The use of biological stimulants promotes plant metabolism, which improves the utilization of mineral fertilizers and plant defense mechanisms against the effect of unfavorable factors. The paper discusses the results of winter wheat seed treatment with ‘Agrimitin’ and a fungicide disinfectant ‘Vintsit Forte’. It has been determined that seed treatment with the preparations promoted the increase of winter wheat productivity. In 2017–2018 after seed treatment with ‘Agrimitin’, the average productivity increase was 0.18 t/ha for the winter soft wheat variety ‘Lydia’ and 0.25 t/ ha for the winter durum wheat variety ‘Lazurit’ compared with the productivity of the control variety. The combined use of preparations promoted the productivity increase on 0.37 t / ha for the winter soft wheat and on 0.44 t /ha for the winter durum wheat. The structural analysis of productivity showed that the use of preparations increased the number of productive stems to 580–648 pcs/ m2 in comparison with 564–621 pcs/m2 of the control variety. A number of kernels per ear and mass of kernels per ear of winter soft wheat after seed treatment had no significant increase. Winter durum wheat showed better effect of seed treatment: ‘number of kernels per ear’ increased from 30.6 pcs. to 32.8 pcs., and ‘mass of kernels per ear’ raised from 1.38 g to 1.55 g. The maximum profitability of winter soft and durum wheat production was identified in the variant with a combined use of the preparations ‘Agrimitin’ + ‘Protravitel’ (184.2% and 208.6% respectively). At the same time, the net income rate of winter soft wheat increased on 3,136 rubles/ha and that of winter durum wheat increased on 4,624 rubles/ha, compared to the the net income rate of the control variety (58145 rubles/ ha and 64796 rubles/ha, respectively).

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie&ndash:Ames&ndash:Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha&minus:1, and an average relative error of &minus:6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.

Winter wheat is one of China's most important staple food crops, and its growth and productivity are influenced by climate. Given its importance, we investigated the influence of excess precipitation under recent climate change on winter wheat in east-central China during 1961–2017. Although annual precipitation in the studied region decreased slightly, it increased during the winter wheat flowering and maturity period (May to June). Concurrently, the number of late growing season sunshine hours decreased. Our results showed that about 44% of the years with excess precipitation and less than normal radiation (16 years) were associated with decreasing winter wheat yields. Furthermore, during most years, precipitation of 50% above normal resulted in large decreases in winter wheat production in Jiangsu and Anhui provinces, some of the wetter parts of the studied region. These results indicated that the grain yield variability of winter wheat was mainly influenced by excess precipitation in May, where precipitation could explain 70%–78% of yield variability in the wet parts. Moreover, excess precipitation can induce Fusarium head blight as well as wheat sprouting of pre-harvest, both affecting the grain quality of winter wheat. Projected increases in precipitation throughout the 21st century in the studied region, warrants further studies of how to maintain the winter wheat production in a changing climate.

Growing spring wheat in Inner Mongolia is challenging because of the short growing period, dry-hot winds, and heat-forced maturity. There are also problems with growing winter wheat varieties, such as frost damage, spring droughts, and “late spring cold”. These factors have restricted efforts to increase yields. In order to address these challenges, this study adopted a “spring wheat winter-sowing” planting model for growing wheat in the Hetao Plain Irrigation District in Inner Mongolia and studied wheat varieties with different vernalization requirements through three consecutive field trials. The effects of different sowing dates were analyzed on seed germination and seedling emergence, growth, material accumulation, and yield formation, and the differences were characterized from traditional spring wheat. The results indicated that delaying the sowing date increased the spring emergence rate of the wheat varieties. The winter-seeded spring wheat germinated and ripened after three and seven days, respectively, earlier than the spring-seeded. The grain yield for the winter-seeded wheat was parallel to the spring-seeded wheat. Compared with the spring-seeded wheat, the winter-seeded wheat displayed less panicles, but greater grains per spike, and a 1000-grain weight. When seeded in winter, Yongliang 4 performed better than Ningdong 11 and Henong 7106 in terms of the emergence rate, material accumulation, and grain yield. The best seeding time for the winter-seeded spring wheat in the Hetao Irrigation District of Inner Mongolia is early November.

Crop production responses to water and soil characteristics is important to understand and manage regional agricultural production. However, the coupling characteristic of these factors was less quantified and it lacks a deep understanding of its effect on the crop production. North China Region (NCR), an important food production region in China but also one of the most water-stressed areas, was selected as the study area. Based on the remote sensing monitoring techniques, region evapotranspiration model, multi-criteria evaluation model, the spatial distribution of winter wheat, actual evapotranspiration (ET), and soil fertility score (SF) was measured in sequence. This study proposed a novel coupling index of water consumption and soil fertility (CWS) to measure the coupling characteristic of ET and SF, and correlated it to the winter wheat yield and area. The results indicated a significant correlation between the CWS and winter wheat production but a non-significant correlation between SF and yield and area. CWS in NCR presented a global pattern of high values in the south but low values in the north, in accordance with that of winter wheat production. Coupling the information of water and soil, the CWS showed a higher correlation to winter wheat yield with the correlation coefficient of 0.588 than solely that of the ET with 0.437. A relative lower correlation of CWS than that of ET implied that ignore of soil fertility but emphasis on the water restrictions would limit the high crop yield and influence the optimal allocation of water and soil resources. Our finding demonstrated the effectiveness of CWS and its higher relation to the winter wheat production in NCR. Information of spatial coupling and mismatching between the winter wheat production and CWS was suggested to be considered as a guide of better spatial optimal allocation of winter wheat.

The article elucidates the necessity of improving the growing technology elements (nutrition systems) of new breeds of winter wheat considering climate changes and contrasting weather conditions of vegetation period of winter wheat. The analysis of winter wheat “Kubus” was conducted in conditions of production in central part of Forest-steppe on the basis of 3 factors experiment. There were used methods of research case and science and practical recommendations for winter wheat growing. During the research both general scientific methods (of dialectics, experiment, analysis and synthesis, hypothesis method) and special methods, among them: field method – the investigation of interaction between the subject of research with weather conditions factors in the central part of Forest-steppe with weather and agrotechnical factors in the central part of Forest-steppe; calculation and weight method – the determination of crop capacity of grain; math statistics: dispersion, correlation and regressive method and graphic data representation in experiment were used. Due to results of research it was revealed that weather conditions of spring and summer period of winter wheat vegetation during the time of experiment, at the hydrothermal coefficient, were contrasting. These facts made it possible to see objectively the plant reaction on growing conditions. It was found out the influence of fertilization system (the main and spring) and the usage of out-of-the root nutrition on the crop capacity of winter wheat during the years of experiment. During the main usage of N30Р60К60 the maximal crop capacity (5,9 t/h) was got by early spring nutrition N30 with variants of compatible usage of out-of-the root nutrition of concentrated solution of copper, nitrogen and sulfur and urea and ammonia admixture. The influence of weather conditions of intensive growing period and plant growth and formation and accumulation of reserve substances in the seed on the crop capacity of winter soft wheat was researched and defined. Analyzing the influence of researched factors the great effect on the crop capacity of winter wheat the usage of spring root and out-of-the root nutrition of sowing at the main fertilizing was revealed. Using the variant without main fertilizing the influence of interaction of compatible usage of root nitric and out-of-the root complex nutrition of winter wheat sowing is increasing. Taking into consideration the results of the research it was found out that levelling of negative influence of weather conditions of spring-summer vegetation of winter wheat can be reduced by the agrotechnical way: implementation of early spring root nutrition of sowing and usage of out-of-the root plant nutrition in the spring planting phase during the main fertilization. The biggest crop capacity of winter wheat (5,9 t/h was got using spring and early spring applying of 30 kg/h ammonium nitrate and compatible out-of-the root nutrition of winter wheat plant (concentrated solution of copper, nitrogen and sulfur and urea and ammonia admixture) with the main fertilizing N30Р60К60.

Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research.