Journal of Horticulture and Plant Research
ISSN: 2624-814X, Vol. 6, pp 11-19
doi:10.18052/www.scipress.com/JHPR.6.11
2019 SciPress Ltd., Switzerland
Submitted: 2018-12-17
Revised: 2019-01-31
Accepted: 2019-02-15
Online: 2019-04-03
Diversity of Weed Species in Farms Kisii Central Sub-County,
Western Kenya
Nyamwamu N. Charles1,a*, Karanja Rebecca2,b, Mwangi Peter3,c
1,2,3Department
of Botany Jomo Kenyatta University of Agriculture and Technology, P. O. Box
62000 Nairobi, Kenya
anyamwamucharles@gmail.com, brebeccakaranja3@gmail.com, cpnmwangi@fsc.jkuat.ac.ke
Tel. +254725566033, Tel. +254722601849, Tel. +254723412269
Keywords: Asteraceae, Abundance, Competition, Importance Value, Smallholder Farmers
Abstract. This study sought to determine species diversity and Importance Values (IV) of weeds in
farms in Kisii Central Sub County, Western Kenya. Eight administrative sub-locations were randomly
selected. Ten farms were selected at equal distance along transect laid across each sub-location.
Quadrant and a line transect laid across each farm were used to collect weed species. Five rectangular
quadrants of 0.5x2m were established in each farm and individuals of each weed species identified
and counted. Importance value (IV) for each weed species was computed from density, frequency
and abundance. Diversity was computed by Shannon index (H’). Twenty four weed species from 22
genera in 10 families were recorded, Family Asteraceae had the highest number of species (6),
followed by Solanaceae and Poaceae with 4 and 3 species respectively. The dominant weed species
were Galinsoga parviflora (IV=241.6%), Pennisetum clandestinum (IV=215.8.7%), Bidens
pilosa (IV=196.7%), Cynodon dactylon (IV=192.4%), Digitaria scalarum (IV=180.8%) and Cyperus
esculentus (172.0%). Weed species diversity was higher (H'=2.81).
1. Introduction
Agricultural weeds are plants that compete with crops for moisture, light, space and nutrients [14],
[21]. Weeds which emerge during the first three months after planting are known to endanger yields
more than those appearing later [10]. Weeds infestation also encourage disease problems, serve as
alternate host for pests, slow down harvesting operation, increase the cost of production and reduce
the market value of crops [1],[21].
[23] studied the weed distribution in maize fields of five Districts of the Punjab, Pakistan and they
found out that weeds were the major challenge farmers were facing in their fields. They recorded that
areas where proper weed knowledge on abundance and distribution was available to farmers’ yields
were better. Understanding abundance and distribution of weed species in the farms is an important
since it helps to determine how weed species changes in response to selection pressures applied by
farmers’ agro-economic practices [20]. Kisii Central Sub County is one of the regions that food crops
are produced for both subsistence and commercial purposes [12]. The region is blessed with fertile
soils and sufficient rains that favour both crops and weeds to thrive.
2. Materials and Methods
2.1 Description of the study area
Kisii Central Sub County is one of the nine Sub Counties of Kisii County in Southwest Kenya.
Temperatures can range from 10ºC to 30ºC. According to [13], Kisii Central Sub County (formerly
Kisii Central district) had a population of 588,000, but with a population growth rate of 3.6% the
population is now over 700,000 (19% of whom live in urban areas. It is one of the most densely
populated Sub Counties in Kenya and covers an area of 317.4Km2. Due to the high population density,
almost all land is put to maximum agricultural use. Land is subdivided within families, meaning that
farm size is reducing and an average farm is only 15,000 m² in area [18], with an average of a quarter
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of an acre allocated for arable farming throughout the Sub County and almost all farmers engage
largely in subsistence and minimal commercial production. The acreage under cash crops in Kisii
Central Sub County is approximately 3,800ha while the area under food crops is about 12,500ha [19].
Livestock production is dominated by dairy and local poultry. Agriculture employs an estimated 80%
of the population either directly or indirectly and the estimated rural poverty is 30% with some areas
having as high as 61% according to Kisii county profile [19].
N
Figure 1: Kisii County, Nyanza Province of Kenya,
where the research was undertaken.
Source: Adopted from Google maps (20/4/2017)
Figure 2: Kisii Sub-County Regions in
Kisii County where the research was
done.
Source: Kisii county profile plan
(20/4/2017)
2.2 Data collection
Eight administrative sub-locations were randomly selected in the sub-county and then grouped into
respective altitude zones based on their altitude levels from the lowest to the highest (T1 to T8). Ten
farms were then selected at an equal 50m distance along a 150m transect laid across each sub-location
in the study area and a survey of the weed species was done. Five quadrants of 0.5x2m were
established in each farm along the laid line transect and collection of the weed species done. The
individuals of each weed species were counted and recorded. Botanical identification of weed species
was done by analysis of vegetative and reproductive parts in reference to guide books and comparing
the weeds with voucher specimen deposited at National Museums of Kenya. Plant taxonomists were
consulted to help identify weed species that could not be identified in the field.
3. Data Analysis
Assessment of phytosociological structure was done using absolute and relative values of density,
frequency, abundance and importance value for each weed species [7]. The following parameters
were computed;
Absolute frequency = number of sampling units with species present/total number of sampling units.
Relative frequency = species absolute frequency/sum of all absolute frequencies x100.
Absolute density = (frequency/100) x Abundance/ no. of quadrants.
Relative density = absolute density of a species/ sum of all absolute densities x 100.
Journal of Horticulture and Plant Research Vol. 6
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Absolute abundance = total number of individuals of a species/total number of sampling units
containing that species x 100.
Importance value = relative frequency + relative density + relative abundance
Weed species diversity was evaluated using Shannon Index (H/) based on natural logarithm which
considers equal weight between abundant and rare weed species. Higher values of the diversity index
obtained will imply greater weed species diversity [26].
4.0 Results
4.1.1 Weed species in the farms
A total of 24 weed species belonging to 12 families were recorded in the farms and the highest
numbers of weed species were recorded by family Asteraceae which dominated greatly with 6 species
followed by family Solanaceae with 5 species and family Poaceae had 3 species while the rest had 2
and 1 species respectively. Most of these weed species occurred in all altitude zones, an indication of
being well adapted to varied ecological conditions and can also withstand weed management practices
like hoeing and hand pulling while other weed species occurred in specific localities of a few altitude
zones. There were weed species families represented by 1 species as shown in Table 1.
4.1.2 Abundance of Weed species
Weed species were classified into families, genera and species names as shown in Table 1.
Table 1: Families, Genera and Species Names.
No.
Family
Genus name
Species name
1
Poaceae
Pennisetum
Digitaria
Cynodon
Bidens
Tagetes
Galinsoga
Conyza
Emilia
Crassocephalum
Commelina
Nicandra
Solanum
P. clandestinum
D. scalarum
C. dactylon
B. pilosa
T. minuta
G. parviflora
C. bonariensis
E. brachycephala
C. vitellinum
C. benghalensis
N. physalodes
S. nigrum
S. incanum
D. stramonium
P. ixocarpa
A. hybridus
A. aspera
G. gynandra
O. latifolia
C. esculentus
C. rotundus
P. aquilinum
L. mollissima
L. martinicensis
2
3
4
Asteraceae
Commelinaceae
Solanaceae
5
Amaranthaceae
6
7
8
Capparaceae
Oxalidaceae
Cyperaceae
9
10
Dennstaedtiaceae
Lamiaceae
Datura
Physalis
Amaranthus
Achyranthes
Gynandropsis
Oxalis
Cyperus
Pteridium
Leonotis
Leucas
No. of
individual
species
3987
3710
4518
3621
3411
5625
2226
1259
445
1212
877
445
1392
864
126
3116
605
319
1525
3774
1084
224
47
42
Total
12,215
16,587
1212
3704
3721
319
1525
4858
224
89
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Number of weed species
7
6
6
5
5
4
3
3
2
2
2
2
1
1
1
1
1
0
Families
Figure 3: Number of weed species in each family in the farms.
4.2 Diversity of weed species in the farms
Diversity of the weed species was calculated using Shannon index (H/);
H/=-∑ pi (lnpi)
Where, H/= diversity
∑= Summation,
pi = Ni/Ntotal,
ln = natural logarithim,
Ni= number of individuals of species i
Ntotal = Total number of individuals in all species
Table 2: Diversity of weed species in the farms
Weed species
Pennisetum clandestinum
Bidens pilosa
Digitaria scalarum
Commelina benghalensis
Tagetes minuta
Galinsoga parviflora
Nicandra physalodes
Amaranthus hybridus
Cynodon dactylon
Solanum incanum
Gynandropsis gynandra
Oxalis latifolia
Conyza bonariensis
Datura stramonium
Solanum nigrum
Cyperus esculentus
Emilia brachycephala
Pteridium aquilinum
Total No. of
individuals
3987
3621
3710
1212
3411
5625
877
3116
4518
1392
319
1525
2226
864
445
3774
1259
224
pi=
Sample/sum
0.089
0.082
0.084
0.027
0.077
0.127
0.019
0.070
0.102
0.031
0.007
0.034
0.050
0.019
0.010
0.085
0.028
0.005
ln(pi)
Pi * ln (pi)
-2.419
-2.50
-2.477
-3.612
-2.564
-2.064
-3.963
-2.659
-2.283
-3.474
-4.962
-3.381
-2.996
-3.963
-4.605
-2.465
-3.576
-5.298
-0.215
-0.202
-0.205
-0.098
-0.197
-0.262
-0.075
-0.186
-0.233
-0.108
-0.035
-0.115
-0.150
-0.075
-0.046
-0.210
-0.100
-0.027
Journal of Horticulture and Plant Research Vol. 6
Crassocephalum vitellinum
445
Leonotis mollissima
47
Leucas martinicensis
42
Physalis ixocarpa
126
Cyperus rotundus
1084
Achyranthes aspera
605
SUM = 44,454
0.010
0.001
0.0009
0.003
0.024
0.014
15
-4.605 -0.046
-6.908 -0.0069
-7.013 -0.0063
-5.801 -0.017
-3.730 -0.092
-4.269 -0.100
TOTAL = -2.807
H/ =2.81
Diversity index H/ =summing up of pi * ln (pi) of each weed species.
4.2.2 Weed species Importance Value
Importance value of each weed species was calculated and tabulated as shown in Table 3.
Table 3: Weed species importance value in farms across the altitude zones.
Botanical name
T1
T2
T3
T4
T5
T6
T7
T8
TOTAL
Galinsoga parviflora
Pennisetum clandestinum
Bidens pilosa
Cynodon dactylon
Digitaria scalarum
Cyperus esculentus
Tagetes minuta
Amaranthus hybridus
Conyza bonariensis
Emilia brachycephala
Solanum incanum
Commelina benghalensis
Oxalis latifolia
Datura stramonium
Nicandra physalodes
Cyperus rotundus
Solanum nigrum
Crassocephalum vitellinum
Achyranthes aspera
Physalis ixocarpa
Leucas martinicensis
Pteridium aquilinum
Gynandropsis gynandra
Leonotis mollissima
30.9
25.8
28.4
28.4
24.8
25.1
18.9
24.1
15.5
11.2
10.4
10.7
14.7
30.3
23.1
22.2
24.0
23.8
19.1
26.1
18.1
12.8
14.9
15.5
12.9
14.9
10.7
11.5
11.2
9.7
-
33.9
30.1
29.2
18.3
26.6
25.6
16.2
13.7
11.0
12.4
15.2
9.4
15.2
10.1
7.3
7.6
18.7
12.3
-
32.4
32.5
23.3
22.9
19.2
21.1
23.9
26.8
16.5
14.6
10.4
16.1
16.5
7.9
21.2
-
34.9
33.5
28.8
28.3
21.9
29.8
19.4
28.2
19.5
13.5
12.8
18.5
10.7
-
20.3
21.7
20.3
19.6
18.4
22.2
17.8
18.4
11.8
8.6
15.2
9.1
13.0
7.4
8.7
13.3
9.0
5.8
-
30.6
29.1
22.9
24.1
24.6
23.7
26.8
20.5
15.2
13.3
12.8
17.9
12.4
14.3
12.0
-
28.3
20.0
21.6
26.8
21.5
24.5
24.5
17.5
12.1
15.5
9.0
15.9
9.3
14.9
4.8
13.0
2.6
4.7
10
3.4
241.6
215.8
196.7
192.4
180.8
172.0
150.4
139.8
133.1
87.8
82.5
82.4
70.4
66.2
65.4
64.1
47.8
47.5
39.1
31.9
21.3
17.0
14.8
10.0
11.1
4.8
6.3
5.0 Discussion
5.1 Abundance of Weed species Families.
A total of 24 weed species belonging to 12 families were recorded in the farms and the highest
numbers of weed species were recorded by family Asteraceae which dominated greatly with 6 species
followed by family Solanaceae with 5 species and family Poaceae had 3 species while the rest had 2
and 1 species respectively as shown in Figure 3. Most of these weed species occurred in all altitude
zones, an indication of being well adapted to varied ecological conditions and can also withstand
weed management practices like hoeing and hand pulling while other weed species occurred in
specific localities of a few altitude zones. There were weed species families represented by 1 species
as shown in Table 3. Similar studies were conducted by [30] and found out the weed communities of
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Volume 6
wheat crop in district Toba Tek Singh, Pakistan and reported 38 weed species \distributed among 35
genera and 17 families were recorded while [17] studied the importance Value Index (I.V.I) of weed
flora of some maize fields of Tehsil Gojra and reported 34 weeds which were distributed among 17
different plant families. In another study, [2] reported twenty three species of 13 families of weeds
of wheat yield from five different localities of village Qambar, District Swat in Pakistan. In a similar
study conducted by [16] in Coffee estates in Kiambu County, Kenya, they reported 19 families
comprising of 47 weed species. [11], also noted that agricultural intensification affects weed species
in arable farms. These results demonstrated that weed species number in arable farms significantly
decreased with extreme altitude levels, similarly to the findings from Central Europe [15] However,
[22] confirmed that species number is negatively correlated with elevation. According to [27], the
number of non-native plant species decreased strongly with elevation extremes. Weed species P.
clandestinum,
G.
parviflora,
B.
pilosa,
D.
scalarum,
C. dactylon and C. bonariensis occur in all altitude zones regardless of prevailing ecological
conditions, an indication of having well adaptive characteristics to various habitats while others like
L. mollissima, L. martinicensis, G. gynandra and P. aquilinum were found to occur in only extreme
altitude levels hence an indication of having limited adaptive features to varied habitats. Weed
management practices in the studied arable farms can be an important factor in determining species
abundance in this study. Low input fields had significantly more species than intensively managed
ones.
5.2 Weed species Diversity
Typical values are generally between 1.5 and 3.5 in most ecological studies, and the index is rarely
greater than 4.0 [26]. The Shannon index increases as abundance of a community increases. The
higher values of H/ indicate greater floristic diversity [26].
The diversity index (H/) obtained was 2.81 as shown in Table 2 and this result agrees with those of
similar studies done in other tropical areas such as that conducted by [3] who obtained diversity index
of 2.53 of weed species in rice fields in Brazil. In the farms, most weed species demonstrated great
capacity to adapt and occur in different altitude zones and tolerance to human management practices.
5.3 Weed species Importance Value
Importance value is the most comprehensive indicator of phytosociology of a given habitat. Table 3
reveals that G. parviflora, P. clandestinum, B. pilosa, C. dactylon, D. scalarum and C. bonariensis
had relatively high I.V and occurred in all altitude zones (T). C. esculentus also scored relatively high
though it did not occur in altitude T2. A. hybridus also recorded a relatively high I.V though it did not
occur in altitude zones T7 and T8. Structural variability in the floristic composition varied across the
altitude zones. G. parviflora ranked as the top scorer at zone T5 and T3 having the Importance Value
of 34.9 and 33.9 respectively. Similarly, P. clandestinum had the highest Importance value (33.5) at
zone T5, while B. pilosa had the highest I.V of 29.2 at zone T3. Weed species E. brachycephala
occurred in all altitude zones except zone (T1) and attained I.V of 87.8. D. stramonium scored I.V.
of 82.5 and was not found in zone T4 and T5. G. gynandra and
L. mollissima occurred only in two zones, T1 and T8 and scoring an aggregate I.V of 14.8 and 10.0
respectively while P. ixocarpa also occurred in two zones T4 and T5 and attained an aggregate I.V
of 31.9. Similarly, L. martinicensis occurred in two altitude zones, T3 and T8 and scored an aggregate
I.V of 21.3. A. aspera, though it occurred only in zone T3, T5 and T8 managed to attain I.V of 39.1.
Weed species with relatively higher I.V had a reasonable stand in all the altitude zones depicting their
widespread occurrence in study area. The Total Importance value summarizes the predominance of
weed species in mathematical terms. In order to obtain potential crop yields from the study area
effective management of these weed species is therefore required. As revealed earlier the
proportionate composition varies from zone to zone, evidently the abundant species like
P. clandestinum, D. scalarum and C. esculentus needs to be addressed because they not only competes
with the crop for nutrients, light and space, but also it increases the cost of production in controlling
them. The seven most dominant weed species were Galinsoga parviflora (IV=241.6%), Pennisetum
Journal of Horticulture and Plant Research Vol. 6
17
clandestinum (IV=215.8%), Bidens pilosa (IV=196.7%), Cynodon dactylon (IV=192.4%), Digitaria
scalarum (IV=180.8%), Cyperus esculentus (IV=172%) and Tagetes minuta (IV=150.4%). G.
parviflora is an American weed, and its centre of origin is considered to be the mountainous area of
Mesoamerica [28]. It is a fast-growing annual herb with the capacity to invade agricultural and other
disturbed areas in most temperate and subtropical regions of the world [9]. It is also highly
competitive and can spread quickly, often being the dominant species in a field and generates
significant economic impact on crop systems, greenhouses, gardens and nurseries [6].
Pennisetum clandestinum is a rhizomatous grass with matted roots and a grass-like or herbaceous
habit, it is resistant to grazing or mowing since it has extensive root network and easily forms new
shoots [5]. It can climb over other plant life, shading it out and produce herbicidal toxins that kill
competing plants [6]. Bidens pilosa commonly known as cobbler's peg is widely distributed in
tropical and subtropical regions of the world and is reported to be a weed of 31 crops. [7], [25]
provided a checklist of noxious weeds in various crops of District Mansehra, Khyber Pakhtunkhwa
Pakistan.
[8] conducted a study and found out reciprocal competition effects between Cynodon dactylon and
most arable crops like maize. D. scalarum is a creeping, perennial grass with long, slender, branching
rhizomes which form a dense mat beneath the soil surface. It occurs as a weed in a wide range of
crops and soils, including those where minimum tillage is practised. It is a common component of
natural grasslands at higher altitudes in East Africa [4]. Cyperus esculentus is a grass-like weed in
the sedge family (Cyperaceae) and it is a major weed of vegetable and row crops in temperate and
tropical regions around the world with extensive underground network of basal bulbs, roots and
rhizomes. It is aggressive in irrigated crops that are maintained at high soil moisture [31] and is
considered a major weed of vegetables, corn, cotton, and peanuts in the southern United States [29].
Tagetes minuta has been reported that it is a weed of 19 crops in 35 countries infesting 10% of maize
fields, and may be particularly severe in low-growing crops such as beans [9]. Its presence in crops
leads to skin irritation to agricultural workers, contaminates milk when there is external contact
between the plant and cattle udders (imparts an objectionable flavour).
6. Conclusion
This study was conducted for determination of diversity, abundance and Importance Value of weed
species in arable farms in Kisii central sub-county. During the study, a total of 24 weed species of 10
different families were reported. These results pointed out seven dominant and major weed species
in the arable farms that have been problematic to crops. The seven dominant weeds were Pennisetum
clandestinum, Bidens pilosa, Digitaria scalarum, Galinsoga parviflora, Cynodon dactylon, Tagetes
minuta and Cyperus esculentus.
Having evaluated weed species diversity in the study area, it is quite easier to formulate long term
weed management guidelines. The dominant weed species, their families and importance values that
contributed to the weed flora were also put into account. In addition, occasional introduction of new
weed species can easily be monitored and noted because of having a record of the weed species
diversity of the previous years. These findings could help predict infestation and thus lead to
improved weed management measures in arable farms especially for smallholder farmers in Kisii
central sub county.
Conflict of Interest
“The author(s) declare(s) that there is no conflict of interest.”
There was no role of the funding sponsors in the design of the study; in the collection, analyses or
interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
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Volume 6
Acknowledgement
The author would like to acknowledge the input, support and guidance accorded by the project study
supervisors, the botany department of Jomo Kenyatta University of Agriculture and Technology, all
the farm managers and management staff of the surveyed farms, the survey biometricians, the
librarians at the museums of Kenya, Mr. Wanjohi, P. K. (University of Eldoret) and botany
departments (Kisii University).
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