Role of AI adoption, AgriTech, and climate variability on agricultural export performance of Pakistan

Achieving Pakistan’s $100 billion export target requires a fundamental transformation of its agricultural sector, which remains constrained by low productivity, limited technological diffusion, and weak integration into global value chains. This study examines the long and short run relations of artificial intelligence adoption and AgriTech infrastructure with agricultural export performance in Pakistan and assesses how climate variability shapes these relationships. Using annual time-series data from 2003 to 2024, the study develops an integrated analytical framework linking AI adoption, AgriTech infrastructure, climate variability, innovation capacity, global demand, exchange rate volatility, and institutional quality to agricultural export performance. The empirical analysis employs the Autoregressive Distributed Lag bounds-testing approach to distinguish long-run relationships from short-run dynamics, with Fully Modified Ordinary Least Squares and Dynamic Ordinary Least Squares) used as robustness checks. The results show that AI adoption, AgriTech infrastructure, and climate variability are the three central technology–environment correlates of agricultural exports: a one-unit increase in the AI Adoption Index is associated with roughly 20% higher agricultural exports, while a comparable increase in AgriTech infrastructure is associated with roughly 18% higher exports; in contrast, a one unit increase in climate variability is associated with about 13% lower exports.