Determinants of adoption of enhanced cashew production technologies among smallholder farmers in Mtwara region, Tanzania

Abstract

The appropriate use of improved technologies in cashew production can lift cashew productivity and income amongst cashew growing countries including Tanzania. The major motive of this study was to assess the adoption determinants of improved technologies in cashew production for enhancing country’s effort of attaining 1,000,000 MT of cashewnut production by 2025 from the current 238,576 MT. Multistage sampling was used to collect cross-sectional data from 760 cashew growers using a semi-structured questionnaire in Tandahimba and Masasi districts. Descriptive statistics were used to analyze the adoption rates whereas a Cragg Double hurdle model was used to analyze the determinant of enhanced cashew technologies. Results showed that the overall adoption of enhanced cashew technologies in Mtwara region was 58%. The findings revealed that the adoption intensity of pesticides application, recommended spacing, and the area under improved cashew trees were 88%, 32%, and 41% respectively. The outcomes revealed by Double Hurdle model indicated that the choice and extent of adoption were influenced by education, off-farm income, farm size, extension contacts, group affiliation, and credit access. Furthermore, cashew tree age, gender, asset endowment, and location influenced only the adoption decision of cashew production technologies. Therefore, the study urges for the inclusive policy agenda that will escalate land allocated for cashew production and off-farm earnings to enhance the adoption and intensity of enhanced cashew technologies. Moreover, results suggest the requirement for policy mediations that will accentuate the extension support and input credit services to heighten espousal of enhanced cashew technology in Tanzania.

Keywords:

• Determinants
• adoption improved cashew
• production technologies
• farmers
• Mtwara
• Tanzania

PUBLIC INTEREST STATEMENT

Embracing farming expertise remains a crucial government strategy to enrich crop production and productivity in guaranteeing food security and exterminating poverty in many African countries. The adoption of numerous agricultural technologies in these countries as commended by agricultural professionals is vital to realize a desired results. Nevertheless, for the farming expertise to be accepted, it must guarantee better productivity to the farmers. In Tanzania, it has been contended that socio-economic, wealthy and institutional factors are the key aspects influencing the adoption of agricultural technologies such as improved planting materials, pesticides application and recommended spacing. Nevertheless, there is scanty information that show how these factors influence the adoption and intensity of multiple cashew production technologies. Henceforth, the present study, attempted to ascertain the driving factors of adopting multiple cashew production technologies. This will helps policy architects to focus on the factors that trigger the adoption and enhance cashew nut productivity.

1. Introduction

Cashew (Anacardium occidentale Linn) is one among the top six high-income generating crops in African countries and ranks ^second after tobacco (Monteiro et al., 2017). Africa is the main producer of raw cashew nut (RCN) producing over 58.4% of the world’s cashew nut in 2020, followed by Asia with 37.9% and Americas with 3.7% (FAOSTAT, 2022). Over 3.1 million smallholder farmers generated income from cashew production in Africa and earned an average of 3148 US$ per ton of shelled cashew nut between 2000 and 2019 (FAOSTAT, 2021). The top five cashew producers in Africa are Ivory Coast, Tanzania, Benin, Mali and Burkina Faso (Tessmann, 2020). In Tanzania Cashew ranks first important cash crop produced mainly for export (BoT, 2018). Over 75% of cashew produced is exported, with exports providing 10–15 percent of the country’s foreign exchange (Mbowe et al., 2019). Also, it’s a prominent source of income for over 667,437 households producing cashew in Tanzania (URT, 2021). Cashew cultivation is subjugated by smallholder farmers with an average farm size of 2–3 acres (Tsafu, 2015). The crop is often intercropped with both annual and perennial crops. The annual crops intercropped mostly with cashew are sesame, maize, cassava, pigeon peas, cowpeas, and groundnuts while coconut is among the perennial crops intercropped with cashew (URT, 2017). While the potential cashew productivity for the matured trees with more than 8 years has been 46 kg/tree/season with improved technologies, farmers had an average productivity of of 11 kg/tree/season with traditional technologies in the 1990s in Tanzania (Kasuga, Katinila et al., 2003). Literature reveals that such low productivity resulted from the use of traditional production practices which include poor technologies and management practices (Kimbi et al., 2020; Mwalongo et al., 2020). To increase productivity, at the beginning of the 1990s, the Cashew Research Project (CRP) based at Tanzania Agricultural Research Institute Naliendele, Mtwara, and development partners invested efforts to introduce enhanced cashew production practices. These efforts involved both distributing improved planting materials and recommended agronomic practices to farmers. Cashew Development Centres (CDCs) were established to serve as training centers for farmers via the available extension officers, sites for the production of improved cashew planting materials as well as demonstration sites and outlet of the research findings to farmers. CDCs were strategically located in the potential cashew growing areas of Tanzania. These were Naliendele, Nanyanga, and Mtopwa located at Mtwara, Tandahimba, and Newala districts in the Mtwara region. The other CDCs were Nyangao, Ngongo, and Nachingwea located at Lindi and Nachingwea districts to serve in the Lindi region and nearby districts, and CDC Nakayaya at Tunduru district to serve the Ruvuma region. The planting materials were distributed freely to farmers through CDCs, training and establishment of village-based cashew nurseries operated by farmers, and also Integrated Cashew Management Program (ICM) approach (Kasuga, Katinila et al., 2003). The planting materials involved polyclonal seeds and seedlings. Again, the ICM program was started to offer cashew growers with a basket of other technology opportunities like pesticides which included both the insecticides and fungicides, pruning, spacing and weeding technologies. ICM is a multidisciplinary approach that was established to deliver cashew growers with a combination of more than one technological choices for increasing cashew farm productivity and the knowledge with which to adapt and develop these technologies. In addition, the intervention introduced an improved marketing system through Warehouse Receipt System (WRS). The WRS signifies a trade arrangement by which produces are kept in an accredited storeroom(s), and the commodity possessors are given receipts to indicate their ownership, worth, category, amount and quality of their produce (Tanzania Warehouse Licensing Board (TWLB), 2013). These interventions aimed to support the government goal of increasing national cashewnut production from 232,700 Mt in 2021 to about 1,000,000 Mt in 2025 (Cashewnut Board of Tanzania (CBT), 2020). Because of this, it is vital to ascertain issues that impede the use of improved cashew technologies to complement government efforts of increasing cashewnut production and productivity in Tanzania.

A number of studies to assess adoption of crop varieties have been conducted in Africa using different methods. For instance, the adoption drivers of maize varieties in Kenya (Wanjira et al., 2021; Willy et al., 2021) and adoption of improved legume varieties in Ethiopia (Asare-Marfo et al., (2016); Dessalegn et al., 2022) as well as adoption of groundnut varieties in Nigeria (Ahmed et al., 2020; Ojo & Ogunyemi, 2014). Nevertheless, most of these studies attempted to assess the aspects prompting the adoption of improved crop varieties while little is known on the intensity of adoption of improved technologies and cashew in Tanzania (Kimbi et al., 2020; Kuboja et al., 2020, 2012; Mwalongo et al., 2020; Tibamanya et al., 2021).

The literature showed that only few scholars studied the adoption of improved cashew production technologies in Africa including Miassi and Dossa (2018)) in Benin, Kidunda et al. (2013) in Tanzania, Nhantumbo et al. (2017) in Mozambique and Dubbert et al. (2021) in Ghana. The authors of these studies focused much on the determinants of adopting improved cashew technologies and paid little attention to the intensity (the degree of use) of these technologies. Majority of these studies assessed the determinant of adopting a single technology (improved planting materials) and reported nothing about the determinant of adopting other technologies which influence cashew production and productivity. The present study fills these gaps by ascertaining factors prompting the adoption of improved cashew technologies and their level of use in Tanzania. The study also assesses the determinant and intensity of adopting other improved cashew production technologies such as pesticides, and recommended spacing. The acquired information from this study serves as a guiding tool for amending the existing approaches used in the improved cashew production technology dissemination in Tanzania and other cashew growing countries.

2. Materials and methods

2.1. Justification of the study in mtwara region

The present study was conducted at Tandahimba and Masasi districts in Mtwara region. Mtwara was selected for this study because it is the top producers of cashewnut in the country producing around 70% of all cashewnut in Tanzania (URT, 2021). In addition, the region has 45.4% of all cashew producers with more than 43.2% of the total area under cashew production in Tanzania (URT, 2021). Further, Mtwara region was among the regions which had more intervention from different cashew stakeholders on Intergrated Cashew Management Practices (ICMP). The economy of Mtwara is dominated by agricultural sector and has employed 90% of the active population living in the region. The second most important economic activity is livestock sector, specifically poutry farming. The region produces both food and cash crops in different seasons of the year. The main cash crops produced in Mtwara region were Cashewnut, Sesame, Pigeon peas, groundnuts and green grams (URT, 2017). Likewise, the major sustenance crops produced in the region were sorghum, cowpeas, cassava, maize, rice, sweet potatoes, bambaranut and vegetables. Mtwara region has three major agro-ecological zones within its respective districts. The first agro-ecology was the inland land plains around Masasi and Nanyumbu districts whereas the coastal plains concealed Mtwara district (URT, 2019) as shown in Figure 1. In addition, the Makonde plateau was another agro-ecology covered mostly Tandahimba and Newala districts (URT, 2016). The average annual precipitation varies from around 800 mm in the inland and central parts to almost 1200 mm in the hummocks and flat terrain near the shore (URT, 2019).

Figure 1. A map of mtwara region showing the study districts.

Source: Field survey (2020)

2.2. Theoretical framework

Adoption of innovative technologies by a crop grower is demarcated as the state in which the technology is used for more than one crop growing season (Ojo & Ogunyemi, 2014). The adoption of improved technologies is guided by diverse concepts (Ghadim & Pannell, 1999). The current study was conceptualized by two concepts namely the diffusion of innovations and utility maximization concepts.

2.2.1. The concept of diffusion of innovations

In the ground, the diffusion of new technology in a given community involves a social practise of conveying the skills or know how among the community members within the society for a specific period of time (Wani & Ali, 2015). Rogers (2010) pinpointed five main stages along which adoption occurs. In line with Rogers (2010), we presumed that crop growers as partakers of farming technologies pass through these stages: knowledge, coaxing, judgment, execution, and validation stage. However, according to Feder and Umali (1993), technology ambiguity among crop grower normally subsidizes over time along these stages, due to the fact that adopters turn out to be competent in using the crop technologies which eventualy changes the production function. In this study, “Adoption” refers to the verdict to use improved cashew planting materials, pesticides and recommended spacing by an individual farmer or household in line with Feder et al. (1985). Here, improved cashew planting materials, pesticides, and recommended spacing were the innovations that the individual household perceived as new (Rogers et al., 1983). It did not matter whether the improved planting materials, pesticides, and recommended spacing were recent as evaluated by time period or invention. In addition, the study is aligned with the fact that adoption was not a continuous situation, as farmers may opt to stop the use of any technology anytime (Negussie & Almaz, 2021).

2.2.2. Utility maximization theory

The level of technology use in a household is in line with utility maximization theory based on Snyder and Nicholson (2008). According to Snyder and Nicholson (2008) adoption of any new technology by a household is motivated by its utility maximization. It means that farming households are anticipated to opt least production cost technologies that derive their maximum satisfaction while expecting to get the utmost benefits. The likelihood of assessing the intensity in this study arouses due to the divisibility of the technologies such as planting materials, pesticides, and recommended spacing as explained by Feder et al. (1985). Therefore, the adoption intensity of improved plating material was measured as the proportion of improved planting materials used to the total materials planted. However, the level of pesticides used was measured as the proportion of cashew trees sprayed with pesticides to the total trees owned while the proportion of cashew trees planted in a recommended spacing was used to asses the intensity of recommended spacing. Generally, the adoption of improved cashew technologies is expected to rise farmers’ yield and returns (Kassa et al., 2021; Mwalongo et al., 2020). This meant that farmers would select improved planting materials, pesticides, and recommended spacing that maximizes their satisfaction. Hence, a farmer faced two options when comparing their satisfactions. The first option was the satisfaction obtained from the use of old cashew technologies represented by $Z_{i0}$ and $Z_{i1}$ was the second option of satisfaction acquired from the the use of improved cashew production technologies, as shown in the first equation;

$Z_{i0}={\delta }_{i0}{X}_{i0}+{\mu }_{i0}$

(1) $Z_{i1}={\delta }_{i1}{X}_{i1}+{\mu }_{i1}$(1)

Where $X$ is the farm-related factors, $\delta$ is the unkown parameter to be estimated, and $\mu$ is the error term. The adoption of recent cashew technology is expected to happen only when $Z_{i1}>Z_{i0}$ (Nahuelhual et al., 2009).

2.3. Empirical method and model specification

Several literatures have recommended diverse techniques of examining contributing factor of technology adoption. Some of these techniques such as probit and logit methods have been widely applied by majority of researchers to model the adoption decision of improved farming technologies (Awotide et al., 2014; Feder et al., 1985; Kimbi et al., 2020; Mwalongo et al., 2020). However, these techniques only clarified the reasons of using and/or not using a certain technology while the degree of technology use was not adquatey explained by these methods (Feder et al., 1985; Beshir, 2014). Hence, alternative approaches of using two-step analytical methods were proposed by scholars to model the decision to use and the extent of using various agricultural technologies. The common methods cited by many scholars were Heckman selection model, Cragg’s double hurdle model, and Tobit model (Awotide et al., 2016; Cragg, 1971; Tobin, 1958). For instantce, Tobit model has been mostly used in various examens to model the espousal decision and intensity of farming technologies (Kassahun, 2021; Nazu et al., 2021; Theophilus et al., 2019). Nevertheless, the model disregards the time interval by assuming that the same factors influence both the decision to use and the extent of using crop technologies, and these factors are mutually estimated (Kassa et al., 2021). On the other hand, Awotide et al., (2014) and Abdullah et al., 2019 have differrentiated the choice to accept a certain technology and the magnitude of its acceptance as two distinct choices made in different period. In this regard, the choice to use an innovation is made first and then the choice involving the extent of using innovation follows (Negussie & Almaz, 2021). The appropriate methods used by many authors to model these kind of decisions were the double hurdle model and heckman two stage procedures (Heckman, 1976; Cragg, 1971). However, this study also have chosen double hurdle model to assess the adoption decision and intensity of using Cashew Production Technologies (CPT). This method as initially proposed by Cragg, (1971) undertakes the autonomous estimation between the adoption decision and the intensity of using cashew technologies. Similar approach has also been used recently by Tibamanya et al. (2021), Solomon et al. (2014), Mahoussi et al., 2021 and Kassa et al. (2021) to asses the extent and intensity of adoption cashew technologies. The model involves two stage decision process, where in stage one the decision of whether to use or not using a certain technology is normally evaluated using either probit or logit models (Oboubisa-Darko, 2015). At this juncture, all observations are included in the analysis including users and non users of modern technologies. Thereafter, the second stage on the extent of adoption is normally modelled by the Truncated regression targerting only users of modern technologies (incessant positive values). At this stage non users of modern technology are dropped in the analysis (Oboubisa-Darko, 2015). The two steps of the double hurdle model and their respective equation have been shown in equation 1 and 2 hereunder;

Step one: The adoption equation. As shown above this step is normally estimated using either probit and logit regression. However, the present study has opted the use of probit model to identify factors guiding farmers’ decision to choose improved cashew technologies in Mtwara region. The probit model was chosen due to its outstanding properties, of assuming a normal distribution than other models, namely Logit and Tobit (Wooldridge, 2002). The probit model has been specified as;

(2) $C_i^{\ast }={\delta }^{\prime }{W}_i+{\mu }_i$(2)

$C_i=\left\{\begin{array}{c}1,if{C}^{\ast }>0\\ 0,if{C}^{\ast }\le 0\end{array}\right\}$

Where $C^{\ast }$ represent a latent variable which equal to 1 when a household i uses Cashew technologies (improved planting materials, pesticides and recommended spacing) and equal to 0 if a household didn’t use. $W^{\ast }$ indicates a vector of explanatory variables affecting the technology engagement (explained in Table 2), $\delta$ is the unkown parameter to be estimated and $\mu$ is the error term which is normally distributed ($\mu N\left(0,1\right))$.

Table 1. Classification of variables in the econometric exploration

Variable name	Description	Expected sign
Dependent variable on adoption equation (first stage)
Improved planting materials	Use of improved planting materials (1 = yes; 0 = no)
Recommended spacing	Used cashew recommended spacing (1 = yes; 0 = no)
Pesticides application	Applied pesticides (1 = yes; 0 = no)
Dependent variable on the adoption intensity (second stage)
Amount of improved cashew tree owned	Proportional of improved cashew trees owned over total cashew trees owned
Amount of pesticides used	Proportional of cashew trees sprayed with pesticides over total cashew trees owned
Amount of trees planted in spacing	Proportional of cashew trees planted in spacing over total cashew trees owned
Independent variables for adoption and intensity equations
Farmer and Household characteristics
Age	Cashew growers’ age (years)	±
Gender	Binary (1 = if male, 0 = if female)	+
Household size	Total number of household members	±
Years of Schooling	Number of years cashew growers spent in formal schools	+
Age of cashew trees	Age of cashew trees since planting (years)	±
Wealth or economic
Farm size	Total farm size with cashew tree (acre)	+
Off-farm income	Binary (1 = if earned off-farm returns, 0 = Otherwise)	+
Asset endowment	Household asset index	+
Institutional and access
Access to extension services	Number of times advised with extension officers	+
Group membership	Binary for 1 = if member to groups and 0 = if nonmember	+
Credit access	Binary for 1 = if received financial help, 0 = Otherwise	+
Geographical residence	Household is located in Tandahimba (1 = yes; 0 = no)	±

Table 2. Cashew growers interviewed in tandahimba and masasi districts (n = 760)

District	Ward	Village	Sample size
Masasi	Chikolopora	Chikolopora	28
		Namyomyo	23
	Chiwale	Chiwale	73
		Lilala	25
		Nanyindwa	25
	Lukuredi	Lukuredi A	50
		Mkoropola	25
		Mraushi	25
	Mnavila	Geuza	50
		Mnavila	25
		Nanyule	53
Tandahimba	Kitama	Kitama Mjini	52
		Mwenge B	25
		Namunda	24
	Malopokero	Malamba	24
		Malopokero	53
		Mbalala	24
	Mdimba	Mdimba	61
		Mtegu	24
		Tukulu	17
	Nanhyanga	Miule	29
		Nanhyanga B	25

The second step of the double hurdle model evaluate only the extent of engaging in a certain activity after using it and disregard non-engaged household. Normally this step is estimated using a truncated regression as indicated hereunder;

(3) $Y_i^{\ast }={\beta }^{\prime }{X}_i+{\epsilon }_i$(3)

$Y_i=\left\{\begin{array}{c}{Y}_i^{\ast }if{Y}_i>0and{C}^{\ast }>0\\ 0if{Y}_i>0and{C}^{\ast }\le 0\end{array}\right.$

Where,

$Y_i$ is the latent variable indicating the observed extent of engaging in activity for household i (propotion of improved cashews owned, or proportion of the spurted cashews with pesticides or proportion of cashews planted in recommended spacing). $X_i$ is a vector of independent variables influencing the extent of engagement (X = W as indicated in Table 2), β is the unknown parameters to be estimated and $\epsilon N(0,{\sigma }^2$) is the error term.

For the empirical analysis the probit and truncated regression as shown in equation 1 and 2 can be expanded into equation 3 and 4 respectively as;

(4) $C_i\left(0,1\right)={\delta }_0+{\delta }_1{W}_1+{\delta }_2{W}_2+{\delta }_3{W}_3+{\delta }_4{W}_4+{\delta }_n{W}_n+{\mu }_i$(4)

(5) $Y_i={\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+{\beta }_3{X}_3+{\beta }_4{X}_4+B_n{X}_n+{\epsilon }_i$(5)

Where X = W (age, gender, year of schooling, access to extension services, off-farm income, cashew tree age, asset endowment). The details of these variables have been explained below.

Since the Cragg double hurdle model nest the Tobit model, then the resolution showing which model to use, or which model fits the data was tested using the likelihood ratio test. To achieve this, the estimate of each enhanced cashew technology was done independently using the Probit, Truncated and Tobit models (Asante et al., 2017; Tibamanya et al., 2021). Thereafter, the log-likelihood values of the models were used to calculate the likehood ratio test (λ) as shown in the equation 5 below;

(6) $\lambda =-2\left(L{L}_{Probit}+L{L}_{Truncated}-L{L}_{Tobit}\right)$(6)

Where; $L{L}_{Probit}$, $L{L}_{Truncated}$ and $L{L}_{Tobit}$ designate the log likehood function of the probit, truncated and tobit regression models respectively. To authenticate the use of the Cragg double hurdle model, the estimated λ for each enhanced cashew technology type tested should be greater than the Chi square (χ²) critical value (Bannor et al., 2020; Ghimire & Huang, 2015). Therefore, based on the likelihood ratio test as shown in Table Appendix 2 the Cragg double hurdle model was preferred in this study because the estimated value of λ was greater than Chi square $\left({\chi }_{0.05}^2\right)$ critical value

2.3.1. Depiction of dependent variables in the double hurdle model

In this examen, the assessed models had three equations with three different dependent variables in each stage. These variables included the use of enhanced cashew planting materials, usage of pesticides, and usage of recommended spacing. The dependent variable for espousal is dichotomous. Adopter in this study is the cashew grower who owned enhanced cashew planting materials or applied pesticides and have planted cashew trees in a recommended spacing for two consecutive seasons. Conversely, the extent of use indicates the proportion of improved cashew trees owned, the percentage of cashew trees planted in recommended spacing, and the proportion of cashew trees sprayed with pesticides for two consecutive season prior to survey in 2020.

2.3.2. Delineation of independent variables castoff in the double hurdle model

A list of covariates that may influence the farmers’ engagement in farming technologies was found from both existing literature and precise features of the agricultural system in Mtwara region. The literature suggested that, farmers’ choice to practice farming expertise was conjectured and predisposed by a collective influence of several aspects including location, sociodemographic and household individualities (Kimbi et al., 2020; Mwalongo et al., 2020; Nhantumbo et al., 2017). The explanatory variables postulated to effect the adoption of improved cashew technologies have been constructed based on the work done by Kidunda et al. (2013), Nhantumbo et al. (2017), and Kimbi et al. (2020). These variables have been grouped into three categories including cashew grower characteristics, market access as well as institutional and location variables.

2.3.3. Cashew grower characteristics

Cashew grower’s characteristics included in the model were age, education, gender, tree age and household size. Farmers’ age indicated knowledge and experience in cashew farming. It was postulated that the higher the age of cashew farmers the higher the knowledge and experience acquired from various use of cashew technologies (Kimbi et al., 2020). Likewise, it was argued that young aged farmers were eager in attempting new farming technologies and long-lasting investiment compared to aged farmers who were risk-averse (Ojo & Ogunyemi, 2014). Years of schooling were measured as the number of years cashew farmers spent in the formal education. It was anticipated to have a affirmative result on the technology espousal (Ojo & Ogunyemi, 2014) since it escalates farmers’ skill to acquire appropriate evidence for evaluating technologies (Mwalongo et al., 2020). Sex was a dummy variable (female = 0, male = 1). Sex is likely to favour adoption positively because men have a priviledge for more information access, properties and training which surges their likelihood in technology adoption (Gebre et al., 2021). Household size was measured in number of active labour in a household. It is predicted to have an optimistic impact on adoption because a household with larger household size is likely to have more workforce readiness that can overcome labour limitations during the inception of novel technology (Mwangi & Kariuki, 2015)

2.3.4. Financial and market variables

In this regard, three variables with mixed effect on adoption and its extent were involved namely farm size, off-farm income and asset endowment. Farm size is measured in hectares (ha) and was an indicator of wealth and social prestige (Giziew, 2014). Therefore, farmers possessing huge farm size are more likely to allocate more land for improved planting materials compared to their counterpart farmers with small farm size (Simtowe et al., 2011). Off-farm income and asset endowment are expected to have a positive effect on technology adoption and its strength. This is because off-farm returns and asset endowment enhance liquidity capital that may finance the purchase of inputs like seeds, seeddlings, pesticides and labour cost (Mwangi & Kariuki, 2015). It was postulated that farmers with off-farm income and asset endowment are likely to adopt new technologies compared to their counterpart without off-farm income and asset endowment.

2.3.5. Institutional and environmental factors

The variables included in this category were extention contacts, access to credit, group membership and geographical location of cashew growers. Access to extension indicated the number of times cashew growers received extension supports per season in 2020. It was hypothesised that the number of extension contacts effects adoption and its extension positively. This is because extension officers supports in generating awareness about the technology availability and its potential benefits that can be accrued from using it (Cole & Fernando, 2021). Access to credit is a dummy variable indicating whether cashew growers received financial assistance in the form of cash or in-kind to support farming operations in 2020. It is projected that farmers who received financial services may increase their chance to adopt improved techonologies due to their increased ability to purchase farm inputs (Kassa et al., 2021; Uaiene & Arndt, 2009). Likewise, Group membership enhances community wealth permitting trust, knowledge and facts altercation amongst farmers.Cashew growers who are members of cooperative societies were more likely to to learn about novel cashew knowledge and increases their likelihood to adopt the new innovations (Nhantumbo et al., 2017). The complete list of variables and their direction have been shown in Table 1.

2.4. Sample selection techniques and size

The study employed a cross-sectional data collected in 2020 while a multi-stage purposive sampling technique was used to pick out districts and wards whereas villages and households were randomly chosen. The choice of districts and wards was grounded on their cashewnut production echelons. In each household, the head of the household was questioned via a semi-structured questionnaire digitized in the mobile data collection platform named surveyCTO. The collected data were analyzed using STATA software version 14. Smallholder cashew farmers were drawn from a sample frame of cashew farmers in the Tandahimba and Masasi districts. The following formula was applied to calculate the sample size as;

(7) $N=a(1-a){\left(\frac{z}{E}\right)}^{2}Deff$(7)

Where;

$a$ = adoption (20%), $z$ = z-score at 95% level of confidence = 1.96, $E$ = level of precision = 5% and $Deff$= Design effect = 3

A total of 760 smallholder cashew farmers were interviewed including 358 in Tandahimba and 374 in Masasi districts respectively as shown in Table 2.

3. Results

3.1. Socio-demographic profile of cashew growers in the study districts

Findings elucidated that cashew farmers (at least 72.35 %) in Tandahimba and Masasi districts attained primary school education level, whose main occupation (at least 98.26%) was farming. Cashew production as an economic crop is mainly (at least 83.08) produced by males and the majority of cashew farmers (73.3%) were members of groups or cooperative societies. In the region, the age distribution was almost normal, where the dominating age group in cashew production ranged between 35–60 years old, for all the districts and most of cashew farmers (85.85%) were married. In addition, results revealed that majorty (73.68%) of cashew growers had good access to extension services in their respective districts as presented in Table 3.

Table 3. Sociodemographic profile of cashew growers in the study districts (n = 760)

Household variable	Categories	% Farmer by districts
		Masasi	Tandahimba	Total
Education	No formal education	12.19	21.79	16.99
	Primary	81.34	72.35	76.85
	Secondary	5.72	5.03	5.38
	Post-secondary	0.75	0.84	0.79
	Total	100	100	100
Marital status	Married	83.9	88	85.85
	Single	14.29	8	11.29
	Divorced	0.78	2.57	1.63
	Widow/widower	1.04	1.43	1.22
	Total	100	100	100
Main occupation	Farming	98.26	98.6	98.42
	Salaried employment	0.75	0.56	0.66
	Business	1	0.84	0.92
	Total	100	100	100
Gender	Female	16.92	16.2	16.58
	Male	83.08	83.8	83.42
	Total	100	100	100
Access to extension services	Yes	82.34	63.97	73.68
	No	17.66	36.03	26.32
	Total	100	100	100
Group membership	Yes	86.4	73.3	79.4
	No	13.6	26.7	20.6
	Total	100	100	100
Age group	<35 years	15.5	10.6	13.2
	35–60 years	65.6	64.0	64.8
	>60 years	19.0	25.4	22.0
	Total	100.0	100.0	100.0

3.2. Types of improved cashew varieties, dissemination strategies and productivity potential per tree

The results revealed that to date there are a total of 54 new cashew varieties released in Tanzania (Table Appendix 1). These varieties were released in three phases. The first phase was the release of 16 cashew varieties in 2006 and the second phase was the release of 22 elite cashew hybrids in 2015. The third phase was the release of 16 dwarf cashew varieties in 2016. The cashew hybrids and the dwarf cashew varieties were yielding higher than the standard cashew variety AC4 which is very high yielding and recode as high as 115 kg/tree/year at age of 25 years. These cashew varieties are produced and disseminated to farmers in the form of polyclonal seeds (botanical seeds) and seedlings (grafted and ungrafted) through Cashew Development Centres (CDCs). Polyclonal seeds are seeds collected from a field planted in triangulation a mixture of different cashew varieties. Farmers are allowed to collect scions from the improved cashew clones available at the CDCs so that they are grafted in the nurseries or in-situ in their fields. Each CDC is therefore comprised of a polyclonal seed garden, scion garden, and nursery. These dissemination strategies are aimed at giving farmers access to many improved cashew varieties and encourage them to plant mixed cashew varieties of different genetic potential per field. The use of diverse genetic materials within a farm assures sustainability of the cashew farms because it reduces the chance of the total crop loss due to outbreaks of new diseases as some of these materials may be tolerant or resistant.

3.3. Cognizance and usage of improved cashew technologies by cashew growers

Results in Table 4 showed different levels of awareness and use for different cashew technologies in Tandahimba and Masasi districts. The information about new technology itself was a primary factor towards its adoption or uptake. The knowledge about technology’s existence, its efficiency, cost, advantages, and its availability were important factor for informed decision making. The awareness campaigns in farming communities were done through participatory research, training, demonstrations, farmers’ field days, farmers’ field school, and radio and Television (TV) promotions. Generally, results disclosed that the overall level of awareness for all improved cashew production technologies in both districts was (79%) while only 58% of cashew farmers used different improved cashew technologies. Generally, the degree of cognizance and application of new cashew innovations were higher in Tandahimba at about 84% and 65% respectively than cashew farmers in Masasi district whose degree of awareness and practise was low at 75% and 45% respectively

Table 4. Cognizance and usage of improved cashew technologies in study districts

Type of technology	Tandahimba (%)		Masasi (%)		Overall (%)
	Awareness	Use	Awareness	Use	Awareness	Use
Cashew weeding	99	97	98	91	98	94
Cashew pruning	98	94	96	82	97	88
Pesticides	86	82	79	74	82	78
Improved planting materials	75	57	68	20	71	38
Weaver ants	78	42	62	18	70	30
Cashew Grafting skills	86	49	70	12	77	29
Recommended spacing	64	36	53	20	58	27
Overall per district (%)	84	65	75	45	79	58

3.4. The espousal intensity of improved cashew technologies in the study districts

The verdicts in Table 5 exhibited that 41% of the entire area under cashew husbandry in the study area was covered by improved cashew trees. The proportional area under improved cashew trees was higher in Tandahimba (52%) than in Masasi district (27%). However, results discovered that the intensity of pesticide use in the study area was very high valued at 88%. Equally, findings indicated that the intensity of cashew tree spacing in the study area was still very low 32% implying that out of 100 cashew tree planted in a farm only 32 trees have been planted in a recommended spacing.

Table 5. The espousal intensity of improved cashew technologies in the study districts

Variable	Tandahimba (n = 204)	Masasi (n = 80)	Total (n = 284)
Total area owned by farmers (acres)	2318	2019	4366
Total area under cashew production (acres)	2209	1811	4020
Total number of cashew trees owned	27,719	21,950	49,669
Number of cashew trees sprayed with pesticides	24,571	18,654	43,225
Number of trees under spacing	11,640	4192	15,832
Total area under improved cashew (acres)	1147	494	1641
% of area under improved cashew trees	52	27	41
% of cashew trees sprayed with pesticides	89	85	87
% of cashew trees under recommended spacing	42	19	32

3.5. Major sources of improved cashew planting material

Table 6 displays the five major sources of planting materials in the study areas. The first source reported by respondents in both districts was family /friends/relatives (72%) followed by Research Institute (17%) and District councils (8%). Other sources reported were cooperative societies/farmer groups and the Cashewnut Board of Tanzania (CBT). In addition, results reported that 96% of the smallholder farmers interviewed showed their willingness to continue using improved planting materials in the study area. These findings show the general acceptability of all the improved cashew planting materials disseminated to cashew growers in the Mtwara region.

Table 6. Main sources of cashew planting materials

	District
Main Sources of planting materials	Masasi (N = 394)		Tandahimba (N = 357)		Total (N = 751)
	Frequency	(%)	Frequency	(%)	Frequency	(%)
Family/friends/relative	330	84	210	59	540	72
Research Institutes	17	4	109	31	126	17
District councils	29	7	27	8	57	8
Cooperatives/Farmer groups	6	2	10	3	16	2
CBT	11	3	1	0	11	1

3.6. Factors limiting the use of improved technologies among cashew farmers

Findings indicated that the major limiting factor in the use of improved cashew technologies was the inadequate information (38%) followed by the Unavailability of inputs (32%). While 10% of the respondents reported that they are not interested in the improved technologies and (7%) of the respondents reported high prices of these technologies as their limiting factor. Other limiting factors reported by respondents were the randomly spaced inherited and/or bought farms (Table 7).

Table 7. Factors limiting farmers from using improved technologies

	Districts
Limiting factors	Masasi (N = 341)		Tandahimba (N = 255)		Total (N = 596)
	Frequency	%	Frequency	%	Frequency	%
Inadequate information	125	36.66	103	40.39	228	38.26
Unavailability of inputs	162	47.51	30	11.76	192	32.21
High prices	20	5.87	19	7.45	39	6.54
Not interested	23	6.74	36	14.12	59	9.9
No motivation	3	0.88	5	1.96	8	1.34
Difficult to learn	2	0.59	8	3.14	10	1.68
Difficult to use	2	0.59	25	9.8	27	4.53
Randomly spaced tree	4	1.17	29	11.37	33	5.54
Total	341	100	255	100	596	100

3.7. The influential factors of espousal of improved cashew technologies

Results presented in Table 8 show the influential factors of adopting cashew technologies as assessed by the probit model. In addition, finding indicates the goodness-of-fit and likelihood ratio index to show model reliability and the extent to which independent variables influences the dependent variable. The verdict showed that the number representing age of the cashew tree was (−0.014) and significant at 5%. The negative sign implies that the chance of cashew growers with old trees to accept enhanced cashew planting materials was lower matched to those with fledgling aged tree.

Table 8. The Probit estimates of the factors of espousal of improved cashew technologies

Variable	Improved planting materials	Pesticides	Recommended Spacing
Farmer and Household characteristics
Age	0.004	−0.003	0.311
Gender	0.046	0.572 (0.12)***	0.218
Household size	0.058 (0.019)**	−0.008	0.067 (0.102)**
Years of Schooling	0.053(0.018)***	0.077 (0.013)***	0.017
Age of cashew trees	−0.465 (−0.012)*	0.063	−0.723 (−0.195)**
Wealth or economic
Farm size/land	0.014	0.012 (0.002)*	−0.002
Off-farm income	0.132 (0.267)**	0.038	0.234
Asset endowment	0.014	0.088	−0.05
Institutional and access
Access to extension	1.158 (0.421)***	0.253	0.66 (0.529)***
Group membership	0.014 (0.164)*	0.035 (0.489)*	0.307
Access to credit	−0.239	0.442 (0.53)**	0.488 (0.244)*
Geographical residence	0.035 (0.322)*	0.211	0.325
Log likelihood	−33.578	217.326	324.521
Number of observation	760	760	658
LR χ^2 (12)	86.861***	35.829**	38.133***
Pseudo R^2	0.116	0.073	0.042

Note: The values in parenthesis are marginal effects and the asterisks ***, ** and * shows Significance levels at 1%, 5% and 10% respectively.

In addition, the finding revealed that the chance of adopting the improved planting was positively influenced by household size and off-farm income by 1.9% and 26.7% respectively at a 5% level of significance. Implying that having extra income sources, for instance, off-farm earnings increase farmers’ investment capacity in improved agricultural activities by 26.7%. Factors such as years of formal training and access to extension had a affirmative effect on the espousal of enhanced cashew planting materials and were significant (p < 0.01). Implying that the more farmers are accessing extension services and expanding their knowledge through formal training the more they are likely to adopt improved cashew planting materials by 42.1% and 1.8% respectively. The coefficients of group membership (0.164) and geographical residence (0.322) were statistically significant (p < 0.1) on the espousal of enhanced cashew planting materials. These findings implied that the chance of cashew farmers joined in groups and residing in the Tandahimba district to adopt ehanced cashew planting material was higher by 32.2% and 16.4% respectively compared to their counterpart farmers in the Masasi district. This might be due to the truth that several interventions such as sites for multiplication of improved planting materials and agricultural extension centers for learning were mostly taking place in Tandahimba.

The outcomes exposed that the espousal of pesticides technology was certainly influenced by gender and years of schooling at a 1% level of significance. Inferring that a male farmer and formal schooling proliferate the possibility of espousing pesticides technology. It was also shown that farm size and group membership had a progressive impact on the espousal of pesticides at a 10% level of significance, meaning that joining a farmer group and owning big farm size escalates the probability of adopting pesticides technology in the study area. The coefficient of credit access was positive and statistically significant (p < 0.05), which implied that cashew growers who had access to financial assistance are more prospective to adopt pesticides technology. Results indicated that contact to extension amenities had a progressive effect on espousal of recommended cashew spacing by 52.9%. Conversely, the coefficient of cashew tree age was (−0.195) denoting that cashew growers with old aged trees are less likely to adopt recommended spacing by 19.5% than those with young aged trees. Results further showed that the marginal effect for credit accessibilty was 0.244 and indicating that cashew growers who have access to financial support increases their chance of espousing recommended spacing technology by 24.4%.

3.8. Factors prompting the degree of espousal of cashew production technologies

Results in Table 9 elucidate the outcomes of the truncated regression model. The model deduced that the coefficient of off-farm income, contact to extension officers, group affiliation significanty effect on the degree of enhanced cashew trees owned. Correspondingly, the amount of pesticides used was significantly affected by the year of schooling, farm size, group attachment, and credit access. Moreover, farm size and credit access factors influence the amount of trees planted in a recommended spacing significantly. The coefficient for off-farm income was 0.037, denoting that the growth of cashew growers’ off-farm earnings inflates the number of improved cashew trees planted per acre. In addition, the coefficient for access to extension service was 0.294, implying that the increase in extension service inflates the number of improved cashew trees by 29.4% per acre. Furthermore, a coefficient of group affiliation was 0.186, suggesting that cashew growers affiliated in groups risestheir magnitude of planting improved cashew trees by 18.6% per acre. Conversely, a coefficient for years of schooling was 0.083, implying that as the number of years spent in the formal shooling increases the amount of pesticides used inflates by 8.3%. However, farm size had a coefficient of 0.058, which meant that as cashew growers increases their farm sizes by 1% the extent of cashew trees squirted with pesticides increases by 5.8% per acre. In addition, the coefficient of credit access was 0.198, indicating that as cashew growers increases their access to financial assistance the amount of cashew trees squirted with pesticides inflates by 19.8%.

Table 9. Truncated estimated prompting the degree of espousal of cashew technologies

Variable	Amount of improved cashew tree owned	Amount of pesticides used	Amount of trees planted in spacing
Farmer and Household characteristics
Age	0.001	0.003	0.214
Gender	0.259	0.235	0.008
Household size	0.078	0.032	0.005
Years of Schooling	0.036	0.007 (0.083)**	0.645
Age of cashew trees	0.023	0.622	0.006
Economic parameter
Farm size/land	0.004	0.006 (0.058)*	0.678 (0.031)**
Off-farm income	0.067 (0.037)**	0.091	0.512
Asset endowment	0.143	−0.445	−0.078
Institutional and access parameters
Access to extension	0.781 (0.294)*	1.087	−7.217
Group membership	0.952 (0.186)***	0.452 (0.027)***	−6.727
Access to credit	−0.395	0.151 (0.198)**	0.4532 (0.002)***
Geographical residence	0.741	−0.029	0.012
Log likelihood	−732.422	124.885	197.394
Number of observation	638	693	519
LR χ^2 (12)	47.96**	36.956**	37.384**
Pseudo R^2	0.064	0.259	0.082
NOTE: The values in parenthesis are marginal effects and the asterisks***, ** and * shows Significance levels at 1%, 5% and 10% respectively.

Finally, the other variables influencing the degree of cashew trees planted in a recommended spacing were farm size (0.031) and credit acces (0.002). These entail that as farm size and credit access increase by 1% the extent of cashew trees planted in recommended spacing inflates by 3.1% and 0.2% respectively.

4. Discussion

To enhance cashew production, productivity, and income generation among smallholders cashew growers in Mtwara region, the use of improved cashew planting materials, pesticides, and recommended spacing are some of the most appropriate technologies that enhance cashew output (Nhantumbo et al., 2017). The descriptive statistics showed that the area under improved cashew trees was estimated at 41% of the total cashew cultivated area in the study districts. The findings implied that more than half of the area under cashew production has been planted with the traditional cashew trees. This was probably because Mtwara is among the traditional cashew growing regions in Tanzania which started cashew production before the release of improved cashew varieties. Thus the chance of owning traditional cashew trees is high compared to non traditional cashew growing areas in Tanzania which purchase improved planting materials from recognized sources.

In addition, the adoption rate of pesticides use and its intensity were 78% and 87% respectively. This implies that out of the total cashew trees owned 87% were applied with pesticides. This was probably because cashew farmers needed to ensure maximum protection and productivity of cashew trees against diseases, insect pests, and notorious weeds that normally affects young leaves, early abortion of young nuts, shoots and deteriorate the nut quality resulting to overall loss of cashew output. Hence, the appropriate use of pesticides on cashew trees can largely reduces production losses per season (Monteiro et al., 2017).

Correspondingly, the verdicts indicated that despite of its importance the intensity of cashew tree planted in a recommended spacing was very low (32%). These results implied that out of 100 trees planted only 32 trees were planted in recommended spacing of 12 metre by 12 meter. Whereas, 68 cashew trees are in a random spaced in a farm (either below or above 12 m) depending on farmers’ need. The recommended spacing is vital for uniform tree canopy, avoiding overcrowding and allowance of enogh light and space (Dendena & Corsi, 2014).

The outcomes disclosed by the double hurdle model indicated that the pesticides use increases amongths the male headed households. These findings show that gender doesn’t significantly intensify the use of improved cashew technologies although it has a positive relationship. These findings showed that males had higher probability of adopting improved technology than females because of their resource ownership. Similar findings were also reported by Mensah et al. (2018) and Addison et al. (2018) who argued that majority of female-headed household had limited access to household resources and were not better-off compared to their counterpart male-headed household. The outcomes reported by Mensah et al. (2018) and Addison et al. (2018) complement to the existing family relationships and practices in the study area (URT, 2016).

The verdicts indicated that household size influences the adoption of both improved planting materials and recommended spacing. Although the findings were not significantly influencing intensification but had a positive association. On adoption, the findings suggest that improved planting materials and spacing are labour-intensive (Solomon et al., 2014). Most of the deprived farming households depend on family labour in their agricultural set-ups (Rehman et al., 2017). This labour limitation may disturb crop output, profit and can dishearten technology adoption. Kidunda et al. (2013) witnessed parallel effects of labor intensive in cashew farming.

In addition, it was revealed that the negative sign on the coeffient of the cashew tree age on the adoption of improved cashew planting materials and recommended spacing suggested the contrary relationship between cashew tree age and adoption of improved cashew planting materials and recommend spacing. This is probably because the higher the age the higher the productivity per tree, thus reduce the chance of replacing aged and productive trees even with improper spacing. In addition, it is costly and labour intensive to re-arrange old random spaced cashew trees into proper spacing within a farm (Dubbert et al., 2021).

Alternatively, it was revealed that the geographical residence of cashew farmers influences only the adoption of improved cashew planting materials and had no effect on its extent. These findings implied that cashew growers residing in Tandahimba district are more likely to adopt improved cashew planting materials by 32.2% compared to their counterpart farmers in the Masasi district. This might be probably due to the proximity of the district with Nanyanga and Mtopwa Cashew Development Centres (CDC) located within and in the nearby district. The centres act as a learning platform and source of improved planting materials for smallholder cashew farmers within and outside the district. This findings are reinforced Mmbando et al. (2021) who revealed a positive relationship between location of magbean farmers and probability of adopting mugbean technologies.

Similarly, years of schooling influenced the use of pesticide and improved planting materials. Meanwhile, it influenced the intensity of pesticides application among cashew farmers in the study area. Schooling adds farmers’ knowledge, approaches and views, and enhances their ability to analyse the pros and cons of any new innovations (Akinbode and Bamire,). The same scenario was also revealed by Kidunda et al. (2013) work who reported positive effect of year of shooling on the log-odds of adopting improved cashew planting materials.

Findings showed that off-farm income had a positive influence on the adoption and intensification of improved planting materials among cashew farmers by 26.7% and 3.7% respectively. Results implied that having an off-farm income escalates the chance of adopting improved cashew planting materials by 26.7% and increases the amount of improved planting materials owned by 3.7%. The returns obtained from off-farm works reduces monetary constraints for the deprived farmer and enhance their ability to buy productive inputs (Huffman, 1980). Majority of the indigenous about 90% in the study districts are smallholder farmers with no well-established off-farm activities (URT, 2021) however, in recent times, cashew production has attracted producers from other sectors like researchers, teachers, and investors with profound off-farm income.

On the other hand, affiliation to farmer groups had a positive sign and significantly influence the adoption and extent of improved cashew planting materials and pesticides application. These findings inferred that farmers’ affiliation in groups increases their probability of adopting the improved planting materials and pesticides application by 16.4% and 48.9% respectively. Additionally, results, indicated that group membership surges the amount of improved planting materials used by 18.6% and rises the amount of trees sprayed with pesticides by 2.7%. The group involvement guarantees their members access to credit, crop production and marketing information (Tripp, 2006). Furthermore, organized groups can easiliy be identified and supported by governmnents and development partners wherever need arises (Daudi et al., 2018; Mwaura et al., 2012; Tchale, 2009).

In addition, cashew growers who possessed bigger farm sizes had more chance of adopting and intensifying pesticides and recommended spacing innovations than their counterparts growers with smaller farm sizes. Various studies have associated technology adoption amongst smallholder farmers and their financial capacitities (Martey et al., 2014). Chandio and Yuansheng (2018) and Oladeji et al. (2015) reported that farmers with bigger farm sizes could be better-off and have higher probability of learning new innovations. Howevers, those who own smaller farm sizes are normally deprived with limited capacity of acquiring crop inputs (Kimbi et al., 2020).

In the same way, credit access influenced the adoption and intensification of pesticides and recommended spacing positively. Access to financial instruments in agriculture influences farmers’ investment choices and provides them with more effective tools to manage risks (Cole & Fernando, 2021). In the study area, it is obvious that pesticides and spacing are among the technologies which can easily be reduced or ignored when financial resources are limited. Farmers cannot observe recommended spacing if they do not have enough money to purchase land. Similarly, they cannot apply enough pesticides with limited finance.

5. Conclusions and recommendations

Improved technologies namely planting materials, pesticides, and spacing are innovations worthy of being distributed throughout the country. Actual adoption and use of enhanced cashew production technologies are vital to guarantee an adequate source of foodstuff and income to all cashew value chain actors. This study examined the espousal of enhanced cashew production technologies amongst smallholder farmers in Mtwara Region. The findings clinched the overall adoption of improved cashew technologies at 58%, however, the adoption of individual technologies varies from 29% for spacing to 97% for weeding technologies. Furthermore, it was deduced that the adoption intensities of improved planting materials (41%) and recommended spacing (32%) were still very low while the intensity of pesticides use in cashew production was very high, estimated at 88%. The revealed factors happened to encourage adoption of improved cashew technologies were gender, household size, household age, location, and age of cashew trees. The adoption and intensity of improved cashew technologies were predisposed by years of schooling, off-farm earnings, farm size, contact to extension amenities, group membership, as well as access to credit. The above results showed that farmer and household characteristics were the main driving factors in the espousal of innovation by cashew growers in the study area while wealthy, institutional and access parameters influenced the espousal intensity of cashew novelties. Based on the results revealed in this study, the subsequent policy measures are suggested for actions to improve cashew production and productivity in Tanzania;

1. To advocate the Integrated Cashew Management Practices (ICMP) to all cashew growing areas in Tanzania. This approach will enhance the concurrent use of all improved cashew production technologies as an essential requirement for increasing productivity.

2. Develop, identify and communicate the appropriate extension messages at scale to all cashew growing areas in Tanzania. This approach will enable cashew growers to locate and understand different sources of enhanced planting materials and their accompanied agronomic packages. The developed and commucated message should packaged into a simple and clear language to the target audience.

3. Adopting the top working strategy to the old and unproductive cashew trees with the improved planting materials. This will enhance the cashew production especially to the traditional cashew growing area in the country.

4. Establish Cashew Development Centres (CDCs) for polyclonal seed production and cashew nurseries in each cashew growing districts. This will dicentralize seed production activitities to the farming communities to ensure a wide and timely access of improved cashew planting materials and other technologies.

5. Strenghthening the institutional support services in all cashew growing areas in the country. Support organization such as Agricultural Marketing Co-operative Societies (AMCOS) and credit service providers enhance market information access, credit inputs and capacitate farmers’ ability to invest in cashew farming.

The recommended approaches proposed will enhance the adoption of improved cashew technologies, and generate economic benefits to smallholder cashew farmers in the study area and country at large.

Acknowledgements

The authors acknowledge the technical support from International Institute of Tropical Agriculture (IITA-Tanzania) for digitizing the survey questionnaire in the mobile data collection platform (surveyCTO). The authors appreciate Local Government Authorities (LGAs) in Mtwara region and farmers who provided information that made this study successful.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was financed by the Government of the United Republic of Tanzania through Cashew Research Program based at TARI-Naliendele, Mtwara

Notes on contributors

Gerald Alex Lukurugu

Gerald Alex Lukurugu is an Agricultural Economist at Tanzania Agricultural Research Institute (TARI) Naliendele, Mtwara, Tanzania His research focuses on seed systems

Serapius Mwalongo

Serapius Mwalongo is an Agricultural Economist from TARI Naliendele, Mtwara

Nicholaus Musimu Kuboja

Nicholaus Musimu Kuboja (PhD) is a Manager for Socioeconomic and Market Research at TARI, Headquarters, Dodoma, Tanzania.

Bakari Rashidi Kidunda

Bakari Rashidi Kidunda is a Centre coordinator for Technology Transfer and Partnerships at TARI Naliendele, Mtwara

Geradina Mzena

Geradina Mzena (PhD) is a cashew breeder and a national coordinator of cashew research program in Tanzania from TARI Naliendele, Mtwara.

Shiferaw Feleke

Shiferaw Feleke (PhD) is an Agricultural Economist working at International Institute of Tropical Agriculture, Dar es Salaam, Tanzania.

Joachim Paul Madeni

Joachim Paul Madeni is a cashew breeder from TARI based at Naliendele, Mtwara.

Peter Albert Masawe

Peter Albert Masawe (PhD) is a cashew breeder and an international consultant in cashew research and development.

Fortunus Anton Kapinga

Fortunus Anton Kapinga (PhD) is a Plant Breeder and Centre Director at TARI Naliendele, Mtwara, Tanzania.

Appendix

Table A1. Improved cashew varieties released in Tanzania and Yield performance for the first 7 years

S/no	Variety name	Year of release	Distinguishing characteristics	Production trend without irrigation or fertilizer application (kg/tree/season)
				Year 4	Year 5	Year 6	Year 7
1	Cashew clone-AC1	2006	Pink flower in colour, Brown colour flushes and Nut shape like kidney which is orange in colour.	9	10	25	24
2	Cashew clone-AC4	2006	Red colour of flushes, orange apple colour, kidney like in shape	11	9	27	22
3	Cashew clone-AC4/17	2006	Pink flower in colour, apple colour is red	4	8	15	16
4	Cashew clone-AC10	2006	Pink flowers, colour flushes are brown, Apple colour is red	4	8	13	10
5	Cashew clone-AC10/129	2006	Pink colour flower, Brown colour flushes, apple yellow	9	19	20	28
6	Cashew clone-AC10/220	2006	Pink colour flower, red colour flushes, orange apple	-	-	-	-
7	Cashew clone-AC14	2006	Pink colour flower, red colour flushes, orange apple	-	-	-	-
8	Cashew clone-AC22	2006	Pink colour flower, Brown colour flushes, red apple	9	8	16	19
9	Cashew clone-AC34	2006	Pink colour flower, green colour flushes, yellow apple	-	-	-	-
10	Cashew clone-AC43	2006	Pink colour flower, brown colour flushes, red apple	8	18	13	25
11	Cashew clone-AZA2	2006	Pink colour flower, brown colour flushes, greenish yellow apple colour	5	13	20	15
12	Cashew clone-AZA17	2006	Pink colour flower, yellow apple colour	8	9	18	19
13	Cashew clone-AZA17/79	2006	Pink colour flower, brown colour flushes, yellow apple colour	4	7	10	14
14	Cashew clone-AZA17/156	2006	Pink colour flower, brown colour flushes, orange apple colour	-	-	-	-
15	Cashew clone-AZA17/158	2006	Pink flower colour, dark brown colour flushes, yellow apple colour	4	9	12	13
16	Cashew clone-AC4/285	2006	Pink flower colour, red colour flushes, orange apple colour	9	16	22	29
17	Cashew Hybrid -H1	2015	Bright green-brown flushes. Immature nuts are purple, which then change to green at maturity.	5.35	5.04	15.2	15.15
18	Cashew Hybrid -H3	2015	Pink brown flushes leave.	6.21	3.8	15.48	17.23
19	Cashew Hybrid -H4	2015	Nut suture projection is behind the apex.	6.69	7.9	13.77	16.52
20	Cashew Hybrid -H5	2015	Angular apple.	7.39	10.17	12.31	19.18
21	Cashew Hybrid -H6	2015	Pink apple spots.	5.18	4.94	10.76	15.79
22	Cashew Hybrid -H7	2015	Light pink Nut eye.	5.54	7.38	8.71	16.07
23	Cashew Hybrid -H8	2015	Spreading plant growth habit. Rounded Apple.	3.72	6.23	8.97	13.06
24	Cashew Hybrid -H9	2015	Strips on young nuts	3.72	6.23	8.97	13.06
25	Cashew Hybrid -H11	2015	Detachment of Nut from the apple is intermediate	8.3	2.21	9.73	17.53
26	Cashew Hybrid -H13	2015	Reddish yellow leaf at senescence	5.52	5.62	10.71	14.01
27	Cashew Hybrid -H14	2015	Medium compactness of Inflorescence	5.53	5.57	9.42	15.35
28	Cashew Hybrid -H15	2015	Deep red Apple. Green young Nuts	5.72	6.32	9	16.62
29	Cashew Hybrid -H16	2015	Circular leaf blade	5.76	8.63	9.64	13.43
30	Cashew Hybrid -H18	2015	Green strips on Apple	4.92	6.62	10.21	13.33
31	Cashew Hybrid -H22	2015	The bases of the Nuts are Angular	7.11	8.25	11.37	16.75
32	Cashew Hybrid -H25	2015	Dark purple Flushes leaves	6.39	7.08	12.66	14.77
33	Cashew Hybrid -H28	2015	Green mid-rib of flushes. Pink young nuts. Green-yellow leaves at senescence	5.28	7.28	8.22	13.82
34	Cashew Hybrid -H29	2015	A cleft on the young nut	6.51	6.49	10.07	19.01
35	Cashew Hybrid -H30	2015	A suture is closer to apex on young nuts	7.49	9.65	13.32	17.39
36	Cashew Hybrid -H31	2015	Light Orange Apple	5.27	6.53	10.09	12.86
37	Cashew Hybrid -H32	2015	Young nuts are purple green	6.72	7.02	13.72	13.91
38	Cashew Hybrid -H33	2015	Purple leaves at senescence	6.6	4.52	9.85	13.62
39	Tanzania Dwarf Cashew-TDC1	2016	Medium in Florence and purple-green young nut	0.64	4.1	3.1	10.4
40	Tanzania Dwarf Cashew-TDC2	2016	Light brown leaf flushes	1.2	2.9	2	10.7
41	Tanzania Dwarf Cashew-TDC3	2016	Nut base and suture are flattened and rounded respectively	1.5	3.9	3	9.6
42	Tanzania Dwarf Cashew-TDC4	2016	Red-brown flushes leaf with pink nut eye	2	4	3.8	10.8
43	Tanzania Dwarf Cashew-TDC7	2016	Brown of mature leaf and deep grooves on the apex of Apple	2	3.5	3.3	9.6
44	Tanzania Dwarf Cashew-TDC8	2016	Wide leaf blade width	1.6	3.9	3.6	9.1
45	Tanzania Dwarf Cashew-TDC9	2016	Pointed nut apex	2.3	3.9	3.9	10
46	Tanzania Dwarf Cashew-TDC10	2016	Dark green mature leaves, large stem girth and flattened apple apex	1.7	4	3.5	8.5
47	Tanzania Dwarf Cashew-TDC12	2016	The yellowish green of mature leaves	0.46	4.2	1.7	8.5
48	Tanzania Dwarf Cashew-TDC13	2016	Red-brown flushes leaf	1.3	2.9	1.2	7.6
49	Tanzania Dwarf Cashew-TDC14	2016	Dense inflorescence, medium number of nuts per panicle, and red apple	2.9	3.4	3.4	12
50	Tanzania Dwarf Cashew-TDC15	2016	A cleft on the young nut and a very large apple	1.8	3.8	2.3	8.9
51	Tanzania Dwarf Cashew-TDC16	2016	Deep red flushes leaf and very small red apple	1.3	2.6	2.1	8.5
52	Tanzania Dwarf Cashew-TDC22	2016	Medium inflorescence and many numbers of nuts per panicle	0.95	3.7	1.9	7.5
53	Tanzania Dwarf Cashew-TDC23	2016	Flattened apple apex	3.2	3.5	3.2	8.8
54	Tanzania Dwarf Cashew-TDC24	2016	Medium in time flowering	1.1	3.4	2.8	6

Source: (Masawe, 2006; Masawe & Kapinga, 2016, 2017)

Table A2.

Probit, Truncated and Tobit regression estimates for improved planting materials, pesticides application and recommended spacing

Variables	Improved planting materials			Pesticides application			Recommended Spacing
	Probit	Truncated	Tobit	Probit	Truncated	Tobit	Probit	Truncated	Tobit
Farmer and Household parameters
Age	0.004	0.001	0.107	−0.003	0.003	0.193	0.311	0.214	0.097
Gender	0.046	0.259	1.606	0.572 (0.12)***	0.235	0.795	0.218	0.008	0.702
HH_size	0.058 (0.019)**	0.078	0.098	−0.008	0.032	0.827 (0.074)***	0.067 (0.102)**	0.005	0.733
Years_Schooling	0.053(0.018)***	0.036	0.004 (0.081)*	0.077 (0.013)***	0.007 (0.083)**	0.181 (0.013)**	0.017	0.645	0.092 (0.25)**
Age of cashew trees	−0.465 (−0.012)*	0.023	0.158	0.063	0.622	0.947	−0.723 (−0.195)**	0.006	0.011
Wealth or economic
Farm size/land	0.014	0.004	0.389	0.012 (0.002)*	0.006 (0.058)*	0.292	−0.002	0.678 (0.31)**	0.812 (0.03)**
Off-farm income	0.132 (0.267)**	0.067 (0.037)**	0.996	0.038	0.091	0.217	0.234	0.512	0.795
Asset endowment	0.014	0.143	0.033 (0.059)**	0.088	−0.445	0.123 (0.046)*	−0.05	−0.078	0.592
Institutional and access
Access to extension	1.158 (0.421)***	0.781 (0.294)*	0.064 (0.077)**	0.253	1.087	0.175	0.66 (0.529)***	−7.217	0.641
Group membership	0.014 (0.164)*	0.952 (0.186)***	0.419	0.035 (0.489)*	0.452 (0.027)***	0.524 (0.82)**	0.307	−6.727	0.037
Access to credit	−0.239	−0.395	0.288 (0.345)***	0.442 (0.53)**	0.151 (0.198)**	0.389 (0.231)***	0.488 (0.244)*	0.453 (0.02)***	0.68 (0.12)**
Geographical residence	0.035 (0.322)*	0.741	0.313 (0.111)**	0.211	−0.029	0.115	0.325	0.012	0.019
Log likelihood	−33.578	−732.422	−118.213	217.326	124.885	240.116	324.521	197.394	211.981
Number of observation	760	738	719	760	693	752	658	519	472
LR χ^2 (12)	86.861***	47.96**	15.31*	35.829**	36.956**	78.251***	38.133***	37.384**	92.418**
Pseudo R^2	0.116	0.064	0.028	0.073	0.259	0.095	0.042	0.082	0.034
Likelihood ratio statistics	314.2***			198.57**			173.91***

Note: The values in parenthesis are marginal effects and the asterisks ***, ** and * shows Significance levels at 1%, 5% and 10% respectively.