Philip K. Thornton

Decision support system for agrotechnology transfer: DSSAT v3

Agricultural decision makers at all levels need an increasing amount of information to better understand the possible outcomes of their decisions to help them develop plans and policies that meet their goals. An international team of scientists developed a decision support system for agrotechnology transfer (DSSAT) to estimate production, resource use, and risks associated with different crop production practices. The DSSAT is a microcomputer software package that contains crop-soil simulation models, data bases for weather, soil, and crops, and strategy evaluation programs integrated with a ‘shell’ program which is the main user interface. In this paper, an overview of the DSSAT is given along with rationale for its design and its main limitations. Concepts for using the DSSAT in spatial decision support systems (for site-specific farming, farm planning, and regional policy) are presented. DSSAT provides a framework for scientific cooperation through research to enhance its capabilities and apply it to research questions. It also has considerable potential to help decision makers by reducing the time and human resources required for analyzing complex alternative decisions.

Understanding options for agricultural production.

Preface. Acronyms. 1. Overview of IBSNAT G. Uehara, G.Y. Tsuji. 2. Data for Model Operation, Calibration, and Evaluation L.A. Hunt, K.J. Boote. 3. Soil Water Balance and Plant Water Stress J.T. Ritchie. 4. Nitrogen Balance and Crop Response to Nitrogen in Upland and Lowland Cropping Systems D.C. Godwin, U. Singh. 5. Cereal Growth, Development and Yield J.T. Ritchie, et al. 6. The CROPGRO Model for Grain Legumes K.J. Boote, et al. 7. Modeling Growth and Development of Root and Tuber Crops U. Singh, et al. 8. Decision Support System for Agrotechnology Transfer: DSSAT v3 J.W. Jones, et al. 9. Modeling and Crop Improvement J.W. White. 10. Simulation as a Tool for Improving Nitrogen Management W.T. Bowen, W.E. Baethgen. 11. The Use of a Crop Simulation Model for Planning Wheat Irrigation in Zimbabwe J.F. MacRobert, M.J. Savage. 12. Simulation of Pest Effects on Crops Using Coupled Pest-Crop Models: The Potential for Decision Support P.S. Teng, et al. 13. The Use of Crop Models for International Climate Change Impact Assessment C. Rosenzweig, A. Iglesias. 14. Evaluation of Land Resources Using Crop Models and a GIS F.H. Beinroth, et al. 15. The Simulation of Cropping Sequences Using DSSAT W.T. Bowen, et al. 16. Risk Assessment and Food Security P.K. Thornton, P.W. Wilkens. 17. Incorporating Farm Household Decision-Making within Whole Farm Models G. Edwards-Jones, et al. 18. Network Management and Information Dissemination for Agrotechnology Transfer G.Y. Tsuji. 19. Crop Simulation Models as an Educational Tool R.A. Ortiz. 20. Synthesis G. Uehara.

Estimating millet production for famine early warning: An application of crop simulation modelling using satellite and ground-based data in Burkina Faso

Abstract Early warning of impending poor crop harvests in highly variable environments can allow policy makers the time they need to take appropriate action to ameliorate the effects of regional food shortages on vulnerable rural and urban populations. Crop production estimates for the current season can be obtained using crop simulation models and remotely sensed estimates of rainfall in real time, embedded in a geographic information system that allows simple analysis of simulation results. A prototype yield estimation system was developed for the thirty provinces of Burkina Faso. It is based on CERES-Millet, a crop simulation model of the growth and development of millet (Pennisetum spp.). The prototype was used to estimate millet production in contrasting seasons and to derive production anomaly estimates for the 1986 season. Provincial yields simulated halfway through the growing season were generally within 15% of their final (end-of-season) values. Although more work is required to produce an operational early warning system of reasonable credibility, the methodology has considerable potential for providing timely estimates of regional production of the major food crops in countries of sub-Saharan Africa.

A rainfall generator for agricultural applications in the tropics

Abstract A new rainfall generator, based on a third-order Markov chain, has been developed to simulate the year-to-year variation in rainfall amount that is observed in the tropics. To do this, some of the parameters of the model are themselves sampled randomly. The model has been fitted to over 40 sites in the tropics. The model was tested in detail for three sites in Central and South America and in the African Sahel. Few significant differences were found between the historical and simulated means and variances of yearly and monthly rainfall amount and raindays per month. There were some problems with simulating wet and dry sequences of longer than 9 days. When historical and simulated rainfall records were used to drive a crop simulation model, there was no significant difference in the mean and variance of maize yield response. The results reported suggest that the rainfall model performs adequately for many applications.

Spatial and temporal variability of rainfall related to a third-order Markov model

Abstract Many types of analysis require rainfall data, which must often be generated to overcome limitations in the historical record. The ability to model outlying rainfall years satisfactorily is particularly important in risk studies. We describe a rainfall generator with this ability, based on a third-order Markov process, and we show how the fitted parameters of the model vary for an illustrative sample of 18 sites with highly contrasting climates. Some of the parameters of the model show patterns that are characteristic of the climate type. The model should thus be suitable for interpolating rainfall data where they do not exist. We also fitted the model to subsets of some long data records to illustrate the marked changes in rainfall characteristics that have occurred in some locations over the last 70 years, and to help understand the nature of these changes.

Fitting a third-order Markov rainfall model to interpolated climate surfaces

Abstract We describe a procedure to link a third-order Markov rainfall model to interpolated monthly mean climate surfaces. Such preliminary surfaces have been constructed for Africa and Latin America and these allow a user to generate daily weather data (rainfall, temperatures and solar radiation) that can be used for purposes of system characterisation and to drive a wide variety of crop and livestock production and ecosystem models. For applications with a time resolution of a few weeks or a growing season, the surfaces described already have substantial utility. We present indicators of reliability both spatially, in map form, and as summaries of goodness of fit for the model parameters. We test the surfaces for goodness of fit for three contrasting sites in the tropics and identify refinements to improve the interpolation methodology.

Using crop models for sustainability and environmental quality assessment

In this article, the question of how various aspects of sustainability can be studied is outlined, with particular reference to ways in which the efficiency of the agricultural research and development process can be enhanced. The role of crop simulation models as useful tools in sustainability research and in the assessment of environmental impact is discussed with reference to some of the collaborative model development and application activities of the International Fertilizer Development Center (IFDC). Despite the fact that much development work is needed on crop simulation models, there are few practical alternatives to address the complex and interactive nature of sustainability issues and environmental impact assessments.

Spatial modeling of risk in natural resource management

Making decisions in natural resource management involves an understanding of the risk and uncertainty of the outcomes, such as crop failure or cattle starvation, and of the normal spread of the expected production. Hedging against poor outcomes often means