Word cloud of crops that respondents modeled

Crop systems models can help researchers estimate the future of food security under climate scenarios. Many crop models are known to exist around the world - for different crops with varying complexities, yet it is not easy to find the right model for the right problem. To better understand the global extent of crop model development and to identify gaps in capabilities, HarvestChoice participated in an initiative to conduct a rapid meta-analysis of crop models using on-line survey to the crop modeling community in the world. Here are the key findings.

Which Model Should I Use...?

Crop models help researchers to simulate the future of food security under climate change scenarios. Many crop models exist around the world, but they are often developed independently and not widely known or used.

A wordle of crop model names retrieved from the survey result

Some models have been developed to represent a single crop or part of a crop production process in detail, whilst others have the capability to model multiple crops in complex rotations and under various management, weather and soil conditions. To better understand the global extent of crop model development and to identify gaps in capabilities, and to determine the geographical coverage and range of crops represented, the Macaulay Land Use Research Institute and IFPRI/HarvestChoice were commissioned by the CGIAR Agriculture and Food Security Challenge Program (CCAFS) to conduct a rapid meta-analysis of crop models for climate change and food security researches using on-line survey to the crop modeling community in the world. For about 1-month of time (September 2010), 141 respondents from 74 countries completed the survey, covering more than 100 crop models.

An improvement in the quality of data used for calibration and testing purposes and as input to the models was seen as one of the most important ways of improving models.

Key Findings

  • An improvement in the quality of data used for calibration and testing purposes and as input to the models was seen as one of the most important ways of improving models.
  • This is associated with a high requirement for improved availability of, and ease of access to shared data sets for calibration and model input.
  • Use of models to improve understanding of processes was seen to be the best outcome, but policy development and climate change mitigation were not seen as key outcomes of model use.
  • There is a paradox in that the main strengths of models were seen to be the detail of process representation, but not the skill in representing observed phenomena.
  • The main strengths of the models were the representation of detailed processes, whereas the robustness in the quality of outputs was rated much lower.
  • For improved modelling of climate change impacts, the best developments in process representation were seen to arise from better understanding and model representation of crop responses to extremes (particularly temperature and water limitations) and to elevated atmospheric carbon dioxide.
  • The main food crops are represented by models, but the focus of application is cereals, maize and rice.
  • Models were seen as being easily transferable to new locations, but limited by the availability of location specific data (e.g. soils, management, and weather).
  • About half of respondents said their models had not been calibrated against elevated CO2 experiments.
  • Model evaluation and testing would be improved by availability of better quality data.
  • Models need to be tested more against extremes of rainfall and temperature.
  • Some models incorporate damage by insect pests, pathogens and physical damage (lodging, frosts, flooding), but there is a need for closer dynamic linking between weather, soil conditions and crop status with the characteristics of the individual form of damage in order to better represent observations.
  • Modelling has been applied in most parts of the world, but the results indicate that the Middle East, Central Asia, African and Russian Federation countries have been under represented by modelling efforts.
  • The quality and level of detail of documentation varies considerably between models, with clear potential for improvement.
  • Funding was seen as the main factor limiting further model development.

Full report can be downloaded at here.

What's Next

When researchers design a new project with crop modeling components, they frequently ask around questions of "Which model simulates this crop?", "Which model works best in this region?", or "Who is the modeling expert on this crop in this region?". The co-authors of the report are planning to work on publishing the survey results as a publicly available/editable online database (think Wikipedia for crop modeling community) that researchers can find answers to the common scoping questions.

Download