Floods are brutal, catastrophic natural hazards which affect most human beings in terms of economy and life loss, especially in the large river basins worldwide. The Indus River basin is considered as one of the world’s large river basins, comprising several major tributaries, and has experienced severe floods in its history. There is currently no proper early flood warning system for the Indus River which can help administrative authorities cope with such natural hazards. Hence, it is necessary to develop an early flood warning system by integrating a hydrodynamic model, in situ information, and satellite imagery. This study used Hydrologic Engineering Center–River Analysis System (HEC–RAS) to predict river dynamics under extreme flow events and inundation modeling. The calibration and validation of the HEC–RAS v5 model was performed for 2010 and 2015 flood events, respectively. Manning’s roughness coefficient (n) values were extracted using the land use information of the rivers and floodplains. Multiple combinations of n values were used and optimized in the simulation process for the rivers and floodplains. The Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09A1, and MOD09GA products were used in the analysis. The Normalized Difference Water Index (NDWI), Modified NDWI1 (MNDWI1), and MNDWI2, were applied for the delineation of water bodies, and the output of all indices were blended to produce standard flood maps for accurate assessment of the HEC–RAS-based simulated flood extent. The optimized n values for rivers and floodplains were 0.055 and 0.06, respectively, with significant satisfaction of statistical parameters, indicating good agreement between simulated and observed flood extents. The HEC–RAS v5 model integrated with satellite imagery can be further used for early flood warnings in the central part of the Indus River basin.

This research investigated the best economically viable power source with the least environmental impact and socially acceptable for the maize crop. Maize is one of the key economic crops in Pakistan. Solar-, electric-, and diesel-powered drip irrigation systems (DIS) were considered for comparative study. We selected 45 sites of maize crop to collect the data, with an area of 1–3 ha, from three divisions. For economic viability, the benefit:cost ratio, life cycle cost, and payback period were calculated, and CO₂ emissions were calculated to assess the environmental impact. The SPSS model was used for one-way ANOVA followed by post hoc and chi-squared tests to check the significance level between all power sources. It was found that the B-C of electric power, solar, and diesel drip irrigation systems was 1.65, 1.52, and 1.44, respectively. Solar, diesel, and electricity power DIS have CO₂ emissions of 0.02, 0.730, and 1.106 tons/ha, respectively. The research concludes that solar power and electric power are the best sources for the environment and economically, respectively. It is recommended that solar power DIS be subsidized, which will help to lower CO₂ emissions and reduce the electricity shortfall in Pakistan.

The global agriculture sector faces many challenges in its mission to meet the increasing demand for food and fiber. Climate change, increasing population growth, emergence of crop diseases, damage to crops from rodents and critters, and shrinking farming land in some regions are among these challenges. Application of agrochemicals has proven to be an efficient answer to some of these challenges. However, the impacts of these products on human health and the environment combined with the increased requirement for sustainable farming requires the development of optimal spraying practices that would balance out all interests and concerns. In this paper, a mathematical model is developed to jointly minimize spraying time and drift losses. The obtained bi-objective mixed integer nonlinear programming model is solved for a case study example published in the crop protection literature. Optimal solutions are obtained using the weighted sum method and the epsilon-constraint approach. The results showed that valid and reasonable solutions can be obtained by selecting the appropriate combination of boom height, nozzle spacing, nozzle type, and tractor travel speed. Useful insights are obtained through various computational experiments.

| Energy is a crucial factor and a capital of a country’s resources leading indicator. World’s energy focus is shifting towards alternate renewable, cheap and environment-friendly resources that can provide long-term sustainability for survival. Due to the fossil fuels depletion and their ever-growing cost, the need to shift focus towards renewable energy resources has increased a lot. To overcome the above-said problems, there is a need to look for a feasible and robust solution for thermal applications in remote areas. Biomass gasification proved to be one of the most efficient and clean waste-to-energy conversion processes in contrast to direct combustion of agricultural waste. Other than controlled burning of biomass, it can be subjected for different purposes like a thermal, synthesis of ethanol, engine running, and for fuel cell applications. An excessive amount of impurities like char, ash, tar, and particulate matter are making these syngas non-feasible for thermal or engine applications. To overcome the above-stated problem, a downdraft gasifier with cyclone separator and hybrid biomass filter was fabricated using locally available materials in the workshop of the Dept. of Farm Machinery and Power at the University of Agriculture, Faisalabad. Uniform distribution of 2.54-5.08 cm 2 sizes of feedstock was used for gasification. Air was used as a gasifying agent. The reactor capacity was 0.19 m 3 . The values of performance parameters for wood waste and corncobs were recorded as fuel consumption rate (0.002195, 0.002323) kg-s -1 , the volume of gas produced (24.56, 26.33) m 3 , operating time (5480, 5164) s, cleaning unit efficiency (81.79, 79.9) % and gas yield (2.04, 2.19) m 3 -kg -1 .