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Abstract: The article explores the role and prospects of artificial intelligence (AI) in addressing global food insecurity. It provides an overview of machine learning (ML) techniques—the core learning component of AI—used to predict food security outcomes and discusses real-world examples as well as recent applications of ML. It further examines the challenges and limitations of ML, including concerns related to data quality and ethical considerations, followed by policy recommendations in crucial areas such as funding, cross-sector collaboration, education, and data standards. Finally, it underscores the importance of recognizing AI as a complementary tool, rather than a standalone solution, in the pursuit of the ultimate goal of achieving a world without hunger.

AbstractWe provide an application of machine learning to identify the distributional consequences of climate change in Malawi. We compare climate impact estimates based on drought indicators established objectively from the k‐means algorithm to more traditional measures. Young women affected by drought were 5 percentage points more likely to be married by 18 than those living in nondrought areas. Our approach generates robust results when varying the number of clusters and definition of treatment status. In some cases, we find the design using k‐means to define treatment is more likely to satisfy the assumptions underlying the difference‐in‐differences strategy than when using arbitrary thresholds. Projections from the estimates indicate future drought risk may lead to larger declines in labor productivity due to women's engagement in early age marriage than other factors affecting their participation rates. Under the extreme representative concentration pathway scenario, drought exposure encourages the exit of 3.3 million women workers by 2100.

AbstractLeveraging advancements in remote data collection and using the Food Insecurity Experience Scale (FIES) as a proxy measure of resilience, we show that machine learning models (such as Gradient Boosting Classifier, eXtreme Gradient Boosting, and Artificial Neural Networks), can predict resilience with relatively high accuracy (up to 81%). Key household‐level predictors include access to financial institutions, asset ownership, the adoption of agricultural mechanization as evidenced by the use of tractors, the number of crops cultivated, and ownership of nonfarm enterprises. Our analysis offers insights to researchers and policymakers interested in the development of targeted interventions to bolster household resilience.

Stochastic plateau production functions provide improved fertilizer recommendations based on multi-year agronomic experiments where weather and other stochastic variables change over time. This research assesses the profitability of no-tillage corn production in northeastern Colorado and determines economically optimal nitrogen fertilizer rates. It also proposes an alternative parameterization of the linear response stochastic plateau model which provides a robustness check against traditional parameterizations. Results show the current use of nitrogen fertilizer in the area exceeds estimated economically optimal levels. This suggests that a reduction in nitrogen use could increase expected profits and simultaneously reduce environmental costs. ; Virginia Tech [58-3012-7-007]; USDA/ARSUnited States Department of Agriculture (USDA)USDA Agricultural Research Service [58-3012-7-007]; Hatch ProjectUnited States Department of Agriculture (USDA) [VA 136247, NC-1034] ; This work was supported by Virginia Tech Cooperative Agreement Number 58-3012-7-007 with USDA/ARS; and by Hatch Project VA 136247 Regional project NC-1034. ; Public domain authored by a U.S. government employee

AbstractDespite increasing scholarly attention to individual aspirations, rural women's aspirations in agriculture remain largely unexplored. With women increasingly central to agricultural work, understanding their aspirations is crucial. Using farm surveys from Ecuador, Kenya, and Rwanda, we investigate the relationship between gender and aspiration formation in agriculture. Similar to previous findings related to education and entrepreneurship, our findings show women have lower ambitions for agricultural production. Addressing the unique challenges they face, gender‐sensitive policies are vital for inclusive, sustainable agriculture. Our study contributes to a broader understanding of the multidimensional factors shaping agricultural outcomes, underscoring the importance of integrating diverse perspectives for effective policy formulation in agriculture.