Transcript Folie 1 - CROP.SENSe.net

VB Standortcharakterisierung
(Cluster B: soil)
Wulf Amelung, Kurt Heil, Andreas Pohlmeier, Stefan
Pätzold, Urs Schmidhalter, Lutz Weihermüller, Gerd Welp
e
„Soil phenotyping“ to improve breeding
 Field experiments must verify breeding success
 But sites are never homogeneous
 Unexplained variances reduce breeding success
Soil Sensing

Optimization of crop management,
Optimizing sampling schemes,
Explaining plant stress
2
Site heterogeneities:
e.g. site for central experiments
Yield: 6.1-9.8 t ha-1
Nmin: 22-90 kg ha-1
?
3
B1: Mapping of soil properties
Optical
sensors
Electromagnetic
sensors
Capacitive
sensors
VIS-NIRS (mobile)
VIS-NIRS (stationary)
EM38
EM38-MK2
EnviroScan
Deviner
Texture
Corg
Nt
CEC
Water content
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5
Equation
Adj. R2,
Sign.
1/clay = 3,06+1/ECa
0,82***
1/clay = 2,29 + 49,01*1/ECa
0,78***
1/clay = 2,23+51,74*1/ECa
0,87***
√clay = 0,26+0,04*√ECa
0,45**
V 0,5 m
Clay = 0,15+0,004*ECa
0,68***
H. 0,5 m
√clay = 0,256+0,05*√ECa
0,51***
1/silt = 1,48+2,32*1/ECa
0,76***
√(Sand+Skeleton) = 0,51+1,09*1/ECa
0,59***
(Sand+Skeleton) = 0,19+3,75*1/ECa
0,54***
EM38- V 1,0 m
MK2
H 1,0 m
√(Sand+Skeleton) = 0,47+2,29*1/ECa
0,64***
V 0,5 m
(Sand+Skeleton) = 0,24+2,36*1/ECa
0,32**
Area,
Tool
N
A15
EM38
N = 12
Mode,
coil distance
V 1,0 m
Dependent
variable
Clay
H 1,0 m
EM38- V 1,0 m
MK2
H 1,0 m
EM38
V 1,0 m
Silt
H 1,0 m
EM38- V 1,0 m
MK2
H 1,0 m
V 0,5 m
H. 0,5 m
EM38
V 1,0 m
H 1,0 m
H. 0,5 m
Sand+
Skeleton
B1: Mapping of soil variety (4 weeks little rain)
Site Dürnast
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B1: Mapping of yield variety
• High relevance for improving breeding success
• Digital maps of (static) soil heterogneity
=> Quantitative mapping of water contents?
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B3: Quantitative EMI?
Calibration needed by
 Electrical
Resistivity
Tomography (ERT)
 Direct Push Injection Logger
(DPIL)
 Cone Penetration Test (CPT)
 Capacity sensors or TDR
After calibration: good estimation of water contents
Robinson et al. (2004)
(R² = 0.87; 0-90cm)
Nüsch et al. (2010)
Nüsch et al. (2010)
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ECa Measurements – Scheyern
3-layer inversion
Quantitative vertical and
horizontal changes are well
reproduced by ECa
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ECa Measurements – Klein Altendorf
HCP 1.0 m (0-1.6 m)
VCP 1.0 m (0-0.8 m)
HCP 0.5 m (0-0.7 m)
VCP 0.5 m (0-0.3 m)
Excellent recordings of physical soil properties
=> Relevance for plant water uptake?
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B4: NMR relaxometry and MRI
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Brownstein-Tarr
equation
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Soil parametes of
B1- B3
Original MRI of barley in
Klein-Altendorf (uL)
Spatial
Mathematical
Reconstruction of
root architecture
assessment
of root
water uptake
=> No nutrients?
Modelling of
water uptake
B1: NIRS reflectance
Laboratory
 Clay content:
R² = 0.84 - 0.90
 Corg, Cinorg, Nt : R² = 0.88 – 0.93
Methods (B1, B3):
Field
 Mathematic
derivation of soil properties from spectral data (PLS, SVM)
Ct %
Ccarb
%
Nt %
2
N
Mean
Range
Error
R
45
45
9.22
5.82
7.24 - 9.99
3.74 - 6.81
0.09
0.09
0.68
0.75
45
0.41
0.14 - 0.50
0.007
0.62
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B3: Corg after local calibration
Arable soils, Germany (n=68)
Corg MIR- PLS [%]
3
RMSECV= 0,07
RPD = 10,1
R² = 0,99
 In the meantime
 Clay content, Fe-content,
 carbonate content
 CEC
 Corg, Nt
 Particulate C
 Available phosphate
2
1
0
0
1
2
3

R² = 0.88-0.99
Corg Elementaranalyse [%]
Bornemann et al., 2010, 2011; SSSAJ
Chamber box design for the field
Rodionov et al., 2014a; STILL
bagged predicted SOC, g kg-1
SOC-prediction depends on soil moisture and
roughness
250
y = 0.74x - 7.98
Radj² = 0.01
RMSEB = 14.20
RPD = 0.12
200
150
100
predicted SOC
50
0
upper 95% confidence
interval
lower 95% confidence
interval
-50
-100
-150
-200
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8
9
10
11
-1
observed SOC, g kg
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13
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Rodionov et al., 2014b; SSSAJ
VIS-NIRS - SOC, g kg -1
Predictions with variable moisture and roughness
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1:1
11
9
y = 0.72x + 2.80; R² = 0.91
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7
9
11
13
Labor - SOC, g kg -1
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Rodionov et al., 2014b; SSSAJ
VIS-NIRS on-the-go (3 km h-1)
SOC predicted on-the-go (g kg -1)
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SOCpred. = 0.8689 SOCelem. anal. + 0.9971
R² = 0.65; n = 188
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12
10
8
6
But this is all surface sensitive (2 mm)
=> Extrapolation to deeper soil?
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8
10
12
14
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SOC elemental analysis (g kg-1)
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Hilberath (arable field)
Gamma
≤ 0.4 m
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Relation 40K-counts / Sand
Unexpected correlations with mineralogy
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Outlook: Flight campaigns
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Dank
… and we could reduce
costs by over 700 Lire if we
do not assess the ground
- BMBF
- MIWFT
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