DSSAT v4.7.5

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

DSSAT 2019

May 20-25, 2019. University of Georgia, Griffin.

AMEI Workshop

University of Florida, April 9-11, 2019

DSSAT 2019 @ Vietnam

Hotel Fortuneland, Can Tho City, Vietnam
March 5-9, 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…

GARDIAN and facilitating data interoperability at CGIAR

The CGIAR online search engine, GARDIAN, is easy to navigate and to perform simple queries to locate data and publications; however, there is a challenge to making these data useable on a large scale.

There is a major gap between the potential value of data collected in agricultural experiments and the value currently obtained through the use of those data. Typically, data collected in experiments are used for the original research purpose only, but a much greater value might be obtained if the data could be combined across locations, time, and management conditions.

Combinations of large datasets could enable scientific advances in such areas as genetic modeling, management optimization, and variety selection, and may potentially reduce the need for collection of additional field experimental data. The CGIAR research centers generate large amounts of data, which could gain value through the application of the FAIR (Findable, Accessible, Interoperable, Reusable) principles, particularly for data which are suitable for quantitative analyses.
Read more…

10th DSSAT Development Sprint

University of Florida | January 07-11, 2019

DSSAT 2018 @ Thailand

Green Lake Resort Hotel, Chiang Mai, Thailand
August 27th – September 1st, 2018

DSSAT 2018 @ Jamaica

University of the West Indies | July 16-27, 2018

9th DSSAT Development Sprint

University of Arizona | July 09-13, 2018

DSSAT 2018

The University of Georgia – Griffin | May 14-19, 2018

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

DSSAT 2018 @ Indonesia

Lampung Tengah, Indonesia | April 9 – 13, 2018

DSSAT 2018 @ Tunisia

Tunis, Tunisia | March 19 – 24, 2018

DSSAT 2018 IITA International Training Program

Ibadan, Nigeria | February 26 to March 2, 2018

Globally Recognized UF/IFAS Crop-Model Researcher Wins National Soil Science Award

November 7, 2017

A University of Florida professor has received a top award from the Soil Science Society of America for helping to develop and apply practices that increase crops, income and help the environment. The L.R. Ahuja Ag Systems Modeling Award goes to a soil scientist, agronomist or crop scientist in recognition of distinguished contributions and their demonstrated impact in the past five to seven years.

“It was a great honor to be recognized for my work in crop modeling and decision-support systems,” said Gerrit Hoogenboom, a UF/IFAS professor of agricultural and biological engineering and a preeminent scholar at the UF/IFAS Institute for Sustainable Food Systems.

Read more about the Award

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 40 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 used for many applications 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 in use for more than 30 years by researchers, educators, consultants, extension agents, growers, private industry, policy and decision makers, and many others in over 150 countries worldwide.  Learn more…

05
May

DSSAT 2019 International Training Program @ Thailand

The DSSAT and MWCropDSS 2019 International Training Program entitled “Efficient and precision agricultural resource utilization under changes with simulation models and GIS” will be held from August 19 through August 24, 2019 at Chiang Mai University in Chiang Mai, Thailand. This workshop is jointly presented by Chiang Mai University, The University of Florida, and the […]

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15
Apr

DSSAT participates in the Agricultural Model Exchange Initiative

A model development workshop was held at the University of Florida April 9-11, 2019 to collaborate on the Agricultural Model Exchange Initiative (AMEI), an open initiative that aims to address challenges for exchanging model units at different granularities between modeling frameworks. Pierre Martre and Cyrille Midingoyi from INRA and Christophe Pradal from CIRAD traveled to […]

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14
Mar

DSSAT 2019 @ Vietnam

The significant rise in the frequency of extreme weather events in recent years has affected agricultural production systems and posture severe problems to farmers, agricultural scientists and extension officers, especially for making decisions at a field level. Hence, it is necessary to strengthen the capacity of regional extension personnel and scientists by providing significant strategies […]

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21
Feb

GARDIAN and facilitating data interoperability at CGIAR

The CGIAR online search engine, GARDIAN, is easy to navigate and to perform simple queries to locate data and publications; however, there is a challenge to making these data useable on a large scale. There is a major gap between the potential value of data collected in agricultural experiments and the value currently obtained through […]

Continue Reading →
21
Feb

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 […]

Continue Reading →