Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

Label_Title

Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

Label_Title

Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

Label_Title

Dear Label_Name,

Greetings! I hope you are well.

I am writing on behalf of an international publishing house, Science and Education Publishing, USA (http://www.sciepub.com).

Science and Education Publishing (SciEP) is a multidisciplinary open access publisher of Journals covering the fields of Science, Technology and Medicine. All SciEP publications are subject to high-quality peer review, editorial and production processes and are freely available online.

Welcome to the April, 2013 edition of SciEP newsletter, providing monthly summaries of articles on science & education related topics of interest to scientist and professionals. The mission of our journals is to provide an open access forum for publishing new findings on issues pertinent to scientific and academic field. We also invite you to submit original research papers, review articles, short reports, or case studies are considered for publication in our Multidisciplinary Journals.

American Journal of Public Health Research
DOI: 10.12691/ajphr-1-1-3

The Implementation of a Tele-Homecare System with Service Oriented Architecture and HL7 Message Transmission Standard
Ching-Sung Wang

American Journal of Medical and Biological Research
DOI: 10.12691/ajmbr-1-1-2

The Influence of Ethanol on Pyruvate Kinases Activity in Vivo, in Vitro, in Silico
Lelevich Sergey Vladimirovich, Khrustalev Vladislav Victorovich, Barkovsky Eugene Victorovich, Shedogubova Tatyana Aleksandrovna

American Journal of Medical Sciences and Medicine
DOI: 10.12691/ajmsm-1-1-1

Clinical and Histological Features in Wegener Granulomatosis
Li Cavoli Gioacchino, Passantino Rita, Bono Luisa, Tortorici Calogera, Ferrantelli Angelo, Giammarresi Carlo, Rotolo Ugo

American Journal of Infectious Diseases and Microbiology
DOI: 10.12691/ajidm-1-1-2

The Curse of Lymphatic Filariasis: Would the Continual Use of Diethylcarbamazine Eliminate this Scourge in Papua New Guinea?
Anne Lanham, Lillian Mwanri

We have released issues of some journals on February 2013.
American Journal of Public Health Research
American Journal of Medical Sciences and Medicine
American Journal of Medical and Biological Research
American Journal of Pharmacological Sciences
American Journal of Clinical Medicine Research
American Journal of Nursing Research
American Journal of Cancer Prevention
American Journal of Medicine Studies
American Journal of Infectious Diseases and Microbiology
American Journal of Cardiovascular Disease Research
American Journal of Biomedical Research
American Journal of Mechanical Engineering
American Journal of Microbiological Research
Journal of Preventive Medicine
American Journal of Food and Nutrition
American Journal of Hypertension Research
American Journal of Sports Science and Medicine
Journal of Cancer Research and Treatment
American Journal of Zoological Research
Journal of Biomedical Engineering and Technology
American Journal of Microbiological Research
Biomedical Science and Engineering

We are pleased to announce that submissions are now being accepted for peer review for all the journals across all our subject areas in SciEP.

Please submit your manuscript via our online submission system:
http://www.sciepub.com/author/signin

To unsubscribe from our mailing list, please click on the following link: unsubscribe

I am looking forward to hearing from you.

Kind regards,

David Rain
Email:join@sciepub.com
Science and Education Publishing (http://www.sciepub.com)

The Influence of Agricultural Technology Adoption on Coffee Productivity: Case of Smallholder Farmers in Mouamenam, Littoral Region of Cameroon

Njoume Ngolle Verdi*, Fomena Neuchy Larissa

Independent Researcher, Bamenda, North West Region, Cameroon

Abstract: : This study examines the influence of agricultural technology adoption on coffee productivity among smallholder farmers in Mouamenam. Using a quantitative cross-sectional design, data were collected from 358 farmers via structured questionnaires. The study focuses on key agricultural technologies including improved coffee seedlings, fertilizer and pesticide use, mechanized tools, and access to extension services. Coffee productivity was measured in kilograms per hectare. Empirical analysis employed Ordinary Least Squares (OLS), log-linear models, Propensity Score Matching (PSM), and Instrumental Variables (IV)/Two-Stage Least Squares (2SLS) to account for potential endogeneity. OLS results indicate that improved seedling adoption (IS) increases productivity by 48.5% (β = 0.485, p < 0.01), mechanized tools (MT) by 32.3% (β = 0.323, p < 0.01), fertilizer and pesticide use (ln_FPU) by 9.2% (β = 0.092, p < 0.01), and extension contacts (ESC) by 12.4% per visit (β = 0.124, p < 0.01). Access to credit (AC) and cooperative membership (Coop) also positively affect productivity (β = 0.211 and β = 0.131, p < 0.01). IV/2SLS results confirm the causal impact of improved seedling adoption (β = 4.286, p = 0.020) and mechanization (β = 0.809, p < 0.01), while fertilizer and pesticide use shows a weak negative effect (β = -1.219, p < 0.10). PSM results further reveal that experience (β = 0.052, p < 0.05) and fertilizer use (β = 8.568, p < 0.01) significantly increase adoption likelihood. Findings underscore that technology adoption, mechanization, and institutional support are key drivers of coffee productivity, while structural constraints and inefficient input use continue to limit potential gains. The study recommends large-scale distribution of improved coffee seedlings, strengthened extension services, expanded credit access, and revitalization of farmer cooperatives to enhance adoption and sustainably increase coffee productivity in Mouamenam.

Keywords: Agricultural technology adoption, Coffee productivity, Smallholder farmers, Propensity score matching, Instrumental variables, Extension services, Cameroon

Received March 10, 2026; Revised April 12, 2026; Accepted April 19, 2026

Citation: Njoume Ngolle Verdi, Fomena Neuchy Larissa, The Influence of Agricultural Technology Adoption on Coffee Productivity: Case of Smallholder Farmers in Mouamenam, Littoral Region of Cameroon. Journal of Applied Agricultural Economics and Policy Analysis, 7 (1). 13-24. January 2026. doi:10.12691/jaaepa-7-1-1

1. Introduction

Agricultural productivity improvement has consistently been linked to the adoption of modern technologies across both developed and developing economies [1] [2]. Traditional agriculture remains inefficient without technological innovation [4]. Also, improved inputs significantly raise farm output [3]. Limited adoption of available technologies constrains productivity among smallholder farmers due to risk and information barriers [5]. Technology adoption remains central to achieving sustainable agricultural growth in Sub-Saharan Africa [6].

Technological stagnation, limited access to modern inputs are major constraint to coffee yield and quality in developing countries [7] [8]. Improved varieties and farm practices boost productivity [9]. According to [10], enhancing technology adoption among smallholder coffee farmers is essential for improving yields, incomes, and long-term sustainability. Coffee production significantly contributes to economic development, and the ideal coffee plantation is characterized by increased yields [11]. As a potential country source, coffee has a European and American market share with competitive prices especially for Arabica and Robusta varieties [12].

Coffee belongs to the Rubiaceae family, and the Coffea arabica species, in particular, is known for its pleasant taste, distinctive aroma, health benefits, and stimulating effects attributed to its high caffeine content [13]. Commercial coffee cultivation began in Yemen during the 14th century. Coffee cultivation is significantly more profitable compared to other crops, offering higher income potential, superior unit value, and robust profitability across various production systems [14] [15]. Coffee farming is notably more labor-intensive than alternative crops, with research highlighting its high labor requirements and substantial gross margins [16].

Coffee production is concentrated in a handful of tropical and subtropical countries. Brazil dominate the global coffee market, producing an estimated 3.0 million metric tons of coffee in 2025, making it the world’s largest producer by a wide margin, followed by Vietnam with around 1.7 million tons and Colombia with roughly 885,000 tons annually. Other key producers include Indonesia (761,000 tons), Ethiopia (482,600 tons), and Honduras (476,300 tons), which together account for a significant share of global output and export earnings.

In Cameroon, coffee is cultivated in West, Northwest, Southwest, and Littoral regions. The country’s production has significantly declined by over 130,000 metric tons in the late 1980s to less than 40,000 metric tons by the year 2005, reflecting long-term structural challenges [17]. Cameroon produces mainly Robusta (about 80%) and Arabica (20%), with smallholder farmers accounting for more than 90% of total production. The International Coffee Organization [18] notes that Cameroon’s yields remain well below global averages, largely due to aging plantations, limited use of improved varieties, and low adoption of modern production technologies.

In response to declining performance, the Cameroonian government has introduced several institutional and legal reforms aimed at revitalizing the coffee sector. The establishment of the Interprofessional Council for Cocoa and Coffee (CICC) under Law No. 2006/025 was intended to regulate production, improve quality, and enhance farmer incomes through better coordination of stakeholders. Additionally, the Ministry of Agriculture and Rural Development (MINADER) has promoted programs such as the National Coffee and Cocoa Development Strategy (2014-2025) to distribute improved seedlings and strengthen extension services. However, as observed by the [6], implementation gaps, weak monitoring, and insufficient funding have limited the effectiveness of these policies, resulting in minimal impact on productivity growth at the farm level.

Despite policy efforts, Cameroon coffee sector continues to face persistent failures and constraints that undermine productivity and competitiveness. Studies by [19] [20] [21] reported that poor access to credit, high input costs, weak farmer organizations, and inadequate extension services are major obstacles to technology adoption in agricultural sector. Also, climate variability, including irregular rainfall and rising temperatures, has further reduced yields, particularly among resource-constrained smallholders [22]. Additionally, market inefficiencies and price volatility discourage reinvestment in coffee farming, prompting many farmers to abandon coffee cultivation for food crops. These challenges underscore the need for localized research, such as in Mouamenam, to understand how agricultural technology adoption can improve coffee productivity and support rural livelihoods.

2. Research Objectives

Main Objective

To investigate the influence of agricultural technology adoption on coffee productivity: Case of smallholder farmers in Mouamenam, Littoral Region of Cameroon

Specific Objectives

i. To investigate the influence of improved coffee seedlings on coffee productivity in Mouamenam.

ii. To assess the effect of fertilizer and pesticide use on coffee productivity in Mouamenam.

iii. To evaluate the effect of mechanized farming tools on coffee productivity in Mouamenam.

iv. To assess the role of extension services and farmer training in enhancing coffee productivity in Mouamenam.

Review of Related Literature

Agricultural Technology Adoption (ATA) and Modern Farm Practices (MFP) have been widely recognized as key determinants of Agricultural Productivity (AP). Early foundational studies by [4] [8] emphasized that traditional agriculture in developing countries is often inefficient without the adoption of new technologies and inputs. Similarly, [3] argued that factors such as access to credit, education, and extension services critically influence farmers’ willingness and capacity to adopt innovations. [5] provided empirical evidence that proper input use, including fertilizers, can significantly boost crop yields, highlighting the high returns to technology adoption in smallholder systems.

In Cameroon, [19] [20] demonstrate that access to improved coffee seedlings, fertilizers, extension services, and efficient farm management significantly enhances productivity among smallholder coffee farmers. Likewise, [21] showed that value addition, cooperative participation, and market access are essential for improving both productivity and profitability within the coffee value chain. Globally, studies in Brazil and Indonesia [11] [12] [14] highlighted the role of irrigation, pruning, and shading practices in raising coffee yields, reinforcing the broader applicability of agronomic technologies for cash crop productivity.

At farm-level, survey data demonstrate that adoption of modern agricultural technologies including improved varieties, fertilizers, and management practices leads to higher yields and income [3] [23]. Specifically, in coffee systems, [24] found that adoption of improved coffee technologies in Ethiopia significantly increased productivity compared to traditional practices. Similarly, [25] revealed that improved coffee seedlings contributed to higher yield potential and resilience against pests and diseases. [26] further showed that access to improved planting material combined with complementary inputs had a strong positive effect on coffee output. Regarding fertilizer and pesticide use, [27] found that appropriate fertilizer application significantly increased crop yields among smallholders, while improper use limited benefits.

Also, mechanized farming tools reduce labour constraints and post-harvest losses, particularly in labour-intensive perennial crops such as coffee [28]. Extension services and farmer training consistently emerge as strong determinants of productivity. [29] demonstrated that effective agricultural extension significantly improves technology adoption and farm performance. In coffee-specific studies, [30] found that farmers receiving regular extension advice achieved higher yields through improved management practices. Similarly, [31] showed that farmer field schools enhanced knowledge, adoption of innovations, and productivity outcomes.

Evidence from Uganda further shows that farmers who adopted recommended coffee management technologies, including improved varieties and soil fertility practices, achieved higher yields compared to non-adopters [32]. In Kenya, [33] found that rejuvenated coffee trees and improved seedlings contributed to improved yield performance, especially when supported by fertilizer application. Studies examining fertilizer and pesticide use also confirm positive productivity effects when inputs are applied correctly; [27] showed that appropriate fertilizer use significantly increased crop yields among East African smallholders, while misuse reduced efficiency.

At the macro level, coffee production and global trade are shaped by structural, institutional, and market dynamics. [7] showed that international agreements and trade policies influence producing countries’ market access and income stability. More recent analyses by [15] demonstrate the dominant role of large agri-food corporations in shaping global coffee markets, with implications for smallholder competitiveness. Regional studies by [16] [22] emphasize the importance of agroforestry practices, labor allocation, and integrated farming systems in enhancing sustainability and efficiency. Collectively, these studies indicate that coffee productivity is not only determined by farm-level technological adoption but also by broader institutional support, market structures, and socio-environmental factors, highlighting the need for comprehensive strategies to improve smallholder outcomes.

Figure 1. Agricultural Technology Adoption and Coffee Productivity

The conceptual framework in figure 1 illustrates the relationship between agricultural technology adoption and coffee productivity. It shows that the adoption of various agricultural technologies specifically improved coffee seedlings, fertilizer and pesticide use, mechanized tools, and extension services and training serves as the independent variable influencing coffee productivity, which is the dependent variable. The diagram suggests that each of these technological interventions contributes to enhancing the efficiency and output of coffee farming. Improved seedlings aim to increase yield potential, fertilizers and pesticides optimize plant health and reduce losses, mechanized tools and irrigation improve farm operations and water management, while extension services and training enhance farmers’ knowledge and skills. Collectively, these factors are hypothesized to positively affect coffee productivity, highlighting the critical role of technology adoption in boosting agricultural performance in Mouamenam.

3. Methodology of the Study

Area of the Study

Mouamenam is situated in Manjo Subdivision identified by the administrative code 5.1.5.3, Moungo Division of the Littoral Region of Cameroon, an area historically known for plantation and smallholder agriculture. Geographically, the locality lies within the humid forest agro-ecological zone of the Littoral region which is favorable for perennial cash crops such as coffee. The area experiences an equatorial climate characterized by high annual rainfall ranging between 2,000 and 3,000 mm, average temperatures of about 24-27°C, and high relative humidity throughout most of the year [34] [35]. These climatic conditions provide adequate moisture for coffee growth and reduce irrigation dependence. The soils in Mouamenam are predominantly volcanic and ferrallitic, derived from Mount Cameroon volcanic deposits, making them relatively fertile and suitable for coffee cultivation. The relief is gently undulating with moderate slopes, allowing traditional coffee farming practices while also accommodating improved agronomic techniques. Mouamenam comprises nine (9) villages Abang, Badjoungué, Mouakoumel, Mouandong, Moumekeng, Ndom, Njoumbeng I and II and Nsoung-Mouasoum linked by feeder roads to Manjo town and nearby markets.

From a socio-economic perspective, Mouamenam is predominantly rural, with agriculture as the main livelihood for the population. Coffee farming constitutes the principal economic activity, practiced mainly by smallholder farmers on family-owned plots averaging less than two hectares [36]. Other crops such as plantain, cassava, maize, and cocoyam are cultivated mainly for subsistence and local trade. Coffee production systems in the area are largely traditional, though varying levels of technology adoption exist, including the use of improved seedlings, fertilizers, pesticides, and limited mechanized tools. Access to extension services is present but uneven, with some farmers benefiting from training programs while others rely on indigenous knowledge [37]. There exist a few number of farmer cooperatives and informal producer groups that support input access and marketing. These physical and socio-economic characteristics make Mouamenam a relevant setting for assessing how technology adoption, mechanization, and extension services influence coffee productivity among smallholder farmers.

Research Design

This study adopts a quantitative cross-sectional research design to examine the influence of agricultural technology adoption on coffee productivity among smallholder farmers in Mouamenam, Littoral Region of Cameroon. A quantitative approach was appropriate because it allows for the measurement of relationships between clearly defined variables and enables hypothesis testing using statistical techniques [38] [39] [40]. Cross-sectional designs are widely used in agricultural economics studies to capture farm-level production and technology adoption behavior at a single point in time, particularly where panel data are unavailable [40]. This design is suitable for achieving the study’s objectives, as it facilitates empirical assessment of how improved seedlings, fertilizer and pesticide use, mechanization, and extension services influence coffee productivity among smallholder farmers.

Target Population of the Study

The target population for this study comprises all smallholder coffee farmers in Mouamenam Canton who are actively engaged in coffee production during the 2024/2025 agricultural season. The canton has a total population of 3,397 inhabitants, comprising 1,591 males and 1,806 females, and is predominantly rural [41]. Smallholder farmers are generally characterized by limited landholdings and reliance on family labor, although farm size thresholds vary across contexts [42]. In this study, smallholders are operationally defined as farmers cultivating less than two hectares of coffee. Focusing on smallholders is justified because they account for the majority of coffee producers in Cameroon and are most affected by constraints related to technology adoption, access to inputs, and extension services [19] [20].

Sampling Technique and Sample Size Determination

A multi-stage sampling technique was employed to select respondents for the study. In the first stage, Mouamenam Canton was purposively selected due to its strong dependence on coffee production and observable variations in technology adoption among farmers. In the second stage, villages within the canton were considered as sampling strata to ensure adequate representation across the study area. In the final stage, simple random sampling was used to select individual coffee farmers from village-level farmer lists obtained from local agricultural extension office located in Moumekeng village. Random sampling minimizes selection bias and enhances the representativeness of the sample [43].

The sample size was determined using [44] formula, which is widely applied in social science and agricultural studies when dealing with finite populations:

where n is the sample size, N is the population size, and e is the level of precision. Using an estimated population of coffee-farming households within the canton and a 5% margin of error, a statistically adequate sample size was obtained to allow for reliable estimation and inference. This approach is consistent with empirical studies on technology adoption in smallholder agriculture [3] [23].

Methods of Data Collection

Primary data were collected using structured questionnaires administered directly to sampled smallholder coffee farmers. The questionnaire captured quantitative information on socio-economic characteristics, farm size, coffee yields, adoption of improved coffee seedlings, fertilizer and pesticide use, mechanized tools, and access to extension services and farmer training. Structured questionnaires are suitable for quantitative studies because they ensure uniformity of responses and facilitate statistical analysis [45]. Secondary data were obtained from reports published by FAO, MINADER, the International Coffee Organization, and previous academic studies to provide contextual and supporting information.

Definition and Measurement of Variables

In Table 1 below, the dependent variable coffee productivity, was measured as total coffee output per hectare (kg/ha), a standard indicator of agricultural productivity used in empirical studies [8] [23]. The key independent variables include adoption of improved coffee seedlings (measured as a binary variable indicating use or non-use), fertilizer and pesticide use (measured by quantity applied per hectare), mechanization (measured by ownership or use of farm tools facilities), and extension services and training (measured by frequency of extension contact or participation in training programs). Control variables were included to account for socio-economic and farm-specific characteristics that may influence productivity, consistent with prior agricultural productivity studies [3] [30].

Table 1. Measurement and Definition of Variables Used in the Models

Econometric Models and Estimation Techniques

The study employed Ordinary Least Squares (OLS) regression to estimate the specified model. OLS is appropriate when the dependent variable is continuous and the objective is to estimate the magnitude and direction of relationships between explanatory variables and productivity [40]. OLS has been extensively used in empirical studies on agricultural technology adoption and productivity due to its simplicity, efficiency, and ease of interpretation [23] [24]. Diagnostic tests, including checks for multicollinearity, heteroscedasticity, and model specification errors, were conducted to ensure the reliability and validity of the estimated results. Where necessary, robust standard errors were applied to correct for heteroscedasticity, following best econometric practice (Greene, 2018).

Baseline Ordinary Least Squares (OLS) Model

To examine the association between technology adoption and coffee productivity in Mouamenam, a baseline Ordinary Least Squares (OLS) regression model is estimated as follows: where CP denotes coffee productivity per hectare and is the error term. Robust standard errors are used to correct for heteroskedasticity.

[picture] | (1.1)

Log-Linear Productivity Model

To allow elasticity interpretation and reduce skewness in the dependent variable, a log-linear model is estimated: in this specification, the coefficients represent semi-elasticities of coffee productivity with respect to technology adoption variables.

[picture] | (1.2)

Propensity Score Matching (PSM) Model

Because farmers self-select into technology adoption, propensity score matching (PSM) is employed to estimate the causal effect of improved coffee seedlings adoption (IS) on coffee productivity.

Treatment Variable

The propensity score is estimated using a logit model:

[picture] | (1.3)

where xi includes age, gender, education, farming experience, farm size, access to credit, household labour, cooperative membership, mechanization, fertilizer use, and extension contacts.

The average treatment effect on the treated (ATT) is computed as:

[picture] | (1.4)

Instrumental Variables (IV) / Two-Stage Least Squares (2SLS) Model

To address potential endogeneity arising from reverse causality and omitted variable bias in technology adoption, an instrumental variables (IV) approach is applied using two-stage least squares (2SLS).

First Stage

[picture] | (1.5a)

where ESC, AC, and Coop serve as instruments, and Zi represents exogenous controls.

Second-Stage Equation (Productivity Equation)

[picture] | (1.5b)

Here, standard errors are made robust to heteroscedasticity, where is the predicted value of technology adoption obtained from the first-stage regression. the dependent variable is the natural logarithm of coffee productivity per hectare.

Estimation Details

Estimation is conducted using two-stage least squares (2SLS). Standard errors are clustered at the village level to account for intra-village correlation. The coefficient on measures the causal effect of technology adoption on coffee productivity. The IV/2SLS estimator corrects for bias due to endogeneity in technology adoption, ensuring that the estimated impact of adoption on coffee productivity reflects causal effects rather than selection bias or omitted variables.

Diagnostic and Robustness Tests

To assess model validity, several diagnostic tests are conducted. Multicollinearity is examined using the variance inflation factor (VIF) following [42]. Although age and farming experience show relatively higher VIF values due to correlation, the mean VIF remains within acceptable limits, indicating no serious multicollinearity. Functional form adequacy is tested using the Ramsey Reset Test [43], which suggests potential misspecification and motivates the use of a log-linear specification. residual normality is assessed using the Shapiro-Wilk Test [43] while strict normality is rejected, this does not compromise inference in large cross-sectional samples.

To address heteroscedasticity, all regressions employ heteroscedasticity-robust standard errors as proposed by [44]. potential endogeneity in technology adoption is handled using an instrumental variables approach estimated by two-stage least squares (2SLS). Instrument relevance is evaluated using first-stage f-statistics, consistent with [45], with values below conventional thresholds, suggesting potential weak instrument concerns. together, these tests support the robustness and credibility of the empirical results.

Analyses and Interpretation of Findings

The descriptive results in Table 2 indicate that coffee productivity among farmers in Mouamenam remains relatively low, with clear differences across households. The average output is about 545 kg per hectare, but values range widely from 119 kg to 1,251 kg per hectare, which indicates strong variation in farm performance.

The level of technology adoption is still limited. Only about 28% of farmers use improved coffee seedlings, while mechanization is used by about 16% of farmers, showing that most producers still rely on traditional methods. Fertilizer and pesticide use is around 65 kg per hectare on average, but the variation is large, meaning usage is not uniform across farmers. Extension services are also weak, with farmers receiving roughly one visit per year, which shows limited technical support.

Table 2. Descriptive Statistics of Study Variables (n=358)

In terms of socio-economic characteristics, farmers are relatively old, with an average age of 55 years and long farming experience of over 30 years. Education is relatively low, with an average of about 6 years of schooling, which may limit understanding and adoption of improved practices. The average farm size is small (1.26 hectares), confirming the dominance of smallholder farming. Access to credit is low (24%), and only about 20% belong to cooperatives, which indicates weak institutional support. Household labor is relatively available, suggesting that labor is not the main constraint.

The OLS results in Table 3 indicate that agricultural technology adoption plays an important role in improving coffee productivity. Improved seedling adoption has a strong positive and significant effect (β = 0.485, p < 0.01), meaning that farmers who adopt improved seedlings obtain higher yields compared to those who do not. Fertilizer and pesticide use also increases productivity (β = 0.092, p < 0.01), while mechanization indicates a strong positive effect (β = 0.323, p < 0.01), confirming that modern inputs improve output. Extension services also contribute positively (β = 0.124, p < 0.01), showing that farmers benefit from technical advice. Access to credit (β = 0.211, p < 0.01) and cooperative membership (β = 0.131, p < 0.01) are also significant, meaning that institutional support helps farmers improve productivity. Education has a positive effect (β = 0.011, p < 0.01), while farming experience has a small negative effect (β = -0.002, p < 0.05), which may reflect the fact that older farmers tend to stick to traditional practices. Gender, farm size, and household labor are not statistically significant. The model explains about 70% of the variation in productivity (R² = 0.704), which shows good explanatory power. Diagnostic tests also show no serious multicollinearity problems. However, normality and specification tests indicate some issues, which is common in cross-sectional farm data and does not affect the main results.

Table 3. Determinants of Coffee Productivity: Log-Linear OLS Estimates

Table 4 presents the propensity score estimation results used to construct comparable groups of adopters and non-adopters. Farming experience increases the likelihood of adoption (β = 0.052, p < 0.05), meaning experienced farmers are more likely to try new practices. Education also has a positive effect, although weakly significant (β = 0.086, p < 0.10), suggesting that schooling helps farmers understand new technologies. Farm size also matters (β = 0.925, p < 0.10), meaning farmers with larger farms are more willing to adopt, likely because they can better manage risks. Fertilizer and pesticide use is strongly significant (β = 8.568, p < 0.01), showing that farmers who already use modern inputs are more likely to adopt improved technologies. Extension contact also increases adoption (β = 0.501, p < 0.10), confirming the importance of information sharing. Gender, credit access, cooperatives, and household labor are not significant, showing that these factors do not strongly influence adoption decisions in the study area. Mechanization has a negative but insignificant effect, which may be due to high cost and limited access. The model fits well with a Pseudo R² of 0.716, indicating strong explanatory power.

Table 4. Propensity Score Model (PSM) Results

Figure 2 presents the standardized percentage bias across covariates before and after propensity score matching, used to assess the quality of balance between technology adopters and non-adopters. The results indicate that matching substantially improves covariate balance, with most variables clustering close to the zero line. Key institutional and production-related variables such as access to credit, cooperative membership, fertilizer and pesticide use, mechanization, extension contacts, and farm size show very small post-matching biases, suggesting that observable differences between the treated and control groups have been largely eliminated. However, moderate residual imbalances persist in education level and farming experience, indicating that adopters tend to possess relatively higher human capital compared to non-adopters. Gender and household labor also show minor remaining differences but remain within acceptable balance thresholds. The standardized bias plot confirms that the matching procedure significantly reduces selection bias and improves comparability between groups, thereby supporting the validity of the propensity score matching approach and strengthening the reliability of the estimated treatment effects.

The IV results in Table 5 confirm that technology adoption has a strong effect on coffee productivity, even after controlling for endogeneity. Improved seedling adoption has a positive and significant effect (β = 4.286, p < 0.05), showing a strong productivity gain among adopters. Mechanization also improves productivity (β = 0.809, p < 0.01), confirming its importance. Fertilizer and pesticide use has a negative and weak effect (β = -1.219, p < 0.10), which may be due to poor application practices or soil degradation. Farming experience remains negative and weakly significant (β = -0.014, p < 0.10). The endogeneity test confirms that adoption is not exogenous (p < 0.01), meaning OLS would be biased. The overidentification test shows that instruments are valid (p = 0.543), although the first-stage results suggest that the instruments are not very strong. Despite this limitation, the results still provide useful evidence.

Table 5. IV/2SLS Regression (causal effects)

Figure 2. PSM Standardized Bias Plot

The clustered OLS/Log-Linear results (Table 6), with standard errors adjusted at the village level (nine villages in Mouamenam), remain highly consistent with the baseline findings and confirm the robustness of the estimated relationships. adoption of improved coffee seedlings (IS) continues to exhibit a strong and statistically significant association with coffee productivity (β = 0.485, p < 0.01), implying that adopters achieve approximately 48 percent higher output per hectare relative to non-adopters, holding other factors constant. Fertilizer and pesticide use (ln_FPU), mechanization (MT), and extension contacts (ESC) all retain positive and statistically significant effects at conventional levels, indicating that both modern inputs and advisory services play critical roles in enhancing productivity in the study area. Access to credit (AC) and Cooperative Membership (Coop) also remain positive and highly significant (p < 0.01), underscoring the importance of institutional support mechanisms in overcoming liquidity and coordination constraints faced by smallholder coffee farmers. Education significantly improves productivity (β = 0.011, p < 0.01), while farming experience shows a weakly negative effect, suggesting diminishing returns to experience in an aging production system characterized by old coffee trees and limited technological renewal. Gender, farm size, and household labor remain statistically insignificant, indicating that productivity differentials are driven primarily by technology adoption and institutional access rather than scale or demographic characteristics.

Table 6. Effects of Technology Adoption on Coffee Productivity (Clustered by Village)

The clustered IV/2SLS results further strengthen the causal interpretation of technology adoption effects after accounting for both endogeneity and intra-village correlation. The coefficient on improved seedling adoption remains positive and economically large (β = 4.286) and is marginally significant at the 10 percent level (p = 0.063), reflecting the reduced precision that typically accompanies village-level clustering with a small number of clusters. mechanization continues to exert a positive and statistically significant effect on productivity (β = 0.809, p < 0.05), while farming experience again shows a weakly negative association. fertilizer and pesticide use loses statistical significance once endogeneity and clustering are addressed, suggesting that its apparent benefits in OLS may partly reflect selection effects rather than true productivity gains. the joint significance of the IV model (wald chi² = 61.95, p < 0.01) indicates strong overall explanatory power. Taken together, the clustered results confirm that technology adoption particularly improved seedlings and mechanization causally enhances coffee productivity in Mouamenam, while also highlighting persistent structural and institutional constraints that limit the effectiveness of complementary inputs at the village level.

4. Discussion of Findings

This study examined the influence of agricultural technology adoption on coffee productivity among smallholder farmers in Mouamenam, Littoral Region of Cameroon. Empirical results from OLS/log-linear regression, propensity score matching, and IV/2SLS estimations consistently indicate that technology adoption significantly enhances coffee productivity, addressing the study’s main objective. In particular, adoption of improved coffee seedlings shows a positive and statistically significant effect on yield in both OLS and clustered regressions, supporting the hypothesis that modern planting materials enhance productivity. This effect is further strengthened in the IV/2SLS estimates, which reveal a larger causal impact after correcting for endogeneity, suggesting that OLS results underestimate the true productivity gains from adoption. These findings are consistent with previous studies [3] [25] [24] that highlight the productivity-enhancing effects of improved varieties in perennial crops.

With respect to the specific objective, the results confirm that farmers using improved seedlings achieve significantly higher output per hectare than non-adopters. The stronger IV estimates further suggest that adoption is constrained by factors such as liquidity, access to information, and risk considerations [5] [23]. Field evidence from Mouamenam supports this, as farmers reported limited access to certified seedlings due to high costs, weak cooperative systems, and inadequate distribution channels. The findings emphasize that improving access to quality planting material is critical for enhancing coffee productivity in the study area.

Regarding the second objective, fertilizer and pesticide use shows a positive and statistically significant association with productivity in the OLS and PSM models, but this effect weakens and turns negative in the IV framework. This divergence suggests that the apparent benefits of chemical inputs in naive models may reflect selection effects rather than true productivity gains. Similar findings have been reported by [27], who emphasized that inappropriate input use limits yield response. In Mouamenam, qualitative observations revealed irregular application rates, limited soil testing, and credit constraints, which likely reduce the effectiveness of fertilizers and pesticides. These results highlight that chemical inputs alone are insufficient and must be combined with training and proper agronomic practices to generate sustainable productivity gains.

The third objective examined the effect of mechanized farming tools on coffee productivity. mechanization exhibits a positive and statistically significant impact across all model specifications, including IV/2SLS, confirming its robustness. This finding is consistent with [8] [16], who showed that labor-saving technologies improve efficiency in labor-intensive perennial crops. In Mouamenam villages, mechanization mainly takes the form of basic tools such as motorized sprayers and processing equipment, which reduce drudgery and post-harvest losses. Given rising rural labor shortages and aging farmer populations, mechanization appears to be a critical pathway for sustaining coffee production in the area.

The fourth specific objective assessed the role of extension services and farmer training. Extension contact shows a strong and statistically significant effect on productivity in the OLS and PSM models and plays an important role as an instrument in the IV estimation. This finding supports the work of [29] [30] [31], who emphasize that extension enhances farmers’ ability to adopt and effectively use new technologies. In Mouamenam, extension services are unevenly distributed across villages, and farmers with regular contact demonstrate better farm management and higher yields. The results therefore validate the hypothesis that extension services are a crucial complement to technology adoption rather than a substitute.

Institutional variables such as access to credit and cooperative membership are also found to significantly enhance productivity in the OLS and Clustered Regressions, confirming the importance of institutional support in technology adoption decisions. These findings are consistent with [3] [21] [26], who argue that credit and collective action reduce adoption barriers. The low cooperative participation rates observed in Mouamenam help explain the generally low adoption levels and reinforce the need for stronger farmer organizations. Socio-demographic factors such as education positively influence productivity, while farming experience shows a negative effect, reflecting diminishing returns to experience in aging systems, as noted by [37]. gender, farm size, and household labor are not statistically significant, suggesting that productivity differences are driven more by technology and institutional access than by scale or demographics, a finding consistent with smallholder-dominated systems in Sub-Saharan Africa.

The hypotheses that technology adoption improves coffee productivity and that institutional and extension support enhance adoption outcomes are strongly supported by the empirical evidence. The consistency of results across multiple econometric techniques strengthens the credibility of the findings and reflects the lived realities of farmers across Mouamenam.

5. Conclusion

This study examined the influence of agricultural technology adoption on coffee productivity among smallholder farmers in Mouamenam, Littoral Region of Cameroon. Using farm-level data and a combination of OLS/log-linear regression, Propensity Score Matching (PSM), and instrumental variable (IV/2SLS) estimation techniques, the study provides robust empirical evidence that agricultural technology adoption particularly improved coffee seedlings and mechanization significantly enhances coffee productivity. In addition, institutional and support factors such as extension services, access to credit, and cooperative membership play an important role in strengthening productivity by facilitating technology adoption and promoting more efficient inputs use. However, the findings also reveal persistent structural constraints, including inefficient fertilizer use, aging plantations, weak farmer organizations, and limited institutional support, which continue to constrain productivity gains. Importantly, the results suggest that productivity challenges in Mouamenam are driven less by land scarcity and more by low and uneven adoption of modern agricultural technologies. Addressing these structural and institutional barriers is therefore essential for revitalizing coffee production, improving rural livelihoods, and promoting sustainable agricultural development in the region.

Policy Recommendations

Based on the findings of this study, several policy and practical recommendations are proposed.

i. First, government agencies and development partners should prioritize the large-scale distribution of certified improved coffee seedlings, alongside targeted plantation rejuvenation programs aimed at replacing aging coffee farms. This would directly address one of the key structural constraints identified in the study.

ii. Second, agricultural extension services should be strengthened and decentralized to ensure regular and effective farmer engagement across all villages. Emphasis should be placed on delivering integrated technology packages rather than promoting isolated input use, thereby improving overall adoption efficiency.

iii. Third, access to affordable credit should be expanded through rural financial institutions and cooperative-based lending schemes. Enhancing financial inclusion will enable farmers to invest in productivity-enhancing inputs and mechanized technologies.

iv Fourth, farmer cooperatives should be revitalized and institutionally supported to improve access to inputs, facilitate knowledge sharing, and strengthen collective marketing systems. This would reduce transaction costs and mitigate adoption risks faced by smallholder farmers.

v. Finally, capacity-building programs should focus on improving farmers’ technical knowledge in areas such as proper fertilizer and pesticide application, soil fertility management, and climate-resilient agricultural practices. Strengthening these competencies is essential for enhancing input efficiency and ensuring long-term sustainability.

vi The effective implementation of these recommendations would significantly improve coffee productivity in Mouamenam and contribute to broader objectives of poverty reduction, rural development, and agricultural transformation in Cameroon.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the support of all smallholder coffee farmers in Mouamenam who participated in this study by providing valuable data and time during the survey process. Their cooperation made this research possible.

The authors also appreciate the support of field enumerators and local agricultural extension officers who assisted in data collection and facilitation during the fieldwork period.

The authors acknowledge the moral support provided by their families during the course of this research.

No specific external funding was received for this research.

Statement of Competing Interests

The authors declare that there are no competing interests, either financial or non-financial, that could have influenced the design of the study, data collection process, analysis, interpretation of results, or preparation of this manuscript.

References

[1]   | Food and Agriculture Organization of the United Nations. The Future of Food and Agriculture: Trends and Challenges. FAO, 2017.
[2]   | World Bank. World Development Report 2008: Agriculture for Development. World Bank, 2008.
[3]   | Feder, Gershon, Richard E. Just, and David Zilberman. "Adoption of agricultural innovations in developing countries: A survey." Economic development and cultural change 33.2 (1985): 255-298.
[4]   | Schultz, Theodore William. "Transforming traditional agriculture." (1964): 212-pp.
[5]   | Duflo, Esther, Michael Kremer, and Jonathan Robinson. "How high are rates of return to fertilizer? Evidence from field experiments in Kenya." American economic review 98.2 (2008): 482-488.
[6]   | World Bank. Transforming Agriculture in Africa and South Asia: Improving Inputs, Markets, and Policies. World Bank, 2020.
[7]   | Akiyama, Takamasa, and Panayotis N. Varangis. "The impact of the International Coffee Agreement on producing countries." The World Bank Economic Review 4.2 (1990): 157-173.
[8]   | Binswanger, Hans P., and John McIntire. "Behavioral and material determinants of production relations in land-abundant tropical agriculture." Economic Development and Cultural Change 36.1 (1987): 73-99.
[9]   | Morris, Michael L., Hans P. Binswanger-Mikhize, and Derek Byerlee. Awakening Africa's sleeping giant: prospects for commercial agriculture in the Guinea Savannah Zone and beyond. World Bank Publications, 2009.
[10]   | FAO. Coffee Production and Productivity: Improving Sustainability in Smallholder Systems. Food and Agriculture Organization of the United Nations, 2021.
[11]   | Musa, Y., A. H. Bahrun, and A. Rini. "Farmers understanding and practices on shading and pruning for Arabica coffee–a survey and analysis on the effects to the yields." IOP Conference Series: Earth and Environmental Science. Vol. 575. No. 1. IOP Publishing, 2020.
[12]   | Wibowo, Ari. "Potensi dan tantangan kopi di era milenial." Warta Pusat Penelitian Kopi Dan Kakao Indonesia 21.2 (2019): 16-23.
[13]   | Aresta, Antonella Maria, and Carlo Zambonin. "Determination of polycyclic aromatic hydrocarbons (PAHs) in coffee samples by DI-SPME-GC/MS." Food Analytical Methods 16.6 (2023): 1009-1016.
[14]   | Turco, Patrícia Helena Nogueira, et al. "Economic profitability in conventional and irrigated coffee production systems in three municipalities in the Marilia region of São Paulo, Brazil." Ciência Rural 47.11 (2017): e20170170.
[15]   | Baronchelli, Adelaide, et al. "Large agri-food corporations in the global staple and cash crops markets: a quantitative analysis of rice and coffee through the virtual water perspective." Environmental Research Letters 19.7 (2024): 074070.
[16]   | Njuki, J. M., et al. "Male vs. female labour in an agroforestry system in the central highlands of Kenya: correcting the misconception." International Journal of Agricultural Resources, Governance and Ecology 3.1-2 (2004): 154-170.
[17]   | Food and Agriculture Organization of the United Nations. FAOSTAT Statistical Database: Crops and Livestock Products. FAO, 2023, https://www.fao.org/faostat.
[18]   | International Coffee Organization. Country Coffee Profile: Cameroon. ICO, 2022, https://www.ico.org
[19]   | Nchare, Amadou. "Analysis of factors affecting the technical efficiency of arabica coffee producers in Cameroon." (2007).
[20]   | Tambi, Mbu Daniel. "Determinants of Coffee Production in Melong Coffee Zone, Cameroon." Journal of Economic & Social Development (1849-6628) 10.1 (2023).

[21]  
Shillie, Peter Ngek, and Mary Juliet Bıme Egwu. "Value Added Agriculture: An Analysis of economic relations in the coffee value chain in the North West Region of Cameroon/value added agriculture: An analysis of economic relations in the coffee value chain in the North West Region of Cameroon." Uluslararası Ekonomi İşletme ve Politika Dergisi 4.2 (2020): 281-296.

[22]   | Traore, Doussou. "Cocoa and coffee value chains in West and Central Africa: constraints and options for revenue-raising diversification." AAACP Paper Series 3 (2009).
[23]   | Doss, Cheryl R. "Analyzing technology adoption using microstudies: limitations, challenges, and opportunities for improvement." Agricultural economics 34.3 (2006): 207-219.
[24]   | Jena, Pradyot Ranjan, et al. "The impact of coffee certification on small‐scale producers’ livelihoods: a case study from the Jimma Zone, Ethiopia." Agricultural economics 43.4 (2012): 429-440.
[25]   | van Asten, Piet JA, et al. "Agronomic and economic benefits of coffee–banana intercropping in Uganda’s smallholder farming systems." Agricultural systems 104.4 (2011): 326-334.
[26]   | Wollni, Meike, and Manfred Zeller. "Do farmers benefit from participating in specialty markets and cooperatives? The case of coffee marketing in Costa Rica1." Agricultural economics 37.2‐3 (2007): 243-248.
[27]   | Kamau, Mercy, Melinda Smale, and Mercy Mutua. "Farmer demand for soil fertility management practices in Kenya’s grain basket." Food Security 6.6 (2014): 793-806.
[28]   | Binswanger, Hans. "Agricultural mechanization: a comparative historical perspective." The World Bank Research Observer 1.1 (1986): 27-56.
[29]   | Anderson, Jock R., and Gershon Feder. "Agricultural extension." Handbook of agricultural economics 3 (2007): 2343-2378.
[30]   | Ragasa, Catherine, and John Mazunda. "The impact of agricultural extension services in the context of a heavily subsidized input system: The case of Malawi." World development 105 (2018): 25-47.
[31]   | Davis, Kristin, et al. "Impact of farmer field schools on agricultural productivity and poverty in East Africa." World development 40.2 (2012): 402-413.
[32]   | Bongers, Ghislaine, et al. "Diversity in smallholder farms growing coffee and their use of recommended coffee management practices in Uganda." Experimental Agriculture 51.4 (2015): 594-614.
[33]   | Wairegi, Lydia WI, et al. "Sustainably improving Kenya's coffee production needs more participation of younger farmers with diversified income." Journal of Rural Studies 63 (2018): 190-199.
[34]   | Molua, Ernest L., and Cornelius M. Lambi. "Climate, hydrology and water resources in Cameroon." CEEPA, Pretoria 37 (2006).
[35]   | Ndenecho, Neba. Ethnobotanic Resources of Tropical Monta: Indigenous Uses of Plants in the Cameroon Highland Ecoregion. African Books Collective, 2010.
[36]   | MINADER. Annual Agricultural Statistics Report. Ministry of Agriculture and Rural Development, Cameroon, 2019.
[37]   | Tambi, Mbu Daniel, and Mah-Soh Glennice Fosah. "ECONOMETRIC MODELLING OF WOMEN EMPOWERMENT AND AGRICULTURAL PRODUCTION IN CAMEROON." Journal of Economic Sciences: Theory & Practice 81.2 (2024).
[38]   | Creswell, John W., and J. David Creswell. Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications, 2017.
[39]   | Saunders, Mark. "Research methods for business students." Person Education Limited (2009).
[40]   | Wooldridge, Jeffrey M. Introductory econometrics a modern approach. South-Western cengage learning, 2016.
[41]   | Belsley, David A., Edwin Kuh, and Roy E. Welsch. Regression diagnostics: Identifying influential data and sources of collinearity. John Wiley & Sons, 2005.
[42]   | Ramsey, James Bernard. "Tests for specification errors in classical linear least-squares regression analysis." Journal of the Royal Statistical Society Series B: Statistical Methodology 31.2 (1969): 350-371.
[43]   | Shapiro, Samuel Sanford, and Martin B. Wilk. "An analysis of variance test for normality (complete samples)." Biometrika 52.3-4 (1965): 591-611.
[44]   | White, Halbert. "A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity." Econometrica: journal of the Econometric Society (1980): 817-838.
[45]   | Staiger, Douglas O., and James H. Stock. "Instrumental variables regression with weak instruments." (1994).