Employing agricultural extension delivery services for improving cocoa bean quality

Abstract

Agricultural extension services are supposed to provide farmers with the know-how and abilities they need to boost productivity, and create premium cocoa beans. In this study, a sample of 384 farmers participated and data collected was analysed using descriptive statistics. The findings reveal that demonstration farms (72.92%) are very effective for disseminating innovations to cocoa farmers. Farmers believe that agricultural extension services play a favourable role in improving the quality of cocoa (Mean = 3.93). Household size, years of farming, off-farm activities, access to credit and access to technical assistance statistically influence cocoa farmers’ choice of extension delivery methods (p < 0.05). Cocoa farmers perceive that agricultural extension services exert positive influence on various aspects of cocoa production (Mean = 3.96). The farmers ranked insufficient knowledge and awareness about the benefits of adopting agricultural extension methods for cocoa bean quality enhancement as their most critical constraint (Mean rank: 1.26). Based on the findings, there should be a concerted effort to promote the establishment of demonstration farms within cocoa-growing regions to serve as practical learning hubs where farmers can observe and learn about new techniques for improving their cocoa bean quality.

Keywords:

• Demonstration farms
• effectiveness
• farmer training
• radio
• rural development

REVIEWING EDITOR:

• George Mudimu, Development Sciences, Marondera University of Agricultural Sciences and Technology, Marondera, Zimbabwe

SUBJECTS:

• Agriculture & Environmental Sciences
• African Studies
• Rural Development
• Sustainable Development

Introduction

Cocoa beans are highly sought after worldwide, particularly by developed nations, because of their numerous economic benefits and uses. Africa is responsible for most of the cocoa production for the global market, which is mainly in Europe and America. Ivory Coast, Ghana, and Nigeria make the most significant contributions to the global market, with Ivory Coast being the largest producer, accounting for up to 39% of global output (Oke et al., 2020). When compared to cocoa from other countries, Ghanaian cocoa beans are exported with a notable quality premium. These premiums contribute to Ghana’s substantial cocoa revenue, which accounts for roughly 30% of the country’s total export earnings and 4% of GDP (Quarmine et al., 2012).

Ghana is known for providing premium-quality cocoa because of the strict quality control procedures that are implemented after production (Levai et al., 2015). Encouraging farmers to enhance the quality of their harvested cocoa beans would be advantageous in order to increase the quantity of high-quality cocoa beans (Antara & Antara, 2015). Cocoa stands out as Ghana’s primary export crop, renowned for its superior quality that commands a premium price on the global market. This industry plays a pivotal role in the country’s economy, contributing significantly to employment, income generation, and foreign exchange earnings (Anang et al., 2011). Despite consistently supplying 98% of grade 1 cocoa beans globally until the 2003/2004 cocoa season, Ghana witnessed a decline in the quality of its cocoa beans from that point onward. Presently, over 80% of Ghana’s cocoa beans are of grade one quality, and with focused attention on post-harvest procedures that impact bean quality, the nation has the potential to achieve close to 100%. Beyond its role in national export revenue, cocoa serves as a vital source of income for numerous individuals in Ghana (Kwadzo, 2017).

Agricultural extension services have long been considered integral to the success of agricultural development initiatives, with a primary focus on enhancing farmers’ productivity and promoting sustainable agricultural practices. According to Al-Sharafat et al. (2012), the historical perspective of agricultural extension involved implementing scientific research findings and applying new knowledge to farming methods through farmer education. Rivera (1997) emphasises that these services not only disseminate information from research centres to farmers but also facilitate a two-way exchange, allowing feedback from farmers to influence research. Swanson (2008) emphasized the crucial role that extension services—often formally organized—play in promoting small-scale agriculture and ensuring personal and national food security.

In the context of the cocoa industry, agricultural extension services become particularly vital by providing cocoa farmers with information and training on best practices for cultivation, harvesting, and post-harvest processing. The role of agricultural extension agents in the cocoa sector is pivotal, aiming to assess farmers’ technical capabilities in managing the complexities of cocoa production (Oke et al., 2020). These specialists monitor developments, ensuring adherence to good agricultural practices. Various instructional methods, including training sessions, seminars, workshops, and field exercises, are employed to educate cocoa farmers. Monthly meetings of cocoa farmer associations serve as platforms for sharing valuable information about cocoa (Oke et al., 2020).

The achievement of superior-quality cocoa beans is contingent on various factors. It has been argued that 80 percent of the quality of cocoa beans is dependent on the correct agronomic practices, which include correct growing methods, good harvesting methods, right pod breaking, good fermentation methods, proper drying methods, and sorting adopted by farmers. Problems militating against the achievement of international quality standards in cocoa are broadly classified into natural factors and structural/financial factors. Natural factors encompass the impact of weather conditions, infestation by pests and rodents, as well as defects in cocoa beans. Structural and financial factors involve the lack of direct extension services, inadequate funding for research activities, elevated levels of chemical residues, the presence of heavy metals, the utilisation of jute sacks for cocoa export, and the mode of shipment (Anang et al., 2011).

Agricultural extension delivery services plays a crucial role in improving farming practices. While previous researches (Khan et al., 2012, Swanson, 2008, Danso-Abbeam et al., 2018, Donkor et al., 2018; Anang et al., 2011; Al-Sharafat et al., 2012; Levai et al., 2015 and Oke et al., 2020) have acknowledged the importance of agricultural extension services in enhancing agricultural practices, farmers’ knowledge, and contributing to community development, there is a lack of specific studies focusing on their impact on cocoa bean quality. Understanding this influence is crucial for ensuring the competitiveness of Ghana’s cocoa industry in the global market. Benjamin et al. (2011) found that farmers in Ghana are knowledgeable about good management practices that can improve cocoa bean quality, but face challenges such as financial constraints and pests. Suh et al. (2020) further emphasized the importance of sustainable management practices, which can positively impact cocoa bean quality. However, Quarmine et al. (2012) highlighted the need for policies to address information asymmetry and incentivize quality production. Toledo-Hernández et al. (2017) suggested that enhancing pollination services can also improve cocoa yields, indicating a potential area for extension services to focus on.

Given the critical importance of cocoa bean quality in determining market value and the multifaceted factors affecting it, such as harvesting, processing, and storage, there is a pressing need to evaluate the effectiveness of agricultural extension services in improving cocoa bean quality. Therefore, this study aims to address this gap by assessing the effectiveness of agricultural extension services in enhancing the quality of cocoa beans. The main objective of the study is to assess the effect of agricultural extension delivery on cocoa bean quality in the Sefwi Boako District, located in the western region of Ghana. The specific objectives include: to access the farmers’ perception of the agricultural extension delivery methods for ensuring cocoa bean quality; to determine the factors influencing farmers’ choice of the agricultural extension delivery methods for ensuring cocoa bean quality; to identify the effect of agricultural extension delivery methods on cocoa bean quality; and to identify the constraints faced by the farmers in the use of agricultural extension delivery methods by extension officers for ensuring cocoa bean quality.

The novelty in this study lies in its comprehensive examination of the relationship between agricultural extension delivery and cocoa bean quality, particularly within the context of Ghana cocoa and the implication it holds for the global market. By assessing farmers’ perceptions of extension delivery methods, determining the factors influencing their choice of these methods, identifying the perceived effect of these methods on cocoa bean quality, and pinpointing the constraints faced by farmers in utilising them effectively, this study offers valuable insights into the dynamics of cocoa production and quality improvement. This multifaceted approach not only addresses a significant gap in the existing literature but also provides practical implications for enhancing cocoa bean quality standards and ensuring the sustainability of cocoa production in Ghana.

Research methodology

Study area

The study was conducted in Sefwi Boako Cocoa District in the Sefwi Wiaso Municipality in the Western North Region of Ghana. Sefwi Boako District is one of the two cocoa districts in the Sefwi Municipality of the Western North Region. This study area was chosen because it contains the highest number of cocoa farmers (MOFA, 2022). It has a tropical rainforest climate, with average annual temperatures between 25 °C and 30 °C. An annual precipitation of between 1524 mm and 1780 mm characterises the region, which experiences moderate to heavy rainfall. September–October and June–July are the two peak months for the double maxima pattern of the climate. In general, there is a lot of humidity, with nighttime highs of about 90% and daytime lows of 75% (GSS, 2014). The unique rainfall pattern in the region is advantageous for the district’s agricultural activities. It has two lengthy wet seasons and a comparatively brief dry season, which runs from December to February and is characterised by low humidity and foggy weather. There are either few or no bush fire outbreaks in the district. Because of the properties of the soil, concentrated downpours of up to 178 mm of rainfall in a single day can cause widespread flooding in certain settlements. Forest ochrosols are the most common soil type in the Sefwi Boako District, making up most of the region’s northern and western regions. High yields are produced in the municipality by these fertile soils, which include forest ochrosols and oxysols, supporting the growth of a variety of cash and food crops like cocoa, palm trees, cola, coffee, cashew, plantains, cocoyam, cassava, and maize (GSS, 2014).

Research design

As De Vaus (2016) emphasizes, a research design serves as the blueprint for data collection, measurement, and analysis. Only quantitative research methods were used in this study, which used a descriptive survey design. The researcher was able to obtain factual information necessary for making decisions by using the descriptive design to collect data from respondents. Through conversations, this method also enabled the researchers to ascertain the subjects’ beliefs, emotions, and behaviours (Creswell, 2014).

Population, sample size and sampling technique

According to GSS (2014), the Sefwi Wiawso Municipality has 139,200 residents, or 5.9% of the region’s total population. There are 49.9 percent of females (69,477) and 50.1 percent of males (69,753) in the population. The percentages of men and women are both marginally greater than the regional averages. The target population of cocoa farmers in the Municipal is nine thousand, four hundred and four (9404).

Glenn (1992) highlighted several ways used in the determination of the sample for a given study, thus, published tables, standardised formulae or a sample from a related study. Consequently, the researcher relied on calculating the sample size using the formula proposed by Yamane (1967). This formula takes into account the desired level of confidence, precision level, and probability of the characteristic being studied. In this case, the researcher aimed for a 95% confidence level, indicating the level of certainty that the sample results accurately represent the population. A precision level of ±5% was chosen, which represents the acceptable margin of error in estimating population parameters. Additionally, a probability of 0.50% was used to ensure that the sample size calculation was conservative and accounted for variability in the population. By applying these parameters to the formula, the sample size was calculated to be 384 cocoa farmers. This sample size was deemed sufficient to provide reliable estimates of the characteristics and behaviours of cocoa farmers in the district. The sample was selected from seven different communities within the district to ensure representation of various geographic areas and farming practices. Care was taken to include a diverse mix of cocoa farmers, considering factors such as farm size, experience, and socio-economic status.

The multistage sampling technique employed in this study was designed to ensure representative sampling of cocoa farmers from various communities within the Sefwi Boako District (Amengor et al., 2022; Yeboah et al., 2023). The process involved several stages to systematically select participants from the target population. Firstly, purposive sampling was utilised to select cocoa-producing communities based on the number of cocoa farmers within each community. This initial step aimed to ensure adequate representation of different geographic areas and farming practices. Secondly, proportionate sampling was employed to determine the number of farmers to be selected from each community. This method involved computing the proportion of farmers in each selected community relative to the total number of cocoa farmers in the district. This ensured that larger communities with more cocoa farmers contributed proportionally more to the sample size. The sampling frame, which contained a comprehensive list of cocoa communities and cocoa farmers, was obtained from the COCOBOD office in the Sefwi Boako District. This sampling frame served as the basis for selecting participants at each stage of the sampling process. Finally, cocoa farmers were selected within each identified community using simple random sampling. This approach ensured that every cocoa farmer in the selected communities had an equal chance of being included in the study, minimizing bias and increasing the representativeness of the sample (Yeboah et al., 2023).

Research instrument and data collection

The study relied solely on primary sources for data collection. Primary data was gathered directly from respondents through individual face to face interviews utilising a structured questionnaire. This structured set of questions was meticulously designed to elicit specific information relevant to the research objectives. Using a structured questionnaire for data collection is a prudent approach, as it allows for systematic gathering of information in a standardised manner. The questionnaire served as a reliable tool for ensuring consistency in the data collection process, as each respondent is presented with the same set of questions. This facilitated comparability of responses across participants, enabling the researchers to draw meaningful conclusions from the data. Additionally, the structured nature of the questionnaire helped ensure that all relevant aspects of the research were adequately addressed, enhancing the comprehensiveness of the data collected (Amengor et al., 2022).

In designing the questionnaire, we incorporated various types of questions to elicit diverse responses from the respondents. One such format was multiple-choice questions, which presented respondents with a range of options to choose from. This format allowed us to gather detailed information on specific topics by offering respondents a selection of predefined answers to each question. Likert scale questions were another key component of the questionnaire, providing respondents with a continuum of choices to indicate their level of agreement or disagreement with a statement. This format enabled us to gauge the intensity of respondents’ opinions on various issues, offering nuanced insights into their perspectives. A scale ranging from one (strongly disagree) to five (strongly agree) was used. Rank statements were included to allow respondents to prioritise their preferences or opinions on the constraints faced by farmers in the use of extension methods for ensuring quality cocoa beans (Yeboah et al., 2023). By asking respondents to rank statements in order of importance or preference, we were able to discern their priorities and preferences. Additionally, binary responses, such as yes or no questions, were incorporated to capture straightforward answers to specific queries. This format facilitated quick and clear responses from respondents, particularly for questions that required a definitive answer. By incorporating multiple-choice questions, Likert scale items, rank statements, and binary responses into the questionnaire, we ensured a comprehensive approach to data collection. This diverse range of question formats allowed for a nuanced understanding of the respondents’ perspectives and provided valuable insights into the research objectives (Yeboah et al., 2023).

Before distributing the questionnaire widely, we conducted a pilot test with a small group of farmers to identify any issues with question clarity, wording, or response options. This preliminary examination aimed to identify any potential issues pertaining to question clarity, wording, or the available response options. Subsequently, we refined the questionnaire based on the feedback garnered from this trial run. We ensured that the research objectives and the specific information sought were clearly elucidated to the respondents. Additionally, comprehensive instructions for completing the questionnaire were provided to ensure uniformity in response collection. The questionnaires were designed to be administered by the researchers, allowing respondents to complete them independently.

Ethical considerations

Ethical approval from a committee was not sought for this study. Reasons being that our study did not involve human participants, the data obtained was fully anonymised and cannot be tracked back to the individual participants. Respondents willingly participated in the research and provided written consent on the questionnaire before they participated in the study. We ensured 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 interview 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. Respondents were given sufficient time to give their responses to the questionnaire, without any pressure or coercion. The study did not involve any questions or activities that could cause physical harm or trigger psychological distress such as stress, anxiety (Hyde-Cooper et al., 2024).

Analytical technique

Data collected was subjected to Stata version 16 and SPSS version 20 for analysis. The socioeconomic characteristics of the respondents were summarised using descriptive statistics, thus, frequency, percentage, mean, and standard deviation. In order to analyse farmers’ preference for the various extension delivery methods used by the extension agents, cocoa farmers were asked to indicate a ‘Yes’ or ‘No’ response to each of the extension delivery methods. The extension delivery methods used for this study were group meetings, farm visits, home visits, local radio talks, durbars and rallies, farmer training and demonstration farms. Frequencies and percentages were used to analyse their response for each of the methods. This analytical approach facilitated a comprehensive assessment of the adoption and perceived effectiveness of each extension method among cocoa farmers.

Farmers’ perspectives on the use of agricultural extension delivery methods in ensuring cocoa bean quality were evaluated using a five-point Likert scale. The scale ranged from 1 to 5, with 1 representing ‘strongly disagree (SD)’, 2 indicating ‘disagree (D)’, 3 denoting ‘neutral (N)’, 4 signifying ‘agree (A)’, and 5 representing ‘strongly agree (SA)’. This structured approach enabled farmers to express their level of agreement or disagreement with the use of various extension delivery methods in enhancing cocoa bean quality. The perception index, in this study was determined using a formula that accounted for the frequency of responses across different categories. Each response option, ranging from ‘Strongly Disagree’ to ‘Strongly Agree’, was assigned a corresponding value. The formula calculated the weighted average of responses by multiplying the frequency of each response category by its assigned value and summing the products. The index provided a quantitative measure of participants’ perceptions regarding the variables under investigation, allowing for a comprehensive analysis of their attitudes and opinions (Yeboah et al., 2023).

To determine the factors influencing farmers’ preferred choice of extension delivery methods to improve cocoa bean quality, the study employed the Multivariate Probit Model (MVP). This statistical approach was selected because it accommodates the analysis of multiple extension delivery services, each representing a binary outcome. It is also able to account for the interdependencies among the various extension services offered to farmers, allowing for a more comprehensive understanding of the factors influencing their choices. With this model, the study aimed to identify the factors that significantly impact farmers’ preferences for specific extension services, shedding light on the key determinants guiding their choices. The decision by a cocoa farmer to prefer an extension delivery method does not preclude a cocoa farmer choosing other methods. More specifically, a cocoa farmer can prefer as many among the extension delivery methods he/she wants to choose. A randomly selected cocoa farmer is likely to choose a particular delivery method if the benefits obtained from it are greater than not choosing. In line with the works of Mulwa et al. (2017) and Kassie et al. (2013), it is assumed that the decision to choose an extension delivery method(s) is governed by a random utility framework. $$Y_i=b_0+b_1{X}_1+b_2{X}_2+\dots .b_n{X}_n+\hspace{0.17em}\epsilon i$$

Where; $Y_i$=Dependent variable (Prefer-1 or Not prefer-0)

$b_{0 }$=Constant

${\epsilon }_i$=error term

$b_1,b_2,b_3\dots \dots \mathit{\text{and}}\mathrm{ }{b}_n$ are parameters to be estimated

$X_1{X}_2{X}_3\dots .X_n$ are the independent variables (age, educational level, sex, years of experience etc)

Table 1 displays the independent variables, measurement and a-prioor expectations used in the multivariate probit model.

Table 1. Independent variables used in the MVP.

Independent variables	Measurement	A-pprior expectations
Age	Age (years)	+
Educational level	Years in formal education	+
Sex	1 = Male, 0 = Female	+/-
Years of experience	Years of experience in cocoa farming	+
Farm size	Farm size of respondent	+
Marital status	1 = Married, 0 = Otherwise	+/-
Household size	Number of persons	+
Land holdings	Acres	+
Off-farm activity	1= Yes, 0= No	+/-
Member of FBO	1= Yes, 0= No	+/-
Years in FBO	1= Yes, 0= No	+/-
Access to inputs	1= Yes, 0= No	+/-
Access to technical assistant	1= Yes, 0= No	+/-
Access to market	1= Yes, 0= No	+/-
Access to credit	1= Yes, 0= No	+/-

Source: Author’s construct, 2023.

The independent variables considered in this study encompass various socioeconomic factors that may influence cocoa farmers’ choice of extension delivery method. Firstly, age is measured in years and is expected to have a positive effect, as older farmers may possess more experience and knowledge in cocoa farming practices. Similarly, educational level, quantified by years of formal education, is anticipated to have a positive effect, as higher education levels may correlate with a better understanding and adoption of modern farming techniques. Sex, represented as a binary variable where ‘1’ denotes male and ‘0’ denotes female, could have either a positive or neutral impact. Years of experience in cocoa farming is expected to positively influence farmers’ choice, as veteran farmers may have honed their skills and accumulated valuable insights over time. Farm size is likely to have a positive association, with larger farms potentially providing greater resources and opportunities for productivity. Marital status, measured as ‘1’ for married and ‘0’ otherwise, may have varied effects, depending on factors such as household dynamics and support systems. Household size, reflecting the number of individuals in the farmer’s household, is expected to have a positive influence, as larger households may contribute more labour and resources to cocoa farming activities. Land holdings, measured in acres, are anticipated to positively affect farmers’ outcomes, as larger land sizes offer greater potential for cultivation and yield. Off-farm activity, indicating engagement in non-agricultural work, could have either a positive or negative impact, depending on how it affects farmers’ ability to invest time and resources in cocoa farming. Membership in a Farmers-Based Organisation (FBO) is expected to have a positive influence, as it may provide access to collective resources, knowledge sharing, and market opportunities. Access to inputs, technical assistance, markets, and credit are all anticipated to have positive impacts, as they contribute to improving farmers’ productivity, profitability, and overall well-being in cocoa farming endeavours (Yeboah et al., 2023; Delia et al., 2012; Adzitey et al., 2020; Kehinde et al., 2020; Teferi & Hernández Yáñez, 2022; Zelalem et al., 2021; Thuo et al., 2014).

To examine the constraints encountered by farmers in adopting agricultural extension delivery methods, we employed the Kendall’s coefficient of concordance (also known as Kendall’s W). Kendall’s W is a rank correlation nonparametric statistic. We evaluated the level of agreement among cocoa farmers concerning the constraints they face.

The Kendall’s statistics (W) can be specified as; $$W=\frac{12\left[\sum T^{2\hspace{0.17em}}-\frac{{\left(\sum T^{2\hspace{0.17em}}\right)}^2}{n}\right]}{n{m}^2\left(n^2-1\right)}$$

Where, w = Kendell’s Coefficient of Concordance; T = sum of ranks for factors being ranked; m = number of respondents; and n = number of factors being ranked.

Results and discussion

The data presented in Table 2 below highlights the distribution of respondents according to their socio-economic characteristics.

Table 2. Socio-economic characteristics of farmers.

Variables	Frequency	Percent
Gender
Male	265	69.0
Female	119	31.0
Age
20–30 years	20	5.2
31–40 years	67	17.4
41–50 years	94	24.5
51–60 years	105	27.3
61 and above	98	25.5
Marital status
Single	24	6.3
Married	250	65.1
Divorced	32	8.3
Widow	43	11.2
Separated	35	9.1
Household size
1–5	151	39.3
6–10	171	44.5
11–15	42	10.9
16–20	13	3.4
21–25	7	1.8
Education
No formal education	134	34.9
Primary	96	25.0
Middle school/JHS	141	36.7
Secondary school level	10	2.6
Tertiary	3	0.78
Landholdings
1–10	332	86.5
11–20	40	10.4
21–30	8	2.1
31–40	4	1.0
Ready Market
Yes	332	86.5
No	52	13.5
Farming years
1–10	62	16.1
11–20	99	25.8
21–30	125	32.6
31–40	73	19.0
41–50	23	6.0
51 and above	2	0.5
Off-farming
Yes	221	57.6
No	163	42.4
Member
Yes	292	76.0
No	92	24
Years in FBO
1-5yrs	279	72.7
6-10yrs	105	27.3
Access to credit
Yes	96	25
No	288	75
Access to inputs
Yes	329	85.7
No	55	14.3
Access to technical assistants
Yes	356	92.7
No	28	7.3

Source: Field Data, 2023.

In Table 2, male respondents comprise the majority (68.3%), with a smaller but still substantial presence of female respondents (30.7%). Farmers exhibit considerable diversity in age, with a notable concentration in the 41–60 age bracket. Marital status among respondents varies, although a substantial majority are married (65.1%). Married individuals may experience distinctive economic responsibilities and constraints compared to their single, divorced, widowed, or separated counterparts (Tey & Brindal, 2012).

The distribution of household sizes is relatively even, with a considerable portion falling within the 6–10 and 1–5 household size categories. Larger households may possess greater labour resources, but they also incur higher demands (Ukolova & Dashieva, 2022). Education levels vary, with a substantial number of farmers having only a primary or middle school education. Given that most farmers own relatively small plots of land (1–10 acres), adopting certain agricultural techniques may pose challenges (Cochran, 2009). A significant portion of respondents (86.5%) have access to farm inputs. Extension programmes play a crucial role in educating farmers about market requirements and facilitating connections with markets (Feder et al., 2011).

Farmers’ years of experience in cocoa cultivation vary, with younger farmers potentially more receptive to modern methods, while experienced farmers may adhere to conventional approaches (Warner & Christenson, 2019). A majority of respondents (57.6%) engage in off-farm activities, implying potential constraints on their time and resources for cocoa cultivation. A substantial proportion of farmers (76.0%) are members of farmer-based organisations (FBOs), providing access to shared information and resources (Frimpong-Manso et al., 2023).

The data reveals a significant divide in farmers’ access to credit, with 25% affirming its availability and 75% reporting a lack thereof. Credit serves as a financial tool that empowers farmers to invest in their agricultural practices (Iskandar et al., 2020). On a positive note, a substantial majority (85.7%) enjoy access to inputs such as seeds and fertilisers, which is vital for fostering agricultural productivity and improving produce quality (Danso-Abbeam et al., 2018). Moreover, a noteworthy 92.7% of farmers have access to technical assistance, indicating a favourable landscape for knowledge transfer and skill development. Technical assistance provides farmers with valuable knowledge and skills (Iskandar et al., 2020).

Preference of extension delivery methods for ensuring cocoa bean quality

The data presented in Table 3 below highlights the distribution of respondents on their preference for extension delivery methods for ensuring cocoa bean quality.

Table 3. Preference for the extension delivery methods.

Delivery Methods	Yes	No
Group Meetings	222 (57.81)	162 (42.19)
Farm Visits	248 (64.58)	136 (35.42)
Home Visits	161 (41.93)	223 (58.07)
Local Radio Talks	254 (66.15)	130 (33.85)
Durbars and Rallies	136 (35.42)	248 (64.58)
Farmer Training	262 (68.23)	122 (31.77)
Demonstration Farms	280 (72.92)	104 (27.08)

Source: Field Data, 2023.

The presented data in Table 3 provides a comprehensive overview of farmers’ preference for the diverse delivery methods employed in agricultural extension services, showcasing the predominant preference of various interactive and hands-on approaches. With the highest perceived efficacy (72.92%), it implies that demonstration farms play a pivotal role in agricultural extension services by serving as practical learning environments where farmers can observe, participate, and gain first-hand experience with innovative farming techniques and technologies. These farms function as living laboratories, allowing farmers to witness the application of advanced agricultural practices, sustainable cultivation methods, and the use of modern technologies. This hands-on approach contributes significantly to knowledge transfer and skill development, enabling farmers to understand the practical nuances of implementing new methods. Demonstration farms not only enhance technical know-how but also build confidence among farmers to adopt and adapt these practices on their own farms. Moreover, they foster a sense of community learning where farmers can share insights and experiences, creating a collaborative environment that accelerates the adoption of best practices (Khan et al., 2009).

The data showing that 68.23% of respondents participate in such programmes demonstrates that farmer training is a prominent and significant component of agricultural extension services. Farmer training initiatives are designed to equip agricultural practitioners with the knowledge, skills, and insights necessary to enhance their farming practices and overall productivity. These training sessions cover a broad spectrum, ranging from the latest technological advancements and sustainable farming methods to market trends and agribusiness management. The high participation rate underscores the recognition among farmers of the value of staying abreast of evolving agricultural practices. Farmer training not only imparts technical expertise but also fosters a sense of empowerment, enabling farmers to make informed decisions, adopt innovative approaches, and navigate challenges effectively. Additionally, these programmes contribute to community building by creating a network where farmers can exchange ideas, share experiences, and collectively contribute to the improvement of local agricultural practices. In essence, farmer training emerges as a linchpin in the continuum of agricultural knowledge transfer, playing a pivotal role in shaping the resilience and success of farming communities. In a study by Mustapha (2017), it was indicated that the use of interpersonal contact by the extension workers with farmers through training was more effective in creating awareness of the improved common bean technologies.

Local radio talks serve as a dynamic and accessible channel within agricultural extension services, with 66.15% of respondents indicating their engagement in this method. Extension services should keep using this strategy and consider doing more frequent radio talks about pertinent topics. Durbars and rallies were perceived as effective by 35.42% of respondents, and 64.58% of the respondents’ perceived durbars and rallies as not effective. Communities can be effectively engaged through these activities, but there is room for improvement. This might entail engaging local leaders, addressing community problems, or improving promotion (Antwi-Agyei & Stringer, 2021; Ankuyi et al., 2023).

Farm visits were perceived as very effective by 64.58% of respondents. This suggests that farm visits are a typically positive experience and a useful way to share knowledge and advice. Extension services must maintain their use of this approach, making certain that the visits’ contents correspond with the requirements of farmers (Lwoga et al., 2011). Mustapha (2017) also found farm visits to be more effective. Home visits, on the other hand, were perceived as very effective by 41.93% of respondents. Even though this proportion is smaller than other approaches, house visits might be an effective way to reach farmers who might find it difficult to attend farm visits or group meetings. Extension services must look for methods to improve the effectiveness of house visits and tailor them to the needs of specific farmers (Tham-Agyekum et al., 2023; Antwi et al., 2022).

Group meetings stand out as a significant and widely embraced delivery method in agricultural extension services, with 57.81% of respondents actively participating in such gatherings. These meetings serve as collaborative forums where farmers come together to discuss, share experiences, and receive valuable information from extension officers, experts, and fellow practitioners. The interactive nature of group meetings fosters community learning, allowing farmers to exchange knowledge, strategies, and best practices. This method not only facilitates the dissemination of crucial agricultural information but also cultivates a sense of belonging and mutual support among farmers. Topics covered in group meetings may include crop management techniques, pest control measures, and innovations in farming technology. The participatory nature of these sessions encourages engagement, questions, and discussions, creating a dynamic learning environment. The popularity of group meetings underscores their effectiveness in fostering a sense of community, building social capital, and contributing to the continuous improvement of agricultural practices within the farming community (Antwi et al., 2022).

Factors influencing farmers’ preference for agricultural extension delivery methods for cocoa bean quality

The results presented in Table 4 provide insights into the factors influencing farmers’ choice of agricultural extension delivery methods for cocoa bean quality improvement.

Table 4. Factors influencing farmers’ preference for the agricultural extension delivery methods for ensuring cocoa bean quality.

Variables	Farm Visits	Farmer Training	Local Radio Talks	Demonstration Farms
Sex	0.21 (0.16)	0.09 (0.16)	0.17 (0.16)	0.16 (0.15)
Age	0.01 (0.01)	−0.01 (0.01)	−0.01 (0.01)	−0.01 (0.01)*
Marital status	0.02 (0.07)	0.03 (0.08)	0.06 (0.07)	0.00 (0.07)
Household size	0.02 (0.02)	0.02 (0.02)	0.04 (0.02)**	0.05 (0.02)***
Level of Education	0.03 (0.08)	0.02 (0.08)	0.02 (0.08)	−0.02 (0.08)
Land holdings	0.02 (0.01)	−0.00 (0.02)	0.01 (0.02)	0.01 (0.01)
Years of farming	−0.03 (0.01)***	0.01 (0.01)	−0.01 (0.01)	−0.00 (0.01)
Off-farm activity	0.00 (0.15)	0.14 (0.16)	0.31 (0.16)**	0.28 (0.15)*
Member of FBO	0.03 (0.17)	0.18 (0.18)	0.16 (0.17)	0.17 (0.17)
Years in FBO	0.02 (0.04)	0.00 (0.04)	−0.02 (0.04)	−0.03 (0.04)
Access to credit	−0.06 (0.16)	0.20 (0.16)	0.30 (0.16)*	0.36 (0.16)**
Access to inputs	0.14 (0.23)	0.21 (0.23)	0.06 (0.23)	0.04 (0.22)
Access to technical assistant	−0.18 (0.33)	−0.42 (0.33)	0.37 (0.35)	0.30 (0.30)
Access to market	0.31 (0.22)	0.04 (0.21)	−0.10 (0.21)	−0.05 (0.21)
Constant	−0.29 (0.72)	−0.22 (0.73)	−1.15 (0.73)*	−1.14 (0.69)*

Source: Field Data, 2023.

Table 4 examines the factors influencing cocoa farmers’ preferences for different agricultural extension delivery methods aimed at ensuring cocoa bean quality. Analysing the results, several key insights emerge. Firstly, demographic factors such as sex and age seem to have minimal influence on farmers’ preferences for extension methods, as evidenced by coefficients close to zero in most cases. However, there are exceptions, such as household size, where larger households show a preference for methods like local radio talks and demonstration farms, indicated by positive and statistically significant coefficients. In larger households, there might be more diverse interests and responsibilities among family members involved in cocoa farming. Local radio talks and demonstration farms could serve as effective methods for disseminating information and learning about cocoa farming practices because they offer accessible and interactive platforms for knowledge sharing. For instance, local radio talks provide a convenient way for multiple household members to access information simultaneously, allowing for broader dissemination of agricultural practices and techniques within the household. Similarly, demonstration farms offer hands-on learning experiences that can accommodate larger groups of family members, enabling them to observe and learn new farming methods together. Additionally, larger households may benefit from the social aspect of extension methods like demonstration farms, where they can interact with other farmers and extension workers, exchange experiences, and build networks within the farming community. This social aspect could contribute to the preference for demonstration farms among larger households, as it provides opportunities for collective learning and collaboration (Frimpong-Manso et al., 2023; Ankuyi et al., 2023; Ukolova & Dashieva, 2022).

Furthermore, engagement in off-farm activities appears to significantly influence preferences for extension methods. Farmers involved in off-farm activities tend to favour methods such as farmer training, local radio talks, and demonstration farms, as indicated by positive and statistically significant coefficients. Farmers involved in off-farm activities may have limited time available for traditional extension methods like farm visits due to their commitments outside of agriculture. As a result, they may prefer extension methods that offer more flexible scheduling, such as farmer training sessions or participation in local radio talks, which can be accessed at convenient times without requiring them to leave their farms for extended periods (Antwi et al., 2022). Secondly, farmer training sessions and local radio talks often provide targeted and practical information that can directly benefit farmers engaged in off-farm activities. These methods may be perceived as valuable opportunities to acquire new skills, knowledge, and insights that can enhance their agricultural practices and improve farm productivity. Moreover, demonstration farms offer hands-on learning experiences that can be particularly appealing to farmers seeking practical demonstrations of innovative farming techniques and technologies. Additionally, farmers involved in off-farm activities may appreciate the social and networking opportunities provided by extension methods like farmer training sessions and demonstration farms. These platforms allow them to interact with fellow farmers, extension workers, and agricultural experts, facilitating knowledge exchange, collaboration, and community-building within the farming community (Ukolova & Dashieva, 2022).

Access to resources plays a crucial role in shaping farmers’ preferences. Access to credit, for example, significantly influences preferences for methods like farmer training, local radio talks, and demonstration farms, with positive coefficients indicating a preference for these methods among farmers with better access to credit. Access to credit provides farmers with the financial means to invest in training programs, attend workshops, or participate in other extension activities that require financial resources. Consequently, farmers with better access to credit may be more inclined to participate in farmer training sessions, as indicated by the positive coefficients. These training sessions offer valuable opportunities for skill development, knowledge acquisition, and exposure to new agricultural techniques and technologies, which can ultimately improve farm productivity and profitability (Akpalu, 2013).

Access to technical assistance has a notable impact, with positive coefficients indicating a preference for methods such as local radio talks and demonstration farms among farmers with better access to technical support. Farmers with better access to technical assistance may prefer extension methods like local radio talks due to the valuable agricultural information and guidance provided through these broadcasts. Technical assistance providers, such as agricultural extension workers or experts, often collaborate with radio stations to disseminate relevant and timely information on various agricultural topics, including pest management, crop cultivation techniques, and market trends. Farmers with access to technical support may be more receptive to these broadcasts, recognising their potential to improve their farming practices and enhance farm productivity (Frimpong-Manso et al., 2023).

Access to markets appears to influence farmers’ preferences, with positive coefficients indicating a preference for farm visits among farmers with better access to markets. According to Akpalu (2013), farmers with better access to markets may prioritise extension methods like farm visits because of their potential to provide direct and personalized assistance tailored to their specific production and marketing needs. Farm visits allow extension workers or experts to interact directly with farmers on their farms, assess their current practices, and provide targeted advice and recommendations to improve crop quality, productivity, and marketability. For farmers with access to markets, the opportunity to receive personalized guidance through farm visits may be particularly appealing as it offers practical solutions to address challenges and capitalize on market opportunities. Furthermore, farmers with better access to markets may recognise the importance of staying informed about market trends, consumer preferences, and quality standards to remain competitive in the marketplace. Farm visits provide an opportunity for extension workers or experts to share valuable market information and insights with farmers, helping them make informed decisions about crop selection, production practices, and marketing strategies to meet market demands and maximize profitability (Frimpong-Manso et al., 2023).

Perception towards the use of agricultural extension delivery service on cocoa beans quality

Table 5 represents farmers’ perception towards the use of agricultural extension delivery service on cocoa beans quality

Table 5. Perception towards the use of agricultural extension delivery service on cocoa beans quality.

Perception statements	Strongly disagree	Disagree	Neutral	Agree	Strongly agree	Mean (SD)
The agricultural extension services I have received have positively influence the quality of my cocoa beans	17(4.4)	65(16.9)	6(1.6)	165(42.9)	131(34.1)	3.86(1.19)
The information and guidance provided by the agricultural extension agents have helped me improve the quality of my cocoa beans	17(4.4)	65(16.9)	17(4.4)	144(37.5)	141(36.7)	3.87(1.22)
I believe that agricultural extension services are essential for enhancing cocoa bean quality	13(3.4)	22(5.7)	53(13.8)	144(37.5)	152(39.6)	4.06(1.01)
The agricultural extension methods I have adopted have resulted in noticeable improvements in the flavour and aroma of my cocoa beans	26(6.8)	56(14.6)	19(4.9)	142(36.9)	141(36.7)	3.85(1.23)
I trust the expertise and knowledge of agricultural extension agents in improving cocoa bean quality	9(2.3)	26(6.8)	51(13.3)	165(43.0)	133(34.6)	4.02(0.96)
The agricultural extension services I have received have helped me adopt better post-harvest practices, leading to improve cocoa bean quality	20(5.2)	57(14.8)	11(2.9)	129(33.6)	167(43.5)	3.98(1.24)
I perceive agricultural extension delivery services as valuable in addressing pest and disease control issues, thereby enhancing the quality of my cocoa beans	24(6.3)	51(13.3)	11(2.9)	138(35.9)	160(41.6)	3.94(1.24)
The agricultural extension services I have implemented have contributed to higher yield of high-quality cocoa beans	33(8.6)	49(12.7)	3(0.8)	148(38.5)	151(39.3)	3.88(1.30)
Overall Mean Index						3.93(1.18)

Source: Field Data, 2023.

The provided data in Table 5 outlines the perceptions of farmers on the use of agricultural extension delivery methods for ensuring cocoa bean quality. The overall mean index is 3.93, indicating a generally positive outlook on the impact of agricultural extension services on cocoa bean quality. The impression is constructive, as it suggests a strong basis for improving the quality of cocoa beans produced by farmers (Onumah et al., 2014; Al-Sharafat et al., 2012; Akpalu, 2013; Anang et al., 2011). The overall sentiment is one of positivity, as farmers affirm a constructive influence on cocoa bean quality resulting from the agricultural extension services received.

Specifically, they highlight the instrumental role of information and guidance provided by extension agents in improving quality. A high mean score of 4.06 further emphasises the strong belief among farmers that agricultural extension services are essential for improving cocoa bean quality. Additionally, farmers express confidence in the expertise of extension agents and recognise the tangible benefits of improved post-harvest practices and pest and disease control, contributing to elevated cocoa bean quality (Iskandar et al., 2020; Bonye et al., 2012; Benjamin et al., 2011).

Farmers acknowledge a positive influence on the quality of their cocoa beans resulting from the agricultural extension services they have received (mean: 3.86). Farmers perceive that the information and guidance provided by agricultural extension agents have been instrumental in improving the quality of their cocoa beans (mean: 3.87). There is a strong belief among farmers that agricultural extension services are essential for enhancing the quality of cocoa beans (mean: 4.06). Farmers indicate that the agricultural extension methods adopted have led to noticeable improvements in the flavour and aroma of their cocoa beans (mean: 3.85). Farmers express a high level of trust in the expertise and knowledge of agricultural extension agents in improving the quality of cocoa beans (mean: 4.02). Farmers perceive that the agricultural extension services they have received have helped them adopt better post-harvest practices, contributing to improved cocoa bean quality (mean: 3.98). This view emphasises how crucial it is to customise post-harvest advice to meet the requirements of everyone (Bello-Bravo & Lutomia, 2023). Farmers recognise the value of agricultural extension delivery services in addressing pest and disease control issues, thereby enhancing the quality of cocoa beans (mean: 3.94). Farmers attribute the implementation of agricultural extension services to a higher yield of high-quality cocoa beans (mean: 3.88). This view highlights how specialised methods are necessary for controlling diseases and pests (Fatty et al., 2021; Kitinoja & Barrett, 2015).

Constraints faced by the farmers in adopting agricultural extension delivery methods towards cocoa bean quality

Table 6 provides an analysis of the constraints that farmers faced when implementing agricultural extension delivery methods aimed at improving the quality of cocoa beans.

Table 6. Constraints faced by the farmers in adopting agricultural extension delivery methods towards cocoa bean quality.

Constraints	Mean rank	Ranking
Lack of access to modern technology and agricultural resources	4.31	4^th
Inadequate access to credit and financial services	4.98	5^th
Insufficient knowledge and awareness about the benefits of adopting agricultural extension methods for cocoa bean quality enhancement	1.26	1^st
Inadequate availability of skilled agricultural extension workers specialized in cocoa farming	1.93	2^nd
Inadequate infrastructure and logistical challenges, such as transportation and storage facilities	7.57	8^th
Limited access to markets and low market prices for cocoa beans	7.58	9^th
Inadequate post-harvest infrastructure and processing facilities	6.21	6^th
Lack of market demands for high-quality cocoa beans	7.56	7^th
Limited availability and accessibility of training and capacity-building programs for farmers on agricultural extension delivery methods	3.60	3^rd
Test Statistics N = 384; Kendall’s Wa = 0.786; Chi-Square = 2413.788; Df = 8; Asymp. Sig. 0.000

Source: Field Data, 2023.

In Table 6, the Kendall’s coefficient of concordance was 78.6%, and at the 1% level, it was statistically significant. This suggests that there was greater agreement among the farmers in the study area concerning the limitations that they had identified. The farmers ranked insufficient knowledge and awareness about the benefits of adopting agricultural extension methods for cocoa bean quality enhancement (mean rank: 1.26) as the most critical constraint. Farmers may not be aware of the potential benefits of agricultural extension services. Addressing this issue requires comprehensive awareness campaigns and education programmes to highlight how these services can improve cocoa quality. By informing farmers about the impact of extension services on cocoa quality, the adoption of these services can be significantly improved (Sinkaiye, 2005; Emmanuel et al., 2016; Feder et al., 2001).

This was followed by the inadequate availability of skilled agricultural extension workers specialising in cocoa farming (mean rank: 1.93), which was ranked as the second most critical constraint. The relatively low mean score indicates that farmers perceive this constraint as significant. The shortage of skilled extension workers hinders the delivery of effective guidance (Birkhaeuser et al., 1991; Khan et al., 2012). To address this, investments in training and the recruitment of specialised extension workers are necessary to meet the specific needs of cocoa farmers. Skilled extension workers can provide tailored guidance and support to cocoa farmers, leading to quality improvement.

Limited availability and accessibility of training and capacity-building programmes for farmers on agricultural extension delivery methods (mean rank: 3.60) and lack of access to modern technology and agricultural resources (mean rank: 4.31) were also ranked as the third and fourth most critical constraints among the farmers in the study area. The relatively low mean score indicates that farmers perceive these two constraints as less critical than some other constraints. However, it is still important. Enhanced access to training and capacity-building programmes can empower farmers to improve their knowledge and skills in cocoa quality enhancement. Farmers’ limited access to modern technology and agricultural resources hinders their ability to adopt improved practices. Access to technology can enhance productivity, pest control, and overall cocoa quality (Bello-Bravo & Lutomia, 2023; Fatty et al., 2021). Addressing this constraint may require government or industry support to provide farmers with access to modern tools and technology, which can enhance the quality of cocoa beans produced.

Poor credit and financial services access (mean rank: 4.98) was ranked as the least important constraint. Other constraints included poor post-harvest infrastructure and processing facilities (mean rank: 6.21), a lack of demand for high-quality cocoa beans in the market (mean rank: 7.56), poor infrastructure and logistical problems like transportation and storage facilities (mean rank: 7.57), and limited access to markets and low cocoa bean prices (mean rank: 7.58) The relatively high mean score suggests that farmers find these constraints substantial. Financial constraints can restrict farmers’ ability to invest in quality-improving practices (Donkor et al., 2018).

Addressing this constraint may involve creating financial support systems, microloans, or cooperative farming to enable farmers to invest in cocoa quality enhancement. The lack of suitable facilities affects the handling and processing of cocoa beans, which is vital for maintaining quality. Investment in such infrastructure is essential to reduce post-harvest losses and improve cocoa quality (Fatty et al., 2021; Tey & Brindal, 2012). Farmers believe that there is a lack of market demand for high-quality beans. Addressing this constraint requires efforts to stimulate demand for premium cocoa beans by promoting their value and quality attributes to buyers and consumers. Lack of proper infrastructure can lead to spoilage and damage. Solving logistical challenges requires investment in transportation, storage, and processing facilities, ensuring that cocoa beans are handled with care to maintain their quality (Agyei et al., 2021; Benjamin et al., 2011; Antara & Antara, 2015).

Conclusion

The findings reveal that demonstration farms are very effective for disseminating innovations to cocoa farmers. Farmers generally see agricultural extension services favorably when it comes to raising the quality of cocoa. Age, household size, years of farming, off-farm activities, access to credit and access to technical assistance statistically influence cocoa farmers’ choice of agricultural extension delivery methods. Cocoa farmers perceive that agricultural extension services exert positive influence on various aspects of cocoa production. The farmers ranked insufficient knowledge and awareness about the benefits of adopting agricultural extension methods for cocoa bean quality enhancement as their most critical constraint.

Based on the findings, there should be a concerted effort to promote the establishment of more demonstration farms within cocoa-growing regions. These farms will serve as practical learning hubs where farmers can observe and learn about new techniques and innovations firsthand. Secondly, agricultural extension services should be tailored to the specific needs and preferences of cocoa farmers. Factors such as age, household size, years of farming experience, engagement in off-farm activities, access to credit, and access to technical assistance should be taken into account when designing extension programs. By customizing these services, stakeholders can maximize their effectiveness and relevance to cocoa farmers. Raising awareness about the benefits of agricultural extension methods for enhancing cocoa bean quality is also essential. Many cocoa farmers perceive insufficient knowledge and awareness as a critical constraint to adopting these methods. To address this, stakeholders should launch awareness campaigns to educate farmers about the positive impacts of agricultural extension services on cocoa production. Workshops, seminars, and educational materials can be utilised to disseminate information and raise awareness among cocoa farmers.

Author contribution

James Aniagyei was involved in the conception and design, analysis and interpretation of the data, the drafting of the paper, revising it critically for intellectual content; and the final approval of the version to be published. John-Eudes Andivi Bakang was involved in the conception and design, analysis and interpretation of the data, the drafting of the paper, revising it critically for intellectual content; and the final approval of the version to be published. Enoch Kwame Tham-Agyekum was involved in the analysis and interpretation of the data, the drafting of the paper, revising it critically for intellectual content; and the final approval of the version to be published. Mark Arhin was involved in the conception and design, analysis and interpretation of the data, revising it critically for intellectual content; and the final approval of the version to be published. Prince Asiedu was involved in the conception and design, analysis and interpretation of the data, and the final approval of the version to be published. All authors agree to be accountable for all aspects of the work.

Disclosure statement

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.

Data availability statement

Data will be made available upon request

Additional information

Notes on contributors

James Aniagyei

James Aniagyei holds an MSc degree in Agricultural Extension and Development Communication from KNUST-Kumasi, Ghana.

John-Eudes Andivi Bakang

John-Eudes Andivi Bakang (PhD) is a Professor and lectures at the Department of Agricultural Economics, Agribusiness and Extension, KNUST-Kumasi, Ghana.

Enoch Kwame Tham-Agyekum

Enoch Kwame Tham-Agyekum (PhD) is a lecturer at the Department of Agricultural Economics, Agribusiness and Extension, KNUST-Kumasi, Ghana.

Mark Arhin

Mark Arhin is a PhD candidate with the University of South Africa, Pretoria.

Prince Asiedu

Prince Asiedu is an MSc student at University of Passau, Germany.