Ms. María José Marques, Autonomous University of Madrid, Spain. Global Symposium on Soil Erosion (GSER19), 15 - 17 May 2019 at FAO HQ.

1. 1
Soil water holding capacity
affected by erosion in olive
groves under semi-arid
climatic conditions
María José Marqués – Autonomous
University of Madrid, Spain

2. Olive groves extension in Spain: 2,697,445 ha
(Ministry of Agriculture, ESYRCE, 2018)
2
Study area
Central Spain
Agricultural Land use:
30-40%
Olive groves: c.a. 5%

3. 3
Mean Annual temperature 13.6 ºC
Accumulated Rainfall 380 mm yr-1

4. Upper parts of slopes Gypsiric Regosols,
Downslope, more developed Cambisol Gypsiric prevails (WRB, 2014)
4
Soil
layers
Bulk
density
g cm-3
Texture pH Electrical
Conductivity
dS m-1 (1:2.5)
Ap
0-17 cm
1.2 Loam 7.5 2.2
Cy1
17-30
cm
1.4 Silty
loam
7.9 2.0
Cy2
>30 cm
1.4 Silty
loam
7.9 2.0
Soil
Average
slope~ 12%

5. Soil loss
Interill erosion rate
1-7 t ha-1 yr-1
Occasional high intensity
events (I10: 55 mm h-1)
yielded
93 t ha-1
5
Bienes R, Marques MJ. 2008. Interrill erosion in a bare soil. An experience of 12
years’. In Proceedings of EUROSOIL 2008 Control No. 2008-A-836-. August 25 –
29, Vienna, Austria.
Sastre B, Barbero-Sierra C, Bienes R., Marqués MJ, García-Díaz A. 2016. Soil loss
in an olive grove in Central Spain under cover crops and tillage treatments, and
farmer perceptions. Journal of Soils and Sediments 1-16. DOI:
http://doi.org/10.1007/s11368-016-1589-9.

6. Previous research in the area
showed that land users are
reluctant to change
conventional tillage by other
sustainable management
practices
They accept soil erosion as
inevitable.
In this semiarid context, their
main concern is related to
water not to soil
6
Marques MJ, Bienes R, Cuadrado J, Ruiz‐Colmenero M, Barbero-Sierra C, Velasco A. 2015. Analysing
Perceptions Attitudes and Responses of Winegrowers About Sustainable Land Management in Central
Spain. Land Degradation & Development Volume 26 (5): 458–467
Barbero-Sierra C, Marques MJ, Ruíz-Pérez M, Bienes R, Cruz-Macéin JL 2016. Farmer knowledge,
perception and management of soils in the Las Vegas agricultural district, Madrid, Spain. Soil Use and
Management 32: 446–454.

7. Aims:
• to establish the relationship between soil erosion,
soil brightness, Water Available Capacity and SOC
• to improve the effectiveness of messages sent to
land users
7
Hypothesis:
• Deeper soil layers are unfavorable for crop production (Bulk
density, organic carbon, water holding capaciy)
• Erosion exposes deeper and paler layers; soil brightness can
be an indicator of soil degradation in shallow gypsic soils

8. 8
Agrarian Extension Center:
IMIDRA. Agri Environmental Research and
Agrarian Extension Center. Madrid. Spain.
Finca La Chimenea.
c.a. 3 ha
12% slope

9. 9
Olive trees planted in 2006; spaced 7 x 7 m
Rainfed
Managed by tillage, 3 to 4 chisel plow per year, 20-30 cm

10. 10
0-10 cm
10-20 cm
20-30 cm
Lab Methods
N=90
•Soil Organic Carbon by Loss
on Ignition Method
(Schulte & Hopkins, 1996)
•Water Available Capacity by
Richard Plates. (< 2mm)
(Richards, 1941)
•Visible –Near infrared soil
spectra (Vis-NIR) by
Spectroradiometer
(ASD LabSpec® 2500)
30 core samples,
Randomly distributed.
April 2018
Sampling
other parameters :
Bulk density and Gypsum content

11. 11
±
Organic
Carbon
a
a
b
±
±
±
Results

12. 12
Water Available
Capacity
a
b
b
±
±
±

13. 13
Reflectance
Wavelenght (nm)
Soil spectra
n=90
Air dried soils
< 2mm
Sentinel-2 band wavelenghts
Spatial resolution 10 m
• B2 490 nm
• B3 560 nm
• B4 665 nm
• B8 842 VNIR
Selected Indices

14. Indices
• Brightness Index (B_I)
• HUE Index (HUE_I)
14
Lab measurements
• SOC
• WAC
Relationships
Regression analysis

15. 15
Regression Summary for Dependent Variable: SOC g/kg
R=,734534 R²=,539541 Adjusted R²=,505433
F(2,27)=15,819 p<,00003 Std.Error of estimate:2,980
Beta St. Err. B St. Err. t(27) p-level
Intercpt -20,95 10,303 -2,03 0,051
BI_I -0,214 0,134 -8,63 5,430 -1,58 0,123
HUE_I 0,650 0,134 15,87 3,292 4,82 <0,001
n=30
n=30, predicted
for 20 cm depth
n=30, predicted
for 30 cm depth
SOC g/kg

16. 16
Regression Summary for Dependent Variable: WAC m3/m3
R=,734902 R²=,540080 Adjusted R²=,506012
F(2,27)=15,853 p<,00003 Std.Error of estimate:,01992
Beta St. Err. B St. Err. t(27) p-level
Intercpt -0,071 0,068 -1,029 0,312
BI_I -0,282 0,135 -0,076 0,036 -2,094 0,045
HUE_I 0,611 0,135 0,099 0,022 4,529 <0,001
n=30
n=30, predicted
for 20 cm depth
n=30, predicted
for 30 cm depth
WAC m3/ m3

17. 17
Lab measurements
• SOC
• WAC
Relationships
Sentinel 2 image
• 15 Nov 2017
Cloud free,
recently tilled
plot
Regression analysis
Factor analysis
+ Gypsum (%)
+ Bulk density (g cm-3)
Indices
• Brightness Index (B_I)
• HUE Index (HUE_I)
= FI

18. Factor Loadings, Factor 1 vs. Factor 2
Rotation: Varimax normalized
Extraction: Principal components
g/cm3
sqr_Gypsum%
SOC g/kg
WHC m3/m3
B_I
HUE_I
SAT_BI_NOV
SAT_FI_NOV
-1,0 -0,8 -0,6 -0,4 -0,2 0,0 0,2 0,4 0,6 0,8 1,0
Factor 1
-1,0
-0,8
-0,6
-0,4
-0,2
0,0
0,2
0,4
0,6
0,8
1,0
Factor2
Factor2
PoroussoilsDensesoils
18
Factor Loadings (Varimax normalized) Extraction: Principal
components (Marked loadings are > ,700000)
Factor 1 Factor 2
Laboratory g/cm3 0,058055 0,737387
sqr_Gypsum% 0,650871 -0,375841
SOC g/kg -0,872260 0,164578
WAC m3/m3 -0,799949 0,274940
ADL Spectra B_I 0,299132 -0,825680
HUE_I -0,865913 0,048621
Sentinel 2 SAT_BI_NOV 0,223494 -0,693619
SAT_FI_NOV -0,634533 0,025732
Expl.Var
63,41%
38.99 % 24,42 %
Factor 1
 Dark soils Pale soils 
n=30 (0-10 cm depth)

19. 19
Factor2
PoroussoilsDensesoils
Factor 1
 Dark soils Pale soils 
Scatterplot (Spreadsheet8 4v*90c)
FACTOR2 = -8,2833E-16-1,895E-16*x
1
2
3
4
5
6
78
9
10
11
12
13
14
1516
17
18
19
20
21
22
23
24
25
26
27
28
29
30
-2,5 -2,0 -1,5 -1,0 -0,5 0,0 0,5 1,0 1,5 2,0 2,5
FACTOR1
DARK BRIGHT
-2,5
-2,0
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
2,0
2,5
FACTOR2
LOWBULKDENSHIGHBULKDENS
0.05
0.1
0.15
5
15
25
WAC
m3/m3
SOC
g/kg
0.15 ± 0.01 a
0.12 ± 0.02 b
0.09 ± 0.01 c
17.1 ± 2.1 a
12.2 ± 2.6 b
8.6 ± 3.6 c
Bulk density
g cm-3
G: 72 ± 9% b
G: 68 ± 12% b
G: 41 ± 19% a
EROSION
1.31
1.13
1.01
0.8 0.9 1 1.1 1.2 1.3 1.4
dense
medium
porous
Different letters
indicate p<0.05

20. Conclusions
20
• SOC and WAC are closely
related
• Brightness models can be
used as a proxy to estimate
erosion and water content
in gypsic soils
• Simple water map
information could be used
to increase awareness
among farmers regarding
erosion
• Brightness images over time
will help to establish
priorities and take decisions
14-16%
10-14%
8-10%
Water Available Capacity 0-10 cm
cm
0-1010-2020-30
L m-2 0-30 cm
13
10
8
11
9
4
8
6
6
22.9 19.6
CV
9-12 %
10-30%
36-48%
31.5∑