Black point disease (BP) severely affects wheat production, and planting disease-resistant varieties is an important measure to mitigate the damage caused by BP. However, the current lack of resistance sources restricts the improvement of BP resistance. To obtain superior resistance sources for BP, this study inoculated 412 wheat germplasm with the dominant pathogen of BP in the Huanghuai wheat region, Bipolaris sorokiniana, and conducted molecular testing using 16 reported BP resistance-related markers. The results showed that among the 412 tested materials, there were no immune or highly resistant germplasm; 4 showed moderate resistance (1.0%), while 95 (23.1%), 209 (50.7%), and 104 (25.2%) exhibited mild susceptibility, moderate susceptibility, and high susceptibility, respectively. Of the 16 markers, 13 showed polymorphism among different materials, with only 3 tightly linked to BP resistance: wmc170, BP-4A-d20, and Xbarc10. These correspond to the resistance loci QBP-Usq.2A, QBB.hau-4A, and QBB.hau-5A, respectively, with QBB.hau-4A exhibiting a better disease resistance effect. Its presence alone or in combination with other loci can significantly reduce the black point rate in wheat (P<0.01). The 4 moderately resistant materials (Shannong 530070, ZC223, 05CV078, and Shannong 38) can serve as resistance sources for improving BP resistance in wheat, and BP-4A-d20 marker can be used for BP resistance breeding.

To understand the evolution of agronomic and quality traits of wheat varieties approved in Jiangsu Province over past decade, thirteen agronomic and quality traits of 90 and 95 wheat varieties approved in Huaibei and Huainan areas of Jiangsu Province from 2014 to 2023 were analyzed. The results showed that the wheat varieties approved in Huaibei from 2014 to 2023 were primarily medium-gluten types, while the focus in Huainan gradually shifted toward medium- and strong-gluten varieties. Local germplasm resources were more frequently utilized in variety breeding within the province compared to external sources, with Zhenmai 9 (17 times), Zhenmai 168 (14 times), Zhengmai 9023 (13 times), and Aikang 58 (11 times) being the most commonly used. The yield, plant height, grain number per spike, and 1 000-grain weight of the approved wheat varieties in both regions showed an upward trend, while the effective panicle number remained stable, and the growth period exhibited a downward trend. There are great differences in the quality changes of wheat varieties between the two places. In the Huaibei region, grain test weight, protein content, wet gluten content, and water absorption increased rapidly. The protein content and wet gluten content of wheat in Huainan showed a downward trend, and were gradually lower than those in Huaibei area. The maximum tensile resistance and tensile area of wheat in the two regions showed a stead trend, while dough stability time showed a downward trend. Correlation analysis revealed that in Huaibei, the correlation between wheat yield and its three components ranked as: 1 000-grain weight>grain number per spike>effective spike number. In Huainan, the correlation ranked as: 1 000-grain weight>effective panicle number>grain number per panicle. This indicates that the main goal of wheat yield breeding in Jiangsu Province in the past ten years was to improve the 1 000-grain weight, and the potential of yield increase can be continuously improved from the aspects of grain number per spike and effective spike number in the future. Huaibei area should try the feasibility of developing strong gluten and weak gluten wheat in different regions, and Huainan area should further introduce new high-quality germplasm resources. While increasing the number of approved varieties, the two regions should take into account the improvement of yield increase rate and the optimization of quality, and breed high-quality and high-yield wheat varieties that meet market demand.

To clarify the composition of glutenin subunits in wheat varieties developed in the Huang-Huai wheat region and their relationship with quality traits, this study used 92 new wheat varieties (lines) as materials to systematically analyze the distribution and combinations of high-molecular-weight glutenin subunits (HMW-GS) and low-molecular-weight glutenin subunits (LMW-GS), as well as their relationships with some quality indicators. The results showed that among the 92 varieties (lines), a total of 9 types of HMW-GS subunits and 15 subunit combinations were detected. The combination with the highest occurrence frequency was 1/7+9/2+12, accounting for 19.57%, while the occurrence frequency of the high-quality subunit combination 1/7+8/5+10 was 8.70%. For LMW-GS, 9 types genes of subunits and 40 combination types were identified. Among them, 72 materials belonged to moderately complex combinations consisting of 2 to 4 subunits genes, and the combination A3d+D3-21 had the highest occurrence frequency (16.30%). Correlation analysis indicated that Glu-B3d was significantly positively correlated with protein content, sedimentation value, and wet gluten content. Cluster analysis classified the materials into three groups, and Group Ⅱ exhibited superior overall quality. The occurrence frequencies of multiple high-quality subunits in Group Ⅱ were higher than those in Group Ⅰ and Group Ⅲ, including 14+15 at the Glu-B1 locus and 5+10 at the Glu-D1 locus of HMW-GS, as well as Glu-A3a (8.33%), A3a/c (54.17%), B3d (37.50%), D3-23 (54.17%), and D3-32 (66.67%) of LMW-GS. In addition, 2+10 at the Glu-D1 locus and D3-22, D3-31 at the Glu-D3 locus were only detected in Group Ⅱ. These results had important reference for wheat quality breeding and genetic improvement in the Huang-Huai wheat region.

Doubled haploid (DH) technology is a biotechnology that rapidly obtains homozygous plants through haploid induction and chromosome doubling, genetic stability in one generation and significantly shortening the breeding cycle. It is widely used in the genetic breeding of various crops such as wheat, corn, and rapeseed, especially a key role in the construction of genetic populations, QTL mapping, and variety improvement. This paper will briefly introduce the method of producing wheat haploid, review the latest progress of DH breeding technology in the field of wheat research, and introduce the application examples of DH technology in accelerating breeding, genetic map construction, nuclear sterility, and microspore genetic transformation, in order to promote the development of DH technology in wheat breeding and further improve the efficiency of wheat breeding.

In order to explore the feasibility of applying the gluten-starch interaction to breed super-gluten wheat variety, 20 F₇recombinant inbred lines (RILs) derived from a cross between Jimai 44 and Jimai 229 and their parents were used as materials, and the farinograph properties of dough were measured. Three RILs with the longer stability time (Group H) and 3 RILs with the shorter stability time (Group L) were selected for further invistigating the gluten protein quantity and quality, starch size distribution and gluten-starch interaction. The results showed that the Group H exhibited higher dough development time and stability time than those of their parents, which can be attributed to their tighter gluten network, higher B-type starch granule proportion and stronger gluten-starch interaction. Correlation analysis showed that dough development time and stability time had significantly positive correlation with gluten index, protein junctions, protein length, B-type starch granule proportion, the ratio of B-type starch granules to A-type starch granules (B/A), B/lacunarity, and B/A/lacunarity, yet significantly negative correlation with lacunarity, A-type starch proportion and A/lacunarity. In summary, compared with Jimai 44 and Jimai 229, the gluten network structure, B-type starch granule proportion, and gluten-starch interaction of Group H were significantly improved. Parameters related to gluten-starch interaction determined using micro-scale flour samples can be used to assess the dough quality at an early generation in wheat breeding.

In order to understand the effects of different types of fertilizers on the photosynthetic characteristics and yield of wheat at the jointing stage, the effects of three different types of fertilizers: F₁ (ternary compound fertilizer, N∶P₂O₅∶K₂O=15∶15∶15), F₂ (wheat compound fertilizer, N∶P₂O₅∶K₂O=26∶10∶9), F₃ (urea) and no topdressing (CK) on wheat photosynthetic indices, dry matter accumulation and yield were studied under the same type and amount of base fertilizer application at the jointing stage. The results showed that the application of F₃ at the jointing stage increased the leaf area index at the middle and early stages of wheat growth compared with F₂, and the F₂ treatment was conducive to maintaining the higher leaf area index at the filling stage. The chlorophyll content of F₂treatment was significantly higher than that of F₁ treatment (P<0.05) at the flowering stage and filling stage, and there was no significant difference when compared with F₃ treatment. The photosynthetic rate of F₂ was the highest. compared to F₃, F₂significantly increased canopy photosynthetically active radiation at flowering and middle of filling stages by 4.74%, 4.18% and 5.83%, 5.09%, respectively in Suixi and Yingshang experimental sites. F₁ can increase the photosynthetic rate compared with F₃, but reduce the photosynthetically available radiation in the canopy. The grain filling rate ranked as F₂>F₁>F₃>CK, and the grain filling rate of CK was significantly lower than that of the treatments. The dry matter accumulation of the F₃ group was higher than that of the F₂ treatment at the booting and flowering stages, while the dry matter accumulation of the F₂ group was higher than that of the F₃ treatment at the maturity stage. The yield of topdressing treatments ranked as F₂>F₁>F₃, with significant differences. Compared to F₁, F₂ increased yield by 7.26% and 8.13%, respectively in Suixi and Yingshang experimental sites. All the findings demonstrated that although topdressing with the same weight of urea increased the leaf area index, chlorophyll content and population dry matter accumulation of wheat at early growth stage, topdressing with specialized compound fertilizer had more advantages in improving photosynthetic indices, grain filling rate and population dry matter accumulation at the middle and later growth stages of wheat. Topdressing with specialized compound fertilizer increased yield when compared to ternary compound fertilizer and urea.

To investigate the effects of different water and nitrogen treatments on photosynthetic characteristics, yield and water-nitrogen use efficiency of drip-irrigated winter wheat during the grain-filling stage, a field experiment was conducted using the cultivar Yinong 22. Three drip irrigation levels 5 250 (W₁), 6 000 (W₂) and 6 750 (W₃) m³·hm^-2and three nitrogen application rates 0 (N₀), 240 (N₁) and 300 (N₂) kg·hm^-2were established to analyze the impacts of water-nitrogen combinations on chlorophyll relative content (SPAD), photosynthetic parameters, leaf area index (LAI), dry matter accumulation, yield and water-nitrogen use efficiency, and the response of each factor to yield was revealed by path analysis. The results showed that irrigation amount and nitrogen application rate significantly affected SPAD, LAI, photosynthetic parameters (C_i, G_s, P_n and T_r) and dry matter accumulation per plant (leaf sheath, grain and stems) from anthesis to 20 days after anthesis. Under W₂N₁ treatment, the above indices did not increase significantly with the increase of water and nitrogen supply. Analysis of yield and water-nitrogen use efficiencies revealed that yields under W₂N₁, W₂N₂, W₃N₁ and W₃N₂ treatments were significantly higher than those under W₁N₀, W₁N₁, W₁N₂, W₂N₀and W₃N₀treatments. The highest yield was achieved under W₂N₂ treatment (8 395.88 kg·hm^-2), followed by that under W₂N₁treatment (8 208.37 kg·hm^-2). Irrigation water use efficiency under W₂N₁ treatment reached 1.37 kg·m^-3, significantly surpassing that of the W₃N₁ (1.22 kg·m^-3) and W₃N₂ (1.23 kg·m^-3) treatments. Precipitation use efficiency was highest under W₂N₂ treatment (33.66 kg·mm^-1·hm^-2), showing no significant difference from the W₂N₁ treatment. Both partial factor productivity of nitrogen (34.20 kg·kg^-1) and agronomic nitrogen use efficiency (6.08 kg·kg^-1) were highest under W₂N₁ treatment. Path analysis showed that photosynthetic characteristics, dry matter accumulation and agronomic traits direct regulated wheat yield, and the direct effect values were 0.889, 0.334 and 0.137, respectively. Water and nitrogen input mainly affected wheat yield through indirect pathway, and the indirect effect value was 1.27. In summary, photosynthetic characteristics are important factors affecting wheat yield, and water and nitrogen inputs affect yield mainly through the indirect drive of photosynthetic characteristics. Under the condition of drip irrigation in the Ili River Valley, taking yield and water and nitrogen use efficiency into consideration, the recommended drip irrigation amount is 6 000 m³·hm^-2, and the nitrogen application rate is 240 kg·hm^-2 (W₂N₁) is the optimal combination.

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.