Growth and Yield Response of Bread Wheat Variety Grown Under Varying Seed Rate and Weeding Time

Growth and Yield Response of Bread Wheat Variety Grown Under Varying Seed Rate and Weeding Time
Growth and Yield Response of Bread Wheat Variety
Grown Under Varying Seed Rate and Weeding Time
1Asfaw Takele, *2Zerhun Jalata, 2Reta Fikadu
1Oromia Agricultural and Natural Resource Office, Horo-Guduru Wollega, Amuru District, Ethiopia.
2Department of Plant Sciences, Faculty of Agriculture, Wollega University, Ethiopia.
Wheat is among the most important staple crop globally. However, constrained by appropriate
agronomic practices. Therefore, the information on the interaction effect of seed rate and
weeding period is useful to identify the effective time of weeding for high yield of wheat. Thus,
the present study conducted at Amuru district of Horro Guduru Zone, Ethiopia in 2019 cropping
season with the aim of identifying optimum seed rate and appropriate time of weeding to improve
production and productivity of bread in the area. The experiment was laid down in randomized
complete block (RCB) design with three replications. The treatment was arranged in factorial
combinations of four weeding intervals (farmer practice, weeding at two weeks after emergence,
three weeks after emergence and four weeks after emergence) and three levels of seed rate (125
kg, 150 kg and 175 kg-1
).The result showed that days to 50% heading, days to maturity and
effective tillers per plant were highly significantly (p<0.01) affected by the interaction effect of
weeding time and seed rate. Moreover, interaction effect of weeding time and seed rate was
significantly (p<0.01) affected the weed above ground dry biomass. Guizotia scabra (22.47%)
with population density (370), Phalaris paradoxa (22.10%) with population (364), Plantago
lanceolata (18.58%) with population density (306), and Bidens piloso L. (8.74%) were the
dominant weed species competing with wheat in the study area. Minimum relative weed density
(26.6%) weed dry biomass (1.7gm) and maximum weed control efficiency (98.08%) was recorded
at weeding four weeks after emergence and 175kgha-1
seed rate. Thus, the finding suggest grain
yield was increased (52.3%) when weeding four weeks after emergence over farmers practice
and 13.75% at 175kg seed rate.
Keywords: Dry matter yield, grain yield, wheat, weed competition, weed relative density
INTRODUCTION
Wheat (Triticum aestivum L.) is believed to have originated
in the Near East and belongs to the family of Poaceae and
the genus Triticum. It is a crop of temperate zone with cool
winters and hot summers being very conducive for its
growth (Violeta et al, 2015).Recent global wheat area
coverage and grain yield production was 214,291,888
hectares and 734,045,174 tons, respectively. The largest
producers are China, India, Russia federation, USA,
France, Canada, Pakistan, Ukraine, Australia and
Germany. The report showed Ethiopia is the largest
producer of wheat in Sub-Saharan Africa with annual
production of 4.2 million tons of yield from an area of
1,748,972 hectares of land (FAOSTAT, 2018 data
http://www.fao.org/faostat/en/#data/QC/visualize) which
indicates globally wheat productivity has reached 3.43 tha-
1as compared to 2.4tha-1 for Ethiopia. Thus reducing the
yield gap is very important.
Most wheat producing area in Ethiopia lie between 6o and
16o N latitude and 35o and 42o E longitudes of an altitude
range from 1500 to 3000 meters above sea level (masl).
*Corresponding Author: Zerhun Jalata, Department of
Plant Sciences, Faculty of Agriculture, Wollega University,
Ethiopia.
E-mail: jaluu_z@yahoo.com
Co-Author Email: asfawtakele68@gmail.com
Research Article
Vol. 7(2), pp. 230-240 September, 2020. © www.premierpublishers.org. ISSN: 2326-
3997
World Research Journal of Agricultural Sciences

Asfaw et al. 231
The most suitable agro-ecological zones, however, fall
between 1900 to 2700 meters above sea level (Bekele et
al., 2000). Production of wheat has grown significantly
over the past two decades following initiatives
implemented to derive agricultural growth and food
security in the country. With increasing production from
around 1.1million tons in1995/96 to 3.9 million tons in
2013/14 which is an average annual growth 7.5 percent
and on the other hand, wheat consumption increases from
2.1 million tons to 4.2 million tons, representing an annual
average growth of 4.2 percent (Samuel et al,
2017).Despite the importance of wheat to the Ethiopian
agriculture, its average yield is still very low as compared
to the world average 3.4 t ha-1 (FAOSTAT, 2018). On the
other hand, the rapid population growth coupled with
increasing urbanization and change in food habits has
resulted in surge makes the wheat demand-supply chain
very volatile (Rosegrant and Agcaoili, 2012).
The main reasons for low productivity of wheat in Ethiopian
and sub-Saharan Africa region is generally attributed to
abiotic and biotic stresses (Wuletaw et al., 2019). Hailu et
al.(1991) indicated the major wheat production constraints
are categorized into two: technical and socio-economic.
The technical constraints include low soil fertility, high
incidence of weeds, pests and diseases, and lack of
improved varieties. Walia et al (2013) reported
inappropriate agronomic practices are some hurdles in
increasing yield of wheat. Different research outputs agree
up on the application of different seed rates had significant
effect on seed quality of bread wheat (Tesfaye, 2015).The
use of inappropriate seed rates by small holder farmers
leads to low yield as compared to research field. This is
due to higher seed rate which leads to higher competition,
shorter spike length and lower number of grains per spike
(Ejaz et al., 2002) and seed rate determines the crop vigor
and ultimately yield (Korres and Froud, 2002).
Reducing seed rate may result in more tillers and spike per
plant and more spikelet per spike but in many cases
reduced grain yield per hectare (Ozturk et al.,
2006).Research results indicated that use of proper seed
rate encourages nutrient availability, proper sun light
penetration for photosynthesis, good soil environment for
uptake of soil nutrients and water use efficiency; and all
necessary for crop vigor and consequently increase the
production and productivity of the crop (Alemayehu,
2015).Therefore, there is a need to determine the optimal
seed rate in each growing area as one of the important
agronomic management to improve production and
productivity of wheat.
In addition to this, weed infestation is another main
bottleneck problem in crop production in Ethiopia,
especially during the rainy season (Hailu et al.1991). The
climate encourages rapid and abundant growth of weeds
and consequently, all agricultural crops are heavily
infested with weeds. Farmers are aware of weed problem
in their fields but often they cannot cope-up with heavy
weed infestation during the peak-period of agricultural
activities this may be because of labor shortage. Hence,
most of their fields are weeded late or left un-weeded.
Such ineffective weed management is considered as the
main factor for low average yield of wheat resulting in
average annual yield loss of 35 % (Esheteu et al.,
2006).Lack of adequate plant population is prone to heavy
weed infestation, which becomes, difficult to control later.
Therefore, the right method of sowing, adequate seed rate,
protection of seed from soil borne pests and diseases etc.,
are very important to obtain proper and uniform crop stand
capable of offering competition to the weeds (
Chandrasekaran et al., 2010).
Optimum seed rate and right time of weeding are important
production factor for higher grain yield and yield
components of bread wheat. In different part of the country
including the study area, weeds grow up speedily and
some weeds species are difficult to control as well as
farmers are using inappropriate seed rate which affects the
wheat grain yield and quality. However; there is little
information available in the study area. Therefore,
research on right time of weeding and optimum seed rate
in bread wheat in the study area would help to provide an
effective time of weeding and identify optimum seed rate
to the study area. Hence, the present research aimed at
determining the optimum seed rate and appropriate
weeding time to increase the production and productivity
of bread wheat as well as estimate the interaction effect of
both weeding time and seed rate on bread wheat yield in
the study area.
MATERIALS AND METHODS
Study area: The experiment was conducted at Amuru
District of Horro Guduru Wollega Zone, Oromia Regional
state, western Oromia. The experimental area is located
387 km away from Addis Abeba (Finfinne) to the west of
Ethiopia and 72 km from Shambi town. The experiment
was conducted at Haro Walo Farmer Training Center
(FTC) under field condition during 2019 rain fed cropping
season. And the area is with an average annual rain fall of
950mm and characterized by 250c maximum and 140c
minimum temperature. The farming system of the
community around the area was characterized by mixed
agriculture. Cereal crops like tef, maize wheat, barley,
niger seed are the major crops farmers grow in the area
for food consumption.

Description of Experimental Materials
Liban bread wheat variety was used in this experiment. It
is well adapted to the study area and was introduced from
Kulumsa Agricultural Research Center.It was released in
2015.Its recommendation shows maturity will take 122-
125, annual rain fall >900mm and 2300-2700m above sea
level is very important for good production. Liben variety
was selected die to its yield advantage and tolerant to
major wheat diseases and suitability to the agro ecology
which is similar to the experimental area (MoANR, 2018).
Moreover, phosphorus and nitrogen fertilizers were
applied uniformly for all plots based on the
recommendation rate.
Treatments and Experimental Design
The experiment consists of two factor combination of three
seed rate treatment levels (125kg, 150kg, and 175kg) per
hectare and four weeding times;( two weeks after
emergency, three weeks after emergency, four weeks
after emergency and farmers practice (Table 1) which was
conducted in randomized complete block design with three
replications in factorial arrangement which was 12(3*4).
Each plot consists of ten rows of 2 m long and 1.5m wide
resulting 3m2. Blocks and plots are separated from each
other by 1m and 0.5m respectively. Seeds were sown in
rows of 20 cm apart by drilling.
Table 1.Descriptions of seed rates and weeding Intervals in factorial treatment combinations
No. Weeding time and Seeding rates kg/ha
1 2WAE +125kg
2 2WAE +150kg
3 2WAE +175kg
4 3WAE+125kg
5 3WAE +150kg
6 3WAE +175kg
7 4WAE +125kg
8 4WAE +150kg
9 4WAE +175kg
10 FP +125kg
11 FP +150kg
12 FP +175kg
WAE= Weeks after emergency, FP=Farmers practice

Asfaw et al. 233
Management of the Experiment
Land preparation was done four times from May up to mid-
July 2019 by using oxen plough. Planting was done on 21
June 2019 by placing the seeds in hand made furrows.
Fertilizer were applied as per the recommended rates. Full
dose of phosphate fertilizer in the form of NPS was applied
equally to all plots at the depth of 2cm below seeds at time
of sowing. While the recommended rate of nitrogen
fertilizer in the form of Urea was applied in split form. Two
third of Nitrogen fertilizer was applied at time of sowing and
one third of remain fertilizer was applied at the four leaf
stage of wheat to all treatment uniformly. Weeds were
managed by hand as per treatment and other agronomic
practices were applied uniformly to all plots following the
recommended practices for the crop. Harvesting was done
when the spikes and leaves turned yellow. Threshing and
winnowing was done simultaneously and manually.
Finally, grain yield assessment was computed from net
plot.
Data Collection: Data was collected on phonological,
growth, yield and yield components. Days to 50% heading
(DH) was recorded as number of days from sowing to
when 50% of the plants in each plot produced head. Days
to 90% physiological maturity (DM) was recorded by
counting the number of days from date of sowing until
when 90% of the plants changed green color to yellowish,
loose its water content and attain to physiological maturity
in each plot. Plant height (PH) was measured by taking
height of ten randomly selected plants from the central
rows of each plot was measured in centimeters from the
ground to the tip of spike, excluding awns at maturity and
means were taken. Effective tiller numbers (ETN) the first
10 x 20 cm area was demarcated after emergency, and
number of plants existed in that area was counted and
recorded (Bekalu and Mamo, 2016).At physiological
maturity, recounting was done on demarked area;because
maximum tillers produced during vegetative phase and
senescence occurs at maturity (Tanguy et al., 2004). Then
the difference between the first and the second was
recorded. From the difference, total number of productive
plants are divided to the first count and recorded as
productive tiller per plant.
Yield and Yield Components: Spike Length (SL) was
measured from ten randomly selected plants of the inner
rows in centimeter on each plot and the mean length was
calculated per plot by measuring from the base to the
upper most part of the spike excluding awns at maturity.
And umber of kernels per spike (NKPS) was also counted
from ten randomly selected plants from the inner rows of
each plot and the mean kernel number was taken at
harvesting. Moreover, thousand kernel weight (g)was
measured after threshing from each plot and kernel
weights was measured with sensitive balance after
adjusting the grain moisture content to 12.5%.Biomass
yield (BY)was measured by weighing the sun dried total
above ground plant biomass (straw + grain) from the net
plot area of each plot. Grain yield (GY) was measured by
taking the weight of the grains threshed from the net plot
area of each plot and converted in to tones per hectare
after adjusting the grain moisture content to
12.5%.Furthermore, harvest Index (HI) of each treatment
was calculated as the percent ratio of grain yield to the total
above ground biomass by using the formula of Donald
(1962).
Weeds Parameters: Weed flora was identified and
recorded using color manuals of weed identification (ATA,
2016). Still those found difficult to identify were recorded
as other weeds. And weed population were counted at 18
days after planting. The population count was taken from
each plot area and converted to per meter square.
Moreover, weed density present in the experimental field
was recorded from each plot in each replication just at
maturity of the crop. Then after, weed aboveground dry
biomass (g) was harvested from each plot independently
from crops during crop harvesting. The harvested weeds
were placed into paper bags separately and drying in
sunlight for three weeks until constant weight measured
and consequently the dry weight was converted into gm-2.
Weed dry weight were subjected to square root
transformation, using the formula √𝑋 + 0.5
2
to ensure
normality before analysis (Merhawit, 2018).
Weed control efficiency (WCE) was calculated to
determine the variation in the dry matter weight
(accumulated due to competition with the wheat plants) of
the treated plots or to estimate the competitive ability of
weeds at different growth stages as compared to the
weedy check (Walia, 2003) and will be computed as
follows: 𝑊𝐶𝐸 =
𝐷𝑀𝐶−𝐷𝑀𝑇
𝐷𝑀𝐶
× 100
Where: DMC is dry matter weight of weeds in weedy check
(control); DMT is dry matter weight of weeds in a treated
plot.
RESULTS AND DISCUSSION
Phenology and Growth Parameters of Wheat
Days to heading: Analysis of variance revealed that the
main effects of time of weeding, seed rate and their
interactions effect has highly significant on days to heading
at (p<0.01 )( Table2).The interaction effects affected that
days to heading by delaying 67 days at 3WAE at 150kgha-
1seed rate. Earliest days to heading (58.67) was recorded
at combination of two weeks after emergency (2WAE) and
175kgha-1seed rate (Table 3).The earliness to heading in
highest seeding rate and early weeding might be due to
the higher competition to resources and late emerging
weeds that are competing for long period of time with
wheat crop. Similarly, Worku (2008) indicates that
increasing the levels of seeding rate decreased the days
to heading consistently.
Days to maturity: The main effect of time of weeding and

World Res. J. Agric. Sci. 234
time of weeding as well as their interaction effect had high
significant (p< 0 .01) effect on physiological maturity (Table
2). The data shows that increasing seed rate decreases
days to physiological maturity (Table 3).This result was in
line with the finding of Dawit et al, (2014) increasing seed
rate from 100kgha-1 to 150kgha-1 decreases physiological
maturity of bread wheat. However; early weeding at 2WAE
with lower seed density also influences days to
physiological maturity (Table 3).. This might be due to late
germination of weed seeds and inter row resource
competition. Late weeding with the interaction of seed rate
also influences days to maturity of bread wheat. The
interaction of 4WAE with 150kgha-1 delays days to
physiological maturity of bread wheat at 112 days (Table
3).
Plant height (cm): Planting density determines the
growing situation by affecting the competition for space
and production resources. However; the current
experiment showed not significantly different in plant
height with varying seed rates, weeding time and their
interaction effect (Table 2).Although it was not significantly
different, plant height influenced by seed rate figuratively.
Maximum plant height (77.17cm) was obtained from 125
kgha-1 followed by 150kgha-1 seed rate and 175kgha-
1indicating a decreasing of plant height with an increasing
seed rate (Table 4.). This is due to high plant density
remains with maximum competition for resources. This
study is in agreement with Tewodros et al., (2017), and
(Baloch et al., 2010).
Yield and Yield Components of Wheat
Effective tiller number: Crop yields are generally
dependent upon many yield contributing agents. Among
these, number of effective tillers is the most important
because of the contribution to final yield. Analyzed data
indicates that main effects of seed rate and weeding time
were highly significantly (p<0.01) affects number of
effective tiller of bread wheat. However; the interaction
effect of seed rate and weeding time has shown not
significant effect on effective tiller number of bread wheat
(Table 2). More number of effective tillers had been
recorded at lower seed rate (125kgha-1) whereas the lower
number of productive tillers of bread wheat were observed
at higher seeding rate (175kgha-1). The result of an
experiment indicates that number of effective tiller
decreases from (6.5- 4.83) when seed rate increased from
125kgha-1 to 175kgha-1 (Table 4).
Table 2: Mean squares of ANOVA for phenology growth and yield components of Wheat at Amuru in 2019.
Df Mean Squares
Source of variation DH DM PH ETN SL NKPS
Replication 2 0.53 0.03 13.40 0.25 1.15 77.19
Weeding time 3 62.62** 28.44** 44.22ns 21.73** 0.25ns 69.81ns
Seed rate 2 49.53** 91.44** 8.90ns 8.58** 0.03ns 21.19ns
Weeding time*seed rate 6 13.01** 6.11** 42.28ns 0.29** 0.18ns 77.19ns
Error 22 0.67 0.03 31.45 0.46 0.33 76.01
Where * and ** shows significant different at 5% and 1% level, respectively, ns= Not significant different, Df=Degree of
Freedom DH = Days to Heading DM = Days to Physiological Maturity PH= Plant Height, ETN = Effective tiller number SL=
Spike Length, NKPS= Number of kernels per Spike.
Table 3: Interaction effect of weeding time and seed rate on days to 50% heading and days to maturity at Amuru
District in 2019.
Means followed by the same letter are not significantly different at 5 % probability levels. LSD = Least significance
difference CV = Coefficient of Variation WAE = Week after emergency, FP=Farmer practice, DH = Days to Heading DM
= Days to Physiological Maturity.
The decrease in the number of tillers beyond the seeding
rate of 125 kg ha-1 might be due to the high competition
among the plants for available resources. This result was
in line with the findings of Intisar et al., (2017) and
Seleiman et al. (2010).Effective number of tillers had been
affected by weeding time. The result revealed that early
Weeding time Seed rate
DH DM
125kg 150kg 175kg 125kg 150kg 175kg
2WAE 60.67c 60.00bc 58.67a 104.00c 106.00d 102.00
3WAE 65.33d 66.00de 59.00ab 104.00c 106.00d 102.00c
4WAE 65.00d 67.00e 60.00bc 106.66e 112f 104.00c
FP 59.00a 59.00ab 59.00ab 106.00d 106d 100.00a
LSD (5%) 1.29 0.28
CV % 1.2 0.2

Asfaw et al. 235
weeding has positive impact on tillering of bread wheat.
Higher number of effective tiller (6.78) recorded at early
weeding two weeks after emergency in contrast to lower
number of tillers (3.44) when it was weeded late 4WAE and
farmer practices (Table 4). The result confirms to Haile and
Girma (2010) report.
Spike length (SL): The analysis of variance for spike
length shows no significant difference among the
treatments (Table 2); however, a relatively higher spike
length of 6.72 cm was recorded from 150kgha-1 seed rate
closely followed by 125 and 175kg ha-1 seed rate which
produced 6.65 cm long spike. Also relative differences
were recorded on the main effect of weeding time. Higher
spike length 6.84cm was recorded on 4WAE (Table 4).
The current experiments coincide with the findings
reported by (Tewodros et al., 2017). The length of spike
plays a vital role in wheat towards the grain per spike and
finally the yield (Shahzad et al., 2007).
Number of kernels per spike (NKPS): Data analyzed
indicates that number of kernels per spike had not
significantly influenced by main effects and their
interaction effects (Table 2). However; maximum number
of kernel per spike 55.4 was recorded at 4WAE and 53.2
was obtained from 175kgha-1 seed rate. Lowest number of
kernels per spike 50.6was obtained from 125kgha-1 seed
rate and 45.6 numbers of seed was counted from farmer’s
practices (Table 4).Similar result was reported by Abiot
(2017).
Thousand kernel weight: The main effect of seed rate
and time of weeding had significant (p<0.05) effect on
thousand kernel weight. However, the interaction effect of
seed rate and weeding time did not show significant effect
on thousand kernel weights ((Table 4). Maximum
thousand kernel weight 29.11gm was recorded from
125kgha-1 seed rate. While minimum thousand kernel
weights was recorded from 175kgha-1 seed rate which was
27.00gm.
Weeding time also influences thousand kernel weights as
two week weeding after emergence of seedling resulted in
29.11gm maximum thousand kernel weight while minimum
kernel weights 26.89gm was recorded from weed check.
Similar finding was reported by Amare and Mulatu (2017)
and Jemal et al., (2015) who reported that increasing
seeding rate significantly decrease 1000-kernel weight.
Biomass Yield (BY): Analysis of variance shows that the
main effect of weeding time and seed rate had highly
significant (p<0.01) effect on above ground dry biomass.
However, the biomass yield was not significantly affected
by the interaction effect by both factors. Highest biomass
yield (2.875kg/plot) was observed at f175kgha-1seeding
rate whereas lower biomass yield (2.4kg/plot) was
obtained from 125kgha-1 seeding rate (Table 4). The
increased in biomass production might be attributed to the
increased plant population due to higher seeding rate and
number of plant. Biomass yield is highly inclined by crop
nutrition and planting density.
The present result is in agreement with the finding of Jemal
et al. (2015) reported that higher biomass yield was
recorded by increasing seed rates from175kgha-1 to
200kgha-1. Higher above ground dry biomass yield
2.961kg was recorded at weeding of four week after
seedling emergence of the wheat (Table 4)..This might be
due to different characteristics of weeds, ecological
condition and crop genetic character. And while lower
biomass yields (1.994kg) was obtained from the control
(Table 4). Mizan et al. (2009) reported that the increased
dry matter weight of the crop was highly governed by the
length of weed free period. Therefore, prolonged weed
competition resulted in reduced biomass accumulation
and shorter spike length and thousand kernels weight,
which ultimately translated into lower grain yield.
Table 4: Main effects of weeding time and seed rate on growth, yield and yield related Characters of Wheat at
Amuru District in 2020
Weeding PH ETN SL NKPS TKW BY GY HI (%)
2WAE 75.62 6.78b 6.84 53.9 29.11b 2.75b 3.76b 39..96b
3WAE 76.38 6.56b 6.71 51.9 28.22b 2.81bc 3.81b 40.74b
4WAE 79.25 6.22b 6.62 55.4 28.78b 2.96c 4.01c 41.12b
FP 73.88 3.44a 6.44 45.6 26.89a 1.99a 22.64a 34.10a
LSD (5%) 5.48ns 0.66 0.56ns 8.5ns 1.15 0.15 1.02 1.26
Seed rate
125kg 77.17 6.5c 6.72 50.6 29.17b 2.4a 3.28a 37.28a
150kg 76.19 5.9b 6.63 51.3 28.58b 2.61b 3.31a 39.40b
175kg 75.45 4.8a 6.62 53.2 27.00a 2.87c 3.80b 40.25b
LSD (5%) 4.74NS 0.57 0.48ns 7.4ns 0.48 0.13 1.85 2.28
CV % 7.4 11.8 8.6 16.9 4.2 6.0 6.2 6.9
Means followed by the same letter are not significantly different at 5 % probability levels. NS= not significant, WAE = Week
after emergency, FP=Farmer practice, PH=Plant height, ETN= Effective Tiller number, SL= Spike length, NKPS= Number
of Kernels per spike TKW=Thousand Kernel Weight, BY=Biomass yield, GY=Grain yield, HI= Harvest Index

Grain yield (Qt): Analysis of variance showed that the
main effect seeding rate and weeding time had highly
significant effect (p < 0.01) on grain yield. However, the
interaction effect of seeding rate and weeding time showed
not significant (p>0.05) effect on grain yield (Table 4). The
highest grain yield (3.80 t ha-1) was obtained at the seeding
rate of 175 kg ha-1 and the lowest grain yield (3.28 tha-1)
was obtained at seeding rate of 125kg ha-1(Table 4). The
maximum grain yield obtained sowing of higher seeding
rate might be due to high density of plants in rows and
increased number of spikes per rows as a result number
of grains and increased spike number in rows. It conforms
to Haile et al. (2013) finding who reported that the lowest
seeding rate (100 kg ha-1) resulted in a grain yield of 3.85
t ha-1, which was significantly lower than the yields
obtained at the other seeding rates (150 and 175 kg ha-1).
Besides this, Hussain et al. (2010) and Abiot (2017)
reported that grain yield increased as seeding rate was
increased from 50 to 150 and from 100 to 150 kg ha-1,
respectively.. Contrarily to this, Amare and Mulatu (2017)
reported maximum grain yield (3.69 t ha-1) from a seed rate
of 100 kg ha-1.
The result also showed (Table 4) highest grain yield 4.0tha-
1 was obtained from weeding four weeks after emergency
(4WAE) while the lower grain yield 2.26tha-1 was recorded
on wheat which was stayed with weedy for long period of
time (FP). Early weeding 2WAE and 3WAE resulted in
relatively not more difference grain yields. But late
weeding decline grain yield from 4014kgha-1 to 2264kgha-
1.The decrease in yield with the increase in the duration of
competition might be the result of increased weed dry
weight and weed population, which might have influenced
the number of productive tillers per meter/square and grain
spike-1.This study was in line with the findings of Merhawit,
(2018) reported wheat grain yield decreased with delays in
weed removal; and vice versa.
Harvest Index (HI in %): The ability of cultivar to convert
the dry matter into economic yield is indicated by its
harvest index. The higher the harvest index value, the
greater the physiological potential of the crop for the
converting dry matter to grain yield. The result showed
harvest index was very highly significantly (p<0.01)
affected by the main effect time of weeding. Highest
harvest index 41.12% was obtained from plots weeded
three weeks after seedling emergence. Minimum harvest
index was recorded from farmer practice (Table 4).
Maximum harvest index 40.25% was observed at
125kgha-1. Lower harvest index 37.28% was obtained from
125kgha-1.
Weed characteristics
Weed flora: The major weed species identified in the
experimental site were Guizoti ascabra (22.47%) with
population density(370), Phalaris paradoxa (22.10%) with
population (364), Plantago lanceolata (18.58%) with
population density (306), and Bidens pilosoL.(8.74%) were
the dominat weed species in the experimental area in
decreasing order while Snowdenia (3), Chrysanthemum
segantum (20), Commel benghalensis L.(20) and Avena
fatua L. weed species were present in low proportion
(Table 5).Thus the study revealed the broadleaved weed
species were more dominating the experimental field than
grass and sedge weed species. Similar findings were
reported by Merhawit (2018) and likewise Burgos et al.,
(2006), reported that broadleaved weed (72%) and Grass
(24%) dominated from the total weed spectrum, whereas
sedges (4%) were minor.
Table 5: Weed population found in the experimental area during 2019 cropping season at Amuru area.
Local name Scientific name Category Population
observed
Relative weed density %
Gargaaraa Eleusine indica (L.) Grass 19 1.2
Cuqii Guizotia scabra Broad leaved 370 22.47
Qorxobbi Plantago lanceolata Broad leaved 306 18.58
Migira saree Phalaris paradoxa Grass 364 22.10
Maxxannee Bidens piloso Broad leaved 144 8.74
Margajabbii Polygonum Broad leaved 93 5.65
Muujjaa Snowdenia Grass 3 0.2
Abba kaasii Chrysanthemum segantum Broadleaved 20 1.21
Waratii Digitaria sanguins Grass 48 2.91
Gutichee Galinsoga paniyflora Broadleaved 130 7.9
Qunnii Cyperesus Sedge 50 3.0
Sinaar Avenafatua L. Grass 26 1.6
- Leucas martinicensis Grass 50 3.0
Gororafardaa Commel benghalensis L. Grass 20 1.21
Total 1643 100

Asfaw et al. 237
Weed relative density: The data indicated that the main
effect of weeding time and seed rate was highly significant
(p < 0.01) effect on relative weed density. However; the
main effect of seed rate and the interaction effect were not
significant (p < 0.05) (Table 6).Maximum relative weed
density (72.7%) was observed on the weedy or farmer
practice. Whereas the lowest relative weed density
(26.6%) was observed on the treatment which was
weeded after four week of seedling emergence. Early
weeding two week after emergency shows higher 40.4%
relative weed density than weeding three weeks (35.0%)
after emergence (Table 7).This implies that increasing
days to be weeded from 22 to 40 days after emergence
reduced weed relative density. However; late weeding
after 45 days of emergency increased weed relative
density to 72.7% (Table 7). It might be due to some weed
species germinate after two week of weeding. Another
study by Mitiku and Dawit (2014) indicated smallest weed
density was recorded at application of topic at 30thday and
highest weed density was recorded on control.
Table 6: Mean squares of ANOVA for weed control
efficiency, weed dry biomass and weed relative
density at Amuru in 2019.
Source of
variation
Df Mean Squares
WCE WDBM WRD
Replication 2 21.32 109.1 644.2
Weeding time 3 18296.0** 171135.4** 3657.9**
Seed rate 2 2.5NS 229.75* 130.8*
Weeding
time*seed rate
6 0.69ns 207.63* 52.7
Error 22 8.25 42.90 166.6
Where * and ** shows significant different at 5% and 1%
level, respectively, ns= Not significantly different,
Df=Degree of freedom, WCE=weed Control Efficiency,
WDBM= Weed Dry Biomass and WRD= Weed Relative
Density.
Table 7: Main effects of weeding time and seed rate on
week after emergency and weed relative density at
Amuru District in 2019.
Weeding WRD WCE(%)
2WAE 40.4b 96.76b
3WAE 35.0ab 96.77b
4WAE 26.6a 98.08b
FP 72.7c 7.03a
LSD (5%) 12.62 2.81
Seed rate
125kg 47.2 74.14
150kg 43.0 74.82
175kg 40.7 75.01
LSD (5%) 10.93ns 2.43ns
CV % 29.6 3.8
Means followed by the same letter are not significantly
different at 5 % probability levels. ns not significant WAE =
Week after emergency WRD= Weed Relative Density,
WCE=weed Control Efficiency,
Weed dry biomass (g): There was significant difference
(p< 0.01) of main effects of weeding time and seed rate on
weed aboveground dry biomass (Table 6). Higher weed
dry weight (106.7g) were recorded at farmer practices
weeded two week after emergence of seedling. Whereas
lower weed dry biomass (1.4g) were obtained at 150kg
seed rate were weeded four week after emergence.
Analyzed data reveals that increasing seed rate from
125kgha-1 to 175kgha-1 had been decreased weed dry
biomass from 28.1g to 20.0g (Table 8).
Increasing seed rate increases intra-row competition for
resources that might reduce weed dry matter accumulation
and late emerging soil seed bank weeds suppressed by
crops. At earlier, hand weeding controlled the emerged
weeds and those that emerged later might have failed to
accumulate sufficient dry matter due to the competition
offered by well grow crop plants. Further, the weed seeds
under soil seed bank that might have been brought to the
upper soil layer by hand weeding, germinated and
emerged later, but were in their initial growth stage
accumulate less dry weight. The result was in agreement
with the findings of Merhawit, (2018) that reported as weed
competition from 15 to 30 DAS had no significant
differences in total dry weight of weed. However, beyond
45 DAS up to weedy check throughout the growing season
increased significantly. This result in harmony with Tyagie
et al.,(2013) of an increase in weed dry weight with
increasing weedy period as a result of prolonged weed
growth period.
Table 8: Interaction effect of weeding time and seed
rate on weed dry biomass at Amuru District in 2019
Weeding time Seed rate
Weed dry biomass
125kg 150kg 175kg
2WAE 2.3a 2.9a 3.0a
3WAE 4.0a 3.0a 2.4a
4WAE 2.0a 1.4a 1.6a
FP 106.7d 89.7c 73.0b
LSD (5%) 11.09
CV % 26.9
Means followed by the same letter are not significantly
different at 5 % probability levels. LSD = Least significance
difference CV = Coefficient of variation WAE = Week after
emergency, FP=Farmer practice
Weed control efficiency (%): There was highly
significance difference (p < 0.01) effect of main effect
weeding time and seed rate on weed control efficiency.
However; the interaction effect of main effects weeding
time and seed rate was not significantly different (Table 6)
Significance differences were observed by various early
weeding, weeding three week after emergence, weeding

after four week of seed emergence compared with farmer
practices. Maximum weed control efficiency (98.08%) was
observed at weeding weeds four week after emergence
which was statistically at par with weedy up to three week
(96.77%) after seedling emergence. While minimum
control efficiency (7.03%) was recorded at farmer
practices (Table 7). Increasing days to weedy from two
week to four week, increases controlling efficiency. Weeds
allowed to in fest more than 45 days after sowing
significantly reduced weed control efficiency. The result
was in agreement with the investigation of Merhawit,
(2018) reported as weed control efficiency decreased with
the increase in duration of the weedy period and increased
with the increase in duration of the weed-free period. Weed
control efficiency was inversely related to the dry matter
accumulated by weed.
CONCLUSION
Right time of weeding and optimum seed rate are most
important agronomic factors influencing production and
productivity of bread wheat. The result revealed that the
interaction effect of weeding time and seed rate of wheat
were highly significantly (p< 0.01) affected days to 50%
heading, days to 90% physiological maturity and effective
number of tillers per plant were highly significantly affected
by seed rate and weeding time. Weeding three weeks after
emergence took the longest period of time (67) days to
heading under 150kgha-1 and shortest days to heading at
125kgha-1 under farmer practices. Long days to maturity
were observed at 125kgha-1 under weeding four week after
emergence. Besides this, higher effective tiller numbers
per plant (6.78, 6.5) was recorded from 125kg seed rate
that weeded at two weeks after emergence, respectively,
And lower numbers (3.4 and 4.8) were recorded from
farmer practice and at 175kg seed rates, respectively.
Similarly, grain yield (4.01 and 3.79 tha-1) was obtained
from 175kg seed rate weeded at four weeks after
emergence.
Furthermore, the results also revealed that interaction
effect of weeding time and seed rate was highly
significantly (p<0.01) affected the weed above ground dry
biomass. Weed above ground biomass produced
maximum 106.7gm/plot at 125kg and minimum (1.4gm)
weight at 150kgha-1 seed rate. Guizotia scabra (22.47%)
with population density (370), Phalaris paradoxa(22.10%)
with population (364), Plantago lanceolata (18.58%) with
population density (306), and Bidens piloso L. (8.74%)
were the dominant weed species competing with wheat in
the study area showing the major weeds consisted of
broad leaved, grass and sedge weeds. And Guizotia
scabra weed had the highest relative density of22.47%.
Moreover, maximum relative weed density (72.7%), weed
above ground dry weight (89.9gm) and minimum weed
control efficiency was observed on farmer practices.
Whereas minimum relative weed density (26.6%), weed
above ground dry biomass (1.7gm) and maximum weed
control efficiency (98.08%) was recorded from weeding
four weeks after emergency. The result suggest that
maximum yield loss due to late weeding after 45days was
43.59% and 40.34% when compared with four weeks after
sawing and 175kgha-1 seed rate. Thus, to prevent more
than 10% yield loss the efficient weed control methods for
wheat variety Leban should be under taken by keeping the
crop weed free between 30-40 DAS with 175kg-1.
ACKNOWLEDGMENTS
We thank the Amuru Woreda Administration Office and
Agricultural and Natural Resource Offices for their
administration support rendered during study period.
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Accepted 18 September 2020
Citation: Asfaw Takele, Zerhun Jalata, Reta Fikadu
(2020). Growth and Yield Response of Bread Wheat
Variety Grown Under Varying Seed Rate and Weeding
Time. World Research Journal of Agricultural Sciences,
7(2): 230-240.
Copyright: © 2020 Asfaw et al. This is an open-access
article distributed under the terms of the Creative
Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium,
provided the original author and source are cited.

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