L. A. Hunt

The DSSAT cropping system model

The decision support system for agrotechnology transfer (DSSAT) has been in use for the last 15 years by researchers worldwide. This package incorporates models of 16 different crops with software that facilitates the evaluation and application of the crop models for different purposes. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. It has one Soil module, a Crop Template module which can simulate different crops by defining species input files, an interface to add individual crop models if they have the same design and interface, a Weather module, and a module for dealing with competition for light and water among the soil, plants, and atmosphere. It is also designed for incorporation into various application packages, ranging from those that help researchers adapt and test the CSM to those that operate the DSSAT-CSM to simulate production over time and space for different purposes. In this paper, we describe this new DSSAT-CSM design as well as approaches used to model the primary scientific components (soil, crop, weather, and management). In addition, the paper describes data requirements and methods used for model evaluation. We provide an overview of the hundreds of published studies in which the DSSAT crop models have been used for various applications. The benefits of the new, re-designed DSSAT-CSM will provide considerable opportunities to its developers and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems at field, farm, and higher levels.

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.

Agronomic data: advances in documentation and protocols for exchange and use

Abstract Data from agronomy experiments are typically collected and stored in a number of minimally documented computer files, with additional information being entered and archived in field books or diaries. Data manipulation is generally cumbersome and error-prone, and data loss is frequent. Modern database technology has the potential to resolve these issues. However, experience gained by an international network of experimenters and crop modellers (the International Benchmark Sites Network for Agrotechnology Transfer; IBSNAT) in using a database for agronomic experiments conducted by many workers at different sites highlighted problems of data entry, quality control, and changing requirements for storage and output variables. In an attempt to minimize these problems, IBSNAT reduced its focus on a central database, but considerably enhanced its effort on the design and use of a set of simple, standard experiment documentation and results files that could be established and edited easily, transferred directly among workers, used as inputs to analytical software and crop models, and read by database and spreadsheet software. The standard files which were developed, and which were used in a software package termed DSSAT V3, have recently been upgraded by a consortium of experimenters and modellers (the International Consortium for Agricultural Systems Applications; ICASA). These new files are described briefly here. The ICASA files constitute an advance in the potential for good documentation and storage of agronomic data, but only partly solve the problem of overall data management and use. There is still need for central and local databases that facilitate both the searching of information from different experiments, and the examination of relationships that may be apparent in a large array of data. A number of such databases have been developed for specific applications, and a few of these are briefly touched upon. In particular, recent work with one large database currently being developed by a number of international Agricultural Research Centers, National Research Organizations, and Universities, (the International Crop Information System, ICIS), is briefly described.

Modeling growth and development of root and tuber crops

Root and tuber crops are physiologically and botanically a diverse group of plants with a common underground storage organ for carbohydrates. Among all the IBSNAT crop simulation models, the root and tuber family of models have the least amount in common. Crop growth simulation models exist for edible aroids, taro (Colocasia esculenta L. Schott) and tanier (Xanthosoma spp.), potato (Solanum tuberosum L.) and cassava (Manihot esculenta L. Crantz). Technically, the economic important products harvested are cassava roots, potato tubers, and aroid corms and cormels. The aroids, potato and cassava models simulate growth and development as affected by environmental factors and cultural practices. All models calculate growth using a capacity model for carbon fixation constrained by solar radiation, temperature, soil water deficit, and nitrogen deficit. They each simulate the effect of soil, water, irrigation, N fertilization, planting date, planting density, row spacing, and the method of planting on plant growth, development and yield. The models assume that during early growth the leaf and stem (petiole in aroids) are the dominant sinks for assimilate. As plants mature most of the assimilate is translocated to storage organs. In the evaluation presented the models show great potential for simulating growth to aid in the interpretation of experimental data, and subsequently, following refinement, help in the evaluation of potential changes in management in diverse environments.

Characterization of Varieties for Performance Related Aspects

Crop physiological knowledge as embedded in computer simulation models of crop growth and development provides a theoretical framework that can be used to analyze information from multi-environment trials (METs). Their use could thus help overcome some of the difficulties associated with conventional approaches to the analyses of MET data. Here, the ‘Cropsim’ model was used to analyze the results from a multi-year study with a historic set of Cimmyt wheat cultivars. The results show that a model in which early development is split into three phases with different photoperiod sensitivity characteristics for each phase accounted well for the differences in anthesis date among recent cultivars, but not for differences among the older materials. The analysis also showed that apparent radiation use efficiency and grain set varied among years and cultivars, and that differences could not be resolved into a number of environmentally invariant characteristics. The current model thus does not incorporate all characteristics that are important for adaptation and high productivity in particular regions. The results show, however, that even now, there are opportunities to use computer simulation of crop growth to help analyze data and so impact on current crop improvement strategies.