1. 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 2 Soil Sensing  Optimization of crop management, Optimizing sampling schemes, Explaining plant stress

3. N min : 22-90 kg ha -1 Yield: 6.1-9.8 t ha -1 Site heterogeneities: e.g. site for central experiments 3?

4. Optical sensors 4 B1: Mapping of soil properties Texture Corg Nt CEC Water content VIS-NIRS (mobile) VIS-NIRS (stationary) Electromagnetic sensors Capacitive sensors EM38 EM38-MK2 EnviroScan Deviner

5. 5

6. Area, N ToolMode, coil distance Dependent variable EquationAdj. R 2, Sign. A15 N = 12 EM38V 1,0 m Clay 1/clay = 3,06+1/ECa0,82*** H 1,0 m1/clay = 2,29 + 49,01*1/ECa0,78*** EM38- MK2 V 1,0 m1/clay = 2,23+51,74*1/ECa0,87*** H 1,0 m√clay = 0,26+0,04*√ECa0,45** V 0,5 mClay = 0,15+0,004*ECa0,68*** H. 0,5 m√clay = 0,256+0,05*√ECa0,51*** EM38V 1,0 m Silt H 1,0 m EM38- MK2 V 1,0 m1/silt = 1,48+2,32*1/ECa0,76*** H 1,0 m V 0,5 m H. 0,5 m EM38V 1,0 m Sand+ Skeleton √(Sand+Skeleton) = 0,51+1,09*1/ECa0,59*** H 1,0 m(Sand+Skeleton) = 0,19+3,75*1/ECa0,54*** EM38- MK2 V 1,0 m√(Sand+Skeleton) = 0,47+2,29*1/ECa0,64*** H 1,0 m V 0,5 m (Sand+Skeleton) = 0,24+2,36*1/ECa 0,32** H. 0,5 m

7. 7 B1: Mapping of soil variety (4 weeks little rain) Site Dürnast

8. 8 B1: Mapping of yield variety High relevance for improving breeding success Digital maps of (static) soil heterogneity => Quantitative mapping of water contents?

9. 9 B3: Quantitative EMI? Robinson et al. (2004) 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. 10 ECa Measurements – Scheyern Quantitative vertical and horizontal changes are well reproduced by ECa 3-layer inversion

11. 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. 12 B4: NMR relaxometry and MRI

13. Brownstein-Tarr equation 13

14. 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 15 NMeanRangeErrorR2R2 C t %459.227.24 - 9.990.090.68 C carb % 455.823.74 - 6.810.090.75 N t %450.410.14 - 0.500.0070.62 Laboratory  Clay content: R² = 0.84 - 0.90  C org, C inorg, N t : R² = 0.88 – 0.93 Field Methods (B1, B3):  Mathematic derivation of soil properties from spectral data (PLS, SVM)

16. B3: Corg after local calibration Arable soils, Germany (n=68) Bornemann et al., 2010, 2011; SSSAJ  In the meantime  Clay content, Fe-content,  carbonate content  CEC  C org, N t  Particulate C  Available phosphate  R² = 0.88-0.99

17. Chamber box design for the field Rodionov et al., 2014a; STILL

18. 18 SOC-prediction depends on soil moisture and roughness Rodionov et al., 2014b; SSSAJ

19. 19 Rodionov et al., 2014b; SSSAJ Predictions with variable moisture and roughness

20. 20 VIS-NIRS on-the-go (3 km h -1 ) But this is all surface sensitive (2 mm) => Extrapolation to deeper soil?

21. Hilberath (arable field) 21 Gamma ≤ 0.4 m

22. Relation 40 K-counts / Sand 22 Unexpected correlations with mineralogy

23. Outlook: Flight campaigns 23

24. Dank 24 … and we could reduce costs by over 700 Lire if we do not assess the ground -BMBF -MIWFT