Wheat Area Research Articles

Analysis of Wheat Export Performance in India

Wheat is a plant which is valued highly for its kernel as an edible part and an important source of carbohydrates. India ranks second position in wheat production in the world with a contribution of roughly 13.53 per cent to global wheat output (Naga Latha et. al., 2022). The study analyses the export performance of wheat and examines the trends in the growth performance of wheat in terms of area, production and productivity, export quantity and export value during 1973-2022 which is a period of 50 years. The analysis reveals that the highest compound growth rate in terms of area was 1.16 per cent, production was 4.49 per cent and productivity was 3.29 per cent respectively. The results of the study on the compound growth rates during the study periods have shown positive and increasing value which indicates high potential for the export of wheat from India. In the analysis of instability, there exists a high positive correlation between the export quantity and value of export in the overall period. Wheat area, production and productivity exhibit higher variability and instability in the overall period. Period II shows lower variability and instability, indicating increased stability. Period I exhibits moderate variability and instability. The overall period has the highest instability, suggesting unpredictable production. Instability indices show high instability at 126.97 per cent in Period I and 138.18 per cent overall, while Period II exhibits moderate instability at 101.94 per cent. Market volatility, trade policies, economic fluctuations, weather patterns and production variability contribute to these fluctuations. As of trend analysis, there is a positive and significant increase. The NPC value of wheat during the overall period was 0.051, it indicates that the commodity is not protected. NPC<1 indicates that the commodity is exportable and possesses export competitiveness and hence the hypothesis i.e. Indian wheat has better competitiveness in the International market.

Beyond perceived risk: COVID-19 vaccination more strongly linked to individual risk perception in wheat areas than rice areas

Vaccines save lives, but vaccine hesitancy remains a global health challenge. That makes it critical to explore the factors that influence whether people get vaccinated. Perceived risk is a pivotal factor in traditional theories of health behavior, such as the Risk Perception Attitude framework and the Health Belief Model. However, studies have found that these models are less predictive of COVID-19 vaccination in some cultures. Studies that test culture by comparing nations are useful, but nations have different healthcare systems and policies that are hard to equate and control for. We addressed this question by testing regional cultural differences in China. Drawing on the rice theory, we compared residents in China's historical rice-farming areas (which are more interdependent) with those in wheat-farming areas (which are more independent). Our survey data from 1872 participants in 29 provinces found that models of perceived risk explained COVID-19 vaccination mainly in historically wheat-farming areas. Conversely, in historically rice-farming areas, perceived risk did not explain vaccination behavior. These findings suggest that cultural factors that are rooted in historical farming practices influence vaccination behaviors. By comparing regions within the same country, we can rule out confounds of national policy and healthcare systems. These insights could help policymakers understand how to tailor vaccination campaigns to people from different cultures.

Mapping 10-m harvested area in the major winter wheat-producing regions of China from 2018 to 2022.

Winter wheat constitutes approximately 20% of China's total cereal production. However, calculations of total production based on multiplying the planted area by the yield have tended to produce overestimates. In this study, we generated sample points from existing winter wheat maps and obtained samples for different years using a temporal migration method. Random forest classifiers were then constructed using optimized features extracted from spectral and phenological characteristics and elevation information. Maps of the harvested and planted areas of winter wheat in Chinese eight provinces from 2018 to 2022 were then produced. The resulting maps of the harvested areas achieved an overall accuracy of 95.06% verified by the sample points, and the correlation coefficient between the CROPGRIDS dataset is about 0.77. The harvested area was found to be about 13% smaller than the planted area, which can primarily be attributed to meteorological hazards. This study represents the first attempt to map the winter wheat harvested area at 10-m resolution in China, and it should improve the accuracy of yield estimation.

Forecasting models for area, production and productivity of wheat in North Gujarat

Forecasting models for area, production and productivity of wheat in North Gujarat

The mutation Asp-376-Glu in the ALS gene confers resistance to mesosulfuron-methyl in Beckmannia syzigachne

Understanding the mechanisms by which weeds develop herbicide resistance is crucial for managing resistance effectively and optimizing herbicide use. Beckmannia syzigachne, a harmful grass weed prevalent in wheat and rice-wheat rotation areas, poses a significant threat to crop productivity. A field herbicide resistance survey identified a resistant population with a new ALS mutation (Asp-376-Glu). The Glu-376-Asp population displayed varying resistance levels to seven ALS herbicides, verified using the dCAPS method. qRT-PCR analysis showed that no significant difference existed in the ALS gene expression between the Asp-376-Glu and S populations. P450 and GST inhibitors failed to reverse resistance to mesosulfuron-methyl, suggesting no involvement of P450- and GST-based metabolic resistance. Molecular docking indicated that the Asp-376-Glu mutation reduces the binding affinity between ALS-inhibitors and BsALS. The findings provide valuable insights into herbicide resistance mechanisms for weed resistance control.

Optimizing cropping patterns and resource allocation for sustainable agricultural development: A case study of Ilam province, Iran

Establishing connections between ecosystems and agricultural mechanization can help address erroneous decision-making in agricultural management and prevent further harm to ecosystems. It is essential for authorities and decision-makers in the agricultural sector to understand ecosystem dynamics and influencing factors to develop appropriate policies for sustainable development. This study focuses on identifying optimal conditions for determining crop patterns and input allocation in Ilam province's agricultural practices, considering technical and energy parameters. Cultivating one hectare of wheat and barley in the region requires an average energy consumption of 1.79 GJ from various sources, with an estimated energy output of 38.04 GJ ha−1 for these crops. The research involved farmers cultivating wheat, barley, fodder maize, and canola in the southern region of Ilam province, covering cities like Abdanan, Dareshahr, and Dehloran. The study suggests adjustments to cropping patterns, including reducing wheat cultivation and increasing irrigated fodder maize cultivation under optimal conditions. It also recommends exploring rainfed cultivation or enhancing the yield of rainfed wheat to offset the reduced wheat cultivation. The goal planning model indicates a shift towards sustainability, with changes in cropping patterns focusing on wheat, barley, fodder maize, and canola crops, including expanding the cultivation areas of irrigated wheat, barley, and fodder maize. Efforts aim to enhance the productivity of irrigated crops to promote sustainability and improve agricultural practices in the province for long-term sustainability goals.

Optimizing cropland use to reduce groundwater arsenic hazards in a naturally arsenic-enriched grain-producing region

In the Hetao Basin, a grain-producing region plagued by naturally occurring arsenic (As) pollution, understanding the role of agricultural cultivation activities in mobilizing As in groundwater is worthwhile. Here we investigated the impact of cropland use characteristics on groundwater As hazards using a model that combines Random Forest (RF) classification with SHapley Additive exPlanation (SHAP). The analysis incorporated eight cropland use characteristics and three natural factors across 1258 groundwater samples as independent variables. Additionally, an optimized cropland use strategy to mitigate groundwater As hazards was proposed. The results revealed that crop cultivation area, especially within a 2500m-radius buffer around sampling points, most significantly influenced the probability of groundwater As concentrations exceeding an irrigation safety threshold of 50 μg/L, achieving an AUC of 0.86 for this prediction. The relative importance of crop areas on As hazards were as follows: sunflower > melon > wheat > maize. Specifically, a high proportion of sunflower area (>30%), particularly in regions with longer cropland irrigation history, tended to elevate groundwater As hazards. Conversely, its negative driving force on groundwater As hazards was more pronounced with the increase in the proportion of wheat area (>5%), in contrast to other crops. Transitioning from sunflower to wheat or melon cultivation in the northeast of the Hetao Basin may contribute to lower groundwater As hazards. This study provides a scientific foundation for balancing food production with environmental safety and public health considerations.

The Carbon Footprint and Influencing Factors of the Main Grain Crops in the North China Plain

The North China Plain (NCP) serves as a critical grain-producing region in China, playing a pivotal role in ensuring the nation’s food security. A comprehensive analysis of the carbon footprint (CF) related to the cultivation of major grain crops within this region and the proposal of strategies to reduce emissions through low-carbon production methods are crucial for advancing sustainable agricultural practices in China. This study employed the lifecycle assessment (LCA) method to estimate the CF of wheat, maize, and rice crops over a period from 2013 to 2022, based on statistical data collected from five key provinces and cities in the NCP: Beijing, Tianjin, Hebei, Shandong, and Henan. Additionally, the Logarithmic Mean Divisia Index (LMDI) model was utilized to analyze the influencing factors. The results indicated that the carbon footprints per unit area (CFA) of maize, wheat, and rice increased between 2013 and 2022. Rice had the highest carbon footprint per unit yield (CFY), averaging 1.1 kg CO2-eq kg−1, with significant fluctuations over time. In contrast, the CFY of wheat and maize remained relatively stable from 2013 to 2022. Fertilizers contributed the most to CF composition, accounting for 48.8%, 48.0%, and 25.9% of the total carbon inputs for wheat, maize, and rice, respectively. The electricity used for irrigation in rice production was 31.8%, which was much higher than that of wheat (6.8%) and maize (7.1%). The LMDI model showed that the labor effect was a common suppressing factor for the carbon emissions of maize, wheat, and rice in the NCP, while the agricultural structure effect and the economic development effect were common driving factors. By improving the efficiency of fertilizer and pesticide utilization, cultivating new varieties, increasing the mechanical operation efficiency, the irrigation efficiency, and policy support, the CF of grain crop production in the NCP can be effectively reduced. These efforts will contribute to the sustainable development of agricultural practices in the NCP and support China’s efforts to achieve its “double carbon” target.

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.

Spatiotemporal Evolution of Winter Wheat Planting Area and Meteorology-Driven Effects on Yield under Climate Change in Henan Province of China.

This study examines the impact of climate change on winter wheat production in Henan Province. The analysis, under the utilization of GLASS LAI data, focuses on shifts in the planting areas of winter wheat. In addition, a comprehensive assessment of the spatiotemporal trends in meteorological factors during the winter wheat growth period has also been conducted. The findings reveal a fluctuating increase in accumulated temperature across Henan Province, ranging from 3145 °C to 3424 °C and exhibiting a gradual rise from north to south. In particular, precipitation patterns from 1980 to 2019 showed limited significant trends, while notable abrupt changes were observed in 1983, 2004, 2009, and 2016. Geographically, southwestern Henan Province experiences greater precipitation than the northeast. Moreover, a fluctuating downward trend in sunshine hours has been observed, gradually decreasing from north to south. The study further highlights an increase in winter wheat planting frequency in the northwestern region of Luoyang and the northeastern part of Zhumadian, contrasted by a decrease in Zhengzhou and Kaifeng. Accumulated temperature is positively correlated with the expansion of winter wheat planting areas (R2 = 0.685), while sunshine hours exert a suppressive effect (R2 = 0.637). Among meteorological factors, accumulated temperature emerges as the most crucial determinant, followed by precipitation, with sunshine hours having a relatively minor influence. Yield demonstrates a positive association with accumulated temperature (R2 = 0.765) and a negative correlation with sunshine hours (R2 = -0.614). This finding is consistent with the impact of meteorological factors on winter wheat production. The results of this study enhance the understanding of how the underlying mechanisms of climate change impact crop yields.

Estimating evapotranspiration and drought dynamics of winter wheat under climate change: A case study in Huang-Huai-Hai region, China

Drought is one of the vital meteorological disasters that influence crop growth. Timely and accurately estimating the drought dynamics of crops is valuable for decision-maker to formulate scientific management measures of agricultural drought risk. In this study, the evapotranspiration and drought dynamics of winter wheat from 1981 to 2020 in the Huang-Huai-Hai (HHH) region of China were evaluated based on long-term multi-source observation data. Four key developmental stages of winter wheat were given attentions: growth before winter stage, overwintering stage, stage of greening-heading, and stage of filling-maturity. The crop water deficit index (CWDI) on a daily scale was established for quantitatively appraising the impacts of drought on winter wheat. Our results indicated that interannual variation in reference crop evapotranspiration (ET0) during the growth season of winter wheat from 1981 to 2020 in the HHH region showed a slight increase trend, with an average of 602.4 mm and obvious spatial differences of decreasing from the Northeast to the Southwest. Over the past forty years, the winter wheat in the HHH region was most severely affected by severe drought, followed by moderate drought, and finally mild drought. In addition, the impacts of drought on winter wheat at different critical growth stages varied greatly. For the growth before winter stage, the winter wheat was mainly threatened by mild, moderate, and severe droughts. For the overwintering stage, the winter wheat was mainly threatened by moderate, severe, and extreme droughts. For the greening-heading stage, the winter wheat was mainly threatened by mild, moderate, severe, and extreme droughts. For the filling-maturity stage, the winter wheat was mainly threatened by mild and moderate droughts. Finally, the impacts of drought on winter wheat during 1981–2020 in the HHH region were revealed to differ extraordinarily in space. In particular, the areas of winter wheat affected by severe drought significantly decreased. However, the areas of winter wheat affected by moderate drought clearly expanded. Our findings provide new insights for further improving climate change impact studies and agricultural drought defense capabilities adapting to continuous environmental change.

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.

Development of Yield Forecast Model in Bread Wheat Using Regression Analysis

Background: Studies highlighted the possibilities of simultaneous crop failures in the world’s “breadbaskets” (wheat) due to heat and 40% of the variability in inter-annual wheat production is already related to temperature extremes. The global yield numbers hide the degree of variability of wheat production, yet several environmental conditions pose a threat to wheat production. Objective: The main objective of the study was to develop a regression model that fitted the dependent variable sufficiently well to account for the total variability. Method: For this, sixty advance lines along with four standard checks were evaluated for fifteen yield-associated traits and eight quality traits during Rabi 2020-21 at the research area of Wheat and Barley section, Department of Genetics and Plant Breeding, CCS Haryana Agricultural University, Hisar. Multiple regression analysis revealed that 98.5% of the variability is explained by the studied morphological and quality traits. Result: The stepwise regression analysis retained a total of seven traits (six morphological and one quality) viz. biological yield per plot, harvest index, grain weight per spike, flag leaf length, main spike weight, number of spikelets per spike and grain appearance score; explaining 97.8 % of the total variability. Conclusion: The seventh model among all, indicated good yield predicting performance without modifying the traits.

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.

Risk Assessment of Spodoptera exempta against Food Security: Estimating the Potential Global Overlapping Areas of Wheat, Maize, and Rice under Climate Change.

Spodoptera exempta, known as the black armyworm, has been extensively documented as an invasive agricultural pest prevalent across various crop planting regions globally. However, the potential geographical distribution and the threat it poses to host crops of remains unknown at present. Therefore, we used an optimized MaxEnt model based on 841 occurrence records and 19 bioclimatic variables to predict the potential suitable areas of S. exempta under current and future climatic conditions, and the overlap with wheat, rice, and maize planting areas was assessed. The optimized model was highly reliable in predicting potential suitable areas for this pest. The results showed that high-risk distribution areas for S. exempta were mainly in developing countries, including Latin America, central South America, central Africa, and southern Asia. Moreover, for the three major global food crops, S. exempta posed the greatest risk to maize planting areas (510.78 × 104 km2), followed by rice and wheat planting areas. Under future climate scenarios, global warming will limit the distribution of S. exempta. Overall, S. exempta had the strongest effect on global maize production areas and the least on global wheat planting areas. Our study offers a scientific basis for global prevention of S. exempta and protection of agricultural crops.

Analyzing the impact of socio-environmental parameters on wheat and barley cultivation areas using the geographical detector model

The national economy of Iranians depends greatly on agriculture. Wheat and barley are two strategic crops in Iran, where their scarcity can adversely affect people's quality of life. Therefore, it is essential to analyze the climatic, humidity-related, and socioeconomic factors affecting the cultivation areas of these crops. This study analyzed the single and mutual effects of 17 parameters on wheat and barley cultivation areas in Iran within the 2007–2020 period. The geographical detector model (GDM) was used in RStudio to analyze the interactive effects of parameters. According to the results, topography had greater effects than other parameters on the cultivation areas of wheat. Spring precipitation had a larger impact with 26% of effects than winter precipitation (SC) with 24% of effects on the cultivation area of wheat. However, winter precipitation and temperature with 42% and 34% effects, respectively, had greater impacts than spring precipitation on the cultivation area of barley. NDVI and SC have the highest impact on the cultivated area of wheat and barley crops. Also, elevation is the most important parameter in wheat cultivation with 54% influence, and this product performs better in high altitudes, on the other hand, elevation is not a vital factor in barley. Also, the winter parameters have a greater effect on wheat than barley, and the average wheat harvest showed high performance in heavy snowfall lands. Also, the results showed that barley has a higher resistance to dry conditions. Moreover, socioeconomic parameters had more effects on barley than on wheat. In other words, livestock cover with 52% of effects had the greatest impact on the cultivation area of barley, whereas population with 36% of effects was ranked the third factor affecting the cultivation area of wheat. According to the interactive results of parameters, the interaction between elevation and NDVI was 88% in wheat. However, the interaction between population and livestock cover was 72% in barely. The optimal results of barley and wheat indicated that both crops had the largest cultivation areas at 22.9–24.9 °C.

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.