Authors: Bruno Basso and Joe T. Ritchie
The Green Revolution, through the adoption of new crop varieties, irrigation, and agrochemicals, saved about 1 billion people from famine by increasing global food production (FAO 2011). We now recognize that these enormous gains in agricultural production were accompanied by harm to agriculture’s natural resource base, jeopardizing our future ability to meet human food, fuel, and fiber needs for a growing population. Earth’s population is projected to increase from ~7 billion in 2011 to ~9 billion in 2050. Given the future challenges to food production and environmental integrity, it is imperative that sustainable land management of agricultural production become an important priority for policy makers. Agricultural crop and soil management practices often cause degradation of the environment, especially the quality of ground and surface water and the fertility of agricultural soils. Clearly, a sustainable framework for developing and improving land use for crop production must be based on long-term and broad-based perspectives.
Sustainable land management is the focus of many research programs, ranging from socioeconomic to ecological, since sustainability is an integrated concept with associated challenges. A multiplicity of factors can prevent production systems from being sustainable; the goals set by a sustainable crop production system may be in conflict with one another, and solutions that work in one site or region with a particular soil, climatic, and socioeconomic setting may not be appropriate in others (Robertson and Harwood 2013). On the other hand, with sufficient attention to indicators of sustainability, a number of practices and policies could be implemented to accelerate the adoption of sustainable practices. Indicators to quantify changes in crop production systems over time at different hierarchical levels are needed for evaluating the sustainability of different land management strategies. Indicators should encompass (1) crop productivity, (2) socioeconomic and ecological well-being, and (3) resource availability.
Approaches for improving land management for the sustainability of crop production should be based on reduced chemical inputs, as well as higher resource use efficiency, enhanced nutrient cycling, and integrated pest management. Modeling is necessary to identify the best approaches because field experiments cannot be conducted with sufficient detail in space and time to find the best land management practices for sustainable crop production across diverse environmental settings. Input from long-term crop and soil management experiments, including measurements of crop yields, soil properties, biogeochemical fluxes, and relevant socioeconomic indicators, is critical to develop and test the models.
Simulation models, when suitably tested in reasonably diverse locations over sufficient time periods, provide a useful tool for finding combinations of management strategies to reach the multiple goals required for sustainable crop production. Models can provide land managers and policy makers with ways to extrapolate experimental results from one location to others where soil, landscape, and climate information is available. When biophysical simulation model results are combined with socioeconomic information, a Decision Support System (DSS) can provide management options for maximizing sustainability goals. Decision Support Systems describe a wide range of computer software programs designed to make site-specific recommendations for pest management (Michalski et al. 1983, Beck et al. 1989), farm financial planning (Boggess et al. 1989), and general crop and land management (Plant 1989). Decision Support System software packages have been designed primarily for use by crop consultants and other specialists who work with farmers and policy makers, although some are used directly by farmers. Users provide site-specific information about soil properties, crop type and management, weather conditions, and other data specific to the software. Typically, a given DSS provides a variety of management options to reach desired sustainability goals.
Process-based models of crop growth and development are integral parts of the most effective DSS models and have been developed and used for more than 40 years, since the advent of high-speed computers. During this time, two scientific teams have integrated such models into DSSs, namely, DSSAT (Tsuji et al. 1998) and APSIM (McCown et al. 1996), and both have proven useful for many groups involved in agricultural research and decision making throughout the world. The International Consortium of Agricultural Systems Applications (ICASA) was formed from several modeling groups to promote the efficient and effective use of functional models for problem solving and decision making (Ritchie 1995). Crop models that simulate crop growth, the timing of critical growth stages, and grain yields have added soil and plant carbon and nitrogen dynamics for different climate, soil, and management conditions (e.g., Parton et al. 1988).
Here, we provide a general overview of crop simulation models followed by a concise description of the model Systems Approach for Land Use Sustainability (SALUS) for evaluating the impact of agronomic management on crop yields, carbon (C) and nitrogen (N) dynamics, and environmental performance. We describe key model components and the minimum data required for simulating crop yields under different management practices. Research at the Kellogg Biological Station Long-Term Ecological Research Site (KBS LTER) provides the opportunity to test models of long-term changes in soil carbon, nitrogen leaching, crop yields, and gaseous emissions from soil. Data from KBS LTER also provide an excellent context for illustrating the utility and limitations of crop models, and we use these data to show two examples of model applications: (1) an evaluation of nitrate leaching as affected by nitrogen fertilizer management in a corn (Zea mays L.) and alfalfa (Medicago sativa L.) rotation and (2) soil carbon dynamics under various tillage systems. We also illustrate spatially connected processes by linking SALUS to digital terrain modeling.
- Basso, B. & Ritchie, J. T. Simulating crop growth and biogeochemical fluxes in response to land management using the SALUS model. in The Ecology of Agricultural Landscapes: Long-Term Research on the Path to Sustainability. (eds Hamilton, S. et al.) 252–274 (Oxford University Press, 2015).