Climate change poses significant challenges to global agriculture, necessitating sustainable practices to ensure food security and mitigate environmental impacts. Climate-smart agriculture (CSA) is a technique that aims to boost agricultural productivity, improve adaptive ability, and reduce greenhouse gas emissions. It includes practices like crop management, sustainable farming, agroforestry, precision farming, and climate-resilient crop varieties. CSA improves soil health, water use efficiency, erosion, carbon sequestration, biodiversity conservation, and resource utilization. Precision agriculture tools like remote sensing and data analytics enable site-specific management decisions. Climate-resilient crop types, such as drought-tolerant, heat-tolerant, and disease-resistant, are crucial for food production under changing climatic conditions. Encouraging laws, institutional frameworks, and financial incentives are essential for CSA implementation. However, more research and collaboration are needed to promote widespread adoption of CSA practices.

To meet the demand for increasing global food production while using limited water resources, crop water stress must be improved in agriculture. Remote-sensing-based plant stress indicators have the benefits of high spatial resolutions, a cheap cost, and short turnaround times. This study discusses the current advancements in agricultural water stress monitoring and irrigation scheduling, some of the challenges that have been met, and the upcoming research needs. Remote sensing systems are prepared to handle the intricate and technical evaluations of agricultural productivity, security, and crop water stress quickly and effectively. We explore the use of remote-sensing systems in the evaluation of crop water stress by looking at the existing research, technologies, and data. This study examines the connection between relative water content (RWC), equivalent water thickness (EWT), and agricultural water stress. Using remote sensing, evapotranspiration, and sun-induced chlorophyll content are examined in connection to crop drought. Spectral indices, remote sensing satellites, and multi-spectral sensing systems, as well as systems that measure land surface temperature, are examined. This critical study focuses on cutting-edge techniques for assessing crop water stress.

Drought is a complex natural disaster with significant implications for agriculture, water resources, and socioeconomic development. Accurate and timely assessment of meteorological drought is crucial for effective management and mitigation strategies. Climate change has led to a rise in climatic anomalies, such as droughts, floods, heatwaves, and cold snaps, which have severe impacts on human well-being and societal patterns. Droughts, which are prolonged periods of limited or absent rainfall, pose significant challenges for sectors like agriculture, energy, and enterprises, especially in economically reliant countries with inadequate water management infrastructure. Drought indicators are essential in meteorology, agriculture, and hydrology for monitoring drought conditions. Accurate drought assessment relies on quantitative index-based comprehensive drought indices, such as India’s Aridity Anomaly Index (AAI), Deciles Index, Percent of Normal Index, Reconnaissance Drought Index (RDI), and the Palmer Drought Severity Index (PDSI). Drought management involves analyzing risk components and using analytical tools for decision making. A decision support system includes institutional, methodological, public, and operational components. Long-term actions include demand reduction through economic incentives, while short-term actions include increasing water supply through wastewater reutilization, inter-basin water conveyance, reservoir construction, and agricultural ponds. Impact minimization is achieved through educational initiatives, reallocating water resources, early warning systems, and insurance programs. Challenges include developing technologies to integrate data sources and create unified indicators, and geospatial decision-support systems facilitate hazard mapping and strategic drought management plans.

The advent of precision agriculture has revolutionized the agricultural sector, emphasizing the utilization of data-driven strategies for decision-making and the optimization of resources. Sensors, encompassing soil, crop, weather, and drone sensors, offer real-time data to facilitate informed decision-making and enhance agricultural outcomes. These sensors facilitate the optimization of irrigation and fertilization and the timely identification of soil-related problems. In addition, they contribute to the surveillance of plant health, the detection of weed infestations, and the monitoring of meteorological conditions. The gathering and management of data play a crucial role in precision agriculture. The advantages encompass decreased utilization of resources, heightened agricultural productivity, a diminished ecological footprint, and better economic viability. Nevertheless, persistent obstacles like technological problems, concerns around data security, and the imperative for advancements in artificial intelligence and machine learning persist.

In this study, we present a comprehensive and detailed analysis to identify and quantify spatial patterns of heavy, very heavy, and extremely heavy rainfall, as well as the trends that have developed over the past seven decades (1951 to 2020) of the monsoon months (June to September) under a warming scenario. We also project these extreme rainfall counts during the near (2036–2060) and late 21st century (2075–2099) for comparison to the historical period (1990–2014). The 5-day maximum rainfall over the provinces of Sindh and Baluchistan is currently about 75% more intense than it would have been without the climate warming by 1.2 °C, and the 60-day rainfall across the basin is currently about 50% more intense. This means that heavy rainfall is now more likely to occur. Due to the high-level of rainfall variability in the area, there is significant uncertainty regarding these estimations, and the causes of the observed changes are not just limited to climate change. However, most of the models and observations we have analyzed for the 5-day rainfall extreme indicate that severe rainfall has been heavier than Pakistan has warmed. According to some of these models, the intensity of the rainfall might have increased by 50% due to climate change under the 5-day event threshold. The model predicts that rainfall intensity will greatly rise in the future for the 5-day event in a climate that is 2 °C warmer than it was in pre-industrial times, while the uncertainty is still quite high for the 60-day monsoon rainfall.

Land use and land cover (LULC) change is a major driver of environmental change in District Bahawalnagar, Punjab. LULC change can lead to changes in soil quality, water availability, and climate, all of which can affect crop yields. LULC change can also lead to the loss of agricultural land, forest land, water bodies, and an increment in urban land that causes climate change and affects the agricultural sector. The study area showed that in the last thirty years, the population increased, built-up land increased, and agricultural land dropped by 30%. The present status of knowledge is reviewed in this paper on the impact of LULC on agricultural production in District Bahawalnagar. The conversion of agricultural land to urban development in District Bahawalnagar has led to a decline in crop yields of an average of 10%. The production of wheat and rice, the two major crops grown in District Bahawalnagar, is influenced by LULC changes. This study also found that the loss of agricultural land has resulted in an increase in soil salinity, which has further reduced crop yields. The detrimental effects of LULC change on agricultural output in District Bahawalnagar can be mitigated by adopting sustainable land management practices. These practices include reforestation, conservation agriculture, and water conservation. The government of Pakistan can also play a role in mitigating the negative impacts of LULC change on agricultural production by developing and implementing land use plans that protect agricultural land from conversion to other uses. More research is required to fully comprehend the effects of LULC and develop effective management strategies. However, LULC is a major challenge that must be addressed if we are to ensure food security in the future.