Abstract
Despite the inherent potential of and merits in adopting modern agricultural technology, the present-day farmer in Sub-Saharan Africa is yet to catch up with the rest of the world in harnessing this potential. To extend the knowledge in the adoption of technology theory, this study examines factors, in particular farmers’ group participation and access to agricultural extension services on farmers’ adoption of modern agricultural technologies (specifically, the use of fertilizers, chemicals and appropriate plant density) and the consequent impact of adopting these agricultural technologies on farmers’ economic performance (income) in the coastal regions of Kenya. Logit regression and multiple linear regression models were used to analyse a sample of 372 smallholder cashew farmers in the Coastal Province of Kenya in 2018. The results show that access to extension services and group membership have statistically significant effects on adopting modern agricultural technologies, namely on fertilizer and pesticide usage and appropriate planting densities. However, fertilizer usage had a negative effect on economic performance while pesticide application showed no effect, and higher planting density had a positive effect. The study recommends that the policy should prioritize extension programs that leverage local platforms such as farmer groups to disseminate agricultural information and economically feasible technologies, such as appropriate cashew planting density – as this was shown to lead to more profitable agribusiness ventures.
1. Introduction
Despite decades of development efforts in the agricultural sector, the twenty-first century Sub-Saharan African farmer still stands disadvantaged in the epicentre of an expanding global economy. Hence, it is challenging to neglect the growing evidence that a paradigm shift – in facilitating access to modern value-adding technology – is needed to improve the livelihoods of vulnerable low-income farmers facing increased competition and risks (FAO, 2011). It has been argued that the adoption of modern technologies is critical in the case of improving the productivity of farmers’ economic performance and the long-term viability of the agricultural sector (Mottaleb, 2018). Despite the advantages of modern technologies, many farmers do not use them. This might be due to several factors correlated with the risks and uncertainties associated with their proper application, farmers’ perceptions and expectations, scale appropriateness and suitability in the prevailing environment (World Bank, 2008) and limited access to extension services and information (FAO, 2011). To extend knowledge in the adoption of technology theory, in this study, we examine factors, in particular farmers’ group participation and access to agricultural extension services, on farmers’ adoption of modern farm technologies (specifically, the use of fertilizers, chemicals, and appropriate plant density) and the consequent impact of adopting these agricultural technologies on farmers’ economic performance (measured in income per acre) in the coastal regions of Kenya.
The effectiveness of the role of extension services in addressing farmers’ information and knowledge gaps and promoting innovations and technologies has often been strongly criticized (Babu et al., 2016; FAO, 2011, 2017; Gautam, 2000; Kondylis et al., 2017). Further, the effectiveness of extension services in improving farmers’ performance often faces criticisms such as the lack of capacity of extension staff to tackle emerging concerns such as climate change, natural resource management and food security (FAO, 2011), generic top-down content to which the farmers cannot relate (FAO, 2017) and inefficacy in reaching most farmers and providing relevant information (Kondylis et al., 2017), especially to the changes in demand for extension (FAO, 2011). On the other hand, a publication by the World Bank asserts the role of extension advisory services in enabling agricultural transformation, and efforts are underway to revive their role in several developing countries (Babu et al., 2016). This assertion might be due to the perception of economic returns by farmers who seek extension services (Elias et al., 2016) and its inherent potential to facilitate information flow – especially as regards modern agricultural technology (Glendenning et al., 2010). Often, information has been sparingly mentioned (if not overlooked) as a principal or contributing factor to farmers’ economic performance. However, it is getting increasingly difficult to ignore the mounting evidence that shows information as a valuable resource similar to land, labour and capital (Balogun et al., 2018; Birner and Anderson, 2007; Liverpool and Winter-Nelson, 2010; Radhakrishnan et al., 2014). Evidence reveals that the more farmers have exposure to sources of information, the more they are likely to adopt innovations, agricultural technologies and best management practices (Singh et al., 2016).
Other than extension, the strong presence of interpersonal agricultural knowledge networks – such as solid self-help, women’s and farmers’ groups – are essential (if not considered more effective) for disseminating relevant information to farmers and possibly facilitating the adoption of modern agricultural technologies (Birner and Anderson, 2007; FAO, 2017; Saint Ville et al., 2016). Recent evidence from Sub-Saharan Africa asserts – farmer groups or cooperative – participation may have a favourable influence on the use of agricultural inputs, as highlighted in a study about the economic performance of agricultural cooperatives in Ethiopia (Sebhatu et al., 2021). As adduced in another recent study conducted in Kenya, efficient farmer groups may economically and socially empower their members by providing various services (Ingutia and Sumelius, 2022) and access to knowledge on new agricultural technologies. Also, increased access to extension support significantly facilitates the adoption of chemical fertilizer and consequently exerts positive influences on food security (Emmanuel et al., 2016). However, to place smallholder farmers in this region in an advantageous position, there is a need to document more empirical evidence on contemporary technology adopters cultivating cash crops – such as cotton, cocoa, tea and cashew – from different cultural contexts within the Sub-Saharan landscape.
The cashew nut plays a vital role in many developing countries. It is a rain-fed cash crop with occasional but beneficial support from irrigation, especially during dry seasons when the cashew is in flower and produces nuts. Most modern cashew plantations practice intercropping, which produces a range of benefits, from improving the food security of small-scale farmers by varying their diet to generating revenues for livelihood, and better optimal utilization of soil resources, especially during the early periods before cashew trees mature to generate income (Costa and Bocchi, 2017). Costa and Bocchi (2017) recommended that cashew require fertilizers at the planting time at a varying rate depending on the tree’s age and yield for optimal growth and productivity. In addition to the recommended use of fertilizers to boost cashew productivity, mitigating against the harmful effects of pests with the application of the required pesticides is highly encouraged. In Africa, the mirid bug, the coreid coconut bug, the cashew stem girdler (a common but usually minor pest of cashew in the Coastal Province of Kenya) and powdery mildew (PMD) adversely affect cashew and can lead to 60-100% yield losses (Costa and Bocchi, 2017; Dendena and Corsi, 2014).
A recent review suggests that West and East Africa have high production potential, with countries like the Ivory Coast, Mozambique and Tanzania cited among the current major producers and processors (Rabany et al., 2015). However, for Kenya, the current under-utilized production and almost inexistent cashew industry and trade, stemming from the privatization and a ban on unprocessed cashew export in 90-ies, limit it only to local consumption. Despite different studies indicating the strong potential of cashew nut production for improving smallholder farmers’ livelihood and enhancing poverty reduction, the sector still lacks proper stimulus (Dendena and Corsi, 2014). In Kenya, farmers value agricultural advice and are willing to share in its cost, even though some evidence suggests that the extension approach adopted in the past had no significant impact on farmer efficiency or crop productivity (Gautam, 2000). Kenya’s Ministry of Agriculture provides extension services to farming communities, including cashew-growing districts but does not provide any extension services specifically on cashew production (Navarra et al., 2017). Expectations are that the farmers’ group facilitate the transfer of know-how to group members. The evidence is missing. Thus, this study examines the impact of agricultural extension support and farmer groups – among other empirically identified factors – on the adoption of new technologies and their consequent impact on the economic performance of the cashew in the coastal regions of Kenya.
Specifically, this study (1) determines the effects of cashew farmers’ group membership and extension support on the adoption of modern agricultural technologies (the use of fertilizers, chemicals and appropriate plant density), and (2) investigates the impact of the adoption of modern farm technologies (the use of fertilizers, chemicals and appropriate plant density) on cashew farmers’ economic performance (income per acre).
Due to the potential for cashew as a cash crop for farmers in the study area, the interest in making the cashew sector vibrant has garnered attention from the Kenyan government and international donors. Although, to the best of our knowledge, no empirical research has been conducted to investigate factors affecting the adoption of modern agricultural technologies and the consequent impact of adopting these agricultural technologies on farmers’ economic performance in the cashew sector. As its main contribution, the paper provides evidence that farmers who receive extension services and are members in groups more probably use modern technologies than other farmers. However, in areas with extensive agriculture, such as the one under consideration in this study, fertilizers do not seem economical. Therefore, the extension services need to disseminate economically feasible technologies, such as appropriate cashew planting density – as shown to help the farmers achieve their economic goals.
2. Conceptual framework and hypotheses
Agricultural technology and innovation adoption might be tracked to the early S-shaped diffusion curve (Tarde, 1903). Rural sociologists later expounded on this concept before its introduction to economics in 1957 by Griliches, as cited by (Toma et al., 2018). Due to different views on technology adoption, tracing its conceptual origin in the literature is quite challenging. Although, once a ‘finger can be placed’ on what the term technology means, defining the adoption or use of technology seems less challenging, as opined by Sanyang et al. (2009). Adoption was defined as the ‘movement of know-how, technical knowledge, or technology from one organization setting to another’ (Sanyang et al., 2009).
This study investigates the use of modern agricultural technologies, namely fertilizer usage, chemical spraying, and appropriate planting densities. Similar indicators have been used in other recent studies evaluating the adoption of agricultural technology (Emmanuel et al., 2016; Läpple et al., 2015; Martey et al., 2014; Meijer et al., 2015; Nata et al., 2014; Sanyang et al., 2009; Senyolo et al., 2018; Wheeler, 2009; Wossen et al., 2017). One of the recent studies conducted in Sub-Saharan Africa argued that smallholder farmers’ inherent capacity to innovate (and possibly adopt new technologies) is strengthened by access to knowledge and information (Emmanuel et al., 2016).
Meijer et al. (2015) categorized various – extrinsic, intrinsic and intervening – factors involved in the decision-making process in the adoption of agricultural innovations. These factors can be traced to information sources, such as input dealers, radio, television, newspapers, extension workers, primary cooperative societies, output buyers or food processors, government demonstrations, village fairs, participation in training, para-technicians and private agencies or NGOs, farmers’ study tours, farmer information and advisory centres (Balogun et al., 2018; Birner and Anderson, 2007; FAO, 2017; Glendenning et al., 2010; Saint Ville et al., 2016).
Although, one of the most-used resources in sharing information has been the farmers themselves, receiving information from others carrying out successful farm practices – or perceived as successful farmers. Empirical studies conducted in rural parts of India reveal that the percentage of farm households accessing information on modern agricultural technology through other progressive farmers was the highest compared to other sources (Birner and Anderson, 2007; FAO, 2017; Glendenning et al., 2010). Also, evidence from rural communities of Saint Lucia in the Caribbean shows the vital role played by ‘peer farmers’ in disseminating new agricultural knowledge; facilitating farmer-to-farmer knowledge exchange; increasing farmer access to information and connecting farmers to sources of support (Saint Ville et al., 2016) with supporting observations from recent reports from Ethiopia and Ghana (FAO, 2017). Furthermore, other studies reveal that farmers’ interactions within their social networks can generate sources of new information that may have a positive influence on agricultural productivity, and this additional information is perceived to be more valuable compared to that offered by governmental extension agents (Balogun et al., 2018; Liverpool and Winter-Nelson, 2010; Saint Ville et al., 2016).
The conceptual framework based on the above empirical studies is illustrated in Figure 1. Based on the previous studies, we expect that group membership and extension services increase the probability of adopting innovative technologies and that this adoption affects economic performance.
Conceptual framework.
Citation: International Food and Agribusiness Management Review 26, 4 (2023) ; 10.22434/ifamr2021.0100
With the inherent potential for the cash crop (cashew) to better position local farmers and the cashew sector as a whole in Kenya economically, it is highly imperative to investigate factors affecting the adoption of modern agricultural technologies and the consequent impact of adopting these agricultural technologies on farmers’ economic performance in the cashew sector. Due to the intense criticism of the agricultural extension services offered in Kenya as lacking a significant impact on productivity (Gautam, 2000), there is a need to re-examine the effect of extension services and other sources where farmers access information such as farmer groups. The hypotheses below were used to carry out the empirical test.
Recent empirical evidence suggests that membership in farmer organizations, amongst other socio-economic factors, positively affects information flow in the adoption of agricultural technologies (Singh et al., 2016). Also, as established farmer groups facilitate technology acquisition and dissemination (Sanyang et al., 2009) as well as trust and social capital generated within farmers’ networks, encouraging uptake of new agricultural knowledge (Saint Ville et al., 2016; Turner et al., 2014). Other evidence from Africa shows that membership in farmer associations is a significant factor influencing fertilizer use (Abebaw and Haile, 2013; Martey et al., 2014) and the adoption of pesticides (Abebaw and Haile, 2013, Denkyirah et al., 2016). On the contrary, an empirical study on the determinants of fertilizer use in Kenya revealed that the probability of adopting fertilizer decreased for farmers who were members of an organization (Makokha et al., 2001). Farmer group membership status significantly positively affects technology adoption (Liverpool and Winter-Nelson, 2010).
H1 Farmers’ group membership positively influences the adoption of modern agricultural technologies (the use of fertilizers, chemicals and appropriate plant density).
In Kenya, the rationale for providing extension services is still relevant. Still, evidence suggests that the extension approach used in the last decade was not effective, sustainable and lacked a significant impact on farmer efficiency or crop productivity (Gautam, 2000). On the contrary, a study established a significant positive relationship between access to extension services and fertilizer usage in Kenya (Makokha et al., 2001). Findings from Sub-Saharan Africa revealed a positive effect of extension support on farmers’ crop yields (Afful and Ayisi, 2016; Elias et al., 2013) which may be linked to the dissemination of new practices and technologies. Also, in Ethiopia, a study revealed that the frequency of extension contact enabled farmers to take up agricultural knowledge and innovations (Elias et al., 2016). Likewise, in Ghana, access to extension services significantly influenced the frequency of pesticide application (Denkyirah et al., 2016). A recent study shows that extension services are a primary driver of knowledge and technology in Kenya (Ingutia and Sumelius, 2022). Some studies affirmed the importance of extension services to significantly influence the adoption of novel technologies (Sanyang et al., 2009; Toma et al., 2018), basal fertilizers (Ouma et al., 2002), pesticides and frequency of pesticide application (Denkyirah et al., 2016). Contrary to previous studies, Bruce et al. (2014) established that farmers who accessed extension services had a lower probability of adopting technologies.
H2 Agricultural extension support positively influences the adoption of modern agricultural technologies (fertilizers, chemicals and appropriate plant density).
As adduced by Ouma et al. (2002), scholars widely accept the vital contribution of using agricultural technologies to agricultural productivity and perhaps the sector’s economic performance. A study conducted in Kenya argued for the increased use of specific agricultural technologies, such as efficient use of fertilizers and improved crop processing and storage, to improve productivity (Makokha et al., 2001). Both extension access and cooperative membership showed a significant positive effect on the technology adoption of improved cassava varieties and a potential to improve agricultural productivity in Nigeria (Wossen et al., 2017). In the Gambia, a comparative study revealed that facilitating technology transfer can boost vegetable production (Sanyang et al., 2009). Sanyang et al. (2009) further highlighted strong evidence for adopting agricultural technologies in contributing to the economic performance of the rice sector when the Gambian government relied on farmers adopting rice irrigation technologies to improve primary production hence meeting local demand. In Ghana, it’s observed that agricultural productivity is limited due to inadequate mechanization and poor use of modern agricultural technologies such as fertilizers and relevant agro-inputs (Martey et al., 2014).
H3 The adoption of modern agricultural technologies fertilizers, chemicals and appropriate plant density influences farmers’ economic performance.
A recent study highlighted that farmer groups positively impact crop yields resulting in increased food security through improved access to fertilizer, insecticides, herbicides and other relevant inputs (Ingutia and Sumelius, 2022). It’s been observed that farmers who are part of a group stand a chance of improving their economic performance through increased disposable income as a result of improved farm productivity and better access to markets (FAO, 2017). Farmers’ knowledge and information depend on the available information farmers are disposed to, the (social) costs of acquiring this information, and information sources, such as farmer groups or social networks or peer-farmer interaction, extension agents, and researchers (Sanyang et al., 2009; Wheeler, 2009). Furthermore, social capital variables such as meeting attendance, decision-making index and self-confidence have significantly influenced farmers’ productivity (Balogun et al., 2018; Saha and Devi, 2014). Farmer group membership status has been shown to significantly affect farmers’ productivity (Balogun et al., 2018) and household welfare (Wossen et al., 2017).
H4 Farmers’ group membership positively influences farmers’ economic performance.
Recent studies from Sub-Saharan Africa revealed that access to extension and advisory services had helped farmers learn new agricultural technologies and subsequently helped farmers switch to more commercial, market-oriented agriculture in Ethiopia (Buehren et al., 2017). Also, a study conducted in Ethiopia showed that extension participation increases farm productivity (Elias et al., 2013). In comparison, another study conducted in South Africa established a direct relationship between extension services received by farmers to farmers’ crop yields (Afful and Ayisi, 2016). In Ghana, a study showed the significant positive effect of extension services on rice productivity (Emmanuel et al., 2016). Although, extension services were shown not to be significantly associated with the likelihood of increasing household food security position (Nata et al., 2014). Scholars have also argued for the possible positive impact of agricultural extension services – an indispensable policy instrument in developing countries – on farmers‘ productivity and household welfare (Wossen et al., 2017).
H5 Agricultural extension support positively influences farmers’ economic performance.
3. Methodology
3.1 Study area
Agriculture is one of the pillars of the Kenyan economy. It contributes one-third of GDP, and this contribution has increased in the past eight years, from 2010 to 2018, by almost 10% and employs 75% of the national labour force (World Bank, 2018). Over 80% of the Kenyan population live in the areas and derive their livelihoods directly or indirectly from this sector. Kenya’s foreign earnings come mainly from black tea, tourism, coffee, and horticultural exports, such as green beans, onions, cabbages, snow peas, green grams, avocados, mangoes, and passion fruit. Even though cashew used to be one of the leading export crops, nowadays, cashew and sesame play a marginal role in national agriculture.
The study was carried out in the main cashew growing areas – Kenya’s Coastal Province, where six counties are named Kwale, Taita Taveta, Mombasa, Kilifi, Lamu and Tana River. The farming systems in the Coastal Province are divided into three cropping systems: annual, biannual and perennial crops and farming activity (calendar) is determined by the rainfall pattern, with cashew as a major perennial crop. Data were drawn from three high cashew density counties, namely: Kilifi County covers an area of 12,246 km2 with a population of 1,109,735 with seven constituencies and 35 wards; Kwale County is on the southern coast of Kenya, occupying a surface area of 8,270 km2 with a population of 649,931, 4 constituencies and 18 wards; Lamu County is located on the northern coast of Kenya and covers an area of 6,273 km2 with a population of 101,539 with two constituencies divided into ten wards. The areas of high cashew density guided the choice of sub-counties of focus; these are:
Kilifi County: Kilifi North, Kilifi South, Magharini and Ganze.
Kwale County: Msambweni, Matuga and Lunga Lunga.
Lamu County: Mpeketoni and Hindi.
3.2 Theoretical model
The re-birth of international donors, NGOs and government interests in the Kenyan cashew sector drives the recent delivery of extension services to farmers to promote agricultural technologies – with the expectation that farmer groups will do the same. The study anticipates that farmer group membership and extension services will enhance farmers’ access to information about agricultural technologies. The improved access to agricultural information is expected to increase farmers’ adoption of new agricultural technologies and consequently improve the economic performance of farmers. In addition, farmer group membership status (Abebaw and Haile, 2013; Balogun et al., 2018; Denkyirah et al., 2016; Ingutia and Sumelius, 2022; Liverpool and Winter-Nelson, 2010; Sanyang et al., 2009; Wossen et al., 2017) and access to extension (Afful and Ayisi, 2016; Buehren et al., 2017; Elias et al., 2013; Emmanuel et al., 2016; Nata et al., 2014; Wossen et al., 2017) have been proven empirically to have a significant effect on agricultural economic performance.
Against this background, the study analysed the simultaneous effect of farmer group membership status and access to an extension service on farmers’ adoption of agricultural technologies (proxied by fertilizer use, chemical spraying and appropriate plant density use) and economic performance (proxied by income). We adopted the logistic regression (logit) model to analyse the use of fertilizers and chemical spraying due to the dichotomous nature of the dependent variables (1 = if a farmer uses fertilizer and chemical spraying; 0 = otherwise).
The logit model is expressed as follows (Gujarati, 1995).
Citation: International Food and Agribusiness Management Review 26, 4 (2023) ; 10.22434/ifamr2021.0100
Where
Multiple linear regression was used as expressed in Equation 2 to analyse the use of plant density (the third proxy for the adoption of agricultural technologies) and as expressed in Equation 3 to analyse the effect of farmer group membership, access to extension services and agricultural technology adoption on the economic performance of farmers (income per acre). The multiple linear regression was used due to the continuous nature of the dependent variables. The multiple linear regression is specified as follows:
Citation: International Food and Agribusiness Management Review 26, 4 (2023) ; 10.22434/ifamr2021.0100
Where Yi is the dependent variable plant density (number of cashew trees per acre) or economic performance (income per acre),
3.3 Data
Data on the following variables were collected and examined in this study for appropriate model design.
Fertilizer use: This study defines this variable as the recommended use of either organic and/or inorganic fertilizers in the study area to boost cashew productivity. The most favoured types of fertilizer, if used, are manure and DAP. Some studies provide empirical evidence on the significant positive impact of fertilizer use on farm output (Bruce et al., 2014), on household food security (Nata et al., 2014) and crop productivity (Emmanuel et al., 2016). Also, fertilizer adoption (both inorganic and manure) was positively influenced by access to extension, membership in an organization, hired labour and off-farm income (Makokha et al., 2001). Although, it was observed that participation in development projects may not always result in increased adoption and fertilizer use intensity (Martey et al., 2014).
Chemical spraying: The chemical spray variable is defined in this study as the use of any agro-chemical input(s) to mitigate pests’ harmful effects. A pest infestation can lead to yield losses if pest infestations are not controlled (Dendena and Corsi 2014, Costa and Bocchi 2017). Amongst the known methods for pest control, the use of chemicals such as pesticides and insecticides has been reported to be the most widely adopted method for pest management by cashew farmers (Dendena and Corsi 2014). Nata et al. (2014) showed that households that used insecticides – an indicator for pest attack – had a decreased production in Ghana.
Planting density: There has been an established relationship between cashew planting density and nut yield, which positively affects farmers’ net incomes (Mangalassery et al., 2019). Mangalassery et al. (2019) further highlighted that a cashew farmer who adopts a high-density planting system might enjoy double-digit yield increases compared to their counterparts who use a relatively lower planting density system. This study defines planting density as the number of cashew trees per acre. The observed span between the trees in the study area was usually 8-12 m, with frequent gaps caused by dead trees.
Extension support: The extension variable expresses if farmers in the study area have access to extension services or not. Some studies have highlighted the relationship between access to extension services and technology adoption (Denkyirah et al., 2016; Ingutia and Sumelius, 2022; Ouma et al., 2002; Sanyang et al., 2009; Toma et al., 2018), while others explored the effect of extension services on crop yields (Afful and Ayisi, 2016; Emmanuel et al., 2016) and farm productivity (Elias et al., 2013).
Group membership: In this study, we define group membership as a farmer who is part of any form of farmer group(s) that support and coordinate members in reaching an agreed goal. This definition aligns with a recent study that highlights the importance of farmer groups in accessing relevant information and other advantages (Ingutia and Sumelius, 2022). Farmers in our target regions are group members such as producer, marketing, savings, social and/or services groups. These groups vary as regarding size and from informal to formal levels of the organization.
Age: Adoption of modern agricultural technologies is influenced by farmers’ characteristics, such as the farmer’s age (in years). Farmers’ age has been shown to significantly influence the decision to use pesticides (Denkyirah et al., 2016) and determined productivity (Balogun et al., 2018). A study established that farmers from 40 years and above have a lower probability of adopting chemical fertilizer (Emmanuel et al., 2016). Other studies found that farmers’ age was not significant to influence technology adoption (Nata et al., 2014) and had a negative effect on innovation (Läpple et al., 2015).
Education: This is defined as the number of famer’s schooling years. This variable (education) has been empirically documented to have a significant positive influence on farmers’ intentions to uptake new technologies (Toma et al., 2018), use pesticides (Denkyirah et al., 2016), adopt chemical fertilizer (Emmanuel et al., 2016) and innovate as they are adduced to process novel information quickly (Läpple et al., 2015). Although, a study showed a contrary result that education had an unexpected negative impact on fertilizer use (Ouma et al., 2002).
Household head gender: Regarding gender, some studies reported that male farmers are more likely to adopt chemical fertilizers (Emmanuel et al., 2016) and pesticides (Denkyirah et al., 2016) compared to women in Ghana. Also, in Ghana, as regards factors affecting fertilizer use intensity, it was established that household heads that are income-earning males were significant influencing factors compared to their counterpart females – who were limited as regards resources hence using their income to meet the nutritional needs of the household (Martey et al., 2014).
Farm size: In this study, farm size was measured in acres. This variable positively impacted farmers’ decisions to adopt chemical fertilizer in Ghana (Emmanuel et al., 2016). Other studies conducted in Ghana showed that the likelihood of farmers adopting fertilizer technology reduced as the farm size increased (Martey et al., 2014), and for farmers with relatively smaller farms, the likelihood of adopting an enhanced rice variety was high compared to their counterparts with larger farms (Bruce et al.,2014). In Nigeria, Balogun et al. (2018) established a significant positive relationship between farm size and productivity of cassava farmers.
Hired labour: This variable measured if a farmer employed the services of additional workers or not. It is well documented that different studies from the literature point to different directions regarding the effect of labour on adopting specific agricultural technologies (Toma et al., 2018). Toma et al. (2018) further observed that farms with more workers per hectare have a higher probability of adopting new technology and continue to use innovations that boost economic performance. Although, scarcity of labour tends to influence a farmer to adopt labour-saving technology (Toma et al., 2018). In Ghana, Bruce et al. (2014) showed that farm labour significantly impacted farm output. In Kenya, hiring of labour was established to be a statistically significant factor influencing the adoption of improved maize variety and the amount of fertilizers farmers use (Ouma et al., 2002).
Off-farm income: In this study, this variable measured whether or not the farmer takes part in any off-farm activity that generates additional income for the farmer. It has been highlighted that when farmers are engaged in any form of off-farm work, this may compete with the time spent on the farm, affecting technology adoption negatively (Toma et al., 2018). Although, Toma et al. (2018) further adduced that when farmers adopt some form of technology, this might free up some time required to carry out some farm activities manually, and this free time may be spent on some other off-farm jobs. A study in Ireland proved that off-farm work has a negative impact on farmers’ innovativeness (Läpple et al., 2015).
Loans: This variable expressed if the farmer has access to credit. Access to credit has been shown to significantly influence farmers’ innovation (Läpple et al., 2015), the decision to use pesticides, and frequency of pesticide application (Denkyirah et al., 2016). It was revealed that access to credit has a complementary relationship with extension services to reduce poverty and a mutual relationship with cooperative membership on technology adoption (Wossen et al., 2017).
Plant sesame: This variable was added to the model as a proxy for diversified agriculture. Sesame is cultivated in intercropping systems with cashew trees in the study area. Intercropping, a cultural practice in the tropics, especially on cashew farms, act as an environmentally sustainable weed management alternative, contributes to the management of soil resources and offers additional income to improve farmers’ livelihood (Dendena and Corsi 2014). Dendena and Corsi (2014) opined that the performance of intercropping on a cashew farm depends on the type of food crop integrated and recommended further investigations. Relevant studies have established the positive impact of planting viable intercrops alongside cashew on farmers’ economic performance (Lawal and Uwagboe, 2017; Sajeev et al., 2014).
3.4 Sampling and data collection
A quantitative structured questionnaire survey was used as the primary research instrument to collect the data. The data was collected by trained local field officers within the EU Trust Fund for Africa-funded project ‘Enhancement of livelihoods in the Kenyan coastal region by supporting Organic and Fair-Trade certification of smallholders’.
A multi-stage sampling approach was designed using stratified sampling as the first step to identify the main cashew growing areas across Kilifi, Kwale and Lamu counties. The target population was estimated at 15,000 local cashew farmers. With 95% level confidence and a 5% margin error, the minimal representative quota sample was set at 375 respondents. Quota sampling was used to collect representative data and to capture also the youth and women components in the study area (the minimum target was 100 male farmers, 25 female farmers, and 25 youth farmers in each county). In addition to quota sampling, the snow-ball method was also used to reach respondents. A pilot survey with ten farmers was conducted, and the questionnaire was adopted according to the respondents’ comments. In total, 417 questionnaires were filled in the field.
The questionnaire for data collection was programmed into an Open Data Kit (ODK) data collection tool and uploaded to the smart mobile/tablet of each of the 16 trained enumerators – project field officers – as the data collection was done electronically between 30 April and 5 May 2018.
The dataset was later cleaned for wrong or missing data to 390 entries. For most farming-related questions, the dataset was further limited to respondents who are owners or co-owners of the farm. Thus, we reached the final number of 372 data entries as the others were not decision-makers. The sample can be smaller for some figures since not all the respondents answered the respective question.
Additional qualitative data were collected with the support of the researchers from the local Pwani University to gain better insight into peer support, networking and peer farmers’ interaction on access to information from cashew farmers in the study area. A semi-structured, unstructured in-depth interview and focus groups were used. Each interview took about thirty minutes to one hour. Respondents for qualitative data collection were: semi-structured interviews with Kwale Agricultural Officer; Kilifi Agricultural Officer; Kilifi Cooperative Officer; 15 unstructured interviews with female cashew farmers; 31 unstructured interviews with male cashew farmers; 2 focus group discussions with local groups of cashew farmers, an interview with a representative of a Cooperative Union and finally an interview with local brokers (middlemen).
3.5 Data analysis
Data were coded, and the STATA software package (StataCorp, College Station, TX, USA) was used to analyse the data set. Descriptive statistics, which show the frequency, mean, standard deviation and percentage of respondents, were used to describe the socio-economic characteristics of the sample.
For this study, the empirical logit model is specified as follows:
Citation: International Food and Agribusiness Management Review 26, 4 (2023) ; 10.22434/ifamr2021.0100
Where Ti is the probability of a farmer adopting the agricultural technologies (i.e. fertilizer and chemical spraying), Gi denotes farmer group membership status, Ei represents access to extension services, Si is a vector of socio-economic characteristics of a farmer (i.e., the gender, age and years of education as well as farmer access to off-farm income), Fi represents a vector of farm characteristics (i.e., farm size, use of hired labour, access to loan, and cultivation of another cash crop – sesame).
For this study, the multiple linear regression adopted is specified as:
Citation: International Food and Agribusiness Management Review 26, 4 (2023) ; 10.22434/ifamr2021.0100
Where Pi represents the dependent variable plant density (Equation 2), the third proxy for a farmer adopting the agricultural technology. As seen in Equation 3, Yi represent the dependent variable income, Gi denotes farmer group membership status, Ei represents access to extension services, Ti is a vector of technology adoption (fertilizer use and chemical spraying), Pi represents the vector of technology adoption plant density, Si is a vector of socio-economic characteristics of a farmer, and Fi represents a vector of farm characteristics.
Logit regression analysis was used to understand the effect of independent variables on the adoption of modern agricultural technologies – such as fertilizer use (4) and chemical spraying (5):
Citation: International Food and Agribusiness Management Review 26, 4 (2023) ; 10.22434/ifamr2021.0100
While a linear regression model was used to investigate planting density and the consequent effect of the adoption of modern technologies on farmers’ economic performance (income per ha). Empirically, the impact of the adoption of modern agricultural technologies on farmers’ profit is specified as in Equation 6 and Equation 7:
Citation: International Food and Agribusiness Management Review 26, 4 (2023) ; 10.22434/ifamr2021.0100
Where Pef denotes economic performance or relative income measured as gross farm income per acre; Age represents the age of farmer in years; Edu denotes number of years spent in school; Hhhead denotes the gender of household head (1 if the household head is a male and 0 otherwise); Farmsize denotes the size of farm in acres; Hlabour represents hired labour (1 if the farmer hires anyone and 0 otherwise); Offfarm denotes off farm income (1 if the farmer generates income outside farming related activities and 0 otherwise); Groupmem denotes group membership (1 if the farmer is part of at least one of any farmer group such as Savings and Credit Cooperative Societies or Village Savings and Loan Associations or production groups or cooperative associations where farmers exchange relevant information as regards know-how, besides the primary function of such groups, and 0 otherwise); Loans denotes loans (1 if the farmer have taken a loan and 0 otherwise); Exten denotes extension support (1 if the farmer received any extension support and 0 otherwise); Plantden denotes planting density (number of trees per acre); Chemspray denotes chemical spraying (1 if the farmer sprays chemical on crops and 0 otherwise); Fertuse denotes fertilizer use (1 if the farmer applies fertilizer and 0 otherwise); Psesame denotes planting sesame (1 if the farmer plants sesame and 0 otherwise);
All observable explanatory variables added to the models employed in this study rest on documented empirical findings which reveal that age, education, gender, farm size, hired labour, off-farm income, group membership, credit access, and extension support have a significant effect on the adoption of agricultural technologies (Elias et al., 2016; Emmanuel et al., 2016; Saint Ville et al., 2016; Turner et al., 2014). Consequently, the adoption of agricultural technologies – with respect to fertilizer usage (Emmanuel et al., 2016) – has been shown to significantly affect farmers’ production levels (Buehren et al., 2017; Dendena and Corsi, 2014; Sajeev et al., 2014; Sanyang et al., 2009). The planting sesame variable was added to the model as a proxy for diversified agriculture. Sesame is often cultivated in intercropping systems with cashew trees in the area as international NGOs together with the local government introduced the crop as an alternative to cassava – which is also commonly planted with cashew trees.
Table 1 provides the descriptive statistics of variables used in the regression models, and the main results are described in the following section.
4. Results and discussion
4.1 Descriptive results
■ Cashew farming systems
Existing cashew trees in the Coastal Province are composed mostly of unknown and poor-quality varieties. The local cashew is highly heterogeneous, resulting in variable yields, nut sizes, nut quality, apple colours, and tree structures. Most of the existing producing trees were planted 20 years ago during a period of government investment in the cashew sector. Other trees are even older and beyond their productive lifetimes.
Currently, due to renewed interest in the cashew sector, new varieties are being brought to the Province. The most typical cashew tree variety brought to the Coastal Province in the form of scions are several lines of Brazilian dwarf. In combination with local rootstock, it can mature relatively early compared to other lines, producing the first fruit within 24 to 26 months. There are other lines as well. However, all lines’ exact scientific name is unknown since imports have not been organized. All such improved trees are still too young to establish practical results on potentially improved yields. There is no unique Kenyan line of cashew trees so far.
Based on our quantitative data, the productive local cashew tree can bear on average 6.4 kg/nuts/year, while the maximum reported productivity from some respondents is around 30 kg/year. Nevertheless, because of the low quality of most trees, which grow without any care, productivity can decrease to about 2-10 kg/tree/year. For the most intensive farmers from our sample, the overall yield can reach 80-120 kg/acre (200-300 kg/ha), which is extremely low compared to other cashew-producing countries, and far less than the Food and Agriculture Organization of the United Nations (FAO) official statistics show. Most cashew farms in the Coastal Province are small, having around 5 acres of land for cashew, and on average less than 20 cashew trees are spread ad hoc around their houses. Adult male farmers usually have the largest area of land available for cashew. There are also old plantations (remains of governmental plantations from the 80-ies) with hundreds of trees in the area. However, all the farmers and the old plantation farm trees were abandoned from intensive cultivation 20 years ago.
The span between the trees is usually 8-12 metres, but with frequent gaps caused by dead trees. The average density of the trees of our respondents is around three trees per acre. The trees are typically overgrown, and their capacity to produce quality nuts is limited. Usually, half of the trees don’t bear any nuts, and of those respondents who own cashew trees, not all harvest cashew. Only a few farmers had started to recuperate old trees or establish new plantations. The typical method of recuperation and re-establishment of a canopy for existing cashew plantations is by cutting the tops of the trees, which stimulates fresh sprouts to emerge. This practice is known as top working. Some farmers have started to plant new, improved varieties with lines available from the government (however, the distribution of a limited number of seedlings happened only twice in the last ten years). There are no advanced plantations that would be regularly pruned and cut to increase productivity. The main cashew inputs include seedlings, fertilizers, manure, and machinery. If we look at practices of pest and disease control and the use of fertilizers, we can observe that the trees are cared for by a minimal number of farmers. The use of fertilizers and pesticides happens once or twice a year, usually when symptoms of diseases start to emerge. However, farmers’ knowledge of using chemicals on their trees is minimal. 80% of the respondents don’t use any fertilizers on the farms (Table 1). The most favoured types of fertilizer, if used, are manure and DAP. The government and international NGOs started to spread the know-how related to the organic methods of cashew farming with the potential to add value to the final certified product. However, the farmers have not initiated the conversion to the organic system, and no farmers were practising organic agriculture at the time of data collection.
Summarized statistics of dependent and independent variables used for Logit and linear regression analysis, n=372.
Citation: International Food and Agribusiness Management Review 26, 4 (2023) ; 10.22434/ifamr2021.0100
The majority of respondents don’t use any agro-chemical inputs on their farms. For those who do use them, the most popular kind is pesticide and fungicide. Around 25% of farmers use hired labour on their farms (usually 2-3 people). Besides the general characteristics of local cashew farming systems, we also analyzed the productivity of local cashew in terms of gross income and associated costs. The average gross income of cashew is extremely low, around 4000 KES or 40 US dollars per year (all expenses estimated in KES, Kenyan shilling, at the following exchange rate: 1 USD = 101.4 KES). The highest costs were associated with fertilizers, agro-chemicals and land preparation. However, it should be noted that only a small proportion of farmers ever spent on these costs.
■ Extension support
The adoption of improved technologies by cashew farmers in the Coastal Province is low. It is estimated that less than 30% of all cashew farmers surveyed have received extension support for their crops, as seen in Table 1. As depicted in Table 2, almost 50% use the services of the government extension officers in the area, while approximately 25% use extension services sourced from Non-Governmental Organizations (NGOs). Only a small number of farmers use private firms (19%) or other farmers (14%).
The contact time between extension staff and farmers was further reduced due to inadequate resources to move regularly from one community to the other. Most extension workers are of retirement age, and the local government cannot attract younger and more enthusiastic field officers anymore. Only 11% of respondents received any training in cashew cultivation (Table 2). Besides two distributions of new seedlings (2008 – 50.000 seedlings; 2014 – 30.000 seedlings), there is no direct support for cashew farmers, only a general crop-cultivation extension service. For instance, the county Agricultural Office runs a system of 17 general-focused Farmer Field Schools in the area.
Summarized descriptive statistics of cashew farmers in Kenya’s coastal region.
Citation: International Food and Agribusiness Management Review 26, 4 (2023) ; 10.22434/ifamr2021.0100
Most of the interviewed farmers have not received any recent training related to cashew. 50% of those who received training received them in the 2016-2018 period. Nevertheless, several international donors and projects targeted the same cashew communities in the area: the most relevant intervention was the project ‘Empowering Women Cashew Farmers’ in Kilifi, implemented by the NGO Self-Help Africa and funded by the Wal-Mart Foundation.
■ Producer group participation
As we learned from the qualitative interviews, more than 20% of the population in the coastal counties belong to producer groups cultivating farm produce such as maize, mango, cashew, cassava, sweet and Irish potatoes, and green grams (legumes), oranges, and vegetables. 10% are involved in marketing groups, 60% are active members of savings’ groups, and the rest belong to social and services groups. However, compared to the rest of Kenya, the Coastal Province seems to lack producer group development and popularity. Besides a few dairy and beekeeping cooperatives, functional marketing cooperatives are almost non-existent among farmers and exist only in the memories of former members.
On the side of smaller farmers, a lack of sustained cooperation has been the major hindrance to the groups prospering, as many think dealing with other farmers limits their ability to generate income. The passivity of members and lack of attendance at any group meetings is another limitation, as the majority of the farmers are hesitant to share information. Youth apathy is also a constraint to joining cooperatives because old people hold leadership positions for extended periods (succession management). The other challenge has been governance issues, with reported malpractice cases among the leadership, which results in non-confidence among the general membership. Our quantitative data confirms that almost 30% of farmers in our target regions are members of a group (Table 1). Belonging to a group seems prevalent, especially in the case of women. This may be because they show better organizational and teamwork skills and are more diligent at fulfilling their requirements, as we have learned from qualitative data. 50% of farmers already in a group meet weekly, which confirms an observation during one of the unstructured interviews with key informants that farmers are used to working and sharing resources in many different informal groupings.
A commonly discussed problem might be the fact that membership in the group is tied to land ownership. However, linking membership to trees’ ownership and not only land might encourage more women and youth to become members. As we learned from interviews with representatives of Cooperative Unions, which support the result shown in table 1, more than 25% of the farmers in the coastal counties belong to a group. However, there is neither any active and wide-reaching national association of cashew producers, a cashew commodity board, nor any export promotion association common in major cashew-producing countries. As we learned, the Kenya Nut Grower Association does not include many cashew producers in the Coastal Province. The main problem frequently discussed is the lack of capital (though the membership contribution fees are very low – usually 20 KES/year) to pay on time to members bringing their cashew production to the cooperative, along with the negative mindset towards cooperatives among farmers on the coast. However, according to our interview with the Cooperative Officer in Kilifi, the local government plans to provide subsidized loans to cooperatives, and renewed interest among farmers can be expected. We also analyzed general trust in society. From our data, we can conclude that most farmers perceive a high level of trust and cooperation (Table 2).
4.2 Regression models results
Table 3 provides the results of regression models analyzing determinants that affect the probability of using fertilizers, chemicals and appropriate planting density by smallholder farmers. It is worthy to note that several control variables were insignificant.
Logit regression models and multiple linear regression for determinants of adopting modern agricultural technologies.a
Citation: International Food and Agribusiness Management Review 26, 4 (2023) ; 10.22434/ifamr2021.0100
■ Determinants of adopting modern agricultural technologies: fertilizer usage
The empirical results show that group membership, planting of sesame, access to loans and use of extension services increase the probability of fertilizer usage, and this confirms our apriori expectation. An increase in farm size decreases the likelihood of fertilizer use. This result is similar to another empirical finding from Kenya that showed a negative relationship between fertilizer use and farm size (Ouma et al., 2002). Unfortunately, it is very difficult to interpret this result. As the cashew sector in the coastal regions of Kenya is undeveloped and since there are no guaranteed markets, farmers with more acres of cashew farmlands might perceive the application of fertilizers as not economical. Further studies are needed to explore this relationship.
Amongst other explanatory variables, the results show that cashew farmers who are members of a group and have access to extension services have a higher probability of taking up the use of fertilizer, which is consistent with empirical evidence from other African countries related to the impact of cooperatives on the adoption of agricultural technology (Abebaw and Haile, 2013). Thus, this confirms our first and second hypotheses: the positive influence of farmers’ group membership and access to extension services on adopting modern agricultural technologies regarding fertilizer usage.
This result affirms the practical merits of extension services as a significant determinant in fertilizer usage, which is in line with a recent empirical study that revealed the impact of agricultural extension services on the adoption of chemical fertilizer in Sub-Saharan Africa (Emmanuel et al., 2016). This is indicative that the Kenyan government and policymakers should pay close attention to the delivery of extension services to cashew farmers in the coastal region to boost the sector’s performance. The result is also consistent with findings from Kenya that adopters of an improved maize variety had better access to extension services – which were mainly sourced from the Ministry of Agriculture and NGOs – compared to their counterpart non-adopters (Ouma et al., 2002).
The results further demonstrate that farmers who are members of a group tend to have a higher probability of adopting agricultural technology. This result supports the empirical evidence that group membership positively affects information flow in adopting farming technologies (Singh et al., 2016; Toma et al., 2016). Information flow among farmers on the coast might arise from other factors – like planting sesame.
The results show that off-farm income, one of the explanatory variables included in the model, has a positive but insignificant relationship with fertilizer usage. A reasonable deduction from this is that farmers might become aware of the potential benefits of organic fertilizer (manure) application whilst interacting with other farmers in the community during other gainful off-farm activities. A previous study conducted in Kenya showed that off-farm income had a significantly positive relationship with manure application (Makokha et al., 2001). It is also worthy to note that a study conducted in Ethiopia established that participating in off-farm activities such as village leadership had a significant positive effect on the likelihood of cooperative membership – which in turn was a significant factor influencing fertilizer use (Abebaw and Haile, 2013). Hired labour showed a negative but insignificant relationship with fertilizer usage; this might be due to the expensive labour requirements in the study area as farmers might direct the services of hired farm workers to other activities on the farm. In Kenya, hired labour for manure application was also insignificant for farmers who only used manure on their farms (Makokha et al., 2001). On the contrary, Ouma et al. (2002) established hired labour as a statistically significant factor affecting the quantity of fertilizers used by farmers in Kenya.
■ Determinants of adopting modern agricultural technologies: chemical spraying
The positive relationship between chemical spraying and group membership (Table 3) indicates the possibility that through some regular meetings, members might access and spread the knowledge about pest control using chemicals. The finding contrasts the recent empirical findings on Ghanaian cocoa farmers’ decisions to use pesticides where membership of a farmer-based organization was statistically significant and negatively influenced the frequency of pesticide application (Denkyirah et al., 2016). As the study expressed ‘that farmers were more aware of insect pest thresholds due to being members of farmer-based organizations, which further indicates that farmer-based organizations are a reliable source of information to farmers.’ Overall, the result is consistent with previous studies that show farmer groups facilitate technology acquisition and dissemination (Sanyang et al., 2009) as well as trust within farmers’ networks encouraging uptake of new agricultural knowledge (Saint Ville et al., 2016; Turner et al., 2014).
However, no statistically significant effect of extension access on the likelihood of applying chemicals in our model. Therefore, we do not accept the second hypothesis as the second hypothesis is related to other technologies but here, we do not accept it as regards chemical spraying. This further differs from the findings of the afore-mentioned study that showed access to extension services was statistically significant and negatively influenced pesticide use – probably due to the introduction of new technologies other than pesticides to farmers by extension agents (Denkyirah et al., 2016). This aligns with findings that show why farmers adopt biological control compared to pesticide spraying (Abdollahzadeh et al., 2015, 2016) as chemicals might be perceived as harmful substances and household heads might not want to endanger family members.
The relationship between access to loans and the adoption of agricultural technology (chemical spraying) was positive but not significant, which is in line with the findings of Emmanuel et al. (2016) that showed that access to formal credit was not significantly related to chemical fertilizer adoption as the authors further argued that credit might facilitate the purchase of agro-chemicals such as pesticides. The possibility that a farmer would purchase and use technologies like pesticides will likely increase if the farmer accesses credit. This positive but not significant relationship can be likened to a study conducted in Kenya that showed that adopters of technology had greater access to credit than non-adopters (Ouma et al., 2002).
■ Determinants of adopting modern agricultural technologies: planting density
The las model results show (Table 3) that group membership and access to extension services increased the number of cashew trees planted per acre (planting density). It is distinct from the number of farmers’ schooling years which negatively affects plant density. The causal effect of accessing extension services to increasing or using appropriate planting density is consistent with empirical evidence from Ghana, where it was observed that there was a significant positive mean difference between farmers who received extension services against their counterparts in terms of the adoption of row planting in rice fields (Emmanuel et al., 2016).
As observed in the study area (Table 1), most farmers did not plant any new trees in the last 30 years. Thus, the average plant density is 10 cashew trees per acre – which is relatively low compared to the recommended plant spacing of 10 by 10 m (100 trees per ha) (FAO, 2021). Sparingly, outlier farms with higher planting densities were observed where farmers did not cut down the old trees or planted new trees in between the old trees. This reflects the observed comments that ‘farmers often do not replant lost trees’ during focus group discussions. This might be due to the additional cost incurred and extra person-hours for managing the nursing period for a new seedling on the farm.
Off-farm income and hired labour showed a positive but insignificant relationship with the number of cashew trees planted per acre. Logically, we deduce that if a farmer gainfully engages farm workers in transplanting and re-establishing cashew trees, this might reduce the frequent gaps caused by dead trees. Loans showed a negative relationship with planting density as farmers with access to credit surprisingly had fewer trees per acre.
■ Impact of adopting modern agricultural technologies on cashew farmers’ economic performance
The results of our multiple linear regression model presented in Table 4 show that economic performance (income per acre) increases with planting density and surprisingly decreases when fertilizers are used. Access to loans negatively affected the economic performance of farmers in the study area. However, this effect was not significant. Probably farmers divert credit received for farming activities to other non-farming-related expenses. The use of pesticides does not show any statistically significant effect on economic performance. Off-farm income showed a significant negative effect on economic performance. That may be explained by the opportunity cost to farmers of investing time in other non-farm activities.
Planting sesame, which represents the practice of intercropping on cashew plantations, showed a positive effect on farm economic performance. In line with recent findings from Africa, intercropping in cashew plantations was empirically proven profitable, offering a safety net and rapid turnaround income for cashew farmers (Lawal and Uwagboe, 2017).
According to the most recent empirical evidence on the impact of different irrigation regimes under varied planting densities on growth, yield and economic return on cashew planting conducted in India, increased planting density was instrumental to improving the raw cashew nut yield and invariably the net economic returns per unit area (Mangalassery et al., 2019). This affirms our findings on the significant positive impact of appropriate planting density per acre on economic performance – hence we accept the third hypothesis that adopting modern agricultural technologies (appropriate plant density) influences farmers’ economic performance.
Access to extension services showed an unexpected negative effect on farmers’ economic performance, but this effect is insignificant. Hence, we do not accept the fifth hypothesis. There might be sundry explanations for this unexpected insignificant effect of access to extension services on farmers’ economic performance: (1) the impact of access to extension services on farmer economic performance is most likely a specific – rather than a universal – influencing element; (2) in this context, probably more successful farmers (experienced farmers with larger farms) get their know-how from other sources and/or self-study – hence, access to extension services may not affect them; (3) similar to (Nata et al., 2014), there is a high possibility of respondent’s social desirability bias behaviour – as farmers might have responded that they access extension services to look socially hospitable during data collection by the trained local field officers within the EU Trust Fund for Africa-funded project and (4) lastly, the extension services in the Kenyan cashew sector is relatively recent with the re-birth of international donors, NGOs and government interests. Hence, translating this recent extension advice – like new information and technology – into higher income needs more time, especially for cashew.
Also, we reject the fourth hypothesis as an insignificant negative relationship between group membership and farmers’ economic performance can be seen from the results. Our study drew empirical evidence from cashew farmers cultivating extensive agriculture in the southern coastal regions, who require more technical information such as appropriate cashew tree density – which might not be easily obtained from farmer groups to improve yield. It is worthy to highlight that the farmers surveyed in this study belong to several groups such as savings associations and other rural groups not necessarily those focused only on cashew production.
Other studies revealed that cooperative membership and other social capital variables have significantly influenced farmers’ productivity (Balogun et al., 2018; Ingutia and Sumelius, 2022; Saha and Devi, 2014; Wossen et al., 2017) but studies investigating the effect on income considering the cost of production are yet missing. The policy implication is to focus resources on facilitating effective cashew-oriented farmer groups to increase information flow – especially the promotion of increased cashew planting density among farmers resident in the three major cashew-dominated counties, and especially to support rigorous studies on how the use of fertilizers and pesticides in the area affects the quality of nuts and the economic performance of smallholder farmers. The extension services should base their services and recommendations on the results of such studies’ results as they aim is to optimize input use and increase economic performance.
Our results indicate that in areas with extensive agriculture, such as the one under consideration in this study, the use of (inorganic) fertilizers is not economical. The cost of investment in fertilizer is higher than the increase in sales. One explanation could be that extension agents discourage the extensive use of chemical fertilizers while promoting organic alternatives such as manure. Hence, farmers tend to use manure – a more affordable choice and probably perceived as more effective. This argument is consistent with Costa and Bocchi’s (2017) advice that cashew trees react better to organic fertilizers owing to the relatively high amount of macro-and micronutrients such as calcium and magnesium that inorganic fertilizers do not provide (Costa and Bocchi, 2017).
Furthermore, manure has been adduced to slowly release nitrogen into the soil, reduce leaching and acidification and perhaps improve soil quality – structure and water content (Dendena and Corsi, 2014). A similar practice was observed in China, where extensive use of chemical fertilizers was discouraged due to the harmful effects on the soil Emmanuel et al. (2016). Lastly, the current underdeveloped market, does not provide premium payment for better quality nuts. Therefore, it will require time and high-quality buyers to appreciate higher quality nuts that might arise from practical fertilizer application. Therefore, the extension services need to provide information specific to the given location based on rigorous studies on input optimization to support the farmers in achieving of their economic goals.
One limitation to this study is the quality of data – especially with no records of any financial indicators by farmers. The spacing of trees had to be cross-checked during data collection since farmers could not specify the exact number of trees per acre. Sometimes even this data was a rough estimation by farmer. Also, we do not know the type of fertilizers farmers use. As cashew farmers are receiving more information on cashew recently and starting to use modern technologies, we recommend future investigation relating to the effect of extension services on farmers’ economic performance in the Kenyan cashew sector.
Results of multiple linear regression (dependent variable: Income in value of KES per acre).a
Citation: International Food and Agribusiness Management Review 26, 4 (2023) ; 10.22434/ifamr2021.0100
5. Conclusions
The present study has examined the impact of farmers’ group participation and access to agricultural extension services on farmers’ adoption of modern agricultural technologies (specifically, the use of fertilizers, chemicals and appropriate plant density) and the consequent impact of adopting these agricultural technologies on farmers’ economic performance (measured in income per acre) in the coastal regions of Kenya. In line with our empirical findings, we conclude that access to extension services and group membership have significant positive effects on adopting modern agricultural technologies, namely fertilizer and pesticide usage and appropriate planting density. It is worthy to note that planting density consequently showed a significant effect on economic performance. The same positive effect can farmers obtain by higher planting density. However, we can observe that the chemical spraying of cashew trees – such as with pesticides – does not affect economic performance, while fertilizer usage has even a negative effect.
Our findings revealed that over the last 5 years, the level of trust and solidarity in the community improved in the coastal regions. Thus, the local government can tap into this social capital to strengthen existing farmer groups further and encourage the formation of new groups – to appropriately use modern technologies to boost the cashew sector’s performance.
Policies should be put in place to promote structures and systems that will facilitate other major stakeholders – such as private firms and NGOs – to deliver extension services that promote the adoption of economic, reasonable, modern agricultural technologies to cashew farmers. This can drive competition in delivering extension services thus raising the possibility of reaching more farmers in Kenya. Furthermore, based on the rising number of female farmers, extension agents should advocate more for the active participation of female farmers in existing farmer groups and the formation of new ones. Information regarding agricultural technology, especially regarding appropriate cashew planting density, should be disseminated – as it has been demonstrated that when farmers use this form of agricultural technology, this leads to more profitable agribusiness ventures and better positioning of Kenya in the global economy.
The farmers’ interest in improving the technology of cashew planting can only be achieved if there is a developed market and demand for high-quality nuts at reasonable prices. Support of investment in processing factories in the area is necessary. The first step in this direction has been taken in the project funded by the EU Trust Fund for Africa, ‘Enhancement of livelihoods in the Kenyan coastal region by supporting organic and Fair-Trade certification of smallholders,’ which supports the establishment of a new large scale cashew processing factory in the area.
Acknowledgements
The authors acknowledge the Internal Grant Agency (IGA) of the Faculty of Tropical Agrisciences, Czech University of Life Sciences Prague for funding this research (IGA number 20213102). A special thanks to the implementers of V4 European Union project ‘enhancement of livelihoods in the Kenyan Coastal Region by supporting Organic and Fair Trade certification of smallholders’ implemented in accordance with the intervention programme ‘conflict prevention, peace and economic opportunities for the youth’ (EUTF05-HoA-KE-18) for data collection.
References
Abdollahzadeh, G., M.S. Sharifzadeh and C.A. Damalas. 2015. Perceptions of the beneficial and harmful effects of pesticides among Iranian rice farmers influence the adoption of biological control. Crop Protection 75: 124-131. https://doi.org/10.1016/j.cropro.2015.05.018
Abdollahzadeh, G., M.S. Sharifzadeh and C.A. Damalas. 2016. Motivations for adopting biological control among Iranian rice farmers. Crop Protection 80: 42-50. https://doi.org/10.1016/j.cropro.2015.10.021
Abebaw, D. and M.G. Haile. 2013. The impact of cooperatives on agricultural technology adoption: empirical evidence from Ethiopia. Food Policy 38 (1): 82-91. https://doi.org/10.1016/j.foodpol.2012.10.003
Afful, D.B. and K.K. Ayisi. 2016. Extension support for grain crop producers under climate variability scenario: implications for extension management in Limpopo Province, South Africa. Journal of Agribusiness and Rural Development 41 (3): 223-235. https://doi.org/10.17306/JARD.2016.49
Babu, S.C., M. Singh, T.V. Hymavathi, K.U. Rani, G.G. Kavitha and S. Karthik. 2016. Improved nutrition through agricultural extension and advisory services: case studies of curriculum review and operational lessons from India. World Bank Group report (94887-IN), pp. 1-38. World Bank, Washington, DC, USA.
Balogun, O.L., I.J. Ogunsina, T.A. Ayo-Bello and O.J. Afodu. 2018. Effects of social capital on productivity of cassava farmers in Ogun State, Nigeria. Journal of Agricultural Sciences 63 (1): 99-112. https://doi.org/10.2298/JAS1801099B
Birner, R. and J.R. Anderson. 2007. How to make agricultural extension demand-driven? The case of India’s agricultural extension policy. Paper 00729. International Food Policy Research Institute (IFPRI), Washington, DC, USA, pp. 1-36. https://doi.org/IFPRI Discussion Paper 00729
Bruce, A.K.K., S.A. Donkoh and M. Ayamga. 2014. Improved rice variety adoption and its effects on farmers’ output in Ghana. Journal of Development and Agricultural Economics 6 (6): 242-248. https://doi.org/10.5897/JDAE2013.0544
Buehren, N., M. Goldstein, E. Molina and J. Vaillant. 2017. The impact of strengthening agricultural extension services: evidence from Ethiopia. Paper 8169. World Bank Policy Research Working Paper, pp. 1-28. https://doi.org/10.1017/CBO9781107415324.004
Costa, S. and S. Bocchi. 2017. Manual for small-scale cashew cultivation in Sierra Leone. University of Milan, Faculty of Agricultural and Food Sciences, Department of Agricultural and Environmental Sciences – Production, Landscape, Agroenergy, Milan, Italy, pp. 1-58.
Dendena, B. and S. Corsi. 2014. Cashew, from seed to market: a review. Agronomy for Sustainable Development 34 (4): 753-772. https://doi.org/10.1007/s13593-014-0240-7
Denkyirah, E.K., E.D. Okoffo, D.T. Adu, A.A. Aziz, A. Ofori and E.K. Denkyirah. 2016. Modeling Ghanaian cocoa farmers’ decision to use pesticide and frequency of application: the case of Brong Ahafo Region. SpringerPlus 5 (1): 1-17. https://doi.org/10.1186/s40064-016-2779-z
Elias, A., M. Nohmi, K. Yasunobu, and A. Ishida. 2013. Effect of agricultural extension program on smallholders’ farm productivity: evidence from three peasant associations in the highlands of Ethiopia. Journal of Agricultural Science 5 (8): 163. https://doi.org/10.5539/jas.v5n8p163
Elias, A., M. Nohmi, K. Yasunobu, and A. Ishida. 2016. Farmers’ satisfaction with agricultural extension service and its influencing factors: a case study in North West Ethiopia. Journal of Agricultural Science and Technology 18 (1): 39-53.
Emmanuel, D., E. Owusu-Sekyere, V. Owusu and H. Jordaan. 2016. Impact of agricultural extension service on adoption of chemical fertilizer: implications for rice productivity and development in Ghana. Wageningen Journal of Life Sciences 79: 41-49. https://doi.org/10.1016/j.njas.2016.10.002
Food and Agriculture Organization of the United Nations (FAO). 2011. Proceedings of the consultation workshop on market-oriented agricultural extension. In: D. Kahan and K. Singh (eds.) Food and Agriculture Organization of the United Nations Rural Infrastructure and Agro-Industries Division Regional Office for Asia and the Pacific, Bangkok, Thailand, RAP Publication 2011/02 (May), pp. 1-42.
Food and Agriculture Organization of the United Nations (FAO). 2017. Success stories on information and communication technologies for agriculture and rural development. In: G. Sylvester (eds.) Food and Agriculture Organization of the United Nations Regional Office for Asia and the Pacific, Bangkok, Thailand, pp. 1-79.
Food and Agriculture Organization of the United Nations (FAO). 2021. Good practices for the establishment of a new cashew farm. Food and Agriculture Organization of the United Nations / Technologies and Practices for Small Agricultural Producers (TECA). Available at: https://teca.apps.fao.org/teca/es/technologies/7461
Gautam, M. 2000. Agricultural extension: the Kenya experience: An impact evaluation. 00-032087. World Bank Publications. Washington, DC, USA, pp. 1-67.
Glendenning, C.J., S. Babu and K. Asenso-Okyere. 2010. Review of agricultural extension in India: Are farmers’ information needs being met? Paper 01048. International Food Policy Research Institute (IFPRI), Washington, DC, USA, pp. 1-35.
Gujarati, D.N. 1995. Basic econometrics. McGraw-Hill, New York, USA.
Ingutia, R. and J. Sumelius. 2022. Do farmer groups improve the situation of women in agriculture in rural Kenya? International Food and Agribusiness Management Review 25 (1): 135-156. https://doi.org/10.22434/IFAMR2020.0142
Kondylis, F., V. Mueller and J. Zhu. 2017. Seeing is believing? Evidence from an extension network experiment. Journal of Development Economics 125: 1-20. https://doi.org/10.1016/j.jdeveco.2016.10.004
Läpple, D., A. Renwick and F. Thorne. 2015. Measuring and understanding the drivers of agricultural innovation: Evidence from Ireland. Food Policy 51: 1-8. https://doi.org/10.1016/j.foodpol.2014.11.003
Lawal, J.O. and E.O. Uwagboe. 2017. Cost effectiveness of intercropping patterns by cashew farmers in Oyo State, Nigeria. International Journal of Forest, Animal and Fisheries Research 1 (1): 27-30.
Liverpool, L.S. and A. Winter-Nelson. 2010. Poverty status and the impact of social networks on smallholder technology adoption in rural Ethiopia. Paper 00970. International Food Policy Research Institute (IFPRI), Washington, DC, USA, pp. 1-25.
Makokha, S., S. Kimani, W. Mwangi, H. Verkuijl and F. Musembi. 2001. Determinants of fertilizer and manure use for maize production in Kiambu District, Kenya. International Maize and Wheat Improvement Center (CIMMYT) and Kenya Agricultural Research Institute (KARI), Mexico, pp. 1-25.
Mangalassery, S., R. Rejani, V. Singh, J.D. Adiga, D. Kalaivanan, T.R. Rupa and P.S. Philip. 2019. Impact of different irrigation regimes under varied planting density on growth, yield and economic return of cashew (Anacardium occidentale L.). Irrigation Science 37 (4): 483-494. https://doi.org/10.1007/s00271-019-00625-7
Martey, E., A.N. Wiredu, P.M. Etwire, M. Fosu, S.S.J. Buah, J. Bidzakin, B.D.K. Ahiabor, and F. Kusi. 2014. Fertilizer adoption and use intensity among smallholder farmers in Northern Ghana: a case study of the AGRA soil health project. Sustainable Agriculture Research 3 (1): 24-36. https://doi.org/10.5539/sar.v3n1p24
Meijer, S.S., D. Catacutan, O.C. Ajayi, G.W. Sileshi and M. Nieuwenhuis. 2015. The role of knowledge, attitudes and perceptions in the uptake of agricultural and agroforestry innovations among smallholder farmers in sub-Saharan Africa. International Journal of Agricultural Sustainability 13 (1): 40-54. https://doi.org/10.1080/14735903.2014.912493
Mottaleb, K.A. 2018. Perception and adoption of a new agricultural technology: Evidence from a developing country. Technology in Society 55: 126-135. https://doi.org/10.1016/j.techsoc.2018.07.007
Nata, J.T., J.W. Mjelde and F.O. Boadu. 2014. Household adoption of soil-improving practices and food insecurity in Ghana. Agriculture and Food Security 3 (17): 1-12. https://doi.org/10.1186/2048-7010-3-17
Navarra, C., C. Borzaga and B. Franchini. 2017. Identifying processes and policies conducive to cooperative development in Africa. An exploratory study on cooperative development projects in the agricultural sector. Summary, concluding remarks and hypotheses for further work. Euricse Research Report 14: 17.
Ouma, J.O., F.M. Murithi, W. Mwangi, H. Verkuijl, M. Geth and H. De Groote. 2002. Adoption of maize seed and fertilizer technologies in Embu District, Kenya. International Maize and Wheat Improvement Center (CIMMYT) and Kenya Agricultural Research Institute (KARI), Mexico, pp. 1-22.
Rabany, C., N. Rullier and P. Ricau. 2015. The African cashew sector in 2015. General trends and country profiles. Analysis of cashew production, processing and trade in Africa. RONGEAD for the African Cashew Initiative, pp. 1-34.
Radhakrishnan, A., S.K. Meti and S.B. Goudappa. 2016. Information management behaviour of Papaya growers of Karnataka: a comparative study. Indian Research Journal of Extension Education 14(2): 31-35.
Saha, B. and R. Devi. 2016. Information management behaviour of traditional fish farmers in Assam. Indian Research Journal of Extension Education 14 (1): 11-16.
Saint Ville, A.S., G.M. Hickey, U. Locher and L.E. Phillip. 2016. Exploring the role of social capital in influencing knowledge flows and innovation in smallholder farming communities in the Caribbean. Food Security 8 (3): 535-549. https://doi.org/10.1007/s12571-016-0581-y
Sajeev, M.V, P.L. Saroj and R. Lakshmisha. 2014. Technology impacts on area, production and productivity of cashew in Dakshina Kannada district, Karnataka. Journal of Plantation Crops 42 (1): 62-69.
Sanyang, S. E., T.-C. Kao and W.-C. Haung. 2009. Comparative study of sustainable and non-sustainable interventions in technology development and transfer to the women’s vegetable gardens in the Gambia. Journal of Technology Transfer 34 (1): 59-75. https://doi.org/10.1007/s10961-008-9084-0
Sebhatu, K.T., T.W. Gezahegn, T. Berhanu, M. Maertens, S. Van Passel, and M. D’Haese. 2021. Exploring variability across cooperatives: economic performance of agricultural cooperatives in northern Ethiopia. International Food and Agribusiness Management Review 24 (3): 397-419. https://doi.org/10.22434/IFAMR2019.0215
Senyolo, M.P., T.B. Long, V. Blok and O. Omta. 2018. How the characteristics of innovations impact their adoption: an exploration of climate-smart agricultural innovations in South Africa. Journal of Cleaner Production 172: 3825-3840. https://doi.org/10.1016/j.jclepro.2017.06.019
Singh, N., P. Malhotra, J. Singh and G. Angad. 2016. Information needs and seeking behaviour of dairy farmers of Punjab. Indian Journal of Dairy Science 69 (1): 98-104.
Tarde, G. 1903. The laws of imitation. Henry Holt and Company, New York, USA.
Toma, L., A.P. Barnes, L.-A. Sutherland, S. Thomson, Burnett F and K. Mathews. 2018. Impact of information transfer on farmers’ uptake of innovative crop technologies: a structural equation model applied to survey data. Journal of Technology Transfer 43 (4): 864-881. https://doi.org/10.1007/s10961-016-9520-5
Turner, R.A., N.V.C. Polunin and S.M. Stead. 2014. Social networks and fishers’ behavior: exploring the links between information flow and fishing success in the Northumberland lobster fishery. Ecology and Society 19 (2): 38. https://doi.org/10.5751/ES-06456-190238
Wheeler, S.A. 2009. Exploring the influences on Australian agricultural professionals’ genetic engineering beliefs: an empirical analysis. Journal of Technology Transfer 34 (4): 422-439. https://doi.org/10.1007/s10961-008-9094-y
World Bank. 2008. World development report 2008: Agriculture for development. The World Bank. Washington, DC, USA, 355. https://doi.org/10.5860/choice.45-4765
World Bank. 2018. Country profile: Republic of Kenya. Available at: https://databank.worldbank.org/views/reports/reportwidget.aspx?Report_Name=CountryProfile&Id=b450fd57&tbar=y&dd=y&inf=n&zm=n&country=KEN%0D
Wossen, T., T. Abdoulaye, A. Alene, M.G. Haile, S. Feleke, A. Olanrewaju and V. Manyong. 2017. Impacts of extension access and cooperative membership on technology adoption and household welfare. Journal of Rural Studies 54: 223-233.
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