NAF
International Working Paper Series
Year 2014
paper n. 14/1
Status of On –farm Water Use Efficiency and Source
of Inefficiency in the Sudan Gezira Scheme
Fadelmola M Elnour
Agricultural Research Corporation, ElObeid Research Station – Sudan [email protected]
Abbas .E. M. Elamin
Agricultural Research Corporation, Monitoring and Evaluation Unit -Sudan
The online version of this article can be found at:
http://economia.unipv.it/naf/
Scientific Board
Maria Sassi (Editor) - University of Pavia
Johann Kirsten (Co-editor)- University of Pretoria
Gero Carletto - The World Bank
Piero Conforti - Food and Agriculture Organization of the United Nations
Marco Cavalcante - United Nations World Food Programme
Luc de Haese - Gent University
Stefano Farolfi - Cirad - Joint Research Unit G-Eau University of Pretoria
Ilaria Firmian -IFAD
Mohamed Babekir Elgali – University of Gezira
Firmino G. Mucavele - Universidade Eduardo Mondlane
Michele Nardella - International Cocoa Organization
Nick Vink - University of Stellenbosch
Alessandro Zanotta - Delegation of the European Commission to Zambia
Copyright @ Sassi Maria ed.
Pavia -IT
[email protected]
ISBN: 978-88-96189-17-7
Status of On –farm Water Use Efficiency and Source of Inefficiency in the Sudan Gezira Scheme
Fadelmola M Elnour1, Abbas .E. M. Elamin2
Abstract
This paper aimed at assessing the economic efficiency and the determinants of inefficiency of
irrigation water in the Gezira scheme. Primary data were collected using multistage stratified random
sampling technique. Simple statistics and stochastic frontier function was used to achieve the stated
objectives. The study showed that water productivity as defined as Kg of out put perm3 of water was
the highest for cotton (0.91kg\m3) and lowest for food crops which include wheat, sorghum and
groundnut. The estimated coefficients of the stochastic frontier function indicated that farmers over –
irrigated both sorghum and groundnut by 17% and wheat by 4%. This means that with the
rationalization use of scare water for the summer crops, it is possible to save and enormous amount of
water which can be used to utilize all sorghum and groundnut growing area, and thus increase total
production or to produce other alterative crops. The study showed that prices of water inputs and out
puts are the major factors that have significant influence on the technical efficiency of irrigation water
while farmers awareness to optimize water use at farm level, participation in FFS and WUA location of
the farm land tenure system are the main factors that have significant influence on the technical
inefficiency for the cultivated crops in the Gezira scheme . the study concluded that policies create
most of the conditions that determine levels of water –use efficiency such as farmer size water
locations and costs, cropping patterns, farmers wariness input subsidies and crop prices . the study
recommend conducting research programs to develop verities with low consumptive water use coupled
conserve soil and water resources. This is., in addition to monitoring of economically optimum crop
water requirement that maximize returns of irrigation water under changing conditions of commodity
prices and efficient water pricing system.
Key words: On –farm Water, Efficiency Water Use, Gezira Scheme, Sudan
1
2
Agricultural Research Corporation, ElObeid Research Station. P.O. Box: 429
Agricultural Research Corporation, Monitoring and Evaluation Unit.
1. Introduction
The increasing scarcity of water for agricultural production around the world is a major cause for
concern. The situation is particularly worrying in West Asia and North Africa (WANA) where the per
capita share of water in many countries has fallen below the water poverty level. With the rapid growth
of the population and the consequent rise in demand for water, water shortages will be an even greater
concern in the coming years. The food security of the poorer countries that depend largely on
agriculture will be particularly threatened. In Sudan, estimates of future water demand and supply
clearly reveal expected substantial deficits. A study by the National Council for Water Resources
projects irrigation requirements by 2020 at 28 bcm, drinking water at 7 bcm and hydroelectric power
storage evaporation at 6.6. Accordingly, demand will outstrip supply by 2020 by 4.1-6.1 bcm. This is
however a conservative estimate based on an assumption restricting the cropped area under irrigation
to 7 million feddans. Under figures of the comprehensive strategy that envisage an additional 10
million feddans and the enormous potential in Sudan to increase the area under irrigation, much higher
water deficits would be expected. Other estimates indicate an expected water deficit of 9.47 bcm by
2025 and a deficit of 20.7 bcm measured according to a water stability threshold requirement of 1000
cm per person (Mukaimir et. al., 1996). More recent data postulate the evolution of Sudan’s water
demand for different uses to reach 48 bcm by 2025 (Ahmed, 1998). This would also mean a
substantially high water deficit in the future (Table 1).
2. Measurement of Technical Efficiency (TE)
There are two common approaches in the literature for estimating the technical efficiency. One
approach based on non- parametric, non-stochastic, linear programming. This suffers from the
criticism that it takes no account of the possible influence of measurement error and other noise in the
data (Coelli, 1995). The second approach uses econometrics to estimate stochastic frontier function,
and to estimate the inefficiency component of the error term. The disadvantage of this approach is that
it imposes an explicit and possibly restrictive functional form on the technology. However, this
approach is chosen here because it permits the estimation of the determinants of inefficiency of the
producing unit, which is the focus of this study.
Table 1 - Summary evolution of water demand for different uses in 1993 and 2025
Demand
for
Population
Demand for
Drinking
Total
(million
Irrigation
water
Others
demand
Year
census)
Water (bcm)
(bcm)
(bcm)
(bcm)
1993
26.9
13.9
0.242
0.908
15.1
1997
27.6
17.5
0.404
1.297
19.2
2000
29.9
24.1
0.545
2.325
27.0
2005
33.9
25.7
0.743
3.128
29.6
2010
38.6
27.1
1.057
3.931
32.1
2015
43.9
30.7
1.443
4.492
36.6
2020
49.9
32.6
1.910
5.052
39.6
2025
56.7
40.3
2.483
5.304
48.0
Source: Ahmed (1998)
Farrell (1957) suggested a deterministic method of measuring the technical efficiency of a firm in an
industry by estimating a frontier production function. Several extensions of Farrell's model have been
made. The most recent being the stochastic frontier models developed by Aigner, Lovel, and Schmidt
(1977) and Meeusen and van den Broeck (1977), which have been used extensively (Coelli, 1995). The
stochastic frontier model assumes an error term with two additive components – an asymmetric
component, which accounts for pure random factors (υi) and one –sided component, which captures the
effects of inefficiency relative to the stochastic frontier (ui).The random factor (υ) is independently and
identically distributed with N (0, σ2υ) while the technical inefficiency effect, (u) is often assumed to
have a half normal distribution |N (0, σ2υ)| .The model is expressed as:
Yi = xi β + (υi - ui)
(1)
TEi = zi δ
(2)
Where xi is the vector of input quantities of the ith firm z is the vector of firm-specific factors
determining the inefficiency. The B and q are unknown parameters to be estimated together with the
variance parameters expressed as σ2 = συ2 + σu2 and γ = σu2 /(σv2 + σu2 ). The parameter, x, has a value
between zero and one such that the value of zero is associated with the traditional response function,
for which the non-negative random variable, uI, is absent from the model. Technical efficiency is
defined as TEi = exp (-ui). It is predicted using the conditional expectation of TEi = exp (-ui), given the
composed error term in equation (1). In this specification, the parameters, β, σ, σu, and γi can be
estimated by the maximum likelihood method, using the computer program, FRONTIER Version 4.1
(Coelli, 1996). This computer program also computes estimates of efficiency.
3. Data and Empirical Model
To assess the efficiency of on-farm water use, fixed-a locatable input model was used to estimate each
model's parameters using the ordinary least squares procedure. Data used in this study are obtained
form a survey in Abdelhakam and Medina blocks of the Central group of the Gezira scheme conducted
in 2007 to examine the technical efficiency of irrigation water and identify the determinants of
inefficiency us in the Gezira scheme. The study area is one of the most productive areas of the Gezira
scheme. The above cross-sectional data for the sample of 193 household is used to estimate a CobbDouglas stochastic production frontier 4. A single equation model is justified since input allocations
and output are observed, implying the general input allocation case where technological relationships
can be estimated directly without explicit assumptions that restrict either behavior or technology (Just,
Zilberman, and Hochman, 1983).
4.
Variables in the Technical Efficiency (TE) Model
A stochastic frontier production function was used to examine the economic efficiency and the
determinants of inefficiency of irrigation water in the Gezira scheme. Water productivity production
function for the selected crops consists of the dependant variable which is the water productivity and
two sets of variables that represent the technical efficiency variables and others responsible for
inefficiencies. The technical efficiency variables include quantitative variables such as the cultivated
area in feddan; product price in SDG/kg, labor used in man-days/feddan, and cost of inputs such as
water, fertilizers and seed in SDG/feddan. The inefficiency variables include qualitative factors that
affect water management such as land tenure (owned, rented or shared), location of the farm from the
distribution points (head, middle, tail), farmers' perceptions such as their participation in farmers field
schools (FFS), participation in WUA, their awareness about the right time of irrigating crops, their
awareness about when to stop irrigation (when the water cover the two-third of the ridge, when the
water cover the top of the ridge, when water reaches the top of the holding boundaries), farmer
perception on crop water requirement (as required, less or more than the required amount), water
availability to the farm in relation to crop requirement (whether it is sufficient or not), similarity of
water at the head and tail of the canal, effective membership in WUA, participation in field days
(participate or not), decision of determining the demanded quantity of water (the farmer or the selected
farmer &/or field inspector), and the quantitative variable which is the total number of irrigation. It is
worth noting that some of the qualitative variables have more than one dummy variable. The number
of the dummy variables depends on the number of factors attributed to the variable in question. These
variables will be subjected to many iterations in the frontier production model to select the most
appropriate factors responsible for the technical efficiency and the sources of inefficiency. Variables in
the frontier production function are as follows:
Y = Water productivity ( out put/ quantity of applied water)
Efficiency variables
X1 = Product price (SDG/kg)
X2 = Water cost (SDG/feddan)
X3 = Fertilizer cost (SDG/feddan)
X4 = Labor used (man-days/feddan)
X5 = Seed cost (SDG/feddan)
Inefficiency variables
X6 = Participation in FFS (Participate=1, otherwise=0)
X7 = Participation in WUA (Participate=1, otherwise=0)
X8 = Land tenure (owned =1, otherwise=0)
X9 = Awareness about the right time of irrigating the crop (aware=1, otherwise=0)
X10 = Awareness about when to stop irrigation (when the water cover the top of the ridge =1, when
water reaches the top of the holding boundaries =0)
X11 = Farmer perception on crop water requirement (as required=1, otherwise=0)
X12 = Location of the farm (head=1, otherwise=0)
X13 = Similarity of water at the head and tail of the canal (similar=1, otherwise =0)
X14 = Membership in WUA (member=1, otherwise=0)
X15 = Participation in field days (participate=1, otherwise=0)
X16 = Farmer perception on the contribution of WUA to efficient use of irrigation water (apparent
contribution =1, has no contribution =0)
X17 = Determination of the demanded quantity of water (the farmer=1, the selected farmer or field
inspector = 0)
X18 = the total number of irrigation
5. On-Farm Water-Use Efficiency in Gezira
Water productivity, defined in technical terms as kg of output per m3 of water, was highest for cotton
(0.91 kg/m3) and lowest for other rotational crops which include wheat (0.30 kg/m3), sorghum (0.27
kg/m3), and groundnut (0.26 kg/m3). Therefore, water yielded more output in cotton production,
compared to sorghum, wheat and groundnut. Each additional m3 of water yielded 0.9 kg of cotton
whereas the output of other crops was much lower for each additional unit of water, 0.3 kg of wheat,
0.26 kg of groundnut and 0.27 kg of sorghum (Table 2). Despite the high water productivity of cotton,
none of the farmers in Abdelhakam block cultivate cotton and very few of them cultivate it in
Elmedina block. Generally, these studies clearly demonstrate the low water productivity in crop
production implying the tendency of farmers to over-irrigate their crops. On the other hand, gross
return per unit of water is following the same pattern.
6.
Results of the stochastic frontier function
As mentioned before, a stochastic frontier production function was used to examine the economic
efficiency and the determinants of inefficiency of irrigation water in the Gezira scheme. Water
productivity production function for the selected crops consists of the dependant variable which is the
water productivity and two sets of independent variables that represent the technical efficiency
variables and others responsible for inefficiencies. When the amount of water required for the crops
was compared with actual amount used, it was found that there was over-irrigation for all crops in the
study area. For the summer crops, farm water use efficiency (FWUE) for sorghum range between
0.516 and 0.997 with an average of 0.83 while for groundnut it ranges between 0.498 and 0.99 with
an average of 0.83. For the winter crop, wheat, farm water use efficiency range between 0.86 and
0.988 with an average of 0.96. These estimates indicate that farmers over-irrigated sorghum and
groundnut by 17% and wheat by 4%. Therefore, to rationalize the use of scarce water for the summer
crops, it is possible to save an enormous amount of water which can be used to expand the sorghum
and groundnut growing area, and thus increase total production, or to produce other crops.
Alternatively, farmers can increase the crop yield considerably under current levels of water use, and
with improved water and crop management practices. Either option can contribute greatly to food
security in Sudan. The estimates of FWUE for sorghum and groundnut indicate a wide technological
gap between the required practices and actual water application. Therefore, improving water-use
efficiency for these crops can contribute greatly to the overall water-use efficiency in the study area,
and offers a high potential for saving water. These results are consistent with the findings of a recent
FAO study which concluded that water productivity seems to be lowest in water-scarce regions of
agriculture-based economies (FAO, 2002).
Table 2 - Gross margin analysis for the main crops in the Gezira scheme
Item
Cotton
Wheat
Groundnuts
Sorghum
Average yield (ton/feddan)
4.09
0.9
1.05
0.95
Average price SDG
208
570
465
550
850.7
513
488
522.5
Total cost (SDG/ feddan)
615
369
277
265
Net benefit (SDG/ feddan)
236
144
211
258
Total labor (man-days/ feddan)
58
7
39
28
14.7
73.3
12.5
18.7
4500
3000
4000
3500
0.19
0.17
0.12
0.15
0.91
0.30
0.26
0.27
0.19
0.17
0.12
0.15
Total Gross Margins (SDG/
feddan)
Gross margin/Man-day
3
Water use (m /feddan)
3
Gross margins/m
3
Water productivity (kg/ m )
3
Water productivity (SDG/ m )
Since water prices in the study area were highly subsidized, they did not have a major quantitative
impact on water allocation. Previous studies showed that water demand is inelastic at low price
changes (Fraiture and Perry, 2002). Because water prices are very low in Sudan, only high increases
in water charges can reduce the amount of water used for irrigation, which in turn will greatly reduce
farmer income. As expected, water cost for sorghum, groundnut and wheat have statistically positive
coefficients of about 0.076, 0.115 and 0.335, respectively. The estimates of the individual coefficients
of the water constraint suggest that an increase in water availability is allocated most heavily to crops
with relatively higher requirements, like groundnut, rather than to crops with relatively low water
requirements, such as sorghum. For this reason, sorghum responds to increased water cost better than
groundnut. In this regard, improvements in water management have the potential to optimize water
use at the farm levels. Thus, sound extension strategies will be needed to increase farmers' awareness
to optimize water use at farm levels, and thus reducing the adverse effects of salinization and water
logging on the productivity of land which are caused by over-irrigation. Thus, by obtaining optimal
water use, it is possible to increase crop productivity in the study area while ensuring the sustainable
use of resources, both water and land.
The elasticity of the farm size is estimated at -0.002 for wheat, indicating that expansion in this winter
crop will negatively affect the available amount of water. For groundnut, the situation may be slightly
different due to the fact that the crop is considered as a summer crop which receive appreciable amount
of water during the rainy season. It is worth noting that the farm size reduces inefficiency, as indicated
by the positive and significant coefficient of the cultivated land. This may be due to low level of
resources and technology that allow efficient operation. While the coefficient on crop establishment
labor is statistically significant for sorghum, the coefficient of fertilizer and seed cost are negative but
significantly different from zero with a coefficient of -0.008 and -0.003. This is unexpected result may
be associated with the water regime. For the inefficiency variables, participation in FFS, participation
in WUA, farmers' perception on crop water requirement and the total number of irrigation are the main
sources of technical inefficiency for sorghum crop with coefficients of -0.016, -0.008, -0.041 and 0.99, respectively. Thus, a policy to increase farmers awareness are expected to reduce inefficiencies
associated with increasing farmer's awareness with respect to irrigation management. For groundnut,
result indicated that approximaty of the farm to the water point or minor canal, farmers 's supervision
to manage water distribution, and the total number of irrigation are the main sources of inefficiency
with coefficients of -0.335, -0.211 and -0.294. In addition to these variables, awareness of farmers
about the right time of irrigation, when to stop irrigation, farmer's perception on crop water
requirement, and effective membership in the WUA are the main sources of inefficiency with
coefficients of -0.030, -0.123, -0.34 and -0.037, respectively.
Some researchers argue that tenancy management such as sharecropping result in an inefficient
allocation of resources as well as reduced incentives to improve agricultural lands (Hayami, et.al.,
1993). The coefficients of the land contract (cultivating own land) is statistically significant and
different from zero. This indicates that the levels of inefficiency associated with sharecropping and
therefore, sharecropping is less efficient. The different levels of inefficiency associated with the land
tenure systems can be explained by the relative degree of restrictions involved and the interaction of
labor and input markets. It is worth noting that sharecropping involves a commitment by both partners
to share the costs of inputs and the benefits of outputs, but places considerable restrictions on the
rights of the sharecropper. Moreover, the sharecropper is responsible for providing labor input to
perform field operations and thus responsible for any sub-optimal use of labor. Therefore, despite the
contribution of the landowner, in terms of inputs, lack of autonomy on the part of the sharecropper in
this partnership explains the inefficiency of sharecropping. The econometric result indicate that land
transaction such as share cropping that involved restriction on farmers' decision making is technically
inefficient compared to owner-cultivated tenure. Finally, farmers who are trained are more efficient
than those who receive no training in the context of improving the technical inefficiency of farming.
This result is consistent with the theory of adoption of innovation as training and increased awareness
of farmer enhances technology uptake and perhaps the returns to adoption.
One can conclude that product price, water prices, farm size, labor used, input used are the major
factors that have significant influence on the technical efficiency of irrigation water while farmers'
awareness to optimize water use at farm levels, participation in FFS, effective participation in WUA,
location of the farm, land tenure system (cultivating own land) are the main factors that have
significant influence on the technical inefficiency for the cultivated crops in the Gezira scheme. The
maximum likelihood estimates for the parameters in the stochastic frontier and inefficiency model are
presented in tables 3 - 5.
Table 3 - Maximum likelihood estimates of the Cobb-Douglas stochastic production frontier
function for sorghum
Variable
Coefficient
Estimate
Std Error
t-ratio
Intercept
β0
7.600
0.451
16.8
Price of sorghum (SDG/kg)
β1
-4.530
0.034
1.4
Water cost (SDG/feddan)
β2
0.076
0.020
3.9
Fertilizer cost (SDG/feddan)
β3
-0.008
0.029
0.273
Labor used (man-days/feddan)
β4
0.033
0.022
1.5
Seed cost (SDG/feddan)
β5
-0.003
0.013
0.241
Intercept
δ0
1.7
0.074
22.4
Participation in FFS (participate=1,
δ1
-0.016
0.035
0.440
δ2
-0.008
0.036
0.221
δ3
0.068
0.055
1.2
δ4
0.063
0.052
1.2
δ5
0.060
0.036
1.7
δ6
-0.041
0.037
1.1
δ7
0.064
0.041
1.6
δ8
-0.992
0.054
18.5
Sigma-squared
0.006
0.002
2.6
Gamma
0.908
0.006
152
otherwise=0)
Participation in WUA (participate=1,
otherwise=0)
Awareness about the right time of
irrigating the crop (aware=1, otherwise=0)
Awareness about when to stop irrigation
(fully aware =1, otherwise =0)
Awareness about when to stop irrigation
(partially aware=1, otherwise =0)
Farmer perception on crop water
requirement (as required=1, otherwise=0)
Who determine the demanded quantity of
water (the farmer=1, otherwise= 0)
Total number of irrigation
Table 4 - Maximum likelihood estimates of the Cobb-Douglas stochastic production frontier
function for groundnut
Variable
Intercept
Price of groundnut (SDG/kg)
Water cost (SDG/feddan)
Area of groundnut area (feddan)
Seed cost (SDG/feddan)
Labor used (man-days/feddan)
Intercept
Participation
in
FFS
(participate=1,
otherwise=0)
Participation in WUA (participate=1,
otherwise=0)
Land tenure (owned =1, otherwise=0)
Awareness about the right time of irrigating
the crop (aware=1, otherwise=0)
Awareness about when to stop irrigation
(aware =1, otherwise =0)
Farmer perception on crop water requirement
(as required=1, otherwise=0)
Location of the farm (head=1, otherwise=0)
Similarity of water at the head and tail of the
canal (similar=1, otherwise =0)
Membership in WUA (member=1,
otherwise=0)
Participation in field days (participate=1,
otherwise=0)
Farmer perception on the contribution of
WUA to efficient use of irrigation water
(apparent contribution =1, has no
contribution=0)
Who determine the demanded quantity of
water (the farmer=1, otherwise = 0)
Total number of irrigation
Sigma- squared
Gamma
Coefficient
β0
β1
β2
β3
β4
β5
δ0
Estimate
6.1
0.058
0.115
0.032
0.018
0.063
-0.100
Std- error
0.821
0.051
0.061
0.020
0.072
0.073
1.6
t-ratio
7.4
1.4
1.9
1.6
0.250
0.861
-0.638
δ1
0.078
0094
0.824
δ2
0.312
0.142
2.2
δ3
δ4
-1.3
0.122
1.4
0.133
0.903
0.917
δ5
0.056
0.099
0.569
δ6
0.125
0.105
1.2
δ7
δ8
-0.335
-0.011
0.158
0.111
-2.1
-0.098
δ9
0.251
0.120
2.1
δ10
-0.137
0.107
-1.3
δ 11
-0.158
0.106
-1.5
δ 12
-0.211
0.118
-1.8
δ 13
-0.294
0.069
0.986
0.308
0.028
0.009
-0.956
0.249
107
Table 5 - Maximum likelihood estimates of the Cobb-Douglas stochastic production frontier function for wheat
Variable
Intercept
Wheat area (feddan)
Labor used (man-days/feddan)
Water cost (SDG/feddan)
Wheat price (SDG/kg)
Intercept
Participation in FFS (participate=1, otherwise=0)
Participation in WUA (participate=1, otherwise=0)
Land tenure (owned =1, otherwise=0)
Awareness about the right time of irrigating the crop (aware=1,
otherwise=0)
Awareness about when to stop irrigation (when the water cover the two-third
of the ridge =1, otherwise =0)
Awareness about when to stop irrigation (when the water cover the top of
the ridge =1, when water reaches the top of the holding boundaries =0)
Farmer perception on crop water requirement (less than required=1,
otherwise=0)
Farmer perception on crop water requirement (more than required=1,
otherwise=0)
Description of water available to the farm in relation to crop requirement
(sufficient=1, not sufficient=0)
Location of the farm (head=1, otherwise=0)
Location of the farm (middle=1, otherwise=0)
Similarity of water at the head and tail of the canal (similar=1, otherwise =0)
Membership in WUA (member=1, otherwise=0)
Participation in field days (participate=1, otherwise=0)
Who determine the demanded quantity of water (the farmer=1, the selected
farmer or field inspector = 0)
The total number of irrigation
Sigma-squared
Gamma
Coefficient
β0
β1
β2
β3
β4
δ0
δ1
δ2
δ3
δ4
Estimate
6.1
-0.002
0.059
0.335
0.078
0.416
0.015
0.023
-0.125
-0.030
Std –error
0.911
0.016
0.068
0.211
0.041
0.287
0.046
0.046
0.192
0.053
t-ratio
6.3
-0.147
0.866
1.6
1.9
1.5
0.334
0.606
0.647
-0.575
δ5
0.028
0.064
0.437
δ6
-0.123
0.092
-1.3
δ7
0.028
0.069
0.398
δ8
-0.340
0.587
-0.580
δ 9
-0.029
0.074
-0.389
δ 10
δ 11
δ 12
δ 13
δ 14
δ 15
0.030
-0.109
-0.028
-0.037
0.042
0.006
0083
0.106
0.078
0.052
0.059
0.052
0.359
-0.1.0
-0.365
-0.716
0.071
0.123
δ 16
-0.280
0.009
0.622
0.226
0.002
0.092
-1.2
5.5
6.8
7. Conclusions
The study identified the reasons for low irrigation efficiency which are associated with poor timing
and lack of uniformity of water applications, leaving parts of the field over- or under-irrigated
relative to crop needs. It also revealed that the operators of irrigation systems face many difficulties
to supply farmers with a timely and reliable delivery of water that would be optimal for on-farm
water efficiency.
The study showed that product price, water prices, farm size, labor used, input used are the major
factors that have significant influence on the technical efficiency of irrigation water while farmers'
awareness to optimize water use at farm levels, participation in FFS, effective participation in
WUA, location of the farm, land tenure system (cultivating own land) are the main factors that have
significant influence on the technical inefficiency for the cultivated crops in the Gezira scheme.
The study showed that water productivity in crop production is low, implying the tendency of
farmers to over-irrigate their crops. Water yielded more output in cotton production compared to
other crops. This indicates that each additional m3 of water yielded 0.9 kg of cotton whereas the
output of other crops was much lower for each additional unit of water. On the other hand, gross
return per unit of water is also high for the same crop following the same pattern.
Though the overall WUE, as defined in this study is low for the rotational crops, indicating that
there is still great potential for improvement to save substantial amounts of water that can solve the
problem of water shortages in other part of the scheme, expand irrigated areas and/or could be
diverted to other uses.
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