Properly designed crop rotation enriches the diversity of the agroecosystem, which has a favorable effect on the environmental properties and crop yield. The experiment assessed winter wheat cultivated under the following crop rotations: A. winter rape + catch crop − spring barley − field pea − winter wheat; B. winter rape − winter wheat + catch crop − field pea − winter wheat; C. winter rape + catch crop − field pea − winter wheat − winter wheat; D. winter rape − winter wheat + catch crop − spring barley − winter wheat. The aim of the study was to investigate: (i) whether the cultivation of wheat in crop rotations following forecrops of rape, pea, barley, and wheat will affect its foliage and photosynthesis; (ii) how the photosynthetic process will affect the wheat yield. At the stem elongation stage (BBCH 36) and at the heading stage (BBCH 58), the following were investigated: foliage features, photosynthetic parameters, and the above-ground biomass; while at the BBCH 89 stage, the grain yield was investigated. It was demonstrated that photosynthesis was most intense under crop rotation A (the highest stomatal conductance, transpiration, intercellular CO₂ concentration, and net assimilation rate ranged from 13.1–29.7 μmol CO₂∙m⁻²·s⁻¹). This was reflected in the above-ground biomass volume (1245–1634 g m⁻²) and grain yield (4.58–7.65 t ha⁻¹). The cultivation of wheat following wheat under crop rotation C and following barley under D had a negative effect on both the foliage and photosynthetic parameters.

Winter wheat is one of the major food crops in China, and timely and effective early-season identification of winter wheat is crucial for crop yield estimation and food security. However, traditional winter wheat mapping is based on post-season identification, which has a lag and relies heavily on sample data. Early-season identification of winter wheat faces the main difficulties of weak remote sensing response of the vegetation signal at the early growth stage, difficulty of acquiring sample data on winter wheat in the current season in real time, interference of crops in the same period, and limited image resolution. In this study, an early-season refined mapping method with winter wheat phenology information as priori knowledge is developed based on the Google Earth Engine cloud platform by using Sentinel-2 time series data as the main data source; these data are automated and highly interpretable. The normalized differential phenology index (NDPI) is adopted to enhance the weak vegetation signal at the early growth stage of winter wheat, and two winter wheat phenology feature enhancement indices based on NDPI, namely, wheat phenology differential index (WPDI) and normalized differential wheat phenology index (NDWPI) are developed. To address the issue of “ different objects with the same spectra characteristics” between winter wheat and garlic, a plastic mulched index (PMI) is established through quantitative spectral analysis based on the differences in early planting patterns between winter wheat and garlic. The identification accuracy of the method is 82.64% and 88.76% in the early overwintering and regreening periods, respectively, These results were consistent with official statistics (R2 = 0.96 and 0.98, respectively). Generalization analysis demonstrated the spatiotemporal transferability of the method across different years and regions. In conclusion, the proposed methodology can obtain highly precise spatial distribution and planting area information of winter wheat 4_6 months before harvest. It provides theoretical and methodological guidance for early crop identification and has good scientific research and application value.

Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation points, and the main producing areas were from south to north. Based on Landsat 8 satellite remote sensing images, the changes in NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and NDWI (Normalized Difference Water Index) were analyzed at different growth stages of winter wheat in 2020. Multiple regression analysis and Back Propagation (BP) neural network machine learning methods were used to establish prediction models for the harvest time of winter wheat at different growth stages. The results showed that the prediction model based on a BP neural network had high accuracy. The RMSE, MAE and MAPE of the training set and the test set were 0.531 and 0.5947, 0.3001 and 0.3104, 0.0114% and 0.0119%, respectively. The prediction model of winter wheat harvest date based on BP neural network was verified in the main winter wheat producing areas of Henan province in 2020 and 2021. The average errors were 1.67 days and 2.13 days, which were less than 3 days, meeting the needs for winter wheat production and harvest. The grain water content of winter wheat at harvest time calculated by the prediction model reached the grain water standard of the wheat combine harvester. Therefore, the prediction of the winter wheat harvest time can be realized based on multiple remote sensing indicators.

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security. However, using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions. Thus, we proposed a new approach to approximating irrigations of winter wheat over the North China Plain (NCP), where irrigation occurs extensively during the winter wheat growing season. This approach used irrigation pattern parameters (IPPs) to define the irrigation frequency and timing. Then, they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat (PRYM–Wheat), to improve the regional estimates of winter wheat over the NCP. The IPPs were determined using statistical yield data of reference years (2010–2015) over the NCP. Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield, with an increase and decrease in the correlation coefficient (R) and root mean square error (RMSE) of 0.15 (about 37%) and 0.90 t ha–1 (about 41%), respectively. The data in validation years (2001–2009 and 2016–2019) were used to validate PRYM–Wheat. In addition, our findings also showed R (RMSE) of 0.80 (0.62 t ha–1) on a site level, 0.61 (0.91 t ha–1) for Hebei Province on a county level, 0.73 (0.97 t ha–1) for Henan Province on a county level, and 0.55 (0.75 t ha–1) for Shandong Province on a city level. Overall, PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years, providing a scientific basis for ensuring regional food security.

Growing technologies and variety selection are crucial for increasing yields and improving the quality of winter wheat grain. The area under winter wheat is the largest in Ukraine, which is associated with the production of high-quality grain. The aim of the study was to determine the influence of weather conditions and variety characteristics on winter wheat plant height, internode length, lodging resistance, productivity factor and yield during the years under study. In the course of the study, the following generally accepted methods were used: systematic approach, system analysis approach, analytical synthesis approach, field approach and statistical approach. The article presents data on the results of research with 20 varieties of soft winter wheat in the conditions of the Training and Practical Centre of Mykolaiv National Agrarian University from 2017 to 2023. Agricultural technology for growing winter wheat varieties is widespread in the steppes of southern Ukraine. The influence of weather conditions and varietal characteristics on lodging resistance and productivity of winter wheat was investigated. The results showed that the optimum plant height of winter wheat varieties ranged from 82.1 to 84.5 cm, and the highest yield was 6.32 t/ha in Staleva and 6.68 t/ha in Duma Odeska. A significant effect of varietal traits on stem length, second and last internode, number of stems formed per 1 m2 , grain weight in the ear and lodging resistance of winter bread wheat was established. Plants of the tested winter soft wheat varieties Staleva, Dyvo, Katarina, Felix, Ozerna, PONTICUS, Faust, Glaucus have a very high (9.0 points) resistance to lodging regardless of the weather conditions of the year. Ukrainian varieties Duma Odeska (6.68 t/ha) and Staleva (6.32 t/ha) achieved higher productivity. The obtained scientific results of the research contribute to the widespread use of the studied winter wheat varieties of this reasonably climatic zone and contribute to further improvement of grain production

Winter wheat is one of the main food crops that ensures the national food security of Ukraine with general production and high-quality grain. The area of winter wheat sowing ranks first in Ukraine, and the production of high-quality grain is of particular relevance. In the technology of its cultivation, the selection of varieties is a decisive factor in increasing yield and improving the quality of grain. The purpose of this study was to determine the influence of weather conditions and variety characteristics during the year on plant height, lodging resistance, and yield of winter wheat. This paper presents data on the results of the research of 20 varieties of soft winter wheat in the conditions of the Research-to-Practice Centre of the Mykolaiv National Agrarian University from 2017 to 2022. During the study, generally accepted methods were used: system approach, system analysis, analysis and synthesis, field and statistical method. The study also analysed the influence of variety characteristics and weather conditions in the years under study on the yield of soft winter wheat. It was established that the optimal plant height of winter wheat varieties ranged within 94.9-100.7 cm, at which the highest grain yield is formed, from 7.09 t/ha in the Staleva variety to 7.73 t/ha in the Duma Odeska variety. An increase in the height of the winter wheat varieties under study by 4.0-6.4% led to a decrease in yield from 4.5 to 20.9%. It was established that the awned varieties of soft winter wheat formed a higher grain yield, compared to the thornless forms, which had a higher plant height and less lodging resistance. The conducted studies confirmed that, on average, the resistance to lodging was higher in the thornless forms of winter wheat plants, which is 0.4 points higher than in the awned ones. The higher average grain yield of soft winter wheat was formed by varieties of spiny forms, which is 5.8% more than that of awned varieties. The obtained results will contribute to the wide application of the varieties of soft winter wheat under study for this soil and climate zone, which will further contribute to the increase in yield and gross grain harvests

As a major world crop, the accurate spatial distribution of winter wheat is important for improving planting strategy and ensuring food security. Due to big data management and processing requirements, winter wheat mapping based on remote-sensing data cannot ensure a good balance between the spatial scale and map details. This study proposes a rapid and robust phenology-based method named &ldquo:enhanced time-weighted dynamic time warping&rdquo: (E-TWDTW), based on the Google Earth Engine, to map winter wheat in a finer spatial resolution, and efficiently complete the map of winter wheat at a 10-m resolution in Henan Province, China. The overall accuracy and Kappa coefficient of the resulting map are 97.98% and 0.9469, respectively, demonstrating its great applicability for winter wheat mapping. This research indicates that the proposed approach is effective for mapping large-scale planting patterns. Furthermore, based on comparative experiments, the E-TWDTW method has shown excellent robustness across lower quantities of training data and early season extraction ability. Therefore, it can provide early data preparation for winter wheat planting management in the early stage.

Winter wheat is the main food crop in the Steppe zone and the main agricultural crop in many countries of the world. The issues of intensifying grain production are inseparable from the production and use of new effective biological products for pre-sowing seed treatment, which positively affect the growth and development of winter wheat plants. The purpose of the study was to establish the effect of seed treatment before sowing with biological products on the productivity of winter wheat varieties. The study presents data on the results of an examination of 10 varieties of winter wheat for seed treatment with biological products in the conditions of the Educational and Scientific Practical Centre of the Mykolaiv National Agrarian University from 2020 to 2022. In the course of the study, generally accepted methods were used: system approach and system analysis, analysis and synthesis, field, and statistical. The influence of seed treatment with biological products and varietal characteristics of winter wheat on productivity was analysed. It was determined that the yield level depended and varied depending on the biological product used for pre-sowing seed treatment and the examined variety. Over the years of the study, biological products have affected the Winter wheat is the main food crop in the Steppe zone and the main agricultural crop in many countries of the world. The issues of intensifying grain production are inseparable from the production and use of new effective biological products for pre-sowing seed treatment, which positively affect the growth and development of winter wheat plants. The purpose of the study was to establish the effect of seed treatment before sowing with biological products on the productivity of winter wheat varieties. The study presents data on the results of an examination of 10 varieties of winter wheat for seed treatment with biological products in the conditions of the Educational and Scientific Practical Centre of the Mykolaiv National Agrarian University from 2020 to 2022. In the course of the study, generally accepted methods were used: system approach and system analysis, analysis and synthesis, field, and statistical. The influence of seed treatment with biological products and varietal characteristics of winter wheat on productivity was analysed. It was determined that the yield level depended and varied depending on the biological product used for pre-sowing seed treatment and the examined variety. Over the years of the study, biological products have affected the density of plants, the coefficient of productive tillering, the number of productive stems, the mass of grain per ear, 1000 grain weight, and the yield of winter wheat varieties. The best results were obtained with the combined use of the examined biological products Azotophyt-R and Phytocide-R. The conducted studies confirmed the feasibility of pre-sowing seed treatment with biological products to optimise the nutrition of plants of winter wheat varieties to form a high grain yield. The scientific achievements obtained will contribute to the widespread use of biological products for seed treatment, ensure rapid and full-fledged growth and development of winter wheat varieties, and further increase grain production and gross harvest