Python-Based Platform to Evaluate Crop/Pest Systems

Lead Organization:

Center for the Analysis of Sustainable Agricultural Systems (CASAS)

Partner Organizations:

Partnerships with appropriate research groups being sought and established as a basis for proposing the actual funding of the software with McKnight and other funders




Globally, the capacity to predict the distribution, phenology, and abundance dynamics of crop, pest, and natural enemy species at local, regional, and global levels is lacking. This capacity is of paramount importance, as it allows assessment of the increasing risk of invasive species and extant pests on crop growth and development and economic impact, and the development of sustainable practices to control pests. Absent such capacity, long-lasting costly control and eradication programs have been initiated against pests without clear understanding of the problem and the effects of weather and other factors on their dynamics or of the impact of or on natural enemies with associate ecological and human health consequences. This capacity is of vital importance because climate change will alter system interactions and outcomes, increasing the complexity of pest management.

CASAS has implemented this capacity in numerous analyses based on methods that fall under the ambit of physiologically based demographic models (PBDMs) of crop production systems. In a nonfunded capacity, CASAS will assist CCRP-funded groups in sub–Saharan Africa in analyzing the effects of weather on the dynamics of the fall armyworm (FAW, an invasive species to Africa) on maize, cotton, and sorghum. The work on FAW is being conducted pro bono outside the project.

The project seeks to explore the potential to develop a unified software platform to assess complex agricultural systems and to make it available to researchers for their independent evaluation of crop/pest/natural enemy problems globally. The proposed system would aid sustainability, help reduce toxic chemical inputs to the environment, and reduce misappropriation of public funds.

Grant Aims:

The overall goal is the development and seamless integration of Pascal-coded system models into a proposed software platform that provides other researchers open access to appropriate weather files to run various models, GIS maps, and analysis of data on local or regional scale, and statistical analyses as appropriate.

Specifically, the project seeks to:

  1. Encourage CASAS and consortium of collaborators ( to work with Python experts (e.g., University of California-Berkeley) to develop required code to implement these goals as unified platform.
  2. Capture underlying commonality of structure in accessible manner and reprogram in high-level language such as Python.

McKnight’s support allows for:

  • Development of complete schema for system
  • Travel by CASAS members to develop how platform components will be integrated

Outputs and Outcomes:

  • Development of general system schema with linkages to open-source statistical (R) and GIS software (GRASS)
  • System holistically able to run on PC-based platform or cloud systems, making it transportable to all areas of world