retention of suboptimal lines longer than desired. This study investigates the potential of univariate and multivariate genomic predictions as an alternative to phenotypic selection for improving WAC. A total of 497 hard winter wheat genotypes were evaluated in multi‐environment advanced yield and elite trials over 8 years (2014–2021). Phenotyping for WAC was done via the solvent retention capacity (SRC) using water as a solvent (SRC‐W). Traits that exhibited a significant correlation (r ≥ 0.3) with SRC‐W and were evaluated earlier than SRC‐W were included in the multivariate genomic prediction models. Kernel hardness and diameter were obtained using the single kernel characterization system (SKCS), and break flour yield and total flour yield (T‐Flour) were included. Cross‐validation showed the mean univariate genomic prediction accuracy of SRC to be r = 0.69 ± 0.005, while bivariate and multivariate models showed an improved prediction accuracy of r = 0.82 ± 0.003. Forward validation showed a prediction accuracy up to r = 0.81 for a multivariate model that included SRC‐W + All traits (SRC‐W, Diameter, SKCS hardness and diameter, F‐Flour, and T‐Flour). These results suggest that incorporating correlated traits into genomic prediction models can improve early‐generation prediction accuracy. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal Frontiers in Plant Science that emphasizes the importance of conserving genetic resources for crop improvement and biodiversity. It discusses the availability of genomic tools and technologies for studying diversity and supporting conservation efforts. The document includes summaries of various research articles on topics such as genetic diversity in olive germplasm, genomic characterization of wheat crop wild relatives, and genetic diversity of Albizia odoratissima. It also acknowledges the lack of reports from Africa but highlights the active work being done in China. The document concludes by stating that while there is a strong molecular emphasis in the research, the challenges of managing genetic resources remain the same. [Extracted from the article]

Background Results Conclusion Leaf area index (LAI) is an important indicator for assessing plant growth and development, and is also closely related to photosynthesis in plants. The realization of rapid accurate estimation of crop LAI plays an important role in guiding farmland production. In study, the UAV‐RGB technology was used to estimate LAI based on 65 winter wheat varieties at different fertility periods, the wheat varieties including farm varieties, main cultivars, new lines, core germplasm and foreign varieties. Color indices (CIs) and texture features were extracted from RGB images to determine their quantitative link to LAI.The results revealed that among the extracted image features, LAI exhibited a significant positive correlation with CIs (r = 0.801), whereas there was a significant negative correlation with texture features (r = −0.783). Furthermore, the visible atmospheric resistance index, the green–red vegetation index, the modified green–red vegetation index in the CIs, and the mean in the texture features demonstrated a strong correlation with the LAI with r > 0.8. With reference to the model input variables, the backpropagation neural network (BPNN) model of LAI based on the CIs and texture features (R2 = 0.730, RMSE = 0.691, RPD = 1.927) outperformed other models constructed by individual variables.This study offers a theoretical basis and technical reference for precise monitor on winter wheat LAI based on consumer‐level UAVs. The BPNN model, incorporating CIs and texture features, proved to be superior in estimating LAI, and offered a reliable method for monitoring the growth of winter wheat. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Late spring coldness (LSC) is the main limiting factor threatening wheat yield and quality stability. Optimal nutrient management is beneficial in mitigating the harms of LSC by improving wheat root physiology. This study proposed a nutrient management strategy that postponed the application of phosphorus (P) and potassium (K), effectively strengthening wheat's defense against LSC. This experiment used the winter cultivar "Yannong19" (YN 19) as plant material for two consecutive years (2021–2022 and 2022–2023). Two fertilizer treatments were used: traditional P and K fertilizers application (R1: base fertilizer: jointing fertilizer = 10:0) and postponed P and K fertilizers application (R2: base fertilizer: jointing fertilizer = 5:5); wheat plants at the anther connective formation stage shifted to temperature-controlled phytotrons for normal (T0, 11 °C/4 h) and low temperatures (T1, 4 °C/4 h; T2, −4 °C/4 h) as treatments of LSC. The results showed that under low temperature (LT) treatment, compared with R1, the R2 treatment increased the concentrations of osmotic adjustment substances (soluble sugars and soluble protein contents by 6.2–8.7% and 3.0–8.9%), enhanced activities of antioxidant enzymes (superoxide dismutase, peroxidase and catalase activities by 2.2–9.1%, 6.2–9.7% and 4.2–8.4%), balanced the hormone concentrations (increased IAA and GA3 contents by 2.8–17.5% and 10.4–14.1% and decreased ABA contents by 7.2–14.3%), and reduced the toxicity (malondialdehyde, hydrogen peroxide content and O2·− production rate by 5.7–12.4%, 17.7–22.8% and 19.1–19.1%) of the cellular membranes. Furthermore, the wheat root physiology in R2 significantly improved as the root surface area and dry weight increased by 5.0–6.6% and 4.7–6.6%, and P and K accumulation increased by 7.4–11.3% and 12.2–15.4% compared to R1, respectively. Overall, the postponed application of P and K fertilizers enhanced the physiological function of the root system, maintained root morphology, and promoted the accumulation of wheat nutrients under the stress of LSC. [ABSTRACT FROM AUTHOR]

The development of water-saving management relies on understanding the physiological response of crops to soil drought. The coordinated regulation of hydraulics and stomatal conductance in plant water relations has steadily received attention. However, research focusing on grain crops, such as winter wheat, remains limited. In this study, three soil water supply treatments, including high (H), moderate (M), and low (L) soil water contents, were conducted with potted winter wheat. Leaf water potential (Ψleaf), leaf hydraulic conductance (Kleaf), and stomatal conductance (gs), as well as leaf biochemical parameters and stomatal traits were measured. Results showed that, compared to H, predawn leaf water potential (ΨPD) significantly reduced by 48.10% and 47.91%, midday leaf water potential (ΨMD) reduced by 40.71% and 43.20%, Kleaf reduced by 64.80% and 65.61%, and gs reduced by 21.20% and 43.41%, respectively, under M and L conditions. Although gs showed a significant difference between M and L, Ψleaf and Kleaf did not show significant differences between these treatments. The maximum carboxylation rate (Vcmax) and maximum electron transfer rate (Jmax) under L significantly decreased by 23.11% and 28.10%, stomatal density (SD) and stomatal pore area index (SPI) under L on the abaxial side increased by 59.80% and 52.30%, respectively, compared to H. The leaf water potential at 50% hydraulic conduction loss (P50) under L was not significantly reduced. The gs was positively correlated with ΨMD and Kleaf, but it was negatively correlated with abscisic acid (ABA) and SD. A threshold relationship between gs and Kleaf was observed, with rapid and linear reduction in gs occurring only when Kleaf fell below 8.70 mmol m−2 s−1 MPa−1. Our findings demonstrate that wheat leaves adapt stomatal regulation strategies from anisohydric to isohydric in response to reduced soil water content. These results enrich the theory of trade-offs between the carbon assimilation and hydraulic safety in crops and also provide a theoretical basis for water management practices based on stomatal regulation strategies under varying soil water conditions. [ABSTRACT FROM AUTHOR]

Drought stress has serious effects on the growth and yield of wheat in both productivity and quality and is an abiotic factor. Traditional methods of crop drought stress monitoring have some deficits. This work has been conducted in order to enhance these conventional methods by proposing a new deep learning approach. This paper has presented a deep learning-based model customized for monitoring drought stress in winter wheat during the critical growth stages. Drought-afflicted winter wheat images were captured at three crucial phases: rising–jointing, heading–flowering, and flowering–maturity. These images are correlated against soil moisture data to construct a comprehensive dataset. DenseNet121 was chosen as the network model since it extracts features from images relating to phenotypes. Several factors, like training methods, learning rate adjustment, and addition of the attention mechanism, are optimized in eight sets of experiments. This provided the final DenseNet-121 model with an average recognition accuracy of 94.67% on the test set, which means that monitoring drought stress during wheat growth's key periods is feasible and effective. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION Global temperature is projected to rise continuously under climate change, negatively impacting the growth and yield of winter wheat. Optimizing traditional agricultural measures is necessary to mitigate potential winter wheat yield losses caused by future climate change. This study aims to explore the variations in winter wheat growth and yield on the Loess Plateau of China under future climate change, identify the key meteorological factors affecting winter wheat growth and yield, and analyze the differences in winter wheat yield and root characteristics under different fertilization depths.Meteorological data from 20 General Circulation Models were applied to drive the Decision Support System for Agrotechnology Transfer model, simulating the future growth characteristics of winter wheat under various fertilization depths. The Random Forest model was used to determine the relative importance of meteorological factors influencing winter wheat yield, root length density and leaf area index. The results showed that temperature and high emission concentration were primary factors influencing crop yield under future climate change. The temperature increase projected from 2021 to 2100 would be anticipated to shorten the phenology period of winter wheat by 2–16 days and reduce grain yield by 2.9–12.7% compared to the period from 1981 to 2020. Conversely, the root length density and root weight of winter wheat would increase by 1.2–10.9% and 0.2–24.1%, respectively, in the future, and excessive allocation of root system resources was identified as a key factor contributing to the reduction in winter wheat yield. Compared with the shallow fertilization treatment (N5), the deep fertilization treatments (N15 and N25) increased the proportion of roots in the deep soil layer (30–60 cm) by 2.7–10.2%. Because of the improvement in root structure, the decline in winter wheat yield under deep fertilization treatments in the future is expected to be reduced by 1.2% to 6.5%, whereas water use efficiency increases by 1.1% to 2.4% compared to the shallow fertilization treatment.The deep fertilization treatment can enhance the root structure of winter wheat and increase the proportion of roots in the deep soil layer, thereby effectively mitigating the decline in winter wheat yield under future climate change. Overall, optimizing fertilization depth effectively addresses the reduced winter wheat yield risks and agricultural production challenges under future climate change. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION Water and nutrients are two main determinants of wheat yield, which are vital for maintaining high crop yields. In the present study, the effects of water and phosphate fertilization on wheat yield, photosynthetic parameters, water productivity and phosphate use efficiency were investigated. Five dryland wheat cultivars from the 1940s to the 2010s that are widely cultivated in Shaanxi Province, China, were used. Experiments were conducted from 2019 to 2022 using two irrigation levels (normal rainfall and no precipitation after the reviving stage) and two phosphorus application levels (0 and 100 kg ha−1).Compared with old cultivars (‘Mazha’), the grain yield of modern cultivars (‘Changhan 58’) was 89.24% higher and was closely correlated with chlorophyll index, leaf area index, photosynthetic rate and tillers. With the replacement of cultivars, the phosphorus content, water potential and phosphatase activity of wheat leaves increased. Considering water–phosphorus interactions, the water use efficiency and phosphorus use efficiency of wheat showed a significant positive correlation.Our findings indicate that modern wheat cultivars are more responsive to phosphorus. Further analysis revealed that modern varieties have evolved two phosphorus absorption strategies in response to phosphorus deficiency – namely, the formation of a phosphorus supply source, which may result in larger numbers of green organs; and an increase in phosphorus sinks, which tended to activation and transport of plant phosphorus. Our results may thus contribute to water conservation, increased yields and the development of strategies for efficient phosphorus fertilization. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Plant height, spike, leaf, stem and grain morphologies are key components of plant architecture and related to wheat yield. A wheat (Triticum aestivum L.) mutant, wpa1, displaying temperature-dependent pleiotropic developmental anomalies, was isolated. The WPA1 gene, encoding a von Willebrand factor type A (vWA) domain protein, was located on chromosome arm 7DS and isolated by map-based cloning. The functionality of WPA1 was validated by multiple independent EMS-induced mutants and gene editing. Phylogenetic analysis revealed that WPA1 is monocotyledon-specific in higher plants. The identification of WPA1 provides opportunity to study the temperature regulated wheat development and grain yield. [ABSTRACT FROM AUTHOR]

Salicylic acid (SA), a vital endogenous hormone, plays a crucial role in plant growth and the response to abiotic and biotic stress. Isochorismate synthase (ICS) and phenylalanine ammonia lyase (PAL) are critical rate-limiting enzymes for SA synthesis. Fusarium head blight (FHB) seriously threatens the safety of wheat production, but increasing the content of SA can enhance FHB resistance. However, the pathway of SA synthesis and regulation in wheat remains unknown. In this study, three wheat ICS (TaICSA, TaICSB, and TaICSD) were identified, and their functions were validated in vitro for isomerizing chorismate to isochorismate. The mutation of one or two homoeoalleles of TaICSA, TaICSB, and TaICSD in the wheat variety 'Cadenza' reduced SA levels under ultraviolet treatment and Fusarium graminearum infection, further enhancing sensitivity to FHB. Overexpression of TaICSA can significantly enhance SA levels and resistance to FHB. To further study SA synthesis pathways in wheat and avoid interference with pathogenicity related genes, the leaves of wild-type Cadenza and different TaICS mutant lines were subjected to ultraviolet treatment for transcriptomic analysis. The results showed that 37 PALs might be involved in endogenous SA synthesis, and 82 WRKY and MYB family transcription factors may regulate the expression of ICS and PAL. These results were further confirmed by RT-PCR. In conclusion, this study expands our knowledge of SA biosynthesis and identifies TaICSA, as well as several additional candidate genes that encode transcription factors for regulating endogenous SA levels, as part of an efficient strategy for enhancing FHB resistance in wheat. [ABSTRACT FROM AUTHOR]

Promoting more floret primordia within a spike to acquire fertile potential during the differentiation and pre-dimorphism phases is critical for increasing the number of fertile florets per spike (NFFs). However, it is yet unknown the physiological mechanism regulating the complex and dynamic process. This study aimed to clarify how intra-spike hormones, pigments, and assimilates coordinate with each other to regulate spike morphology and then floret primordia development. A two-year field experiment was conducted with two winter wheat genotypes: N50 (big-spike with greater NFFs) and SM22 (mediumspike with fewer NFFs). We monitored high temporal and spatial-resolution changes in the number and morphology of floret primordia within a spike, as well as in intra-spike hormones, pigments, and assimilates. Our results revealed that the big-spike genotype had more NFFs than the medium-spike genotype, not only because they had more spikelets, but also because they had greater NFFs mainly at central spikelets. More floret primordia at central spikelets had sufficient time to develop and acquire fertile potential during the differentiation phase (167-176 d after sowing, DAS) and the pre-dimorphism phase (179 DAS) for the big-spike genotype than the medium-spike genotype. Floret primordia with fertile morphology during the pre-dimorphism phase always developed into fertile florets during the dimorphism phase. Those early-developed floret primordia most proximal and intermediate to the rachis in the big-spike genotype developed faster than the medium-spike genotype. Correspondingly, the spike dry matter and pigments (chlorophyll a, chlorophyll b, carotene, and carotenoids) content during 170-182 DAS, auxin (IAA) and cytokinin (CTK) content on 167 DAS were significantly higher in the big-spike genotype than in the medium-spike genotype, while jasmonic acid (JA) content was significantly lower in the big-spike genotype compared to the medium-spike genotype during 167-182 DAS. Since the significant differences in intra-spike hormone content of the two genotypes appear earlier than those in dry matter and pigments, we propose a possible model that helped the N50 genotype (big-spike) to form more fertile florets, taking the intra-spike hormone content as a signaling molecule induced assimilates and pigments synthesis, which accelerated the development of more floret primordia during the differentiation phase and then acquired fertile potential during the pre-dimorphism phase, finally improved the NFFs. Our high temporal and spatial-resolution analysis provides an accurate time window for precision cultivation and effective physiological breeding to improve the number of fertile florets in wheat. [ABSTRACT FROM AUTHOR]

Accurate nitrogen (N) nutrition diagnosis is essential for improving N use efficiency in crop production. The widely used critical N (Nc) dilution curve traditionally depends solely on agronomic variables, neglecting crop water status. With three-year field experiments with winter wheat, encompassing two irrigation levels (rainfed and irrigation at jointing and anthesis) and three N levels (0, 180, and 270 kg ha 1), this study aims to establish a novel approach for determining the Nc dilution curve based on crop cumulative transpiration (T), providing a comprehensive analysis of the interaction between N and water availability. The Nc curves derived from both crop dry matter (DM) and T demonstrated N concentration dilution under different conditions with different parameters. The equation Nc = 6.43T 024 established a consistent relationship across varying irrigation regimes. Independent test results indicated that the nitrogen nutrition index (NNI), calculated from this curve, effectively identifies and quantifies the two sources of N deficiency: insufficient N supply in the soil and insufficient soil water concentration leading to decreased N availability for root absorption. Additionally, the NNI calculated from the Nc-DM and Nc-T curves exhibited a strong negative correlation with accumulated N deficit (Nand) and a positive correlation with relative grain yield (RGY). The NNI derived from the Nc-T curve outperformed the NNI derived from the Nc-DM curve concerning its relationship with Nand and RGY, as indicated by larger R2 values and smaller AIC. The novel Nc curve based on T serves as an effective diagnostic tool for assessing winter wheat N status, predicting grain yield, and optimizing N fertilizer management across varying irrigation conditions. These findings would provide new insights and methods to improve the simulations of water-N interaction relationship in crop growth models. [ABSTRACT FROM AUTHOR]

To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model's accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a "red shift" of the green peak and a "blue shift" of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops. [ABSTRACT FROM AUTHOR]

The main difference in fodder qualities of winter rye, triticale and wheat grains is the content of anti-nutritive substances: water-soluble pentosans and, accordingly, the viscosity of the aqueous extract of grain. Lack of scientific information about the content of antinutrients in the grain of winter cereal crops determines the relevance of the research; and the possibility to develop ways of using it and volumes of production for fodder purposes determines study's innovativeness. The paper was set out to make a comparative assessment of the winter rye, winter triticale and winter wheat grain quality. On average for three years, the protein content of winter wheat grain was 11.5%, winter triticale – 10.0% and winter rye – 8.5%. In terms of starch content, winter wheat grain differs significantly from winter rye grain (by 0.9 percentage points more); winter triticale (by 0.8 percentage points more). On average, over three years, phosphorus in winter rye grain was 0.02 percentage points less than in wheat grain and 0.03 percentage points less than in triticale grain. Winter triticale and wheat grains hardly vary in the content of arabinoxylans and the viscosity of the aqueous extract. The results can be used in fodder production and animal husbandry. [ABSTRACT FROM AUTHOR]

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability. [ABSTRACT FROM AUTHOR]

Accurate and timely prediction of crop yields is crucial for ensuring food security and promoting sustainable agricultural practices. This study developed a winter wheat yield prediction model using machine learning techniques, incorporating remote sensing data and statistical yield records from Henan Province, China. The core of the model is an ensemble voting regressor, which integrates ridge regression, gradient boosting, and random forest algorithms. This study optimized the hyperparameters of the ensemble voting regressor and conducted an in-depth comparison of its yield prediction performance with that of other mainstream machine learning models, assessing the impact of key hyperparameters on model accuracy. This study also explored the potential of yield prediction at different growth stages and its application in yield spatialization. The results demonstrate that the ensemble voting regressor performed exceptionally well throughout the entire growth period, with an R2 of 0.90, an RMSE of 439.21 kg/ha, and an MAE of 351.28 kg/ha. Notably, during the heading stage, the model's prediction performance was particularly impressive, with an R2 of 0.81, an RMSE of 590.04 kg/ha, and an MAE of 478.38 kg/ha, surpassing models developed for other growth stages. Additionally, by establishing a yield spatialization model, this study mapped county-level yield predictions to the pixel level, visually illustrating the spatial differences in land productivity. These findings provide reliable technical support for winter wheat yield prediction and valuable references for crop yield estimation in precision agriculture. [ABSTRACT FROM AUTHOR]

Protection of high-yielding winter wheat (WW) with fungicides increases the productivity of nitrogen (N) present in the soil–crop system during the growing season. As a consequence of the action of fungicides, the nitrogen gap (NG) reduces. This hypothesis was verified on the basis of data from a field experiment conducted with WW during three growing seasons (2013/2014; 2014/2015, 2015/2016) in Poland. The field experiment included two crop protection systems (CP): (i) CP-0—without fungicides and CP-F—with fungicides and (ii) six N doses increased gradually by 40 kg N ha−1 from 0 to 240 kg N ha−1. The grain yield (GY) of WW treated with fungicides was significantly higher than that of the unprotected. The difference in yields between both CP systems was 17.3% on a plot fertilized with 200 kg N ha−1 (9.13 vs. 11.2 t ha−1). The fungicide yield gap increased progressively with Nf doses from 0.76 t ha−1 in the Nf control plot to 2.17 t ha−1 in the fertilized with 200 kg ha−1. The use of fungicides increased the amount of N in grain (Ngr) from 15 kg N ha−1 in the control N plot to 51 kg N ha−1 in the plot with 200 kg N ha−1. The main source of additional N in grain (Ngr) was inorganic N released from the soil (Ng89) during the WW growing season. The maximum Ng89 values were 64.4 and 83.0 kg N ha−1. These values corresponded to Nf doses of 94.4 and 80.8 kg N ha−1. The Ng89 of 70.1 kg N ha−1 conditioned 100-percentage Nf recovery. As a consequence, the prediction reliability of GY and Ngr was highest when Ng89 was used as a predictor. The net increase in the absolute NG size in response to increasing N input was significantly slower and therefore smaller in fungicide-protected than in unprotected WW. It can be concluded that the use of fungicides due to the increase in inorganic N productivity in the soil–crop system reduces the potential threat of N dispersion into the environment. In the light of the results obtained, it should be concluded that the fungicidal protection of crop plants should be treated as a factor significantly reducing the nitrogen gap and, thus, the yield gap. [ABSTRACT FROM AUTHOR]

Biogas digestate is increasingly used in organic farming to improve soil nutrient supply and sustainably increase yields. However, biogas digestate can also lead to environmentally relevant N2O emissions. The benefits, opportunities, and risks associated with the use of digestate as a fertilizer in organic farming are a subject of ongoing debate, in part due to a lack of conclusive experimental results. A field trial conducted in southern Germany examined the short-term and long-term impacts of digestate fertilization on winter wheat yield, nitrogen use efficiency, and N2O-N emissions. The four-year results from the years 2019 to 2022 are presented. Digestate was applied with a nitrogen input of up to 265 kg ha−1, with 129 kg ha−1 NH4+-N. The application of digestate resulted in a significant increase in wheat yield, with an average increase of 53% (2019) to 83% (2022) compared to the unfertilized control. It is notable that the treatment applied for the first time did not reach the yield of the long-term fertilized treatment, with a yield gap of 0.5 to 1.2 Mg ha−1 (6% to 15%). The highest N2O-N emissions (up to 3.30 kg ha−1) in the vegetation period from spring to autumn were measured in the long-term fertilized treatment. However, very high N2O-N emissions (up to 3.72 kg ha−1) also occurred in two years in winter in the unfertilized treatment. An increase in soil inorganic N stocks and N2O-N emissions was observed following the wheat harvest and subsequent tillage in all treatments. No significant differences were identified between the fertilizer treatments with regard to product-related emissions. The experimental results demonstrate that N2O-N emissions are not solely a consequence of N fertilization, but can also be attributed to tillage, post-harvest practices, and previous crops, with considerable variability depending on weather conditions. The experimental data provide comprehensive insight into the influence of cultivation, soil characteristics, and meteorological conditions on N2O-N emissions at an agricultural site in southern Germany. [ABSTRACT FROM AUTHOR]

Timely and accurate predictions of winter wheat yields are key to ensuring food security. In this research, winter wheat yield prediction models for six provinces were established using a random forest (RF) model. Two methods were employed to analyze feature variables. RF partial dependence plots were generated to demonstrate the nonlinear relationships between the feature variables and yield, and bivariate Moran's I was considered to identify the spatial associations between variables. Results showed that when environmental data from key growth periods were used for prediction model establishment, the root mean square error (RMSE) varied between 200 and 700 kg/ha, and the coefficient of determination (R2) exceeded 0.5. Feature variable analysis results indicated that the longitude, latitude, topography and normalized difference vegetation index (NDVI) were important variables. Below the threshold, the yield gradually increased with increasing NDVI. Bivariate Moran's I results showed that there was zonal distribution of meteorological elements. Within a large spatial range, the change in environmental variables due to the latitude and longitude should be accounted for in modeling, but the influence of collinearity between the feature variables should be eliminated via variable importance analysis. [ABSTRACT FROM AUTHOR]

Barley crops are at risk of being attacked by several leaf diseases. Net blotch, brown rust, Rhynchosporium and Ramularia leaf spot are among the most widespread and can cause severe attack and yield losses. Two trial protocols targeting Ramularia and net blotch, respectively, have been tested in several countries in 2021 and 2022. Ramularia trials were situated in Germany, Ireland, Scotland, and Denmark. The net blotch trials were placed in Denmark, Belgium, the UK, Germany, Finland, and France. In the two protocols, 12–13 different fungicide solutions including co-formulations of DMIs, SDHIs, QoIs, and multi-site inhibitors have been tested to compare efficacy and yield responses. Against Ramularia leaf spot, the fungicides were applied at GS 47–51 and against net blotch at GS 37–45. In six trials, the efficacy against Ramularia leaf spot was scored. The results showed a superior control from the co-formulation fluxapyroxad + metyltetraprole (78–100% control), but also solo mefentrifluconazole and the mixtures fluxapyroxad + mefentrifluconazole performed well (average 74–76% control). The mixture fluxapyroxad + metyltetraprole provided the best yield increase followed by Ascra Xpro. Folpet as a solo solution was inferior. Following the net blotch protocol, only three trials developed enough disease to rank the different fungicides; however, in five trials ranking against brown rust was also possible. Most treatments gave very good control of net blotch, and brown rust (> 80% control). The mixture fluxapyroxad + metyltetraprole delivered the best control against all diseases overall. Average yield responses from eight trials showed very similar increases from the tested fungicides. [ABSTRACT FROM AUTHOR]

Food and feed production worldwide heavily relies on wheat (Triticum aestivum). However, current agricultural practices face numerous challenges including a shortage of land for cultivation, a desire to reduce the use of chemical pesticides and fertilizers, and the development of resistance towards employed pesticides and virulence towards host resistance in the most widely grown varieties. In this paper, we demonstrate based on the literature that cultivating wheat variety mixtures generally leads to increased yield and yield stability across years and environments, reduced severity of multiple diseases, and a decreased risk of lodging before harvest compared to the cultivation of pure stands. Moreover, mixtures may delay fungicide resistance development and increase genetic diversity, ultimately prolonging the durability of resistance genes. Furthermore, growing mixtures may lower the risk of crop failure due to more extreme weather events and lead to better utilization of water and nutrients. We discuss a Danish case study advocating the cropping of wheat varieties in mixtures as an example of how variety mixtures can be utilized in integrated pest management strategies. The study shows that if all involved stakeholder groups promote the adoption of variety mixtures, a major uptake by farmers can be reached, potentially reducing the dependency on pesticides in current cropping schemes. [ABSTRACT FROM AUTHOR]

The precision monitoring of film-mulched winter wheat growth facilitates field management optimization and further improves yield. Unmanned aerial vehicle (UAV) is an effective tool for crop monitoring at the field scale. However, due to the interference of background effects caused by soil and mulch, achieving accurate monitoring of crop growth in complex backgrounds for UAV remains a challenge. Additionally, the simultaneous inversion of multiple growth parameters helped us to comprehensively monitor the overall crop growth status. This study conducted field experiments including three winter wheat mulching treatments: ridge mulching, ridge–furrow full-mulching, and flat cropping full-mulching. Three machine learning algorithms (partial least squares, ridge regression, and support vector machines) and deep neural network were employed to process the vegetation indices (VIs) feature data, and the residual neural network 50 (ResNet 50) was used to process the image data. Then the two modalities (VI feature data and image data) were fused to obtain a multi-modal fusion (MMF) model. Meanwhile, a film-mulched winter wheat growth monitoring model that simultaneously predicted leaf area index (LAI), aboveground biomass (AGB), plant height (PH), and leaf chlorophyll content (LCC) was constructed by coupling multi-task learning techniques. The results showed that the image-based ResNet 50 outperformed the VI feature-based model. The MMF improved prediction accuracy for LAI, AGB, PH, and LCC with coefficients of determination of 0.73–0.92, mean absolute errors of 0.29–3.89 and relative root mean square errors of 9.48–12.99%. A multi-task MMF model with the same loss weight distribution ([1/4, 1/4, 1/4, 1/4]) achieved comparable accuracy to the single-task MMF model, improving training efficiency and providing excellent generalization to different film-mulched sample areas. The novel technique of the multi-task MMF model proposed in this study provides an accurate and comprehensive method for monitoring the growth status of film-mulched winter wheat. [ABSTRACT FROM AUTHOR]

There is growing interest in using canopy temperature (Tc), including crop water Stress index (CWSI), for irrigation management. However, Tc varies greatly in one day, while soil water content (SWC) varies little, which may lead to different conclusions on whether irrigation is needed based on CWSI at different times. For this end, Tc of winter wheat was continuously monitored, and the data of such environmental factors as atmospheric temperature and soil water content (SWC) were simultaneously collected. CWSI was calculated based on empirical formulation and Tc and CWSI were generalized based on the normalization formulation. The correlation SWC between Tc and CWSI before and after generalization was compared and error analysis was based on SWC theoretical formula. The results showed: (1) the accuracy of SWC retrieval by Tc and CWSI increased firstly and then decreased with time during the day. The optimal time for Tc monitoring SWC was between 10:00 ~ 16:00 (R2 > 0.72) and the optimal time for CWSI monitoring SWC was between 9:00 ~ 18:00 (R2 > 0.69). (2) CWSI and Tc were mapped based on the relationship between crop water stress and soil water deficit and normalized canopy temperature expressions characterized the relationship between crop water stress and soil water deficit. (3) The accuracy of inversion of SWC by mapping Tc from 18:00 ~ 8:00 is increased from 0.5 ~ 0.6 to 0.7 ~ 0.8; the accuracy of soil water content inversion by mapping CWSI from 18:00 ~ 8:00 was improved from 0.2 ~ 0.4 to 0.4 ~ 0.6. (4) The theoretical expression of SWC deduced based on CWSI also proves that considering the relationship between crop water stress and soil water deficit change can effectively reduce the relative error from 30 to 5% in the morning and evening. This study contributes to the understanding of the reason why the correlation between Tc and SWC varies greatly during the day and solves the time-limited problem of thermal infrared remote sensing monitoring of crop water stress. [ABSTRACT FROM AUTHOR]

Accurate modeling of site-specific crop yield response is key to providing farmers with accurate site-specific economically optimal input rates (EOIRs) recommendations. Many studies have demonstrated that machine learning models can accurately predict yield. These models have also been used to analyze the effect of fertilizer application rates on yield and derive EOIRs. But models with accurate yield prediction can still provide highly inaccurate input application recommendations. This study quantified the uncertainty generated when using machine learning methods to model the effect of fertilizer application on site-specific crop yield response. The study uses real on-farm precision experimental data to evaluate the influence of the choice of machine learning algorithms and covariate selection on yield and EOIR prediction. The crop is winter wheat, and the inputs considered are a slow-release basal fertilizer NPK 25–6–4 and a top-dressed fertilizer NPK 17–0–17. Random forest, XGBoost, support vector regression, and artificial neural network algorithms were trained with 255 sets of covariates derived from combining eight different soil properties. Results indicate that both the predicted EOIRs and associated gained profits are highly sensitive to the choice of machine learning algorithm and covariate selection. The coefficients of variation of EOIRs derived from all possible combinations of covariate selection ranged from 13.3 to 31.5% for basal fertilization and from 14.2 to 30.5% for top-dressing. These findings indicate that while machine learning can be useful for predicting site-specific crop yield levels, it must be used with caution in making fertilizer application rate recommendations. [ABSTRACT FROM AUTHOR]

Rice–wheat rotation cropland is one of the most important agroecosystems in South China, the escalation of conflict between food demand augment and water supply shortage increased with climate change. Water use efficiency plays a more significant role in optimising water and carbon management. Thus, the diurnal and seasonal variations of water use efficiency were assessed by the 3‐year eddy covariance observations in the Shouxian National Observatory, a typical rice–wheat rotation station. The results revealed a 'U'‐shaped diurnal pattern of water use efficiency for winter wheat (Triticum aestivum L.) and rice (Oryza sativa L.). Seasonal water use efficiency had two peaks with the highest in the winter wheat‐growing season. The average water use efficiency for the rice–wheat rotation cropland was 2.85 g C kg−1 H2O over the whole year with 2.62 and 3.11 g C kg−1 H2O for winter wheat and rice, respectively. However, gross primary productivity and evapotranspiration of rice were higher than those of winter wheat. Temperature, photosynthetically active radiation were the principal impact factors of water use efficiency in the rice‐growing season. Comparatively, soil water and vapour pressure deficit dominated the water use efficiency changes in the winter wheat‐growing season. Our analyses can help understand the water use requirements for carbon assimilation on rice–wheat rotation cropland on the field scale. [ABSTRACT FROM AUTHOR]

A simulation study was conducted for assessing the climate change impact on wheat cultivars (HD2967 and PBW725) under RCP 4.5 and RCP 6.0 scenarios for four agroclimatic zones (AZ) of Punjab. The yield trend during 70 years (2025–95) using the CERES‐Wheat model was assessed for different sowing windows (end October to end November). The maximum/minimum temperature and rainfall, respectively, during the season varied between 25–27°C/9–12°C and 27–103 mm (AZ II), 24–27°C/8–13°C and 37–105 mm (AZ III), 24–26°C/9–12°C and 20–80 mm (AZ IV) and 23–26°C/9–12°C and 30–71 mm (AZ V). The climatic conditions largely vary across the state, because of which only AZs II, III and V were found productive for wheat crop with most of the years lying in the high yield (>5000 kg/ha) category. The sowing of HD2967 during mid to end November would be suitable adaptation strategy for wheat growers in the state. [ABSTRACT FROM AUTHOR]

The genus Clavibacter (family Microbacteriaceae) includes gram‐positive actinobacterial species infecting diverse agricultural crops such as alfalfa, maize, pepper, potato, tomato and wheat. Clavibacter tessellarius, causing bacterial mosaic of wheat, was reported for the first time in 1976 in Nebraska (United States). Since then, the pathogen was thought to be restricted to North America (Canada and the United States). While bacterial strains suspected to be C. tessellarius have occasionally been isolated from wheat samples outside North America, occurrence of the pathogen in the Old World has not yet been confirmed by the plant protection authorities. In spring 2020 (January to May) dozens of wheat fields with young plants showing leaf chlorosis and mild mosaic symptoms were surveyed and sampled in southern Iran. Among 192 surveyed wheat fields, gram‐positive, orange‐pigmented actinobacterial strains were isolated from samples of 53 fields. Altogether, 61 bacterial strains were identified as C. tessellarius based on the phenotypic features, PCR‐based molecular tests and pathogenicity assays. Multilocus sequence analysis of five housekeeping genes (atpD, dnaK, gyrB, ppk and rpoB) using 10 representative strains from diverse geographic areas in southern Iran confirmed the phylogenetic status of the strains within C. tessellarius. Our data indicate that the bacterial mosaic pathogen occurs in four southern Iranian provinces including Bushehr, Fars, Kerman and Khuzestan. Thus, occurrence of C. tessellarius outside North America is confirmed. Potential risks associated with the emergence of the pathogen in the Old World, and the need for strict quarantine measures in the area, is further discussed. [ABSTRACT FROM AUTHOR]

Accurate mapping of winter wheat provides essential information for food security and ecosystem protection. Deep learning approaches have achieved promising crop discrimination performance based on multitemporal satellite imagery. However, due to the high dimensionality of the data, sequential relations, and complex semantic information in time-series imagery, effective methods that can automatically capture temporal-spatial features with high separability and generalizability have received less attention. In this study, we proposed a U-shaped CNN-Transformer hybrid framework based on an attention mechanism, named the U-Temporal-Spatial-Transformer network (UTS-Former), for winter wheat mapping using Sentinel-2 imagery. This model includes an "encoder-decoder" structure for multiscale information mining of time series images and a temporal-spatial transformer module (TST) for learning comprehensive temporal sequence features and spatial semantic information. The results obtained from two study areas indicated that our UTS-Former achieved the best accuracy, with a mean MCC of 0.928 and an F1-score of 0.950, and the results of different band combinations also showed better performance than other popular time-series methods. We found that the MCC (MCC/All) of the UTS-Former using only RGB bands decreased by 4.53 %, while it decreased by 13.36 % and 35.02 % for UNet2d-LSTM and CNN-BiLSTM, respectively, compared with that of all the band combinations. The comparison demonstrated that the proposed UTS-Former could capture more global temporal-spatial information from winter wheat fields and achieve greater precision in terms of local details than other methods, resulting in high-quality mapping. The analysis of attention scores for the available acquisition dates revealed significant contributions of both beginning and ending growth images in winter wheat mapping, which is valuable for making appropriate selections of image dates. These findings suggest that the proposed approach has great potential for accurate, cost-effective, and high-quality winter wheat mapping. [ABSTRACT FROM AUTHOR]

Unprecedented precipitation deficits in the 2022-2023 growing season across the primary wheat-producing region in the United States caused delays in winter wheat emergence and poor crop growth. Using an integrated approach, we quantitatively unraveled a 37% reduction in wheat production as being attributable to both per-harvested acre yield loss and severe crop abandonment, reminiscent of the Dust Bowl years in the 1930s. We used random forest machine learning and game theory analytics to show that the main driver of yield loss was spring drought, whereas fall drought dominated abandonment rates. Furthermore, results revealed, across the US winter wheat belt, the La Niña phase of the El Niño Southern Oscillation (ENSO), increased abandonment rates compared to the El Niño phase. These findings underscore the necessity of simultaneously addressing crop abandonment and yield decline to stabilize wheat production amid extreme climatic conditions and provide a holistic understanding of global-scale ENSO dynamics on wheat production. [ABSTRACT FROM AUTHOR]