Hostname: page-component-77c89778f8-5wvtr Total loading time: 0 Render date: 2024-07-23T16:09:08.900Z Has data issue: false hasContentIssue false

GIS and simulation technologies for assessing cropping systems management in dry environments

Published online by Cambridge University Press:  30 October 2009

Claudio O. Stockle
Affiliation:
Associate Professor, Biological Systems Engineering Department, Washington State University, Pullman, WA 99164-6120.
Get access

Abstract

The long-term productivity, sustainability, and environmental impact of cropping systems cannot be assessed adequately using conventional agronomic experiments. This pap erdiscusses the use of computer-based technologies as support tools for this type of assessment, including crop growth simulation models, weather generators, geographical information systems, and risk assessment and economic models. Comprehensive systems that integrate these technologies are just emerging, offering great potential for the analysis of agricultural systems in the future.

Type
Selected Papers from the U.S.-Middle East Conference on Sustainable Dryland Agriculture
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Badini, O. 1994. Assessment of agroclimatologic variability and crop production risk in Burkina Faso. M.S. thesis. Program in Environmental Sciences and Regional Planning, Washington State Univ., Pullman.Google Scholar
2.Boote, K.J., Jones, J.W., Hoogenboom, G., Wilkerson, G.G., and Jagtap, S.S.. 1989. PNUTGRO, peanut crop growth simulation model. Journal No. 8420. Florida Agric. Exp. Sta., Gainesville.Google Scholar
3.Burrough, P.A. 1986. Principles of Geographical Information Systems for Land Resources Assessment. Clarendon Press, Oxford, England.CrossRefGoogle Scholar
4.Cabelguenne, M., and Debaeke, R.. 1995. Manuel d'utilisation du modèle EWQTPR (EPIC-Phase TEMPS REEL). Institut National de la Recherche Agronomique, Station d'Agronomie, Toulouse/Auzville, France.Google Scholar
5.Campbell, G.S. 1990. CLIMGEN, weather generator software. Dept. of Crop and Soil Sci., Washington State Univ., Pullman.Google Scholar
6.Deybe, D. and Stockle, C.O.. 1992. Methodological framework for analyzing production and resource management alternatives for sustainability of agricultural watersheds in developing countries. Agric. Economics Staff Paper A.E 92–2. Dept. of Agric. Economics, Washington State Univ., Pullman.Google Scholar
7.Hoogenboom, G., White, J.W., Jones, J.W., and Boote, K.J.. 1991. BEANGRO, dry bean crop growth simulation model. Journal No. N-00379. Florida Agric. Exp. Sta., Gainesville.Google Scholar
8.Jackson, B.S., Arkin, G.F., and Hearn, A.B.. 1990. COTTAM, a cotton plant simulation model for an IBM PC microcomputer. MP 1685. Texas Agric. Exp. Sta., College Station.Google Scholar
9.Jones, C.A., and Kiniry, J.R. (eds). 1986. CERES-Maize, a Simulation Model of Maize Growth and Development. Texas A&M Univ. Press, College Station.Google Scholar
10.Jones, J.W., Boote, K.J., Hoogenboom, G., Jagtap, S.S., and Wilkerson, G.G.. 1989. SOYGRO, soybean crop growth simulation model. Journal No. 8304. Florida Agric. Exp. Sta., Gainesville.Google Scholar
11.Kropff, M.J., and van Laar, H.H. (eds). 1993. Modelling Crop-Weed Interactions. CAB International, Wallingford, England.Google Scholar
12.Ndlovu, L.S. 1994. Development and evaluation of a weather generator for crop simulation models. Ph.D. dissertation. Dept. of Biological Systems Engineering, Washington State Univ., Pullman.Google Scholar
13.Ndlovu, L.S., Stockle, C.O., Campbell, G.S., and Saxton, K.E.. 1993. Estimating evapotranspiration using generated weather data. ASAE Paper No. 93-2586. Amer. Soc. Agric. Engineers, St. Joseph, Michigan.Google Scholar
14.Pala, M., Stockle, C.O., and Harris, H., (in press). Validation of CropSyst for two durum wheat cultivars under different water and nitrogen regimes in a Mediterranean type of environment. Agricultural Systems.Google Scholar
15.Richardson, C.W., and Wright, D.A.. 1984. WGEN: A model for generating daily weather variables. ARS-8. U.S. Dept. of Agric., Agric. Research Service, USDA National Technical Information Service, Springfield, Virginia.Google Scholar
16.Rosenthal, W.D., Vanderlip, R.L., Jackson, B.S., and Arkin, G.F.. 1989. SORKAM, a grain sorghum crop growth model. MP 1669. Texas Agric. Exp. Sta., College Station.Google Scholar
17.Sharpley, A.N., and Williams, J.R. (eds). 1990. EPIC, Erosion/Productivity Impact Calculator: 1. Model Documentation. Technical Bull. No. 1768. U.S. Dept. of Agric., Temple, Texas.Google Scholar
18.Stapper, M. 1984. SIMTAG, a simulation model of wheat genotypes. Dept. of Agronomy and Soil Sci., Univ. of New England, Armidale, Australia, and International Center for Agricultural Research in the Dry Areas, Aleppo, Syria.Google Scholar
19.Stockle, C.O., and Nelson, R.. 1994. CropSyst, cropping systems simulation model. User's Manual. Dept. of Biological Systems Engineering, Washington State Univ., Pullman.Google Scholar
20.Stockle, C.O., Martin, S.A., and Campbell, O.S.. 1994. CropSyst, a cropping systems simulation model: Water/nitrogen budgets and crop yield. Agric. Systems 46:335359.CrossRefGoogle Scholar
21.Virginia Polytechnic Institute and State University. 1990. Proceedings of the Conference on Application of Geographic Information Systems, Simulation Models, and Knowledge-based Systems for Landuse Management. Dept. of Agric. Engineering, VPI&SU, Blacksburg.Google Scholar