Agriculture Reference
In-Depth Information
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 ecologi-
cal well-being, and (3) resource availability.
Approaches for improving land management for the sustainability of crop pro-
duction 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 measure-
ments of crop yields, soil properties, biogeochemical fluxes, and relevant socioeco-
nomic 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 manage-
ment 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 pro-
vide 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 scien-
tific 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 cli-
mate, 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, car-
bon (C) and nitrogen (N) dynamics, and environmental performance. We describe
