Crop Modelling

There is an evident gap between agricultural information needed by scientists, policy makers, development professionals, and the information created by the result of a traditional approach regarding records of collected harvests.

Crop modeling and simulation allows the decomposition of complex non-linear interactions among production factors. When combined with environment and policy sciences, it strengthens agriculture decision-making.

Crop modeling and simulation is particularly needed to monitor agriculture under climate change that is already characterized by reduced precipitation and increased frequency of drought and salinity. As a series of drought shocks might result in frequent crop failure resulting in land abandonment.

Resilience at the regional level consists in policy implementation of effective response strategies at national and regional levels. In agriculture, resilience might be based on both optimization of practices and sustainable transfer of technology and skills.

ICBA research is targeted towards initiatives that enable policy makers in Middle East and North Africa region to perform agriculture resource planning on national scales. ICBA work aims to:

  1. Valorize advances in crop modeling and simulation to produce agriculture dashboard for policy makers.
  2. Transfer agriculture modeling and monitoring tools and data to a wider range of users in the region.

The approach is based on the use of a suite of state-of-the art crop-environment modeling tools for estimating and predicting yields of cereals.

ICBA works on real time monitoring and simulating crop models under extreme climate conditions such as its work under the ‘Application of near real time monitoring systems for irrigated agriculture in MENA’ project targeting Jordan, Iraq, Tunisia, and Yemen.

ICBA pursues agriculture monitoring and crop management optimization under climate variability and climate change such as its work in Tunisia under the MAWRED project during which wheat and barley crop growth, development and production for the most widely grown cultivars Karim and Arbi, respectively were simulated. Subsequently, genetic coefficients of 50 other genotypes of wheat and barley are being adjusted based on consistent data on plant canopy attributes, phenology, leaf area index and plant nutrients assimilation and remobilization.