Winter Wheat Area Research Articles

Automatic mapping of winter wheat planting structure and phenological phases using time-series sentinel data

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.

Trade-off between the future water resource utilization and grain production in a water-deficient region from the perspective of the Water−Land−Grain nexus

The utilization and future evolution of water resources in water-deficient regions will have a significant impact on sustainable agricultural development. The trade-off between water resource utilization and grain production in the Daqing River Basin (DRB) was explored, and the characteristics of the future evolution of the water − land − grain system was analyzed. The main planting patterns of farmland in the DRB are winter wheat, spring maize, summer maize, or a winter wheat − summer maize rotation. The total water consumption and supply are 61.6 × 108 and 54.4 × 108 m3, respectively. The water supply gap is approximately 7.2 × 108 m3, which is mainly supplemented by the overexploitation of groundwater. Under the current land use structure, grain production in the DRB has achieved self-sufficiency, but excessive water consumption in agricultural production has led to severe groundwater overexploitation. Despite the exploration and implementation of a fallow land policy in the DRB, the quantity of water resources conserved currently falls significantly short in comparison to the extent of groundwater overexploitation. From the trade-off between water resource utilization and grain production, if winter wheat areas were left completely fallow, or winter wheat planting regions establish three crops in two years or a single crop in a year, the total amount of irrigation water that could be saved was 18.4 × 108, 7.5 × 108, and 15.2 × 108 m3, although the grain production capacity would be reduced by 31 %, 12 %, and 24 %, respectively. This study proposes prioritizing fallow areas in the winter wheat planting regions with high water demand located in the Diandong Plain, and changing the current system of two crops a year to three crops in two years or a single crop a year. The implementation of this approach is anticipated to alleviate the strain on water resources. To achieve the control of groundwater overexploitation and balance the supply and demand of water resources, it is necessary to continue to expand the fallow area of winter wheat in the eastern and southern plains of Baiyangdian by approximately 25.3 × 104 hm2, or increase the amount of water diverted from outside the basin by approximately 7.20 × 108 m3. By 2030, the DRB will face the dual pressures of both water and grain security, with a water resource gap of 13.8 × 108 m3. It is therefore necessary to increase inter-basin water transfer or continue to expand the fallow area of winter wheat in the future.

Identification and Analysis of Long-Term Land Use and Planting Structure Dynamics in the Lower Yellow River Basin

Accurate identification of the spatio-temporal planting structure and analysis of its driving factors in an irrigation district are the important bases for scientific and reasonable utilization of irrigation water resources. In pursuit of this goal, the training sample migration method combined with the random forest algorithm were used to classify land use and planting structure over 2001–2022 in the lower Yellow River Basin. Moreover, an econometric regression model was applied to quantify the driving factors of the change in the crop-planted area. The results illustrated that the identification method has relatively high accuracy in identifying historical periods of land use and planting structures, with the average kappa coefficient equating to 0.953. From 2001 to 2022, the area of cultivated land was the largest, with the proportion of the total area increasing from 45.72% to 58.12%. The planted area of winter wheat–summer maize rotation increased from 74.84% to 88.11% of the cultivated land. While the planted area of cotton declined by 96.36%, about 50% of cotton planting was converted to the winter wheat–summer maize rotation planting. The government policies about grain purchase and storage were the dominant factors for the change in the crop-planted area. This resulted in an increase of 63.32 × 103 ha and 63.98 × 103 ha in the planted area of winter wheat and summer maize, respectively. The findings are of great significance to the allocation of water resources in irrigation districts of the lower Yellow River Basin.

Long-term (2013–2022) mapping of winter wheat in the North China Plain using Landsat data: classification with optimal zoning strategy

ABSTRACT Long-term mapping of winter wheat is vital for assessing food security and formulating agricultural policies. Landsat data are the only available source for long-term winter wheat mapping in the North China Plain due to the fragmented landscape in this area. Although various methods, such as index-based methods, curve similarity-based methods and machine learning-based methods, have been developed for winter wheat mapping based on remote sensing, the former two often require satellite data with high temporal resolution, which are unsuitable for Landsat data with sparse time-series. Machine learning is an effective method for crop classification using Landsat data. Yet, applying machine learning for winter wheat mapping in the North China Plain encounters two main issues: 1) the lack of adequate and accurate samples for classifier training; and 2) the difficulty of training a single classifier to accomplish the large-scale crop mapping due to the high spatial heterogeneity in this area. To address these two issues, we first designed a sample selection rule to build a large sample set based on several existing crop maps derived from recent Sentinel data, with specific consideration of the confusion error between winter wheat and winter rapeseed in the available crop maps. Then, we developed an optimal zoning method based on the quadtree region splitting algorithm with classification feature consistency criterion, which divided the study area into six subzones with uniform classification features. For each subzone, a specific random forest classifier was trained and used to generate annual winter wheat maps from 2013 to 2022 using Landsat 8 OLI data. Field sample validation confirmed the high accuracy of the produced maps, with an average overall accuracy of 91.1% and an average kappa coefficient of 0.810 across different years. The derived winter wheat area also has a good correlation (R2 = 0.949) with census area at the provincial level. The results underscore the reliability of the produced annual winter wheat maps. Additional experiments demonstrate that our proposed optimal zoning method outperforms other zoning methods, including Köppen climate zoning, wheat planting zoning and non-zoning methods, in enhancing wheat mapping accuracy. It indicates that the proposed zoning is capable of generating more reasonable subzones for large-scale crop mapping.

The G143S mutation in cytochrome b confers high resistance to pyraclostrobin in Fusarium pseudograminearum.

Wheat crown rot (WCR), primarily caused by Fusarium pseudograminearum has become more and more prevalent in winter wheat areas in China. However, limited fungicides have been registered for the control of WCR in China so far. Pyraclostrobin is a representative quinone outside inhibitor (QoI) with excellent activity against Fusarium spp. There is currently limited research on the resistance risk and resistance mechanism of F. pseudograminearum to pyraclostrobin. Here, we determined the activity of pyraclostrobin against F. pseudograminearum. The EC50 values ranged from 0.022 to 0.172 μg mL-1 with an average EC50 value of 0.071 ± 0.030 μg mL-1. Four highly pyraclostrobin-resistant mutants were obtained from two sensitive strains by ultraviolet (UV) mutagenesis in the laboratory. The mutants showed decreased mycelial growth rate and virulence as compared with the corresponding wild-type strains, indicating that pyraclostrobin resistance suffered a fitness penalty in F. pseudograminearum. It was found that the high resistance of four mutants was caused by the G143S mutation in Cytb. Molecular docking analysis also further confirms that the G143S mutation in Cytb decreased the binding affinity between pyraclostrobin and Cytb. The resistance risk of F. pseudograminearum to pyraclostrobin could be low to medium. Although a mutation at the G143S position of Cytb could potentially occur, this mutation decreases the fitness of the mutant, which may reduce its survival in the environment. Therefore, the negative consequences of a possible mutation are lower. This makes pyraclostrobin a good candidate for controlling crown rot in wheat. © 2024 Society of Chemical Industry.

GACDNet:Mapping winter wheat by generative adversarial cross-domain networks with transformer integration for zero-sample extraction

Accurate extraction of winter wheat area is essential for wheat yield estimation. Remotely sensed images have limited coverage, are taken at different times, from different angles and in different geographical areas, and the spectral information of the same feature varies from image to image. Machine learning methods are very popular in extracting winter wheat planting area, there are differences between different images, namely the distribution differences between source domain data and target domain data, the results obtained by these methods directly applied to other areas are not satisfactory. To achieve cross regional extraction of winter wheat area, we propose generative adversarial cross-domain networks for image classification under the same image type. The cross-domain network proposed comprises a generative network and a feature extractor. The generative network creates diverse and acceptable samples from the initial input data, constrained by the contrast loss. The feature extractor converts the initial input data and the generated data into a high-level representation. Two time series datasets were constructed and a series of experiments were conducted on these datasets, obtaining high-precision and optimal classification results. Sample training was performed using 70%, 50%, 30% and 10% of the data on both datasets. The study results demonstrated the high performance and cross-domain capability of our network. Furthermore, the winter wheat cultivation area of the entire Zhoukou city was extracted, yielding results with an accuracy of 94.23%. Extensive experiments and applications on the dataset demonstrate that our method is feasible and reliable.

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

Drought Risk Assessment of Winter Wheat at Different Growth Stages in Huang-Huai-Hai Plain Based on Nonstationary Standardized Precipitation Evapotranspiration Index and Crop Coefficient

Soil moisture plays a crucial role in determining the yield of winter wheat. The Huang-Huai-Hai (HHH) Plain is the main growing area of winter wheat in China, and frequent occurrence of drought seriously restricts regional agricultural development. Hence, a daily-scale Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI), based on winter wheat crop coefficient (Kc), was developed in the present study to evaluate the impact of drought characteristics on winter wheat in different growth stages. Results showed that the water demand for winter wheat decreased with the increase in latitude, and the water shortage was affected by effective precipitation, showing a decreasing trend from the middle to both sides in the HHH Plain. Water demand and water shortage showed an increasing trend at the jointing stage and heading stage, while other growth stages showed a decreasing trend. The spatial distributions of drought duration and intensity were consistent, which were higher in the northern region than in the southern region. Moreover, the water shortage and drought intensity at the jointing stage and heading stage showed an increasing trend. The drought had the greatest impact on winter wheat yield at the tillering stage, jointing stage, and heading stage, and the proportions of drought risk vulnerability in these three stages accounted for 0.25, 0.21, and 0.19, respectively. The high-value areas of winter wheat loss due to drought were mainly distributed in the northeast and south-central regions.

Characterization of a wheat stable QTL for spike length and its genetic effects on yield-related traits

Spike length (SL) is one of the most important agronomic traits affecting yield potential and stability in wheat. In this study, a major stable quantitative trait locus (QTL) for SL, i.e., qSl-2B, was detected in multiple environments in a recombinant inbred line (RIL) mapping population, KJ-RILs, derived from a cross between Kenong 9204 (KN9204) and Jing 411 (J411). The qSl-2B QTL was mapped to the 60.06–73.06 Mb region on chromosome 2B and could be identified in multiple mapping populations. An InDel molecular marker in the target region was developed based on a sequence analysis of the two parents. To further clarify the breeding use potential of qSl-2B, we analyzed its genetic effects and breeding selection effect using both the KJ-RIL population and a natural mapping population, which consisted of 316 breeding varieties/advanced lines. The results showed that the qSl-2B alleles from KN9204 showed inconsistent genetic effects on SL in the two mapping populations. Moreover, in the KJ-RILs population, the additive effects analysis of qSl-2B showed that additive effect was higher when both qSl-2D and qSl-5A harbor negative alleles under LN and HN. In China, a moderate selection utilization rate for qSl-2B was found in the Huanghuai winter wheat area and the selective utilization rate for qSl-2B continues to increase. The above findings provided a foundation for the genetic improvement of wheat SL in the future via molecular breeding strategies.

Technology for Production of Wheat Doubled Haploid via Maize Pollen Induction—Updated Review

Chromosome elimination resulting in haploids is achieved by rapid loss of chromosomes from one parent during the zygote stage and is an important procedure to produce doubled haploid (DH) lines in plants. During crosses between an emasculated wheat (Triticum aestivum L.) and maize (Zea mays L.) as pollen donors, the complete loss of maize chromosomes results in wheat haploid embryos. Through embryo rescue and chromosome doubling processes, pure lines with stable traits can be quickly obtained. The technique is called the “Wheat × Maize System”. Although this technology is not new, it remains a practical approach to date. In order to optimize and improve this technology and to achieve its maximum potential in the winter wheat area of China, this paper reviews the previous and ongoing research and technical procedures for the production of wheat DH lines via the maize pollen induction and presents outlooks on DH research and its application in wheat breeding.

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Spatiotemporally mapping winter wheat is imperative for informing and shaping global food security policies. Traditional mapping methods heavily rely on sufficient and reliable samples obtained through labor-intensive fieldwork and manual sample collection. However, these methods are time-consuming, costly, and lack timely and continuous data collection. To address these challenges and fully leverage remote sensing big data and cloud computing platforms like Google Earth Engine (GEE), this paper developed an algorithm for Auto-Generating Winter Wheat Samples for mapping (AGWWS). The AGWWS utilizes historical samples to determine the optimal migration threshold by measuring Spectral Angle Distance (SAD), Euclidean Distance (ED), and Near-Infrared band Difference Index (NIRDI). This facilitates the auto-generation of winter wheat sample sets for the years 2000, 2005, 2010, 2015, and 2021. Approximately two-thirds of the samples were allocated for training, with the remaining one-third used for validating the mapping method, employing the One-Class Support Vector Machine (OCSVM). The Huang–Huai–Hai (HHH) Plain, a major winter wheat production region, was selected to perform the algorithm and subsequent analysis on. Different combinations of the hyper-parameters, gamma and nu, of the OCSVM based on the Gaussian Radial Basis Function Kernel were tested for each year. Following correlation analysis between the winter wheat area derived from the generated maps and the national statistical dataset at the city level, the map with the highest corresponding R2 was chosen as the AGWWS map for each year (0.77, 0.77, 0.80, 0.86, and 0.87 for 2000, 2005, 2010, 2015, and 2021, respectively). The AGWWS maps ultimately achieved an average Overall Accuracy of 81.65%. The study then explores the Non-Grain Production of Winter Wheat (NGPOWW) by analyzing winter wheat change maps from 2000–2005, 2005–2010, 2005–2010, and 2015–2021 in the HHH Plain. Despite an overall increase in the total planted area of winter wheat, the NGPOWW phenomena has led to concerning winter wheat planting marginalization. Compensatory winter wheat areas are notably situated in mountainous and suburban cultivated lands with low qualities. Consequently, despite the apparent expansion in planted areas, winter wheat production is anticipated to be adversely affected. The findings highlight the necessity for improved cultivated land protection policies monitoring the land quality of the compensation and setting strict quota limits on occupations.

The selected agrotechnical factors affecting the occurrence of black grass Alopecurus myosuroides in winter wheat crops in north-western Poland

Alopecurus myosuroides of the family Poaceae is a highly competitive species for winter cereal crops, particularly for barley and wheat, less frequently for root vegetables or rapeseed. This study aimed to determine the predominant factor or group of factors affecting the cover of A. myosuroides in winter wheat field depending on the tillage systems, time, and amounts of herbicides used. The following variables differentiating the quantitative contribution of A. myosuroides in the analyzed fields were assumed: plough or no-plough tillage, date of biocidal application, and the total amount of herbicides (active substances) used. When spring-only herbicide was applied, the lowest A. myosuroides cover, not exceeding 25%, was observed in the margins of winter wheat fields. The coverage of A. myosuroides, reaching 25–100% of the winter wheat area, regardless of the tillage system, was found when the herbicides from the group of sulfonylurea derivatives (acetolactate synthase ALS inhibitors) were frequently used. Winter wheat infestation with A. myosuroides was significantly lower when ploughing was applied. The highest cover of A. myosuroides, reaching 50–100% of the area, was observed only in the central part of the analyzed winter wheat fields, following the application of herbicides in autumn and spring or in autumn only.

Historic winter wheat yield, production, and economic value trends in Kansas, the “Wheat State”

AbstractYear‐to‐year weather variability and technologies (fertilizer, irrigation, herbicides, conservation tillage, varieties, mechanization, and others) are among factors that affect winter wheat (Triticum aestivum L.) yields in water‐limited cropping systems. The objective of these analyses was to quantify winter wheat yield trends, variability, relationship with weather, yield gap, and value in the “Wheat State,” Kansas. Two data sources were used for this study, that is, annual winter wheat yield reported for selected counties from the United States Department of Agriculture and the Kansas State University wheat variety performance trial near Colby, Garden City, Hays, and Tribune, Kansas. Results of the study showed a decline in harvest area and inflation‐adjusted market value per area of winter wheat beginning in the 1930s. However, both the survey and research data showed continuous increase in winter wheat yield at rates of 3–180 kg ha−1 year−1 that varied by irrigation, location, data source, or time range considered, except for a few years where yield stagnated or declined. Despite increased productivity, results indicated an increase in annual winter wheat yield variation over time. A positive correlation was observed between nonirrigated wheat yield and all months of precipitation except for January, February, and June. October through December precipitation provided the best positive correlation with nonirrigated wheat yield. Wheat yields were negatively correlated with high temperature in months that coincided with wheat flowering and maturity. The yield gap between actual and potential wheat yield was estimated at 15%–55%. The decreasing revenue per land area may be one of the reasons for decreasing wheat planted acreage over time. We concluded producers need to increase adoption of latest technology (varieties with drought and heat tolerance) and management to obtain yield potential. Research should investigate methods to increase the stability of state‐wide wheat production and financial return.

Mapping the Planting Area of Winter Wheat at 10-m Resolution Using Sentinel-2 Data and Multimodel Fusion Method

Mapping the Planting Area of Winter Wheat at 10-m Resolution Using Sentinel-2 Data and Multimodel Fusion Method

A multitemporal index for the automatic identification of winter wheat based on Sentinel-2 imagery time series

ABSTRACT Timely and accurate monitoring of the spatial distribution of wheat is crucial for wheat field management, growth monitoring, yield estimation and prediction. In this study, a multitemporal index, termed the winter wheat mapping index (WWMI), was constructed for automatic winter wheat mapping on the basis of Sentinel-2 enhanced vegetation index (EVI) time series and wheat phenological features. Henan, an important winter wheat production province in China, was selected as the study area. Zhumadian, the primary wheat-growing city in Henan, was the test area. Both empirical and automatic threshold (Otsu) methods were adopted to calculate the optimal threshold of the WWMI. The performance of WWMI in winter wheat mapping was compared at object-oriented and pixel-based levels. The proposed WWMI separated winter wheat and nonwinter wheat areas well, thus achieving highly accurate winter wheat mapping. In Zhumadian, the empirical threshold method performed better than the Otsu method, but the former relied on official statistics to iteratively adjust the WWMI threshold. In Henan, the mapping accuracy achieved by the Otsu method was higher than that achieved by the empirical threshold method, with mean relative errors (MREs) of 6.78% and 19.87% at the municipal and county levels, respectively. This was because, compared with the empirical threshold method, the Otsu method did not rely on official statistics and adaptively determined the optimal threshold of the WWMI for each city in Henan, thus fully considering wheat growth state differences in different cities. In addition, the object-oriented WWMI performed better than the pixel-based WWMI in wheat mapping. The results further demonstrated the feasibility of combining the WWMI with the Otsu method for automatic winter wheat mapping at large extents, which will provide a theoretical basis for identifying other food crops.

Uncovering the primary drivers of regional variability in the impact of climate change on wheat yields in China

Responses of wheat yield in different regions to separate and joint changes in mean temperature (Tmean), precipitation (Prec), and solar radiation (Rad) are the foundation for sustainable development under climate change; however, these remain uncertain. Here, we quantified the response of wheat yield variation to changes in Tmean, Prec, and Rad by building multiple regression models, de-trending analysis, and MK trend tests. Multi-site meteorological data was obtained from different wheat growing areas and corresponding wheat yield data from provinces. Our findings showed that the effects of different climatic elements on wheat yield are spatially heterogeneous by region, and the combined effects of climatic elements are higher than those of single elements. Climate change caused an increase of 1% and 4.52% in winter and winter-spring wheat areas, respectively, and a loss of 3.72% in spring wheat areas. In addition, the effect of single meteorological factors on wheat yield varied depending on their rate of change. Rad and Prec are the main drivers of wheat yield variability; in winter, spring and winter-spring wheat areas, Rad significantly impacted wheat yield by 0.89%, −1.18%, and 2.46%, respectively, and Prec caused changes of −0.18%, −1.54%, and 1.33%, respectively. Considering normal and extreme climate fluctuations, we found that the contribution of climate change to wheat yield changes in different wheat-growing regions reached 26.1%, 18.6%, and 16.7%, respectively. Our results indicate that the overall climatic conditions in the spring wheat region are moving in an unfavourable direction, while Prec variability has become the main limiting factor for wheat production in the current winter and winter-spring wheat regions. These comprehensive and detailed findings provide a reference for the development of more accurate adaptation strategies and management measures in different regions. Going forward, the results of this study can be better applied to the development of region-specific climate-smart agricultural practices and extended to other crops and regions to better understand the broader impacts of climate change on global food security.

The assimilation surface of winter wheat under the effect of seed treatment with biological preparations

Purpose. To establish the peculiarities of the winter wheat assimilation apparatus development under the effect of seed treatment with biological preparations.
 Methods. Research was conducted using field, laboratory and mathematical methods in 2019–2022 in the stationary crop rotation of the Department of Crop Production of the National Academy of Sciences of Ukraine on typical podzolized chernozems in the Right Bank Forest Steppe of Ukraine. The peculiarities of the formation of the assimilation area of winter wheat under the application of biological preparations for seed treatment were studied with the use of the following preparations: Binok Zerno, Rizomax, Trichodermin, Planoriz, Urozhai Start. N32P32K32 fertilizer and complex granular fertilizer DuraSOP actibition were applied in seedbed preparation. The leaf area and glumes were calculated using the Petiole Pro program. The area of the stem was determined by the formula of the external area of the cylinder.
 Results. The flag leaf is quite important for the formation of a high yield of winter wheat, since its loss or damage significantly reduces the flow of photosynthetic assimilates into the plant. In particular, in the earing phase (BBCH 57), on average its area was 7.68 thousand m2/ha and good indicators of the formation of the area of the flag leaf were in the treatments with Binok Zerno and Harvest Start, both separately and in combination. In the next phase of development – flowering (BBCH 65) – the patterns were preserved, although the combination of Binok Zerno in a complex with fertilizer Harvest Start was better – 9.24 thousand m2/ha. A decrease in the total assimilation area was observed as a result of the gradual dying off of the leaves of the lower tiers, a reduction in the possibility of photosynthesis of the flag leaf and a decrease in the area of the ear involved in assimilation. In particular, on average according to the experiment, in the earing phase (BBCH 57), the total assimilation area of winter wheat was 51.9 thousand m2/ha; in the flowering phase (BBCH 65) 51.8 and in the phase of milk grain ripeness (BBCH 75) 29.3 thousand m2/ha. The total area of the assimilation surface in the earing phase (BBCH 57) was the largest for the combined seed treatment with the biological preparation Binok Zerno and the fertilizer Urozhai Start – 61.7 thousand m2/ha. In the future, in the phases of flowering (BBCH 65) and milk ripeness of grain (BBCH 75), the obtained patterns were preserved and the best treatment was the combined application of Binok Zerno and Harvest Start. By the end of the vegetation season, the concentration of the main photopigments in the leaves of winter wheat decreased In the phase of milk ripeness of the grain (BBCH 75), the content of chlorophyll a was 11.01 mg/g and chlorophyll b 3.93 mg/g. However, at earlier stages of wheat ontogenesis, the content of chlorophyll b in leaves increased, while chlorophyll a per gram of leaf dry matter decreased. It was established that the average content of chlorophyll a was 11.6 mg/g during the earing phase (BBCH 57), while the content of chlorophyll b was 4.09 mg/ha. In the flowering phase (BBCH 65), their content was 11,4 and 4.14 mg/ha, respectively. It was determined that the complex seed treatment with Binok Zerno + Harvest Start together with fertilizer N32P32K32 + Actibion ensured a higher content of chlorophylls – 16.3; 16.0 and 15.4 mg/g of dry matter, respectively.
 Conclusions. The application of the studied biological preparations for the seed treatment of winter wheat seeds contributes to a significant increase of the indicators of the total assimilation area of plants throughout the vegetation season, in particular its components, such as the area of flag leaves, blumes and stems, as well as the content of photosynthetic pigments. Established regularities of plant growth and development are important factors for optimizing crop cultivation technology in the conditions of the Right Bank Forest Steppe of Ukraine.

Determining the ecological compensation standards based on willingness to accept (WTA) for intensive agricultural production areas: A case in China

Ecological compensation is an effective way to encourage farmers to use minimal chemical fertilizers by paying for their losses when agricultural planting methods change. In this research, we conducted field surveys and created a conceptual framework to determine the compensation standard based on farmers' willingness to accept (WTA) and ecosystem service values in a rotational double cropping area of winter wheat–summer maize, Huantai County, China. The results showed that the farmers’ average WTA on the chemical fertilizer reduction application fluctuates within a range of 250.00–7740.00 CNY/hm2. The limiting compensation standards range from 362.74 to 4823.43 CNY/hm2 and the ceiling standard is from 367.29 to 4851.83 CNY/hm2 by reducing chemical fertilizer application from 5% to 95%. Huantai County has a potential of 15%–35% for chemical fertilizer reduction, and the compensation standard is the lowest (362.74 CNY/hm2) when 25% of the current chemical fertilizer usage is reduced. Our research provided a valid approach to determining ecological compensation standards toward the aims of efficiency and sustainability for protecting farmland ecology and highlighting the critical ecosystem service changes caused by the farmland fertilizer reduction program in China.

Tracking the spatio-temporal change of the main food crop planting structure in the Yellow River Basin over 2001–2020

Accurate extraction of crop planting structure is the fundamental for estimating regional crop yield and crop water consumption in the Yellow River Basin (YRB). However, the planted area of the main food crops in the YRB in recent 20 years is still unclear. Therefore, the MODIS image data was used to extract the growing area of the main food crops (winter wheat, summer maize, spring wheat and spring maize) in the YRB over 2001–2020 by the combination of the differential algorithm, spectral mutation algorithm and threshold method. The results revealed that the combined method could be used to track the planted area of the main food crop in the YRB, with R2 and RMSE values ranged from 0.51 to 0.82 and 11.01 to 68.92 × 103 hm2, respectively. The planted area of winter wheat and summer maize in the down-stream irrigation district accounted for 74.7% and 86.7% of the total in the YRB, respectively. Inner Mongolia and Ningxia autonomous regions are the main producing areas for spring wheat and spring maize, accounting for 73.8% and 83.5% of the total in the YRB, respectively. The planted area of winter wheat, summer maize and spring maize had an increasing trend with the rate of 1.440 × 104 hm2 per year, 2.375 × 104 hm2 per year and 2.257 × 104 hm2 per year over 2001–2020. Whereas, the planted area of spring wheat had a decreasing trend with the rate of −2.019 × 104 hm2 per year. Implementation of water-saving irrigation measures, variations of diverted water from the Yellow River, local policies improvement and agricultural market might be the main driving forces for the changes of the planted area for food crops. The results can provide a fundamental agricultural information dataset for further study of regional crop water consumption in the YRB.

Estimating daily minimum grass temperature to quantify frost damage to winter wheat during stem elongation in the central area of Huang-Huai plain in China.

Frost damage to winter wheat during stem elongation frequently occurred in the Huang-Huai plain of China, leading to considerable yield losses. Minimum Stevenson screen temperature (STmin) and minimum grass temperature (GTmin) have long been used to quantify frost damage. Although GTmin has higher accuracy than STmin, it is limited in application due to the lack of data. Therefore, this study aimed to select appropriate environmental variables to estimate GTmin, as well as to quantify the frost damage. Shangqiu, a frost-prone winter wheat area in the central Huang-Hui plain, was selected as the study area. From the descriptive statistics of ST, air relative humidity (RH), wind speed (WS), cloud fraction (CF), and volumetric soil water content (VWC) during temperature decreasing and increasing, seven variables significantly correlated with GTmin were selected, including STmin, maximum reduction of ST (RST), maximum increase of ST (IST), minimum RH during temperature increasing (RHmin), WS at STmin occurrence (WS), minimum VWC during temperature decreasing (VWCmin), and nightly CF. Multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and K-nearest neighbor (KNN) were adopted for estimating GTmin based on the various combinations of the variables. Results showed the more variables, the higher the accuracy for the MLR and SVR. However, this pattern was not always true for the KNN and RF. The KNN based on STmin, RST, IST, RHmin, and WS achieved the highest accuracy, with R2 of 0.9992, RMSE of 0.14 ℃, and MAE of 0.076 ℃. The overall classification accuracy for frost damage identified by the estimated GTmin reached 97.1% during stem elongation of winter wheat from 2017 to 2021. The integrated frost stress (IFS) index calculated by the estimated and measured GTmin maintained high linear fitting accuracy. The KNN with fewer variables demonstrated good applicability at the regional scale.