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.

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.

To clarify how sowing method and nitrogen (N) rate regulate dry matter (DM) and nitrogen accumulation, remobilization, and their vertical distribution within a wheat canopy, a split-plot field experiment was conducted in Zhaoxian Experimental Base of Shijiazhuang Academy of Agriculture and Forestry Sciences from 2022 to 2024. A winter wheat cultivar Lunxuan 103 was used as the test material. Two planting methods of uniform tridimensional sowing and conventional strip sowing were set up in the main plot, and four nitrogen application rates of 0, 180, 240 and 300 kg·hm^-2 were set up in the subplot. At anthesis and maturity, aboveground organs were sampled by canopy layers at 20 cm intervals from top to bottom (H1-H4) to quantify DM and N accumulation, pre-anthesis remobilization amount and efficiency, and their contributions to grain. The results indicate that compared with conventional row planting, tridimensional uniform sowing significantly increased plant dry weight at anthesis, pre-anthesis DM and nitrogen transport, transport efficiency, and contribution to grain yield, while post-flowering DM, nitrogen accumulation, and contribution to grain yield were somewhat reduced. The allocation ratio of DM and nitrogen to stem and leaf sheath and leaves increased at anthesis, whereas the allocation ratio to spike rachis and glume decreased at maturity. Compared with conventional strip sowing, under uniform tridimensional sowing, the pre-flowering DM and nitrogen transport in stem and leaf sheath of layers H1 to H4 increased by 14.84% and 21.97%, 24.19% and 16.89%, 16.85% and 28.27%, 11.31% and 24.27%, respectively; for leaves, pre-flowering DM and nitrogen transport increased by 33.65% and 17.63%, 16.17% and 10.63%, 28.16% and 8.20%, and 23.81% and 22.57%, respectively. The increase in pre-flowering DM and nitrogen transport efficiency of stem and leaf sheath in different vertical layers under tridimensional uniform sowing compared to conventional strip sowing followed the order H2>H4>H1>H3 and H3>H2>H1>H4, respectively; for leaves, the increase in pre-flowering DM ranked as H3>H1>H4>H2. As nitrogen application increased, grain weight, dry matter transport, and dry matter and nitrogen transport efficiency in both planting methods first increased and then decreased, with the maximum observed under the 240 kg·hm^-2 nitrogen application; post-flowering DM accumulation, grain nitrogen accumulation, and nitrogen transport generally showed an upward trend. In summary, uniform tridimensional sowing mainly promotes increases in grain dry weight and nitrogen accumulation by enhancing the redistribution capacity of dry matter and nitrogen in the middle and upper layers (stem and leaf sheath of H2 and H3) and the middle and lower layers (leaves of H3 and H4). Under the conditions of this experiment, 240 kg·hm^-2 was the optimal nitrogen application rate.

To investigate the effects of potassium polyacrylate (K-PAM) application rates on the grain filling characteristics and yield formation of black hulless barley, a randomized block design was employed using the black hulless barley variety Liuling as the test material. Five K-PAM application rates (0, 6, 12, 18, and 24 kg·hm^-2, labeled as CK, T1, T2, T3, and T4, respectively) were carryed out for field experimenk. The results indicated that, compared with CK, application of K-PAM significantly increased the 1 000-grain weight of black hulless barley, with the increase rate of 0.92%, 10.64%, 5.27%, and 8.03% for T1, T2, T3, and T4 treatments, respectively. Under the T2 treatment, the maximum grain filling rate (R_max) of black hulless barley was significantly higher than that of CK, with an increase rate of 7.56%, grain yield was increased by 17.61%, the number of effective panicles was increased by 20.92%, and grains per panicle was increased by 22.42%. Thus, the application of K-PAM may help black Highland barley seeds to fill, promote the synergistic enhancement of panicle number, panicle grains number and grain weight, and increase grain yield. Under the conditions of this experiment, the optimal application rate of K-PAM is 12 kg·hm^-2.

To further explore the evolution patterns of wheat varieties in the dryland areas of the Huanghuai region, 67 wheat varieties approved by the state from 2006 to 2023 were used as experimental materials. The variation analysis, linear analysis, correlation analysis, path analysis and cluster analysis were conducted on the main agronomic traits (plant height, effective spike number, grain number per spike, thousand-grain weight and yield) and quality traits (protein content, wet gluten content, stability time, test weight and water absorption rate) of the wheat varieties in the dryland of the Huanghuai region. The results showed that the coefficient of variation of stability time of the wheat varieties in the dryland of the Huanghuai region was the largest, reaching 72.31%, while the coefficients of variation of other agronomic and quality traits were relatively small (2.23%-14.37%). Plant height and thousand-grain weight showed a gradually decreasing trend, with an average annual decrease of 0.21 cm and 0.03 g, respectively; grain number per spike, effective spike number and yield showed a steady upward trend, with an average annual increase of 0.11 grains, 4.71×10⁴ spikes·hm^-2 and 28.73 kg·hm^-2, respectively; protein content and wet gluten content showed a decreasing trend year by year, with an average annual decrease of 0.04% and 0.03%, respectively; stability time, test weight and water absorption rate showed a slow increasing trend, with an average annual increase of 0.2 min, 1.48 g·L^-1 and 0.04%, respectively. The results of correlation analysis showed that yield was significantly (P<0.05) or extremely significantly (P<0.01) positively correlated with thousand-grain weight, grain number per spike, and effective spike number, while plant height was significantly negatively correlated with yield; protein content was extremely significantly positively correlated with wet gluten content, and the test weight was significantly positively correlated with the water absorption rate. Path analysis indicated that the direct contribution to yield ranked as effective spike number (0.488)>thousand-grain weight (0.356)>grain number per spike (0.303)>plant height (-0.069). The cluster analysis classified the 67 varieties into four categories according to quality indicators. Class I varieties had the best performance in stability time and test weight; Class II varieties had the highest average values of water absorption rate, wet gluten content, and protein content; Class Ⅲ contained the most varieties, with the lowest average values of protein content and stability time among the four groups; Class IV varieties have the lowest test weight. In conclusion, for wheat breeding in the dryland of the Huanghuai region, it is necessary to appropriately reduce plant height while increase effective spike number, grain number per spike, and thousand-grain weight to enhance yield. At the same time, attention should be paid to improving the quality of the varieties to achieve high yield and quality.

To investigate the effects of different irrigation methods on the carbon balance of the wheat field ecosystem and to propose a water-saving, emission-reducing, and high-yield irrigation model suitable for wheat production in the North China Plain, three irrigation methods were set up: traditional surface irrigation (FI), alternate furrow irrigation (ABI), and micro-sprinkler irrigation (MSI), and three micro-sprinkler irrigation quotas of 20 mm (MSI20), 30 mm (MSI30), and 40 mm (MSI40) under the MSI conditions were set up to explore the impacts of different irrigation methods and micro-sprinkler irrigation quotas on soil respiration, net carbon value of the farmland ecosystem, and soil carbon emission efficiency in wheat fields. Compared with the FI treatment, the MSI treatments reduced soil respiration rate before heading stage but increased it during the grain-filling stage. The ABI treatment had a lower soil respiration rate due to the lower irrigation amount. The carbon emissions from irrigation power consumption accounted for 60.3% to 75.8% of the total carbon emissions from production materials of all treatments, being the main carbon source in irrigation agriculture inputs. Compared with the FI treatment, the average yields of the MSI20 and MSI30 treatments increased by 6.5% and 8.1%, respectively over the two years, and the net carbon value of the wheat field ecosystem was increased by 20.2% and 15.5%, respectively, and soil carbon emission efficiency was increased by 9.4% and 2.9% in 2022-2023, respectively. Although the ABI treatment achieved similar or even higher net carbon values of the wheat field ecosystem and soil carbon emission efficiency, its yield decreased by 5.1% to 9.8% when compared to that under FI treatment. Under micro-sprinkler irrigation conditions, the yield of winter wheat did not increase significantly with the increase of micro-sprinkler irrigation quota. Moreover, in the case of heavy rainfall during the middle of grain-filling stage, the yield, net carbon value of the wheat field ecosystem, and soil carbon emission efficiency with higher micro-sprinkler irrigation quota (MSI40 treatment in 2022-2023) were significantly reduced compared to other micro-sprinkler irrigation treatments. Under the conditions of this experiment, considering water conservation, emission reduction, yield, and environmental benefits, micro-sprinkler irrigation quotas of 20 mm is the best irrigation management model.

Hulless barley is a crop of significant economic and ecological value in the Qinghai-Tibet Plateau. Establishing a core germplasm resource bank based on its nutritional quality is crucial for promoting genetic improvement and the efficient utilization of hulless barley germplasm resources. In this study, a total of 286 hulless barley germplasm resources were selected, and a core germplasm subset was constructed based on 15 quality traits, including total flavonoid content. A multidimensional combination strategy was employed, incorporating two genetic distances, three sampling methods, seven sampling ratios, and eight clustering methods. By comparing the mean difference percentage, variance difference percentage, range conformity rate, and coefficient of variation change rate for various subsets, the most optimal sampling strategy for constructing the hulless barley core germplasm was identified. This strategy involved Euclidean distance, a multiple clustering deviation sampling method, a 10% sampling ratio, and the shortest distance method. This resulted in the establishment of a core germplasm bank consisting of 28 hulless barley germplasms based on nutritional quality traits. To validate the representativeness of the constructed core germplasm, key statistical parameters, including the mean, variance, range, coefficient of variation, diversity index, principal component analysis, and clustering analysis, were analyzed for both the original germplasm and the selected core germplasm across the 15 nutritional quality traits. The results showed no significant difference in the mean values of the 15 nutritional quality traits between the core and original germplasm. Moreover, the variance of each trait in the core germplasm was not lower than that of the original germplasm. The average diversity index conformity rate for the core germplasm reached 93.12%. Principal component analysis revealed that the cumulative contribution rate of the core germplasm was 82.04%, compared to 64.34% for the original germplasm. Cluster analysis further classified the 28 core germplasms into five distinct groups. The findings indicate that the core germplasm constructed in this study exhibits significant heterogeneity. It effectively retains most of the genetic information of the original germplasm while also representing the original germplasm adequately. This core germplasm can be utilized for further research on the nutritional quality of hulless barley and will contribute to the efficient use of germplasm resources.

In order to explore the evaluation methods and indicators of nitrogen efficiency at the maturity stage of spring wheat in Xinjiang and to screen high nitrogen-efficient germplasm resources, providing reference for breeding wheat varieties with high nitrogen-use efficiency, twenty spring wheat varieties widely cultivated in Xinjiang were used as research materials, and four nitrogen fertilizer levels were set up: 0 kg·hm^-2 (N0), 100 kg·hm^-2 (N1), 200 kg·hm^-2 (N2), and 300 kg·hm^-2 (N3). Eleven traits were investigated under different nitrogen levels, such as stem dry weight, leaf dry weight, weight per spike, grain number per spike, grain yield, plant height, and SPAD value of wheat at maturity. Principal component analysis (PCA) and membership function were used to comprehensively evaluate wheat varieties and classify nitrogen efficiency types. The results showed that, compared with N2 and N3 treatments, N0 and N1 treatments significantly resulted in reduced plant height during the wheat maturity stage, thinner stems and leaves, and lower grain yield; The coefficients of variation for most traits under N0 and N1 treatments were higher than those under N2 and N3. Correlation analysis showed that stem weight was significantly positively correlated with spike weight and grain weight under the four nitrogen levels, and spike number per unit area was negatively correlated with stem weight, spike weight, grain weight per spike, and 1 000-grain weight. Four principal components were extracted under each nitrogen level across two years, with cumulative contribution rates ranging from 75.63% to 82.20%. Based on the comprehensive nitrogen-use efficiency index (G value), the tested wheat varieties were classified into four categories: nitrogen-efficient type, nitrogen-inefficient type, low N fertilizer application nitrogen-efficient type, and high N fertilizer application nitrogen-efficient type. Combining G value and grain yield, seven high-yield and nitrogen-efficient varieties were identified, including Hechun 137, Liangchun 1242, Liangchun 1354, Xinchun 38, Xinchun 47, Xinchun 48, and Xinchun 6.

In order to clarify the impact of coupling canopy morphology with other modal information on estimating winter wheat leaf chlorophyll content (LCC), two wheat varieties, Shinong 086 and Hemai 2020 were used as materials. Four nitrogen application levels (0, 120, 240, and 360 kg·hm^-2) were set up in fields. This study used a low-altitude unmanned aerial vehicle (UAV) remote sensing platform equipped with a multispectral camera to obtain spectral data of winter wheat at key growth stages. The canopy morphology effect was calculated in combination with the digital elevation model (DEM), and Random Forest Regression (RF), Categorical Boosting (Catboost), and eXtreme Gradient Boosting (XGBoost) were applied to analyze the contribution of spectral information, index information, and canopy morphology information, among other multi-modal data, to the monitoring of LCC in winter wheat. The results showed that compared with spectral and vegetation index features, canopy morphology could improve the monitoring accuracy of LCC (r²=0.66), and the importance of DEM was superior to that of slope. Among the three machine learning algorithms, the LCC model constructed by Catboost could achieve high monitoring accuracy both in the case of single feature and multi-source feature coupling, especially when a single canopy morphology feature was used as the input variable. Multi-source feature coupling does not improve the monitoring accuracy of LCC, but the coupling of other features with canopy morphology features can improve the monitoring accuracy to a certain extent.

This study investigated the effects of different compost substitution ratios for chemical fertilizers and planting densities on wheat stem lodging resistance, and explored the feasibility of using vegetation indices derived from UAV-based multispectral imagery for lodging prediction, aiming to provide scientific guidance for fertilizer-density management and lodging forecasting in wheat production. Using the wheat cultivar Huaimai 43, a split-plot design was implemented during 2022-2024 with four compost substitution levels［0(T0), 10% (T1), 20% (T2), 30% (T3)］ and three planting densities (D1: 1.5 million plants·hm²; D2: 3.0 million plants·hm²; D3: 4.5 million plants·hm²). Stem lodging resistance traits were measured, and UVA multispectral images were acquired for lodging prediction. The results demonstrated that, under the same planting density, as the proportion of compost substitution increases, the height of wheat plants showed a trend of first decreasing and then increasing, the center of gravity height showed a fluctuating trend, and all were at the lowest in the T2 treatment;the lodging resistance index, fresh weight, mechanical strength, the number and area of large and small vascular bundles in the second basal internode, as well as hemicellulose, cellulose and lignin contents in basal internodes all first increased and then decreased, and peaked under T2. Under the same compost substitution level, as planting density increased, plant height and center of gravity height showed an increasing trend; whereas the lodging resistance index, fresh weight, mechanical strength, and the number and area of large and small vascular bundles in the second basal internode decreased. In contrast, hemicellulose, cellulose and lignin contents in basal internodes showed a trend of first increased and then decreased, reaching maxima under D2. Correlation analysis revealed that the lodging resistance index showed the strongest association with vegetation indices. Accordingly, key vegetation indices (NDVI, RVI, TVI, DVI, and RDVI) were selected to construct lodging resistance prediction models using three regression methods: Stepwise Multiple Linear Regression (SMLR), Partial Least Squares Regression (PLSR), and Principal Component Regression (PCAR). The SMLR model achieved the highest accuracy, with calibration and validation R² values of 0.43 and 0.76, respectively. In conclusion, a compost substitution ratio of 20% combined with a planting density of 1.5 million plants·hm^-2 conferred superior lodging resistance in wheat. The SMLR-based model utilizing vegetation indices effectively predicted the lodging resistance index.