Location: Natural Resource Management Research2009 Annual Report
1a. Objectives (from AD-416)
Objective 1: Determine the environmental and economic impacts of cover crop and cover crop mixtures in semiarid cropping systems. Objective 2: Develop dynamic cropping systems to help meet bio-energy production needs and increase economic returns while enhancing natural resource quality. Objective 3: Develop multiple enterprise systems that integrate crops and livestock to economically optimize the quality and quantity of agricultural products while maintaining or enhancing soil quality indicators. Objective 4: Develop and identify management principles common to integrated agricultural systems across production regions that reduce risks, improve competitiveness, and promote environmental stewardship. Objective 5: Understand the best ways new production technologies and management systems should be delivered so producers can more easily adopt them.
1b. Approach (from AD-416)
Multiple methodologies will be used depending on the specific objectives because of the complexity of this integrated agricultural systems research project. Objective 1 will use a modified crop matrix where different cover crops are seeded into a common residue to evaluate the above- and below-ground impact of cover crops on subsequent crops. Objective 2 will use small plot techniques, crop rotation, economic analysis, and modeling techniques to develop economically feasible management strategies for biofuels and an Eddy Covariance System to measure CO2 flux as a surrogate for environmental impact of biomass crops. Integration of crops and livestock, Objective 3, will compare the performance of livestock when grazing annual crops in the fall to livestock performance when grazing perennial grasses in the fall. In the first 3 objectives, common data collected will include soil properties, biomass accumulation and soil water use. In addition, data on the impact of bio-char will be collected in Objective 2 and livestock production data will be collected in Objective 3. Economic analysis will be conducted as appropriate. The approaches for objectives 4 and 5 are not field based. Objective 4 will continue an existing roundtable that brings national and international leaders together to share ideas, concepts, and philosophies of integrated systems. Objective 5 will use a non-ARS collaborator to conduct surveys and coordinate with ARS to develop metrics that enhance the adoption of integrated agricultural technology by producers.
3. Progress Report
Objective 1: • Conducted late-summer seeding of cover crop treatments into dry pea stubble. Documented cover crop establishment, aboveground biomass production, water use, and nutritional quality for all treatments. Soil samples collected and processed from select treatments prior to seeding and again at peak cover crop biomass to document change in soil attributes. Objective 2: • Field plots were established for all sub-objectives. Pot culture experiment for bio-char (Sub-objective 2.1) will be initiated in August (FY09). Perennial crops established for phase 1 of Sub-objective 2.2, and establishment and yield data collected. Perennial crop (alfalfa) established for Sub-objective 2.3. Plots established and simulations run for Sub-objectives 2.4 and 2.5. Objective 3: • Livestock and agronomic data collected for Sub-objectives 3.1 and 3.2. Production costs determined for Sub-objective 3.3. Objective 4: • Fourth meeting in workshop series held. Planning initiated for 5th meeting. Information regarding drivers in Southeastern U.S. agricultural production systems has been published as a book chapter and a comparison between production systems in the Southeastern and Northeastern U.S. is being developed into a peer-reviewed manuscript. Objective 5: • Collaboration is continuing between ARS locations in Watkinsville, GA, Mandan, ND, and North Carolina State University on developing a metric to measure customer use of ARS technology.
1. Five year on-farm study documents significant increases in soil carbon under switchgrass. Environmental outcomes associated with switchgrass production require information on net greenhouse gas emissions, of which data on soil carbon change is a key component. To date, nearly all information on soil carbon change under switchgrass have been based on either modeled assumptions or small plot research. A five year on-farm study was conducted to determine change in soil carbon on 10 switchgrass fields in North Dakota, South Dakota, and Nebraska. Across sites, soil carbon increased significantly at the 0-1 ft and 0-4 ft soil depths, with accrual rates of 0.5 and 1.3 ton C/ac/yr, respectively. This study underscored the potential of switchgrass to serve as a carbon-negative bioenergy crop within the central and northern Great Plains.
2. Corn-soybean tillage economics. While no-till and minimum tillage systems could provide environmental benefits, use of these practices for corn and soybean production in the Northern Corn Belt has been limited due to the perception that they are less profitable than conventional tillage practices. Economic analysis was conducted at the Northern Great Plains Research Laboratory using data from a 7-year field study comparing 8 different tillage practices conducted near Morris, MN by researchers at the North Central Soil Conservation Research Laboratory. This research showed that average net returns could be increased by as much as $37 per acre by switching from moldboard plow tillage to a minimum tillage fall residue management system. Furthermore, all but one of the minimum tillage and no-till systems evaluated were less risky than the two conventional tillage systems commonly used in the area. This research provides information to producers that minimum tillage and no-till systems can be economically viable alternatives to conventional tillage practices, and could lead to greater adoption of these systems in the northern Corn Belt.
3. Drivers of Agricultural Production Systems. Understanding factors (drivers) that influence the structure of agricultural production systems is important for producers, researchers and policy makers. ARS scientists from four different locations are holding producer orientated workshops in different regions of the country to identify ‘Principles of Integrated Agricultural Systems’. One of the major outcomes, of the four workshops which have been held to this point, is identification of social, economic, technological and environmental drivers of agricultural systems. Identification of these drivers provides producers, researchers and policy makers the ability to identify factors that shape agricultural systems, a way to evaluate the impact different drivers have on shaping agricultural systems and a means to initiate assessment of the future direction of their agricultural system,
4. Land Allocation Model (LAM). One of the most basic and expensive resources in agricultural enterprises is land. Optimally allocating this valuable resource among different enterprises can be difficult and has substantial impacts on profitability and sustainability of the production system. LAM is the Land Allocation Model, a matrix model developed by the Northern Great Plains Research Laboratory, USDA-ARS to evaluate factors that drive land allocation decisions. The model utilizes producer derived information to evaluate environmental, economic, production and producer preference factors that underlie land allocation decisions between enterprises. The model utilizes information that is relevant and easily accessed by producers and ARS is currently working on developing an Excel spreadsheet version of the model. Although the current matrix only incorporates four enterprises, the matrix was designed so that additional benefits and parameters could be included. The model provides a means for producers to optimize land allocation, evaluate factors that impact land allocation and calculate specific net returns that may shift land allocation between enterprises.
5. Stella Model to Test Agricultural Sustainability. One of the major challenges for agriculture is to develop sustainable production systems. However, testing the sustainability of different production systems at a field scale is difficult because of enormous resource requirements. The Northern Great Plains Research Laboratory collaborated with scientists at other locations to develop a computer model in a Stella® modeling environment that compared the sustainability of conventional crop system, an extensive range livestock system and an integrated crop-livestock system. Different indicators were developed for the social, economics and environmental aspects of sustainability. Each of the systems affected the indicators differently illustrating that there is no single best system to enhance agricultural sustainability. The model gives us the opportunity to explore interactions within various production systems, and determine the relative impact of management inputs on indices.Liebig, M.A., Schmer, M.R., Vogel, K.P., Mitchell, R. 2008. Soil Carbon Storage by Switchgrass Grown for Bioenergy. BioEnergy Research. 1:215-222.