By Leonard, JA; Kudra, AB; Baijukya, F; Tryphone, GM (2022).

Greener Journal of Agricultural Sciences

Vol. 12(1), pp. 75-85, 2022

ISSN: 2276-7770

Copyright ©2022, the copyright of this article is retained by the author(s)

https://gjournals.org/GJAS

 

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Common Weeds Found in Selected Cassava Farms in Eastern Zone of Tanzania

 

 

Joseph A. Leonard1; Abdul B. Kudra1; Frederick Baijukya2; George M. Tryphone1

 

 

1Department of Crop Science and Horticulture, Sokoine University of Agriculture,

 P. O. Box 3005 Chuo Kikuu, Morogoro, Tanzania.

2Internatioal Institute of Tropical Agriculture, P. O. Box 34441 Dar Es Salaam, Tanzania.

 

 

 

ARTICLE INFO

ABSTRACT

 

Article No.: 022422022

Type: Research

Full Text: PDF, HTML, PHP, EPUB

 

A field study was conducted at Kiimbwanindi village, Mkuranga district and Ilonga village, Kilosa district. Coast and Morogoro regions of Tanzania, respectively to identify the common weeds affecting cassava fields. A total of 24 random 1 m × 1 m quadrat were placed in each cassava field where by all weed species found in each quadrat were identified to a species level. During weed identification, weed density, uniformity and frequency were calculated according to Thomas methodology and used to determine weeds’ relative abundance. Also, a composite soil samples were collected based on random sampling procedure at a depth of 0 to 50 cm from each field before land preparation and analysed in the laboratory in order to determine the amount of nutrient content available in the soil. A total of 22 weeds species belonging to 16 families were identified, whereby out of these 14 were broad leaved weeds, 6 grassy weeds, 1 mushroom and 1 sedged weed belonging to 10 perennial and 12 annual weeds plant. During weed identification, Cyperus rotundus and Echinochloa colona were the most abundantweed species while Dactyloctenium aegyptium, Portulaca oleracea, Agaricus sp and Bidens pilosa were the least occurred weed species.  Perennial weeds Cyperus rotundus, Echinochloa colona, Trichodesma zeylanicum, Reissantia sp, Mucuna pruriens and Commelina benghalensis found to be the mostly abundant weed species due to their ability to adapt into various soil types and their ability to reproduce as compared to other weeds. The study recommended that, research toward new or improved weed control measures is needed and also more survey work is needed on a regular basis to identify possible weed population shifts.

 

Accepted:  03/03/2022

Published: 04/04/2022

 

*Corresponding Author

Tryphone GM

E-mail: muhamba@ sua.ac.tz

 

Keywords: Weed, Thomas methodology, Relative Abundance, Weed density.

 

 

 

 

 


INTRODUCTION

 

Weed is any plant that originated in a natural environment and in response to imposed or natural environments evolved, and continues to do so, as an interfering associate with crops of interest and its activities. Weeds can also be defined as the plants, which grow where they are not wanted that is objectionable or interferes with the activities or welfare of man (Rana and Rana, 2016; Conrad et al., 2018). The interference of weeds in the farm, they interface with the utilization of natural resources, harmful, dangerous, prolific, persistent, resistant, competitive, even poisonous, and economically detrimental and can grow under adverse climatic conditions (Rana and Rana, 2016). Weeds have the following characteristics; they have long seed life in soil, they are quick in emergence, they have ability to survive and prosper under the disturbed conditions of a cropped field, they have rapid early growth and they do not have any special environmental requirements for their seeds to germination. Example Rana and Rana (2016), explained that Cyperus rotundus which have 78% viability can propagate through tubers.

Weeds can be classified using taxonomic key (Scientific identification method) where by all the visible characteristics of the plant that remain roughly constant among all individuals within a specific species are identified. Due to that, Rana and Rana (2016) mentioned, there are at least 450 families of flowering plants and over 350,000 different species, in which only about 3,000 of them have been used by humans for food. Fewer than 300 species have been domesticated, and of these, there are about 20 that stand between humans and starvation. Other classification methods are based on life history, habitat and morphology or plant type (Rana and Rana, 2016).

Cassava is highly susceptible to weed infestation especially from perennial weeds. This is because of its initial slow growth rate, wide plant spacing used on its production and the long maturity period of between 12 and 18 months (Chikoye et al., 2001; Howeler, 2007; Ekeleme et al., 2019). According to Olorunmaiye et al., (2013), cited by Ekeleme et al., (2019), in West Africa, environments where cassava is growing tend to be dominated by perennial weed species such as Imperata cylindrica, Chromolaena odorata, Panicum maximum, Cyperus rotundus, and Mimosa invisa (Ekeleme et al., 2019). Also, Reshma et al. (2016) reported cassava requires good weed management during the first three to four months after planting, as it has a tendency of exhibiting slow initial growth and incomplete canopy cover. When the field is kept free from weeds for the first several weeks after planting, it gives the cassava a competitive edge that allows it to out compete weeds that would emerge later in the season (Reshma et al., 2016; Ekeleme et al., 2019).

Since weeds vary not only in their ability to compete with crops and reduce yields but also vary in their response to different management strategies, thus it has been reported that, in Africa, the annual cost of weed control has been estimated to be $ 4.3 billion (Kayeke et al., 2018). Also, Rana and Rana (2016) and Ekeleme et al. (2019), reported that, in cassava production, weeding activities take 50 to 80 percent of the total cassava production budget and poor and improper weeding has been reported to cause cassava root yield losses ranging from 40%-90% (Chikoye et al., 2001; Ekeleme et al., 2019). Therefore, proper weed management, and accurate weed identification (information on weed species diversity, frequency of occurrence, competitive ability and abundance) is the first step to successfully managing these weeds in a proper timing and using improved technologies.

 

 

MATERIALS AND METHODS

 

Description of the study sites

 

The study was conducted in Eastern zone of Tanzania where by two fields were selected, one at Ilonga village, Kilosa district, Morogoro region (6°46’ 27” S, 37°2’14” E, and 479.95 m asl) and another at Kiimbwanindi village, Mkuranga district Coastal region (7°12’19” S, 39°20’38” E and 93.87 m asl). At Ilonga Kilosa, the district experiences the mean annual temperature of about 25°C with an average of eight months of rainfall starting from October to May (Kajembe et al., 2013; Zakayo, 2015). According to Zakayo (2015) stated that the rainfall distribution at Kilosa site is bimodal, with short rains begins from October to January, followed by long rains starting from mid-February to May. While at Kiimbwanindi Mkuranga, average monthly temperature ranges from 18.8 °C during the coolest months of July and August to the highest monthly means of 31.9 °C to 32.6 °C during the hot season from December to March (Mkuranga, 2009). Relative humidity ranges from 67-70 % from August to October and increasing to 82 % during the wettest month of April, and the site is experiencing bi-modal rainfall pattern; form March to May (the main wet season) with averaged 550 mm of rain and November to December (short rains) with averaged 235 mm of rain (Mkuranga, 2009; RCO, 2011). 

 

Sampling procedure

 

A survey of weeds in the selected cassava fields of 861 square meters each at Ilonga, and Kiimbwanindi was conducted between November 2019 and April 2020. A total of 24 quadrats (1 m × 1 m) were placed at random in each cassava field. In each quadrat all available weed species were identified to a species level, counted and recorded. Clear pictures of these weeds (including those weeds found out of the quadrats) were taken for records. The position of each field sampled was recorded using a Global Positioning System (GPS) whereby the information on history of cultivation, methods of land preparation, fertilizer application, troublesome and most challenging weeds and weed management practices were also collected.

 

Data collection

 

Weed data: Data collected include weed species, density, frequency, uniformity and relative abundance of each weed found within a placed 1 m × 1 m quadrat.

 

Soil data: At each studied field, a minimum of six soil samples collected at the depth of 0-20 cm and 21-50 cm separately, were collected at a zigzag sampling procedure and then bulked to form one composite sample for each depth and each field. These soil samples were collected before land preparation and taken to IITA analytical soil laboratory to be analysed for the assessment of the soil fertility status (physical and chemical properties) of the studied area.

 

Data analysis

 

Weed data analysis: For the determination of relative abundance (RA) of each species in the Thomas method, five quantification measures were used, these are weed frequency, relative frequency, uniformity, relative uniformity, and mean field density (Thomas, 1985). The following are procedures and formulae used to determine the RA  as describe by by Thomas (1985).

 

       i.          Weed density of each weed species was obtained by taking the number of real plants in each field/quadrat divide by the number of fields/quadrats. Thus

 

Dki =                                               (i)

 

Where by

 

Dki = density (number of plants or spikes/panicles/m2) of the species k in field i 

Zj = number of plants/spikes/panicles in each 1m2 sample

n = number of fields

 

     ii.          Weed frequency of each weed species was obtained by taking the ratio of the number of fields where the species was present, to the total number of fields. Thus

Fk =                                    (ii)

 

Where by

Fk = frequency of the species k

Yi = present (1) or absence (0) of the species k in the field i and n = number of fields

 

    iii.          Weed uniformity indicates the percentage of quadrats infested by a species and is an estimate of the area infested by a weed. Thus

 

Uk =                                (iii)

 

Where by

Uk = field uniformity value for species k,

Xij = presence (1) or absence (0) of species k in quadrat j in field i, and

m = number of quadrats per field.

 

  iv.            Relative abundance is the overall evaluation of the importance of each species with respect to others, and the RA of each weed species was obtained by the formula

 

                 (iv)

 

Where by

RAk = the relative abundance of species k

RFk = (the frequency of species k / sum of all frequencies of all species) × 100

RUk = (uniformity of species k / sum of all uniformity values of all species) × 100

RDk = mean density of species k / sum of mean field densities of all species) × 100

 

Soil data analysis: Soil pH, amount of N, P, K, Ca and Mg nutrients were analysed in the laboratory at IITA in Dar es Salaam to assess fertility status following the soil analysis procedures stated by Jones (2001) and Jones (2012). These data were helpful in proper weed identification and in the study of the best weed management combination.

 

 

RESULTS

 

Physical and chemical characteristics of the soils at studied sites

 

Soil chemical characteristics and particle size class (0 to 20 cm and 21 to 50 cm deep) at experimental sites in 2019 are shown in Table 1. The soil of the two sites were found to be silty clay loam (24% cay, 15% silt and 61% sand) with sufficient available phosphorus, total nitrogen and exchangeable potassium (5.07 ppm, 0.25% and 0.67 cmolckg-1), respectively and pH of 5.88 at Ilonga. Also, there was loamy sand (12% clay, 3% silt and 85% sand) with sufficient available phosphorus, total nitrogen and exchangeable potassium (10.79 ppm, 0.25% and 0.23 cmolckg-1), respectively and pH of 5.47 at Kiimbwanindi.

This soil condition was optimal hence the locations support the cassava production (Soil staff, 1993).

 

 

 


Table 1: Soil characteristics of the studied sites

Parameter

Method used

Ilonga

Kiimbwanindi

Range suitable for
cassava production

Rated according to:

0-20 cm

21-50 cm

0-20 cm

21-50 cm

pH (in H2O)

pH meter

5.88

5.61

5.47

5.24

4.5 - 7.0

CIAT (2011)

OC (%)

Walkley - Black

1.48

1.26

0.66

0.3

4.0 - 10.0

Landon (2014)

P (mgkg-1)

Bray 1

5.07

2.86

10.79

5.86

< 4.2

Howeler (2002)

N (%)

Kjeldahl

0.25

0.2

0.25

0.2

0.20 - 0.50

Landon (2014)

Ca (cmolckg-1)

Ammonium Acetate Extraction pH 7.0

10.8

10.82

2.21

0.93

1.0 - 5.0

CIAT (2011)

Mg (cmolckg-1)

2.57

2.66

0.9

0.32

0.40 - 1.00

CIAT (2011)

K (cmolckg-1)

0.67

0.38

0.23

0.17

0.15 - 0.25

CIAT (2011)

Na (cmolckg-1)

0.08

0.12

0.04

0.03

< 2

Howeler (2002)

Cu (mgkg-1)

DTPA Extraction pH 7.3

2.04

2.24

0.6

0.83

0.3 - 0.8

Motsara and Roy (2008)

Zn (mgkg-1)

0.59

0.39

2.07

1.18

1.0 - 3.0

Motsara and Roy (2008)

Mn (mgkg-1)

21.5

21.31

26.61

26.92

1.2 - 3.5

Motsara and Roy (2008)

Fe (mgkg-1)

55.35

50.56

20

22.39

4.0 - 6.0

Motsara and Roy (2008)

 

Textural class

Hydrometer

SCL

SCL

LS

SL

 

 

SCL = silty clay loam soil, LS = loamy sand soil and SL= sandy loam soil

 

 

RESULTS

 

 


Weed species, families and their lifecycle 

 

The results of the occurred weeds in the surveyed fields are presented in table 2 and table 3 for Ilonga, Kilosa site and Kiimbwanindi, Mkuranga site, respectively. A total of 22 weed species belonging to 16 families were identified. These 16 weed families include Poaceae with five species, Asteraceae and Fabaceae with two species each and Agaricaceae, Apocynaceae, Boraginaceae, Commelinaceae, Convolvulaceae, Cyperaceae, Euphorbiaceae, Lamiaceae, Nyctaginaceae, Phyllanthaceae, Portulacaceae, Celastraceae, and Malvaceae had one species. Out of these identified weed species, 14 were broadleaf weeds, 6 grassy weeds and 1 sedge weed.

 


 

 

Table 2: Weed species, families, life cycle and plant morphology found at Ilonga, Kilosa site during the 2019/2020 planting season

Sn

Scientific   name

Family

Life cycle

Morphology

1

Agaricus sp

Agaricaceae

Annual

Convex cup mushroom

2

Asclepias syriaca

Apocynaceae

Perennial

Broad leaved

3

Bidens pilosa

Asteraceae

Annual

Broad leaved

4

Boerhavia erecta

Nyctaginaceae

Annual/Perennial

Broad leaved

5

Commelina benghalensis

Commelinaceae

Perennial

Broad leaved

6

Corchorus olitorius

Malvaceae

Annual

Broad leaved

7

Cynodon nlemfuensis

Poaceae

Perennial

Grass

8

Cyperus rotundus

Cyperaceae

Perennial

Sedge

9

Dactyloctenium aegyptium

Poaceae

Annual

Grass

10

Echinochloa colona

Poaceae

Annual

Grass

11

Euphorbia hirta

Euphorbiaceae

Annual

Broad leaved

12

Ocimum gratissimum

Lamiaceae

Perennial

Broad leaved

13

Phyllanthus amarus

Phyllanthaceae

Annual

Broad leaved

14

Portulaca oleracea

Portulacaceae

Annual

Broad leaved

15

Trichodesma zeylanicum

Boraginaceae

Annual

Broad leaved

16

Cynodon plectostachyus

Poaceae

Perennial

Grass

17

Tridax procumbens

Asteraceae

Perennial

Broad leaved

 

 

Table 3: Weed species, families, life cycle and plant morphology found at Kiimbwanindi, Mkuranga site during the 2019/2020 planting season

Sn

Scientific   name

Family

Life cycle

Morphology

1

Commelina benghalensis

Commelinaceae

Perennial

Broad leaved

2

Cynodon plectostachyus

Poaceae

Perennial

Grass

3

Cyperus rotundus

Cyperaceae

Perennial

Sedge

4

Digitaria sp

Poaceae

Annual

Grass

5

Ipomoea sp

Convolvulaceae

Perennial

Broad leaved

6

Mucuna pruriens

Fabaceae

Annual

Broad leaved

7

Reissantia sp

Celastraceae

Perennial

Broad leaved

8

Tephrosia sp

Fabaceae

Perennial

Broad leaved

 

 

 


Weed density, uniformity, frequency and relative abundance

 

Table 4 shows the result of density, uniformity, frequency and relative abundance of weeds found in the selected farm at Ilonga, Kilosa site. Cyperus rotundus occurred in highest mean field densities followed by Echinochloa colona while Bidens pilosa, Portulaca oleracea and Agaricus sp were least in density. Cyperus rotundus, Echinochloa colona and Trichodesma zeylanicum were the highest  abundant species and the most disturbing weed species at the studied site.  

Table 5 shows the result of density, uniformity, frequency and relative abundance of weeds found in the selected farm at Kiimbwanindi, Mkuranga site. Reissantia sp had thehighest mean field densities followed by Mucuna pruriens while other weed species found to be very minimal. Reissantia sp, Mucuna pruriens, Cyperus rotundus and Commelina benghalensis were the highest occurred and the most disturbing weed species at this site.

Table 6 shows the result of density, uniformity, frequency and relative abundance of weeds found in the studied areas. Cyperus rotundus had  high mean field densities of 130 plants m-2 followed by Echinochloa colona which occurred in 39.4 plants m-2 while Euphorbia hirta, Ipomoea sp, Dactyloctenium aegyptium, Bidens pilosa, Portulaca oleracea and Agaricus sp were least in density in descending order ranging from 0.06 to 0.02 plants m-2 mean field densities. The most widespread weed species in terms of frequency was Cyperus rotundus, Cynodon plectostachyus and Commelina benghalensis. Cyperus rotundus with 87.59% relative abundance, Echinochloa colona with 41.19% relative abundance, Trichodesma zeylanicum with 23.24% relative abundance and Reissantia sp with 20.65% relative abundance were the highest occurred and the most disturbing weed species in the studied sites.

 


 

 

Table 4: Mean field density (MFD), relative mean density (RD), frequency (F), relative frequency (RF), uniformity (U), relative uniformity (RU) and relative abundance (RA) of weed species collected during the 2019/2020 season at Ilonga village, Kilosa.

SN

Weed species

MFD
(plant/m2)

RD (%)

F (%)

RF (%)

U (%)

RU (%)

RA (%)

1

Cyperus rotundus

258.25

71.27

100.00

5.88

100.00

22.64

99.80

2

Echinochloa colona

78.79

21.75

100.00

5.88

100.00

22.64

50.27

3

Trichodesma zeylanicum

18.58

5.13

100.00

5.88

87.50

19.81

30.82

4

Cynodon plectostachyus

1.92

0.53

100.00

5.88

25.00

5.66

12.07

5

Commelina benghalensis

1.21

0.33

100.00

5.88

25.00

5.66

11.88

6

Corchorus olitorius

0.29

0.08

100.00

5.88

16.67

3.77

9.74

7

Ocimum gratissimum

1.00

0.28

100.00

5.88

12.50

2.83

8.99

8

Boerhavia erecta

0.63

0.17

100.00

5.88

12.50

2.83

8.89

9

Asclepias syriaca

0.54

0.15

100.00

5.88

12.50

2.83

8.86

10

Phyllanthus amarus

0.17

0.05

100.00

5.88

12.50

2.83

8.76

11

Tridax procumbens

0.17

0.05

100.00

5.88

8.33

1.89

7.82

12

Euphorbia hirta

0.13

0.03

100.00

5.88

8.33

1.89

7.80

13

Cynodon nlemfuensis

0.46

0.13

100.00

5.88

4.17

0.94

6.95

14

Dactyloctenium aegyptium

0.08

0.02

100.00

5.88

4.17

0.94

6.85

15

Bidens pilosa

0.04

0.01

100.00

5.88

4.17

0.94

6.84

16

Portulaca oleracea

0.04

0.01

100.00

5.88

4.17

0.94

6.84

17

Agaricus sp

0.04

0.01

100.00

5.88

4.17

0.94

6.84

 

Table 5: Mean field density (MFD), relative mean density (RD), frequency (F), relative frequency (RF), uniformity (U), relative uniformity (RU) and relative abundance (RA) of weed species collected during the 2019/2020 season at Kiimbwanindi village, Mkuranga.

SN

Weed species

MFD
(plant/m2)

RD (%)

F (%)

RF (%)

U (%)

RU (%)

RA (%)

1

Reissantia sp

6.17

37.95

100.00

12.50

91.67

33.33

83.78

2

Mucuna pruriens

5.00

30.77

100.00

12.50

79.17

28.79

72.06

3

Cyperus rotundus

1.75

10.77

100.00

12.50

33.33

12.12

35.39

4

Commelina benghalensis

2.46

15.13

100.00

12.50

20.83

7.58

35.20

5

Digitaria sp

0.42

2.56

100.00

12.50

25.00

9.09

24.16

6

Tephrosia sp

0.17

1.03

100.00

12.50

8.33

3.03

16.56

7

Cynodon plectostachyus

0.17

1.03

100.00

12.50

8.33

3.03

16.56

8

Ipomoea sp

0.13

0.77

100.00

12.50

8.33

3.03

16.30

 

 

Table 6: Frequency (F), relative frequency (RF), uniformity (U), relative uniformity (RU), mean field density (MFD), relative mean density (RD), and relative abundance (RA) of the 22 weed species collected during the 2019/2020 season in selected cassava fields in Eastern zone, Tanzania.

Sn

Weed

F (%)

RF (%)

MFD
(plant/m2)

RD (%)

U

(%)

RU (%)

RA (%)

1

Cyperus rotundus

100

8.00

130

68.67

66.67

10.92

87.59

2

Echinochloa colona

50

4.00

39.40

20.81

100.00

16.38

41.19

3

Trichodesma zeylanicum

50

4.00

9.29

4.91

87.50

14.33

23.24

4

Reissantia sp

50

4.00

3.08

1.63

91.67

15.02

20.65

5

Mucuna pruriens

50

4.00

2.50

1.32

79.17

12.97

18.29

6

Commelina benghalensis

100

8.00

1.83

0.97

22.92

3.75

12.72

7

Cynodon plectostachyus

100

8.00

1.04

0.55

16.67

2.73

11.28

8

Digitaria sp

50

4.00

0.21

0.11

25.00

4.10

8.21

9

Corchorus olitorius

50

4.00

0.15

0.08

16.67

2.73

6.81

10

Ocimum gratissimum

50

4.00

0.50

0.26

12.50

2.05

6.31

11

Boerhavia erecta

50

4.00

0.31

0.17

12.50

2.05

6.21

12

Asclepias syriaca

50

4.00

0.27

0.14

12.50

2.05

6.19

13

Phyllanthus amarus

50

4.00

0.08

0.04

12.50

2.05

6.09

14

Tridax procumbens

50

4.00

0.08

0.04

8.33

1.37

5.41

15

Tephrosia sp

50

4.00

0.08

0.04

8.33

1.37

5.41

16

Euphorbia hirta

50

4.00

0.06

0.03

8.33

1.37

5.40

17

Ipomoea sp

50

4.00

0.06

0.03

8.33

1.37

5.40

18

Cynodon nlemfuensis

50

4.00

0.23

0.12

4.17

0.68

4.80

19

Dactyloctenium aegyptium

50

4.00

0.04

0.02

4.17

0.68

4.70

20

Bidens pilosa

50

4.00

0.02

0.01

4.17

0.68

4.69

21

Portulaca oleracea

50

4.00

0.02

0.01

4.17

0.68

4.69

22

Agaricus sp

50

4.00

0.02

0.01

4.17

0.68

4.69

 

 

 


A weed compendium

 

All weeds found in the selected farms at both sites were recorded. These weeds were then identified to species level, their habit and life cycle.  A total of 57 weeds belongs to 28 families were identified within and out of the random placed quadrats Appendix 1. Sample of weed pictures found at a studied sites are present below in Figure 1:

 



 

Text Box: a)     Bidens pilosa

 

 Description: E:\GJOURNALS\Downloads\2022\Feburary\022422022 Leonard et al\Proof\fig1.jpg

Text Box: b) Ageratum conyzoides

 

 Description: E:\GJOURNALS\Downloads\2022\Feburary\022422022 Leonard et al\Proof\fig2.jpg

Text Box: c) Euphorbia heterophylla

 

 Description: E:\GJOURNALS\Downloads\2022\Feburary\022422022 Leonard et al\Proof\fig3.jpg

Text Box: d) Commelina benghalensis

 

 Description: E:\GJOURNALS\Downloads\2022\Feburary\022422022 Leonard et al\Proof\fig4.jpg

Figure 1: Some of the weeds found at studied sites

 

 

 


DISCUSSION

 

Weed density, uniformity, frequency and relative abundance from the studied sites

 

In the studied areas where cassava was grown, perennial weeds such as Cyperus rotundus tend to dominate with high density (130 plantsm-2) as compared to annual weeds. This might be attributed by the reproductive ability of these perennial species, their ability to make use of the available resources in the soil and history of previous cropping systems and weed management practices. Similar results were reported by Olorunmaiye et al. (2013) who reported the high presence of perennial weeds in cassava filed.

 

Commelina benghalensis, Cynodon species and Cyperus rotundus had highest frequency. Frequency describes the percentage of the fields that are infested with weeds in which having high frequency is the indication of the availability of these weeds in cassava fields. Similar results to this were reported by Ekeleme et al. (2019), who stated that, environments where cassava is growing tend to be dominated by perennial weed species such as Imperata cylindrica, Panicum maximum, Cyperus rotundus, and Mimosa invisa.

 

Echinochloa colona, Reissantia sp, Trichodesma zeylanicum, Mucuna pruriens and Cyperus rotundus showed the highest uniformity than other weeds. Weed uniformity indicate how even these weeds are, across the fields. Weeds like Cynodon nlemfuensis, Dactyloctenium aegyptium, Bidens pilosa, Portulaca oleracea and Agaricus sp had the lowest uniformity which indicate they were only found in patches.

 

In this study, Commelina benghalensis, Cynodon spp and Cyperus rotundus were highly in frequency but varied in their relative abundance. Cyperus rotundus was highly abundant weed (87.59%) followed by Echinochloa colona (41.19%) and Trichodesma zeylanicum (23.24%). This highly abundance of these weeds is the due to their high density and frequency which reflect their dominance to the fields. The reasons that made Cyperus rotundus to be abundant might be due to its ability to grow in almost every soil type over a range of soil moisture, pH and elevation as it grows best in moist fertile soils and also frequent cultivation has been suggested to promote/favour its growth.  Similar results were reported by Olorunmaiye et al. (2013) that Cyperus rotundus can grow over a high range of soil types. Reissantia sp, Mucuna pruriens and Commelina benghalensis having 20.65%, 18.29% and 12.72%, respectively were also highly abundant weed species, this might be due to their ability to reproduce both sexually and asexually and highly adapted on the areas having temperature ranging from 30° C to 35° C (Webster et al., 2005) similar to that of studied sites. 

 

 

CONCLUSION

 

The study played an important role in identifying common weed species that are mostly found in cassava fields in Eastern zone Tanzania, and hence proven that, perennial weeds Cyperus rotundus, Echinochloa colona, Trichodesma zeylanicum, Reissantia sp, Mucuna pruriens and Commelina benghalensis are the mostly and abundantly occurring weed species with intrinsic adaptive characteristics compared to other species. Thus, this study document probably the first-time common weed and its behaviour as influenced by density, uniformity, frequency and relative abundance as they are associated with cassava production systems in Eastern zone Tanzania.

Based on the above-mentioned study findings, the following have been recommended, firstly more survey work is needed on a regular basis to identify possible weed population shifts, secondly research toward new or improved control measures is needed and lastly farmers should be trained on weed management practices for increased cassava yield to optimum level. 

 

 

CONFLICT OF INTEREST

 

I declare no potential conflict of interest.

 

 

CONTRIBUTIONS OF AUTHOR

 

The experiment, collection of data, data analysis, and the write-up of the manuscript was carried out by Joseph Adonia Leonard. The supervisors of this study were Abdul Kudra, George Tryphone and Frederick Baijukya. The final manuscript read and approved by all authors.

 

 

ACKNOWLEDGMENT

 

This is part of MSc. Research Dissertation by Joseph Adonia Leonard funded by International Institute of Tropical Agriculture (IITA) under the African Cassava Agronomy Initiative (ACAI) project.

 

 

REFERENCES

 

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Appendix 1: Other Weeds found in the selected cassava farms during the 2019/2020 season at Ilonga, Kilosa and Kiimbwanindi, Mkuranga in Eastern Zone of Tanzania

Sn

Scientific name

Family

Life cycle

1

Agaricus sp

Agaricaceae

Annual

2

Amaranthus viridis L.

Amaranthaceae

Annual

3

Celosia trigyna L.

Amaranthaceae

Erect annual plant

4

Annona senegalensis

Annonaceae

Perennial

5

Asclepias syriaca

Apocynaceae

Perennial

6

Acanthospermum hispidium

Asteraceae

Branched annual plant

7

Ageratum conyzoides L.

Asteraceae

Annual plant

8

Bidens Pilosa L.

Asteraceae

Annual plant

9

Conyza sp

Asteraceae

Annual plant

10

Emilia javanica L.

Asteraceae

Annual herb

11

Kleinia sp

Asteraceae

Perennial herbs

12

Launaea cornuta

Asteraceae

Herbaceous perennial plant

13

Tridax procumbens L.

Asteraceae

Annual, sometimes perennial

14

Markhamia obtusifolia

Bignoniaceae

Perennial

15

Trichodesma zeylanicum

Boraginaceae

Annual plant

16

Reissantia sp

Celastraceae 

Perennial

17

Gloriosa superba L.

Colchicaceae

Perennial

18

Commelina benghalensis L.

Commelinaceae

Herbaceous perennial plant,

19

Bonamia mossambicensis

Convolvulaceae

Perennial

20

Ipomoea sp

Convolvulaceae

Perennial

21

Jacquemontia sp

Convolvulaceae

Perennial

22

Merremia tridentate L.

Convolvulaceae

Perennial herb

23

Cucumis sp

Curcubitaceae

Perennial plant

24

Cyperus rotundus

Cyperaceae

Perennial

25

Kylinga erecta

Cyperaceae

Creeping perennial glabrous sedge

26

Acalypha ciliate

Euphorbiaceae

Annual herb

27

Euphorbia heterophylla L.

Euphorbiaceae

Annual plant

28

Euphorbia hirta L.

Euphorbiaceae

Annual herb

29

Phyllanthus amarus

Euphorbiaceae

Annual plant

30

Mucuna pruriens

Fabaceae

Annual

31

Senna hirsuta L.

Fabaceae

Herbaceous perennial shrub

32

Tephrosia sp

Fabaceae 

Perennial

33

Cenchurus mitis

Gramineae

Annual crop

34

Cynodon dactylon L.

Gramineae

Perennial grass

35

Cleodendrum johnstonii

Lamiaceae

Perennial

36

Ocimum sp

Lamiaceae

Perennial

37

Cassia mimosoides L.

Leguminaceae

Annual, or short-lived perennial herb

38

Corchorus aestuan L.

Malvaceae

Annual or perennial herb

39

Corchorus olitoris L.

Malvaceae

Annual or biennial herb

40

Hibiscus surattensis L.

Malvaceae

Climbing annual plant

41

Melochia corchorifolia L.

Malvaceae

Spreading perennial plant

42

Waltheria indica L.

Malvaceae

Perennial plant

43

Corchorus olitorius

Malvaceae 

Annual 

44

Boerhavia diffusa L.

Nyctaginaceae

Herbaceous perennial plant

45

Boerhavia erecta L.

Nyctaginaceae

Erect annual to perennial plant

46

Cynodon nlemfuensis

Poaceae

Perennial

47

Cynodon plectostachyus

Poaceae

Perennial

48

Dactyloctenium aegyptium

Poaceae

Annual

49

Digitaria sp

Poaceae

Annual

50

Echinochloa colona

Poaceae

Annual

51

Portulaca oleraceae L.

Portulacaceae

Annual

52

Agathisanthemum bojeri

Rubiaceae

Shrubby perennial herb

53

Richardia scabra L.

Rubiaceae

Annual plant

54

Spermacoce pusilla

Rubiaceae

Prostrate annual

55

Tecca leontopetaloides L.

Teccaceae

Perennial herb

56

Lantana camara L.

Verbenaceae

Perennial

57

Tribulus terrestris L.

Zygophyllaceae

Annual plant

 

 

 

Cite this Article: Leonard, JA; Kudra, AB; Baijukya, F; Tryphone, GM (2022). Common Weeds Found in Selected Cassava Farms in Eastern Zone of Tanzania. Greener Journal of Agricultural Sciences, 12(1): 75-85.