1st International Congress on Environmental Modelling and Software - Lugano, Switzerland - June 2002
Keywords
decision support systems, modeling, parameterization, plant genotype
Start Date
1-7-2002 12:00 AM
Abstract
Few farmers and ranchers adopt agricultural software such as decision support systems (DSS). While numerous decision aids are available, most are too difficult for producers to use, exclude components (e.g., economic budgeting, weeds, multicriteria decision analysis) necessary for meaningful use on farms and ranches, and usually suffer from poor understanding by scientists of producer needs and how they process information. The USDA-ARS Great Plains Framework for Agricultural Resource Management (GPFARM) decision support system has been developed that integrates a graphical user interface, data from farms and ranches, soil-plant-weed-water-N-erosion simulation modules, an economic analysis package, and a multicriteria decision making (MCDM) toolbox. The purpose is to assist U.S. Great Plains producers in selecting alternative management scenarios for whole farm and ranch systems that are economically viable and environmentally sound. A major user requirement for GPFARM is to make the DSS as easy and quick to set up and use as possible. This means that plant parameters must be supplied to the user. Developing this parameter database for a large regional area differing in climate, soils, and management practices is made very difficult both by the known genotype by environment interaction (G X E) and the uncertainty in the variability (and distribution) of most parameters. This paper addresses the work, and complications, of creating a crop parameter database focusing on winter wheat (Triticum aestivum L.). One important plant parameter (thermal time from sowing to maturity) and predicting grain yield (the result of the entire parameter database) are both examined from the perspective of the G X E interaction. Some conclusions drawn from this analysis are: 1) for both thermal time and yield, the relative rankings of varieties were not consistent whether considering within or between treatments across years, showing the difficulty of simulating the G X E interaction, and 2) selected parameters must be set for at least dryland and irrigated conditions to better capture the G X E interaction.
Parameterizing GPFARM: An Agricultural Decision Support System for Integrating Science, Economics, Resource Use, and Environmental Impacts
Few farmers and ranchers adopt agricultural software such as decision support systems (DSS). While numerous decision aids are available, most are too difficult for producers to use, exclude components (e.g., economic budgeting, weeds, multicriteria decision analysis) necessary for meaningful use on farms and ranches, and usually suffer from poor understanding by scientists of producer needs and how they process information. The USDA-ARS Great Plains Framework for Agricultural Resource Management (GPFARM) decision support system has been developed that integrates a graphical user interface, data from farms and ranches, soil-plant-weed-water-N-erosion simulation modules, an economic analysis package, and a multicriteria decision making (MCDM) toolbox. The purpose is to assist U.S. Great Plains producers in selecting alternative management scenarios for whole farm and ranch systems that are economically viable and environmentally sound. A major user requirement for GPFARM is to make the DSS as easy and quick to set up and use as possible. This means that plant parameters must be supplied to the user. Developing this parameter database for a large regional area differing in climate, soils, and management practices is made very difficult both by the known genotype by environment interaction (G X E) and the uncertainty in the variability (and distribution) of most parameters. This paper addresses the work, and complications, of creating a crop parameter database focusing on winter wheat (Triticum aestivum L.). One important plant parameter (thermal time from sowing to maturity) and predicting grain yield (the result of the entire parameter database) are both examined from the perspective of the G X E interaction. Some conclusions drawn from this analysis are: 1) for both thermal time and yield, the relative rankings of varieties were not consistent whether considering within or between treatments across years, showing the difficulty of simulating the G X E interaction, and 2) selected parameters must be set for at least dryland and irrigated conditions to better capture the G X E interaction.
