Climate change significantly threatens pulse crop production, particularly C? crops like chickpeas, pigeonpeas, lentils and blackgram, which are highly sensitive to rising temperatures and erratic weather patterns. Even brief exposure to heat stress during critical growth stages can cause substantial yield losses. Integrating advanced information technologies and geospatial tools into agricultural decision-making is crucial to mitigate these challenges. The Decision Support System for Agrotechnology Transfer (DSSAT) has emerged as a powerful crop simulation model that aids in assessing and mitigating climate change impacts on pulse crops. This review explores the role of DSSAT in climate-smart agriculture by integrating soil, weather and crop management data for precise yield predictions. By simulating diverse climatic scenarios, DSSAT facilitates the development of adaptive strategies, including selecting heat-tolerant genotypes, optimizing sowing dates and improving irrigation and nutrient management. Moreover, DSSATs’ ability to evaluate extreme weather events such as droughts and heatwaves enhances its application in risk assessment and sustainable agricultural planning. Despite its advantages, challenges such as data availability, model calibration and regional specificity hinder its widespread adoption. Integrating DSSAT with remote sensing, Artificial Intelligence (AI) and Machine Learning (ML) algorithms further enhances its predictive capabilities, making it a more robust tool for climate-resilient pulse production. By leveraging DSSATs’ potential, policymakers, researchers and farmers can develop climate-smart strategies to ensure sustainable pulse production and food security under changing climatic conditions.
