Use of LTE data for cross-scale cropping system modelling

Date: 29th May 2018

Use of LTE data for cross-scale cropping system modelling and

assessment

Rothamsted, 21st – 23rd May 2018

Frank A Ewert

Leibniz Center for Agricultural Landscape Research (ZALF) e.V., Germany

INRES-Crops Science, University of Bonn, Germany

Background

Use of LTEs for modelling

Selected results

Challenges and opportunities

2

Crop and cropping system models

Crop models

Radiation interception

Assimilation

Allocation

Organ growth

Leaves, stems, ... Roots

Respiration

Phen

olo

gy

Weather and CO2

Soil

H2O

, N

an

d C

Pla

nt

NE

T


Assimilation

Allocation

Organ growth


Respiration

Phen

olo

gy

Weather and CO2

Soil

H2O

, N

an

d C

Pla

nt

NE

T


Assimilation

Allocation

Organ growth


Respiration

Phen

olo

gy

Weather and CO2

Soil

H2O

, N

an

d C

Pla

nt

NE

T


Assimilation

Allocation

Organ growth


Respiration

Phen

olo

gy

Weather and CO2

Soil

H2O

, N

an

d C

Pla

nt

NE

T

Physiological processes

Crop

Soil

Atmosphere

Man

agem

ent

Bre

edin

g

3


0

500

1000

1500

2000

2500

3000

0 50 100 150 200 250 300

Day of the year

g m

-2

0

2

4

6

8

LA

I

Stems

Roots

Grains

Total Biomass

LAI

• Dynamic

• Deterministic

• Process-based

• Numerical simulation

• Temperature

• Precipitation

• Radiation

• CO2

• …

Climate

• Irrigation

• Fertilization

• Varieties

• Sowing date

• Pests, diseases

• Rotation

• Tillage

• …

Management

Soil

• H20

• C, N, P, K

FactorsVariables Model type

4

Crop 1 Crop nCrop 2

time

Crop

Soil

Atmosphere

Man

agem

ent

Bre

edin

g

Crop model Cropping system model


5

Main areas of model application

Research Teaching Decision support Communication

Virtual phenotyping

Functional Structural Plant Modelling

Observational data analysis

Impact assessment

Genetic modelling

Integrated modelling

Model comparison and improvement

Farming systems

Markets

Yield gap ananlysis

Land use

Crop

Soil

Organism

Organ

…

6

Scaling crop system models

Sources of uncertainty

Input data• Climate, soil, management

• Genetic characteristics

Model• Structure

• Parameters

Output data• Post processing

• Observations

Aspect of scale

Asseng et al, 2015

Ewert et al, 2013

Example, wheat yield

Bonn

NRW

Germany

Global

7

Crop modelling and LTE

• Time series (but changes in M, G)

• (Spacial variabilitymultiple sites)

• Rotations

• Climate, CO2

• Soil characteristis

• Crop characteristics

• Management (NPK, manure, …)

• E (C+S) x M E… Environment

M… Management

C… Climate

S… Soil (NPK, C, H2O,…)

G… Genotype

Model inputs (potentially) available from LTE

• G x E

• G x M

• G x E x M

Data

Broadbalk Winter Wheat

experiment, Rothamsted

Johnston and Poulton (2018)

Goudriaan and Zadoks

(1995) in Fuhrer (2003)

8

Examples of using LTE data for modelling

Modelling soil properties

Example: soil C

Multi-site experiments

Example: soil C

Example: RothC with data from

LTE Rothamsted, spring barley

Johnston et al., 2009 in Johnston and Poulton (2018)

Sim und Obs organic carbon

9

Müncheberg, D Bad Lauchstädt, D


Few studies to model

soil and crop processes

Kersebaum, (2007)

10

Treatment effects

reproduced by all

models and

observation


Few studies to model soil and crop

processes multi-model simulations

Crop-specific (normalised mean absolute) errors of

yield sims across all models, sites and treatments.

Kollas et al., (2015)

5 Locations

11

? Modelling long time

series of crop and soil

variables

?? Parameterisation for

changing varieties?


Yield change and variability

of winter wheat in the long

term fertilization experiment,

Dikopshof, Bonn, 1953-2013Rueda-Ayala, et al., (2018)

12


? Modelling long time

series of crop (and soil)

variables;

Example phenology

Direct comparison of winter

wheat varieties grown at

Dikopshof, Bonn, 1952-2013

31 M

ay 2

016

New cultivar

(Tommi)Old cultivar

(Heines VII)

Dikopshof, Bonn (D), 1953-2013

Rezaei, et al., (2018)

13

Challenges and opportunities (scaling)

• Soil

• Climate

• Management

• Crop rotations

Ewert et al, 2014

Up-scaling crop/soil responses

• Sampling

• Aggregation

Phenology observation stations (>6000),

1959-2009https://eoimages.gsfc.nasa.gov/images

14


Up-scaling

• Sampling

Zhao et al, 2016

Teixeira et al., 2015

Time

Space

Cohort

s

a) b) c)

Time

Space

Cohort

s

Time

Space

Cohort

s

a) b) c)

Ewert, 2004

Aggregation of rotation

a) Mi… Monocrop, initialised per a

a) Mc… Monocrop, continouos

b) Rs … Crop rotation, instance

c) Rs … Crop rotation, aggregate


Teixeira et al., 2015

Aggregation of rotation

Example: New Zealand

Effect of aggregation

methods depends on:

• Crop

• Output variablw

Autumn wheat Silage maize Forage kale Green-feed wheat

Mi… Monocrop, initialised

Mc… Monocrop, continouos

Rs … Crop rotation, instance

Rs … Crop rotation, aggregate


17

Challenges and opportunities

The Use of Long Term Field Experiments in Soil Research – The example of

Bonares, M Grosse Tuesday, 11.40-12.30

• Soil

• Climate

• Management

• Crop rotations

Aggregation / Sampling of rotation

18

Fair principles, data management and sharing, repositories and LTE data

Tuesday 15.45-17.00 (C Hoffmann, The Bonares Repository)

Often analogue

Metadata sporadic

Low accessibility

Difficult to cite & reuse

Archiving unsafe

Standards

203 LTFEs

Running times >20 a

BonaRe Data Repository is „FAIR plus“follows the FAIR principles AND data are freely available

Recent data situation

19

Challenges and opportunities (outlook)

Sensing (FLEX/Sentinel 3),

ZALF-Drone)

Farm and landscape

Monitoring

Adjacent experiments

Date: 29th May 2018

Use of LTE data for cross-scale cropping system modelling and

assessment

Rothamsted, 21st – 23rd May 2018

Frank A Ewert

Leibniz Center for Agricultural Landscape Research (ZALF) e.V., Germany

INRES-Crops Science, University of Bonn, Germany

Thank you

Documents

Use of LTE data for cross-scale cropping system modelling