Agricultural Water Resources Management Research Articles

Integrated multi-objective chance-constrained fuzzy interval linear programming model with principal component analysis for optimizing agricultural water resource management under uncertainties

Integrated multi-objective chance-constrained fuzzy interval linear programming model with principal component analysis for optimizing agricultural water resource management under uncertainties

Deficit Irrigation-Based Improvement in Growth and Yield of Quinoa in the Northwestern Arid Region in China

Given the current global water scarcity issues, which particularly affect arid regions such as northwestern China, it is crucial to find crop planting patterns that result in efficient water resource utilization. Quinoa, as a drought-resistant and highly nutritious crop, has garnered significant attention from agricultural researchers in recent years. From 2019 to 2020, a series of experimental studies were conducted under non-mulching drip irrigation conditions to investigate the growth adaptability and the response to different irrigation levels of quinoa in an arid region in northwestern China. A comparative analysis of quinoa’s dry matter accumulation, yield, thousand-grain weight, harvest index, and water use efficiency under varying irrigation levels revealed that increasing irrigation significantly enhanced quinoa’s dry matter accumulation and yield. By optimizing the irrigation strategies, we found that the water-saving practice of initiating moderate irrigation in the sensitive water-demanding stages (flowering and fruiting) of quinoa increased the yield. The experiment results showed that, in 2020, the optimal irrigation amount was 3675 m3·ha−1 during a 14-day irrigation cycle, meeting quinoa’s growth requirements while improving water resource utilization efficiency. This study not only provides a scientific basis for the efficient cultivation of quinoa in the arid regions of northwestern China, but also offers new insights into and technical support for agricultural water resource management in the region, contributing to the sustainable development of agriculture in arid areas.

Study on the Significance and the Drivers of Indigenous Traditional Knowledge in Agricultural Water Resource Management in Water Scarcity Purulia District of West Bengal

Abstract- The paper focuses on the Indigenous traditional knowledge as a vital resource of society with multidimensional use. We can see the application of this knowledge in the field of agriculture, water resource management, disease treatment etc. Agricultural water resource management is a big challenge in the drought prone and topographically diversified Purulia district. This district is the homeland of different indigenous people. Tribal community dominated Purulia district have its own cultural heritage, still today the indigenous people are using their ancestor’s knowledge in different arena of life. This article tries to find out the different traditional and local methods, techniques and tools of agricultural water resource management and their significance in the field of efficient water resource management, it also focuses on the factors which attempt to drive away this local knowledge from grass root level.

Water Footprint Assessment of Agricultural Crop Productions in the Dry Farming Region, Shanxi Province, Northern China

Exploring the crop production water footprint and their driving factors is of significant importance for management of agricultural water resources. However, how do we effectively assess the total agricultural water consumption and explore the significance of their driving factors, i.e., population, economy, and agricultural production conditions, using a backpropagation neural network (BPNN)? It is still ambiguous. Water consumption for crops during the growing season is explicitly explored by way of water footprint indicators (green water footprint, WFPg, and blue water footprint, WFPb). This study provides new insights into the factors driving the changes in crop production water footprint in Taiyuan City over the period of 2005–2021. Simulations of crop evapotranspiration using the CROPWAT model were quantified. The results showed that Taiyuan City has a low crop yield level below the average level of China, with the highest crop yield in maize. The crop production water footprint in Taiyuan City showed a non-linearly decreasing trend over time. The average annual crop production water footprint was 187.09 × 103 m3/kg in Taiyuan City, with the blue water footprint and green water footprint accounting for 63.32% and 36.68%, respectively. The crop production water footprint in the west and north of Taiyuan City was significantly higher than those in other areas, accounting for 42.92% of the total crop production water footprint. Oilseed crops contributed most to the total crop production water footprint, accounting for 47.11%. The GDP and total sown area of crops were more important for the changes in WFPb. Agricultural machinery power and agriculture-to-non-agriculture ratio were more important for the changes in WFPg. Agricultural machinery power and GDP were more important for the changes in IWFP. In-depth analysis of the factors driving the changes in crop production water footprint is dramatically important for agricultural decision makers to mitigate water resource pressure in Taiyuan City.

Gender roles and ethnicity in agricultural water management: a case study of Tinh Bien District, An Giang Province, Vietnam

Women form a crucial part of Vietnam’s agricultural labor force and contribute to national economic development. Still, often the nature of their contribution is inadequately recognized by broad sectors of society. Due to entrenched gender roles and norms, women’s influence over agricultural activity decision-making at the household and institutional levels is limited and poorly acknowledged. This paper explores women’s participation at a household and local level in agricultural water resources management. This research draws upon field studies conducted in Tinh Bien district, An Giang province, Vietnam, a predominantly wetlands environment but with some prominent low hills that present different challenges for water management. The study employed focus group discussion, in-depth interviews, and participant observations to gather data at households and higher levels of governance. The findings highlight that despite being central to socio-economic production across multiple aspects of water wetlands management, women’s voices are overlooked for inclusion within management institutions. It was noted that Khmer minority women tend to be better represented at the household level than Kinh women, but both Khmer and Kinh women’s decision-making power over wetlands and water management at higher institutional levels is still limited.

Spatial Insights into Drought Severity: Multi-Index Assessment in Małopolska, Poland, via Satellite Observations

This study focuses on the assessment of drought severity, employing a comparative analysis between the normalized multi-band drought index (NMDI; calculated using Sentinel-2 imagery) and the combined drought indicator (CDI). The research aims to pinpoint the most accurate and reliable remote sensing techniques, which reflect ground-based measurements, thereby enhancing the precision of drought monitoring systems. By investigating the specific area of Małopolska, the study not only contributes to the global knowledge base on drought assessment methodologies but also addresses the regional needs for improved drought management practices. Through an exhaustive analysis correlating satellite-derived indices with in situ meteorological data, this research elucidates the potential of integrating NMDI and CDI for a comprehensive understanding of drought dynamics in Małopolska. In particular, the indices depict different dynamics of drought levels, as well as the location of regions more prone to its occurrence. The findings are poised to advance drought monitoring capabilities, offering significant insights for agricultural sustainability and water resource management in the region.

Assessing the accuracy of OpenET satellite-based evapotranspiration data to support water resource and land management applications

Remotely sensed evapotranspiration (ET) data offer strong potential to support data-driven approaches for sustainable water management. However, practitioners require robust and rigorous accuracy assessments of such data. The OpenET system, which includes an ensemble of six remote sensing models, was developed to increase access to field-scale (30 m) ET data for the contiguous United States. Here we compare OpenET outputs against data from 152 in situ stations, primarily eddy covariance flux towers, deployed across the contiguous United States. Mean absolute error at cropland sites for the OpenET ensemble value is 15.8 mm per month (17% of mean observed ET), mean bias error is −5.3 mm per month (6%) and r2 is 0.9. Results for shrublands and forested sites show higher inter-model variability and lower accuracy relative to croplands. High accuracy and multi-model convergence across croplands demonstrate the utility of a model ensemble approach, and enhance confidence among ET data practitioners, including the agricultural water resource management community.

Effects of land use changes on green water along upper and middle reaches of Huaihe River Basin

Study regionThe Upper and Middle Reaches of the Huaihe River Basin Study focusThe examination of the ramifications of alterations in land utilization on terrestrial water resources holds paramount importance in the realm of agricultural water resource management. Despite progress in understanding the cumulative impacts of land use on terrestrial water bodies, the understanding of the exact impacts of a single land use on terrestrial water bodies is still insufficient. In this investigation, the SWAT model was employed to meticulously delve into the ramifications of modifications in land use on the quantity of green water in the middle and upper stretches of the Huaihe River, with a focus on simulating the hydrological cycle. Furthermore, this inquiry introduces a novel coefficient of impact to scrutinize the repercussions of specific land use changes on terrestrial water bodies. New hydrological insights for the regionThe outcomes of the study unveil that albeit the spatial expanse of certain land use categories is restricted, their repercussions on terrestrial water bodies reverberate extensively. Ultimately, the classification of forest land and agricultural acreage underwent refinement, incorporating considerations of vegetation cover and crop typology. The inquiry establishes that the extent of forest land and the genre of crops cultivated on agricultural land stand as pivotal determinants of terrestrial water dynamics.

Estimation of Evapotranspiration from the People’s Victory Irrigation District Based on the Data Mining Sharpener Model

Reasonable evaluation of evapotranspiration (ET) is crucial for optimizing agricultural water resource management. In the study, we utilized the Data Mining Sharpener (DMS) model; the Landsat thermal infrared images were sharpened from a spatial resolution of 100 m to 30 m. We then used the Surface Energy Balance System (SEBS) to estimate daily ET during the winter wheat growing season in the People’s Victory Irrigation District in Henan, China. It was concluded that the spatiotemporal patterns of land surface temperature and daily evapotranspiration remained consistent before and after sharpening. Results showed that the R2 value between the ET of 30 m spatial resolution and the value by eddy covariance method reached 0.814, with an RMSE of 0.516 mm and an MAE of 0.245 mm. All of these were higher than those of 100 m spatial resolution (R2 was 0.802, the RMSE was 0.534 mm, and the MAE was 0.253 mm). Furthermore, the daily ET image with a 30 m spatial resolution exhibited clear texture and distinct boundaries, without any noticeable mosaic effects. The changes in surface temperature and ET were more consistent in complex subsurface environments. The daily evapotranspiration of winter wheat was significantly higher in areas with intricate drainage systems compared to other regions. During the early growth stage, daily evapotranspiration decreased steadily until the overwintering stage. After the greening and jointing stages, it began to increase and peaked during the sizing period. The correlation between net solar radiation and temperature with ET was significant, while relative humidity and soil moisture were negatively correlated with ET. Throughout the growth period, net solar radiation had the greatest effect on ET.

Blockchain-driven incentive mechanism for agricultural water-saving: A tripartite game model

Implementing water-saving agriculture is an important measure to alleviate water resource shortage, improve agricultural ecological environment, and achieve sustainable agricultural development. However, both water-saving service companies (WSSCs) and farmers often lack the motivation to adopt water-saving practices due to the high implementation costs involved. To overcome this shortcoming, this study aims to design a blockchain-driven incentive mechanism based on the evolutionary game theory, in which the government, WSSCs and farmers are game subjects and the idea of cooperative incentive for the government is introduced. Through analyzing the complex interactions among various stakeholders and agricultural water-saving incentives, some findings of this study are obtained. Firstly, the usage of blockchain technology (BT) in the incentive mechanism for agricultural water-saving is reflected in supervision and additional default costs, which promote the sustainable cooperation among government, WSSCs and farmers in varying degrees. Secondly, the additional default costs brought by the usage of BT have a deterrent effect on WSSCs and farmers, which makes them to actively implement water-saving practices. Thirdly, the additional default cost brought by BT has become the main basis for the government to choose BT supervision. As the additional default cost increases, the government are not more inclined to use BT. It provides valuable insights into motivating diverse stakeholders to actively participate in water-saving practices, promoting sustainable water resources management in agriculture.

Estimating reference crop evapotranspiration using optimized empirical methods with a novel improved Grey Wolf Algorithm in four climatic regions of China

Accurate estimation of reference crop evapotranspiration (ETo) is crucial for agricultural water management. As the simplified alternatives of the Penman-Monteith equation, empirical methods have been widely recommended worldwide. However, its application is still limited to parameters localization varied with geographical and climatic conditions, therefore developing an excellent optimization algorithm for calibrating parameters is very necessary. Regarding the above requirement, the present study developed a novel improved Grey Wolf Algorithm (MDSL-GWA) to optimize the most recommended ones among three types of ETo methods. After the optimization performance comparison among Least Square Method (LSM), Genetic Algorithm (GA), Grey Wolf Algorithm (GWA), and MDSL-GWA in four climatic regions of China, this study found that the Priestley-Taylor (PT) method was the best radiation-based (Rn-based) method and achieved better performance in temperate continental region (TCR), mountain plateau region (MPR), and temperate monsoon region (TMR) than other types. While the temperature-based (T-based) Hargreaves-Samani (HS) method performed best in subtropical monsoon region (SMR), further attaching better performance among the same type in TMR and TCR, while the Oudin method was the best T-based method in MPR. Moreover, the Romanenko method was better humidity-based (RH-based) in TCR and MPR, whereas the Brockamp-Wenner method exhibited higher in SMR and TMR. Furthermore, despite intelligence optimization algorithms significantly enhancing original ETo methods, the MDSL-GWA achieved best performance and outperformed other algorithms by 4.5–29.6% in determination coefficient (R2), 4.7–27.3% in nash-sutcliffe efficient (NSE), 3.7–44.4% in relative root mean square error (RRMSE), and 3.1–56.2% in mean absolute error (MAE), respectively. After optimization, the MDSL-GWA-PT was the most recommended ETo method in TMR, TCR, and MPR, and the median values of R2, NSE, RRMSE, and MAE ranged 0.907–0.958, 0.887–0.925, 0.083–0.103, and 0.115–0.162 mm, respectively. In SMR, the MDSL-GWA-HS produced the best ETo estimates, with median values of R2, NSE, RRMSE, and MAE being 0.876, 0.843, 0.112, and 0.146 mm, respectively. In summary, this study recommended the best ETo method and algorithms using accessible data in four climatic regions of China, which is helpful for decision-making in effective management and utilization of regional agricultural water resources.

Spatiotemporal Variation in the Meteorological Drought Comprehensive Index in the Beijing–Tianjin–Hebei Region during 1961–2023

It is crucial to investigate the characteristics of meteorological drought in the Beijing–Tianjin–Hebei (BTH) region to improve the accuracy of agriculture and water resource monitoring and management. In this study, using instrumental observation data from 85 meteorological stations in the BTH region during 1961–2023 derived from the National Meteorological Information Center, we first calculated the meteorological drought comprehensive index (MCI) and analyzed the spatiotemporal characteristics of the MCI. In the BTH region, the MCI intensity from May to June was the most severe in the intraseasonal variation. The trend of the decreasing drought intensity in May–June has occurred for the past 60 years. The southern region in the BTH region was more likely to experience droughts. Next, the spatial patterns of the top two EOF modes of the May–June MCI were depicted. The primary spatial pattern of the BTH, which was characterized by consistent changes in the MCI throughout the entire BTH region, could be represented by the first mode’s R2 of 69.01%. Then, we compared the spatial pattern of the MCI intensity under different return periods. Using the May–June MCI of 1961–2023, the drought intensity gradually increased from northwest to southeast for the 10-, 20-, and 50-year return periods. However, the drought intensity decreased and then increased from northwest to southeast based on the 1991–2023 MCI. Notably, a 20-year return period of severe drought affected Beijing and northern Hebei in 2023. Finally, we discussed the linkages of drought in the BTH region and atmospheric circulation/sea surface temperature (SST) anomalies, which were calculated using the Pearson correlation coefficient and wavelet coherence. We suggest that the MCI variations in the BTH region may be related to the SST anomalies of the Indian Ocean in 1961–1990 and the Pacific Ocean in 1991–2023, respectively. The abovementioned studies have enlightened us to focus on predicting the Pacific SST for drought, which will facilitate agricultural production and water resource management in the BTH region.

Mean transit time of water bodies in a typical soil-plant-atmosphere continuum of the subtropical monsoon region

The mean transit time (MTT) is a good indicator of water cycle processes. We know little about the MTT of different water bodies within the soil-plant-atmosphere continuum (SPAC) in the subtropical monsoon region. We estimated the MTT of stratified soil water at different depths as well as the xylem water and leaf water in typical Cinnamomum camphora woodland located in Changsha City from March 2017 to October 2019. The main methods used in this study included the stable isotope technology, the linear mixed model and the sine wave fitting method. The results showed that the stable isotopes were more depleted in summer and enriched in winter for different water bodies within the SPAC. The δ2H values of soil water gradually decreased as depth increased. The δ2H values of xylem water closely resembled those of soil water, but the δ2H values of leaf water were more positive and exhibited larger variation. Results of the linear mixed model indicated that the lower MTT values of soil water and plant water occurred between June and September, while the higher values were often observed around January and from April to May. The precipitation replenishment exhibited a significant negative correlation with the MTT. The MTT of soil water generally increased with depth, although preferential flow could enhance the replenishment of deeper soil water and subsequently reduce the MTT. The mean MTT values of xylem water and leaf water were similar. Results of the sine wave fitting method showed that the young water fraction (Fyw) of soil water gradually decreased as depth increased, while the MTT of soil water gradually increased as depth increased. The Fyw and MTT of xylem water were lower and higher than those of leaf water, respectively. Both the mean MTT values of soil water based on the linear mixed model or the sine wave fitting method increased from the surface to the deeper soil layers. The former exhibited a smaller variation range and the latter showed a larger variation range. The mean MTT value of xylem water based on the linear mixed model was 2.4 days less than that of leaf water, while the MTT value of xylem water in the sine wave fitting method was 87.4 days higher than that of leaf water. These differences may be due to the parameterization of "new/young water", the uncertainty of results, and the effect of evaporative fractionation. This study contributes to a better understanding of water transport and consumption processes within the SPAC and provides valuable insights for agricultural production and water resources management in the subtropical monsoon region.

The Decreased Availability of Soil Moisture and Canopy Conductance Dominate Evapotranspiration in a Rain-Fed Maize Ecosystem in Northeastern China

Evapotranspiration (ET) determines the crop productivity in rain-fed agriculture. Global climate change alters the trade-off between soil water supply and atmospheric demand, energy partitioning, and community biophysical and structural properties; however, the interactive effects of these biotic and abiotic factors on ET and its components remain unclear. ET was measured in 2005–2020 in a rain-fed maize ecosystem in northeastern China using the eddy covariance method. By decomposing ET into transpiration (T) and evaporation (E) with the Shuttleworth–Wallace model, we investigated the abiotic and biotic interactive effects on ET and its components at annual levels. Results showed that available energy and albedo exhibited no significant time-series trends, but the Bowen ratio exhibited an increasing trend. Precipitation exhibited no significant trends; however, soil water content (SWC) decreased with time, accompanied by significantly increased air temperature (Ta) and a vapor pressure deficit (VPD). The ET decline was controlled by T, rather than E. The T decline was mainly controlled by canopy conductance and SWC. CO2 concentrations and the VPD exhibited indirect effects on T by reducing canopy conductance, while Ta and precipitation had indirect effects on T by reducing SWC. Our results indicated that decreasing ET may be more severe with crop physiological adaptability for a decreased SWC. Aiming to enhance water resource efficiency, the practice of returning crop residues to the field to reduce soil evaporation, coupled with adjusting the sowing time to mitigate the risk of seasonal droughts during critical growth stages, represents an effective strategy in agricultural water resource management.

Quantification of the water exchange in an agroforestry system under the background of film-mulching drip irrigation of farmland

Water availability is the most critical constraint for the healthy and sustainable development of agroforestry systems in arid and semi-arid regions; consequently, it is essential to clarify soil water dynamics and quantify water exchange within agroforestry systems. In this study, a two-year field experiment and a HYDRUS-2D numerical simulation model were used to clarify the characteristics of the two-dimensional distribution of soil water and the horizontal water exchange in an agroforestry system under the background of film-mulching drip irrigation of farmland. The results showed that the HYDRUS-2D for agroforestry systems had high accuracy in calculating the water flow and capturing the dynamic water exchange in various soil layers. The model simulation results showed that there were differences in soil water content between forest belt and farmland, and the two-dimensional distribution of soil water content showed vertical water transport and horizontal water exchange in the agroforestry system. During the growing season, the horizontal water flow exchange was mainly from the forest belt side and the farmland side to the agroforestry transition zone, with inflows of 61.3 mm and 199 mm at depths of 0–100 cm, respectively. A long-term severe "water stress zone" developed at a depth of about 0–40 cm in the forest belt, so the 0–40 cm soil depth was the main area of water competition in the agroforestry system. The results could provide a reference for the long-term healthy development of agroforestry systems and the management of agricultural water resources in oasis-desert systems under film-mulching drip irrigation conditions.

Statistical and deep learning models for reference evapotranspiration time series forecasting: A comparison of accuracy, complexity, and data efficiency

Reference evapotranspiration (ETo) is an essential variable in agricultural water resources management and irrigation scheduling. An accurate and reliable forecast of ETo facilitates effective decision-making in agriculture. Although numerous studies assessed various methodologies for ETo forecasting, an in-depth multi-dimensional analysis evaluating different aspects of these methodologies is missing. This study systematically evaluates the complexity, computational cost, data efficiency, and accuracy of ten models that have been used or could potentially be used for ETo forecasting. These models range from well-known statistical forecasting models like seasonal autoregressive integrated moving average (SARIMA) to state-of-the-art deep learning (DL) algorithms like temporal fusion transformer (TFT). This study categorizes monthly ETo time series from 107 weather stations across California according to their length to better understand the forecasting models' data efficiency. Moreover, two forecasting strategies (i.e., recursive and multi-input multi-output) are employed for machine learning and DL models, and forecasts are assessed for different multi-step horizons. Our findings show that statistical forecasting models like Holt-Winters' exponential smoothing perform almost as well as complex DL models. Unlike statistical models, DL models generally suffer from low data efficiency and perform well only when enough data is available. Importantly, although the computational costs of most DL models are higher than statistical methods, this is not the case for all. Considering computational cost, data efficiency, and forecasting accuracy, our findings point to the superiority of the neural basis expansion analysis for interpretable time series forecasting (N-BEATS) architecture for univariate ETo time series forecasting. Moreover, our results suggest Holt-Winters and Theta methods outperform SARIMA – the most employed statistical model for ETo forecasting in the literature – in accuracy and efficiency.

How Does Agricultural Water Resources Management Adapt to Climate Change? A Summary Approach

This editorial paper takes the form of a concise report and delves into a critical and intricate issue essential for the sustainability of agriculture. It centers on the intricate relationship between agri-cultural water resource management and agronomical practices, as well as their ability to adapt to the impacts of climate change while ensuring both the quantity and quality of crop yields. Specifically, this paper serves as a synopsis of how the far-reaching consequences of climate change for water resources impact agricultural production. It also highlights primary adaptation strategies for managing agricultural water resources, as drawn from the existing literature. Such strategies are designed to counteract the potentially adverse impacts of climate change on the rural sector. Fur-thermore, this brief report offers a valuable overview of the 17 selected papers featured in this Special Issue (SI) on Water, published by MDPI. These papers serve as exemplars of cutting-edge approaches to adaptability in water resource management and resilient crop production systems, as these fields attempt to thrive in an ever-changing environmental landscape.

Contributions of women's collective farming to women's agency in the Upper Indus Basin in the face of climate change

A consortium managed project in the Upper Indus Basin of Gilgit-Baltistan, Pakistan, supported the creation of women's collective farming. The component of the project related to water resource management for agriculture was implemented in Hunza district, where in three sites, the communities encouraged women farmers to engage in the pilot interventions related to innovative water resource management technologies and improved farming practices to overcome climate-induced issues for farmers. This paper analyzes the situation of women as collective farmers and its contributions to women's agency. The study used a gender analysis framework, and data was collected through quantitative and qualitative research methodologies in the project site areas. The study's findings show that climate change has created issues more for women farmers being at the forefront in handling and tackling the impacts through various measures. It shows that collective farming and networking is an appropriate tool for women to challenge norms and stereotypes that hinder them from being part of the decision-making at various levels. Furthermore, it helps women to minimize drudgery by sharing burdens, using appropriate technology, and sharing their successes and issues while working together. Based on the findings, this study suggests that policy decisions to implement such initiatives at a more macro level will help involve women at various levels of decision-making, minimize their drudgery, expand the choices of women farmers, and overall contribute to the agency.

Different types of meteorological drought and their impact on agriculture in Central China

Challenges in drought monitoring hinder social development. In this study, a robust drought monitoring framework composed of ten indicators was established based on the standard precipitation actual evapotranspiration (SPAEI) index calculated using the maximum entropy production (MEP) model. Additionally, the relationship between meteorological drought and agricultural development was analyzed. The results indicated that the seasonal SPAEI performed better than the standardized precipitation evapotranspiration index (SPEI) in terms of drought area, vegetation drought, and drought identification in Central China. The annual drought duration was between 3.4 and 4.5 months, and the annual drought frequency ranged from 1.1 to 1.9 times per year. The main drought pattern was divided into persistent light drought and short-term severe drought, with the latter dominating crop yields in Central China. Although persistent light drought contributes more to drought, short-term severe drought has a greater impact on crop yields. Thoroughly investigating regional drought patterns and assessing their impacts on agriculture will contribute to a better understanding of drought characteristics and provide valuable guidance for regional agricultural water resource allocation and management.

Analysing the Impact of the Southwest Monsoon on Kharif Rice Acreage in the Raipur District Using Microwave Remote Sensing

This study utilized Synthetic Aperture Radar (SAR) data from Sentinel-1 to estimate the kharif paddy acreage in the Raipur district. The unique capability of microwaves to penetrate clouds enabled the mapping of paddy fields even during the monsoon season. Supervised classification based on training samples was employed to delineate paddy fields. The results demonstrated that the estimated paddy area closely matched the reported area, with error percentages of 5.3% and 8.6% in 2017 and 2019, respectively. The analysis of data from 2011 to 2019 revealed a consistent decline in both paddy acreage and rainfall during that period. Furthermore, the block-level analysis indicated significant spatial variations, with the Arang blocks having the largest paddy cover and the Raipur block the least. Moreover, a strong correlation was observed between southwest monsoon rainfall and the kharif rice cultivation, with paddy acreage decreasing during SW monsoon deficit years. These findings shed light on the impact of monsoons on rice cultivation and can guide agricultural planning and water resource management strategies in the region.