Nextgen Agricultural System Models and Use Cases: the Economic Data Challenge
Abstract:
I will overview and update the study of Next Generation Agricultural Systems Data, Models and Knowledge Products carried out by the Agricultural Model Inter-comparison and Improvement Project (AgMIP) (Agricultural Systems 2017 special issue). The study used a set of use cases for agricultural systems models to evaluate their status and strategies for development of the next generation of emerging models. Data were found to be a critical limitation to model improvement. I will use the case of agricultural greenhouse gas mitigation to focus on economic data and model challenges and strategies to support private (i.e., on-farm) and public policy decision-making.
Bio:
John Antle is a professor in the Department of Applied Economics at Oregon State University, Corvallis, Oregon. He received the Ph.D. in Economics from the University of Chicago. Previously he was an assistant and associate professor at UC Davis and a professor at Montana State University. His public service includes Senior Economist on the President's Council of Economic Advisers, lead author for IPCC assessment reports, and a member of the National Research Council’s Board on Agriculture and Natural Resources. He is a Fellow and past President of the Agricultural and Applied Economics Association, and has received numerous research awards. He is the developer of a widely used technology impact assessment model, the Tradeoff Analysis Model (TOA-MD), and a founding member of the Executive Committee of the Agricultural Model Inter-comparison and Improvement Project (AgMIP). His current research focuses on the sustainability of agricultural systems in developing and industrialized countries. His latest book is Sustainable Agricultural Development: An Economic Perspective (Palgrave-Macmillan 2020).
Summary:
AgMIP: Global Agricultural Model Intercomparison project
Bridges gap between data, models and users of agricultural models
Inspired by the Climate Model Intercomparison Project (CMIP)
Areas:
Sustainable Farming
Crop model intercomparison
Global and Regional Agriculture Assessments
NextGen Knowledge Products, Models and Data
Flow
Use-cases (small farms, commercial farms, policy)
Pre-competitive data and models (shared by community)
Base models, understanding of crops and pests
Competitive data and knowledge products
Early warning, ag monitoring, nutrition tracker, mobile sensors and big data
Hard problems: agricultural practices, crops very heterogeneous
Yields in US vary a lot, globally much more
Agricultural systems are very complex, design requires many tradeoffs
While improving sustainability, must meet farmers’ profit goals (they can’t go bankrupt in the process)
Economic analysis of agricultural systems
Internal validity: captures a single economic systems
Prediction in a different but observable environment
Prediction in a different, never observed environment (e.g. new climate)
Approach:
Randomized Controlled Trials extract causal mechanisms
Observational data is limited in quality:
Large sampling bias
Few samples in contexts where given technique doesn’t work, many where it does: hard to identify the causal dynamics
Unobserved heterogeneity
How farmers treat crops
Details of environment surrounding the farm
Trials/Observational approach is limited:
Cannot freely intervene on real systems
Dimensionality of real systems is too large
Proposed alternative:
Combination of expert, observational and modeled data
Hybrid structural models:
Process-based models (simulation of crop behavior, environment, soil, etc.)
Behavioral models (farmer decisions)
Prediction for future scenarios
Requires participatory process to envision future scenarios
Climate
Ag technologies and practices
Propose range of plausible future worlds
Pathways forward: New Data Infrastructure for Agriculture
Many challenges in coordinating community
Data ownership
Voluntary vs Mandatory
Soft vs Hard Infrastructure
Alternative: remote sensing