Large language models can help boost food production, but be mindful of their risks

Coverage of ChatGPT-style large language models (LLMs) in the media has focused on their eye-catching achievements, including solving advanced mathematical problems and reaching expert proficiency in medical examinations. But the gradual adoption of LLMs in agriculture, an industry which touches every human life, has received much less public scrutiny. In this short perspective, we examine risks and opportunities related to more widespread adoption of language models in food production systems. While LLMs can potentially enhance agricultural efficiency, drive innovation, and inform better policies, challenges like agricultural misinformation, collection of vast amounts of farmer data, and threats to agricultural jobs are important concerns. The rapid evolution of the LLM landscape underscores the need for agricultural policymakers to think carefully about frameworks and guidelines that ensure the responsible use of LLMs in food production before these technologies become so ingrained that policy intervention becomes challenging.

Can Cooperative Supports and Adoption of Improved Technologies Help Increase Agricultural Income? Evidence from a Recent Study

Global climate change may result in major environmental issues that have already affected and will continue to affect agricultural sector in the future. A continuing effort to utilize and adopt new agricultural technologies is necessary to mitigate climate change and increase agricultural income. Agricultural cooperatives are gradually being used in emerging countries to encourage improved technology and reduce food insecurity and poverty. This research analyses the influence of cooperative supports (CS) and technology adoption (TA) on agricultural income in Pakistan. It applied the propensity score matching (PSM) technique to evaluate the productivity on survey data from 498 wheat growers to conduct counterfactual analysis for farmers in Pakistan. In addition, a dual selection model (DSM) was applied to resolve the bias in sample selection caused by observed and unobserved aspects of survey data. The results showed that, contrasted with non-membership and non-adopters, growers who joined CS and TA could boost agricultural income by 2.78% and 2.35%, respectively. Stimulatingly, the influence of less-revenue farmers on agricultural income was more substantial than that of high-income farmers. Agricultural income of growers who attached cooperatives and adopted improved agricultural technology enhanced by 5.45% and 4.51%, respectively. These results, among others, emphasize the optimistic role of growing CS and TA in boosting wheat farmer’s income. The findings of the study showed strong relationships among education, age, skill, training, gender with CS and TA, and agricultural income. Overall, this study can be helpful in conducting similar studies in other emerging/developing countries for wheat or any other crop growers.