An official website of the United States government.
Our data sets will provide information on the potential for diverse organic systems to improve soil quality and manage carbon (C) and nitrogen (N). This research will allow farmers to reduce greenhouse gases (GHG)s and manage nutrient cycling to enhance soil fertility while providing ecosystem services. Emissions data, coupled with C and N inputs, fossil fuel use, and emissions estimates from livestock will enhance GHG models for farming systems. <P>Our long-term goal is to have farmers adopt management practices that integrate cover crops, tillage practices, organic amendments and livestock to improve soil quality, utilize nitrogen efficiently and reduce greenhouse gas emissions from soil and farm machinery.<P> Supporting objectives include: 1. Quantify and model GHG emissions and C sequestration in long-term and reduced tillage organic systems with varying manure application, crop rotation, and tillage intensity. 2. Identify and quantify, for example, the keystone microbial community members that control nitrification and denitrification in different organic farming systems. 3. Facilitate and evaluate the adoption of organic management practices that restore, maintain and enhance soil quality and contribute to climate change mitigation. 4. Via this collaboration between WSU and Purdue University, we will establish a national research and education program centered on dissemination of information on the importance of soil biology in organic production systems. <P>This research will be disseminated via refereed journals, presentations at professional meetings, farmer field days, teaching modules and extension materials. Research will be evaluated by our stakeholders.
Non-Technical Summary: <BR>Critical knowledge gaps exist in on-farm and basic research that include the identification of best management practices (BMP)s for retention of carbon (C) and nitrogen(N)inputs from plant and animal amendments in soil and adaption of these identified BMPs across a range of climatic conditions. Timing of field operations, tillage, and the selection of crops and amendments all have a critical effect on nutrient cycling, yields and greenhouse gas emissions (GHG). Biological indicators of soil quality such as C sequestration can be used as a metric to compare a range of management practices that enhance soil conservation and contribute to climate change mitigation. Despite the potential to use soil quality as a dynamic measure, there are few soil quality data sets available that compare different organic management systems. We will compare five organic cropping systems, providing a unique opportunity to study the effects of key management practices (tillage intensity, amendment type, and livestock integration) on GHG emissions, C sequestration and cycling of N and C. Organic systems are reliant upon mineralization of N from organic sources for fertility. The process of mineralization is microbially driven and leads to additional microbial processes, nitrification and denitrification, which produce nitrous oxide (N2O). Therefore, we will quantify and identify microorganisms that control nitrification and denitrification. Agricultural systems are a source of GHGs: carbon dioxide (CO2), N2O, and methane (CH4). Best management practices that sequester C and improve nitrogen use efficiency in organic systems will reduce emissions of CO2 and N2O. The majority of CH4 in agricultural systems comes from livestock production. Therefore, integration of livestock reduces the need for off-farm sources of fertility but requires BMPs that reduces the potential for loss of CH4 and N2O from livestock. Our research will provide farmers, researchers and the public with information on the potential of diverse organic systems to improve soil quality, reduce GHG emissions, and enhance nutrient cycling while providing ecosystem services. Our data sets on emissions, coupled with C and N inputs from amendments and cover crops, fossil fuel use in farming systems, and estimated emissions from livestock will bolster GHG emissions models for farming systems. Our long-term goal is to have farmers adopt management practices that integrate cover crops, tillage practices, organic amendments and livestock to improve soil quality, utilize nitrogen efficiently and reduce greenhouse gas emissions from soil and farm machinery. <P> Approach: <BR> Gas samples will be taken from the Long-term Organic Vegetable Systems, Organic Reduced Tillage Experiments and the Purdue Organic Cover Crop studies using an infrared gas analyzer (IRGA, LI-COR 7100). Nitrous oxide rates will be taken throughout the growing season to coincide with major field operations. An additional set of N2O measurements will be taken after temperatures are below 0?C when the top 3-5 cm of soil freezes. Thaw events will be sampled when temperatures have warmed enough to thaw the surface soil. We will sample a minimum of two freeze thaw cycles to determine if N2O flux decreases after the first freeze thaw event. Surface carbon dioxide efflux will also be measured. Soil samples will be taken to a 30 cm depth for analysis of inorganic N, and nitrifier/denitrifier community analysis using qPCR and 454 sequencing. We will estimate carbon sequestration and turnover in each organic management system. The mean duration of C sequestration after the adaption of a best management practice such as integration of cover crops will be estimated as the percentage change in the annual rate of soil organic C. A, 350 d laboratory incubation will be conducted to estimate the portion of total soil C in each of three C pools: active, slow, and resistant and the turnover rate of each pool. Soils for the laboratory incubation will be sampled randomly to a 30-cm depth. A three pool nonlinear model will be fit in SAS NLIN to estimate the size of the active and slow pools of C and their turnover rates. Acid hydrolysis will be used to estimate the resistant pool of carbon. Based on exploratory data analysis we will use appropriate transformatiosn for running mixed models (PROC MIXED; SAS 2002) or consider non-parametric tools such as classification and regression trees. In order to tie signi?cant differences in the soil chemical and gas flux properties to the relative abundance of bacterial and archaea taxa within a treatment, we will use repeated measures ANOVAs followed by a Tukey's HSD post-hoc test. Because of the complex nature of the data, for analysis we will use a method where data are considered for their level of normality and transformed as needed prior to any statistical analysis. Means by which research and extension activities will be monitored. Implementation of a strong project and data management plan will ensure accomplishment of proposal objectives and milestones. The lead PD, Ann-Marie Fortuna will provide oversight and administer the project including budget coordination and project reporting. She will hold quarterly project update meetings via conference call or interaction via the web. In the 3rd year, a comprehensive survey and evaluation of all participants (n?200) will be performed by WSU's Social and Economic Sciences Research Center. The survey questions will focus on knowledge gained with respect to greenhouse gas emissions from farm practices as well as any management changes made during the course of the project.