Gerrit Hoogenboom, a professor at the University of Florida speaks to participants during a pass workshop on Decision Support System for Agrotechnology Transfer (DSSAT) at the IITA Campus in Ibadan, Nigeria.
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 agronomic field trial.
The scientists are drawn from the International Institute of
Tropical Agriculture (IITA), Wageningen University (WUR), University of Florida
(UF) and the African Plant Nutrition Institute (APNI). They are part of the
ACAI team that has been developing AKILIMO decision support tools using on
field research data and simulation models to create a robust prediction engine
that provides site-specific recommendation for cassava agronomy.
Both LINTUL and DSSAT are designed to predict crop growth and yields as affected by genotype, soil and daily and seasonal climate variations in temperature, solar radiation and rainfall. However, DSSAT is more data-intensive by simulating in great details dry matter allocation to different plant organs against simpler dry matter partitioning pattern used in LINTUL.
So far, LINTUL has been used to predict water-limited yield used in the AKILIMO prediction engine together with GIS data to provide recommendations
The team held a virtual workshop in June to evaluate the performance of the two models using similar geospatial and crop data. Given the different parameterization, the two models could perform differently under varying agro-ecological conditions.
According to ACAI and IITA Data Scientist Meklit Chernet, it
is important to understand the strengths and the limitations of the two models
to improve the prediction of AKILIMO decision support.
The team is currently calibrating and investigating the
models using the field data collected by ACAI in order to perform side by side