Open Access This article is
- freely available
Agriculture 2018, 8(2), 21; doi:10.3390/agriculture8020021
What Prompts Agricultural Innovation in Rural Nepal: A Study Using the Example of Macadamia and Walnut Trees as Novel Cash Crops
Centre for Development and Environment, University of Bern, Hallerstrasse 10, 3012 Bern, Switzerland
HELVETAS Swiss Intercooperation, Jhamshikhel, Dhobi Ghat, Lalitpur, GPO Box 688, 44600 Kathmandu, Nepal
HELVETAS Swiss Intercooperation, Av. Julius Nyerere No. 1213, 1100 Maputo, Mozambique
Correspondence: email@example.com; Tel.: +41-44-368-65-00
Received: 12 December 2017 / Accepted: 22 January 2018 / Published: 2 February 2018
Agricultural innovations are important, especially as climatic conditions around the world have been subject to increasing change over the past decades. Through innovation, farmers can adapt to the changing conditions and secure their livelihoods. In Nepal, 75% of the population depends upon agriculture, which is impacted by climate change, migration, and feminisation. In this context, it is important to understand what drives a household to start agricultural innovation to increase its economic benefits and resilience in the face of multiple pressures. We sought a comprehensive understanding of these drivers by investigating the determinants of rural innovation, using macadamia and walnut trees as examples of novel, potentially commercialised cash crops. After conducting an in-depth household survey that divided farmers into those who cultivate nuts and those who do not, we analysed the socio-economic and cultural characteristics of each category using statistical tests and a multiple logistic regression. Our results show that the individual variables of ethnicity, wealth and “years of experience with fruit trees” correlate significantly with nut cultivation. The results of the multiple regression suggest that “years of experience with tree cultivation” and “having an income through fruit trees” most influence nut cultivation. Overall, we conclude that nut cultivation is an accepted and promising cash crop mostly grown by wealthier households, and that, for poor, landless, or female-headed households to benefit, alternative business models and new policies must be explored and developed. We further suggest that this is also true for other nut or other cash crop trees that have gained recent attention in Nepal such as almond, hazelnut, or pecan farming.
Keywords:agriculture; innovation; livelihood; macadamia; Nepal; walnut
Agricultural practices have been subject to transformation and adaptation throughout human history, as farmers successfully innovate in response to environmental and socio-economic challenges . The great growth in yields and increased agricultural productivity in the second half of the 20th century was obtained as a result of many factors, including the extensive mechanization of operations and use of fertilisers [2,3]. This “green revolution” has heavily shaped agriculture: the introduction of modern varieties of different crops led to higher yields that have helped ease global food shortages, among other benefits, but not all innovations have had purely positive results: some have led to increased pressure on soils and the environment through the extended use of chemicals [3,4]. As we show below, agriculture in Nepal has been somewhat untouched by this “green revolution” and many farmers still farm only for subsistence or to turn only a modest profit . We set out to investigate how Nepalese agriculture currently operates, and whether innovative crop choices and growing methodologies could help farmers adapt to the pressures of local climate changes and labour changes. We chose to focus on nut tree cultivation, which is a relatively new, innovative crop that holds potential to help farmers adapt and thrive in the new climate.
In the past decades, farmers have increasingly had to adjust to the additional variable of global climate change. These changes have local impacts, including a shift in the elevation and extent of climate zones, which impacts the type of crops that farmers can expect to grow each season and affects the long-term viability of tree crops [5,6,7,8]. In Nepal, maximum temperatures have been increasing since 1960 and precipitation patterns have shifted in combination with a decrease in in the summer period . The effects are expected to be both negative (e.g., loss of useful growing land due to drought or floods or temperature shifts) and positive (e.g., warming of previously unusable or less-usable lands allowing farmers to plant crops that previously would not have survived); however, depending on the locality, people who will be exposed to the worst of the impacts are usually those least able to cope with the associated risks [10,11,12].
Therefore, there is an increasing urgency for a stronger focus on adapting agriculture to future climate changes to avoid food scarcity . Inadequate income and inadequate food sources are main components of poverty, which also leads to political instability [14,15]. In recent years, tensions between the urban and rural areas were created in part through poverty, which was one of the drivers for the civil war in 1996–2006 . Agricultural innovations can increase the well-being of the rural areas and could form the basis of a stable political situation . Both technical and institutional innovations are needed in agriculture to ensure self-sufficiency and adequate incomes in rural areas under the expected climate changes [16,17,18]. In Nepal, over 75% of the population depends upon agriculture for self-sufficiency and income . The most commonly cultivated crops are rice, maize, and millet; the specific crop choice usually depends upon the elevation of the farm in question [6,8].
Farmers have started to adopt different strategies to earn a livelihood as they adapt to some changes already (e.g., changes in temperature or a shift in precipitation patterns) [7,8,9]. These strategies include abandoning traditional crops, delaying sowing, leaving land fallow, and migrating, in addition to seeking temporary opportunities further away [20,21,22,23,24]. Male-out migration, the most popular off-farm livelihood strategy, especially for deprived households, increases financial flows into rural areas and improves food security on a short-term basis [25,26,27,28]. At the same time, the missing labour also undermines food sovereignty in the agriculture-based economy: the lack of adequate labour to produce labour-intensive subsistence crops has resulted in a growing dependence on food imports, as some communities and households are unable to meet their own food needs [26,28,29]. This threatens the access of food to the poor, especially to nutritious food, and is also of concern to overall economic stability [25,26,27,28,29,30].
Some agricultural innovation is already occurring in Nepal. Vegetable cultivation in areas with market access has increased markedly; Brown and Kennedy  show that selling vegetables has a gross margin that is 10 times higher than that of selling staple crops. Additionally, as part of the complex farming systems on Nepalese hillsides, enhanced agroforestry has improved livelihoods [32,33,34]. Farmers have increased the volume of plantations of fruit trees, especially those of high-value crops such as coffee [35,36,37,38]. Nut trees are also playing an increasingly important role in agroforestry, and offer additional benefits when compared to fruit and coffee trees: besides producing a nutritious ready-to-eat food, nut trees crops are non-perishable, robust, low-volume, high-value, easy to handle, and in high demand [39,40]. Because of their benefits and potential to meet nutritional and commercial needs, we chose to investigate this aspect of agricultural innovation.
Different authors have determined that a variety of economic and social factors drive the adoption of agroforestry systems globally and in Nepal [41,42,43,44]. Aase et al.  explored innovation in different geographical Himalayan settings, finding that natural and individual resources and networks of organisations support innovation across the regions. Other studies focus on general adaptive practices such as multiple cropping, water management or income diversification through off-farm work [46,47,48]. Present research does not, however, discuss the specific motivations and specific variables that impact the farmers’ decisions to choose or not to choose agricultural innovations and livelihoods. With this paper, we aim to bridge this gap by investigating the cultivation of macadamia and walnut trees as an example of the ways by which Nepalese farmers adopt new crop systems; the cultivation of nut trees is considered representative of innovation and adoption of rural livelihood strategies in general.
We chose to focus on nut crops in Nepal as they are relatively new to farmers there and represent an innovative livelihood strategy where they are grown. Macadamia and walnut are the most widespread of the different nut trees in Nepal . According to our observations, the commercialisation of both value-chains is in its infancy as both nuts only gained popularity in recent years . Understanding the motivation for and success of this new strategy of nut cultivation was a major motivation for the Helvetas project, to which this study is linked, as there is missing research specifically on nut cultivation in Nepal . Macadamia (Macadamia integrifolia) are newcomers and were introduced to Nepal in 1970 by a joint project of the Food and Agriculture Organization of the United Nations, and the Australian and Nepalese governments. The most suitable areas for growing macadamia in Nepal are the tropical and sub-tropical zones at a mean elevation of 60–1700 m [5,49,50]. In the temperate and cold zones at an elevation of 1200–3000 m, two types of walnuts (Juglans regia L.) are present: the indigenous hard-shell walnut tree and the soft-shell walnut tree, which was introduced to Nepal and is comprised mainly of cultivars and populations selected for crop production [51,52]. There is no clear history about the introduction of soft-shell walnuts to Nepal. During our survey, several farmers said that their grandfathers introduced the well-producing Kashmiri walnut when they came back from working in India.
Our research project consisted of four parts. First, we conducted an extensive, semi-structured household survey in the field to characterise those farmers who cultivate tree nuts (“nut growers”) and those householders who do not (“non-nut growers”). Secondly, the quantitative data, collected from closed-answer questions and other observations (see below) was analysed using statistics and a multiple logistic regression analysis to identify the factors and variables that drive farmers to choose walnuts or macadamia nuts within their respective growing regions. Thirdly, we conducted in-depth analyses of the farmers’ answers to open-ended questions and their stories in order to add commentary and details to the statistical results. Finally, we use the insights and data collected above to derive recommendations for future nut cultivators, development partners and policymakers.
2. Materials and Methods
2.1. The Study Areas
The field-based study was conducted in different districts of Nepal and focuses on the cultivation of two cash-crops, macadamia and walnuts. The locations of macadamia and walnut growers do not overlap; the two nuts require different growing conditions and therefore are found in different climatic settings . Walnuts occur across most of the temperate and cold zones of Nepal: we had many districts to choose from for our field study. We chose to focus on the Jumla district, in the Mid-Western Development Region, as representative of a walnut-growing region, because, in our pre-visit, we had found enough farmers who were growing walnuts over the whole district, who had more than one tree. Also, the local governmental research centre had also initiated their own small-scale research activities with walnuts and had therefore shown interest in our study . We were able to obtain 144 interviews there. Macadamia are less common but are found in locations across the sub-tropical zone of Nepal; in order to obtain a similar number of interviews, we identified five different districts that had the most macadamia nut growers and obtained a total of 135 interviews (Figure 1).
2.2. Data Collection
We designed and pre-tested a semi-structured interview based upon on the rural livelihood system approach and the nine-square Rural Livelihood System mandala [53,54,55]. We also ensured that statistically significant answers and results from similar surveys on the adaptation of agricultural innovations were integrated into our survey [41,42,45,46]; we sought to understand if variables known to impact decisions about agricultural practices in other contexts also applied in Nepal, and with regards to nut cultivation.
The survey was carried out from November 2014 to May 2016, and was paused for two months after April 2015 due to the series of strong earthquakes that struck Nepal. We found the participants by beginning the study in known nut-growing villages, and then following referrals from farmers to others who were growing nuts nearby. We chose one person from each household, usually the head (often, but not always, male), to interview. In total, we interviewed 279 household heads that we divided into four groups: (1) walnut growers (n = 89), (2) macadamia growers (n = 69), (3) non-walnut growers (n = 55), and (4) non-macadamia growers (n = 66). The first two categories, which we referred to collectively as “nut growers”, represent farmers “adopting a new livelihood strategy”. The latter two categories represented farmers “not adopting a new livelihood strategy” and are collectively referred to in this paper as “non-nut growers”.
The extensive survey that we created consisted of three parts that in turn focused on specific elements of a household that are known to be relevant and/or could be statistically significant factors in agricultural innovation. Each section included open and closed questions that allowed for descriptive statistics and the extraction of qualitative information from the participants’ narratives.
Section 1 was the collection of general information on households regarding educational, financial and social background (characterisation of households, 50 questions).
Section 2 consisted of questions considering direct questions about the specifics of nut cultivation (36 questions).
Section 3 focused on questions regarding the emotional context of the respondent and his or her family (44 questions).
To allow for a precise analysis of the responses, we divided the answers into quantitative and qualitative variables, and the latter included data on emotional aspects of decision-making. The quantitative answers revealed characteristics that were indicative of the interviewees and their households. The qualitative answers are descriptive. To constitute the dataset on emotional aspects, we asked the household heads questions around themes such as which needs had to be met for their happiness, what worried them, and what they were aspiring for in their lives.
2.3. Data Analysis
We took our survey data and divided it into three parts for analysis: (1) the characterisation of the household, (2) the drivers for growing nuts, and (3) the narratives. For the whole analysis, we used R, a language and environment for statistical computing and graphics [56,57].
For the first part, we analysed the characteristics of the households using general statistical functions: the standard of deviation for non-categorical variables and a two-tailed Fisher’s exact test to determine whether there was a dependence between the first and the second variable, the first variable being “nut grower” and the second being “non-nut grower”. We then applied a two-tailed Fisher’s exact test to determine if there was a non-random association between a pair of variables e.g., the variable “nut grower” and “non-nut grower” and the variable gender (“man” and “woman”). This distribution was then used to compute the p-value as “the probability of observing more extreme data than the actually-obtained data”, where “extreme” means that the probability of each of these possible values is smaller than the observed one. The null hypothesis is then rejected if the p-value is smaller than a pre-specified significance level. Most authors accept 0.05 as the significance level and it is marked as “*”, while 0.01 is marked with “**”, and 0.001 with “***” .
For the second part, addressing the drivers that prompt farmers to adopt nuts, we applied a multiple logistic regression analysis to determine the effect of the variables on the dependent nominal variable to grow nuts . In our study, this was the degree of relationship between the driver’s influence such as ethnicity, age, land size, and the decision of the farmers to become a nut grower (i.e., adopt a new livelihood strategy). The model is specified as follows: let be the response variable which indicates whether individual has cultivated nuts () or not (), for , where is the number of farmers interviewed. Let be the probability that (and therefore the probability that is ) for individual . The explanatory variables are denoted by for individual , where represents the number of variables in the dataset studied. The logistic regression model is therefore:for , where are the regression coefficients and is an error term.
The goal of the following analyses was to study which variables influenced the production of nuts. From the quantitative set of variables, gender, ethnicity, poverty rating, growing fruit trees, and growing fodder trees, were treated as categorical variables, which means that the answers of respondents were summarised and represented as categories rather than numbers. The quantitative variables were called quantitative as they expressed answers on an ordinal scale, i.e., number of school years, numbers of fruit trees planted, number of poverty rating—with the exception of gender, which is nominal.
In the qualitative set of variables, all the variables were binary except for the variable: “Shared decision for big investment (man/woman)”, which was treated categorically (the three possible answers were “the man”, “the woman”, or “shared decision making”). The qualitative variables were further divided into a set of data focused on variables related to emotional characteristics; we felt that a question such as “what do you need for happiness?” differed from “how do you get your income?” or “what do you know about nuts?” The emotional variables focused on three aspects of the respondents, as mentioned in Section 2.1, that is, the needs for happiness, their worries in life, and their aspirations. Each set of qualitative variables was treated individually and independently of the others.
The third part, the narratives, was extracted from the individual interviews. These were selected according to their usefulness in contributing to the overall understanding of the motivation for nut cultivation. They reflected the opinions of individual farmers that either perished in the statistical analysis or that were given as additional, qualitative reflections during the survey.
The multiple logistic regression analysis encountered two limitations: the limited number of surveys taken compared to the number of variables present, and the fact that some variables were categorical. Moreover, only the variables without any missing values could be consulted for multiple logistic regression, due to the limited number of surveys taken. These variables were chosen by careful analysis, considering all the other variables with missing values, to ensure that no important information was missed with this step.
Additionally, with the topography of Nepal ranging from accessible, flat plains to deeply-fissured, mountainous terrain, the social complexity in Nepal is immense, and the respondents of our five ethnic backgrounds were divided into 14 sub-groups. Combined, these factors made it impossible to select a group of farmers that truly represented the whole population of Nepal. The answers of respondents might also have been influenced by their expectations when a research team was visiting. We were aware of this fact and used common sense to investigate answers that did not match the context. Additionally, the different perspectives of each of the research team members, given their own experience, ethnic backgrounds and education, may have influenced the answers obtained; by using such a thorough survey, we reduced these risks to a minimum. Finally, the team members’ skills in translating the local language into English may have influenced the results, and therefore careful assessment of each answer and its translation was a requirement prior to the statistical analyses.
3. Results and Discussion
3.1. Characterisation of the Households (Results of Part 1 of the Data Analysis)
The socio-economic characteristics of the households are depicted below for walnut and non-walnut growers and macadamia and non-macadamia growers (Table 1).
These characteristics of the households are expected to influence the decision-making around new livelihood strategies. To derive the influences, we looked at each variable individually (Table 1). Since no adjustment was performed for multiple testing, one should be careful with the interpretation of the individual p-values. Overall, there were three main significance finds: (1) The ethnic groups called Dalits were least likely (p < 0.001) and Brahmin were most likely to grow nuts (p < 0.01). (2) A wealthy farmer was very likely to grow nut trees (p < 0.001), while a very poor farmer was not likely (p < 0.01) to grow nut trees. (3) Finally, there were significantly more farmers who grew nut trees in combination with fruit trees than growing them without fruit trees (p < 0.001).
The result that Dalits were less likely to grow nuts is no surprise: Dalits are often less wealthy with less land and smaller incomes. This context does not allow them to take the necessary risks to invest time and money into a new, possibly uncertain, livelihood strategy. In comparison, Brahmins in our study area were more likely to grow nuts, which can be explained by the sufficient financial assets available to them that increase their tolerance of failure. This is also linked to the quality and nature of the land that they farm. Although it would have been too complicated to quantify differences in land type, Brahmins tend to own better, more fertile land on which there are pockets suitable for tree cultivation, whilst Dalits (who are not traditionally farmers) tend to own or farm less productive, more marginal land. Lastly, nut growers rely most often on their experience with fruit trees to help them cultivate nuts, as the third results shows.
3.2. Analysis of Drivers for Growing Nuts as a Novel Livelihood Strategy (Results of Part 2 of the Data Analysis)
The aim of the logistic regression was to deduce the variables that influenced the decision by a farmer to grow nuts. The results of the multiple logistic regression are presented in Table 2. Pre-results including all variables (without adjusted p-values) are given in the supplementary material (Table A1 and Table A2).
In the quantitative set of variables, “years of experience with tree cultivation” had a significant impact on nut production, since the adjusted p-value was below the significance value (p < 0.001). The outcome of the qualitative set of variables showed that “having an income through fruit trees” had an influence on whether a farmer cultivates nuts (p < 0.001). Looking at the emotional set of variables, “financial means” and “spiritualty/religion” (factors needed for a happy life) and “gaining social reputation” (an aspiration) had a positive influence on tree cultivation (p < 0.05).
Due to the small size of the sample, the outcomes must be interpreted carefully. These p-values give an idea as to which variables might influence whether a farmer grows nuts; however, they cannot be combined, as the three datasets were tested independently. The quantitative result “years of experience with trees” could be interpreted that farmers who grow any cash crop tree, and may even have an income from it, trust their ability to cultivate nut trees successfully and therefore choose to grow them. The result from the qualitative data set “having an income through fruit trees” supports this interpretation: fruit farmers have positive experiences with other tree crops and are therefore more likely to opt for the new but similar livelihood strategy. “Financial means” or “religion/spirituality”, as a way to happiness, and “social reputation” as an aspiration (the results of the emotional data set) are significant on a lower level, and were therefore not interpreted further. Moreover, although ethnicity, poverty ranking, and “years of experience with tree growing” in general may influence the decision-making process, as the simple statistical pairwise analysis shows in Table 1, these were not detected in the result of the multiple logistic analysis. This could be an objection to the suitability of approach of the logistic regression or it can be attributed to the small number of respondents. With more observations, we could test the variables jointly and hope to obtain stronger conclusions. We therefore suggest that the results of the logistic variable be treated with care and that the results of the simple statistical evaluation and the narratives be consulted for additional insights.
The answers of the logistic regression are not completely reflected in similar publications. The recent study of Cedamon et al.  for example finds that household income, migration and caste have an influence on the adaptation of agroforestry practices, while Dhakal et al. show in their study in the Terai plains, that institutional support and infrastructure development promote agroforestry while farm size, labour force and farming inputs are restraining factors . Soft variables such as needs for happiness, worries or aspirations were not tested in other comparable studies to the best of our knowledge.
3.3. Narratives about the Motivation for Nut Cultivation (Part 3 of the Data Analysis)
The aim of the narrative was to get more individual, detailed insights into the nut cultivation and to discuss the results of the statistical analyses above. The narratives were given by each household head during the interviews as extra explanations for a closed question or as answers to open questions. We analysed the narrative answers only after the statistical assessment was complete, and did so in order to illuminate those results. In Table 3, we summarise the motivation expressed by the nut growers to cultivate nuts. “Curiosity/trial”, “financial motivation” and “own consumption” were mentioned the most. Non-nut growers said that they are not aware of nuts’ economic value and that they do not have the knowledge needed and/or available land.
In Jumla district, where apple cultivation is promoted by the government, walnut is mostly grown in combination with apple trees. Farmers expressed that “I have walnuts to diversify my income. If my apple trees get damaged due to hail, I still have walnuts” and “Walnuts are less susceptible to certain diseases and pests”. Other farmers stated that the “Walnuts will sell all year around due to its non-perishability” and “I planted my first tree so that my children [will] stop climbing to the trees of my neighbours to take nuts”.
In the macadamia districts, farmers said: “Now my neighbours have asked me for small trees as they see that my tree is bearing nuts”. Another farmer reported, “My wife has seen how much our neighbours gets paid for one kilo of nuts; therefore, she went to the nursery and worked in return for a small macadamia tree”. However, macadamias are sometimes liked too much: “I planted some trees close to the road to school hoping that the school children stop climbing up my trees on my land but rather climb the ones along the road”, and, “Two years ago, some of my newly planted macadamia trees were taken out of the ground during the night. Now I have planted them all in sighting distance of my house or the temple to discourage people stealing them”.
A handful of older farmers from both areas expressed: “I’m getting old. Soon, I will not be able to work physically hard. With nut trees, I will only have to pick the nuts. Therefore, the nut trees will be my pension”. A farmer from Syangja reported: “Many young men have left our village to find work abroad. We are short in labour, which lead to more barren fields. Therefore, our community has bought 200 macadamia trees and planted them last year instead of finger millet. Once planted, there will be very little physical labour required”.
Regarding the feminisation of agriculture that is caused by the male-out migration, over eighty percent of the farmers agreed that: “If the woman has knowledge, there is no problem for her to cultivate nuts”. The reality showed that the domestic work burden of women (unpaid care) often hampers their engagement in income-generating farm tasks. Instead of this being a barrier to nut cultivation, however, men planning to migrate could plant the trees before they go. Our interviews and research showed that nuts appeared to require little labour once they were established. Women and other householders who were left behind could focus on other farm and domestic tasks, only requiring assistance with the nuts in harvest time and perhaps with pruning. Nuts represent a nutritious, sustainable and low-labour crop to many households.
4. Conclusions and Recommendations
Nepal faces climate challenges, including shifting climate zones, and economic changes, such as migration from the rural areas to cities. Cultivation of certain nuts and other cash crops, provides a potential way to adapt agricultural practices to meet these and other challenges . As Barrueto et al.  have shown, the climate of Nepal is suitable for both macadamia and walnut value-chains at present and under projected climate change scenarios even though growing zones will undergo a regional and elevational shift, as introduced above.
According to the Government of Nepal , the national agricultural strategy emphasises supporting farmers to move from subsistence to commercial farming. The commercialisation of walnuts could lead to the substitution in the markets of locally-produced walnuts for those that are currently imported from neighbouring countries . The commercialisation of macadamia has the potential to supply both local and international markets . Moreover, land can be used more efficiently: macadamia growers reported that they have started to intercrop macadamia with coffee. This is a promising strategy, as income is diversified between two crops and coffee is expected to give higher yields [63,64]. We recommend further applied research to explore the best conditions for the intercropping of coffee with macadamia and other cash crops in Nepal.
Furthermore, with this investigation, we have shown that nut cultivation is a novel livelihood strategy that represents the first steps by farmers into crop commercialisation in the areas of Nepal that we studied. Nut production is already an accepted livelihood strategy for men and women, which diversifies and stabilises incomes. Curiosity, economic benefits, own consumption, and low labour are the most commonly mentioned reasons for starting nut cultivation; nuts provide a good source of food and potential income to farmers, and requires minimal continuous labour (in contrast to labour-intensive crops such as millet). Nut cultivation could also lead to the increased resilience of rural communities, as poverty is reduced and nutrition increased.
Barriers to production remain. Our research also revealed that women, poor, and landless farmers do not have the financial means, risk-bearing capacity, land to grow trees, or skills needed to assess the risks and benefits of this crop. This is in line with research from Aase et al.  who found that farm size influences the agricultural innovation, as well as water availability and active national nongovernmental organisations. Oli et al.  also found that land size was one apparent determinant for whether a farmer grows trees; in their research gender, neither [potential] income nor ethnicity were significant influences on farmers’ decisions.
Many farmers also appear to lack knowledge about the benefits of their involvement in these value-chains. Several pilot projects and workshops to teach farmers to grow macadamia nuts have already been done, with some success. We therefore recommend that development partners and policymakers explore inclusive business models, i.e., models that pay heed to weaker members of the rural communities, such as those mentioned above (women, poor, and/or landless). Such models could also benefit from the involvement of the private sector in the promotion of these value-chains, and in the creation of an environment that enables an inclusive commercialisation of these promising crops: walnuts and macadamia could be produced both for local and international markets, and for home consumption . They would provide a reliable, sustainable source of subsistence for farmers with the possibility of additional income from sales. Finally, we suggest that additional research be conducted into the suitability of other nuts (almonds, pecans, hazelnuts) for production in Nepal. Together, these nuts could fulfil the dual purpose of increasing income and food security for rural areas in Nepal under present and future climatic conditions.
The authors thank HELVETAS Swiss Intercooperation Nepal and the Centre for Development and Environment of the University of Bern, Switzerland, for their funding and academic guidance.
A.K.B. developed and executed the livelihood survey in Nepal. J.M. and T.K. guided that process. A.K.B. performed the statistical analysis and interpretation. A.K.B. was the overall lead in the paper, while T.H. advised the structure and overall content of this paper, including revisions.
Conflicts of Interest
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
Table A1. Pre-result logistic regression after using command drop1 in R (ETH Zurich, Zurich, Switzerland).
|Quantitative Set of Variables||df||Deviance||Pr(>Chi)||Significance Code|
|Number of people in the household||1||278.72||0.111377|
|Migration to district of close family members||1||276.22||0.851546|
|Migration to country of close family members||1||278.75||0.109039|
|Available men working on land||1||276.19||0.971917|
|Available woman working on land||1||279.01||0.092608|
|Land size (ha)||1||283.85||0.005615||**|
|Poverty of the respondent||4||288.53||0.014938||*|
|Farmer cultivates fruit trees||1||276.19||0.964421|
|Farmer cultivates fodder trees||1||278.63||0.117849|
|Years of experience with trees||1||301.67||0.000000||***|
|Qualitative Set of Variables|
|Income through Cereals||1||309.36||0.273575|
|Income through Legumes||1||316.5||0.003881||**|
|Income through Potatoes||1||314.23||0.013766||*|
|Income through Vegetables||1||316.05||0.004979||**|
|Income through Fruits||1||342.68||0.000000||***|
|Income through Dairy (Milk, Ghee, Eggs)||1||308.3||0.70245|
|Income through Chicken||1||309.12||0.327879|
|Income through Livestock||1||311.81||0.055848|
|Income through Remittance||1||309.48||0.250627|
|Income through Pension||1||308.27||0.734097|
|Income through Widow/War allowance||1||308.95||0.374114|
|Income through off farm income||1||308.57||0.521749|
|Income through medical plants||1||308.95||0.374887|
|Practice of work load share (man/woman)||1||310.08||0.165634|
|Knowledge regarding health benefits of nuts||1||313.54||0.02037||*|
|Shared decision for big investment (man/woman)||2||318.27||0.006359||**|
|Emotional Set of Variables|
|Nature of Needs for Happiness|
|Education of children||1||287.59||0.0184484||*|
|Wellbeing of children||1||282.19||0.6947026|
|Lack of food||1||282.07||0.8653194|
|Not having enough when old||1||284.66||0.1054408|
|Aspirations for Future|
|Education of children||1||285.43||0.0655168|
|Acquire skills / knowledge||1||288.42||0.0115337||*|
* Most authors accept 0.05 as the significance level and it is marked as “*”, while 0.01 is marked with “**”, and 0.001 with “***”.
Table A2. Full Result logistic regression with adjusted p-values.
|Quantitative Set of Variables||Adjusted p-Value||Significance Code|
|Number of people in the household||1|
|Migration to district of close family members||1|
|Migration to country of close family members||1|
|Available men working on land||1|
|Available woman working on land||1|
|Land size (ha)||0.05987|
|Poverty of the respondent||0.18456|
|Farmer cultivates fruit trees||1|
|Farmer cultivates fodder trees||1|
|Years of experience with trees||0.000013||***|
|Qualitative Set of Variables|
|Income through Cereals||1|
|Income through Legumes||0.058216|
|Income through Potatoes||0.165195|
|Income through Vegetables||0.069711|
|Income through Fruits||0.0000000675||***|
|Income through Dairy (Milk, Ghee, Eggs)||1|
|Income through Chicken||1|
|Income through Livestock||0.558478|
|Income through Remittance||1|
|Income through Pension||1|
|Income through Widow/War allowance||1|
|Income through off farm income||1|
|Income through medical plants||1|
|Practice of work load share (man/woman)||1|
|Knowledge regarding health benefits of nuts||0.224073|
|Shared decision for big investment (man/woman)||0.082672|
|Emotional Set of Variables|
|Nature of Needs for Happiness|
|Education of children||0.295174|
|Worries of Respondents|
|Wellbeing of children||1|
|Lack of food||1|
|Not having enough when old||1|
|Aspirations for Future|
|Education of children||0.982753|
|Acquire skills / knowledge||0.1960731|
* Most authors accept 0.05 as the significance level and it is marked as “*”, while 0.01 is marked with “**”, and 0.001 with “***”.
- Turner, B.L., II; Brush, S.B. Comparative Farming Systems; The Guilford Press: New York, NY, USA, 1987; pp. 11–48. ISBN 0-89862-780-X. [Google Scholar]
- Cavallo, E.; Ferrari, E.; Coccia, M. Likely Technological Trajectories in Agricultural Tractors by Analysing Innovative Attitudes of Farmers. Int. J. Technol. Policy Manag. 2015, 15, 158–177. [Google Scholar] [CrossRef]
- Evenson, R.E.; Gollin, D. Assessing the Impact of the Green Revolution, 1960 to 2000. Science 2003, 300, 758–762. [Google Scholar] [CrossRef] [PubMed]
- Farmer, B.H. Perspectives on the ‘Green Revolution’ in South Asia. Mod. Asian Stud. 1986, 20, 175–199. [Google Scholar] [CrossRef]
- Barrueto, A.K.; Merz, J.; Hodel, E.; Eckert, S. The suitability of Macadamia and Juglans for cultivation in Nepal: An assessment based on spatial probability modelling using climate scenarios and in situ data. Reg. Environ. Chang. 2017. [Google Scholar] [CrossRef]
- Maharjan, K.L.; Joshi, N.P. Effect of Climate Variables on Yield of Major Food-Crops in Nepal: A Time-Series Analysis. Clim. Chang. Agric. Rural Livelihood 2013, 127–137. [Google Scholar] [CrossRef]
- Ranjitkar, S.; Sujakhu, N.M.; Merz, J.; Kindt, R.; Xu, J.; Matin, M.A.; Ali, M.; Zomer, R.J. Suitability Analysis and Projected Climate Change Impact on Banana and Coffee Production Zones in Nepal. PLoS ONE 2016, 11, e0163916. [Google Scholar] [CrossRef] [PubMed]
- Malla, G. Climate Change and Its Impact on Nepalese Agriculture. J. Agric. Environ. 2008, 9, 62–71. [Google Scholar] [CrossRef]
- McSweeney, C.; Lizcano, G.; New, M.; Lu, X. The UNDP climate change country profiles: Improving the accessibility of observed and projected climate information for studies of climate change in developing countries. Bull. Am. Meteorol. Soc. 2010, 91, 157–166. [Google Scholar] [CrossRef]
- Adger, W.N.; Huq, S.; Brown, K.; Conway, D.; Hulme, M. Adaptation to climate change in the developing world. Prog. Dev. Stud. 2003, 3, 179–195. [Google Scholar] [CrossRef]
- Gornall, J.; Betts, R.; Burke, E.; Clark, R.; Camp, J.; Willett, K.; Wiltshire, A. Implications of climate change for agricultural productivity in the early twenty-first century. Philos. Trans. R. Soc. B 2010, 365, 2973–2989. [Google Scholar] [CrossRef] [PubMed]
- Smit, B.; Wandel, J. Adaptation, adaptive capacity and vulnerability. Glob. Environ. Chang. 2006, 16, 282–292. [Google Scholar] [CrossRef]
- Howden, S.M.; Soussana, J.F.; Tubiello, F.N.; Chhetri, N.; Dunlop, M.; Meinke, H. Adapting agriculture to climate change. Proc. Natl. Acad. Sci. USA 2007, 104, 19691–19696. [Google Scholar] [CrossRef] [PubMed]
- Sharma, K. The Political Economy of Civil War in Nepal. World Dev. 2006, 34, 1237–1253. [Google Scholar] [CrossRef]
- Sapkota, P.; Keenan, R.J.; Paschen, J.-A.; Ojha, H.R. Social production of vulnerability to climate change in rural middle hills of Nepal. J. Rural Stud. 2016, 48, 53–64. [Google Scholar] [CrossRef]
- Chhetri, N.; Chaudhary, P.; Tiwari, P.R.; Yadaw, R.B. Institutional and technological innovation: Understanding agricultural adaptation to climate change in Nepal. Appl. Geogr. 2012, 33, 142–150. [Google Scholar] [CrossRef]
- Rodima-Taylor, D.; Olwig, M.F.; Chhetri, N. Adaptation as innovation, innovation as adaptation: An institutional approach to climate change. Appl. Geogr. 2012, 33, 107–111. [Google Scholar] [CrossRef]
- Smithers, J.; Blay-Palmer, A. Technology innovation as a strategy for climate adaptation in agriculture. Appl. Geogr. 2001, 21, 175–197. [Google Scholar] [CrossRef]
- Central Bureau of Statistics (CBS). National Population and Housing Census 2011: General and Social Characteristics Tables; National Report; Central Bureau of Statistics: Kathmandu, Nepal, 2012; Volume 1, p. 262.
- Bhattarai, B.; Beilin, R.; Ford, R. Gender, Agrobiodiversity, and Climate Change: A Study of Adaptation Practices in the Nepal Himalayas. World Dev. 2015, 70, 122–132. [Google Scholar] [CrossRef]
- Gentle, P.; Maraseni, T.N. Climate change, poverty, livelihoods: Adaptation practices by rural mountain communities in Nepal. Environ. Sci. Policy 2012, 21, 24–34. [Google Scholar] [CrossRef]
- Manandhar, S.; Vogt, D.S.; Perret, S.R.; Kazama, F. Adapting cropping systems to climate change in Nepal: A cross-regional study of farmers’ perception and practices. Reg. Environ. Chang. 2011, 11, 335–348. [Google Scholar] [CrossRef]
- Sugden, F.; Maskey, N.; Clement, F.; Ramesh, V.; Philip, A.; Rai, A. Agrarian stress and climate change in the Eastern Gangetic plains: Gendered vulnerability in a stratified social formation. Glob. Environ. Chang. 2014, 29, 258–269. [Google Scholar] [CrossRef]
- Sujakhu, N.M.; Ranjitkar, S.; Niraula, R.R.; Pokharel, B.K.; Schmidt-Vogt, D.; Xu, J. Farmers’ Perceptions of and Adaptations to Changing Climate in the Melamchi Valley of Nepal. Mt. Res. Dev. 2015, 36, 15–30. [Google Scholar] [CrossRef]
- Bhandari, P. Relative Deprivation and Migration in an Agricultural Setting of Nepal. Popul. Environ. 2004, 25, 475–499. [Google Scholar] [CrossRef]
- Sunam, R.; Adhikari, J. How does Transnational Labour Migration Shape Food Security and Food Sovereignty? Evidence from Nepal. Antropol. Forum 2016, 26, 248–261. [Google Scholar] [CrossRef]
- Sunam, R.K.; McCarthy, J.F. Reconsidering the links between poverty, international labour migration, and agrarian change: Critical insights from Nepal. J. Peasant Stud. 2016, 43, 39–63. [Google Scholar] [CrossRef]
- Gartaula, H.; Niehof, A.; Visser, L. Shifting perceptions of food security and land in the context of labour out-migration in rural Nepal. Food Secur. 2012, 4, 181–194. [Google Scholar] [CrossRef]
- Gartaula, H.; Patel, K.; Johnson, D.; Devkota, R.; Khadka, K.; Chaudhary, P. From food security to food wellbeing: Examining food security through the lens of food wellbeing in Nepal’s rapidly changing agrarian landscape. Agric. Hum. Values 2016, 34, 573–589. [Google Scholar] [CrossRef]
- Jaquet, S.; Shrestha, G.; Kohler, T.; Schwilch, G. The Effects of Migration on Livelihoods, Land Management, and Vulnerability to Natural Disasters in the Harpan Watershed in Western Nepal. Mt. Res. Dev. 2016, 36, 494–505. [Google Scholar] [CrossRef]
- Brown, S.; Kennedy, G. A case study of cash cropping in Nepal: Poverty alleviation or inequity? Agric. Hum. Values 2005, 22, 105–116. [Google Scholar] [CrossRef]
- Amatya, S.M.; Newman, S.M. Agroforestry in Nepal: Research and practice. Agrofor. Syst. 1993, 21, 215–222. [Google Scholar] [CrossRef]
- Garforth, C.J.; Malla, Y.B.; Neopane, R.P.; Pandit, B.H. Socioeconomic Factors and Agro-Forestry Improvements in the Hills of Nepal. Mt. Res. Dev. 1999, 19, 273–278. [Google Scholar]
- Pandit, B.H.; Shrestha, K.K.; Bhattarai, S.S. Sustainable Local Livelihoods through Enhancing Agroforestry Systems in Nepal. J. For. Livelihood 2014, 12, 47–63. [Google Scholar]
- Acharya, B.; Dhakal, S.C. Profitability and Major Problems of Coffee Production in Palpa District, Nepal. Int. J. Appl. Sci. Biotechnol. 2014, 460–463. [Google Scholar] [CrossRef]
- Devkota, L.N. Deciduous Fruit Production in Nepal; FAO Regional Office for Asia and the Pacific: Bangkok, Thailand, 1999; Available online: http://www.fao.org/docrep/004/ab985e/ab985e09.htm (accessed on 20 September 2017).
- Government of Nepal (GoN). Average Cost of Production and Gross Profit of Fruit Farming in Nepal 2071/072 (2014/15); Government of Nepal, Ministry of Agricultural Development, Department of Agriculture, Agribusiness Promotion and Marketing Development Directorate, Market Research & Statistics Management Program: Kathmandu, Nepal, 2015; p. 152. Available online: http://www.doanepal.gov.np/downloadfile/FRUITS%20book_1444370660.pdf (accessed on 16 February 2017).
- Tiwari, K.P. Agricultural Policy Review for Coffee Promotion in Nepal. J. Agric. Environ. 2010, 11, 138–147. [Google Scholar] [CrossRef]
- Ros, E. Health Benefits of Nut Consumption. Nutrients 2010, 2, 652–682. [Google Scholar] [CrossRef] [PubMed]
- Souci, S.W.; Fachmann, W.; Kraut, H. Food Composition and Nutrition Tables; Medpharm Scientific Publishers: Stuttgart, Germany, 2000. [Google Scholar]
- Neupane, R.P.; Sharma, K.R.; Thapa, G.B. Adoption of agroforestry in the hills of Nepal: A logistic regression analysis. Agric. Syst. 2002, 72, 177–196. [Google Scholar] [CrossRef]
- Oli, B.N.; Treue, T.; Larsen, H.O. Socio-economic determinants of growing trees on farms in the middle hills of Nepal. Agrofor. Syst. 2015, 89, 765–777. [Google Scholar] [CrossRef]
- Webb, E.L.; Dhakal, A. Patterns and drivers of fuelwood collection and tree planting in a Middle Hill watershed of Nepal. Biomass Bioenergy 2011, 35, 121–132. [Google Scholar] [CrossRef]
- Cedamon, E.; Nuberg, I.; Pandit, B.H.; Shrestha, K.K. Adaptation factors and futures of agroforestry systems in Nepal. Agrofor. Syst. 2017, 1–17. [Google Scholar] [CrossRef]
- Aase, T.H.; Chapagain, P.S.; Tiwari, P.C. Innovation as an Expression of Adaptive Capacity to Change in Himalayan Farming. Mt. Res. Dev. 2015, 33, 4–10. [Google Scholar] [CrossRef]
- Jones, L.; Boyd, E. Exploring social barriers to adaptation: Insights from Western Nepal. Glob. Environ. Chang. 2011, 21, 1262–1274. [Google Scholar] [CrossRef]
- Onta, N.; Resurreccion, B.P. The Role of Gender and Caste in Climate Adaptation Strategies in Nepal: Emerging Change and Persistent Inequalities in the Far-Western Region. Mt. Res. Dev. 2011, 31, 351–356. [Google Scholar] [CrossRef]
- Tiwari, K.R.; Rayamajhi, S.; Pokharel, R.K.; Balla, M.K. Determinants of the Climate Change Adaptation in Rural Farming in Nepal Himalaya. Int. J. Multidiscip. Curr. Res. 2014, 2, 2321–3124. [Google Scholar]
- Berg, C. Prospects of Fruit Growing in Palpa District, Nepal; Report; Swiss Federal Institute of Technology: Zurich, Switzerland, 1985; p. 146. [Google Scholar]
- Upadhyay, M.P.; Joshi, B.K. Status of Plant Genetic Resources in Nepal. In Plant Genetic Resources in SAARC Countries: Their Conservation and Management; Government of Nepal: Kathmandu, Nepal, 2003; pp. 297–422. [Google Scholar]
- Forestry Nepal. Juglans Regia. 2016. Available online: http://www.forestrynepal.org/resources/trees/juglans-regia (accessed on 15 February 2016).
- Jackson, J.K. Manual of Afforestation in Nepal; Forest Research and Survey Centre, Ministry of Forests and Soil Conservation: Kathmandu, Nepal, 1994; Volume 2, pp. 277–280. Available online: http://www.dfrs.gov.np/downloadfile/Manual%20volume%202%20reduced_1450252224.pdf (accessed on 9 February 2017).
- Department for International Development (DFID). Sustainable Livelihoods Guidance Sheets; UK Department for International Development: London, UK, 1999. Available online: http://www.eldis.org/vfile/upload/1/document/0901/section2.pdf (accessed on 20 November 2017).
- Baumgartner, R.; Högger, R. Search of Sustainable Livelihood Systems—Managing Resources and Change; Sage Publications: New Delhi, India, 2004; p. 382. ISBN 076199808X. [Google Scholar]
- Eyhorn, F. Organic Farming for Sustainable Livelihoods in Developing Countries? The Case of Cotton in India; Hochschulverlag AG: Zurich, Switzerland, 2007; p. 223. ISBN 978-3-7281-3152-2. [Google Scholar]
- R Core Team. The R Project for Statistical Computing; Wirtschaftsuniversität Wien: Vienna, Austria, 2017; Available online: https://www.r-project.org/ (accessed on 23 January 2017).
- Venables, W.N.; Smith, D.M. An Introduction to R, Version 3.2.2; Network Theory Ltd.: London, UK, 2016; Available online: https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf (accessed on 16 December 2016).
- McDonald, J.H. Handbook of Biological Statistics; Sparky House Publishing: Baltimore, ML, USA, 2014; Available online: http://www.biostathandbook.com/HandbookBioStatThird.pdf (accessed on 11 February 2017).
- Dhakal, A.; Cockfield, G.; Maraseni, T.N. Evolution of agroforestry based farming systems: A study of Dhanusha District, Nepal. Agrofor. Syst. 2015, 86, 17–33. [Google Scholar] [CrossRef]
- Barrueto, A.K.; Merz, J.; Clot, N.; Hammer, T. Climate Changes and Their Impact on Agricultural Market Systems: Examples from Nepal. Sustainability 2017, 9, 2207. [Google Scholar] [CrossRef]
- Government of Nepal (GoN). Agriculture Development Strategy (ADS) 2015 to 2035; Government of Nepal, Ministry of Agricultural Development: Kathmandu, Nepal, 2015; p. 363. Available online: http://www.moad.gov.np/downloadfile/final ADS report_1440476970.pdf (accessed on 3 March 2017).
- Government of Nepal (GoN). Statistical Information on Nepalese Agriculture 2013/2014; Government of Nepal, Ministry of Agricultural Development: Kathmandu, Nepal, 2014; p. 222. Available online: http://www.moad.gov.np/en/content.php?id=332 (accessed on 3 March 2017).
- Perdoná, M.J.; Soratto, R.P. Higher yield and economic benefits are achieved in the macadamia crop by irrigation and intercropping with coffee. Sci. Hortic. 2015, 185, 59–67. [Google Scholar] [CrossRef]
- Perdoná, M.J.; Soratto, R.P. Arabica Coffee–Macadamia Intercropping: A Suitable Macadamia Cultivar to Allow Mechanization Practices and Maximize Profitability. Agron. J. 2016, 108, 2301–2312. [Google Scholar] [CrossRef]
- Barrueto, A.K. Final Project Report—Foster Climate Resilience of Horticultural Tree Crops and Value Chains; Internal Report; HELVETAS Swiss Intercooperation: Zurich, Switzerland, 2017. [Google Scholar]
Figure 1. Overview of study areas in Nepal.
Table 1. Characteristics of households (standard deviation in brackets).
|Variable||Walnut Growers||Non-Walnut Growers||Pr (>|z|)||Macadamia Growers||Non-Macadamia Growers||Pr (>|z|)||Tot Nut||Tot Non-Nut||Pr (>|z|)|
|Age||44.5 (13.9)||35 (13.7)||-||52.6 (14.9)||48.2 (17.2)||-||48.1 (14.9)||42.2 (17)||-|
|School years||5.5 (4.4)||5.4 (4.5)||-||6.4 (4.2)||5.2 (4.4)||-||5.9 (4.3)||5.3 (4.4)||-|
|Number of people in the household||6.2 (2.3)||5.9 (2.9)||-||5.7 (2.9)||5 (2.3)||-||5.9 (2.6)||5.4 (2.6)||-|
|Migration to district of close family members||0.5 (1)||0.3 (0.6)||-||0.4 (0.9)||0.3 (0.7)||-||0.5 (1)||0.3 (0.6)||-|
|Migration to country of close family members||0.1 (0.3)||0.1 (0.3)||-||0.5 (0.7)||0.4 (0.6)||-||0.3 (0.5)||0.3 (0.5)||-|
|Available men working on land||1.2 (0.8)||1.3 (1.1)||-||1.6 (1.2)||1.4 (0.8)||-||1.4 (1)||1.3 (0.9)||-|
|Available woman working on land||1.5 (0.8)||1.5 (1)||-||1.7 (1)||1.6 (0.7)||-||1.6 (0.9)||1.6 (0.8)||-|
|Land size (ha)||1.5 (2.1)||0.6 (0.5)||-||0.9 (1.2)||0.6 (0.5)||-||1.2 (1.7)||0.6 (0.5)||-|
|Poverty rating||Very poor||8||19||0.00||0||1||0.49||8||20||0.00|
|Farmer grows fruit trees||80||34||0.00||65||59||0.36||145||93||0.00|
|Farmer grows fodder trees||12||0||0.00||51||48||1||63||48||1|
|Years of experience with trees||15.2 (8.1)||3.7 (5.1)||-||15.9 (7.3)||12.5 (8.2)||-||15.5 (7.8)||8.5 (8.2)||-|
* Nepal has 126 caste/ethnic groups reported in 2011 . Chhetri is the largest group 16.6% of the total population of 26,494,504, followed by the Brahman-Hill (12.2%). Dalits, Janajati and Giri make less than 1% of the population.
Table 2. Result logistic regression with adjusted p-values.
|Variable||p-Values Adjusted||Significance Code|
|Quantitative||Years of experience with trees||0.0000||***|
|Qualitative||Income through fruit trees||0.0000||***|
|Emotional||Need for happiness: Financial||0.0350||*|
|Need for happiness: Religion/Spirituality||0.0001||*|
|Future aspiration: Social reputation||0.0129||*|
* Most authors accept 0.05 as the significance level and it is marked as “*”, while 0.01 is marked with “**”, and 0.001 with “***”.
Table 3. Motivation to grow nuts or not to grow nuts.
|Motivation to Grow Nuts||Walnut (%)||Macadamia (%)||Total (%)|
|Copy from neighbour||9||1.4||5.7|
|Motivation not to Grow Nuts|
|Unaware of economic value||32.7||57.6||46.3|
|No quality sapling||9.1||7.6||8.3|
|Long gestation period||1.8||0||0.8|
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).