Niger Basin Focal Project Coordination Workshop

Niger Basin Focal Project
Coordination:
Chiang Mai workshop, 18 September 2009
Jean Charles CLANET
Andrew OGILVIE

Large transboundary basin
• 4183 km
• 2.1 M km² / 1.2 M km²
• 10 countries
Country Basin size per country Proportion Proportion
(km ²) of basin of country
w ithin w ithin
country basin (% )
(% )
Benin 44,967 3,5 38,7
Burkina Faso 86,919 6,8 31,5
Cameroon 86,381 6,8 18,4
Côte d’Ivoire 23,550 1,9 7,3
Guinea 98,095 7,7 39,9
Mali 263,168 20,7 20,9
Niger 87,846 6,9 7,4
Nigeria 562,372 44,2 61,5
Tchad 19,516 1,5 1,5
TO L Active
TA 1,272,814 100 -
Basin

BFP NIGER Coordination: Jean Charles CLANET & Andrew OGILVIE – IRD/G-EAU

Spanning range of agroclimatic zones

From <50mm in North to >4500mm in South


Prone to extreme demographic expansion
Evolution of Niger Basin population 2005-2050 according to various UN DESA scenarios

450 000 000

400 000 000 384 036 651

350 000 000 Constant-fertility variant
High variant
Medium variant
300 000 000
Low variant
246 388 996
Population

250 000 000

215 273 326
200 000 000

186 656 464
150 000 000

100 000 000
94 506 856

50 000 000

-
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year

Population density
(Source: D Kaczan based on SEDAC data)


Due to high fertility
• Future population trends will
depend on speed of fertility
decrease and HIV/AIDS
prevalence

Sources: Tabutin and Guengan


Subject to extreme poverty
• 8 in Low development category
UNDP HDI
• Generalised poverty where
education, roads, electricity,
health, water sector are
underdeveloped


Often regarded as water poor


Complex wider context


WP2: Rainfall distribution
300
300 700
730
520
200 450 Mean annual rainfall (average 1951-1990),
200
100 and monthly rainfall for wet and dry years
100
0
0 J M M J S
400 J M M J S
1700
300
300 1350 1050
30 0 900
200 1200 200

20 0 1100
100
100
0 10 0
J M M J S 0
0 J M M J S
J M M J S

• Uneven water distribution
– Significant rainfall in South and up to 13° N
– Quarter of basin under Sahel and Semi-arid climate


Rainfall variability
• Seasonal and inter-
annual variations
• Recent drought and
future uncertainties

Cartographie SIG, C. Dieulin, 2009, IRD/ HSM


Impact on river flows

• Peculiar hydrology
• 3 major « châteaux
d’eau » in South of
basin
Source: Marquette, Zwarts et al, FAO


Advances in basin hydrology
Main Niger sub-basins and annual hydrographs
for wet and dry years • Ability to
predict
Discharges in m3/s
changes in
flow from
B C rainfall
predictions
• Impact of
A D dams,
E climate
change,
F land use
change etc


Hydrogeology…

• Large uncertainties
over GW reserves
• 5-50 mm/year GW
recharge depending
on location and land
use


Towards water accounting

Gretp1984.shp
0
0 - 17422
17422 - 21228
21228 - 25547
25547 - 31439


Water use: green water
• Substantial rainfall
(except North)
• But short and erratic
rainy season


WP3: Blue water use and irrigation
• Blue water largely
under-exploited
• Irrigation largely
under-developed
• Reliance on rainfed
agriculture


Irrigation zones and systems

• Irrigation along river • Small dams
• Inner Delta • Recession flood
• Nigeria dams, fadamas, • Lowlands
Sokoto

Nigeria’s greater control over water
• Nigeria as leader in
dam construction
and irrigation

types de périmètres Burkina Mali Niger Nigeria total

grands périmètres 8000 62500 13000 69 000 152500

PIV publique 3000 9500 12500

PIV collectif 6000 8000 14000

PIV individuel 4000 50000 161 700 215700

petit privé 30000 30000

agroindust 4000 4500 8500

décrue 60000 12000 723 000 795 000

submers cont 85000 85 000

basfonds subm cont 9000 22000 18 500 49 500

total 34000 281500 75000 972 500 390500

Source (Association Régionale de l'Irrigation et du Drainage en Afrique de l'Ouest et du Centre 2004)
FAO 1992 et JICA 1993 pour le Nigéria


Vast potential for irrigation


WPr of selected irrigation zones
Superficie
Nom des Pluviométrie Superficie intensité
Typologie APPIA Pays fleuves Date de réalisation moyenne par Culture principale
périmètres moyenne annuelle équipée(en ha) culturale %
exploitant (ha)

Lata T1 Niger 756 1991 227,0 1,00 Riz 200

Kamaka T2 Mali 451 1994 16,0 0,41 Riz

Sinah T2 Mali 449 1997 49,0 1,00 Riz 100

Saba 1 T3 Mali 410 2001 35,0 2,50 Riz 100

B1 T1 Mali 383 1951 576,7 20,00 Riz 151

Djidian T1 Mali 553 1950 298,0 12,00 Riz 100

Boundoum T1 Sénégal 250 1991 262,0 1,70 Riz

Nakambe/ bagre T1 Burkina Faso 910 1974 680,0 1,00 Riz 200

Vallée du Kou T1 Burkina Faso 943 1970 1260,0 1,00 Riz 200

Sakoira T3 Niger 379 1992 3,6
WPr of market gardening activitivies and rice
0,20 Oignon 180
Source: APPIA
Tera T2 Niger 382 1981 46,0 1,00 oignon, tomate 125

Gamkale T4 Niger 526 1980 200,0 0,16 choux, poivron, laitue 200

Mbida T2 Niger 334 1997 17,0 0,07 Niébé 200

Keur Mbir Ndao T4 Sénégal 326 1966 20,0 0,08 Oignon

Mbawane T4 Sénégal 366 1974 40,0 1,20 Oignon

pomme de terre,
Titao Burkina Faso 588 1999 4,5 0,06
T3 oignon 200


WPr in wet and dry season
Wet season Dry season
3000.0 2000.0
Gross Inflow (mm/ha) I rrigation (mm/ha)
Evapot ranspirat ion (mm/ha) 1800.0
2500.0 Ev apotranspiration (mm/ha)
1600.0

1400.0
2000.0
1200.0

1500.0 1000.0

800.0
1000.0
600.0

400.0
500.0
200.0

0.0 0.0
Bargodaga Kamaka Sinah Saba1 Djidian B1 N10 Lata B1 N10 Sakoira Tera Gamkale Mbida
Irrigation Scheme Irrigation Scheme

25000 Irrigated inflow (m3) 1.00 35000.0 6.0
Irrigation inflow (mm/ha)
Yield (Kg)
WP (Kg/m3) 0.90 Gross Value Product ($/ha)

Gross Valur Product/ Irrigation inflow
30000.0
WP ($/m3)
5.0
20000 0.80

W ate r Productivity (kg/m 3)

Water productivity ($/m3)
25000.0
0.70
4.0
15000 0.60
20000.0
Yie ld (kg)

0.50 3.0
15000.0
10000 0.40
2.0
0.30 10000.0

5000 0.20
1.0
5000.0
0.10

0 0.00 0.0 0.0
Kamaka B1 Sinah Djidian N10 Saba1 Bargodaga Lata B1 N10 Sakoira Tera Gamkale Mbida

Irrigation Schemes Irrigation Scheme


Identified constraints


WP3: Rainfed agriculture


Rainfed Water productivity
1999, good year

kg grain per ton applied water: Intercepted rainfall

kg grain per ton depleted water:
Evapotranspirable water

• Maps of rainfed WPr
Leached soils
Heavy rainfall
according to CPWF definition
Rainfed cereals = marginal crops • Difficult/dangerous to
Major crops = lowland rice
interpret
=> return to theory

Increasing rain utility
• Within context of increasing strain on water resources need to ensure water is used
efficiently and to produce most value (food, energy, water supply, environment)
strive to increase total utility of water (increase/better water depending activities, save
water and assign it to activities showing a deficit). In agriculture, “more crop per drop”

• Rainfed agriculture differs somewhat as rain is not necessarily the limiting factor, even in
Sahel (Breman and Cissoko, 1998)
• Rainfed agriculture also faces two constraints:
– cannot reduce applied water (the rainfall is an environmental data );
– cannot try to reduce depleted water (actual ET is an environmental function which
controls the moisture and rain parameters (Monteny and Casenave, 1989)

• To increase the direct utility of the rain one can only improve the rainfed production process
– where rain falls in excess, reduce its noxiousness;
– where it falls insufficiently, improve its efficiency (RUE);
– if rain is adequate, reduce the other limiting factors


Measuring direct utility of rain
• Rain is a necessary condition to rainfed production but not necessarily a (limiting) factor
• WProd does not inform the level of utility of rain in general, and must be reserved for
activities where water is really a factor (such as irrigation, or rainfed in arid zone).
• In rainfed agriculture, this rain utility can be measured in various indicators:
– Average yield : productivity of the “rain field” (= land) assigned to rainfed crop
– Average food production per rural capita (allows to judge satisfaction of the needs,
and labour productivity)
– Population living of rain resources (human production of the " rain field")
– Land use assigned to the rainfed crops

•very low rainfed land
use in Guinea and arid
zone (<5%) rainfed =
marginal activity
• relatively low global
rainfed land-use (<15%)
• some districts in
Niger, and Nigeria >25%
– Rain-use efficiency (RUE) when the rain is a limiting factor

Rain as a limiting factor

max. phytomass (t MS/ha)
12
Savanna grass production becomes 10

dependent to variations in rainfall 8 perennial grass, slope, latitude
11-12°N
above 10°N (below 800mm) 6
perennial grass, slope, latitude
4
(Fournier 1991). 6-9°N
2

0
400 600 800 1000 1200 1400 1600
rainfall of the year (mm)

t de céréales par habitant rural (Cereal ton/capita)
Usual droughts (1986): 0,50
1984 1986 1988 1994
no effect if zone >800mm 0,45
small effect 500-800mm 0,40
great effect <500mm 0,35
= (insufficiency) 0,30
0,25
0,20
FS
Heavy droughts (1984) 0,15
great effect <1000mm 0,10
(insufficiency) 0,05
-
sahélien<500 soudanosahel nord soud centre sud
500-600mm 600-800 soudanien soudanien
800-1000 >1000 mm

RUE when rain is limiting factor
• Relevant in areas of rainfall under 600mm for pearl millet, 700 mm for sorghum (at the
beginning of cycle), 800 m for maize, 900 mm for tubers, 1000 mm for rice (approximately).
• How to measure it?
Actual ET is not an universal water index, as dependent on ETP and ETM Prod/ETa is not
relevant RUE index
RUE index: water satisfaction index: actual ET/potentiel ET or a better water indicator
(IRESP). The higher the water index, the higher the yield
RUE index: relative yield (actual yield/potential yield) at a standard deficit (IRESP 0,5 or
actET/potET 0,75)
• Increase RUE by
1) increasing water
Relative yield satisfaction index (= reduce
1 deficit = synchronising offer
and demand)
System B System A
2) increasing relative yield in
case of water index <1, and
reducing drought resistance
0 Water index
IRESP 0,5 1
(minimising actual ET of zero
IRESP, yield)
0
ActualET/potential ET


WP3: Fisheries


Inner Delta fisheries
Delta amont (Juilet à octobre 1995)
Surface 15000
inondée
2
(km ) 0,0093x
10000 y = 36,637e
2
R = 0,9629

5000

0
0 200 400 600 800
Hauteur d'eau à la station de Mopti (cm)
100 Total catch
(tonsx1000)
80

60

40

20

0
500 1000 1500 2000 2500
Inflow Mopti (m3/s, July-November)


Marginal WPr in Inner Delta fisheries

Fish marketed in Mopti ( t; t+1) according to flood index (t)

25000
y = 157.47x - 483

20000 R2 = 0.72

15000

10000

5000

0
20 40 60 80 100 120 140
Flood index (in days)

Abstraction of some volume to the river flood decreases the fish catch,
about 28 tonnes/y for 1 m3/s during the flood period.
Data for 1988-2005 from Mali administration, processed by CP 72


Constraints to livelihood in fisheries

socio economic environment of poor countries
(school, health, domestic water, credit)

poor productive assets

sometimes a lack of landuse rights

environmental change


Fisheries in the Niger basin

• Major drivers of change:
– changes in hydrologic regime
• rainfall variability and climate change,
• construction of reservoirs and water abstraction
– increased pressure on ressource,
• increased total population and boom on fish demand by
urban markets,
• increased fishers population


WP3: Livestock


Livestock Water Productivity
• LWP= ∑ Value of production and services (V)
Quantity of water withdrawn for production and services

LWP= Vv + Vl + Vf + Vt + Vcp
Qefn + Qefc + Qerr + Qecm
Animal products and services: meat (v), milk (l), manure (f), traction
and transport (t), leather & skin (cp)
Water in animal feed (natural, fn; cultivated; fc); crop residues (rr),
drinking water (cm)

• Data gathering complete for V, finalising calculations for
water from animal feed and crop residues (Crop water use)
• Continue determining options to improve systems and LWP
where necessary (questions over how to interpret LWP)


Livestock distribution


Evolution of stock numbers 1978-2050
Evolution annuelle du cheptel (bovins et petits ruminants) en zone agroclimatique
aride du système pastoral du BFN de 1978 à 2008

450000

400000

350000

300000
Nombre (Tête)

250000

200000

150000

100000

50000

0

Année
Bovins Petits Ruminants

Evolution annuelle du cheptel (bovins, petits ruminants et camelins) en zone
agroclimatique aride du système pastoral du BFN de 2008 à 2050

18000000

16000000

14000000

12000000
Nombre (Tête)

10000000

8000000

6000000

4000000

2000000

0

Année
Bovins Petits Ruminants Camelins


Pastoral & trade movements

Clanet, 2009


Leglislation surrounding pastoralism
DISPOSITIFS Benin Burkina Cam. Côte Iv Guinée Mali Niger Nigeria2 Tchad

Locaux (us et coutumes) X X X X X X X X X

Nationaux (Législatifs : textes, lois codes, X X X X X X X X X
décrets, schémas directeurs … 1987 1984 1983 1982 1995 1996 1997 1979 1960

Gestion quotidienne L>N L>N L>N L>N L>N L>N L>N L>N L>N

Bassin - - - - - - - X -

Régionaux
•CEDEAO
(x) x x x X X X
•CEBV
x x x x x x -
•CILSS
x x x x x x x - x

Dispositifs focalises sur : Migrants Aména- N.r. Aména- Prati- Code Aména- Sectoriel Hydrau
gements gements ques A-P gements lique

1- PRASET : Projet Régional d'Appui au Secteur de l'Elevage Transhumant, GTZ, Niamey, 1997
2- Hors CEDEO
3- CEDAO : Communauté des Etats de l’Afrique de l’Ouest
4- CILSS : Comité inter-états de lutte contre la sécheresse au Sahel
5- CEBV : Communauté du bétail et de la Viande

Mobilités pastorales transfrontalières besoin: « sécuriser le foncier pastoral » (PRASET, Niamey,
GTZ, 1997).

Legislation exists but rarely applied. Local customs take precedence over national law

WP4: Institutional context

• Transboundary dependance
• Lack of transboundary and
national water management


Institutional difficulties
Bénin BF Cam C.I. Gui Mali Niger Nigeria Tchad
Droit/Politi Permis Droits
que Concession Titre foncier ationaliste Propriété Propriété Propriété Pas clair
d’occuper occupation

GIRE Projet PAGIRE Gire = Cellule GIRE
Principes
adopté Plan directeur Pris en compte À améliorer Principe
pilote reconnus défi

Décentralis
ation Inachevé,
6 dép. 13 régions 10 provinces début Collectivités 8 régions 789 LGAs Difficile
déficit

Gouv/Cout Reconnaiss Reconnaiss Reconnaiss Reconnaiss Reconnaiss
umier ance Chefs consultés À améliorer ance ance ance Pas reconnu Très mauvais ance

Genre PDFA PDFA - - PDFA PDFA - PDFA -
signé signé signé signé signé

• Recent
institutions/decentralisation
• Budding IWRM
• Uneven recognition of
traditional law

D’après 2iE, EIER-ETSHER

Importance of institutions at local level
• Impact on WPr
via land tenure
• Existing systems
based on
traditional
law/customs
Users type Proprietor Claimant Authorized User Authorized Entrant
- State
- ‘Maître des eaux, des terres, des pâturages’ Exploitation unity (e.g. with
Lineage member: Undifferentiated actor :
Property right in the Inner discretionary power on water, land or pasture fishery right)
prescribed right for individual or herd
Delta access or outsider (with a temporal
pasture access without particular status
- Chiefs (village, family, lineage or production right for extraction)
unit)
Access X X X X

Withdrawal X X X

Management X X

Exclusion X


Legal pluralism issue

- Legal plurality: overlap of traditional
and modern water/land rights
- Decentralisation, IWRM, NGOs add to
this plurality
- Creates more authority structures
& levels and set of rules
- Case by case study required, some
mixtures work better than others


WP1: Water and poverty at a national scale

Falkenmark WPI
HDI -0.21 n/a

SVI 0.07 -0.47

GSI -0.18 -0.08
Headcount
Ratio 0.26 -0.34

CSIRO.

Issue no. 1: What and where is poverty?

Child mortality

Child morbidity universally agreed-to metric
No Hot spot
(stunting)

Household
wealth

CSIRO.

Issue no. 2: Accounting for causes of
poverty

•Derive weightings from the data

• Spatially explicit modelling – heterogeneous coefficients for a
heterogeneous problem

CSIRO.

Outcomes: e.g. Central Mali
For each hotspot, we identified the most serious water constraint
• Water poverty manifests in different ways in different places

Central Mali and the Inner Delta

Poverty definitions are
crucial → use multiple
metrics simultaneously
and compare results

CSIRO.

Modelled outcomes: Central Mali
Wealth Morbidity Mortality
(Constant) -0.16539 -1.71010 *** 0.14522 ***
Population density (people/km2) 0.00137 ** 0.00040 -0.00004
Population (people) 0.00000 0.00000 0.00000
Telephones (proportion) - 0.74888 -0.10982
Electricity (proportion) - -0.39659 0.10017
NPP (produced) (tonnes/0.25° cell) - - -
Access (’00 km) 0.04319 0.17222 -0.01578
Education (years) 0.22160 *** 0.20625 * -0.03104 **
Forest Cover (proportion) -0.17428 0.04348 0.00325
Cattle density (units/km2) 0.00001 0.00443 0.00055
Chicken density (units/km2) -0.00024 0.00031 -0.00029
Sheep density (units/km2) -0.00081 * -0.00172 0.00025 *
Goat density (units/km2) -0.00107 -0.00457 0.00013
pig density (units/km2) -0.01497 -0.06780 -0.00559
Unprotected water (proportion) -0.20029 *** 0.32213 -0.00789
Water Access (minutes) -0.00282 0.02090 ** 0.00038
Dams (’00 km) - - -
Irrigation (percent) -0.00631 *** 0.01206 * -0.00090
Precipitation (mm/yr) - - -
TARWR (m3/yr/km2/person) 0.00871 0.01864 -0.00406
Drought Economic Risk (decile) 0.01388 * 0.03840 * 0.00311
Human footprint (1-100 index) 0.00120 0.00667 0.00059
Malaria prevalence (parasite ratio) -0.18819 ** -0.26380 -0.06278 *
Moran’s I for residuals -0.025 -0.002 -0.011
Akaike information criterion -144.31 37.22 -321.88
Aprox. Pseudo adj. R2 0.81 0.63 0.60
Spatial weights matrix 2 nearest neighbors 3 nearest neighbors 1 nearest neighbor
CSIRO. Sample size 83 83 83

Outcomes: Considerable variation between hotspots

Considerable disparity between results analysed for child
mortality and child stunting – warrants using multiple
metrics
• All findings based on statistical correlations, not observed causality

North West Nigeria:
• Water quality (access to protected sources) is the primary water-
related poverty correlate. 1% improvement is associated with a 1.1%
decrease in child mort. rates
• Secondary evidence: Irrigation has been beneficial as well as water
access
• Education: 1yr improvement in average schooling attainment is
associated with a 0.6% decrease in child mort. rates.

CSIRO.

Outcomes: variation between hotspots
Utility of the
Poverty Water poverty Non-water TARWR
Hotspot Measure of poverty variables poverty variables variable
Water access
Moderate –
North west Unprotected water Education
All three metrics child mortality
Nigeria Irrigation Livestock
only
TARWR

Central Mali
Education Limited – not
and the Inner Child mort. only Unprotected water
Livestock significant
Delta

Limited – child
East Burkina Unprotected water Education
All three metrics morbidity only,
Faso Dams Environ. damage
contrary signs

Education
Population
East Nigeria and Limited – child
Irrigation density
north Wealth index only mortality only,
Dams Malaria
Cameroon contrary signs
Drought risk
Environ. damage
South and Access to towns
Limited –
central Nigeria Education
All three metrics Unprotected water contrary signs,
(‘wealth Electricity
CSIRO. small effect
hotspot’) Telephones

WP5: Intervention potential


AgWat & poverty
• Sample agricultural problems:
– access to water
– poor soil fertility
– pests
– crop diseases
– lack of inputs
– access to markets

• Improvements needed in:
– Awareness raising, information
and communication
– Training and capacity building
– Equipment
– Legal and administrative
frameworks
– Finances
– Cooperation and information
exchange


Physical interventions

Source: UNESCO

Adapt demand to water supply Conservation tillage and conservation agriculture
(photosensitivity, better decision making currently not possible in semi-arid conditions of
for sowing, extensivity), West and Center Africa (very strong competition
with other crop residues uses)
Adapt supply to crop demand (runoff control Intensification does not enhance RUE, except
and water harvesting, rooting) through organic matter inputs.
Enhancing tolerance to supply-demand gap in Supplemental irrigation during short dry-spells and
deficit (rooting management, drought beginnings of humid seas in intensive farming
resistance) or excess (drainage tolerancy) only

SENEGAL à BAKEL 16.74
8 9 10 11 12 13 14 1 I22 3 4 5
J2
6 7

Flow predictions
13.95
22
MAN TALI
23
AN
24 25 26 27
NIGER à 15 ULIKOURO 18
28
KO 16 17 19 20 21
11.16
SE G E
LIN U
36 37 38 39 40 41 42 29 30 31 32 33 34 35

latitude
8.37
50 51 52 53 54
FOMI 55 56 43 44 45
H5
46 47 48 49
² 5.58

• Predictions in August for Sept-Oct rainfall used 64

78
65

79
66

80
67

81
68

82
69

83
G5
70

84
57

71
58

72
59

73
60

74
61

75
62

76
63

77
2.79

to predict flow data 92 93 94 95 96
0
97 98
500
85
1000 km
86 87 88 89 90 91
0

-2.79

• Used for Manantali dam in Senegal. Extension to -21.09 -18.28 -15.47 -12.66 -9.84 -7.03 -4.22 -1.40
longitude
1.41 4.22 7.04 9.85 12.66 15.47

Niger river upstream of Inner Delta Figure 1 : Location of Bakel in Senegal basin and
Koulikouro in Niger and zones used for predictions in
• Spatiotemportal uncertainty for rainfall the Arpege model
predictions (7 days feasible)
3000 3000 3000 m3/s

fleuve SENEGAL à Bakel
m3/s calage m3/s calage calage
2500 2500 2500
2000 2000 2000
1500 1500 1500
1000 1000 1000
500 prévu = f ( J2 ) 500 prévu = f ( G5 ) 500 prévu = f ( H5 )
observé année observé observé
année prévu en temps réel
année
0 prévu en temps réel 0 prévu en temps réel 0
1975 1980 1985 1990 1995 2000 2005 2010 1975 1980 1985 1990 1995 2000 2005 2010 1975 1980 1985 1990 1995 2000 2005 2010

fleuve NIGER à Koulikouro
5000 calage 5000
m3/s calage
m3/s
4000 4000

3000 3000

2000 2000

1000 prévu = f ( G5 ) 1000 prévu = f ( I2 )
observé année observé année
0 0 prévu en temps réel
1975 1980 1985 1990 1995 2000 2005 2010 1975 1980 1985 1990 1995 2000 2005 2010

Modèle : ARPEGE 3 forcé Modèle : ARPEGE 4.6 forcé Modèle : ARPEGE 4.5 couplé
Figure 2 : results for natural river flow in sept-oct at Bakel in Senegal river basin and Koulikoro in Niger river basin, obtained from the
successive versions of ARPEGE


Future threats


Climate change modelling
• High uncertainty
• Increase in T°C,
in variability and
extreme events,
later start of
rainy season,
HADCM2 – A2 scenario Variability of discharges for some basins near 2080 in regard dry spells, and
of the average 1966-1995
overall more
rain in Central
part of WA &
decrease in
West
• Variation in
yields

Water development and IWRM
• Dam building
– Impact on local
people
– Impact d/s
– Against scientific
advice

BFP NIGER Niger
Coordination: Jean Charles CLANET & Andrew OGILVIE – IRD/G-EAU

Initial conclusions
• Water and poverty: complex relation, yet to be proved…
• Large potential for WPr improvements but above 800mm
rainfall, water rarely a limiting factor. Under 800mm rainfall,
water is only one variable. Others more significant
• Difficult to implement (weak economy, reliance on aid, diversity
of ethnicities/languages, insecurity).
• Improvements in water management need to be accompanied
by institutional and cultural changes to support them. Also
investment, markets, microfinance… Easier to import cheap
products than invest in national agriculture?
• Large potential for irrigation (x 000s ha), rainfed agriculture,
livestock, integrated systems (fisheries, agroforestry)
• Large scale hydraulic investment – complicate situation not
opposite…


Further insights
• Wider causes of poverty need to be addressed
– Eg. impacts of improvements in education
• Literacy improvements should also alleviate demographic
pressure and future water « stress »


Thank you for your attention


Niger Basin Focal Project Coordination Workshop

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More from Water, food and livelihoods in River Basins: Basin Focal Projects

More from Water, food and livelihoods in River Basins: Basin Focal Projects (20)

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Niger Basin Focal Project Coordination Workshop