Search

Introduction: Ancient water harvesting systems, such as those from the Indus Valley Civilization (~3500 BCE), have been vital for irrigation and climate resilience, especially in arid regions. One such prominent system in South Asia, called tank irrigation, initially thrived through community management but declined post-independence due to colonial policies and neglect in Sri Lanka and India. This study evaluates current policy frameworks and rehabilitation programs to enhance the resilience of these systems in India, develop strategies for their protection and adaptation to climate change, and integrate global lessons for sustainable development. Methods: A systematic meta-analysis of grey literature was conducted to aggregate data on policy constraints. Policy analysis involved detailed investigations of relevant documents, regulations, and comparative analyses of frameworks at regional and national levels. Pilot projects on tank rehabilitation were assessed through reported case studies and field surveys to gauge impact. Thematic analysis was used to explore the global potential of these systems in climate resilience and overall environmental sustainability. Results: The analysis showed that pilot projects for tank rehabilitation had limited success in achieving sustainability under current climate conditions. Tank irrigation systems are crucial for adapting to extreme weather, including floods, droughts, and heat waves, replenishing groundwater, reducing soil erosion, and ensuring reliable water supplies. Traditional water harvesting technologies support 17 Sustainable Development Goals (SDGs), including clean water access, hunger reduction, gender equality, and climate action. Integrating AI and machine learning in water management benefits disaster response, while eco-tourism aids system maintenance and cultural awareness. Discussion: The study underscores the need for policy reforms to enhance tank rehabilitation and institutional arrangements. It calls for increased beneficiary participation and constitutional recognition of current practices. Strategic, national-scale assessments and resilience targets are recommended to improve the effectiveness of such water harvesting systems in mitigating natural hazards and enhancing environmental services. [ABSTRACT FROM AUTHOR]

The scarcity of climatic data is the biggest challenge for developing countries, and the development of models for reference evapotranspiration (ET0) estimation with limited datasets is crucial. Therefore, the current investigation assessed the efficacy of four machine learning (ML) models, namely, linear regression (LR), support vector machine (SVM), random forest (RF), and neural networks (NN), to predict ET0 based on minimal climate data in comparison with the standard FAO‐56 Penman‐Monteith (PM) method. The data on daily climate parameters were collected for the period 2000−2021, including maximum and minimum temperatures (Tmax and Tmin), mean relative humidity (RH), wind speed (WS), and sunshine hours (SSH). The performance of the developed models considering different input combinations was evaluated by using several statistical performance measures. The results showed that the SVM model performed better than the other ML models during training (R2 = 0.985; mean absolute error [MAE] = 0.170 mm/day; mean square error [MSE] = 0.052 mm/day; root mean square error [RMSE] = 0.229 mm/day; mean absolute percentage error [MAPE] = 5.72%) and testing stages (R2 = 0.985; MAE = 0.168 mm/day; MSE = 0.050 mm/day; RMSE = 0.224 mm/day; MAPE = 5.91%) under full dataset scenario. The best performance of the models to estimate was with Tmax, RH, Ws, SSH, and Tmin. The results of the current study are substantial as it offers an approach to estimate ET0 in semi‐arid data‐scarce region. Core Ideas: Four machine learning models (linear regression [LR], support vector machine [SVM], random forest [RF], and neural network [NN]) were assessed to estimate ET0 using minimal climate data.The combination of two, three, and four input variables showed promising results for ET0 estimation using SVM under full and limited dataset conditions.The SVM model outperformed the other models in terms of accuracy by giving high values of R2 (0.86–0.98) and lowest values for MAE (0.168–0.529), RMSE (0.22–0.70), and MAPE (5.91%−18.13%).The RF model could not predict the ET0 precisely with poorest values of R2 (0.79–0.89), MAE (0.51–0.682), RMSE (0.63–0.85), and MAPE (17.65%−23.04%). [ABSTRACT FROM AUTHOR]

Precise computation of evapotranspiration is critical for an efficient irrigation planning and managing agriculture water. This investigation aimed to study the performance of regression techniques and machine learning (ML) techniques in estimating daily reference evapotranspiration (ET0) and compare them to the ET0 computed by the Penman–Monteith (PM-56) technique of "Food and Agriculture Organization" (FAO). The study used the meteorological data of the Pusa Institute, ICAR-IARI, New Delhi, which is located in a semi-arid climatic zone, for a period of 31 years (1990–2020). Six regression techniques were used, including multiple linear regression (MLR), elastic net regression (ENR), ridge regression (RDR), lasso regression (LASSOR), partial least square regression (PLSR), and Poisson regression (POR). Four machine learning models, namely radial basis function (RBF), M5Tree, locally weighted learning (LWL), and gradient boosted tree (GBT), were also evaluated for predicting daily ET0. The M5Tree model outclassed all other models in predicting daily ET0, with a mean absolute error (MAE), mean squared error (MSE), root-mean-squared error (RMSE), R-squared (R2), mean absolute percentage error (MAPE), and Willmott's index (d) of 0.088, 0.018, 0.136, 0.994, 3.073%, and 0.914, respectively, in training of the models. While during testing period, M5Tree model gave MAE, MSE, RMSE, R2, MAPE and d values as 0.114, 0.16, 0.382, 0.949, 3.946% and 0.988, respectively. During training phase, the GBT model reported MAE, MSE, RMSE, R2, MAPE and d as 0.097, 0.021, 0.145, 0.993, 3.257% and 0.998, respectively, which were slightly poor than the M5Tree model. Further, the GBT model showed the best performance during model testing, with MAE, MSE, RMSE, R2, MAPE, and d of 0.126, 0.053, 0.230, 0.982, 4.166%, and 0.995, respectively. Additionally, the POR model performed the worst in predicting daily ET0 values, as evidenced by the prediction error statistics. In conclusion, the developed ET0 model may be utilized to precisely predict ET0 in semi-arid region for efficient irrigation scheduling, especially in the absence of weighing type field lysimeter. [ABSTRACT FROM AUTHOR]

Wheat (Triticum aestivum L.) is regarded as an important staple food crop globally and is sensitive to high temperature (HT) during reproductive stages in comparison to vegetative stages. High-temperature stress is a major abiotic constraint and is projected to reduce crop yield there by threatening food security. A field investigation entitled "Effect of high-temperature stress on yield of various wheat varieties at different growth stages" was conducted at the Students Instructional Farm of the Acharya Narendra Deva University of Agriculture and Technology Kumarganj, Ayodhya (UP) during Rabi season of 2021-22. The experiment was planned under SPD (Split plot Design) with three replications and treatment consisting of three dates of sowing viz; D1 (15th November), D2 (30th November), and D3 (15th December). Results indicated that growth attributes such as plant height, number of tillers plant-1, dry matter plant-1, and membrane stability index were significantly affected by the date of sowing. When late sown variety was planted timely all the growth attributes showed a drastic reduction with respect to late sowing (15th December) However, V1 and V2 showed comparatively good performance in terms of growth attributes when sown early D1 and D2. Performance of V3 was very good as compared to V1 and V2 when sown delayed D3 i.e. facing high temperature stress. Time of sowing decreased substantially almost in all the yield components measured viz; the number of ear bearing tillers-1, number of grains ear-1, ear length (cm), grain yield plant-1 (gm), biological yield plant-1 (g), and test weight (g) and harvest index which caused severe reduction of yield in V1 and V2. Overall growth attributes physiological traits and yield, as well as yield components of the wheat crop, was adversely affected by the time of sowing due to the onset of high temperature during crop growth and particularly grain filling. Variety i.e., V3 (Kundan) reduced the detrimental effect of heat stress by improving physiological traits which ultimately helped in obtaining higher yield. [ABSTRACT FROM AUTHOR]

This research investigation aims to evaluate the performance of the Right Main Canal (RMC) of the Bhimsagar medium irrigation scheme in Rajasthan, India, employing water delivery and water use indicators, such as adequacy, equity, dependability, relative water supply, and relative irrigation supply. The three minors chosen were Ratanpura at the head, Chaplada at the middle, and Maraita II at the tail reach on RMC. The volume of water diverted into the selected minors from the right main canal was measured during crop season, and crop water demand in the command area of each minor was computed using the CROPWAT 8.0 software. Overall, RMC adequacy was adequate at the head, and middle reach minors but inadequate for the tail reach minor. The water distribution system was determined to be unreliable regarding water supply concerning time, with a dependability value of 0.49. Overall equity was determined to be good to fair for the head and middle reach minors but poor for tail reach minor due to disparity in water distribution. In 2013–14, RWS and RIS showed insufficiency in the command system. The average values of RWS and RIS were found to be 1.00 and 1.43, respectively. [ABSTRACT FROM AUTHOR]