toward next generation crop models...toward next generation crop models james w. jones crop modeling...

Toward Next Generation Crop

Models James W. Jones

Crop Modeling for Agriculture and Food Security

International Crop Modeling Symposium

Berlin, Germany

15-17 March 2016

Source: Monica Ozores-Hampton

Comments on history of crop modeling

• What fueled/limited progress?

• What are we learning?

Example Efforts Contributing to Next Generation

Models

• Model Intercomparison Projects (MIPs)

• Data, big data

• Genetics, gene-based modeling

What are we learning?

Laying the foundation for next generation

models

Outline

Crop Modeling for Agriculture and Food Security Berlin, Germany: 15-17 March 2016

Early innovators/innovations, visionaries

Fueled by technological development

Funding inconsistencies

Not fully embraced by traditional agronomic

researchers

Families or “tribes” of modelers

Long period of limited progress, stagnation

…

Nature Climate Change paper by Rotter et al. 2011.

titled “Crop-Climate Models Need an Overhaul”


History - Characterized by:

Modeling linkage between transpiration and growth (C. T. de

Wit, Wageningen University, 1958.

Modeling evapotranspiration & soil water dynamics (H.

Penman, J. Monteith, J. T. Ritchie, 1972, others)

Photosynthesis and growth (R. S. Loomis, W. G. Duncan, F.

Penning de Vries, de Wit et al.*)

Phenological development (J. Hesketh, H. Nix, J. Ritchie)

Soil nutrient dynamics and uptake (H. van Keulen, D.

Godwin, W. J. Parton)

Integrated models (SUCROS, CERES models, EPIC, AFRC-

Wheat, DSSAT-CSM, CENTURY, APSIM, STICS, …)


Early Innovators / Innovations

*Bouman et al., 1996. (C. T. de Wit, H. van Keulen, F. Penning de Vries, R. Rabbinge, J. Goudriaan, M. Kropft, M. van Ittersum, etc.)

My first paper, 1972, Jones, Hesketh, Kamprath. N balance for crop models: A first approximation. Crop Science.

Mathematical modeling in other fields

Development of computers, enabling solutions

to nonlinear dynamic system models

Development & proliferation of personal

computers

Remote sensing, satellite technologies

Internet, communication technologies, www

{Molecular genetics technologies}


Development Fueled by Technological Progress

Incremental progress to new plateaus funded by immediate

crises or initiatives • Unexpected large Soviet Union wheat purchase in 1972

• US Soil & Water Conservation act (1980)

• Overuse of pesticides; funding for integrated pest management (e.g.,

national CIPM project in US)

• USAID-funded IBSNAT project to develop systems approaches for

technology transfer (1983-93); DSSAT foundation continues now

• Systems Analysis and Simulation for Rice Production (SARP), 1984

• Climate change assessment of impacts and adaptation, starting in late

1980s (IPCC assessments)

• Australia support of APSIM, starting in 1991 continuing now

• EU research funding for policy support (SEAMLESS project, 2005-9)

• AgMIP and MACSUR crop modeling initiatives; starting in 2010

But, slow, incomplete embracing of crop modeling by

“mainstream” agricultural researchers/experimentalists


… also by Funding & Institutional Initiatives

Uncertainty, understanding and reducing it

Open, discoverable, accessible, and usable

data

Incorporating modern genetic information in

crop models


Brief Overview of Three AgMIP Efforts

Asseng, Ewert et al. 2013.

Nature Climate Change

Ensemble of models

predicted yields

accurately even if

uncalibrated (given

only phenology info.)

No individual model

predicted all sites

accurately.

Crop models are more uncertain than most would

have thought. Why? • Model structure, functions vary and contribute considerably

• Parameter definitions and values vary, difficult to harmonize

• A number of high impact journal articles are showing new ways of

quantifying uncertainties due to model structure, parameters,

Inputs, measurement errors (e.g., Wallach et al. 2016. Estimating

model prediction error … Submitted).

Most crop models have been developed using a limited

range of conditions, e.g., for weather & soils in region

where model was developed

More extensive datasets are needed to develop and test

models for more reliable simulations


What Are We Learning?



data


crop models



Data: the Foundation of the Knowledge Chain

12

Modeling system knowledge chain. Infrastructure includes technical, institutional,

and organizational aspects (adapted from Lokers & Janssen 2014).

13

There is a Data Gap

• More extensive data are necessary to achieve robust models

• Major gap between potential value of data collected in

research and the value currently obtained through their use

• Typically, data collected in agronomic experiments are used

for the original research purpose only

• Vastly greater value can be obtained if data were combined

across locations, time, and management conditions

• Data are needed to provide the science base for next

generation models of agricultural systems and decision

support systems

• US federally-funded projects are required to make data

openly available; no existing process

• NARDN objectives:

• Create distributed network for harmonized crop, livestock data

• Devise common metadata for those systems

• Develop tools for discovering, accessing, and using the data

• Develop tools, procedures for researchers to contribute data

• Develop plan for long-term network operation

National Agricultural Research Data Network, NARDN

Characteristics of NARDN Project

15

• Emphasis on core sets of data defined by researchers;

main portal at the US National Agricultural Library (NAL)

• Uses ICASA Data Standards for crops (~30 years

experience) as a Data Dictionary (White et al., 2014)

• New definition of livestock core data, data dictionary

• Demonstrated by AgMIP to work for different crop

models (e.g., running APSIM and DSSAT models with

same inputs, assumptions)

• Active contributions by researchers, initially 13 core

states; open to all

• ARS endorsement and support for data portal at NAL

• Endorsed by international data initiatives and private

sector collaborators

16

Virtual Research Lab: Network of Networks



data


crop models



Genetic Coefficients – genotype-specific parameters (GSPs) for

simulating variations among cultivars/hybrids

First used in CERES maize and wheat models (J. T. Ritchie and

others)

Key GSPs defined for depicting phenological development

differences (phase durations, daylength sensitivities) and yield

formation

Most crop models now have these, BUT there are major

differences among models in definitions/use

Difficult to estimate, limiting applicability of models

No direct link to modern genetic information, and field data are

required to parameterize GSPs for each cultivar

Next generation crop models must provide a better way to

include gene effects (gene-based crop model)


Genetics and Crop Models

Current approaches – develop relationships between GSPs

and QTLs (e.g., White and Hoogenboom, 1996, 2003;

Messina et al., 2006; etc.)

Why not continue this?

• Current models do not include GSPs for all processes and traits that

we now know are under genetic control (examples from this study)

• May need to modify environmental effects, interactions, in the model

• Current crop models are not ideally structured to make all of the

changes that are needed.

• Major changes may be needed, but code may be reusable

• Although some existing crop models are modular, new modules are

needed that are designed based on what we are now learning about

genetic control of processes and so that new modules can be easily

modified as more is learned, fine granularity

Need for a gene-based model

MR is a GSP (genetic coefficient)

g(P,G) includes two GSPs (slope and critical day length)

f(T) has been assumed to be the same for all genotypes

(e.g., base and optimal temperature thresholds did not

vary across genotypes)

RF thus depends on G; E={T,P} for a particular day t

Geneticists and bioinformatics specialists in UF’s NSF

gene-based modeling project told us we were wrong

They were correct; we had developed this model over 20

years ago

Incorporating Genetic Effects RF(t|G,T,P) = MR(G) * f(T) * g(P,G)


Two Models for Rate of Development

Toward First Flower

Hwang et al., Agricultural Systems (in review)

1. In the DSSAT CROPGRO-Bean Model

RF(t|G,T,P) = MR(G) * f(T) * g(P,G)

𝑅𝐹 𝑡 = 0.029 + 7.5 · 10−4 𝑇𝑀𝐸𝐴𝑁 𝑡 − 21.35 − 7.3 · 10−6 𝑆𝑅𝐴𝐷 𝑡 − 18.31 − 2.2 · 10−3 𝐷𝐿 𝑡 − 12.7

−3.3 · 10−4 𝑇𝑀𝐸𝐴𝑁 𝑡 − 21.35 𝐷𝐿 𝑡 − 12.7

+9.8 · 10−4 · 𝑇𝐹1 + 1.7 · 10−3 · 𝑇𝐹2 − 3.9 · 10−4 · 𝑇𝐹3 + 2.0 · 10−4 · 𝑇𝐹4

−1.5 · 10−4 · 𝑇𝐹5 + 8.9 · 10−4 · 𝑇𝐹6 − 5.3 · 10−4 · 𝑇𝐹7 − 3.1 · 10−4 · 𝑇𝐹8

−3.4 · 10−4 · 𝑇𝐹9 − 9.7 · 10−5 · 𝑇𝐹10 + 2.6 · 10−4 · 𝑇𝐹11 − 6.6 · 10−5 · 𝑇𝐹12

+𝑇𝐹2 −3.6 · 10−5 𝑇𝑀𝐸𝐴𝑁 𝑡 − 21.35

+𝑇𝐹3 6.7 · 10−5 𝑇𝑀𝐸𝐴𝑁 𝑡 − 21.35 − 1.1 · 10−3 𝐷𝐿 𝑡 − 12.7

+𝑇𝐹5 5.5 · 10−5 𝑇𝑀𝐸𝐴𝑁 𝑡 − 21.35

+𝑇𝐹7 −2.6 · 10−4 𝐷𝐿 𝑡 − 12.7

+𝑇𝐹12(−6.4 · 10−6 𝑆𝑅𝐴𝐷 𝑡 − 18.31 − 3.9 · 10−4 𝐷𝐿 𝑡 − 12.7 ) [7]

2. Mixed effects dynamic model, geneticists

Phaseolus Vulgaris L. (Common Bean)

184 Recombinant Inbred Lines, 5 locations

Mixed Effects dynamic development rate model

Equation (2) from last slide


GxE Model for Time to First Flower

Significant temperature – daylength interactive effects on

development rate/time of first flower

Significant effects of 12 quantitative trait loci (QTL) on

development variations among genotypes

From 75 to 80% variability among 184 genotypes grown in 5

environments were explained by GxE dynamic model

And concluded:

Need to revise functional form of development rate model

Need to develop fine granularity modules for dynamic

processes (as traits)

Develop an evolutionary pathway toward Next Generation

models as knowledge is gained

More collaboration among crop modelers, geneticists, and

bioinformatics may lead to transformations in models

We found that

New activity to compare different gene-based phenology

models and gene incorporation into 12 rice crop models

Co-led by T. Li (IRRI), X. Yin (WUR), and T. Lefarge

(CIRAD)

Data on about 300 lines grown across different

environments are available from IRRI

Additional data from CIRAD/IRRI to be made available

Evaluate phenology components of existing models, using

genetic information to inform each, and a new non-linear

mixed effects dynamic model


AgMIP Rice Modeling Team

Intercomparison of models for specific crops

Harmonizing databases, model inputs

Model improvement; gene-based modeling

Incorporating pest and disease damage in crop models

Crop rotations, residue management

Expanding models to include under-served crops (e.g.,

vegetables, fruit, pasture, trees)

Incorporation of nutrient composition in crop models (ILSI)

Incorporation of ozone effects

Spatial modeling (e.g., for regional & global assessments)

Coupling crop, livestock & household economic models for

farming systems analyses


AgMIP, MACSUR Contributions to Next Generation

Crop Modeling

Early crop modelers can feel pride in getting this far, but

humility and a new generation of models are needed.

Open, discoverable, accessible, and usable data are

essential; More extensive datasets are needed to develop and

test models for more reliable, robust simulations

Technologies continue to enable new models, new

capabilities and application opportunities

• Molecular genetics

• Low powered sensors, rapid phenotyping platforms

• Internet of things

User demands for information derived from models will grow

for specific use-cases

Should establish requirement for publicly-funded agronomic

research to include modeling component and make data

openly available


Prospects for Next Generation Crop Models

Adopt a data culture across the research to application chain

Include math and modeling courses in degree programs

Include interdisciplinary experience in graduate agricultural

science research programs

Emphasize transdisciplinary research, teams incorporating

geneticists, field experimentalists, modelers, pathologists,…

Provide funding opportunities & incentives for incorporating

modeling components into research

Continue to build community of science; but not silos

Embrace diversity of approaches

Laying Foundation for Next Generation Models

Thank You


toward next generation crop models...toward next generation crop models james w. jones crop modeling...

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