Original by Susan McCarthy, National Agricultural Library, Ming Chan, National Agricultural Library, and Cheryl Porter, University of Florida in Research and Science Sep 08, 2021. Crop researchers are hungry for data to feed their crop models. There is a wealth of historical data that’s inaccessible because today’s crop model software applications cannot easily interpret it. USDA’s […]
08
Sep
03
Sep
IFDC Hosts 15th Crop Modelling Development Sprint
Original by IFDC Staff | August 9, 2021 From July 26 to July 30, 2021, IFDC hosted the 15th Decision Support System for Agrotechnology Transfer (DSSAT) Development Sprint. The development sprint had three goals: improve DSSAT and its associated tools and databases; provide a forum for communications and information exchange among developers, users, and others interested in modeling and […]
17
Aug
15th Hybrid DSSAT Development Sprint, July 26-30, 2021
During the last week of July the first “Hybrid” DSSAT Development Sprint was held, hosted by Dr. Upendra Singh and Dr. Willingthon Pavan at the International Fertilizer Development Center in Muscle Shoals, Alabama. In-person participants included five representatives from the University of Florida and three representatives from IFDC, while there were around 18 on-line participants […]
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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
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 […]
