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Magboul et al., Sudan Academy of Sciences Journal-Special Issue (Climate Change), Vol. 11 , 2015, 62-73
ISSN 1816-8272
Copyright © 2015 SAPDH
Impact Assessment of Climate Change on the Livelihoods
of Pastoral Communities in Sudan’ Butana Region:
A Multidimensional Tradeoff Analysis
Abdelhamed M. Magboul1, Abbas E. M. Elamin1*, Imad-eldin A. Ali-Babiker2,
Abdelmotalib A. Ibnoaf3 and Faisal M. El-Hag2
Abstract
This study aimed at assessing the impact of climate change on the livelihoods of
pastoral communities in Sudan’ Butana Region. Emphasis was put on the
forecasted climate factors such as annual rainfall, minimum and maximum
monthly temperatures and their impacts on the grazing system, milk productivity
and livelihoods. Three States; Gadarif, Gezira and Khartoum states, out of five
states constituting Butana region were chosen. A comprehensive questionnaire
and farm survey were conducted in 2013. About 203 pastoralists’ households, 100
from Gadarif, 53 from Gezira and 50 from Khartoum were chosen and
interviewed. Simple and multiple linear regression was used to assess the impact
of the climate factors on milk productivity. The forecasted value of milk
productivity in 2030 was used as a livestock productivity parameter in Tradeoff
Analysis model for Multi-Dimensional Impact Assessment (TOA-MD) to assess
the impacts of climate change on the livelihoods of pastoral communities. Results
showed that Gezira State was mostly suffering from climate variability and
change, as reflected in significant increases in minimum and maximum monthly
temperatures and significant decrease in annual rainfall. This negatively affected
rangelands productivity, grazing systems, with significant reduction in animal
productivity. Monthly minimum and maximum temperatures had the highest
effect on milk productivity than that of annual rainfall. The Gedarif expansion in
Butana was considered as the part mostly suffering from high losses in forecasted
milk productivity by 2030 accompanied by high losses in forecasted pastoralist’
income. It was recommended that urgent intervention to rehabilitate rangelands,
encourage pastoralist to change their herd structure into productive stock and keep
sheep or goats in the herd composition should be advocated.
Keywards: Climate Change, Multidimensional Tradeoff, Pastoral, livelihoods.
Introduction
Livestock production is an important component in the Sudanese economy.
Animal wealth was responsible for 19.3% of the GDP and over 80% of rural
households in Sudan depend on both pastoral and agricultural activities for their
livelihoods (MFNE, 2005). Sudan ranks top in terms of livestock population in the
Arab World and comes second to Ethiopia among the African countries.
Livestock population was estimated at about 103 million heads (Osman et al.,
1
Planning, Monitoring and Evaluation, Agricultural Research Corporation (ARC), Wad Medani, Sudan,
*Corresponding Author email: [email protected].
2
Dryland Research Centre (DLRC), Agricultural Research Corporation (ARC), Soba, Khartoum, Sudan.
3
Faculty of Commerce, Economics and Social Studies, Al Neelain University, Khartoum, Sudan.
62
2012). Those include 39, 30, 29 and 4 million heads of sheep, goats, cattle and
camels, respectively (MAF, 2011). Within the geographical distribution of total
livestock in the country 7.2, 5.3, 4.7, 2.3 and 1.3 million are found in the Gezira,
Kassala, Gadarif, Nile River and Khartoum states, respectively. These five states
sharing the Butana region contribute about 20.7 million heads (about 20% of the
total animals in the country, Table 1). In addition, Butana region hosts
approximately 8.2 million heads of livestock during the rainy season.
Table 1: Total Livestock populations (million heads) in the states
sharing the Butana region
State
Cattle
Sheep
Goats
Camels
Totals
Gezira
2.40
2.50
2.10
0.12
7.20
River Nile
0.08
1.01
1.20
0.01
2.30
Khartoum
0. 25
0.44
0.64
0.001
1.30
Kassala
0.96
2.02
1.67
0. 67
5.30
Gadarif
1.04
2.14
1.06
0. 33
4.65
Totals
4.80
8.08
6.71
1.15
20.74
Country’s Total 29.4
39.14
30.45
4.62
103.57
Source: Osman et al., 2012
According to satellite images, the total area of the Butana is about 81,567 km², of
which 62% is located in Gadarif and Nile River States. About 21% of the Butana
area is under cropland, 41% under rangeland cover and about 29% is bare land,
which are desert and semidesert areas. About 63% and 24% of Butana bare land is
in the Nile River and Kassala States, respectively (Table 2). Fig. 1 shows the
agro-climatic zones of the Butana region by State.
Table 2. Total area, land use (1000 km²) and %
share of each state in Butana area
Gadarif Gezir Kassala Khartou River Total
land
a
m
Nile
use
Bare land 0.69
0.26
5.65
2.19
14.91 23.64 (29%)
Cropland 10.11
1.98
0.52
3.5
1.03
17.11 (21%)
Grassland 12.98
4.31
5.74
4.40
5.64
34.92 (41%)
Tree
3.86
0.74
0.66
1.45
1.4
10.30 (9%)
cover
Total
27.63
7.29
11.97
11.53
23.06 81.48
land use
(34%)
(9%)
(15%)
(14%)
(28%) (100%)
Total
27.63
7.31
12.00
11.56
23.07 81.57
Butana
(34%)
(9%)
(15%)
(14%)
(28%) (100%)
area
Source: Osman et al., 2012
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63
Figure 1. Butana region
Climate change and variability is a predominant phenomenon in semidesert, arid
and semiarid zones and droughts are becoming more frequent and more severe.
Since the early 1980s, due to the reduction in the quantity and uneven distribution
of the rainfall, there is an observed fluctuation of the rainfall, long dry spells, drop
in relative humidity and rise of temperature. Elhag (2006) stated that Gezira and
Khartoum states were subjected to a significant reduction in their monthly and
annual rainfall during the period 1996-2006. A consensus clearly emerged among
pastoralists in the region that climate has been changing over the past few decades
and has adversely affected the productivity of Butana's rangelands. This has
resulted in the steady deterioration of both the productivity and biological
diversity of the Butana rangelands. Furthermore, Butana hosts other pastoralists
from drought-affected areas in other parts of the Sudan. This has intensified
pressure on its fragile and deteriorating resource base and further exacerbating the
vulnerability of its pastoralists. This study attempted to assess trends and changes
in climate factors such as annual rainfall, minimum and maximum monthly
temperatures over time in Butana region, impacts of climate change on the
grazing system and animal productivity and on livelihoods of the pastoral
communities in Butana area.
Materials and Methods
Site selection and data collection: Three States namely; Gadarif, Gezira and
Khartoum were chosen out of the five States constituting Butana area (Figure 1).
Gezira State had the highest share in almost all species of animals, about 35% of
animals in Butana region, and it is the most affected area by climate change as
demonstrated by the decreasing trend of rainfall and rise of temperatures over the
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ISSN 1816-8272
last five decades. It is worth mentioning that Gezira and Gadarif contribute about
57% of the total animal wealth in Butana region. Khartoum represented the
second most-affected area by climate change. Gadarif ranks second in terms of
cattle and sheep but with less camels and goats compared with Kassala State.
A comprehensive questionnaire and a farm survey were conducted in 2013 under
the project entitled “Enhancing Climate Change Adaptation in Agriculture and
Water Resources in the Greater Horn of Africa” (ECAW). About 203 Pastoralists
households, 100 from Gadarif, 53 from Gezira and 50 from Khartoum were
directly interviewed.
Regression analysis: Simple regression analysis was used to estimate the trend of
the historical climate data to detect whether there is a climate change in the
Butana region as postulated by annual rainfall and monthly minimum and
maximum temperatures during the period 1961-2013 recorded in five weather
stations in Butana region. The linear trend is simply represented by the following
equation:
Where:
Ty = quantity of the climate variable in question i.e., annual rainfall, minimum or
maximum monthly temperature in the main weather stations in Butana region.
a = intercept or estimated value when x equals to zero
b = slope of the line or average change in Y per unit of time
x = time factor in years from 1961 to 2013 used to forecast the estimated values
of rainfall and temperatures into 2030.
Again, a multiple linear regression was used to assess the impact of the climate
factors on annual milk productivity per unit of four animal species in the three
States of the study area. The multiple regression equation is expressed as follow:
Where y is the annual milk productivity, i represents the three chosen States
namely Gadarif, Gezira and Khartoum, j is the observations within each state,
represent the coefficients of the variables,
represents the intercept term,
and ε is an error term.
T and R represent the historical data of temperature and rainfall in the Butana
states, which are used to forecast the estimated value of milk productivity per unit
for the four animal species in 2030 and then used the forecasted livestock
productivity in Tradeoff Analysis model for Multi-Dimensional Impact
Assessment (TOA-MD).
TOA-MD model: The development and application of relatively simple and
reliable methods for assessing the impacts of climate change at the agricultural
system and/or household level are needed to provide timely recommendations on
the potential impacts of alternative technologies and policies. This study used the
TOA-MD impact assessment model (Antle and Valdivia 2006; Antle and Ogle,
ISSN 1816-8272
65
2011; Antle, 2011). The model uses statistical description of a heterogeneous farm
population to simulate adoption and impacts of a new technology or change in
environmental conditions (Seth, 2012). TOA-MD model was used to assess the
impact of technology and climate changes using economic, environmental and
social indicators. Claessens et al. (2012) pointed that this model has been used for
the analysis of technology adoption, payments for environmental services and set
up and interpreting of climate change applications. The model simulates
technology adoption with associated economic, environmental and social
outcomes in a heterogeneous farm population for regional impact assessment. The
methodology uses the surveys, experimental and modeled data to assess simulated
management practices that are typically available in countries where semisubsistence systems are important, combined with future socio-economic
scenarios based on new scenario pathway concepts being developed by the
climate change and impact assessment modeling communities (Hugo, 2011). The
model includes the following parameters:
Population, which targets the pastorals households in Butana region. About 203
respondents from three States (Gedarif, Gezira and Khartoum) out of five
stretching into the Butana region.
Systems scenarios: where system1 describes the current economic situation of
pastoralists, which refers to base climate and base technology, and system2
describing a changing economic situation of pastoralists in the future (in 2030)
with reference to change in climate and base technology. The model captured the
adoption rate of those pastoralists who continue using the existing technologies of
2012 in the future (2030) despite changes in climate.
Strata: three strata, 1, 2 and 3 target the pastoralists in Gadarif, Gezira and
Khartoum States with 100, 53 and 50 sampled respondents, respectively.
Subsystems: include types of livestock activities; cattle, sheep, goats and camels.
Source of data for system1 was the survey data while the data input for system2
was the projection with respect to the change in milk production due to change in
climate factors.
Types of data: included farm household data such as area/ha, farm size in ha,
family size, average milk production in liters, prices of milk in SDG/liter at the
current price and cost data (average variable costs), standard deviation of net
return from livestock; and an outcome variable represented by a poverty line of
USD 1/person/day.
Outcomes variables and indicators: These included poverty rate, defined as the
percentage of farm population living on less than USD 1 per day, net loss, defined
as the percentage of farm income losses as a result of climate change impacts, and
the adoption rate, defined as the number of households or the percentage of farm
population from the sample continuing to adopt the prevailing technology.
The model hypothesizes that only milk productivity will be affected as a result of
climate change while the herd size, operation costs and farm size will remain the
same during the period 2012 to 2030.
TOA-MD is used as a climate impact assessment tool to measure: a) the impact of
climate change without adaptation, i.e., assuming all farmers use the base
technology (system1) or the impact of climate change, when farmers choose
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ISSN 1816-8272
whether to adopt the adapted technology under the perturbed climate. In this
respect, a comparison was made between pastoralists' economic and social
situations under public rangeland and the prevailing technology options on the
one hand, and facilities provided by the Butana Development Agency (BDA) to
develop the local public rangelands, on the other hand. This included
establishment of small protected grass areas to use in the dry season, reseeding of
some preferred grasses and water harvesting techniques.
There is no definite farm size for trans-humans whereby animals are
predominantly grazed in public rangelands. For this reason, returns and costs were
calculated on the basis of herd size and then converted to per farm size based on
the estimated areas surrounding the pastoralist villages. It is also likely that the
impact of climate change on productivity is underestimated because the effects of
increasing climate and weather variability have not been included, and this is one
of the biggest constraints in the rain-fed agriculture.
Results and Discussion
Results showed that Gezira state was subjected to significant reduction in monthly
and annual rainfall during the period 1961-2013. This indicated that Gezira state
was the state mostly affected and impacted by climate change. With the exception
of Gadarif, which was subjected to non-significant increases in the annual and
monthly rainfall, other states were subjected to non-significant decreases in the
annual and monthly rainfall (Table 3).
With respect to temperatures, the five states were subjected to a high significant
increase in winter (November to February) minimum temperatures with variable
changes in summer and autumn temperatures. The River Nile state recorded
insignificant increase in summer (March - June) minimum temperature while,
along with Gedarif, recording rising minimum temperatures in autumn (Table 4).
Moreover, the Gezira, Gedarif and the River Nile states were subjected to highly
significant increases in the maximum temperature during the three seasons;
summer, winter and autumn. Kassala and Khartoum, on the other hand, were
subjected to significant increases in maximum temperature during winter (Table
5).
Table 3. Trends of the annual and monthly rainfall
and their significance levels
Location
Annual rainfall
Monthly rainfall
Trend
P-value
Trend
P-value
ns
-0.136
0.128ns
Khartoum
-1.226
0.128
Kassala
-0.109
0.803ns
-0.047
0.811ns
Gezira
-1.761
0.016*
-0.22
0.016*
ns
0.180
0.104ns
-0.037
0.611ns
Gadarif
1.623
0.104
River Nile
-0.260
0.611ns
ns = not significant; * = significant (p = 0.05)
ISSN 1816-8272
67
Table 4. Trends of minimum temperature and their significance levels
Minimum temperature
State
Summer
Winter
Autumn
Trend
P-value
Trend P-value
Trend
P-value
Khartoum state
0.030
0.000** 0.058 0.010**
-0.023
0.167ns
Kassala state
0.025
0.004** 0.039 0.000**
0.014
0.054ns
ns
Gezira state
0.013
0.059
0.047 0.003**
-0.006
0.520ns
Gadarif state
0.033
0.000** 0.031 0.000**
0.024
0.000**
River Nile state
0.012
0.09 ns
0.058 0.01**
0.015
0.008**
ns = not
significant; *’** = significant (p = 0.05,0.01)
Source: calculated by author
Table 5. Trends of maximum temperature and their significance levels
State
Maximum temperature
Summer
Winter
Autumn
Trend
P-value
Trend
P-value
Trend
P-value
Khartoum state
-0.023
0.316ns
0.089
0.01*
-0.064
0.037*
Kassala state
0.011
0.077ns
0.024
0.010*
-0.007
0.589ns
Gezira state
0.026
0.000**
0.036
0.000** 0.024
0.004**
Gedarif state
0.034
0.000**
0.029
0.000** 0.031
0.000**
River Nile state
0.032
0.000**
0.032
0.000** 0.036
0.000**
ns = not significant; *,** = significant (at p = 0.05, 0.01)
The amount of milk per each species of animal during the period 1961-2013
(FAO, 2006 and survey data, 2013) for the three States; Gedarif, Gezira and
Khartoum was regressed on the amount of annual rainfall and minimum and
maximum monthly temperature for the same period to trace the effect of these
climatic factors on the grazing system reflected in milk productivity. Monthly
maximum and minimum temperatures affected milk productivity more than the
annual rainfall. This was reflected by the higher negative coefficients of monthly
maximum and minimum temperatures compared with the coefficients of annual
rainfall (Table 6).
Gedarif was the state with highest suffering from increases in minimum and
maximum temperatures. In spite of a rising, though insignificant, forecasted
annual rainfall, Gedarif has encountered high losses in forecasted milk
productivity by 2030 as a result of the impacts of these climate factors (Tables 6
and 7).
Annual milk production in Gedarif in 2030 is expected to decrease to 86%, 93%
and 99% for sheep, goats and camels, respectively, from its base-system level in
2012 as a result of climate change, although, cattle milk is expected to increase to
102%. In the Gezira, milk production is expected to decrease to 72% and 87% for
sheep and goats, respectively of the base-system levels, but milk productivity will
simultaneously increase to 107% and 101% for cattle and camels, respectively.
Production of milk in Khartoum will witness increases to 104%, 115% and 101%
for sheep, cattle and camels respectively, but it will decrease to 96% for goats.
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ISSN 1816-8272
The forecasted minimum temperatures depict increases by 5%, 4% and 3% by
2030 in Gedarif, Gezira and Khartoum States, respectively, over their base-period
levels. Annual rainfall is also forecasted to increase by 17% in Gedarif
(102.84mm) while it would decrease to 60.8% (-139.9) and 52.4% (-89.12mm) by
2030 in the Gezira and Khartoum States, respectively (Table 7).
The simulation model showed large negative impacts of climate change on the
milk productivity in Butana region as demonstrated by high income declines of
the respondent herders by 2030 due to reduction in milk production (Table 8).
Increased severity of climate factors (minimum and maximum temperatures) and
the relatively high operating costs of animal grazing and drinking-water provision
had resulted in high positive annual losses in net income in 2030 (SDG1445.56
and SDG282.16 thousands in Gedarif and Gezira). Further, there were positive
annual net losses in farm income (616SDG and 2.3 thousands in Gedarif and
Gezira) and annual net losses in incomes as a percent of mean net farm returns
(23.08% and 9% in Gedarif and Gezira). On the other hand, Khartoum recorded
negative annual net losses for the same items. These findings indicated that
Khartoum State would gain under climate change situations due to the
combination of insignificant change in climate factors (maximum and minimum
temperatures) and cost-effective animal production (Table 8).
Table 6. Estimated coefficients for milk productivity for the four animal
species in the three states
Animal species
Gedarif
Coefficient
Significant
Camels
Constant
236.62
0.000**
Maximum temperature
0.041
0.392 ns
Minimum temperature
0.076
0.867 ns
Annual rainfall
0.001
0.725 ns
Cows
Constant
2154.27
0.000**
Maximum temperature
-16.04
0.252 ns
Minimum temperature
-48.65
0.000**
Annual rainfall
-0.083
0.078*
Goats
Constant
743.38
0.002**
Maximum temperature
4.52
0.607ns
Minimum temperature
-35.16
0.000 **
Annual rainfall
-0.068
0.021*
Sheep
Constant
671.26
0.000**
Maximum temperature
-11.16
0.097*
Minimum temperature
-9.91
0.094 *
Annual rainfall
0.011
0.248 ns
ns = not significant; *,** = significant (p = 0.05,0.01)
ISSN 1816-8272
69
Gezira
Coefficient
Khartoum
Significant Coefficient Significant
236.59
0.447
0.085
0.002
0.000**
0.416ns
0.827 ns
0.411 ns
237.09
0.37
-0.34
-0.003
0.000**
0.268 ns
0.909 ns
0.285ns
2452.75
-50.7
-7.24
0.157
0.000**
0.000**
0.580ns
0.072 *
1764.90
-23.04
-19.06
0.032
0.000**
0.082*
0.098 *
0.781 ns
1063.44
-31.43
8.56
0.051
0.000**
0.000**
0.276 ns
0.307 ns
787.91
-9.67
-14.97
0.004
0.003**
0.179 ns
0.023 ns
0.952ns
1051.23
-32.71
9.48
0.045
0.000**
0.000**
0.172 ns
0.236 ns
825.41
-13.30
-12.54
0.009
0.001**
0.043 *
0.034 *
0.605ns
Table 7. Summary of the average data used in the TOA-MD sensitivity and
scenario analysis
Table 8. Impacts of climate change on Pastoralists in Butana area (losses and
gains in SDG/year)
Items
Total gains in income of respondent herders
Total losses in income of respondent herders
Net losses income of respondent herders
Gains in farm income
Losses in farm income
Net losses in farm income
Gains in incomes as a percent of mean net farm returns
Losses in incomes as a percent of mean net farm returns
Net losses in incomes as a percent of mean net farm returns
Source: TOA-MD model results
Gedarif
801.12
2246.67
1445.56
3.41
9.57
6.16
12.79
35.87
23.08
Gezira
624.21
906.36
282.16
5.10
7.40
2.30
19.90
28.90
9.00
Khartoum
457.25
335.30
-121.95
3.78
2.77
-1.01
29.42
21.57
-7.85
(Herders income in 000 SDG); USD 1 = SDG 5.7
Results of the analysis showed the simulated adoption rate of prevailing
technologies in grazing animals and using public rangelands in Butana area under
the situation of prevailing climatic conditions (System 1) and that of climate
change (System 2) as a function of the opportunity cost of changing from System
1 to System 2. The rate that would occur if pastorals are behaving economically
rational and maximizing expected returns to their rangelands near their villages
(farms) is the point where the curves crossed the horizontal axis. The simulation
results designated adopters at 35.3% out of the sampled respondents of the
population of pastoral households (Fig.2).
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70
Fig. 2. Adoption rate and opportunity cost of prevailing technologies.
The results provide predicted annual net returns per farm in SDG in relation to the
adoption rate of available technologies in grazing animals and using the public
rangelands in Butana region under the situations of base and change climate. The
baseline poverty rates were at the zero adoption rates. However, the annual net
return per farm would decrease from 23.913 to 23.58 thousand SDG under climate
change at the economically efficient rates of adoption (Figure 3).
Fig. 3. Net returns per farm versus adoption rate of prevailing technologies
NRFM1_A = Net returns per farm for system1: with animal production at the base climate situations.
NRFM2_A = Net returns per farm for system2: with animal production under changing climate
NRFM_A = Net Returns per farm for systems 1& 2: with base animal production methods and climate
change situation.
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71
Results of the analysis depict predicted poverty rates as a percentage of the
respondent pastorals' households in relation to the adoption rate of prevailing
technologies in grazing animals using the public rangelands in Butana area under
the situations of base changing climate. Taking the baseline poverty rates at zero
adoption rates, the poverty rate at a poverty line of USD 2 per person per day
would increase from 43.9% to 71.4% of the respondent pastoralists of Butana
region under climate change situations and economically efficient rates of
adoption (Figure 4).
Fig. 4. Poverty rate and adoption rate of prevailing technologies
POVERY1_A = Poverty rate for system1: when animal production at the base climate situations.
POVERT2_A = Poverty rate for system2: when animal production under change climate situations.
POVERTY_A = Poverty rate for system 1&2: when animal production at the base and under change
climate situations.
Conclusions
The Gezira part of Butana was the area mostly suffering from both climate
variability and climate change. This was manifested as significant increases in
minimum and maximum monthly temperatures, and a significant decrease in
annual rainfall with negative impacts on public rangelands’ productivity and the
grazing system leading to a highly significant reduction in animal production.
Much higher effect of monthly minimum and maximum temperatures on milk
productivity than that of annual rainfall was depicted. The Gedarif State part of
the Butana region was the mostly suffering from high losses in forecasted milk
productivity by 2030 accompanied by high losses in forecasted pastoralist’
income as a result of the impact of rising temperatures despite some increase in
rainfall. Therefore, urgent intervention to rehabilitate rangelands, encourage
pastoralist to improve their herd structure to favor productive animals and to
diversify their herd through keeping sheep or goats should be undertaken.
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72
Acknowledgements
This work was part of the Project “Enhancing Climate Change Adaptation in
Agriculture and Water Resources in the Greater Horn of Africa” (ECAW) which
was funded by the International Development Research Center (IDRC) Grant No.
106552-003. The advice of Dr. Antle, John M. of Oregon State University and Dr.
Roberto Valdivia of Montana State University is highly appreciated.
References
Antle, J.M. 2011. “Parsimonious Technology Impact Assessment.” American
Journal of Agricultural Economics. Vol 93(5): 1292-131.
Antle, J.M, S. Ogle. 2011. “Influence of Soil C, N2O and Fuel Use on GHG
Mitigation with No-till Adoption”. Climatic Change. Vol. 111:609-625.
Antle, J.M., Valdivia, R. 2006. “Modelling the Supply of Ecosystem Services in
Agriculture: A Minimum-data Approach.” Australian Journal of Agricultural
and Resource Economics 50: 1–15.
Claessens, L., J.M. Antle, J.J. Stoorvogel, R.O. Valdivia, P.K. Thornton, M.
Herrero (2012). A method for evaluating climate change adaptation strategies
for small-scale farmers using survey, experimental and modeled data.
Agricultural Systems 111 (2012) 85–95.
Elhag, Muna Mohamed. 2006. Causes and Impact of Desertification in the Butana
Area of Sudan. PhD thesis Department of Soil, Crop and Climate Sciences,
Faculty of Natural and Agricultural Sciences, University of the Free State
Bloemfontein, South Africa p 31.38
FAO Stat, 2006 Website www.fao/org.net.
Hugo, R. 2011. Ex-Ante Economic and Ecosystem Service Potential of Simulated
Conservation Practices in Ghana Using A Minimum Data Approach. M.Sc.
thesis Department of Agricultural Economics College of Agriculture Kansas
State University Manhattan, Kansas 2011.
MAF. 2011. Ministry of Agriculture and Forest (MAF) Reports,Khartoum, Sudan.
MFNE. 2005. Ministry of Finance and National Economy (MFNE) Reports,
Khartoum, Sudan.
Osman, A. K., El Wakeel, A. S. and ElGameri, M. A. 2012. Ecological Zonation
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thesis Oregon State University. P 13.
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Heifer International

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