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

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

D_ki = (i)

Where by

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

Z_j = 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

F_k = (ii)

Where by

F_k = frequency of the species k

Y_i = 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

U_k = (iii)

Where by

U_k = field uniformity value for species k,

X_ij = 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

RA_k = the relative abundance of species k

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

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

RD_k = 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 cmol_ckg^-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 cmol_ckg^-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 (cmol_ckg^-1)

Ammonium Acetate Extraction pH 7.0

10.8

10.82

2.21

0.93

1.0 - 5.0

CIAT (2011)

Mg (cmol_ckg^-1)

2.57

2.66

0.9

0.32

0.40 - 1.00

CIAT (2011)

K (cmol_ckg^-1)

0.67

0.38

0.23

0.17

0.15 - 0.25

CIAT (2011)

Na (cmol_ckg^-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

22.39

4.0 - 6.0

Motsara and Roy (2008)

Textural class

Hydrometer

SCL

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

RESULTS

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

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

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.

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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.