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.

Land use–land cover (LULC) alteration is primarily associated with land degradation, especially in recent decades, and has resulted in various harmful changes in the landscape. The normalized difference vegetation index (NDVI) has the prospective capacity to classify the vegetative characteristics of many ecological areas and has proven itself useful as a remote sensing (RS) tool in recording vegetative phenological aspects. Likewise, the normalized difference built-up index (NDBI) is used for quoting built-up areas. The current research objectives include identification of LULC, NDVI, and NDBI changes in Jhelum District, Punjab, Pakistan, during the last 30 years (1990–2020). This study targeted five major LULC classes: water channels, built-up area, barren land, forest, and cultivated land. Satellite imagery classification tools were used to identify LULC changes in Jhelum District, northern Punjab, Pakistan. The perception data about the environmental variations as conveyed by the 500 participants (mainly farmers) were also recorded and analyzed. The results depict that the majority of farmers (54%) believe in the appearance of more drastic changes such as less rainfall, drought, and decreased water availability for irrigation during 2020 compared to 30 years prior. Overall accuracy assessment of imagery classification was 83.2% and 88.8% for 1990, 88.1% and 85.7% for 2000, 86.5% and 86.7% for 2010, and 85.6% and 87.3% for 2020. The NDVI for Jhelum District was the highest in 1990 at +0.86 and the lowest in 2020 at +0.32; similarly, NDBI values were the highest in 2020 at +0.72 and the lowest in 1990 at −0.36. LULC change showed a clear association with temperature, NDBI, and NDVI in the study area. At the same time, variations in the land area of barren soil, vegetation, and built-up from 1990 to 2020 were quite prominent, possibly resulting in temperature increases, reduction in water for irrigation, and changing rainfall patterns. Farmers were found to be quite responsive to such climatic variations, diverting to framing possible mitigation approaches, but they need government assistance. The findings of this study, especially the causes and impacts of rapid LULC variations in the study area, need immediate attention from related government departments and policy makers.

AquaCrop is a water-driven model that simulates the effect of environment and management on crop production under deficit irrigation. The model was calibrated and validated using three databases and four irrigation treatments (i.e., 100%ET, 80%ET, 70%ET, and 50%ET). Model performance was evaluated by simulating canopy cover (CC), biomass accumulation, and water productivity (WP). Statistics of root mean square error (RMSE) and Willmott’s index of agreement (d) showed that model predictions are suitable for non-stressed and moderate stressed conditions. The results showed that the simulated biomass and yield were consistent with the measured values with a coefficient of determination (R²) of 0.976 and 0.950, respectively. RMSE and d-index values for canopy cover (CC) were 2.67% to 4.47% and 0.991% to 0.998% and for biomass were 0.088 to 0.666 ton/ha and 0.991 to 0.999 ton/ha, respectively. Prediction of simulated and measured biomass and final yield was acceptable with deviation ˂10%. The overall value of R² for WP in terms of yield was 0.943. Treatment with 80% ET consumed 20% less water than the treatment with 100%ET and resulted in high WP in terms of yield (0.6 kg/m³) and biomass (1.74 kg/m³), respectively. The deviations were in the range of −2% to 11% in yield and −2% to 4% in biomass. It was concluded that AquaCrop is a useful tool in predicting the productivity of cotton under different irrigation scenarios.

Drip irrigation has long been proven beneficial for fruit and vegetable crops in Pakistan, but the only barrier in its adoption is the high cost of installation for small landholders, which is due to overdesigning of the system. In the present study, the cost of a conventional drip irrigation system was reduced by redesigning and eliminating the heavy filtration system (i.e., hydrocyclon, sand media, disc filters (groundwater source), pressure gauges, water meters, and double laterals).Purchasing the drip system from local vendors also reduced the cost. Field trials were conducted during 2015 and 2016 to observe the productive and economic effects of low-cost drip irrigation on vegetables (potato, onion, and chilies) and fruits (olive, peach, and citrus). The low-cost drip irrigation system saved 50% cost of irrigation and increased 27–54% net revenue in comparison with the furrow irrigation system. Further, water use efficiency (WUE) was found from 3.91–13.30 kg/m³ and 1.28–4.89 kg/m³ for drip irrigation and furrow irrigation systems, respectively. The physical and chemical attributes of vegetables and fruits were also improved to a reasonably good extent. The present study concluded that low-cost drip irrigation increased the yield by more than 20%, as compared with traditional furrow irrigation, and thus, it is beneficial for the small landholders (i.e., less than 2 hectares).

The present study aimed at investigating scientific irrigation scheduling (SIS) for the sustainable production of olive groves. The SIS allows farmers to schedule water rotation in their fields to abate crop water stress and maximize yields, which could be achieved through the precise monitoring of soil moisture. For this purpose, the study used three kinds of soil moisture sensors, including tensiometer sensors, irrometer sensors, and gypsum blocks for precise measurement of the soil moisture. These soil moisture sensors were calibrated by performing experiments in the field and laboratory at Barani Agricultural Research Institute, Chakwal in 2018 and 2019. The calibration curves were obtained by performing gravimetric analysis at 0.3 and 0.6 m depths, thereby equations were developed using regression analysis. The coefficient of determination (R²) at 0.3 and 0.6 m depth for tensiometer, irrometer, and gypsum blocks was found to be equal to 0.98, 0.98; 0.75, 0.89; and 0.82, and 0.95, respectively. After that, a drip irrigation system was installed with the calibrated soil moisture sensors at 0.3 and 0.6 m depth to schedule irrigation for production of olive groves as compared to conventional farmer practice, thereby soil moisture profiles of these sensors were obtained to investigate the SIS. The results showed that the irrometer sensor performed as expected and contributed to the irrigation water savings between 17% and 25% in 2018 and 2019, respectively, by reducing the number of irrigations as compared toother soil moisture sensors and farmer practices. Additionally, olive yield efficiencies of 8% and 9%were observed by the tensiometer in 2018 and 2019, respectively. The outcome of the study suggests that an effective method in providing sustainable production of olive groves and enhancing yield efficiency.

Aim of study: A field experiment was executed, under rainfed conditions from 2014-15 to 2017-18, to study the role of cowpea (Vigna unguiculata L.) in rainwater harvesting to enhance the wheat (Triticum aestivum L.) yield.Area of study: Rain-fed area of Pothwar region, Punjab, Pakistan.Material and methods: We designed three treatments (T1: control; T2: cowpea grown after conventional tillage and incorporated into soil to act as “green manure”; and T3: grown without any tillage practice, cut with sickle and spread as “mulch”). The effect of these treatments on soil moisture conservation was studied against conventional farmer’s practice, wherein no host crop is grown before wheat sowing.Main results: Available soil water remained highest in T2 during first three years when sufficient rainfall was received contrary to fourth year with low rainfall. The results revealed that cowpea biomass of 15.2 t/ha and 13.72 t/ha, from T2 and T3 respectively, were produced during 2015 corresponding to 213 mm rainfall. Whereas, these quantities increased to 25.69 t/ha and 24.29 t/ha during 2017 with 387 mm of rainfall. The study revealed that net income from wheat crop under T2 was Rs 13000 and Rs 9000 per hectare higher than that of control during the first two years respectively. Contrarily, net income from T2 was found negative and benefit-cost ratio reduced to 0.79 when very low rainfall was received during the last year.Research highlights: Use of cowpea as green manure gave maximum net return if sufficient rainfall is received during decomposition of cowpea and hence recommended for in-situ rainwater harvesting.