Land Health Surveillance

Harnessing science and technology to provide

Reliable, comparable, and locally relevant information on land health status, risks and intervention outcomes

Global Research Project 4: Land HealthWorld Agroforestry Centre (ICRAF)

Land Health

The capacity of land to sustain delivery of essential ecosystem services (the benefits people obtain from ecosystems)

Widespread degradation is reducing productivity, impeding development , damaging the environment

Reliable data on land and soil functional properties

The things we really want to know about land and soil

Nutrient supply and retentionWater infiltration and storageAbility to resist erosionCarbon stocks and sequestration potentialTillage and engineering properties

Woody coverWildlife habitatHydrological functioning

Land health surveillance

AfricaSoils.net

Randomization of Sentinel Site locations stratified by climate

African Soil Information

Service

AfricaSoils Sentinel Site based on the Land Degradation

Surveillance Frameworka spatially stratified, hierarchical, randomized sampling framework

Sentinel site (100 km2)

16 Clusters (1 km2)

10 Plots (1000 m2)

4 Sub-Plots (100 m2)

Sampling plot (1 000 m2)sub-plots (100 m2)

Infrared Spectroscopy for rapid soil characterization

• Rapid

• Low cost

• Reproducible

• Predicts many soil functional properties

What soil properties affect spectral shape?

Soil IR fundamentals

1 = Fingerprint region e.g Si-O-Si stretching/bending2 = Double-bond region (e.g. C=O, C=C, C=N3 = Triple bond (e.g. C C, C N)≡ ≡4 = X–H stretching (e.g. O–H stretching)NIR = Overtones; key features clay lattice and water OH; SOM affects overall shape

Soil infrared calibrations

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Cost surfaces, etc.

CovariatesRemote Sensing (RS) and Spatial Data

Elevation

Vegetation

Hydrology

Topographical properties

Climate

Landsat

Legacy data

ASTER

Quickbird

MODIS

500 m

250 m

28.5 m

15 m

2.4 m

0.6 m

Loca

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Examples from UNEP-ICRAF West Africa Drylands Project

10 km

Reg

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160 km

Spectral prediction of TOC, POC, Charcoal-C

Australian and Kenya soils

Janik LJ, Skjemstad JO, Shepherd KD and Spouncer LR (2007) The prediction of soil carbon fractions using mid-infrared-partial least square analysis. Journal of Australian Soil Research 45(2): 73–81.

TOC: alkyl–CH2 stretching modes; carbohydrate overtones of the –COH stretch; carboxylic acid–COOH; amide I and II bands; alkyl–CH2 deformation; aromatic–CH in plane deformation; carbohydrate–COH stretch.CHAR: C=C skeletal vibrations; phenolic, or COO stretching vibrations; ring C–H in plane deformations

Spectral prediction of SOC in global alfisols

Kamau-Rewe, M., Rasche, F., Cobo, J.G., Dercon, G., Shepherd, K.D., Cadisch, G. (2011). Generic prediction of soil organic carbon in Alfisols using diffuse reflectance Fourier transformed mid-infrared spectroscopy. Soil Science Society of America Journal 75: 2358–2360.

Not treated (NT) and with mineral signature subtracted 550Δ

Spectral signatures respond to management-induced changes in soil functional properties

NARL long-term experiment, Kenya

Spectral differences in SOC quality in aggregates

Verchot, L.V., Dutaur, L., Shepherd, K.D., Albrecht, A. 2011. Organic matter stabilization in soil aggregates: Understanding the biogeochemical mechanisms that determine the fate of carbon inputs in soils. Geoderma 161: 182–193.

Microaggregates had similar spectra despite large texture differences

Luero 35% sandTeso 76% sand

The meso- and macro- aggregates, and macro-aggregates were enriched in carboxylic-C and aromatic-C, indicating the importance of OM decomposition and plant-derived C in the stabilization of larger aggregates.

• Vibrations of H-bonded hydroxyl O-H in phenols • Asymmetric and symmetric aliphatic-C CH3 and CH2

stretching.• C = O stretching of carboxyl and ketones• Aromatic C = C conjugated with C = O and/or COO-

Aliphatic C–H deformation of CH2 or CH3 groups • C-O stretching of polysaccharide

Spectral prediction of C mineralization rates in SOC fractions

Mutuo PK, Shepherd KD, Albrecht A, and Cadisch G (2006) Prediction of Carbon Mineralization Rates from Different Soil Physical Fractions Using Diffuse Reflectance Spectroscopy. Soil Biology & Biochemistry 38:1658–1664.

Extending to X-ray and laser technology

Aggregate stability using laser diffraction particle size analysis

ResultsResults ((

Mineral Semi-quant (%)

QuartzMontmorilloniteMicroclineKaoliniteHematiteIlmeniteGibbsite

76.94.03.210.22.92.00.8

Quantitative x-ray diffraction spectroscopy

ResultsResults ((Characterise sites relative to AfSIS population

ResultsResults ((Spectral applications in long-term trials• Increase sample density (IR interpolation)

• Time series samples?• Interpret treatment effects on SOC functional groups (IR)

•Develop generalizable spectral indices of across-site and management-induced soil quality

•Examine treatment effects on aggregate stability (LDPSA)

•Characterize sites in tems of: spectral similarity (IR); mineralogical analysis (XRD); AfSIS population sample (IR, reference measures)