use of lte data for cross-scale cropping system modelling
TRANSCRIPT
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
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
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
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
Physiological processes
Crop
Soil
Atmosphere
Man
agem
ent
Bre
edin
g
3
Crop and cropping system models
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
Crop and cropping system models
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
Examples of using LTE data for modelling
Few studies to model
soil and crop processes
Kersebaum, (2007)
10
Treatment effects
reproduced by all
models and
observation
Examples of using LTE data for modelling
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?
Examples of using LTE data for modelling
Yield change and variability
of winter wheat in the long
term fertilization experiment,
Dikopshof, Bonn, 1953-2013Rueda-Ayala, et al., (2018)
12
Examples of using LTE data for modelling
? 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
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
Challenges and opportunities (scaling)
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
Challenges and opportunities (scaling)
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