K.D. Munugoda,T.S. Talagala,S.M.C.U.P. Subasinghe,D.S. Hettiarachchi,A.T. Cooray
Choice Modeling for the Commercial Cultivation of Underutilized Aromatic Plants for Producing Mosquito Repellents: Targeting Rural Sector Income Generation
Medicinal and aromatic plants,bioeconomy,rural economy,indigenous ethnobotanical knowledge,mosquito control
Tropical countries face considerable economic losses due to mosquito-borne diseases which can be effectively combatted using plant-based mosquito repellents. Therefore, using a questionnaire survey, we selected the 25 top-ranked common but underutilized aromatic plants with mosquito repellent ability in Sri Lanka to investigate the rural sector’s willingness to cultivate and supply them. Cinnamomum verum, Citrus aurantiifolia, Citrus sinensis, Citrus reticulata, Aegle marmelos, and Ocimum tenuiflorum were the common species thus identified. The willingness to cultivate and supply aromatic plants with mosquito repellent ability varied between 88% and 60%. The Chi-squared test indicated a significant association between gender and willingness to cultivate and supply these plants. Men had a higher willingness (82%). Persons formally educated up to elementary school level had the highest willingness (85%). The willingness from households with many non-income-generating members was 100%. The random forest model developed in this study identifies farmers’ willingness to cultivate and supply aromatic plants with mosquito repellent properties. It was trained using an upsampling strategy. Our findings aid in understanding the scenarios involved with introducing, cultivating, and supplying aromatic plants.
Choice Modeling for the Commercial Cultivation of Underutilized Aromatic Plants for Producing Mosquito Repellents: Targeting Rural Sector Income Generation

Tropical countries face considerable economic losses due to mosquito-borne diseases which can be effectively combatted using plant-based mosquito repellents. Therefore, using a questionnaire survey, we selected the 25 top-ranked common but underutilized aromatic plants with mosquito repellent ability in Sri Lanka to investigate the rural sector’s willingness to cultivate and supply them. Cinnamomum verum, Citrus aurantiifolia, Citrus sinensis, Citrus reticulata, Aegle marmelos, and Ocimum tenuiflorum were the common species thus identified. The willingness to cultivate and supply aromatic plants with mosquito repellent ability varied between 88% and 60%. The Chi-squared test indicated a significant association between gender and willingness to cultivate and supply these plants. Men had a higher willingness (82%). Persons formally educated up to elementary school level had the highest willingness (85%). The willingness from households with many non-income-generating members was 100%. The random forest model developed in this study identifies farmers’ willingness to cultivate and supply aromatic plants with mosquito repellent properties. It was trained using an upsampling strategy. Our findings aid in understanding the scenarios involved with introducing, cultivating, and supplying aromatic plants.

As a result of the COVID-19 pandemic, most countries, including those of Asia, have faced numerous challenges with downturned economic, social, and environmental sectors derailing their development paths on a never-before-seen scale (Yap 2020). Therefore, finding new strategies to manage and overcome those hurdles is necessary. The European Union developed the bioeconomy concept, recognizing its sustainability and capacity to develop the future, making the earth more pleasant to inhabit (Fritsche et al. 2020). It is defined as the sustainable use of land, fisheries, and aquaculture environments and their conservation into food, feed, and fiber (McCormick and Kautto 2013). Certain governments, such as Argentina, Germany, Malaysia, South Africa, Netherlands, and the United States, are also searching for appropriate methods with minimal negative environmental impacts to aid people who lost their livelihoods and faced revenue decline. Adopting the bioeconomy concept fulfils this requirement, benefiting those producing agricultural and forestry commodities (Fritsche et al. 2020; São Simão 2019). Medicinal and aromatic plants (MAPs) are herbal medications primarily used for therapeutic, aromatic, and culinary purposes as components of cosmetics, pharmaceuticals, health foods, and other natural health products (Brinckmann et al. 2022; Devika 2021). Sri Lanka, which is of high biodiversity, bears a number of these plants (Jayakody and Jayasekera 2013). The unique properties of aromatic plants, derived from secondary metabolites are exploited in the manufacture of widely used products such as spices, flavoring agents, perfumes, medicine, and biocides. Aromatic plants also supply raw materials to produce various essential industrial chemicals (Devika 2021). Most people continue to rely on traditional medicine or ethnomedicine for routine healthcare (Che et al. 2017). Manufacturing and use of traditional medicine in China, India, and Africa are among the most extensively practiced systems globally (Che et al. 2017). Mosquito-borne diseases, such as dengue fever, have become an epidemic in Asia, causing a profound impact on daily life (Ali et al. 2018). They affect the national economy by reducing the workforce and increasing medical expenses. Numerous preventive measures suggested by the health sector to combat mosquito-borne diseases became ineffective due to the limitations of large-scale vector control tactics (Dahmana and Mediannikov 2020). Therefore, household insect repellent consumption has considerably increased in the past years (Sirisena and Noordeen 2016). Though synthetic mosquito repellents such as DEET are effective, they can cause problems such as environmental danger, impacts on non-target organisms, and an increase in mosquito resistance (Burfield and Reekie 2005). Studies revealed that DEET could also be toxic to humans and some other organisms (Burfield and Reekie 2005; Rowland et al. 2004). Chemical compounds in the aerial parts of certain MAPs, such as citronella, lavender, cinnamon, rosemary, and basil can also repel insects other than mosquitoes (Burfield and Reekie 2005; Lee 2018). Under these circumstances, developing safe and effective repellents is possible and essential to controlling mosquito bites. Countries in Europe and Asia dominate international trade in MAPs, accounting for 68% of global imports and exports (Türkekul and Yildiz 2021). Asia accounts for 36% and 25% of MAPs exports and imports, respectively (Türkekul and Yildiz 2021). Most MAPs are still collected from the wild in their natural habitats (Chandra et al. 2022; Grigoriadou et al. 2019). However, the contribution of the MAP trade to the economy of Sri Lanka is not significant (Gamage et al. 2021), despite having a large number of MAPs growing in different parts of the country (Jayakody and Jayasekera 2013). In addition, many of these plants remain underexploited because the knowledge of their specific properties, such as mosquito repellent ability, is confined only to traditional ayurvedic practitioners and botanical experts. Most of these plant species are also found in rural regions (Weerasinghe et al. 2017). If a proper mechanism was established, villagers could earn an additional income by collecting materials required for the commercial production of mosquito or other insect repellents. Since these plants can also be grown in home gardens, their cultivation and collection can serve to increase the revenue of rural communities with no threat to the existence of natural habitats and forests (Shackleton et al. 2011). However, little is presently known about Sri Lankan people’s willingness to grow and supply aromatic plants, and the significant factors influencing their willingness are unclear. Choice modeling helps determine the relationship between influencing characteristics and the choices made by the community in the commercial cultivation of aromatic plants. This study was aimed at identifying the distribution of underutilized but commonly available MAPs with mosquito repellent properties in Sri Lanka and the willingness of the rural sector to cultivate and supply those plants, along with the factors significantly influencing their choice to earn an extra income. The results will benefit policymakers in implementing new income generation methods under the bioeconomy concept.

A pre-tested, structured questionnaire survey was administered to experts in plant science and folk medicine practitioners to identify the underutilized aromatic plants with mosquito-repelling characteristics. The first section of the questionnaire was aimed at obtaining demographic information of the respondents and their experience in herbal science. The second section obtained the list of underutilized plants with mosquito repellent ability that are available in the country. Thirty experts with substantial plant science knowledge with a degree in a botany-related subject or at least 15 years of experience as a curator of an herbal garden were selected for the survey. The institutes of the experts were subjectively chosen based on reputation. Those 30 comprised curators (3), research officers (3), lecturers (16), and professors (8) who are attached to botanic gardens, research institutes, institutes relevant to herbal medicine, and universities. Of them, 14 were men ranging from 28 to 65 years of age, and 16 were women ranging from 24 to 58 years. In addition, 50 practitioners of rural folk medicine participated in this study, 10 from each of five districts (Anuradhapura, Badulla, Gampaha, Hambantota, and Kurunegala). They were randomly selected from the registered folk medicine practitioners list at the Department of Ayurveda, Nawinna, Sri Lanka. This group included 46 men aged 30 to 72 years, and four women aged 35 to 57 years.

The underutilized plants with mosquito repellent properties listed by experts and folk medicine practitioners were ranked to be used in a second survey (homestead survey). However, plants producing aromatic oils in parts other than the leaves were removed from this list to ensure continuity and sustainability of harvesting. The conservation status of the remaining plants was checked by the red data book published by the International Union for Conservation of Nature (IUCN 2015) to eliminate those that cannot be commercially used. The remaining plant species were ranked based on the counts in the questionnaire survey of experts and practitioners (Eq. (1)). The 25 top-ranked plant species were selected for the rest of the study. Voucher specimens (KMM01-USM14) were prepared for the verification of these species at the National Herbarium, Peradeniya, Sri Lanka. These specimens are kept at the Centre for Forestry and Environment, University of Sri Jayewardenepura, Sri Lanka. Scientific names of all plant species were verified with World Flora Online (WFO 2023). where ais the total count of a particular species in the experts’ surveybis the total count of a particular species in the folk medicine practitioners’ surveyxis the total number of experts interviewed, andyis the total number of folk medicine practitioners interviewed.

After selecting the 25 top-ranked plants, another survey was conducted using a pre-tested, structured questionnaire to gather information about the perception of the village community on the cultivation of those medicinal and aromatic plants. The questionnaire was divided into four main sections. The first section sought information on the respondents’ demographic characteristics, including income and educational status (Table 1). The second section aimed to gather information on the cultivation of agronomic crops in the home gardens, and family members’ contribution to managing those crops. Section three gathered information on the presence of each of this study’s 25 top-ranked aromatic plants and other medicinal and aromatic plants, and their current financial benefits. The last section was used to gather information on the willingness of farmers to cultivate aromatic plants commercially. Participants for the household survey were selected from all three major climate zones of Sri Lanka using six administrative districts, Batticaloa, Hambantota (dry zone), Matara, Rathnapura (wet zone), and Badulla and Kurunegala (intermediate zone) (Fig. 1). Two divisional secretariat divisions (DS divisions) were randomly selected from each district, and four villages were randomly selected from each DS division. Twenty randomly chosen participants whose home garden is more than 1 acre (0.4 ha) were selected from each village for the questionnaire survey. Thereby, 480 homesteads in 24 villages were surveyed from November 2019 to April 2020. The questionnaire was administered in each divisional secretariat division by a trained team of men and women living in the same division who were familiar with the culture of that particular area. During interviews, they used the comfortable language of the respondents (Sinhala or Tamil).

The availability of the 25 top-ranked aromatic plant species in the six selected districts, as revealed by the homestead questionnaire survey, was analyzed with a heatmap and ordered columns using hierarchical clustering. This made it easy to compare the similarities and dissimilarities among districts and the distribution of selected aromatic plants with mosquito repellent properties. The hierarchical clustering was performed on the matrix with 28 rows (observations) and six columns (variables). Observations are the plant species types, and variables are the six districts. The matrix cell value represents species counts. The distance between observations and variables was measured using the Euclidean distance, and hierarchical clustering was performed using the ward method. For statistical modeling, the random forest (Breiman 2001) and logistic regression (Menard 2002) approaches were used. They are two of the most heavily used methods for binary classification. The popularity of the two methods results from their simplicity in implementation and high prediction accuracy. The factors associated with willingness to grow and supply those aromatic plants were first tested by binary logistic regression. Then, a statistical model was developed to predict the probability of willingness using binary logistic regression and random forest machine learning algorithm. The prediction performance of the model was evaluated using the out-of-sample classification accuracy. All statistical data analysis was performed using R software (v4.1.1; R Core Team 2021). Random forest is a tree-based ensemble supervised machine learning algorithm (Breiman 2001), which trains multiple decision tree models separately to make final predictions. It is robust to outliers and overfitting and can deal with large datasets. The random forest model makes no prior assumptions about the model and is quite powerful in capturing both linear and non-linear relations between the relationships. The reason for using both logistic regression and the random forest approach in our analysis is as follows: the interpretability power is high in the logistic regression approach compared to random forest. On the other hand, the random forest model’s prediction accuracy is high compared to the logistic regression approach for our dataset. The random forest package in R was used to train the random forest model (Liaw and Wiener 2002). Altogether, ten explanatory variables were used in binary logistic regression to test the willingness of participants to cultivate and supply aromatic plants with mosquito repellent properties; among them, the continuous variables were the participants’ age, number of non-income-generating family members, monthly income, land size, number of family members contributing to agriculture-based activities, and number of paid workers for the agriculture-related activities in the same home garden. Gender of participants (2 levels: men, women), education (6 levels: no schooling (NS), elementary school (ES), middle school dropouts (MD), middle school (MS), high school (HS), and diploma/degree (DD)), district (6 levels: Badulla, Batticaloa, Hambantota, Kurunegala, Matara, and Rathnapura), and availability of agronomic crops in the same home garden (2 levels: yes and no) were the categorical variables. First, a Chi-squared test was performed to determine whether an association existed between the dependent and explanatory variables. Then, the binary logistic regression analysis was used to model the relationship between the binary response and explanatory variables and to identify the statistical significance of each independent variable. Y was considered to have a value of 1, if there is a willingness to grow aromatic plants and 0 otherwise. X = {X1, X2, X3, …, Xk} denote k as number of independent variables. A key assumption in logistic regression is a linear relationship between the continuous independent variables and the logit of the outcome. We checked this assumption by plotting the log odds of the dependent variable and the continuous independent variables.

Our dataset encountered the class imbalance problem because the number of people willing to grow and supply aromatic plants was substantially higher than the number of people unwilling to do so. Therefore, two sampling procedures were used to address the issue of class imbalance: (i.) eliminate some data from the majority class (undersampling) and (ii.) augment the minority class with intentionally generated or duplicated data (oversampling). Oversampling and undersampling are two common techniques used to address the class imbalance problem. One particular advantage of oversampling is that no data is removed. The data were divided into training and test sets at an 8 : 2 ratio before creating the prediction model. The training data was used to train the model, and the test set was used to assess its out-of-sample classification accuracy. Overall accuracy is the ratio of the number of predictions to the total number of observations in the out-of-sample. The accuracy of the out-of-sample prediction was evaluated using four measures: (i) overall accuracy, (ii) sensitivity, (iii) specificity, and (iv) F-measure (Table 2). The sensitivity of a positive class measures how well it was predicted, which is computed as in Eq. (2). The specificity summarizes how well the negative class was predicted (Eq. (3)). For imbalanced classification, precision–recall measures are widespread. The F-measure was introduced by combining precision and recall into a single metric as given by Eqs. (4), (5), and (6).

The survey of experts and practitioners revealed 127 aromatic plant species with mosquito repellent properties, belonging to 58 families. Though it was expected to use the 25 top-ranked species after the ranking approach, 28 were selected for further analysis since a few species had the same rank (Table 3). The Rutaceae family had 14 species, the highest, while Fabaceae and Lamiaceae had 11 and 8 species, respectively. Among the 28 species, 13 were introduced (Table 3), and those are extensively growing in the wild or cultivated in home gardens in Sri Lanka (POWO 2023; Senaratna 2001; The National Red List 2020). The invasiveness of those species is not recorded (de Vlas and de Vlas-de Jong 2014).

The hierarchical clustering depicts the distribution of the plant species in the six districts and the similarities between those districts based on plant mention distribution patterns (number of people mentioning) (Fig. 2). According to that, Matara and Hambantota districts share the most similarities. Badulla is markedly different from the other districts, with a significantly greater species richness or diversity of selected aromatic plants than other districts.

The Chi-squared test indicated a statistically significant association between gender and willingness (p = 0.013). Men demonstrated higher willingness than women (Fig. 3a), accounting for 82% of the total number of male participants. Participants who cultivated agronomic crops in their home gardens had a greater willingness (81%) than those who did not cultivate such plants (Fig. 3b). Those who completed only elementary school (ES) education showed the highest willingness (85%) to cultivate and supply aromatic medicinal plants (Fig. 3c), which could be due to the difficulty of obtaining lucrative employment with their low level of education. Those who did not reveal their education status had the lowest willingness (68%). Participants in the Kurunegala district showed the highest percentage of willingness, 88% (Fig. 3d), followed by both Hambantota and Rathnapura districts (85%), Batticaloa (84%) and Matara (79%). Badulla district had the lowest willingness, accounting for 60%. The Chi-square test results revealed a significant association between the district and willingness to grow and supply aromatic plants (p = 0.000).

Participants with 4 or more members contributing to agriculture-related activities had the highest willingness, 100% (Fig. 4a), and those without such members had the lowest willingness (73%). Those with 4–5 paid workers used for agriculture-related activities in the home gardens had 100% willingness (Fig. 4b). Willingness was 100% when more than five non-income-generating members were present in the surveyed households (Fig. 4c). Statistically significant differences were not seen between the willingness to cultivate the aromatic plants with mosquito repellent properties and the number of family members, agri-contributing employees, and income (Table 4). However, the willingness of all those three categories was above 72%.

To grow and supply aromatic plants with mosquito repellent properties, respondents (N = 684) requested four types of assistance. Among these requests, 346 (51%) are for financial assistance, 288 (42%) are for awareness and training, 36 (5%) are for the creation of market opportunities, and 14 (2%) are for materials such as seeds, seedlings, and fertilizers.

The age of participants who were willing and unwilling, respectively, to cultivate and supply aromatic plants with mosquito repellent properties was similar, although the latter had slightly more variation (Fig. 5a). The variability of the monthly income of those willing to cultivate and supply aromatic plants was higher than the other group (Fig. 5b), though statistically not significant (Table 4). A difference was not observed between those two categories and land size (Fig. 5c).

The logistic regression model revealed that district and gender substantially impacted the participants’ willingness to cultivate and supply aromatic plants (Table 4). The results also suggest that men are more likely to report willingness to engage in these activities than women (odds ratio (OR): 0.78, 95%; confidence interval (CI): 0.16–1.40). Compared to Badulla, the willingness was higher in the other five districts. The findings further showed that the respondent’s age, education, and economic status were insignificant determinants.

Both logistic regression and the random forest technique were employed to train the models’ predictiveness to predict the probability of willingness to grow aromatic plants with mosquito repellent properties. Both models were trained with three different sampling setups: (i) imbalance data, (ii) upsample data, and (iii) downsample data. The out-of-sample accuracy measures (Table 5) confirmed that a random forest trained using an upsampling strategy had the best prediction accuracy.

Various studies have highlighted the importance of native plants in repelling insects such as mosquitoes. Karunamoorthi et al. (2009) identified five plant species traditionally used as mosquito repellents in Western Ethiopia. Pålsson and Jaenson (1999) reported using eight plant species of six families to repel mosquitoes in Bissau, Guinea. Kweka et al. (2008) identified five plant species belonging to four families in Tanzania used for the same purpose. Though a few studies were conducted in Sri Lanka on the cosmetic properties of plants used in folk medicine (Gamage et al. 2021; Nirmalan 2017), information is lacking on underutilized aromatic plants with mosquito repellent properties. Out of 127 potential aromatic plants identified in this study, we selected the 25 top-ranked species for this study due to time limitations and the commercial and conservation status of some plants. However, three of the ranks were shared by two species each, resulting in 28 species. Previous surveys also revealed that those 28 species of aromatic plants were widely distributed in rural areas (Kankanamlage et al. 2014; Napagoda et al. 2019). Therefore, there is a high potential for their commercial use, which will bring extra income to rural populations. According to our survey findings, the presence of the selected plant species was significantly lower in Batticaloa compared to the other five districts. A study by Perera (2012) revealed that the most common plant species in Batticaloa belong to the families Euphorbiaceae, Sapotaceae, Annonaceae, Ebenaceae, Melastomataceae, and Sapindaceae. The plants identified in this study do not belong to any of those families. Many villages in the Batticaloa district were recently established to resettle war refugees by clearing the native flora, which could also have contributed to the low presence of this study’s plant species of interest. The Badulla district showed the highest availability of the selected plant species. Although both Badulla and Kurunegala are located in the intermediate climate zone, Badulla has a higher elevation. Retaining medicinal plant species in home gardens could also be high in the home gardens of the Badulla district due to higher awareness of the medicinal properties of these plants among the locals (Jayasuriya 2008). The EDA approach of our study confirmed that willingness is not associated with the rest of the tested explanatory variables, but gender and district were significant according to the statistical model. However, Truelove et al. (2015) found that the willingness of Sri Lankan paddy farmers to adopt new farming techniques is strongly linked with land size, land ownership, farming experience, their attitude towards risks, and availability of market information, while gender is non-significant. Herath and Takeya (2003) conducted a logit analysis on factors determining adaption of intercropping farming systems among rubber smallholders in Sri Lanka. They found that farmers’ awareness, level of extension services received, education level, and experience farming other crops have significant positive impacts. In contrast, social participation in agriculture, family size, experience with farming rubber, and the nature of the land were not significant. Dias et al. (2020) found that farmers’ age, household size, access to credit facilities, extension services, technology, type of primary employment, and land size were influential factors in adopting protected agriculture in the dry zone of Sri Lanka. This inconsistency among significant variables could be due to the class imbalance classification problem. The number of willing participants (N = 384) in our study was also much higher than that of non-willing participants (N = 96). Thus, it may be possible to obtain more reliable and effective results in future studies by expanding the study region and utilizing diverse sampling methods. Nevertheless, we managed to develop a prediction algorithm that can identify any participant’s willingness from the six selected districts and is available at https://srilanka.shinyapps.io/grower/. The help file for this app is available at https://smart-research.github.io/grower/. This link provides usage instructions for the app, along with a video tutorial.

The findings of our study demonstrate the potential of certain aromatic plants used in traditional medicine for developing mosquito repellents. The willingness of farmers to cultivate and supply such plant materials to earn an additional income is high. Therefore, policymakers can act on developing pathways to encourage and support farmers so that the government mandate in increasing the country’s bioeconomy can also be served.