Team Members:
Person Name | Person role on project | Affiliation |
---|---|---|
Meha Jain | Principal Investigator | University of Michigan, Ann Arbor, US |
Nishan Bhattarai | Postdoc Researcher | University of Oklahama, Norman, USA |
Vijesh Krishna | Collaborator | CIMMYT, Mexico City, Mexico |
Amy Lerner | Collaborator | Universidad Nacional Autónoma de Mexico, Mexico City, Mexico |
Climate change, including warming temperatures, changing rainfall patterns, and more extreme events, is threatening agricultural production across the globe. This is particularly true for grain crops across the tropics, which have been shown to be negatively impacted by changes in climate. While a significant body of research has focused on how farmers may adapt to climate change, less work has acknowledged that climate is only one factor that farmers respond to. Changing policies and market fluctuations, among other factors, likely also play a strong role in explaining agricultural transitions. Yet, it is unclear whether agricultural transitions in response to non-climatic factors lead to maladaptation to longer-term changes in climate. This proposal examines agricultural transitions, their drivers, and potential climate change impacts across Mexico for the nation’s main grain crop, maize. We focus on maize because it is one of the crops projected to be most negatively impacted by climate change, and we focus on Mexico because it is one of the nations that will face some of the greatest negative impacts of climate change, it is the seventh largest producer of maize worldwide, and maize is critical for the nation’s food security. Previous studies have suggested that maize production has greatly transitioned across the country over the last several decades, with abandonment of production in some parts of the country and concentration in others. Simultaneously, farmers who continue to grow maize have altered their cropping strategies by expanding irrigation, adjusting planting time, altering tillage practices, and/or changing crop variety. To date it remains unclear why such transitions have occurred, and how climate change will impact these new maize landscapes in the future.
In this proposed project, we will use a combination of remote sensing, census data, and household surveys to understand transitions in Mexican maize production over the last 20 years. Specifically, we will (1) quantify maize transitions by mapping crop and landcover type across our study region from 2000 to the present. We will also examine if farmers have altered their maize planting strategies, by mapping maize sowing date, crop duration (an indicator of maize variety), tillage practices, and evapotranspiration (an indicator of irrigation use). We will (2) link these data products with climate, policy, and price data and use econometric regression methods to assess the causes of maize transitions over the last 20 years. Finally, we will (3) use crop model simulations and climate projections to assess whether these transitions are adaptive to future changes in climate.
Project Documents
Year | Authors | Type | Title |
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2022 | Publications | Mei, W., H. Wang, D. Fouhey, W. Zhou, I. Hinks, J. M. Gray, D. Van Berkel, M. Jain (2022). Using Deep Learning and Very High-Resolution Imagery to Map Smallholder Field Boundaries. Remote Sensing. 14(13): 3046. | |
2020 | NASA LCLUC Science Team Presentation | Policy, market, and climate change impacts on maize production in Mexico |