The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.

This study was conducted to investigate the effects of various irrigation water (W) and nitrogen (N) levels on growth, root-shoot morphology, yield, and irrigation water use efficiency of greenhouse tomatoes in spring–summer and fall–winter. The experiment consisted of three irrigation water levels (W: 100% of crop evapotranspiration (ETc), 80%, and 60% of full irrigation) and three N application levels (N: 100%, 75%, and 50% of the standard nitrogen concentration in Hoagland’s solution treatments equivalent to 15, 11.25, 7.5 mM). All the growth parameters of tomato significantly decreased (p < 0.05) with the decrease in the amount of irrigation and nitrogen application. Results depicted that a slight decrease in irrigation and an increase in N supply improved average root diameter, total root length, and root surface area, while the interaction was observed non-significant at average diameter of roots. Compared to the control, W₈₀ N₁₀₀ was statistically non-significant in photosynthesis and stomatal conductance. The W₈₀ N₁₀₀ resulted in a yield decrease of 2.90% and 8.75% but increased irrigation water use efficiency (IWUE) by 21.40% and 14.06%. Among interactions, the reduction in a single factor at W₈₀ N₁₀₀ and W₁₀₀ N₇₅ compensated the growth and yield. Hence, W₈₀ N₁₀₀ was found to be optimal regarding yield and IWUE, with 80% of irrigation water and 15 mM of N fertilization for soilless tomato production in greenhouses.

Pakistan is facing a severe energy crisis due to its heavy dependency on the import of costly fossil fuels, which ultimately leads to expansive electricity generation, a low power supply, and interruptive load shedding. In this regard, the utilization of available renewable energy resources within the country for production of electricity can lessen this energy crisis. Livestock waste/manure is considered the most renewable and abundant material for biogas generation. Pakistan is primarily an agricultural country, and livestock is widely kept by the farming community, in order to meet their needs. According to the 2016–2018 data on the livestock population, poultry held the largest share at 45.8%, followed by buffaloes (20.6%), cattle (12.7%), goats (10.8%), sheep (8.4%), asses (1.3%), camels (0.25%), horses (0.1%), and mules (0.05%). Different animals produce different amounts of manure, based upon their size, weight, age, feed, and type. The most manure is produced by cattle (10–20 kg/day), while poultry produce the least (0.08–0.1 kg/day). Large quantities of livestock manure are produced from each province of Pakistan; Punjab province was the highest contributor (51%) of livestock manure in 2018. The potential livestock manure production in Pakistan was 417.3 million tons (Mt) in 2018, from which 26,871.35 million m³ of biogas could be generated—with a production potential of 492.6 petajoules (PJ) of heat energy and 5521.5 MW of electricity. Due to its favorable conditions for biodigester technologies, and through the appropriate development of anaerobic digestion, the currently prevailing energy crises in Pakistan could be eliminated.

Urban aquifers are experiencing increasing pressures from climate change, land-use change, and abstraction, consequently, altering groundwater levels and threatening sustainable water availability, consumption, and utilization. Sustainability in such areas requires the adaptation of groundwater resources to these stressors. Consequently, this research made projections about future climate, land use, and abstraction, examines how these drives will affect groundwater levels, and then proposes adaptation strategies to reduce the impact on Lahore’s groundwater resources. The objectives are achieved using an integrated modeling framework involving applications of Soil Water Assessment Tool (SWAT) and MODFLOW models. The results indicated a projected rise in T_min by ~2.03 °C and T_max by ~1.13 °C by 2100 under medium (RCP 4.5) and high-end (RCP 8.5) scenarios, respectively. Future precipitation changes for mid, near and far periods are projected to be −1.0%, 25%, and 24.5% under RCP4.5, and −17.5%, 27.5%, and 29.0% under RCP8.5, respectively. The built-up area in the Lahore division will dominate agricultural land in the future with an expansion from 965 m² to 3716 km² by the year 2100 under R1S1 (R2S2) land-use change scenarios (significant at p = 5%). The future population of the Lahore division will increase from 6.4 M to 24.6 M (28.7 M) by the year 2100 under SSP1 (SSP3) scenarios (significant at p = 5%). Groundwater level in bult-up areas will be projected to decline from 185 m to 125 m by 2100 due to increasing groundwater abstraction and expansion in the impermeable surface under all scenarios. In contrast, agricultural areas show a fluctuating trend with a slight increase in groundwater level due to decreasing abstraction and multiple recharge sources under combined scenarios. The results of this study can be a way forward for groundwater experts and related institutions to understand the potential situation of groundwater resources in the Lahore division and implement adaptation strategies to counteract diminishing groundwater resources.

The reuse of lignocellulosic biomaterials as a source of clean energy has been explored in recent years due to the large amount of waste that involves human activities, such as those related to agriculture and food. The anaerobic digestion (AD) of plant-based biomass for bioenergy production poses a series of challenges that new technologies are attempting to solve. An improved decomposition of recalcitrant lignocellulose together with an increase in biogas production yield are the main objectives of these new approaches, which also seek the added value of being environmentally friendly. Recent research has reported significant progress in this regard, offering promising outcomes on the degradation of lignocellulose and its subsequent transformation into biomethane by specialized anaerobic microorganisms, overcoming the drawbacks inherent to the process and improving the yield of methane production. The future of the agri–food industry seems to be heading towards the implementation of a circular economy through the introduction of strategies based on the optimized use of lignocellulosic residues as a source of clean and sustainable energy.