niger basin focal project

Coordination:

Jean Charles CLANET

Andrew OGILVIE

Niger Basin Focal Project

Chiang Mai workshop, 18 September 2009

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

Country Basin size per country (km²)

Proportion of basin within country (%)

Proportion of country within 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

TOTAL Active Basin

1,272,814 100 -

Large transboundary basin

• 4183 km

• 2.1 M km² / 1.2 M km²

• 10 countries


Spanning range of agroclimatic zones

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


Evolution of Niger Basin population 2005-2050 according to various UN DESA scenarios

384 036 651

246 388 996

215 273 326

94 506 856

186 656 464

-

50 000 000

100 000 000

150 000 000

200 000 000

250 000 000

300 000 000

350 000 000

400 000 000

450 000 000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Population

Constant-fertility variant

High variant

Medium variant

Low variant

Prone to extreme demographic expansion

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


Due to high fertility

• Future population trends willdepend on speed of fertilitydecrease 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


0

100

200

300

J M M J S �

700450

0

100

200

300

J M M J S �

1050900

0

100

200

300

J M M J S �

12001100

0

100

200

300

400

J M M J S �

17001350

0

100

200

300

J M M J S �

730520

Mean annual rainfall (average 1951-1990),

and monthly rainfall for wet and dry years

WP2: Rainfall distribution

• 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


Main Niger sub-basins and annual hydrographs

for wet and dry years

A

B C

DE

F

Discharges in m3/s

Advances in basin hydrology

• Ability to

predict

changes in

flow from

rainfall

predictions

• Impact of

dams,

climate

change,

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.shp00 - 1742217422 - 2122821228 - 2554725547 - 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

• Inner Delta

• Nigeria dams, fadamas,

Sokoto

• Small dams

• Recession flood

• Lowlands


Nigeria’s greater control over water

• Nigeria as leader in

dam construction

and irrigation

390500972 5007500028150034000total

49 50018 500220009000basfonds subm cont

85 00085000submers cont

795 000723 0001200060000décrue

850045004000agroindust

3000030000petit privé

215700161 700500004000PIV individuel

1400080006000PIV collectif

1250095003000PIV publique

15250069 00013000625008000grands périmètres

totalNigeriaNigerMaliBurkina types de périmètres

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

200

pomme de terre,

oignon0,064,51999588Burkina Faso

T3Titao

Oignon1,2040,01974366SénégalT4Mbawane

Oignon0,0820,01966326SénégalT4Keur Mbir Ndao

200Niébé0,0717,01997334NigerT2Mbida

200choux, poivron, laitue0,16200,01980526NigerT4Gamkale

125oignon, tomate1,0046,01981382NigerT2Tera

180Oignon0,203,61992379NigerT3Sakoira

200Riz1,001260,01970943Burkina FasoT1Vallée du Kou

200Riz1,00680,01974910Burkina FasoT1Nakambe/ bagre

Riz1,70262,01991250SénégalT1Boundoum

100Riz12,00298,01950553MaliT1Djidian

151Riz20,00576,71951383MaliT1B1

100Riz2,5035,02001410MaliT3Saba 1

100Riz1,0049,01997449MaliT2Sinah

Riz0,4116,01994451MaliT2Kamaka

200Riz1,00227,01991756NigerT1Lata

intensité

culturale %Culture principale

Superficie

moyenne par

exploitant (ha)

Superficie

équipée(en ha)Date de réalisation

Pluviométrie

moyenne annuelle Pays fleuvesTypologie APPIA

Nom des

périmètres

WPr of market gardening activitivies and riceSource: APPIA


WPr in wet and dry season

0

5000

10000

15000

20000

25000

Kamaka B1 Sinah Djidian N10 Saba1 Bargodaga Lata

Irrigation Schemes

Yield (kg)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Water Productivity (kg/m3)

Irrigated inflow (m3)

Yield (Kg)

WP (Kg/m3)

0.0

5000.0

10000.0

15000.0

20000.0

25000.0

30000.0

35000.0

B1 N10 Sakoira Tera Gamkale Mbida

Irrigation Scheme

Gross Valur Product/ Irrigation inflow

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Water productivity ($/m3)

Irrigation inflow (mm/ha)

Gross Value Product ($/ha)

WP ($/m3)

0.0

200.0

400.0

600.0

800.0

1000.0

1200.0

1400.0

1600.0

1800.0

2000.0

B1 N10 Sakoira Tera Gamkale Mbida

Irrigation Scheme

I rrigation (mm/ha)

Evapotranspiration (mm/ha)

0.0

500.0

1000.0

1500.0

2000.0

2500.0

3000.0

Bargodaga Kamaka Sinah Saba1 Djidian B1 N10 Lata

Irrigation Scheme

Gross Inflow (mm/ha)

Evapotranspirat ion (mm/ha)

Wet season Dry season


Identified constraints


WP3: Rainfed agriculture


1999, good year

Leached soilsHeavy rainfallRainfed cereals = marginal cropsMajor crops = lowland rice

kg grain per ton applied water: Intercepted rainfall

Rainfed Water productivity

• Maps of rainfed WPraccording to CPWF definition

• Difficult/dangerous to interpret

=> return to theory

kg grain per ton depleted water:

Evapotranspirable water


• 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

Increasing rain utility


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

– Rain-use efficiency (RUE) when the rain is a limiting factor

•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 as a limiting factor

Savanna grass production becomes

dependent to variations in rainfall

above 10°N (below 800mm)

(Fournier 1991).

0

2

4

6

8

10

12

400 600 800 1000 1200 1400 1600

rainfall of the year (mm)

max. phytomass (t MS/ha)

perennial grass, slope, latitude

11-12°N

perennial grass, slope, latitude

6-9°N

t de céréales par habitant rural

-

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

0,45

0,50

sahélien<500 soudanosahel500-600mm

nord soud600-800

centresoudanien800-1000

sudsoudanien>1000 mm

1984 1986 1988 1994Usual droughts (1986):no effect if zone >800mmsmall effect 500-800mmgreat effect <500mm= (insufficiency)

Heavy droughts (1984)great effect <1000mm(insufficiency)

FS

(Cereal ton/capita)


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)

Water index

IRESP, ActualET/potential ET

IRESP

0

Relative yield

1

1

0

System B System A

0,5

• Increase RUE by

1) increasing water

satisfaction index (= reduce

deficit = synchronising offer

and demand)

2) increasing relative yield in

case of water index <1, and

reducing drought resistance

(minimising actual ET of zero

yield)


WP3: Fisheries


Inner Delta fisheries

y = 36,637e0,0093x

R2 = 0,9629

0

5000

10000

15000

0 200 400 600 800

Hauteur d'eau à la station de Mopti (cm)

Surface

inondée

(km2)

Delta amont (Juilet à octobre 1995)

0

20

40

60

80

100

500 1000 1500 2000 2500

Inflow Mopti (m3/s, July-November)

Total catch

(tonsx1000)


Abstraction of some volume to the river flood decreases the fish catch,

about 28 tonnes/y for 1 m3/s during the flood period.

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

y = 157.47x - 483

R2 = 0.72

0

5000

10000

15000

20000

25000

20 40 60 80 100 120 140

Flood index (in days)

Data for 1988-2005 from Mali administration, processed by CP 72

Marginal WPr in Inner Delta fisheries


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 1978-2050Evolution annuelle du cheptel (bovins et petits ruminants) en zone agroclimatique

aride du système pastoral du BFN de 1978 à 2008

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

Année

Nombre (Tête)

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

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

16000000

18000000

Année

Nombre (Tête)

Bovins Petits Ruminants Camelins


Pastoral & trade movements

Clanet, 2009


Leglislation surrounding pastoralism

Hydraulique

SectorielAména-gements

CodeA-P

Prati-ques

Aména-gements

N.r.Aména-gements

MigrantsDispositifs focalises sur :

x-xxxxxxx

-xxxxxx

XXXxxx(x)

Régionaux

•CEDEAO

•CEBV

•CILSS

-X-------Bassin

L > NL > NL > NL > NL > NL > NL > NL > NL > NGestion quotidienne

X1960

X1979

X1997

X1996

X1995

X1982

X1983

X1984

X1987

Nationaux (Législatifs : textes, lois codes, décrets, schémas directeurs …

XXXXXXXXXLocaux (us et coutumes)

TchadNigeria2NigerMaliGuinéeCôte IvCam. BurkinaBeninDISPOSITIFS

� Mobilités pastorales transfrontalières �� besoin: « sécuriser le foncier pastoral » (PRASET, Niamey,

GTZ, 1997).

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

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

• Recent institutions/decentralisation

• Budding IWRM

• Uneven recognition of traditional law

-PDFA

signé

-PDFA

signé

PDFA

signé

--PDFA

signé

PDFA

signé

Genre

ReconnaissanceTrès mauvaisPas reconnu

Reconnaissance

Reconnaissance

ReconnaissanceÀ améliorerChefs consultés

Reconnaissance

Gouv/Cout

umier

Difficile789 LGAs8 régionsInachevé,

déficitCollectivitésdébut10 provinces13 régions6 dép.

Décentralis

ation

PrincipeÀ améliorerPris en compteCellule GIREGire =

défiPlan directeur

Principes reconnus

PAGIRE adopté

Projet

pilote

GIRE

Pas clairDroits

occupationPropriétéPropriétéPropriété�ationalisteTitre foncier

Permis d’occuper

Droit/Politi

que

TchadNigeriaNigerMaliGuiC.I.CamBFBénin

Concession

D’après 2iE, EIER-ETSHER


Importance of institutions at local level

• Impact on WPr

via land tenure

• Existing systems

based on

traditional

law/customs

XExclusion

XXManagement

XXXWithdrawal

XXXXAccess

Undifferentiated actor :

individual or herd

without particular status

Exploitation unity (e.g. with

fishery right)

or outsider (with a temporal

right for extraction)

Lineage member:

prescribed right for

pasture access

- State

- ‘Maître des eaux, des terres, des pâturages’

discretionary power on water, land or pasture

access

- Chiefs (village, family, lineage or production

unit)

Property right in the Inner Delta �

Authorized EntrantAuthorized UserClaimantProprietorUsers type �


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

CSIRO.CSIRO.

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.CSIRO.

No universally agreed-to metric

Issue no. 1: What and where is poverty?

Child mortality

Child morbidity

(stunting)

Household

wealth

Hot spot

CSIRO.CSIRO.

•Derive weightings from the data

Issue no. 2: Accounting for causes of

poverty

• Spatially explicit modelling – heterogeneous coefficients for a

heterogeneous problem

CSIRO.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

Poverty definitions are

crucial → use multiple

metrics simultaneously

and compare results

Central Mali and the Inner Delta

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

Sample size 83 83 83

CSIRO.CSIRO.

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.CSIRO.

Outcomes: variation between hotspots

Poverty

Hotspot Measure of poverty

Water poverty

variables

Non-water

poverty variables

Utility of the

TARWR

variable

North west

NigeriaAll three metrics

Water access

Unprotected water

Irrigation

TARWR

Education

Livestock

Moderate –

child mortality

only

Central Mali

and the Inner

Delta

Child mort. only Unprotected waterEducation

Livestock

Limited – not

significant

East Burkina

FasoAll three metrics

Unprotected water

Dams

Education

Environ. damage

Limited – child

morbidity only,

contrary signs

East Nigeria and

north

Cameroon

Wealth index onlyIrrigation

Dams

Education

Population

density

Malaria

Drought risk

Environ. damage

Limited – child

mortality only,

contrary signs

South and

central Nigeria

(‘wealth

hotspot’)

All three metrics Unprotected water

Access to towns

Education

Electricity

Telephones

Limited –

contrary signs,

small effect


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

Adapt demand to water supply(photosensitivity, better decision makingfor sowing, extensivity),

Adapt supply to crop demand (runoff control and water harvesting, rooting)

Enhancing tolerance to supply-demand gap in deficit (rooting management, droughtresistance) or excess (drainage tolerancy)

Source: UNESCO

Conservation tillage and conservation agriculture currently not possible in semi-arid conditions ofWest and Center Africa (very strong competitionwith other crop residues uses)

Intensification does not enhance RUE, exceptthrough organic matter inputs.

Supplemental irrigation during short dry-spells andbeginnings of humid seas in intensive farmingonly


Flow predictions• Predictions in August for Sept-Oct rainfall used

to predict flow data

• Used for Manantali dam in Senegal. Extension to Niger river upstream of Inner Delta

• Spatiotemportal uncertainty for rainfall predictions (7 days feasible)

-2.79

0

2.79

5.58

8.37

11.16

13.95

16.74

-21.09 -18.28 -15.47 -12.66 -9.84 -7.03 -4.22 -1.40 1.41 4.22 7.04 9.85 12.66 15.47

longitude

latitu

de

85

71

57

43

29

15

114

28

42

56

70

84

9897

83

69

55

41

27

1312

26

40

54

68

82

9695

81

67

53

39

25

1110

24

38

52

66

80

9493

79

65

51

37

23

98

22

36

50

64

78

92 87

73

59

45

31

17

32

16

30

44

58

72

86 89

75

61

47

33

19

54

18

32

46

60

74

88 91

77

63

49

35

21

76

20

34

48

62

76

90

²

SELINGUE

FOMI

SENEGAL à BAKEL

MANANTALI

J2

G5

I2

H5

NIGER à KOULIKOURO

0 500 1000 km

Modèle : ARPEGE 3 forcé Modèle : ARPEGE 4.6 forcé Modèle : ARPEGE 4.5 couplé

fle

uve S

EN

EG

AL

à B

ake

lfle

uve

NIG

ER

à K

oulik

ou

ro

0

500

1000

1500

2000

2500

3000

1975 1980 1985 1990 1995 2000 2005 2010

prévu = f ( J2 )

observéprévu en temps réel

m3/s

année

calage

0

500

1000

1500

2000

2500

3000

1975 1980 1985 1990 1995 2000 2005 2010

prévu = f ( G5 )observéprévu en temps réel

m3/s

année

calage

0

500

1000

1500

2000

2500

3000

1975 1980 1985 1990 1995 2000 2005 2010

prévu = f ( H5 )

observé

prévu en temps réel

m3/s

année

calage

0

1000

2000

3000

4000

5000

1975 1980 1985 1990 1995 2000 2005 2010

prévu = f ( G5 )observé

m3/s

année

calage

0

1000

2000

3000

4000

5000

1975 1980 1985 1990 1995 2000 2005 2010

prévu = f ( I2 )observéprévu en temps réel

m3/s

année

calage

Figure 1 : Location of Bakel in Senegal basin and

Koulikouro in Niger and zones used for predictions in

the Arpege model

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


HADCM2 – A2 scenario Variability of discharges for some basins near 2080 in regard

of the average 1966-1995

Climate change modelling

• High uncertainty

• Increase in T°C,

in variability and

extreme events,

later start of

rainy season,

dry spells, and

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

Nig

er


• 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, diversityof 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 notopposite…

Initial conclusions


Further insights

• Wider causes of poverty need to be addressed

– Eg. impacts of improvements in education

• Literacy improvements should also alleviate demographicpressure and future water « stress »


Thank you for your attention

niger basin focal project

Design

south bfp nigercoordination

basin bfp niger coordination

landuse bfp niger coordination

guenganbfp nigercoordination

nigriabfp nigercoordination

ird hsm bfp niger coordination

water nigeria

faobfp niger coordination