
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13495-0
10.1016/j.heliyon.2024.e37464
e37464
Research Article
Determinants of maize farmers willingness to pay for private extension services in Ejisu municipality, Ghana
Bakang John-Eudes Andivi
Wongnaa Camillus Abawiera
Tham-Agyekum Enoch Kwame ektagyekum@knust.edu.gh
⁎
Fatimatu Suraju
Obeng Jonathan Annorhene
Nsafoah Elsie Boatemah
Antwi Michael Asiedu
Department of Agricultural Economics, Agribusiness and Extension, KNUST, Kumasi, Ghana
⁎ Corresponding author. ektagyekum@knust.edu.gh
05 9 2024
15 9 2024
05 9 2024
10 17 e3746419 1 2024
2 8 2024
4 9 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
In recent times, private sector involvement in extension services delivery in Ghana has increased. We assessed farmers' willingness to pay (WTP) for private extension services. We used quantitative techniques and selected 385 farmers through multistage sampling technique. Farmers’ had a good perception of private extension services because of its flexibility, availability, value for money, and acceptable rates. Income, secondary occupation, marital status, extension contact, amount of maize sold every season, and land tenure system have an effect on farmers' WTP and the amount to pay. Government policies should continue to encourage and support private sector participation in providing extension services.

Keywords

Determinants
Maize farmers
Private extension services
Willingness to pay
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pmc1 Introduction

Agriculture is the predominant economic activity in sub-Saharan Africa and serves as the primary source of food, money, and employment for the rural people [1]. In sub-Saharan Africa (SSA), it is primarily rural and characterised by smallholders who produce small, fragmented plots of land [2]. It plays a pivotal role in the socio-economic landscape of Ghana, employing a substantial portion of its population (MoFA, 2018), primarily within smallholder family farms characterised by traditional farming methods [3].

The significance of agriculture is also underscored by its contribution to the nation's Gross Domestic Product (GDP), which accounts for a substantial share and serves as a crucial revenue source for the government [4]. In fact, approximately 70 % of the labour force is actively involved in the agricultural sector, and agriculture alone provides 50–60 % of the government's revenue ([5]; MoFA, 2018).

While Ghana's agriculture predominantly comprises smallholder family farms, these operations often employ rudimentary technologies [2]. Subsequently, improving agricultural productivity and sustainability becomes a paramount concern [6]. This aspiration is closely tied to the field of agricultural extension [3], which acts as a bridge connecting farmers with essential knowledge and skills. Extension services are designed to empower farmers, influence their attitudes, and build their capacity to make informed decisions regarding agricultural management [7].

Agricultural extension services have a diverse function in the transformation of farming communities [8]. It involves training farmers, disseminating new technologies, and facilitating various aspects of agricultural development, from organising farmers to market their produce to fostering networks with institutions aimed at improving productivity and livelihoods [9]. According to Khan et al. [10], it improves the abilities of farmers, makes it easier to spread technology, and cause changes in the attitudes of farmers. Extension services contribute to the development of the communities by stimulating the growth of social and human resources, and advocating for sustainable use of natural resources [11]. In Ghana, it is associated with the enhancement of farmers' technical and managerial abilities, well-being and higher earnings [12]. Extension education provides farmers with practical information to effectively address daily issues. Extension services strive to enlighten farmers and report their issues to researchers to raise their level of productivity and living conditions [8]. According to Davis et al. [13], extension agents play a crucial role in addressing contemporary challenges like climate change and globalisation.

Similar to numerous other developing countries, the Ministry of Food and Agriculture in Ghana oversees the provision of agricultural extension services in Ghana [14]. As the services are deemed social services, they are rendered without charge; thus, the government bears the entire financial burden associated with their provision [15]. The notion is that extension services are necessary to facilitate farmers' access to information for agricultural transformation [16]. However, while traditional public extension services have played a vital role, there has been a significant change in response to practical restrictions such as increasing expenses, limited resources, and changing views on the government's involvement in extension services [14,17]. There have been various advocacy activities for increased farmer involvement in the extension process and a more diversified range of services, including information on markets and income opportunities [18].

It is believed that this transition can facilitate the involvement of private-sector entities and non-governmental organisations (NGOs) in the provision of agricultural extension services [14]. In Ghana, there are a number of private extension services working actively in various districts and municipals. For instance, 1) Syecomp Business Services offers agricultural technology solutions and advisory services to farmers and agribusinesses in Ghana, 2) Agri-Impact Consult provides consulting and extension services to farmers, focusing on sustainable agricultural practices and market access, 3) Farmerline offers digital agricultural extension services, providing farmers with timely information on weather forecasts, market prices, and best agronomic practices through mobile technology, 4) Esoko is a digital platform that provides market information, weather forecasts, and agricultural advisory services to farmers and other stakeholders in the agricultural value chain, 5) AgriCensus is a data-driven platform that offers market intelligence and analysis for agricultural commodities, including prices, production forecasts, and trade data, and 6) AgroCenta is an agribusiness platform that connects smallholder farmers to markets, finance, and extension services, facilitating access to inputs, information, and buyers. The potential of private agricultural extension services to supplement the current public service structure has become more evident [19].

Privatisation has yielded positive outcomes in various sectors, such as telecommunications, banking, and education. Private extension services play a significant role in agricultural development, particularly in providing advisory services to farmers (Ali, 2011). The quality of these services is generally perceived as moderate to high, and there is a positive attitude towards increased private sector involvement ([20]; Kaur, 2013). Non-governmental organisations (NGOs) have also played a significant role in providing quality extension services compared to traditional public extensions [21,]. Moreover, the inclusion of the private sector as a source to provide extension means farmers will have to pay for the services they receive [22].

The limited success of public extension services in meeting the complex demands of contemporary farming practices emphasizes the need for a more effective and demand-responsive extension system [21]. Farmers in Ghana, for instance, have historically relied on face-to-face interactions, either in groups or individually, to access agricultural information from extension workers. As the number of farming families has grown, however, the decline in extension workers has left the current public extension system struggling to provide timely and efficient services [12].

While the role of agricultural extension services in promoting knowledge, innovation, and development among farmers is evident, a critical question arises regarding the approach through which these services should be delivered. Traditional public extension services have been pivotal in disseminating knowledge and technologies to farmers. However, the inefficiencies and challenges of the public extension services have led to a growing interest in privatised extensions. There is increasing interest in private extension services, which have the potential to offer customised, timely, and demand-driven support to farmers, thereby potentially bridging the gap between public extension services and the evolving needs of farmers. Crucially, the effectiveness of private extension services depends on farmers' willingness to pay for them (Oladele et al., 2008; [21]). The adoption, sustainability, and optimisation of the extension landscape of private extension services largely depend on farmers' willingness to pay for these services [22]. The question of whether farmers are willing to pay for private extension services and what factors influence this willingness is a fundamental challenge.

The privatisation of agricultural extension services has become a topic of interest in recent research, with studies such as Atinaf et al. [23], Sylla et al. (2019), Abate et al. [24], Aydogdu et al. [25], and Beyene [26] contributing to this area. However, there remains a literature gap regarding the willingness of maize farmers in Ghana to pay for private extension services. While Akinnagbe et al. [27] surveyed agricultural extension workers in Nigeria, revealing mixed views on privatisation, and Gebreegziabher and Mezgebo [28] explored factors influencing farmers' readiness to financially support privatisation in Ethiopia, there is limited empirical evidence on the attitudes of maize farmers in Ghana towards cost-sharing agricultural extension services. Understanding the factors that influence farmers' willingness to pay for private extension services, as examined by Wordofa [29] and Tolera et al. [30] in other contexts, is crucial for designing effective extension programmes tailored to the needs and preferences of Ghanaian maize farmers. Therefore, this study aims to fill this gap by investigating the determinants of willingness to pay for private extension services among maize farmers in Ghana, thereby providing insights for policy formulation and extension programme design in the country.

To optimise the extension service landscape, it is crucial to identify the key determinants of farmers' willingness to pay for private extension services. The study specifically seeks to answer the following questions: 1) What is the perception of farmers towards private extension services? 2) What are the factors influencing farmers’ willingness and the amount farmers are willing to pay for private extension services? 3) What are the constraints on accessing extension services?

This study's contribution lies in its examination of farmers' willingness to pay for private extension services, a topic of increasing relevance given the changing landscape of agricultural extension provision. It also holds significant relevance to the global delivery of extension services, particularly in the context of the increasing role of the private sector in extension service provision. Understanding farmers' perceptions and readiness to invest in private services provides insights applicable to diverse agricultural systems around the world. As countries grapple with the challenge of enhancing agricultural productivity and livelihoods, particularly in the face of evolving environmental, social, and economic dynamics, research on private extension services offers practical implications for shaping more responsive and effective extension strategies on a global scale. Thus, this study contributes to the broader discourse on enhancing extension services' delivery and impact, offering valuable lessons and considerations for policymakers, practitioners, and stakeholders worldwide.

2 Methodology

2.1 Study area and design

The study area was the municipality of Ejisu-Juaben, found in Ghana's Ashanti region. It is situated about 20 km from Kumasi. The proximity to Kumasi suggests potential economic and infrastructural linkages that might influence the agricultural practices in the area and the spatial relationship could impact factors such as market access, transportation, and the availability of resources (https://ejima.gov.gh/). The study primarily adopted quantitative research methods. This indicates a systematic and structured approach to data collection and analysis, likely involving numerical data, statistical tools, and measurable variables. The emphasis on quantifiable information suggests a focus on obtaining objective, numerical insights into the characteristics of the maize farmers, their perceptions and the factors that influence their willingness to pay for private extension services.

2.2 Population, sample size and sampling technique

The study population, comprising 10,974 maize farmers, was identified based on comprehensive data available at the municipal assembly office. Thus, the target population was all smallholder maize farmers in the municipality. To obtain a representative subset for detailed examination, a sample size of 385 farmers was carefully selected. We used the Yamane's formula to arrive at the sample size. This study employed a multistage sampling technique that involved a systematic process to ensure representation and randomness in the selection of study participants. We purposively chose Ejisu as a focal area initially because of its significant maize farming activities within the Ashanti region. We then selected maize farming communities within Ejisu using a simple random sampling approach. The ballot method ultimately led to the selection of four communities: Ejisu, Donyina, Kwaso, and Achinakrom. In the final stage of sampling, individual maize farmers were selected from the chosen communities using a simple random sampling technique. We obtained a list of registered maize farmers within the municipality from the Ministry of Food and Agriculture office to facilitate this process. We again used the ballot method to randomly select specific farmers, ensuring a diverse representation of maize farmers across the selected communities. This rigorous sampling process aimed to capture a comprehensive and unbiased sample of maize farmers for the study, enhancing the validity and generalisability of the findings.

2.3 Research instrument and data collection

The study primarily relied on primary data obtained directly from the respondents through a structured questionnaire survey. This approach ensured the collection of accurate and relevant information directly from the farmers in the municipality. The questionnaire developed was structured into three sections to cover the specific objectives of the study. First of all, the questions sought to elaborate on the personal, household, and farm characteristics of respondents. The second section focused on questions related to farmers’ willingness to pay for private extension services and the amount they are willing to pay. Thirdly, the questionnaire sought to ask farmers about their perceptions on private extension services. By utilising this method, the researchers gathered comprehensive data that provided insights into the areas.

Before commencing with the main data collection, a pre-testing phase was conducted in Essienimpong, a community located within the municipality. This pre-testing aimed to evaluate the questionnaire's clarity, comprehensiveness, and suitability for the study's objectives. A sample of 10 maize farmers participated in the pre-test, providing feedback on the questionnaire's structure, wording, and relevance to their farming practices. Based on the feedback received, necessary adjustments and refinements were made to the questionnaire to ensure its effectiveness and appropriateness for the target population. This pre-testing phase helped identify any potential issues or ambiguities in the questionnaire, allowing researchers to fine-tune it before initiating the actual data collection process.

We collected data for this study in February 2023 and it took a period of three (3) weeks. During the data collection process, the researchers visited the farmers either at their homes or at their farm sites, facilitating direct engagement and interaction with the participants. By meeting the farmers in their natural settings, researchers could observe firsthand the conditions in which they operate, including their farming practices, the environment, and any specific issues they faced. Moreover, this approach helped build rapport and trust between the researchers and the farmers, encouraging open and candid communication. As a result of this direct engagement, the study achieved a remarkable 100 % response rate, as all selected farmers willingly participated and provided valuable insights into their farming activities. This hands-on approach not only ensured the collection of high-quality data but also fostered a collaborative relationship between the researchers and the farming community, enhancing the overall validity and reliability of the study findings.

2.4 Ethical considerations

We acquired informed consent or permission from each participant, ensuring that their consent was voluntary, explicit, and provided without any kind of pressure, bribery, or misinformation. We provided a clear and comprehensive description and explanation of the research objectives to them, elucidating the nature of the data being collected and emphasising its anonymity and confidentiality. We provided the participants with a clear explanation of our methodology and the intended use and dissemination of the study findings. We provided a comprehensive explanation of the advantages or potential drawbacks linked to involvement in the study. This was completed prior to the commencement of the actual data collection process. Consequently, they provided their signed consent after being fully informed.

2.5 Data analysis

Both descriptive and inferential statistics were employed for the purpose of data analysis using SPSS version 20 and Stata version 17. We used descriptive statistics such as frequency, percentage, mean and standard deviation to assess the socio-economic characteristics of the maize farmers. Frequency and percentage distributions were utilized to present categorical data, providing insights into the distribution of different characteristics among the farmers. Meanwhile, mean and standard deviation calculations offered measures of central tendency and variability, respectively, for continuous variables such as age, household income, education and farm size.

We asked sixteen questions about farmers' perceptions of private extension services. Services such as liaising with agricultural equipment, assisting with bank loan applications, ensuring a market for produce, etc. We used a five-point Likert scale, which included categories for strongly agree (5), agree (4), neutral (3), disagree (2), and strongly disagree (1). After the study, we calculated the outcomes and then calculated the mean score of these statements by multiplying the relevant frequencies by each assigned number on the five-point Likert scale and dividing by the number. We created the overall perception index for these statements by summing the individual mean scores of each statement and dividing by the total number of statements submitted. Farmers with high scores were considered to have good perceptions of private extension, whereas those who received a low score were considered to have negative perceptions of private extension.

2.6 Model construction

The study employed the Cragg Double Hurdle model with reference to previously established factors influencing farmers' willingness to pay and the amount they are willing to pay. We conducted a likelihood ratio test to validate the model's appropriateness, ensuring the distinctness of the willingness to pay and the payment amount. This decision led to the utilisation of three different models for analysis: the Heckman, Tobit, and Cragg Double Hurdle models. Among these, the Cragg Double Hurdle model emerged as the most suitable for examining the determinants of maize farmers' willingness to pay for private extension services and the corresponding payment amount. Findings demonstrating that the factors influencing farmers' willingness to pay were different from those affecting the payment amount supported this decision. This model operates based on two separate stochastic processes governing the decisions to adopt and the willingness to pay for a given strategy. By employing this model, the study explored the factors that influence maize farmers' willingness to pay and the amount they are willing to pay [31]. Importantly, we established that the decision to be “willing to pay” and the determination of the payment amount occurred in two distinct stages. Furthermore, the factors influencing these two decisions were not identical. Some variables significantly influenced the discrete decision (willingness to pay) but not the continuous decision (payment amount), and vice versa. We discovered that various variables had an influence on these discrete and continuous decisions [32].

We found that the same set of variables could influence the discrete decision (willingness to pay) and the continuous decision (payment amount) differently. The insignificant Mill's ratios in the Heckman models also indicated that the results supported the independence assumption. This indicated that selection bias was not significant in the sample, supporting the suitability of estimating the two models separately. The study's application of the Cragg two-step model found relevance in estimating the determinants of crop-livestock diversification. The use of the Cragg Double Hurdle model has been widespread in the academic literature and applied across various research areas, including marketing, economics, and social science studies. In this context, it was employed to analyse the decision-making behaviour of maize farmers concerning their willingness to pay and the corresponding payment amount for private extension services [32,33].

We used the Double Hurdle Model because there are two separate decisions that a respondent must make. First is Participation Decision (First Hurdle): Whether to participate in an activity (e.g., willingness to pay for private extension services) and second is the Intensity Decision (Second Hurdle): The extent of participation (e.g., the amount willing to be paid). There are clearly two distinct stages in the decision-making process. The two stages have different determinants and error structures. There is no indication that the explanatory variables are endogenous, thus, the DHL was used without the need for instrumental variables. In the context of farmers' willingness to pay for private extension services, we assumed that the explanatory variables (e.g., age, income, farm size) are not correlated with the error terms in both stages, the DHL is sufficient. The primary interest was basically on understanding the two-stage decision-making process without the complication of addressing endogeneity.

The double hurdle model is then expressed as;f(Y|x)=[1−Φ(χγ)]1[y=0][Φ(χγ)Φ(χβ/σ)φ(Y−χβσ)/σ]1[Y>0]

The double hurdle model is then expressed asf(Y|x)=[1−Φ(χγ)]1[y=0][Φ(χγ)Φ(χβ/σ)φ(Y−χβσ)/σ]1[Y>0]

To determine the variables influencing maize farmers' willingness to pay and the amount for private extension among our respondents, binary probit regression analysis was employed for the first hurdle and the amount farmers are willing to pay was measured using the multiple linear regression (Ordinary Least Square) in the second hurdle. The willingness to pay took a binary value, 1 for yes and 0 for no [34] while the amount to pay was a continuous variable.

Table 1 explains the various variables in detail, thus, measurement, a-prior expectations and the literature supporting the variables.Table 1 Measurement of variables.

Table 1Variables	Measurements	A-prior expectations	Source	
Dependent Variables			Atinaf et al. [23]; Abate et al. [24]; Aydogdu et al. [25]; Akinnagbe et al. [27]; Gebreegziabher and Mezgebo [28]; Wordofa [29]	
Willingness to pay for private extension services	1 if Yes, 0 if No		
Amount Maize farmers are willing to pay	Actual amount willing to pay (Ghana cedis)		
Independent Variables			
Sex	1 if male, 0 if female.	+/−	
Age	Actual age in years	+	
Household size	Number of household members	+	
Marital Status	1 if married and 0 if unmarried	+/−	
Years of Education	Actual years of schooling	+	
Income of Farmer	Actual earnings per season in Ghana Cedis (GHC)	+	
Farm size	Actual farm size of farmer in acre	+	
Farm Experience	Actual years in farming	+	
Land tenure System	1 if owned, 0 if otherwise	+/−	
Maize sold per season	Actual maize sold per season (Kg)	+	
Other Occupation of Farmer	1 if yes, 0 if Otherwise	+/−	
Extension Contact	1 if Yes, 0 if No	+/−	

While this study provides valuable insights into farmers' willingness to pay for private extension services, some limitations are present. The data collected through survey responses are subject to self-report bias, where participants may provide responses that align with social desirability or perceived expectations rather than their true attitudes or behaviours. The study's cross-sectional design provides a snapshot of farmers' willingness to pay at a specific point in time, limiting the ability to assess changes in attitudes or behaviours over time. While the study examines various socio-economic factors influencing willingness to pay, we may have overlooked other relevant variables that could further elucidate farmers' decision-making processes. While farmers may express a willingness to pay for private extension services, actual payment behaviour may differ due to financial constraints, competing priorities, or other unforeseen factors not captured in the study. However, we addressed these limitations through rigorous methodological approaches to enhance the robustness and applicability for future research on farmers' willingness to pay for private extension services.

3 Results and discussion

3.1 Socioeconomic characteristics of farmers

An overview of the socioeconomic characteristics of the respondents is given in Table 2. The independent variable “sex” has a mean value of 0.369. This value suggests that, on average, approximately 37 % of the participants in the study are male, while the remaining percentage, which totals 63.1 %, represents female participants. This reflects the gender distribution within the study population, with males constituting a smaller proportion compared to females.Table 2 Socioeconomic characteristics of maize farmers showing measurements.

Table 2Independent Variables	Mean	Std. Div.	Min.	Max.	
Sex	0.369	0.483	0	1	
Age	49.664	8.287	20	78	
Household Size	4.351	0.909	2	9	
Marital Status	0.891	0.312	0	1	
Years of Education	9.026	1.412	0	16	
Income of Farmer	1554.805	1796.904	300	19.500	
Farm Size	6.332	5.728	1	35	
Farm Experience	19.877	8.861	4	50	
Land Tenure System	0.844	0.363	0	1	
Maize Sold per Season	10.239	10.448	4	97	
Other Occupation of Farmer	0.725	0.447	0	1	
Extension Contact	0.304	0.461	0	1	
Source: Field Data, 2023

The average age of maize farmers is approximately 49.66 years. This suggests that a sizeable proportion of maize producers are relatively above their productive age. Younger farmers are more receptive to adopting innovative farming technologies due to their prospective exposure to modern agricultural practices [35,36]. This reaffirms the notion that age plays a significant role in determining farmers' readiness to incorporate new farming methods and technologies.

With an average household size of 4.35 members per household, maize farmers in the study area potentially have access to a cost-effective labour force. The relatively larger household sizes suggest that farmers may benefit from having more family members available to participate in agricultural activities. This abundance of labour within the household could contribute to reduced labour costs and increased efficiency in farm operations. Additionally, larger household sizes may imply greater social support networks and shared responsibilities, which can have a positive impact on agricultural productivity and overall household well-being. Therefore, leveraging the available labour from larger household sizes could be an advantageous strategy for maize farmers to optimise their farming activities and enhance household livelihoods [28].

Marital status, with a mean of 0.891, suggests a predominantly married farming population. This suggests that married individuals make up a significant proportion of the maize farming community. The predominance of married farmers in farming households could have implications for household dynamics, resource management, and decision-making processes. Married people may benefit from shared responsibilities, resource pooling, and mutual support from their spouses, which can have a positive influence on agricultural productivity and household well-being [35]. The average educational attainment among farmers, which stands at 9.026 years, suggests a moderate level of education, approximately equivalent to completing junior high school. This implies that, on average, farmers in the study have received education beyond the elementary or primary level but may not have attained secondary education or higher. This educational background could influence their understanding of agricultural practices, engagement with extension services, and adoption of modern farming techniques [37].

With a mean income of 1554.805 Ghana cedis, there is evident variability in the financial capacities among the farmer group. This indicates that while some farmers may have relatively higher incomes, others may have lower incomes, reflecting the diverse economic circumstances within the community. Such a disparity in income levels could have an impact on farmers' access to resources, investment capacity in agricultural inputs, and adoption of advanced farming technologies. The analysis of farm size data reveals that the average farm size for cultivating maize is 6.33 acres, with a range from a minimum of 1 acre to a maximum of 35 acres. This variation in farm sizes suggests that there are different levels of agricultural production capacity among farmers in the study area. Farmers with larger land holdings may have the potential for increased maize production and possibly higher incomes. Conversely, those with smaller landholdings may face limitations in terms of production scale and profitability [27].

The average of 19.88 years spent in farming suggests that maize farmers in the study area possess significant experience in maize cultivation. This extensive farming experience implies that farmers have acquired a wealth of practical knowledge and skills related to maize production over the years. Such accumulated expertise is likely to contribute to more informed decision-making, effective crop management practices, and better overall productivity in maize farming. Additionally, this level of experience may enable farmers to adapt to various challenges and changes in agricultural conditions, enhancing their resilience and ability to sustainably manage their maize farms [35].

The land tenure system has a mean value of 0.844, indicating that the majority of farmers in the study area are landowners. This indicates that most maize farmers have secure rights to the land they cultivate, which can have significant implications for agricultural productivity, investment decisions, and long-term planning. Farmers, as landowners, have greater incentives to invest in improving their land and adopting sustainable farming practices, knowing that they have a stake in future returns from their agricultural activities. Additionally, secure land tenure can facilitate access to credit, as land can serve as collateral for loans, enabling farmers to invest in inputs, technologies, and other resources to enhance their maize production [25].

Farmers’ average amount of sold maize per season, 10.239 bags, reflects the farmers' productivity and their ability to generate income from maize cultivation. The mean values for variables such as the presence of other occupations (0.725) indicates that a significant portion of maize farmers may engage in diverse economic activities beyond farming, highlighting their diversified livelihood strategies. This diversification could influence their resilience to agricultural risks and enhance their overall economic well-being. Moreover, the mean value for extension contact (0.304) indicates the frequency of interaction between farmers and agricultural extension agents or services. The value suggests a relatively less frequent engagement with extension services. A more frequent extension contact can enhance farmers' productivity, profitability, and resilience to various agricultural challenges [36].

3.2 Willingness to pay

An overview of maize farmers’ willingness to pay and the amount they are willing to pay is given in Table 3. The data reveals that a significant majority of respondents, comprising 91.16 %, expressed a willingness to pay for private extension services, while only a small proportion, 8.8 %, indicated otherwise. This strong inclination towards financial contribution for private extension services shows the perceived value and importance attributed to such services among the surveyed population [23]. The high percentage of farmers willing to invest in private extension services suggests a recognition of the potential benefits they can accrue from access to tailored agricultural advice, technical support, and innovative practices offered by private extension providers [24]. Additionally, this finding implies a degree of readiness among farmers to take ownership of their agricultural development and invest in resources that could enhance their productivity, profitability, and resilience to various agricultural challenges. According to a study by Sumo et al. [38], majority of the rice farmers expressed their willingness to pay for private extension services.Table 3 Maize farmers’ willingness to pay.

Table 3Variables	Yes		No		
Frequency	Percent	Frequency	Percent	
Willingness to pay for private extension services	351	91.16	34	8.8	
	Mean	Std. Div.	Min.	Max.	
Amount maize farmers are willing to pay	88.153	182.195	50	2000	
NB: 1 USD as at the time of data collection was 12.15 Ghana cedis (GHC).

Source: Field Data, 2023

The mean amount of 88.153 Ghana cedis (US$7.26) reflects the average financial commitment that maize farmers in Ghana are willing to make towards private extension services. This amount serves as a significant insight into the typical level of monetary contribution expected from the maize farmers. A comparison with other studies reveals variations in farmers' willingness to pay (WTP) for extension services across different countries. For instance, Sumo et al. [38] found a higher average WTP of US$11.21 per farm visit among farmers for privatised extension services. This figure surpasses the charges reported in Bangladesh and Tanzania, indicating potentially greater value placed on extension services by farmers in certain contexts. However, the average WTP in Ghana, as observed in this study, falls below US$17.00, as reported by Otishua et al. [39], and even further below US$26.99, as documented by Farinde and Atteh [40] among yam farmers in Nigeria. These disparities may stem from various factors such as differences in income levels, farm sizes, agricultural practices, and the perceived value of extension services within each agricultural context. While the average WTP of 88.153 Ghana cedis provides a baseline, it also suggests a need for affordability and accessibility considerations in designing extension programs.

3.3 Maize farmer's perception on private extension services

The data in Table 4 indicates a consistently positive perception among respondents towards private extension services across various aspects. Respondents rated their perception of private services as highly favourable, with an overall mean score of 4.56. Specifically, respondents perceived private services to be flexible (mean = 4.52), providing relevant and up-to-date information (mean = 4.53), easily accessible (mean = 4.54), available when needed (mean = 4.58), and responsive to changes in farming practices (mean = 4.56). Moreover, respondents believed that private services would effectively link farmers to value-added actors (mean = 4.58) and ready markets (mean = 4.58), with reasonable service fees (mean = 4.52) and effective communication (mean = 4.59). Respondents also thought that private services would improve crop productivity, profitability, and value growth (mean = 4.58), by providing specialized production information (mean = 4.54), helping farmers find markets and loans (means = 4.64 and 4.56), communicating with farm equipment (mean = 4.61), and holding health talks (mean = 4.44). These constructive perceptions are supported by prior studies, such as Loki [41], Shausi et al. [42], and Sumo et al. [38], which highlighted the high value placed by farmers on private extension services.Table 4 Perception of farmers on private extension services.

Table 4Statements	SD (%)	D (%)	N (%)	A (%)	SA (%)	Mean	
I perceive private services to be flexible.	2(0.52)	1(0.26)	42(10.9)	90(23.4)	250(64.9)	4.52	
I perceive private services to provide relevant and up to date information.	4(1.0)	1(0.25)	34(8.8)	95(24.7)	251(65.2)	4.53	
Private services will be easily accessible.	0	0	46(11.9)	87(22.6)	252(65.5)	4.54	
I perceive private services will be available when I need assistance.	1 (0.25)	0	29(7.5)	101(26.2)	254(66)	4.58	
Private services will be responsive and adaptive to changes in farming practices.	0	1.001	41(10.6)	85(22.1)	258(67)	4.56	
I perceive private services to link farmers to value added actors.	1(0.001)	0	34(8.8)	88(22.9)	262(68)	4.58	
I perceive services to link farmers to ready market.	0	1(0.001)	37(9.6)	86(22.3)	261(67.8)	4.58	
I perceive services fees to be reasonable.	0	3(0.8)	51(13.2)	73(19)	258(67)	4.52	
I perceive services to communicate with farmers effectively.	0	20.5	297.5	9424.4	260(67.5	4.59	
I perceive services to make a positive impact on productivity and profitability.	0	2(0.5)	33(8.6)	9023.4	260(67.5	4.58	
I perceive services will help in developing value for produce.	0	0	37(9.6)	89(23.1)	259(67.3)	4.58	
I perceive the service will provide specialized information on production.	0	0	44(11.4)	88(22.9)	253(65.7)	4.54	
I perceive the services to help me secure market for produce.	0	0	17(4.4)	106(27.5)	262(68)	4.64	
I perceive service will help secure loans from bank.	0	0	47(12.2)	74(19.2)	264(68.6)	4.56	
I perceive service will liaison with farm machinery.	0	0	32(8.3)	85(22)	268(69.6)	4.61	
I perceive services will provide health talk.	0	2(0.5)	63(16.3)	83(21.5)	237(61.6)	4.44	
Overall index						4.56	
NB: SD-Strongly Disagree, D-Disagree, N-Neutral, A-Agree, SA-Strongly Agree.

Source: Field Data, 2023

3.4 Determinants of farmers' willingness to pay for private extension

The study's findings (Table 5) offer insightful information on the variables affecting farmers' willingness to pay for private extension and the amount to pay. From the results above, the following factors showed significant relationships; marital status, income, secondary occupation, extension contact, amount of maize sold, age, gender, household size, farm size and years of schooling. Oladele [37] affirmed that farmers' socio-economic characteristics like level of education, income, farm size and farming experience had direct relationship to their willingness to pay for agricultural extension services,Table 5 Determinants of farmers' willingness to pay for private extension.

Table 5Willingness	Amount WTP	Model	
	Probit	Truncated	Tobit	Heckman	
Variables	Coef.	dy/dx	SE	Coef.	SE	Coef.	SE	Coef.	SE	
Age	0.002	0.001	0.012	−4.413***	1.467	−4.413***	1.467	−3.908	8.455	
Sex	−0.134	−0.037	0.18	−37.955**	19.34	−37.955*	19.34	6.3	126.188	
Marital status	0.944***	0.321	0.246	−47.033	38.279	−47.033	38.278	−464.41***	544.691	
Income	0.30***	0.04	0.14	−0.01	0.007	−0.01	0.007	0.051***	0.084	
Household size	−0.43	−0.012	0.094	32.696***	12.557	32.696***	12.557	46.597	72.531	
Farm size	0.005	0.001	0.017	7.871***	1.904	7.871***	1.904	4.000	12.075	
Farm experience	−0.006	−0.002	0.12	0.665	1.37	0.665	1.37	2.022	8.085	
Years of education	−0.066	−0.018	0.059	25.082***	6.875	25.082***	6.875	47.365	48.421	
Other occupations	0.323*	0.092	0.189	27.664	23.224	27.664	23.224	−84.817*	190.139	
Extension contact	0.583***	−0.144	0.2	−26.92	19.779	−26.92	19.779	143.874***	235.89	
Maize sold per season	0.27*	0.007	0.016	1.384*	0.825	1.384	0.825	−5.194*	9.424	
Land tenure system	0.769***	0.249	0.203	−8.359	27.695	−8.359	27.695	−307.781***	394.247	
Pseudo R2 = 0.361; Number of obs. = 385; Log likelihood = −170.43864	
NB: ***,**,* indicate significance levels at 1 %,5 %, and 10 % respectively.

Source: Authors' Construct, 2023

From the results, marital status is significant at 1 %. Being married increases the probability of willingness by 94.4 percentage points. This suggests that marital status plays a substantial role, possibly due to shared responsibilities and decision-making within a family unit. Conversely, Mwaura et al. [43] indicated that marital status was not significant in influencing the willingness to pay for neither crop nor animal husbandry. As anticipated, the WTP decision for private extension is positively related to farmer income at a significant level of 1 %. This highlights the importance of financial stability in fostering farmers' readiness to engage in agricultural extension programs. As a result, maize farmers' WTP for private extension increases with each unit rise in income. As a result, maize farmers who earn more can pay more for private extension services. This is so because WTP and income are significantly related. With more income, maize farmers' household financial situations are stable, boosting their willingness to pay for private extension services. The study is in line with Varin [44], Aydogdu [45], and Sumo et al. [38], who also discovered that income had a favourable impact on farmers' willingness to pay for extension services.

Additionally, having a second job is important because farmers who work outside of their maize farms are more willing to pay for private extension services. Farmers engaged in other occupations besides farming have a 32.3 % higher probability of being willing to pay for extension services. This suggests that diversified income sources may positively influence farmers' openness to extension programmes. Farmers who have had previous extension contact experience a 58.3 % increase in the probability of willingness. This finding suggests a noteworthy relationship between past engagement with extension services and a higher likelihood of farmers being receptive to future initiatives. Farmers who have previously interacted with extension services may have gained firsthand knowledge about the benefits of such programmes, leading to a more favourable perception. They might have witnessed improvements in farming practices, crop yields, or overall farm management, encouraging a positive attitude towards future engagements. To enlighten farmers about agricultural productivity-enhancing technologies, extension contacts are crucial. According to Kassie et al. (2020), extension agents should provide demonstration plots where farmers can practice new farm technologies and receive hands-on training. This will encourage farmers to adopt new technologies. According to the findings of Gelgo [46], Djokoto et al. [47], as well as Wongnaa and Babu [48], meeting with extension officers more frequently increases the likelihood that farmers will adopt agricultural technologies.

The amount of maize sold each season has a considerable positive link with the WTP. This demonstrates that a 1 % increase in the quantity of maize farmers sold each season increased their WTP by 27 %. WTP for private extension services therefore rises along with yield. Farmers with higher yields are those who make effective use of private services and adhere to all farming guidelines provided by the private services, thereby becoming familiar with new techniques and technology. Ghimire et al. [49] discovered that among rural farm households in Central Nepal, yield has a favourable impact on the adoption of enhanced rice varieties. The system of land tenure is also related favourably to the willingness of maize farmers to pay for commercial extension services. Farmers who are land owners are more eager because they do not have to pay rent on it, but those who rent farms are discouraged from signing up for private extension services.

The negative coefficient for age in the willingness to pay (WTP) analysis indicates that, on average, as individuals get older, the amount of money they are willing to pay decreases. It implies that younger farmers may be more willing or able to invest money, while older individuals might express less enthusiasm or financial commitment [38]. This is a natural conclusion as older farmers possess a greater wealth of agricultural expertise accumulated over time through experimentation and would consequently be reluctant to invest in extension services that offer less, if any, further information to their existing repertoire [50]. Being male is associated with a substantially lower willingness to pay (WTP). This outcome, which has a high level of statistical significance (p 0.01), emphasizes the value of taking gender dynamics into account when determining preferences and financial commitments. Being a woman farmer relates to a higher willingness to pay. Oladele et al. (2008) also indicated that being a male farmer will result in a drop in the likelihood of wanting to pay for extension services.

The positive coefficient related to household size suggests that larger household sizes are associated with higher levels of willingness to pay. This outcome is statistically robust (p < 0.001), indicating that as the size of the household increases, so does the likelihood of a higher WTP. According to the findings, households with a greater number of members demonstrate a higher inclination to invest in private extension services compared to those with fewer members. Large households likely necessitate a greater amount of food and would therefore be inclined to utilise private extension services in order to enhance output. This would enable them to achieve both their household consumption needs and their income creation objectives [38]. This result contradicts that of Gebreegziabher and Mezgebo [28], who documented an inverse correlation between the number of people in a home and farmers' willingness to pay for privatised agricultural extension services in Tigray, Ethiopia. Nevertheless, it aligns with the findings of Namyenya et al. [51], who discovered a direct correlation between the number of individuals in a family and the willingness to pay for irrigation water among rice farmers in Uganda.

The presence of a positive coefficient for farm size suggests a direct correlation between the size of the farm and the monetary value it is willing to allocate. There is a strong and statistically significant association between the size of farms and the willingness to pay (WTP), with larger farms being associated with greater levels of WTP (p < 0.001). It indicates that farmers who have larger agricultural operations may have a greater appreciation for the topic being discussed, which results in a greater inclination to invest. Moreover, in agrarian societies, farm size is frequently employed as a measure of affluence. The findings presented here imply that households with larger farms may exhibit a greater inclination towards enhancing their agricultural output by availing themselves of suitable advisory services [28]. The results of this study align with those of Angella et al. [52], who suggested that the correlation between farm size and willingness to pay is likely due to the fact that farmers with greater land holdings also grow larger rice plots at the Doho Rice Irrigation Scheme in Uganda.

Years of schooling had a beneficial impact on maize farmers' WTP, indicating that education level significantly affects their willingness to pay for private extension services. People with extensive educational backgrounds will invest in the services since they have a bigger financial capacity and a better comprehension of what the private sector has to offer [38]. Farmers with a higher level of education are more inclined to comprehend, analyse, and implement the new knowledge they obtain from private suppliers of extension services. The findings align with those of Shausi et al. [42], who observed a favourable correlation between education and farmers' willingness to pay (WTP) for sustainable agricultural land utilisation in the GAP-Harran Plain of Turkey. The quantity of maize sold each season is positively significant at a 1 % level, indicating that it has a considerable effect on farmers' willingness to pay. This suggests that an increase in the number of units of maize sold each season will raise the WTP for private extension services. These results contradict those of Oladele et al. (2008), who found that an increase in the number of crops sold each season will result in a drop in the likelihood that people will be willing to pay for extension services.

4 Conclusion

The research has shown an intricate web of maize farmers' perceptions, which indicates that overall, farmers’ perceptions of the various activities that would be carried out by the private extension services would be a good concept. Our findings on factors influencing WTP demonstrate that maize farmers must overcome the first hurdle, i.e., whether they are willing or not willing, and then decide how much they are willing to pay for private extension services. The findings from the study indicate that there is a very high willingness on the part of farmers to pay for private extensions. Marital status is a significant factor influencing farmers' willingness to pay for private extension services. Being married increases the probability of a willingness to pay. Farmers' income and willingness to pay have a positive relationship. Farmers engaged in other occupations besides farming demonstrate a higher probability of willingness to pay for extension services. Diversified income sources positively influence farmers' openness to extension programmes. Previous extension contact significantly increases the probability of willingness to pay. The amount of maize sold each season has a considerable positive link with willingness to pay, suggesting that WTP for private extension services rises along with yield. The land tenure system is also related favourably to farmers' willingness to pay for commercial extension services, with landowners showing more eagerness. Older farmers express a lower willingness to pay. Being male is associated with a substantially lower willingness to pay. Larger household and farm sizes positively impact willingness to pay. Higher levels of education have a beneficial impact on farmers' willingness to pay. The quantity of maize sold each season is positively significant, suggesting that an increase in maize sales raises the willingness to pay for private extension services.

Based on the research findings, the following recommendations can be made. Government policies should continue to promote and support private sector involvement in providing extension services. This can be done through incentives such as tax breaks, subsidies, or grants for private extension service providers. Efforts should be made to enhance the infrastructure for delivering extension services, both public and private. This includes improving access to information and communication technologies (ICTs) in rural areas to facilitate the dissemination of agricultural knowledge and advice. Extension service providers, both public and private, should tailor their services to meet the specific needs and preferences of maize farmers. This may involve offering flexible and responsive services that address farmers' concerns and challenges. There should be increased collaboration and partnerships between government agencies, private sector actors, non-governmental organisations (NGOs), and research institutions to ensure a coordinated and holistic approach to extension service delivery. Government and development partners should explore innovative financing mechanisms to make private extension services more affordable and accessible to smallholder farmers. This may include subsidizing extension service costs for marginalised and low-income farmers. Recognising the positive impact of previous extension contacts on farmers' willingness to pay, extension services should prioritise regular and meaningful interactions with farmers. Programmes related to yield improvement should be emphasised, considering the considerable positive link between the amount of maize sold per season and the willingness to pay.

Further research could explore the gender dynamics influencing farmers' willingness to pay for private extension services. This could involve investigating the barriers faced by female farmers in accessing and benefiting from extension services, as well as identifying strategies to address gender disparities in extension service delivery. Undertaking longitudinal studies to assess the long-term impact of private extension services on maize farmers' productivity, profitability, and livelihoods. This would provide valuable insights into the effectiveness and sustainability of private extension models over time.

Data availability statement

Data will be made available upon request.

CRediT authorship contribution statement

John-Eudes Andivi Bakang: Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Camillus Abawiera Wongnaa: Supervision, Software, Resources, Project administration, Investigation, Data curation. Enoch Kwame Tham-Agyekum: Writing – review & editing, Writing – original draft, Visualization, Supervision, Formal analysis, Data curation. Suraju Fatimatu: Visualization, Validation, Project administration, Methodology, Conceptualization. Jonathan Annorhene Obeng: Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Elsie Boatemah Nsafoah: Visualization, Validation, Resources, Project administration, Conceptualization. Michael Asiedu Antwi: Writing – original draft, Visualization, Validation, Supervision, Resources, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare no conflicts of interest regarding the research conducted and the findings presented in this study. This research was conducted in an unbiased manner and without any financial or non-financial interests that could potentially influence the results or interpretation of the data. The authors are committed to upholding the highest standards of academic integrity and transparency in their research endeavours.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e37464.
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