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Title: Systems approach critical to agroecosystems management

Author
item Sherrod, Lucretia
item Ahuja, Lajpat
item SCHIPANSKI, MEAGAN - Colorado State University
item PETERSON, GARY - Colorado State University

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/28/2015
Publication Date: 11/17/2015
Citation: Sherrod, L.A., Ahuja, L.R., Schipanski, M., Peterson, G.A. 2015. Systems approach critical to agroecosystems management. Poster session presented at the ASA-CSSA-SSSA Annual Meeting, Minneapolis, MN.

Interpretive Summary: Conventional tillage wheat-fallow is the still the predominate dryland cropping system in the Central Great Plains of the U.S. This system is vulnerable to excessive soil erosion which resulted in excessive organic matter loss. A no-till cropping systems experiment was established in 1985 to identify systems that maximize the use of precipitation and minimize the time in summer fallow by collecting data across gradients of 1)PET sites, 2)soils (across landscapes) and 3) cropping intensities. The 4th variable is time whereby the minimum length of the study is derived by the longest rotation in the study. Each phase of each cropping system is represented each year. Since the maximum rotation length is 4 years all systems are back to their starting phase in year 12 and 24, (1997 and 2009) of the study, which permitted a comprehensive evaluation of the annualized production for each of the cropping systems. By looking at the cropping system at various scales (landscape within PET sites, averaged over landscapes comparing PET sites, or averaged over all PET sites and landscapes) we can determine which systems have the highest yields and if they are influenced by one or more of these environmental factors. The time factor of this study allows for the comparison of wet vs. dry years and the ability to assess the yield potential and thus the economic sustainability of a system at a process level. These databases across gradients of climatic zones, gradients of soil productivity, and gradients of cropping intensity provides a data source to improve soil and crop models.

Technical Abstract: Sustainable dryland agriculture in the semi-arid Great Plains of the U.S. depends on achieving economic yields while maintaining soil resources. The traditional system of conventional tillage wheat-fallow was vulnerable to excessive soil erosion which resulted in excessive organic matter loss. No-till has allowed for increased cropping and less fallow. Optimizing the cropping system depends on environmental factors such as mean annual precipitation (MAP) and potential ET (PET) along with soil types working in concert with robust systems management. A long term cropping systems experiment was established in 1985 to identify systems that maximize the use of precipitation by collecting data across gradients of 1)PET sites, 2)soils (across landscapes) and 3) cropping intensities. The 4th variable is time whereby the minimum length of the study is derived by the longest rotation in the study. Each phase of each cropping system is represented each year. Since the maximum rotation length is 4 years all systems are back to their starting phase in year 12 and 24, (1997 and 2009) of the study, which permitted a comprehensive evaluation of the annualized production for each of the cropping systems. Systems can be compared within a PET site and soil or pooled soils within each location or over all locations and soils. This study has been designed to 1) determine if cropping system sequences with fewer summer fallow periods are feasible; 2) quantify the relationships among climate, soil type, and cropping sequences; 3) quantify the long-term effects of no-till on soil structural stability, microbial populations, and nutrient cycling along with organic C and N; 4) identify cropping systems that will minimize soil erosion; and 5) develop a data base across climatic zones that will allow the economic assessment of the different management systems and provide a long-term data source to improve soil and crop models.