Search Results (440)

Accurate regional crop classification, acreage estimation, yield prediction, and crop water requirement assessment are essential for effective agricultural planning and market forecasts. This study uses an integrated geospatial and statistical approach to assess major winter crops wheat and sugarcane cultivation in Ghotki District, Pakistan, from 2017/18 to 2022/23. It combines satellite data from Landsat 8 and Sentinel-2, ground truthing, and crop reporting records to analyze key factors such as cultivation area, crop gradients, vegetation health, normalized difference vegetation index (NDVI)-based wheat and sugarcane yield models, crop water requirements, and total irrigation water consumption. Results showed that wheat cultivation areas ranged from 15% to 19%, with the highest coverage observed in the 2021/22 winter season. Sugarcane cultivation ranged from 6% to 10%, peaking in the 2018/19 season. A strong linear association between NDVI and wheat yield (R² = 0.86) was observed. Wheat and sugarcane yield predictions utilized linear regression, and robust linear regression models, all of which were validated by the findings. Irrigation water demand for the winter season was calculated at 1887 million cubic meters (MCM) in 2017/18, with 1357 MCM supplied by the Sindh Irrigation Drainage Authority (SIDA). By 2020/21, water demand reached 2023 MCM, while SIDA’s supply was 1357 MCM. These results highlight the significance of integrating geospatial analysis with statistical records to provide timely, reliable estimates for cropped areas, yield forecasting, vegetation dynamics, and irrigation planning. The proposed methodology contributes a scaleable solution for informed decision-making in agricultural and water resource management, applicable across other districts in Pakistan and on a global scale. Full article

Wheat is a key staple crop globally, essential for food security and sustainable agricultural development. The results of this study highlight how innovative monitoring techniques, such as UAV-based multispectral imaging, can significantly improve agricultural practices by providing precise, real-time data on crop growth. This study utilized unmanned aerial vehicle (UAV)-based remote sensing technology at the wheat experimental field of the Hebei Academy of Agriculture and Forestry Sciences to capture the dynamic growth characteristics of wheat using multispectral data, aiming to explore efficient and precise monitoring and management strategies for wheat. A UAV equipped with multispectral sensors was employed to collect high-resolution imagery at five critical growth stages of wheat: tillering, jointing, booting, flowering, and ripening. The data covered four key spectral bands: green (560 nm), red (650 nm), red-edge (730 nm), and near-infrared (840 nm). Combined with ground-truth measurements, such as chlorophyll content and plant height, 21 vegetation indices were analyzed for their nonlinear relationships with wheat growth parameters. Statistical analyses, including Pearson’s correlation and stepwise regression, were used to identify the most effective indices for monitoring wheat growth. The Normalized Difference Red-Edge Index (NDRE) and the Triangular Vegetation Index (TVI) were selected based on their superior performance in predicting wheat growth parameters, as demonstrated by their high correlation coefficients and predictive accuracy. A random forest model was developed to comprehensively evaluate the application potential of multispectral data in wheat growth monitoring. The results demonstrated that the NDRE and TVI indices were the most effective indices for monitoring wheat growth. The random forest model exhibited superior predictive accuracy, with a mean squared error (MSE) significantly lower than that of traditional regression models, particularly during the flowering and ripening stages, where the prediction error for plant height was less than 1.01 cm. Furthermore, dynamic analyses of UAV imagery effectively identified abnormal field areas, such as regions experiencing water stress or disease, providing a scientific basis for precision agricultural interventions. This study highlights the potential of UAV-based remote sensing technology in monitoring wheat growth, addressing the research gap in systematic full-cycle analysis of wheat. It also offers a novel technological pathway for optimizing agricultural resource management and improving crop yields. These findings are expected to advance intelligent agricultural production and accelerate the implementation of precision agriculture. Full article

Fusarium head blight (FHB) is a serious fungal disease of wheat and other small cereal grains, significantly reducing grain yield and producing mycotoxins that affect food safety. There is a need for disease detection technologies to determine the right time to apply fungicides, as FHB infection begins before visible symptoms appear. Using multispectral remote sensing by an unmanned aircraft system (UAS), wheat plants were observed under field conditions infested with FHB and simultaneously protected with fungicides sprayed with four different types of nozzles, as well as corresponding control plots infested with FHB only. The results showed that the levels of deoxynivalenol (DON) differed significantly between the five treatments, indicating that the control had the highest DON concentration as no fungicide treatment was applied. This study revealed that the assessment of the normalized difference vegetation index (NDVI) after FHB infection could be useful for predicting DON accumulation in wheat, as a significant negative correlation between DON and NDVI values was measured 24 days after anthesis. The decreasing NDVI values at the end of the growth cycle were expected due to senescence and yellowing of the wheat spikes and leaves. Therefore, significant differences in the NDVI were observed between three measurement points on the 13th, 24th, and 45th day after anthesis. Additionally, the green normalized difference vegetation index (GNDVI) and normalized difference red-edge index (NDRE) were in significant positive correlation with the NDVI at 24th day after anthesis. The use of appropriate measurement points for the vegetation indices can offer the decisive advantage of enabling the evaluation of very large breeding trials or farmers’ fields where the timing of fungicide application is particularly important. Full article

This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models. Full article

As an important physiological indicator, wheat canopy temperature (CT) can be observed after flowering in an attempt to predict wheat yield and quality. However, the relationship between CT and wheat yield and quality is not clear. In this study, the CT, photosynthetic rate (Pn), filling rate, wheat yield, and wheat quality of 68 wheat lines were measured, in an attempt to establish a connection between CT and yield and quality and accelerate the selection of new varieties. This experiment used an infrared imaging camera to measure the CT of wheat materials planted in the field in 2022. Twenty materials with significant temperature differences were selected for planting in 2023. By comparing the temperature trends in 2022 and 2023, it is believed that materials 4 and 13 were cold-type materials, while materials 3 and 11 were warm-type materials. The main grain filling period of cold-type materials occurs in the middle and late stages of the grain filling period and the Pn and the thousand-grain weights of cold-type materials were higher than those of warm-type materials. Similarly, under continuous rainy conditions, cold-type materials had a higher protein and wet gluten contents, while warm-type materials had higher sedimentation values and shorter formation times. Full article

Traditional methods for crop data collection are labor-intensive, inefficient and, more costly compared to remote sensing (RS) techniques. This study aims to identify key climatic variables influencing maize and wheat yields and develop predictive models while also evaluating the performance of the CropWatch cloud yield prediction model (CW_YPM) in major agricultural regions of Ethiopia. Climate data from 54 meteorological stations spanning 2000–2021 were analyzed. RS data, including NDVI from MODIS at 250 m resolution, agroecological zones, and observed crop yield data, were utilized for model prediction and validation. Correlation analysis and a stepwise modeling approach with multiple regression models were applied. The results revealed regional variations in the effects of climatic parameters on yields, with vapor pressure deficits showing negative correlations and rainfall exhibiting positive correlations. Non-linear models generally outperformed linear models in yield prediction—using both climate-only (CO) and combined climate-NDVI data. The best CO model for maize in the Horo Guduru area achieved an RMSE of 0.392 tons/ha, an R² of 0.94, and an index of agreement (d) of 0.984. Incorporating NDVI improved accuracy, with the best maize model in the Illu Ababor area achieving an RMSE of 0.477 tons/ha, an R² of 0.91, and d of 0.976. CW_YPM also performed effectively across the study area. This research highlights the value of integrating critical climatic variables with the NDVI to enhance crop yield forecasting in Ethiopia, thereby-supporting agricultural planning and food security initiatives. Full article

Nitrogen dioxide (NO₂) impacts climate, air quality, soil nitrogen fixation, and crop production, yet its transboundary impacts remain unclear. This study combines 15 global datasets to assess nitrogen’s transboundary impacts on crop yields and soil health. We use machine learning to develop yield prediction models for major grain crops (maize, rice, soybean, and wheat) affected by NO₂. Our findings indicate stable soil nitrogen fixation in China from 2015 to 2020, although overgrazing and deforestation may cause declines. Increasing soil total nitrogen content by 0.62–2.1 g/kg can reduce NO₂ by 10–30%. Our research indicates that the current agricultural environments for major grain crops (58.5–94.2%) have already exceeded the NO₂ concentration range that crops can tolerate, particularly in regions near northern urban clusters. This highlights the need for regional interventions, such as precision nitrogen fertilizer management, to enhance both soil nitrogen fixation and crop yields. Scenario analysis suggests that NO₂ control can boost maize and rice yields in a greener context, while increasing total nitrogen content improves wheat and soybean yields. This provides a solution for advancing sustainable agriculture by linking nitrogen cycle management with improved crop yields and environmental sustainability. Full article

Agricultural insurance is by far the most popular risk management tool used in Iran. Despite many years of experience, Iran’s current insurance policy has not managed to protect all producers in the sector. The basic principle of whole-farm insurance consists of pooling all the insurable risks of a farm into a single policy and overcoming most of the major impediments to existing policies. This study aimed to evaluate the benefits of whole-farm insurance (WFI) in Zanjan province of Iran. This study employed historical farm-level and county-level data from 1982 to 2021 to estimate yield and price density functions and predict future values. Parametric and non-parametric approaches were utilized to calculate farmers’ expected compensation and guaranteed and simulated revenues. The premium rates were then calculated using the PQH simulation and Cholesky decomposition and compared under three scenarios: the single-crop, double-crop, and triple-crop options. Finally, farmers’ welfare benefits were compared under the three scenarios with the no-insurance case. The results demonstrate that WFI provides lower loss ratios compared to yield insurance and crop-specific insurance. Furthermore, producer welfare can be improved when they insure at least one crop compared to no-insurance. For example, the welfare benefits of insuring wheat, barley, alfalfa, wheat–barley, wheat–alfalfa, barley–alfalfa, and barley–alfalfa in terms of cost reduction to producers at 75% coverage are 8.8, 1.8, 2.9, 1.2, 0.9, and 1.8, respectively. Therefore, we recommend that the Iranian Agricultural Insurance Fund adopts WFI as a new risk management tool. This policy has the potential to decrease insurance premiums and administrative costs while improving the certainty equivalents and benefits to farmers through crop insurance. Full article

Accurate crop yield prediction is crucial for formulating agricultural policies, guiding agricultural management, and optimizing resource allocation. This study proposes a method for predicting yields in China’s major winter wheat-producing regions using MOD13A1 data and a deep learning model which incorporates an Improved Gray Wolf Optimization (IGWO) algorithm. By adjusting the key parameters of the Convolutional Neural Network (CNN) with IGWO, the prediction accuracy is significantly enhanced. Additionally, the study explores the potential of the Green Normalized Difference Vegetation Index (GNDVI) in yield prediction. The research utilizes data collected from March to May between 2001 and 2010, encompassing vegetation indices, environmental variables, and yield statistics. The results indicate that the IGWO-CNN model outperforms traditional machine learning approaches and standalone CNN models in terms of prediction accuracy, achieving the highest performance with an R² of 0.7587, an RMSE of 593.6 kg/ha, an MAE of 486.5577 kg/ha, and an MAPE of 11.39%. The study finds that April is the optimal period for early yield prediction of winter wheat. This research validates the effectiveness of combining deep learning with remote sensing data in crop yield prediction, providing technical support for precision agriculture and contributing to global food security and sustainable agricultural development. Full article

The fungus Puccinia triticina Eriks (Pt) is the cause of leaf rust, one of the most damaging diseases, which significantly reduces common wheat yields. In Pt-resistant adult plants, an APR-type resistance is observed, which protects the plant against multiple pathogen races and is distinguished by its persistence under production conditions. With a more complete understanding of the molecular mechanisms underlying the function of APR genes, it will be possible to develop new strategies for resistance breeding in wheat. Currently, mainly APR genes, such as Lr34, Lr46, and Lr67, are principally involved in resistance breeding as they confer durable resistance to multiple fungal races occurring under different climatic and environmental conditions. However, the mechanisms underlying the defence against pathogens mediated by APR genes remain largely unknown. Our research aimed to shed light on the molecular mechanisms related to resistance genes and miRNAs expression, underlying APR resistance to leaf rust caused by Pt. Furthermore, the present study aimed to identify and functionally characterize the investigated miRNAs and their target genes in wheat in response to leaf rust inoculation. The plant material included hybrid forms of wheat from the F₂ and BC₁F₁ generations, obtained by crossing the resistance cultivar Glenlea (CItr 17272) with agriculturally important Polish wheat cultivars. Biotic stress was induced in adult plants via inoculation with Pt fungal spores under controlled conditions. The RT-qPCR method was used to analyze the expression profiles of selected APR genes at five time points (0, 6, 12, 24, and 48 hpi). The results presented here demonstrate the differential expression of APR genes and miRNAs at stages of leaf rust development at selected timepoints after inoculation. We analyzed the expression of three leaf rust resistance genes, using different genetic backgrounds in F₂ and BC₁F₁ segregation materials, in leaf tissues after Pt infection. Our goal was to investigate potential differences resulting from the genetic background found in different generations of hybrid forms of the same parental forms. Gene ontology analysis predicted 190 target genes for tae-miR5384-3p and 167 target genes for tae-miR9653b. Our findings revealed distinct expression profiles for genes, with the highest expression levels observed mainly at 6, 24, and 48 hpi. The candidate gene Lr46-Glu2 displayed an upregulation, suggesting its potential involvement in the immune response against Pt infection. Full article

Food sustainability has become a major global concern in recent years. Multiple complimentary strategies to deal with this issue have been developed; one of these approaches is regenerative farming. The identification and analysis of crop type phenology are required to achieve sustainable regenerative faming. Earth Observation (EO) data have been widely applied to crop type identification using supervised Machine Learning (ML) and Deep Learning (DL) classifications, but these methods commonly rely on large amounts of ground truth data, which usually prevent historical analysis and may be impractical in very remote, very extensive or politically unstable regions. Thus, the development of a robust but intelligent unsupervised classification model is attractive for the long-term and sustainable prediction of agricultural yields. Here, we propose FastDTW-HC, a combination of Fast Dynamic Time Warping (DTW) and Hierarchical Clustering (HC), as a significantly improved method that requires no ground truth input for the classification of winter food crop varieties of barley, wheat and rapeseed, in Norfolk, UK. A series of variables is first derived from the EO products, and these include spectral indices from Sentinel-2 multispectral data and backscattered amplitude values at dual polarisations from Sentinel-1 Synthetic Aperture Radar (SAR) data. Then, the phenological patterns of winter barley, winter wheat and winter rapeseed are analysed using the FastDTW-HC applied to the time-series created for each variable, between Nov 2019 and June 2020. Future research will extend this winter food crop mapping analysis using FastDTW-HC modelling to a regional scale. Full article

Recurrent selection for early vigour traits in wheat (Triticum aestivum L.) has provided an opportunity to generate competitive biotypes to suppress agronomically important weeds. Quantifying the potential benefits of competitive genotypes, including yield improvement and reduced frequency of herbicide application when incorporated into a long-term rotation, is vital to increase grower adoption. In this simple economic model, we evaluated a weed-suppressive early vigour genotype utilising on-farm experimental results and simulation analysis to predict gross margins for a seven-year wheat-canola rotation in southeastern Australia. The model applied a local weather sequence and predicted wheat production potential, costs and benefits over time. An early vigour wheat genotype was compared to commercial wheat cultivars for weed control, yield and actual production cost. With respect to weed control, three scenarios were evaluated in the model: standard herbicide use with a commercial cultivar (A), herbicide use reduced moderately by inclusion of an early vigour wheat genotype and elimination of the postharvest grass herbicide (B) or inclusion of an early vigour wheat genotype and withdrawal of both postharvest grass and broadleaf herbicides (C). Cost savings for the use of a competitive wheat genotype ranged from 12 AUD/ha in scenario B to 40 AUD/ha in scenario C, for a total saving of 52 AUD/ha. The model generated annual background gross margins, which varied from 300 AUD/ha to 1400 AUD/ha based on historical weather conditions, production costs and crop prices over the 30-year period from 1992 to 2021. The benefits of lower costs for each of the three scenarios are presented with rolling seven-year average wheat–canola rotation gross margins over the 30-year period. The limitations of this model for evaluation of weed suppression and cost benefits are discussed, as well as relative opportunities for adoption of early vigour traits in wheat. Full article

The Normalized Difference Vegetation Index (NDVI) is an important remote sensing index that is widely used to assess vegetation coverage, monitor crop growth, and predict yields. Traditional NDVI calculation methods often rely on multispectral or hyperspectral imagery, which are costly and complex to operate, thus limiting their applicability in small-scale farms and developing countries. To address these limitations, this study proposes an NDVI estimation method based on low-cost RGB (red, green, and blue) UAV (unmanned aerial vehicle) imagery combined with deep learning techniques. This study utilizes field data from five major crops (cotton, rice, maize, rape, and wheat) throughout their whole growth periods. RGB images were used to extract conventional features, including color indices (CIs), texture features (TFs), and vegetation coverage, while convolutional features (CFs) were extracted using the deep learning network ResNet50 to optimize the model. The results indicate that the model, optimized with CFs, significantly enhanced NDVI estimation accuracy. Specifically, the R² values for maize, rape, and wheat during their whole growth periods reached 0.99, while those for rice and cotton were 0.96 and 0.93, respectively. Notably, the accuracy improvement in later growth periods was most pronounced for cotton and maize, with average R² increases of 0.15 and 0.14, respectively, whereas wheat exhibited a more modest improvement of only 0.04. This method leverages deep learning to capture structural changes in crop populations, optimizing conventional image features and improving NDVI estimation accuracy. This study presents an NDVI estimation approach applicable to the whole growth period of common crops, particularly those with significant population variations, and provides a valuable reference for estimating other vegetation indices using low-cost UAV-acquired RGB images. Full article

The timely and reliable prediction of crop yields on a larger scale is crucial for ensuring a stable food supply and food security. In the last few years, many studies have demonstrated that deep learning can offer reliable solutions for crop yield prediction. However, a key challenge in applying deep-learning models to crop yield prediction is their reliance on extensive training data, which are often lacking in many parts of the world. To address this challenge, this study introduces TrAdaBoost.R2, along with fine-tuning and domain-adversarial neural network deep-transfer-learning strategies, for predicting the winter wheat yield across diverse climatic zones in the USA. All methods used the bidirectional LSTM (BiLSTM) architecture to leverage its sequential feature extraction capabilities. The proposed transfer-learning approaches outperformed the baseline deep-learning model, with mean absolute error reductions ranging from 9% to 28%, demonstrating the effectiveness of these methods. Furthermore, the results demonstrate that the semi-supervised transfer-learning approach using the two-stage version of TrAdaBoost.R2 and fine-tuning achieved a superior performance compared to the domain-adversarial neural network and standard TrAdaBoost.R2. Additionally, the study offers insights for improving the accuracy and generalizability of crop yield prediction models in diverse agricultural landscapes across different regions. Full article

Background: The tiller number is a key agronomic trait for increasing the yield potential of wheat (Triticum aestivum L.). A number of quantitative trait loci (QTLs) and key genes controlling tillering have been identified, but the regulatory mechanisms remain unclear. Methods: In this study, we utilized the dwarf-monoculm mutant (dmc) obtained from the ethyl methane sulfonate (EMS)-treated wheat cultivar Guomai 301. The F₂ populations were constructed using the dmc mutant crossed to multiple tiller parents. The F₂ populations were surveyed for tillering traits at the critical fertility stage for genetic analyses. The extreme-tillering-phenotype plants from the F₂ population were used to construct mixing pools that were analyzed by a wheat 55K SNP array. The tillering genes of dmc were mapped using the wheat 55K SNP array combined with transcriptomic data. Results: The results showed that the genetic phenotype of dmc is controlled by two dominant genes. The tillering genes of dmc were mapped on the 60–100 Mb region of chromosome 5B and the 135–160 Mb region of chromosome 7A. A total of sixteen candidate genes associated with the tillering trait of dmc were identified. Two candidate genes, TraesCS5B02G058800 and TraesCS7A02G184200, were predicted to be involved in indole acetic acid (IAA) response and transport, which were considered as potential regulatory genes. Conclusions: This study elucidated the genetic basis of the dmc mutant and provided two valuable reference genes for studying the development and regulatory mechanisms of wheat tillering. Full article