Livestock–Water Interactions: The Case of Gumara Watershed in the Upper Blue Nile Basin, Ethiopia

Mengistu Alemayehu Asfaw

Department of Crop and Animal SciencesHumboldt Universität zu Berlin

Outline

• Introduction Problem statement Objectives

• Materials and Methods Description of study area Study design and treatments Statistical analysis

• Results and Discussion Livestock water productivityCollective management on communal grazing lands Determinants of good pasture condition

• Conclusions and Recommendations2

The Ethiopian Highlands

3

Rugged mass of mountains covering 40% of the country’s land area

Have moderate temp. and adequate rainfall

80% of the human & 78% of the livestock population of the country concentrate here

Mixed Farming Systems in the Highlands

4

Farm power and manure

CropsCrop residue

Livestock

Integrated mixed crop-

livestock farming

Multi-functions of livestock in mixed farming

• Nutritious products for home consumption

• Income source from livestock sales

• Asset accruing functions

• Renewable farm power source

• Manure

5

At National level• Livestock make 45% of the total

agricultural GDP (Behnke and Metaferia, 2011)

Farm resource base of the mixed farming

1. Land tenure system• Land is under state

ownership• Farmers have use right• Grazing is communal

Due to increasing rural population– Land scarcity is critical– Pasture area is marginalized

6

2. Water scarcity-Rain fed farming practice- Highly seasonal- Erratic rainfall- No water harvesting

technology

3. Feed scarcity

- Heavy reliance on crop

residues

- Over-exploitation of

communal grazing lands- Critical during cropping period

A need to increase resource productivity

in a sustainable manner

The present study focused

much on water productivity

Specific Objectives

1) Refine the methodology for assessing LWP in the

framework of Life Cycle Assessment

2) Assess LWP in the mixed farming systems of the

Ethiopian highlands

7

3) Explore the impact of collective management on

sustaining pasture ecosystem and land degradation

4) Identify the determinant factors influencing good

pasture condition

Assessing LWP in mixed farming systems, Ethiopia

1.1 MATERIALS AND METHODS

Study site - Gumara watershed was

selectedReasons• Part of a big project in

the Nile basin• Represents different

mixed farming systems• Availability of

hydrological information

9

Major features• Topography varies from

rolling rugged mountains to vast flat lands

• Altitude ranges between 1780-3740 m above sea level

• Rainfall distribution is uni-modal (1300-1500mm) in 3-4 months with low temperature

Study Design

Three distinct scenarios of mixed farming systems

i) Rice/noug based farming complex (RNF)

• Crop residues and aftermath grazing – major feed resource base

• Livestock species- Cattle and equine

10

Study Design…

ii)Tef/finger millet based farming complex (TMF)• Crop residues,

pastureland and aftermath grazing – major feed resources

• Livestock species- Cattle, equine, sheep, goats

• Equines are used as pack animals 11

Study Design…

iii) Barley/potato based farming complex (BPF)• Grazing land- major

feed resource base• Livestock species-

Sheep, cattle, equine• Use of horse and mule

for ploughing cropland

12

Determination of LWP• LWP was determined

using the framework of Life cycle assessment (LCA) and water foot printing concept

13

n

k

n

i

n

waterdepleted

lossmortalitybenefitslivestockLWP

1

1

LCA is used to compile inventory in a defined system boundary (from cradle to farm gate –in the present study)

The water foot print accounting was based on LCA frame of the herd's productive life time (birth to end of productive life)

• Out puts (milk, meat)• Services (draught power)• Asset (stock capital)• Manure

Valued in monetary

terms

Depleted water –water used in livestock and no longer available for reuse in the domain

water for• Feed production (pasture and crop

residues)• Drinking water• hygiene and processing

Data Collection

In applying LWP to Gumera watershed– 62 farmers were

monitored for about 1.5 years

– Sample farmers were stratified based on their wealth status

14

Wealth status (Poor, Medium and Rich)

Stratification criteria• Land holding• Livestock holding• Annual grain harvest• Additional income

Statistical analysis

T-test analysis – for comparing early off-take (at 2 years of age) and late off-take (at 4 years of age)

15

Yij=µ+Si+Eij where; Yij=response variable such

as LWP, water use; µ=the overall mean, Si = Livestock speciesEij= error term.

Yijk=µ+Fi+Wj+(F*W)ij+Eijk where; Yijk=response variable such as LWP, water use; µ=the overall mean, Fi=ith farming system, Wj=jth wealth status of smallholder farmers, (F*W)ij=interaction between farming

system and wealth status, Eijk= error term.

Farming system

N CWP± se (USD m-3)

LWP± se (USD m-3)

Water use±se (m3

kg-1 lwt)

RNF 12 0.46±0.01a 0.057±0.003 b

50.6±2.5b

TMF 27 0.38±0.01b 0.066±0.002 a

42.7±1.7a

BPF 23 0.33±0.01c 0.066±0.002a

42.4±1.9a

Mean 0.39±0.01 0.063±0.003

45.2±2.0

F-test ** ***

Table 1. LWP and CWP under three different mixed farming systems.

1.2 RESULTS AND DISCUSSION

16

More water loss

CWP-crop water productivity; LWP-livestock water productivity; USD- United States Dollars

20% additional

water

Wealth status

N CWP2± se (USD

m-3)

LWP± se (USD m-3)

Water use± se (m3 kg-1

lwt)Poor 23 0.37±0.01

b0.060±0.003b

46.8±2.1ab

Medium 23 0.38±0.01b

0.058±0.002 b

48.0±1.9b

Rich 16 0.43±0.01a

0.072±0.003 a

40.9±2.2a

Mean 0.39±0.01 0.063±0.003

45.2±2.1

F-test ** ** *

Table 2. LWP across wealth status of smallholder farmers in Gumara watershed.

1.2 RESULTS AND DISCUSSION

17CWP-crop water productivity; LWP-livestock water productivity; USD- United States Dollars

1.2 RESULTS AND DISCUSSION

Off-take

type

N LWP± se

(USD m-3)

Sale income± se

(USD TLU-1)

Water use± se (m3

kg-1 lwt)

Early 62 0.09±0.003 272.1±2.3 13.2±0.6

Late 62 0.068±0.001 265.3±1.2 29.6±1.0

Mean 0.079±0.002 268.7±1.7 21.4±0.8

t-test ** ** **

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Table 3. LWP under two off-take managements.

Reduced by >50%

LWP- livestock water productivity; USD- United States Dollars; TLU- tropical livestock unit

1.2 RESULTS AND DISCUSSION

Livestock

species

N Liv.

no./hh

LWP±se (USD

m-3)

Water use±se

(m3 kg-1 lwt)

Small ruminant 50 5.3 0.053±0.002b 37.9±5.7b

Cattle 62 5.9 0.077±0.002a 37.6±5.0b

Equine 44 1.4 0.037±0.002c 143.2±5.9a

Mean 0.057±0.002 67.4±

F-test ** **

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Table 4. LWP for different livestock species

LWP – Livestock water productivity; USD- United States Dollars

Impact of Collective Management on Communal Grazing Lands

Study Design

Parameter

GLM type

Restricted communal

Private holding

Freely open communal

Grazing duration (days/month)

12 10 30

Resting season August –November; May - June

July-October

No resting

Dominant grazer species

oxen cattle Cattle, sheep and

equine

21

Table 6. Description of different types of grazing land management (GLM).• Three types of Grazing

Land Management (GLM) under two slope gradients (<10%, 15-25%)

The GLMs are:

I. restricted communal

GLM

II. private holding GLM

III. freely open communal

GLM

• Identified villagers are recognized as members to have use right

• The grazing land management is governed by local by-laws

• Only fixed number of animals are allowed for grazing

• Open for livestock in the village

• Kept by a farm household for making hay and afterward grazing

• Vegetation attributes: -Hrebacious biomass yield- Ground cover

determined along a 50m transect line in three replications

• Runoff and soil loss: - measured from 18 plots each

with 4x2 m2 demarcated using galvanized iron sheet

Soil moisture and bulk density- Samples taken from each plot

Data Collection

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23

Stocking density, stocking rate and carrying capacity

•Dry matter yield per ha•Daily feed intake of animals -

using average animal weight method (Pratt and Rasmussen, 2001)•Grazing duration• Livestock number •Area of grazing land

Data Collection

Stocking density - is the actual number of livestock grazing on specific area of the pasture for specified period of time Stocking rate- is the number of

livestock grazing on the entire of the pastureland for the entire grazing period

Carrying capacity - is the maximum number of livestock that can be supported by a unit of grazing land for the entire grazing period without harm in the long term

Statistical analysis

Parametric and non-parametric analysis were runuing a 3x2 factorial design

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Yij=µ+Gi+Sj+(G*S)ij+Eijk where; Yij=response variable; µ=the overall mean, Gi=ith type of GLM, Sj=jth slope of grazing land, (G*S)ij=interaction between GLM and slope, Eijk= error term.

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Restricted communal private holding Freely open communal 0

5

10

15

20

25

30

0

0.5

1

1.5

2

2.5 Stocking density

Carrying capacity

Stoking rate

biomass removed by livestock

GLM type

Stoc

king

rate

(TLU

/ha)

Annu

al b

iom

ass r

emov

ed (t

/ha)

46% of the herbage biomass is removed

80% of the herbage biomass is removed

2. 2 RESULTS AND DISCUSSION

2. 2 RESULTS AND DISCUSSION

Measured

parameter

Restricted communal

GLM

Private holding GLM Freely open communal

GLM

SEM

<10% slope 15-25%

slope

<10% slope 15-25%

slope

<10% slope 15-25%

slope

HBY (t DM/ha)3.9ab 2.8 bc 5.2 a 2.7 c 2.8 bc 2.5 c 0.3

GCw (%) 85.0a 76.4 a 87.6 a 78.3 a 44.3 b 42.7 b 4.6

26HBY – aboveground herbaceous biomass yield; GCw - ground cover after end of wet season; SEM – standard error of mean

Table 5. Vegetation attributes across different types of GLM

Measured

parameter

Restricted

communal GLM

Private holding

GLM

Freely open

communal GLM

SEM

<10%

slope

15-25%

slope

<10%

slope

15-25%

slope

<10%

slope

15-25%

slope

RO (mm) 172.3d 167.3d 343.5b 255.9c 284.2c 491.3a 27.0

SL (t/ha) 6.1e 14.0c 6.4e 10.9d 24.5b 31.7a

Runoff and Soil Loss

Restricted communal GLM

•Reduce surface runoff by more than 40%

•Curb the rate of soil erosion by more than 50%

27RO = cumulative surface runoff per year; SL= annual soil loss; SEM – standard error of mean

2. 2 RESULTS AND DISCUSSIONTable 6. Runoff and soil loss as affected by different types of GLM

Table7. Bulk density and soil moisture

Measured

parameter

Restricted communal

GLM

Private holding GLM Freely open

communal GLM

SEM3

<10%

slope

15-25%

slope

<10%

slope

15-25%

slope

<10%

slope

15-25%

slope

SM (%)1 34.5a 24.3cd 29.4b 26.3bc 26.8bc 22.6d 1.1

BD (g/cm3)2 0.82c 1.02ab 0.87bc 0.94abc 1.06a 1.08a 0.03

2. 2 RESULTS AND DISCUSSION

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Determinant Factors to Good Pasture Condition of Restricted Communal

Grazing Land

3.1 Study area and design

• A cross-sectional study was carried out in barley/potato based farming system

• 42 villages were randomly selected

• 140 smallholder farmers were selected using multistage sampling technique 30

• Explanatory variables to pasture condition• 7 variables were used to explain the dependent variable

• Area of communal grazing land• Area of restricted grazing land • Area of cropland at household level• Oxen number in a village• Livestock density in a village • Pasture resting period • Soil fertility

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3.1 Data collection

• Proxy indicators to pasture condition (PROGRAZE manual, 1996)

• Herbage DM yield using a quadrat,

• Legume proportion, • Digestibility (Tilley and Terry,

1963 )• Carrying capacity/stocking rate

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3.1 Data collection

• Binary dependent variable -logistic regression model

• For DMY – Ordinary Least Squares (OLS) method was used

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3.2 Statistical analysis

3. 2 RESULTS AND DISCUSSION

Explanatory variable OLS Logit

DM yieldLegume

proportion

digestibility Ratio of carrying

capacity to

stocking rate

Area of communal grazing land -0.01928 0.0661 0.6337 0.0660

Area of restricted grazing land 0.02906 -0.00640 2.1685* 2.0641**

Area of cropland -0.55043 -3.5360* -1.0045 -0.2110

Oxen number 0.00282 -0.00221 -0.0560** -0.0507**

Livestock density -0.00205 -0.0123 -0.00874 -0.00375

Pasture resting period 0.05221*** -0.00640 0.0563 0.0245

Soil fertility 0.38756 4.2194*** 11.8126* 2.7193

Intercept -6.29490*** 5.1111 -12.4499 -6.3015

Log-likelihood functions ad-R2= 0.74 -109.496 -103.944 -116.256

Model chi-square - 23.8368 38.933 37.150234 * significant at 10% level; ** significant at 5% level; *** significant at 1% level

Table 8. Logit regression coefficients of variables affecting pasture condition

CONCLUSIONS AND RECOMMENDATIONS

• CWP was higher than LWP

• LWP varied across different farming systems and wealth status

• Cattle had higher LWP due to more values of the multiple functionalities and better feed utilization efficiency

• Early off-take management scenario increased LWP

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• Livestock mortality – is one of the main causes to decrease LWP

• Overstocking is the major problem that aggravates overgrazing and eventually reduces LWP

• Management of communal grazing land can be improved using local institutions and policy supports

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CONCLUSIONS AND RECOMMENDATIONS

37

THANK YOU FOR YOUR ATTENTION

38

Conceptual framework of livestock–water interactions to assess LWP (Peden et al.

2007)

Fig. 4. LWP conceptual frame work 39

Data Collection

Determination of LWP

40

n

k

n

j

n

m

n

l

ljk

n

i

n

j

j

n

j

jii

DGmSDET

MSCPOLWP

1 1 11

1 11

)*(

Fig.1. Quadratic relationship between soil loss and runoff on each rainfall event.

0 2 4 6 8 10 12 14 16 180

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

f(x) = − 0.00222302453627081 x² + 0.0982490535924658 x − 0.144825590981227R² = 0.875409588032303

Run off, mm

Soil

loss

, ton

/ha

2. 2 RESULTS AND DISCUSSION

41