Determinants of Adoption of USAID Markets II Project’s Agricultural Technology by Smallholder Farmers in Southwest, Nigeria.

By Ogunjobi, VO (2024).

Greener Journal of Agricultural Sciences

ISSN: 2276-7770

Vol. 14(1), pp. 23-32, 2024

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Determinants of Adoption of USAID Markets II Project’s Agricultural Technology by Smallholder Farmers in Southwest, Nigeria.

Ogunjobi, V.O.

Department of Project Management Technology, Federal University of Technology Akure, Ondo State, Nigeria.

ARTICLE INFO	ABSTRACT
*Article No.:* 020124018 *Type: Research* *Full Text:* PDF, PHP, HTML, EPUB, MP3	This study was conducted to assess the determinants of adoption of agricultural technologies introduced by MARKETS II project to small-holder farmers in Southwest, Nigeria. Specifically, the study assessed the level of adoption of USAID MARKETS II project’s technologies and the also the factors determining the adoption of these technologies. Multistage sampling procedure was used to select a sample size of 525 farmers, out of which 254 were project participants and 271 were non- participants of the project. Questionnaires were used to collect the data from the respondents. The results were analysed using frequencies and percentages and Binary Logit regression. The results revealed that factors that determined the adoption of technologies introduced by MARKETS II project included educational status, farm size, farming experience, membership in cooperative societies, annual income and number of extension visits. However, farming experience had a negative relationship. Also, the agricultural technologies introduced by MARKETS II project were well adopted by the farmers as all the mean values (3.0833 to 4.6909) were greater than the weighted mean (3). The study therefore recommends that smallholder farmers should be encouraged to join cooperative societies as this was shown to boost their exposure to new technology and special educational facilities should also be provided for the farmers for them to achieve a higher level of education as this has implications for technology adoption.
*Accepted:* 03/02/2024 *Published:* 07/02/2024
Corresponding Author Dr.* (Mrs) Ogunjobi, V.O. *E-mail:* voogunjobi@ futa.edu.ng
*Keywords: Agricultural technology, smallholder farmers, MARKETS II project, adoption.*

1. INTRODUCTION

Agriculture plays a central role in stimulating economic growth, reducing poverty, and improving food and nutrition security in the world. Globally, the agricultural sector is an important area for economic development while economic history provides evidence that agricultural revolution is a fundamental requirement for economic growth (Stewart, 2000; Adesina, 2012; Alexandratos & Bruinma, 2012; Dada, 2014; Okunlola, 2019). A strong and efficient agricultural sector would enable a country to feed its populace, generate employment, earn foreign exchange and provide raw materials for industries. The agricultural sector has a multiplier effect on a nation's socio-economic and industrial structure because of the multifunctional nature of agriculture (Ogen, 2004; Dada, 2014, Mosa et al., 2023). Smallholder agriculture means cultivating land primarily by farmers who own less than five hectares. It is an important sector especially in developing countries, because it contributes to food security and poverty alleviation.

The United States Agency for International Development (USAID) is the world's premier international development agency and a catalytic actor driving development results. USAID leads international development and humanitarian efforts to save lives, reduce poverty, strengthen democratic governance and help people progress beyond assistance. The objective of USAID is to support partners to become self-reliant and capable of leading their own developmental programmes. USAID and Chemonics International worked together in the Maximizing Agricultural Revenue and Key Enterprises in Targeted Sites (MARKETS) II project through large-scale commercial buyers and agricultural lending banks to help smallholders access training and high-quality inputs, such as seeds and fertilizers. MARKETS II launched in April 2012 to promote sustainable agriculture development via increasing private sector participation and investment, introduction of improved technology, raising income, increasing employment, attaining food security, and reducing poverty (USAID, 2013). Agricultural projects may be technology- oriented, which are to change the technical production potential; to broaden the resource base; to improve post harvest distribution; or institution building, at the Government level, project-management level and or the farmers’ level (Vernon & Yujiro, 2014). The United States Agency for International Development (USAID) Maximizing Agricultural Revenue and Key Enterprises in Targeted Sites (MARKETS) II project is an example of such projects. The USAID MARKETS-II project is a demand driven agriculture systems facilitation project that took place in 26 states in Nigeria. The project was to assist farmers define their needed quality and quantity of inputs and also to mobilize firms such as seed and fertilizer firms, farm implements providers, extension services and credit providers to collaborate with government extension agents to provide training and capacity building programmes to the farmers. The project was also implemented to scale up agricultural technology interventions across different value chains while contributing to more inclusive, resilient and sustainable agricultural development (USAID,2017).

Problem statement

In Nigeria's agricultural system, a notable aspect is the significant share of farm production managed by smallholder farmers. Typically, these farmers own between 1 to 3 hectares and have restricted access to advanced farming technologies. (Ogunlela & Mukhtar, 2009, Nchuchuwe & Adejuwon, 2012). Many smallholder farmers lack the infrastructure and technical resources to improve yields and adopt modern production practices and marketing strategies, therefore there is a need for technologies that can help smallholder farmers gain the necessary tools and resources to improve their productivity and resilience to risk (Mosa et al., 2023).

The Nigerian agricultural sector has suffered from years of poor management, inconsistent and poorly implemented government policies and projects (Amos, 2018, Ogunleye et al., 2018). It is also characterized and surrounded by illiterate farmers who live in rural areas, producing over 90 percent of the total food consumed and other agricultural products (Ogunjobi et al., 2022). The farmers’ educational status gives them little or no room for improvement through scientific research, innovation and different agricultural development projects.

This study therefore is to evaluate the determinants of adoption of technology by smallholder farmers in Southwest, Nigeria, with special reference to the Maximizing Agricultural Revenue and Key Enterprises in Targeted Sites (MARKETS) 11 project. This is in view to maximizing the developmental outcomes of agricultural projects which will in turn stimulate economic growth, reduce poverty and increase food security consequent to achieving sustainable development goals.

METHODOLOGY

The study area for this research is Southwest Nigeria. Southwest, Nigeria is one of the geopolitical zones of Nigeria. It is made up of six States: Ekiti State; Lagos State; Ogun State; Ondo State; Osun state; and Oyo State. However, the study was limited to Ondo and Oyo states, because they were the intervention states of the MARKETS II project. The research design that was used for this study is the descriptive research design which involved survey research. In this survey research, the researcher selected a sample of respondents from the population and administered structured copies of questionnaire to them. Primary data were used for this study. Structured questionnaires were used to collect the primary data from the respondents.

The population of the study comprises all cocoa, cassava and aquaculture farmers in the areas of intervention of MARKETS-II project in Southwest, Nigeria (Ondo, and Oyo states). The participants and non- participants of the project in these states that are involved in farming and processing of the value chains.

Multistage sampling procedure was used to carry out the sampling in stages. In the first stage, purposive sampling method was used to select Ondo and Oyo states out of the 6 states in Southwest Nigeria. These states were chosen because they were the areas of intervention of the USAID MARKETS II project. At the second stage, purposive sampling was also used to select the MARKETS II project intervention towns. At the third stage, stratified sampling technique was used to divide the population into groups based on the value chain (cocoa, cassava and aquaculture), the state and towns of intervention (35), then using the random sampling technique, a sample of 15 farmers were chosen per town.

Model Specification

Binary Logistic Regression model

Due to the dichotomous nature of the independent variable, the binary logistic regression model was employed to assess how a set of independent variables such as gender, age, household size, level of education, farm size, farming experience, annual income (farm and non- farm), access to credit and membership in cooperative society determined adoption of the agricultural technologies introduced by the MARKETS II project. This model has been used by (Hazra and Gogtay, 2017; Owusu, 2017). Adoption of MARKETS II technologies was conceptualized as bivariate, taking the value of 1 for respondents that adopted and 0 for no adoption. This was used as the dependent variable. Demographic variables as well as other variables were used as independent variable and specified explicitly in the model as:

Y = β₀ + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₄ + β₅X₅ + β₆X₆ + β₇X₇ + β₈X₈ + β₉X₉+ U.…………(i)

Where; Y = Adoption of MARKETS II agricultural technologies (1 = Yes, 0 = otherwise)

β₀ = Constant

X₁ = Age of the farmer (years)

X₂ = Gender of the farmer (Male = 1 : Female = 0).

X₃ = Educational status of the farmer (Number of years).

X₄ = farm size (hectares)

X₅= household size

X₆ = farming experience (Number of years)

X₇ = Membership of cooperatives (Yes = 1: No = 0)

X₈ = Annual Income (Amount in Naira)

X₉ = Access to credit (Yes = 1: No = 0)

X₁₀= Number of visits by extension agents

U = Error term

(i) Five point Likert Scale

A five point likert scale was used to assess the level of adoption of agricultural technology introduced by the MARKETS-II project: Very low = 1; low = 2; moderate = 3; high = 4; very high = 5.

The likert scale measuring instrument is represented by the formula:

͞X = ∑fx/N

Where ͞X = mean score

∑ = summation sigh

f = frequency

x = number of nominal value of each response category

N = number of respondents

5+4+3+2+1 = 3

Therefore the weighted mean is 3

Decision rule: Any mean value greater or equal to 3 means positive level of adoption as shown in Table 1.

Table 1: Likert Scale interpretation using mean score

Value	range allocation	Innovation adoption status
0.1-	1.0	Not adopted
1.1-	2.0	Slightly adopted
2.1-	3.0	Moderately adopted
3.1-	4.0	Mostly adopted
4.1-	5.0	Completely adopted

Adapted from Mohammad et al. (2014), Owusu-Manu et al. (2017)

Source: Field Survey, 2023

RESULTS AND DISCUSSION

Socio- Economic Characteristics of Respondents

Table 2 presents the Socio- economic characteristics of the respondents, both participants and non- participants of the MARKETS II project. It provided descriptive information on age, gender, educational qualification, marital status, family size, farming experience, land size, and membership of respondents in cooperative societies.

(i). Age of Respondents

Table 2 reveals the age distribution of the respondents, highlighting that the bulk of them fall within the 40 to 49 age range. Specifically, 27.2% of participants and a higher percentage of 36.2% of non-participants were within this age bracket. Notably, 12.2% of participants were under 20 years old, a segment not represented among the non-participants, suggesting a younger demographic involvement in the MARKETS II project. Additionally, 7.9% of participants and 1.5% of non-participants were aged between 20 and 29 years. The 30 to 39 age group comprised 23.2% of participants and 25.1% of non-participants, while 18.9% of participants and 30.3% of non-participants were between 50 and 59 years old. Those above 60 years constituted 10.6% of participants and 7% of non-participants.

This data indicates that a greater proportion of participants are under the age of 50 compared to non-participants, suggesting that participants are generally younger and more of them are in their prime productive years. This age factor could be influential in the easier adoption of technology among participants. These observations align with the research findings of Oladapo et al. (2012), Fanola and Fakayode (2014), Mazza et al. (2015), Balogun et al. (2016) and Akudugu et al., (2023).

(ii). Gender of Respondents

According to the data presented in Table 2, the majority of both participant and non-participant farmers were male, with 65.2% of participants and 72% of non-participants being men. Conversely, women accounted for 34.8% of participant farmers and 28% of non-participants. These statistics indicate a higher representation of male farmers in both groups. This trend suggests that the farming of crops like cocoa and cassava, as well as aquaculture in Southwest Nigeria, is predominantly undertaken by men, possibly due to the demanding nature of these agricultural activities. This observation is in line with the findings from studies conducted by Oluwatusin (2014), Mazza et al. (2015), Abidogun et al. (2019) and Falana et al., (2023).

, which also highlight the male-dominated aspect of these agricultural sectors.

(iii). Educational Qualification

The educational attainment of the respondents, as outlined in Table 2, indicates varied levels of education between participants and non-participants. Specifically, 6.7% of participants and a higher 15.9% of non-participants had no formal education. Delving deeper, 23.2% of the participants completed primary education, while this figure was 33.6% among non-participants. Secondary education was attained by 30.7% of participants and 18.8% of non-participants. Vocational education was pursued by 5.9% of participants and 9.2% of non-participants. Notably, 33.2% of participants advanced to tertiary education, compared to 22.6% of non-participants. These figures suggest that participants generally had a higher level of education, with a greater percentage achieving tertiary education, whereas non-participants were more likely to have no formal education. This trend aligns with Mazza et al. (2015) Balogun et al. (2016) and Sanusi et al., (2023) who observed that beneficiaries of the FADAMA project were typically more educated than non-beneficiaries. This difference in education levels could have implications for the adoption of technology and the effective management of agricultural businesses.

(iv). Marital Status

According to the data outlined in Table 2, the marital status of the respondents indicates that a significant proportion, consisting of 74.4% of participants and 93% of non-participants, were married. This high percentage of married individuals suggests a responsible approach to both farm management and accurate questionnaire completion. Among the participants, 21.6% were single and 2.4% were either divorced or separated. In contrast, 3% of the non-participants were single and 4.1% were divorced. The predominance of married respondents in this survey potentially reflects positively on the availability of family labor. This correlation is consistent with the findings of Balogun et al. (2011), Mazza et al. (2015) and Makka et al., (2023) who noted that a high rate of married farmers indicates a commitment to work diligently for the welfare of their families.

(v). Household size

Examining how respondents are distributed based on the size of their households shows that the majority, comprising 62.6% of participants and 57.9% of non-participants, have households consisting of 4 to 6 individuals. Following this, 27.6% of participants and 15.9% of non-participants have between 7 and 10 members in their families. Additionally, households with 1-3 persons account for 9.8% of participants and 24% of non-participants. This trend suggests that the respondents have the advantage of family assistance, which potentially reduces their reliance on hired labour. This concept aligns with the findings presented by Balogun et al. (2011) and and Makka et al., (2023).

(vi). Farming experience

Farming experience is crucial for the effectiveness of agribusiness ventures. Table 2 categorizes respondents by their years of farming experience, highlighting differences between participants and non-participants. A significant 37% of the participants reported having 16 to 20 years of farming experience. In contrast, the majority of non-participants had 11 to 15 years of experience. Among participants, a small proportion (2.8%) had less than 5 years of experience, 9.4% had 6 to 10 years, 13.4% had 21 to 25 years, and another 13.4% had over 25 years of experience. For non-participants, 17% had under 5 years of experience, 20.3% had 11 to 15 years, 20.7% had 16 to 20 years, 8.9% had 21 to 25 years, and 9.6% had more than 25 years of experience in farming. This distribution reflects the notion posited by Balogun et al. (2011) and Anyasi et al., (2023) that greater farming experience enhances a farmer's ability to make more informed production decisions, thereby boosting their knowledge and productivity.

(vii). Farm Size

Table 2 presents data on the farm sizes of respondents in hectares. Among the participants, only 3.1% managed less than 1 hectare, whereas for non-participants, this figure was 12.6%. A significant majority (93.7%) of the participants had farms ranging from 1 to 5 hectares, reflecting the MARKETS II project's focus on smallholder farmers. A smaller portion, 2.8%, had farms between 6 and 10 hectares, and a mere 0.4% managed farms between 11 and 15 hectares. Similarly, the largest group of non-participants comprised those with farms of 1 to 5 hectares. Among the non-participants, 9.2% had farms of 6 to 10 hectares. It's noted that none of the respondents possessed farms larger than 15 hectares. This finding supports Okunlola's (2019) research, which highlighted the dominance of smallholder farmers in Nigeria's agricultural production system, responsible for a substantial part of agricultural output.

(viii). Membership of cooperatives

Cooperative societies play a crucial role in providing capital to farmers, thereby enhancing their income. As indicated in Table 2, membership in a cooperative society is notably higher among participants of the MARKETS II project, with 72.4% being members, compared to 57.2% of non-participants. This higher membership rate among participants suggests a stronger engagement in agricultural projects. Farmers believe that being part of a cooperative society affords them greater access to agricultural information, more affordable inputs, and improved extension services. This observation aligns with the research conducted by Balogun et al. (2011) and Ojiagu and Uchenna (2015).

Table 2: Socio- economic Characteristics of Respondents

	Participants		Non-Participants
Characteristics	Frequency	Percentage (%)	Frequency	Percentage (%)
Age (Years)
Below 20	31	12.2	0	0.0
20-29	20	7.9	4	1.5
30-39	59	23.2	68	25.1
40-49	69	27.2	98	36.2
50-59	48	18.9	82	30.3
60 and above	27	10.6	19	7.0
Total	254	100.0	271	100.0
Gender
Male	166	65.2	195	72.0
Female	88	34.8	76	28.0
Total	254	100.0	271	100.0
Educational Qualification
Primary school	59	23.2	91	33.6
Secondary School:	78	30.7	51	18.8
Vocational/ Technical	15	5.9	25	9.2
OND	45	17.7	37	13.7
HND	13	5.2	16	5.9
BSc, BA, BEd, BTech	27	10.6	8	3.0
Informal	17	6.7	43	15.9
Total	254	100.0	271	100.0
Marital status
Married	189	74.4	252	93.0
Single	55	21.6	8	3.0
Divorce/ Separated	6	2.4	11	4.1
Others	4	1.6	0	0.0
Total	254	100.0	271	100.0
Family size
1-3	25	9.8	65	24.0
4-6	159	62.6	157	57.9
7-10	70	27.6	43	15.9
Total	254	100.0	271	100.0
Farming experience (years)
Below 5	7	2.8	46	17.0
6-10	24	9.4	55	20.3
11-15	61	24.0	64	23.6
16-20	94	37.0	56	20.7
21-25	34	13.4	24	8.9
25 and above	34	13.4	26	9.6
Total	254	100.0	271	100.0
Land size (hectares)
<1	8	3.1	34	12.6
1-5	238	93.7	212	78.2
6-10	7	2.8	25	9.2
11-15	1	0.4	0	0.0
16-20	0	0.0	0	0.0
21-25	0	0.0	0	0.0
Above 25	0	0.0	0	0.0
Total	254	100.0	271	100.0
Membership of cooperatives
No	70	27.6	116	42.8
Yes	184	72.4	155	57.2
Total	254	100.0	271	100.0

Source: Field Survey, 2023

Table 3: Binary logistic regression estimates of the determinants of adoption of MARKETS II project’s agricultural technology

Code	Variables		Coefficient	S.E.	Sig.	Odds Ratio
X₁		Age	-1.569	1.291	.224	.208
X₂		Gender	.398	.436	.361	1.489
X₃		Educational status	1.109*	.669	.097	3.032
X₄		Farm Size	.339***	.114	.003	1.404
X₅		Household size	-.852	.850	.316	.426
X₆		Farming Experience	-3447***	1.265	.006	.032
X₇		Membership in Cooperative	1.825***	.409	.000	6.203
X₈		Annual Income	.002**	.001	.027	1.002
X₉		Access to Credit	.741*	.408	.069	2.099
X₁₀		Extension visit Constant	.002* -1.904	.001 1.252	.027 .128	1.002 .149
Model Chi- square			97.629
Hosmer- Lemeshow test:
Chi- square			2.938
Significance			.938
Cox and Snell R²			.355
Nagelkerke R²			.494
Overall predicted percentage correct			76.9

*, **, *** Significant at 10, 5, and 1 percent levels respectively. Source: Field Survey, 2023

Table 4:Level of adoption of agricultural technologies introduced by MARKETS-II project

Technologies introduced by MARKETS-II Mean ͞(X)				Adoption status
1.	Access to New cultivation practices		4.2585	Completely adopted
2.	Access to New processing techniques		3.9750	Mostly adopted
3.	Access to New Marketing and storage techniques		3.6841	Mostly adopted
4.	Access to Market outlet for easy sale of farm produce		3.8095	Mostly adopted
5.	Access to sales at Farm gate		3.6636	Mostly adopted
6.	Access to Middle men to facilitate marketing		3.5114	Mostly adopted
7.	Access to Local market		3.6644	Mostly adopted
8.	Access to Farmers’ association		4.1276	Completely adopted
9.	Access to Government buying agents		3.3356	Mostly adopted
Technology introduced specifically for Cocoa
10.	Provision of club to break open pods	4.2919		Completely adopted
11.	linking cocoa farmers with major procuring and processing companies	4.2261		Completely adopted
12.	Provision of raised drying platform	4.4667		Completely adopted
13.	Support to become certified under international cocoa standard	4.2122		Completely adopted
14.	Access to Training on integrated pest management	4.0151		Completely adopted
15.	Access to training on pruning and phyto sanitary management	4.6727		Completely adopted
Technology introduced specifically for Cassava
16.	New agronomic management practices	4.1519		Completely adopted
17.	Training on optimal spacing	4.6909		Completely adopted
18.	Training on timely use of NPK, pesticide and herbicide	4.2837		Completely adopted
19.	Training on intercropping	4.5683		Completely adopted
Technology introduced specifically for Aquaculture
20.	Access to Improved water testing	3.0833		Mostly adopted
21.	New feeding techniques	4.3333		Completely adopted
22.	Training on pond water sanitation	3.2291		Mostly adopted
23.	Provision of improved smoking kilns for processing	3.5833		Mostly adopted
	Source: Field Survey, 2023

CONCLUSION

The study assessed the factors that determined the adoption of agricultural technologies introduced by MARKETS II project to smallholder farmers in Southwest, Nigeria. Based on the results, the factors that determined the adoption of technologies introduced by MARKETS II project included educational status, farm size, farming experience, membership in cooperative societies, annual income and number of extension visits. However, farming experience had a negative relationship. The results further show that the strongest determinant of the smallholder farmers’ adoption of technology was membership in cooperative society among all the other determinants. Also, the results show that the agricultural technologies introduced by MARKETS-II project were well adopted by the farmers.

Recommendation

Based on the findings and conclusion of this study the following recommendations were made:

i). Smallholder farmers should be encouraged to join cooperative societies as this was shown to boost their exposure to new technology.

ii). Special educational facilities should also be provided for the farmers for them to achieve a higher level of education.

iii). Extension agents should be made to visit the farmers as at when due.

Acknowledgement

The support provided by Tertiary Education Trust Fund (TETFund) through Institution – Based Research (IBR) enabled us to conduct data collection, analysis, and interpretation, as well as cover expenses related to research materials, participant recruitment, and travel, where applicable. Their investment in this work has significantly contributed to the quality and impact of our research findings.

REFERENCES

Abidogun, O. G., Olajide B. R., Amujoyegbe, B. J., Bamire, A. S., Kehinde, A. D. & Gaya, I.(2019). Gender Involvement in Cocoa Farming Activities in South West Nigeria. Ife Journal of Agriculture. 31(1), 104-117.

Adesina, A. (2012). Agricultural transformation agenda: Repositioning agriculture to drive Nigeria’s economy. Abuja: Nigeria Federal Ministry of Agriculture and Rural Development.

Akudugu M.A, Nkegbe P.K., Wongnaa C.A., Millar K.K. (2023). Technology adoption behaviors of farmers during crises: What are the key factors to consider? Journal of Agriculture and Food Research, Volume 14, 100694, ISSN 2666-1543,

Alexandratos, N. & Bruinsma, J. (2012). World agriculture towards 2030/2050: The 2012 Revision. Global Perspective Studies Team ESA Working Paper No. 12-03 June 2012 Agricultural Development Economics Division Food and Agriculture Organization of the United Nations.

Anyasi, C. F., Ajah, J., and Idu, E. E. (2023). Small-Scale Crop Farmers’ Access to Farm Inputs in the North Central Zone, Nigeria. Direct Research Journal of Agriculture and Food Science: Vol. 8 8 (8), pp.288-294, ISSN 2354-4147

Amos, T. T. (2018). Agricultural production and productivity: The game changing food security paradigm in Nigeria. Inaugural Lecture Series 95 Federal University of Technology, Akure, Nigeria.

Balogun O.L., SA Yusuf, BT Omonona, VO Okoruwa (2011). Social capital and microcredit effects on poverty among the rural households in South west states, Nigeria. Journal of Agricultural and Biological Science.

Balogun, O. L., Ogunsina,I. J. Ayo-Bello T. A., Afodu O.J.(2018). Effect of social capital on productivity of cassava farmers in Ogun State, Nigeria. Journal of Agricultural Sciences, Belgrade, 63. 99-112. 10.2298/JAS1801099B.

Dada A.D. (2014). Evaluation of Technological Capability and Innovations in the Nigerian Cassava Processing Industry. An Unpublished Ph.D. Thesis Submitted to the African institute for Science policy and Innovation (AISPI), Faculty of Technology, Obafemi Awolowo University, Ile-Ife, Nigeria, 2014.

Falana, A.R., Aderounmu, A.F., Ayandokun, A.E., Adesokan, F.B., Majolagbe, M.O., Adelusi, F.T., Oke, O.O. and Ojo-Fakuade, F. (2023). Level of Awareness of Silkworm Rearing among Farmers in Ido Local Government Area, Ibadan, Oyo State, Nigeria. Nigerian Agriculture Journal ISSN: 0300-368X 54 (1). 149-155.

Fanola, A. and Fakayode S. B. (2014) Awareness and effects of climate change on cocoa production in Ondo State, Nigeria International Journal of Applied Agricultural and Apicultural Research Faculty of Agricultural Sciences, LAUTECH, Ogbomoso, Nigeria. IJAAAR 10 (1&2): 91-97

Hazra, A., & Gogtay, N. (2017). Biostatistics Series Module 9: Survival Analysis. Indian Journal of Dermatology, 62, 251 - 257

Makka, T. B., Murtala, N., Sani, M. H., & Haruna, U. (2023). Socio-economic characteristics of irrigated upland tomato (lycopersicon esculentum) farmers in Gombe state, Nigeria. Journal of Agripreneurship and Sustainable Development, 6(2), 140–161. https://doi.org/10.59331/jasd.v6i2.443

Mazza M., Ekumankama O.O., & Okezie, C.A. (2015). Effect of Second National Fadama Development Project on Farmers Productivity in Imo State, Nigeria. Journal of Natural Sciences Research, 5, 69-74.

Mosa S. S. and László, Berényi. 2023. E-Governance in Agriculture: A Framework on Digital Technology Adaptation by Smallholder Farmers. In Central and Eastern European eDem and eGov Days 2023 (CEEeGov 2023), September 14, 15, 2023, Budapest, Hungary. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3603304.3603341

Muhammad Waseem Riaz Marral¹, Muhammad Shahid Ibni Zamir² and Shahid Iqbal (2014). Impact of Phosphorous Levels and Different Tillage Systems on Yield and Economics of

Nchuchuwe, F. F. & Adejuwon, K. D. (2012). The challenges of agriculture and rural development in Africa: The case of Nigeria. International Journal of Academic Research in Progressive Education and Development, 1 (3).

Ogbodo, I. H., Aguaguiyi, F. N., Nwafor M. O., G.O. Umebali, E.E. (2021). Determinants of Adoption of Improved Agricultural Technologies among Fadama Rice Farmer Cooperative Societies in Enugu State, Nigeria University-Led Knowledge and Innovation for Sustainable Development Nnamdi Azikiwe University Book Series on Sustainable Development.

Ogen, O. (2003). Patterns of economic growth and development in Nigeria since 1960 In: S.O. Arifalo and Gboyega Ajayi (Eds) (2003). Essays in Nigerian contemporary history. Lagos First Academic Publishers.

Ogunjobi V.O., Ojo O.J., Adebambo H.O. (2022) - Factors Determining Participation of Farmers In Agricultural Projects: Evidence From USAID Markets II Project In Southwest Nigeria Quest Journals Journal of Research in Agriculture and Animal Science. Volume 9 ~ Issue 7: 01-07 ISSN (Online) : 2321-9459

Ogunlela, Y. & Mukhtar, A. A. (2009). Gender issues in agriculture and rural development in Nigeria: The role of women. Humanity and Social Sciences Journal, 4 (1).

Ogunleye, O., Ajibola A., Enilolobo O. & Shogunle, O. (2018). Influence of road transport infrastructure on agricultural sector development in Nigeria. Logistics & Sustainable Transport, 9 (1), 39-50. doi: 10.2478/jlst-2018-0004.

Ojiagu N. C. & Uchenna C. O. (2015). Effects Of Membership Of Cooperative Organisations And Determinants On Farmer Members’ Income In Rural Anambra State, Nigeria International Journal Of Scientific & Technology Research 4(8), ISSN 2277-8616 28 IJSTR©2015 www.ijstr.org

Okunlola O. J. (2019). Transforming Nigerian Agriculture: Agricultural extension in the saddle. Federal University of Technology, Akure (FUTA) 112th Inaugural Lecture.

Oladapo, A., Shittu, A.M., Agbonlahor, M.U. and Fapojuwo O.E (2012) credit use and production efficiency of cocoa farms in Ondo State, Nigeria. Nigerian Journal of Agricultural Economics (NJAE) 3(1), 69 – 77

Oluwatusin, F. M. (2014). The perception of and adaptation to climate change among cocoa farm households in Ondo state, Nigeria. Academic Journal of Interdisciplinary Studies MCSCR Publishing, Rome-Italy 3 (1), 286- 295.

Owusu, M. (2017) The Determinants of Foreign Direct Investment Inflows in Ghana. Kwame Nkrumah University of Science and Technology, Kumasi. The International Business & Economics Research Journal, 12 (5), 573-584.

Owusu-Manu, D., Torku, A., Pärn, E., Addy, M., & Edwards, D. (2017). An Empirical Assessment of Innovation Practices of Quantity Surveying Firms in Ghana. Journal of Construction Project Management and Innovation, 7 (Supplement), 1843–1858. https://doi.org/10.36615/jcpmi.v7iSupplement.14.

Sanusi, L., Sanusi, J., Ibrahim, S., & Nawaf, A. (2023). Determination of Heavy Metals Concentrations in Soil and Tomato Plant (Solanum lycopersicom) from Ajiwa Fadama farms, Katsina State. UMYU Scientifica, 2(4), 39–44. https://doi.org/10.56919/usci.2324.005

Stewart, R. (2000). Welcome address at the proceedings of the 7th world sugar farmers conference, Durban.

USAID (2013). Maximizing Agricultural Revenue and Key Enterprises in Targeted Sites (MARKETS-II). 2013 First update April 2012 – March 2013. United States Agency for International Development (USAID) report.

USAID (2017). Maximizing Agricultural Revenue and Key Enterprises in Targeted Sites (MARKETS-II). 2017 Final update April 2012 – March 2017. United States Agency for International Development (USAID) report.

Vernon, R. & Yujiro, H. (2014). Strategies for agricultural development. Agricultural Experiment Station Scientific Journal Paper Series, pp. 129-148.

Cite this Article: Ogunjobi, VO (2024). Determinants of Adoption of USAID Markets II Project’s Agricultural Technology by Smallholder Farmers in Southwest, Nigeria. Greener Journal of Agricultural Sciences, 14(1): 23-32.