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.

Agricultural production in the United States is undergoing marked changes due to rapid shifts in consumer demands, input costs, and concerns for food safety and environmental impact. Agricultural production systems are comprised of multidimensional components and drivers that interact in complex ways to influence production sustainability. In a mixed-methods approach, we combine qualitative and quantitative data to develop and simulate a system dynamics model that explores the systemic interaction of these drivers on the economic, environmental and social sustainability of agricultural production. We then use this model to evaluate the role of each driver in determining the differences in sustainability between three distinct production systems: crops only, livestock only, and an integrated crops and livestock system. The result from these modelling efforts found that the greatest potential for sustainability existed with the crops only production system. While this study presents a stand-alone contribution to sector knowledge and practice, it encourages future research in this sector that employs similar systems-based methods to enable more sustainable practices and policies within agricultural production

Technological advances have contributed to impressive yield gains and have greatly altered US agriculture. Selective breeding and directed molecular techniques address biological shortcomings of plants and animals and overcome environmental limitations. Improvements in mechanization, particularly of power sources and harvest equipment, reduce labor requirements and increase productivity and worker safety. Conservation systems, often designed to overcome problems introduced from other technologies, reduce negative impacts on soil and water and improve the environmental sustainability of production systems. Advances in information systems, largely developed in other disciplines and adapted to agriculture, are only beginning to impact US production practices. This paper is the fourth in the series of manuscripts exploring drivers of US agricultural systems. While development of technology is still largely driven by a need to address a problem, adoption is closely linked with other drivers of agricultural systems, most notably social, political and economic. Here, we explore the processes of innovation and adoption of technologies and how they have shaped agriculture. Technologies have increased yield and net output, and have also resulted in decreased control by producers, increased intensification, specialization and complexity of production, greater dependence on non-renewable resources, increased production inputs and hence decreased return, and an enhanced reliance on future technology. Future technologies will need to address emerging issues in land use, decline in work force and societal support of farming, global competition, changing social values in both taste and convenience of food, and increasing concerns for food safety and the environment. The challenge for farmers and researchers is to address these issues and develop technologies that balance the needs of producers with the expectations of society and create economically and environmentally sustainable production systems.

The temporally variable light environment of natural plant canopies presents distinct limitations to carbon assimilation, partially as a result of the photosynthetic induction requirement that develops when leaves are shaded. This study was undertaken with soybean (Glycine max L.) leaves to further identify factors contributing to the activation state of the fast component of induction during low photosynthetic photon flux density (PPFD) periods. Determination of pool sizes of carbon reduction cycle intermediates at low light and upon return to saturating light indicated that different limitations to photosynthetic activity arise over the time course of a 10-minute low PPFD period. Photosynthetic activity upon reillumination was limited by the regeneration of ribulose 1,5-P(2). There was an increase in the levels of fructose 1,6-P(2), sedoheptulose 1,7-P(2), triose-P, ribose 5-P, and ribulose 5-P pools, indicating inactivation of stromal enzymes, most notably fructose 1,6-bisphosphatase, sedoheptulose 1,7-bisphosphatase, and ribulose 5-P kinase. The fast-induction component was the most important factor limiting assimilation during rapid, brief light transients, during which the decay of the slow component was minimal. This may be particularly significant for upper leaves in soybean canopies that generally experience very rapid light transients.