Wheat (Triticum aestivum L.) is one of the world’s primary food crops, and timely and accurate yield prediction is essential for ensuring food security. There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for wheat yield prediction in arid regions remain unclear. This study was conducted to (1) assess the performance of widely recognized remote sensing indices to predict wheat yield at different growth stages, (2) evaluate the predictive accuracy of different yield predictive machine learning models, (3) determine the appropriate growth period for wheat yield prediction in arid regions, and (4) evaluate the impact of climate parameters on model accuracy. The vegetation indices, widely recognized due to their proven effectiveness, used in this study include the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Atmospheric Resistance Vegetation Index (ARVI). Moreover, four machine learning models, viz. Decision Trees (DTs), Random Forest (RF), Gradient Boosting (GB), and Bagging Trees (BTs), were evaluated to assess their predictive accuracy for wheat yield in the arid region. The whole wheat growth period was divided into three time windows: tillering to grain filling (December 15–March), stem elongation to grain filling (January 15–March), and heading to grain filling (February–March 15). The model was evaluated and developed in the Google Earth Engine (GEE), combining climate and remote sensing data. The results showed that the RF model with ARVI could accurately predict wheat yield at the grain filling and the maturity stages in arid regions with an R² > 0.75 and yield error of less than 10%. The grain filling stage was identified as the optimal prediction window for wheat yield in arid regions. While RF with ARVI delivered the best results, GB with EVI showed slightly lower precision but still outperformed other models. It is concluded that combining multisource data and machine learning models is a promising approach for wheat yield prediction in arid regions.

The command area of the Rakh branch canal grows wheat, sugarcane, and rice crops in abundance. The canal water, which is trivial for irrigating these crops, is conveyed to the farms through the network of canals and distributaries. For the maintenance of this vast infrastructure; the end users are charged on a seasonal basis. The present water charges are severely criticized for not being adequate to properly manage the entire infrastructure. We use the residual value to determine the value of the irrigation water and then based on the quantity of irrigation water supplied to farm land coupled with the infrastructure maintenance cost, full cost recovery figures are executed for the study area, and policy recommendations are made for the implementation of the full cost recovery system. The approach is unique in the sense that the pricings are based on the actual quantity of water conveyed to the field for irrigating crops. The results of our analysis showed that the canal water is severely under charged in the culturable command area of selected distributaries, thus negating the plan of having a self-sustainable irrigation system.

The estimation of the groundwater (GW) potential in irrigated areas is crucial for the sustainable management of water resources in order to ensure its sustainable use. This study was conducted in a selected area of the Chaj doab, Punjab, Pakistan, to quantify the impacts of the pumping and the recharge on the aquifer therein. To that end, a groundwater flow model (MODFLOW) and a groundwater recharge model (WetSpass) were coupled to assess the conditions of the aquifer. The model was calibrated manually on twelve-year data (2003–2014) against the observed groundwater levels, and it was validated with five-year data (2015–2019). Three main scenarios (divided into ten subscenarios) were simulated for the future prediction of the groundwater: Scenario-I (to assess the impact of the pumping if the prevailing conditions of the years from 2003 to 2019 were to continue until 2035); Scenario-II (to assess the impact of the pumping on the aquifer by increasing the pumping capacity by 25, 50, 75, and 100% for the coming 10 years); and Scenario-III (to assess the impact on the aquifer of the decrease in the average groundwater recharge from the river by 50% by following the same pumping trend). The Scenario-I results show that there would be an 18.1 m decrease in the groundwater table at the end of the year 2035. The Scenario-II results predict decreases in the water table by 2.0, 5.5, 9.8, and 14.3 m in the year 2029 as a result of increases in the pumping capacity of 25, 50, 75, and 100%, respectively. The results of Scenario-III show that, with the decrease in the recharge from the rainfall, there would be a 0.7 m decrease in the water table, and that, from open-water bodies, there would be a 2.4 m decrease in the water table. These results are very helpful for determining the recharge and discharge potential of the aquifer.

To meet the demand for increasing global food production while using limited water resources, crop water stress must be improved in agriculture. Remote-sensing-based plant stress indicators have the benefits of high spatial resolutions, a cheap cost, and short turnaround times. This study discusses the current advancements in agricultural water stress monitoring and irrigation scheduling, some of the challenges that have been met, and the upcoming research needs. Remote sensing systems are prepared to handle the intricate and technical evaluations of agricultural productivity, security, and crop water stress quickly and effectively. We explore the use of remote-sensing systems in the evaluation of crop water stress by looking at the existing research, technologies, and data. This study examines the connection between relative water content (RWC), equivalent water thickness (EWT), and agricultural water stress. Using remote sensing, evapotranspiration, and sun-induced chlorophyll content are examined in connection to crop drought. Spectral indices, remote sensing satellites, and multi-spectral sensing systems, as well as systems that measure land surface temperature, are examined. This critical study focuses on cutting-edge techniques for assessing crop water stress.

Agriculture is the major economic sector in Asian countries and the majority of their population depends on it. In addition to the largest irrigation system in the Indus basin, Pakistan is suffering from water shortages that are affecting the overall crop production of the country. Different resource conservation technologies (RCTs) such as precision land leveling (PLL), raised bed planting (RBP), and different high-efficiency irrigation systems (HEISs) can be opted for better water productivity. In this study, the potential of these RCTs has been explored to enhance production and save irrigation water through their sustainable adoption. Based on studies by different researchers, water savings up to 47% and yield increases up to 35% have been reported under PLL, while water savings up to 50% and about 10–33% yield increases were observed under RBP. Similarly, under different HEISs, water savings up to 80% and yield increases up to 53% have been reported compared with crops sown under conventional farming. Based on the findings of the researchers regarding RCTs, these have been proved as progressive sowing techniques for better productivity under the limited available water scenario. The detailed review in this paper concludes that RCTs resulting in the improvement of gravity irrigation methods, viz., PLL and RBP, have a great potential of adoption and water productivity improvement at the regional scale in developing countries such as Pakistan, while high-cost HEISs can also be promoted at limited scale among progressive farmers for high-value agriculture.

Drought is a complex natural disaster with significant implications for agriculture, water resources, and socioeconomic development. Accurate and timely assessment of meteorological drought is crucial for effective management and mitigation strategies. Climate change has led to a rise in climatic anomalies, such as droughts, floods, heatwaves, and cold snaps, which have severe impacts on human well-being and societal patterns. Droughts, which are prolonged periods of limited or absent rainfall, pose significant challenges for sectors like agriculture, energy, and enterprises, especially in economically reliant countries with inadequate water management infrastructure. Drought indicators are essential in meteorology, agriculture, and hydrology for monitoring drought conditions. Accurate drought assessment relies on quantitative index-based comprehensive drought indices, such as India’s Aridity Anomaly Index (AAI), Deciles Index, Percent of Normal Index, Reconnaissance Drought Index (RDI), and the Palmer Drought Severity Index (PDSI). Drought management involves analyzing risk components and using analytical tools for decision making. A decision support system includes institutional, methodological, public, and operational components. Long-term actions include demand reduction through economic incentives, while short-term actions include increasing water supply through wastewater reutilization, inter-basin water conveyance, reservoir construction, and agricultural ponds. Impact minimization is achieved through educational initiatives, reallocating water resources, early warning systems, and insurance programs. Challenges include developing technologies to integrate data sources and create unified indicators, and geospatial decision-support systems facilitate hazard mapping and strategic drought management plans.