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Grasslands have an underground biomass component that serves as a carbon (C) storage sink. Switchgrass (Panicum virgatum L.) has potential as a biofuel crop. Our objectives were to determine biomass and C partitioning in aboveground and belowground plant components and changes in soil organic C in switchgrass. Cultivars Sunburst and Dacotah were field grown over 3 yr at Mandan, ND. Aboveground biomass was sampled and separated into leaves, stems, senesced, and litter biomass. Root biomass to 1.1‐m depth and soil organic C to 0.9‐m depth was determined. Soil C loss from respiratory processes was determined by measuring CO2 flux from early May to late October. At seed ripe harvest, stem biomass accounted for 46% of total aboveground biomass, leaves 7%, senesced plant parts 43%, and litter 4%. Excluding crowns, root biomass averaged 27% of the total plant biomass and 84% when crown tissue was included with root biomass. Carbon partitioning among aboveground, crown, and root biomass showed that crown tissue contained approximately 50% of the total biomass C. Regression analysis indicated that soil organic C to 0.9‐m depth increased at the rate of 1.01 kg C m−2 yr−1 Carbon lost through soil respiration processes was equal to 44% of the C content of the total plant biomass. Although an amount equal to nearly half of the C captured in plant biomass during a year is lost through soil respiration, these results suggest that northern Great Plains switchgrass plantings have potential for storing a significant quantity of soil C.

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Techniques to reliably calibrate computer models are needed before the models can be applied to help solve natural resource problems. The USDA‐ARS Root Zone Water Quality Model (RZWQM) is a comprehensive simulation model designed to predict hydrologic and chemical response, including potential for ground‐water contamination, of agricultural management systems. RZWQM Version 3.2 was calibrated and evaluated at sites in Iowa, Minnesota, Missouri, Nebraska, and Ohio as part of the Management Systems Evaluation Areas (MSEA) project and at a site near Sterling in northeastern Colorado. Soil horizon description and a description of the physical and hydraulic properties of the soil were required to initialize the model. Calibration for nutrient cycling involved adjusting the model coefficients for mineralization, infiltration, and denitrification. Initial N pool sizes were estimated using medium to long‐term computer simulations. Maximum N uptake rate, plant respiration, specific leaf area, and the effect of age at the time of propagule development and senescence were used to calibrate the plant production and yield component. To match the observed results for soil water, N, and plant growth, an iterative approach for calibrating the model was followed. When done methodically, total biomass estimates were within 5%, yield estimates were within l0%, and N uptake was within 20% of field measurements. Calibration of the C and N dynamics module produced results that were generally within 20 to 50 kg ha−1 of measured values for soil profile NO−3‐N. Independent evaluations of the calibrated model focused on four indicator output variables related to plant growth—total biomass, yield, N uptake, and N in the soil profile. Predictions matched the observed data in most cases. The crop model is very sensitive to plant N content. Even small errors in simulating N uptake levels can result in substantial errors in estimates of yield and total aboveground biomass. The model predicted biomass and yield well on irrigated and most dryland management systems and adequately simulated crop variables at various positions along the landscape.

Agriculture has been very successful in addressing the food and fiber needs of today's world population. However, there are increasing concerns about the economic, environmental and social costs of this success. Integrated agricultural systems may provide a means to address these concerns while increasing sustainability. This paper reviews the potential for and challenges to integrated agricultural systems, evaluates different agricultural systems in a hierarchical systems framework, and provides definitions and examples for each of the systems. This paper also describes the concept of dynamic-integrated agricultural systems and calls for the development of principles to use in developing and researching integrated agricultural systems. The concepts in this paper have arisen from the first in a series of planned workshops to organize common principles, criteria and indicators across physiographic regions in integrated agricultural systems. Integrated agricultural systems have multiple enterprises that interact in space and time, resulting in a synergistic resource transfer among enterprises. Dynamic-integrated agricultural systems have multiple enterprises managed in a dynamic manner. The key difference between dynamic-integrated agricultural systems and integrated agricultural systems is in management philosophy. In an integrated agricultural system, management decisions, such as type and amount of commodities to produce, are predetermined. In a dynamic-integrated system, decisions are made at the most opportune time using the best available knowledge. We developed a hierarchical scheme for agricultural systems ranging from basic agricultural production systems, which are the simplest system with no resource flow between enterprises, to dynamic-integrated agricultural systems. As agricultural systems move up in the hierarchy, their complexity, amount of management needed, and sustainability also increases. A key aspect of sustainability is the ability to adapt to future challenges. We argue that sustainable systems need built-in flexibility to achieve this goal.

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yield of 9.0 Mg ha Ϫ1 , and sustained yields this great on dryland sites have not been documented in the study Switchgrass (Panicum virgatum L.) has been identified as a potenarea.