Pakistan is among the countries that are highly vulnerable to climate change. The country has experienced severe floods and droughts during recent decades. The agricultural sector in Pakistan is adversely affected by climate change. This systematic review paper set out to analyze the existing literature on adaptation measures at the farm level toward climate change in Pakistan. Adopting a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, a total of 62 articles were identified from the Web of Science and Scopus databases. The review paper indicates that the main adaptation strategies adopted by farmers are as follows: changing cropping practices, changing farm management techniques, advanced land use management practices, and nonagriculture livelihood options. Further, this review shows the factors influencing the farmer’s adaptation measures to climate change. Influencing factors were examined and classified into three groups: demographic, socioeconomic, and resources and institutional. Barriers hindering farmers’ adaptive capacity were identified as lack of access to information and knowledge, lack of access to extension services, lack of access to credit facility, and lack of farm resources.

The Crop Water Stress Index (CWSI) is a useful tool for evaluating irrigation scheduling and achieving water conservation and crop yield goals. This study examined the CWSI under different water stress conditions for the scheduling of wheat crop irrigation and developed indices using the leaf canopy temperature in Faisalabad, Pakistan. The experiments were conducted using a randomized, complete block design and four irrigation treatments with deficit levels of D_0%, D_20%, and D_40% from the field capacity (FC) and D_100% (100% deficit level). The CWSI was determined at pre-heading and post-heading stages through the lower baseline (fully watered crop) and upper limit (maximum stress). These baselines were computed using the air temperature and canopy temperature of plant leaves and the vapor pressure deficit (VPD). The CWSI for each irrigation treatment was calculated and the average seasonal CWSI value for the whole season was used to develop the empirical relationships for scheduling irrigation. The relationships between the air canopy temperatures and the VPD resulted in slope (x) = −0.735 and interception (c) = −0.8731 as well as x = −0.5143 and c = −1.273 at the pre- and post-heading stages, respectively. The values of the CWSI for the treatment at deficit levels of D_0%, D_20%, D_40%, and D_100% were found to be 0.08, 0.61, 0.20, and 0.64, respectively. The CWSI values developed in this study can be effectively used to promote better the monitoring of irrigated wheat crops in the region.

The live storage of Pakistan’s major reservoirs, such as the Tarbela reservoir, has decreased in recent decades due to the sedimentation load from the Upper Indus Basin, located in High Mountain Asia. The government of Khyber Pakhtunkhwa took the initiative in 2014 and introduced the Billion Tree Afforestation Project (BTAP). They planted one billion trees by August 2017, mostly in hilly areas. In 2018, the Government of Pakistan also launched a project of 10 billion trees in five years. We assessed the effect of different land-use and land-cover (LULC) scenarios on the water yield and sediment load in the Tarbela reservoir of Pakistan. The soil and water assessment tool (SWAT) model was used to predict the impacts of the LULC changes on the water yield and sediment load under three distinct scenarios: before plantation (2013), after planting one billion trees (2017), and after planting ten billion trees (2025). The model calibration and validation were performed from 1984 to 2000 and 2001 to 2010, respectively, using the SUFI2 algorithm in SWAT-CUP at the Bisham Qila gauging station. The statistical evaluation parameters showed a strong relationship between observed and simulated streamflows: calibration (R² = 0.85, P_BIAS = 11.2%, NSE = 0.84) and validation (R² = 0.88, P_BIAS = 10.5%, NSE = 0.86). The validation results for the sediment load were satisfactory, indicating reliable model performance and validity accuracy (R² = 0.88, P_BIAS = −19.92%, NSE = 0.86). Under the LULC change scenarios, the water yield’s absolute mean annual values decreased from 54 mm to 45 mm for the first and second scenarios, while the third scenario had an estimated 35 mm mean annual water yield in the Tarbela reservoir. The sediment load results for the second scenario (2017) showed a 12% reduction in the sediment flow in the Tarbela reservoir after 1 billion trees were planted. In the third scenario (2025), following the planting of 10 billion trees, among which 3 billion were in the Tarbela basin, the sediment load was predicted to decrease by 22%. The overall results will help to inform the water managers and policymakers ahead of time for the best management and planning for the sustainable use of the water reservoirs and watershed management.

In recent years, there has been an increasing concern regarding the impact of pesticide exposure on pollinators, particularly honeybees (Apis mellifera L.). This concern arises from their crucial role in maintaining ecological balance and global food production. Therefore, an extensive investigation has been conducted to explore the intricate relationship between pesticides and the biological development of honey bees in the Rahim Yar Khan region. This study assessed the impact of four pesticides (Emamectin benzoate, Chlorpyrifos, Imidacloprid, and Acetamiprid) on honey bee eggs, larvae, and adult bees under controlled laboratory conditions. The pesticides were applied at concentrations of 10%, 30%, and 50%, as per regional agricultural recommendations. A zero-toxicity control was also included for comparison. Toxicity evaluations were conducted through contact exposure, and Probit and regression analyses were performed using SPSS software to comprehensively assess the toxicity profiles. The study revealed significant adverse effects on the immediate behavioral responses of A. mellifera following pesticide exposure. These effects included heightened agitation, narcotic-like symptoms, audible hovering, crawling, ceased food-sharing behavior, and reduced proboscis extension. Chlorpyrifos exhibited the highest toxicity against adult bees, while Emamectin Benzoate had the least toxicity. Regarding honey bee eggs, Chlorpyrifos, Imidacloprid, and Acetamiprid were notably more toxic, whereas Emamectin Benzoate exhibited the least toxicity. The impact on larvae varied across developmental stages and pesticides, with Imidacloprid, Chlorpyrifos, and Acetamiprid causing significant mortality, while Emamectin Benzoate showed lower toxicity. The study highlights that Emamectin Benzoate demonstrates lower toxicity compared to other insecticides. This emphasizes the importance of balancing effective pest management with the preservation of pollinator health. The findings underscore the need for informed and sustainable approaches to pesticide use, taking into consideration the potential repercussions on honeybee development and behavior.

Evapotranspiration is essential for precise irrigation and water resource management. Previous literature suggested that eddy covariance (EC) systems could directly measure evapotranspiration in agricultural fields. However, the eddy covariance method remains difficult for routine use, due to its high cost, operational complexity, and relatively multifaceted raw data processing. An alternative method is the flux variance (FV) method, which can estimate the sensible heat flux using high-frequency air temperature measurements by fine-wire thermocouples, at relatively low-cost and with less complexity. Additional measurements of the net radiation and soil heat flux permit the extraction of latent heat flux through the energy balance closure equation. This study examined the performance of the FV method and the results were compared against direct eddy covariance measurements. Data were collected from November 2018 to July 2019, covering seasonal variations. Due to the method’s limitation, only the data under unstable conditions were used for the analysis and days with rainfall were omitted. The results showed that the FV-estimated sensible heat flux was in good agreement with that of eddy covariance in the seasons of winter 2018 and summer 2019. The best agreement between the estimated and measured sensible heat fluxes was observed in the summer, with R² = 0.83, RMSE = 34.97 Wm⁻² and RE = 8.20%. The FV extracted latent heat flux was in good agreement with that measured by EC for both seasons. The best result was obtained in the summer, with R² = 0.92, RMSE = 23.12 Wm⁻², and RE = 6.37%. Overall estimations of sensible and latent heat fluxes by the FV method were in close relation with the eddy covariance data.

A method to estimate the sensible heat flux (H) for unstable atmospheric condition requiring measurements taken in half-hourly basis as input and involving the land surface temperature (LST), H_LST, was tested over a tall and dense aspen stand. The method avoids the need to estimate the zero-plane displacement and the roughness length for momentum. The net radiation (Rn) and the latent heat flux (λE) dominated the surface energy balance (SEB). Therefore, λE was estimated applying the residual method using H_LST as input, λE_R-LST. The sum of H and λE determined with the eddy covariance (EC) method led to a surface energy imbalance of 20% Rn. Thus, the reference taken for the comparisons were determined forcing the SEB using the EC Bowen ratio (BREB method). For clear sky days, H_LST performed close to H_BREB. Therefore, it showed potential in the framework of remote sensing because the input requirements are similar to current methods widely used. For cloudy days, H_LST scattered H_BREB and nearly matched the accumulated sensible hear flux. Regardless of the time basis and cloudiness, λE_R-LST was close to λE_BREB. For all the data, both H_LST and λE_R-LST were not biased and showed, respectively, a mean absolute relative error of 24.5% and 12.5% and an index of agreement of 68.5% and 80%.