Assessment of Water Productivity Enhancement and Sustainability Potential of Different Resource Conservation Technologies: A Review in the Context of Pakistan

Abstract

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.

Estimation of Crop Water Productivity Using GIS and Remote Sensing Techniques

“…It is based on METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration). The Landsat-image-based process operates on the Google Earth Engine system [10][11][12]15,16,20,21,32,33]. The temporal resolution is 16 days with a spatial resolution of 30 m. Using the Penman-Monteith equation for alfalfa crop reference, EEFlux includes North American Land Data Assimilation System (NLDAS) for reference ET (ETr) estimation [32][33][34].…”

“…The Landsat-image-based process operates on the Google Earth Engine system [10][11][12]15,16,20,21,32,33]. The temporal resolution is 16 days with a spatial resolution of 30 m. Using the Penman-Monteith equation for alfalfa crop reference, EEFlux includes North American Land Data Assimilation System (NLDAS) for reference ET (ETr) estimation [32][33][34]. The years 1998, 2017, 2014-2015, and 2016-2017 were selected based on the low and high flows which were obtained from the flow data by using the frequency analysis.…”