Growth Stages Of Wheat Research Articles

Nitrogen Fate of Chemical Fertilizer and Green Manure-Wheat Rotation Using 15N Tracer Technique

To investigate the accumulation, distribution, and translocation of nitrogen (N) from chemical fertilizers and green manure in wheat plants, we conducted an experiment using leguminous green manure crop hairy vetch and wheat as test materials. During the anthesis and maturity stages of wheat, the 15N enrichment and Nitrogen content of wheat plant samples were determined in different organs, and 15N enrichment and nitrogen content of soil samples were measured at maturity. The accumulation and distribution characteristics of nitrogen from different sources during the wheat growth stages were investigated to explore the impacts of green manure and chemical fertilizers on wheat growth and nitrogen uptake. The results showed that nitrogen from chemical fertilizers in the soil-plant system exhibited the following pattern: plant absorption > soil residue > loss, while nitrogen from green manure showed: soil residue > loss > plant absorption. Wheat had higher absorption of nitrogen from chemical fertilizers compared to green manure. The absorption of nitrogen from chemical fertilizers by wheat ranged from 46.00% to 54.08%, while the absorption of nitrogen from green manure ranged from 20.71% to 28.21%. The residual nitrogen from green manure in the soil (38.47% to 58.22%) was higher than that from chemical fertilizers (31.44% to 40.69%), and the nitrogen loss from green manure (21.07% to 33.31%) was higher than that from chemical fertilizers (5.22% to 22.56%). Supply of nitrogen from the two sources could coordinate the distribution of nitrogen in various organs and increase the proportion of nitrogen allocation in grains. This was beneficial for facilitating nutrient transfer to reproductive organs.

Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.

Optimizing wheat prosperity: innovative drip irrigation and nitrogen management strategies for enhanced yield and quality of winter wheat in the Huang-Huai-Hai region.

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. 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 indicated that compared to CK, 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 irrigations was 1.6% higher than CK, and other metrics 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). 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.

Chlorophyll content and yield responses for withholding irrigation and Si foliar spray at normal and Nano source under different critical growth stages of wheat

Chlorophyll content and yield responses for withholding irrigation and Si foliar spray at normal and Nano source under different critical growth stages of wheat

GSP-AI: An AI-Powered Platform for Identifying Key Growth Stages and the Vegetative-to-Reproductive Transition in Wheat Using Trilateral Drone Imagery and Meteorological Data.

Wheat (Triticum aestivum) is one of the most important staple crops worldwide. To ensure its global supply, the timing and duration of its growth cycle needs to be closely monitored in the field so that necessary crop management activities can be arranged in a timely manner. Also, breeders and plant researchers need to evaluate growth stages (GSs) for tens of thousands of genotypes at the plot level, at different sites and across multiple seasons. These indicate the importance of providing a reliable and scalable toolkit to address the challenge so that the plot-level assessment of GS can be successfully conducted for different objectives in plant research. Here, we present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets. In the study, we first established an open Wheat Growth Stage Prediction (WGSP) dataset, consisting of 70,410 annotated images collected from 54 varieties cultivated in China, 109 in the United Kingdom, and 100 in the United States together with key climatic factors. Then, we built an effective learning architecture based on Res2Net and long short-term memory (LSTM) to learn canopy-level vision features and patterns of climatic changes between 2018 and 2021 growing seasons. Utilizing the model, we achieved an overall accuracy of 91.2% in identifying key GS and an average root mean square error (RMSE) of 5.6 d for forecasting the flowering days compared with manual scoring. We further tested and improved the GSP-AI model with high-resolution smartphone images collected in the 2021/2022 season in China, through which the accuracy of the model was enhanced to 93.4% for GS and RMSE reduced to 4.7 d for the flowering prediction. As a result, we believe that our work demonstrates a valuable advance to inform breeders and growers regarding the timing and duration of key plant growth and development phases at the plot level, facilitating them to conduct more effective crop selection and make agronomic decisions under complicated field conditions for wheat improvement.

Effect of Foliar Fertilization on the Physiological Parameters, Yield and Quality Indices of the Winter Wheat

The main purpose of the paper is to highlight the impact of foliar fertilization during the various growth stages of winter wheat and its role in achieving high-quality and superior production. Foliar fertilizers play a crucial role in calibrating and forming active growth intervals correlated with productivity factors. The research was conducted over a two-year period using the Andrada winter wheat variety. It involved the application of four different foliar fertilizers: basic mineral fertilization, FoliMAX Orange (two treatments and three treatments), FoliMAX CerealsMIX (two treatments and three treatments), and Microfert U (three treatments). Depending on the treatment, two or three applications of foliar with foliar fertilizers were carried out at various stages of winter wheat development, including pre-flowering and grain formation. The research method used to record physiological parameters was non-destructive (the leaves were not detached from the plant) and was based on the use of the CIRAS-3 foliar gas analyzer, which simultaneously determines several physiological and environmental indicators. As an indicator read by the device, photosynthesis was chosen for leaf temperature and leaf transpiration, correlated with production and quality indices. By applying the foliar fertilizer treatments to the wheat culture, we managed to increase production and improve the quality. After using different foliar fertilizers, the assimilation and physiological parameters had higher values of over 30 μmolm−2·s−1. Foliar fertilization applied to the winter wheat variety resulted in an average production increase between 450 and 765 kg·ha−1 and quality indices with a protein percentage between 11.5 and 12.6%, gluten content between 21.5 to 24.0% and the Zeleny index between 29.0 and 39.0%, and the mass of one thousand grains was between 48.0 and 50.5 g.

Impacts of Long-term Application of Fertilizer and Manures on Physico-chemical Properties, Phosphorus Uptake and Crop Yield at Different Growth Stages of Wheat

A field experiment was conducted on wheat in a sandy loam soil (Typic Haplustept) at IARI farm, New Delhi during 2018-19 in a long-term fertilizer experiment to study the physico-chemical properties of soil, crop yield, phosphorus uptake and phosphorus availability. The experiment was designed with seven different treatments, replicated thrice in a randomized complete block design. The treatments details were T1: Control, T2: 100%N, T3: 100%NP, T4: 100%NPK, T5: 150% NPK, T6: 100%NPK+5t FYM and T7: 100% NPK+ Zn-5 kgha-1. The results revealed that pH and electrical conductivity were highest under control and lowest recorded under 100% NPK+FYM. Available phosphorus content at all four stages was wheat significantly greater under NPK+FYM followed by 150% NPK treatment and lowest under nitrogen alone (100% N) and unfertilized treatment (control). Initially available phosphorus in soil ranged from 13.5 to 39.8 and 15.4 to 37.9 kg ha-1 at 0-15 cm and 15-30 cm depth, respectively. At the tillering stage, available P was 20.7 to 43.7 and 16.5 to 38.1 kg ha-1 at 0-15 cm and 15-30 cm depth, respectively. At panicle emergence, it was 24.1 to 48.4 kg ha-1 and 18.6 to 42.3 at 0-15 cm and 15-30 cm depth. At harvest of wheat crop, available P ranged from 20.5 to 41.8 and 16.5 to 39.1 kg ha-1 at 0-15 cm and 15-30 cm depth, respectively. Total phosphorus uptake and crop yield significantly increased under 150% NPK treatment followed by 100% NPK+FYM.

Model for Detecting Boom Height Based on an Ultrasonic Sensor for the Whole Growth Cycle of Wheat

Ultrasonic feedback energy is affected by the variety, planting, and growth state of crops; therefore, it is difficult to find applications for this energy in precision agriculture systems. To this end, an ultrasonic sensor was mounted in a spray boom height detection system. Winter wheat was used as the test object to obtain feedback energy values for the spray boom height from the top of the wheat in the field during six critical growth stages: the standing stage, the jointing stage, the booting stage, the heading stage, the filling stage, and the maturity stage. The relationship between the actual value of the height from the spray boom at the top of the wheat (Habw) and the detected value of the height from the spray boom at the top of the wheat (Hdbw) was analyzed. A spray boom height detection model based on the ultrasonic sensor during the full growth cycle of wheat was determined. Field validation tests showed that the applicability of the spray boom height detection distance (Dd) of the spray boom height detection model proposed in the present study was 450~950 mm. Within the applicable Dd range, the detection error of the detection model was ≤50 mm during the full growth cycle. This study provides a method for constructing a boom height detection model based on the whole growth cycle of wheat, which improves the reliability and accuracy of ultrasonic boom height detection for different wheat growth stages. The proposed method solves the problem of low accuracy of repeated detection of low-cost ultrasonic sensors in different environments and can provide technical support for improving field applications of the boom height control system based on ultrasonic sensors.

Combining Texture, Color, and Vegetation Index from Unmanned Aerial Vehicle Multispectral Images to Estimate Winter Wheat Leaf Area Index during the Vegetative Growth Stage

Leaf Area Index (LAI) is a fundamental indicator of plant growth status in agronomy and environmental research. With the rapid development of drone technology, the estimation of crop LAI based on drone imagery and vegetation indices is becoming increasingly popular. However, there is still a lack of detailed research on the feasibility of using image texture to estimate LAI and the impact of combining texture indices with vegetation indices on LAI estimation accuracy. In this study, two key growth stages of winter wheat (i.e., the stages of green-up and jointing) were selected, and LAI was calculated using digital hemispherical photography. The feasibility of predicting winter wheat LAI was explored under three conditions: vegetation index, texture index, and a combination of vegetation index and texture index, at flight heights of 20 m and 40 m. Two feature selection methods (Lasso and recursive feature elimination) were combined with four machine learning regression models (multiple linear regression, random forest, support vector machine, and backpropagation neural network). The results showed that during the vegetative growth stage of winter wheat, the model combining texture information with vegetation indices performed better than the models using vegetation indices alone or texture information alone. Among them, the best prediction result based on vegetation index was RFECV-MLR at a flight height of 40 m (R2 = 0.8943, RMSE = 0.4139, RRMSE = 0.1304, RPD = 3.0763); the best prediction result based on texture index was RFECV-RF at a flight height of 40 m (R2 = 0.8894, RMSE = 0.4236, RRMSE = 0.1335, RPD = 3.0063); and the best prediction result combining texture and index was RFECV-RF at a flight height of 40 m (R2 = 0.9210, RMSE = 0.3579, RRMSE = 0.1128, RPD = 3.5575). The results of this study demonstrate that combining vegetation indices and texture from multispectral drone imagery can improve the accuracy of LAI estimation during the vegetative growth stage of winter wheat. In addition, selecting a flight height of 40 m can improve efficiency in large-scale agricultural field monitoring, as this study showed that drone data at flight heights of 20 m and 40 m did not significantly affect model accuracy.

Identifying Crop Growth Stages from Solar-Induced Chlorophyll Fluorescence Data in Maize and Winter Wheat from Ground and Satellite Measurements

Crop growth stages are integral components of plant phenology and are of significant ecological and agricultural importance. While the use of remote sensing methods for phenology identification in cropland ecosystems has been extensively explored in previous studies, the focus has often been on land surface phenology, primarily related to the start and end of the growing season. In contrast, the monitoring of crop growth within an agronomic framework has been limited, particularly in the context of recently developed solar-induced chlorophyll fluorescence (SIF) data. Additionally, some critical growth stages have not received adequate attention or evaluation. This study aims to assess the utility of SIF data, collected from both ground and satellite measurements, for identifying critical crop growth stages within the realm of remote sensing phenological estimation. A comparative analysis was conducted using enhanced vegetation index (EVI) data at the Shangqiu site in the North China Plain from 2018 to 2022. Both SIF and EVI time-series data, obtained from ground and satellite sources, undergo a comprehensive phenological estimation framework encompassing pre-processing, modeling, and transition characterization. This approach involves reconciling time-series phenological patterns with crop growth stages, revealing the necessity of redefining the mapping relationship between these two fundamental concepts. After preprocessing the time-series data, the framework incorporates the phenological modeling process employing two double logistic models and a spline model for comparison. Additionally, it includes phenological transition characterization using four different methods. Consequently, each input dataset undergoes an assessment, resulting in 12 sets of estimations, which are compared to select the ideal estimation portfolio for identifying the growth stages of maize and winter wheat. Our findings highlight the efficacy of SIF data in accurately identifying the growth stages of maize and winter wheat, achieving remarkable results with an R-square exceeding 0.9 and an RMSE of less than 1 week for key growth stages (KGSs). Notably, SIF data demonstrate superior accuracy, robustness, and sensitivity to phenological events when compared to EVI data. This study establishes an estimation portfolio utilizing SIF data, involving the Gu model, a double logistic model, as the preferred phenological modelling method together with various compositing methods and transition characterization methods, suitable for most KGSs. These findings create opportunities for future research aimed at enhancing and standardizing crop growth stage identification using remote sensing data for a wide range of KGSs.

Dual sampling linear regression ensemble to predict wheat yield across growing seasons with hyperspectral sensing

In the domain of crop yield assessment, the combination of remote sensing and advanced regression techniques has gained significant attention. This study focused on predicting wheat yield across growing seasons using hyperspectral reflectance data. The performance of two decision tree-based algorithms, deep forest (DF) and random forest (RF), as well as a novel ensemble method called dual sampling linear regression ensemble (DSLRE), was evaluated. Canopy hyperspectral reflectance data were collected at two stages of wheat growth: early grain filling (EGF) and mid-grain filling (MGF), in three distinct growing environments. Prediction accuracy was assessed using the Pearson correlation coefficient (PCC) and mean absolute error (MAE). The results showed that the DF algorithm outperformed the RF algorithm in most scenarios. In modeling framework A, DF achieved the highest accuracy at the MGF stage using truncated reflectance (PCC = 0.60, MAE = 0.82 t/ha). In modeling framework B, DF performed better than RF in three out of four conditions, while RF exhibited higher prediction accuracy during the EGF stage using raw reflectance (PCC = 0.59, MAE = 1.48 t/ha). Moreover, the DSLRE algorithm outperformed DF and RF in six of the eight modeling scenarios and demonstrated robustness in coping with noisy features. The heritability analysis indicated that DSLRE predictions effectively captured the effects of genetic factors on yield variation. These findings illustrate the significance of the proposed DSLRE algorithm in improving yield prediction, providing valuable guidance for effective crop yield management.

A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing

Diagnosing water conditions timely and accurately is crucial for seasonal irrigation scheduling in crop production. The purpose of this study was to establish robust water deficit models of winter wheat in different growing seasons by combining unmanned aerial vehicle (UAV) multispectral and thermal images. In 2021 and 2022, a water deficit field experiment on winter wheat was carried out in Hebei Province, China. Five-band multispectral and thermal images were obtained with a UAV at six key growth stages of winter wheat. Fourteen vegetation indices (VIs) and two thermal indices (TIs) were calculated. Simultaneously, wheat stomatal conductance and soil water content were measured. On this basis, normalized stomatal conductance (NGS) and effective water content (EWC) were calculated. TIs had the highest correlation with NGS and EWC at early growth stages, whereas ratio vegetation index (RVI), modified simple ratio index (MSR) and normalized difference vegetation index (NDVI) were highly correlated at later stages. Partial least squares (PLS), support vector machine (SVM) and gradient boosting decision tree (GBDT) were used to predict NGS and EWC for each growth stage, with the data of 2021 as the training set and the data of 2022 as independent test set. In general, GBDT outperformed PLS and SVM, and NGS was better predicted than EWC. Including VIs and TIs effectively improved the estimation accuracy of the predictive models. The test set results of the NGS and EWC models built by GBDT achieved the best performance in flowering stage (coefficient of determination (R2) = 0.88, root mean square error (RMSE) = 0.08, normalized root mean square error (NRMSE) = 14.7%) and filling stage (R2 = 0.90, RMSE = 0.05, NRMSE = 15.9%), respectively. The models of the post-heading stage were better than those of the pre-heading stage for both NGS and EWC. This study provides a robust method for diagnosing water stress only using UAV remote sensing data.

Evapotranspiration Partitioning and Estimation Based on Crop Coefficients of Winter Wheat Cropland in the Guanzhong Plain, China

Accurate estimation and effective portioning of actual evapotranspiration (ETa) into soil evaporation (E) and plant transpiration (T) are important for increasing water use efficiency (WUE) and optimizing irrigation schedules in croplands. In this study, E/T partitioning was performed on ETa rates measured using the eddy covariance (EC) technique in three winter wheat growing seasons from October 2020 to June 2023. The variation in the crop coefficients (Kc, α, and KHc) were quantified by combining the ETa and reference evapotranspiration rates using the Penman–Monteith, Priestley–Taylor, and Hargreaves equations. In addition, the application of models based on the modified crop coefficient (Kc, α, and KHc) was proposed to estimate the ETa rates. According to the obtained results, the average cumulative ETa, T, and E rates in the three winter wheat growth seasons were 471.4, 265.2, and 206.3 mm, respectively. The average T/ETa ratio ranged from 0.16 to 0.72 at the different winter wheat growth stages. Vapor pressure deficit (VPD) affected the ETa rates at a threshold of 1.27 KPa. The average Kc, α, and KHc values in the middle stage were 1.34, 1.54, and 1.21, respectively. The measured ETa rates and ETa rates estimated using the adjusted Kc, α, and KHc showed regression slope coefficients of 0.96, 0.99, and 0.96, and coefficients of determination (R2) of 0.92, 0.93, and 0.90, respectively. Therefore, the Priestley–Taylor-equation-based adjusted crop coefficient is recommended. The adjusted crop-coefficient-based models can be used as valuable tools for local policymakers to effectively improve water use.

Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR

BackgroundThe metrics for assessing the yield of crops in the field include the number of ears per unit area, the grain number per ear, and the thousand-grain weight. Typically, the ear number per unit area contributes the most to the yield. However, calculation of the ear number tends to rely on traditional manual counting, which is inefficient, labour intensive, inaccurate, and lacking in objectivity. In this study, two novel extraction algorithms for the estimation of the wheat ear number were developed based on the use of terrestrial laser scanning (TLS) in conjunction with the density-based spatial clustering (DBSC) algorithm based on the normal and the voxel-based regional growth (VBRG) algorithm. The DBSC involves two steps: (1) segmentation of the point clouds using differences in the normal vectors and (2) clustering of the segmented point clouds using a density clustering algorithm to calculate the ear number. The VBRG involves three steps: (1) voxelization of the point clouds, (2) construction of the topological relationships between the voxels as a connected region using the k-dimensional tree, and (3) detection of the wheat ears in the connected areas using a regional growth algorithm.ResultsThe results demonstrated that DBSC and VBRG were promising in estimating the number of ears for different cultivars, planting densities, N fertilization rates, and growth stages of wheat (RMSE = 76 ~ 114 ears/m2, rRMSE = 18.62 ~ 27.96%, r = 0.76 ~ 0.84). Comparing the performance of the two algorithms, the overall accuracy of the DBSC (RMSE = 76 ears/m2, rRMSE = 18.62%, r = 0.84) was better than that of the VBRG (RMSE = 114 ears/m2, rRMSE = 27.96%, r = 0.76). It was found that with the DBSC, the calculation in points as units permitted more detailed information to be retained, and this method was more suitable for estimation of the wheat ear number in the field.ConclusionsThe algorithms adopted in this study provide new approaches for non-destructive measurement and efficient acquisition of the ear number in the assessment of the wheat yield phenotype.

Unrevalling phenotypic diversity of root system architecture in ancient wheat species versus modern wheat cultivars

Understanding the phenotypic variability in root system architecture and root-shoot relationships across different growth stages of wheat is of utmost importance for the improvement of genotypes with enhanced nutrient uptake and resource-use efficiency. This study focused on identifying variations and relationships in the root and shoot characteristics of seven modern cultivars and twelve ancient wheat accessions of different species, including T. monococcum, T. turanicum, T. polonicum, T. mirabile, T. durum, and T. aestivum, during the early vegetative growth and stem elongation stages. The results demonstrated significant phenotypic variation among the genotypes for shoot traits, root biomass, rooting depth, R/S ratio, and seminal and nodal root numbers. When considering both growth stages, the dry land-adapted cultivar ‘Taner’ and ancient wheat species, such as T. turanicum (2) and T. monococcum (1) accessions, exhibited deeper roots, which can enhance access to water in drought-prone areas. Furthermore, it was observed that modern wheat cultivars and T. turanicum accessions exhibited increased root biomass, suggesting a higher allocation of resources towards root growth, which could potentially enhance nutrient uptake. Conversely, T. monococcum accessions and T. mirabile revealed lower root biomass compared to other ancient species and modern cultivars. Additionally, the unrooted cluster analysis based on root biomass, rooting depth, and root to shoot ratio at both growth stages indicated a distinct separation of T. monococcum accessions and T. mirabile from other genotypes. Overall, these findings underscore the importance of phenotypic diversity in root traits for crop improvement and adaptation to varying environments. Identifying genotypes with desirable root characteristics can enhance nutrient and water uptake efficiencies, leading to increased crop productivity and sustainability.

Molecular diagnostic assay for pre-harvest detection of Tilletia indica infection in wheat plants.

The current study describes a new diagnostic method for the rapid and accurate detection of Tilletia indica, the pathogen accountable for causing Karnal bunt (KB) disease in wheat. This method uses quantitative real-time polymerase chain reaction (qPCR) and a primer set derived from glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene of T. indica to identify the presence of the pathogen. The qPCR assay using this primer set was found highly sensitive, with a limit of detection (LOD) value of 4 pg of T. indica DNA. This level of sensitivity allows for the detection of the pathogen even in cases of different growth stages of wheat, where no visible symptoms of infection on the wheat plants can be seen by naked eyes. The study also validated the qPCR assay on ten different wheat cultivars. Overall, this study presents a valuable molecular tool for rapid, specific and sensitive detection of KB fungus in wheat host. This method has practical applications in disease management, screening of wheat genotypes against KB and can aid in the development of strategies to mitigate the impact of Karnal bunt disease on wheat production.

Impacts of climate change on winter wheat net primary production: the regulatory role of crop management.

The Huang-Huai-Hai Plain (3HP) is the main agricultural area in China. Although climate change (CC) and crop management (CM) are considered factors affecting the winter wheat net primary production (NPP) in this region, their effects remain unclear. In the present study, we evaluated the relative contributions of CC and CM to winter wheat aboveground NPP (ANPP) in the 3HP and the relationships between climatic factors and ANPP using the first-order difference method from 2000 to 2020. CM had a greater influence on the ANPP of winter wheat than did CC. However, the relative contribution of CM to ANPP gradually decreased in humid and dry sub-humid regions with the development of winter wheat. Furthermore, in areas characterized by low temperatures and limited precipitation, CC became the dominant factor contributing to ANPP, indicating that varieties resilient to drought and cold should be selected in these regions. Minimum and average temperatures were the dominant factors driving spatiotemporal variations in ANPP during the early stage of winter wheat growth, whereas maximum temperature constrained growth throughout the winter wheat growth cycle. When winter wheat entered the vigorous growth stage, precipitation and solar radiation replaced temperature as the driving factors influencing winter wheat growth. The results of the present study provide guidance for optimizing winter wheat crop management in the 3HP. © 2023 Society of Chemical Industry.

Wheat cultivar replacement drives soil microbiome and microbial cooccurrence patterns

While wheat domestication is reported to influence the soil microbial community, few studies have evaluated the influence of cultivar replacement in modern breeding on both bacterial and fungal communities. Especially, few studies reported the bacterial-fungal interkingdom association by analysis of taxa co-occurrence or co-exclusion between different wheat growth stages. In this study, we selected major wheat cultivars from different decades to investigate their genetic relatedness, plant traits, soil bacterial and fungal communities in the rhizosphere and proximal root zone, and the relationships between them. Our results indicated that host selection had the greatest impact on bacterial and fungal communities compared to growth stage and sampling location (P<0.001). At flowering, the soil microbial community in the genotype group consisting of the 1950 s (W50 s) and 1960 s (W60 s) cultivars could be clearly distinguished from those in later genotype groups. Plant traits explained the largest source of variation in microbial β-diversity (12.8–20.6%) (P=0.01), with plant height, aboveground dry matter, leaf area per plant and specific root length being associated with the divergence in microbial composition or quantity among cultivars. The cultivar from the 1970 s (W70 s) enriched a greater number of microbial taxa with the highest relative abundance, suggesting that old cultivar could be considered as a source of cultivar-microbe interaction. The cultivar from the 2000 s (W00 s) enriched taxa from the bacterial genus Nocardioides and increased the fungal phylum Glomeromycota in the rhizosphere. At three growth stages, W00 s root-zone exhibited the highest bacteria/fungi ratio (B/F) and contained more phosphorus cycle-related bacterial phoD-genes than W50 s and W60 s. The co-occurrence network revealed more operational taxonomic units (OTUs) from the bacterial order Rhizobiales in the largest module of W00 s. The increased B/F ratio and the aforementioned taxa are reported to be involved in soil nitrogen and phosphorus availability, suggesting that contemporary cultivar may recruit beneficial bacteria and fungi while weaken the association with other fungi. These findings contribute to the development of microbiome-based breeding strategies for sustainable wheat farming.

Drought Priming and Subsequent Irrigation Water Regimes Enhanced Grain Yield and Water Productivity of Wheat Crop

The most important factor impacting wheat production is water stress that occurs during the reproductive growth stage. Therefore, the plant responses and water productivity as affected by drought priming were investigated during Rabi seasons 2021 and 2022. The field trials were conducted in the research field of the Department of Irrigation and Drainage, Faculty of Agricultural Engineering, Sindh Agriculture University, Tandojam. The Hamal-BNS wheat variety was subjected to differing irrigation water regime levels (40%, 50% and 60% of soil water holding capacity, SWHC) after being subjected to drought priming, irrigation water recovery (water closure period) and drought priming. There were six treatments: (1) DPP-40 (drought priming plants at 40% of SWHC), (2) DPP-50, (3) DPP-60, (4) CTP-40 (controlled treated plants at 40% of SWHC), (5) CTP-50 and (6) CTP-60. During the experiment period, soil moisture content was significantly affected by the different treatments at various growth stages of wheat. The results indicated that winter wheat pre-exposed to drought priming attained a stress imprint that improved the subsequent deficit water levels which occurred during the later plant growth stage as demonstrated by the progress of test weight, grain yield, plant level water use efficiency and irrigation water use efficiency as well as relative yield compared to CTP-50 (control treatment). Under the irrigation water regime levels during the post-anthesis period, primed wheat plants sustained grain yield and higher relative yield than wheat plants without priming due to the better irrigation water regime for drought-primed wheat plants. Similarly, primed wheat plants consumed 18.3% less irrigation water as compared to non-primed plants, which significantly increased plant level WUE and irrigation WUE and decreased dry biomass and root development of drought-primed wheat plants. Therefore, to conserve fresh water for other field crops and increase water productivity in the Sindh province, it is recommended that drought priming is used during the early growth period of wheat plants as a successful irrigation method.

Prediction of Grain Yield and Gluten Content in Winter Bread Wheat Based on Nutrient Content in Plant Parts during the Critical Cereal Window

Reliable prediction of winter bread wheat grain yield (GY) and its qualitative parameters (crude protein (CP) and wet gluten (GL) content, wet gluten yield (GLY)) requires evaluation of the plant nutritional status in the Critical Cereal Window (CCW). The reliability of the forecast depends on the dedicated plant characteristics and the correct selection of the diagnostic plant parts. This hypothesis was verified in a one-factor field experiment carried out in the 2013/2014, 2014/2015, and 2015/2016 growing seasons. The field experiment included applying 0, 40, 80, 120, 160, 200, and 240 kg N ha−1. The N, P, K, Ca, Mg, Fe, Mn, Zn, and Cu content in wheat was determined in two growth stages: (i) beginning of booting (BBCH 40) and (ii) full flowering (BBCH 65). The evaluated plant components included the leaves and stem for BBCH 40 and the flag leaf, leaves, stem, and ear of BBCH 65. Grain yields were very high, significantly responding to the increased rates of fertilizer nitrogen (Nf), with a maximum yield of 11.3 t ha−1 achieved in 2014 (N rate of 209 kg N ha−1), 13.7 t ha−1 in 2015, and 8.6 t ha−1 in 2016 (N rate of 240 kg N ha−1). The CP and GL content also increased linearly in accordance with the Nf rates. At the beginning of the booting stage, the GY forecast based on the content of nutrients in the leaves or the stem was 94%. Meanwhile, a slightly higher yield prediction was obtained for leaves during the full flowering stage (95%). The key nutrients comprised K, Ca, and Mn, accounting for 93% of the GY variability. The accuracy of the GL prognosis at BBCH 40, regardless of the plant part, exceeded 99%. Three nutrients, namely, P, Mg, and Zn, explained 98% of the GL variability, and the GLY forecast was high (97%). Both wheat traits depended on Zn, which buffered the action of N and Mg. At the full flowering stage, the highest, yet slightly weaker, predictions of GL and GLY were obtained for leaves (95% and 92%, respectively). At this stage of winter wheat growth, the significant role of Zn and K and the buffering effect of Cu on the action of both nutrients was apparent. The obtained results unequivocally confirm that the game for winter wheat grain yield occurs within the Critical Cereal Window. In addition, the end result depends on the plant’s N supply during this period and the nutritional status of other nutrients. Application of 40–80 kg N ha−1 fertilizer critically impacted the GY and technological quality. Moreover, micronutrients, including Zn and Cu, influence the GY, GL, and GLY considerably. At the beginning of the booting phase (BBCH 40), winter wheat leaves serve as a highly reliable plant component indicator for evaluating nutrient content and quantitative (GY, GLY) and qualitative (GL) characteristics of grain. Moreover, analysis conducted during BBCH 40 allows the farmer to correct the nutritional status of the wheat, taking into account N and other nutrients as necessary.