Household-Level Factors Affecting the Climate Change Adaptation in Agriculture in Central and Western Nepal

Shree Kumar Maharjan1*

1 Graduate School of International Development and Cooperation (IDEC), Hiroshima University, 739-0046 Hiroshima Prefecture, Higashihiroshima, Kagamiyama, 1-3-2, Japan.

*Corresponding Author: Shree Kumar Maharjan, Graduate School of International Development and Cooperation (IDEC), Hiroshima University, 739-0046 Hiroshima Prefecture, Higashihiroshima, Kagamiyama, 1-3-2, Japan, TEL: +81-8062698826; FAX: +81-8062698826;

Citation: Shree Kumar Maharjan (2019) Household-Level Factors Affecting the Climate Change Adaptation in Agriculture in Central and Western Nepal. SciEnvironm 2: 138.

Copyright:© 2019 Shree Kumar Maharjan. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Received date: July 13, 2019; Accepted date: July 22, 2019; Published date: July 25, 2019.


Climate change impacts are observed in all livelihood sectors primarily in agriculture and forestry. Multiple factors have influenced the climate vulnerabilities and adaptations in agriculture at the household level. This study focused on the factors affecting adaptation in agriculture in Madi and Deukhuri valleys of Central and Western Nepal. The systematic random sampling technique was applied to select 154 households in Madi and 150 households in Deukhuri. The main purpose of the study was to analyze the socio-economic factors that either influence or restrain the farmers’ adaptation to climate change at the household level by applying linear probability model. Based on analysis, it is revealed that crop diversity, education, training and total land holding (acre) were positively significant for adaptation choices the study sites. Rest of the variables were not significant though indicated positive as expected except age, occupation, ethnicity, family size, and access to credit.


adaptation, agriculture, climate, factors, Nepal.


Climate change is a natural phenomenon. However, it is severely impacting the livelihood of the people, especially poor and marginalized farmers. It has further aggravated the poverty in rural Nepal at individual, households and community levels due to adverse impacts [1]. There is a growing recognition that the impacts of climate change are closely connected to poverty though it seems distant and marginal as compared to poverty alleviation and economic development [2]. The farmers have experienced the change in agriculture and their surroundings because of increased temperature and erratic patterns of rainfall, which is expected to be more unpredictable and uncertain in the days to come. Adaptation is the most efficient way out for them to face the extreme weather conditions associated with climate variations and to minimize the negative impacts of climate change [3].

The impacts, responses and adaptive capacity of the farmers to the climate change are specific to the socio-economic and geographical contexts. Adaptation is a process, action or outcome in a system at different levels (household, community, group, sector, region, country) to deal with, manage or adjust to the changing conditions, stresses, hazards or opportunities [4]. Among these levels, adaptation at the household and community scales is important by managing the locally available resources including the crops, livestock, land and water through different livelihood choices, strategies and options [5]. It enhances the adaptive capacity and helps in reducing vulnerability. The studies carried out in South Asia clarified that the factors that increase the vulnerability to climate change also help the farmers in building resilience or adaptive capacity [6,7].

Government of Nepal (GoN) has emphasized on adaptation in the national and local plans and priorities [3], which require consolidated efforts of all the stakeholders at all levels including the farmers, poor and marginalized people, though their contributions to the climate change is minimal. Dixit [7] emphasized on the plural institutions and incremental solutions at the local, regional and national scales. There are multiple factors including the policy and institutional, socio-economic, physical, natural and human capital that have been directly or indirectly supporting or influencing and also restraining climate change adaptation [1]. Regmi et al [3] emphasized on analysis of such influencing factors at the farm or household level. Piya et al [8] highlighted the bottom-up assessment of these factors or determinants based on actual and direct interaction with the affected people and communities.

There are some researches that have analyzed the factors influencing or supporting adaptation in different socioeconomic and geographical contexts applying different methodologies and models. Smit and Wandel [4] highlighted the social, cultural, political and economic forces to determine adaptive capacity. Regmi et al [3] applied Logit Regression Model to determine the factors affecting the probability of adaptation strategies at the household level. Piya et al [8] conducted a quantitative analysis of factors/determinants decisive in household adaptation including the socio-economic variables, perceptions and institutional factors. Most of the studies analyzing the factors or determinants were highly concentrated in Africa such as Maddison [9], Hassan and Nhemachena [10], Leggesse et al [11] among others. Very few of such studies [8,12] have analyzed the factors influencing adaptation in Nepalese context. Legesse et al [11] affirmed that less emphasis given on the analysis of factors influencing adaptation strategies of smallholder farmers to adapt to climate variability and change. Likewise, Sujakhu et al [13] highlighted limited research on climate impacts on local livelihoods and responses of the farming communities to address the impacts.

The studies analyzing the factors/determinants in household adaptation decisions are still relatively less, though studies on adaptation in general increased drastically in recent decades. Piya et al [8] affirmed the limited studies at household level. It is important to understand and analyze the factors that facilitate or restrain the choices of particular adaptation at the household level. The current study analyzes and compares these factors at the household level in central and western Nepal. It aims to derive the socio-economic characteristics of the farmers on climate change adaptation at the household level and to analyze the factors of climate change adaptation at the local level.

Conceptual framework

The sustainable livelihood framework is the most insightful and commonly used framework despite its limitations in assessing vulnerability of rural livelihoods to climate change. Most of the vulnerability assessments account for the livelihoods and factors that either influence or constrain in the adaptation [2]. This framework is also motivated from sustainable livelihood framework, which basically explains the natural and anthropogenic causes triggered climate risks and vulnerabilities impacting the livelihoods of the people. Over exploitation of available livelihood resources and increased anthropogenic interventions further aggravated the climate risks and vulnerabilities at the household and community level. In contrary, increased climatic risks and vulnerabilities further destroyed the resources [1]. In that sense, these livelihood resources can also be considered as the factors which either support the adaptation or further increase the climate risks and vulnerabilities. Adaptation is one of options to reduce the impacts of climate change through adaptation choices/strategies and adaptation policies/plans. Multiple factors influence these adaptation strategies/choices and policies/plans either enhancing or restraining it in the process. The current study mainly concentrated on these factors at the national and local levels including the household level in the study sites. The possible factors are included in the framework. All of these listed factors may not be applicable in this study and analysis (Figure 1). The current study concentrates on analyzing the factors at the local level particularly focusing on household level factors such as age, gender, education, occupation, ethnicity, family size, economically active members per household, land holding size at the household level through questionnaire survey. Age, gender, ethnicity and family size are included in the social factors, whereas occupation and land holding size are included in the economic factors since these factors assist in the economy of the household. Likewise, the economically active members per household is considered as the human capital.

Figure 1: The conceptual framework of the research.


Study sites

The study was conducted in Madi valley of Chitwan district and Deukhuri valley of Dang District. The wards 4, 5 and 6 of Madi valley, which was under the jurisdiction of Baghauda Village Development Committee (VDC) prior to declaration of the Madi Municipality in 2015. The Sisahaniya Rural Municipality was selected as the study site in Deukhuri Valley (Figure 2). Chitwan district was identified as the high climate vulnerability district and Dang district was considered as the low climate vulnerability district [14]. However, Nepal Climate Change Support Programme (NCCSP) was implemented in Dang district, but not in Chitwan district. One of the districts with the implementation of local adaptation plan of action and one without any such local adaptation plans were selected as the study sites. Both the valleys were dominated by the Tharu ethnic group. Tharu is one of the largest ethnic groups scattered across Tarai region of Nepal and some states of Uttar Pradesh and Uttarakhand states of Nepal [15]. This ethnic group is known for their traditional skills and knowledge, who adapted effectively in the region during the time of Malaria outbreak in 1950s [16]. The study sites within these valleys were slightly different in the composition of households. The study site in Madi valley has mixed ethnicity, but the site in Deukhuri has mostly Tharu people (more than 95%).

Figure 2: Map of the districts showing the administrative boundaries and frequently flood affected areas including the study sites.

Source: UN-NIP (2017) (

Data and Sampling

The data collection was carried out through the household survey using semi-structured questionnaire in July/August 2016 and July/August 2017 in Madi valley, Chitwan district and Deukhuri Valley, Dang district respectively. The households for the survey were selected through the systematic random sampling technique. Additionally, the focus group discussions (FGDs) comprising of 12-15 people, with the varying age from 20-60 years were also carried out. The FGDs were supportive to triangulate the information and affirm the households affected from the impacts. The list of the households was gathered from the municipality and rural municipality offices in Madi and Deukhuri valleys respectively. We engaged the B.SC. Ag. Students from Agriculture and Forestry University (AFU) and trained NGO enumerators for the household surveys in the valleys. The enumerators were oriented, and they practiced the questionnaire for couple of days prior to the survey. Furthermore, the published papers and unpublished reports of government and non-government organizations were also consulted in the research process.

Empirical Model and selection of the variables

This study intended to analyze the household level factors that influence the climate adaptation in the selected valleys. We found different researches that have used different empirical models for analyzing the household factors/determinants in adaptation decisions in different parts of the world. Piya et al [8] applied multivariate probit model (MVP) to analyze the factors affecting the household decisions in adaptation since this model is suitable to account the correction structure among the choice variables. Other researchers such as Hassan & Nhemachena [10] also used MVP in their studies. Likewise, Maddison [9] and Deressa et al [17] applied Heckman sample selection probit model in determining the household decisions for adaptation. Likewise, Bryan et al [5] applied binary and multinomial choice models. They used binary model to estimate the factors influencing farmers’ perceptions and multinomial logit (MNL) model to analyze the factors affecting these perceptions, which permits the analysis of multiple responses over the chosen base category. Regmi et al [3] applied logistic regression model to analyze the binary and dichotomous choices of the adaptation. Since the nature of outcome variables in the current study was also binary and dichotomous, we also applied the logistic regression model, which is also known as probability linear model, with the selection of the explanatory variables listed in Table 1.

Table 1: Explanatory variables selected for the model

S.N.VariablesUnitMeanStandard DeviationExpected sign
1. Age (age) Number of years 39.86 13.93 +/-
2. Gender (gender) Dummy; = 1 if female, 0 otherwise 0.49 0.50 +/-
3. Education (edu) Number of years of schooling 4.52 2.59 +
4. Occupation (occu) Dummy; = 1 if Agriculture, 0 otherwise 0.65 0.47 +/-
5. Ethnicity (ethnicity) Dummy; = 1 if Tharu, 0 otherwise 0.68 0.47 +/-
6. Family size (fam_siz) Number of family members 6.21 2.63 +
7. Economically active member (econ_active) Number of members active economically 3.95 1.85 +
8. Source of information related to CC (source_info) Dummy; = 1 if Yes, 0 otherwise 0.55 0.49 +
9. Knowledge of CC policy (know_ccpolicy) Dummy; = 1 if Yes, 0 otherwise 0.13 0.34 +
10. Total Land holding (tl_land_acre) Land holding in acre (1 ha = 2.47 acre) 1.07 1.05 +
11. Access to credit (access-credit) Dummy; =1 if Yes, 0 otherwise 0.96 0.17 +
12. Crop Diversity (crop_div) Number of crops cultivated per household in a year 14.02 4.7 +
13. Training and awareness (training) Dummy; =1 if Yes, 0 otherwise 0.71 0.45 +

The outcome response of the farmers on adaptation choices have two possible values; Y = 1, if the farmer has multiple adaptation choices and Y = 0, if the farmer has no adaptation choices. In this case, the value of outcome variable, in which the probability of binary response can be defined as

Yi=1; P (Yi=1)= Pi

Yi=0; P( Yi=0)=1-Pi

Where Pi = E (Y = 1/x) =π(x) represents the conditional mean of Y given certain values of X. In the dichotomous data the conditional mean must be in between less than or equal to 1 and greater than or equal to zero (i.e., 0≤E(Y l x≥1). The probability of the farmer with multiple adaptation choices as expressed by Hosmer and Lemeshow [18] will be

The simplified logistic regression model is given by the equation

A logit transformation of π(x) is central, which is defined as

Where Yi is a binary dependent variable (1, if farmers practice multiple adaptation choices, 0 otherwise), xi represents the explanatory variables used in the model, βi is the parameters to be estimated, ∈i represents error term, e (exp) represents the base of the natural logarithms.

Results and Discussion

Socioeconomic characteristics

The descriptive analysis of socioeconomic characteristics including age, gender, education, occupation, ethnicity, family size, economically active members per household, land holding size etc. of the households were carried out in the study areas which are related to the vulnerability and adaptive capacity. The average age of the respondents in Madi was 41, ranging from 16 to 93 whereas it was 38 in Deukhuri ranging from 16 to 70. Maximum respondents were married (88% in Madi and 92% in Deukhuri) and dependent in agriculture (61% in Madi and 70% in Deukhuri) in both sites. In terms of ethnicity, 40% respondents were Tharu in Madi whereas 96% respondents in Deukhuri were Tharu. Deukhuri valley has comparatively higher economically active members per household than in Madi valley.


Madi valley has comparatively higher average irrigated and unirrigated land than Deukhuri valley. The average land affected per household was also higher in Madi valley. But the irrigation facility appeared to be better in Deukhuri valley. We also witnessed the number of irrigation canals made up of cement in the valley, whereas the irrigation canals in Madi valley were mostly traditional. In terms of source of information and knowledge related to climate policies were more or less same in both valleys. The crop diversity, livestock diversity (types) and training participated by the respondents were higher in Deukhuri valley (Table 2).

Table 2: The mean, standard deviation and range of the selected variables in both valleys

S.N.VariablesMadi valleyDeukhuri valley
MeanStd. Dev.RangeMeanStd. Dev.Range
1. Age 41.03 16.10 16-93 38.66 11.19 16-70
2. Gender 0.49 0.50   0.49 0.50  
3. Education 4 2.56   5.06 2.52  
4. Occupation 0.61 0.48   0.70 0.45  
5. Ethnicity 0.40 0.50   0.96 0.18  
6. Family size 6.19 2.64 2-17 6.23 2.62 2-20
7. Economically active members 3.77 1.77 1-8 4.12 1.91 1-12
8. Source of information 0.60 0.49   0.54 0.49  
9. Knowledge of CC policy 0.09 0.28   0.18 0.38  
10. Total land holding (acre) 1.08 1.06 0-5.00 1.05 1.04 0.19-10.02
11. Access to credit 0.96 0.19   0.97 0.16  
12. Crop diversity 12.99 5.95 1-31 15.06 2.75 1-21
13. Training and awareness 0.57 0.49   0.86 0.34  

Linear probability model

The total of observations for analysis was 273 out of 304 in the data set due to some missing data particularly on irrigated lands, unirrigated lands and affected lands. The likelihood log of the analysis was -111.06877 for the comparison of nested models. The likelihood ratio chi-square of 62.85 with the p-value of 0.0001 which indicates that our model as a whole fit significantly better. In the Table 3, the coefficients, their standard errors, the z-statistics, associated p-values and the 95% confidence interval of the coefficients are presented. The analysis shows that four variables namely crop diversity, education, training and total land holding (acre) were statistically significant for deciding adaptation choices in the study sites. All of these variables (education, total land holding, crop diversity and training) were positively significant for the adaptation choices. However, the coefficients of age, occupation, ethnicity, family size, access to credit have negative sign though we expected the family size and access to credit to be positive. Not all members in the family actively engaged in agriculture which might be the reason of negative sign of family size. There are multiple reasons of borrowing credits such as for festivals, education of the children, whenever family members get sick etc. That is why, the access to credit also appeared negative though it was expected to be positive. Piya et al [8] also found the negative influence of the households with access the credit in their study. The access to credit was reported in subsistence needs, not in productive investment. Though the study sites were different (hills and inner Tarai), the communities have more or less same reasons for accessing the credits from different sources in rural Nepal. Rest of the variables such as gender, economically active members, source of information, knowledge of climate policies/plans were not significant, though appeared positive as expected.

Table 3: Linear probability model analysis of factors affecting adaptation in the study sites

adapt_choicesCoef.Std. Err.zP>|z|[95% Conf. Interval][95% Conf. Interval]
age -.0068082 .0157422 -0.43 0.665 -.0376624 .024046
gender .5673333 .3586351 1.58 0.114 -.1355786 1.270245
edu .1917972 .0776573 2.47 0.014* .0395916 .3440028
occu -.1026955 .3732023 -0.28 0.783 -.8341585 .6287676
ethnicity -.385051 .3964773 -0.97 0.331 -1.162132 .3920302
fam_siz -.1928492 .132671 -1.45 0.146 -.4528795 .0671812
econ_active .1100619 .1950748 0.56 0.573 -.2722777 .4924014
source_info .1540385 .3525761 0.44 0.662 -.536998 .845075
know_ccpolicy .162888 .5415921 0.30 0.764 -.8986129 1.224389
tl_land_acre .050302 .0204852 2.46 0.014* .0101517 .0904523
access_credit -.8450484 1.454104 -0.58 0.561 -3.695039 2.004942
crop_div .1522679 .0398446 3.82 0.000** .074174 .2303619
liv_div .214212 .1429951 1.50 0.134 -.0660533 .4944772
training and awareness .9070397 .3551025 2.55 0.011* .2110516 1.603028
_cons -1.516839 1.750436 -0.87 0.386 -4.947632 1.913953

** significant at P=0.01; * significant at P=0.05, Source: Field Survey 2016 and 2017

The crop diversity was positively significant (P<0.01) for adaptation choices since the crop diversity included the diversity of cereals, pulses, fruits, vegetables and some perennial and medicinal species including the bamboo, Ipil-Ipil (Leucaena leucocephala), Bakaino (Melia azedarach), Colocasia, sugarcane, Mentha, Groundnut, Dhaicha (Sesbania bispinosa) etc. The multiple crop species with different root systems and canopies in the given land dissipate the climate risks thus, supportive towards the adaptation choices. That is the reason mixed-farming, agro-forestry and intercropping is more beneficial and adapted than the monocropping. Integration of multiple crops including trees in the farms counter the adverse impacts of climate change through adaptation [19]. Amenu [20] emphasized on increasing crop diversity to adapt to climate change impacts. Diversifying the crops have different but positive response to the climatic shocks, pests and diseases. Crop diversification itself can also be considered as the adaptation choice/strategy. However, in this study we have considered the crop diversity as the factors based on the number of crops being cultivated by the households in the last fiscal calendar.

Likewise, education, training and total land holding (acre) were positively significant as the education and training helped the farmers to understand the climate risks, impacts and different adaptation choices appropriate to the locality and their households through formal and informal education and learnings. Regmi et al [3] also found that the training was positively significant towards adaptation, however, education wasn’t significant in their study. The study carried out by Maddison [9] emphasized that the educated farmers are more likely to respond through different adaptation choices than the experienced farmers though they are more familiar and knowledgeful about the local climatic conditions. Likewise, more land holdings provide better opportunities to cultivate and diversify more crops for enhanced adaptation. However, proper land management is important since mismanagement and intensive tillage continuously disturb the land that might lead to climate vulnerability [19]. Having a big land but keeping it abandoned and barren may not be supportive to adaptation. Instead, it leads the climate vulnerability. Comarazamy et al [21] affirmed that anthropogenic land-cover and land-use changes including use of land for agriculture have extreme climate impacts. The climate change impacts on the water and land availability for rain-fed agriculture [2]. They have analyzed the climate change impacts on poverty, land degradation and loss of biodiversity in their study. For every one-unit change in crop diversity, the log odds of adaptation choices would change positively by 0.1522. Likewise, for every unit change in education and training, the log odds of adaptation would change by 0.191 and 0.907 respectively. In case of total land holding, it was 0.503.

Both of these research sites were located in the inner Tarai districts with the same climatic conditions and majority of dominating Tharu ethnic people. However, the climate vulnerabilities and intensity of the climate change impacts may not be similar. The vulnerability assessment carried by the Ministry of Environment (MoE) in 2010 also categorized Dang as low-risk district and Chitwan as the high-risk district in terms of overall vulnerability assessment. Tharu people have been living in the region since their ancestry adapting the changing climatic contexts. Since there is no any significant difference between study sites (one with 96% Tharu and another with mixed ethnicity), it can be anticipated that other communities who migrated from the hills after abolition of malaria outbreak in 1950s have also adapted in the localities. However, it is observed that most of the migrated people have settled in comparative better areas close to the roads and market centers that are less prone to climate impacts than the ethnic inhabitants in both sites.

There are no any noteworthy observations with respects to implementation of NCCSP in Dang district as compared to Chitwan district. Number of government and non-government organizations including cooperatives and communities in Madi valley were actively engaged in agriculture and climate related activities including adaptation strategies such as early warning systems, evacuation centers, construction of water ponds, irrigation canals, embankments in the rivers, culverts like in Deukhuri valley. Only the knowledge and awareness of the farmers about climate policies/plans was comparatively higher in Deukhuri valley as compared to Madi valley since they had more opportunities to participate and engaged in the LAPA process since NCCSP in the district. However, other factors are practically same in both study sites. The first phase of NCCSP mostly focused on awareness raising and capacity building for development of the local plans in mid and far-western Nepal, which needs to be implemented in the district. Regmi et al [22] highlighted the constraints of LAPA implementation in relation to the issues of governance, planning, capacity and also resources. Furthermore, change in the administrative boundaries from village development committees (VDCs) to rural municipalities and municipalities after the election in 2017 added difficulties in the implementation of local adaptation plans developed [23]. However, the government is in the process of developing next phase of NCCSP for further strengthening and expansion throughout the country, which will be supportive for farmers to implement the adaptation choices at the community and household level. It is important to consider these factors associated for effective and efficient implementation of adaption strategies/choices and policies/plans as well [1].

Conclusion and Recommendations

Climate change impacts are observed by the farmers in both study sites and they have attempted to address the impacts through adaptation choices. The study sites in Madi valley has mixed ethnicity, whereas Deukhuri has more than 95% Tharus. Maximum respondents were married and dependent on agriculture for their livelihood in both sites. Linear probability model was used to analyze the binary or dichotomous outcome variable with number of explanatory variables. Crop diversity, education, training and total land holding (acre) were positively significant for adaptation choices in the studied households in the study sites. Rest of the variables were not significant though indicated positive as expected except age, occupation, ethnicity, family size, and access to credit. No any noteworthy difference in understanding climate change and adaptation choices in these sites at the household level. It is recommended to maintain and enhance the crop diversity in the total land holding through crop diversification, mixed farming, agro-forestry and integration of fruits, perennial crops in the same unit of land. The education and training on agricultural practices and climate adaptation are influential in adaptation choices, thus the government and other stakeholders should focus on additional training and educational activities to strengthen the climate adaptation.


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