Researchers with Agricultural & Resource Economics and Geographical Sciences are collaborating to develop decision-making tools for farmers to adapt to a changing climate and improve productivity
Image Credit: Edwin Remsberg
In a nearly $10 million project funded by the United States Department of Agriculture National Institute of Food and Agriculture (USDA-NIFA), UMD researchers are partnering with project lead Kansas State University (KSU), Oklahoma State University (OSU), and the USDA Agricultural Research Service (USDA-ARS) El Reno to improve soil health, nutrient and water use efficiencies, and overall agricultural productivity in the Southern Great Plains states. As a part of this multi-institutional consortium known as the Rainfed Agricultural Innovation Network (RAIN), the Department of Geographical Sciences in UMD’s College of Behavioral & Social Sciences are leading a modeling team that will develop a real-time decision making system for farmers based on conditions of the season, while Bob Chambers in the Department of Agricultural & Resources Economics in UMD’s College of Agriculture & Natural Resources will try to make sense of the recommendations and discuss implications for agricultural productivity.
“It’s an interesting phenomenon for productivity, because Kansas and Oklahoma have had massive drops in their measured productivity since 1960,” says Chambers. “In fact, they have gone from being essentially productivity leaders to productivity laggers in agriculture.”
This phenomenon is exactly what the entire RAIN project is designed to address, explains César Izaurralde, research professor with Geographical Sciences. “As a research network, we want to examine how to make improvements in nutrient and water use efficiency and try to improve the sustainability of agricultural production in that region, and at the same time try to improve total factor productivity in terms of what you put in and what you get out.”
Total factor productivity is unique in agriculture, and is a major focus of Chambers’ work. In other sectors, all inputs are controlled by the industry. But in agriculture, there are certain inputs provided by weather and climate that are not in the producer’s control, but that have significant impacts on productivity year to year. “It is driven by Mother Nature,” says Chambers, “and we want to be able to put those numbers into the calculations so that we can ultimately describe growth. It is hard to describe growth patterns when we haven’t been able to sort out weather patterns to determine what is actually growth and what isn’t.”
With weather patterns noticeably changing across regions, these questions are becoming even more important. Incorporating this data into the calculations is something that hasn’t been historically done, but is a major goal for Chambers. Due to his expertise in this field, UMD recently hosted an Organization for Economic Cooperation and Development (OECD) working group meeting in agriculture, gathering international experts from around the world to discuss the standardization of global agricultural productivity numbers. Chambers is continuing this work in this new collaboration hoping to make sense of the data and make predictions for future productivity in the Southern Great Plains region. “We need to know how to make the system in semi-arid regions like this more resilient to changing weather patterns so we can sustain and improve productivity,” says Izaurralde.
With this in mind, the project has a major goal of incorporating new emerging technology and monitoring techniques like ground and remote sensing to track crop production in real time and provide solutions to not only properly plan and strategize for the growing season, but also to adapt to changes in weather and other conditions. Researchers and Extension specialists from KSU, OSU, and USDA-ARS El Reno are gathering data in soil quality, soil fertility, soil physics, microbiology, agronomy, and meteorology to help inform best practices and uncover new strategies for animal and crop production that can work in the region. All of this information goes directly into the decision-making models that Izaurralde and Varaprasad Bandaru, associate research professor with Geographical Sciences, are developing.
“Usually we use historical data to calibrate our models in order to make predictions on crop yields and environmental performance under a variety of weather, technology, and policy scenarios. In this project, we want to use our models more proactively to try to get some near real-time predictions at field and regional scales,” says Izaurralde. “These new sensing and modeling tools will give farmers access to relevant agronomic information necessary to make real-time decisions. Integrating all these tools is where the best solutions will occur.”
The models are already being initialized with the conditions of the new field experiments in Kansas and Oklahoma and will be used to assess agricultural and soil conditions during the project to continue to improve their accuracy. “We are excited to be able to hand farmers something at the end of this project that can streamline their access to new agricultural technologies, improve and diversify production in the region, and ultimately allow them to plan and adapt their strategies throughout the season,” says Izaurralde.
“This work is truly multi-institutional and multi-disciplinary,” adds Chambers, “with physical and social scientists, agronomists and economists all looking at production across the Southern Plains from a variety of angles to figure out how to best improve productivity.”
For more information on this project, visit the Rainfed Agriculture Innovation Network (RAIN) at their website, www.rainfedag.org.