Crop Distribution Research Articles

The influence of regional industrial structure on the suitability of biomass energy development: A case study of 30 counties and cities in a cold region of China

The development of the biomass economy contributes to economic growth and social development. The study of biomass energy economy from the perspective of industrial structure type has the significance of promoting the optimization and upgrading of industrial structure, promoting the development of green industry and other aspects. It helps to promote the sustainable development of the economy and society. And industrial structure is a major component of the socio-economic system. Therefore, it is necessary to study the impact of material energy supply and demand from the perspective of regional industrial structure type. The objective of this study is to explore the relationship between the type of industrial structure and the development of biomass energy from the perspective of the economic direction, so as to fill the gap in this research perspective. Based on the three major industries generally regarded to constitute GDP - agriculture, industry, and service - the regression results of STATA software were used to calculate biomass potential according to crop distribution status. Using the ArcGIS analysis tool, the biomass break-even model related to resource supply-demand has been used to obtain the break-even status of the research area under six industrial structures in the past 20 years. According to research results, the break-even degree of biomass energy supply is between 0.109 and 1.236 when the industrial structure types of counties and cities are service-agriculture-industry and industry-agriculture-service. This is suitable for biomass energy development. When the industrial structure type of counties and cities is service-industry-agriculture, the break-even degree of biomass energy supply is between -0.428 and 0.277, which is not suitable for biomass energy development.

Spatial and temporal distribution of Scirtothrips dorsalis (Thysanoptera: Thripidae) and their natural enemies in Florida strawberry fields.

Given the recent invasion of Scirtothrips dorsalis Hood in North America, there is limited information regarding their distribution and population dynamics in cultivated small fruit crops. Therefore, we investigated the spatial and temporal distribution of S. dorsalis and their natural enemies in commercially produced strawberry fields in Florida. During 2 consecutive strawberry production seasons, 4 and 6 geographically separated strawberry fields were sampled and were divided into grids with 30-40 sampling points per field. At each sampling point, 4-5 leaf and flower samples were collected, and sticky traps were deployed. We quantified the occurrence of S. dorsalis as well as potential natural enemies, including Orius spp., Geocoris spp., and other predators such as long-legged flies. During both years, most of the S. dorsalis and natural enemies were found on field borders, and counts progressively diminished further into the interiors of plots and away from field edges. Cluster and outlier analysis revealed that S. dorsalis formed statistically significant clusters and that these "hot spots" remained in the same general locations throughout the season. There was a strong relationship between the occurrence of natural enemies and the presence of S. dorsalis, but the number of natural enemies was generally low compared to S. dorsalis. Our results indicate that targeting field borders for chemical control or planting strawberries away from natural areas containing potential alternative hosts for thrips may be an effective strategy for reducing agricultural inputs; however, future field assessments are needed to determine if these methods could replace the treatment of entire fields.

Cross-Regional Crop Classification Based on Sentinel-2

Accurate crop classification is of vital importance for agricultural water management. Most researchers have achieved crop classification by model optimization in the same temporal and regional domain by adjusting the value of input features. This study aims to improve the accuracy of crop classification across temporal and spatial domains. Sentinel-2 satellite imagery is employed for crop classification training and prediction in selected farming areas of Heilongjiang Province by calculating vegetation indices and constructing sequential input feature datasets. The HUNTS filtering method was used to mitigate the influence of cloud cover, which increased the stability and completeness of the input feature data across different years. To address the issue of shifts in the input feature values during cross-scale classification, this study proposes the hypothesis testing distribution method (HTDM). This method balances the distribution of input feature values in the test set even without knowing the crop distribution, thereby enhancing the accuracy of the classification test set. The results indicate that the HTDM significantly improves prediction accuracy in cases of substantial image quality variance. In 2022, the recognition accuracy for crop types at all farms processed by the HTDM was above 87%, showcasing the strong robustness of the HTDM.

Cross-Modal Segmentation Network for Winter Wheat Mapping in Complex Terrain Using Remote-Sensing Multi-Temporal Images and DEM Data

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.

Annual winter crop distribution from MODIS NDVI timeseries to improve yield forecasts for Europe

Crop yield forecasts allow policy makers to anticipate market behaviour and regulate prices. Annual updates on which crops are grown where can improve crop yield forecast accuracy. Existing efforts to map crops across the European Union resulted in late-season map availability or short time series that do not meet forecasting requirements. We propose a new approach to retrieve annual winter crop maps and improve forecasting efforts by identifying pixels with dominant winter crop signals using moderate resolution imagery. These pixels are distinguished from summer crop signals based on their senescence date. When this date precedes the theoretical maturity date of a winter crop, expressed in GDD, the pixel is labelled as having a dominant winter crop signal. Our 2018 map accurately identified 77% and 83% of dominantly winter-crop area, when compared to farmers’ declaration data and a high-resolution crop map for Europe, respectively. While the resulting annual winter crop maps underestimated winter crop area, derived region-specific annual NDVI profiles better described winter crop phenology as compared to the use of static maps. Regression analysis between these regional NDVI profiles and statistical wheat yield data indicates that our annual maps help explain more yield variability than static maps, with an RMSE reduction of 3% for the EU27 as whole. The proposed approach is applicable to long historical timeseries and provides maps before the end of the agricultural season. Those maps positively impact crop yield description, notably in eastern, northern, and northeastern European regions.

Preparation of a thematic map of agroecological crops using Google Earth

In a context where agroecology has become an increasingly relevant alternative to promote sustainable and environmentally friendly agricultural practices, this project seeks to provide an efficient tool for mapping and visualizing agroecological crops in a specific geographic area, allowing farmers, researchers, and planners to evaluate and promote sustainable agricultural practices. This project will describe the methodology used for creating the thematic map, which will include data collection on agroecological crops through surveys and records, as well as the processing and analysis of this information within the Google Earth environment. The accuracy and reliability of the mapping technology used will be evaluated, ensuring that the results are dependable and precise. The obtained results will provide a clear and accessible visual representation of the spatial distribution of agroecological crops, thereby facilitating informed decision-making for agricultural planning and the promotion of sustainable practices. The ultimate goal is to contribute to the promotion and practical application of agroecology by providing a useful and easily accessible tool for mapping agroecological crops in a specific geographic area. The hope is to foster interest in sustainable agriculture and encourage the adoption of environmentally friendly practices in the agricultural sector, thus contributing to a more sustainable and equitable future in agriculture.

Temporally transferable crop mapping with temporal encoding and deep learning augmentations

Detailed maps on the spatial and temporal distribution of crops are key for a better understanding of agricultural practices and for food security management. Multi-temporal remote sensing data and deep learning (DL) have been extensively studied for deriving accurate crop maps. However, strategies to solve the problem of transferring crop classification models over time, e.g., training the model with data for a recent year and mapping back to the past, have not been fully explored. This is due to the lack of a generalized method for aggregating optical data with regard to the irregularity in annual clear sky observations and the scarcity of multi-annual crop reference data to support a more generalized DL model. In this study, we tackled these challenges by introducing a method namely Temporal Encoding (TE) to capture the irregular phenological information. Subsequently, we adapted and integrated two methods, i.e., Random Observations Selection (ROS) and Random Day Shifting (RDS) to simulate the variability of temporal sparsity as well as the shifts of crop phenology over different years. We tested this approach with a 1-dimensional Convolutional Neural Network (1D-CNN) and a Transformer Network models. Our results for both classifiers showed that models trained with crop reference data from 2018 and a dense time series of Landsat 7/8 and Sentinel-2 A/B data can be transferred with little decreases in accuracy to map 12 consecutive years from 2010 to 2021. The Transformer Network was slightly more accurate, while the 1D-CNN was much three times faster. Furthermore, the proposed models could achieve similar performances in the same years with and without fully available satellite information. The TE with ROS and RDS appears well suited for improving temporal transferability to support long term historic crop mapping.

CROPGRIDS: a global geo-referenced dataset of 173 crops

CROPGRIDS is a comprehensive global geo-referenced dataset providing area information for 173 crops for the year 2020, at a resolution of 0.05° (about 5.6 km at the equator). It represents a major update of the Monfreda et al. (2008) dataset (hereafter MRF), the most widely used geospatial dataset previously available, covering 175 crops with reference year 2000 at 10 km spatial resolution. CROPGRIDS builds on information originally provided in MRF and expands it using 27 selected published gridded datasets, subnational data of 52 countries obtained from National Statistical Offices, and the 2020 national-level statistics from FAOSTAT, providing more recent harvested and crop (physical) areas for 173 crops at regional, national, and global levels. The CROPGRIDS data advance the current state of knowledge on the spatial distribution of crops, providing useful inputs for modelling studies and sustainability analyses relevant to national and international processes.

Integrating Optical and SAR Time Series Images for Unsupervised Domain Adaptive Crop Mapping

Accurate crop mapping is crucial for ensuring food security. Recently, many studies have developed diverse crop mapping models based on deep learning. However, these models generally rely on a large amount of labeled crop samples to investigate the intricate relationship between the crop types of the samples and the corresponding remote sensing features. Moreover, their efficacy is often compromised when applied to other areas owing to the disparities between source and target data. To address this issue, a new multi-modal deep adaptation crop classification network (MDACCN) was proposed in this study. Specifically, MDACCN synergistically exploits time series optical and SAR images using a middle fusion strategy to achieve good classification capacity. Additionally, local maximum mean discrepancy (LMMD) is embedded into the model to measure and decrease domain discrepancies between source and target domains. As a result, a well-trained model in a source domain can still maintain satisfactory accuracy when applied to a target domain. In the training process, MDACCN incorporates the labeled samples from a source domain and unlabeled samples from a target domain. When it comes to the inference process, only unlabeled samples of the target domain are required. To assess the validity of the proposed model, Arkansas State in the United States was chosen as the source domain, and Heilongjiang Province in China was selected as the target domain. Supervised deep learning and traditional machine learning models were chosen as comparison models. The results indicated that the MDACCN achieved inspiring performance in the target domain, surpassing other models with overall accuracy, Kappa, and a macro-averaged F1 score of 0.878, 0.810, and 0.746, respectively. In addition, the crop-type maps produced by the MDACCN exhibited greater consistency with the reference maps. Moreover, the integration of optical and SAR features exhibited a substantial improvement of the model in the target domain compared with using single-modal features. This study indicated the considerable potential of combining multi-modal remote sensing data and an unsupervised domain adaptive approach to provide reliable crop distribution information in areas where labeled samples are missing.

Exploring the Climate and Topography of Olive Orchards in Extremadura, Southwestern Spain

The olive tree is a significant woody crop in the Mediterranean basin, with the primary olive-growing areas located in the south and southwest of the Iberian Peninsula. Climate plays a crucial role in crop distribution, viability, production, and yield. The climatic and topographical conditions of Extremadura in southwestern Spain, were analysed to characterise and group the olive-growing areas. To achieve this, a Geographic Information System (GIS) was developed to locate all registered olive tree plots. The orientations and elevations of these were analysed, along with the average, minimum, and maximum temperatures, precipitation, and frost for the period of 1990–2021. The study reveals that olive groves in Extremadura are primarily situated at elevations ranging from 200 to 600 m above sea level. Additionally, it was found that the least common orientation is towards the north. Temperatures are not a constraint for olive growing, except in the mountainous areas in the north of the region; however, rainfall can be a limiting factor. In fact, 50% of the olive-growing area receives less rainfall than is considered adequate for optimal production. Frost days and the low probability of severe frosts are not a problem for olive growing in this region, except in the highest parts of the region. The scale of this study, at plantation level, will enable the implementation of measures aimed at climate change adaptation and mitigation.

Water and Nitrogen Dynamics in Drip Fertigated Tomato for Water of Different Qualities under Polyhouse Conditions


 Water and NO3-N dynamics in the soil during the growing season is an important tool in improving the nitrogen management and environmental protection. HYDRUS-2D has been widely used to predict the water and NO3-N distribution in the soil. The objective of this study was to simulate the water and NO3-N distribution in the soil under drip fertigated tomato irrigated with different water qualities under polyhouse conditions. Field data were collected on spatial and temporal distribution of water and available NO3-N during growing season. The model was calibrated for the hydraulic conductivity and parameters were used for the validation of the model. The model performance in simulating the water and NO3-N was evaluated by using coefficient of determination (R2), root mean square error (RMSE), index of agreement and Nash–Sutcliffe model efficiency (NSE). For both calibration and validation, the higher values of R2 from 0.70 to 0.99 for water distribution and 0.70 to 0.96 for NO3-N distribution showed that observed and predicted values are highly correlated. The value of RMSE ranges from 0.004 to 0.0016 for water and 0.002-0.006 for NO3-N distribution. The index of agreement value varied from 0.86-0.98 for water distribution and 0.89-0.99 for NO3-N distribution. The values of NSE (nearer to 1) i.e. 0.17 to 0.98 for water distribution and -0.09 to 0.94 for NO3-N distribution show that HYDRUS-2D was predicting with good accuracy. From these results, it can be concluded that the model performs well for predicting the water and NO3-N distribution in the tomato crop irrigated with different water qualities under polyhouse conditions.

Investing in the Pioneering Varieties as an Agreement to Attaining Economic Take-off on Smallholder Seed Firms in West Africa

Farmers everywhere depend on access to good quality seed, which is fundamental to their crop production systems. Most governments have made significant investments in strengthening national agricultural production capacities, yet farmers in developing countries still face difficulties in accessing the quality seed of the varieties that they require. Guaranteeing farmers’ access to quality seed can only be achieved through a viable seed supply system that can multiply and distribute seeds, which have been produced or preserved. This is better achieved by the private sector, but medium- to large-scale international seed companies concentrate on high-value crops and avoid dealing in the self-pollinating, open-pollinating and vegetative-propagated varieties on which most smallholder farmers depend for their food security as the biology of these crops makes it easy for farmers to keep their seeds for several seasons. However, smallholders are often seen as the driving force of economic growth, poverty reduction and food security. This is also true for smallholder seed enterprises, which, in the absence of large companies, provide a valid alternative for the production and distribution of food security crops. This paper reviews case studies on smallholder seed enterprise in Côte d’Ivoire, as well as relevant world literature in order to identify key issues that facilitate or constrain the development of the seed sector. The final section provides some guidelines on policy design and implementation to promote the development of sustainable seed enterprises at different stages in the evolution of national seed sectors. It provides examples of good practices and hence seeks to assist governments in identifying ways in which they can support the development of smallholder seed enterprises that will provide the most appropriate varieties to smallholder farmers in their efforts to boost food production.

Spatial Distribution and Influencing Factors of Daylily Cultivation in the Farming–Pastoral Ecotone of Northern China

The farming–pastoral ecotone in northern China is an ecologically vulnerable area with low-quality arable land, and cash crops are an important economic source for local farmers. Although local governments have introduced supportive policies, there are still several factors that hinder the implementation of the policies: there is a lack of sufficient research on the distribution of specialty crops, and the driving factors for agricultural planting structure adjustment are not yet clear. In this study, the specialty cash crop of the daylily planting industry in Yunzhou District, in the Farming–Pastoral Ecotone in northern China, was selected as the research object. Field surveys were conducted to collect sample points and village-level survey data, which were further combined with Sentinel-1 and Sentinel-2 data, and vegetation indices. Support vector machine (SVM) and random forest (RF) classifiers were utilized to identify daylilies and compare the accuracy using different combinations of input data. Furthermore, the classification results were counted by village, and spatial autocorrelation was used to analyze the spatial distribution pattern of daylilies. Finally, in conjunction with the village-level survey data, Spearman correlation analysis, multiple regression trees (MRT), and random forests were employed to explore the driving factors of daylily cultivation. The results indicate that using an RF classification tree of 300 resulted in the optimal method, as it achieved the highest accuracy for crop classification. The overall accuracy and daylily classification accuracy were 94.6% and 94.75%, respectively. Daylily distributions were mainly concentrated near the Sanggan River, urban areas, and the tourism industry. The distribution area of daylilies in each village was concentrated in 13.4–38.8 hm2. Spatial clustering showed more aggregation of low–low and high–high types. Labor force and daylily yield were identified as the most significant influencing factors. Further analysis of the different regions revealed the importance of industry support policies and technical training. This study provides data to support the distribution of specialty crops in Yunzhou District and a technical basis for adjusting agricultural planting structures.

WPS:A whole phenology-based spectral feature selection method for mapping winter crop from time-series images

Accurately obtaining the spatial distribution and planting patterns of crops is very important for agricultural planning and food security. At present, time-series images have been proved to be an effective tool to characterize crop seasonal growth patterns, and identifying crop information by measuring the time-series similarity between unknown classes and known crop phenology curves is also considered to be a useful way. However, the existing methods of selecting feature ignore the connection between each phenological stage of crops and the unique growth rules of the whole phenology, which makes it difficult to select time-series spectral features that are potentially important for crop mapping. In order to make up for this problem, a Whole Phenology-based Spectral Feature Selection (WPS) method was proposed. The aim was to select the time-series feature sets with great differences among winter crops from a large number of spectral features, so as to improve the mapping accuracy of winter rapeseed and winter wheat. Firstly, spectral separability between all classes is calculated. Secondly, the key phenological periods of winter crops were selected according to the importance of temporal features, and the spectral feature sets with high separability were selected according to the key phenological periods. Finally, a Time-weighted Dynamic Time Warping (TWDTW) algorithm was used to generate the winter rapeseed and winter wheat maps of two cities in the middle and lower reaches of the Yangtze River. The mapping accuracy of the two crops is more than 92%, which matches the crop planting area well. The research shows that combining the WPS method with the TWDTW mapping method has great potential to accurately map crop types based on satellite time-series images.

Response of the cultivation suitability of Pu'er tea (Camellia sinensis var. assamica) to climate conditions and change in China.

Crop cultivation suitability plays a vital role in determining the distribution, quality, and production of crop and can be greatly affected by climate change. Therefore, evaluating crop cultivation suitability under climate change and identifying the factors influencing it can optimize crop cultivation layout and improve production and quality. Based on comprehensive datasets including geographical distribution points, climate data, soil characteristics, and topography, our study employed the MaxEnt model to simulate the potential distribution of Pu'er tea (Camellia sinensis var. assamica) cultivation suitability in Yunnan Province from 1961 to 2020. Furthermore, we assessed the consistency between the simulated suitable areas and the actual production of Pu'er tea. The results showed that precipitation of the warmest quarter, precipitation of the driest month, and average temperature in January were the three dominant environmental variables affecting the cultivation distribution of Pu'er tea. The high suitable areas for Pu'er tea cultivation in Yunnan Province were mainly distributed in the western and southern regions, accounting for 13.89% of the total area of Yunnan Province. The medium suitable areas are mainly distributed in the central and western regions of Yunnan Province, accounting for 20.07% of the total area of Yunnan Province. Over the past 60years, the unsuitable area for Pu'er tea has increased, while the suitable area has shown a trend of migration to the southwest. Changes in precipitation and temperature were found to be the main drivers of the changes in the distribution of suitable areas for Pu'er tea. We also found a mismatch between the cultivation suitability and the actual production of Pu'er tea. Our study provides an accurate assessment and zoning analysis of the suitability of Pu'er tea cultivation in Yunnan Province, which can help optimize the layout of Pu'er tea cultivation and reduce potential climate risks.

Evolution of Crop Planting Structure in Traditional Agricultural Areas and Its Influence Factors: A Case Study in Alar Reclamation

This research provides a comprehensive analysis of the spatiotemporal evolution of the regional cropping structure and its influencing factors. Using Landsat satellite images, field surveys, and yearbook data, we developed a planting structure extraction model employing the classification regression tree algorithm to obtain data on the major crop cultivation and structural characteristics of Alar reclamation from 1990 to 2023. A dynamic model and transfer matrix were used to analyze temporal changes, and a centroid migration model was used to study spatial changes in the cropping structure. Nonparametric mutation tests and through-traffic coefficient analysis were utilized to quantify the main driving factors influencing the cropping structure. During the period of 1990–2023, the cotton area in the Alar reclamation region expanded by 722.08 km2, while the jujube exhibited an initial increase followed by a decrease in the same period. The primary reasons are linked to the cost of purchase, agricultural mechanization, and crop compatibility. In the Alar reclamation area, cotton, chili, and jujube are the primary cultivated crops. Cotton is mainly grown on the southern side of the Tarim River, while chili cultivation is concentrated on the northern bank of the river. Over the years, there has been a noticeable spatial complementarity in the distribution and density of rice and cotton crops in this region. In the Alar reclamation, the main factors influencing the change in cultivated land area are cotton price, agricultural machinery gross power, and population. Consequently, implementing measures such as providing planting subsidies and other policy incentives to enhance planting income can effectively stimulate farmers’ willingness to engage in planting activities.

Boosting crop classification by hierarchically fusing satellite, rotational, and contextual data

Accurate early-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patterns and their spatio-temporal variability present significant challenges. While current deep learning-based methods show promise in crop type classification from single- and multi-modal time series, most existing methods rely on a single modality, such as satellite optical remote sensing data or crop rotation patterns. We propose a novel approach to fuse multimodal information into a model for improved accuracy and robustness across multiple crop seasons and countries. The approach relies on three modalities used: remote sensing time series from Sentinel-2 and Landsat 8 observations, parcel crop rotation and local crop distribution. To evaluate our approach, we release a new annotated dataset of 7.4 million agricultural parcels in France (FR) and the Netherlands (NL). We associate each parcel with time-series of surface reflectance (Red and NIR) and biophysical variables (LAI, FAPAR). Additionally, we propose a new approach to automatically aggregate crop types into a hierarchical class structure for meaningful model evaluation and a novel data-augmentation technique for early-season classification. Performance of the multimodal approach was assessed at different aggregation levels in the semantic domain, yielding to various ranges of the number of classes spanning from 151 to 8 crop types or groups. It resulted in accuracy ranging from 91% to 95% for the NL dataset and from 85% to 89% for the FR dataset. Pre-training on a dataset improves transferability between countries, allowing for cross- domain and label prediction, and robustness of the performances in a few-shot setting from FR to NL, i.e., when the domain changes as per with significantly new labels. Our proposed approach outperforms comparable methods by enabling deep learning methods to use the often overlooked spatio-temporal context of parcels, resulting in increased precision and generalization capacity.

Soil water distribution and water productivity in red cabbage crop using superabsorbent polymeric hydrogels under different drip irrigation regimes

The water issue has emerged as a significant barrier to agricultural productivity, particularly in dry and semiarid agro-ecologies, particularly during the winter season. Superabsorbent hydrogels have been identified as effective tools for improving crop and water productivity in arid locations by enhancing moisture retention. The application of hydrogel to soil is commonly done as a basal application, and its effectiveness is heavily influenced by the moisture level present in the soil. Therefore, a two-year field study was carried out to study the effects of various hydrogels and their application techniques and to assess the efficacy of recently developed cross–linked hydrogels (SPG 1118 and Pusa hydrogel) on soil water distribution (both spatial and temporal), water productivity and profitability of red cabbage. The experiment consisted of two irrigation regimes [full irrigation (100% ETc) and limited irrigation (75% ETc)] assisted in main plots, with two type of hydrogels (Pusa Hydrogel and SPG 1118) and two methods of application [root dipping and soil application of hydrogels and Control with no application of hydrogel] assisted in sub plots. The field experiment was conducted using spilt plot design with three replications. The analysis of soil water distribution patterns reveals that the utilization of hydrogels has resulted in a notable increase of 3–6% in soil water content (SWC) under full irrigation conditions, as compared to plots subjected to limited water conditions. In comparison to plots without hydrogel application, the plots treated with SPG 1118 and Pusa hydrogel exhibited enhanced soil water content of 5–6% and 2–4%, respectively. Additionally, the application of hydrogels in the root dipping showed increased water availability in the crop's root zone compared to soil application in throughout the crop season. Thus, hydrogels with reduced irrigation application resulted in significantly higher crop water productivity (14%) and irrigation water productivity (8 – 14%) compared to full irrigation levels. Likewise, root dipping of SPG 1118 led in considerable improvement in CWP (12–14%) and IWP (11–12%) over no hydrogel applied plots. In relation to profitability, the adoption of full irrigation in conjunction with SPG 1118 hydrogel resulted in an 8% increase in the benefit cost ratio compared to the control plots. Hence, the use of the unique SPG 1118 hydrogel through direct application to the roots, whether under conditions of full or limited irrigation, presents an inventive approach to address the growing issues of water shortages and sustainability in countries experiencing water stress.

Comparison of CWSI and Ts-Ta-VIs in moisture monitoring of dryland crops (sorghum and maize) based on UAV remote sensing

Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture. Utilizing unmanned aerial vehicle (UAV) remote sensing, we explored the applicability of an empirical crop water stress index (CWSI) based on canopy temperature and three-dimensional drought indices (TDDI) constructed from surface temperature (Ts), air temperature (Ta) and five vegetation indices (VIs) for monitoring the moisture status of dryland crops. Three machine learning algorithms (random forest regression (RFR), support vector regression, and partial least squares regression) were used to compare the performance of the drought indices for vegetation moisture content (VMC) estimation in sorghum and maize. The main results of the study were as follows: (1) Comparative analysis of the drought indices revealed that Ts-Ta-normalized difference vegetation index (TDDIn) and Ts-Ta-enhanced vegetation index (TDDIe) were more strongly correlated with VMC compared with the other indices. The indices exhibited varying sensitivities to VMC under different irrigation regimes; the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment (r=−0.93). (2) Regarding spatial and temporal characteristics, the TDDIn, TDDIe and CWSI indices showed minimal differences Over the experimental period, with coefficients of variation were 0.25, 0.18 and 0.24, respectively. All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops, but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event. (3) For prediction of the moisture content of single crops, RFR models based on TDDIn and TDDIe estimated VMC most accurately (R2>0.7), and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples, with R2 and RMSE of 0.62 and 14.26%, respectively. Thus, TDDI proved more effective than the CWSI in estimating crop water content.

Potentiality of Landsat-9 for early-season mapping of winter garlic and winter wheat

ABSTRACT Landsat-9 is the latest satellite of the Landsat program and provides useful agricultural remote sensing data. The earlier the crop distribution data is obtained, the greater the value of the data. However, whether Landsat-9 image is suitable for the early-season mapping of winter garlic and winter wheat is a problem worthy of attention. Therefore, this study evaluates the potential to use two Landsat-9 images, acquired on 13 December 2021 and 30 January 2022, for the early-season identification of winter garlic and winter wheat in China. According to J-M (Jeffries-Matusita) distances, we evaluated the separability of winter garlic, winter wheat, and other ground objects based on the two Landsat-9 images. Then, winter garlic and winter wheat were extracted by using unsupervised classification method, i.e. the IsoData and K-means clustering algorithms, and supervised classification method, i.e. the Random Forest (RF), and Support Vector Machine (SVM) algorithms. The separability between garlic and wheat in January is stronger than that in December. The classification overall accuracy based on the Landsat-9 image on 30 January 2022 is 92.03% with a kappa coefficient of 0.87 using the SVM algorithm. This period is about 4 months earlier than the crop harvest period. Landsat-9 images have good potentiality for early-season mapping of winter garlic and winter wheat.