Gerrit Hoogenboom

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.

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.

Contribution of agrometeorology to the simulation of crop production and its applications

Weather has a significant impact on crop growth and development. This paper presents an overview of crop modeling and applications of crop models, and the significance of weather related to these applications. To account for the impact of weather and climate variability on crop production, agrometeorological variables are one of the key inputs required for the operation of crop simulation models. These include maximum and minimum air temperature, total solar radiation, and total rainfall. Most models use daily data as input, because variables at a smaller time scale are usually unavailable for most locations. It is important to define standard file formats for weather and other input data; this will expand the applicability of weather data by different models. Issues related to missing variables and data, as well as locations for which no data are available, need to be addressed for model applications, as it can affect the accuracy of the simulations. Weather generators can be used to stochastically generate daily data when data are missing or long-term historical data are unavailable. However, the use of observed weather data for model input will provide more precise crop yield simulations, especially for tropical regions. Many of the crop models have been applied towards strategic and tactical management decision making as well as yield forecasting. The predicted variability of crop yield and related variables as well as natural resource use is mainly due to the short- and long-term variation of weather and climate conditions. The results produced by the models can be used to make appropriate management decisions and to provide farmers and others with alternative options for their farming system. The crop models have been used extensively to study the impact of climate change on agricultural production and food security. Recently, they have also been applied towards the impact of climate variability and the effect of El Nino/Southern Oscillation (ENSO) on agricultural production and food security. It is expected that, with the increased availability of computers, the use of crop models by farmers and consultants as well as policy and decision makers will increase. Weather data in the form of historical data or observations made during the current growing season, and short-, medium-, and long-term weather forecasts will play a critical role in these applications.

The CROPGRO model for grain legumes.

The CROPGRO model is a generic crop model based on the SOYGRO, PNUTGRO, and BEANGRO models. In these earlier crop models, many species attributes were specified within the FORTRAN code. CROPGRO has one set of FORTRAN code and all species attributes related to soybean, peanut, or drybean are input from external ‘species’ files. As before, there are also cultivar attribute files. The CROPGRO model is a new generation model in several other ways. It computes canopy photosynthesis at hourly time steps using leaf-level photosynthesis parameters and hedge-row light interception calculations. This hedgerow approach gives more realistic response to row spacing and plant density. The hourly leaf-level photosynthesis calculations allow more mechanistic response to climatic factors as well as facilitating model analysis with respect to plant physiological factors. There are several evapotranspiration options including the Priestley-Taylor and FAO-Penman. An important new feature is the inclusion of complete soilplant N balance, with N uptake and N2-fixation, as well as N deficiency effects on photosynthetic, vegetative and seed growth processes. The N2-fixation option also interacts with the modeled carbohydrate dynamics of the plant. CROPGRO has improved phenology prediction based on newly-optimized coefficients, and a more flexible approach that allows crop development during various growth phases to be differentially sensitive to temperature, photoperiod, water deficit, and N stresses. The model has improved graphics and sensitivity analysis options to evaluate management, climate, genotypic, and pest damage factors. Sensitivity of growth processes and seed yield to climatic factors (temperature, CO2, irradiance, and water supply) and cultural management (planting date and row spacing) are illustrated.

The impact of climate variability and change on crop yield in Bulgaria

During the recent decade, the problem of climate variability and change, due to natural processes as well as factors of anthropogenetic origin, has come to the forefront of scientific problems. The objective of this study was to investigate climate variability in Bulgaria during the 20th century and to determine the overall impact on agriculture. There was no significant change in the mean annual air temperature. In general, there was a decrease in total precipitation amount during the warm-half of the year, starting at the end of the 1970s. Statistical multiple regression models, describing the relationship between crop yield, precipitation, and air temperature were also developed. Several transient climate change scenarios, using global climate model (GCM) outputs, were created. The Decision Support System for Agrotechnology Transfer (DSSAT) Version 3.5 was used to assess the influence of projected climate change on grain yield of maize and winter wheat in Bulgaria. Under a current level of CO 2 (330 ppm), the GCM scenarios projected a decrease in yield of winter wheat and especially maize, caused by a shorter crop growing season due to higher temperatures and a precipitation deficit. When the direct effects of CO 2 were included in the study, all GCM scenarios resulted in an increase in winter wheat yield. Adaptation measures to mitigate the potential impact of climate change on maize crop production in Bulgaria included possible changes in sowing date and hybrid selection.

Development of a neural network model to predict daily solar radiation

Many computer simulation models which predict growth, development, and yield of agronomic and horticultural crops require daily weather data as input. One of these inputs is daily total solar radiation, which in many cases is not available owing to the high cost and complexity of the instrumentation needed to record it. The aim of this study was to develop a neural network model which can predict solar radiation as a function of readily available weather data and other environmental variables. Four sites in the southeastern USA, i.e. Tifton, GA, Clayton, NC, Gainesville, FL, and Quincy, FL, were selected because of the existence of longterm daily weather data sets which included solar radiation. A combined total of 23 complete years of weather data sets were available, and these data sets were separated into 11 years for the training data set and 12 years for the testing data set. Daily observed values of minimum and maximum air temperature and precipitation, together with daily calculated values for daylength and clear sky radiation, were used as inputs for the neural network model. Daylength and clear sky radiation were calculated as a function of latitude, day of year, solar angle, and solar constant. An optimum momentum, learning rate, and number of hidden nodes were determined for further use in the development of the neural network model. After model development, the neural network model was tested against the independent data set. Root mean square error varied from 2.92 to 3.64 MJ m−2 and the coefficient of determination varied from 0.52 to 0.74 for the individual years used to test the accuracy of the model. Although this neural network model was developed and tested for a limited number of sites, the results suggest that it can be used to estimate daily solar radiation when measurements of only daily maximum and minimum air temperature and precipitation are available.

Modeling Growth, Development, and Yield of Grain Legumes using Soygro, Pnutgro, and Beangro: A Review

The interactions between plants and their environment involve an elaborate collection of biological, physical, and chemical processes. To better understand the responses of crops to their environments, computer models are being used to study both the simple and complex aspects of this system.

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.

Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation

Advanced information on crop yield is important for crop management and food policy making. A data assimilation approach was developed to integrate remotely sensed data with a crop growth model for crop yield estimation. The objective was to model the crop yield when the input data for the crop growth model are inadequate, and to make the yield forecast in the middle of the growing season. The Cropping System Model (CSM)–Crop Environment Resource Synthesis (CERES)–Maize and the Markov Chain canopy Reflectance Model (MCRM) were coupled in the data assimilation process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and vegetation index products were assimilated into the coupled model to estimate corn yield in Indiana, USA. Five different assimilation schemes were tested to study the effect of using different control variables: independent usage of LAI, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), and synergic usage of LAI and EVI or NDVI. Parameters of the CSM–CERES–Maize model were initiated with the remotely sensed data to estimate corn yield for each county of Indiana. Our results showed that the estimated corn yield agreed very well with the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) data. Among different scenarios, the best results were obtained when both MODIS vegetation index and LAI products were assimilated and the relative deviations from the NASS data were less than 3.5%. Including only LAI in the model performed moderately well with a relative difference of 8.6%. The results from using only EVI or NDVI were unacceptable, as the deviations were as high as 21% and −13% for the EVI and NDVI schemes, respectively. Our study showed that corn yield at harvest could be successfully predicted using only a partial year of remotely sensed data.