Crop-livestock adaptation to climate change based on modelling and remote-sensing
Good livestock management and policies are crucial to maintaining economic stability while addressing the challenges of climate change. Yet the policies and practices guiding the industry vary by region and nation.
We will assess how crop-livestock practices impact farm performance regionally under different climate change scenarios. Coupling existing multiple-scaling and remote-sensing techniques with advanced biophysical models we will evaluate drivers of yield variability for feed systems in Argentina, Uruguay, and Australia.
This project will hold three workshops. The workshops aim to assess how crop-livestock management practices impact regional farming performance under different climate change scenarios. Technologies, which will be used to assess the yield variability for diverse forage cropping systems, include existing model-scaling and remote-sensing techniques developed in Argentina (the most advanced biophysical models in the world). These assessments will take place in Argentina, Uruguay, and Australia. The workshops will also strengthen crop-livestock management skills for the three countries in different areas: systems modeling (Australia), remote-sensing system (Argentina), and livestock systems analysis (Uruguay).
The project will be conducted collaboratively with livestock industry decision-makers who will perform foresight exercises to explore alternative futures. This will facilitate the development of forage crop regional management and preparation for environmental impacts.
An important part of this project was to spend time together discussing the application of remote sensing data to crop model development. However, due to COVID19 restrictions, the team has been not able to travel, so we carried out several online meetings to advance with the project milestones. Please have a look to some researchers in action!
Partners from the University of Buenos Aires at 7:30 AM (Argentinian time) and from the University of Southern Queensland and University of Tasmania at 8:30 PM (Australian time)
University of Southern Queensland (Australia), University of Buenos Aires (Argentina), University of Entre Rios (Argentina), Instituto Nacional de Investigación Agropecuaria (Uruguay), Association of Regional Consortiums of Agricultural Experimentation (Argentina and Uruguay)
Council on Australia Latin America Relations (COALAR) Department of Foreign Affairs and Trade, Australian Government.
- Uncertainty decomposition in crop models
- Bestiapop - A python package for climate data extraction, processing and visualisation in crop models
- APSIM Potato model development - Mapping potato yield variability under diferent G*E*M scenarios in Tasmania
- Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement
- University of Entre Rios