Search Results (44)

Urbanization is causing a decrease in agricultural land. This leads to changes in cropping patterns, irrigation water availability, and water allowance. Therefore, change in cropping pattern, irrigation water availability, and water allowance were investigated in the Multan region of Pakistan using remote sensing and GIS techniques. The multi-temporal Landsat images with 30 m resolution were acquired for both Rabi (winter) and Kharif (summer) seasons for the years of 1988, 1999 and 2020. The image processing tasks including layer stacking, sub-setting, land use/land cover (LULC) classification, and accuracy assessment were performed using ERDAS Imagine (2015) software. The LULC maps showed a considerable shift of orchard area to urban settlements and other crops. About 82% of orchard areas have shifted to urban settlements and other crops from 1988 to 2020. The LULC maps for Kharif season indicated that cropped areas for cotton have decreased by 42.5% and the cropped areas for rice have increased by 718% in the last 32 years (1988–2020). During the rabi season, the cropped areas for wheat (Triticum aestivum L.) have increased by 27% from 1988 to 2020. The irrigation water availability and water allowance have increased up to 125 and 110% due to decrease in agricultural land, respectively. The overall average accuracies were found as 87 and 89% for Rabi and Kharif crops, respectively. The LULC mapping technique may be used to develop a decision support system for evaluating the changes in cropping pattern and their impacts on net water availability and water allowances. Full article

Water management is a long-standing source of dispute between the riparian states of Karnataka and Tamil Nadu. Recently, these disputes have intensified due to impacts from climate change and Bangalore’s rapid growth to megacity status. Despite well-defined national water governance instruments, competition between state actors and limited access to reliable hydrometric data have led to a fragmented regulatory regime, allowing unchecked exploitation of surface and groundwater resources. Meanwhile, subsidised energy for groundwater pumping incentivises the unsustainable irrigation of high-value, water-intensive crops, resulting in overextraction and harm to aquatic ecosystems. Here, we employ a water–energy–food–environment (WEFE) nexus approach to examine the socio-political, economic, and environmental factors driving unsustainable irrigation practices in the Cauvery River Basin (CRB) of Southern India. Our methodology integrates spatially explicit analysis using digitised irrigation census data, theoretical energy modelling, and crop water demand simulations to assess groundwater use patterns and energy consumption for irrigation and their links with governance and economic growth. We analyse spatio-temporal irrigation patterns across the whole basin (about 85,000 km²) and reveal the correlation between energy access and groundwater extraction. Our study highlights four key findings. First, groundwater pumping during the Rabi (short-rain) season consumes 24 times more energy than during the Kharif (long-rain) season, despite irrigating 40% less land. Second, the increasing depth of borewells, driven by falling water table levels, is a major factor in rising energy consumption. Third, energy input is highest in regions dominated by paddy cultivation. Fourth, water pumping in the Cauvery region accounts for about 16% of India’s agricultural energy use, despite covering only 4% of the country’s net irrigated area. Our study reinforces the existing literature advocating for holistic, catchment-wide planning, aligned with all UN Sustainable Development Goals. Full article

Under the 1960 Indus Water Treaty, Pakistan owned the Western rivers (Indus, Jhelum, and Chenab) and India the Eastern rivers (Ravi, Suleimanki, and Beas). Pakistan’s per capita water availability will reduce from 5260 m³ to less than 1000 m³ by 2025, causing water stress. The Indus Basin’s water availability was examined at inflow and outflow gauges between 1991 and 2015. The Indus Basin inflow and outflow gauges indicated exceptionally low and high flows before, during, and after floods. Lower flow values vary greatly for the Indus, Chenab, and Jhelum rivers. During Rabi and Kharif, the Indus and Chenab rivers behaved differently. Lower flows (Q90 to Q99) in Western Rivers are more periodic than higher flows (Q90 to Q99) and medium flows (Q90 to Q99). The outflow gauge Kotri reported 35% exceedance with zero flows during pre-flood and post-flood seasons and 50% during flood season, indicating seasonal concerns. Outflow and inflow both fell, particularly after the year 2000, according to data collected over a longer period (1976–2015). Low storage and regulating upstream capacity caused the Indus Basin outflow to reach 28 MAF (million acre feet) between 1976 and 2015, which is 70% more than the permitted 8.6 MAF downstream Kotri gauge. For 65 percent of the year, the Indus Basin does not release any water downstream of Kotri. As a result, the ecosystem relies on an annual influx of at least 123 MAF to sustain itself, and an outflow of 8.6 MAF from the Indus Basin necessitates an inflow of 113.51 MAF. At high-flow seasons, the Indus Basin experiences devastating floods, yet it dries out at a frightening rate before and after floods. The preservation of ecosystems and riparian zones downstream depends on the large environmental flows in eastern rivers. This is achievable only by fully implementing IWT and improving water management practices at western rivers. Full article

Heavy rainfall significantly impacts agriculture by damaging crops and causing substantial economic losses. The Paravanar River Basin, a coastal river basin in India, experiences heavy rainfall during the monsoon season. This study analyzed both ground-level rainfall measurements and farmers’ experiences to understand the effects of heavy rainfall on agriculture. Rainfall data from nine rain gauge locations were analyzed across three cropping seasons: Kharif 1 (June to August), Kharif 2 (September to November), and Rabi (December to May). To determine the frequency of heavy rainfall events, a detailed analysis was conducted based on the standards set by the India Meteorological Department (IMD). Villages near stations showing increasing rainfall trends and a higher frequency of heavy rainfall events were classified as vulnerable. The primary crops cultivated in these vulnerable areas were identified through a questionnaire survey with local farmers. A detailed analysis of these crops was conducted to determine the cropping season most affected by heavy rainfall events. The impacts of heavy rainfall on the primary crops were assessed using the Delphi technique, a score-based crop risk assessment method. These impacts were categorized into eight distinct types. Among them, yield reduction, waterlogging, crop damage, soil erosion, and crop failure emerged as the most significant challenges in the study area. Additional impacts included nutrient loss, disrupted microbial activity, and disease outbreaks. Based on this evaluation, risks were classified into five categories: low risk, moderate risk, high risk, very high risk, and extreme risk. This categorization offers a framework for understanding potential consequences and making informed decisions. To address these challenges, the study recommended mitigation measures such as crop management, soil management, and drainage management. Farmers were also encouraged to conduct a cause-and-effect analysis. This bottom-up approach raised awareness among farmers and provided practical solutions to reduce crop losses and mitigate the effects of heavy rainfall. Full article

Analyzing the spatial and temporal trends in cropping patterns and intensity on a larger scale is essential for implementing timely policy decisions and strategies in response to climate change and variability. By converting cropping intensity estimates, we can compute net and gross production values, indirectly indicating food security status in the study region. This study compared the utility of optical (MOD13Q1) and SAR (Sentinel 1A) datasets for determining cropping patterns and associated intensity estimates across multiple agricultural seasons from 2019 to 2023, with spatial resolutions of 250 m and 20 m, respectively. The analysis revealed that the highest and lowest gross cropped areas using Sentinel 1A data were 55.85 lakh hectares (2022–2023) and 52.88 lakh hectares (2019–2020), respectively. For MODIS data, the highest and lowest gross cropped areas were 62.07 lakh hectares (2022–2023) and 56.87 lakh hectares (2019–2020). Similarly, the highest and lowest net sown areas using Sentinel 1A data were 43.71 lakh hectares (2022–2023) and 41.76 lakh hectares (2019–2020), and for MODIS data, the values were 48.81 lakh hectares (2022–2023) and 46.39 lakh hectares (2019–2020), respectively. Regardless of the datasets used, the highest gross and net cropped areas were reported in Tiruvannamalai district and the lowest in Kanchipuram district. Thiruvarur district reported the highest cropping intensity, while Sivagangai district had the lowest. Among all seasons, the rabi season accounted for the maximum area, followed by the kharif and summer seasons. The study concluded that single cropping (51%) was the dominant cropping pattern in Tamil Nadu, followed by double cropping (31%) and triple cropping (17%) in both datasets. Sentinel 1A data showed better performance in estimating gross and net cropped areas than optical data, with deviations ranging from 7.02% to 11.01%, regardless of the year and cropping estimates derived. The results indicated that the spatial resolution of the datasets was not a significant factor in determining cropping patterns and intensity on a larger scale. However, this may differ for smaller study areas. Full article

Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global agricultural targets, labour-intensive surveys for yield estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed crop phenology and biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key agricultural parameters, such as leaf area index (LAI), soil and plant analyser development (SPAD) chlorophyll, and grain yield of maize. The study’s findings demonstrate that during the kharif season, the wide dynamic range vegetation index (WDRVI) showcased superior correlation coefficients (R), coefficients of determination (R²), and the lowest root mean square errors (RMSEs) of 0.92, 0.86, and 0.14, respectively. However, during the rabi season, the atmospherically resistant vegetation index (ARVI) achieved the highest R and R² and the lowest RMSEs of 0.83, 0.79, and 0.15, respectively, indicating better accuracy in predicting LAI. Conversely, the normalised difference red-edge index (NDRE) during the kharif season and the modified chlorophyll absorption ratio index (MCARI) during the rabi season were identified as the predictors with the highest accuracy for SPAD chlorophyll prediction. Specifically, R values of 0.91 and 0.94, R² values of 0.83 and 0.82, and RMSE values of 2.07 and 3.10 were obtained, respectively. The most effective indices for LAI prediction during the kharif season (WDRVI and NDRE) and for SPAD chlorophyll prediction during the rabi season (ARVI and MCARI) were further utilised to construct a yield model using stepwise regression analysis. Integrating the predicted LAI and SPAD chlorophyll values into the model resulted in higher accuracy compared to individual predictions. More exactly, the R² values were 0.51 and 0.74, while the RMSE values were 9.25 and 6.72, during the kharif and rabi seasons, respectively. These findings underscore the utility of UAV-based multispectral imaging in predicting crop yields, thereby aiding in sustainable crop management practices and benefiting farmers and policymakers alike. Full article

This study conducted to evaluate catchment storage and command relationship and water use strategies under supplemental irrigation for sustainable rainfed agriculture in the semi-arid regions of Rajasthan, India. In southern Rajasthan, a small category of farmers is above 78%, the potential evapotranspiration is greater than the average rainfall with prevailing arid conditions, and rainfed agriculture is a challenging task. An agricultural micro watershed of 2.0 ha evaluated to establish a catchment storage command area (CSC) relationship and micro irrigation system as an effective water use strategy. The significant results indicate that a farm pond with a storage capacity of 560 m³ with permanent lining (cement + brick) is sufficient to harvest runoff water from a 2.0 ha catchment under the rainfall conditions of below normal (up to 50% deficit), long-term average, and wet years. Harvested rainwater can be used to irrigate a command area of even up to 1.0 ha, with supplemental irrigation of 5 cm in both the seasons of kharif as well as rabi. The two crops, maize (Zea mays) in the kharif season and coriander (Coriandrum sativum) in the rabi season, were significantly profitable with supplemental irrigation by adopting a drip irrigation system. Full article

A comprehensive study spanning three kharif and rabi seasons (2018–2019, 2020–2021, and 2021–2022) was conducted to investigate the intricate interactions among different cropping sequences and their impacts on cumin yield, financial outcomes, and soil microbial dynamics. The experiment was designed using a randomized block design, comprising eight distinct treatment combinations, each replicated three times. The results revealed compelling insights into the potential of specific cropping sequences to enhance multiple aspects of agricultural sustainability. The results revealed that the highest cumin yield (averaging 592 kg ha⁻¹ over the three years) was achieved when cumin was cultivated subsequent to pearl millet, showcasing significant increases of 14.28% and 23.07% over the cumin–fallow and cumin–cotton cropping systems, respectively. When it came to cumin equivalent yield, the cumin–cotton cropping sequence (985 kg ha⁻¹) emerged as the most favorable, closely followed by cumin–groundnut (968 kg ha⁻¹). Furthermore, analyzing net realizations and benefit–cost ratios demonstrated that the cumin–pearl millet cropping sequence stood out with the maximum values (₹88,235 ha⁻¹ and 2.7, respectively), followed by the cumin–mung bean cropping system (₹84,164 ha⁻¹ and 2.47, respectively). Among the various cropping sequences studied, cumin–mung bean, cumin–cluster bean, cumin–pearl millet and cumin–groundnut were recorded as statistically similar in terms of soil microbial enzymatic activities viz. fluorescein diacetate (FDA), alkaline phosphatase (ALP), dehydrogenase activity (DHA), and microbial biomass carbon and were at par over the cumin–sorghum, cumin–sesame, cumin–cotton and cumin–fallow cropping systems. These findings emphasize the significance of strategic crop sequencing for sustainable agriculture practices that simultaneously optimize productivity and maintain soil health. Full article

Bottle gourd (Lagenaria siceraria) is a well-known cucurbit with an active functional ingredient. A two-year field experiment was carried out at the Research Farm of Seed Science and Technology, CCS HAU, Hisar, in a randomized block design during the Kharif season (Kharif is one of the two major cropping seasons in India and other South Asian countries, heavily reliant on monsoon rains with the other being Rabi) and the summer season. Five different crossing periods (CP), viz. CP1, CP2, CP3, CP4, and CP5, were considered to illustrate the effects of agro-climatic conditions on the quality and biochemical components of two bottle gourd parental lines and one hybrid, HBGH-35. The average mean temperature for the Kharif season in 2017 was 31.7 °C, and for the summer season, it was 40.1 °C. Flowers were tagged weekly from the start of the crossing period until the end and harvested separately at different times. The fruits harvested from different crossing periods under different environmental conditions influenced the bottle gourd’s qualitative and biochemical traits and showed significant variations among the five crossing period environments. A positive significance and correlation were observed between weather variables and different biochemical characteristics. Henceforth, the CP₄ crossing period at a temperature of 31.7 °C retained high-quality seed development, which may be essential in enhancing agricultural productivity and the national economy. Full article

Fall armyworm (FAW) Spodoptera frugiperda is currently being considered as a serious insect pest in maize that causes significant yield losses worldwide. Silicon (Si) and plant growth regulators (PGRs) are known to induce resistance against biotic and abiotic stresses thereby enhancing the yield. This study was conducted to determine the influence of Si and PGRs on the incidence and damage of FAW on maize (Zea mays L.) under field condition. The experiment was conducted in both Kharif and Rabi seasons using a randomized complete block design with three replications and treatments. Various combinations of foliar silicic acid (FSA) and two PGRs such as gibberelic acid (GA₃) and jasmonic acid (JA) were tested to study their effects on FAW incidence and maize yield. The application of FSA at 2mL/plant + GA₃ at 0.5 mg/plant recorded the lowest number of larvae per plant (0.39 larva/plant) with the lowest damage score of 2.55 (Davis scale). The percent infestation was also low for the same treatment, i.e., 34.14 percent infestation with the highest percent reduction over control (56.43%). The highest yield (58.39 q/ha) and cost–benefit ratio (1:2.34) was recorded for FSA at 2 mL/plant + GA₃ at 0.5 mg/plant, which was considered as the best treatment. This study demonstrated that exogenous application of Si along with PGRs has significant negative effect on field incidence of FAW and enhanced the yield of maize. Full article

Rice-fallow areas have significant potential to sustainably increase agricultural intensification to address growing global food demands while simultaneously increasing farmers’ income by harnessing the residual soil moisture in rainfed ecologies. Assam is the largest rice-growing belt in northeast India during kharif; however, for the next rabi season, an average of 58% of the rice areas remain uncultivated and are described as rice-fallow (Kharif, rabi and zaid are the crop seasons in the study area. The kharif season refers to the monsoon/rainy season and corresponds to the major crop season in the region extending from June to October. The rabi season refers to the winter season extending from November to April, and the zaid season is the summer crop season from April to June). Unutilized rice-fallow areas with optimum soil moisture for a second crop were identified over three consecutive years using multiple satellite data (optical and radar) for the state of Assam and an average accuracy of 92.6%. The reasons governing the existence of rice-fallow areas were analyzed, and an average of 0.88 million ha of suitable rice-fallow areas, based on soil moisture availability, were identified. Targeted interventions were carried out in selected locations to demonstrate the potential of sustainable cropping intensification. Maize, with best management practices, and a yield between 5.5 and 6 t/ha, was demonstrated as a successful second crop during the rabi season in selected areas with optimum residual soil moisture after the kharif paddy harvest. This study highlights the significance of geospatial technology to effectively identify and target suitable rice-fallow areas for cropping intensification and to enhance productivity and profitability. Full article

The global demand for food is growing with the population and urbanization, which puts pressure on water resources, which need assessing and quantifying water requirements. Adopting efficient irrigation methods to optimize water use is essential in this situation. In this study, crop water productivity (CWP) of major crops in the Rohri canal command area was estimated by the ratio of yield and actual evapotranspiration (ET_a). Analyzing the CWP of major crops, water scarcity challenges can be tackled by selecting the most feasible irrigation methods. However, ET_a was calculated and aggregated for all four stages of the crop growth period: initial, crop development, flowering stage, and maturity seasons. The crop yield data were obtained from the districts’ agricultural statistics. For this purpose, evapotranspiration products of Landsat 5 and 8 were downloaded from Earth Engine Evapotranspiration Flux (EEFlux). Landsat images were processed in a GIS environment to calculate ET_a. The approach suggests developing a CWP database for major crops like wheat, cotton, and rice to improve irrigation water management. The objectives of this study are to estimate and analyze the difference in the CWP and evapotranspiration of major crops for the Rabi and Kharif seasons with high and moderate flows during 1998–2019. It comprises nine districts of Sindh that come under the Rohri Canal command area. To analyze the difference in CWP between the Rabi and Kharif seasons for all study crops of the seasons of Rabi (2014–2015 and 2016–2017) and Kharif (1998 and 2017). The growing periods for wheat, cotton, and rice in the Rohri Canal command area are 160, 195, and 180 days, respectively. The estimated ET_a of the Rohri canal command area and CWP were in good agreement with the literature-reported values. Hence, enhanced agricultural productivity can be achieved by making considerable investments to improve agricultural research and extension systems. Full article

Drought is one of the most challenging disasters that impact the natural and cultural ecosystems across the world, especially in the climate dependent sectors of arid and semi-arid areas. The aim of this article is to share the experiences gained and enhance the readers’ awareness on the status of drought and process of the early warning systems (EWS) in south India. Drought status of three agroecologically different states is included in this article, such as Kerala, Tamil Nadu and Telangana. As far as Tamil Nadu is concerned, Karur, Thuthukudi, Krishnagiri, Namakkal, Trichy and Thirunelveli districts are water scarce compared to other districts in the state. The districts such as Wayanad, Thiruvananthapuram, Idukki and Palakkad in Kerala have received lesser rainfall compared to the other parts of the state during the period 1981 to 2019. In Telangana, the mandals such as Nagarkurnool, Jogulamba-Gadwal, Wanaparthy, Mahabubnagar Nalgonda and Yedadri are frequently hit by dry spells and droughts. As a case study, weather early warning dissemination, carried out at Parambikulam Aliyar basin, Coimbatore, Tamil Nadu, during Khariff and Rabi seasons, using IMDs medium and extended range forecast is also elaborated in particular in the article. As far as the accuracy of forecast is concerned, probability of false detection (false alarm rate) was found to be 0.81 for Khariff and 0.30 for Rabi season, indicating the need for better performance in the accuracy of dry spell early warning, disaster preparedness and response. In-spite of this, access to early warning has supported the farmers during harvest and land preparation with a utility score of 72% and 59%, respectively. In Parambikulam Aliyar basin, remote sensing products such as MODIS-NDVI, NDWI and TWI was also used to identify the real-time progression of monthly vegetative condition for Kharif and Rabi seasons. NDVI values were used to monitor the district level vegetation condition and compared it with the drought year 2016, the difference in area under barren land was 76% less during Khariff, 2021 and 44% during Rabi, 2021.This study is a compilation of lessons learned from different states and the existing knowledge and practice in early warnings, and recommends the need for a holistic approach in drought and dry spell monitoring along with better accuracy and dissemination to minimize climate-related shocks in agriculture. Full article

Soil moisture deficit is an essential element in the estimation of irrigation demands, both spatially and temporarily. The determination of temporal and spatial variations of soil moisture in a river basin is challenging in many aspects; however, distributed hydrological modelling with remote sensing inputs is an effective way to determine soil moisture. In this research, a water demand module was developed for a satellite-based National Hydrological Model—India (NHM-I) to estimate distributed irrigation demands based on soil moisture deficits. The NHM-I is a conceptual distributed model that was explicitly developed to utilize the products from remote sensing satellites. MOD13Q1.5 data were used in this study to classify paddy and irrigated dry crops. Along with the above data, the DEM, Leaf Area Index, FAO soil map, and crop characteristics data were also used as inputs. The NHM-I with water demand module was evaluated in the Damodar river basin, India, from 2009 to 2018. The integrated NHM-I model simulated the irrigation demands effectively with remote sensing data. The temporal analysis reveals that soil moisture deficits in the Kharif season varied annually from 2009 to 2018; however, soil moisture deficits in the Rabi season were almost constant. The 50% Allowable Moisture Depletion (AMD-50) scenario can reduce the irrigation demand of 1966 MCM compared to the Zero Allowable Moisture Depletion (AMD-0) scenario. The highest annual irrigation demand (8923 MCM) under the AMD-50 scenario occurred in the 2015–2016 season, while the lowest (6344 MCM) happened in 2013–2014 season. With a new water demand module and remote sensing inputs, the NHM-I will provide a platform to assess spatial and temporal irrigation demands and soil moisture. Full article

The Indus Water Treaty allocated the water of the Ravi River to India, and India constructed the Thein Dam on the Ravi River. This study investigates the water availability of the Ravi Riverfront for both pre-dam and post-dam scenarios augmented with pre-flood, flood, and post-flood sub-scenarios. The study also investigates river water availability for low and high magnitudes (Flow Duration Curves) and its linkages with climate change. The modified Mann–Kendall, Sen’s slope estimator, and Pearson correlation were used to investigate the river flows. It was found that there is a remarkable decrease in the river water by −36% of annual mean flows as compared to the pre-dam scenario. However, during the flood season, it was −32% at the riverfront upstream (Ravi Syphon Gauge). The reduction in water volume was found as 2.13 Million Acre Feet (MAF) and 1.03 MAF for maximum and mean, respectively, in the Rabi (Winter) season, and 4.07 MAF and 2.76 MAF for max and mean, respectively, in the Kharif (Summer) season. It was also revealed that 180–750 cusecs of water would be available or exceeded for 90% to 99% of the time at Ravi Riverfront during the flood season. The high flows were mainly controlled by temperature in the pre-dam scenario; presently, this water is stored in the Thein Dam reservoir. In contrast, the precipitation role is significant in the post-dam scenario, which means that the flows in the Ravi River are mainly due to base flow contributions and precipitation. This study is the first step in analyzing the river water availability of the Ravi Riverfront, which will ultimately address the associated problems and their solutions to decision-makers. Additionally, implementing an eco-friendly riverfront promotes urban sustainability in developed urban areas, such as Lahore City, and will lead to a comfortable and healthy lifestyle; this will only be possible with water availability in the Ravi Riverfront reach. Full article