A process-based modeling approach to forecast and optimize agronomic systems

Designing Optimum Genetic Improvement & Agronomic Systems, Ames, IA, November 29th , 2016

Sotirios ArchontoulisAssistant Professor of Integrated Cropping Systems

Agronomic system: soils + crop-cultivars + climate + management

Planting Harvesting

Grain fill periodflowering

Leaf area index

Soil water

NO3 + NH4

Biomass

Because of the complexity in crop yield, scientists break it down to its components and study only a single component without putting it back together

Very important but usually ignored

Optimizing the agronomic system

What? Yield N-loss Irrigation and nutrients Profits Yield and profits and N-loss

How? Statistical models Process-based models

Type of optimization? Static Dynamic

Optimize or track the system and respond accordingly?

Process-based modeling (APSIM software)

EvaporationRunoff

Drainage Decomposition MineralizationImmobilization

NitrificationDinitrification

Leaching

PhenologyLight interceptionBiomass accumulationBiomass partitioningBiomass retranslocation Leaf area development SenescencePlant death

Water uptakeNutrients uptake

rain temperature radiation

Carry over effects

Grains Stover

INPUTS

INPUTS

OUTPUTS

APSIM has become one of the world’s foremost farming system model > 3,000 licenced users > 100 countries

Holzworth et al. 2014 Environmental Modeling & Software 62: 327-350

Open source codeFree to downloadhttp://www.apsim.info/

ISU work with the APSIM model

Teaching (since 2012) Research (since 2013) Extension (since 2014)

On-going or finished studies Yield – N relationships Manure effects on yields Corn hybrids Soybean varieties Soil water Tile drainage CO2 emissions Rye cover crop Prairies modeling Soil organic carbon Root dynamics Plant growth Soil temperature Biochar effects Erosion Rotation effects N fixation

Databases for Iowa & USA ~ 500 meteorological files (each has 32 years)~ 350 soil profiles (source: web soil survey)~ 60 soybean cultivars (maturity 00 to 9)~ 20 corn hybrids (maturity 85 to 125) ~ 7 workshops

Next workshop: June 2017

The APSIM model and past performance in Iowa Water and Nitrogen limitations included Pest/diseases/phosphorus (not yet)

Puntel , Sawyer, et al., 2016 Front. Plant Science

Continuous corn in central Iowa

Full N

Zero N

Sequential analysis; includes residue, water and nitrate carry overs

How can we optimize the system? Since APSIM has the appropriate algorithms we can optimize the system for NUE

Plant breeders/ Crop physiologists

SoilScientists

Farmers Simulation experiments

Define & test Hypotheses

Optimize the system for each field separately

Statistics (objective functions)Synchronize Demand/Supply

Increase Yield

Decrease N loss

Design field experimentsDefine new hypotheses

Outputs for evaluation

Agronomists

Design and run experiments creation of databases

Farmers (management)

Breeders &physiologists(hybrids)

Crop rotation (previous crop)Cover crops Time of N applicationAmount & type of N applicationManurePlant density & sowing time

PhotosynthesisCanopy stay greenN mobilization and NHIN concentration of tissueRoot architecture (depth x rld) Sink capacity

Soil scientists SOMN inhibitors

Replicates Sum

Factor Level

221010??

??????

??35 years weather data????

Simulation model

Results

Simple exercise

Objective:Increase grain yieldDecrease N leaching

Approach:Explore N management- Rate - Time

Replications:30 historical years

Location:Ames, IA

0 75 150 225 300

0 75 150 225 300

N A M Jun Jul

N A M Jun Jul

May application 180 kg N/ha

Results

Simple exercise

Objective:Increase grain yieldDecrease N leaching

Approach:Explore N management- Rate - Time

Replications:30 historical years

Location:Ames, IA

0 75 150 225 300

0 75 150 225 300

N A M Jun Jul

N A M Jun Jul

May application 180 kg N/ha

optimum

What did we learn?

Yes it works but we need more than

this!

Variability (due to weather) quite high

Risks and logistics

Variability in hybrids was ignored

Example of a static, process-based optimization tool for soybeans

Inputs

http://agron.iastate.edu/CroppingSystemsTools/soybean-decisions.html

Overview of the FACTS platform (dynamic tool)FACTS is a publically available web-tool that deals with production and environmental aspects of cropping systems

Forecast tool Assessment tool

(during the growing season) (before/after the growing season)

perform what-if scenario analysis to identify management practices with the highest profits and lowest environmental impacts

Year 2016 (learn) Multi-year (inform 2017 decisions)

2015 proof-of concept (8 fields) 2016 FACTS publically available (20 fields)

Why we do it Provide quantitative

answers to questions that farmers ask

Improve science behind predictive models

FACTS sites and measurements in 20166 locations (3 with tile drainage)10 corn experiments10 soybean experiments

High resolution measurements; Frequency from 30 minutes up to 1 week

FACTS approach: http://crops.extension.iastate.edu/facts/

Publically availableNo feesNo subscription

Measurements and simulations both presented

Website information 2: soil water and nitrogen

Website information 6: crop yield prediction

Combine measured

Yield Prediction Error (last forecast)

Yield Prediction Error (first forecast)

FACTS web-visits

FACTS assessment tool under development

What-if management scenario analysis – example Ames corn 2016

2016Management

AlternativeManagement

% Yield Change

% N-loss(season)

35,000 plants/acre

5,000 pl/acre more 0.3 -1.8

5,000 pl/acre less -1.3 1.7

May 5th **Planting

12 days earlier planting -5.3 -4.7

12 days later planting 7.7 -7.1

111 RM, 1st week of Sept maturity

Hybrid (7 more days to maturity) 6.9 0

Hybrid (5 less days to maturity) -10.6 -2.4

150 lbs N/acre as UAN At planting

Split N (planting & V6) 0.12 -30.3

PrePlant N application -0.93 23.8

50 lbs/acre more N-rate 0.17 29.1

50 lbs/acre less N-rate -7.3 -15.4

Diagnose / learn from the past optimize the system

Initial soil profile nitrogen and water: Ames corn 2016

2016Values

AlternativeValues

% Yield Change

% N-loss(season)

30 lbs N03-N/acreon January 1

15 lbs less N over the profile -2.1 -12.5

15 lbs more N over the profile 0.2 10.1

3 feet water tabledepth on January 1

4 feet water table depth -8.9 -19.6

2 feet water table depth 0.7 38.6

Diagnose / learn from the past

Alternative management options: Ames soybean 2016 weather

2016Management

AlternativeManagement

% Yield Change

% N-loss(season)

137,000plants/acre

20,000 pl/acre more 1.4 -1.5

20,000 pl/acre less -1.9 1.5

May 6th

Planting 12 days earlier planting -10.0 30.3

12 days later planting -1.6 -10.6

2.7 MG, 1st week of Sept maturity

Variety (10 more days to maturity) 7.8 -4.5

Variety (6 less days to maturity) -15.6 12.1

Summary - % corn yield changes 2016Alternative Management NW NE Central SW SE

More plants 1.1 0.8 0.5 0.1 0.2

Less plants -3.1 -2.2 -1.2 -0.6 -0.6

Earlier Planting -2.7 1.8 -3.3 -3.1 -4.5

Later Planting -4.6 -6.9 2.6 0.7 3.1

Longer Hybrid 6.2 10.7 9.1 10.2 12.1

Shorter Hybrid -12.2 -12.2 -11.4 -12.8 -10.6

Split N 0 0 0 0 0

Pre-Plant N -0.1 0 -0.5 0 -0.1

50 lbs more N 0 0 0.1 0 0

50 lbs less N -9.9 -0.7 -3.7 0 -0.3

Next: “Big runs” to inform 2017 decisions Factor Level

Soil profile water at harvest 5

Soil nitrate at harvest 6

N-rate 20 (every 10 lbs/ac) to estimate EONR

N-time 5 (Fall, Spring, Planting, Split, Late)

N-type 4 (UAN, NH3, NH4NO3, UREA)

Hybrids 20 (maturity x sink capacity)

Planting date 15

Seeding rate 6

Row spacing 2

Tillage type x timing 10 = 5 x 2

Replications (weather years) 40 = 37 (historical) + 3 (forecast )

Sum 17,280,000,000

Model Outputs

Yield

N-loss

Input cost

Profits

Frost risks

FACTS website

X 6 sites Cover cropsResidue managementUpdate the analysis

every 2nd week to decrease uncertainty in weather

Final Reflection

Process-based models are powerful tools, but ….

Optimizing the agronomic system while making lots of assumptions for unknown variables is quite risky

Static optimization tools can aid decision making (select cultivar, decide planting date)

Optimized solutions might not be applicable in practice

Dynamic forecasting and assessment / diagnostic tools (e.g. FACTS) provide the necessary information for the farmers to optimize their cropping system/operation!

Challenges and opportunities: link breeding efforts to cropping systems modeling tools

Acknowledgments

SponsorsDepartment of Agronomy, ISU ANR, Extension ISUIowa Soybean AssociationSoybean Research CenterPlant Science Institute USDA- NIFA (1004346)USDA-NRCSClimate Corp, Monsanto DuPont Pioneer

Main collaborators Mike Castellano Mark Licht

@ArchontoulisLab

Lab-website: http://faculty.agron.iastate.edu/sarchont/FACTS-website: http://crops.extension.iastate.edu/facts/

Email: sarchont@iastate.edu