INTEGRATION OF REMOTELY SENSED DATA INTO CROP MODEL TO ADAPT CLIMATE CHANGE IN AGRICULTURE SECTOR IN PAKISTAN

Abstract

This comprehensive study investigates the efficacy and impacts of climate-smart agricultural practices in Punjab, Pakistan, focusing on rice-wheat and cotton-wheat cropping systems as a meta-analysis. The research employed a multi-faceted approach, combining field studies, economic analyses to evaluate various climate-smart practices and their potential for enhancing agricultural sustainability in the region. The study examined five key climate-smart practices: Alternate Wetting and Drying (AWD) and Direct Seeded Rice (DSR) for rice cultivation, Zero Tillage for wheat, ridge sowing for cotton, and raised bed planting for wheat. Each practice was evaluated for its agronomic performance, resource use efficiency, economic viability, and environmental impact. Results demonstrated that AWD in rice cultivation led to water savings of 15-25% and reduced methane emissions by 30-70% compared to conventional flooding practices. Economic analysis revealed a 7% increase in net profit for AWD. DSR showed even more promising economic outcomes, with a 15% increase in total revenue and a 32% increase in net profit, accompanied by significant reductions in water use and labor requirements. Zero Tillage wheat demonstrated substantial benefits, including a 24% increase in net profit and improved soil health. In the wheat-cotton cropping system, ridge sown cotton and raised bed wheat both showed improvements in water use efficiency and yield potential, with economic analyses indicating increases in net profit of 22% and 24% respectively. The study also employed advanced remote sensing techniques, utilizing a Random Forest algorithm to estimate rice crop areas in Punjab. This method yielded a total rice area estimate of 2,703,586 hectares, which was 3.79% higher than official estimates from the Crop Reporting Service. The spatial resolution and accuracy of this approach enabled detailed, district-level analysis of rice cultivation patterns. Based on the crop area estimations, the study quantified GHG emissions from rice cultivation. Total CH4 emissions were estimated at 252.79 Gg, with significant variations observed across districts. This analysis provides crucial data for targeting emission reduction strategies in high-emission areas. Finally, the research modelled optimal sowing dates for wheat under changing climate, considering temperature increases of 1°C and 1.5°C. Results indicated that delayed sowing dates, ranging from 6 to 14 days depending on the location and temperature increase scenario, xiv could help to maintain or improve wheat yields under warming conditions. This finding highlights the importance of adaptive management strategies for climate change. Comprehensive approach of this research, combining field-level practices with landscape-scale analysis and future climate modeling, provides a robust framework for understanding and addressing the complex challenges facing Punjab's agricultural sector. The findings offer valuable insights for policymakers, agricultural extension services, and farmers, emphasizing the potential of climate-smart practices to enhance productivity, resource efficiency, and environmental sustainability simultaneously. Furthermore, the study underscores the importance of location-specific recommendations and the integration of advanced technologies in agricultural planning and management under changing climatic conditions.