Global climate change leads to frequent occurrence of low-temperature stress (LTS), which poses a serious threat to global food security. Here, environment-control phytotron experiments were conducted on cold-responsive cv. XM26 and cold-tolerant cv. YN19 during the anther differentiation period. Six LTS levels (4, 2, 0, -2, -4, -6 °C) and a control treatment (10 °C) were set to study the effects of different levels of LTS on wheat seed-setting characteristics and yield. LTS significantly decreased grain number per spike, 1 000-grain weight, and grain yield per plant (GYPP) of the two wheat cultivars. Each spike's grain number and weight distribution showed a quadratic curve, and the near-medium dominance of grain development was not affected by temperature. The grain number percentage and grain weight of wheat at different grain positions were G2 (2nd grain position) ≥ G1 (1st grain position) > G3 (3rd grain position) > G4 (4th grain position), in which G3 and G4 grain positions were more sensitive to LTS. In summary, LTS during the anther differentiation in wheat mainly led to a decrease in GYPP by significantly reducing the number and weight of inferior grains. Improving wheat cultivation measures and promoting the development of inferior grains are significant ways to prevent disasters and increase wheat quality and productivity in the future.

Lodging is a crucial factor that limits wheat yield and quality in wheat breeding. Therefore, accurate and timely determination of winter wheat lodging grading is of great practical importance for agricultural insurance companies to assess agricultural losses and good seed selection. However, using artificial fields to investigate the inclination angle and lodging area of winter wheat lodging in actual production is time-consuming, laborious, subjective, and unreliable in measuring results. This study addresses these issues by designing a classification-semantic segmentation multitasking neural network model MLP_U-Net, which can accurately estimate the inclination angle and lodging area of winter wheat lodging. This model can also comprehensively, qualitatively, and quantitatively evaluate the grading of winter wheat lodging. The model is based on U-Net architecture and improves the shift MLP module structure to achieve network refinement and segmentation for complex tasks. The model utilizes a common encoder to enhance its robustness, improve classification accuracy, and strengthen the segmentation network, considering the correlation between lodging degree and lodging area parameters. This study used 82 winter wheat varieties sourced from the regional experiment of national winter wheat in the Huang-Huai-Hai southern area of the water land group at the Henan Modern Agriculture Research and Development Base. The base is located in Xinxiang City, Henan Province. Winter wheat lodging images were collected using the unmanned aerial vehicle (UAV) remote sensing platform. Based on these images, winter wheat lodging datasets were created using different time sequences and different UAV flight heights. These datasets aid in segmenting and classifying winter wheat lodging degrees and areas. The results show that MLP_U-Net has demonstrated superior detection performance in a small sample dataset. The accuracies of winter wheat lodging degree and lodging area grading were 96.1% and 92.2%, respectively, when the UAV flight height was 30 m. For a UAV flight height of 50 m, the accuracies of winter wheat lodging degree and lodging area grading were 84.1% and 84.7%, respectively. These findings indicate that MLP_U-Net is highly robust and efficient in accurately completing the winter wheat lodging-grading task. This valuable insight provides technical references for UAV remote sensing of winter wheat disaster severity and the assessment of losses.

Vernalization is necessary for winter wheat to flower. However, it is unclear whether vernalization is also required for spring wheat, which is frequently sown in fall, and what molecular mechanisms underlie the vernalization response in wheat varieties. In this study, we examined the molecular mechanisms that regulate vernalization response in winter and spring wheat varieties. For this purpose, we determined how major vernalization genes (VRN1, VRN2, and VRN3) respond to vernalization in these varieties and whether modifications to histones play a role in changes in gene expression. We also identified genes that are differentially regulated in response to vernalization in winter and spring wheat varieties. We found that in winter wheat, but not in spring wheat, VRN1 expression decreases when returned to warm temperature following vernalization. This finding may be associated with differences between spring and winter wheat in the levels of tri-methylation of lysine 27 on histone H3 (H3K27me3) and tri-methylation of lysine 4 on histone H3 (H3K4me3) at the VRN1 gene. Analysis of winter wheat transcriptomes before and after vernalization revealed that vernalization influences the expression of several genes, including those involved in leucine catabolism, cysteine biosynthesis, and flavonoid biosynthesis. These findings provide new candidates for further study on the mechanism of vernalization regulation in wheat.

Grain yield and quality as well as the weed infestation of winter wheat grown after potatoes, peas and winter wheat were evaluated in the study. The experiment was established in a system of randomized blocks, in three replications. The experimental results were statistically processed via the analysis of variance method. Coefficients of Pearson’s linear correlation between grain yield and its components, grain quality parameters, and the number and air-dry weight of weeds were also calculated. Grain yields of winter wheat were higher when it was grown after potato and pea than after winter wheat. In addition, winter wheat grains harvested from plots with potato and pea as previous crops had a higher total protein content and a higher sedimentation index than those harvested from plots with winter wheat as the previous crop. However, the study years affected the protein, gluten and starch contents of winter wheat grain to a greater extent than the previous crops did. A higher number of weeds with a higher air-dry weight was recorded on the post-winter wheat than on the post-potato and post-pea plots. Negative values of correlation coefficients were computed between the number of weeds and their air-dry weight and grain yield, number of spikes, grain weight per spike, 1000 grain weight, total protein content of the grain, and the value of sedimentation index as well as between weed number and wet gluten content of the grain. | Grain yield and quality as well as the weed infestation of winter wheat grown after potatoes, peas and winter wheat were evaluated in the study. The experiment was established in a system of randomized blocks, in three replications. The experimental results were statistically processed via the analysis of variance method. Coefficients of Pearson’s linear correlation between grain yield and its components, grain quality parameters, and the number and air-dry weight of weeds were also calculated. Grain yields of winter wheat were higher when it was grown after potato and pea than after winter wheat. In addition, winter wheat grains harvested from plots with potato and pea as previous crops had a higher total protein content and a higher sedimentation index than those harvested from plots with winter wheat as the previous crop. However, the study years affected the protein, gluten and starch contents of winter wheat grain to a greater extent than the previous crops did. A higher number of weeds with a higher air-dry weight was recorded on the post-winter wheat than on the post-potato and post-pea plots. Negative values of correlation coefficients were computed between the number of weeds and their air-dry weight and grain yield, number of spikes, grain weight per spike, 1000 grain weight, total protein content of the grain, and the value of sedimentation index as well as between weed number and wet gluten content of the grain.

Knowledge of the responses of winter wheat yield to meteorological dryness/wetness variations is crucial for reducing yield losses in Henan province, China’s largest winter wheat production region, under the background of climate change. Data on climate, yield and atmospheric circulation indices were collected from 1987 to 2017, and monthly self-calibrating Palmer drought severity index (sc-PDSI) values were calculated during the winter wheat growing season. The main results were as follows: (1) Henan could be partitioned into four sub-regions, namely, western, central-western, central-northern and eastern regions, based on the evolution characteristics of the time series of winter wheat yield in 17 cities during the period of 1988–2017. Among them, winter wheat yield was high and stable in the central-northern and eastern regions, with a remarkable increasing trend (<i>p</i> < 0.05). (2) The sc-PDSI in February had significantly positive impacts on climate-driven winter wheat yield in the western and central-western regions (<i>p</i> < 0.05), while the sc-PDSI in December and the sc-PDSI in May had significantly negative impacts on climate-driven winter wheat yield in the central-northern and eastern regions, respectively (<i>p</i> < 0.05). (3) There were time-lag relationships between the sc-PDSI for a specific month and the atmospheric circulation indices in the four sub-regions. Furthermore, we constructed multifactorial models based on selected atmospheric circulation indices, and they had the ability to simulate the sc-PDSI for a specific month in the four sub-regions. These findings will provide scientific references for meteorological dryness/wetness monitoring and risk assessments of winter wheat production.

The possible influence of global climate changes on agricultural production is becoming increasingly significant, necessitating greater attention to improving agricultural production in response to temperature rises and precipitation variability. As one of the main winter wheat-producing areas in China, the temporal and spatial distribution characteristics of precipitation, accumulated temperature, and actual yield and climatic yield of winter wheat during the growing period in Shanxi Province were analysed in detail. With the utilisation of daily meteorological data collected from 12 meteorological stations in Shanxi Province in 1964–2018, our study analysed the change in winter wheat yield with climate change using GIS combined with wavelet analysis. The results show the following: (1) Accumulated temperature and precipitation are the two most important limiting factors among the main physical factors that impact yield. Based on the analysis of the ArcGIS geographical detector, the correlation between the actual yield of winter wheat and the precipitation during the growth period was the highest, reaching 0.469, and the meteorological yield and accumulated temperature during this period also reached its peak value of 0.376. (2) The regions with more suitable precipitation and accumulated temperature during the growth period of winter wheat in the study area had relatively high actual winter wheat yields. Overall, the average actual yield of the entire region showed a significant increasing trend over time, with an upward trend of 47.827 kg ha⁻¹ yr⁻¹. (3) The variation coefficient of winter wheat climatic yield was relatively stable in 2008–2018. In particular, there were many years of continuous reduction in winter wheat yields prior to 2006. Thereafter, the impact of climate change on winter wheat yields became smaller. This study expands our understanding of the complex interactions between climate variables and crop yield but also provides practical recommendations for enhancing agricultural practices in this region

In order to further improve the utility of unmanned aerial vehicle (UAV) remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching, ridge–furrow full mulching, and flat cropping full mulching in winter wheat. Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV. Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks (ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out. The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status. The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75, a root mean square error of 8.40, and a mean absolute value error of 6.53. Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remote-sensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification. The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.

Field experiments were carried out at the Experimental Station of Vytautas Magnus University Agriculture Academy in Lithuania (54° 53' 3.26", 23° 50' 33.25") during 2017-2018. Six winter wheat varieties were studied in the experiment: 'Skagen' (control), 'Julius', 'Edvins', 'Artist', 'Aron' and 'Evina', which were sown on September 15. Preceding crop – winter rape. Seed rate – 4.5 million ha-1. The aspects of the dynamics of photosynthesis pigments in winter wheat leaves depending on variety is analysed in the article. Physiological activity of the plant, the growth and development are the most important moments decisive the accumulation of these pigments in the plant. Thus, the general condition of the plant might be described by the composition and content of photosynthesis pigments chlorophyll a, b and carotenoids. Spectrophotometric Wettstein method and “Genesys” 6 spectrophotometer were used for determination of the content of photosynthesis pigments (chlorophyll a, b and carotenoids) in green leaf mass in 96 % ethyl alcohol extract. The accumulated amounts of photosynthetic pigments (chlorophylls a, b and carotenoids) in different varieties of winter wheat leaves differed. Winter wheat variety 'Aron' accumulated the highest contents of photosynthetic pigments during the tillering and stem elongation stages, meanwhile during the heading – anthesis and early milky maturity stages winter wheat varieties 'Artist' and 'Skagen' demonstrated the best results. The LAI of different varieties of winter wheat differed in the field experiment. Significantly higher LAI was defined for winter wheat variety Artist (0.9) after the resumption of vegetation (BBCH 24–27) and for 'Skagen' (4.0) at the end of vegetation (BBCH 70–73). The highest LAI of winter wheat is determined during the heading - anthesis stage (varieties 'Aron', 'Edvins', 'Skagen'). During the growing season, the lowest LAI was observed for winter wheat variety 'Evina'.