vb standortcharakterisierung (cluster b: soil) wulf amelung, kurt heil, andreas pohlmeier, stefan...
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VB Standortcharakterisierung (Cluster B: soil)
Wulf Amelung, Kurt Heil, Andreas Pohlmeier, Stefan Pätzold, Urs Schmidhalter, Lutz Weihermüller, Gerd Welp
2
„Soil phenotyping“ to improve breeding
Field experiments must verify breeding success
But sites are never homogeneous
Unexplained variances reduce breeding success
e
Soil Sensing
Optimization of crop management,
Optimizing sampling schemes,
Explaining plant stress
Nmin: 22-90 kg ha-1 Yield: 6.1-9.8 t ha-1
Site heterogeneities: e.g. site for central experiments
3
?
4
Opticalsensors
B1: Mapping of soil properties
Texture Corg Nt CEC Water content
VIS-NIRS (mobile)VIS-NIRS (stationary)
Electromagnetic sensors
Capacitive sensors
EM38EM38-MK2
EnviroScanDeviner
50 0.2 0.4 0.6 0.8 1cumulative response
-2
-1.6
-1.2
-0.8
-0.4
0
dept
h of
inve
stig
atio
n [m
]
vertical 1.18 m
vertical 0.71 m
vertical 0.32 m
horizontal 1.18 m
horizontal 0.71 m
horizontal 0.32 m
-0.25
-0.5
-0.9
-1.1
-1.8
Area,N
Tool Mode,coil distance
Dependentvariable
Equation Adj. R2, Sign.
A15N = 12
EM38 V 1,0 mClay
1/clay = 3,06+1/ECa 0,82***
H 1,0 m 1/clay = 2,29 + 49,01*1/ECa 0,78***EM38-MK2
V 1,0 m 1/clay = 2,23+51,74*1/ECa 0,87***
H 1,0 m √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***
EM38 V 1,0 mSilt
H 1,0 mEM38-MK2
V 1,0 m 1/silt = 1,48+2,32*1/ECa 0,76***
H 1,0 m
V 0,5 m
H. 0,5 m
EM38 V 1,0 mSand+Skeleton
√(Sand+Skeleton) = 0,51+1,09*1/ECa 0,59***
H 1,0 m (Sand+Skeleton) = 0,19+3,75*1/ECa 0,54***EM38-MK2
V 1,0 m √(Sand+Skeleton) = 0,47+2,29*1/ECa 0,64***
H 1,0 m
V 0,5 m(Sand+Skeleton) = 0,24+2,36*1/ECa
0,32**
H. 0,5 m
7
B1: Mapping of soil variety (4 weeks little rain)
Site Dürnast
8
B1: Mapping of yield variety
• High relevance for improving breeding success• Digital maps of (static) soil heterogneity
=> Quantitative mapping of water contents?
9
B3: Quantitative EMI?
0 100 200 300 400Relative Location
0.4
0.6
0.8
1
EC
a [d
S m
-1]
Early morningMid day
Repeated measurements within a day
0 10 20 30 40Relative Location
14
16
18
20
22E
Ca
[mS
m-1
]
Person 1Person 2
Person 3Person 4
Influence of operator (Instrument B)
0 10 20 30 40Relative Location
15
20
25
EC
a [m
S m
-1]
Instrument BInstrument A
Comparison of two instruments (EM38-DD)
Robinson et al. (2004)
Nüsch et al. (2010)Nüsch et al. (2010)
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 (R² = 0.87; 0-90cm)
10
ECa Measurements – Scheyern
Quantitative vertical and horizontal changes are well reproduced by ECa
3-layer inversion
11
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?
12
B4: NMR relaxometry and MRI
Brownstein-Tarr equation
13
Original MRI of barley in Klein-Altendorf (uL)
Mathematical Reconstruction of root architecture
Modelling of water uptake
Soil parametes of B1- B3
Spatial assessment of root water uptake=> No nutrients?
15
B1: NIRS reflectance
N Mean Range Error R2
Ct % 45 9.22 7.24 - 9.99 0.09 0.68Ccarb % 45 5.82 3.74 - 6.81 0.09 0.75
Nt % 45 0.41 0.14 - 0.50 0.007 0.62
Laboratory
Clay content: R² = 0.84 - 0.90
Corg, Cinorg, Nt : R² = 0.88 – 0.93
Field Methods (B1, B3):
Mathematic derivation of soil properties from spectral data (PLS, SVM)
B3: Corg after local calibration
Arable soils, Germany (n=68)
Corg Elementaranalyse [%]
0 1 2 3
Co
rg M
IR-
PL
S [
%]
0
1
2
3 RMSECV= 0,07RPD = 10,1R² = 0,99
Bornemann et al., 2010, 2011; SSSAJ
In the meantime Clay content, Fe-content, carbonate content CEC Corg, Nt
Particulate C Available phosphate
R² = 0.88-0.99
Chamber box design for the field
Rodionov et al., 2014a; STILL
18
SOC-prediction depends on soil moisture and roughness
7 8 9 10 11 12 13-200
-150
-100
-50
0
50
100
150
200
250
f(x) = 0.742173147467258 x − 7.98315504665408R² = 0.0160195640571011
predicted SOCLinear ( predicted SOC)upper 95% confidence intervallower 95% confidence interval
observed SOC, g kg-1
bagg
ed p
redi
cted
SO
C, g
kg-
1
Rodionov et al., 2014b; SSSAJ
19Rodionov et al., 2014b; SSSAJ
Predictions with variable moisture and roughness
7 9 11 137
9
11
13
f(x) = 0.720809754672823 x + 2.79683456666252R² = 0.912532894844378
Labor - SOC, g kg -1
VIS-
NIR
S -
SO
C, g
kg
-1
y = 0.72x + 2.80; R² = 0.91
20
SOC elemental analysis (g kg-1)
6 8 10 12 14 16
SO
C p
redi
cted
on-
the-
go (
g kg
-1)
6
8
10
12
14
16SOCpred. = 0.8689 SOCelem. anal. + 0.9971
R² = 0.65; n = 188
VIS-NIRS on-the-go (3 km h-1)
But this is all surface sensitive (2 mm)=> Extrapolation to deeper soil?
Hilberath (arable field)
21
Gamma≤ 0.4 m
Relation 40K-counts / Sand
22
Unexpected correlations with mineralogy
Outlook: Flight campaigns
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24
Dank … and we could reduce costs by over 700 Lire if we do not assess the ground - BMBF
- MIWFT