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Nextgen Agricultural System Models and Use Cases:

The Economic Data Challenge

John M. Antle

Professor of Applied Economics

Oregon State University

tradeoffs.oregonstate.edu

Alphabet Modeling Talk, Nov 9 2021

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Themes

Observations and updates from the AgMIP NextGen project

The economic data challenge: greenhouse gas mitigation & sustainbility

Pathways forward

Discussion

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AgMIP NextGen Project: bridging the gap between data, models and users

AgMIP research team + stakeholder workshop (BMGF)

Agricultural Model Inter-comparison and Improvement Project (agmip.org)

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AgMIP NextGen Project

Computational agricultural science can accelerate innovation & improve decision making (simulation experiments vs field experiments)

Data the most important limitation to model improvement & use

Knowledge products needed to connect end-users (in science and in decision making) with data and models (demand-driven)

Private-public partnerships needed to support pre-competitive and competitive science, data, model, and knowledge-product development

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These are very hard problems: diversity, heterogeneity

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These are very hard problems: complexity

“Circular” gain-based system (Basso et al., Ag Systems 2021)

What’s the goal? Profitability vs greenhouse gas mitigation vs sustainability?

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How to put in the economics and evaluate tradeoffs at field, farm and landscape scales?

Basso and Antle Nature Sustainability 2020

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Economic analysis of agricultural systems

Evaluation paradigm (Heckman JEP 2010)

P1: implemented interventions in the environment where they are observed (the problem of internal validity in ex post evaluation)

P2: implemented interventions in a different but observable environment

(the problem of external validity in ex post evaluation)

P3: evaluation of new interventions in environments never historically observed

(the ex ante evaluation problem).

P2 and P3 require models satisfying “Marshak’s Maxim”: minimally sufficient structure needed to identify the impact of the intervention

“P3 is the problem that economic policy analysts have to solve daily. Structural econometrics addresses this problem. The program evaluation approach* does not.”

*program evaluation approach = estimation of treatment effects without specification of a structural model based on economic theory

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Implications for economic analysis of ag systems

Key methodological challenges linked to observability of key phenomena: inputs, outputs & prices (cost of production)

The identification problem(s)
Unobserved heterogeneity
Prediction of system performance with new technologies in new environments

RCTs (alone) don’t work: diversity, heterogeneity, complexity

Solution: combine expert, observational and modeled data

Hybrid structural models

Process-based models (experimental, observational)
Behavioral models (observational, expert)

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Identification problem in non-experimental data:

Heckman’s argument for structural models to solve P3 requires strong assumptions of parameter invariance not valid for new technologies or future exogenous variables

Even if “Marshak’s Maxim” satisfied, economic behavior often leads to failed “identification in the data”, i.e., failure of common support condition required for identification of counterfactuals

Solution: combine mechanistic models with better data & statistical models to identify structure of counterfactuals

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Example: “Identification” problem due to lack of common support in non-experimental data

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Unobserved heterogeneity? Or bad data?

In ag systems, many elements of “unobserved heterogeneity” are not time invariant ⇒ fixed-effects estimators do not solve bias problems

E.g., planting date, soil moisture at planting time that determine crop variety and fertilizer use

Key problem is inaccurate and incomplete data

Lack of accurate data on most management inputs and cost of production, and timing of input use

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Prediction of system behavior with new technologies in new environments

Two key elements:

Prediction of exogenous variables “out of sample”

Use participatory scenario methods (e.g., as in climate research)

Representation of new technologies

Use “hybrid structural models” that satisfy Marshak’s Maxim and overcome counterfactual identification problem

Use better observational data that overcome bias problems from unobserved heterogeneity and incomplete data

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Prediction of system behavior with new technologies in new environments: greenhouse gas mitigation

Dryland wheat system in PNW
Wheat-fallow => wheat-camelina system with no-till cultivation
Hybrid model: LCA + DNDC + TOA-MD

Antle et al. Mit. Adapt. Global Change 2018

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Prediction of system behavior with new technologies in new environments: greenhouse gas mitigation

Antle et al. Mit. Adapt. Global Change 2018

Adoption and impact depend on:

Crop prices

Biofuel market development

Policy support

Large uncertainties:

C rates

Markets & policy

Implications for current policy debate ... are data and modeling tools adequate?

AgMIP => need model ensemble

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Pathways forward: building a new data infrastructure

We can envision a system that would meet the needs of private and public use cases ...

Basso and Antle, Nature Sustainability 2020

But many issues to overcome:

Data “market” failure
Data ownership
Voluntary vs mandatory
Soft & hard infrastructure

To avoid data ownership issues, build real-time site-specific data with remotely-sensed and other public data?

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Thanks for the opportunity to share my work with you!

References:

Antle, J.M. 2019. Data, Economics, and Computational Agricultural Science. American Journal of Agricultural Economics Volume 101, Issue 2, March 2019, Pages 365–382.

Antle, J.M., S. Cho, H. Tabatabaie and R. Valdivia. 2018. Economic and Environmental Performance of Dryland Wheat Systems in a 1.5 Degree C World. Mitigation and Adaptation Strategies for Global Change 24(2):165-180.

Antle, J., J. Jones and C. Rosenzweig. 2017. Next Generation Agricultural System Data, Models and Knowledge Products: Synthesis and Strategy. Agricultural Systems 155: 179-185.

Basso, B. and J.M. Antle. 2020. Digital Agriculture to Design Sustainable Agricultural Systems. Nature Sustainability 3 (April 2020): 254-256.

Basso, B, J.W. Jones, J.M. Antle, R.A. Martinez-Feria and B. Verma. 202. Enabling circularity in grain production systems with novel technologies and policy. Agricultural Systems 193 (Oct 2021):

Capalbo, S.M., J.M. Antle and C. Seavert. 2017. Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making. Agricultural Systems 155: 191-199.