To investigate the effects of spermidine on the starch composition and gelatinization characteristics of wheat grains, using the wheat varieties Xinong 538 and Lankaoaizao 8 as experimental materials, four spermidine spray concentrations (0.25, 0.75 and 1 mmol·L^-1) were set up at grain-filling stage. The indicators of starch composition, granule size distribution, and gelatinization characteristics of wheat grains were determined and analyzed. The results showed that compared with spraying clear water CK, spraying 0.25 and 0.75 mmol·L^-1 spermidine significantly increased the 1 000-grain weight and yield of the two varieties. After the application of spermidine, the amylose content of Xinong 538 and Lankaoaizao 8 decreased and increased, respectively. The amylopectin ratio showed a decreasing trend and a downward increase followed by a decrease with the increase of the application concentration. Overall, spermidine spraying reduced the quantity and volume of A type starch granules in both varieties and increased the quantity and volume of B type starch granules. High concentration (1 mmol·L^-1) of spermidine reduced, increased and decreased the peak viscosity, final viscosity, and breakdown of Xinong 538 and Lankaoaizao 8, respectively. 0.75 mmol·L^-1 spermidine increased the peak viscosity, trough viscosity, breakdown, final viscosity, and setback of Lankaoaizao 8, while increased the breakdown, setback, peak time, and pasting temperature of Xinong 538. Overall, under the conditions of this experiment, the application of 0.75 mmol·L^-1 arginine spray significantly increased the 1 000-grain weight and yield of wheat, and by influencing the amylose content, amylose/amylopectin ratio and type B starch particle composition of wheat, increased the peak viscosity and breakdown of wheat grain starch, thereby enhancing the gelatinization characteristics of wheat and ultimately improving the quality of wheat starch. It is a relatively suitable spraying concentration in wheat production.

To investigate the effects of biochar application duration on the growth and yield of spring wheat, a randomized block design experiment was conducted using the cultivar Xinchun 37 as material. Two nitrogen treatments (no nitrogen and 255 kg·hm^-2) and three biochar treatments(no biochar, one-year biochar application, and five-years biochar application, with an application rate of 20 t·hm^-2) were set up. Differences in photosynthetic parameters, relative chlorophyll content, dry matter accumulation and translocation, grain filling dynamics, yield, and yield components of spring wheat under different treatments were analyzed. The results showed that compared with no biochar application, both sole biochar application and biochar combined with nitrogen fertilizer significantly increased the net photosynthetic rate of spring wheat after 1- and 5-year biochar application, promoted the accumulation of dry matter in stems, leaves, and spikes at all growth stages, and enhanced the allocation of dry matter to grains at maturity, thereby increasing grain yield. Overall, the effects of 5-year biochar application were better than those of 1-year. Specifically, the yield under the treatment of 5-year biochar combined with nitrogen fertilizer reached 7 508.91 kg·hm^-2, which was 37.56% higher compared to the treatment without nitrogen fertilizer or biochar. Orthogonal partial least squares-discriminant analysis revealed significant differences in net photosynthetic rate, effective panicle number, and yield of spring wheat under different biochar application durations, whether applied alone or combined with nitrogen. In conclusion, biochar application can effectively promote the growth and yield of spring wheat, and the longer the application duration, the more pronounced the promoting effect.

To adapt to the future climate change and increased carbon emissions in the Longzhong region, this study proposes optimization strategies for the production management of rain-fed spring wheat. Based on field experimental data from Dingxi, Gansu, collected from 2014 to 2017, the Root Zone Water Quality Model 2 (RZWQM2) was calibrated and validated for this region. Using this model, climate projections under the RCP4.5 and RCP8.5 scenarios for the period 2025-2060 in Northwest China were analyzed. Different sowing dates and CO₂ concentration gradients (370, 400, 500 and 600 μmol·mol^-1) were applied to evaluate the impact of future climate change on spring wheat yield and LAI. The results show that, The model accurately simulated the leaf area index (LAI) and yield of spring wheat, with R₂ values reaching 0.913 and 0.992, respectively. Under the RCP4.5 scenario with moderate warming, when CO₂ concentration exceeds 500 μmol·mol^-1, significant yield increases were observed. The average yield increases for early sowing (March 7), conventional sowing (March 19), and late sowing (March 31) were 29.3%, 28.9%, and 24.7%, respectively. Under the RCP8.5 scenario, high temperatures, reduced radiation, and precipitation fluctuations weakened the CO₂ fertilization effect, resulting in a yield increase of only 20.9%-27.5%, with significant interannual variability. The sowing date effect was also prominent, with early sowing maintaining the highest and most stable yield and LAI in both climate scenarios, while late sowing, impacted by combined heat and drought stress, showed the highest yield fluctuation. In conclusion, under the future higher CO₂ concentrations, through coordinating reasonable early sowing and water and fertilizer management the risks of high temperature and water stress can be alleviated to increase the LAI and yield of spring wheat in Longzhong dryland.

In order to explore the spatiotemporal variation characteristics of wheat quality in Shandong Province and identify key meteorological factors, the quality traits of 673 Jimai 22 samples collected from 105 counties and districts in Shandong Province from 2018 to 2024 were analyzed. The spatiotemporal variation characteristics of wheat quality were examined by analysis of variance (ANOVA), and the relationship between wheat quality traits and meteorological factors was analyzed by the stepwise regression method. The high-quality possibility distributions were visualized by geographic information system (GIS). The results showed that Jimai 22 in Shandong Province generally exhibited medium-strong gluten and medium gluten characteristics, with relatively high protein content and wet gluten content, but moderate processing properties such as stability time and maximum tensile resistance. In terms of temporal variation, wheat quality showed significant annual fluctuations, and the proportion of medium-strong gluten and medium gluten wheat increased from 53.5% in 2018 to 87.6% in 2024. In terms of spatial pattern, there were obvious differences in wheat quality among ecological areas, and the quality of wheat in Jiaodong was the best, followed by the southwestern of Shandong Province. Meteorological factors analysis indicated that the anthesis-maturating period was the key period determining wheat quality. During this period, the daily temperature range was significantly correlated with test weight, stability time, and maximum tensile resistance; the maximum temperature was significantly negatively correlated with protein content and sedimentation index; the rainfall was significantly positively correlated with wet gluten content; and the average temperature was significantly negatively correlated with sedimentation index and development time. Jimai 22 showed good adaptability in Shandong Province. Jiaodong had the potential for developing medium-strong gluten wheat, while the southwestern in Shandong Province was suitable for developing medium gluten wheat.

Addressing the challenges of inadequate utilization of spectral feature variations among wheat cultivars and the limited generalization capacity of conventional ensemble learning methods for yield prediction, this study proposed a novel Extreme Stacked Generalization (ESG) algorithm. The ESG method was designed to enhance prediction accuracy and stability across multiple cultivars and years by dynamically optimizing feature selection and model integration, thereby resolving micrometer-level spectral discrepancies among cultivars. The study utilized canopy hyperspectral reflectance and yield data collected over two wheat growing seasons (2018—2020), containing the early and middle grain-filling stages. A dual-validation framework was employed, comprising independent cross-year validation (Framework 1) and multi-period data fusion validation (Framework 2). The performance of the ESG algorithm was benchmarked against Ridge Regression (RR), K-Nearest Neighbors (KNN), Random Forest (RF), and a standard Stacked Generalization (SG) algorithm, using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as evaluation metrics. Results demonstrated that the ESG algorithm significantly outperformed all other models, reducing the prediction RMSE to a range of 1.01-1.31 t·hm^-2. The stability of cross-year predictions was notably improved, with the range of error fluctuation decreasing by 18.9%, indicating strong environmental adaptability and cultivar-discriminating capabilities. Furthermore, the middle grain-filling stage was identified as the optimal prediction window, achieving a RMSE of 1.01 t·hm^-2. This study concludes that the ESG algorithm can effectively adapt to the specific spectral characteristics of different wheat cultivars and environmental variations, enabling robust and stable yield prediction across diverse cultivars and growing years.

To address the limitations of existing wheat biomass estimation models, such as insufficient generalizability in field environments and discontinuity in application due to growth-stage-specific modeling, this study focused on winter wheat from the jointing to filling stages in Henan Province. Multi-spectral UAV data were used to construct a 34-dimensional feature set, including reflectance from 10 spectral bands, 15 vegetation indices, 8 texture features, and quantified growth stages (jointing=1, booting=2, filling=3). Features were selected through correlation analysis (ranked by absolute correlation coefficient values), and four machine learning algorithms—Linear Regression (LR), Random Forest (RF), LightGBM, and K-Nearest Neighbors Regression (KNN)—were employed to develop a unified model for estimating above-ground biomass (AGB) across multiple growth stages. Model parameters were optimized by progressively increasing the number of input features. The results showed that the quantified growth stage had the highest correlation with AGB (correlation coefficient of 0.80), while the near-infrared band and the red-edge 740 nm band were identified as critical spectral features (with correlation coefficients of 0.48 and 0.44, respectively). The Random Forest (RF) model achieved the highest accuracy with 18 input features (growth stage + 2 band reflectance values + 8 vegetation indices + 7 texture features), with a coefficient of determination (R₂) of 0.87, a root mean square error (RMSE) of 291.2 g·m^-2, and a normalized root mean square error (nRMSE) of 12.8% on the test set. These findings demonstrate that a unified model for estimating AGB in winter wheat across multiple growth stages can be constructed using multi-spectral UAV data and field samples. This approach significantly enhances the model’s generalizability in practical field production environments and effectively addresses application challenges during transitional growth stages.

To address the challenges of time consumption, low accuracy, and inefficiency in the localization calibration of yield formation parameters for spring wheat in the APSIM model under dryland conditions, a Chaos Particle Swarm Optimization (CPSO) algorithm was employed. Based on meteorological data from Dingxi City, Gansu Province, spanning 1971 to 2023, as well as yield data extracted from the Dingxi Statistical Yearbook for the periods 1971—2013 and 2022—2023, along with field-measured data collected from Mazichuan Village, Anding District, Dingxi City, between 2014 and 2021, key parameters influencing spring wheat yield were calibrated using the CPSO algorithm. The results showed that after parameter optimization with CPSO, the root mean square error (RMSE) decreased from 39.21 kg·hm^-2 to 24.64 kg·hm^-2; the normalized RMSE (NRMSE) dropped from 2.32% to 1.65%; and the modeling efficiency (ME) increased from 0.965 to 0.991. Therefore, through parameter optimization with CPSO, the fitting degree of APSIM model to spring wheat yield was significantly improved, and the model had better adaptation to Dingxi City, Gansu Province.

To compare the deposition patterns of spray droplets on different parts of wheat plants and their impact on the field control efficacy against wheat powdery mildew during daytime and nighttime operations of agricultural drones, a 20% tebuconazole·fluopyram SC was used as the test agent to conduct daytime and nighttime drone spraying trials. Parameters such as droplet deposition amount, droplet density, and coverage in the wheat canopy, as well as the control efficacy against wheat powdery mildew 7 and 14 days after treatment, were analyzed. The results showed that under the same spray volume and droplet size, daytime operations resulted in higher droplet deposition and coverage compared to nighttime, while droplet density was higher at night than during the day, averaging 2.27% higher. Analysis of deposition effects across different canopy layers revealed that droplet density, coverage, and deposition amount decreased sequentially in the flag leaf, penultimate leaf, third leaf from the top, and fourth leaf from the top, with all parameters being higher on the leaf surface than the underside. A 600 mL·hm^-2 application rate of 20% tebuconazole·fluopyram SC demonstrated good efficacy against wheat powdery mildew, with nighttime operations yielding better results than daytime. When the spray volume was 45 L·hm^-2 and droplet size was 50 μm, the efficacy reached its peak, with 57.73% control efficacy from daytime spraying and 72.10% from nighttime spraying 14 days after treatment. These findings provide references for the rational setting of agricultural drone operation parameters and scientific basis for effective control of wheat powdery mildew.

Phospholipase D (PLD) is a type of important lipid metabolic enzyme that is widely involved in signal transduction, membrane structure remodeling, and stress responses, especially playing a crucial role in plant immune defense and abiotic stress. To understand the functions of the wheat PLD family members, this study utilized bioinformatics for genome-wide identification, combined with conservation domain analysis to identify and classify the wheat PLD gene family members. Phylogenetic analysis and the expression patterns analysis of these genes were comprehensively conducted. A total of 51 TaPLD genes were identified in wheat, which can be classified into 6 subfamilies: α, β, φ, δ, κ and ζ. Among them, the α subfamily has the most members and the highest sequence conservation. Each TaPLD contains a basic HKD (His-Lys-Asp) motif sequence of the PLD domain. Most TaPLD are predicted to be located in the cytoplasm, while a few of them are located in the chloroplast, endoplasmic reticulum, or vacuole. TaPLD genes are mainly distributed on chromosomes 1, 5, and 7. Moreover, there are various gene duplication phenomena within the wheat genome and among species. Transcriptome data analysis showed that 11 TaPLD genes are specifically expressed in anther. After inoculation with wheat powdery mildew fungus, powdery mildew pathogen and stripe rust pathogen, multiple genes such as TaPLDα9, TaPLDα10, TaPLDα11 and TaPLDβ5 were induced to express. After low-temperature treatment, TaPLDζ1, TaPLDζ2 and TaPLDζ3 were significantly induced to express. After drought and high-temperature treatments, TaPLDα9, TaPLDα10 and TaPLDα11 were significantly upregulated, and the relative expression levels of the three genes were higher under drought treatment. These data provide a systematic revelation of the molecular characteristics of wheat PLD genes and their potential roles in growth and development, immune defense and abiotic stresses, offering new perspectives for the study of the molecular mechanisms of wheat growth, disease resistance, and stress tolerance.

Cytokinins (CKs) play a pivotal regulatory role in plant growth, development and stress responses, with isopentenyltransferases (IPTs) serving as critical rate-limiting enzymes in CKs biosynthesis. To investigate the role of wheat IPT genes in plant responses to low-temperature stress, this study cloned the TaIPT9A gene (TraesCS7A02G560300) from the tiller nodes of the cold-resistant winter wheat cultivar Dongnong Dongmai 1(Dn1). Bioinformatics analysis, low-temperature-induced promoter activity assays, and functional characterization were conducted. The TaIPT9A gene was overexpressed in the cold-sensitive rice cultivar Longjing 11 via Agrobacterium-mediated transformation to preliminarily analyze its cold resistance function. The results showed that TaIPT9A has a coding sequence (CDS) of 1 362 bp, encoding 453 amino acids. Phylogenetic analysis revealed that wheat IPT proteins are most closely related to those of Aegilops tauschii. The TaIPT9A promoter contains cis-acting elements responsive to low temperature, light, ABA and MeJA, and its activity was induced by cold stress. Compared to wild-type rice, TaIPT9A-overexpressing plants exhibited greener leaves and better growth under low-temperature stress. Additionally, these plants showed significantly increased proline (Pro) content, catalase (CAT) activity, and upregulated expression of antioxidant enzyme-related genes (OsCATC, OsSOD1, OsSOD2 and OsAPX1) as well as cold resistance-related genes (OsCATC and OsDREB1A). These findings suggest that TaIPT9A enhances cold tolerance in rice seedlings.