Vol.2, No.3, 238-247 (2011)
Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/AS/
Agricultural Scienc es
Soil fertility effec t on water producti vity of maize in the
upper blue nile basin, Ethiopia
Teklu Erkossa*, Seleshi Bekele Awulachew, Denekew Aster
International Water Management Institute, Addis Ababa, Ethiopia; *Corresponding Author: t.erkossa@cgiar.org
Received 1 July 2011; revised 28 July 2011; accepted 5 August 2011.
Maize (Zea mays) is among the major cereals
grown in the high rainfall areas of the sub-
Saharan Africa’s (SSA) such as the Ethiopian
part of the Blue Nile basin. However, its pro-
ductivity is severely constrained by poor soil,
water and crop management practices. This
study simulated water productivity of the crop
under varying soil fertility scenarios (poor, near
optimal and non limiting) using hybrid seeds
under rainfed conditions using the FAO Aqua-
Crop model. The result indicated th at grain yield
of maize increased from 2.5 tons·ha–1 under
poor to 6.4 and 9.2 tons·ha–1 with near optimal
and non-limiting soil fertility conditions. Corres-
pondingly, soil evaporation decreased from 446
mm to 285 and 204 mm, while transpiration
increased from 146 to 268 and 355 mm. Conse-
quently, grain water productivity was increased
by 48% and 54%, respectively, with the near
optimal and non-limiting soil fertility conditions.
The water productivity gain mainly comes from
reduced evaporation and increased transpi-
ration without significantly affecting water left
for downstream ecosystem serv ices. Therefore,
this has a huge implication for a basin scale
water management planning for various pur-
Keywords: AquaCrop; Simulation; Water
Productivity; Soil Fertility; Nitisols
Agricultural water management for food and liveli-
hood security is a major concern in the face of persistent
poverty and rampant environmental degradation in the
Sub Saharan Africa (SSA). About 97% of agricultural
land in SSA is under rainfed system [1], which will re-
main the dominant source of food production in the near
future [2]. However, crop yield from rainfed agriculture
in the region remains meager (around 1 t·ha–1) [3]. This
suboptimal performance is due to management problems
rather than low potential of the agro-ecosystem [4,5]. In
the tropical environment, various abiotic and biotic fac-
tors including climatic conditions such as temperature,
rainfall, season length and fertility affect crop productiv-
ity [6]. There are evidences showing that rainfed agri-
culture generates among the world’s highest yields in
several regions of the world [7]. Yields in commercial
rainfed agriculture in the sub-humid and humid tropical
regions may exceed 5 - 6 tons·ha–1 [5]). However, due to
the widespread nutrient depletion in agricultural soils
exacerbated by improper land use, yield and water pro-
ductivity in the rainfed systems in many SSA countries
is decreasing or stagnating [7]. Drechsel [8] suggests
that nutrient depletion is the chief biophysical factor
limiting small-scale production in Africa.
In the upper part of the Blue Nile basin, sever land
degradation, exacerbated by lack of external inputs such
as improved seeds and fertilizers lead to low agricultural
productivity. Hitherto, expansion of cultivated land has
been the major strategy to cope with the low productivity,
population expansion and increased demand for food.
However, this strategy is challenged as the agriculturally
suitable lands are almost used up, especially in the high-
lands. Therefore, technological interventions are indis-
pensable to overcome the biophysical constraints and
enhance land and water productivity in the area.
With its total annual production and productivity ex-
ceeding all other cereals (23.24% of 13.7 Million tons),
and second after tef (Eragrostis tef) in area coverage
(16.12% of the 8.7 million hectares), maize (Zea mays)
is one of the most important crops grown in Ethiopia [9];
[10]. It is the most extensively cultivated food crops and
main source of calorie in the Ethiopian part of the Blue
Nile basin [11]. With the introduction of the hybrid seeds
and the high yielding open pollinated varieties, and the
increasing local demand, the importance of the crop may
increase even further. However, the current national av-
T. Erkossa et al. / Agricultural Science 2 (2011) 238-247
Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/AS/
erage yield is about 2 tons·ha–1 [10], which is much
lower than its productivity in industrialized countries
such as USA (8 - 9 tons·ha–1) [12], the developing worlds’
average (3 tons·ha–1) and the yield recorded under dem-
onstration plots in Ethiopia (5 - 6 tons ha-1) [9].
According to Tanner and Sinclair [13], in situations
where yield is less than 40% - 50% of potential, non-
water factors such as soil fertility limit yield and crop
water productivity per unit of evapotranspiration. In the
Ethiopian part of the Blue Nile basin, land degradation
and nutrient depletion, lack of access to improved tech-
nologies such as seeds and fertilizers, and poor weed and
pest control practices are among the major factors de-
pressing the water productivity of maize [11]. At the
basin scale, water is a scarce resource, which should be
utilized efficiently. This is becoming pressing issue with
the looming effects of climate change, increased water
demand due to population growth and economic devel-
The idea of producing more with less water led to the
evolution of the concept of water productivity (WP),
which is a robust measure to assess the ability of agri-
cultural systems to convert water into food and other
useful products [14]. Agricultural WP is defined as the
ratio of the net benefits from crop, forestry, fishery, and
livestock to the amount of water required to produce
those benefits [15]. Crop WP is the physical mass of
production or its economic value measured against gross
inflows, net inflow, depleted water, process depleted
water, or available water [15,16]. Crop WP can be en-
hanced by increasing the yield per unit area of land by
using better agronomic practices and improved crop va-
rieties. This study assessed the effects of soil fertility
levels on water productivity of maize and the water bal-
ance of the maize based farming system in the Ethiopian
part of the Blue Nile basin.
2.1. Location and Biophysical Settings
The study was conducted in the Abbay river basin,
which is situated in the north-central and western parts
of Ethiopia. The basin is situated in the upper part of the
Blue Nile Basin and is one of the three major sub-basins
of the Nile basin draining from Ethiopia (Figure 1).
High bio- physical variability (elevation, slope, climate
and soil type) characterizes the basin. However, only
four soil types including Nitisols, Leptosols, Luvisols
and Vertisols cover over 80% of the area [17]. In re-
sponse to the biophysical variability, diverse farming
systems have evolved but covering 23% of the area, the
maize based farming system is the second largest after
the tef based system (Figure 2). Maize is widely grown
also in other farming systems in the basin as the second
or third crop. The study focused on the Nitisols area,
which covers about 70% of the 4.4 million hectares of
the maize based farming systems.
2.2. The Maize Based Farming System
Maize is the dominant crop in this farming system,
which is situated in the southwestern part of the Abbay
Basin, but a number of other crops like tef, wheat (Triti-
cum durum Desf.), barley (Hordeum Vulgare), and finger
millet (Eleusine coracana) pulses, oil crops and vegeta-
bles like potatoes (Colcus edulis) are also widely grown
as the second or third crop depending on the local cir-
cumstances. In addition, root and tuber crops are grown
with some fruit trees like citrus, mango (Magnifera in-
dica) and banana (Musa acuminate). Although nutrient
depletion through soil erosion by water and crop uptake
is prevalent, not many farmers use the optimal type and
quantity of fertilizers. The use of manure as fertilizer is
restricted to backyards [19]. In addition, the use of im-
proved seeds is minimal.
2.3. Analytical Tool and Data Capturing
The FAO Aqua Crop model Version 3 [21,22] was
used to simulate the grain and biomass productivity of
maize as well as the water balance of the farming system.
The climatic, soil characteristics (rooting depth, texture
and hydraulic characteristics) and crop variables were
the inputs to the model. The model was validated using
daily weather and crop data obtained from research cen-
ters located within or just at the boundary of the basin
(Table 1). For simulation, ten years monthly average
rainfall, minimum and maximum temperature, relative
humidity, dew point temperature, wind speed at 2 m
above the ground, bright sunshine hours and radiation
data obtained from the National Meteorological Services
Agency (NMSA) were used. The Reference Evapotran-
spiration (ETo) for both the validation and simulation
phases was estimated using the ETo Calculator [21],
based on daily minimum and maximum temperature and
wind speed data obtained from the weather stations. The
average atmospheric CO2 concentration (369.41 ppm by
volume) measured for the year 2000 at Mauna Loa Ob-
servatory in Hawai [21] was used as a reference default
value. Soil profile data from the agricultural research
centers [23,24] and basin master plan study [25], were
used for the validation and simulation phases, respec-
ively. t
T. Erkossa et al. / Agricultural Science 2 (2011) 238-247
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Figure 1. The three sub-basins of the Ethiopian Nile basin. Source: [18].
Table 1. Location of the research stations used for model vali-
Location Latitude Longitude Altitude (m·asl)
Adet 11˚16N 37˚28E 2080
Ambo 8˚58N 37˚52E 2130
Bako 9˚06N 37˚09E 1650
Fogera 11˚55N 37˚41E 1810
Pawe 11˚14N 36˚03E 1050
Openly accessible at
2.3.1. Description of the Model
AquaCrop was developed to replace the approach de-
veloped by Doorenbos and Kassam [29] (FAO Irrigation
& Drainage Paper no. 33) to determine the yield re-
sponse to water for field, vegetable and tree crops [21,
22]. Among the significant departures of the model from
its precursors is that it separates 1) the ET into soil
evaporation (E) and crop transpiration (T) and 2) the
final yield (Y) into biomass (B) and harvest index (HI)
[22]. The separation of ET into E and T avoids the con-
founding effect of the non-productive consumptive use
of water (E) while the separation of Y into B and HI al-
lows the distinction of the functional relations between
the environment and B from those between environment
and HI. The use of this relation (Equation 1) avoids the
confounding effects of water stress on B and on HI.
T is the crop transpiration (mm) and WP is the water
productivity parameter (kg of biomass m–2 and per mm
of cumulated water transpired over the period in which
the biomass is produced).
In addition, the model performs a daily water balance
that includes all the incoming and outgoing water fluxes
(infiltration, runoff, deep percolation, evaporation and
transpiration) and changes in soil water content [27].
2.3.2. Calibration of the Model
The model has been parameterized and tested for
T. Erkossa et al. / Agricultural Science 2 (2011) 238-247
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Figure 2. Farming systems of the Abbay basin. Source: [20].
maize and many other crops [24,28]. Studies show that it
is able to simulate the canopy cover (CC), biomass de-
velopment and grain yield of different maize cultivars
grown under varying water availability conditions [28].
This lead to the establishment of conservative parame-
ters for the crop, which were used by Heng et al. [27] to
validate the model’s performance and robustness under
local conditions. Heng et al. [27] compared simulated
parameters including canopy development, biomass ac-
cumulation, grain yield, evapotranspiration (ET) and
water use efficiency (WUE) against their corresponding
field measurements under a wide range of environments
including rainfed and irrigated conditions. In the same
way, this study used the conservative parameters estab-
lished by Hsiao et al. [28] and validated the model under
local conditions.
2.3.3. Validation of the Model
A range of statistical methods and visual techniques
can be used to assess the goodness-of-fit of a given
model and to compare the performance of a suite of
models, based on the specific context of the problem
[29]. In this study, due to lack of measured data, the
model was validated for grain yield only; using data
from research stations in and around the basin. The re-
search stations applied the recommended rates of nitro-
gen and phosphorus, which varied from station to station,
and this was considered as near optimal since micronu-
trients were not applied. Retaining the conservative pa-
rameters [28], planting dates, seeding rates, and cultivar
growth characteristics (days to flowering and days to
maturity) for each site were used to estimate grain yield
for 17 locations and year combinations in the maize
based farming system. The model output was compared
with the measured grain yield data obtained from the
research stations [30,31]. Combined graphical and statis-
tical approaches were followed for the validation as sug-
gested by Bellocchi et al. [32]. Yang et al. [33] argued
that any of the Relative Root Mean Square Error (RM-
SE), coefficient of efficiency (E), mean absolute error
(MAE) and paired t-test could lead to the same conclu-
sion. Consequently, this study used RMSE and E (Equa-
T. Erkossa et al. / Agricultural Science 2 (2011) 238-247
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tions 2 and 3) to examine the robustness of the model,
and [34] (Equation 3)
where E and O are the estimated and observed yield
(ton·ha–1), respectively, and N and O are the number of
observations and the mean of the measured yield
(ton·ha–1) in that order.
The RRMSE represents a measure of the mean devia-
tion between observed and simulated values, which in-
dicates the absolute model uncertainty [27], where as the
coefficient of efficiency (E) shows how much the overall
deviation between observed and simulated values depart
from the overall deviation between observed values (Oi)
and their mean value (O). The value of E can range
from to +1, and the model estimation efficiency in-
creases as E gets closer to +1 [27].
2.4. Simulating Crop Yield and Water
For brevity, the maize based farming system was con-
sidered as a huge homogenous field, so that the input
data could be averaged over the whole area. Thus, ten
years and seven locations (Figure 3) average weather
data was used together with soil profile data averaged
over the locations.
2.4.1. Simulating Crop Yield
While the conservative crop parameters were retained,
the planting date, seeding rate and days to flowering and
maturity were set based on the data from the research
centers. Three soil fertility scenarios were considered
1) Poor—representing the traditional no fertilizer use;
2) Near optimal—representing the use of the recom-
mended rates of nitrogen and phosphorus fertilizers and;
3) No n-limiting—representing the use of the recom-
mended rate of both fertilizers together with the other
necessary macro and micronutrients as well as treatment
of other limiting factors such as soil acidity.
The soil profile data representing the Nitisols in the
area was obtained from the basin master plan study
document [25]. Average planting date (June 2) which
corresponds with the date on which the rainfall in five
successive days was at least 40mm was considered. As
the moisture content at planting was not known, the
simulation was run from 1 January, when permanent
wilting point could be assumed.
3.1. Model Validation
AquaCrop was developed to predict crop productivity
as a function of water availability under varying soil
fertility conditions. In the upper part of the Blue Nile
basin, the model revealed that the rainfall at all the sites
considered for validation was adequate to grow maize
without significant sign of moisture stress throughout the
growing stages. The graphic presentation shows that the
model simulation results do not perfectly match with the
measured grain yield. The estimates are inconsistently
higher or lower than the measured for all locations and
years (Figure 4). However, the RRMSE percentage was
Figure 3. Average monthly reference evapotranspiration (ETo) of the weather stations used for simulation.
T. Erkossa et al. / Agricultural Science 2 (2011) 238-247
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Figure 4. Estimated and measured grain yield of maize under near optimal soil fertility conditions.
low (8.1%) and the model efficiency (E) was 0.98,
which is very close to +1, indicating that the model is
able to predict the productivity of maize grown under
the conditions of the research stations where near op-
timal soil fertility conditions were maintained. This
indicates that AquaCrop can predict the productivity
of maize grown on Nitisols in the maize based farm-
ing system in the upper part of the Blue Nile basin.
3.2. Crop Productivity and Water Balance
3.2.1. Crop Productivity
The results indicate that moisture availability was not a
limiting factor in the area as the predicted biomass
productivity was 100% of the amount that could be
produced under well-watered conditions (Ta b l e 2 ). This
is because the area received a total of 1451 mm rainfall
during the growing period. However, changing soil
fertility level caused a considerable variation in the
biomass produced such that 39%, 75% and 100% of the
biomass yield that could be potentially achievable under
well-fertilized conditions were obtained under the poor,
near optimal and non-limiting soil fertility situations,
respectively. Improving soil fertility enhances crop pro-
ductivity by increasing canopy and root growth and
development, which respectively increase photosynthesis
and water and nutrients uptake by the crop.
The estimated average biomass yield increased from
7.5 to 19.3 tons·ha–1 when the soil fertility level changed
from poor to none limiting. Similarly, the corresponding
increase for grain yield was from 2.5 to 9.2 tons·ha–1.
This agrees with Steduto et al. [35] who suggested that
improvements in soil fertility and management of rain-
Table 2. Estimated performance of maize grown under different
soil fertility status on Nitisols in Abbay basin.
Soil fertility conditionsPoor Near optimal Non limiting
Biomass (ton·ha–1) 7.5 14.3 19.2
Grain yield (ton·ha–1)2.5 6.4 9.2
Biomass produced
(reference to well
watered) (%)
100 100 100
Biomass produced
(reference to well
fertilized) (%)
39 75 100
Biomass Water
productivity (kg·m–3)5.1 5.3 5.4
Grain water
productivity (kg·m–3)1.7 2.4 2.6
water to reduce evaporation and diverting more flows to
transpiration might double or even quadruple crop yield.
The result substantiates also the findings of Breman et al.
[36] who based on model analysis and field experiments
concluded that nutrient limitations set a stronger ceiling
on yield than water availability for arid and semiarid re-
In the highlands of Ethiopia, soil fertility depletion
due to soil erosion, continuous cultivation and removal
of nutrients in crop harvests is a priority problem that
challenges crop productivity [37]. Consequently, soil
fertility improvement was suggested as priority interven-
tions for increased crop water productivity than water
related interventions [38,39].
The use of hybrid seeds, applying recommended rates
of nitrogen and phosphorus fertilizers, and implementing
row planting as recommended by Tenaw et al. [40] can
increase yield by three fold as compared to the current
T. Erkossa et al. / Agricultural Science 2 (2011) 238-247
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harvest. Enhancing soil fertility including all the necessary
macro and micronutrients, and treating soil acidity may
further augment productivity up to more than four times
the current situation. This prediction is valid for the 3.03
million hectares of Nitisols (70% of the 4.4 million
hectares maize based farming systems) in the upper Blue
Nile basin. Supposing that 50% of the Nitisols area is
planted to hybrid maize annually, up to 9.7 million tons
and 13.9 million tons of maize grain can be obtained
under the near optimal and non-limiting soil fertility
conditions, respectively (Figure 5).
3.2.2. The Water Balance
Soil fertility levels affected the water balance compo-
nents, except runoff and infiltration (Ta b l e 3). On aver-
age, the area received a total of 1451 mm of rainfall
during the growing period (June to October) out of which
Figure 5. Biomass and grain production expected from
50% of the Nitisols area in the maize based farming
Table 3. Effect of the soil fertility conditions on water balance.
Soil fertility conditions
Water balance compo-
nents Poor Near optimal Non limiting
Evaporation (Ea) 446 285 204
Transpiration (Ta) 146 268 355
(ETa) 592 553 559
Percent (Ta/ETa) 25 48 64
Runoff 593 593 593
Infiltration 858 858 858
Drainage 276 311 304
Figure 6. Effect of soil fertility on partitioning of Evapotran-
spiration to its components by maize
59% infiltrated into the soil and the rest (593 mm) was
lost as surface runoff (Annex 2). Of the infiltrated water,
276 mm, 311 mm and 304 mm drained from the rooting
zone as deep percolation under poor, near optimal and
non-limiting soil fertility conditions, respectively. The
balance was either productively used as transpiration (Ta)
or was lost as soil evaporation (Ea). Within the same soil
fertility condition, the potential transpiration (Tx) and
actual transpiration (Ta) were nearly the same indicating
a negligible moisture stress. However, the soil fertility
levels affected the balance between Ea and Ta (Figure 6).
While the monthly Ea remained over 50 mm throughout
the growing period under conditions of poor soil fertility,
it diminished to 15 mm in August with near optimal and
to nil in August and September when soil fertility was
not limiting (Annex 1). In sharp contrast to the case with
Ea, Ta increased with enhanced soil fertility conditions
from a total of 146 mm under poor to 355 mm under
non-limiting soil fertility conditions with a correspon-
ding 25%, 48% and 64% share of the ETa. This is due to
the enhanced canopy growth, which almost fully covered
the soil surface by the end of August and early Septem-
ber under the non- limiting soil fertility conditions lead-
ing to maximum Ta and minimum Ea while a substantial
part of the soil was still exposed to evaporation due to
constrained canopy cover under poor soil fertility situa-
tion. Therefore, improving soil fertility decreases un-
productive losses and enhances beneficial consumption
or deep percolation that recharges ground water. This
confirms Cooper et al. [41] who suggested that applica-
tion of fertilizer might be one option to enhance water
use efficiency of crops as it allows a rapid growth of the
canopy that shades the soil surface, thereby reducing the
proportion of the total water that is evaporated.
3.3. Crop Water Productivity
Improving soil fertility situation from poor to near op-
timal and non-limiting conditions increased grain water
T. Erkossa et al. / Agricultural Science 2 (2011) 238-247
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productivity by 48 and 54%, respectively (Figure 7).
This agrees with the findings of Stewart [42] who indi-
cated that soil fertility is the component of a manage-
ment system that affects water use efficiency and ex-
plained that a complete and balanced fertility program
helps to produce a crop with roots that exploit more soil
volume for water and nutrients in less time. Field and
pot experiments with millet [43] and Sorghum [44] in
Niger also confirmed that improved soil fertility en-
hances water use efficiency. In this connection, Vegh et
al. [45] reported increased water use efficiency of maize
with increasing phosphorus fertilizer rates.
3.4. Potential Use of the Excess Water
The condition of improving water management for
farming systems in these high rainfall areas rests largely
on managing the excess water that is lost to unproduc-
tive losses, mainly evaporation and runoff. While evapo-
ration can be significantly converted to transpiration by
enhancing crop canopy cover as discussed earlier, part of
the water lost as surface runoff (593 mm) from the
farming system can be harvested for domestic uses, live-
stock or to grow a second or even a third crop, depend-
ing on the type of crop to be grown and water manage-
ment methods to be adopted. There are ranges of crops
that can be considered, but vegetables like potato (So-
lanum tuberosum) and onion (Allium moly) or shallot
(Alliiim ascalonicum Linnare) are among the crops
widely grown during the off-season under traditional
small-scale irrigation in the area. Legumes such as
chickpea (Cicer arietinum) and lentil (Lens culinaries
Medik) can also be grown with residual soil moisture
and supplementary irrigation, and these can improve soil
fertility for the next crop in addition to their contribution
to the increased cropping intensity.
During the main rainy season, soil fertility is the ma-
jor yield-limiting factor in the maize based farming sys-
tem of the upper part of the Blue Nile basin. Improving
soil fertility and the use of high yielding maize varieties
can significantly improve water productivity by reducing
evaporation loss and increasing transpiration. While this
does not affect the quantity, it may improve the quality
of downstream flow as the increased canopy cover can
also reduce soil erosion and sediment load. The in-
creased deep percolation may also augment water avail-
ability in the basin due to increased ground water re-
charge. If rain water harvesting is considered, cropping
intensity can be increased. This can further enhance soil
fertility if legumes are used during the dry season with
supplemental irrigation. However, the feasibility of this
should be confirmed through socio-economic investiga-
tions before implementation.
This paper is part of the “Improved water and land management in
the Ethiopian highlands and its impact on downstream stakeholders
dependent on the Blue Nile” project output, supported by the Chal-
lenge Program on Water and Food. A consortium of national and inter-
national institutions implemented the project. Thanks are due to the
researchers at national and regional agricultural research centers in-
cluding Bako, Ambo and Adet as well as the study on livestock water
productivity conducted at Fogera for providing us with the crop, soil
and weather data used for validation of the model. Especial thanks to
Dr. Michael Blümmel, Dr. Mosissa Worku, Dr. Yihenew G. Selassie
and Mr. Gudeta Napier for allowing us use their data.
[1] Rockstrom, J., Folke, C., Gordon, L., Hatibu, N., Jewitt,
G., de Vries, F.P., Rwehumbiza, F., Sally, H., Savenije, H.
and Schulz, R. (2004) A watershed approach to upgrade
rainfed agriculture in water scarce regions through Water
System Innovations: An integrated research initiative on
water for food and rural livelihoods in balance with eco-
system functions. Physics and Chemistry of the Earth
Parts ABC, 29, 1109-1118.
[2] Parr, J.F., Stewart, B.A., Hornick, S.B. and Singh, R.P.
(1990) Improving the sustainability of dryland farming
systems: A global perspective. In: Singh, R.P., Parr, J.F.
and Stewart, B.A., Eds., Advances in Soil Science, Vol.
13, Dryland Agriculture Strategies for Sustainability,
Springer Verlag, New York, 1-8.
[3] Rockström, J. (2001) Green water security for the food
makers of tomorrow: Windows of opportunity in drou-
ght-prone savannahs. Water Science and Technology, 43,
[4] Agarwal, A. and Narain, S. (1997) Dying wisdom. Centre
for Science and Environment, Thomson Press, Faridabad.
[5] Rockström, J. and Falkenmark, M. (2000) Semiarid crop
production from a hydrological perspective-gap between
potential and actual yields. Critical Reviews of Plant
Science, 19, 319-346
[6] Bellon, M.R. (2001) Participatory research methods for
technology evaluation: A manual for scientists working
with farmers. Cimmyt, Mexico D.F.
[7] Wani, S.P., Sreedevi, T.K., Rockström, J. and Rama-
krishna, Y.S. (2009) Rainfed agriculturePast trends
and future prospects. In: Wani, S.P., Rockström, J. and
Oweis, T., Eds., Rainfed Agriculture: Unlocking the Po-
tential, Comprehensive Assessment of Water Manage-
ment in Agriculture Series, CAB International, Walling-
[8] Drechsel, P., Giordano, M. and Gyiele, L. (2004) Valuing
nutrition in soil and water: Concepts and techniques with
examples from IWMI studies in developing world. IWMI
Research Paper 82, International Water Management In-
stitute (IWMI), Sri Lanka.
[9] Benti, T., Kebede, M., Legesse, W., Mosisa, W. and Leta,
T. (1997) Reflections on the successful achievements of
T. Erkossa et al. / Agricultural Science 2 (2011) 238-247
Copyright © 2011 SciRes. Openly accessible at http://www.scirp.org/journal/AS/
hybrid maize breeding program in Ethiopia. In: Ransom,
J.K., Palmer, A.F.E., Zambezi, B.T., Muduruma, Z.O.,
Waddington, S.R. Pixley, K.V. and Jewell, D.C., Eds.,
Maize Productivity Gains through Research and Techno-
logy Dissemination, Proceedings of the Fifth Eastern and
Southern Africa Regional Maize Conference, Arusha,
Tanzania, June 3-7 1996, 67-71.
[10] Central Statistics Authority (CSA) (2008) The federeal
democratic republic of Ethiopia, central statisticsl agency,
agricultural sample survey 2007/2008. Report on Area
and Production of Crops. Statistical Bulletin 417, Vol. 1.
[11] Tolessa, D., Preez, C.C.D. and Ceronio, G.M. (2007)
Effect of tillage system and nitrogen fertilization on yield
and yield components of maize in Western Ethiopia.
South African Journal of Plant and Soil, 24, 63-69.
[12] Comprehensive Assessment of Water Management in
Agriculture (2007) Water for food, water for life: A com-
prehensive assessment of water management in agricul-
ture. Earthscan and Colombo: International Water Man-
agement Institute, London.
[13] Tanner, C.B. and Sinclair, T.R. (1983) Efficient water use
in crop production: Research or research? In: Taylor,
H.M., Jordan, W.A. and Sinclair, T.R., Eds., Limitations
to Efficient Water Use in Crop Production, American So-
ciety of Agronomy, Madison.
[14] Kijne, J.W., Tuong, T.P., Bennett, J., Bouman, B. and
Oweis, T. (2003) The challenge program on water and
food consortium. Ensuring food security via improve-
ment in crop water productivity. Challenge Program on
Water and Food: Background Papers to the Full Pro-
posal, Sri Lanka.
[15] Molden, D. (1997) Accounting for water use and produc-
tivity. SWIM Paper 1. International Water Management
Institute, Sri Lanka.
[16] Molden, D. and Sakthivadivel, R. (1999) Water account-
ing to assess use and productivity of water. Water Re-
sources Development, 15, 55-71.
[17] Teklu, E., Awulachew, S.B., Amare, H. and Aster, D.
(2009) Impacts of improving water management of
smallholder agriculture in the Upper Blue Nile basin. In:
Awulachew, S.B., Teklu E., Smakhtin, V.K. and Fernando,
A., Eds., Improved Water and Land Management in the
Ethiopian Highlands: Its Impacts on Downstream Stake-
holders Dependent on the Blue Nile, Intermediate Results
Dissemination Workshop held at the International live-
stock Research Institute (ILRI), Addis Ababa, 5-6 Febru-
ary 2009
[18] Seleshi B.A., Denekew, A., Loulseged, M., Loiskandl, W.,
Ayana, M. and Alamirew, T. (2007) Water resouces and
irrigation development in Ethiopia, Colombo, Sri Lanka.
International Water Management Institute, Working Pa-
per 123.
[19] Teklu, E. and Gezahegn, A. (2003) Indigenous knowl-
edge and practices for soil and water management in East
Wollega, Ethiopia. In: Wollny, C., Deininger, A., Bhan-
dari, N., Maass, B., Manig, W., Muuss, U., Brodbeck, F.
and Howe, I., Eds., Technological and Institutional In-
novations for Sustainable Rural Development, Deutscher
[20] Seleshi, B.A. (2010) Improved water and land manage-
ment in the Ethiopian Highlands and its impact on down-
stream stakeholders dependent on the Blue Nile. Interna-
tional Water Management Institute, CPWF Project Re-
port, Project Number, submitted to CGIAR Challenge
Program for Water and Food.
[21] Raes, D., Steduto, P., Hsiao, Th. and Fereres, E. (2009)
AquaCrop—The FAO crop model for predicting yield
response to water: II. Main algorithms and soft ware de-
scription. Agronomy Journal, 101, 438-447.
[22] Steduto, P., Hsiao, T.C., Raes, D. and Fereres, E. (2009)
AquaCrop—The FAO crop model to simulate yield res-
ponse to water: I Concepts. Agronomy Journal, (In Press).
[23] Abayneh, E., Demeke, T. and Ashenafi, A. (2006) Soils
of adet agricultural research center and its testing sites.
National Soil Research Center (NSRC) Soil Survey and
Land Evaluation Section, Soil Survey Internal Report
[24] Wakene, N. (2001) Assessment of important physico-
chemical propoerties of Nitosols under different man-
agement systems in Bako area, Western Ethiopia. M.Sc
Theisis, School of Graduate Studies, Alemaya Unviersity,
[25] BCEOM (1998) Abbay river basin master plan pro-
ject—Phase 2: Agriculture.
[26] Doorenbos, J. and Kassam, A.H. (1979) Yield response to
water. Irrigation and Drainage Paper No. 33, FAO, Rome,
[27] Heng, L.K., Hsiao, Th., Evett, S., Howell, T. and Stedut,
P. (2009) Validating the FAO AquaCrop model for irri-
gated and water deficient field maize. Agronomy Jour-
nal, 101, 488-498. doi:10.2134/agronj2008.0029xs
[28] Hsiao, T.C., Heng, L.K., Steduto, P., Rojas-Lara, B., Raes,
D. and Fereres, E. (2009) AquaCrop—Th FAO crop
model to simulate yield response to water: III. Parame-
terization and testing for maize. Agronomy Journal, 101,
448-459. doi:10.2134/agronj2008.0218s
[29] Berk, R.A., Fovell, R.G., Schoenberg, F. and Weiss, R.E.
(2001) The use of statistical tools for evaluating com-
puter simulations—an editorial essay. Climatic Change,
51, 119-130 doi:10.1023/A:1012241427060
[30] Mosissa, W. (1999) Stability of maize genotypes across
locations and soil Fertility levels. M.Sc Thesis, School of
Graduate Studies of Alemaya University, Alemaya,
[31] Yihenew, G.S.M. (2004) Modeling of nitrogen and phos-
phorus fertilizer recommendations for maize (Zea mays
L.) grown on Alfisols of Northwestern Ethiopia. Ph.D
Dissertation, Kasetsart University, Thailand.
[32] Bellocchi, G., Mike, R., Marcello, D. and Keith, M.
(2009) Validation of biophysical models: Issues and
methodologies. Agronomy for Sustainable Development,
30, 109-130. doi:10.1051/agro/2009001
[33] Yang, J., Greenwood, D.J., Rowell, D.L., Wadsworth,
G.A. and Burns, I.G. (2000) Statistical methods for
evaluating a crop nitrogen simulation model, N-ABLE.
Agricultural System, 64, 37-53.
[34] Nash, J.E. and Sutcliffe, J.V. (1970) River flow forecast-
ing through conceptual models, Part 1, A discussion of
principles. Journal of Hydrology, 10, 282-290.
[35] Steduto, P., Jacob, W. K., Hanjra, M., Prem, A. and Bin-
draban, S. (2007) Pathways for increasing agricultural
T. Erkossa et al. / Agricultural Science 2 (2011) 238-247
Copyright © 2011 SciRes. http://www.scirp.org/journal/AS/Openly accessible at
water productivity, water use and productivity in a river
[36] Breman, H., Groot, J.J.R. and van Keulen, H. (2001)
Resource limitations in Sahelian agriculture. Global En-
vironmental Change, 11, 59-68.
[37] Smaling, E.M.A., Nandwa, S.M. and Janssen, B.H. (1997)
Soil fertility is at stake. In: SSSA, Eds., Replenishing Soil
Fertility in Africa, Soil Science Society of America Spe-
cial Publication, Madison, 47-61.
[38] Aregawi, B. (1989) Research on multipurpose tree spe-
cies in Ethiopia. Proceedings of the IAR/ICP, AF Na-
tional Agroforestry Workshop, Awasa, 14-18 November
1988, 29-32.
[39] ICRAF (1990) Agroforestry: Potentials and research
needs for the Ethiopian Highlands No. 21. In: Hoekstra,
D.A., Torquebiau, E. and Bishaw, B., Eds., ICRAF, Nai-
[40] Tenaw, W., Waga, M., Beirtukan, M., Tolessa, D., Tesfa,
B., Berhanu, A., Hussein, M.A.and Tewodros, M. (2001)
Development of appropriate cultural practices for maize
production in Ethiopia, In: Mandefro, N., Tanner, D. and
Twumasi-Afriyie, S., Eds., Enhancing the Contribution
of Maize to Food Security in Ethiopia, Proceedings of the
Second National Maize Workshop of Ethiopia, Addis
Ababa, 12-16 November 2001.
[41] Cooper, P.J.M., Gregory, P.J., Tully, D. and Harris, H.C.
(1987) Improving water use efficiency of annual crops in
the rainfed farming systems of West Asia and North Af-
rica. Experim ental Agricu lture, 23 , 113-158.
[42] Stewart, W.M. (2001) Balanced fertilization increases
water use efficiency, news and views. The Potash and
Phosphate Institute (PPI) and the Potash & Phosphate In-
stitute of Canada (PPIC).
[43] Gregory, P.J. (1988) Plant and management factors af-
fecting the water use efficiency of dryland crops. In:
Unger, P.W., et al., Eds., Challenges in Dryland Agricul-
ture: A Global Perspective, Texas A & M University
Press, College Station, 171-175.
[44] Hundekar S.T., Badanur, V.P. and Saranagamath, P.A.
(1999) Effect of crop residues in combination with fertil-
izers on soil properties and sorghum yield. Fertilizer
News, 44, 59-63.
[45] Vegh, K.R., Szundy T., Rajkai K. and Tischner T. (1998)
Roots, phosphorus uptake and water use efficiency of
maize genotypes. Acta Agronomica Hungarica, 46, 35-