




































 AMERICAN INTERNATIONAL JOURNAL OF AGRICULTURAL STUDIES 6(1) (2022), 20-25  

20 

 

      AGRICULTURAL STUDIES 

                                                               AIJAS VOL 6 NO 1 (2022) P-ISSN 2641-4155   E-ISSN 2641-418X 
                                                  

                                                                                                                        Available online at www.acseusa.org      

                                                                                                                                     Journal homepage: https://www.acseusa.org/journal/index.php/aijas 
                                                                                                                                         Published by American Center of Science and Education, USA 

DETERMINANTS OF OUTPUT OF CASSAVA (MANIHOT 

SPECIES) PRODUCTION IN ABUJA, NIGERIA 

 

 Olugbenga Omotayo Alabi  (a)1   Godbless Friday Safugha  (b )    
 

(a) Professor, Department of Agricultural-Economics, University of Abuja, PMB 117 Gwagwalada-Abuja, Abuja, Nigeria; E-mail: 
omotayoalabi@yahoo.com 
(b) M.Sc. Development Economics (Agricultural Economics), Department of Agricultural-Economics, University of Abuja, PMB 117 Gwagwalada-

Abuja, Abuja, Nigeria; E-mail: gsafugha@gmail.com 
 

 
A R T I C L E I N F O 
 

 

Article History: 
 

Received: 6th September 2022  

Accepted: 19th October 2022 

Online Publication: 23rd October 2022 

 

Keywords: 

Determinants of Output, Cassava (Manihot 

species) Production, Abuja, Nigeria 

 

JEL Classification Codes:  

       

Q1, Q3, Q5 

 
 

 

 
 

 
  

 
A B S T R A C T 

 
This research study focused on determinants of the output of cassava (Manihot species) production in 

Abuja, Nigeria. Multi-stage method of sampling was used. One hundred (100) cassava farmers were 

sampled and selected. Primary data were obtained through the use of a well-designed and also well-

structured questionnaire. Data were analyzed using econometrics and statistical tools. The results show 

that 87% of cassava producers were between 31 to 50 years of age. About 72% had formal education 

and were literate. Averagely, they had 4.76 hectares of cassava farmland. The estimated gross margin 

(GM) and net farm income (NFI) of cassava production per hectare were 1,464, 162.72 Naira and 1, 
453, 752.49 Naira respectively. This implies that cassava production was profitable and worthwhile. 

Farm size and fertilizer input were statistically significant factors influencing the output of cassava 

production. Age, labour input, and cassava cuttings were statistically significant factors influencing the 

output of cassava production. While chemical input statistically and significantly influenced the output 

of cassava production. The constraints facing cassava producers were the unavailability of improved 

cassava cuttings, the high cost of farm inputs, insecurity, inadequate extension services, and inadequate 

finances. The research study recommends that improved cassava cuttings should be made available to 

farmers for increased productivity. Extension officers should be employed to disseminate innovations, 
research findings, and new farm technologies to cassava farmers. Credit or loan facilities should be 

made available to cassava producers at low-interest rates. 

 
 

© 2022 by the authors. Licensee ACSE, USA. This article is an open-access article distributed under 
the terms and conditions of the Creative Commons Attribution (CC BY) license 

(http://creativecommons.org/licenses/by/4.0/).                           

 

INTRODUCTION 

Cassava (Manihot species) belongs to Euphorbiaceae, and it a major and important source of carbohydrate. It is second 

important staple food crop after maize in terms of energy or calories consumed. Nigeria is the largest producer of cassava 

in the world with estimated production of 60,001,531 tonnes in 2020 (FAO, 2020). Cassava production in Nigeria in 2018 

and 2019 were 55, 867, 727 tonnes and 59, 411, 510 tonnes respectively. The total land area for cassava production in 

Nigeria was 7,737, 846 ha, with annual yield of 77, 543hg/ha, and the average yield of cassava was 10.6 tonnes per hectare 

in 2020 respectively (FAO, 2020). The tuber of cassava contains 2% protein, 62% water content, 20 – 30 % starch, 1 – 2% 

fiber, traces of minerals, and vitamins (Akerele et al., 2018).  Cassava tolerates wide ranges of climatic and soil conditions; 

it yields properly on poor soils with low rainfall. Cassava has tolerance to drought and has capacity to yield under marginal 

soil conditions. In Nigeria, cassava can be grown in all ecological zones, and when moisture is available, it is planted all the 

year round. Cassava can be consumed when properly prepared and processed. Cassava products include: flour, chips, starch, 

pellets, alcohol, and adhesives, Cassava products are vital raw materials for the following industries: livestock feed, wood, 

textile, confectionary, pharmaceuticals, soft drink and food, and ethanol/alcohol industries respectively. They are tradeable 

in international markets, and plays a significant role in increasing income, and food production in Nigeria (Aboajah et al., 

2018). Cassava especially the roots and leaves when compares with other staple food can generates more cash incomes and 

provides calories for largest number of farming households (Sanusi et al., 2020).  About 250 million people in sub-Saharan 

Africa (SSA) derive their daily calories or energy from cassava, the leaves are consumed as vegetables (Oladoyin et al., 

                                                      
1Corresponding author: ORCID ID: 0000-0002-8390-9775 

© 2022 by the authors. Hosting by ACSE. Peer review under responsibility of American Center of Science and Education, USA.  

https://doi.org/10.46545/aijas.v6i1.264 
 

To cite this article: Alabi, O. O. ., & Safugha, G. F. (2022). DETERMINANTS OF OUTPUT OF CASSAVA (MANIHOT SPECIES) PRODUCTION IN 

ABUJA, NIGERIA. American International Journal of Agricultural Studies, 6(1), 20–25. https://doi.org/10.46545/aijas.v6i1.264 

https://doi.org/10.46545/aijas.v6i1.264
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://orcid.org/0000-0002-8390-9775
https://orcid.org/0000-0003-3306-3035


Alabi & Safugha, American International Journal of Agricultural Studies 6(1) (2022), 20-25 

  

21 
 

2022). It is forecasted that by 2025, close to 62% of global cassava production will come from sub-Saharan Africa (Okorie 

et al., 2021). Cassava has many uses and this made the crop a potential and major foreign exchange earner in Nigeria. 

Cassava is cultivated by smallholder farmers who are resource poor farmers, having low resources. Cassava ensures food 

security, plays significant role in alleviating poverty, and helps in environmental protection. Cassava production over the 

years is faced with problems such as poor storage facilities, pests and diseases, price fluctuations, urbanization, and low 

capitalization. The high costs of processing, production, transportation, and the deficit in infrastructures in Nigeria makes it 

difficult to add value to cassava in terms of quality, safety, quantity and shelf life, as this can encourage export and increase 

foreign earnings.  

 

Objectives of the Study 

This research study focused on determinants of output of cassava (Manihot species) production in Abuja, Nigeria. 

Specifically, the objectives were: 

 Identify the socio-economic profiles of cassava farmers, 

 Determine the profitability, costs and returns of cassava production, 

 Evaluate factors influencing or affecting output of cassava production, and 

 Determine the constraints faced by cassava farmers in the study area. 

 

MATERIALS AND METHODS 

The research study was conducted in Abuja, Nigeria. Abuja is located between Latitudes 90 4│20|| North and Longitudes 70 

29│28|| East. Abuja has three weather conditions annually, they are: rainy season, dry season and the harmattan period. The 

brief harmattan period comes in between the rainy and dry seasons. Abuja falls within the savannah zone vegetation, the 

vegetation in the territory are classified into three (3) savannah types: firstly, grassy savannah; secondly, savannah 

woodland; and thirdly, the shrub savannah. Abuja has population of about 776,298 people (NPC, 2006). The population of 

Abuja in 2022 is about 3,652,000 people which is 5.43% increase over the population of 3,464,000 people in 2021. The 

people are engaged in agricultural production activities. They are involved in animal production and growing crops. Crops 

grown include: cassava, maize, millet, soybean, garden egg, beans, rice, yam, groundnut, sorghum. Animal reared include: 

poultry, goats, sheep, cattle, rabbit and turkey. Multi-stage method of sampling was used. Sample size of 100 cassava farmers 

were selected. Data obtained were those from primary sources. Data were collected through the use of well-designed and 

also well-structured questionnaire. The questionnaire was administered to the cassava farmers through the help of well-

trained enumerators. Data were analyzed using the following analytical tools: 

 

Descriptive Statistics: This involves the use percentages, mean, range and frequency-distributions. This was used 

specifically to achieve objective one (i).  

 

Farm Budgetary Technique: The gross margin model is stated thus: 

𝐺𝑀 = 𝑇𝑅 − 𝑇𝑉𝐶 … … … … … … … … … … (1) 

        𝐺𝑀 = ∑ 𝑃𝑖𝑄𝑖
𝑛
𝑖=1 − ∑ 𝑃𝑗𝑋𝑗 … … … … … … (2)𝑚

𝑗=1  

    𝑁𝐹𝐼 = 𝑇𝑅 − 𝑇𝐶 … … … … … . (3) 

    𝑁𝐹𝐼 = ∑ 𝑃𝑖𝑄𝑖
𝑛
𝑖=1 − [∑ 𝑃𝑗𝑋𝑗

𝑚
𝑗=1 + ∑ 𝐺𝐾𝑘

𝑘=1 ] … … … . (4) 

Where 

𝑃𝑖  = Price of Cassava (
𝑁

𝐾𝑔
), 

𝑄𝑖 = Quantity of Cassava (Kg), 

𝑃𝑗 = Price of Factor Inputs (
𝑁

𝑈𝑛𝑖𝑡
), 

𝑋𝑗 = Quantity of Factor Inputs (Units),  

𝑇𝑅 = Total Revenue obtained from Sales from Cassava (N), 

𝑇𝑉𝐶 = Total Variable Cost (N), 

𝐺𝐾 = Cost of all Fixed Inputs (Naira)  

𝑁𝐹𝐼 = Net Farm Income (Naira)  
This was used specifically to achieve objective two (ii). 

 

Financial Analysis: Gross margin ratio according to Ben-Chendo et al. (2015) is defined as:  

   𝐺𝑟𝑜𝑠𝑠 𝑀𝑎𝑟𝑔𝑖𝑛 𝑅𝑎𝑡𝑖𝑜 =
𝐺𝑟𝑜𝑠𝑠 𝑀𝑎𝑟𝑔𝑖𝑛

𝑇𝑜𝑡𝑎𝑙 𝑇𝑒𝑣𝑒𝑛𝑢𝑒
… … … … . (5) 

The operating ratio (OR) according to Olukosi and Erhabor (2015) is defined as:  

   𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑅𝑎𝑡𝑖𝑜 =
𝑇𝑉𝐶

𝐺𝐼
… … … … … … … … (6) 

Where, 

𝑇𝑉𝐶 = Total Variable Cost (Naira), 

𝐺𝐼 = Gross Income (Naira), 

The rate of return per naira invested (RORI) in cassava production is defined as: 

   𝑅𝑂𝑅𝐼 =
𝑁𝐼

𝑇𝐶
… … … … … … … … … … (7) 

 



Alabi & Safugha, American International Journal of Agricultural Studies 6(1) (2022), 20-25 

  

22 
 

Where,    

𝑅𝑂𝑅𝐼 = Rate of Return per Naira Invested (Unit)  

𝑁𝐼 = Net Income (Naira)  

𝑇𝐶 = Total Cost (Naira)  

This was used specifically to achieve objective two (ii). 

 

Cobb-Douglas Production Function Model: The model is defined as follows: 

𝐿𝑜𝑔 𝑌 = 𝛼0 + 𝛼1𝐿𝑜𝑔 𝑋1 + 𝛼2𝐿𝑜𝑔 𝑋2 + 𝛼3𝐿𝑜𝑔 𝑋3 + 𝛼4𝐿𝑜𝑔 𝑋4 + 𝛼5𝐿𝑜𝑔 𝑋5 + 𝛼6𝐿𝑜𝑔 𝑋6 + 𝑈𝑖…………(8)  

𝑌 = Output of Cassava (Kg),  
𝑋1 = Age of  Cassava Farmers in Years,  
𝑋2 = Farm Size in Hectares  

𝑋3 = Labour − Input in Mandays  

𝑋4 = Fertilizer − Input in Kg  

𝑋5 = Cassava − Cuttings in Kg  

𝑋6 = Chemical − Input in Litres  

𝑈𝑖 = Error Term,   
𝛼1 − 𝛼6 = Regression Coefficients,  
𝛼0 = Constant Term,  
This was used specifically to achieve objective three (iii). 

 

Principal Component Model: The constraints faced by cassava farmers were subjected to principal component analysis. 

This was used specifically to achieve objective four (iv). 

 

RESULTS AND DISCUSSIONS 

Socio-Economic Profiles of Cassava Farmers 

The summary statistics of socio-economic profiles of cassava farmers are presented in Table 1. Gender classifications show 

that 71% of cassava farmers were male, while 29% were female. This signifies that cassava farming was dominated by male 

counterparts, this might be due to strength and rigors involve in activities of cassava farming. Majority (87%) cassava 

producers were between 31 to 50 years of age. This age range of cassava producers are likely to be more energetic and be 

willing to take risks in cassava farming. The average age of cassava producers was 42 years. This implies that cassava 

producers were active, resourceful, and energetic in their youthful age. Age of cassava producer’s influences physical work 

and productivity, as cassava farming is believed to be labour intensive. Furthermore, 72% of cassava producers attended 

formal education and were literate, while 28% had non-formal education. Education increase farmers’ understanding and 

knowledge of new farm technologies, and it is a significant factor that facilitates adoption of improved farm technologies 

among cassava producers. In addition, 84% of cassava producers had between 1 to 10 years’ experience in cassava farming. 

Farmers with long years of experience in cassava farming would be more conversant with the problems and this would 

increase the farmer’s level of acceptance of innovations and new ideas as a method of overcoming the constraints (Ashaye 

et al., 2018). The average farm size was 4.76 hectares, this signifies that cassava producers were smallholder, resource poor, 

small-scale farmers. Averagely, there are 5 people per household, this signifies that availability of family labour for activities 

of cassava production and this will reduce amount spent on hired labour.   

 

Table 1. Socio-Economic Profiles of Cassava Farmers 

 

Socio-Economic Profiles Frequency  Percentage Mean 

Gender 
Male 

Female 

Age in Years 
31 – 40 

41 – 50  

51 – 60  
Educational Level in Years 

Non-Formal 

Tertiary 
Secondary 

Primary 

Experience in Farming Years 
1 – 5 

6 – 10  

11 – 15  
16 – 20  

Farm Size in Hectares 
1 – <5 

5 – <10 

10 – <15 
15 – <20  

Size of Household (Units) 

1 – <5  

 
71 

29 

 
50 

37 

13 
 

28 

31 
23 

18 

 
21 

63 

14 
02 

 
73 

19 

05 
03 

 

67 

 
71.00 

29.00 

 
50.00 

37.00 

13.00 
 

28.00 

31.00 
23.00 

18.00 

 
21.00 

63.00 

14.00 
02.00 

 
73.00 

19.00 

05.00 
03.00 

 

67.00 

 
 

 

 
 

42.00 

 
 

 

 
 

 

 
 

7.85 

 
 

 
 

4.76 

 
 

 

5.00 



Alabi & Safugha, American International Journal of Agricultural Studies 6(1) (2022), 20-25 

  

23 
 

 
 
 

Source: Field Survey (2021) 

 

Financial Position and Profitability Analysis among Cassava Producers per Hectare 

The financial analysis, costs and returns, profitability of cassava production per hectare was presented in Table 2. The costs 

incurred and revenue obtained in cassava production per hectare was based on the prevailing market price as the time of the 

field survey. The total variable cost (TVC) estimated was 89,966.96 Naira and this accounted for about 89.63% of total cost 

involved in cassava production per hectare. The total variable cost includes: cost of labour (16.49%), cost of fertilizer 

(29.35%), cost of cassava cuttings (21.49%), transportation (05.65%), rent on land (12.86%), loading and offloading cost 

(03.79%). The total fixed cost was estimated at 10, 410. 23 Naira and this accounted for 10.37% of total cost of cassava 

production per hectare.  The total cost of cassava production per hectare was evaluated at 100, 377.19 Naira. The total 

revenue was calculated at 1, 554, 129.68 Naira per hectare. The gross margin and net farm income of cassava production 

per hectare were 1,464, 162.72 Naira and 1, 453, 752.49 Naira respectively. This means that cassava production was 

profitable. The gross margin ratio was calculated at 0.94, this implies that for every one (1) Naira invested in cassava 

production per hectare, 94 kobo covered interest, profits, taxes, depreciation, and expenses. Operation ratio in financial 

analysis is used to measure operating efficiency and financial position of an enterprise. It is preferable and worthwhile to 

have low values of operating ratio for an enterprise. The calculated operating ratio was 0.058, this signifies that 5.8 % of 

returns from cassava produce was used to cover cost of cassava sold and other operating expenses. The calculated rate of 

returns was 14.48, this signifies that for every one (1) Naira invested in cassava production 1448 kobo was realized. 

 

 Table 2. Financial Analysis, Costs and Returns, Profitability of Cassava Production per Hectare 

 
Variable Value (N) Percentage 

(a) Variable Cost 

Cost of Labour 
Cost of Fertilizer 

Cost of Cassava Cuttings 

Transportation Cost 
Rent on Land 

Loading/Offloading Cost 

(b) Total Variable Cost 
(c) Fixed Cost 

Depreciation of Assets/Farm Tools 

Taxes 
Interest 

(d) Total Fixed Cost 

(e) Total Cost of Production 
(f) Total Revenue 

(g) Net Farm Income(NFI) 

(h) Gross Margin 
(i) Gross Margin Ratio 

(j) Operating Ratio 

(k) Rate of Return on Investment 

 

16,550.00 
29,456.09 

21,576.87 

05,670.00 
12,907.00 

03,807.00 

89,966.96 
 

5,500.23 

3,709.00 
1,201.00 

10,410.23 

100,377.19 
1,554,129.68 

1,453,752.49 

1,464,162.72 
0.94 

0.058 

14.48 

 

16.49 
29.35 

21.49 

05.65 
12.86 

03.79 

89.63 
 

05.47 

03.70 
01.20 

10.37 

100.00 

Source: Field Survey (2021) 

 

Determinants of Output of Cassava Production 

The result of Cobb-Douglas production function model showing factors influencing output of cassava production was 

presented in Table 3. The exogenous factors under considerations were age, farm size, labour-input, fertilizer-input, cassava 

cuttings and chemical – input. The regression coefficients of all predictor variables were positive and significant. Farm size 

(𝑋2) and fertilizer input (𝑋4) were statistically significant regressor variables influencing output of cassava production at 

(𝑃 < 0.01). A 1% increase in fertilizer factor input will lead to 22.98% increase in output of cassava production. Age (𝑋1), 

labour-input (𝑋3), and cassava cuttings (𝑋5) were exogenous variables influencing output of cassava production at (𝑃 <
0.05). As cassava producers advanced in age, additional of one year in age will lead to 12.46% increase in output of cassava 

production. Also, chemical input (𝑋6) was statistically significant at (𝑃 < 0.10). The return to scale is the summation of all 

elasticities of production for predictor factors included in the Cobb-Douglas production function model. The return to scale 

was calculated at 1.373, which means increasing return to scale, this means that for every additional unit to production inputs 

in cassava production will lead to more than proportionate increase in output of cassava production. The coefficient of 

multiple determinations  (𝑅2) was 0.891, this means that 89.1% of variations in output of cassava production was explained 

by the predictor variables included in the Cobb-Douglas production function model. The F-value of 247.82 was significant 

at (𝑃 < 0.01), this signifies that the model is of good fit. This result is similar to findings of Nandi et al. (2011) who reported 

that farm size, labour input, and cassava cuttings had positive coefficients and were statistically and significantly predictor 

factors influencing output of cassava production. 

 

 

 

 

5 – <10  

10 – <15  
Total  

22 

11 
100 

22.00 

11.00 
100.00 



Alabi & Safugha, American International Journal of Agricultural Studies 6(1) (2022), 20-25 

  

24 
 

Table 3. Result of Multiple Regression Analysis of Cobb-Douglas Production Function Model 

 
Variable Parameter Regression 

Coefficient 

Standard  

Error 

t-Statistics 

Age (𝑿𝟏) 

Farm Size (𝑿𝟐) 

Labour-Input (𝑿𝟑) 

Fertilizer-Input (𝑿𝟒) 

Cassava-Cuttings (𝑿𝟓) 

Chemical-Input (𝑿𝟔) 
Constant 

𝛼1 

𝛼2 

𝛼3 

𝛼4 

𝛼5 

𝛼6 

𝛼0 

0.124660** 

0.157761*** 
0.100184** 

0.22985*** 

0.27828** 
0.48236* 

8.9927** 

0.04516 

0.04370 
0.00041 

0.06162 

0.10344 
0.24485 

3.55442 

2.76 

3.61 
2.51 

3.73 

2.69 
1.97 

2.53 

RTS = 1.373 

𝑹𝟐 = 0.891 

𝐀𝐝𝐣𝐮𝐬𝐭𝐞𝐝 𝑹𝟐  = 0.852 

F-Value  = 247.82*** 

    Source: Data Analysis (2021) 

   *-Significant at( 𝑃 < 0.10)    **-Significant at (𝑃 < 0.05)     

    ***-Significant at (𝑃 < 0.01)     

 

Problems Facing Cassava Producers in the Area of Study 

The constraints facing cassava producers were subjected to principal component model or factor analysis and was presented 

in Table 4. Constraints facing cassava farmers with Eigen-values greater than one or unity were retained and used for further 

analysis by the model. Problems with Eigen values less than one or unity were discarded by the model. Unavailability of 

improved cassava cuttings was ranked 1st with Eigen-value of 1.9013 and this problem explained 18.24% of all constraints 

retained in the model. High cost of farm input was ranked 2nd among all constraints retained in the model, and this constraint 

explained 17.35% of all constrained retained in the model. All the retained problems in the model explained 69.53% of all 

constraints facing cassava producers that was included in the principal component analysis. The chi-square value of 671.27 

was statistically significant at (𝑃 < 0.01), this signifies that the model is of good fit.  

 

Table 4. Principal Component Analysis of Constraints Facing Cassava Farmers 

 
Constraints Eigen-Value Difference Proportion Cumulative 

Unavailability of Improved Cassava 

 Cuttings 
High Cost of Farm Input 

Lack of Extension Services 

Inadequate Finances 
Insecurity 

1.9013 

 
1.827 

1.724 

1.702 
1.535 

0.623 

 
0.304 

0.207 

0.167 
0.094 

0.1824 

 
0.1735 

0.1224 

0.1148 
0.1022 

0.1824 

 
0.3559 

0.4783 

0.5931 
0.6953 

Bartlett Test of Sphericity 
KMO  

Chi Square 

Rho   

 
0.7221 

671.27*** 

1.00000 

   

Source: Computed from Data Analysis (2021) 

***-Significant at (𝑃 < 0.01) 

 

CONCLUSIONS 

This research study has established that cassava production is profitable and worthwhile in the area of study. The cassava 

producers were resourceful, active, energetic, and young farmers. The mean age was 42 years. Most cassava producers had 

formal education and were literate. They had long years of experiences in cassava farming and are smallholder, resource 

poor, small scale farmers. The gross margin and net farm income of cassava production per hectare were 1,464,162.72 Naira 

and 1,453,752.49 Naira respectively. Financial analysis shows gross margin ratio and operation ratio of 0.94 and 0.058 

respectively. The statistical and significant predictor variables influencing output of cassava production were age of cassava 

producers, farm size, labour-input, fertilizer input, cassava cuttings and chemical-input. The constraints faced by cassava 

producers were unavailability of improved cassava cuttings, high cost of farm inputs, and lack of extension services, 

inadequate finances, and insecurity. Based on the results, the following points were recommended: 

 Improved cassava cuttings should be made available for cassava producers for increased productivity. 

 Extension officers should be employed by government to disseminate innovations, research findings, and new farm 

technologies to cassava producers. 

 Credit or loan facilities should be made available by government to cassava producers at low interest rate. 

 Farm inputs such as fertilizers, chemicals, and land should be adequately provided for cassava producers for 

increased productivity. 

 Security should be provided to protect lives and properties of farmers’ family, farm produce and farm land. 

 
 

 

Author Contributions: Conceptualization, O.O.A. and G.F.S.; Methodology, O.O.A. and G.F.S; Software, O.O.A. and G.F.S; Validation O.O.A. and 
G.F.S; Formal Analysis, O.O.A. and G.F.S.; Investigation, O.O.A. and G.F.S.; Resources, O.O.A. and G.F.S.; Data Curation, O.O.A. and G.F.S.; Writing 

– Original Draft Preparation, O.O.A. and G.F.S.; Writing – Review & Editing, O.O.A. and G.F.S.; Visualization, O.O.A. and G.F.S.; Supervision, O.O.A. 

and G.F.S.; Project Administration, O.O.A. and G.F.S.; Funding Acquisition, O.O.A. and G.F.S. Authors have read and agreed to the published version of 
the manuscript. 



Alabi & Safugha, American International Journal of Agricultural Studies 6(1) (2022), 20-25 

  

25 
 

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 

groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Acknowledgments: N/A. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   

 

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 https://doi.org/10.22161/ijeab/3.2.32 

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