Author
Steiner, Jean | |
Coleman, Samuel | |
Starks, Patrick | |
Garbrecht, Jurgen |
Submitted to: Southern Conservation Agricultural Systems Conference
Publication Type: Abstract Only Publication Acceptance Date: 10/31/2012 Publication Date: N/A Citation: N/A Interpretive Summary: Abstract only. Technical Abstract: The USDA Agricultural Research Service (ARS) is coordinating ten well-established research sites as a Long Term Agro-ecosystem Research (LTAR) Network. The goal of the LTAR is to sustain a land-based infrastructure for research, environmental management testing, and education, that enables understanding and forecasting of the nation’s capacity to provide agricultural commodities and other ecosystem goods and services under ever-changing environmental and resource-use conditions. The LTAR site for the Southern Plains will be led by the Grazinglands Research Laboratory, El Reno, Oklahoma. Resilience of Great Plains agricultural systems under variable climate conditions is essential. Research across the spectrum of cropland, pastureland, and prairie that is characteristic of the Great Plains is needed to identify sustainable forage-based production systems that are adaptable across enterprise types, from large-scale commercial livestock operations that dominate production and economics to small farms that dominate the landscape, particularly in the southeastern portions of the region. Anticipated research impacts include production systems that support vibrant rural economies, promote biological diversity (soil, plant, and animal), reduce greenhouse gas emissions, and increase soil organic matter, with corresponding positive impacts on carbon sequestration, water and air quality and agricultural sustainability. Developing knowledge and tools to support the diverse agricultural systems that comprise the fabric of the Southern Plains (SP) landscape in the face of complex interactive climate, policy, and economics drivers requires transdisciplinary science conducted over decades to provide understanding that is scalable in time and space. |