Wheat Region Research Articles

Haplotype Analysis and Gene Pyramiding for Pre-Harvest Sprouting Resistance in White-Grain Wheat.

The Huanghuai winter wheat region, China's primary wheat-producing area, predominantly cultivates white-grained wheat. Pre-harvest sprouting (PHS) significantly impacts yield and quality, making the breeding of PHS-resistant varieties crucial for ensuring China's wheat production security. This study evaluated the PHS rate of 344 white-grained wheat varieties over two consecutive growing seasons (2022/2023 and 2023/2024). Furthermore, it analyzed the effects of allelic variations and their combinations in six genes (Tamyb10, TaDFR, TaMKK3-A, TaGASR34, Tasdr, and TaMFT) on PHS resistance. Results revealed average PHS rates of 66.1% and 64.4% for the two growing seasons, with coefficients of variation of 39.1% and 40.2%, respectively, and a narrow-sense heritability of 0.72. These findings indicate substantial genetic variation and relatively high genetic stability within the tested materials. Among the six molecular markers examined, the superior haplotype GS34-7Bb exhibited the lowest average PHS rate (41.9%) over two growing seasons, demonstrating the strongest PHS resistance. Analysis of different haplotype combinations identified two advantageous genotypes for PHS resistance in white-grained wheat: TaMKK3-Ab + GS34-7Bb + Tasdr-2Aa + TaMFT-A1b (average PHS rate: 20.8%) and TaMKK3-Ab + GS34-7Bb + Tasdr-2Ab + TaMFT-A1b (average PHS rate: 34.2%). Notably, the distribution frequency of superior haplotypes of PHS-related genes and these two advantageous haplotype combinations showed varying degrees of decline over time.

Fungal Pathogen Diversity and Fungicide Resistance Assessment in Fusarium Crown Rot of Wheat in the Huanghuai Region of China.

Fusarium crown rot (FCR) poses a major threat to wheat production in the Huanghuai wheat region of China. This study aims to enhance understanding of pathogen populations causing FCR, focusing on their pathogenicity, trichothecene genotypes, and fungicide resistance. During the 2022-2023 growing seasons, we collected 1820 fungal isolates from 233 locations in this region. Our results identified Fusarium pseudograminearum, Fusarium graminearum, and Fusarium asiaticum as the primary pathogens, with F. pseudograminearum exhibiting the highest virulence. Three trichothecene genotypes were identified, including nivalenol, 3-acetyldeoxynivalenol, and 15-acetyldeoxynivalenol. No correlation was observed between trichothecene genotype and virulence, except in F. asiaticum. Antifungal assays demonstrated that all six tested fungicides effectively inhibited F. pseudograminearum, with fludioxonil being particularly effective. Field surveys identified isolates resistant to difenoconazole and pyraclostrobin. Laboratory analysis also revealed strains with FpSdhC1A83V and FpSdhC1S80N mutations conferring resistance to cyclobutrifluram. These findings offer critical insights for developing effective control strategies to manage FCR.

Research progress on the impact of climate change on wheat production in China.

It is crucial to elucidate the impact of climate change on wheat production in China. This article provides a review of the current climate change scenario and its effects on wheat cultivation in China, along with an examination of potential future impacts and possible response strategies. Against the backdrop of climate change, several key trends emerge: increasing temperature during the wheat growing season, raising precipitation, elevated CO2 concentration, and diminished radiation. Agricultural disasters primarily stem from oscillations in temperature and precipitation, with the northern wheat region being mostly affected. The impact on wheat production is manifested in a reduction in the area under cultivation, with the most rapid reduction in spring wheat, and a shift in the center of cultivation to the west. Furthermore, climate change accelerates the nutritional stage and shortens phenology. Climate change has also led to an increase in yields in the Northeast spring wheat region, the Northern spring wheat region, the Northwest spring wheat region, and the North China winter wheat region, and a decrease in yields in the middle and lower reaches of the Yangtze River winter wheat region, the Southwest winter wheat region, and the South China winter wheat region. To cope with climate change, Chinese wheat can adopt adaptation strategies and measures such as breeding different wheat varieties for different wheat-growing regions, implementing differentiated farmland management measures, promoting regional ecological farmland construction, and establishing scientific monitoring and early warning systems. While future climate change may stimulate wheat yield potential, it could cause climate-induced issues such as weeds, diseases, and pests worsen, thereby posing challenges to the sustainability of farmland. Moreover, it is essential to conduct comprehensive research on pivotal areas such as the microscopic mechanism of climate change and wheat growth, the comprehensive influence of multiple climate factors, and the application of new monitoring and simulation technologies. This will facilitate the advancement of related research and provide invaluable insights.

Mild drought conditions at the tillering stage promote dry matter accumulation and increase grain weight in drip-irrigated spring wheat (Triticum aestivum L.).

In order to elucidate the physiological mechanism of post-flowering assimilate transport regulating the formation of yields in arid regions and to provide technological support for further water-saving and high yields in the wheat region in Xinjiang, we conducted a study on the effects of different fertility periods and different degrees of drought and re-watering on the post-flowering dry matter accumulation and transport of spring wheat and the characteristics of grain filling. In two spring wheat growing seasons in 2023 and 2024, a split-zone design was used, with the drought-sensitive variety Xinchun 22 (XC22) and drought-tolerant variety Xinchun 6 (XC6) as the main zones and a fully irrigated control during the reproductive period [CK, 75%~80% field capacity (FC)], with mild drought at the tillering stage (T1, 60%~65% FC), moderate drought at the tillering stage (T2, 45%~50% FC), mild drought at the jointing stage (J1, 60%~65% FC), and mild drought at the jointing stage (J2, 45%~50% FC) as the sub-zones. The dry matter accumulation of the aboveground parts of wheat (stem sheaths, leaves, and spikes), the transfer rate and contribution rate of nutrient organs, the maximum filling rate (Vmax), and the mean filling rate (Vmean) increased significantly after re-watering in the T1 treatment, and decreased with the deepening of the degree of water stress. The 13C isotope tracer results also showed that the T1 treatment increased the distribution rate of 13C assimilates in the grain at maturity. Correlation and principal component analyses showed that grain weight was highly significantly and positively correlated with stem sheath, leaf, and spike dry matter accumulation, amount of nutrient organ post-flowering transports, transport rate, contribution rate, the onset and the termination time of the rapid growth period, Vmax, and Vmean, and stem sheath and spike dry matter accumulation had a direct effect on grain weight. While the two varieties performed differently among the treatments, both exhibited optimal performance in the T1 treatment. In conclusion, mild drought at the tillering stage (60%-65% FC) was the best model for water conservation and high yield of wheat under the conditions of this trial.

Accumulation of beneficial haplotypes in Huang-Huai-Hai wheat region and its application in molecular breeding

The Huang-Huai-Hai wheat region (HHHR) is characterized by the largest cultivation area and yield among all the major wheat-producing regions in China. Over the past 70 years, significant advances in wheat breeding have been achieved in this region, resulting in high and stable yields as well as improved disease resistance. However, there is a notable deficiency in the systematic molecular-level analyses of wheat breeding advantages in HHHR. To bridge this gap, we used a Wheat 55K SNP array to evaluate 384 accessions from a core collection of wheat germplasms across China to systematically analyze the distribution patterns of beneficial haplotypes associated with traits related to yield and powdery mildew resistance specific to HHHR. Our findings indicate that varieties from HHHR demonstrate significantly superior performance in terms of yield-related traits and powdery mildew resistance compared to those from other wheat regions. Using genome-wide association studies (GWAS) analysis, we identified the QTNs associated with both grain yield and powdery mildew resistance. Importantly, beneficial haplotypes were found at significantly higher frequencies in the HHHR than in other wheat-growing regions. Based on these haplotypes, the MFP-a gene was identified as potentially regulating jasmonic acid synthesis while also playing a role in grain development and conferring powdery mildew resistance. Furthermore, identity by descent (IBD) analysis revealed specific conserved genomic segments that have become fixed through selective breeding practices in HHHR, which may serve as invaluable resources for the targeted enhancement of yield and disease resistance traits in other wheat-growing areas. Finally, using the Aimengniu breeding lineage as a case study, we elucidated the genetic basis underlying the key founder parental formations utilized in breeding programs. This study not only provides essential references and guidance for future molecular breeding initiatives in China but also has implications for enhancing wheat production worldwide.

Satellite Imagery, Big Data, IoT and Deep Learning Techniques for Wheat Yield Prediction in Morocco

In the domain of efficient management of resources and ensuring nutritional consistency, accuracy in predicting crop yields becomes crucial. The advancement of artificial intelligence techniques, synchronized with satellite imagery, has emerged as a potent approach for forecasting crop yields in modern times. We used two types of data: spatial data and temporal data. Spatial data are gathered from satellite imagery and processed using ArcGIS to extract data about crops based on several indices like NDVI and NWDI. Temporal data are gathered from agricultural sensors such as temperature sensors, rainfall sensor, precipitation sensor and soil moisture sensor. In our case we used Sentinel 2 satellite to extract vegetation indices. We have used IoT systems, especially Raspberry Pi B+ to collect and process data coming from sensors. All data collected are then stored into a NoSQL server to be analysed and processed. Several machine learning and deep learning algorithms have been used for the processing of crop recommendation system, such as logistic regression, KNN, decision tree, support vector machine, LSTM, and Bi-LSTM through the collected dataset. We used GRU deep learning model for the best performance, the RMSE and R2 for this model was 0,00036 and 0,99 respectively.The main contribution of our paper is the development of a new system that can predict several crop yields, such as wheat, maize, etc, using IoT, satellite imagery for spatial data and the use of sensors for temporal data. We are the first paper that has combined spatial data and temporal data to predict crop yield based on deep learning algorithms, unlike other works that uses only remote sensing data or temporal data. We created an E-monitoring crop yield prediction system that helps farmers track all information about crops and show the result of prediction in a mobile application. This system helps farmers with more efficient decision making to enhance crop production. The main production regions of wheat in Morocco are in the rainfed areas of the plains and plateaus of Chaouia, Abda, Haouz, Tadla, Gharb and Saïs. We studied three main regions well known for wheat production which are Rabat-Salé, Fez-Meknes, Casablanca-Settat.

Charting the Course of Invasion: Ensemble Species Distribution Models Predict the Range Expansion of a Newly Invasive Aphid Pest Metopolophium festucae cerealium in North America

Invasive species, including insect pests, pose significant threats globally. Ongoing global environmental changes may exacerbate the threats, by potentially favoring range expansion of invasive pests, altering native ecosystems, and damaging valuable crops. To reduce the spread and impact of invasive pests, monitoring and identification of their suitable habitats in the context of global environmental change (e.g., ongoing changes in land use and climate) is essential. This study examines the current and future potential habitat suitability of a newly invasive aphid, Metopolophium festucae cerealium, in North America, focusing on its potential expansion into wheat growing regions in North America. Using occurrence data collected during a decade of surveying from ∼450 sites in the Pacific Northwest (PNW) where the aphid has invaded, with the help of an ensemble modeling framework, we predicted the habitat suitability for M. f. cerealium for North America under various climate scenarios and land use conditions. The results indicate a high likelihood of further eastward and southward expansion from the PNW, particularly in wheat and cereal crop-producing regions, posing a threat to crop production. The key environmental drivers include cropland percentage, temperature, and precipitation, suggesting potential impacts of future environmental change. The study underscores the importance of considering not only climatic factors but also host plant presence and agricultural practices in pest management strategies.

Semantic Segmentation Model-Based Boundary Line Recognition Method for Wheat Harvesting

The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0~1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester.

Unraveling the complex interactions between ozone pollution and agricultural productivity in China's main winter wheat region using an interpretable machine learning framework

Surface ozone has become a significant atmospheric pollutant in China, exerting a profound impact on crop production and posing a serious threat to food security. Previous studies have extensively explored the physiological mechanisms of ozone damage to plants. However, the effects of ozone interactions with other environmental factors, such as climate change, on agricultural productivity at the regional scale, particularly under natural conditions, remain insufficiently understood. In this study, we employed an interpretable machine learning framework, specifically the eXtreme Gradient Boosting (XGBoost) algorithm enhanced by SHapley Additive exPlanations (SHAP), to investigate the influence of ozone and its interactions with environmental factors on crop production in China's primary winter wheat region. Additionally, a structural equation model was developed to elucidate the mechanisms driving these interactions. Our findings demonstrate that ozone pollution exerts a significant negative effect on winter wheat productivity (r = −0.47, P < 0.001), with productivity losses escalating from −12.28 % to −22.09 % as ozone levels increase. Notably, the impact of ozone is spatially heterogeneous, with western Shandong province identified as a hotspot for ozone-induced damage. Furthermore, our results confirm the complexity of the relationship between ozone pollution and agricultural productivity, which is influenced by multiple interacting environmental factors. Specifically, we found that severe ozone pollution, when combined with high aerosol concentrations or elevated temperatures, significantly exacerbates crop productivity losses, although drought conditions can partially mitigate these adverse effects. Our study highlights the importance of incorporating the interactive effects of air pollution and climate change into future crop models. The comprehensive framework developed in this study, which integrates statistical modeling with explainable machine learning, provides a valuable methodological reference for quantitatively assessing the impact of air pollution on crop productivity at a regional scale.

Updating soil organic carbon for wheat production with high yield and grain protein

Soil organic carbon (SOC) is crucial for mitigating global warming and significantly impacts crop production. While the relationship between SOC and wheat yield is well-documented, its effect on wheat grain protein content, which is essential for food security and human health, remains unclear. This study gathered management data from wheat farmers and collected plant and soil samples in the Huang-Huai winter wheat region, China’s primary wheat-growing area, from 2015 to 2022. Boundary line analysis was used to quantify the responses of wheat yield and protein content to variations in SOC. Our findings reveal that increases in SOC significantly enhance wheat yield and protein content. The highest yields, reaching up to 10,848 kg ha–1, and a maximum protein content of 17.3 % were observed in soils with SOC ranging from 7.8–18.1 g kg–1, and high-yielding, high-protein wheat exhibited higher spike numbers and grain weights and more efficient nutrient accumulation from soil or fertilizer to shoots. Optimizing SOC levels to produce high-yielding, high-protein wheat could substantially reduce nitrogen (N), phosphorus (P), and potassium (K) fertilizer use by 9.42×104, 0.70×104, and 3.66×104 Mg per year, decrease greenhouse gas emissions by 3.36 Mt CO2 eq and generate an economic benefit of 2.77 billion USD. In conclusion, our study expands the understanding of SOC’s role in crop production beyond crop yield, providing valuable insights for producing high-yielding, high-protein wheat.

Characterization of flag leaf morphology identifies a major genomic region controlling flag leaf angle in the US winter wheat (Triticum aestivum L.)

Key messageMulti-environmental characterization of flag leaf morphology traits in the US winter wheat revealed nine stable genomic regions for different flag leaf-related traits including a major region governing flag leaf angle.Flag leaf in wheat is the primary contributor to accumulating photosynthetic assimilates. Flag leaf morphology (FLM) traits determine the overall canopy structure and capacity to intercept the light, thus influencing photosynthetic efficiency. Hence, understanding the genetic control of these traits could be useful for breeding desirable ideotypes in wheat. We used a panel of 272 accessions from the hard winter wheat (HWW) region of the USA to investigate the genetic architecture of five FLM traits including flag leaf length (FLL), width (FLW), angle (FLANG), length–width ratio, and area using multilocation field experiments. Multi-environment GWAS using 14,537 single-nucleotide polymorphisms identified 36 marker-trait associations for different traits, with nine being stable across environments. A novel and major stable region for FLANG (qFLANG.1A) was identified on chromosome 1A accounting for 9–13% variation. Analysis of spatial distribution for qFLANG.1A in a set of 2354 breeding lines from the HWW region showed a higher frequency of allele associated with narrow leaf angle. A KASP assay was developed for allelic discrimination of qFLANG.1A and was used for its independent validation in a diverse set of spring wheat accessions. Furthermore, candidate gene analysis for two regions associated with FLANG identified seven putative genes of interest for each of the two regions. The present study enhances our understanding of the genetic control of FLM in wheat, particularly FLANG, and these results will be useful for dissecting the genes underlying canopy architecture in wheat facilitating the development of climate-resilient wheat varieties.

Temporal and spatial patterns of extreme heat on wheat in China under climate change scenarios

Revealing the spatial-temporal pattern of extreme heat on staple crops is crucial for proposing adaptation strategies to mitigate climate change-related agricultural risks. Studies in this field generally focus on the reproductive stage and rely on a single-staged threshold temperature to construct extreme heat indicators, which particularly neglect the vegetative stage of wheat. Therefore, to measure the extreme heat risks more scientifically across the entire life cycle of wheat, our study defines a new comprehensive extreme heat index (CEHI) that considers specific thresholds in both the reproductive and vegetative stages. In general, under three climate scenarios (RCP2.6, RCP4.5, and RCP8.5), approximately 20 % of the wheat-planting regions in China, especially in winter wheat regions such as the North China Plain, the Sichuan Basin, and the Xinjiang Tarim Basin, are projected to face high levels of extreme heat. Meanwhile, from 2010 to 2099, the average growth rates of extreme heat in China under RCP2.6, RCP4.5, and RCP8.5 scenarios are approximately 0.08, 0.06, and 0.1, respectively. By the century's end, the proportion of wheat-planting regions experiencing high and very high levels (CEHI≥0.4) of extreme heat is projected to increase from 18.0 %, 17.9 %, and 18.4 % to 21.4 %, 25.1 %, and 28.9 % under RCP2.6, RCP4.5, and RCP8.5 scenarios. Among them, RCP8.5 has the highest extreme heat severity on wheat in China, followed by RCP4.5, while RCP2.6 has minimal severity. Under the RCP8.5 scenario, the proportions of very high, high, moderate, low, and very low levels of extreme heat are 3.4 %, 18.5 %, 16.7 %, 14.9 %, and 46.5 %, respectively. Meanwhile, our study also emphasizes that although higher-latitude spring wheat regions will experience a significantly increasing trend in extreme heat, this may not spell long-term damage to wheat. Therefore, with consideration of varied temperature sensitivities across wheat growth stages, our study indicates that CEHI serves as an effective method to comprehensively and scientifically assess extreme heat on wheat. Furthermore, based on the regional and varietal differences in extreme heat under climate change, our study highlights the importance of developing region- and variety-specific policies to ensure the sustainability of wheat.

Genetic diversity and population structure of wheat landraces in Southern Winter Wheat Region of China

BackgroundWheat landraces are considered a valuable source of genetic diversity for breeding programs. It is useful to evaluate the genetic diversity in breeding studies such as marker-assisted selection (MAS), genome-wide association studies (GWAS), and genomic selection. In addition, constructing a core germplasm set that represents the genetic diversity of the entire variety set is of great significance for the efficient conservation and utilization of wheat landrace germplasms.ResultsTo understand the genetic diversity in wheat landrace, 2,023 accessions in the Jiangsu Provincial Crop Germplasm Resource Bank were used to explore the molecular diversity and population structure using the Illumina 15 K single nucleotide polymorphism (SNP) chip. These accessions were divided into five subpopulations based on population structure, principal coordinate and kinship analysis. A significant variation was found within and among the subpopulations based on the molecular variance analysis (AMOVA). Subpopulation 3 showed more genetic variability based on the different allelic patterns (Na, Ne and I). The M strategy as implemented in MStratv 4.1 software was used to construct the representative core collection. A core collection with a total of 311 accessions (15.37%) was selected from the entire landrace germplasm based on genotype and 12 different phenotypic traits. Compared to the initial landrace collections, the core collection displayed higher gene diversity (0.31) and polymorphism information content (PIC) (0.25), and represented almost all phenotypic variation.ConclusionsA core collection comprising 311 accessions containing 100% of the genetic variation in the initial population was developed. This collection provides a germplasm base for effective management, conservation, and utilization of the variation in the original set.

Population genetic analyses highlight an eastward dispersal of Puccinia striiformis f. sp. tritici from the Shaanxi province to the North China Plain

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a common airborne wheat disease. The frequent occurrence at a large scale in China has caused significant yield losses and poses a considerable threat to food security. To effectively manage and forecast the disease, a comprehensive understanding of the long-distance migration patterns of Pst is essential. Shaanxi province, situated in close proximity to the northwestern epidemic areas in China, plays a crucial role as a key overwintering region for Pst. However, it remains uncertain whether Pst, after winter reproduction in Shaanxi province, can extend its spread to the primary wheat regions in the North China Plain. In this study, during February and June 2022, a total of 302 Pst samples were collected from Shaanxi province and the North China Plain. Thirteen pairs of simple sequence repeat (SSR) markers were adopted to analyze the population genetic structure. It was observed that both genetic and genotypic diversities exhibited a discernible decline from the Shaanxi to the North China Plain. Moreover, Shaanxi displayed a close genetic relationship with Henan and Shandong, whereas Henan exhibited the most substantial population exchange with Shaanxi. Further analysis revealed that Shaanxi served as the primary inoculum of Pst in the investigated region, and the spread of Pst to Henan and Shandong originated from Shaanxi. As a result, the epidemics in Shandong further led to the prevailing of the disease in Hebei. Our study enhances the understanding of the epidemiological patterns of wheat stripe rust in the springtime prevalent regions of China, and it provides insights for future disease management.

Comparative analysis of wheat and barley yield performance across temperate environments

ContextA comparative analysis of grain yield performance of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) under different environments constitutes a way to identify restrictions in yield formation between both crop species. However, most of the previous studies on these field crops have been carried out in Mediterranean environments. ObjectiveThis study aims to compare the grain yield performance of wheat and barley across a wide range of temperate environmental conditions. MethodsWheat and barley yield databases from the national network of cultivar evaluation of Argentina were assembled. The database included 191 wheat cultivars (with early-, mid- and late-flowering length cycle), 140 barley cultivars, and 44 combinations of sites and years of the Argentine wheat and barley belt region. Average heading date variation among environments and crops was no more than 10 days. Crop grain yield comparisons were carried out using a regression analysis, benchmarking within each site-year the grain yield of each cultivar against the average grain yield of all crop cultivars (i.e., environmental index). ResultsThere were no differences in grain yield between wheat and barley for all data, but the behavior between crop species changed with the length of the wheat cycle (i.e., early-, mid-, or late-flowering cultivars). Barley portrayed a greater grain yield than early-flowering wheat under low-yielding environments (< 6060 kg ha−1), but this advantage vanished as the yield environment improved. Mid-flowering wheat showed a similar grain yield to barley across temperate environments. Lastly, late-flowering wheat outperformed barley mainly in high-yielding environments (> 6138 kg ha−1). Grain number m−2 was the main numerical component that explained variability in grain yield. The relative contribution of the yield numerical components differed between species, having barley lower grain number m−2 but a greater grain weight than wheat. ConclusionsThe comparison of the grain yield performance between species represents a strategy to adjust the rotation system, being a critical factor in considering the variability in the crop growth cycle.

Fhb9, a major QTL for Fusarium head blight resistance improvement in wheat

Fusarium head blight (FHB), mainly caused by Fusarium graminearum, is one of the most devastating diseases of wheat worldwide. Identification and validation of major quantitative trait loci (QTLs) for FHB resistance without negative effects on agronomic traits is critical to success in breeding FHB-resistant cultivars. In this study, a stable major QTL on chromosome arm 2DL was identified by evaluating a recombinant inbred line (RIL) population derived from Shi4185×Shijiazhuang 8 in both field and greenhouse experiments. QTL mapping and pedigree analyses indicated that the 2DL QTL is the same QTL as QFhb-2DL previously identified in Ji5265, therefore, designated Fhb9. Four kompetitive amplicon sequence polymorphism (KASP) markers were developed based on exome capture sequencing data to enhance marker density in the Fhb9 region, and it was delimited to an interval between single nucleotide polymorphism (SNP) markers KASP-12056 (533.8) and KASP-525 (525.9 Mb) explained 26.0-30.1% of the phenotypic variation. Analysis of the geographic distribution of the Fhb9 resistance allele suggested that it originated from Huang-Huai winter wheat region in China, and very low frequency of Fhb9 in modern Chinese cultivars reveals that it has not been widely deployed in breeding programs. Field and greenhouse evaluation of yield-related traits of near-isogenic lines (NILs) contrasting in Fhb9 alleles indicated that Fhb9 resistance allele did not show any adverse effects on those traits. Fhb9 showed an additive effect on enhancing FHB resistance with Fhb1. Therefore, Fhb9 is a valuable major QTL for improving FHB resistance in wheat and the near-diagnostic markers developed in this study will facilitate its deployment in wheat breeding programs.

Upregulation of TaHSP90A transcripts enhances heat tolerance and increases grain yield in wheat under changing climate conditions.

Plants have certain adaptation mechanisms to combat temperature extremes and fluctuations. The heat shock protein (HSP90A) plays a crucial role in plant defence mechanisms under heat stress. In silico analysis of the eight TaHSP90A transcripts showed diverse structural patterns in terms of intron/exons, domains, motifs and cis elements in the promoter region in wheat. These regions contained cis elements related to hormones, biotic and abiotic stress and development. To validate these findings, two contrasting wheat genotypes E-01 (thermo-tolerant) and SHP-52 (thermo-sensitive) were used to evaluate the expression pattern of three transcripts TraesCS2A02G033700.1, TraesCS5B02G258900.3 and TraesCS5D02G268000.2 in five different tissues at five different temperature regimes. Expression of TraesCS2A02G033700.1 was upregulated (2-fold) in flag leaf tissue after 1 and 4h of heat treatment in E-01. In contrast, SHP-52 showed downregulated expression after 1h of heat treatment. Additionally, it was shown that under heat stress, the increased expression of TaHSP90A led to an increase in grain production. As the molecular mechanism of genes involved in heat tolerance at the reproductive stage is mostly unknown, these results provide new insights into the role of TaHSP90A transcripts in developing phenotypic plasticity in wheat to develop heat-tolerant cultivars under the current changing climate scenario.

Insight into the herbicide resistance patterns in Lolium rigidum populations in Tunisian and Moroccan wheat regions.

Rigid ryegrass (Lolium rigidum Gaud.) is one of the most troublesome weeds in Moroccan and Tunisian cereal crop fields. In total, 19 rigid ryegrass field populations were randomly selected in northern wheat crop areas of Morocco and Tunisia to examine the patterns of herbicide resistance to acetolactate synthase (ALS)- and acetyl-CoA carboxylase (ACCase)-inhibiting herbicides. Greenhouse experiments confirmed reduced sensitivity to ALS- and/or ACCase-inhibiting herbicides in all L. rigidum populations. The occurrence of target-site resistance (TSR) was tested using high-throughput genotyping. The advent of next-generation sequencing (NGS) has enabled easy identification of causal mutations and confirmed the presence of ALS and ACCase mutations at specific codons conferring TSR. Thirteen populations showed resistance to ALS-inhibiting herbicides associated with point mutations in positions Pro-197-Thr, Pro-197-Ser, Pro-197-Leu, Pro-197-Gln and Trp-574-Leu, while resistance to ACCase-inhibiting herbicides was detected in 18 populations in positions Asp-2078-Val, Trp-2027-Cys, Ile-1781-Leu, Gly-2096-Ala, and Ile-2041-Asn of the enzymes conferring TSR. Additionally, dose-response experiments with pyroxsulam applied after the inhibition of cytochrome P450 monooxygenase by malathion showed an increase in sensitivity in two out of seven highly resistant (HR) rigid ryegrass populations. This demonstrates the presence of non-target-site resistance (NTSR) in some ryegrass populations. Further evidence of NTSR was investigated in dose-response experiments with pyroxsulam, following pretreatment with the glutathione S-transferase (GST) inhibitor 4-chloro-7-nitrobenzoxadiazole (NBD-Cl), which partially reversed resistance in only a few individuals of two L. rigidum populations. Hence, our study confirms the existence of multiple and cross-resistance to ALS- and ACCase-inhibiting herbicides in L. rigidum from Morocco and Tunisia with both TSR and NTSR mechanisms. These results emphasize local resistance management as an important tool to detect and mitigate gene flow from rigid ryegrass populations where resistance has evolved.

Climate change enhances stability of wheat-flowering-date

The stability of winter wheat-flowering-date is crucial for ensuring consistent and robust crop performance across diverse climatic conditions. However, the impact of climate change on wheat-flowering-dates remains uncertain. This study aims to elucidate the influence of climate change on wheat-flowering-dates, predict how projected future climate conditions will affect flowering date stability, and identify the most stable wheat genotypes in the study region. We applied a multi-locus genotype-based (MLG-based) model for simulating wheat-flowering-dates, which we calibrated and evaluated using observed data from the Northern China winter wheat region (NCWWR). This MLG-based model was employed to project flowering dates under different climate scenarios. The simulated flowering dates were then used to assess the stability of flowering dates under varying allelic combinations in projected climatic conditions. Our MLG-based model effectively simulated flowering dates, with a root mean square error (RMSE) of 2.3 days, explaining approximately 88.5 % of the genotypic variation in flowering dates among 100 wheat genotypes. We found that, in comparison to the baseline climate, wheat-flowering-dates are expected to shift earlier within the target sowing window by approximately 11 and 14 days by 2050 under the Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 climate scenarios, respectively. Furthermore, our analysis revealed that wheat-flowering-date stability is likely to be further strengthened under projected climate scenarios due to early flowering trends. Ultimately, we demonstrate that the combination of Vrn and Ppd genes, rather than individual Vrn or Ppd genes, plays a critical role in wheat-flowering-date stability. Our results suggest that the combination of Ppd-D1a with winter genotypes carrying the vrn-D1 allele significantly contributes to flowering date stability under current and projected climate scenarios. These findings provide valuable insights for wheat breeders and producers under future climatic conditions.

Precipitation and Temperature Maxima: A Study across the Southern Great Plains Winter Wheat Region

Abstract With the importance of agriculture to the southern Great Plains (SGPs), accurate knowledge of growing season (GS) temperatures and precipitation is critical. Previous research into GS precipitation and temperature maxima leads to the development of the asynchronous difference index (ADI) which identified positive and negative ADI GSs (March–September). The goal of this research is to further investigate the ADI within a specific agricultural region of the SGP, the winter wheat region, and to quantify the temporal evolution of temperature and precipitation during positive and negative ADI GSs. For this, Global Historical Climatology Network (GHCN) daily station data were analyzed across the GS (March–September) from 1900 to 2020. Results show that differences appear in the temperature and precipitation fields when comparing positive and negative GSs. Namely, positive (negative) ADI GSs show positive (negative) precipitation and negative (positive) temperature anomalies early in the GS, with these anomalies flipping in the later portion of the GS. Further, the results of this work show that the depicted changes in temperature and precipitation during positive and negative ADI GSs impact winter wheat yields. Overall, these results analyze the implications of positive and negative ADI GSs on the SGP climate, namely, the impact of the seasonal variability of daily maximum temperature and precipitation on agriculture.