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United States Department of Agriculture

Agricultural Research Service

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Research Project: Developing Sustainable 'IN-ROW' Management Practices for Weed Management and Soil Nitrogen Retention

Location: Crops Pathology and Genetics Research

2013 Annual Report


1a.Objectives (from AD-416):
Develop alternative "in-row" weed management practices that both provide effective weed management, do not negatively impact grapevine growth and juice, and improve soil nitrogen (N) retention by minimizing inorganic N leaching and nitrous oxide (N2O) emissions.


1b.Approach (from AD-416):
Establish three 'in-row' weed management practices: 1)a standard herbicide application; 2)an under-the-vine cover crop, and 3)furrow cultivation with compost. We will focus on surveying changes in both weed seedbank and weed community compostion, measure annual soil N leaching and N2O emissions, and address peak periods of N2O loss during fertigation. This project is part of GRACEnet, a national network of USDA/ARS researchers contributing data from studies on land use and greenhouse gas emissions to develop biogeochemical models and best management practices to minimize N loss from agricultural systems.


3.Progress Report:

The agreement was established in support of objective 4 of the in-house project, the goal being to investigate the impacts of vineyard practices on soil microbial ecology. The project's goal is to develop "in-row" weed management practices that both provide effective weed management, do not negatively impact grapevine growth and juice, and improve soil nitrogen retention by minimizing inorganic nitrogen leaching and nitrous oxide emissions.

The biweekly measurements for inorganic nitrogen pools, soil water content and temperature, and greenhouse gas emissions were completed. All soil samples have been assessed for nutrient content. Weed communities, grape yield, harvest characteristics and pruning biomass were characterized. Necessary reports were issued to the funding agency. The data are currently being analyzed. Findings indicate ‘pulse’ events such as cultivation, compost addition, fertigation, irrigation and rainfall are major periods of greenhouse gas emissions. When cultivation occurred just after compost addition, nitrous oxide (N2O) and carbon dioxide (CO2) emissions increased and were greater than the cover crop and herbicide treatments. When rainfall occurred immediately after this cultivation, N2O and CO2 emissions were also greater than the other treatments, highlighting the interactive effects of management (i.e., cultivation and compost) and rainfall on greenhouse gas emissions. When treatments were irrigated, both N2O and CO2 were greatest from the cultivated treatment, followed by the cover crop and herbicide treatments, respectively. Although the cultivated treatment emitted more greenhouse gases (GHGs), further analysis will determine the net carbon footprint of each respective treatment. The cover crop and herbicide treatments tended to have greater nitrate leaching than the cultivated treatment. Grape juice characteristics such as pH, titratable acidity, yield, and total soluble solids did not differ among treatments. Temporal dynamics of leaching differed among treatments, suggesting management practices could be adjusted over time to minimize these losses. Data from this study will be incorporated into the GRACEnet (Greenhouse gas Reduction through Agricultural Carbon Enhancement network) database to develop predictive models of greenhouse gas emissions in response to agricultural management. Furthermore, findings from this study will be included in the development of an interactive process model for grower use assessing GHG emissions associated with different management practices, a project that was awarded to the California Sustainable Winegrowing Alliance by California Department of Food and Agriculture (CDFA) Specialty Crops Block Grant (SCBG), "Field Testing a Carbon Offset and Greenhouse Gas Emissions Model for California Wine Grape Growers to Drive Climate Protection and Innovation”. The main focus in FY2013 was to adapt published procedures for both quality control of GHG data and subsequent model selection, and then create a publicly available model in R programming language to streamline data analysis and GHG emissions models. The progress made on this project has been adequate and meets all expected milestones, as agreed upon with the funding body.


Last Modified: 10/25/2014
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