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

23

24

Dank … and we could reduce costs by over 700 Lire if we do not assess the ground - BMBF

- MIWFT