Authors: Danika Mosher*, East Tennessee State University, Monica Yeliss Ayala, East Tennessee State University, April D. Bledsoe, East Tennessee State University, Mitchell S Ogden, East Tennessee State University, T. Andrew Joyner, East Tennessee State University, Ingrid E. Luffman, East Tennessee State University
Topics: Natural Resources, Agricultural Geography, Mountain Environments
Keywords: coffee, species distribution modeling, agroforestry, climate change, Central America, coffee rust
Session Type: Poster
Start / End Time: 8:00 AM / 9:40 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download
Coffea arabica has far-reaching impacts on the societies of today by affecting the productivity of workers and the global economy. However, many issues threaten the strength of the coffee industry at its source: the growing regions. In Guatemala, a country where coffee-growing is a significant economic driver, climate change impacts farmers resulting in losses in crop yield and arable land. To mitigate these effects, species distribution modeling (SDM) can identify areas presently suitable for coffee growing and project suitability into the future using climate change projection models known as representative concentration pathways (RCPs). Along with determining the coffee plant suitability in Guatemala, the potential to apply sustainable agroforestry techniques must be considered. Introducing shade trees and alley cropping to farms has benefits, including added supplemental income and food as well as replenished soil nutrients. Comparison of present coffee plant distribution to future possible distribution alongside distribution for shade trees, such as Inga vera and Musa paradisiaca, can provide useful information for farm management and climate change mitigation.