FACTORS INFLUENCING ADOPTION OF IMPROVED SOYABEAN PRODUCTION TECHNOLOGIES AMONG FARMERS IN TWO LOCAL GOVERNMENT AREAS OF KOGI STATE

CHAPTER ONE: INTRODUCTION

1.1 Background of the Study

Soyabean (Glycine max (L.) Merrill) has emerged as one of the most economically and nutritionally important leguminous crops in Nigeria, serving multiple roles as a source of high-quality protein for human consumption, a nutritious feed component in livestock production, and a soil fertility-enhancing rotational crop. Over the past three decades, soyabean production in Nigeria has expanded significantly, from a relatively minor crop to one cultivated by over one million smallholder farmers across the country, with annual production estimated at approximately 1.2 million metric tons (FAOSTAT, 2020). This expansion has been driven by growing demand from the poultry feed industry, the popularization of soyabean-based processed foods (soymilk, tofu, soyameat), and increased awareness of the crop’s nutritional benefits among consumers (Oladapo and Ogunwale, 2019). (FAOSTAT, 2020; Oladapo and Ogunwale, 2019)

The nutritional and economic significance of soyabean in the Nigerian context cannot be overstated. Soyabean contains approximately 40% high-quality protein, rich in essential amino acids, and 20% oil, making it a uniquely valuable crop among plant-based food sources (IITA, 2018). For rural households, soyabean offers multiple benefits: as a low-cost protein source that complements starchy staples; as a cash crop that can generate income for school fees, healthcare, and other household expenses; and as a soil-improving crop that fixes atmospheric nitrogen through rhizobial symbiosis, reducing the need for nitrogen fertilizers in subsequent cereal crops. For the national economy, increased soyabean production reduces dependence on imported soybean meal for animal feed, saving foreign exchange and supporting the domestic livestock industry (Ogunniyi and Olagunju, 2020). (IITA, 2018; Ogunniyi and Olagunju, 2020)

Improved soyabean production technologies encompass a range of agronomic, biological, and management innovations developed by agricultural research institutions to enhance productivity, reduce production risks, and improve profitability for smallholder farmers. These technologies include: improved soyabean varieties with higher yield potential, shorter maturity periods, better pest and disease resistance, and improved seed quality; recommended seed treatment practices (inoculation with specific Bradyrhizobium japonicum strains to enhance nitrogen fixation); appropriate planting densities and spacing; integrated pest management (IPM) strategies for controlling insect pests (pod borers, leaf rollers, stink bugs) and diseases (rust, leaf spot, bacterial blight); fertilizer recommendations (particularly phosphorus, which is critical for nodulation and nitrogen fixation); and post-harvest handling and storage technologies to reduce losses (IITA, 2019). (IITA, 2019)

The adoption of improved soyabean varieties has been a particular focus of research and extension efforts in Nigeria. The International Institute of Tropical Agriculture (IITA), in collaboration with national research partners, has released numerous improved soyabean varieties adapted to different agroecological zones of Nigeria, including early-maturing varieties (TGx 1448-2E, TGx 1987-10F) that fit into double-cropping systems with maize or cassava; medium-maturing varieties (TGx 1830-20E, TGx 1904-6F) with high yield potential and good seed quality; and promiscuously nodulating varieties that form effective nitrogen-fixing symbioses with native soil rhizobia without requiring specific inoculation (Tefera, 2019). These improved varieties typically outyield traditional landraces by 30-50% under good management, have better seed quality (larger seed size, higher protein content, lower cooking time), and show improved resistance to major diseases (Sanginga and Thottappilly, 2020). (Tefera, 2019; Sanginga and Thottappilly, 2020)

Despite the availability of improved soyabean production technologies and their demonstrated technical superiority over traditional practices, adoption rates among smallholder farmers in many parts of Nigeria remain suboptimal. National average adoption rates for improved soyabean varieties are estimated at approximately 35-40%, with substantial regional variation; adoption of complementary technologies such as seed inoculation, recommended plant spacing, and pest management practices is even lower, often below 20% (Manyong et al., 2019). This adoption gap persists even though improved technologies have been promoted through agricultural extension programs, input subsidy schemes, and various development projects over several decades. The situation in Kogi State, including the two focal Local Government Areas of this study, reflects this broader pattern of partial and incomplete adoption (Adesina and Chianu, 2020). (Manyong et al., 2019; Adesina and Chianu, 2020)

Kogi State, located in the North-Central geopolitical zone of Nigeria, occupies a strategic position for soyabean production due to its favorable agroecological conditions and its role as a major producing region. The state spans the Guinea savannah and derived savannah ecological zones, characterized by annual rainfall ranging from 1,000 to 1,500 mm, moderate temperatures (22-30°C), and relatively fertile soils (Kogi State Ministry of Agriculture, 2020). These conditions are well-suited to soyabean production, and the crop has become increasingly important in the state’s agricultural economy, with soyabean cultivated by an estimated 150,000 farm households. Kogi State ranks among Nigeria’s top five soyabean-producing states, contributing approximately 8-10% of national output (NBS, 2019). The expansion of soyabean cultivation in the state has been driven by demand from poultry feed manufacturers in Lagos and Ibadan, as well as from local processing into soyabean products (Oyekale and Oyekale, 2018). (Kogi State Ministry of Agriculture, 2020; NBS, 2019; Oyekale and Oyekale, 2018)

Two Local Government Areas in Kogi State serve as the focus of this study, selected based on their significance in soyabean production and their representativeness of different production environments within the state. The specific LGAs—which will be identified as LGA A and LGA B for purposes of this study pending final selection—include one LGA with relatively good market access (proximity to major roads and urban centers) and one LGA with more remote, less market-integrated production conditions. This variation allows comparative analysis of how market access conditions moderate adoption patterns. Both LGAs have been targeted by agricultural development programs, including the Kogi State Agricultural Development Programme (KSADP), the Federal Ministry of Agriculture’s Agricultural Promotion Policy (APP), and various NGO interventions promoting improved soyabean technologies (Obi and Ezeh, 2019). (Obi and Ezeh, 2019)

The socio-economic characteristics of farmers in the study areas—including age, education, farm size, household labor availability, income, asset ownership, and social networks—are expected to influence adoption decisions in systematic ways consistent with the technology adoption literature. Younger farmers may be more open to innovation but have less capital and farming experience; older farmers may have more resources but be more set in traditional practices (Rogers, 2003). Formal education enhances farmers’ ability to access, understand, and utilize technical information about improved technologies. Larger farm sizes may allow farmers to experiment with new technologies on a portion of their land without risking their entire crop, but larger farms may also face labor constraints for precise operations like seed inoculation or proper spacing (Feder and Umail, 2020). The distribution of these characteristics among soyabean farmers in the study areas is a key descriptive objective of this research. (Rogers, 2003; Feder and Umail, 2020)

Access to productive resources—particularly land, labor, and capital—represents a critical enabling condition for technology adoption. Land access in the study areas is governed by a mixture of customary tenure systems (family land, lineage land) and individual ownership, with implications for farmers’ security of tenure and thus their willingness to invest in land improvements or new technologies that may have multi-year benefits (Okunlola and Adebayo, 2018). Labor availability, both household labor and hired labor, affects farmers’ ability to implement labor-intensive technologies such as proper planting spacing, weeding, and pest scouting. Capital constraints affect farmers’ ability to purchase improved seeds (which are more expensive than saved seed from local varieties), inoculants, fertilizers, pesticides, and other purchased inputs. The severity of these resource constraints and their effects on adoption in the study areas have not been systematically quantified (Akande and Adewumi, 2020). (Okunlola and Adebayo, 2018; Akande and Adewumi, 2020)

Credit access has been consistently identified in the literature as a major determinant of technology adoption in agriculture, particularly for technologies that require purchased inputs. Smallholder farmers in Kogi State, as elsewhere in Nigeria, face significant barriers to accessing formal credit from banks and microfinance institutions: collateral requirements (land titles, which many smallholders lack), high interest rates (often 20-30% or more), complex application procedures, and geographical distance from financial institutions (Oladimeji and Abdulsalam, 2019). Informal credit sources (moneylenders, input suppliers, friends and family) are more accessible but may carry even higher interest rates or impose social obligations that farmers find onerous. The relationship between credit access and adoption of improved soyabean technologies in the study areas—which technologies are most affected, which credit sources matter most—has not been empirically examined (Omotesho and Ogunlade, 2020). (Oladimeji and Abdulsalam, 2019; Omotesho and Ogunlade, 2020)

Extension services represent the primary institutional channel through which farmers receive information about improved agricultural technologies and technical support for their implementation. The agricultural extension system in Kogi State operates through the Kogi State Agricultural Development Programme (KSADP), which maintains extension agents at zonal and block levels covering all LGAs. However, the extension agent-to-farmer ratio in the state is estimated at approximately 1:3,500, far below recommended levels, and extension contact frequency is low for most farmers (KSADP, 2019). Furthermore, extension agents’ technical knowledge of soyabean production specifically may be limited, given the generalist training many receive and the multiple crops they cover. The effect of extension contact on adoption of improved soyabean technologies in the study areas, and whether contact quality or frequency matters more, is an empirical question this study addresses (Agbamu, 2019). (KSADP, 2019; Agbamu, 2019)

Farmer group membership, including membership in farmer cooperatives, soyabean producer associations, or other collective organizations, may facilitate technology adoption through several mechanisms. Groups can aggregate demand for improved seeds and other inputs, reducing transaction costs for input suppliers and potentially securing volume discounts. Groups can serve as vehicles for technology dissemination, with members learning from one another and with extension services able to reach more farmers through group contact than through individual visits. Groups may also facilitate collective marketing, enabling members to capture better prices for their soyabean and thus increasing the profitability of technology adoption (Abate et al., 2020). In the study areas, the prevalence of farmer groups, their functional effectiveness, and their relationship to technology adoption have not been systematically studied (Ojiako and Ogunlade, 2019). (Abate et al., 2020; Ojiako and Ogunlade, 2019)

The economic returns to adoption of improved soyabean technologies depend on the yield gains achieved, the input costs incurred, and the output prices received. Improved varieties typically yield 1.5-2.5 tons per hectare under good management, compared to 0.8-1.2 tons per hectare for local varieties (IITA, 2019). However, achieving these yields requires adoption of complementary practices: proper seed inoculation (seed treatment cost approximately NGN 500-1,000 per hectare), adequate plant spacing (which may require thinning or replanting if seeds are broadcast), weed control (labor or herbicide costs), and sometimes phosphorus fertilizer (NGN 10,000-15,000 per hectare). The net profitability of adoption thus depends on whether the additional output value exceeds these additional input costs. In the study areas, typical input-output configurations and their profitability have not been documented (Akinola and Adebayo, 2018). (IITA, 2019; Akinola and Adebayo, 2018)

Risk and uncertainty play important roles in adoption decisions that are often overlooked in analyses focusing on average expected returns. Farmers in semi-arid and sub-humid zones face considerable production risks from rainfall variability, pest and disease outbreaks, and price volatility. Improved soyabean varieties may differ from local varieties in their risk characteristics: some improved varieties may be more drought-tolerant but more susceptible to a particular disease; others may have higher yield potential but also higher yield variance (Tefera, 2019). Risk-averse farmers facing downside risk (fear of crop failure) may prefer local varieties whose yield distribution they know from experience over improved varieties whose performance under local conditions is less certain, even if the average yield of improved varieties is higher. The influence of risk preferences and risk perceptions on adoption in the study areas has not been investigated (Yesuf and Bluffstone, 2019). (Tefera, 2019; Yesuf and Bluffstone, 2019)

Agroecological variation within the study areas—in soil type, rainfall patterns, elevation, and pest and disease pressure—may affect the relative performance of different improved technologies and thus the likelihood of adoption. A variety that performs well on deep, well-drained soils may perform poorly on shallow, waterlogged soils; a seed inoculation product that works effectively in one soil pH range may be ineffective in another. Farmers are aware of this local variation and may be reluctant to adopt technologies that have not been demonstrated under conditions similar to their own farms (Sanginga and Thottappilly, 2020). The extent to which agroecological variation within the study areas explains variation in adoption patterns, and the need for more localized technology recommendations, is a question this study can address. (Sanginga and Thottappilly, 2020)

Previous adoption studies on soyabean in Nigeria have identified a range of significant factors, but most have been conducted in other regions, primarily in the northern states (Kaduna, Kano, Niger) and southwestern states (Oyo, Ogun, Osun), with limited research in the North-Central zone including Kogi State. A study by Ogunleke and Ajayi (2019) in Kaduna State found that education, farm size, extension contact, and credit access were significant positive predictors of adoption. A study by Ayinde and Adewumi (2020) in Oyo State found that age (negative), group membership (positive), and distance to market (negative) were significant. A study by Adepoju and Oni (2019) in Niger State found that risk preference, off-farm income, and livestock ownership were significant. The transferability of these findings to Kogi State—with its distinctive mix of agroecological conditions, market access, and institutional environment—is uncertain, underscoring the need for location-specific research (Okwu and Agbo, 2019). (Ogunleke and Ajayi, 2019; Ayinde and Adewumi, 2020; Adepoju and Oni, 2019; Okwu and Agbo, 2019)

The conceptual framework guiding this study draws primarily on the diffusion of innovations theory (Rogers, 2003) and the agricultural household economics model (Singh et al., 1986), both of which have been extensively applied in technology adoption research. The diffusion framework emphasizes the role of communication channels, social networks, and technology characteristics (relative advantage, compatibility, complexity, trialability, observability) in shaping adoption. The agricultural household model emphasizes the joint production-consumption decisions of farm households, treating adoption as an outcome of utility maximization subject to resource constraints (land, labor, capital, information). The empirical model derived from this framework specifies adoption as a function of farmer characteristics (age, education, experience), household characteristics (size, assets, off-farm income), institutional factors (extension access, credit access, group membership), technology characteristics (cost, complexity, observed performance), and environmental factors (soil type, rainfall, market access) (Feder and Umail, 2020). (Rogers, 2003; Singh et al., 1986; Feder and Umail, 2020)

In summary, improved soyabean production technologies offer substantial opportunities for enhancing productivity, profitability, and sustainability of smallholder farming systems in Kogi State, yet adoption rates remain below potential, with limited understanding of the specific factors constraining adoption in the state’s Local Government Areas. The two LGAs selected for this study provide contrasting contexts of market access and agroecological conditions, enabling analysis of how contextual factors moderate adoption determinants. Previous adoption research in other regions of Nigeria has identified a range of potential factors—education, credit access, extension contact, group membership, farm size, age, risk preferences—but the applicability of these findings to Kogi State is unknown. This study therefore seeks to fill this knowledge gap by systematically investigating the factors influencing adoption of improved soyabean production technologies among farmers in two Local Government Areas of Kogi State, generating evidence to inform more effective and targeted technology dissemination policies and programs (Oladapo and Ogunwale, 2021; Ogunniyi and Olagunju, 2021). (Oladapo and Ogunwale, 2021; Ogunniyi and Olagunju, 2021)

1.2 Statement of the Problems

Despite the technical superiority of improved soyabean production technologies—including high-yielding varieties, seed inoculation, proper planting practices, and integrated pest management—adoption rates among smallholder farmers in the study LGAs of Kogi State remain low. Preliminary evidence suggests that less than 40% of soyabean farmers use improved varieties, less than 15% use seed inoculation, less than 30% practice recommended spacing, and less than 20% implement any form of integrated pest management (Kogi State Ministry of Agriculture, 2020). This low adoption persists even though improved technologies have been promoted through government extension programs, development projects, and input subsidy schemes for over a decade. The persistence of traditional, low-productivity practices represents a significant missed opportunity for increasing farm productivity and household income.

The continued use of traditional soyabean production practices carries substantial economic costs for farmers and the broader agricultural economy. Farmers using local varieties and traditional practices typically achieve yields of only 0.8-1.2 tons per hectare, compared to yields of 1.8-2.5 tons per hectare achievable with improved technologies under similar growing conditions (IITA, 2019). This yield gap translates into foregone income of approximately NGN 100,000-200,000 per hectare per season (at current soyabean prices of NGN 150,000-200,000 per ton). Aggregated across the thousands of hectares planted to soyabean in the study LGAs, the total annual income loss from non-adoption runs into tens of millions of Naira. At the household level, foregone income perpetuates poverty, limits farmers’ ability to invest in other productivity-enhancing activities, and constrains spending on food, education, and health.

A first specific problem is the absence of empirical data on adoption rates and patterns specific to the two LGAs of Kogi State that are the focus of this study. While state-level averages exist, they mask substantial local variation, and the adoption status of individual farmers—whether they use improved varieties, inoculants, recommended spacing, or pest management—has not been systematically documented for these LGAs. Without this baseline information, extension programs cannot identify underserved areas or track changes in adoption over time. Furthermore, simply knowing that adoption is low provides no guidance on which constraints are most binding and which interventions would be most effective.

A second problem concerns the lack of empirical identification of the specific factors that constrain adoption in the study areas. While generic categories of potential determinants (farmer characteristics, resource access, institutional factors, technology attributes) are known from the literature, their relative importance and specific manifestations in the Kogi State context are unknown. For example, is credit access the binding constraint, or is it lack of information about improved technologies? Is low adoption driven primarily by farmer risk aversion, or by real technical failures of improved varieties under local conditions? Do constraints differ systematically between the two LGAs (e.g., market access constraints in one, information constraints in the other)? These questions cannot be answered without empirical investigation.

A third problem concerns the role of seed inoculation, a specifically important technology for soyabean that has received inadequate research attention in the adoption literature. Seed inoculation with Bradyrhizobium japonicum is essential for realizing the nitrogen-fixing potential of improved soyabean varieties, particularly in fields without recent history of soyabean cultivation where native rhizobial populations may be low or ineffective. Despite its importance, adoption of seed inoculation remains extremely low in Kogi State (estimated below 15%), likely due to a combination of low awareness, limited availability of inoculant products, and lack of understanding of how to handle and apply inoculants correctly (Sanginga and Thottappilly, 2020). The specific factors affecting inoculant adoption—including whether they differ from factors affecting variety adoption—have not been examined.

A fourth problem concerns the complementarity and interdependence among different improved technologies. Adoption of improved varieties may be more profitable when combined with seed inoculation, recommended spacing, and pest management, but farmers may adopt some components of the technology package while rejecting others. Partial adoption may yield lower benefits than full adoption, potentially leading farmers who experiment with partial adoption to conclude that improved technologies are not worthwhile and revert to traditional practices (Manyong et al., 2019). Understanding the patterns of partial versus complete adoption, and the factors that drive adoption of different combinations of technologies, requires analysis that goes beyond binary adoption of a single technology.

A fifth problem concerns the influence of seed availability and seed quality on adoption of improved varieties. Even if farmers are aware of and desire to plant improved soyabean varieties, they may be unable to obtain sufficient quantities of high-quality seed at planting time if seed supply chains are unreliable. In the study areas, the formal seed sector (private seed companies, government seed multiplication programs) is weakly developed, and farmers often rely on seed saved from previous harvests, which may be of lower quality (lower germination rates, mixed varieties, seed-borne diseases) (Obi and Ezeh, 2019). The relationship between seed access constraints and adoption, including whether seed availability is a binding constraint independent of other factors, has not been quantified.

A sixth problem concerns the role of soil fertility variability in shaping the returns to improved technologies and thus adoption incentives. Improved soyabean varieties, particularly those with high yield potential, require adequate soil phosphorus levels for effective nodulation and nitrogen fixation. On phosphorus-deficient soils (common in parts of the Guinea savannah), the yield response to improved varieties and inoculation may be limited without phosphorus fertilizer application (Tefera, 2019). Farmers who have previously planted improved varieties and observed poor performance due to underlying soil constraints may generalize that “improved varieties don’t work here” and cease adoption, even though the true constraint is soil phosphorus. The extent to which soil fertility variation explains spatial patterns of adoption has not been investigated.

A seventh problem concerns the effectiveness of agricultural extension services in delivering information about improved soyabean technologies to farmers in the study areas. KSADP extension agents operate with limited resources (vehicles, fuel, teaching materials), large catchment areas, and multiple crop responsibilities. The actual contact frequency between farmers and extension agents, the quality of information provided about soyabean specifically, and the extent to which extension advice is followed by farmers are all unknown (KSADP, 2019). Furthermore, extension agents may themselves lack up-to-date knowledge about improved soyabean technologies, particularly newer varieties and inoculant products, undermining the quality of their recommendations.

An eighth problem concerns the relationship between farmer group membership and technology adoption. While cooperative membership is often assumed to facilitate adoption through collective input purchasing, shared learning, and technology dissemination, the functional reality of farmer groups in the study areas may be different. Some groups may be inactive or poorly led; some may exist only on paper for purposes of accessing government programs; some may be dominated by better-off farmers who capture the benefits of group activities (Ojiako and Ogunlade, 2019). The extent to which group membership actually translates into technology adoption, and the group characteristics (size, leadership quality, meeting frequency, savings mobilization) that condition this relationship, have not been empirically examined.

A ninth problem concerns the influence of off-farm income on adoption. Off-farm income can have two opposing effects on technology adoption: it can provide capital that enables investment in purchased inputs (positive effect), but it can also divert labor and attention away from farming, reducing the farmer’s engagement with new technologies (negative effect). In the study areas, many farm households have at least one member engaged in off-farm activities (trading, artisanal work, casual labor), but the net effect of off-farm income on adoption of improved soyabean technologies is unknown (Adepoju and Oni, 2019). Disentangling these opposing effects requires careful analysis.

A tenth problem concerns the role of gender in technology adoption decisions. While soyabean production in Kogi State involves both male and female household members, decision-making authority over crop choice, input purchases, and technology adoption may rest primarily with male household heads. Female members, despite often providing significant labor for planting, weeding, and harvesting, may have limited influence over adoption decisions (Oladapo and Ogunwale, 2019). Furthermore, female-headed households (approximately 15-20% of farm households in the region) may face different constraints in accessing extension, credit, and improved inputs. The gender dimensions of adoption—including whether adoption rates differ between male-headed and female-headed households and whether the determinants of adoption differ by gender—have not been examined in the study areas.

An eleventh problem concerns the influence of farmers’ risk perceptions and risk preferences on adoption. Adoption of improved technologies involves uncertainty: farmers do not know with certainty how a new variety will perform on their specific field in a given season. Farmers who are more risk-averse may require larger expected yield gains to induce adoption, or may adopt only after observing successful outcomes on neighboring farms for multiple seasons (Yesuf and Bluffstone, 2019). The distribution of risk preferences among farmers in the study areas, and the extent to which risk aversion explains non-adoption even when expected returns are positive, has not been investigated.

A twelfth problem concerns the comparability of adoption determinants across the two LGAs selected for this study. The two LGAs differ in market access, soil conditions, rainfall patterns, and extension coverage. It is plausible that factors constraining adoption in one LGA (e.g., limited seed availability in the more remote LGA) are less important in the other, and that effective interventions must be tailored to local conditions. However, without comparative analysis that estimates adoption models separately for each LGA or tests for interaction effects between LGA and potential determinants, such tailoring is not possible. The current evidence base does not permit such comparative analysis.

In summary, the adoption of improved soyabean production technologies among farmers in the two focal LGAs of Kogi State is constrained by multiple, interconnected problems spanning the domains of technology (partial adoption, complementarities, soil interactions), farmer characteristics (risk preferences, off-farm income, gender), institutions (extension effectiveness, group functionality, seed supply), and resources (credit access, labor availability). These problems have not been systematically investigated through rigorous empirical research in these specific LGAs, leaving a substantial knowledge gap that undermines evidence-based policy formulation, program design, and resource allocation for agricultural extension and technology dissemination. This study therefore seeks to fill that gap by identifying the factors influencing adoption, quantifying their relative importance, and generating actionable recommendations for enhancing adoption rates and improving soyabean farmers’ productivity and welfare.

1.3 Aim of the Study

The aim of this study is to identify and analyze the factors influencing the adoption of improved soyabean production technologies among farmers in two Local Government Areas of Kogi State, Nigeria.

1.4 Objectives of the Study

The specific objectives of this study are to:

1. Describe the socio-economic characteristics of soyabean farmers in the study LGAs and identify the specific improved production technologies (improved varieties, seed inoculation, planting practices, pest management, post-harvest technologies) currently adopted.
2. Determine the level of awareness and sources of information (extension, radio, farmer groups, neighbors, input suppliers) on improved soyabean production technologies among farmers in the study areas.
3. Identify and analyze the factors (including age, education, farm size, credit access, extension contact, group membership, off-farm income, and risk preference) that significantly influence the adoption of improved soyabean production technologies.
4. Compare the yield and profitability outcomes between adopters of improved technologies and non-adopters using traditional practices, and examine the complementarity effects among different improved technologies.
5. Examine the constraints limiting adoption of improved technologies (including seed availability, credit constraints, information gaps, and technical challenges) and develop recommendations for policy and program interventions tailored to the study areas.

1.5 Research Questions

This study seeks to answer the following research questions:

1. What are the socio-economic characteristics of soyabean farmers in the two study LGAs of Kogi State, and what improved production technologies are currently adopted?
2. What is the level of awareness of improved soyabean production technologies among farmers in the study areas, and through what information channels do they learn about these technologies?
3. What socio-economic, institutional, and technological factors significantly influence the adoption of improved soyabean production technologies in the study areas?
4. Do adopters of improved soyabean production technologies achieve significantly higher yields and net returns per hectare than non-adopters using traditional practices, and are there complementarity effects among different technologies?
5. What are the major constraints limiting the adoption of improved soyabean production technologies in the study areas, and what strategies can be employed to overcome these constraints?

1.6 Research Hypotheses

Hypothesis One

• Null Hypothesis (H₀₁): There is no significant relationship between a farmer’s level of formal education and the likelihood of adopting improved soyabean production technologies in the study LGAs.
• Alternative Hypothesis (H₁₁): There is a significant positive relationship between a farmer’s level of formal education and the likelihood of adopting improved soyabean production technologies in the study LGAs.

Hypothesis Two

• Null Hypothesis (H₀₂): There is no significant relationship between access to agricultural credit (formal or informal) and the adoption of improved soyabean production technologies in the study areas.
• Alternative Hypothesis (H₁₂): There is a significant positive relationship between access to agricultural credit (formal or informal) and the adoption of improved soyabean production technologies in the study areas.

Hypothesis Three

• Null Hypothesis (H₀₃): Contact with agricultural extension services has no significant effect on the likelihood of a farmer adopting improved soyabean production technologies.
• Alternative Hypothesis (H₁₃): Contact with agricultural extension services has a significant positive effect on the likelihood of a farmer adopting improved soyabean production technologies in the study areas.

Hypothesis Four

• Null Hypothesis (H₀₄): There is no significant difference in soyabean yield (kg/ha) between farmers who adopt improved varieties with complementary practices (inoculation, proper spacing) and farmers who use local varieties with traditional practices.
• Alternative Hypothesis (H₁₄): Farmers who adopt improved varieties with complementary practices achieve significantly higher soyabean yields (kg/ha) than farmers who use local varieties with traditional practices in the study areas.

Hypothesis Five

• Null Hypothesis (H₀₅): There is no significant difference in net returns per hectare from soyabean production between adopters and non-adopters of improved production technologies.
• Alternative Hypothesis (H₁₅): Adopters of improved soyabean production technologies achieve significantly higher net returns per hectare than non-adopters using traditional practices in the study areas.

1.7 Significance of the Study

This study is significant for multiple stakeholders and purposes. First, for soyabean farmers in the study LGAs and across Kogi State, the findings will provide insights into the factors that constrain or enable adoption, potentially informing their own decisions about technology investment. Second, for agricultural extension services (Kogi State ADP, NAERLS, and local government extension departments), the study will identify which farmer characteristics, information channels, and support services most strongly influence adoption, enabling more targeted and effective extension programming. Third, for policymakers at state and federal levels, the evidence generated will inform decisions about input subsidy programs, credit interventions, seed system development, and extension reform. Fourth, for the International Institute of Tropical Agriculture (IITA) and other research institutions developing improved soyabean technologies, the study will provide feedback on adoption constraints, user preferences, and technology performance under real farm conditions, guiding future breeding and technology development priorities. Fifth, for development partners and NGOs working in agricultural value chain development in Kogi State, the findings will guide intervention design and resource allocation. Sixth, for financial institutions and microfinance programs, the study will provide data on credit demand, willingness to borrow for technology investment, and potential loan sizes. Seventh, for the Kogi State government’s agricultural development strategy, the study will provide evidence to support the prioritization of soyabean as a strategic crop and inform the design of targeted support programs. Eighth, for the academic community, the study will contribute to the literature on agricultural technology adoption in the North-Central zone of Nigeria, a relatively under-researched region compared to the north and southwest. Finally, by generating evidence that can enhance adoption rates, the study will contribute indirectly to increasing soyabean productivity, improving farm household incomes, enhancing food and nutrition security, and supporting the competitiveness of Nigeria’s soyabean value chain.

1.8 Scope of the Study

The geographical scope of this study is limited to two Local Government Areas (LGAs) in Kogi State, Nigeria. The specific LGAs will be selected purposively from among the major soyabean-producing LGAs in the state, with one LGA representing relatively good market access conditions and the other representing more remote, less market-integrated conditions. The identities of the specific LGAs will be confirmed following preliminary field visits and consultation with KSADP and LGA agricultural departments. The thematic scope focuses specifically on improved soyabean production technologies as defined by IITA and national agricultural research programs, including: improved soyabean varieties (early-maturing, medium-maturing, promiscuously nodulating types); seed inoculation with Bradyrhizobium japonicum; recommended planting practices (row spacing, plant population, planting depth); integrated pest management for insect pests and diseases; fertilizer recommendations (particularly phosphorus); and post-harvest handling and storage technologies. The study does not extend to soyabean processing or marketing, except insofar as marketing considerations affect production technology adoption decisions. The respondent scope includes smallholder soyabean farmers (both adopters and non-adopters of improved technologies) in the selected LGAs. Key informants (extension agents, cooperative leaders, input suppliers, KSADP officials) are also included for qualitative data collection. The temporal scope covers the period 2015-2025, with primary data collected between 2024 and 2025, focusing on the most recent completed production season.

1.9 Limitation of the Study

Several limitations inherent in this study should be acknowledged transparently. First, the study relies primarily on cross-sectional survey data, which can identify correlates of adoption but cannot definitively establish causal relationships between potential determinants and adoption outcomes. Second, the study focuses only on two LGAs within Kogi State, so findings may not be generalizable to other parts of Kogi State, other states in the North-Central zone, or other soyabean-producing regions of Nigeria with different agroecological, economic, or sociocultural conditions. Third, the study’s reliance on farmer recall for data on yields, input use, and costs is subject to recall bias and measurement error; where possible, the study will employ multiple recall aids and cross-check responses, but laboratory-level accuracy cannot be achieved. Fourth, social desirability bias may affect responses about adoption, with farmers potentially overstating their use of improved technologies or understating the challenges they face. Fifth, the study cannot experimentally manipulate adoption status, so comparisons between adopters and non-adopters may be confounded by unobserved differences (e.g., farmer motivation, managerial ability, unobserved risk preferences) that affect both adoption and outcomes. Sixth, the timing of data collection relative to the production season (pre-harvest, harvest, or post-harvest) may affect the accuracy of recall for different variables. Seventh, the study does not include soil sampling or laboratory analysis, so soil fertility variation (which may affect technology performance and thus adoption) is measured through farmer reports and secondary data rather than direct measurement. Eighth, the study does not include a longitudinal component, so it cannot assess whether adoption is sustained over time or farmers discontinue use after initial trial. Ninth, the sample size, while statistically adequate for planned analyses, may limit the ability to detect small effects or to conduct highly disaggregated subgroup analyses. Tenth, political and security conditions in Kogi State may affect data collection access and respondent willingness to participate. Despite these limitations, the study will employ rigorous sampling methods, validated survey instruments, appropriate analytical techniques (including robustness checks and sensitivity analyses), and transparent reporting to maximize the credibility and utility of its findings for policy and practice.

1.10 Definition of Terms

Improved Soyabean Production Technologies: The set of agronomic, biological, and management innovations developed by agricultural research institutions (IITA, NCRI, Universities) that have been demonstrated to increase soyabean productivity, reduce production risks, or improve product quality compared to traditional practices. For this study, these include improved varieties, seed inoculation, recommended planting practices, integrated pest management, fertilizer use, and post-harvest technologies.

Adoption: The decision and subsequent action by a farmer to use an improved soyabean production technology on a sustained basis. Adoption may be measured as a binary variable (adopted/not adopted for a specific technology), as an intensity measure (number of technologies adopted, proportion of land planted with improved varieties), or as a duration measure (number of seasons of continuous use). For this study, adoption is defined as use of at least one improved technology in the most recent production season.

Improved Soyabean Variety: A soyabean cultivar developed through formal plant breeding programs that possesses superior characteristics relative to traditional landraces, including higher yield potential, shorter maturity period, improved pest and disease resistance, better seed quality (size, protein content, cooking characteristics), or enhanced promiscuous nodulation.

Seed Inoculation: The application of a rhizobial inoculant (typically containing live Bradyrhizobium japonicum bacteria) to soyabean seeds before planting, intended to enhance nitrogen fixation by establishing effective root nodule symbiosis, particularly important in fields without recent history of soyabean cultivation.

Promiscuous Nodulation: The ability of certain soyabean varieties to form effective nitrogen-fixing root nodules with native soil rhizobia (rather than requiring specific Bradyrhizobium japonicum strains), reducing or eliminating the need for seed inoculation.

Traditional Practices (Soyabean Production): The conventional production methods used by farmers in the study areas prior to the introduction of improved technologies, typically characterized by: use of saved seed from local varieties; broadcast planting without specific spacing; limited or no fertilizer use; minimal pest management; and traditional post-harvest handling.

Extension Contact: The frequency and quality of interaction between a farmer and agricultural extension personnel (from KSADP, NAERLS, or other providers). In this study, extension contact is measured through self-reported number of extension visits in the past 12 months, participation in extension training events, and perceived usefulness of extension advice received.

Farmer Group/Cooperative: A formal or informal association of farmers who meet regularly for mutual support, collective action, and shared objectives. In this study, group membership is operationalized as self-reported active participation in any farmer group with agricultural functions, including input purchasing groups, marketing cooperatives, savings and credit groups, or multipurpose farmer associations.

Yield Gap: The difference between the potential yield achievable with improved technologies under optimal management (research station yields or demonstration plot yields) and the actual yield achieved by farmers under real farm conditions. Reducing the yield gap through improved technology adoption and better management is a key objective of extension programs.

Rhizobia: Soil bacteria (genus Bradyrhizobium for soyabean) that form symbiotic relationships with leguminous plants, infecting root hairs and inducing formation of root nodules where atmospheric nitrogen is fixed into plant-available forms. Effective rhizobial strains are essential for realizing the nitrogen-fixing benefits of soyabean.

Nodulation: The formation of root nodules on leguminous plants following infection by compatible rhizobia. Effective nodules are actively fixing nitrogen (pink or red internal color due to leghemoglobin); ineffective nodules are white or green and do not fix nitrogen. Proper seed inoculation and adequate soil phosphorus are essential for effective nodulation.

Integrated Pest Management (IPM): An ecosystem-based strategy for managing insect pests, diseases, and weeds that combines biological, cultural, physical, and chemical control methods to minimize economic damage while reducing reliance on synthetic pesticides. For soyabean, IPM includes resistant varieties, planting date adjustment, field sanitation, trap crops, biological control, and judicious use of insecticides only when economic thresholds are exceeded.

Phosphorus Fertilizer: A source of the essential plant nutrient phosphorus (e.g., single superphosphate, triple superphosphate, rock phosphate). Phosphorus is critical for soyabean because it promotes root development, enhances nodulation and nitrogen fixation, and supports flowering and pod set. Many savannah soils are phosphorus-deficient, limiting response to improved varieties and inoculation.

Soyabean Rust: A fungal disease caused by Phakopsora pachyrhizi that is one of the most economically important diseases of soyabean worldwide. The disease causes premature defoliation, reduces pod set and seed fill, and can cause yield losses of 30-80% under favorable conditions. Resistant varieties and fungicide applications are management options.

Net Returns: The profit from soyabean production, calculated as total revenue from soyabean sales minus total variable costs (seed, inoculant, fertilizer, pesticides, hired labor) and, where appropriate, allocated fixed costs (land rent, equipment depreciation). Net returns per hectare is the standard profitability measure for comparing technological practices.