Abstracts: To investigate the functional characteristics of low-temperature tolerance-related NAC transcription factors in wheat, a low-temperature-tolerant variety Mazhamai and a low-temperature-sensitive variety Jimai 22 were used for transcriptome sequencing, and 31 candidate NAC transcription factors potentially associated with low-temperature tolerance were screened. Bioinformatics analysis showed that 12 of the TaNAC proteins are basic proteins (pI>7), while the others are acidic proteins (pI<7). All proteins are hydrophilic and mainly localized in the nucleus. The secondary structure was dominated by random coils. Collinearity analysis revealed that there were extensive collinear relationships between NAC transcription factors in wheat and rice. Promoter analysis indicated that the promoter regions of these NAC transcription factor genes contained various cis-acting elements involved in stress responses and hormone signaling, among which TaNAC048-A1 harbored the most abundant regulatory elements. Under low-temperature stress, the expression levels of TaNAC048-D1,TaNAC048-A1,TaNAC015-B4,TaNAC039-D1,TaNAC104-D2,TaNAC048-B1,TaNAC001-B1, and TaNAC001-A1 were significantly up-regulated in Mazhamai. In Jimai 22, TaNAC048-D1, TaNAC039-D1, and TaNAC104-D2 were significantly up-regulated, while TaNAC048-B1 was significantly down-regulated, and the remaining four genes showed no significant changes. The transcriptome sequencing data were verified by qRTPCR. These TaNAC transcription factor genes may play important roles in the response of wheat to low-temperature stress.

Based on the continuous 37 year (1984-2020) phenological observation data of winter wheat at the National Agricultural Meteorological Observation Station in Tongren City, Guizhou Province, and combined with the meteorological observation data of the corresponding period, the changes and response relationships of the phenological periods of winter wheat and agricultural climate factors were systematically analyzed using kernel density estimation, climate trend analysis, correlation analysis, and multiple linear regression model. The results showed that the phenological periods of winter wheat in Tongren City from 1984 to 2020 generally showed a phased delay trend, among which the tillering period had the largest delay ［10.9 d· (10 a)^-1］. The duration of key growth stages was characterized by an extension at early stage ［vegetative growth period extended by 5.6 d· (10 a)^-1］, a shortening in the middle stage［vegetative and reproductive growth concurrent period shortened by -10.8 d· (10 a)^-1］, and relative stability at late stag ［reproductive growth period changed by 0.2 d· (10 a)^-1］. During the growth period of winter wheat, the agricultural climate elements generally showed an increase in temperature, a decrease in sunshine, and an increase in the fluctuation of water and heat conditions. Correlation analysis indicated that accumulated temperature and ground temperature were significantly positively correlated with the phenological period day numbers of sowing, emergence and three-leaf stage (P<0.001), while relative humidity and sunshine duration were significantly negatively correlated with the phenological periods of heading, flowering and milk-ripen stage (P<0.05 or P<0.01), yet the maturity period had no significant correlation with each climate factor. The results of multiple regression analysis showed that the phenological periods of winter wheat were most sensitive to accumulated temperature and ground temperature, followed by daily temperature range, precipitation and relative humidity, and less sensitive to sunshine duration. This study revealed the response characteristics of the phenological periods of winter wheat in the northeastern part of Guizhou Province to climate change, which can provide agricultural meteorological basis for the breeding and cultivation management of winter wheat varieties in the region.

To clarify the effects of planting density on yield formation in different spring wheat cultivars, a field experiment was conducted using three spring wheat cultivars, Liaochun 18, Liaochun 43, and Shenmai 155, under five planting densities of 3.75 million, 5.25 million, 6.75 million, 8.25 million, and 9.75 million plants·hm^-2, designated as D1, D2, D3, D4, and D5, respectively. The effects of planting density on grain yield and its components, SPAD value, dry matter accumulation and distribution, leaf area index (LAI), grain to leaf ratio, and photosynthetic characteristics were compared among cultivars. The results showed that planting density significantly affected grain yield and its components, with significant variety and planting density interaction effects. With the increase of planting density, the number of effective spikes per unit area of the three varieties spring wheat showed a significant increasing trend, while the number of grains per spike and 1 000-grain weight generally showed a decreasing trend, and the yield showed an initial increase and then a decrease. The yield of Liaochun 18 and Shenmai 155 reached the highest under the D4 treatment, which were 8 206.62 and 5 057.74 kg·hm^-2, respectively, the yield of Liaochun 43 reached the highest under the D2 treatment (7 191.61 kg·hm^-2). With the increase of planting density, the dry matter transport before flowering, the dry matter transport efficiency before flowering, the contribution rate of dry matter before flowering to grain yield, the dry matter accumulation after flowering, the contribution rate of dry matter after flowering to grain yield, and the SPAD value of flag leaves all showed a trend of increasing first and then decreasing, and all reached the maximum at D3 or D4 treatments. The LAI, grain number per leaf, grain weight per leaf, intercellular CO₂ concentration of flag leaves, net photosynthetic rate, stomatal conductance, and transpiration rate all showed an initial increase and then decrease trend with the increase of planting density. In conclusion, the optimum planting density was 8.25 million plants·hm^-2 for Liaochun 18 and Shenmai 155, and 5.25 million plants·hm^-2 for Liaochun 43.

Gamma-aminobutyric acid (GABA) synthesis in plants is primarily catalyzed by glutamate decarboxylase (GAD), a key enzyme in GABA metabolism that is involved in regulating reactive oxygen species (ROS) accumulation and stress responses. In this study, genome-wide identification of the wheat TaGAD gene family members was performed, followed by analyses of their structural characteristics, miRNA targeting relationships, expression responses to exogenous hormones including IAA, MeJA, ABA, SA, and H₂O₂, as well as expression profiles of the GAD gene family induced by biological stress from stripe rust infection. The results showed that a total of 19 TaGAD genes were identified in the wheat genome, distributed across 7 chromosomes. Analysis of gene structure and conserved motifs revealed that most members of this family contained the Pyridoxal_deC conserved domain. miRNA target gene prediction suggested that the expression of TaGAD genes might be regulated at the translational level by miRNAs. Transcriptome analysis indicated that TaGAD9-3B, TaGAD11-3D, TaGAD4-2B, and TaGAD2-2A exhibited relatively high expression levels at different developmental stages and in various tissues of wheat, and their expression was significantly altered under drought and high-temperature stresses. Quantitative real-time PCR analysis demonstrated that the transcript levels of TaGAD genes varied under exogenous substance treatments and stripe rust infection. Among them, the expression of TaGAD4-2B was most significantly up-regulated by IAA induction, with an increase of more than 300 fold, while TaGAD7-3A showed the highest expression change (24 fold up-regulation) under SA induction. Upon infection by the stripe rust physiological race CYR32, the expression levels of TaGAD15-4A and TaGAD19-4D were up-regulated more prominently, reaching 13 fold and 12 fold increases respectively, which were significantly higher than those of other family members. Through systematic identification, characteristic analysis, expression profiling and quantitative detection of stress responses of the wheat TaGAD gene family, this study lays a foundation for further in-depth research on the functions of TaGAD family members in wheat.

Based on the daily meteorological data from 1994 to 2023 and the growth period data of drought-saline wheat from agrometeorological experimental stations in the main coastal drought-saline wheat planting areas of Cangzhou, Hebei Province, this study systematically analyzed the variation characteristics of precipitation, sunshine, and thermal resources during the whole growth period and key growth stages of drought-saline wheat, and identified the key meteorological factors affecting its yield by using methods including linear trend estimation, Mann-Kendall (MK) trend test and mutation test, and Pearson correlation analysis. The results showed that the precipitation during the entire growth period of coastal drought-saline wheat in Cangzhou, Hebei Province showed an increasing trend. Combined with the precipitation in the summer of the sowing year and before sowing, natural precipitation in all years could meet the water demand of drought-saline wheat. However, there were significant differences among various growth stages. The proportions of years in which precipitation could meet the water demand during the sowing-overwintering stage, overwintering-regreening stage, regreening-heading stage, and heading-maturity stage are 76.7%, 66.7%, 1.4%, and 26.7%, respectively. The sunshine duration during the whole growth period and grain-filling stage of the wheat showed an increasing trend. A significant mutation in the sunshine duration during the grain-filling stage occurred in 2009, and the annual sunshine duration before and after the mutation differed by 358 h. The annual average sunshine duration during the grain-filling stage was 9.1 h·d^-1, indicating sufficient sunlight. The accumulated temperature above 0 ℃ during the growing season showed a significant increasing trend, with 76.7% of the years reaching over 2 000 ℃. For the negative accumulated temperature below 0 ℃, 50% of the years were within -200 ℃; however, the winter minimum temperature presents a significant decreasing trend, and 43.3% of the years drop below -15 ℃. The diurnal temperature range during the filling stage fell between 11.5-13.5 ℃ in 90% of the years, with a significant reduction observed in the 2010-2016 period. The number of days with the daily maximum temperature exceeding 32 ℃ during the filling stage increased significantly, while the number of days with the daily maximum temperature exceeding 35 ℃ decreases. The annual yield of coastal drought-saline wheat in Cangzhou, Hebei Province was significantly positively correlated (P<0.05) with the precipitation during the whole growth period, summer precipitation in the sowing year, pre-sowing precipitation, precipitation during the regreening to heading stage, and sunshine duration during the whole growth period and grain-filling stage. Water is the main limiting factor for coastal drought-saline wheat in Cangzhou, Hebei Province. It is necessary to improve water use efficiency through precision irrigation and rainwater harvesting technologies, and match cold-resistant, drought-tolerant, and early-maturing varieties to reduce climate risks.

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 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.

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.

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.