The rapidly changing climatic scenario is demanding periodic evaluation of groundwater quality at the temporal and spatial scale in any region for its effectual management. The statistical, geographic information system (GIS), geostatistical, and map overlay approaches were applied for investigating the spatio-temporal variation in groundwater quality and level data of 242 monitoring wells in Punjab, Pakistan during pre-monsoon and post-monsoon seasons of the years 2015 and 2016. The analysis indicated the higher variation in data for both the seasons (pre-monsoon and post-monsoon) as coefficient of variation (CV) values were found in the range of 84–175% for groundwater quality parameters. Based on the t-test values, the marginal improvement in groundwater electrical conductivity (EC), sodium absorption ratio (SAR) and residual sodium carbonate (RSC) and decrease in groundwater level (GWL) were observed in 2016 as compared to 2015 (p = 0.05). The spatial distribution analysis of groundwater EC, SAR and RSC indicated that the groundwater quality was unfit for irrigation in the lower south-east part of the study area. The groundwater level (GWL) was also higher in that part of the study area during the pre-monsoon and post-monsoon seasons in 2015 and 2016. The overlay analysis also indicated that the groundwater EC, RSC and GWL values were higher in south-east parts of the study area during pre-monsoon and post-monsoon seasons of 2015 and 2016. Hence, there is an instant need to apply groundwater management practices in the rest of the region (especially in the lower south-east part) to overcome the future degradation of groundwater quality.

In recent decades, the fortunes of energy economies have been closely linked in Pakistan. A major energy inefficiency issue was found in Pakistan due to the mismatch between horsepower (HP) requirements and bore depth. Keeping this in view, a total of 194 tubewells were chosen for an energy audit in the Multan region, Pakistan. The Terrameter SAS 4000 was used to measure the accurate demand of the head during the resistivity surveys at all of the selected locations. The results showed that the tubewell sets were installed arbitrarily at high power, irrespective of the provided flow and head, and these pumps used more energy for their flow. The results revealed that the efficiency of the tubewell sets increased from 35 to 54%, from 55 to 80%, from 49 to 80%, and from 48 to 75% for centrifugal pumps with electric motors and diesel engines and for turbines with electric motors and diesel engines, respectively. A weighted overlay analysis indicated that the efficiency of tubewells covering 838.12, 1131.8, and 2077.1 km² for centrifugal pumps with electric motors, diesel engines, and turbines, respectively, was enhanced for the study area. Similarly, the energy saved for the study area covered 1423.8, 1161.1, and 1131.1 km², as shown by the overlay analysis. The results revealed that the annual energy saving was found of 3486 kw for 194 tubewells, resulted in the saving of USD 0.204 million in operational costs over one year. The overall results indicate the strong need to adopt proper investigations of the head and power requirements before installing a system in the study area.

The precise monitoring of nitrogen (N) is an effective strategy for enhancing the crop yield per unit of land, but it involves field-level soil and crop data. The two years of experimental study were conducted during the cotton growing seasons of 2018 and 2019 at the Agriculture Research Farm of the Department of Agricultural Engineering, Bahauddin Zakariya University, Multan. The Nitrogen Fertilizer Optimization Algorithm (NFOA) was formulated based on the observed data for cotton lint yield (CLY) and GreenSeeker Normalized Difference Vegetation Index (GSNDVI) during the growing stages of cotton. The precision nitrogen application rate-based green seeker (PNAR) G.S for cotton was identified as 150-165 kg/ha. A linear relationship was observed between CLY (R2 = 0.80) for cotton with the GSNDVI. The average nitrogen requirement (N_req) using (PNAR) G.S was determined through the nitrogen fertilizer optimization algorithm (NFOA). The N_req was found to be 0.013 kg/kg for cotton. Precision N management originating from handheld crop sensors (GreenSeeker) may be helpful in decision-making for site-specific in-season N fertilizer management to enhance crop yield.