DSSAT v4.7.5

Latest Version: 4.7.5 (April 2019) | Free of charge!

DSSAT 2021

May 17-22, 2021. University of Georgia, Griffin.

14th DSSAT Development Sprint

Virtual Meeting | January 11-15, 2021

17720420720_05c8543ce5_zDSSAT International Training Workshop 2021

May 17-22, 2021 (Updated)
The University of Georgia at Griffin, Georgia, USA

Jointly presented by the DSSAT Foundation, University of Florida, University of Georgia, and the International Fertilizer Development Center – the workshop will cover the operation of DSSAT Version 4.7.5, description of the DSSAT-Cropping System Model, CSM and its modules, and the science embedded in the models, minimum data requirements and experimental data collection for systems simulation, integration of crop simulation models with database management and GIS, and the application of the DSSAT-CSM model to improve management of cropping systems.

Learn more about the workshop Workshop Brochure

13th DSSAT Development Sprint

Virtual Meeting | July 20-24, 2020

The DSSAT crop modeling ecosystem

The DSSAT crop modeling ecosystem is one of the oldest and most widely used crop modeling platforms across the world. The success of DSSAT is based on the inclusiveness and participatory approach that has been used since the original development of the CERES and CROPGRO family of models and the emphasis on sharing data and model code. DSSAT is not just a software program, but an ecosystem of:

• Crop model users;
• Crop model trainers;
• Crop model developers;
• Models for the most important food, feed, fiber, and fuel crops;
• Tools and utilities for data preparation;
• Minimum data for model calibration and evaluation; and
• Application programs for assessing real-world problems.

Download the chapter in PDF More info/buy the book

Advances in crop modelling for a sustainable agriculture

This collection summarises key advances in crop modelling, with a focus on developing the next generation of crop and whole-farm models to improve decision making and support for farmers.
Chapters in Part 1 review advances in modelling individual components of agricultural systems, such as plant responses to environmental conditions, crop growth stage prediction, nutrient and water cycling as well as pest/disease dynamics. Building on topics previously discussed in Part 1, Part 2 addresses the challenges of combining modular sub-systems into whole farm system, landscape and regional models. Chapters cover topics such as integration of rotations and livestock, as well as landscape models such as agroecological zone (AEZ) models. Chapters also review the performance of specific models such as APSIM and DSSAT and the challenges of developing decision support systems (DSS) linked with such models. The final part of the book reviews wider issues in improving model reliability such as data sharing and the supply of real-time data, as well as crop model inter-comparison.

Read More… Watch the informative video Buy the bookDownload brochure

12th DSSAT Development Sprint

University of Florida | January 06-10, 2020

DSSAT 2019 @ South Africa

ARC Headquarters in Pretoria, South Africa
September 30th – October 4th, 2019

DSSAT 2019 @ Pakistan

PMAS Arid Agriculture University, Rawalpindi, Pakistan

Sep 4-5, 2019

DSSAT 2019 @ Thailand

Chiang Mai University in Chiang Mai, Thailand
August 19th – August 24th, 2019

WEBINAR

Date: 29 August 2019
Time: 08:30-9:30 AM Eastern Time

11th DSSAT Development Sprint

IFDC | July 15-19, 2019

Cross-continental disease and crop modeling collaborations to beat back wheat blast

Cross-continental collaborations facilitated by the CGIAR Platform for Big Data in Agriculture thrive to beat back the threat of wheat blast in Brazil and Bangladesh.

Wheat blast disease is a major threat to smallholder farmers. The disease was first discovered in Brazil in 1985. Decades later it escaped from South America when it crept its way across the ocean and appeared in Bangladesh in 2016. Wheat blast outbreaks are linked to the right climate conditions. More accurate weather forecasts, coupled with disease models are key for farmers to adapt to the threat of the disease. Effective forecasting and warning systems can also help farmers avoid unnecessary fungicide use, thereby saving them money and reducing environmental risks.

Because the disease is new, knowledge of wheat blast epidemiology and modeling was limited in Bangladesh. That’s why scientists at the International Maize and Wheat Improvement Center (CIMMYT) reached out to Professor Jose Mauricio Fernandes, a Crop Pathologist, and Mr. Felipe de Vargas, a Computer Scientist, within the Universidade de Passo Fundo (UPF) in Brazil… Read more…

CRAFT: A New Spatial Yield Forecasting Tool

The CCAFS Regional Agricultural Forecasting Toolbox (CRAFT) is a software platform designed for yield forecasting at spatial resolutions of either 5 or 30 arc-minutes using an ensemble modeling approach. Currently the DSSAT, APSIM, and SARRA-H crop simulation models have been implemented for nine important food and feed crops using the AgMIP IT tools. CRAFT was an initiative of CCAFS and was developed in partnership with the Asia Risk Center, Washington State University, and the University of Florida.

Read More about CRAFT Download CRAFT v3.4

ICASA Data Standards Version 2.0

icasa_v2_paper

Researchers increasingly seek to integrate results from multiple experiments. The ICASA V2.0 standards allow flexible description of field experiments. Major categories of data are management, soil, weather and crop responses. The standards may be implemented in diverse digital formats. Planned improvements emphasize data quality and appropriate usage.

Access at ScienceDirect
Download in PDF

Improving Soil Fertility Recommendations in Africa

The new book gives a detailed description of the application of DSSAT in simulating crop and soil processes within various Agro-ecological zones in Africa. The book provides examples of the application of DSSAT models to simulate nitrogen applications, soil and water conservation practices including effects of zai technology, phosphorus and maize productivity, generation of genetic coefficients, long-term soil fertility management technologies in the drylands, microdosing, optimization of nitrogen x germplasms x water, spatial analysis of water and nutrient use efficiencies and, tradeoff analysis.

Read Online Buy at Amazon
 

What is DSSAT?

Decision Support System for Agrotechnology Transfer (DSSAT) is software application program that comprises dynamic crop growth simulation models for over 42 crops. DSSAT is supported by a range of utilities and apps for weather, soil, genetic, crop management, and observational experimental data, and includes example data sets for all crop models. The crop simulation models simulate growth, development and yield as a function of the soil-plant-atmosphere dynamics. DSSAT has been applied to address many real-world problems and issues ranging from genetic modeling to on-farm and precision management to regional assessments of the impact of climate variability and climate change. DSSAT has been used for more than 30 years by researchers, educators, consultants, extension agents, growers, private industry, policy and decision makers, and many others in over 174 countries worldwide.  Learn more…

13
Jun

Computer software helps solve what-if questions in agriculture

Original by Ashley N Biles for CAES News Anyone familiar with agriculture knows that a successful harvest largely relies on environmental factors. An especially hot summer with no rain in sight or poor soil quality can cause as many problems as a late cold snap right in the middle of planting season. Often farmers must rely on trial […]

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21
May

DSSAT 2021 International Training Program at the University of Georgia

DSSAT Foundation – The University of Georgia – Griffin Campus, Griffin, Georgia USA Scientists from across the world met from May 17-22, 2021 on the University of Georgia Griffin Campus to learn about the latest version of the Decision Support System for Agrotechnology Transfer (DSSAT) computer software program. The DSSAT crop modeling ecosystem helps researchers […]

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16
Feb

Transforming crop simulation models into gene-based models

Dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.  Dynamic crop simulation models are tools that predict the phenotype (i.e. observable characteristics) of plants grown in specific environments. In these models, genotypic differences among cultivars are represented by empirical […]

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08
Feb

Young Malian researchers gear up for grand challenges with crop modeling

Budding agricultural researchers are being trained in crop modeling to take on pressing and ever-present challenges of land, climate and food security. In a recent training program at ICRISAT-Mali, eight students learned to model agrosystems with DSSAT software, taking into account smallholder farming constraints. Crop modeling is a process that describes different stages of crop […]

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29
Jan

Advancing crop growth models using genotype-specific parameters

Incorporating genotype-specific parameters and realistic trait physiology will advance crop growth models. Plant breeders face an urgent mission: of adapting crops to climate change and feeding an increased world population. Crop models can help breeders select cultivars and cultivar traits for different target environments. For instance, models can be used to evaluate traits (e.g., life […]

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15
Jan

14th Virtual DSSAT Development Sprint

The 14th DSSAT Development Sprint was held from January 11-15, 2021 as a Virtual Meeting due to continuing Covid-19 Pandemic. The DSSAT Development Sprint was hosted by the Institute for Sustainable Food Systems and the Department of Agricultural & Biological Engineering of the University of Florida. One of the main goals of the DSSAT Development […]

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01
Dec

Reuse, don’t lose, process-based models and components

A new system automatically transforms existing process-based crop models into different languages and simulation platforms. This new approach, described in in silico Plants, will improve the reproducibility, exchange and reuse of process-based crop models (PBM). PBM are increasingly implemented as autonomous components describing each biophysical process. According to lead author, Dr. Cyrille Ahmed Midingoyi, researcher […]

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