1

Factors influencing soil organic matter content in human-

disturbed soils

Edoardo A.C. CostantiniConsiglio per la ricerca e la sperimentazione in agricoltura, CRA-ABP, Firenze, Italy

edoardo.costantini@entecra.it

European Society for Soil Conservationhttp://www.essc.sk/

Outline

Why soil organic carbon mattersCauses of variationsEffects of land use changesMonitoring: the Agroscenari projectEffects of soil managementSensor based monitoring to detecteffects of mangement variations.

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Soils provide provisioning and regulating ecosystem services relevant to major challenge areas of 1)climate change adaptation and mitigation, 2)food and energy security, 3)water protection, 4)biotechnology for human health,5)ecological sustainability, and 6)slowing of desertification.

Soil and ecosystem services

Organic matter pivotal functions

nutritional function in that it serves as a source of N, P for plant growthbiological function in that it profoundly affects the activities of microflora and microfaunal organismsphysical and physico-chemical function in that it promotes good soil structure, thereby improving tilth, aeration and retention of moisture and increasing buffering and exchange capacity of soils

Soil organic matteris a key indicator of soil health

Soil threats

• Contamination

• Sealing

• Compaction

• Salinisation

• Water and wind erosion

• Floods and landslides• Decline in organic

matter content• Loss of biodiversity

23% of Italian soils has less than 1% SOC in the first 30 cm (JRC, 2010)

Causes of variations: lithology and SOC

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a) Marine sediments, coastal and deltaic deposits, calcarenites and residual soil deposits;b) Alluvial and lacustrine deposits, clayey formationsc) Effusive formations, sandstone, metamorphic schist, clayey sandstone, marls and marly-pelitic turbidited) Lagoons and slope deposits;e) Calcareous and dolomitic rocks, intrusive and metamorphic non-schist rocks

Morphology and SOC

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a) Levelled lowlands b) Medium and low rolling hillsc) High rolling hillsd) Steep low hillse) Steep high hillsf) Low mountaing) High mountain

Climate and SOC

9

dry xeric xeric ustic udicUdometric Regimes

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

SOC

Con

tent

(dag

kg-1

)

Mean Mean±0.95 Conf. Interval

thermic mesicThermometric Regimes

1.4

1.5

1.6

1.7

1.8

1.9

2.0

SOC

Con

tent

(dag

kg-1

)

Mean Mean±0.95 Conf. Interval

Climate change and SOC(1978-1990 vs 1991-2006)

200 0 200100 Km

not arables -109 - 00 - 88 - 115

Index of Climate Impact on SOC Variations in Arable Lands

0 150 300 450 60075 Km

0.28 - 0.550.55 - 0.620.62 - 0.650.65 - 0.680.68 - 0.750.75 - 1.01

MAT VARIATIONS (°C)

-620 - -400-400 - -300-300 - -200-200 - -100-100 - 00 - 307 0 150 300 450 60075 Km

MAP VARIATIONS (mm/y)

Relatively higher in meadows, lower in agricultural areas, not significant in forests

Land use and SOC

arable lands meadows forestsLand Uses

1.21.41.61.82.02.22.42.62.83.03.2

SOC

Con

tent

(dag

kg-1

) Mean Mean±0.95 Conf. Interval

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Basic statistics on soil organic matter content (SOMC g.100g-1) and land use type in Italy

Land use

Mean Standard

deviation Sites (n)

SOMC<1.72

(% of sites)

SOMC<3.5

(% of sites)

Paddies 2.02 1.68 135 43 96

Vineyards 2.09 1.94 2,105 48 91

Vegetables 2.11 1.87 236 44 93

Agricultural mixed 2.15 1.87 605 48 90

Row-crops 2.23 1.84 11,084 38 90

Olive tree groves 2.29 1.87 1,816 41 85

Urban soils 2.32 1.93 102 43 85

Orchards 2.34 1.54 568 36 85

Meadows 3.04 3.35 1,982 30 75

Agroforestry 3.80 6.10 454 27 71

Wetlands 3.94 4.50 17 47 65

Mediterranean macchia 4.95 5.85 254 24 52

Rangeland 5.09 5.01 1,665 19 53

Woodlands 6.00 5.87 2,493 12 38

All land uses 2.95 3.41 23,516 34 80

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[1] Data from the national soil database.

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Irrigation

Basic statistics on soil organic matter content (SOMC g.100g-1) and land use type with respect to irrigation in the regions of central and southern Italy

Land use SOMC Standard error Sites (n)

Vegetables irrigated 1.90 ±0.09 109

Vegetables not irrigated 2.38 ±0.28 80

Row-crops irrigated 1.96 ±0.04 1,517

Row crops not irrigated 2.06 ±0.03 2,288

Orchards irrigated 2.39 ±0.12 277

Orchards not irrigated 2.80 ±0.13 289

Vineyards irrigated 2.05 ±0.08 405

Vineyards not irrigated 2.06 ±0.09 438

Olive tree groves irrigated 2.00 ±0.07 472

Olive tree groves not irrigated 2.08 ±0.05 855

Agricultural mixed irrigated 1.67 ±0.16 37

Agricultural mixed not irrigated 2.04 ±0.11 153

Meadows irrigated 2.24 ±0.19 217

Meadows not irrigated 2.48 ±0.17 392

All land uses irrigated 2.03 ±0.11 3,040

All land uses not irrigated 2.27 ±0.12 4,495

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Land use and SOC variations

150 0 15075Kilometers

< 50 very low

50 - 100 low

100 - 150 medium

150 - 200 high

> 200 very high

-357

.2

-299

.8

-242

.3

-184

.8

-127

.4

-69.

9

-12.

5

45.0

102.

5

159.

9

Estimation error (Mg hm-2)

0%7%

13%20%27%34%40%47%54%61%

Perc

ent o

f obs

150 0 15075Kilometers

< 50 very low

50 - 100 low

100 - 150 medium

150 - 200 high

> 200 very high

-314

.4

-252

.5

-190

.6

-128

.7

-66.

8

-4.9

56.9

118.

8

Estimation error (Mg hm-2)

0%

9%

18%

27%

35%

44%

53%

62%

Perc

ent o

f obs

150 0 15075Kilometers

< 50 very low

50 - 100 low

100 - 150 medium

150 - 200 high

> 200 very high-3

46.7

-304

.4-2

62.0

-219

.7-1

77.4

-135

.0-9

2.7

-50.

4-8

.034

.376

.711

9.0

Estimation error (Mg hm-2)

0%6%

12%18%24%30%35%41%47%53%59%

Perc

ent o

f obs

Agricultural Cambisols

Tipology Percentage

CalcaricVerticEutricFluvic

DystricOthers

48,011,510,710,54,5

15,8

Vertic Cambisol

Land use and organic carbon

Land use and microbic biomass

Biomassa microbica

0102030405060

0 30 60 90 120 150(mg C 100g-1)

cm

MacchiaPrato Coltivato

Land use and humification

DH (%)0

10

20

30

40

0 10 20 30 40 50 60cm

FrumentoPascoloMacchia

SOM variation caused by land use change

0102030405060708090

100%

Mac

chia

-frum

ento

Mac

chia-

pasc

olo

Mac

chia

-med

icaM

acch

ia-p

rato

st.

Mac

chia

-frum

ento

Mac

chia

-pra

to st

.

Soveria S.Vicarello

S. Quirico

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Soil resilience and land uses changes

2 vineyards: different age, same soil

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Year 2010 vs 1995

Total arthropods

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0

25

50

75

100

125

150

175

200

P1 P2 P3 P4 P5 P6 P7 P8

Art

ropo

di to

tali

(N)/c

arot

a di

terr

eno

area sperimentale

Artropodi del suolo totali (N)

2011 2012

Biological diversity 2011

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0

1

2

3

4

5

6

7

8

9

0

20

40

60

80

100

120

P1 P2 P3 P4 P5 P6 P7 P8

n°ta

xa

QB

Sar

QBSar 2011

QBS tot n taxa

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0

1

2

3

4

5

6

7

8

9

0

20

40

60

80

100

120

P1 P2 P3 P4 P5 P6 P7 P8

n°ta

xa

QB

Sar

QBSar 2012

QBS tot n taxa

Biological diversity 2012

0 150 300 450 60075Km

no soil28 - 6060 - 9090 - 120120 - 150150 - 180180 - 241

Carbon Stock Mg/ha 1979-1988

0 150 300 450 60075Km

no soil28 - 6060 - 9090 - 120120 - 150150 - 180180 - 241

Carbon Stock Mg/ha 1989-1998

0 150 300 450 60075Km

no soil28 - 6060 - 9090 - 120120 - 150150 - 180180 - 241

Carbon Stock Mg/ha 1999-2008

Total CS 3.32 Pg Mean CS 107 Mg hm–2

Total CS 2.74 Pg Mean CS 88 Mg hm–2

Total CS 2.93 Pg Mean CS 95 Mg hm–2

Monitoring SOC spatial and temporal changes

Monitoring SOC spatial and temporal changes

Strategies:Periodical sampling of benchmark sites

Agroscenari project (MAAF): Space for time sampling (climosequences)Resampling of legacy data

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Aim and relevance of the project

To identify threshold values of climatic indices, able to separate significant SOC variations in space and time, within four typical Mediterranean crop systemsTo map the areas most sensitive to past and future climate changes

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►Climatic parameters from the national grid (30x30 km), ► monthly values from 1961 to 2010, ► downscaling to a 1 km grid by Geographical Weighted Regression using auxiliary variables, i.e., distance from the coast and relieves, elevation, dominant wind direction, (leeward/windward effect), latitude and longitude. ► two reference long-term climates: 1961-1990 (t1) and 1981-2010 (t2)► De Martonne index (IDM) as reference climate indicator

Materials and methods

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►147 soils sampled along climatic gradients (climosequences) ► 3-4 replications according to local variability►SOC analysed with Springer and Klee (g dag-1)► IDM values of the sites in each climosequenceclustered in 2 groups and SOC of the sites in the 2 clusters submitted to analysis of variance► 69 legacy sites, surveyed in the years 1960-2000, resampled and analysed in 2011. ► Land use permanence checked by remote sensing analysis. Uniform SOC analysis► ∆SOC minimum detectable differences assessment

Materials and methods

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Po valley

resampled total23 62

Soil

Chromic Luvisol, loam

Croppingsystem

row crops and forage rotation

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resampled total3 23

Campania

Soil

Vitric Andosol, medial

Croppingsystem

olive treegroves

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resampled total14 30

Sardinia

Soil

Chromic Luvisol,

sandy loam

Croppingsystem

permanentmeadows

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resampled total29 32

Soil

Calcic Vertisol, clay

Sicily

Croppingsystem

cereals

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Organic carbon and climatic indices (1961-2010)

ZONE MAP MAT IDM AI MST SAI

cor p-value cor p-value cor p-value cor p-value cor p-value cor p-value

Po valley 0.42 2*10-6 -0.34 1*10-4 0.4 5*10-6 0.38 1*10-5 -0.25 0.004 -0.21 0.02

Campania 0.26 0.01 -0.3 0.004 0.4 2*10-4 0.3 0.004 -0.29 0.007 -0.24 0.02

Sardinia 0.31 0.002 -0.1 0.3 0.28 0.005 0.3 0.003 0.2 0.06 -0.21 0.03

Sicily 0.47 2*10-8 -0.37 1*10-5 0.46 3*10-8 0.42 4*10-7 -0.13 0.11 -0.37 1*10-5

SAI soil aridity index (dry-days/year)= 44,53+[MAT]*7,31-[MAP]*0,06-[AWC_50]*0,23

MST mean soil temperature (50 cm)= [MAT]+(([FC]*100)-20,7)/7,9

AI FAO UNEP= [MAP]/[ETOPENMAN-MONTEITH]

IDM De Martonne = [MAP]/[MAT+10]

SOC vs long term IDM in the 4 cropping systems

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zone variable mean st.err.

Po valleyIDM 39.63 0.41SOC 1.44 0.08

CampaniaIDM 39.73 0.12SOC 2.27 0.16

SardiniaIDM 19.64 0.08SOC 1.02 0.06

SicilyIDM 22.44 0.26SOC 1.24 0.07

Y= -0.38 +0.05x

Y= 0.09 +0.05xY= -0.63 +0.08x

Y= -6.87 +0.23x

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Climatic thresholds in space: Po valley

cluster sites SOC IDM Threshold IDM

zone n mean mean diff. meanmean diff. + 1.96 st. err.

Po valley 2 30 1.31 0.24** 36.43 6.841 56 1.55 42.83

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Climatic thresholds in space: Campania

cluster sites SOC IDM Threshold IDM

zona n mean mean diff. meanmean diff. + 1.96 st.err.

Campania2 42 1.97 0.62*** 38.28 3.131 44 2.58 41.17

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Climatic thresholds in space: Sardinia

cluster sites SOC IDMThreshold

IDM

zona n mean mean diff. meanmean diff. + 1.96 st.err.

Sardinia2 36 1.13 0.23*** 20.63 2.161 46 0.90 18.64

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Climatic thresholds in space: Sicily

cluster sites SOC IDMThreshold

IDM

zona n mean mean diff. meanmean diff. + 1.96 st.err.

Sicily2 50 1.12 0.39** 20.81 3.781 53 1.35 24.08

The climate change: t1(1961-1990) vs t2 (1981-2010)

Zone MAP MAT IDM

t1 t2meandiff. t1 t2

meandiff. t1 t1

meandiff.

Po valley 940 841 -99* 12 12.6 0.6*** 43.3 37.4 -5.9**

Campania 1009 869 -140** 14.7 15.2 0.5*** 41.1 34.6 -6.5**

Sardinia 573 506 -67** 16 16.4 0.4** 22 19.2 -2.8**

Sicily 557 608 51 ns 16.5 16.9 0.4*** 21.2 22.7 1.5 ns

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Spatially aggregated mean annual temperature trend

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Spatially aggregated total precipitation trend

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Sensitive areas in the Po valley

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Sensitive areas in Campania

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Sensitive areas in Sardinia

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Sensitive areas in Sicily

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Climatic thresholds validation in time: SOC vs IDM in t1 and t2

anova test(SOC-IDM)*t Pr (>F)

po 0.47sar 0.85sic 0.96

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SOC minimum detectable differences

zone t1 t2

Yearsbetweensamplings(mean) MDD df

Meandifference

Po Valley 1.55 1.45 18 0.33 40 -0.12 ns

Campania 2.27 2.38 30 1.31 4 0.10 ns

Sardinia 0.93 1.27 23 0.7 24 0.34 ns

Sicily 1.27 1.15 18 0.19 52 -0.11 ns

zone t1 t2

Years betweensamplings(mean) MDD df

Mean difference

Po Valley 1.90 1.52 15 0.79 10 -0.37 ns Campania 2.27 2.38 30 1.31 4 0.10 ns Sardinia 1.07 1.40 25 0.51 18 0.33 ns

Sicily - - - - - -

MDD in the sensitive areas

MDD in all areas

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Conclusions

SOC actually varies with long term climate in the 4 studied cropping systems:

IDM thresholds along climosequences correspond to significant SOC variationThresholds and SOC variations are different in each cropping systemMost sensitive soils: Vitric Andosols of CampaniaSOC variations are less than pronounced that climatic variations

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Conclusions and perspectives

Climatic thresholds were surpassed with time in 3 out of 4 regions, with the exception of SicilyThe SOC vs IDM relationship did not change with timeSOC content in the resampled sites did not significantly change

High local variability? Change of management practices in the same cropping system?

"We are dealing with 10 global issues at the moment: food security; availability of water;

climate change; energy demand; wastedisposal; extinction of biodiversity; soil

degradation and desertification; poverty; political and ethnic instability; and rapid

population increase. The solution to all of these lies in soil

management" Rattal Lal, Ohio Agricultural Research and Development Center

Measures to combat SOC decline

Conservation agriculture

Crop rotation can increase organic mattercontent, lower fertilizers and groudwater

pollution, and reduce runoff

Cover crop and stubbles

The maintenance of crop residues on the surface protects soil from impact of raindrops and runoff.

Conservation tillage

Conservation tillage

Conservation tillage

Grass covers, water ways

Inter-connected threads Terraces

Breaking the slope

Contour cultivation

reduces runoff and enhances water harvesting

Summary of the effect of farming practices on soil characteristics

a) Soil physical characteristics

b) Soil chemical characteristics

c) Soil biological characteristics

The transition phase of conservation agriculture

First phase: improvement of tillage techniques; second phase: improvement of soil conditions and fertility; third phase: diversification of cropping pattern; fourth phase: the integrated farming system is functioning smoothlySource: FAO, 2004

Monitoring organic carbon variations due to changes in management.

Spatial interpolation with the use of

Soil Proximal Sensing

Conservation agriculture(green and organic manure, shallow ploughing, crop rotation)

Traditional farming (no roation, mineral fertilization, deep ploughing)

STUDY AREA

Traditional farming

Conservation agriculture

OC analysed by laboratory method

OC analysed by Vis-NIR

spectroscopy

Vis-NIR spectrometer

Quick and non-destructive analysis to estimate several soil parameters (OC, clay, CaCO3, CSC, iron oxides).

30 samples

(15 Vis-NIR and 15 traditional)

=

about 5 samples/hectare

OC prediction models from Vis-NIR, calibrated on more than100 soil samples

In the 2 studied fields:

Gamma-ray spectroradiometry

Total counts of gamma-rays

emitted from the soil (about 0-40

cm)

Gamma-rays emitted from the radionuclides:

40K238U232Th

Gamma-rays are related to: parent material mineralogy, clay content, moisture, organic matter, stoniness, etc.

CORRELATION MODEL STRONGLY SITE SPECIFIC!

Geostatistics

(Ordinary kriging)

Measured and interpolated gamma-rays total counts

Gamma-rays maps were used as co-variates to interpolate organic carbon content within the fields!

FINAL RESULT: Predicted map of carbon stock

CARBON STOCK

Traditional farming field:

33.2 tons/hectare

(very heterogeneous with very high and very low OC

content!)

Conservative farming field:

36.0 tons/hectare

(more homogeneous)

(+ 2.8 tons/hectare)

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Any question?