Work Package 4
2024 – 2028

Crop Growth Simulation

University of Bonn

BADC, BUET, SRDI, TH Köln, LIST Luxembourg

WP-4 utilizes DSSAT and other crop models to simulate productivity and dynamic moisture-stress forecasting. It integrates field observations with satellite data to provide actionable insights for crop choice and irrigation scheduling.

Scientific Objectives

01

Calibrate and validate DSSAT models for major rice varieties and secondary crops in Bangladesh.

02

Simulate crop yield response to varying irrigation intervals and fertilizer applications.

03

Develop dynamic moisture-stress indicators to predict potential yield gaps in real-time.

04

Evaluate the impact of climate change (salinity, heat stress) on future crop productivity.

Execution Framework

T1. Crop Model Parameterization

Gathering genetic and environmental parameters for accurate growth simulation.

  • Compilation of historical crop yield data and management records from SRDI and DAE.
  • Direct measurement of leaf area index (LAI) and biomass in pilot field trials.
  • Determination of crop-specific genetic coefficients for local rice cultivars.
  • Setup of soil profile parameters based on regional SRDI soil surveys.

T2. Yield Gap and Stress Analysis

Identifying factors limiting agricultural productivity.

  • Simulation of potential, attainable, and actual yields across different agro-ecological zones.
  • Quantification of yield losses due to water stress and excessive soil salinity.
  • Evaluation of the benefits of supplemental irrigation during dry spells in the Aman season.
  • Analysis of nitrogen-use efficiency and its interaction with water availability.

T3. Forecasting and Decision Support

Translating model outputs into agricultural advisories.

  • Development of seasonal yield forecasting models based on climate outlooks.
  • Generation of irrigation scheduling recommendations based on predicted soil moisture levels.
  • Scenario analysis of shifting sowing/planting dates to mitigate climate risks.
  • Integration of crop status indicators into the WebAIS Digital Twin platform.

Project Milestones

MS-1

DSSAT models calibrated for the top 5 rice varieties in the pilot regions.

MS-2

Regional yield gap analysis completed for baseline conditions.

MS-3

Real-time moisture-stress forecasting module operational in WebAIS.

MS-4

Final adaptation strategy report for climate-resilient cropping patterns.

Our Team

The multidisciplinary scientific team driving innovation and technical excellence in this work package.

Mohammad Mizanur Rahman

Mohammad Mizanur Rahman

WP-Lead