This document summarizes a presentation on indicators for desertification monitoring at local scales in Tunisia. Case studies were conducted at multiple arid zone observatories using direct observations and modeling methods. Key findings included increasing soil salinity and declining groundwater levels in irrigated areas, as well as changes in landscape features, vegetation, and socioeconomic indicators like population trends and household incomes over time. Simulation models were developed to evaluate past land use changes and predict future desertification risks under different scenarios to support decision making.

1. UNCCD 2nd Scientific Conference
Economic assessment of desertification, sustainable land management and
resilience of arid, semi-arid and dry sub-humid areas
9-12 April 2013 - Bonn, Germany
Session 4.3 : Indicators for DLDD and SLM
Case studies of desertification indicators evaluation at local level in Arid
Zones of Tunisia using direct observations and modeling methods
Presented by:
Rachid Boukchina & Mongi Sghaier
Research team: Ouessar M., Fetoui M., Ouled Belgacem A., Khatteli H. and Taamallah H.
Institute of Arid Lands (IRA) – Tunisia
rachid.boukchina@ira.rnrt.tn www.ira.rnrt.tn

2. Overview
The ecosystems and the agro-systems of the
arid zone of Tunisia are the most affected
systems by desertification process in the
country. The constraints faced by these
systems are both natural and anthropogenic.
These constraints and others create new
ecological, socio-economic conditions for which
the traditional modes of natural resource
management are ineffective.
In the last decades much effort has been put
to combat desertification in arid area of
Tunisia. Several measures aimed at achieving
agro-pastoral development on these systems
have failed for a variety of reasons including
the lack of environmental knowledge.
2

3. Monitoring Desertification at local scale
In Tunisia, until recently there was no effort put
in place to monitor impacts of anti-
desertification management and to evaluate
trends in desertification process.
In this context and with the support of national
and international partners the Institute of Arid
Lands (IRA) has implement an research
programmes (UNESCO, DYPEN, CAMELIO,
ROSELT/OSS, DNSE/OSS) for monitoring
desertification at local scale (Arid Zone
Observatories Network).
3

4. Scheme of integration of the local environmental observation in the
Monitoring Evaluation national design in Tunisia
N.C.S.D
National scale
M. Environment
UNCCD Focal point
Regional S.
développement
régionaux de
décisionnels
, CRDA etc…
régionaux,
conseils
Centres
Arid Zones Observatory
développement)…
( comités locaux
décisionnels
Ouara
Local S.
Bou Oued
Centres
locaux
Faouar J’bil Hedma Menzel Graguer
Jeffara
Dekouk
Sidi Toui
de
Habib
Roselt /OSS
Biodiversity

5. ROSELT/OSS Menzel Habib observatory
Main characteristics
Localisation Lower Meridionales Plains
Area 113100 ha
Climate Lower arid Mediterranean
stage with mild winters
ecosystems Steppes and agrosystem
Steppes : Rhanterium
suaveolens, Arthrophytum
Vegetation
scoparium, Artemisia
campestris
Fauna Very scarce
Population 11330 inhab.
Economic Pastoralisme , agriculture (cereal
Activities cropping, arboriculture),
5

6. ROSELT/OSS Menzel Habib observatory
Bou Hedma
Localisation lower southern plains
Area 75000 ha
arid inferior stage with mild
Climate winters
Steppe and
ecosystems woodland savanna
Vegetation Acacia raddiana
Fauna the antelopes Addax and Oryx
Population 15000 inhab.
Economic Agriculture (cereal cropping,
Activities arboriculture) livestock production,
6

7. Monitoring approaches
ROSELT/OSS Methodological Guidebook
1. Bio-physical data set 2. Socio-economic data set
Climate: Human population:
rainfalls number
meteorological data location
Soil: education
pedology, organization
surface conditions, Economic parameters:
soil fertility Farm income
Water: Non-farm income
groundwater salinity, Infrastructure:
Evaporation soil erosion control devices;
Vegetation & Fauna: Water management;
yields, roads,
spatial distribution, schools, . . .
flora & fauna diversity.
7

8. ROSELT/OSS framework: main outputs
Establishment of Environmental indicators:
Reference states (To) Diachronic/synchronic
- Bio-physical data - Historical data
-Socio-economic data - Official statistics
- Remote sensing
Simulation models/ Socio-economic indicators
Decision Support Tools
8

9. 50
0
100
150
P(mm) 200
250
300
350
400
1971-72
1972-73
Drought period
1973-74
1974-75
1975-76
1976-77
1977-78
1978-79
1979-80
1980-81
* Sep 2012 to Mar 2013
1981-82
1982-83
1983-84
1984-85
1985-86
1986-87
1987-88
1988-89
1989-90
1990-91
1991-92
1992-93
1993-94
1994-95
years and 14 surplus years (P>110% of P)
1995-96
1996-97
1997-98
1998-99
1999-00
2000-01
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
Environmental indicators: Annual rainfall
2008-09
2009-10
2010-11
2011-12
2012-13
P
was 143 mm and showed 19 years of deficit (P<80% of P), 4 normal
Great inter-annual variation: between 1971 and 2013*, mean rainfall (P)

10. Environmental Indicators: Land Use Change (LUC)
1976 (T0)
April 1976: (Escadafal, 2006) April 1989:
Transect SE / SO (gray): Drought 1987 – 1989
Plain is dominated by steppes Decrease of annual crops (red)
Rhanterium suaveolens (beige) and and appearance of mobile sand
annual crops (red). dune (yellow).

11. Environmental Indicators: Land Use Change (LUC)
Year 2000 Year 2009
The comparison of the evolution of land use in MH observatory between
1976 (baseline year) and 2009 reveals: rangelands have been
transformed into rainfed and irrigated cropping lands:
• the increase of rainffed cropped lands (olive trees) with water
harvest management;
• the expansion of irrigated land using saline groundwater; and
• good vegetation quality in protected area (Rhanterium steppes).

12. Environmental Indicators: Landscape
Répartition des zones morphologiques (Ghram, 2007).
Unité Superficie (ha) % Total
"disappearing“ of
Terrasse de glacis 655 1 mobile sand dune
Bas-fonds 2660 2
Cône de déjection 913 1
Garaas 1669 1
Glacis d'ablation 6392 6
Plaine 84455 75
Sebkha 1534 1
Versant 14722 13
Total 113000 100

13. Environmental Indicators: anti-desertification management
Progress (ha) in implementation of soil erosion and water harvest
techniques between 1987 and 2009 (CRDA-Gabès, 2010):
- Water budget at watershed scale (infiltration, evaporation, runoff)
- Soil conservation (erosion, fertility)

14. Environmental Indicators: Groundwater salinity
TDS (g/L)
<3 3 to 5.5 >6
Nb Wells 2 38 29
(%) (3) (55) (42)
From 1987 to 2005, the total number of
exploited wells for agriculture irrigation
decreased from 237 to 69 .
14

15. Environmental Indicators: Soil quality in irrigated land
CE (ms/cm)
0
0 5 10 15 20 25
-10
-20
Depth (cm)
-30
-40 4 cropping seasons
-50
-60
-70
-80
-90
Reference plot 2 cropping seasons
Significant trends have been noted in soil salinity in irrigated land.
Measure made during different cropping seasons indicate an increase in
soil electric conductivity from close to 1 to 22 mS /cm. High EC values are
the result of submersion irrigation.
15

16. Socio-economic indicators: Population & growth rates
The Menzel Habib population dynamics during 55 years show three phases:
- from 1956-1966 decrease of population
- from 1966 to 1984 increase of population (36%)
- from 1984 to 2010: (11974 to 11330 hab. ) and during
two decades a negative growth rates were observed for men

17. Socio-economic indicators: Age structure
Year 2004
Year 1996 Year 2010
The comparison of the age structure between 1996 and 2010
reveals a small family size and a gradual declining of the
population: migration (Gabes city).

18. Socio-economic indicators: households income
Poverty line (378 TD/year/P)
Farm income: 14 %
Non-farm income: 49 %
Livestock production: 37 %

19. Socio-economic Indictors: Land Tenure
In Menzel Habib observatory, trends in agriculture speculation vary
with land tenure:
• Cereal cropping under communal governance
• Olive trees cropping after land privatisation

20. Flora Indictors: Haddej & Bou Hedma NP
200 mm
158 mm
120 mm
52 mm
Protection period since 1980
The results obtained show that the specific flora diversity parameters (plant
richness, Shannon-Weaver and equitability indexes) double inside compared
to outside of the parks. The majority of species, which develop only inside
the parks and are very rare outside (non protected area). The results show
the significant link between variation in vegetation frequency and rainfall
levels (P < 0.05). Ouled Belgacem, A. et al. (2009)

21. Importance
Non-farm income
Agriculture investment
Population
Livestock
production
1960
1984 1994 2004 Temps

22. Simulation Models for Desertification Monitoring
SIEL (Loireau et al. 2007) SIELO (Fetoui et al. 2012)
- Bio-physical / Socio-economic data -Landsat: Land Change Cover (LCC)
- Historical data / Official statistics -Landscape use
- Remote sensing
Local Environmental Information system for
Information Systems operational desertification
ROSELT/ OSS SIEL model monitoring at Local Scale
SIELO model
Diagnostic of natural
Spatialized& prediction
resource indicators
of desertification Specific (Landscape) &
of future evolutions Global Indicators of
risks / scenarios
resource use desertification risks
22

23. SIEL & SIELO tests : Menzel Habib observatory & O. Oum Zessar Watershed
Different land use scenarios controlled by farming
activities and integrating the stakeholder
perspectives were simulated and results confirm the
pastor vocation of Menzel Habib observatory and
highlight the risks to be embedded following the
intensification of olive trees, cultivation of cereals
and irrigation. Sghaier et al. 2008

24. Conclusion
 ROSELT/OSS concept framework has been sufficiently tested and is
operational for Tunisian Observatories ant the obtained outputs
converge to the UNCCD strategic objectives (1 and 2);
 Modeling land use scenarios change and its impact in local scale are
helpful to investigate the interactive mechanism between land use
system and desertification process; and
 However there are several difficulties and challenges facing the need
establishment of reference status, indicators decision tools for the
local scale:
• the complexity of the desertification (temporal and spatial
variation) ;
• multiple indicators are needed to represent different forms of
natural resources degradation.
• work remains to improve the efficacy of methods for model
development, testing, validation, and application.