About

Kathy Baylis is a professor at the Department of Geography at UC Santa Barbara. Her research explores how agricultural, trade, and conservation policy affects human and environmental outcomes. She is involved in studying the interactions between human activities and environmental systems, with a focus on policy impacts and sustainability issues.

Research topics

• Economics
• Political Science
• Business
• Environmental science
• Natural resource economics
• Geography
• Environmental resource management
• Agroforestry
• Artificial Intelligence
• Computer Science
• Forestry
• Economic growth
• Ecology
• Agricultural economics
• Environmental planning
• Psychology
• Management science
• Risk analysis (engineering)

Selected publications

• Zero-shot inference strategies for smallholder (<0.1 ha) agriculture field delineation with the Segment Anything foundation model

  Science of Remote Sensing · 2026-04-18 · 3 citations

  preprintOpen access

  Accurate mapping of agricultural field boundaries is crucial for enhancing outcomes like precision agriculture, crop monitoring, and yield estimation. However, extracting these boundaries from satellite images is challenging, especially for smallholder farms and data-scarce environments. This study explores the Segment Anything Model (SAM) to delineate agricultural field boundaries in Bihar, India, using 2-meter resolution SkySat imagery without additional training. We evaluate SAM's performance across three model checkpoints, various input sizes, multi-date satellite images, and edge-enhanced imagery. Our results show that SAM correctly identifies about 58% of field boundaries, comparable to other approaches requiring extensive training data. Using different input image sizes improves accuracy, with the most significant improvement observed when using multi-date satellite images. This work establishes proof of concept for using SAM and maximizing its potential in agricultural field boundary mapping. Our work highlights SAM's potential in delineating agriculture field boundary in training-data scarce settings to enable a wide range of agriculture related analysis.

  PublisherDOI

• Why Didn't I Get a Payout? Understanding Farmer Choices, Index Insurance, and Basis Risk

  Applied Economic Perspectives and Policy · 2026-05-07

  article

  ABSTRACT Index insurance, while heralded as a potential solution to alleviate poverty and food insecurity among agricultural households, has its own set of challenges, notably basis risk. Basis risk is the discrepancy between the insurance payout and losses incurred, posing a significant deterrent to the adoption of index insurance. This study investigates the effect of basis risk training on the uptake of rainfall index insurance among rural farmers in Senegal, employing an educational simulation game as a pedagogical tool. Our results suggest that a lack of transparency and a misunderstanding of basis risk contribute to low participation and a lack of trust in index insurance.

  PublisherDOI

• Zero-shot inference strategies for smallholder ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg" display="inline" id="d1e304"> <mml:mo>&lt;</mml:mo> </mml:math> 0.1 ha) agriculture field delineation with the Segment Anything foundation model

  Science of Remote Sensing · 2026-04-18

  articleOpen access
  PublisherDOI

• Official estimates of global food insecurity undercount acute hunger

  Nature Food · 2025-12-12 · 2 citations

  articleOpen access

  The Integrated Food Security Phase Classification (IPC) system is the official global method for classifying food insecurity. As of 2023, international agencies and governments use IPC analyses to allocate more than US$6 billion of humanitarian assistance annually. Here we evaluate data from approximately 1 billion people in more than 10,000 IPC subnational analyses conducted between 2017 and 2023. We find that IPC estimates understate the extent and severity of crises. Our primary estimates indicate that IPC subnational analyses underestimate the number of acutely hungry people in the world, missing approximately one in five. We find evidence of under-classification around the IPC threshold that determines whether an area is classified as ‘stressed’ or ‘in crisis’—a threshold meant to trigger deployment of humanitarian resources. Contrary to widely held assumptions, our findings suggest that IPC analyses are conservative; the prevalence and severity of acute hunger is probably considerably higher than global estimates indicate. The Integrated Food Security Phase Classification system is the global method for classifying food insecurity severity and allocating humanitarian aid. An evaluation of 10,000 Integrated Food Security Phase Classification subnational analyses from 2017 to 2023 indicates that food severity might have been undercounted.

  PublisherOA PDFDOI

• Five Lessons for Closing the Last Mile: How to Make Climate Decision Support Actionable

  Earth s Future · 2025-07-31 · 2 citations

  articleOpen access1st authorCorresponding

  Abstract Climate shocks are increasing, threatening global agricultural production and food security. But a more extreme climate allows for improved predictions and enables advisory services that allow farmers, ranchers and consumers to respond effectively. To date, there is limited uptake of forecasts. How can we make sure these predictions are valued by and valuable for users of agro‐climatic forecasts? Over the past two years, we held over 40 interviews with food system stakeholders to identify their needs and shortcomings of existing decision support. In this Commentary, we combine these findings and nascent modeling efforts with existing literature to characterize five lessons for improving the uptake and utilization of predictive tools for last mile users in the agrifood system. Given the explosion of machine learning prediction efforts across many applications, we believe our lessons are broadly applicable to forecasting models intended for decision support. Improved accuracy alone does not necessarily lead to improved decision support, and the trust required to motivate action.

  PublisherOA PDFDOI

• A Model of the Model: Unpacking CGE Results on Carbon Leakage

  National Bureau of Economic Research · 2025-12-01 · 1 citations

  reportOpen accessSenior author

  Computational general equilibrium (CGE) models can evaluate detailed tax reforms, trade restrictions, or environmental policy.These models can capture many complexities, but these complexities can make results difficult to interpret.Analytical general equilibrium (AGE) models provide better intuition and interpretation but cannot capture relevant complexities.We propose a method that employs AGE models to understand CGE models -a "model of the model".We apply this idea to climate policy and carbon leakage -the increase in emissions elsewhere.Our AGE models identify seven key economic determinants of leakage within any one outcome.We then unpack results from three existing CGE models.

  PublisherDOI

• Impact forecasting for humanitarianism: Opportunities and challenges

  Proceedings of the National Academy of Sciences · 2025-07-14 · 1 citations

  letterOpen access1st authorCorresponding
  PublisherDOI

• Is field size an indicator of farm size in smallholder-dominated croplands?

  2025-09-21

  preprintOpen access
  PublisherDOI

• Does humidity matter? Prenatal heat and child health in South Asia

  Science Advances · 2025-12-19 · 2 citations

  articleOpen access

  Heat extremes pose substantial health risks during pregnancy and early childhood. High humidity exacerbates heat strain, but its long-term effects on health remain poorly understood. We compare the effect of prenatal exposure to extreme humid heat versus heat alone on child growth in South Asia, where high rates of child stunting meet rapidly accelerating hot-humid extremes. After adjusting for sociodemographic, seasonal, and spatial confounders, we use within-community variation in children's ages to isolate the impact of prenatal exposures. We find that hot-humid exposures are much more detrimental to health than hot temperatures alone, with the potential to increase stunting in South Asia by over 3 million children by 2050. These findings underscore the importance of accounting for humidity when estimating and localizing climate change impacts.

  PublisherDOI

• Investigating the performance of high-resolution subseasonal precipitation forecasts in support of food insecurity early warning

  Environmental Research Climate · 2025-04-09 · 1 citations

  articleOpen accessSenior author

  Abstract Anticipating precipitation extremes across sub-Saharan Africa can help mobilize interventions, trigger anticipatory actions, and promote beneficial actions like water harvesting. Reliable crop model forecasts can help identify when and where food aid interventions can be most beneficial. To date, however, there has been little research evaluating the utility of rainfall forecasts. This study, therefore, assesses the efficacy of the Subseasonal Consortium database (SubC) for use in a regional crop water balance model—the Water Requirement Satisfaction Index (WRSI)—in east Africa. We find that combining two dekads (20 days) of statistically downscaled and bias-corrected SubC precipitation data with climatological information delivers improved estimates of end-of-season conditions over a 17-year test period.&amp;#xD;Our results show that SubC forecasts provide a 35-55% reduction in end-of-season WRSI Root Mean Squared Error in 60% of the east African agropastoral areas during the short rains, with the highest accuracy being in areas most vulnerable to inconsistent precipitation timing and quantities. Across the 17 tested seasons, 1999/00 to 2015/16, use of the SubC either improved or did not degrade the accuracy of WRSI prediction compared to a benchmark model for over 70% of the seasons and for 90% of the study region. In general, the improved accuracy provided by two dekads of SubC forecast is nearly equivalent to what can be attained with one dekad of “perfect” forecast (i.e., observation data). In effect, a 20 day forecast provides a 10-day advance in our early warning capabilities. During extreme events, such the 2005/2006 drought in east Africa, the SubC-driven WRSI could provide advanced warning of poor cropping conditions and potential crop failure up to 3 months before the end of the season. Overall, these improvements provide earlier and more accurate estimates of likely seasonal water balance outcomes, and allow for the identification of where interventions may be needed.

  PublisherDOI

Frequent coauthors

• Daniel C. Miller

  26 shared

• Pablo J. Ordoñez

  Inter-American Development Bank

  24 shared

• Jonathan Coppess

  University of Illinois Urbana-Champaign

  17 shared

• Jordi Honey‐Rosés

  University of British Columbia

  16 shared

• Eeshani Kandpal

  16 shared

• Thomas Heckelei

  University of Bonn

  15 shared

• Nikolas Merten

  The Ohio State University

  14 shared

• Mary Mutemi

  14 shared

Labs

• Kathy Baylis LabPI

Education

• Ph.D., Agricultural and Resource Economics

  University of California at Berkeley

  2003