Presented by Mengistu Alemayehu at ILRI, Addis Ababa, 18 April 2013

1. Livestock–Water Interactions: The
Case of Gumara Watershed in the
Upper Blue Nile Basin, Ethiopia
Mengistu Alemayehu Asfaw
Department of Crop and Animal Sciences
Humboldt Universität zu Berlin

2. Outline
• Introduction
 Problem statement
 Objectives
• Materials and Methods
 Description of study area
 Study design and treatments
 Statistical analysis
• Results and Discussion
 Livestock water productivity
Collective management on communal grazing lands
 Determinants of good pasture condition
• Conclusions and Recommendations
2

3. 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

4. Mixed Farming Systems in the
Highlands
4
Integrated
mixed crop-
livestock
farming

5. 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)

6. 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

7. 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

8. Assessing LWP in mixed farming
systems, Ethiopia

9. 1.1 MATERIALS AND METHODS
Study site
- Gumara watershed was
selected
Reasons
• 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

10. 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

11. 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

12. 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

13. 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
lossmortalitybenefitslivestock
LWP
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

14. 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

15. 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 species
Eij= 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.

16. 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

17. Wealth status N CWP2 se
(USD m-3)
LWP se
(USD m-3)
Water use se
(m3 kg-1 lwt)
Poor 23 0.37 0.01b 0.060 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
17
CWP-crop water productivity; LWP-livestock water productivity; USD- United States
Dollars

18. 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 ** ** **
18
Table 3. LWP under two off-take managements.
Reduced
by >50%
LWP- livestock water productivity; USD- United States Dollars;
TLU- tropical livestock unit

19. 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 ** **
19
Table 4. LWP for different livestock species
LWP – Livestock water productivity; USD- United States Dollars

20. Impact of Collective Management on
Communal Grazing Lands

21. 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

22. •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
22

23. 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 timeStocking 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

24. Statistical analysis
Parametric and non-parametric analysis were run
uing a 3x2 factorial design
24
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.

25. 25
0
0.5
1
1.5
2
2.5
0
5
10
15
20
25
30
Restricted communal private holding Freely open communal
Stockingrate(TLU/ha)
GLM type
Stocking density
Carrying capacity
Stoking rate
biomass removed
by livestock
Annualbiomassremoved(t/ha)
46% of the
herbage
biomass is
removed
80% of the herbage biomass is removed
2. 2 RESULTS AND DISCUSSION

26. 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 76.4a 87.6a 78.3a 44.3b 42.7b 4.6
26
HBY – 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

27. 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%
27
RO = cumulative surface runoff per year; SL= annual soil loss; SEM – standard error of
mean
2. 2 RESULTS AND DISCUSSION
Table 6. Runoff and soil loss as affected by different types of GLM

28. 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
28

29. Determinant Factors to Good Pasture
Condition of Restricted Communal
Grazing Land

30. 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

31. • 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
31
3.1 Data collection

32. • 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
32
3.1 Data collection

33. • Binary dependent variable -logistic
regression model
• For DMY – Ordinary Least Squares (OLS)
method was used
33
3.2 Statistical analysis

34. 3. 2 RESULTS AND DISCUSSION
Explanatory variable OLS Logit
DM yield
Legume
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

35. 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
35

36. • 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
36
CONCLUSIONS AND
RECOMMENDATIONS

37. 37
THANK YOU FOR YOUR
ATTENTION

38. 38

39. Conceptual framework of livestock–water
interactions to assess LWP (Peden et al.
2007)
Fig. 4. LWP conceptual frame work 39

40. Data Collection
Determination of LWP
40
n
k
n
j
n
m
n
l
ljk
n
i
n
j
j
n
j
jii
DGmSDET
MSCPO
LWP
1 1 11
1 11
)*(

41. Fig.1. Quadratic relationship between soil loss and runoff on each rainfall event.
y = -0.002x2 + 0.098x - 0.144
R² = 0.875
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 5 10 15 20
Soilloss,ton/ha
Run off, mm
2. 2 RESULTS AND DISCUSSION
41