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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18
Nov

Crop modeling takeaways from the 2020 CGIAR Convention

This is one of a series of blogs written by our Youth in Data delegates who participated in the 2020 CGIAR Convention on Big Data in Agriculture. The global event was held virtually 19-23 October 2020. Feature photo: Alfonso Cortés / CIMMYT. During my experience as a Youth in Data 2020 delegate at the 2020 CGIAR […]

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22
Jun

Scientists from IITA, Wageningen and University of Florida are Optimizing Crop Models for Improved Predictions

The African Cassava Agronomy Initiative (ACAI) project scientists are working on optimizing crop models to improve the prediction of the AKILIMO decision support tools. The team is evaluating the performance of the Light Interception and Utilization (LINTUL) and Decision Support System for Agrotechnology Transfer (DSSAT) to predict the attainable yield of cassava under well managed […]

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14
Jan

12th DSSAT Development Sprint at the University of Florida

The 12th DSSAT Development Sprint was held from January 6-10, 2020 at the University of Florida and co-hosted by the Institute for Sustainable Food Systems and the Department of Agricultural & Biological Engineering. One of the main goals of the DSSAT Development Sprint is to improve the science of the Cropping System Model (CSM) and […]

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09
Jan

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 […]

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