1 Introduction

1.1 Cash crops agriculture

Agriculture holds global significance for its impact on employment, food security, farmers’ financial capacity, and livelihoods [1]. It employs over 25% of the global workforce, serving as the primary income source in many low- to middle-income countries [2]. Agriculture contributes to economic development, constituting 4% of the global Gross Domestic Product (GDP), with some of the least developed countries relying on it for over 25% of GDP. Additionally, agriculture has the potential to uplift 80% of the world’s disadvantaged population in rural areas, engaged in farming [3]. According to [4], over 90% of the world’s 570 million farms are operated by individuals or families, relying predominantly on family labour. In Sub-Saharan Africa and Asia, about 80 % of the farmland, which varies in size from about a hectare to 10 hectares, is managed by small-scale farmers [5]. These observations underscore the pivotal role of agriculture in both developed and developing economies, in enhancing food security and improving income and communities’ livelihoods.

Cash crop production entails farming of different crops like coffee, tea, cocoa, cashew nuts, sisal, cotton, sugarcane, and cloves primarily for sale and profit, as opposed to personal consumption or sustenance [6, 7]. Cash crop farming plays a pivotal role in agricultural practices, especially in the global south, serving a dual function by significantly contributing to both food security and serving as a crucial income source for smallholder farmers [1]. These crops, play a vital role in improving farmers’ investments in agricultural inputs, technology, and infrastructure, food purchase diversification, improving overall dietary intake, and nutritional well-being, thus contributing to their stable and resilient livelihood [7]. Likewise, cash crops form the backbone of agriculture in numerous nations and play a substantial role in creating employment, contributing to the country’s exports, generating tax revenues, and contributing to the GDP [8]. For example, coffee, cotton, and tea production constitutes a substantial portion of low-income countries export revenue and supports millions of smallholder farmers globally by providing essential income and employment [9,10,11,12]. These observations suggest that cash crops are crucial for the prosperity and sustainability of smallholder farmers in the Global South, linking food security with economic well-being.

1.2 Cash crops agriculture in Tanzania

In Tanzania, cash crops, like coffee, tea, tobacco, sisal, and cashew nuts are vital economic pillars, offering livelihoods, export revenue, and employment. They also play a pivotal role in national development, driving economic growth, and improving the well-being of smallholder farmers and the country [13, 14]. For instance, coffee contributes about five percent to total exports, generating around United States Dollars (USD) 100 million annually, whereby, over 90% of its output is from smallholder farmers, providing employment to 400,000 families, impacting 2.4 million citizens [15]. Similarly, tea, provides indirect support to approximately 2 million people, creating direct employment for around 50,000 individuals, and contributing to around 45 million of annual foreign exchange income [14]. Tea cultivation involves an estimated 32,000 smallholder farmers, with average tea farms covering less than 3.5 acres [14, 16, 17]. Figure 1 shows the distribution of major cash crops across key growing regions in Tanzania.

Fig. 1
figure 1

Geographical distribution of major cash crops in Tanzania (researcher’s construct, 2024)

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While cash crop farming holds crucial importance for smallholder farmers in Tanzania and the entire nation, both the cultivation of these crops and the broader agricultural sector face numerous challenges. These obstacles encompass inadequate infrastructure, for instance, deficient rural roads and irrigation systems, limited accessibility to financial and productive resources, insufficient extension services, restricted access to farming technology, and escalating population growth leading to heightened land demand. Other challenges, include climate change-induced shocks causing unpredictable weather patterns, constrained access to market information and overall market entry, and a lack of private sector investments hindered by restricted access to long-term funds, insufficient capacity, business expertise, and policies discouraging agribusiness expansion [18,19,20]. These challenges have the potential to exert adverse effects on cash crop agriculture performance and the overall performance of smallholder farmers.

1.3 Farmers’ participation in contract farming

To tackle the mentioned cash crop farming challenges faced by farmers, contract farming is considered a viable solution. Contract farming plays a crucial role in addressing agricultural challenges by providing a structured framework for collaboration between farmers and suppliers, in most cases agri-processors. Through farming contractual agreements, farmers receive support in the form of inputs, technology, and market access, reducing production risks. This model enhances productivity, ensures a stable income for farmers, and promotes sustainable agricultural practices. Furthermore, contract farming fosters economic stability, improves supply chain efficiency, and facilitates the adoption of modern farming techniques [21, 22]. In this regard, farmers’ participation in contract farming is likely to contribute to addressing challenges such as market uncertainties, input accessibility, and income volatility, thus contributing to the overall resilience and development of cash crop farming and the agricultural sector at large.

Evidence from the literature indicates varying levels of contract farming participation by farmers engaged in various crop productions in Tanzania. For example, [23] report that in Tanzania, more than 70% of smallholder farmers participate in contract farming across various agricultural subsectors, including cash crop farming. Another example, as reported in [24], is that about seven percent of sisal farmers and 49% of sugarcane producers allocated their land to engage in contract farming across Tanzania. Moreover, other studies indicate that over 50% of smallholder tea farmers engage in green leaf tea contract farming in specific areas of the Southern Highlands of Tanzania [16, 17]. Based on this nuanced observation on participation, it is apparent that, limited engagement in contract farming is likely to lead to reduced performance among farmers in terms of both food and cash crop output and overall livelihoods [21].

Differences in engagement in contract farming in Tanzania and beyond can be attributed to various factors, including farmers education level, several socio-economic factors, and the state of the crop, that is whether it is a perishable crop or not [25]. Studies by [26,27,28,29] found that literacy level positively influences farmers participation in contract farming. The positive relationship contributes to improved understanding, negotiation, and compliance with contracts, while also enhancing decision-making, risk management, and adaptability, which are critical factors for successful contract farming participation. Some socio-economic variables positively effecting farmers’ participation in contract farming, including farmers’ age, gender, household income, land ownership status and land size, access to market information, experience in farming and contract farming, marital status, membership in cooperatives, gender, and quality certification [26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44]. Moreover, literature indicates that low levels of education can limit farmers' engagement in contract farming in developing countries, Tanzania included, as they are likely to exhibit limited understanding of contracts, low negotiation power, and adoption of required practices [25, 45, 46].

Despite the acknowledged importance of literacy level in contract farming participation alongside other socio-economic variables, there is limited evidence to establish the influence of literacy level on contract farming participation in Tanzania's cash crop agriculture context. Since farmers' literacy is central to this study, we adapt the Human Capital Theory as a relevant framework to understand the influence of literacy levels on participation. This theory posits that investments in education enhance individuals’ productivity and economic potential, leading to better income and well-being [47, 48]. This entails that higher literacy levels are likely to equip individuals with the knowledge and competencies needed to undertake specific tasks, which in turn enhances their economic outcomes, such as increased income and better employment opportunities [49]. Likewise, educated individuals not only possess better technical skills but also have the critical thinking abilities necessary to adapt to changing market conditions and adopt new technologies [50]. In line with theory, education equips individuals with critical skills, knowledge, and adaptability, enabling them to effectively engage in economic activities like contract farming. In the context of cash crop farming, literacy empowers farmers to navigate complex contracts, implement sustainable practices, and make informed decisions, aligning with the theory's premise that education improves economic outcomes [46, 51, 52].

This paper aims to examine the strength of the association between higher levels of farmers' literacy, alongside selected socio-economic variables, and the likelihood of farmers participating in contract farming. Understanding this connection is essential, as literacy is expected to enhance farmers' abilities to negotiate fair agreements, adopt innovative technologies, and contribute to the success and sustainability of contract farming in cash crop agriculture, using green leaf tea as an illustrative case [26, 53]. The importance of this paper lies in its potential to influence policies related to promoting farmers' literacy levels alongside selected socio-economic variables and economic empowerment within smallholder agriculture, with a focus on cash crop farming, advancing knowledge on literacy dynamics in agriculture, supporting rural development plans, and offering empirical data for testing hypotheses in the context of contract farming in smallholder agriculture in Tanzania. Additionally, insights from this study may potentially be replicated in different settings in the global south, for instance, in other countries in Africa, Latin America, Asia, Oceania, and beyond.

2 Materials and methods

A summary of the study’s methodology is provided in Fig. 2, with specific details for each element discussed in the subsequent paragraphs.

Fig. 2
figure 2

Summary of study methodology (researcher’s construct, 2024)

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2.1 Research design

This paper follows a quantitative methodological approach, whereby logistic regression was specifically used to assess the probability of smallholder farmers engaging in contract farming within cash crop agriculture. The investigation focused on factors such as farmer's literacy level as well as socio-economic variables, including farmer’s age, gender, household income, land size, access to market information, experience in contract farming, and technology access. By employing this approach, the study aimed to uncover trends and patterns in the contract farming participation levels of the studied population, serving as the foundation for testing the null hypothesis.

2.2 Data collection and processing

This study employed primary quantitative data gathered through a cross-sectional survey involving 393 smallholder tea farmers from 37 diverse communities across three districts in the Southern Highlands region of Tanzania. A closed-ended questionnaire was utilized in this survey. The districts encompassed Busokelo and Rungwe in the Mbeya region, as well as Njombe District Council in the Njombe region. Random sampling was conducted based on distinct clusters, specifically 20 wards and 37 villages, considering smallholder farmers' involvement or non-involvement in tea contract farming during the 2022 tea farming season.

2.3 Variables measurement

Output variable The output variable in this study is Contract Farming Participation (CFP) during the 2022 green leaf tea production season in selected regions (Mbeya and Njombe regions) in Tanzania. CFP is measured as a dichotomous variable, where farmers engaged in contract farming are denoted by '1,' while those not participating are denoted by '0' (1 = Engaged, 0 = Not Engaged). Connectedly, the dichotomous nature of CFP, led to the adoption of binary logistic regression model of data analysis. Binary logistic regression analysis is well-suited for predicting the likelihood of participation in the 2022 green leaf tea production season in the two selected regions [54, 55].

Exogeneous variables In this study, the selection of exogeneous variables aligns closely with the fundamental principles of Human Capital Theory, encompassing literacy level of farmers, along with various socio-economic factors such as gender, age, household income, land size, access to market information, experience in contract farming, and access to technology, were used as predictor variables.

Literacy level is measured based on three level of education: no formal education; primary education for those with some and completed primary school (standard seven); secondary education for those who completed form four or beyond. In the context of Tanzania rural settings, farmers who have completed secondary school education and beyond are considered to have relatively higher literacy levels compared to those who attained lower levels or those who did not attend to school, as such, they are likely to exhibit higher standard of living because of their ability to diversify their incomes [56]. This observation further suggests that basic education, characterized by non-skilled learning, holds particular significance as a measure of human capital, especially in rural areas of Tanzania. The adapted measurement approach is consistent with Human Capital Theory, which posits that education contributes to improved economic outcomes [47, 48]. In this regard, it is likely that farmers with higher literacy levels may possess better abilities to understand, negotiate, and navigate the complexities of contract farming agreements. The measurement of literacy levels along with other socio-economic variables is detailed in Table 1.

Table 1 Independent variables measurement
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2.4 Contract farming participation (CFP) estimation model

Applying the dependent and independent variables detailed earlier, we opted for the binary logistic regression model to ascertain the probability of farmers engaging in contract farming based on farmer's literacy level and other socio-economic variables. This model was selected due to ability to predict the likelihood of participation while considering the binary nature of the dependent variable. The model specifications, including variables and coefficients, are expounded upon below.

$${\text{ln}(\text{CFP}}_{\text{z}}) =\text{ f}\left(\text{FALL},\text{SEV}\right)$$
(1)

whereby: \({\text{ln }(\text{CFP}}_{\text{j})}\)= Log-odds (likelihood) of a zth farmer participating in contract farming which is represented as 1, indicating the log-odds of farmer participation, while 0 signifies the log-odds of non-participation. These log-odds values fall within the range of 0 to 1, mapping onto the broader scale of -∞ to + ∞

Whereby FALL = Farmer’s Literacy Level for the zth respondent. FALL has four categories which is defined below with their respective dummy variable definition. EDU1 = No formal education (1 = zth respondent have no formal education; 0 = Otherwise), EDU2 = Primary education (1 = zth respondent have primary education; 0 = Otherwise), EDU3 = Secondary education or higher (1 = zth respondent have secondary education or higher; 0 = Otherwise).

SEV = Social-Economic Variables for the zth respondent.

In Eq. 1, the logit transformation broadens the range of predicted values from -∞ to + ∞. To calculate the likelihood of participation within the constrained range of 0 to 1, Eq. 1 can be re-expressed as Eq. 2, thereby converting log-odds into probabilities.

$${\text{Probability }(\text{CFP}}_{\text{z}}) =\frac{\text{exp}(\text{ln}({\text{CFP}}_{\text{z}})) }{[1 +\text{ exp}(\text{ln}({\text{CFP}}_{\text{z}})]}$$
(2)

whereby \({\text{Probability }(\text{CFP}}_{\text{z}})\) = The likelihood of the zth farmer engaging in contract farming. \(\text{exp}(\text{ln}({\text{CFP}}_{\text{z}})\) = The exponential function of the logit, precisely reverse the logit transformation by returning the value to the original odds scale. \(1 +\text{ exp}(\text{ln}({\text{CFP}}_{\text{z}})\) = The result obtained by adding 1 to the exponential function of the logit. \(\frac{\text{exp}(\text{ln}({\text{CFP}}_{\text{z}})) }{[1 +\text{ exp}(\text{ln}({\text{CFP}}_{\text{z}})]}\) = The calculation of the probability (CFPz) involves dividing the exponent of the logit by the sum of the exponent and 1. This process ensure that the resulting probability is confined within the range of 0 to 1. Therefore, the estimation of probability (CFPz) is achieved by employing the logistic function on the log-odds, thereby ensuring its adherence to the 0 to 1 range.

However, as shown in Table 2, Social-Economic Variables (SEV) is a composite of various selected variables, thus, their disaggregation is shown in equation (iii).

$$\text{SEV}=\text{ f}\left(\text{FAGE},\text{FA},\text{ HHI},\text{ LAS},\text{ AMINF},\text{ CFE},\text{ AFATE}\right)$$
(3)

whereby: FAGE = Farmer’s Gender for the zth respondent. FA = Farmer’s age for the zth respondent. HHI = Household annual income for the zth respondent. LAS = Land size for the zth respondent for the zth respondent. AMINF = Access to market information for the zth respondent. CFE = Contract farming experience for the zth respondent. AFATE = Access to farming technology for the zth respondent.

Table 2 Respondents distribution location, gender and literacy level
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After breaking down SEV in (iii) equation (i) with disaggregated FALL may be re-written into equation (iv).

$${\text{ln }(\text{CFP}}_{\text{z}})=\text{ f}\left(\text{EDU}1,\text{ EDU}2,\text{ EDU}3,\text{FAGE},\text{FA},\text{ HHI},\text{ LAS},\text{ AMINF},\text{ CFE},\text{ AFATE}\right)$$
(4)

Moreover, after reformulation of equation (i) into equation (iv) to consider the breaking down the SEV in equations (iii), then to discern the influence of FALL and SEV indicators on the log-odds of farmers' participation in contract farming, equation (iv) can be reformulated structurally as equation (v).

$$\text{ln}\left({\text{CFP}}_{\text{z}}\right)= {\upbeta }_{0}+{\upbeta }_{1 }{\text{EDU}1}_{\text{zx}}+{\upbeta }_{2 }{\text{EDU}2}_{\text{zx}}+{\upbeta }_{3 }{\text{EDU}3}_{\text{zx}}+ {\upbeta }_{4}{\text{FAGE}}_{\text{zx}}+{\upbeta }_{5}{\text{FA}}_{\text{zx}}+{\upbeta }_{6 }{\text{HHI}}_{\text{zx}}+{\upbeta }_{7 }{\text{LAS}}_{\text{zx}}+{\upbeta }_{8}{\text{AMINF}}_{\text{zx}}+{\upbeta }_{9}{\text{CFE}}_{\text{zx}}+{\upbeta }_{10 }{\text{AFATE}}_{\text{zx}}+{\upepsilon }_{\text{z}}$$
(5)

whereby: z represents the farmer’s identity, ranging from 1 to n. x denotes the disaggregated variable, ranging from 1 to n. β1 signifies the regression coefficient. ϵ denotes the error term.

2.5 Data analysis

The obtained data underwent cleaning in Excel before being imported into IBM SPSS Statistics Version 26 for descriptive analysis while inferential statistical analysis was done using STATA Version 15. Descriptive analysis of predictor and outcome variables was conducted and presented by using tables. Relevant assumptions, precisely validity, reliability, normality, significant outliers tests, multicollinearity test, Box-Tidwell test, and overall model fitness were examined and satisfied. This model was selected in line with other studies with dichotomous outcome variables, like those undertaken by [27,28,29]

3 Results

3.1 Descriptive results

Results from this research indicate that the percentage of participants was slightly greater in Njombe District Council (35%) when contrasted with Busokelo (33%) and Rugwe (32%). Furthermore, the overall percentage of female participants (47%) was slightly lower than that of male participants (53%). Furthermore, literacy levels varied amongst respondents, with the majority (52%) having primary education followed by those with secondary education or higher (36%), while 11 % had no formal education (see the details in Table 2).

Connectedly, the age range of the respondents varied from a minimum of 20 years to a maximum of 80 years. In connection to this, the average age of the respondents was 47 years, slightly below the average age of African farmers engaged in agricultural activities, which is estimated to be around 60 years [57]. Similarly, the average size of households is 5.2 individuals, exceeding the national average for Tanzania Mainland, which stands at 4.6 persons [58]. Concerning involvement in contract farming, the study unveils that 70% of smallholder tea farmers actively participate in contract farming, whereas the remaining 30% opt not to engage in this specific farming arrangement.

3.2 Inferential statistical results

Model assumptions before conducting the binary logistic regression analysis, we ensured that critical assumptions relevant to this model were tested and met. Firstly, test of significant outlier was done in STATA 15 using least likely method whereby 10 influential points were observed and excluded in further analysis, making the number of observations used to be 383 instead of 393. Box-Tidwell regression model was run to establish linear relationship between continuous variables (farmer’s age, income, land size and experience) and logit transformation of the dependent variable the test of which was passed at P > 0.05 in all four cases (see the details Table 3).

Table 3 Box-tidwell regression model
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A binary logistic regression model was conducted to assess the relationship between the predictors and the outcome variable. The model aimed at testing the null hypothesis to predict the probability of participation in contract farming within cash crops agriculture, considering literacy level and selected socio-economic variables. The analysis included 383 observations after excluding 10 influential outliers identified in a preliminary outlier test. The overall model was statistically significant, as indicated by the Likelihood Ratio Chi-Square (LR chi2) value of 63.8 with 9 degrees of freedom, and a p-value of 0.0000. This p-value suggests that the predictors, taken together, significantly contribute to explaining the variance in the outcome variable. The Log Likelihood for the fitted model was − 198.65079, which reflects the fit of the model to the data. The Pseudo R2 value was 0.1384, indicating that approximately 13.84% of the variance in the outcome variable is explained by the model. While this suggests a moderate level of explanatory power, it also indicates that there are likely other factors influencing the outcomes that were not included in this model. The binary logistic regression analysis model fitting, and parameter estimates results are presented in Table 4.

Table 4 Binary logistic regression results
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4 Discussion

The results in Table 4 display parameter estimates, showing the likelihood of participation in contract farming according to structural equation (v). The subsequent paragraphs delve into the interpretation and discussion of these results.

4.1 Farmer’s literacy level effect on contract farming participation

The study results indicate that a higher literacy rate among farmers has a significant positive influence on their engagement in contract farming within the cash crops agriculture sector, with farmers with secondary education or higher depicted higher likelihood of participation (β = 1.849; P = 0.000) than farmers with primary education (β = 1.054; P = 0.006). This implies that farmers with high literacy level are more likely to participate in contract farming compared to those with lower literacy levels. The statistically significant positive relationship between farmers' literacy levels and their likelihood of engaging in contract farming aligns with the core principle of Human Capital Theory which contents that investments in education and skills development enhance individuals' productivity and economic potential [47]. Relatedly, higher coefficient to the higher education level indicates that higher literacy levels substantially increase the probability of farmers participating in contract farming. This suggests that literacy equips farmers with the necessary skills and knowledge to better understand, negotiate, and comply with the complexities of contract farming agreements, thereby facilitating their engagement in green leaf farming. This also emphasize that the identified positive relationship between literacy and engagement in contract farming holds crucial implications for agricultural policies and interventions in Tanzania.

Moreover, this finding aligns with theoretical expectations, suggesting a positive relationship between higher literacy levels and farmers' involvement in contract farming within the cash crops agriculture fraternity. Furthermore, this result resonates with findings from other studies conducted by scholars, such as [26,27,28], which also explored the relationship between education levels and participation in contract farming across various agricultural value chains. The study’s results consistency with previous studies strengthens the cumulative knowledge in the field and reinforces the notion that literacy plays a consistent and pivotal role in shaping farmers' decisions, particularly in the context of contract farming across various agricultural value chains.

Likewise, the finding underscores the role of literacy in informed decision-making among farmers. Literate individuals are more likely to grasp the complexities of contract farming arrangements, enabling them to make strategic choices that align with their agricultural objectives. This insight suggests that promoting literacy among farmers could contribute to increased participation in contract farming due to enhanced comprehension of contract farming dynamics and the ability to tap into its opportunities. This, in turn, is likely to foster more sustainable and beneficial agricultural practices and overall improved farmer’s livelihood.

Beyond the immediate agricultural context, the significant positive association between literacy levels and engagement in contract farming suggests potential socio-economic benefits for farming communities in Tanzania and beyond. Contract farming often involves a more systematic approach to agricultural production and marketing, with the potential to improve farmers' income and overall livelihoods. This too aligns with broader socio-economic development goals, emphasizing the interconnectedness of literacy and economic empowerment.

4.2 Influence of other social-economic variables on contract farming participation

From a different perspective, evidence from literature indicate that, there other several socio-economic variables beyond literacy level which influence farmers participation in contract farming. Some of these variables explored in the subsequent paragraph of this study, include farmer’s age, gender, household income, land size, access to market information, experience in contract farming, and technology access.

The study findings reveal that the gender of the farmer has a significant positive influence (β = 0.949; P = 0.000) on contract farming participation in cash crops agriculture. The positive coefficient shows that being male increases the likelihood of contract farming participation while being female decreases it. This result aligns with theoretical expectation which suggest a positive relationship between these variables. Furthermore, this finding aligns with previous studies for instance by [31,32,33], all of whom identified a positive relationship between these variables. The identified gender-based disparities suggests complex gender dynamics in contract farming participation in Tanzanian cash crops agriculture. This finding prompts a revaluation of agricultural policies to address gender-specific barriers like resources access, market information access, and decision-making power, highlighting the need for nuanced interventions tailored to influence female farmers participation.

Likewise, the study results show a significant positive effect of farm size (β = 0.862; P = 0.000) on farmers' participation in contract farming within Tanzanian cash crops agriculture. This result suggest that a unit increase in farmers cash crop farm size, is likely to elevate the odds ratio of contract farming participation in the cash crops sector. In practice this implies that farmers with large farm size are likely to engage in contract farming to benefit from the economies of scale associated with large farms such as, access to inputs, faming technology, extension support, and products markets. This finding aligns with theoretical expectations, which posit a positive relationship between this variables and contract farming participation. Similarly, this result resonates with previous studies like that of [26, 28, 36], all of whom identified a positive association between this variable and contract farming participation.

Other variables, including household income, access to market information, contract farming experience, and access to farming technology on farmers' participation in contract farming within Tanzanian cash crops agriculture shown a positive but insignificant effect on participation likelihood at P = 0.05 (see the details in Table 4). The positive association of these variables resonate with previous studies. For instance, studies by [26, 28, 34,35,36,37,38,39,40,41,42,43,44] revealed a positive association between these variables. The positive influence suggests these variables may influce contract farming participation, but their statistical insignificance makes them less important than significant factors in this study like literacy, gender, and land size, which policymakers should prioritize.

Besides, the study results indicate adverse but statistically insignificant effect of the farmer's age on involvement in contract farming within cash crops agriculture (β = − 0.020; P = 0.083). The negative influence suggests a reduced likelihood of older farmers participating in contract farming compared to the young counterparts. This outcome contradicts the anticipated positive relationship between these variables. Additionally, it diverges from earlier studies, such as those conducted by [28,29,30] which found a positive association between these factors. The unexpected relationship revealed in this study challenges previously established positive association between age and contract farming participation. This deviation highlights the importance of understanding how age interacts with literacy and human capital factors in influencing farmers' decisions in the cash crops sector.

5 Conclusion and recommendations

This study investigated the effect of farmers' literacy, along with other socio-economic variables, on their engagement in contract farming within Tanzania's cash crops agriculture, framed within the Human Capital Theory. The study provides nuanced insights into the determinants of farmers' participation in contract farming within Tanzanian cash crops agriculture. Based on the study findings and discussion, we conclude that a higher literacy level among farmers significantly positively contributes to their active participation in contract farming within the cash crops agriculture sector. This aligns with the human capital theory, which suggests that higher knowledge, skills, and experience, entailing quality human resource are likely to enhance economic outcomes such as income, productivity, and participation in activities like contract farming. This finding emphasizes the importance of literacy level in understanding the contract farming dynamics, in the wake to improve their performance for instance in terms of improved production and productivity, market access, income and overall livelihood. Likewise, gender and land size significantly positively influence likelihood of higher participation. Other socio-economic factors, specifically household income, access to market information, contract farming experience, and access to farming equipment positively influence farmers' participation in contract farming but their influence is not statistically significant as that of literacy level, gender, and farm size. Conversely, gender indicate negative effects on participation likelihood but the same is not statistically significant. These findings underscore the multifaceted nature of factors influencing contract farming participation, emphasizing the need for targeted interventions, policies and practices change.

We recommend that the government and other development partners to prioritise on literacy programs tailored to farmers, as higher literacy levels significantly contribute to increased participation. This can be achieved through targeted adult education initiatives that focus on contract literacy and financial management. Likewise, policies should ensure equitable access to resources, such as land and market information, particularly for female farmers, who face significant barriers as such limiting their participation in various economic activities, including contract farming. Similarly, implementing gender-sensitive policies and supporting women’s access to these resources will help bridge participation gaps. Additionally, supporting older farmers through age-inclusive training and resources can mitigate the negative effects of age on participation likelihood. Finally, while income, market information, and farming experience positively influence participation, their statistical insignificance suggests the need for integrated strategies that address multiple barriers simultaneously, ensuring that these factors collectively enhance participation outcomes. Future research could explore the specific characteristics of different localities, uncovering the complex relationships between literacy, socio-economic factors, and the dynamics of contract farming.