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Winter wheat is a significant global food crop, and it is crucial to monitor its distribution for better agricultural management, land planning, and environmental sustainability. However, the distribution style of winter wheat planting fields is not consistent due to different terrain conditions. In mountainous areas, winter wheat planting units are smaller in size and fragmented in distribution compared to plain areas. Unfortunately, most crop-mapping research based on deep learning ignores the impact of topographic relief on crop distribution and struggles to handle hilly areas effectively. In this paper, we propose a cross-modal segmentation network for winter wheat mapping in complex terrain using remote-sensing multi-temporal images and DEM data. First, we propose a diverse receptive fusion (DRF) module, which applies a deformable receptive field to optical images during the feature fusion process, allowing it to match winter wheat plots of varying scales and a fixed receptive field to the DEM to extract evaluation features at a consistent scale. Second, we developed a distributed weight attention (DWA) module, which can enhance the feature intensity of winter wheat, thereby reducing the omission rate of planting areas, especially for the small-sized regions in hilly terrain. Furthermore, to demonstrate the performance of our model, we conducted extensive experiments and ablation studies on a large-scale dataset in Lanling county, Shandong province, China. Our results show that our proposed CM-Net is effective in mapping winter wheat in complex terrain. [ABSTRACT FROM AUTHOR]

Supply chain disruptions, a pandemic, and war in Ukraine have exposed faultlines in a globalised food system that depends on a few staple crops grown in a few exporting regions and transported to consumers around the world. In the UK, just three crops, (wheat, barley, and oilseed rape), account for 75 per cent of the UK's 4.5 million hectares of arable land whilst the country imports around half its food—nearly 40 per cent—from just four EU countries (The Netherlands, Ireland, Germany, and France). Poor diets contribute to one in seven deaths in the UK, 63 per cent of the population is overweight or obese and health inequality is increasing between the poorest and most affluent regions. The food security and health of the UK population is therefore dependent on a small number of locally grown crops, vulnerable supply chains, and an unhealthy, obesogenic diet. The UK food system must diversify if it is to become food and nutritionally secure, meet its climate and biodiversity goals and have a healthy and active population. Climate-resilient and nutritious underutilised crops can help diversify the UK agrifood system, but research and investment in them is sporadic, piecemeal, and unfocused. In this paper, we compare two approaches to identifying potentially suitable underutilised crops for the UK. The first, based on UK Department for Environment, Food and Rural Affairs (Defra) Project CH0224, was delivered through literature and database searches and the expertise of growers, advisers, breeders, seed suppliers, processors, traders, and researchers. The second used the CropBASE digital knowledge base for underutilised crops. The two approaches produced no single crop that was common to both shortlists. We propose that the analytical and predictive tools derived from CropBASE could be combined with local knowledge and expertise from the Defra project to provide a common framework for the identification of underutilised crops that are best suited to local UK circumstances now and in climates of the future. [ABSTRACT FROM AUTHOR]

In order to eliminate the limitations of traditional winter wheat yield prediction methods, the prediction models based on machine learning are used to improve the accuracy of winter wheat yield prediction. In this study, by collecting a large amount of domestic literature about wheat growth characteristics, the irrigation amount, fertilization amount, soil nutrient status, planting density, maximum leaf area index (LAImax), maximum aboveground dry matter accumulation (Dmax) and yield (Y) were chosen to develop the learning models. Using the data of the irrigation amount, fertilization amount, soil nutrient status and planting density as the training set, the regression prediction models (Gaussian process regression mode, linear regression model, regression tree mode and support vector machine model) were used to train and learn the data of the LAImax, Dmax and Y, respectively. The results show that the Gaussian regression model has the best precision compared to the other models. The coefficients of determination (R2) of the learning results of the Gaussian regression model for the LAImax, Dmax and Y are 0.9, 0.93 and 0.86, and the root mean square error (RMSE) is 0.57, 1125.1 and 640.41. Based on the data of the irrigation amount, nitrogen application amount, potassium application amount, phosphorus application amount, organic matter content, total nitrogen content, alkali-hydrolyzable nitrogen content, available phosphorus content, available potassium content and planting density, the method proposed in this paper can reliably predict the LAImax, the Dmax and Y of winter wheat. The results also have certain reference significance for the yield prediction of other crops. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper focuses on the problems of inaccurate extraction of winter wheat edges from high-resolution images, misclassification and omission due to intraclass differences as well as the large number of network parameters and long training time of existing classical semantic segmentation models. This paper proposes a lightweight winter wheat planting area extraction model that combines the DeepLabv3+ model and a dual-attention mechanism. The model uses the lightweight network MobileNetv2 to replace the backbone network Xception of DeepLabv3+ to reduce the number of parameters and improve the training speed. It also introduces the lightweight Convolutional Block Attention Module (CBAM) dual-attention mechanism to extract winter wheat feature information more accurately and efficiently. Finally, the model is used to complete the dataset creation, model training, winter wheat plantation extraction, and accuracy evaluation. The results show that the improved lightweight DeepLabv3+ model in this paper has high reliability in the recognition extraction of winter wheat, and its recognition results of OA, mPA, and mIoU reach 95.28%, 94.40%, and 89.79%, respectively, which are 1.52%, 1.51%, and 2.99% higher than those for the original DeepLabv3+ model. Meanwhile, the model's recognition accuracy was much higher than that of the three classical semantic segmentation models of UNet, ResUNet and PSPNet. The improved lightweight DeepLabv3+ also has far fewer model parameters and training time than the other four models. The model has been tested in other regions, and the results show that it has good generalization ability. The model in general ensures the extraction accuracy while significantly reducing the number of parameters and satisfying the timeliness, which can achieve the fast and accurate extraction of winter wheat planting sites and has good application prospects. [ABSTRACT FROM AUTHOR]

This paper focuses on the problems of omission, misclassification, and inter-adhesion due to overly dense distribution, intraclass diversity, and interclass variability when extracting winter wheat (WW) from high-resolution images. This paper proposes a deep supervised network RAunet model with multi-scale features that incorporates a dual-attention mechanism with an improved U-Net backbone network. The model mainly consists of a pyramid input layer, a modified U-Net backbone network, and a side output layer. Firstly, the pyramid input layer is used to fuse the feature information of winter wheat at different scales by constructing multiple input paths. Secondly, the Atrous Spatial Pyramid Pooling (ASPP) residual module and the Convolutional Block Attention Module (CBAM) dual-attention mechanism are added to the U-Net model to form the backbone network of the model, which enhances the feature extraction ability of the model for winter wheat information. Finally, the side output layer consists of multiple classifiers to supervise the results of different scale outputs. Using the RAunet model to extract the spatial distribution information of WW from GF-2 imagery, the experimental results showed that the mIou of the recognition results reached 92.48%, an improvement of 2.66%, 4.15%, 1.42%, 2.35%, 3.76%, and 0.47% compared to FCN, U-Net, DeepLabv3, SegNet, ResUNet, and UNet++, respectively. The superiority of the RAunet model in high-resolution images for WW extraction was verified in effectively improving the accuracy of the spatial distribution information extraction of WW. [ABSTRACT FROM AUTHOR]

The main purpose of the paper is to highlight the impact of foliar fertilization during the various growth stages of winter wheat and its role in achieving high-quality and superior production. Foliar fertilizers play a crucial role in calibrating and forming active growth intervals correlated with productivity factors. The research was conducted over a two-year period using the Andrada winter wheat variety. It involved the application of four different foliar fertilizers: basic mineral fertilization, FoliMAX Orange (two treatments and three treatments), FoliMAX CerealsMIX (two treatments and three treatments), and Microfert U (three treatments). Depending on the treatment, two or three applications of foliar with foliar fertilizers were carried out at various stages of winter wheat development, including pre-flowering and grain formation. The research method used to record physiological parameters was non-destructive (the leaves were not detached from the plant) and was based on the use of the CIRAS-3 foliar gas analyzer, which simultaneously determines several physiological and environmental indicators. As an indicator read by the device, photosynthesis was chosen for leaf temperature and leaf transpiration, correlated with production and quality indices. By applying the foliar fertilizer treatments to the wheat culture, we managed to increase production and improve the quality. After using different foliar fertilizers, the assimilation and physiological parameters had higher values of over 30 μmolm−2·s−1. Foliar fertilization applied to the winter wheat variety resulted in an average production increase between 450 and 765 kg·ha−1 and quality indices with a protein percentage between 11.5 and 12.6%, gluten content between 21.5 to 24.0% and the Zeleny index between 29.0 and 39.0%, and the mass of one thousand grains was between 48.0 and 50.5 g. [ABSTRACT FROM AUTHOR]

The knowledge of interactions among crop genotypes, soil types, and crop management is essential for precision agriculture. This paper explores these interactions through the analysis of 27 years of winter wheat trials, with 276 unique varieties tested across seven distinct soil types and more than 8000 plots. The study investigates how different winter wheat crop varieties respond to varying soil types and preceding crops. The findings revealed a significant interaction between variety, soil type, and preceding crop. With only a few exceptions, the highest-yielding varieties were predominantly the most recently developed. The ranking of the varieties exhibited inconsistency across the various soil types, implying that a variety yields differently when cultivated in different soil types. Furthermore, the influence of preceding crops on yield varied with soil type. This suggests that taking field-specific soil variation and the preceding crop into account during variety selection may improve the yield potential. Furthermore, the study highlights consistent yield increases due to advancements in breeding programs, with yearly increases ranging from 0.05 to 0.1 t/ha per year across all soil types. Integration of insights from genetics, soil attributes, and management practices demonstrates how farmers can increase productivity. [ABSTRACT FROM AUTHOR]

Waterlogging harms winter wheat growth. To enable accurate monitoring of agricultural waterlogging, this paper conducts a winter wheat waterlogging monitoring study using multi-source data in Guzhen County, Anhui Province, China. The hydrological model HYDRUS-1D is coupled with the crop growth model WOFOST, and the Ensemble Kalman Filter is used to assimilate Sentinel-1 inversion soil moisture data. According to the precision and continuity of soil moisture, the damage of winter wheat waterlogging were obtained. The experimental results show that the accuracy of the soil moisture is improved after data assimilation compared with that before data assimilation, and the Nash–Sutcliffe efficiency (NSE) of the simulated soil moisture values at three monitoring sites increased from 0.528, 0.541 and 0.575 to 0.752, 0.692 and 0.731, respectively. A new waterlogging identification criterion has been proposed based on the growth periods and probability distribution of soil moisture. The proportion, calculated from this identification criterion, of the waterlogging wheat farmland in total farmland shows a high correlation with the yield reduction rate. The correlation coefficient of the waterlogging farmland proportion and the yield reduction rate in 11 towns of Guzhen County reaches 0.78. Through the synchronization of geography, agriculture and meteorology, the framework shows great potential in waterlogging monitoring. [ABSTRACT FROM AUTHOR]

The crop drought risk assessment is an important basis for mitigating the effects of drought on crops. The study of drought using crop growth models is an integral part of agricultural drought risk research. The current Decision Support System for Agrotechnology Transfer (DSSAT) model is not sufficiently sensitive to moisture parameters when performing simulations, and most studies that conduct different scenario simulations to assess crop drought vulnerability are based on the site-scale. In this paper, we improved the moisture sensitivity of the Crop Environment Resource Synthesis System (CERES)-Wheat to improve the simulation accuracy of winter wheat under water stress, and then we assessed the drought intensity in the Beijing-Tianjin-Hebei region and constructed a gridded vulnerability curve. The grid vulnerability curves (1 km × 1 km) were quantitatively characterized using key points, and the drought risk distribution and zoning of winter wheat were evaluated under different return periods. The results show that the stress mechanism of coupled water and photosynthetic behavior improved the CERES-Wheat model. The accuracy of the modified model improved in terms of the above-ground biomass and yield compared with that before the modification, with increases of 20.39% and 11.45% in accuracy, respectively. The drought hazard intensity index of winter wheat in the study area from 1970 to 2019 exhibited a trend of high in the southwest and low in the southeast. The range of the multi-year average drought hazard intensity across the region was 0.29–0.61. There were some differences in the shape and characteristic covariates of the drought vulnerability curves among the different sub-zones. In terms of the cumulative loss rates, almost the entire region had a cumulative drought loss rate of 49.00–54.00%. Overall, the drought risk index decreased from west to east and from north to south under different return periods. This quantitative evaluation of the drought hazard intensity index provides a reference for agricultural drought risk evaluation. [ABSTRACT FROM AUTHOR]

Artificial irrigation is critical for improving soil moisture conditions and ensuring crop growth. Its irrational deployment can lead to ecological and environmental issues. Mapping and understanding the changes in irrigated areas are vital to effectively managing limited water. However, most researchers map irrigated areas with a single data resource, which makes it hard to detect irrigated signals in complex situations. The case study area for this paper was China's winter wheat region, and an irrigated area map was generated by analyzing the effects of artificial irrigation on crop phenological characteristics and soil moisture time series. The mapping process involved three steps: (1) generating a basic irrigated map by employing the ISODATA classification method on the Kolmogorov–Smirnov test irrigation signals from the microwave remote sensing data and reanalysis data; (2) creating the other map with the maximum likelihood ratio classification and zoning scheme on the phenological parameters extracted from the NDVI time series; and (3) fusing these two maps at the decision level to obtain the final map with a higher spatial resolution of 1 km. The map was evaluated against existing irrigated area data and was highly compatible with GMIA 5.0. The overall accuracy (OA) was 73.49%. [ABSTRACT FROM AUTHOR]

In order to effectively obtain the winter wheat growing area in a large part of the Guanzhong plain, this paper proposes a random forest Guanzhong plain winter wheat extraction algorithm based on spatial features of neighborhood samples using the 250 m resolution spectral imager (MERSI) of the FY-3 satellite as the data source. In this paper, first, the training and validation samples were obtained by constructing a neighborhood sample space sampling model, then the study area was classified using an integrated learning random forest Classifier, and finally the classification data obtained from different time phases were fused using voting game theory to obtain the final classification result map. The land use change and winter wheat distribution change from 2011 to 2014 were also analyzed. The experimental results showed that the overall accuracy of winter wheat obtained after random forest fusion processing was the highest compared with the traditional algorithm, reaching 98.63%. At the same time, LANDSAT 8 images were used to obtain the distribution of winter wheat, and the distribution areas obtained from MERSI data and LANDSAT 8 images were generally consistent in terms of spatial distribution as shown by the distribution areas at the county scale. [ABSTRACT FROM AUTHOR]

Our economic analysis aimed to evaluate the profitability of winter bread wheat production based on two fundamental aspects. The first was the grainprotein content as a criterion for determining grain prices. The other was a comparative simulation of production profitability relying on grain production costs in 2015 and 2022. We used the results of a field experiment conducted in 2014 and 2015 involving winter bread wheat fertilised with nitrogen applied at progressive increments of 40 kg N ha−1 within arange from 0 to 240 N ha−1 with or without fungicide protection. We assumed that experimental factors significantly affected both the yield and the market value of grain, and hence the profitability conditioned by wheat prices on global markets. The working hypothesis of this paper is: wheat production profitability has not changed in the face of a global energy crisis. Our analysis shows that growing bread wheat generates profit when inputs are high: these inputs include high nitrogen rates and full crop protection. The real grain selling price guarantees production profitability. We should consider that, in the circumstances of a global energy crisis, the world should possibly switch to baking products from low-protein flour. Only upon such an assumption can the expenditure on fertilisers and fungicides be significantly reduced. [ABSTRACT FROM AUTHOR]

Plant nitrogen concentration (PNC) is a traditional standard index to measure the nitrogen nutritional status of winter wheat. Rapid and accurate diagnosis of PNC performs an important role in mastering the growth status of winter wheat and guiding field precision fertilization. In this study, the in situ hyperspectral reflectance data were measured by handheld SVC HR−1024I (SVC) passive field spectroradiometer and PNC were determined by the modified Kjeldahl digestion method. Continuous wavelet transform (CWT), successive projection algorithm (SPA) and partial least square (PLS) regression were combined to construct an efficient method for estimating winter wheat PNC. The main objectives of this study were to (1) use CWT to extract various wavelet coefficients under different decomposition scales, (2) use SPA to screen sensitive wavelet coefficients as independent variables and combine with PLS regression to establish winter wheat PNC estimation models, respectively, and (3) compare the precision of PLS regression models to find a reliable model for estimating winter wheat PNC during the growing season. The results of this paper showed that properly increasing the decomposition scale of CWT could weaken the impact of high-frequency noise on the prediction model. The number of wavelet coefficients has been significantly reduced after screened by SPA. The PNC estimation model (CWT–Scale6–SPA–PLS) based on the wavelet coefficients of the sixth decomposition scale most accurately predicted the PNC (the determination coefficient of the calibration set (Rc2) was 0.85. Root mean square error of the calibration set (RMSEc) was 0.27. The determination coefficient of the validation set (Rv2) was 0.84. Root mean square error of the validation set (RMSEv) was 0.28 and relative prediction deviation (RPD) was 2.47). CWT-Scale6-SPA-PLS can be used to predict PNC. The optimal winter wheat PNC prediction model based on CWT proposed in this study is a reliable method for rapid and nondestructive monitoring of PNC and provides a new technical method for precision nitrogen management. [ABSTRACT FROM AUTHOR]

The response of winter wheat (Triticum aestivum L.) to the application of different rates of nitrogen (N) and phosphorus (P) on different agro-climatic zones (ACZs) has not been well studied in Afghanistan. The objectives of this study were to (1) determine the impact of soil and climate on the responses of wheat to N and P fertilization, (2) quantify the specific N and P response of winter wheat for different ACZs, and (3) determine the economical application rates of N and P for farmers for each considered ACZs. This paper evaluates the effects of nitrogen levels (NL) at 35.28, 65, 95, and 120 kg N ha−1 and phosphorus levels (PL) at 0, 50, 70, and 90 kg P2O5 ha−1, respectively, in four locations (L) for two growing seasons (GS), on both yield and quality characteristics of winter wheat. Soil pH was the main environmental parameter affecting straw yield (SY), grain yield (GY), protein content (PC), and protein yield (PY). Winter wheat SY, GY, PC, and PY increased significantly (p < 0.05) with PL rates up to 50 kg P2O5 ha−1 and with NL rates up to 120 kg N ha−1. NL was the most important parameter in determining PC, thus showing potential for further improvement in N management. The highest marginal rate of return was used as an index for the farmers to accept site-specific N and P fertilizer recommendations. [ABSTRACT FROM AUTHOR]

Crop production is one of the major interactions between humans and the natural environment, in the process, carbon is translocated cyclically inside the ecosystem. Data assimilation algorithm has advantages in mechanism and robustness in yield estimation, however, the computational efficiency is still a major obstacle for widespread application. To address the issue, a novel hybrid method based on the combination of the Crop Biomass Algorithm of Wheat (CBA-Wheat) to the Simple Algorithm For Yield (SAFY) model and the transfer learning method was proposed in this paper, which enables winter wheat yield estimation with acceptable accuracy and calculation efficiency. The transfer learning techniques learn the knowledge from the SAFY model and then use the knowledge to predict wheat yield. The main results showed that: (1) The comparison using CBA-Wheat between measured AGB and predicted AGB all reveal a good correlation with R2 of 0.83 and RMSE of 1.91 t ha−1, respectively; (2) The performance of yield prediction was as follows: transfer learning method (R2 of 0.64, RMSE of 1.05 t ha−1) and data assimilation (R2 of 0.64, RMSE of 1.01 t ha−1). At the farm scale, the two yield estimation models are still similar in performance with RMSE of 1.33 t ha−1 for data assimilation and 1.13 t ha−1 for transfer learning; (3) The time consumption of transfer learning with complete simulation data set is significantly lower than that of the other two yield estimation tests. The number of pixels to be simulated was about 16,000, and the computational efficiency of the data assimilation algorithm and transfer learning without complete simulation datasets. The transfer learning model shows great potential in improving the efficiency of production estimation. [ABSTRACT FROM AUTHOR]

Drought is one of the most common natural threats to agricultural production worldwide. Few studies have studied the effects of agricultural practices on drought mitigation at a regional scale over a long period. This paper analyzes the spatiotemporal characteristics in the agricultural drought-affected area change index (ADAC), which was developed to assess the drought mitigation. The linear regression method was used to investigate the impact factors on the change of ADAC in the three main winter wheat provinces of northern China. The results showed that the average ADAC during the main growing season in the study area was approximately −61.5% over the past 38 years, which indicated a great decrease of the agricultural drought-affected area. The significant decreasing trends of ADAC values across the study area during 1981–2000 could be explained by the area percentage equipped for irrigation (APEI) by 49.2–89.7%. There was a lack of pronounced change trends of ADAC during 2001–2018, implying that the positive effects of irrigation infrastructure in the plain area might reach a plateau under the constraints of available water resources, and other agricultural practices need to be investigated in the future. This research provides helpful decision information on drought adaptation management and water conservation project planning. [ABSTRACT FROM AUTHOR]

The application of remote sensing techniques and prediction models for soybean rust (SBR) monitoring may result in different fungicide application timings, control efficacy, and spraying performance. This study aimed to evaluate the applicability of a prediction model as a threshold for disease control decision-making and to identify the effect of different application timings on SBR control as well as on the spraying technology. There were two experimental trials that were conducted in a 2 × 4 factorial scheme: 2 cultivars (susceptible and partially resistant to SBR); and four application timings (conventional chemical control at a calendarized system basis; based on the prediction model; at the appearance of the first visible symptoms; and control without fungicide application). Spray deposit and coverage at each application timing were evaluated in the lower and upper region of the soybean canopy through quantitative analysis of a tracer and water-sensitive papers. The prediction model was calculated based on leaf reflectance data that were collected by remote sensing. Application timings impacted the application technology as well as control efficacy. Calendarized system applications were conducted earlier, promoting different spray performances. Spraying at moments when the leaf area index was higher obtained poorer distribution. None of the treatments were capable of achieving high spray penetration into the canopy. The partially resistant cultivar was effective in holding disease progress during the crop season, whereas all treatments with chemical control resulted in less disease impact. The use of the prediction model was effective and promising to be integrated into disease management programs. [ABSTRACT FROM AUTHOR]

Rapidly developing remote sensing techniques are shedding new light on large-scale crop growth status monitoring, especially in recent applications of unmanned aerial vehicles (UAVs). Many inversion models have been built to estimate crop growth variables. However, the present methods focused on building models for each single crop stage, and the features generally used in the models are vegetation indices (VI) or joint VI with data derived from UAV-based sensors (e.g., texture, RGB color information, or canopy height). It is obvious these models are either limited to a single stage or have an unstable performance across stages. To address these issues, this study selected four key wheat growth parameters for inversion: above-ground biomass (AGB), plant nitrogen accumulation (PNA) and concentration (PNC), and the nitrogen nutrition index (NNI). Crop data and multispectral data were acquired in five wheat growth stages. Then, the band reflectance and VI were obtained from multispectral data, along with the five stages that were recorded as phenology indicators (PIs) according to the stage of Zadok's scale. These three types of data formed six combinations (C1–C6): C1 used all of the band reflectances, C2 used all VIs, C3 used bands and VIs, C4 used bands and PIs, C5 used VIs and PIs, and C6 used bands, Vis, and PIs. Some of the combinations were integrated with PIs to verify if PIs can improve the model accuracy. Random forest (RF) was used to build models with combinations of different parameters and evaluate the feature importance. The results showed that all models of different combinations have good performance in the modeling of crop parameters, such as R2 from 0.6 to 0.79 and NRMSE from 10.51 to 15.83%. Then, the model was optimized to understand the importance of PIs. The results showed that the combinations that integrated PIs showed better estimations and the potential of using PIs to minimize features while still achieving good predictions. Finally, the varied model results were evaluated to analyze their performances in different stages or fertilizer treatments. The results showed the models have good performances at different stages or treatments (R2 > 0.6). This paper provides a reference for monitoring and estimating wheat growth parameters based on UAV multispectral imagery and phenology information. [ABSTRACT FROM AUTHOR]

Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping. [ABSTRACT FROM AUTHOR]

Intra-field heterogeneity of soil properties, such as soil organic carbon (SOC), nitrogen (N), phosphorous (P), exchangeable cations, pH, or soil texture, is a function of complex interactions between biological factors, physical factors, and historic agricultural management. Mapping the crop growth and final yield heterogeneity and quantifying their link with soil properties can contribute to an optimization of amendment/fertilizer application and crop yield in a management variable zones (MVZ) approach. To this end, we studied a field of 17 ha consisting of four former fields that were merged in early 2017 and cropped with winter wheat in 2018. Historical management practices data were collected. The topsoil characteristics were analyzed by grid-based sampling and kriged to create maps. We tested the capacity of a multispectral MicaSense® RedEdge-MTM camera sensor embedded on an unmanned aerial vehicle (UAV) to map in-season growth of winter wheat. Relating several vegetation indices (VIs) to the plant area index (PAI) measured in the field highlighted the red-edge NDVI (RENDVI) as the most suitable to follow the crop growth throughout the growing season. The georeferenced final grain yield of the winter wheat was measured by a combine harvester. The spatial patterns in RENDVI at three phenological stages were mapped and analyzed together with the yield map. For each of these images a conditional inference forest (CI-forest) algorithm was used to identify the soil properties significantly influencing these spatial patterns. Historical management practices of the four former fields have induced significant heterogeneity in soil properties and crop growth. The spatial patterns of RENDVI are rather constant over time and their Spearman rank correlation with yield is similar along the growing season (r ≃ 0.7). Soil properties explain between 87% (mid-March) to 78% (mid-May) of the variance in RENDVI throughout the growing season, as well as 66% of the variance in yield. The pH and exchangeable K are the most significant factors explaining from 15 to 26% of the variance in crop growth. The methodology proposed in this paper to quantify the importance of soil parameters based on the CI-forest algorithm can contribute to a better management of amendment/fertilizer inputs by stressing the most important parameters to take into consideration for site-specific management. We also showed that heterogeneity induced by the soil properties can be described by a crop map early in the season and that this crop map can be used to optimize soil sampling and thus amendment/fertilizer management. [ABSTRACT FROM AUTHOR]

Sewage sludge management for fertilizer purposes can be a step in the circular phosphorus (P) economy. Using microbial solubilization in manufacturing fertilizers from recycled materials is an innovative approach with the potential to increase P compounds' bioavailability, and fertilizers from sewage sludge ash and P-solubilizing bacteria are promising products of this technology. In addition to P and a range of macronutrients, these fertilizers contain small amounts of micronutrients and potentially toxic elements. This paper discusses the effects of fertilizer on iron (Fe) and aluminum (Al) content in soil, test plants (spring or winter wheat; grain and straw), weeds and post-harvest residues, based on field experiments. Treatments with conventional P fertilizers (superphosphate, phosphorite) and without P fertilization provided references. The tested biofertilizers containing the Bacillus megaterium or Acidithiobacillus ferrooxidans strain had no effect on total Fe and Al content in the soil or on the concentration of these elements in plant biomass when applied at P doses up to 35.2 kg ha–1. Fe and Al levels in grain did not suggest a potential risk to consumers. [ABSTRACT FROM AUTHOR]

Wastes of biological origin from wastewater treatment systems and slaughterhouses contain substantial amounts of phosphorus (P) with high recovery potential and can contribute to alleviating the global P supply problem. This paper presents the performance of fertilizer (AF) and biofertilizer (BF) from sewage sludge ash and animal blood under field conditions. BF is AF incorporated with lyophilized cells of P-solubilizing bacteria, Bacillus megaterium. In the experiments with spring or winter wheat, the biobased fertilizers were compared to commercial P fertilizer, superphosphate (SP). No P fertilization provided an additional reference. Fertilizer effects on wheat productivity and on selected properties of soil were studied. BF showed the same yield-forming efficiency as SP, and under poorer habitat conditions, performed slightly better than AF in increasing yield and soil available P. Biobased fertilizers applied at the P rate up to 35.2 kg ha–1 did not affect the soil pH, did not increase As, Cd, Cr, Ni, and Pb content, and did not alter the abundance of heterotrophic bacteria and fungi in the soil. The findings indicate that biobased fertilizers could at least partially replace conventional P fertilizers. Research into strain selection and the proportion of P-solubilizing microorganisms introduced into fertilizers should be continued. [ABSTRACT FROM AUTHOR]

Drought has a significant impact on agricultural production, but the evolution of drought in the North China Plain (NCP) and its impact on winter wheat yield remain unclear. In this paper, we used the Standardized Precipitation Evapotranspiration Index (SPEI) and combined with the Standardized Yield Residual Series (SYRS) and the Vegetation Condition Index (VCI) to study the spatiotemporal distribution of drought at different time scales and its impact on winter wheat yield. We found that: (1) The southern NCP was dominated by mild drought at the 1-month scale. In the northern NCP, mild drought was observed at the short-time scale and moderate drought at the medium- and long-time scales; (2) the frequency of mild drought was high and the frequency of moderate drought was a step lower. Moreover, drought risks were increasing in the north-central, eastern, and northeastern NCP, while the southern, west-central, southeastern, and northwestern NCP were becoming wetter (at the 6- and 9-month scales); (3) the maximum correlation coefficients were concentrated in the seedling to the greening stages. (4) Drought affected winter wheat with a lag of approximately 3 months, and the VCI was the most sensitive to the short-term SPEI. [ABSTRACT FROM AUTHOR]

The purpose of this work is to present a new method for estimating the parameters of the biomass of agricultural crops based on Earth remote sensing (ERS) data. The method includes mathematical models and algorithms estimation and has been tested on the example of spring wheat sowing. Sowing biomass parameters are the basis for making management decisions aimed at obtaining a given crop yield. Currently, for these purposes, vegetation indices are most widely used. It is impossible to estimate the physical parameters of the crop sowing biomass using these indices, due to their scalar form and lack of dimension. The paper develops a classical approach to the problem of estimating the parameters of the state of agricultural crops, in which remote sensing data are considered as an indirect measurement of the estimated parameters. The basis for the implementation of the estimation method is the dynamic model of biomass parameters and the remote sensing model, which reflects the relationship between the spectral reflection parameters and the estimated parameters of the crop biomass. The parameters of the dynamic model and the remote sensing model are refined by selective ground measurements in separate elementary sections of the field. The difference between this article and previous works of a similar nature lies in the fact that agricultural crops with a more complex morphological structure are considered as the object of evaluation. In addition, such an important feature of agricultural objects as their spatial distribution is considered here. To take it into account, a new type of mathematical models is used, in which spatial coordinates are introduced. Due to the significant complication of modeling and estimation algorithms based on such models, simpler approximation schemes are proposed. The advantage of the proposed approach is that the assessment is considered as a dynamic process that meets the content of the task of monitoring crops. [ABSTRACT FROM AUTHOR]

Monitoring crop areas and yields is crucial for food security and agriculture management across the world. In this paper, we mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation (PROBA-V) products in the North China Plain (NCP). First, the daily 100-m land surface reflectance was generated by fusing the PROBA-V 100-m and 300-m S1 products. Our results show that the blended data exhibited high correlations with the referenced data (0.71 ≤ R2 ≤ 0.94 for the red band, 0.50 ≤ R2 ≤ 0.95 for the near-infrared band, and 0.88 ≤ R2 ≤ 0.97 for the shortwave infrared band). The time-series Normalized Difference Vegetation Index (NDVI) derived from the synthetic reflectance was then clustered for winter wheat identification. The overall classification accuracy was between 78% and 87%, with a kappa coefficient above 0.57, which was 10%–20% higher than the classification accuracy using the 300-m data. Finally, a light use efficiency model was employed to estimate the biomass and yield. The estimation results were closely related to the field-measured biomass and yield, with high R2 and low root mean square errors (RMSE) (0.864 ≤ R2 ≤ 0.871 and 168 ≤ RMSE ≤ 191 g/m2 for biomass; and 0.631 ≤ R2 ≤ 0.663 and 41.8 ≤ RMSE ≤ 62.8 g/m2 for yield). This paper shows the strong potential of using PROBA-V 100-m data to enhance the spatial resolution of PROBA-V 300-m data and because the proposed framework in this study was based only on the relatively high spatio-temporal resolution PROBA-V data and achieved favorable results, it provides a novel approach for crop areas and yields estimation utilizing the relatively new data set. [ABSTRACT FROM AUTHOR]

Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure--up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data. [ABSTRACT FROM AUTHOR]

Reflectance anisotropy is a signal that contains information on the optical and structural properties of a surface and can be studied by performing multi-angular reflectance measurements that are often done using cumbersome goniometric measurements. In this paper we describe an innovative and fast method where we use a hyperspectral pushbroom spectrometer mounted on a multirotor unmanned aerial vehicle (UAV) to perform such multi-angular measurements. By hovering the UAV above a surface while rotating it around its vertical axis, we were able to sample the reflectance anisotropy within the field of view of the spectrometer, covering all view azimuth directions up to a 30° view zenith angle. We used this method to study the reflectance anisotropy of barley, potato, and winter wheat at different growth stages. The reflectance anisotropy patterns of the crops were interpreted by analysis of the parameters obtained by fitting of the Rahman-Pinty-Verstraete (RPV) model at a 5-nm interval in the 450-915 nm range. To demonstrate the results of our method, we firstly present measurements of barley and winter wheat at two different growth stages. On the first measuring day, barley and winter wheat had structurally comparable canopies and displayed similar anisotropic reflectance patterns. On the second measuring day the anisotropy of crops differed significantly due to the crop-specific development of grain heads in the top layer of their canopies. Secondly, we show how the anisotropy is reduced for a potato canopy when it grows from an open row structure to a closed canopy. In this case, especially the backward scattering intensity was strongly diminished due to the decrease in shadowing effects that were caused by the potato rows that were still present on the first measuring day. The results of this study indicate that the presented method is capable of retrieving anisotropic reflectance characteristics of vegetation canopies and that it is a feasible alternative for field goniometer measurements. [ABSTRACT FROM AUTHOR]

The leaf chlorophyll content is one of the most important factors for the growth of winter wheat. Visual and near-infrared sensors are a quick and non-destructive testing technology for the estimation of crop leaf chlorophyll content. In this paper, a new approach is developed for leaf chlorophyll content estimation of winter wheat based on visible and near-infrared sensors. First, the sliding window smoothing (SWS) was integrated with the multiplicative scatter correction (MSC) or the standard normal variable transformation (SNV) to preprocess the reflectance spectra images of wheat leaves. Then, a model for the relationship between the leaf relative chlorophyll content and the reflectance spectra was developed using the partial least squares (PLS) and the back propagation neural network. A total of 300 samples from areas surrounding Yangling, China, were used for the experimental studies. The samples of visible and near-infrared spectroscopy at the wavelength of 450,900 nm were preprocessed using SWS, MSC and SNV. The experimental results indicate that the preprocessing using SWS and SNV and then modeling using PLS can achieve the most accurate estimation, with the correlation coefficient at 0.8492 and the root mean square error at 1.7216. Thus, the proposed approach can be widely used for winter wheat chlorophyll content analysis. [ABSTRACT FROM AUTHOR]

The rapid and non-destructive monitoring of the canopy leaf nitrogen concentration (LNC) in crops is important for precise nitrogen (N) management. Nowadays, there is an urgent need to identify next-generation bio-physical variable retrieval algorithms that can be incorporated into an operational processing chain for hyperspectral satellite missions. We assessed six retrieval algorithms for estimating LNC from canopy reflectance of winter wheat in eight field experiments. These experiments represented variations in the N application rates, planting densities, ecological sites and cultivars and yielded a total of 821 samples from various places in Jiangsu, China over nine consecutive years. Based on the reflectance spectra and their first derivatives, six methods using different numbers of wavelengths were applied to construct predictive models for estimating wheat LNC, including continuum removal (CR), vegetation indices (VIs), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural networks (ANNs), and support vector machines (SVMs). To assess the performance of these six methods, we provided a systematic evaluation of the estimation accuracies using the six metrics that were the coefficients of determination for the calibration (R2 C) and validation (R2V) sets, the root mean square errors of prediction (RMSEP) for the calibration and validation sets, the ratio of prediction to deviation (RPD), the computational efficiency (CE) and the complexity level (CL). The following results were obtained: (1) For the VIs method, SAVI(R1200, R705) produced a more accurate estimation of the LNC than other indices, with R2C, R2V, RMSEP, RPD and CE values of 0.844, 0.795, 0.384, 2.005 and 0.10 min, respectively; (2) For the SMLR, PLSR, ANNs and SVMs methods, the SVMs using the first derivative canopy spectra (SVM-FDS) offered the best accuracy in terms of R2C, R2V, RMSEP, RPD, and CE, at 0.96, 0.78, 0.37, 2.02, and 21.17, respectively; (3) The PLSR-FDS, ANN-OS and SVM-FDS methods yield similar accuracies if the CE and CL are not considered, however, ANNs and SVMs performed better on calibration set than the validation set which indicate that we should take more caution with the two methods for over-fitting. Except PLS method, the performance for most methods did not enhance when the spectrum were operated by the first derivative. Moreover, the evaluation of the robustness demonstrates that SVM method may be better suited than the other methods to cope with potential confounding factors for most varieties, ecological site and growth stage; (4) The prediction accuracy was found to be higher when more wavelengths were used, though at the cost of a lower CE. The findings are of interest to the remote sensing community for the development of improved inversion schemes for hyperspectral applications concerning other types of vegetation. The examples provided in this paper may also serve to illustrate the advantages and shortcomings of empirical hyperspectral models for mapping important vegetation biophysical properties of other crops. [ABSTRACT FROM AUTHOR]

Timely and nondestructive monitoring of leaf area index (LAI) using remote sensing techniques is crucial for precise and efficient management of crops. In this paper, a new spectral index (SI) for estimating LAI of winter wheat (Triticum aestivum L.) is proposed on the basis of field hyperspectral measurements. A simple index based on the empirical relationships between LAIs and SIs of all available two-waveband combinations from hyperspectral data is developed by considering the difference between reflectance values at 760 and 739 nm (DSIR760-R739 = R760 - R739). Among published and newly developed SIs, DSIR760-R739 exhibited a significant and strong linear relationship with LAI and showed outstanding performance in LAI assessments. The permissible bandwidths for broad-band DSIR760-R739 investigated using simulated reflectance were 5 nm for both 760 and 739 nm center wavelengths. The results indicate that the linear regression model based on the narrow-band and broad-band DSIR760-R739 is a simple but accurate method for timely and nondestructive monitoring of LAI. [ABSTRACT FROM AUTHOR]

The challenge of converting global agricultural food, fiber and energy crop cultivation into an ecologically and economically sustainable production process requires the most efficient agricultural management strategies. Development, control and maintenance of these strategies are highly dependent on temporally and spatially continuous information on crop status at the field scale. This paper introduces the application of a process-based, coupled hydro-agroecological model (PROMET) for the simulation of temporally and spatially dynamic crop growth on agriculturally managed fields. By assimilating optical remote sensing data into the model, the simulation of spatial crop dynamics is improved to a point where site-specific farming measures can be supported. Radiative transfer modeling (SLC) is used to provide maps of leaf area index from Earth Observation (EO). These maps are used in an assimilation scheme that selects closest matches between EO and PROMET ensemble runs. Validation is provided for winter wheat (years 2004, 2010 and 2011). Field samples validate the temporal dynamics of the simulations (avg. R2 = 0.93) and > 700 ha of calibrated combine harvester data are used for accuracy assessment of the spatial yield simulations (avg. RMSE = 1.15 t·ha-1). The study shows that precise simulation of field-scale crop growth and yield is possible, if spatial remotely sensed information is combined with temporal dynamics provided by land surface process models. The presented methodology represents a technical solution to make the best possible use of the growing stream of EO data in the context of sustainable land surface management. [ABSTRACT FROM AUTHOR]

This paper describes the setting and results of a real-time experiment of irrigation scheduling by a time series of optical satellite images under real conditions, which was carried out on durum wheat in the Haouz plain (Marrakech, Morocco), during the 2012/13 agricultural season. For the purpose of this experiment, the irrigation of a reference plot was driven by the farmer according to, mainly empirical, irrigation scheduling while test plot irrigations were being managed following the FAO-56 method, driven by remote sensing. Images were issued from the SPOT4 (Take5) data set, which aimed at delivering image time series at a decametric resolution with less than five-day satellite overpass similar to the time series ESA Sentinel-2 satellites will produce in the coming years. With a Root Mean Square Error (RMSE) of 0.91mm per day, the comparison between daily actual evapotranspiration measured by eddy-covariance and the simulated one is satisfactory, but even better at a five-day integration (0.59mm per day). Finally, despite a chaotic beginning of the experiment-the experimental plot had not been irrigated to get rid of a slaking crust, which prevented good emergence-our plot caught up and yielded almost the same grain crop with 14% less irrigation water. This experiment opens up interesting opportunities for operational scheduling of flooding irrigation sectors that dominate in the semi-arid Mediterranean area. [ABSTRACT FROM AUTHOR]

Numerous epidemiological studies have highlighted the positive effects on health of wholegrain bakery products made from 'old' common wheat (Triticum aestivum L.) varieties. However, 'old' common wheat varieties display poor rheological properties, and there is limited information on its free asparagine (ASN) content, the main precursor to acrylamide during the baking process. This paper evaluates the effects of two seeding density levels (SD: 200 and 350 seed m−2), three nitrogen levels (NL: 35, 80 and 135 kg N ha−1), and two sulfur levels (SL: 0 and 6.4 kg S ha−1) towards improving the grain yield (GY), rheological characteristics, and ASN content of 14 'old' common wheat varieties. SL and SD treatments significantly increased GY without decreasing the protein content (PC), while NL significantly increased the PC without affecting GY. The dough strength (W) increased significantly with increasing SL and NL but was significantly reduced with increasing SD. ASN significantly increased by 111% as NL increased from 35 to 135 kg ha−1, while ASN significantly decreased by 85.1% with the SL treatment. The findings show that 135 kg N ha−1 combined with 6.4 kg S ha−1 can improve the technical performance of 'old' wheat wholegrain flours while maintaining the ASN as low as possible. [ABSTRACT FROM AUTHOR]

Wheat breeding continues to be an important component of agricultural innovations in the Great Plains region of the US. This paper reviews Oklahoma's wheat breeding program over the past several decades with an emphasis on the economic aspects and planning of future breeding strategies. Over the past century, Oklahoma's wheat yields have increased but the productivity gains have been offset in part due to increased yield variability. The shift to improved wheat varieties has resulted in increased susceptibility to plant disease and pest pressure. While a few varieties introduced over the past few decades have dominated the wheat seed market, recent trends indicate producers are adopting a more diverse range of wheat varieties. Producers' concerns have expanded beyond demands for high yielding varieties to include more market oriented needs to increase protein content and test weight. This paper identifies several key policy instruments that Oklahoma stakeholders and policy makers should consider when planning future breeding strategies. A critical need is to assist Oklahoma producers in adapting to climate change by developing drought and heat resistant wheat varieties. Marketing new varieties would be improved and revenue increased if more innovative pricing structures were adopted to better align producers' willingness to pay with seed development costs, which could then be re-invested in breeding. Research budgets, which have remained stagnant in real terms, will need to be increased to adequately address producers' needs into the 21st century. [ABSTRACT FROM AUTHOR]

Accurate mapping of agricultural fields is needed for many purposes, including irrigation decisions and cadastral management. This paper is concerned with the automated mapping of cropland strips that are common in the North China Plain. These strips are commonly 3–8 m in width and 50–300 m in length, and are separated by small ridges that assist with irrigation. Conventional surveying methods are labor-intensive and time-consuming for this application, and only limited performance is possible with very high resolution satellite images. Small Unmanned Aircraft System (sUAS) images could provide an alternative approach to ridge detection and strip mapping. This paper presents a novel method for detecting cropland strips, utilizing centimeter spatial resolution imagery captured by sUAS flying at low altitude (60 m). Using digital surface models (DSM) and ortho-rectified imagery from sUAS data, this method extracts candidate ridge locations by surface roughness segmentation in combination with geometric constraints. This method then exploits vegetation removal and morphological operations to refine candidate ridge elements, leading to polyline-based representations of cropland strip boundaries. This procedure has been tested using sUAS data from four typical cropland plots located approximately 60 km west of Jinan, China. The plots contained early winter wheat. The results indicated an ability to detect ridges with comparatively high recall and precision (96.8% and 95.4%, respectively). Cropland strips were extracted with over 98.9% agreement relative to ground truth, with kappa coefficients over 97.4%. To our knowledge, this method is the first to attempt cropland strip mapping using centimeter spatial resolution sUAS images. These results have demonstrated that sUAS mapping is a viable approach for data collection to assist in agricultural land management in the North China Plain. [ABSTRACT FROM AUTHOR]

The presented paper deals with the issue of selecting a suitable system for monitoring the winter wheat crop in order to determine its condition as a basis for variable applications of nitrogen fertilizers. In a four-year (2017–2020) field experiment, 1400 ha of winter wheat crop were monitored using the ISARIA on-the-go system and remote sensing using Sentinel-2 multispectral satellite images. The results of spectral measurements of ISARIA vegetation indices (IRMI, IBI) were statistically compared with the values of selected vegetation indices obtained from Sentinel-2 (EVI, GNDVI, NDMI, NDRE, NDVI and NRERI) in order to determine potential hips. Positive correlations were found between the vegetation indices determined by the ISARIA system and indices obtained by multispectral images from Sentinel-2 satellites. The correlations were medium to strong (r = 0.51–0.89). Therefore, it can be stated that both technologies were able to capture a similar trend in the development of vegetation. Furthermore, the influence of climatic conditions on the vegetation indices was analyzed in individual years of the experiment. The values of vegetation indices show significant differences between the individual years. The results of vegetation indices obtained by the analysis of spectral images from Sentinel-2 satellites varied the most. The values of winter wheat yield varied between the individual years. Yield was the highest in 2017 (7.83 t/ha), while the lowest was recorded in 2020 (6.96 t/ha). There was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha). [ABSTRACT FROM AUTHOR]

Root system architecture (RSA) is a target for breeding crops with effective nutrient and water use. Breeding can use populations designed to map quantitative trait loci (QTL). Here we non-invasively phenotype roots and leaves of the 16 foundation parents of two multi-parent advanced generation inter-cross (MAGIC) populations, covering diversity in spring (CSIRO MAGIC) and winter (NIAB MAGIC) wheats. RSA components varied after 16 days in the upgraded, paper-based imaging platform, GrowScreen-PaGe: lateral root length 2.2 fold; total root length, 1.9 fold; and seminal root angle 1.2 fold. RSA components total and lateral root length had the highest root heritabilities (H2) (H2 = 0.4 for CSIRO and NIAB parents) and good repeatability (r = 0.7) in the GrowScreen-PaGe. These can be combined with leaf length (H2 = 0.8 CSIRO; 0.7 NIAB) and number (H2 = 0.6 CSIRO; 0.7 NIAB) to identify root and shoot QTL to breed for wheats with vigorous RSA and shoot growth at establishment, a critical phase for crop productivity. Time resolved phenotyping of MAGIC wheats also revealed parents to cross in future for growth rate traits (fastest: Robigus–NIAB and AC Barrie–CSIRO; slowest Rialto–NIAB and G204 Xiaoyan54–CSIRO) and root: shoot allocation traits (fast growers grew roots, notably laterals, quicker than leaves, compared to slow growers). [ABSTRACT FROM AUTHOR]

Site-specific weed management requires sensing of the actual weed infestation levels in agricultural fields to adapt the management accordingly. However, sophisticated sensor systems are not yet in wider practical use, since they are not easily available for the farmers and their handling as well as the management practice requires additional efforts. A new sensor-based weed detection method is presented in this paper and its applicability to cereal crops is evaluated. An ultrasonic distance sensor for the determination of plant heights was used for weed detection. It was hypothesised that the weed infested zones have a higher amount of biomass than non-infested areas and that this can be determined by plant height measurements. Ultrasonic distance measurements were taken in a winter wheat field infested by grass weeds and broad-leaved weeds. A total of 80 and 40 circular-shaped samples of different weed densities and compositions were assessed at two different dates. The sensor was pointed directly to the ground for height determination. In the following, weeds were counted and then removed from the sample locations. Grass weeds and broad-leaved weeds were separately removed. Differences between weed infested and weed-free measurements were determined. Dry-matter of weeds and crop was assessed and evaluated together with the sensor measurements. RGB images were taken prior and after weed removal to determine the coverage percentages of weeds and crop per sampling point. Image processing steps included EGI (excess green index) computation and thresholding to separate plants and background. The relationship between ultrasonic readings and the corresponding coverage of the crop and weeds were assessed using multiple regression analysis. Results revealed a height difference between infested and non-infested sample locations. Density and biomass of weeds present in the sample influenced the ultrasonic readings. The possibilities of weed group discrimination were assessed by discriminant analysis. The ultrasonic readings permitted the separation between weed infested zones and non-infested areas with up to 92.8% of success. This system will potentially reduce the cost of weed detection and offers an opportunity to its use in non-selective methods for weed control. [ABSTRACT FROM AUTHOR]

Sentinel-2 imagery is an unprecedented data source with high spatial, spectral and temporal resolution in addition to free access. The objective of this paper was to evaluate the potential of using Sentinel-2 data to map winter crops in the early growth stage. Analysis of three winter crop types—winter garlic, winter canola and winter wheat—was carried out in two agricultural regions of China. We analysed the spectral characteristics and vegetation index profiles of these crops in the early growth stage and other land cover types based on Sentinel-2 images. A decision tree classification model was built to distinguish the crops based on these data. The results demonstrate that winter garlic and winter wheat can be distinguished four months before harvest, while winter canola can be distinguished two months before harvest. The overall classification accuracy was 96.62% with a kappa coefficient of 0.95. Therefore, Sentinel-2 images can be used to accurately identify these winter crops in the early growth stage, making them an important data source in the field of agricultural remote sensing. [ABSTRACT FROM AUTHOR]

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data. [ABSTRACT FROM AUTHOR]

Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Multimodal images fusion has the potential to enrich the information gathered by multi-sensor plant phenotyping platforms. Fusion of images from multiple sources is, however, hampered by the technical lock of image registration. The aim of this paper is to provide a solution to the registration and fusion of multimodal wheat images in field conditions and at close range. Eight registration methods were tested on nadir wheat images acquired by a pair of red, green and blue (RGB) cameras, a thermal camera and a multispectral camera array. The most accurate method, relying on a local transformation, aligned the images with an average error of 2 mm but was not reliable for thermal images. More generally, the suggested registration method and the preprocesses necessary before fusion (plant mask erosion, pixel intensity averaging) would depend on the application. As a consequence, the main output of this study was to identify four registration-fusion strategies: (i) the REAL-TIME strategy solely based on the cameras' positions, (ii) the FAST strategy suitable for all types of images tested, (iii) and (iv) the ACCURATE and HIGHLY ACCURATE strategies handling local distortion but unable to deal with images of very different natures. These suggestions are, however, limited to the methods compared in this study. Further research should investigate how recent cutting-edge registration methods would perform on the specific case of wheat canopy. [ABSTRACT FROM AUTHOR]

Generally, there is an inconsistency between the total regional crop area that was obtained from remote sensing technology and the official statistical data on crop areas. When performing scale conversion and data aggregation of remote sensing-based crop mapping results from different administrative scales, it is difficult to obtain accurate crop planting area that match crop area statistics well at the corresponding administrative level. This problem affects the application of remote sensing-based crop mapping results. In order to solve the above problem, taking Fucheng County of Hebei Province in the Huanghuaihai Plain of China as the study area, based on the Sentinel-2 normalized difference vegetation index (NDVI) time series data covering the whole winter wheat growth period, the statistical data of the regional winter wheat planting area were regarded as reference for the winter wheat planting area extracted by remote sensing, and a new method for winter wheat mapping that is based on similarity measurement indicators and their threshold optimizations (WWM-SMITO) was proposed with the support of the shuffled complex evolution-University of Arizona (SCE-UA) global optimization algorithm. The accuracy of the regional winter wheat mapping results was verified, and accuracy comparisons with different similarity indicators were carried out. The results showed that the total area accuracy of the winter wheat area extraction by the proposed method reached over 99.99%, which achieved a consistency that was between the regional remote sensing-based winter wheat planting area and the statistical data on the winter wheat planting area. The crop recognition accuracy also reached a high level, which showed that the proposed method was effective and feasible. Moreover, in the accuracy comparison of crop mapping results based on six different similarity indicators, the winter wheat distribution that was extracted by root mean square error (RMSE) had the best recognition accuracy, and the overall accuracy and kappa coefficient were 94.5% and 0.8894, respectively. The overall accuracies of winter wheat that were extracted by similarity indicators, such as Euclidean distance (ED), Manhattan distance (MD), spectral angle mapping (SAM), and spectral correlation coefficient (SCC) were 94.1%, 93.9%, 93.3%, and 92.8%, respectively, and the kappa coefficients were 0.8815, 0.8776, 0.8657, and 0.8558, respectively. The accuracy of the winter wheat results extracted by the similarity indicator of dynamic time warping (DTW) was relatively low. The results of this paper could provide guidance and serve as a reference for the selection of similarity indicators in crop distribution extraction and for obtaining large-scale, long-term, and high-precision remote sensing-based information on a regional crop spatial distribution that is highly consistent with statistical crop area data. [ABSTRACT FROM AUTHOR]

This paper focuses on the agronomic evaluation of a synthetic NPK (N in the form of urea, P and K in the form of phosphate monopotassium) fertilizers blended with four types of pine (Pinus sylvestris L.) wood biochar prepared at different thermal regimes (300 °C, 400 °C, 600 °C and 700 °C). The evaluation of benefits was done based on crop nutritional status and soil fertility. The pot experiment was set up with fertile Haplic Luvisol fertilized with 1.85 g kg−1 of blends of biochar (1.25 g) with urea (310 mg) and KH2PO4 (290 mg), which is equivalent to 500 kg ha−1 (biochar ~67.6%; N ~7.8%; P ~3.6%; K ~4.7%) applied before sowing. Only NPK blends made with biochar containing 75% or 85% carbon increased the biomass of 27-day old wheat seedlings from 12% to 20% in comparison to NPK applied alone. These blends raised the content of Mn and Fe in plants but decreased the contents of Ca and Mg. All the tested mixtures enhanced soil fertility by increasing the content of humic acids. Additionally, the content of potentially phytotoxic phenolic compounds was lower. In general, the addition of biochar to NPK fertilizer did not show a negative effect on crop quality. The overall results of the study suggest that the application of low doses of biochar to synthetic fertilizer can benefit crops and can support soil fertility. [ABSTRACT FROM AUTHOR]

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer's and user's accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase's and reviving phase's data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere. [ABSTRACT FROM AUTHOR]

Yield is one of the primary concerns for any farmer since it is a key to economic prosperity. Yield productivity zones—that is to say, areas with the same yield level within fields over the long-term—are a form of derived (predicted) data from periodic remote sensing, in this study according to the Enhanced Vegetation Index (EVI). The delineation of yield productivity zones can (a) increase economic prosperity and (b) reduce the environmental burden by employing site-specific crop management practices which implement advanced geospatial technologies that respect soil heterogeneity. This paper presents yield productivity zone identification and computing based on Sentinel-2A/B and Landsat 8 multispectral satellite data and also quantifies the success rate of yield prediction in comparison to the measured yield data. Yield data on spring barley, winter wheat, corn, and oilseed rape were measured with a spatial resolution of up to several meters directly by a CASE IH harvester in the field. The yield data were available from three plots in three years on the Rostěnice Farm in the Czech Republic, with an overall acreage of 176 hectares. The presented yield productivity zones concept was found to be credible for the prediction of yield, including its geospatial variations. [ABSTRACT FROM AUTHOR]