EFFECT OF PLANT DENSITY OF COWPEA AND NITROGEN
FERTILIZER ON YIELD AND YIELD RELATED TRAITS OF THE
COMPONENT CROPS UNDER SORGHUM-COWPEA
INTERCROPPING SYSTEM AT KOBO, NORTHERN ETHIOPIA
M.Sc. THESIS
BERHANE SIBHATU
AUGUST 2015
HARAMAYA UNIVERSITY, HARAMAYA
EFFECT OF PLANT DENSITY OF COWPEA AND NITROGEN
FERTILIZER ON YIELD AND YIELD RELATED TRAITS OF THE
COMPONENT CROPS UNDER SORGHUM-COWPEA
INTERCROPPING SYSTEM AT KOBO, NORTHERN ETHIOPIA
A Thesis Submitted to the Postgraduate Program Directorate
(School of Plant Sciences)
HARAMAYA UNIVERSITY
In Partial Fulfillment of the Requirements for the Degree of
MASTER OF SCIENCE IN AGRICULTURE (AGRONOMY)
By
Berhane Sibhatu
August 2015
Haramaya University, Haramaya
HARAMAYA UNIVERSITY
Postgraduate Program Directorate
We hereby certify that we have read and evaluated this Thesis titled ‘Effect of Plant Density of
Cowpea and Nitrogen Fertilizer on Yield and Yield Related Traits of the Component Crops
under Sorghum-Cowpea Intercropping System at Kobo, Northern Ethiopia’ prepared under
our guidance by Berhane Sibhatu. We recommend that it be submitted as fulfilling the thesis
requirement.
Ketema Belete (PhD)
----------------------------------Major Advisor
----------------------Signature
----------------Date
Taye Tessema (PhD)
----------------------------------Co-advisor
-----------------------Signature
-----------------Date
As a member of the Board of Examiners of the MSc Thesis Open Defense Examination, we
certify that we have read and evaluated the Thesis prepared by Berhane Sibhatu and examined
the candidate. We recommend that the thesis be accepted as fulfilling the Thesis requirements
for the Degree of Master of Science in Agriculture (Agronomy).
---------------------------------Chairperson
----------------------Signature
--------------------Date
---------------------------------Internal Examiner
-----------------------Signature
---------------------Date
---------------------------------External Examiner
-------------------------Signature
----------------------Date
Final approval and acceptance of the Thesis is contingent up on the submission of its final
copy to the Council of Graduate Program (CGP) through the candidate’s department/ school
graduate council (DGC or SGC).
ii
DEDICATION
I dedicate this thesis manuscript to MY PARENTS for their consistent and unreserved
encouragement, and their dedicated partnership in the success of my life.
iii
STATEMENT OF THE AUTHOR
By my signature below, I declare and affirm that this Thesis is my own work. I have followed
all ethical and technical principles of scholarship in the preparation, data collection, data
analysis and compilation of this Thesis. Any scholarly matter that is included in the Thesis has
been given recognition through citation.
This Thesis is submitted in partial fulfillment of the requirements for a Master of Science
degree at the Haramaya University. The Thesis is deposited in the Haramaya University
Library and is made available to borrowers under the rules of the library. I solemnly declare
that this Thesis has not been submitted to any other institution anywhere for the award of any
academic degree, diploma or certificate.
Brief quotations from this Thesis may be made without special permission provided that
accurate and complete acknowledgement of the source is made. Requests for permission for
extended quotations from or reproduction of this Thesis in whole or in part may be granted by
the Head of the School or Department when in his or her judgment the proposed use of the
material is in the interest of scholarship. In all other instances, however, permission must be
obtained from the author of the Thesis.
Name: Berhane Sibhatu Gebregziabher
Signature: _____________
Date of Submission: _______________________
School/Department: Plant Sciences (Agronomy)
iv
BIOGRAPHICAL SKETCH
The author, Berhane Sibhatu Gebregziabher, was born in 1984 at Degua-Tembien woreda,
south eastern zone of Tigray, Ethiopia. He attended his elementary and junior education at
Adi-Edaga and Hagereselam schools, respectively, from 1993-1998. He attended his
secondary school at Atse-yohannes Comprehensive Secondary School in Mekelle from 19992002. Then he joined Hawassa University (the then Debub University) in 2002 and graduated
with Bachelor of Science Degree in Plant sciences in July 2006. After graduation, he was
recruited by the Bureau of Agriculture at the central zone of Tigray, Tanqua-Abergelle woreda
and served there as Agroforestry expert for two years. The author was then employed by
Tigray Agricultural Research Institute (TARI) and assigned to work at Abergelle Agricultural
Research Center (AbARC) in 2009 and served as Junior and Assistant Researcher till June
2012. After that, he worked for Ethiopian Institute of Agricultural Research under Mehoni
Agricultural Research Center as Assistant Researcher-II until he joined the Haramaya
University School of Graduate Studies in 2014 to pursue his MSc Degree in Agriculture
(Agronomy).
v
ACKNOWLEDGEMENTS
I am highly honored to express my indebtedness to my major advisor, Dr. Ketema Belete and
co-advisor, Dr. Taye Tessema, for their enthusiastic and cheerful advice and guidance
spending their precious time from the inception to the completion of this thesis manuscript.
I gratefully acknowledge Integrated Striga Control (ISC) project for the financial support of
this study and Ethiopian Institute of Agricultural Research (EIAR) for facilitating the process
and giving me the opportunity to pursue my MSc degree. My thanks extend to Haremaya
University that facilitated me with all the necessary educational needs for the successful
completion of my study. Along with this, the support of Mehoni, Abergelle and Mekelle Soil
Research Centers and Kobo Agricultural Research sub-Center was invaluable and deserves
special thanks. I am thankful to Mehoni Agricultural Research Center staff members for their
cooperation during this thesis work. Specially, I am deeply indebted to my colleagues Mr.
G/meskel G/korkos and Mr. Berhane Hailu for their moral as well as endless physical support
during the whole experimental hardship. No words to express how much I am thankful to
them. Besides, I would like to express special thanks to Mr. Daniel Fitwi and Mr. Haile Adane
for their unforgettable help in providing me transport services and field work supports.
I would like to extend my appreciation and sincere thanks to Mr. Efrem Tarik, Mr. Hintsa
Meressa, Mr. Fantaye Belay and other staff members of Abergelle Agricultural Research
center and also to Kobo Agricultural Research Sub-center staff members for their generous
cooperation during the field work. I am grateful to Mr. H/slassie G/meskel for his unreserved
suggestions and assistance in obtaining information relevant to my research work. In line with
this, I acknowledge to my friends Mr. Adhena Meselle and Mr. Gidena Tasew for the special
and memorable time that I spent with them during my stay at Haramaya University campus
and their technical support during field work.
I affectionately thank my father Kes Sibhatu Gebregziabher, my mother Birhan Abera , and
my brothers and sisters who offered me courage and hospitality during my study period.
Above all, I praise the almighty God for keeping me healthy to successfully accomplish the
course work as well as the field research work.
vi
ACRONOMYS AND ABBREVIATIONS
AATF
African Agricultural Technology Foundation
ANOVA
Analysis of Variance
BNF
Biological Nitrogen Fixation
CSA
Central Statistical Agency
EIAR
Ethiopian Institute of Agricultural Research
ETB
Ethiopian Birr
FAO
Food and Agricultural Organization
FYM
Farm Yard Manure
GMV
Gross Monetary Value
IITA
International Institute of Tropical Agriculture
ILRI
International Livestock Research Institute
ISC
Integrated Striga Control
ISM
Integrated Striga Management
LER
Land Equivalent Ratio
LAI
Leaf Area Index
MoA
Ministry of Agriculture
MoARD
Ministry of Agriculture and Rural Development
MoFED
Ministry of Finance and Economic Development
MA
Monetary Advantage
SSA
Sub-Saharan Africa
TJAI
Thomas Jefferson Agricultural Institute
UNDP
United Nations Development Program
UN-EUE
United Nations Emergencies Unit for Ethiopia
USA
United States of America
USDA
United States Department of Agriculture
vii
TABLE OF CONTENTS
STATEMENT OF THE AUTHOR
iv
BIOGRAPHICAL SKETCH
v
ACKNOWLEDGEMENTS
vi
ACRONOMYS AND ABBREVIATIONS
vii
LIST OF TABLES
xi
LIST OF TABLES IN THE APPENDIX
xiii
ABSTRACT
xiv
1. INTRODUCTION
1
2. LITERATURE REVIEW
4
2.1. Overview of Sorghum and Cowpea Crops
2.1.1. Sorghum production
2.1.2. Cowpea production
4
4
6
2.2. Concepts and Main Aspects of Intercropping System
8
2.3. Benefits of Intercropping
9
2.3.1. Effect of intercropping in soil fertility
2.3.2. Effect of intercropping on soil properties
2.3.3. Soil and water management
2.3.4. Reduction of pest and disease incidence
2.3.5. Insurance against crop failure
2.3.6. Improvement of forage quality
2.3.7. Efficient resource utilization and yield advantage
9
10
11
11
13
14
14
2.4. Disadvantages of Intercropping
15
2.5. Spatial and Temporal Arrangements of Intercrops
16
2.6. Evaluation of Productivity and Efficiency of Intercropping
17
2.7. Effect of Plant Density in Intercropping System
18
2.8. Effect of Nitrogen on Cereal-Legume Intercropping
19
viii
TABELE OF CONTENTS (Continued)
3. MATERIALS AND METHODS
21
3.1. Description of Experimental Area
21
3.2. Experimental Materials
21
3.3. Treatments and Experimental Design
21
3.3.1. Treatments
3.3.2. Field experimental design
3.4. Field Management
21
22
23
3.5. Data Collection
25
3.5.1. Soil sample collection and analysis
3.5.2. Sorghum component
3.5.2.1. Crop phenology
3.5.2.2. Growth parameters
3.5.2.3. Yield related traits and yield
3.5.3. Cowpea component
3.5.3.1. Crop phenology
3.5.3.2. Growth parameters
3.5.3.3. Yield related traits and yield
3.5.4. Striga component
3.5.5. Productivity of the intercropping system
3.6. Data Analysis
25
25
25
26
26
27
27
27
28
29
29
30
4. RESULTS AND DISCUSSION
31
4.1. Soil Analysis
31
4.2. Sorghum Component
32
4.2.1. Crop phenology
4.2.1.1. Days to 50% heading
4.2.1.2. Days to 50% flowering
4.2.1.3. Days to 90% physiological maturity
4.2.2. Growth parameters
4.2.2.1. Plant height
4.2.2.2. Panicle length
4.2.2.3. Leaf area plant-1
4.2.2.4. Lea area index
4.2.3. Yield related traits and yield
ix
32
32
32
33
34
34
34
35
36
37
TABELE OF CONTENTS (Continued)
4.2.3.1. Panicle weight
4.2.3.2. Thousand kernel weight
4.2.3.3. Above ground dry biomass
4.2.3.4. Grain yield
4.2.3.5. Harvest index
37
37
38
39
40
4.3. Cowpea Component
41
4.3.1. Crop phenology
4.3.1.1. Days to 50% flowering
4.3.1.2. Days to 50% pod setting
4.3.1.3. Days to 90% physiological maturity
4.3.2. Growth parameters
4.3.2.1. Leaf area plant-1
4.3.2.2. Leaf area index
4.3.2.3. Plant height
4.3.2.4. Nodule number plant-1 (To be in growth parameter)
4.3.2.5. Number of branches plant-1 (To be in growth parameter)
4.3.3. Yield related traits and yield
4.3.3.1. Number of pods plant-1
4.3.3.2. Number of seeds pod-1
4.3.3.3. Hundred seed weight
4.3.3.4. Harvest index
4.3.3.5. Grain yield
4.3.3.6. Above ground dry biomass
41
41
42
43
43
43
43
44
45
46
47
47
47
48
49
50
51
4.4. Striga Population
52
4.5. Total Land Productivity and Gross Return Evaluation
53
5. SUMMARY AND CONCLUSIONS
58
6. REFERENCES
60
7. APPENDICES
76
x
LIST OF TABLES
Table
page
1. Major soil characteristics of the experimental site before planting
32
32
33
33
35
35
38
38
40
40
42
42
46
46
49
49
51
51
52
52
55
55
2. Main effects of plant densities of cowpea and N-rates on days to 50%
heading, days to 50% flowering and days to 90% physiological maturity of
intercropped sorghum with cowpea
3. Main effects of plant densities of cowpea and N-rates on plant height,
panicle length plant-1, leaf area, and leaf area index of intercropped
sorghum with cowpea
4. Main effects of plant densities of cowpea and N-rates on panicle weight
plant-1, thousand kernel weight, above ground dry biomass and harvest
index (%) of intercropped sorghum with cowpea
5. Interaction effects of palnt densities of cowpea and N-rates on grain yield
(kg ha-1) of intercropped sorghum with cowpea
6. Main effects of plant densities of cowpea and N-rates on phenology of sole
and intercropped cowpea with sorghum
7. Main effects of plant densities of cowpea and N-rates on growth parameters
of cowpea planted in sole and intercropped with sorghum
8. Main effects of plant densities of cowpea and N-rates on nodule number
plant-1, number of branches plant-1, number of pods plant-1, number of seeds
pod-1, hundred seed weight and harvest index of sole and intercropped
cowpea with sorghum
9. Interaction effects of plant densities of cowpea and N-rates on grain yield
(kg ha-1) of intercropped cowpea with sorghum
10. Interaction effects of plant densities of cowpea and N-rates on above
ground dry biomass (kg ha-1) of intercropped cowpea with sorghum
11. Interaction effects of plant densities of cowpea and N-rates on total land
equivalent ratio (LER)
xi
12. Interaction effects of plant densities of cowpea and N-rates on gross
monetary value (GMV) of the intercrops
56
56
57
57
13. Interaction effects of plant densities of cowpea and N-rates on monetary
advantage (MA) of the intercrops
xii
LIST OF TABLES IN THE APPENDIX
Appendix Table
page
1. Monthly and yearly total annual rainfall (mm) for 2010-2014 of Kobo area
77
2. Mean monthly and annual minimum temperature (0C) for the year 2010-2014 at
Kobo area
77
3. Mean monthly and annual maximum temperature (0C) for the year 2010-2014 at
Kobo area
78
4. Ratings for organic matter (OM), total N, available phosphorus, cation exchange
capacity (CEC), electrical conductivity (EC) and soil pH
79
5. Analysis of variance (ANOVA) for days to 50% heading, days to 50% flowering and
days to 90% maturity of sorghum as affected by plant densities of cowpea and Nrates
79
6. Analysis of variance for leaf area plant-1 (cm2), leaf area index, panicle length plant-1
(cm) and plant height (cm) of sorghum as affected by plant densities of cowpea and
N-rates
80
7. Analysis of variance for sorghum panicle weight plant-1 (g), thousand kernel weight
(g), dry biomass yield (kg ha-1), grain yield (kg ha-1) and harvest index (%) as
affected by plant densities of cowpea and N-rates
81
8. Analysis of variance for phenological parameters of cowpea compoenet as affected
by plant densities of cowpea and N-rates
81
9. Analysis of variance for leaf area (cm2), leaf area index, and plant height (cm) of
cowpea component as influenced by cowpea densities and N rates
82
10. Analysis of variance for cowpea nodule number plant-1, number of branches plant-1,
number of pods plant-1, number of seeds pod-1, hundred seed weight (g), grain yield
(kg ha-1), above ground dry biomass yield (kg ha-1), and harvest index (%) as
influenced by plant densities of cowpea and N-rates
83
11. Analysis of variance for sorghum partial LER, cowpea partial LER, total LER,
sorghum MV (ETB ha-1), cowpea MV (ETB ha-1) and GMV (ETB ha-1) as influenced
by cowpea densities and N rates
84
xiii
Effect of Plant Density of Cowpea and Nitrogen Fertilizer on Yield and
Yield Related Traits of the Component Crops under Sorghum-Cowpea
Intercropping System at Kobo, Northern Ethiopia
ABSTRACT
Sorghum productivity is limited in the study area mainly due to deterioration of soil fertility
and continuous cropping of sorghum. Hence, an experiment on sorghum/cowpea
intercropping was conducted in 2014/15 cropping season to assess the effect of density of the
intercropped cowpea and nitrogen fertilizer rates on yield and yield related traits of sorghum
and cowpea as well as to determine the appropriate plant density of cowpea and nitrogen
fertilizer rate that maximize the productivity of the intercrop system. Treatments consisted of
factorial combinations of three cowpea densities (50, 75 and 100%) and four rates of nitrogen
(0, 20.5, 41 and 61.5 kg N ha-1) accompanied with sole sorghum and sole cowpea with three
replications in randomized complete block design (RCBD). The analysis of variance result
indicated that phenological data of sorghum and cowpea were not significantly influenced due
to main effects of cowpea density and nitrogen rates, their interactions and cropping system.
Number of seeds pod-1 of cowpea, and panicle weight and HI of sorghum showed an increased
trend up to 41 kg N ha-1. Similarly, 100 seed weight of cowpea, plant height, panicle length
and dry biomass yield of sorghum as well as leaf area and LAI of both crops showed an
increased trend as N level increased. LAI and 100 seed weight of cowpea were affected due to
cowpea density in which the highest value of LAI was recorded at high population density
while at lowest cowpea density for the highest 100 seed weight. In addition, grain yield of
sorghum, grain yield and dry biomass of cowpea were influenced due to interaction effect. The
highest grain yield of sorghum (2370.40 kg ha-1) and cowpea (821.33 kg ha-1) were due to
combination of 41 kg N ha-1 + 75% sole cowpea density, and 20.5 kg N ha-1 + 100% of sole
cowpea density, respectively. The 20.5 kg N ha-1 + 100% sole cowpea density gave the highest
dry biomass (2375 kg ha-1) of cowpea as it did for grain yield. The analysis of variance result
showed that sole sorghum gave 24.84% higher dry biomass and 31.03% grain yield advantage
over the intercropped. Moreover, sole cowpea produced 45.22% dry biomass, 58.34% grain
yield and 27.86% higher nodules plant-1 than intercropped cowpea. LER was more than unity
apart from 0 kg N ha-1 + 50% sole cowpea density, which showed that intercropping of
sorghum and cowpea is advantageous in many instances rather than planting each of the
crops sole. The highest LER (1.46) and GMV (14001.70 ETB ha-1) were achieved due to 41 kg
N ha-1 + 75% sole cowpea density, and this can be tentatively recommended for the study
area.
Key words: cowpea, economic analysis, intercropping, Nitrogen, plant population, sorghum
xiv
1. INTRODUCTION
In Sub-Saharan countries, like Ethiopia, where the small-scale farming dominates the overall
national economy, agricultural production and productivity is very poor. The factors
attributing for poor productivity are recurrent droughts, environmental degradation, poor
infrastructure in quality and quantity, and backward cultural practices. Considerable loss also
occurs to the produce due to poor practices of post-harvest handling and limited use of
appropriate postharvest technologies (MoFED, 2006).
Low soil fertility, particularly N and P deficiencies are among the major biophysical
constraints affecting agriculture in Sub-Saharan Africa. According to Sanchez et al. (1997),
soil fertility depletion in smallholder farmers' holdings is the fundamental biophysical root
cause of declining per capita food production in the region. It has also been reported by
different investigators that continuous cropping, removal of field crop residues for animal feed
and over grazing between cropping seasons with little or no external inputs have reduced the
capacity of arable lands and threatened the sustainability of food production systems in the
densely populated humid and sub humid highlands of East Africa (IITA, 1992).
Therefore, it is necessary to adopt improved and sustainable technologies in order to guarantee
improvements in food productivity and thereby food security (Landers, 2007). Such
technologies include the use of integrated soil fertility management practices (ISFM) which
have intercropping cereals with legumes as one of its main components (Sanginga and
Woomer, 2009). This practice is an attractive strategy to smallholder farmers for increasing
productivity and labour utilization per unit of area of available land through intensification of
land use (Seran and Brintha, 2010).
Intercropping is one of the intensive cropping systems which ensure sustainable utilization of
limited land resources (Tesfa et al., 2001). It is the practice of growing two or more crops
simultaneously in the same field. By growing more than one crop at a time in the same field
farmers maximize water use efficiency, maintain soil fertility, and minimize soil erosion
which are the serious drawbacks of monocropping (Hoshikawa, 1991). In most instances
2
intercropping offers the advantage of increasing yield, nutritional diversity and net income.
Hence, it is important for the development of sustainable food production systems, particularly
in cropping system with limited external inputs (Getachew et al., 2006).
Farmers in different parts of the world intercrop different crops according to their preference
based on social and biological needs (Andrew and Kassam, 1983). A survey in Ethiopia
particularly in the northwestern Ethiopia (Amhara Region) indicated that in subsistence
economy the farmers use a combination of crops grown on a piece of land. The most important
crop mixtures used by farmers in the area are sorghum (Sorghum bicolor (L.) Moench)-chick
pea (Cicer arietinum L.), sorghum-faba bean (Vicia faba L.), finger millet-rape seed (Brassica
juncea L.), field pea (Pisum sativum L.)-faba bean, maize (Zea mays L.)-rape seed, maizepotato (Solanum tuberosum L.), and maize-faba bean (UNDP, 1996).
Sorghum [Sorghum bicolor (L) Moench] which belongs to the grass family Poaceae is among
the dominant staple cereals for the majority of Ethiopians. It is a crop dominated by resource
poor smallholders and typically produced under adverse conditions (Wortmann et al, 2009). In
the country sorghum is grown in almost all regions occupying an estimated total land area of
1.68 million ha and its national average productivity of 22.83 qt/ha (CSA, 2014). It is a major
crop in Kobo (north Wollo) district as the agro-ecology of the area best fits for the crop. It
covered an estimated total land area of 37,645.25 ha and produced an average productivity of
17.73 qt/ha in north Wollo which is less than the national average productivity (CSA, 2014). It
is used as human food, animal feed and fire wood. However, its productivity is limited due to
deterioration in soil fertility, shortening of the length of fallow, expansion of production into
marginal lands with little use of external soil amendments and the increasing trend towards
continuous cultivation of cereal monocrops in place of traditional rotation and intercropping
systems, and lack of agricultural inputs (UNDP, 1996). Furthermore, Striga is one of the main
biotic constraints that cause serious yield loss of sorghum and is commonly occurring on low
nitrogen-fertility soils and under low rainfall ecologies throughout Amhara (particularly in
Wollo) and Tigray regions (especially from Humara to shire, Central and Southern zone
Tigray) (Shank, 1996).
.
3
Cereal-legume intercropping is among the approaches that induces sorghum productivity.
Sorghum - cowpea intercropping is among the most important farming system for small scale
farmers in Arid and Semi-Arid Lands of sub-Sahara African (Karanja et al., 2014). Cowpea
[Vigna unguiculata (L) Walp] is grown extensively in the low lands and mid-altitude regions
of Africa (particularly in the dry savanna) sometimes as sole crop but more often intercropped
with cereals such as sorghum or millet (Agbogidi, 2010a). Intercropping with cowpea leads to
higher sorghum grain yield which may be related to the benefits of N fixation under cowpea
intercropping as well as to induction of suicidal germination of Striga seeds away from the
host roots.
Legumes with effective Biological Nitrogen Fixation (BNF) can be grown with less applied N
fertilizer. Appropriate fertilization with respect to type, amount, time, and method of fertilizer
application can increase the advantage of intercropping (Undie et al., 2012).
Due to the rising cost of chemical fertilizers, intercropping legumes with cereals is particularly
important in countries where the cost of N fertilizer is high and / or availability of fertilizer is
limited. Rebka et al. (2013) reported that a few of farmers in north Wollo practiced sorghum
intercropping (10.7%), and use improved varieties of sorghum (9%). According to their report,
farmers believed that the use of fertilizer for sorghum production is important. However,
unaffordable high costs, the low fertilizer response of landraces and the extreme drought at the
end of growing seasons limit the use of inorganic fertilizer by famers. Thus, to reduce these
constraints, a research on sorghum-cowpea intercropping system under effective management
of N fertilizer application and cowpea plant density in the study area to get quantitative
information on the productivity of the system, its influence on yield and yield related traits of
the component crops, and its effect on Striga control have to be conducted.
Therefore, this experiment aimed to assess the effect of density of the intercropped cowpea
and nitrogen fertilizer rates on yield and yield related traits of sorghum and cowpea; and also
to determine the appropriate plant density of cowpea and nitrogen fertilizer rate that maximize
the productivity of the intercrop system.
4
2. LITERATURE REVIEW
2.1. Overview of Sorghum and Cowpea Crops
2.1.1. Sorghum production
Sorghum is classified under grass family of Poaceae. It is perennial by nature and hence, a
very suitable multi-cut forage crop, but where the end product is grain it is grown as an annual
rainfed crop (Poehlman and Sleper, 1995). The annual wild and domesticated sorghum are
diploid (2n=2x=20) and are of tropical origin C4 crop (Dicko et al., 2006). It is generally selfpollinating, but 5-15% of plants can out-cross, and the flowers open during the night or early
morning. Those at the top of the panicle open first and it takes approximately 6 to 9 days for
the entire panicle to flower (Laidlaw and Godwin, 2009).
Ethiopia is the center of origin and diversity of Sorghum bicolor (L.) Moench (Mesfin and
Tileye, 2013). The crop has spread to other parts of Africa, India, Southeast Asia, Australia
and the United States (Mesfin and Tileye, 2013). It is an important staple food crop in Africa,
South Asia, and Central America (AATF, 2011; FAO, 2012). It is a major food and nutritional
security crop to more than 100 million people in horn of Africa, owing to its resilience to
drought and other production constrains (Gudu, 2013).
Generally, sorghum is produced in many countries of the world. It is the fifth major cereal
crop in the world in terms of tonnage after maize, wheat, rice and barley (FAO, 2012). In the
year 2013/14, sorghum production in the world was 60.46 million tons, and its production in
2014/15 was estimated to be 62.02 million tons which could represent an increase of 1.56
million tons or a 2.58% in sorghum production in the world (USDA, 2015). Even though the
productivity of sorghum varied across the different parts of the world, it showed an increment
in area coverage and productivity (AATF, 2011).
5
In Ethiopia, sorghum is adapted to a wide range of environment, and hence can be produced in
the highlands, medium altitude and lowlands. It grows most importantly in the moisture
stressed parts where other crops can least survive and food insecurity is rampant (Adugna,
2007). In lowlands of Ethiopia, especially in the lowlands of eastern Ethiopia and in the north
and north-eastern parts of the country where the climate is characterized by unpredictable
drought and erratic rainfall, sorghum is one of the most important cereal crops planted as food
insurance (Degu et al., 2009).
In Ethiopia, during 2013/14, sorghum accounted for 13.52% of the total of 79.38% area
allotted to cereal crop production (tef, maize, sorghum, and wheat). Similarly, during the same
year, sorghum took 15.22% (38,288,701.03 qt) of the total production (215,835,225.61 qt) of
major cereals (maize, tef, wheat, and sorghum). In that year, sorghum is the most important
staple food crop ranked 3rd in area coverage after tef and maize. Similarly, it ranked 4th in total
production after maize, tef and wheat, respectively (CSA, 2014). In the country sorghum is
grown in almost all regions occupying an estimated total land area of 1.68 million ha and its
national average productivity of 22.83 qt/ha (CSA, 2014). Sorghum is known for its versatility
and diversity, and is produced over a wide range of agro-ecological zones. Main sorghum
producing regions are Oromia, Amhara, Southern Nations and Nationalities People (S.N.N.P.)
and Tigray. The leading sorghum producing zones are East and West Hararge in Oromiya,
North Gondar and North Shoa in Amhara (Demeke and Di Marcantonio, 2013).
Sorghum has multiple uses of grain, forage, fodder and biofuel sources. In Asia and Africa,
sorghum grain is consumed by humans or as animal feed; stalks are used as animal fodder, or
as housing material. In case of Ethiopia, the grain is used for human food like
porridge,”injera”, “Kitta”, “Nifro”, infant food, syrup, and local beverage such as “Tella”,
and “Areke” (MoA, 2010). Sorghum grain has a high nutritive value, with 70-80%
carbohydrate, 11-13% protein, 2-5% fat, 1-3% fiber, and 1-2% ash. Protein in sorghum is
gluten free and thus, it is a specialty food for people who suffer from celiac disease (intolerant
to food with gluten), including diabetic patients and is a good substitute for cereal grains such
as wheat, barley, and rye (Dial, 2012).
6
Thus, sorghum is an important food and feed crop. The livelihoods of millions of subsistence
farmers depend on sorghum production. However, its production and productivity is limited
by a number of abiotic and biotic stresses. In Ethiopia, sorghum productivity faces constraints
that limit its yielding potential. Waddington et al. (2009) reported the ten constraints with the
greatest estimated yield losses by farming system combination. The constraints include leaf,
stem, and panicle pest; weed competition, N-deficiency, crop establishment difficulties, Striga,
drought (water deficit), soil physical degradation, soil fertility depletion, cold stress/ frost
damage, and inadequate farmer production and utilization knowledge training. Way forward to
solve these constraints should focus on environmentally friendly and economically feasible
integrated research approaches with prominent achievements on sorghum yield improvement.
2.1.2. Cowpea production
Cowpea belongs to the family Fabaceae and has alternate names like southern peas, black eye
peas, crowder peas (Clark, 2007). It is a warm-season; annual legume that exhibits a wide
range of growth habits and self-pollinating crop with conspicuous flowers; borne on short
pedicels (Sheahan, 2012).
Its origin and domestication occurred in Africa near Ethiopia and subsequently was developed
mainly in the farms of the African Savannah. It is an indigenous crop that has evolved from
the native wild types and its genetic diversity is greater than that of any other crop in the dry
African Savannah (IFAD, 2000). It is a legume widely adapted and grown throughout the
world, especially in the tropics and subtropics and has become a part of the diet of about 110
million people. It is a heat-loving and drought-tolerant crop and is also more tolerant to
infertile and acid soils (Clark, 2007).
The major cowpea growing countries are Nigeria, Niger, Mali, Senegal, Togo, Benin, Ghana,
Chad in West Africa; Tanzania, Somalia, Kenya, Zambia, Zimbabwe, Botswana and
Mozambique in eastern and southern Africa; India, Pakistan, Sri Lanka, the Philippines,
Bangladesh, Indonesia and China in Asia; and Brazil, West Indies, Cuba and southern USA in
America (Mahalakshmi et al., 2007). Burkina Faso and Cameroon are the other prominent
7
producers in Africa (Nedumaran et al., 2013). Generally, area under cowpea has gone up with
many new countries contributing to total production from the last decade onwards.
In Ethiopia, cowpea is becoming among the most commonly cultivated lowland pulses. It is
grown in rift valley areas of Ethiopia for its fodder and grain value (dual purpose) (Ayana et
al., 2013). Similarly, different genotypes of cowpea are adapted well to the semi-arid lowlands
of Northern Ethiopia (Solomon and Kibrom, 2014).
Cowpea is commonly cultivated as a nutritious and highly palatable food source in the
southern United States, Africa, Asia, and throughout the tropics and subtropics. The seed is
reported to contain 24% crude protein, 53% carbohydrates, and 2% fat. It then considered as
the poor man’s major source of protein (Agbogidi, 2010b). In short, cowpea has the potential
to serve as a key legume species for intensifying the crop and livestock production systems by
supplying protein in human diets and fodder for livestock, as well as bringing nitrogen into the
farming system through biological fixation, and suppress weeds, especially Striga (Clark,
2007; Ayana et al., 2013; Agza et al., 2012).
However, biotic (weeds, insect-pests) and abiotic (infertile soil, moisture stress) factors are the
main challenges in cowpea production (TJAI, 2010). As to Ethiopia, at the farm level,
productivity appears to be severely constrained by three major factors (MoARD, 2008). Those
factors include: (i) limited or no use of chemical fertilizers (e.g. phosphates); (ii) very limited
availability of improved varieties (mostly grown from unimproved cultivars with low genetic
potential); and (iii) the use of conventional agronomic practices (e.g., sub-optimal crop
rotations, poor seed bed preparation, inappropriate planting density).
This phenomenon implies that further research strategies are mandatory to alleviate problems
in cowpea production. The International Institute of Tropical Agriculture (IITA) and the
International Livestock Research Institute (ILRI) have been working together to develop
improved dual-purpose (for food and feed) cowpea varieties with resistance to biotic and
abiotic stresses and better nutritional attributes (Kristjanson et al., 2001), but not yet still
sustainable promising technologies have been achieved to alleviate the constraints.
8
2.2. Concepts and Main Aspects of Intercropping System
The forms of agriculture and cropping system found throughout the world are the results of
variation in local climate, soil, economics and social structure. Water balance, radiation,
temperature and soil conditions are the main determinants of the physical ability of crops to
grow and cropping system to exist (Harwood, 1975). Therefore, the cropping system varies
from place to place in the world. It is important to design improved system for a given agroecological situation based on their superiority over the existing systems which is adapted by
the farmers of the area in terms of their biological productivity and stability of production with
the least harm to the ecosystem. Farmers generally take decisions on the technologies to be
adopted on the basis of cost, risk, and return. In small farms, the farmers raise crops as risk
minimizing measures against total crop failures and to get different produces to take of their
family’s food, income, etc. Attractive strategy for increasing productivity and labour
utilization per unit area of available land is to intensify land use. This can be done by growing
several crops simultaneously or in succession with each other in farms devoted to short
maturing annual crops (Seran and Brintha, 2010).
Among the cropping systems, intercropping is practiced by the small-scale farmers in subSaharan Africa. It is a type of mixed cropping and defined as the simultaneous cultivation of
more than one crop species on the same piece of land (Hauggaard-Nielsen et al., 2008).
For the success of intercropping system several aspects need to be taken into consideration
before and during the cultivation process (Seran and Brintha, 2010). Those considerations
include maturity of crop, compatible crops, time of planting and plant density. The choice of
compatible crops depends on the plant growth habit, land, light, water and fertilizer utilization
(Brintha and Seran, 2009). When two or more crops are grown together the peak period of
growth of components do not coincide so as to make their major demands on resources at
different times. Plant competition could be minimized not only by spatial arrangement, but
also by choosing compatible crops which are able to exploit soil nutrients (Seran and Brintha,
2010).
9
2.3. Benefits of Intercropping
Most of farmers in the tropics and sub tropics are small holders who lack the financial capacity
to purchase agricultural inputs. As a result, they prefer multiple cropping as a means of
increasing production per unit of time (Willey, 1979a). Some of the main reasons for choosing
intercropping include the following advantages.
2.3.1. Effect of intercropping in soil fertility
Growing agricultural crops implies that nutrients (N, P, K, etc.) are removed from the soil by
the crop. Nutrient removal results decline of soil fertility when replenishment with inorganic
or organic nutrient inputs is inadequate (Talawar and Rhoades, 1998). According to Tisdale et
al. (1995) one of the greatest challenges for our generation will be to develop and implement
soil, water and nutrient management technologies that enhance the quality of soil, water and
air.
Intercropping is one of the options available to maintain soil fertility and crop yields. This is
attained when a cereal crop (such as maize or sorghum) or a tuber crop (such as cassava) is
grown in association with a pulse (beans, peas, etc). For example, maize-cowpea intercropping
increases the amount of N, P, and K contents compared to mono crop of maize (Dahmardeh et
al., 2010). After the intercrop is harvested, decaying roots and fallen leaves provide N and
other nutrients for the next crop. This residual effect of the pulse crop on the next crop is
largest when the remains of the pulse are left on the field and ploughed under after harvest.
However, when a large amount of N is removed in the grain harvest, more N is removed from
the field than fixed by the pulse crop. Thus, soil depletion can still occur in a grain-pulse
intercrop when the nutrients taken up by the crops are not replaced with manure or fertilizers
(Giller, 2001). Generally, legumes contribute to maintaining the soil fertility via N fixation,
which is increased in intercrops due to the more competitive character of the cereal for soil
inorganic N (Adigbo et al., 2013).
10
The benefits of legume intercrop with respect to N are direct transfer of N from the legume to
the cereal during the current intercrop and residual effects when the fixed N becomes available
on the sequential crops after the senescence of the legume and the decomposition of residues.
The direct transfer of N to companion crops occurs mainly by excretion of N from the legume
nodules, representing an immediate source of N to the cereal. Thus, the use of legumes in
mixtures contributes some N to the cereal component and some residual nitrogen to the
following crops (Adu-Gyamfi et al., 2007).
2.3.2. Effect of intercropping on soil properties
Mostly the effects of including a legume in a crop rotation or as part of an intercropping
system are accepted to be via the N2 fixed from the atmosphere by symbiotic microbes (Paynel
et al., 2008). However, there appears to be evidence that the positive influence of the legume
should be attributed to a combination of effects on all nutrients and the soil physical
properties. The legume crop does influence the N and P in the soil, as well as the exchangeable
cations and the soil organic carbon (SOC) (Fischler et al., 1999). Moreover, Siddig et al.
(2013) studied that sorghum-groundnut intercropping gave the lowest pH (6.53) and the
highest N (0.054%) and organic matter (0.68%) and consequently an increase in electrical
conductivity (0.21dsm-1).
Fischler et al. (1999) found that there were sustained improvements in soil physical properties
due to the crotalaria green manure combined into the maize-bean cropping system. They noted
a small decrease in bulk densities and increase porosity as well as increased water infiltration.
It is generally understood that the roots produced by legumes are associated with a high
microbial activity and therefore increased microbial biomass in the soil. This results in
increased soil aggregation. Some of the legume species used, particularly in hedgerows or with
perennial legume crops are also deep rooted. This assists in the recycling of nutrients and
particularly in bringing them up from the deeper soil layers (Snapp et al., 1998).
Under intercropping systems there is also an improvement in the soil organic carbon (SOC).
The SOC pool can be increased by mixed cropping, cover crops and crop rotations. It is
11
usually not evenly distributed through the soil, so aggregation “hot spots” form. The increased
SOC thus related to increased aggregation by a dependent on the deposition rates in the soil
(Makumba et al., 2006) which will vary according to the other environmental conditions in
different regions.
2.3.3. Soil and water management
In semi-arid areas, the soil evaporation is high in typical farmers’ field. Under these high
evaporative demand situations, the useful water for transpiration and thus conversion to
biomass is extremely low. However, if intercropping was to be practiced, there would be a
higher coverage of the soil by the second crop and so it forms a sort of green or living mulch.
While it will use water for transpiration, this will also produce a crop and thus be useful water
and at the same time decrease the bare soil surface evaporation. This would then also increase
the water use efficiency of the combined crop (Walker and Ogindo, 2003).
The process whereby the soil surface is shaded quicker under the intercropping system is due
to the faster growth of the leaves (Walker and Ogindo, 2003). This earlier closure of the crop
canopy reduced the amount of solar radiation reaching the soil surface. In addition,
intercropping systems control soil erosion by preventing rain drops from hitting the bare soil
where they tend to seal surface pores, prevent water from entering the soil and increase surface
runoff (Seran and Brintha, 2010). Kariaga (2004) found that in maize-cowpea intercropping
system, cowpea acts as best cover crop and reduced soil erosion. Similarly, sorghum-cowpea
intercropping reduced runoff by 20-30% compared with sorghum sole crop and by 45-55%
compared with cowpea monoculture (Reddy and Reddi, 2007).
2.3.4. Reduction of pest and disease incidence
The worsening of most insect problems has been associated with the expansion of
monocultures at the expense of the natural vegetation, thereby decreasing local habitat
diversity (Lithourgidis et al., 2011). However, intercropping cereal and legume crops reduce
pests, diseases and suppress weeds (Sanginga and Woomer, 2009). Crops grown
12
simultaneously enhance the abundance of predators and parasites, which in turn prevents the
build-up of pests, thus minimizing the need of using expensive and dangerous chemical
insecticides. Sekamatte et al. (2003) reported that intercropping maize with soybean,
groundnut, and common beans reduced significantly termite attack and the consequent loss in
grain yield of maize compared with maize monoculture, whereas it increased the nesting of
predatory ants in maize fields.
Intercrops also reduce disease incidence. According to Fininsa and Yuen (2002) slower
disease progress rate and lower incidence and severity of bean common bacterial blight
occurred on beans planted with maize or sorghum in row, mixed and broadcast intercropping
than on bean planted alone; the incidence was reduced by 36% and severity by 20% in
intercropping compared to sole cropping. Similarly, both mixed and row intercropping of
maize and bean significantly decreased incidence and severity levels of bean common
bacterial blight and rust diseases compared with sole cropping (Chemeda, 1996). Besides, faba
bean mixed with maize reduced the epidemics of chocolate spot and increased faba bean grain
yield (Samuel et al., 2008).
Weed control is an important aspect in intercropping because chemical control is difficult once
the crops have emerged. A study by Bilalis et al. (2010) showed that intercropping maize with
legumes considerably reduced weed density in the intercrop compared with maize pure stand
due to decrease in the available light for weeds in the maize-legume intercrops, which led to a
reduction of weed density and weed dry matter yield compared with sole crops. Several
studies were conducted to control Striga. Accordingly, Khan et al. (2007, 2008) demonstrated
that intercropping maize or sorghum with the fodder leguminous Desmodium uncinatum
(Jacq.) DC. and D.intortum (Mill.) Urb, significantly reduced S.hermonthica infestation and
increased grain yield. Similar studies in Kenya indicate that intercropping with cowpeas
between the rows of maize significantly reduced Striga numbers when compared to within the
maize rows (Odhiambo and Ransom, 1993). Moreover, finger millet (Eleusine coracana)
intercropped with green leaf desmodium (Desmodium intortum) reduced Striga hermonthica
counts in the intercrops than in the monocrops (Midega et al., 2010). Fasil et al. (2005) also
reported related findings on sorghum-cowpea intercropping where Striga emergence was
13
lower under intercrops than sole crops. Generally, various studies have shown that
intercropping cereals, mainly with legumes such as cowpea (Vigna unguiculata), peanut
(Arachis hypogaea) and green gram (Vigna radiate) can reduce the number of Striga plants
(Carsky et al., 1994). Potentially they might be acting as traps crops, stimulating suicidal
Striga germination or the microclimate under the crop canopy may be altered and interfere
with Striga germination and development (Parker and Riches, 1993). It is also hypothesized
that nitrogen fixed by the legumes might interact with Striga growth, as increasing the amount
of available nitrogen can reduce Striga densities (Pieterse and Verkleij, 1991).
As Striga is more favor in less fertile soil, a system that would improve soil fertility to
increase yield as well as reduce Striga infestation will be also of double advantage. Good soil
management practices involving the use of crop residues and organic manure have been
effective control measure against Striga. Vogt et al. (1991) observed that Striga infestation
decreased with increasing organic matter of the soil and that organic matter content seemed to
be the most important factor which preserved the soil fertility. Since soil microbial biomass
flourishes better in a medium rich in organic matter, organic or inorganic soil amendments
may increase soil suppressiveness to S.hermonthica and also improve soil conditions to
increase yield of subsequent cereal. Soil fertility and soil moisture management should be an
integral part of any Striga control strategy (Babiker, 2007). Soil fertility could be increased by
including legumes as intercrops, in rotation with cereals or by adding farm-yard manure,
incorporation of crop residues immediately after harvest and/or inorganic fertilizers.
2.3.5. Insurance against crop failure
Intercropping is more stable than mono-cropping. The stability under intercropping can be
attributed to the partial restoration of diversity that is lost under mono-cropping. From this
point of view, intercropping provides high insurance against crop failure, especially in areas
subject to extreme weather conditions such as frost, drought, flood, and overall provides
greater financial stability for farmers, making the system particularly suitable for laborintensive small farms. Thus, if a single crop may often fail because of adverse conditions such
14
as frost, drought, flood, or even pest attack, farmers reduce their risk for total crop failure by
growing more than one crop in their field (Lithourgidis et al., 2011; Solank et al., 2014).
2.3.6. Improvement of forage quality
Combining the growth of cereal forages with other crops capable of increasing the protein
content of the ration has great nutritional and financial value. Intercropping beans with wheat
improved forage dry matter and percentage of dry matter compared with bean sole crop and
also enhanced crude protein, neutral detergent fibre content, and water-soluble carbohydrates
compared with beans and wheat sole crops (Lithourgidis and Dordas, 2010). Furthermore,
intercropping legumes with maize significantly reduced neutral detergent fibre and acid
detergent fibre content, increasing digestibility of the forage. It is evident from the above that
intercrops of maize with legumes can substantially increase forage quantity and quality and
decrease the requirements for protein supplements compared with maize sole crops
(Javanmard et al., 2009). Similarly, maize and cowpea intercrops gave higher total forage dry
matter digestibility than maize or cowpea sole crops and led to increased forage quality (crude
protein and dry matter digestibility concentration) than maize monoculture and higher watersoluble carbohydrate concentrations than sole cowpea (Dahmardeh et al., 2009). Moreover,
Getachew et al. (2013) on forage legumes- maize intercropping found that intercropping
significantly enhanced stover crude protein content, crude protein yield and total fodder
protein yield by 20, 18 and 39%, respectively, as compared to the sole cropping.
2.3.7. Efficient resource utilization and yield advantage
Resources are utilized efficiently under compatible intercrops. When two or more crops that
differs interms of rooting systems, pattern of water and nutrient demand, and above ground
habit are planted together, water, nutrients and sunlight are used more efficiently. For
example, intercropping maize with cowpea has been reported to increase light interception in
the intercrops, reduce water evaporation, and improve conservation of the soil moisture
compared with maize alone (Ghanbari et al., 2010).
15
Yield advantages have been recorded in many legume-cereal intercropping systems, including
soybean-sorghum (Hayder et al., 2003), sorghum-cowpea (Tauro et al., 2013) and cowpeamaize (Eskandari and Ghanbari, 2010). The reasons of yield advantage of intercropping are
mainly that environmental resources such as water, light and nutrients can be utilized more
efficiently in intercropping than in the respective sole cropping systems (Liu et al., 2006). As
noted by Willey (1991) and Tadesse et al. (2012) the underlying principle of better
environmental resource use in intercropping is that if crops differ in the way they utilize
resources when grown together, they can complement each other and make better combined
use of resources than when they are grown separately. Normally, complementary use of
resources occurs when the component species of an intercrop use qualitatively different
resources or they use the same resources at different places or at different times (Tofinga et al.,
1993).
By using the land equivalent ratio (LER) one is able to compare the production of the
intercrop and express it relative to the yield from a sole crop of the dominant species (Beets,
1982). For example, the intercrop of maize and soybean had an advantage of 32% over the
sole cropping systems (Solomon et al., 2014). This means that if the maize was planted alone
on a piece of land, it would need 32% more land surface area to produce the same yield as
produced by the intercropping system. This clearly shows that the inclusion of the beans with
the maize on one land gave an advantage to the concurrent maize production and did not
reduce it in any way. Many studies on sorghum-legume (Karanja et al., 2014) and maizelegumes (Seran and Brintha, 2010; Tilahun et al., 2012) showed that intercropping systems
have a higher productivity than the sole cereal systems across semi-arid areas of Africa.
2.4. Disadvantages of Intercropping
Intercropping has many advantages as some of them listed above. However, it has some
disadvantages such as the selection of the appropriate crop species and the appropriate sowing
densities, including extra work in preparing and planting the seed mixture and also extra work
during crop management practices. The selection of an appropriate intercropping system for
each case is quite complex as the success of intercropping systems depend much on the
16
interactions between the component species, the available management practices, and the
environmental conditions (Lithourgidis et al., 2011)
A serious disadvantage in intercropping is also thought to be difficult with practical
management, especially when the component crops have different requirements for fertilizers,
herbicides, and pesticides. Additional cost for separation of mixed grains and lack of
marketing of mixed grains, problems at harvest due to lodging, and grain loss at harvest also
can be serious drawbacks of intercropping. Depending on crops mixed, competition for light,
water and nutrients, or allelopathic effects that may occur between mixed crops may reduce
yields (Carruthers et al., 2000). Thus, it needs selection of appropriate crops, planting rates,
and changes in the spatial arrangement of the crops to reduce competition.
Moreover, mechanization is a major problem in intercropping. Machinery used for sowing,
weeding, fertilizing, and harvesting are made for big uniform fields. Harvesting remains a
great problem. In the developing countries, the work needed in the field is mainly done by
hand with simple tools because intercropping is very labour intensive. In these countries,
however, where manual labour is plentiful and cheap, it is not necessary to invest in expensive
machinery especially for intercropping. But, for intercropping on a large scale basis,
mechanization is generally believed to be impossible or inefficient (Vandermeer, 1989).
2.5. Spatial and Temporal Arrangements of Intercrops
Interspecific competition in intercropping can affect growth, development and yield of each
component crop due to differences in species and microclimate, and mutual shading (Rashid,
2003). This unfavorable and unfair acquisition of resources resulting from inter-specific
competition is needed to be alleviated through agronomic management approaches, such as
spatial and temporal arrangements of intercrops.
Spatial arrangement is the systematic apportioning of the farm area or any growing surface for
crop production. In multiple cropping by intercropping, the intercrop can be planted in any of
the following ways: with in the rows of the main crop, between the rows of the main crop and
17
in replacement series (Bareja, 2011). When two or more crops are growing together, each must
have adequate space to maximize cooperation and minimize competition between them.
Improved resource utilization and hence, increased yield can be also achieved with proper
manipulation of time of planting. For example, the study carried out by Amujoyegbe and
Elemo (2013) revealed that staggering planting date takes advantage of peak resource demands
and reducing competition between crops.
Identifying the optimal spatial and temporal arrangements and selection of effective,
compatible and adaptable legume crops, depending on the natural endowments of localities
could be an important prerequisite for successful intercropping. Fasil et al. (1997) reported
that one row of legume every two rows of sorghum was an optimum arrangement both in
terms of reduction in parasitic weed incidence and increase in cereal yield. Similar study in
another environment showed that alternate row planting of sorghum and legumes with
staggered planting of the crops (planting legumes intercrops 3-4 weeks after the cereal) was
found more productive and led to overall reduction in Striga infestation (Fasil, 2002).
Similarly, an experiment conducted in the north western Ethiopia highlands indicated that
highest LERs (2 at Motta and 1.5 at Adet), highest N use efficiency, and gross monetary
advantage were obtained when a planting pattern of 1:1 maize: faba bean alternate rows were
used (Tilahun et al., 2012). In line with this, an experiment with different spatial arrangements
of pearl millet - cowpea intercrop showed that one row of millet to one row of cowpea (with
millet planted 2 weeks before cowpea) yielded better than the other spatial arrangements
(Yirzagla, 2013). Hence, spatial and temporal arrangement of crops is critical in determining
the growth and yield of intercrops.
2.6. Evaluation of Productivity and Efficiency of Intercropping
Different indices have been suggested for evaluating productivity and efficiency per unit area
of land of cereal and legume intercropping systems (Palaniappan, 1985). Among them, land
equivalent ratio (LER) is the most widely used relative index to evaluate the efficiency and
productivity of intercropping (Willey, 1991).
18
The LER could be used either as an index of biological efficiency to evaluate the effects of
various agronomic variable (e.g. fertility levels, density and spacing, comparison of cultivars
performance, relative time of sowing, and combinations) on an intercrop system in a locality
or as an index of productivity across geographical locations to compare a variety of intercrop
systems. It is defined as the area that a sole crop has to occupy in order to produce the same
amount of yield as its component in the intercrop (Mead and Willey, 1980). The LER
compares the yield of each part of the intercrop to the yield of that same species grown alone
as a sole crop. The equation is as follows:
LERT = LERa + LERb + ……….. + LERn
Where LERT is the total LER and the subscripts a, b, to n give partial LERs for each different
species that is a component of the intercropping system. Each of these partial LERs can be
calculated as follows:
LERa = YIa / YSa
etc
Where YIa is the mass yield per unit area of the first species ‘a’ in the intercrop and YSa is the
mass yield per unit area of the same species when grown as a sole crop (Tsubo et al., 2005).
The LERT value greater than one (LER>1) shows that intercropping has a yield advantage
over the sole cropping of each crop individually. It also indicates that if the sole cropping was
used a larger piece of land would be required to produce the same total yield that was
produced under the intercropping system (Tsubo et al., 2005).
2.7. Effect of Plant Density in Intercropping System
Plant density or plant spacing describes the space left between plants when planting a garden,
field, and other landscaping plants. The more closely spaced plants are, the higher the density.
The planting density of certain plants can be described by the number of plants within a given
unit of area (Hamel, n.d).
In intercropping situation, total population (sum of population of the component crops) and
component population (population of each component) have to be distinguished. The seeding
19
rate of each crop in intercrop has to be adjusted to optimize plant density. According to the
report of Seran and Brintha (2010) if full rate of each crop were planted, neither would yield
well because of intense overcrowding. Morgado and Willey (2003) reported that dry matter
yield accumulation of individual maize plant decreased with increase in bean plant population.
In contrast, Omae et al. (2014) reported that biomass and grain yield of cowpea increased at
high density of cowpea on millet-cowpea intercropping but not significant effect on biomass
yield and grain yield of millet. It is evident from various workers that intercropping gives
higher yield advantage when total population in the system is higher than that of sole crops
(Willey, 1979b). Similarly, Oroka (2012) reported that maximum density of intercrops
resulted in maximum forage dry matter yield of rice and cowpea on rice-cowpea intercropping.
Another report by Egbe (2010) found that the competitive ratio of soybean increased (0.76 1.15) with increasing density of the soybean in the intercrop combinations, indicating higher
competitiveness at higher densities than the sorghum component, while the competitive ratio
of sorghum had the opposite response (1.23 - 0.76). Prasad and Brook (2005) reported that
with increasing maize density at soybean-maize intercropping, rates of accumulation of dry
matter and leaf area index also increased the latter, resulting in decreasing transmission of
light to the intercropped soybean. The studies above are clear indication of the challenge that
comes in knowing how much to reduce the seedling rates.
2.8. Effect of Nitrogen on Cereal-Legume Intercropping
Fertilizers are more efficiently used in an intercropping system, due to the increased amount of
nutrients taken up. Ahmed and Rao (1982) on maize-soybean intercrop reported that LER
showed a yield advantage of between 42 and 64% for the intercrop with different amounts of
N fertilizer. If no inorganic N fertilizer was added, then the intercrop gave a slightly lower
yield, however with some applied N, the yield was above the sole maize production.
Therefore, as the returns on N application were higher under the maize-bean intercropping
system, it is more economically viable and should be recommended. Ogutu et al. (2012) also
indicated that interaction of Cropping system × N fertilizer × Location increased seedling
growth rate, seed vigor, seedling dry matter and other seed quality parameters on maize –
common bean based intercropping system. They also reported that addition of N fertilizer was
20
significant for shoot length, 1000 seed weight and root length of bean. Moreover, they
concluded that N fertilizer application is not necessary in pure bean seed production as the
crop is capable of fixing its own N by Rhozobium bacteria but starter fertilizer is necessary
where soil is suspected to be of low soil fertility in terms of N and P. Jat et al. (2014) on
maize-mung bean intercropping similarly reported that number of cobs plant-1, length of cob
plant-1, number of grains cob-1 and 1000-grain weight were greatly influenced by the
integrated N management. Moreover, Oroka (2012) on rice-cowpea intercropping reported that
forage dry matter yield of both rice and cowpea had linear relationship with N. High N
increased dry matter yield of the crops. The higher dry matter observed under high N rates
could be due to increase in foliage of the crops resulting in accumulation of photo-assimilate.
This author reported that LER ranged from 1.41 to 3.47 indicated the intercrops had 41% to
247% forage dry matter yield of their sole crops due to application of N fertilizer.
21
3. MATERIALS AND METHODS
3.1. Description of Experimental Area
The experiment was carried out under rain-fed conditions in Kobo Agrcultural Research Subcenter testing site in 2014/15 cropping season. It is situated at an altitude of 1468 meter above
sea level (m.a.s.l), and at latitudes of 12009’ N and longitudes of 39o38’ E including eutric
vertisol soil type. The soil had particle size distribution of 21% sand, 33% silt and 46% clay,
and moderately alkaline pH (7.61), organic matter (1.62%), total nitrogen (0.0784%) and
available P (17.02 mg kg-1). Rainfall during the growing season of five months was 690.5 mm,
which is more or less similar to the five years average rain fall (652.28 mm). The mean
maximum and minimum temperatures (2010-2014) were 30.24 0C and 15.32 0C, respectively.
The area is characterized by bimodal rainfall pattern with a short rainy season (belg) from
February to April and a long rainy season (kirmet) from June to September with a peak in
August.
3.2. Experimental Materials
The materials used in this experiment were Bekur variety of cowpea which was released by
Sirinka Agricultural Research center; and Striga-resistant sorghum variety, P-9401, known
locally as Gobiye and also released in northern Ethiopia by Sirinka Agricultural Research
Center (Fasil and Verkleij , 2004) . Urea and triple super phosphate (TSP) fertilizers were also
included in the experimental materials.
3.3. Treatments and Experimental Design
3.3.1. Treatments
The treatments consisted of four levels of N fertilizer rates: 0, 20.5, 41 and 61.5 kg ha -1. Urea
(46% N) was used as source of N. In addition, the treatments included plant densities of
22
cowpea intercropped with recommended rate of sorghum variety. The plant densities were
50%, 75%, and 100% of the recommended sole plant density of cowpea (83,333 plants ha-1,
60x20 cm). Sorghum was planted at recommended population density (66667 plants ha-1,
75x20cm). Sole cowpea and sorghum at recommended plant densities ha-1 were included in the
treatments.
The treatment (T) combination comprised:
T1. Sorghum (100%) + 50% cowpea + 0 kg N ha-1
T2. Sorghum (100%) + 50% cowpea + 20.5 kg N ha-1
T3. Sorghum (100%) + 50% cowpea + 41 kg N ha-1
T4. Sorghum (100%) + 50% cowpea + 61.5 kg N ha-1
T5. Sorghum (100%) + 75% cowpea + 0 kg N ha-1
T6. Sorghum (100%) + 75% cowpea + 20.5 kg N ha-1
T7. Sorghum (100%) + 75% cowpea + 41 kg N ha-1
T8. Sorghum (100%) + 75% cowpea + 61.5 kg N ha-1
T9. Sorghum (100%) + 100% cowpea + 0 kg N ha-1
T10. Sole sorghum (100%) + 100% cowpea + 20.5 kg N ha-1
T11. Sorghum (100%) + 100% cowpea + 41 kg N ha-1
T12. Sorghum (100%) + 100% cowpea + 61.5 kg N ha-1
T13. Sole sorghum (66667 plants ha-1) + 41 kg N ha-1 (recommended fertilizer)
T14. Sole Cowpea (83,333 plants ha-1)
3.3.2. Field experimental design
23
The combined treatments were laid out in randomized complete block design (RCBD)
consisting of two factors replicated thrice in factorial arrangement with path width of 1 m
between plots and 1.5 m between blocks. The gross size of experimental plot was 3.75 m x 4
m (15 m2) accommodating five rows of sorghum planted at a spacing of 75 cm between rows
and 20 cm between plants in both sole and intercropped sorghum plots; and four rows of
cowpea having 75 cm inter-row spacing. Net sampling plot size was 2.25 m x 3.6 m in all the
cases, in which the two outer most rows and 0.2 m row length at both ends considered as
borders leaving three middle rows for sorghum and two rows for cowpea with the length of 3.6
m for sampling.
3.4. Field Management
Land preparation was done at the beginning of June with tractor and leveled before planting.
Sorghum was planted at a spacing of 75 x 20 cm while the required intra-row spacing of
cowpea was made by adjusting spacing between cowpea. Sorghum was planted on July 20,
2014 cropping season. Cowpea was planted three weeks later after sorghum at 1:1 sorghumcowpea spatial arrangement. This is according to the report of Fasil (2002) which showed that
alternate row planting of sorghum and legumes with staggered planting of the crops (planting
legumes intercrops 3-4 weeks after the cereal) was found to be more productive and lead to
overall reduction in Striga infestation. Cowpea was planted with intra-row spacing of 13 cm,
17 cm and 25 cm based on the treatments which represented 100%, 75% and 50% of the
recommended cowpea planting density, respectively.
Nitrogen fertilizer in the form of urea (46% N) was applied in two doses, i.e., half of the
quantity applied as band application at planting and the remaining half was top-dressed at knee
height growth stages of sorghum. It was applied for sorghum rows only at 5 cm away from the
sorghum. The full dose of P (46 kg P2O5 ha-1) was applied as band application in the form of
triple super phosphate (TSP) at planting time for both sorghum and cowpea rows in sole and
intercrop cases. In-situ soil moisture conservation practice (tied ridging) was done for all plots
to harvest water. The ridges were constructed at a spacing of 75 cm and a height of 30 cm tied
at 2.5 m distance. The seeds were sown by hand in the rows as uniformly as possible and
24
covered with soil manually. Thinning of seedlings was done two weeks after emergence at
spacing of 20 cm for sorghum and at 20 (for sole), 13, 17, and 25 cm intra row-spacing of
cowpea. Three and two seeds hill-1 were planted for sorghum and cowpea, respectively. Then,
after emergence it was thinned to one seedling hill-1 for each crop. All other necessary field
management practices like weeding and chemical spray (karate) for cowpea seedlings were
carried out equally for all experimental units.
25
3.5. Data Collection
3.5.1. Soil sample collection and analysis
Soil sample at a depth of 0-30 cm was taken from five random spots diagonally across the
experimental field using auger before planting. The collected soil samples were composited to
one sample. The bulked soil samples were air dried, thoroughly mixed and ground to pass 2
mm sieve size before laboratory analysis. Then the samples were properly labeled, packed and
transported to the laboratory. After that, soil organic carbon, total N, soil pH, available P,
cation exchangeable capacity (CEC), electrical conductivity (EC) and texture were analyzed at
Mekelle Soil Research Center laboratory.
The soil pH was measured in the supernatant suspension of a 1: 2.5 soil to water ratio using a
standard glass electrode pH meter (Rhoades, 1982). The Walkley and Black (1934) method
was used to determine the organic carbon (%). Total N was determined using Kjedahl method
as described by Bremner and Mulvaney (1982). Available P (mg kg-1) was determined by
employing the Olsen et al. (1954) method using ascorbic acid as the reducing agent. The
cation exchange capacity (CEC) in cmol (+) kg-1 was measured using 1M-neutral ammonium
acetate method (Jackson, 1967). Electrical conductivity (EC) was done in the soil to water
suspension of 1:5 (Jackson, 1967). The soil particle size distribution was determined using the
Bouyoucos hydrometer method (Bouyoucos, 1962).
3.5.2. Sorghum component
3.5.2.1. Crop phenology
Data on days to 50% heading, days to 50% flowering and 90% maturity of sorghum were
recorded by counting the number of days from planting to the time when 50% and 90% of the
plots reached each of phenological stage, respectively.
26
3.5.2.2. Growth parameters
Five random plants plot-1 from the net plot area were taken to measure leaf area plant-1 (cm2).
Physiologically well performed three leaves plant-1 were considered. It was determined at 50%
heading using the method described by Sticker et al. (1961) as: Leaf area = leaf length of the
leaves x maximum width of leaf x 0.75. Where, 0.75= correction factor for sorghum
Leaf area index (LAI) was measured from five random plants plot-1 in which it was calculated
as the ratio of unit leaf area per unit ground from the net plot according to Watson (1958)
where unit leaf area =leaf area x No of leaves /plant. Five random plants from each net plot
were also taken to measure panicle length (cm) when the plants reached 90% physiological
maturity. Similarly, plant height (cm) was measured from five randomly taken plants of each
net plot at 90% physiological maturity from the ground level to the base of the panicle. The
five randomly taken plants from the net plot area were tagged to take data of all these growth
parameters.
3.5.2.3. Yield related traits and yield
Panicles of five sorghum plants, where data for sorghum growth parameters were taken, were
bulked and mixed together at 12.5% adjusted moisture level (using Digital Grain Moisture
Meter, Moistexss7satake) to determine panicle weight plant-1 (g). From the bulked seed of net
plot yield adjusted at 12.5% moisture level, 1000 kernels were counted using electrical seed
counter (MARK: watech International Thatcham, Berkshire RG 19 4QD, England) to
determine thousand kernel weight (g). It was measured using electronic sensitive balance
(Mark: Sartorius; TUV product service; Schutzart IP65SARTORIUS AGGOTTIIUGEN,
Germany). Grain yield (kg) was obtained from all plants of net plot area. It was determined
using sensitive balance after the grain had been dried, threshed, cleaned and adjusted to 12.5%
moisture level. Then, it was converted to kg ha-1 basis. Similarly, the above ground biomass
(kg) was measured after the plants from the net plot area were harvested and sun dried till
constant dry weight was attained. Harvest index was also computed as ratio of grain dry
weight to above ground dry biomass expressed in percentage.
27
3.5.3. Cowpea component
3.5.3.1. Crop phenology
Data on days to 50% flowering, 50% pod setting and 90% maturity of cowpea were recorded
from the net plot area from date of planting when 50% and 90% plants plot-1 reached their
respective phenological stages.
3.5.3.2. Growth parameters
Leaf area plant-1 (cm2) of cowpea was determined by stacking the leaf laminae of the five
sample plants on the table at 50% flowering stage and a cork borer of known area was driven
through them to cut out discs. Ten physiological well performed leaves plant-1 were sampled.
The complete disc were counted and weighed both when fresh and after drying. The remaining
parts after cutting of discs were combined as per the discs. The weights obtained were used to
determine the leaf area as described by Ibrahim et al. (2014):
a*n 
A 
* W
 w 
Where:
A = Total leaf area plant-1 (cm2); a = area of individual discs (cm2); n = number of discs taken;
w= weight of dry n discs (g) and W= Total dry weight of leaves plant-1 (g).
Similar to sorghum, leaf area index (LAI) of cowpea was calculated as the ratio of unit leaf
area per unit ground from the net plot according to Watson (1958) where unit leaf area =leaf
area x No of leaves plants-1. Five randomly taken plants from the net plot area were used to
determine plant height (cm). It was measured from the ground level to the apex of the main
stem at 90% physiological maturity stage of the crop with a standard meter rule.
28
Effective nodule number plant-1 was collected at the time of 50% flower initiation. Data were
taken by digging out the roots of five plants randomly from the net plot by removing the soil
clods carefully and leaving the nodules on the root. The remaining soil on the root was washed
using gently running pure water on a sieve so that the root nodules were cleaned and then
carefully removed separately from the tap root region and lateral roots. Effective nodules from
sampled plants were counted based on their colour (pink colour) and the mean value of the
five plants was recorded as the number of effective nodules expressed on plant basis. Number
of branches plant-1 was determined by counting the branches from five randomly sampled
plants of the net plot. It refers to the primary branches on the main stem plant-1 harvested at
maturity. The five randomly taken plants from the net plot area were tagged to take data of leaf
area, leaf area index, plant height and number of branches while other five plants were
randomly taken for number of effective nodules.
3.5.3.3. Yield related traits and yield
The five randomly taken plants, where data of leaf area, leaf area index, plant height and
number of branches were taken, were also used to collect data of number of pods plant-1 at
physiological maturity and number of seeds pod-1. Number of seeds pod-1 was determined
from 15 pods taken from the five sampled plants of the net plot. Similarly, grain yield sample
taken from net plot area adjusted at 10.5% moisture level (using Digital Grain Moisture Meter,
Moistexss7satake) was used to measure hundred seed weight (g) using electronic sensitive
balance
(Mark:
Sartorius;
TUV
product
service;
Schutzart
IP65SARTORIUS
AGGOTTIIUGEN, Germany).
All plants of the net plot area were harvested to determine the grain yield (kg) of cowpea. The
data were taken at 10.5% moisture level using electronic sensitive balance (Mark: Sartorius;
TUV product service; Schutzart IP65SARTORIUS AGGOTTIIUGEN, Germany) and then
converted in kg ha-1 basis. Like to sorghum, the above ground dry biomass weight (kg) was
measured using sensitive balance after the net plot area plants had been harvested and sundried
till constant dry weight was attained. Similar to the grain yield, it was converted in to kg ha-1
29
basis. Furthermore, harvest index was computed as ratio of grain dry weight to above ground
dry biomass expressed in percentage.
3.5.4. Striga component
Number of Striga shoots emerged per net plot area were planned to be counted at weekly
intervals from the time of emergence of Striga seedling.
3.5.5. Productivity of the intercropping system
According to Willey (1991), productivity of the intercropping system was determined by
calculating the land equivalent ratio (LER) and the economic analysis using gross monetary
value (GMV). Land equivalent ratio (LER) was used to evaluate the productivity of intercrops
compared with mono-crops. It was calculated according to Mead and Willey (1980):
LER 
Yab Yba

Yaa Ybb
Where;
Yab= yield per unit area of crop a in the intercrop
Yaa= yield per unit area of crop a in sole crop
Yba= yield per unit area of crop b in the intercrop
Ybb= yield per unit area of crop b in sole crop; a = sorghum; b = cowpea
Gross monetary value (GMV) and monetary advantage (MA) were calculated from the yield
of sorghum and cowpea in order to evaluate the economic advantage of intercropping as
compared to sole cropping (Willey, 1979a). The monetary value was determined by the
existing local market price of produce (sorghum and cowpea) during the harvest period.
Accordingly, GMV was calculated by multiplying yields of the component crops by their
respective current market price. MA described by Willey (1979a) was calculated as:
30
Monetary Advantage (MA )  value of combined inercrops yield *
LER  1
LER
Hence, to estimate the GMV of component crops, sorghum grain yield was valued at an
average existing local market price of Birr 4.50 kg-1 and cowpea at Birr 5.50 kg-1 in Kobo
district from November 2014 to February 2015.
3.6. Data Analysis
The collected data were subjected to the analysis of variance (ANOVA) using the SAS
computer package version 9.1 (SAS Institute, 2004). Mean separation was carried out using
least significance difference (LSD) test at 5% probability level as described in Gomez and
Gomez (1984).
31
4. RESULTS AND DISCUSSION
4.1. Soil Analysis
Analysis of soil samples before planting was carried out for the major soil physical and
chemical properties at soil laboratory of Mekelle Soil Research Center (Table 1). The results
in Table 1 indicated that the soil comprised total N of 0.0784%. According to Berhanu (1980)
it has low level of N. The analysis in Table 1 also showed that characterizing the available P
content of the composite soil sample in the study area was rated as high (17.02 mg kg-1)
available P (Olsen et al., 1954). The organic matter content of the soil is taken as a crude
measure of fertility status. It is estimated indirectly from the organic carbon determination
(OM% = 1.72 X % OC). The composite soil sample gave 1.62% soil OM which rated as low.
This was in agreement with findings of Tekalign (1991) who reported that soils having OM
value in the range of 0.86-2.59% are considered low (Appendix Table 4). Thus, it needs
supplement or addition of materials that increase the organic matter in the soil. Moreover, the
pH value of the experimental soil was 7.61. According to Tekalign (1991), soils having pH
value in the range of 7.4 to 8.0 are considered moderately alkaline soils. The cation exchange
capacity of the study area was 48.8 Cmol kg-1 (Table 1). This showed high capacity of the soil
to retain cations in exchangeable form for the plant. According to Hazelton and Murphy
(2007), the electrical conductivity of the soil (0.18 ds m-1) also indicated that the experimental
soil was non-saline. The soil of the experimental site has a proportion of 21% sand, 33% silt,
and 46% clay that can be texturally classified as clay soil (Table 1).
32
Table 1. Major soil characteristics of the experimental site before planting
Soil parameter
Total N
Available P
Organic Matter
Soil pH
EC
CEC
Particle Size distribution
Sand
Silt
Clay
Textural class
Unit
%
mg kg-1
%
ds m-1
cmol kg-1
Value
0.0784
17.02
1.65
7.61
0.18
48.80
%
%
%
21
33
46
CLAY
4.2. Sorghum Component
4.2.1. Crop phenology
4.2.1.1. Days to 50% heading
The analysis of variance (Appendix Table 5) showed that neither the main effects (cowpea
density and nitrogen) nor their interaction significantly affected days to 50% heading of
sorghum. Days to 50% heading of intercropped sorghum did not also differ from sole cropped.
This might be most probably due to the compatible nature of the component crops for limited
growth resources. This finding agreed with the work of Adem (2006) on sorghum-cowpea
intercropping where days to 50% heading did not differ significantly between the sole crop
and the intercropped cereal.
4.2.1.2. Days to 50% flowering
The days to 50% flowering of sorghum was not significantly affected by the main effects, their
interaction and cropping system (Appendix Table 5). The sole and intercropped sorghum plant
flowered at the range of 77.89 – 79 days from time of planting (Table 2). This non-significant
effect could be due less contrasting effect of the intercropped cowpea on sorghum for the
growth resources. In agreement to this result, Yesuf (2003) and Eybo (2007) on sorghum–
33
haricot bean and sorghum–fababean intercropping, respectively, reported no statistically
significant difference on days to 50% flowering of sorghum.
4.2.1.3. Days to 90% physiological maturity
Same to days to 50% heading and flowering, days to 90% physiological maturity was not
significantly differed by the main effects, their interaction and cropping system (Appendix
Table 5). This lack of significance difference could be most probably due to less competitive
effect of the associated cowpea on sorghum for limited growth resources till physiological
maturity. It matured at 108 – 110 days starting from its planting time (Table 2). Yesuf (2003)
and Sisay (2004) reported non-significant effect of sorghum-bean, and sorghum-green gram
intercrop on days to 50% maturity of sorghum, respectively, which was in line with the current
result.
Table 2. Main effects of plant densities of cowpea and N-rates on days to 50% heading, days
to 50% flowering and days to 90% physiological maturity of intercropped sorghum with
cowpea
Treatments
Days to 50%
heading
Days to 50%
flowering
Cowpea densities
50%
74.00
78.83
75%
74.50
78.58
100%
74.00
78.58
LSD (P=0.05)
NS
NS
-1
N-rates (kg ha )
0
73.56
77.89
20.5
73.78
78.33
41
74.78
79.44
61.5
74.56
79.00
LSD (P=0.05)
NS
NS
CV (%)
2.00
2.26
Cropping system
Sole sorghum
75.03
79.20
Intercropped sorghum 74.17
78.67
LSD (P=0.05)
NS
NS
CV (%)
1.49
1.29
NS= not significantly different from each other at P<0.05; LSD= least
CV= Coefficient of variation
Days to 90%
maturity
109.93
108.92
107.98
NS
108.19
108.64
108.97
109.98
NS
1.99
108.67
108.94
NS
1.18
significant difference;
34
4.2.2. Growth parameters
4.2.2.1. Plant height
Plant height of intercropped sorghum was affected significantly (P<0.05) only due to the effect
of N (Appendix Table 6). The tallest plant height (121.38 cm) of intercropped sorghum was
recorded due to application of 61.5 kg ha-1 and was statistically at par with the plant height
(120.19 cm) obtained due to application of 41 kg ha-1(Table 3). As N increased from nil to
61.5 kg ha-1, plant height also increased from 113.11 cm to 121.38 cm. This increment could
be due to the vital role of N in enlargement of vegetative growth of plant parts. In agreement
with this result, Abebe et al. (2013) reported significant variation in plant height of
intercropped maize due to integrated N fertilizer application on maize-soybean intercropping
where plant height of maize increased as the level of fertilizer increased.
The plant height of intercropped (117.60 cm) and sole sorghum (115.70 cm) were not
significantly different which could be attributed to less competition of growth resources
between the intercrops. This result was in agreement with the study by Biruk (2007) who
reported that the main effect of common bean planting densities, interaction effect and the
cropping system had no significant effect on plant height of sorghum intercropped with
common bean. Moreover, Demesew (2002) on maize-haricot bean found non-significant effect
of intercropping of haricot bean on the maize height.
4.2.2.2. Panicle length
As indicated in Appendix Table 6, N resulted in significant variation (P<0.05) in panicle
length of intercropped sorghum, but there was no significant effect due to cowpea density. The
highest panicle length (28.08 cm) was recorded when 61.5 kg N ha-1 was applied which was
statistically at par with the panicle length recorded at 41 kg N ha-1 (Table 3). Similar to plant
height, increasing trend in N fertilizer yielded in corresponding increment of panicle length.
Hence, application of 61.5 kg N ha-1 exceeded to a plot receiving nil fertilizer by 10.38%.
Application of N fertilizer improved panicle length as compared to nil fertilizer. This might be
most probably due to the attributes of N fertilizers to increase the vegetative growth of crops.
35
Similary, Eyob (2007) on sorghum-faba bean intercropping study reported that an increasing
rate of N resulted in highly significant difference (P<0.01) of intercropped sorghum panicle
length.
The ANOVA result in Appendix Table 6 also showed that the panicle length of sole and
intercropped sorghum was not significantly different. This was associated with compatible
nature of the intercrops under intercropping condition.
Table 3. Main effects of plant densities of cowpea and N-rates on plant height, panicle length
plant-1, leaf area plant-1 and leaf area index of intercropped sorghum with cowpea
Treatments
Plant height
(cm)
Panicle length
plant-1 (cm)
Leaf area
plant-1(cm2)
Leaf area
index
Cowpea densities
50%
117.44
27.18
3022.60
2.02
75%
117.30
27.00
2853.60
1.90
100%
117.07
26.53
2693.30
1.80
NS
NS
NS
NS
LSD (P0.05)
-1
N-rates (kg ha )
0
113.11 c
25.44 b
2406.20 b
1.60 b
20.5
114.41 bc
26.39 ab
2657.60 b
1.77 b
41
120.19 ab
27.71 a
3122.00 a
2.08 a
61.5
121.38 a
28.08 a
3240.20 a
2.16 a
5.83
1.76
309.29
0.21
LSD (P0.05)
CV (%)
5.08
6.68
11.07
11.09
Cropping system
Sole sorghum
115.70
27.37
3156.70
2.10
Intercropped
117.60
26.91
2856.50
1.91
sorghum
NS
NS
NS
NS
LSD (P0.05)
CV (%)
3.50
4.68
5.33
5.35
Means with the same letter (s) in the same column are not significantly different at P0.05;
NS= Non-significant; LSD= least significant difference; CV= coefficient of variation
4.2.2.3. Leaf area plant-1
Leaf area was highly significantly affected (P<0.01) due to N fertilizer application. The
highest leaf area was obtained due to application of 61.5 kg N ha-1 (Table 3). It was
36
progressively increased as level of N increased. Those results insured the importance of N in
stimulating and enhancing the photosynthetic and metabolic activities of plants which
reflected on the increase in the vegetative growth of sorghum. A report from Wondimu et al.
(2005) suggested that the higher leaf, stem and panicle dry mass of sorghum with N
fertilization could obviously be due to the positive effect of N on canopy development as a
result of alterations in leaf area development. Consistent with the current result, other
intercropping studies on sorghum- cowpea (Adem, 2006) and on sorghum-faba bean (Eyob,
2007) reported that leaf area of intercropped sorghum was significantly affected by N. Ayoola
and Agboola (2002) similarly reported that application of inorganic fertilizers and FYM
increased growth and expansion of leaves in intercropped maize.
In contrast to N effect, cowpea density, interaction effect as well as the cropping system did
not significantly influence (P<0.05) the leaf area. In line with this result, Biruk (2007) found
that leaf area of intercropped sorghum was not significantly influenced by the main effect of
common bean planting densities and interaction effect as well as the cropping system. In
addition, Dechasa (2005) studied that beans planting density had no significant effect on
sorghum leaf area. Moreover, Demesew (2002) on sorghum-haricot bean intercropping
reported that the cropping system did not significantly affect the leaf area of the sorghum.
4.2.2.4. Lea area index
Leaf area index (LAI) was varied significantly (P<0.01) due to the main effect of N; however,
the variation due to cowpea density and interaction effect was non-significant (Appendix
Table 6). The applied levels of N affected sorghum LAI in a pattern similar to their respective
effects on leaf area. Accordingly, the highest numerical value of LAI was obtained due to
application of 61.5 kg N ha-1 (Table 3). This result indicated that application of 61.5 kg N ha-1
revealed an increment of 35% over the nil fertilizer. The increase in LAI with N supply could
be due to the effect of N on the rate of leaf expansion and reduced rate of leaf senescence
(Muchow, 1988), and so far few studies have been focused upon this aspect (Eyob, 2007 ;
Mahmoud et al., 2013). Likewise, Abebe et al. (2013) reported that integrated N fertilizer
application on soybean-maize intercropping significantly affected LAI of maize.
37
As indicated above, cropping system did not affect LAI significantly. This could be most
likely due to absence of aggressive nature of intercropped cowpea on sorghum. This finding
complements to the work of Biruk (2007) who reported that the main effect of common bean
planting densities and interaction effect as well as the cropping system did not significantly
affect LAI of intercropped sorghum under sorghum-common bean intercropping system.
4.2.3. Yield related traits and yield
4.2.3.1. Panicle weight
ANOVA result showed the significant effect of N but the non-significant effect of cowpea
density, interaction and cropping system on panicle weight (Appendix Table 7). The 0 kg N
ha-1 gave significantly the lowest panicle weight as compared to 41 and 61.5 kg N ha-1 (Table
4). This could be due to the role of N in enhancing the seed holding capacity of the panicle.
This result concurred with the finding of Jat et al. (2014) on maize – mung bean intercropping
which indicated that grain weight cob-1 of maize was significantly affected due to N fertilizer.
Similarly, Biruk (2007) reported that panicle weight of intercropped sorghum was
significantly affected by N application but neither the common bean planting density nor
interaction effect and cropping system had significant influence on it under sorghum-common
been intercropping. Furthermore, Adem (2006) on sorghum-cowpea intercropping showed that
neither the component density nor their interaction and cropping system significantly affected
sorghum panicle weight. This non-significance was attributed to reduction of inter specific
competition for growth resources.
4.2.3.2. Thousand kernel weight
The analysis of variance (Appendix Table 7) showed that the main effects, their interaction
and cropping system did not cause any significant variation on 1000 kernel weight of
intercropped sorghum. This could be associated with the compatible use of growth resources
of the intercrops without affecting each other. In conformity with this result, Biruk (2007) on
38
sorghum-common bean intercropping reported that neither of the component crop planting
density nor N as well as the interaction effect influenced 1000 kernel weight of sorghum.
Similarly, Agete (2008) found that cropping system did not cause significant difference
(P<0.05) in 1000 kernel weight of sorghum on sorghum-forage legumes intercropping.
Solomon et al. (2014) made a study on maize-soybean intercropping and found that 1000
kernel weight of maize was not significantly affected due to cropping system. However, Eyob
(2007) found a significant difference among the N-rates and also among the component crop
planting densities on sorghum 1000 kernel weight.
Table 4. Main effects of plant densities of cowpea and N-rates on panicle weight plant-1,
thousand kernel weight, above ground dry biomass and harvest index of intercropped sorghum
with cowpea
Treatments
Panicle weight
plant-1 (g)
Thousand kernel
weight (g)
Dry biomass
(kg ha-1)
Harvest
index (%)
42.46
40.58
42.38
NS
25.81
25.62
25.38
NS
3745.00
3782.89
3764.97
NS
46.94 b
51.68 a
47.43 b
3.84
39.42 b
25.35
3579.50 c
44.02 c
20.5
41
41.52 ab
43.56 a
25.70
25.86
3641.10 bc
3847.70 ab
50.60 ab
53.94 a
61.5
42.73 a
25.51
3988.90 a
46.17 bc
2.73
6.70
NS
5.43
236.39
6.42
4.43
9.31
44.12
41.81
27.00
25.94
4699.50 a
3764.30 b
51.11
48.68
Cowpea densities
50%
75%
100%
LSD (P=0.05)
N-rates (Kg ha-1)
0
LSD (P=0.05)
CV (%)
Cropping system
Sole sorghum
Intercropped sorghum
LSD (P=0.05)
NS
NS
842.46
NS
CV (%)
5.89
5.91
5.67
3.85
Means with the same letter (S) in the same column are not significantly different at P0.05;
NS= non-significant; LSD= least significant difference; CV= coefficient of variation
4.2.3.3. Above ground dry biomass
39
The statistical analysis showed that N showed highly significant (P<0.01) effect and cropping
system had significant effect on above ground dry biomass of intercropped sorghum; whereas,
the main effect of cowpea density and the interaction were not significant (Appendix Table 7).
Application of N fertilizer resulted in progressive increase in dry matter yield of intercropped
sorghum (Table 4). Thus, plots received 61.5 kg N ha-1 out yielded by 11.44% dry biomass
over those which did not get any N fertilizer. Unlike to the other N rates, 41 kg N ha-1 was
statistically at par with 61.5 kg N ha-1 on influencing the dry biomass yield. The vigor of
above ground part of sorghum plants due to high N enable them to harvest ample solar
radiation, which resulted in the corresponding increment of photosynthetic rate. This higher
photosynthetic rate also results in higher accumulation of dry matter. Related to this finding
Jat et al. (2014) found significant effect of N fertilizer on stover yield of maize under maizemungbean intercrops. Likewise, Abebe et al. (2013) reported that the pooled mean for
biological yield of maize varied significantly due to integrated N fertilizer on soybean-maize
intercropping.
Dry biomass yield of sorghum had shown significant effect due to cropping system (Appendix
Table 7). Sole sorghum had significantly higher dry biomass yield (4699.50 kg ha-1) than the
intercropped sorghum. This might be because of free access of sole sorghum to growth
resources with no competition. This result was in line with that of Siddig et al. (2013) who
reported that dry matter weight of sorghum was significantly influenced due to sorghumground nut intercropping.
4.2.3.4. Grain yield
Sorghum grain yield was significantly (P<0.05) affected by cropping system and cowpea
density (Appendix Table 7). Further, N and interaction effect showed highly significant
difference (P<0.01) on this parameter. As displayed in Table 5, the highest grain yield
(2370.40 kg ha-1) was obtained from the combination of 41 kg N ha-1 and 75% sole cowpea
density which was significantly the highest, while the lowest grain yield of 1523.30 kg ha-1was
obtained from the unfertilized plots intercropped with 50% sole cowpea density.
40
Appendix Table 7 also indicated that cropping system had significant (P<0.05) effect on grain
yield. Sole sorghum exceeded by 31.03% of the intercropped sorghum yield (Table 5). The
most probable reason for this variation could be due to interspecific competition for resources
like soil nutrients, sunlight and water in the intercropped sorghum. Similarly, Karanja et al.
(2014) reported that yield reductions involving one or all intercropping components in
intercropping could be associated to interspecific competition for nutrients, moisture and/or
space. Similar result was also reported by Getachew et al. (2013) on maize-forage legumes
intercropping where cropping system affected grain yield. Moreover, Abraha (2013) on maizeforage legumes (lablab and cowpea) intercropping indicted that grain yield of sole maize
yielded the highest (3056 kg ha-1 ) and lower (2305 kg ha-1 ) for maize- cowpea integration.
Table 5. Interaction effects of plant densities of cowpea and N-rates on grain yield (kg ha-1) of
intercropped sorghum with cowpea
Cowpea
densities
50%
75%
100%
Intercrop mean
Sole sorghum
N rates (kg ha-1)
0
1523.30 e
1630.00 de
1569.60 e
20.5
1569.60 e
2070.00 b
1884.60 bcd
41
1879.60 bcd
2370.40 a
1972.10 bc
61.5
2065.40 b
1745.40 cde
1713.30 cde
1832.78 b
2401.40 a
Sole X Intercrop
Density X
Nitrogen
LSD (P=0.05)
291.58
492.67
CV (%)
9.40
6.62
Means with the same letter (s) are not significantly different at P0.05; LSD= least significant
difference; CV= coefficient of variation
4.2.3.5. Harvest index
The analysis of variance (Appendix Table 7) revealed that the interaction effect and cropping
system did not cause significant difference in harvest index (HI) of intercropped sorghum
which agreed to the finding of Abebe et al. (2013) on soybean-maize intercropping system
where harvest index of maize was no significantly affected by cropping system. On the other
41
hand, significant difference (P<0.05) was observed due to the main effects. As presented in
Table 4, the most outstanding HI (51.68%) of intercropped sorghum was obtained from 75%
of sole cowpea density and was statistically superior to the others. The lowest HI (46.94%)
had been seen from plots received 50% sole cowpea density (Table 4). Comparable reports by
Eyob (2007) on sorghum-faba bean detected that HI of intercropped sorghum (34%) was
influenced due to planting density and this also agrees with finding of Karanja et al (2014) on
sorghum-cowpea intercropping. Table 4 showed that the highest and lowest HI had been due
to 41 kg N ha-1 and nil N, respectively. It is true that N enhances vegetative growth of plants.
The proportional conversion of the biomass to grain yield amplified the HI of plants. Thus,
high HI indicates the presence of good partitioning of dry matter to grain yield.
4.3. Cowpea Component
4.3.1. Crop phenology
4.3.1.1. Days to 50% flowering
The ANOVA result (Appendix Table 8) showed that neither of the main effects nor their
interaction and the cropping system had significant effect on days to 50% flowering. The
reason for this non-significant difference could be due to low resource competition among the
component crops that could affect the days to flowering. This result was in agreement with the
work of Agete (2008) on forage legumes intercropped with sorghum where days to 50%
flowering of legumes were not significantly affected by main effects of intecropping patterns
and forage legumes, and their interaction. Similar study by Ibrahim et al. (2014) showed that
days to 50% flowering of cowpea did not vary due to sorghum-cowpea intercropping.
Likewise, the result was in conformity with the findings of Tilahun et al. (2012) and Eyob
(2007). On the other hand, Biruk (2007) reported days to 50% flowering of common bean was
significantly (P<0.05) affected by main effect of common bean planting density, common
bean varieties and the cropping system under sorghum-common bean intercropping where sole
cropped bean flowered earlier than the intercrop and also when it intercropped at its lowest
density.
42
4.3.1.2. Days to 50% pod setting
Similar to days to 50% flowering, the analysis of variance (Appendix Table 8) revealed nonsignificant difference due to the main effects, interaction effect and cropping system on days
to 50% pod setting of cowpea. This might be ascribed to absence of shading and competition
for available resources of intercropped sorghum on cowpea. This current result agreed with the
work of Zerihun (2011) who reported no significant difference in days to pod initiation of
soybean due to integrated N fertilizer application on maize-soybean intercropping. Similarly,
Ajeigbe et al. (2005) reported that pod setting did not significantly vary due to cowpea-cereals
intercropping.
Table 6. Main effects of plant densities of cowpea and N-rates on phenology of sole and
intercropped cowpea with sorghum
Treatments
Days to 50%
flowering
Days to 50%
pod setting
Days to 90%
physiological
maturity
Cowpea densities
50%
58.82
65.17
90.58
75%
58.66
66.33
90.08
100%
58.74
66.42
89.67
LSD (P=0.05)
NS
NS
NS
N-rates (kg ha-1)
0
58.69
65.67
89.89
20.5
58.58
65.78
90.00
41
58.79
65.89
90.22
61.5
58.90
66.55
90.33
LSD (P=0.05)
NS
NS
NS
CV (%)
1.37
2.28
1.26
Cropping system
Sole cowpea
58.01
65.33
88.95
Intercropped cowpea
58.74
65.97
90.11
LSD (P=0.05)
NS
NS
NS
CV (%)
3.18
3.68
0.93
NS= non-significant; LSD= least significant difference; CV= Coefficient of variation
43
4.3.1.3. Days to 90% physiological maturity
Cowpea planting density, N, their interaction and cropping system did not significantly affect
days to 90% physiological maturity of intercropped cowpea (Appendix Table 8). The crop
reached its physiological maturity with in mean days of 88.95 – 90.58. In agreement with this
result, Eyob (2007) reported that main effect of component planting density, N and their
interaction did not show significant effect on 90% physiological maturity of faba bean in
sorghum-faba bean intercropping.
4.3.2. Growth parameters
4.3.2.1. Leaf area plant-1
The ANOVA result (Appendix Table 9) showed that leaf area of intercropped cowpea was
significantly affected (P<0.05) by N but not due to cowpea density, cropping system and
interaction effect. The highest leaf area (2053.40 cm2) was recorded when 61.5 kg N ha-1 was
applied. It was clearly seen that leaf area increased as the N rate increased progressively.
Increase in leaf area with increment in N rates could be ascribed to superior cell expansion,
more rapid cell division and parallel augmented photosynthate construction. In agreement with
this result, Adem (2006) reported that N caused significant difference on leaf area of cowpea
on sorghum – cowpea intercropping where the highest leaf area was obtained due to the
highest N rate. Similar to this finding Biruk (2007) also reported that leaf area of common
bean on sorghum-common bean intercropping had been differently affected by neither of the
common bean planting density nor cropping system and the interaction effect.
4.3.2.2. Leaf area index
The analysis of variance revealed that cowpea density and N rates showed significant effect on
LAI but non-significant effect due to interaction and cropping system (Appendix Table 9).
Increasing cowpea density caused a corresponding increment of LAI (Table 7). Accordingly,
the highest LAI (1.90) was recorded due to 100% of sole cowpea density and it was
44
significantly higher than 50 and 75% of sole cowpea densities. The smallest LAI (0.96) had
been recorded on plots getting 50% of the recommended cowpea density and was significantly
inferior to the other cowpea densities. Treatments with high densities resulted in higher LAI
because of lower ground area occupied by a plant which ultimately increased the LAI. This
current result was in line with the finding of Adem (2006) on sorghum-cowpea intercropping
where the highest LAI was recorded due to the highest component density.
Nitrogen affected LAI of intercropped cowpea in the same pattern as to leaf area. The largest
LAI (1.58) was recorded due to 61.5 kg N ha-1 and produced 23.44% LAI over the unfertilized
plots (Table 7). The result indicated that N fertilizer application could enhance nutrient
availability which probably increase the photosynthetic efficiency and consequently increased
the vegetative growth and development. The current result agrees with the finding of Abebe et
al. (2013) on soybean-maize intercropping where the highest LAI was found to be at
application of the highest integrated N fertilizer.
Similar to this current result, Biruk (2007) reported that cropping system did not significantly
affect LAI of common bean on sorghum-common bean intercropping. Furthermore, Ibrahim et
al. (2014) found that LAI of cowpea was not significantly affected due to sorghum-cowpea
cropping system.
4.3.2.3. Plant height
Neither of the main effects (cowpea density and nitrogen) nor their interactions and cropping
system had significant influence on plant height of intercropped cowpea (Appendix Table 9).
This might be attributed to less shading effect of sorghum and competition on the intercropped
cowpea. This result was in line with that of Biruk (2007) who reported that planting density of
common bean did not significantly affect plant height of common bean intercropped with
sorghum. Similarly, Demesew (2002) reported non-significant effect of N-rate on plant height
of haricot bean intercropped with maize. This result also confirmed the finding of Ibrahim et
al. (2014) who reported that plant height of cowpea did not show significant response due to
45
sorghum-cowpea intercropping. Likewise, Abraha (2013) reported non-significant difference
between the height of lablab-maize and sole lablab.
4.3.2.4. Nodule number plant-1 (To be in growth parameter)
The analysis of variance showed that cowpea planting density as well as the interaction effect
did not significantly affect nodule number of intercropped cowpea (Appendix Table 10). This
result agreed with the study of Eyob (2007) who found that number of nodules of intercropped
faba bean on sorghum-faba bean intercropping was not significantly influenced due to
component crop planting density and interaction of main effects of planting densities and N
rates.
Regardless of cowpea density and interaction effect, N and cropping system significantly
influenced the nodule number plant-1 of cowpea (Appendix Table 10). The maximum nodule
number plant-1 of intercropped cowpea (13.80) was recorded from plots received nil N and
was significantly higher than any of the N levels while the lowest (9.87) was recorded from
61.5 kg N ha-1 (Table 8). Increasing rate of N reduced number of nodules plant-1. The reason is
justified by many researchers. According to the report of Herridge (1982) and Noel et al.
(1982), higher N in the soil depresses nodulation and N fixation through inhibition of thread,
slowing of nodule growth, inhibition of fixation with the established nodules, and more rapid
senescence of nodules when either NO3- or NH4+ is added. Similar report by Cenpukdee and
Fukai (1991) further indicated that additional N directly antagonizes Rhizobium N2 fixation in
the legume. Moreover, Lucius and Vanslayke (2010) pointed out that the bacteria use available
form of N compounds, when reach in the soil, and therefore, when supplied with available
nitrogenous compounds, they fail to use atmospheric N.
The sole cowpea produced significantly higher nodule number compared to the intercropped
cowpea (Table 8). The possible reason for the lower nodule number in intercropped cowpea
could be due to the shading effect of sorghum that hinders N-fixation. Similar result on maizesoybean intercropping could possibly be due to the shading effects of maize that significantly
reduced light interception potential of the associated soybean and reduced the photosynthetic
46
assimilate (Abebe et al., 2013). Reduced assimilate might be resulted in limited food supply
for associated Rhizobium bacteria, and consequently their atmospheric fixation capacity were
diminished (Tisdale et al., 1995). The result was in harmony with report of Eyob (2007) on
sorghum-faba bean intercropping indicated that nodule number of faba bean was significantly
affected due to application of N and cropping system (where sole faba bean registered more
number of nodules than intercropped faba bean). Likewise, Tamado and Eshetu (2000)
reported that intercropping significantly affected haricot bean number of nodules plant-1 where
the lowest number was recorded in intercropping.
Table 7. Main effects of plant densities of cowpea and N-rates on growth parameters of
cowpea planted in sole and intercropped with sorghum
Treatments
Leaf area
plant-1(cm2)
Leaf area
Index
Plant
height
(cm)
Number
of nodules
plant-1
Number of
branches
plant-1
Cowpea densities
50%
1797.80
0.96 c
51.99
11.49
3.14
75%
1811.80
1.42 b
51.70
11.70
3.02
100%
1848.00
1.90 a
54.23
12.25
2.76
LSD (P=0.05)
NS
0.19
NS
NS
NS
N-rates (kg ha-1)
0
1653.80 b
1.28 c
52.23
13.86 a
3.13
20.5
1674.90 b
1.35 b
52.37
12.46 b
2.99
41
1894.70 ab
1.50 ab
52.66
11.05 c
2.88
61.5
2053.40 a
1.58 a
53.29
9.87 c
2.87
LSD (P=0.05)
306.36
0.21
NS
1.24
NS
CV (%)
17.22
15.36
6.08
10.75
15.26
Cropping system
Sole cowpea
2225.50
1.66
52.87
16.37 a
4.00
Intercropped
1819.10
1.43
52.64
11.81 b
2.97
cowpea
LSD (P=0.05)
NS
NS
NS
3.93
NS
CV (%)
7.15
5.31
3.74
7.94
20.10
Means with the same letter (s) in the same column are not significantly different at P0.05;
NS= Non-significant; LSD= least significant difference; CV= Coefficient of variation
4.3.2.5. Number of branches plant-1 (To be in growth parameter)
47
None of the main effects, their interaction effect and cropping system caused any effect on the
number of branches plant-1 of intercropped cowpea (Appendix Table 10). This could be most
likely due to existence of less competition for available resources. In line with this result,
Agete (2008) revealed non-significant effect of intercropping on branch number plant-1 of
legume forage in sorghum- legume forages intercrop. In contrast, Zerihun (2011) on maizesoybean intercropping found that number of primary branches plant-1 showed significant
variation due to the effect of soybean varieties and integrated N fertilizer application.
4.3.3. Yield related traits and yield
4.3.3.1. Number of pods plant-1
Neither of the main effects nor their interaction and cropping system did have a significant
effect on this parameter. According to Table 8, the number of pods plant-1 ranged from 12 to
14.33. In agreement to this result, Solomon et al. (2014) reported that cropping system did not
have significant effect on number of pods plant-1 on maize-soybean intercropping system.
Despite to the current result, Karanja et al. (2014) noticed that number of pods plant-1 varied
significantly due to cowpea plant density and cropping system on sorghum-cowpea
intercropping system where the highest value was obtained at sole cowpea.
4.3.3.2. Number of seeds pod-1
The ANOVA result in Appendix Table 10 showed that number of seeds pod-1 of intercropped
cowpea was affected by neither of cowpea density nor the interaction of the main effects. This
was possibly due to the reason that population differences in cowpea did not aggravate for
competition of available growth resources. In agreement with the present result, Minale et al.
(2001) reported that a non-significant effect on the number of seeds pod-1 was observed in the
maize-faba bean intercropping. Besides, Dechasa (2005) on sorghum-bean intercropping
found that the main effect of planting density showed non-significant difference on number of
seeds pod-1. Similar finding was reported by Solomon et al. (2014) on maize-soybean
48
intercropping system where the number of seeds pod-1 was not significantly affected due to
soybean planting densities.
Nitrogen and cropping system had significant effect on the number of seeds (Appendix Table
10). Application of N fertilizer enhanced seeds holding capacity of a pod. Application of no
fertilizer produced lower number of seeds pod-1 than the others. Application of N has positive
effect on number of seeds pod-1. However, Eyob (2007) on sorghum-faba bean and Zerihun
(2011) on maize-soybean intercropping found that fertilizer application did not cause
significant difference on number of seeds pod-1 of faba bean and soybean, respectively.
Number of seeds pod-1 of cowpea was significantly reduced when intercropped with sorghum.
Sole cropped cowpea produced more number of seeds pod-1 than the intercropped cowpea.
This might be because of absence of more competition for resources (light, moisture,
nutrients) in the sole cropped cowpea. Ajeigbe et al. (2005) also reported similar result where
more seeds were obtained from sole cowpea. However, Biruk (2007) on sorghum-common
bean intercropping found that number of seeds pod-1 of common bean did not vary
significantly in terms of cropping system.
4.3.3.3. Hundred seed weight
Hundred seed weight of intercropped cowpea was significantly affected by cowpea density
and N but not by interaction effect and cropping system (Appendix Table 10). The highest
seed weight (20.82 g) was recorded on 50% cowpea planting density and was statistically at
par with 75% of sole cowpea density. This result showed that hundred seed weight of
intercropped cowpea decreased as population density increased. This could be due to high
intra and inter row competition of growth resources in the densely populated plants. In line
with this result, Adem (2006) reported that 100 seed weight of cowpea varied due to main
effect of component density on sorghum-cowpea intercropping where the highest seed weight
was obtained from the lowest plant density.
Increasing in N fertilizer rate led to an increment of seed weight. Hence, application of 61.5 kg
N ha-1 gave the maximum seed weight (21.11 g) and was statistically at par with others with
49
the exception of plots received nil fertilizer. In line with this result, Minale et al. (2001)
reported that 100 seed weight of faba bean on maize-faba bean intercropping was significantly
affected by fertilizer (nitrogen and P2O5) application where 1000 seed weight was increased
with increasing fertilizer rates. Similarly, Saleem et al. (2015) on maize-mungbean
intercropping reported that 1000 grain weight of mungbean was significantly affected by
fertilizer amendments.
Table 8. Main effects of plant densities of cowpea and N-rates on nodule number plant-1,
number of branches plant-1, number of pods plant-1, number of seeds pod-1, hundred seed
weight and harvest index of sole and intercropped cowpea with sorghum
Treatments
Cowpea densities
50%
75%
100%
LSD (P=0.05)
N-rates (kg ha-1)
0
20.5
41
61.5
LSD (P=0.05)
CV (%)
Cropping system
Sole cowpea
Intercropped cowpea
No of pods
plant-1
No of seeds
pod-1
100 seed weight
(g)
Harvest
index (%)
12.86
13.21
12.91
NS
9.12
9.06
8.54
NS
20.82 a
19.78 ab
18.94 b
1.34
27.25
29.01
28.80
NS
12.69
14.33
12.94
12.00
NS
17.17
7.53 b
8.77 ab
9.94 a
9.39 a
1.24
14.27
18.45 c
19.58 ab
20.25 ab
21.11 a
1.55
8.00
26.06
30.25
28.54
28.57
NS
14.42
13.40
13.98
12.21 a
8.91 b
20.79
19.85
41.32 a
28.36 b
LSD (P=0.05)
NS
2.31
NS
7.08
CV (%)
7.18
6.23
6.63
5.78
Means with the same letter (s) in the same column are not significantly different at P0.05;
NS= non-significant; LSD= least significant difference; CV= Coefficient of variation
4.3.3.4. Harvest index
In the present study, significant difference was not observed due to main effects and
interaction effect on HI of cowpea but the effect due to cropping system was significant
(Appendix Table 10). With regard to this, higher HI (41.32%) was recorded on sole cropped
50
than the intercropped cowpea (Table 8). The reason for high percentage of HI in sole cowpea
could be due to partitioning of more dry matter to seed yield. Consistent to the obtained result,
Karikari et al. (1999) reported that the effect of intercropping with maize or sorghum on HI of
Bambara groundnut was significant. Correspondingly, Saleem et al. (2015) on maizemungbean intercropping reported that HI of mungbean significantly varied due to cropping
system where sole mungbean had higher HI than the intercrop. Generally, improved HI
represents increased physiological capacity to mobilize photosynthates and translocate them
into organs having economic yield. This is, therefore, a fact that the economic yield of a
cropping system is determined by the harvest index (HI) (ratio of grain yield to above ground
biomass). The higher the HI is, the higher the dry matter conversion efficiency to the grain
(Karanja et al., 2014).
4.3.3.5. Grain yield
The main effects and their interaction revealed highly significant (P<0.01) effect on grain
yield (Appendix Table 10). The highest grain yield (821.33 kg ha-1) was obtained when 20.5
kg N ha-1 added to 100% of sole cowpea density. It was significantly higher than the other
combinations. On the other hand, the lowest grain yield had been produced when nil N
fertilizer was applied to 50% of sole cowpea density (Table 9). In conformity with this result,
Solomon et al. (2014) on maize-soybean intercropping reported that soybean planting density
significantly affected grain yield of intercropped soybean. This result was also consistent with
other related research on millet-cowpea intercropping where the highest cowpea yield was
produced from high cowpea density (Omae et al. 2014).
The statistical analysis in Appendix Table 10 also indicated that cowpea grain yield ha-1 had
significant difference due to cropping system. Sole cowpea had higher grain yield (1315.67 kg
ha-1) than the intercropped (548.11 kg ha-1). This implied that the intercropped cowpea yield
was reduced by 58.34% of sole cropped (Table 9). This might be because of competition for
light and other environmental growth resources. A report by Fisher et al. (1986) indicated that
competition for light had an effect on bean yield in maize-bean intercropping. Similarly, Egbe
(2010) reported that shading by the taller plants in mixture could reduce the photosynthetic
51
rate of the lower growing plants and thereby reduce their yields. In agreement with the current
result, Solomon et al. (2014) reported that sole cropped soybean produced higher yields than
when intercropped with maize. Similar result was also reported by Abebe et al. (2013) who
found that the effect of cropping systems considerably influenced grain yield of intercropped
soybean varieties when compared with their respective sole crops.
Table 9. Interaction effects of plant densities of cowpea and N-rates on grain yield (kg ha-1) of
intercropped cowpea with sorghum
Cowpea densities
50%
75%
100%
Intercrop mean
Sole cowpea
0
448.67 d
486.33 cd
505.33 bcd
20.5
452.33 d
512.67 bcd
821.33 a
N-rates (kg ha-1)
41
61.5
580.33 bc
510.33 bcd
606.33 b
552.00 bcd
573.33 bc
528.33 bcd
548.11 b
1315.67 a
Density X Nitrogen
Sole X Intercrop
LSD (P=0.05)
119.22
287.45
CV (%)
12.84
8.78
Means with the same letter (s) are not significantly different at P<0.05; = least significant
difference; CV= Coefficient of variation
4.3.3.6. Above ground dry biomass
The ANOVA result (Appendix Table 10) showed highly significant (P<0.01) difference due to
cowpea planting densities and cropping systems. Moreover, interaction of main effects
significantly (P<0.05) influenced dry biomass of intercropped cowpea. In contrast, N did not
have significant (P<0.05) effect.
The interaction of 20.5 kg N ha-1 and 100% sole cowpea planting density produced the highest
dry biomass (2375 kg ha-1) and it was statistically at par to combinations of 41 kg N ha-1 with
50% and 100% sole cowpea planting densities (Table 10). The lowest dry biomass had been
recorded when no fertilizer was applied to 50 and 75% of sole cowpea densities as well as
when 20.5 kg N ha-1 was applied to 50% of sole cowpea density (Table 10). In line to the
current result, Adem (2006) reported that dry biomass of cowpea was significantly affected by
52
interaction of main effects on sorghum-cowpea intercropping. Furthermore, Omae et al.
(2014) on millet-cowpea intercropping reported that biomass of cowpea was influenced due to
cowpea density where the highest biomass yield was gained from high cowpea density.
The dry biomass of sole crop was significantly higher than the intercropped cowpea. Sole
cropped cowpea out yielded the intercropped cowpea by 56.83%. The increment in dry
biomass production of sole cropped cowpea might be attributed to absence of competition and
thus, more dry matter accumulation in stem, branches and leaves matter as a result of its good
vegetative cover to harvest ample solar radiation important for its photosynthesis. This result
was in conformity with the findings of Biruk (2007) and Karanja et al., (2014) who reported
that sole cropped gave higher dry biomass yield than the intercropped. Likewise, Getachew et
al. (2013) reported that dry biomass of forage legumes was significantly affected due to
cropping system when intercropped with maize.
Table 10. Interaction effects of plant densities of cowpea and N-rates on above ground dry
biomass (kg ha-1) of intercropped cowpea with sorghum
Cowpea
densities
50%
75%
100%
Intercrop mean
Sole cowpea
N-rates (kg ha-1)
0
1758.30 d
1750.00 d
1977.90 bcd
20.5
1729.10 d
1856.00 cd
2375.00 a
41
2108.30 abc
1891.50 cd
2255.00 ab
61.5
1795.80 cd
2046.00 bcd
1783.30 cd
1943.80 b
3048.50 a
Density X Nitrogen
Sole X Intercrop
LSD (P=0.05)
343.05
590.74
CV (%)
10.42
6.12
Means with the same letter (s) are not significantly different at P<0.05; LSD= least significant
difference; CV= Coefficient of variation
4.4. Striga Population
The current study is part of the ISC (Integrated Striga Control) project. Sorghum-legumes
intercropping have been used as method of Striga management. Thus, observation on Striga
population was made during the study. Accordingly, Striga infestation (emergence) was not
53
observed in the experimental area during the implementation period of the experiment though
the experimental area was known as Striga block as it was infested with Striga seeds for the
purpose of developing Striga resistant varieties. The absence of Striga emergence could be
most likely due to the Striga resistance potential of sorghum variety (Gobiye), the soil
moisture conservation practice (tied ridge) and N fertilizer used in the experiment. However,
other non-resistant varieties planted nearby to the experimental area were infested with Striga,
which confirms that the area had Striga seed banks. This result was consistent with the report
of Gebisa and Gressel (2007) which indicated that Striga resistant sorghum cultivars supported
low number of emerged Striga plants and when complemented with ISM packages (water
conservation and N fertilizers) were nearly free of Striga. According to the report, use of only
resistant varieties is immediately challenged by the Striga seed bank. Another study by Adem
(2006) showed that Striga was not emerged due to the effect of tied ridging, resistant sorghum
variety (Birhan) and N fertilizer in sorghum-cowpea intercropping system. Similar findings
were also reported by Fasil et al. (2005) on sorghum-cowpea intercropping where lower Striga
count were recorded at sorghum-legumes intercropped than sole sorghum. Thus, use of
integrated approaches, i.e. the use of Striga resistant sorghum variety, N fertilizer, moisture
conservation practice (tied ridge) and intercropping system as a whole were very effective
against Striga weed.
4.5. Total Land Productivity and Gross Return Evaluation
The productivity of this experiment was evaluated by land equivalent ratio (LER) and gross
monetary value (GMV) as indices. Thus, the analyzed data in Appendix Table 11 depicted that
land equivalent ratio (LER) was highly significantly affected (P<0.01) by N, interaction of
main effects and cropping system. Moreover, cowpea density had significant (P<0.05) effect
on LER.
The highest LER (1.46) had been achieved when 41 kg N ha-1 was applied to 75% sole
cowpea density. Statistically, it was at par with the interaction of 20.5 kg N ha-1 and 100% sole
cowpea density. Nevertheless, the lowest LER (0.98) was recorded on plots having 50% of
sole cowpea density with no N fertilizer (Table 11). Concerning to the result in Table 11, in all
54
interactions the LER was more than unity except in the treatment that received 50% of the
recommended cowpea with no N fertilizer application validating that limited soil fertility
significantly reduces the productivity of intercropping system. LER more than unity implied
that intercropping of sorghum and cowpea is advantageous in many instances rather than sole
planting.
Tauro et al. (2013) showed that when the LER > 1, intercropping is advantageous because
environmental resources are used more efficiently for plants growth and LER < 1, there is
disadvantage as environmental resources utilized less efficiently. Moreover, yield advantages
have been recorded in many legume-cereal intercropping systems, including soybean-sorghum
(Hayder et al., 2003), and cowpea-maize (Eskandari and Ghanbari, 2010). The reason of yield
advantage of intercropping are mainly that environmental resources such as water, light and
nutrients can be utilized more efficiently in intercropping than in the respective sole cropping
systems (Liu et al., 2006). Intercropping also gives higher yield advantage when total
population in the system is higher than that of sole crops (Willey, 1979b).
The data for LER (1.19) in Table 11 showed that intercropping gave 19% advantage in
efficiently utilizing land than planting the crops sole. The sole cropping of either sorghum or
cowpea would require 0.19 more unit of land to get the same yield obtained from the
intercropping system. This result agreed with the report of Chemeda (2003) who found up to
28% higher total productivity increase of maize-bean intercropping compared with pure stand.
Getachew et al. (2013) also reported that intercropping gave a 45%, 29%, and 21% yield
advantages than planting sole crops on maize-forage legumes intercropping system.
55
Table 11. Interaction effects of plant densities of cowpea and N-rates on total land equivalent
ratio (LER) of the intercropped sorghum and cowpea
Cowpea
densities
50%
75%
100%
Intercrop mean
0
0.98 e
1.05 de
1.04 de
N rates (kg ha-1)
20.5
41
61.5
1.00d e
1.23 c
1.25 c
1.26b c
1.46 a
1.16 cd
1.42 ab
1.26 bc
1.12 cde
1.19
Density X Nitrogen
LSD(P=0.05)
0.17
CV (%)
8.35
Means with the same letter (s) are not significantly different, NS= non-significant; LSD= least
significant difference; CV= Coefficient of variation
The analysis for GMV showed highly significant (P<0.01) difference due to the influence of
N, interaction effect and cropping system. GMV was also significantly (P<0.05) affected by
cowpea density (Appendix Table 11). Values of GMV appeared in the same pattern as to LER.
Thus, the highest GMV (14001.70 ETB ha-1) was due to combination of 41 kg N ha-1 and 75%
of sole cowpea density which was statistically equivalent to the combination of 20.5 kg N ha-1
and 100% sole cowpea density. On the other hand, the least GMV (9322.70 ETB ha-1) was
obtained from plots planted with 50% of sole cowpea density with nil N fertilizer (Table 12).
Generally, based on this result of economic analysis, intercropping of cowpea with sorghum
was more advantageous than sole crop. In agreement to this, Yesuf (2003) reported that sole
crops (sorghum and haricot bean) gave less GMV than the intercropped. Moreover, Solomon
et al. (2014) reported that the GMV of intercrops was higher than sole maize on maizesoybean intercropping.
56
Table 12. Interaction effects of plant densities of cowpea and N-rates on gross monetary value
(ETB ha-1) of sorghum-cowpea intercrops
Cowpea
densities
50%
75%
100%
Intercrop mean
0
9322.70 e
10009.80 ed
9842.50 ed
N rates (kg ha-1)
20.5
41
9551.10 ed
11650.00 bc
12134.70 bc 14001.70 a
12998.00 ab 12027.70 bc
61.5
12101.20 bc
10890.40 cd
10615.80 cde
11262.13
Density X Nitrogen
LSD (P=0.05)
1553.60
CV (%)
8.15
Means with the same letter (s) are not significantly different, NS= non-significant; ETB=
Ethiopian birr; LSD= least significant difference; CV= Coefficient of variation
Regarding to monetary advantage (MA), it varied significantly in terms of main effects and
their interaction. Similar to LER, the maximum and minimum MA was recorded in the same
trend. The maximum MA (4382.80 ETB ha-1) was obtained due to combination of 41 kg N ha1
and 75% sole cowpea density. The minimum MA (-223.60 ETB ha-1) had been seen in plots
received 50% cowpea with nil N fertilizer (Table 13). This indicated that no MA of
intercropping incurred, here. The negative value indicates a loss of Birr 223.60 ha-1 from
unfertilized intercrops. Solomon et al. (2014) found that the highest MA was obtained at high
planting density of soybean on maize – soybean intercropping system. Similarly, the current
result depicted that statistically at par MA was obtained at high planting densities (Table 14).
Related work by Abebe et al. (2013) on soybean-maize intercropping also reported that
integrated fertilizer application with various proportions of NP with FYM significantly
increased MA over the unfertilized intercrops.
57
Table 13. Influence of interaction effects of plant densities of cowpea and N-rates on monetary
advantage (ETB ha-1) of sorghum and cowpea intercrops
N rates (kg ha-1)
0
20.5
41
61.5
-223.60 e
-19.40 de
2152.00 c
2452.00 bc
476.80 de
2508.80 bc
4382.80 a
1464.40 cd
399.00 de
3832.70 ab
2475.00 bc
1151.60 cde
Density X Nitrogen
LSD(P=0.05)
1549.30
CV (%)
52.15
Means with the same letter (s) are not significantly different, NS= non-significant, LSD= least
significant difference; CV= Coefficient of variation
Cowpea
densities
50%
75%
100%
Generally, the LER and GMV analysis showed that sorghum-cowpea intercropping was highly
superior to and more advantageous over sole cropping. Intercropping is more reliable for
sustainable and environmentally safe cereal crop production as to N fixation nature of cowpea
and its compatible nature. It also provides high insurance against crop failure, especially in
areas subject to extreme weather conditions such as frost, drought, flood, and overall provides
greater financial stability for farmers, making the system particularly suitable for laborintensive small farms (Lithourgidis et al., 2011). Furthermore, intercropping led to greater
land utilization and increased the net return over sole crops (Khola and Singh, 1996).
Likewise, Tauro et al. (2013) reported that exporting crop residues in all cropping systems led
to nutrients mining while incorporating cowpea residues gave positive N balance. The authors
also demonstrated that intercropping produced a sustainable cropping system through BNF
and sparing of P with in the systems.
58
5. SUMMARY AND CONCLUSIONS
The production and productivity of sorghum in the study area is limited mainly due to
deterioration of soil fertility and continuous cropping of sorghum. Hence, this experiment was
conducted in Kobo district in 2014/15 cropping season on sorghum-cowpea intercropping to
assess the effect of plant densities of the intercropped cowpea and N fertilizer rates on yield
and yield related traits of sorghum and cowpea as well as to determine the appropriate plant
density of cowpea and nitrogen fertilizer rate that maximize the productivity of the intercrop
system. Treatments comprised factorial combinations of three cowpea densities (50, 75 and
100% of sole cowpea) and four levels of N rates (0, 20.5, 41 and 61.5 kg N ha-1) accompanied
with sole sorghum and sole cowpea laid out in randomized complete block design (RCBD)
with three replications.
The main effect of N significantly influenced plant height, panicle length, above ground dry
biomass yield, leaf area and leaf area index of sorghum. Increasing of N rate showed an
increment of above ground dry biomass yield, but an increment of the other parameters up to
41 kg N ha-1. Phenological data of sorghum were not significantly influenced (P<0.05) due to
main effects, their interactions and cropping system. Grain yield of sorghum was affected due
to main effects, their interaction and cropping system. The highest grain yield (2370.40 kg ha1
) was obtained from the combination of 41 kg N ha-1 and 75% sole cowpea density. The
highest dry biomass (3988.90 kg ha-1) of intercropped sorghum was produced from the highest
N-rate. Sole sorghum had significantly higher dry biomass yield (4699.5 kg ha-1) than the
intercropped sorghum (3764.3 kg ha-1).
With regard to cowpea component, the main effects, their interaction and cropping system did
not cause significant influence on the phenological data of cowpea. Leaf area and LAI showed
statistically significant response to N where an increased trend was observed with increase in
N rates. LAI was also significantly affected due to cowpea density. Application of N and
cropping system showed significant effects on nodule number plant-1 and number of seeds
pod-1. Increasing rate of N reduced number of nodules plant-1 while increased the number of
seeds pod-1. Concerning to 100 seed weight, it was significantly differed due to cowpea
59
density and N effect where highest seed weight was recorded at the lowest cowpea planting
density. Like that of number of seeds pod-1, an increment of N level increased 100 seed weight
of cowpea. The main effects as well as their interaction and cropping system significantly
affected the grain yield. As a result, the highest grain yield (821.33 kg ha-1) was obtained when
20.5 kg N ha-1 combined with 100% of sole cowpea density. In this study, the intercropped
cowpea yield was drastically reduced by 58.34% as compared to sole cropped cowpea. As
explained in grain yield, the main effects as well as their interaction and cropping system
significantly affected the dry biomass yield of intercropped cowpea. Thus, the highest dry
biomass yield (2375.00 kg ha-1) was obtained when 20.5 kg N ha-1 combined with 100% of
sole cowpea density.
The LER and GMV analysis showed that sorghum-cowpea intercropping was highly superior
to and more advantageous over sole cropping. The highest LER (1.46) and GMV (14001.70
ETB ha-1) were achieved at 41 kg N ha-1 + 75% sole cowpea density while the lowest LER
(0.98) and GMV (9322.70 ETB ha-1) were recorded on plots having 50% of sole cowpea
density without N fertilizer.
In general, the system is more reliable for sustainable and environmentally safe cereal crop
production due to the N fixation nature of cowpea and its compatible nature. It is also
important to suppress Striga weed infestation. There was no Striga emergence more probably
due to use of integrated approaches, i.e. the use of Striga resistant sorghum variety, N
fertilizer, moisture conservation practice (tied ridge) under intercropping system. This is,
therefore, sorghum-cowpea intercropping was proved to be more productive and efficient
system in utilizing land compared to sole cropping with carefully managed N fertilizer and
cowpea plant density. Hence, combination of 41 kg N ha-1 and 75% sole cowpea density can
be tentatively recommended for the study area.
Finally, further research experiments on sorghum-legumes intercropping under different
planting proportions, planting patterns and dates integrated with application of organic and
inorganic fertilizers should be given exhaustive consideration to identify best technology for
sustainable sorghum production.
60
6. REFERENCES
AATF [African Agricultural Technology Foundation]. 2011. Feasibility Study on Striga
Control in Sorghum. Nirobi, African Agricultural Technology Foundation. ISBN 9966775-12-9.
Abebe Zerihun, Sharma, J.J., Dechasa Nigussie and Fred, K. 2013. The effect of integrated
organic and inorganic fertilizer rates on performances of soybean and maize
component crops of a soybean/maize mixture at Bako, Western Ethiopia. African
Journal of Agricultural Research, 8(29), 3921-3929.
Abraha Lemlem. 2013. The effect of intercropping maize with cowpea and lablab on crop
yield. Herald Journal of Agriculture and Food Science Research, 2 (5):156 – 170.
Adem Mohammed, 2006. Effects of component density of Striga resistant sorghum and
cowpea, and N rates on productivity of intercropping system in North Wollo, Ethiopia.
MSc Thesis, Haramaya University, Haramaya, Ethiopia.
Adigbo, S.O., Iyasere, E., Fabunmi, T.O., Olowe, V.I.O., and Adejuyigbe, C.O. 2013. Effect
of spatial arrangement on the performance of cowpea/maize intercrop in derived
Savannah of Nigeria. American Journal of Experimental Agriculture, 3(4):12-16.
Adugna, A., 2007. The role of introduced sorghum and millets in Ethiopian Agriculture. SAT
EJournal, vol. 3. ICRISAT, India.
Adu-Gyamfi, J.J., Myaka, F.A., Sakala,W.D., Odgaard, R., Vesterager, J.M. 2007. Biological
nitrogen fixation and nitrogen and phosphorus budgets in farmer-managed intercrops
of maize-pigeonpea in semiarid southern and eastern Africa. Plant Soil, 295:127-136.
In: Lithourgidis, A.S., Dordas ,C.A., Damalas, C.A. and Vlachostergios, D.N. Annual
intercrops: an alternative path way for sustainable agriculture. Review article,
Australian Journal of Crop science, 5(4):396-410.
Agbogidi, O. M. 2010a. Screening six cultivars of cowpea (Vigna unguiculata (L.) Walp) for
adaptation to soil contaminated with spent engine oil. Journal of Environmental
Chemistry and Ecoloxicology, 7: 103 – 109.
Agbogidi, O.M. 2010b. Response of six cultivars of cowpea (Vigna unguiculata (L.) Walp.) to
spent engine oil. African Journal of Food Science and Technology, 1(6):139-142.
61
Agete Gerena. 2008. Intercropping of Forage Legumes with Sorghum in the Central Rift
Valley, Ethiopia. MSc Thesis, Haramaya University, Haramaya, Ethiopia.
Agza, B., Kasa, B., Zewdu, S., Aklilu, E. and Alemu, F., 2012. Animal feed potential and
adaptability of some cowpea (Vigna unguiculata) varieties in North West lowlands of
Ethiopia. Wudpecker, Journal of Agricultural Research, 1: 478-483.
Ahmed, S. and M.R. Rao. 1982. Performance of Maize-Soybean intercrop combination in the
tropics: Results of a multiplication study. Field Crops Research, 5:147-161.
Ajeigbe, H.A., Singh, B.B and Oseni,T.O. 2005. Cowpea-cereal intercrop productivity in the
Sudan Savana Zone of Nigeria as affected by planting pattern, crop variety and pest
management. African Crop Science Journal, 13(4): 269-279.
Amujoyegbe, B. J. and K. Elemo. 2013. Productivity of maize /cowpea intercrop as influenced
by time of introducing cowpea and nitrogen fertilizer rates in southwestern Nigeria.
Agricultural Science Research Journal, 3(7):186-193.
Andrew, D.J and A.H. Kassam. 1983. The importance of multiple cropping in increasing
world food supply. In: Papendic, R.I. (ed.) Multiple Cropping. American Society of
Agronomy, 677 South Segoe Road Madison, Wisconsin 53711. PP. 7-32.
Ayana Etana, Estefanos Tadesse, Ashenafi, Mengstu, Abubeker Hassen. 2013. Advanced
evaluation of cowpea (Vigna unguiculata) accessions for fodder production in the
central rift valley of Ethiopia. Journal of Agricultural Extension and Rural
Development, 5(3): 55-61.
Ayoola, O.T. and Agboola, A.A., 2002. Influence of cassava planting pattern and organicinorganic fertilizer sources on the growth and yield of maize in cassava – maizemelon intercrop in South west Nigeria. Journal of Agricultural Research, 3(2): 161168.
Babiker, A. G. T., 2007. Striga: The Spreading Scourge in Africa. Regular Plant Grow and
Development, 42: 74-87.
Bareja, B.G. 2011. Towards an Informed Application in Agriculture and Related
Sciences.(http://www.cropsreview.com/row-planting.html). Accessed on May 15/2014.
Beets, W.C. 1982.
Boulders.
Multiple Cropping and Tropical Farming Systems. Westview Press,
62
Berhanu Debele. 1980. The physical criteria and their rating proposed for land evaluation in
the highland region of Ethiopia. Land Use Planning and Regulatory Department,
Ministry of Agriculture, Addis Ababa, Ethiopia.
Bilalis, D., Papastylianou, P., Konstantas, A., Patsiali, S., Karkanis, A. and Efthimiadou, A.
2010. Weed-suppressive effects of maize-legume intercropping in organic farming.
International Journal of Pest Management, 56:173-181
Biruk Tesfaye. 2007. Effects 0f Planting Density and Varieties of Common Bean (Phaseolus
vulgaris L.) Intercropped with Sorghum (Sorghum bicolor L.) on Performance of the
Component Crops and Productivity of the System in South Gondar, Ethiopia. MSc
Thesis, Haramaya University, Haramaya, Ethiopia.
Bouyoucos, J. 1962. Hydrometer method improved for making particle size analysis of soil.
Agronomy Journals, 54:464-465.
Bremner, J.M. and C.S. Mulvaney. 1982. Methods of soil analysis of total Nitrogen. Part 2Chemical and micro-biological properties. Agronomy, 9 (2):595-624.
Brintha, I. and Seran, T. H. 2009. Effect of Paired Row Planting of Radish (Raphanus sativus
L.) Intercropped with Vegetable Amaranths (Amaranths tricolor L.) on Yield
Components of Radish in Sandy Regosol. Journal of Agricultural Science, 4:19-28.
Carruthers, K, Prithiviraj, B, Fe, Q, Cloutier, D, Martin, R.C, Smith, D.L. 2000. Intercropping
corn with soybean, lupin and forages: yield component responses. European Journal
of Agronomy, 12:103-115.
Carsky, R.J., L. Singh and R. Ndikawa. 1994. Suppression of Striga hermonthica on sorghum
using a cowpea intercrop. Experimental Agriculture, 30: 349-358.
Cenpukdee, U and S. Fukai. 1991. Effect of nitrogen supply on Cassava /pigeon pea
intercropping with three contrasting Cassava cultivars. Fertility Research, 29: 275-280.
Chemeda Fininsa. 1996. Effect of intercropping bean with maize on bean common bacterial
blight and rust diseases. International Journal of Pest Management, 42(1):51-54.
Chemeda Fininsa. 2003. Relationship between bacterial blight severity and bean yield loss in
pure stand and bean-maize intercropping systems. International Journal of pest
Management, 49(3): 177-185.
Clark, Andy (ed.). 2007. Managing cover crops profitably, 3rd edition. Sustainable Agriculture
Network, Beltsville, MD.
63
CSA (Central Statistics Agency). 2014. Report on Area and Production of Major Crops
(Private Peasant Holdings, Meher Season): Agricultural Sample Survey. Central
Statistics Agency, Addis Ababa, Ethiopia.
Dahmardeh, M., Ghanbari, A., Syasar, B. and Ramrodi , M. 2009. Intercropping maize (Zea
mays L.) and cow pea (Vigna unguiculata L.) as whole-crop forage: Effects of planting
ratio and harvest time on forage yield and quality. Journal of Food and Agricultural
Environment, 7:505-509.
Dahmardeh, M, A. Ghanbari, B.A. Syahsar and M. Ramrodi. 2010. The role of intercropping
maize (Zea mays L.) and cowpea (Vigna unguiculata L.) on yield and soil chemical
properties. African Journal of Agricultural Research, 5:631-636.
Dechasa Hirpa, 2005. Effect of moisture conservation methods and Plant Density of
Component Crops on Performance of Sorghum/bean intercropping in Meiso district,
West Hararghe. MSc Thesis, Haramaya University, Haramaya, Ethiopia.
Degu, E., A. Debello, and K. Belete. 2009. Combining ability study for grain yield and yield
related traits of grain sorghum (Sorghum bicolor L.) in Ethiopia. Acta Agronomica
Hungarica, 57 (2): 175-184.
Demeke M. and Di Marcantonio, F. 2013. Analysis of incentives and disincentives for
sorghum in Ethiopia. Technical notes series, MAFAP, FAO, Rome.
Demesew Mengesha. 2002. Effect of Planting Density of Haricot bean and Nitrogen
Fertilization on Productivity of Maize and Haricot bean Additive Intercrop System.
MSc Thesis, Haramaya University, Haramaya, Ethiopia.
Dial, H.L. 2012. Plant guide for sorghum (Sorghum bicolor L.). USDA-Natural Resources
Conservation Service, Tucson Plant Materials Center, Tucson, AZ.
Dicko, M.H., H. Gruppen, A.S.Traore, A.G.J. Voragen and W.J.H. van Berkel. 2006. Sorghum
grain as human food in Africa: relevance of content of starch and amylase activities.
African Journal of Biotechnology, 5 (5): 384-395.
Egbe, O.M. 2010. Effects of plant density of intercropped soybean with tall sorghum on
competitive ability of soybean and economic yield at Otobi, Benue State, Nigeria.
Journal of Cereals and Oilseeds, 1(1):1 – 10.
64
Eskandari, H., A. Ghanbari. 2010. Environmental resource consumption in wheat and bean
intercropping: Comparison of nutrient uptake and light interception. Notulae Scientia
Biologicae, 2(3):100-103.
Eyob Kahsay. 2007. Effect of Sorghum Planting Density and Nitrogen Rates on Productivity
of Faba bean (Vicia faba L.)/ Sorghum (Sorghum bicolor L. Moench) Intercropping
System at Wukro Maray, Central Zone of Tigray, Ethiopia. MSc Thesis, Haramaya
University, Haramaya, Ethiopia.
FAO (Food and Agricultural Organization). 2012. Database of agricultural production. FAO
Statistical Databases (FAOSTAT). In: Rebeka Gebretsadik. Integrating Sorghum
[Sorghum bicolor (L.) Moench] Breeding and Biological Control Using Fusarium
oxysporum Against Striga hermonthica in Ethiopia. Doctoral Dissertation, University
of KwaZulu-Natal, South Africa.
Fasil Reda. 2002. Striga hermonthica in Tigray (Northern Ethiopia). Prospects for control and
improvement of productivity through mixed cropping. Ethiopia Agricultural Research
Organization, Addis Ababa, Ethiopia. 118p.
Fasil Reda, Gebremedhin Woldewahid, Beyenesh Zemichael and Wondimu Bayou. 1997. An
integrated approach to the control of Striga in sorghum. Pest Management Journal of
Ethiopia 1:43-49.
Fasil Reda and Verkleij, J.A.C. 2004. The biology and control of Striga: a review. Pest
Management Journal of Ethiopia, 8: 1–13. In: Hussien,T. Distribution of two Striga
species and their relative impact on local and resistant sorghum cultivars in East
Ethiopia. Wiley Inter Science, Trop Sci. 2006, 46(3), 147–150.
Fasil Reda, Verkleij, J. A. C., and Ernst,W. H. O. 2005. Intercropping for the Improvement of
Sorghum Yield, Soil Fertility and Striga Control in the Subsistence Agriculture Region
of Tigray (Northern Ethiopia). Journal of Agronomy and Crop Science, 191:10—19
Fininsa, C. and Yuen, J. 2002. Temporal progression of bean common bacterial blight
(Xanthomonas campestris pv. phaseoli) in sole and intercropping systems. European
Journal of Plant Pathology, 108: 485–495.
Fischler, M, Wortmann, C.S. and Feil, B. 1999. Crotalaria (C. ochroleuca G. Don.) as a green
manure in maize-bean cropping systems in Uganda. Field Crops Research, 61: 97-107.
65
Fisher, N.M, Layoock, D, Masyansa, B.S.K. and Owuor, J.O. 1986. Report on mixed cropping
experiments in the West of Kenya during the 1975 season. Field Beans, 11(43): 367370.
Gebisa Ejeta and Gressel, J. (eds). 2007. Integrating New Technologies for Striga Control:
Towards Ending the Witch-Hunt. World Scientific Publishing Co. Pte. Ltd., Singapore.
Getachew Agegnehu, Amare Ghizaw, Woldeyesus Sinebo, 2006. Yield performance and landuse efficiency of barley and faba bean mixed cropping in Ethiopian high lands.
European Journal of Agronomy, 25: 202-207.
Getachew Bekele, Ketema Belete and Sharma, J.J. 2013. System Productivity of Forage
Legumes Intercropped with Maize and Performance of the Component Crops in
Kombolcha, Eastern Ethiopia. East African Journal of Sciences, 7 (2): 99-108.
Ghanbari , A., Dahmardeh, M., Siahsar, B.A, and Ramroudi ,M. 2010. Effect of maize (Zea
mays L.) - cowpea (Vigna unguiculata L.) intercropping on light distribution, soil
temperature and soil moisture in the environment. Journal of Food and Agricultural
Environment, 8:102-108.
Giller, K. E. 2001. Nitrogen Fixation in Tropical Cropping Systems, 2nd Edition, CABI,
Wallingford. 423p.
Gomez, K. A. and A. A. Gomez. 1984. Statistical procedures for agricultural research, 2nd
edition. John Viley and Sons Inc., New York.
Gudu, S., E.O. Ouma, A.O. Onkware, E.J. Too, B.A. Were, J.O. Ochuodho, C.O. Othieno, J.R.
.Okalebo, J. Agalo and S.M. 2013. Preliminary Participatory On-farm Sorghum
Variety Selection for Tolerance to drought, Soil Acidity and Striga in Western Kenya.
Maina Moi University, Kenya First Bio-Innovate Regional Scientific Conference
United Nations Conference Centre (UNCC-ECA), Addis Ababa, Ethiopia.
Hamel, Gregory. n.d. Planting density. (http://www.ehow.com/facts_7520978_plantingdensity.html). Accessed on March 8, 2014
Harwood, R.R. 1975. Farmer oriented research aimed at crop intensification. Proceedings of
the cropping system Work Shop, March 18-20, IRRI, Los Bonos, Philippines, Pp: 1231.
66
Hauggaard-Nielsen, H., Jornsgaard, B., Kidane , J. and Steen Jensen, E. 2008. Grain legume–
cereal intercropping: The practical application of diversity, competition and facilitation
in arable and organic cropping systems. Renewable Agriculture and Food Systems,
23(1): 3–12.
Hayder Ghulam, Suhail Mumtaz, Aslam Khan, Sherin Khan. 2003. Maize and Soybean
Intercropping under Various Levels of Soybean Seed Rates. Asian Journal of Plant
Science, 2(3): 339-341.
Hazelton, P. and Murphy, B. 2007. Interpreting soil test results: What do all the numbers
mean? 2nd Edition. CSIRO Publishing. Australia, 152p.
Herridge, D.F. 1982. Use of the ureide technique to describe the nitrogen economy of field
grown soybeans. Plant Physiology, 70: 7-11.
Hoshikawa, K. 1991. Significance of Legume Crops in intercropping, the Productivity and
Stability of Cropping System. In: Johanson, C., K.K. Lee and K.L. Saharawat, (Eds.).
Phosphorus Nutrition of Grain Legume in the Semi-Arid Tropics. ICRISAT. Pp. 173176.
Ibrahim, H.A, Singh, L., and Babaji, G.A. 2014. Evaluating yield performance of cowpea
varieties under sole and intercropping with sorghum at Bauchi, Nigeria. Ethiopian
Journal of Environmental Studies and Management, 7:709-719.
IFAD (Food and Agriculture organization of the United Nations). 2000. Applied and adaptive
research on cowpea in semi- arid zones of West Africa. Executive Board-Sixty ninth
sessions, Rome, 3-4 May, 2000. Pp: 18-20
IITA (International Institute of Tropical Agriculture). 1992. Sustainable food production in
Sub-Saharan Africa I. IITA’s Contributions. IITA, Ibadan Nigeria. 208pp.
Jackson, M.L. 1967. Soil Chemical Analysis. Prentice-Hall of India, New Delhi.
Jat, P.C., Rathore, S.S. and Sharma. R.K. 2014. Effect of Integrated Nitrogen Management
and Intercropping Systems on Yield Attributes and Yield of Maize. Indian Journal of
Hill Farming, 27(1):52-56
Javanmard , A., Nasab, A.D.M., Javanshir, A., Moghaddam , M. and Janmohammadi, H.
2009. Forage yield and quality in intercropping of maize with different legumes as
double cropped. Journal of Food and Agricultural Environment, 7:163-166.
67
Karanja, S. M., Kibe, A. M., Karogo, P. N. and Mwangi, M. 2014. Effects of Intercrop
Population Density and Row Orientation on Growth and Yields of Sorghum - Cowpea
Cropping Systems in Semi-Arid. Rongai, Kenya, Journal of Agricultural Science, 6
(5): 1-43.
Kariaga, B.M. 2004. Intercropping Maize with Cowpeas and Beans for Soil and Water
Management in Western Kenya. Proceedings of the 13th International Soil
Conservation Organization Conference, July 2004, Conserving Soil and Water for
Society, Brisbane, pp: 1-5.
Karikari, S.K., O. Choba and B. Molosiwa. 1999. Effect of intercropping bambara groundnut
on pear millet, sorghum and maize in Botswana. African Crop Science Journal, 7(2):
143-152.
Khan, Z.R., Midega, C.A.O., Hassanali, A., Pickett, J.A. and Wadharns, L.J. 2007.
Assessment of different legumes for the control of Striga hermonthica in maize and
sorghum. Crop Science, 47: 730- 736.
Khan, Z.R., Pickett, J.A., Hassanali, A., Hooper, A.M. and Midgea, C.A.O. 2008. Desmodium
species and associated biochemical traits for controlling Striga species: Present and
future prospects. Weed Research, 43: 302- 306.
Khola, O.P.S and D. P. Singh. 1996. Comparative Performance of Swedge rape (Brassica
napus) and Ethiopian mustard (Brassica carinata) in intercropping with French bean
(Phaseolus vulgaris). Indian Journal of Soil Conservation, 24 (1): 68-73.
Kristjanson, P., Tarawali, S., Okike, I., Singh, B.B., Thornton, P.K., Manyong, V.M., Kruska,
R.L. and Hoogenboom, G. 2001. Genetically Improved Dual-purpose Cowpea:
Assessment of Adoption and Impact in the Dry Savanna Region of West Africa. ILRI
Impact Assessment Series 9. ILRI (International Livestock Research Institute),
Nairobi, Kenya. 68 pp.
Laidlaw, H. K. C. and Godwin, I. D., 2009. Sorghum Compendium of Transgenic Crop Plants.
Chichester, UK. : John Wiley & Sons, Ltd.
Landers, J. N. 2007. Tropical Crop–livestock Systems in Conservation Agriculture: The
Brazilian Experience. Integrated Crop Management, Vol. 5. FAO. Rome.
68
Lithourgidis, A.S. and Dordas, C.A. 2010. Forage yield, growth rate, and nitrogen uptake of
faba bean intercrops with wheat, barley, and rye in three seeding ratios. Crop Science,
50:2148-2158.
Lithourgidis, A.S, Dordas, C.A., Damalas, C.A. and Vlachostergios, D.N. 2011. Annual
intercrops : an alternative path way for sustainable agriculture. Australian Journal of
Crop Science, 5(4): 396-410.
Liu, J.H., Z.H. Zeng, L.X. Jiao, Y.G. Hu, Y. Wang, H. Li. 2006. Intercropping of different
silage maize cultivars and alfalfa. Acta. Agron. Science, 32:125- 130.
Lucius, L. and Van Slayke. 2010. Fertilizers and crop production. Agriobioss press, New
Delhi, India.
Mahalakshmi,V., Ng,Q., Lawson, M. and Ortiz, R.. 2007. Cowpea (Vigna unguiculata L.)
Walp.) core collection defined by geographical, agronomical and botanical descriptors.
NIAB, Plant Genetic Resources: Characterization and Utilization, 5 (3): 113 – 119.
Mahmoud. E.A., Ramadan.B.S.H., Bekheet. M.A. and Gomaa .M.A. 2013. Effect of Nitrogen
Fertilization and Plant Density on Productivity and Quality of Sweet Sorghum.
American-Eurasian Journal of Agriculture & Environmental Sci., 13 (5): 654-659.
Makumba, W, Janssen, B, Oenema, O, Akinnifesi, F.K, Mweta, D. and Kwesiga, F. 2006. The
long-term effects of a gliricidia-maize intercropping system in Southern Malawi, on
gliricidia and maize yields and soil properties.
Agriculture, Ecosystems and
Environment, 116: 85-92.
Mead, R. and Willey, R.W. 1980. The concept of a ‘land equivalent ratio’ and advantages in
yields from intercropping. Experimental Agriculture, 16:217-228.
Mesfin Teshome and Tileye Feyissa. 2013. Genetic Diversity of Wild Sorghum (Sorghum
bicolor ssp. verticilliflorum (L.) Moench) Germplasm from Ethiopia as Revealed by
ISSR Markers. Asian Journal of Plant Sciences, 12: 137-144.
Midega, C.A.O., Khan, Z.R., Amudavi, D.M., Pittchar, J. and Pickett, J.A. 2010. Integrated
management of Striga hermonthica and cereal stem borers in finger millet [Eleusine
coracana (L.) Gaertn.] through intercropping with Desmodium intortum. International
Journal of Pest Management, 56:145-151.
69
Minale Liben, Tilahun Tadesse and Alemayehu Assefa. 2001. Determination of nitrogen and
phosphorus fertilizer levels in different maize-faba bean intercropping pattern in North
Western Ethiopia. pp. 513-518. Seventh Estern and Southern Africa Regional Maize
Conference 11th-12th February, 2001.
MoA (Ministry of Agriculture). 2010. Animal and Plant Health Regulatory Directorate. Crop
variety register issue no 13. Addis Ababa, Ethiopia.
MoARD (Ministry of Agriculture and Rural Development). 2008. Crop Variety Registration.
Crop Development Department, Addis Ababa, Ethiopia.
MoFED (Ministry of Finance and Economic Development). 2006. Ethiopia: Building on
Progress Plan for Accelerated and Sustained Development to End Poverty 2005/62009/10. September 2006.
Morgado, L.B. and Willey, R.W. 2003. Effects of plant population and nitrogen fertilizer on
yield and efficiency of maize-bean intercropping. Pesq. agropec. bras., Brasília,
38(11):1257-1264.
Muchow, R.C. 1988. Effect of nitrogen supply on the comparative productivity of maize and
sorghum in a semi-arid tropical environment. I. Leaf growth and leaf nitrogen. Field
Crops Research, 18, 1-16.
Nedumaran, S., Abinaya, P., Shraavya, B., Rao, P. and Bantilan, C. 2013. Grain Legumes
Production, Consumption and Trade Trends in Developing Countries-An Assessment
and Synthesis. ICRISAT Economics Discussion paper series, p.3. ICRISAT
Patancheru,
India.(http://oar.icrisat.org/6428/7/Nedumeran_etal_2013_IEDPS_3.pdf.
Accessed on May 8/ 2014
Noel, K.O., M. Carneol and W.J. Brill. 1982. Nodule Protein Synthesis and Nitrogenase
Activity of Soy bean Exposed to Fixed Nitrogen. Plant Physiology, 70: 1236-1241.
Odhiambo, G.D., and Ransom, J.K. 1993. Effect of dicamba on the control of Striga
hermonthica in maize in western Kenya. African Crop Science Journal 1:105-110.
Ogutu, M.O., Muasya, R. and Ouma, G. 2012. Effects of nitrogen fertilizer applications in a
bean – maize based intercropping systems and locations on seed quality of common
bean in Western Kenya. International Research Journal of Agricultural Science and
Soil Science, 2(11): 481-492.
Olsen, S.R., Cole, V.,Walanbe, C.V. and L.A. Dean. 1954. Estimation of Available
phosphorus in soils by extraction with Sodium Bicarbonate. USA Circular NO 939.
70
Omae, H., Saidou, A.K. and Tobita, S. 2014. Improving Millet-Cowpea Productivity and Soil
Fertility with Crop Rotation, Row Arrangement and Cowpea Density in the Sahel,
West Africa. American-Eurasian Journal of Agriculture & Environmental Science, 14
(2): 110-115.
Oroka, F. O. 2012. Density and Nitrogen effects on Forage Dry Matter Yield and Productivity
Indices of Crop Residues in Rice-Cowpea Intercropping. Journal of Biology,
Agriculture and Healthcare, 2 (8):92-98.
Palaniappan, S.P. 1985. Cropping systems in the tropics, principles and management. Wiley
Eastern Limited. Tamil Nadu Agricultural University. India. Pp. 75-80.
Parker, C., C.R., Riches. 1993. Parasitic Weeds of the World: Biology and Control. CAB
International, Wallingford, UK, P. 332.
Paynel, F, Lesuffleur, F, Bigot, J, Diquélou, S and Cliquet, J.B. 2008. A study of N transfer
between legumes and grasses. Agron. Sustainable Development, 28:281-290.
Pieterse, A.H., J.A. Verkleij. 1991. Effect of Soil Condition on Striga Development –a Review
.In: J.K. Ransom., L.J. Musselman., A.D. Worsham and C. Parker. (Eds.), Striga.
Proceedings of the Fifth International Symposium of Parasitic Weeds, April 1991.
CIMMYT, Nairobi, Kenya, pp. 329-339.
Poehlman, J.M. and Sleper, D.A. 1995. Breeding Field Crops, 4th ed. Oxford and IBM
pub.Co. New Delhi, India. 494p.
Prasad, R. B. and Brook, R. M. 2005. Effect of varying maize densities on intercropped maize
and soybean in Nepal. Experimental Agriculture, 41:365–382.
Rashid, A. 2003. Effect of planting geometry and legumes intercropping system on the yield
of sorghum. Asian Journal of Plant Science, 2:361-364.
Rebka Gebretsadik, H. Shimelis, M.D. Laing, P. Tongoona, N. Mandefro. 2013. A diagnostic
appraisal of the sorghum farming system and breeding priorities in Striga infested
agro-ecologies of Ethiopia. Doctoral Dissertation, University of KwaZulu-Natal, South
Africa.
Reddy, T. Y. and Reddi, G. H. S. 2007. Principles of Agronomy. Kalyani Publishers, India.
Pp: 468-489.
Rhoades, J.D. 1982. In Methods of Soil Analysis, Part 2. 2nd Edition (A.L. Page. Miller and
D.R. Keeney, Eds.). American Society of Agronomy. Madison, USA.
71
Saleem, R., Z. I. Ahmad, M. A. Anees, A. Razzaq and A. Saleem. 2015. Productivity and land
use efficiency of maize-mungbean intercropping under different fertility treatments.
Sarhad Journal of Agriculture, 31(1): 37-44.
Samuel Sahile, Chemeda Fininsa, Sakhuja, P.K., and Seid Ahmed. 2008. Effect of mixed
cropping and fungicides on chocolate spot (Botrytis fabae) of faba bean (Vicia faba) in
Ethiopia. Crop Protection, 27(2):275–282.
Sanchez, P.A., K.D. Shepherd, M.J. Soul, F.M. Place, R.J. Buresh and A.M.N. Zac. 1997. Soil
fertility replenishment in Africa. Replenishing soil fertility Africa, SSSA, Special
Publication 51. SSSA, Madison, USA.
Sanginga, N. and Woomer P. L. 2009. Integrated Soil Fertility Management in Africa:
Principles, Practices and Development Process. Tropical Soil Biology and Fertility
Institute of the International Centre for Tropical Agriculture. Nairobi. 263pp.
SAS (Statistical Analysis System) Institute. 2004. SAS User Guides, Version 9.1. SAS Inc.
Cary. North Carolina, USA.
Sekamatte, B.M., Ogenga-Latigo, M. and Russell-Smith, A. 2003. Effects of maize-legume
intercrops on termite damage to maize, activity of predatory ants and maize yields in
Uganda. Crop Protection, 22:87-93.
Seran, H.T. and Brintha, I. 2010. Review on maize Based Intercropping. Asian Network for
Scientific Information, Journal of Agronomy, Pp.1-11.
Shank, R. 1996. Striga: The Parasitic Weed and its Relation to Poverty. UN-EUE, Addis
ababa, Ethiopia. (http://www.africa.upenn.edu/EUE/Striga396.html). Accessed on
April 7, 2014
Sheahan, C.M. 2012. Plant guide for cowpea (Vigna unguiculata). USDA-Natural Resources
Conservation Service, Cape May Plant Materials Center, Cape May, NJ.
(http://plants.usda.gov/plantguide/pdf/pg_viun.pdf ) . Accessed on March28/2014.
Siddig. A., Mohamed, Ali, Adam.A.Mohamed, Ali.H.Bahar and Abdulmohsin. R.
Khairelseed. 2013. Effects of Sorghum (Sorghum bicolor L.) Moench) and Groundnut
(Arachis hypogaea L.) İntercropping on some soil chemical properties and crop yield
under rain-fed conditions. Journal of Science and Technology, 3:1, 1-74.
72
Sisay Tekle. 2004. Effect of planting pattern and proportion of green gram (Vigna radiate (L)
wilczec) on productivity of sorghum/ green gram intercropping system in Kewet
wereda. Amhara Region. MSc Thesis, Haramaya University, Haramaya, Ethiopia.
Snapp, S.S., Mafongoya, P.L.,Waddington, S. 1998. Organic matter technologies for
integrated nutrient management in smallholder cropping systems of southern Africa.
Agriculture, Ecosystems and Environment, 71: 185-200.
Solanki, G., Lohra, N., Lohra,J. and Solanki, R. 2014. An Alternative Pathway for Sustainable
Agriculture: Annual Intercrops-A Review. Journal of Science, 4(3): 184-186.
Solomon, G. and Kibrom, G. 2014. Forage production potential and nutritive value of cowpea
(Vigna unguiculata) genotypes in the northern lowlands of Ethiopia. Journal of
Agricultural Research and Development, 5(4): 066-071.
Solomon Kebebew Waktola, Ketema Belete and Tamado Tana. 2014. Productivity evaluation
of maize - soybean intercropping system under rain fed condition at Bench-Maji
Zone, Ethiopia. Sky Journal of Agricultural Research, 3(9):158 – 164.
Sticker, F.C., S.Wearden and A.W. Pauli. 1961. Leaf Area Determination in Grain Sorghum.
Agronomy Journal, 53: 187-188.
Tadesse, T., Liben, M., and Asefa, A. 2012. Role of maize (Zea mays L.)- fababean (Vicia
faba L.) intercropping planting pattern on productivity and nitrogen use efficiency of
maize in northwestern Ethiopia highlands. International Research Journal of
Agricultural Science and Soil Science, 2(3): 102-112.
Talawar, S. and Rhoades, R.E. 1998. Scientific and local classification and management of
soils. Agriculture and Human Values, 15: 3-14.
Tamado Tana and Eshetu Mulatu. 2000. Evaluation of Sorghum, Maize and Common Bean
Cropping System in East Hararghe, Eastern Ethiopia. Ethiopian Journal of
Agricultural Science, 17: 33-45.
Tauro. T. P. J., Adu-Gyamfi, Dhliwayo. D. K. C. and Heng.L. K. 2013. Nitrogen and
phosphorus budgets for sorghum and cowpea production under simulated sole- and
intercropping systems in low- and medium-P soils. African Journal of Agricultural
Research, 8(9): 727-735.
73
Tekalign Tadese. 1991. Soil, plant, water, fertilizer, animal manure and compost analysis.
Working Document No. 13. International Livestock Research Center for Africa, Addis
Ababa, Ethiopia.
Tesfa, B. Tolessa, D., Setegn, G. Tamado, T., Negash, G. and Tenaw W. 2001. Development
of Appropriate Cropping Systems for Various Maize Producing Regions of Ethiopia.
Second National Maize Workshop of Ethiopia. 12-16 November, 2001. Addis Ababa.
Tilahun Tadesse, Minale Liben and Alemayehu Asefa. 2012. Role of maize (Zea mays L.)fababean (Vicia faba L.) intercropping planting pattern on productivity and nitrogen
use efficiency of maize in northwestern Ethiopia highlands. International Research
Journals, 2(3):102-112.
Tisdale, S.L., Nelson, W.L., Beaton, J.D. and Havlin, J.L. 1995. Soil Fertility and Fertilizers,
5th edition, Macmillan, New York, USA.
TJAI (Thomas Jefferson Agricultural Institute). 2010. Cowpea: a versatile legume for hot, dry
conditions. Columbia, MO.In: Sheahan, C.M. 2012. Plant guide for cowpea (Vigna
unguiculata). USDA-Natural Resources Conservation Service, Cape May Plant
Materials Center, Cape May, NJ. (http://plants.usda.gov/plantguide/pdf/pg_viun.pdf ).
Accessed on March 28, 2014
Tofinga, M.P., Paolini, R. and Snaydon, R.w. 1993. A study of root and shoot interactions
between cereals and peas in mixtures. Journal of Agricultural Science, 120:13-24.
Tsubo, M, Walker, S and Ogindo HO, 2005. A simulation model of cereal–legume
intercropping systems for semi-arid regions I. Model development. Field Crops
Research, 93: 10–22.
Undie, U. L., D. F. Uwah and E. E. Attoe. 2012. Effect of Intercropping and Crop
Arrangement on Yield and Productivity of Late Season Maize/soybean Mixtures in the
Humid Environment of South Southern Nigeria. Journal of Agricultural Science, 4:4,
1-98.
UNDP (United Nations Development Program). 1996. Sustainable Agriculture and
Environmental Rehabilitation Program (SAERP). Stastical Master Book on Sectorial
Conditions and Activities in the Amhara Regional State. Volume II. Joint production
of the Amhara Regional Council and Economic Commission for Africa. UNDP/ECA.
SAERP /WARDIS Project, northern Ethiopia. PP. 35-45.
74
USDA (United States of Department of Agriculture). 2015. World Sorghum Production
2014/2015. https://www.worldsorghumproduction.com/ Accessed on April 8, 2015
Vandermeer, J.H. 1989. The Ecology of Intercropping. Cambridge University Press,
Cambridge, UK.
Vogt, W.; J. Sauerborn and M. Honisch. 1991. Striga hermonthica, Distribution and
Infestation in Ghana and Togo on Grain crops. Proceedings of fifth International
Symposium of Parasitic Weeds, June 24th - 30th. Nairobi, Kenya. pp. 372- 377.
Waddington, S. R., Li, X., Dixon, J., Hyman, G. and Vicente, M. C. 2009. Getting the focus
right: production constraints for six major food crops in Asian and African farming
systems. Food Security, 2(1) : 27-48.
Walker, S. and Ogindo, H.O. 2003. The water budget of rainfed for maize and bean intercrop.
Physics and Chemistry of the Earth, 28: 919-926.
Walkley, A. and Black, C.A. 1934. An examination of different methods for determining soil
organic matter and the proposed modification by the chromic acid titration method.
Soil Sciences, 37: 29-38.
Watson, D.J. 1958. The dependence of net assimilation rate on leaf area index. Ann. Bot.
Land, N.S. 22: 37-54.
Willey, R.W. 1979a. Intercropping, Its importance and research Needs. I. Competition and
Yield Advantages. Field Crop Abstract, 32: 1-10.
Willey, R.W. 1979b. Intercropping, Its importance and research Needs. II. Agronomy and
research approach. Field Crop Abstract, 32: 78-85.
Willey, R.W. 1991. Evaluation and presentation of intercropping advantage. Experimental
Agriculture, 21: 119-123.
Wondimu Bayu, N. F.G. Rethman and P. S. Hammes. 2005. Growth and yield compensation
in sorghum (Sorghum bicolor L. Moench) as a function of planting density and
nitrogen fertilizer in semi-arid areas of northeastern Ethiopia. South African Journal of
Plant and Soil, 22(2): 76-83.
75
Wortmann, C., Mamo, M., Mburu, C., Letayo, E., Abebe, G., Kayuki, K. C. 2009. Atlas of
Sorghum Production in Eastern and Southern Africa. In: Lithourgidis, A.S, Dordas,
C.A., Damalas, C.A. and Vlachostergios, D.N. Annual intercrops : an alternative path
way for sustainable agriculture. Review article: Australian Journal of Crop Science,
5(4): 396-410.
Yesuf Mohammed. 2003. Effect of Planting Arrangement and Population Densities of Haricot
Bean on Productivity of Sorghum/Haricot Bean Additive Mixtures. MSc Thesis,
Haramaya University, Haramaya, Ethiopia.
Yirzagla, J. 2013. Effect of spatial arrangement on the performance of Pearl millet-Cowpea
Intercrop. Journal of Advances in Agricultural Science and Technology 1 (3): 028-031.
Zerihun Abebe. 2011. System Productivity as Influenced by Integrated Organic and Inorganic
Fertilizer Application in Maize/Soybean Intercrop at Bako, Western Ethiopia. MSc
Thesis, Haramaya University, Haramaya, Ethiopia.
76
7. APPENDICES
77
Appendix Table 1. Monthly and yearly total annual rainfall (mm) for 2010-2014 of Kobo area
Year
2010
2011
2012
2013
2014
Total
Jan.
0.0
0.6
0.0
18.8
0.0
19.4
Feb.
10.5
0.0
0.0
1.0
10.7
22.2
Mar.
Apr.
4.5
18.9
47.7
26.2
59.4
156.7
65.1
30.2
35.8
57.5
31.7
220.3
May
June
Jul.
Aug.
Sept.
Oct.
72.3
1.8
245.4 310.5 45.9
14.7
79.5
0.0
69.0
176.0 10.8
13.4
19.2
43.5
53.3
291.8 43.0
12.0
4.9
4.0
179.8 176.9 44.0
30.4
153.0 4.5
154.2 255.8 190.1 85.9
328.9 53.8
701.7 1211
333.8 156.4
Source: Kobo Agricultural Research sub-center
Nov.
0.0
26.1
0.0
0.0
22.8
48.9
Dec.
0.5
0.0
4.0
0.0
3.8
8.3
Total Rain
fall
771.20
424.5
550.3
543.5
971.9
3261.4
Mean
652.28
Appendix Table 2. Mean monthly and annual minimum temperature (0C) for the year 2010-2014 at Kobo area
Year
2010
Jan.
13.3
Feb.
15.3
Mar.
16.2
Apr.
17.7
May
17.8
June
20.3
Jul.
18.8
Aug.
17.1
Sept.
16.3
Oct.
13.7
Nov.
11.0
Dec.
11.1
Average
15.72
2011
13.2
13.0
14.1
16.6
17.3
18.9
19.1
17.1
15.7
13.3
14.0
10.4
15.23
2012
11.7
12.0
14.8
16.8
16.4
17.9
17.9
16.7
15.7
11.9
13.0
12.0
14.73
2013
13.0
12.7
16.5
17.6
18.5
19.3
17.6
17.1
15.4
14.1
13.0
11.4
15.52
2014
11.8
15.1
15.6
16.8
17.5
19.1
18.4
17.0
15.8
13.3
13.0
11.2
15.38
Mean
12.6
13.62
15.44
17.1
17.5
19.1
18.36
17
15.78
13.26
12.8
11.22
15.32
Source: Kobo Agricultural Research sub-center
78
Appendix Table 3. Mean monthly and annual maximum temperature (0C) for the year 2010-2014 at Kobo area
Year
2010
Jan.
26.9
Feb.
29.7
Mar.
29.1
Apr.
31.5
May
32.6
June
35.4
Jul.
31.5
Aug.
29.7
Sept.
30.1
Oct.
30.3
Nov.
28.0
Dec.
26.5
Average
30.11
2011
26.2
29.0
28.3
31.7
31.6
34.4
32.5
29.6
31.2
30.8
27.0
26.8
29.93
2012
27.5
30.4
29.9
29.9
32.8
34.7
31.9
28.6
30.7
28.4
29.0
28.4
30.18
2013
28.4
29.3
31.0
31.3
33.7
34.3
31.6
29.6
30.8
29.2
29.0
27.7
30.49
2014
27.6
28.6
30.2
31.8
31.2
34.6
33.7
30.2
30.8
29.6
29.0
28.6
30.49
Mean
27.32
29.4
29.7
31.24
32.38
34.68
32.24
29.54
30.72
29.66
28.4
27.6
30.24
Source: Kobo Agricultural Research sub-center
79
Appendix Table 4. Ratings for organic matter (OM), total N, available phosphorus, cation exchange capacity (CEC), electrical
conductivity (EC) and soil pH
OM (%)a
Total N
(%)b
Available P
(mg kg-1)c
CEC
(cmol (+)/kg)d
> 40
Rating
Rating
very high
EC
(dS/m)d
<2
pHa
Rating
Non-saline
> 5.17
> 0.25
> 10
25-40
High
2–4
Slightly saline
< 4.5
Very strongly acid
2.59-5.17
0.12-0.25
5-10
12-25
Medium
4–8
Moderately
saline
4.5-5.2
Strongly acid
0.86-2.59
0.01-0.12
<5
6-12
Low
8–16
Highly saline
5.3-5.9
Moderately acid
< 0.86
< 0.01
<6
Very Low
> 16
Extremely
saline
6.0-6.6
Slightly acid
6.7-7.3
Neutral
7.4-8.0
Moderately alkaline
> 8.0
Strongly alkaline
< 4.5
Very strongly acid
Source: aTekalign (1991), b Berhanu (1980), c Olsen et al. (1954), d Hazelton and Murphy (2007)
80
Appendix Table 5. Analysis of variance (ANOVA) for days to 50% heading, days to 50%
flowering and days to 90% maturity of the intercropped sorghum as affected by plant densities
of cowpea and N-rates
Sources of
df
Mean squares
variation
DH
DF
MD
Replication
2
3.08
1.58
19.33 **
Cowpea density
2
1.00
0.25
11.32
(A)
N-rate (B)
3
3.15
4.30
5.20
AXB
6
0.48
1.66
9.55
Error
22
1.23
3.16
4.70
CV (%)
2.00
2.26
1.99
Cropping system
Replication
2
2.87
0.81
11.54
Sole Vs Intercrop
1
1.11
0.43
0.11
Error
2
1.23
1.04
1.64
CV (%)
1.49
1.29
1.18
df= degree of freedom; DH= days to 50% heading; DF= days to 50% flowering; MD= days to
90 Physiological maturity
Appendix Table 6. Analysis of variance for leaf area plant-1 (cm2), leaf area index, panicle
length plant-1 (cm) and plant height (cm) of the intercropped sorghum as affected by plant
densities of cowpea and N-rates
Sources of
df
Mean squares
variation
PH (cm)
PL (cm)
LA (cm2)
LAI
Replication
2
19.89
2.35
69593.83
0.03
Cowpea
2
0.41
1.35
325441.08
0.15
density (A)
N-rate (B)
3
152.59*
13.27*
1380134.17**
0.61**
AXB
6
13.15
1.78
185973.44
0.08
Error
22
35.53
3.23
100085.97
0.04
CV (%)
5.08
6.68
11.07
Cropping
system
Replication
2
10.16
3.21
59530.66
0.03
Sole Vs
1
5.45
0.32
135216.09
0.06
Intercrop
Error
2
16.71
1.61
25721.97
0.01
CV (%)
3.50
4.68
5.33
df= degree of freedom; LA= Leaf area; LAI= Leaf area index; PL=panicle length; PH= plant
height;
**, * Significant at 1% and 5% probability level, respectively
81
Appendix Table 7. Analysis of variance for sorghum panicle weight plant-1 (g), thousand
kernel weight (g), dry biomass yield (kg ha-1), grain yield (kg ha-1) and harvest index (%)
Sources of
variation
df
PWt
(g)
1.67
13.53
1000KWt
(g)
0.61
0.54
Mean squares
DBY
GY
(kg ha-1)
(kg ha-1)
46800.64
30802.83
4311.90
134096.90*
HI (%)
Replication
2
60.60
Cowpea density
2
81.73*
(A)
N-rate (B)
3
29.12*
0.43
320221.04**
375465.51** 178.14*
AxB
6
16.27
0.57
98615.93
128082.92 **
44.81
Error
22
7.84
1.93
58466.00
29651.47
20.55
CV (%)
6.70
5.43
6.42
9.40
9.31
Cropping system
Replication
2
5.84
0.65
42619.40
44835.02
19.18
Sole Vs Intercrop
1
8.03
1.70
1312020.14*
484998.74*
8.86
Error
2
6.40
2.45
57506.69
19666.94
3.69
CV (%)
5.89
5.91
5.67
6.62
3.85
df= degree of freedom; PWt= panicle weight; 1000KW= Thousand kernel weight; GY= Grain
yield; DBY= dry biomass Yield; HI= harvest index;
**,* Significant at 1% and 5% probability level, respectively
Appendix Table 8. Analysis of variance for phenological parameters of cowpea component as
affected by plant densities of cowpea and N-rates
Sources of variation
df
Replication
2
Sole Vs Intercrop
1
Cowpea density (A)
2
N-rate (B)
3
AXB
6
Error
22
CV (%)
Cropping system
Replication
2
Sole Vs Intercrop
1
Error
2
CV (%)
DF= days to 50% flowering; DPS= Days
Physiological Maturity; df= degree of freedom;
* Significantly different at 5% probability level
DF
3.69*
0.80
0.08
0.17
0.64
0.65
1.37
Mean squares
DPS
9.53*
0.61
5.86
1.43
0.71
2.25
2.28
DPM
0.36
2.02
2.53
0.37
0.68
1.30
1.26
6.71
11.29
0.42
0.80
0.61
2.02
3.45
5.84
0.69
3.18
3.68
0.93
to 50% pod setting; DPM= days to 90%
82
Appendix Table 9. Analysis of variance for leaf area (cm2), leaf area index, and plant height
(cm) of cowpea component as influenced by plant densities of cowpea and N-rates
Sources of
df
Mean squares
variation
LA (cm2)
LAI
Ph (cm)
Replication
2
108808.69
0.06
2.41
Cowpea density (A)
2
8062.64
2.63**
22.91
N-rate (B)
3
326294.90*
0.18*
2.00
AxB
6
127385.26
0.08
18.37
Error
22
98199.31
0.05
10.23
CV (%)
17.22
15.36
6.08
Cropping system
Replication
2
41358.35
0.02
6.42
Sole Vs Intercrop
1
247822.73
0.08
0.08
Error
2
20909.44
0.01
3.88
CV (%)
7.15
5.31
3.74
df= degree of freedom; LA= Leaf area; LAI= Leaf area index; Ph= Plant height;
**,* Significantly different at 1% and 5% probability level, respectively
83
Appendix Table 10. Analysis of variance for cowpea nodule number plant-1, number of branches plant-1, number of pods plant-1,
number of seeds pod-1, hundred seed weight (g), grain yield (kg ha-1), above ground dry biomass yield (kg ha-1), and harvest index
(%) as influenced by plant densities of cowpea and N-rates
Sources of
df
Mean squares
variation
NNPP
NBPP NPPP
NSPP
HSWt (g) GY (kg ha-1) DBY(kg ha-1)
HI (%)
Replication
2
1.33
0.46
6.55
7.29*
1.90
15996.36
90222.94
51.14
Cowpea density
2
1.83
0.46
0.43
1.22
10.64*
36445.53**
254182.83**
11.07
(A)
N-rate (B)
3
26.93**
0.13
8.63
9.61**
11.33*
26012.96**
93892.18
26.89
AxB
6
2.34
0.31
3.41
1.87
4.42
28601.71**
146164.81*
29.14
Error
22
1.61
0.21
4.98
1.61
2.52
4956.66
41100.68
16.72
CV (%)
10.75
15.26
17.17
14.27
8.00
12.84
10.42
14.42
Cropping system
Replication
2
2.41
0.51
2.36
0.69
1.16
1899.08
1388.98
1.68
Sole Vs Intercrop
1
31.10*
1.48
0.77
16.40*
1.33
883722.53** 2309265.29** 252.07*
Error
2
1.25
0.49
1.44
0.43
1.82
6694.68
20498.79
4.06
CV (%)
7.94
20.10
8.98
6.23
6.33
8.78
5.59
5.78
df= degree of freedom; NNPP= nodule number plant-1; NBPP= number of branches plant-1; NPPP= number of pods plant-1; NSPP=
number of seeds pod-1; HSWt= hundred seed weight; GY=grain yield; DBY= dry biomass yield; HI= harvest index;
**,* Significantly different at 1% and 5% probability level, respectively
84
Appendix Table 11. Analysis of variance for sorghum partial LER, cowpea partial LER, LER, sorghum MV (ETB ha-1), cowpea
MV (ETB ha-1), GMV (ETB ha-1) and MA (ETB ha-1) as influenced by cowpea densities and N rates
Source of
variation
df
Sorghum
partial
LER
0.04**
0.02*
Cowpea
partial
LER
0.03
0.02**
LER
Sorghum
MV
Mean squares
Cowpea
MV
GMV
MA
Replication
2
0.01
623757.24
483889.92
2130564.15
839453.77
Cowpea
2
0.05*
2715462.21*
1102477.22**
3756036.69*
4147155.79*
density (A)
N-rate (B)
3
0.07**
0.02**
0.13**
7603176.57 **
786892.13**
12419111.21** 12152775.73**
AXB
6
0.02**
0.02**
0.05**
2593679.05**
865201.82**
4312897.16** 4509611.95**
Error
22
0.01
0.01
0.01
600442.23
149939.09
841807.79
837136.38
CV (%)
10.01
13.47
8.35
9.40
12.84
8.15
52.15
Cropping
system
Replication
2
0.002
0.001
0.001
907932.37
57445.78
252366.65
Sole Vs
1
0.079*
0.505** 0.504**
9821186.16*
26732733.19** 4193292.40**
Intercrop
Error
2
0.002
0.001
0.001
398243.77
202520.72
31996.55
CV (%)
4.45
5.27
2.27
6.62
8.78
2.04
df= degree of freedom; LER= land equivalent ratio; MV= monetary value; GMV= gross monetary value;
**,* Significantly different at 1% and 5% probability level, respectively