Farmers’ Awareness and Adaptation Measures to Climate Change in Lai Chau Province, Vietnam

Farmers’ Awareness and Adaptation Measures to Climate Change in Lai Chau Province, Vietnam

Published: 2023.09.27
Accepted: 2023.09.27
72
Faculty of Economics and Rural Development, Vietnam National University of Agriculture
Department of Agricultural and Resource Economics, Kangwon National University
Center for Agrarian Systems Research and Development (CASRAD)

ABSTRACT

Climate change is a worldwide environmental threat that has an impact on all economic sectors, especially on agriculture. In Vietnam, Lai Chau province is the most vulnerable province to climate change because of its high exposure to extreme events as well as limited capacity to adapt. The objectives of this study are to explore farmers’ awareness of climate change, adaptation measures, and identify the key factors affecting the farmers’ adaptation decision in Lai Chau province, Vietnam. The study used survey data of 200 farmers and secondary data to explore the research objectives by using the risk matrix and a multivariate probit model. The results showed that most farmers believed the presence of climate change and the major causes are anthropogenic activities. In the context of climate change, 78.5% of the farmers made adaptations to their farming. The farmers used seven adaptation methods and they were likely to combine simultaneously several strategies to manage climate risks. Experience, farm size, farmer association’s membership, credit, extension, distance to market and risk perception had a significant impact on the farmers’ decision to adaptation practices. Findings of the study suggest that government should encourage farmers to expand production and promote the implementation of the “land accumulation” program while providing information regarding climate change scenarios, seasonal variability, and information about the impacts of climate change.

Keywords: Awareness of Climate Change, Adaptation Strategy, Lai Chau Province, Multivariate Probit Model.

INTRODUCTION

The effects of climate change are being felt throughout the world and it has manifested in the increased volatility of extreme weather events (David et al., 2019). Changing climate is one of the most complicated challenges since it has negative impacts on various areas including the economy, environment, human health, and livelihoods (Bruce and Thomas 2018). Climate change is occurring on a global scale, however developing countries suffer most from its negative consequences due to their low level of adaptation (Elizabeth et al., 2009; Abid et al., 2015). Vietnam is highly vulnerable to climate change because of a combination of geographic and climatic factors, as well as socioeconomic structure. According to David et al., (2019), Vietnam is the world’s sixth most climate change-vulnerable country with frequent exposure to climate-related hazards such as drought, floods, and salinization.

Lai Chau is the highest vulnerable province influenced by climate change in the upland areas of Vietnam (Anshory and Herminia 2010). Many factors contribute to the high level of vulnerability in this mountain region, including high poverty rates, limited resources to respond, complex terrain; livelihoods of people rely heavily on agriculture and forestry; and the communities are mainly ethnic minorities with backward farming skills (Martin 2003; Thomas et al., 2010). The reality shows that climate change and agriculture are highly correlated. Climate change’s rapid pace has a far-reaching impact on the agro-ecosystem and productivity (Hatfield et al., 2011; Naveen 2019). The consequences of climate change are more conspicuous for smallholders, who are heavily dependent on agriculture (Rashid and Charles 2008). Meanwhile, the majority of households in Lai Chau province rely on agriculture for their income. Therefore, it is critical to take adaptive practices to lessen the negative impacts of global warming on farmers and agriculture.

The agricultural sector must adjust to the negative impacts of climate change to protect farmers’ livelihoods (IPCC 2008). Nicholas et al., (2012) argued that climate change adaptation is an effective farm-level measure that can minimize climate vulnerability by better preparing farmers and their farming for the climate change’s effects, preventing expected damage, and assisting them to cope with bad events. Several factors are commonly used to investigate farmers’ adaptive behavior such as age, education level, size of household, farm income, credit, information, etc. Farmers’ awareness of climate change plays a significant role in decision-making regarding adaptation (Deressa et al., 2011; Fosu-Mensah et al., 2012; Hoa et al., 2013).

Literature on the adoption of agricultural conservation methods have discovered a link among environmental change awareness, attitudes toward climate risks, and the willingness to implement potential solutions (Linda et al., 2008). As Howden et al. (2008) emphasized, farmers would not adapt to climate change if they were unaware of its existence or do not perceive it as a hazard to their livelihoods. Thus, for better understanding of farmers’ awareness to climate change and the way in which they perceive climate change, the types and extent of adaptation measures used by farmers are crucial to promote successful adaptations in the agricultural sector (Mertz et al., 2009; Nicholas et al., 2012).

According to Anthony and Dagmar (2008), policies to promote climate change adaptation need to be based on the beneficiaries’ cooperation. If those people disagree with policymakers and regulators, the implementation of proposed policies will fail. In addition, Slovic (2000) points out that awareness of risk is subjective and varies between individuals and regions. The results of the risk awareness studies in one country cannot be applied to another due to different cultural and economic contexts. Derived from the above reality, this study was undertaken. The specific objectives of the study are: (1) To clarify awareness of climate change of the farmers in Lai Chau province, (2) To identify farmer’s adaptation measures and determinants of farmer’s adaptation decision; (3) and to propose some recommendations to boost successful adaptation to climate change in the coming time.

LITERATURE REVIEW

Climate change

According to the IPCC (2014), climate change can be identified by “changes in the state of the climate that can be identified by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer.” The IPCC further explains that natural internal processes as well as external factors like solar cycle modulation, volcanic eruptions, and anthropogenic changes in atmospheric composition can contribute to climate change. The prime characteristic of climate change are rises in the mean of temperature; changes in cloud cover and rainfall; melting ice caps and snow cover reduced; increases in ocean temperature and sea acidity; and increases in droughts, floods, violent weather patterns (UNFCCC, 2007; IPCC, 2014).

The three principal components of greenhouse gases are nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4). Increased levels of these gases have resulted in more heat being retained in the global atmosphere, the heat that normally generally goes back into space. This extra heat has caused the greenhouse effect that ensuing climate change (UNFCCC 2007). Edward et al. (2014) argued that 97% or more of climate scientists conclude that global warming is happening, and it is primarily the result of human activity. This assertion is supported by The US National Climate Assessment (2018), since the 20th century’s middle, observed warming is extremely likely caused by human activities. The main causes for CO2 emissions are fossil fuel burning, deforestation and mechanization.

The total amount of greenhouse gas emissions in Vietnam in 2019 was 259 million tons of CO2 equivalent. Among the sectors, energy emerged as the largest contributor, responsible for 155.23 million tons of CO2 equivalent. Manufacturing and construction ranked second with emissions of 73.86 million tons, followed closely by agriculture with 69.30 million tons of CO2 equivalent (Our World in Data, 2022). In agricultural practices, N2O emissions are caused by the application of fertilizers on soils, and CH4 is released from livestock and rice farming.

Adaptation to climate change

Adaptation to climate change entails making the necessary adjustments and changes to reduce the detrimental impact of climate change or capitalize on the positive ones (UNFCCC 2007). In human systems, adaptation aims to mitigate or avoid harm while also capitalizing on advantageous opportunities. Human intervention in natural systems can aid adaptation to predicted climate changes and their consequences (IPCC 2014). Thus, adaptation refers to activities taken by farmers to respond to changing climatic conditions to mitigate negative consequences or capitalize on any opportunities that may arise.

For farmers, adaptation is improving the production capacity of crops and livestock in the face of climate change by applying appropriate technical advances. The risk of agricultural failure and reduced productivity of crops and livestock can be reduced with adaptation and improved the resilience of plants, animals, and agricultural systems to climate change’s consequences (Sen et al., 2015). According to Burton et al. (2003), the adaptation process includes learning about risks, evaluating response options, selecting adaptation measures, mobilizing resources, undertaking adaptation measures, and revising options to suit specific circumstances. Many studies have found farmers often used some common adaptation measures to respond to climate change, including the use of crop switching, improved crop varieties, mixed cropping, crop rotation, changing planting dates, changing production techniques, applying soil conservation techniques, using irrigation, and income source diversification (Elizabeth et al., 2009; Fosu-Mensah et al., 2012; Hoa et al., 2013; Abid et al., 2015; Son et al., 2015; Marie et al., 2020).

Factors affecting the farmers’ adaptation decision

The adoption of adaptation measures to climate change by farmers is determined by their awareness of climate change, demographics (age, education, family size, farming experience), socio-economic characteristics (farm size, income, distance to market), and institutional factors (access to credit service, extension service, membership of farmer association) (Elizabeth et al., 2009; Fosu-Mensah et al., 2012; Abid et al., 2015; Sunny and Sidana 2018; Marie et al., 2020).

Awareness of climate change: Farmers’ perception of global warming attributes and perception toward risk are critical in adaptation decision-making (Abid et al., 2015). Risk awareness is crucial to characterize the problem and determine appropriate behaviors to handle it. Appropriate risk perception is critical and is a requirement for choosing an appropriate adaptation strategy (Arbuckle et al., 2015). Therefore, there is a positive link between farmers’ risk perceptions and adaptive actions.

Farming experience: Experience in farming is a significantly determinant of technology adoption (Abid et al., 2015). More experienced farmers have better risk-bearing ability, information and understanding on climatic changes, as well as the best crop and livestock management methods to adapt (Nhemachena and Hassan, 2007). Thus, farmers who have more experience are more likely to employ climate change adaptation measures.

Farm size: Farm size has a positive impact on new science and technology as larger farms tend to adapt faster than smaller farms (Fosu-Mensah et al., 2012). Farmers with larger farmland sizes are more likely to have a greater ability to try and invest in methods of climate risk mitigation (Sunny and Sidana, 2018). Especially, farm size is positively related to variety change as farmers are willing to use a portion of their land to grow new types.

Membership of farmer association: Farmer association provides its members with a platform to connect and exchange ideas, as well as information on agricultural production management and training and workshops. The group allows farmers and stakeholders to share ideas on how to improve yields and construct climate resilience (Onyeneke et al., 2019; Marie et al., 2020). Therefore, membership of farmer association is expected to positively impact farmers’ adaptation to climate change.

Access to credit: Access to lending facilities is important when adapting to new technology as access to cash allows farmers to easily buy inputs such as improved seeds, fertilizers (Fosu-Mensah et al., 2012). In addition, some adaptation strategies have high costs and so, when farmers have access to credit, it will create favorable conditions for the adoption process. Therefore, availability of credit helps farmers strengthen their financial position and thus they can easily go for new adaptation (Sunny and Sidana, 2018).

Access to extension: Scholars have reported that there is a positive association between agricultural extension access and climate change adaptation. Extension services are a valuable resource for information on new agricultural technologies. It brings information on implementing innovations and better farm management practices (Elizabeth et al., 2009; Fosu-Mensah et al., 2012).

Distance to market: Distance from farmhouse to the market reduces farmers’ adoption of climate adaptation practices and technologies since adaptation necessitates inputs, which are typically purchased at the markets. As a result, the more time it takes farmers to reach the market where adaptation inputs are sold, the lower their ability to adapt to climate change (Nhemachena and Hassan 2007; Abid et al., 2015). Distance to the main market is expected to negatively impact farmers’ adaptation to climate change.

Based on the discussed factors on the adoption behavior of the farmers towards climate change, the study proposed the conceptual framework in Figure 3 for detailed analysis.

METHODOLOGY

Data collection

Primary data collection: Farming household survey questionnaire was used as the primary data collecting method. A structured questionnaire was used comprising both closed and open-ended questions. This study purposely selected four districts, i.e., Muong Te, Phong Tho, Tan Uyen, and Than Uyen, based on their vulnerability, agricultural importance, and the severity of damage caused by climate change events (Figure 4). Random sampling was used for farming household survey and 50 households were selected to represent the total farming household in each district. The data collection mainly focuses on farmers’ socio-economic characteristics, their awareness of climatic trends, climate change consequences, and their adaptability to climate change.

Secondary data collection: Secondary data was gathered from various documents such as journal articles, books, reports, Lai Chau province’s annual reports, Lai Chau Statistical Yearbook, and other related resources.

Data analysis

Regression model: The econometric models including binary probit, binary logit and multinomial probit are often used to analyze the elements driving farmer adaptation to climate change (Elizabeth et al., 2009; Abid et al., 2015; Son et al., 2015; Sunny and Sidana, 2018). The application of these models provides the understanding of the factors that influence adaptation to climate change. These models, however, may limit our understanding by considering adaptation strategies separately and/or not considering the interdependence of farmers’ adaptation measures. Through the survey, we found that farmers in Lai Chau province have numerous adaptation strategies and they tend to use simultaneously several strategies to mitigate climate risks. Thus, empirical models should consider the simultaneous adaptation decisions and therefore the authors such as Nhemachena and Hassan (2007), Mulwa et al. (2017), and Onyeneke et al., (2019) have shown that multivariate probit model (MVP), in this case, is more suitable. The advantage of using the MVP model is that it explicitly recognizes and controls for potential correlation among adaptation options. By contrast, the univariate tool is susceptible to biases because it ignores common factors that could be unobserved and unmeasured but affect the various adaptation measures. Thus, MVP method provides an improved estimation (more precise estimates) of the relationship between adaptation options and the elements that explain it.

The study, therefore, used MVP to analyze the factors affecting farmers’ choice of adaptation measures. The MVP simultaneously models the effect of a set of independent variables on each of the different adaptation practice while permits the error terms of each adaptation strategy to be freely correlated.

There are 7 climate change adaptation practices as dependent variables in the study as following:

where Yij (j=1,…,7) represents the climate change adaptation practices adopted by the ith producer (i=1,…,200); βi is the vector of model parameters; X is the vector of explanatory variables; and εi is the error term that has a multivariate normal distribution distributed with zero mean, unitary variance and an (n × n) correlation matrix (Mulwa et al., 2017; Onyeneke et al., 2019).

Dependent variables: From the climate change adaptation literature and the responses gathered, the study identified seven common adaptation measures: Y1= Crop/livestock switching (yes = 1, no = 0); Y2= Intercropping (yes = 1, no = 0); Y3= Crop rotation (yes = 1, no = 0); Y4= Using improved/new varieties (yes = 1, no = 0); Y5= Adjusting production techniques (yes = 1, no = 0); Y6= Changing planting dates (yes = 1, no = 0); Y7= Diversify income sources (yes = 1, no = 0).

Independent variables: The choice of predictors based on the existing literature review, conceptual framework, and data availability. These variables are exogenous to climate change adaptation: X1= farming experience (years); X2= farm size (ha); X3= membership of farmer association (yes = 1, no = 0); X4= access to credit (yes = 1, no = 0); X5= extension access (yes = 1, no = 0); X6= distance to market (km); X7= risk perception (1= high risk; 0= low risk).

Cross-sectional data in econometric analysis is usually associated with heteroskedasticity and multicollinearity problems. Multicollinearity among independent variables can lead to inaccurate parameter estimates. Therefore, this study used pair-wise correlation to examine the correlation between each pair of independent variables and determined the Variance Inflation Factor (VIF) for each of the independent variables. The result showed that all pair-wise correlation coefficients < 0.5, the VIFs do not reach the convectional thresholds of 5. Thus, the analysis may not appear to be problematized by multicollinearity. And the study estimated a robust model to address the possibilities of heteroskedasticity.

RESULTS AND DISCUSSIONS

Farmers’ awareness about climate variables (temperature, rainfall, drought, and flooding)

The survey findings showed that a large number of farmers believed that the temperature had increased (87%). Interviewed farmers also claimed that temperature seems to decrease during the winter season and raised in the number of extremely hot days during the summer season. Because of rising high temperatures in the summer, there was an increase in drought, selection of an increase in droughts up to 80.5%. The farmers’ awareness of temperature change appears in line with observed scientific data from Lai Chau province (an increasing trend in temperature, from 20.2°C in 2005 to 20.9°C in 2020). Regarding the precipitation trends, 48.5% of farmers reported an increase in rainfall, while 31.5% reported a decrease. The observed scientific data prove that farmers’ perception was appropriate. Actual rainfall is unevenly distributed among districts in the study area, of which Than Uyen district tended to decrease slightly while Muong Te district tended to increase slightly. Besides, farmers claimed that the rain has become more and more complicated, intense, and unpredictable, heavy rain is concentrated in a short space of time, leading to increased flooding in the rainy season. Therefore, a wealth of the respondents (76.5%) perceived an increase in floods and landslides. The details are presented in Figure 5.

Farmer’ awareness of causes of climate change

Table 1 shows that a large number of farmers (66%) attributed climate change to human-related causes. About 68.5% of respondents believed that the appearance of many factories and industrial zones is the primary cause of climate change. Urbanization is a secondary cause (66.5%). The third cause is population growth contributed to climate change (59%). Poor management of natural resources (land, forest...) and agricultural production ranks last with only 28%. This is a point worth noting because many farmers believe that agricultural production has no effect or has little influence on climate change. Because they are not aware of this problem, the situation of using too much chemical fertilizer is still happening, and they are not really paying attention to sustainable farming methods.

Perception of farmers towards effect of climate change on agricultural production

Every year, heavy rains, storms, flash floods, and landslides, which caused significant agricultural losses, seriously destroy irrigation systems, and it costs a lot to overcome the consequences. Over 70% of respondents attributed the temperature and rainfall changing reduced yields, the rise in weed infestation, an increase in pests/insects, and disease outbreaks. Motha (2011) argued that during critical growth phases, most plants and animals are sensitive and vulnerable to the direct effects of high temperatures, decreased rainfall, flooding, and freezes. Other indirect effects on crops and animals include influences on soil processes, nutrient dynamics, and pest organisms. Farmers also reported that harmful pests such as the rice-feeding ear-cutting caterpillars, fungi, black cutworms, among others, are multiplying and spreading because of increasing temperatures and shifting precipitation patterns.

Climate change adaptation strategies of farmers

The survey results show that 153 respondents (out of 200) reported having adopted climate change adaptation methods. The most commonly practiced strategies were “intercropping” (53.0%), “using improved varieties” (41.5%), “crop/livestock switching” (41.0%), and “adjusting production techniques” (40.5%). The other three strategies mentioned, namely, “crop rotation”, “diversify income sources” and “changing planting date”, have the lower adoption rate. Other strategies are “taking water and soil protection measures (digging ditches, planting forests)” and “manage water usage (reuse, use water sparingly) .” However, only 4.5% of farmers used these strategies, therefore this study focused on seven adaptation measures from Y1 to Y7.

Lai Chau farmers have utilized seven main adaptation strategies and they are likely to use a combination of methods to manage climate risks rather than using one. Recent findings of climate change adaptation research agree that farmers often use multiple adaptation methods to reduce climate risks (Mulwa et al., 2017; Onyeneke et al., 2019). Table 4 shows that all 21 pair correlations were positive. This suggests that the adaptation methods were complementary, in other words, these measures were used at the same time. The 16 pairs of adaptation strategies had statistically significant correlation coefficients, while 5 pairs were not, and those were related mainly to method Y7. This implies that Y7_ Diversify income sources was often used alone. Because when farmers must choose this way, it means they will not have much time for the farm, not invest more in agriculture than at the beginning.

Table 5 shows that, in general, the methods farmers used to respond to climate change are appropriate and effective. Crop/livestock Switching, Adjusting Production Techniques, and Using Improved Varieties reached high efficiency with the ratings of 3.52, 3.46, and 3.43 points, respectively. The last one is Changing Planting Dates with only 2.89 points. It is worth noting that the level of success seems to be influenced by how deeply farmers engage with these strategies. In one group, farmers who comprehensively implement these strategies as integral parts of their short or long-term plans achieve higher efficiency. This highlights their thorough understanding of the strategies and their commitment of substantial resources to successful implementation. In contrast, another group only partially comprehends and applies these strategies, resulting in lower efficiency. This suggests that while they might have some awareness of the adaptive measures, their understanding could be limited, or they might not allocate significant resources to carry out these strategies.

Table 6 shows the costs of implementing adaptation measures and these costs are divided into 3 groups. The first group includes crop/livestock switching and adjusting production techniques, these measures are highly effective but also require a high level of investment. The second group is the middle group, including intercropping, crop rotation, using improved varieties, and diversified income sources, which have average efficiency and moderate cost. And the last group has changed the planting date method, which does not require many resources, but requires long experience in agriculture as well as the ability to predict the situation. However, this method had low efficiency and was not as desired by farmers. Since most farmers in Lai Chau have low and middle income, the measures in the second group have been widely used by farmers.

Determinants of farmers’ adaptation decisions

The study found that the participants had an average of 24 years in farming practice. The average farm size in this study area was 1.35 ha, and 38% of households were the membership of farmer association. In addition, 67% and 83% of participants had access to credit/loan and extension service, respectively. The distance from farms to market are various and the mean distance was 7.73 km. The study further shows that 67% of the participants considered the risk of climate change as high.

An MVP model was used to analyze the determinants of farmer adaptation decisions by using seven predictors. The likelihood ratio test was used to assess the appropriateness of MVP. The result of likelihood ratio test (Chi2 = 206.6, P < 0.01) was statistically significant. This implies that the use of MVP was appropriate and had a strong explanatory power. In addition, this study used the Akaike Information Criterion (AIC) to evaluate how well a model fits the data set. For linear regression model, statisticians estimate R2 and for binary logit/probit or multinominal logit/probit model, they estimate Pseudo R2 to determine the goodness-of-fit of the models. However, MVP does not have these criteria, so this study used Akaike Test to compare different possible models and identify which one is the most appropriate for the data. The model with the lowest AIC value is the preferred model. Table 7 demonstrates that the model used in this study with 7 explanatory variables, which has the smallest AIC, was the most suitable.

Table 8 shows the MVP model coefficients, which reveals the direction of effect of explanatory variables. The coefficient on farming experience has positively signed for all the adaptation strategies. Especially, farming experience significantly encouraged Y4_Using improved varieties, Y5_Adjusting production techniques, and Y6_Changing planting dates. This finding indicates that higher experience producers are more likely to adapt to climate change. Greater farming experience farmers have more farming management skills, techniques, and better judgement on adaptation to adverse weather situations. Similarly, Nhemachena and Hassan (2007), Abid et al., (2015) described that adopting climate change adaptation practices significantly correlate with farming experience.

Except for Y7_Diversify income sources, farm size influences the choice of all remaining strategies in a positive and statistically significant way. This means the amount of farmland has a positive effect on farmers who are using a climatic change adaptation strategy. The result implies that large landholdings increase the probability of using adaptation methods to cope with global warming. This is consistent with some research that reported the adoption of new technologies and the size of the farm have a beneficial link (Fosu-Mensah et al., 2012; Abid et al., 2015; Sunny and Sidana 2018). Larger landholdings’ farmers are more likely to invest in climate change adaptation methods.

The coefficient of farmer association participation is positive and statistically significant in influencing Y1_Crop/livestock switching, Y3_Crop rotation, Y4_Using improved varieties, Y5_Adjusting production techniques, and Y6_Changing planting dates. This means that farmers who participate in farmer association increase the probability of adopting these adaptation measures. This may be the result of farmers’ groups sharing of experiences, and ideas on how to increase yields and exchanging information about improved technology, as well as building resilience to climate risks (Onyeneke et al., 2019; Marie et al., 2020). Therefore, being a part of a farmers group can help farmers increase social learning and knowledge transfer about agriculture and climate change adaptation practices. Boansi et al., (2017) also found that membership of these types of groups helps to increase adopting climate risk management strategies.

Access to credit has significantly increased Y1_Crop/livestock switching, Y2_Intercropping, Y5_Adjusting production techniques, and Y7_Diversify income sources. It is because these adaptation strategies require a certain level of financial for adoption that farmers with greater access to credit use them more often than their poorer counterparts who have difficulty in credit access. This result is similar to those from Fosu-Mensah et al. (2012), Sunny and Sidana (2018) which also found a positive link between loan access and climate change adaptation. However, when asked more deeply, farmers said that getting a loan still faces difficulties such as procedures and collateral. These findings point to the importance of improved institutional support in encouraging adaptation practices in smallholder farming communities to relieve the negative effects of climate change.    

The coefficient of access to extension is positively and statistically significantly related to several adaptation strategies. Extension increases the probability of Y2_Intercropping, Y3_Crop rotation, Y4_Using improved varieties, and Y5_Adjusting production techniques. This result suggests that these climate change adaptation methods are more likely adopted by producers who have accessed and received extension education. Agricultural extension offers knowledge and information about better farming techniques and technologies, and it could also be a valuable source related to climatic risk and climate change information. Thus, farmers with greater access to the extension services will be better able to obtain information on climate risk management information as well as improved technology and techniques, climate-smart practices. This corroborates the findings of Elizabeth et al. (2009), and Fosu-Mensah, et al. (2012) on determinants of climate change adaptation measures applied by farmers. Therefore, farmers’ adaptability to the adverse impacts of climate change should be augmented through the provision of frequent support and timely information from extension services.

Distance to the market significantly decreased the likelihood of adopting Y1_Crop/livestock switching, Y3_Crop rotation, Y4_Using improved varieties, Y5_Adjusting production techniques. This factor determines the ability of farmers to access inputs and production materials like seeds, fertilizers, pesticides, machines, and especially for improved varieties and the materials needed to build the barn. Therefore, the further away farmers are from the market, the less likely they are to be able to obtain the supplies required for farming and climate risk management. Market access also encourages farmers to produce extra food and cash crops that can be easily transported to markets, which boosts their income and enables them to adapt to the effects of climate change. Nhemachena and Hassan (2007), and Abid et al. (2015) indicated that farmers face higher cost for transportation and then increase the difficult level to buy production inputs and to market their agro-products.

The perception of risk is positively and significantly related to use all the adaptation strategies except for Y6_ Changing planting dates. People who consider climate change as a high risk are more likely to take adaptive measures than those who see it as a low risk. Communities at high risk from climate change-related hazards were identified in this study. These communities were typically located on hill land, which was also vulnerable to natural disasters (e.g., landslides, mudslides, soil loss). Azadia et al. (2019) argued that if farmers are not percept of the climate risks, they will not respond to them. In addition, the greater the public’s understanding of the level of risk they face, the more support to relevant adaptation strategies they provide (Abid et al., 2015). Thus, public awareness campaigns in reaction to extreme weather events and global warming, as well as education on post-disaster actions should be offered.

CONCLUSION AND POLICY IMPLICATION

Using farm-level data from four districts in Lai Chau province, this study examines farmers’ awareness and strategies for adapting to climate change. Many farmers acknowledged the increases in temperature, rainfall, drought, flash flooding, and landslides. They believed that changes in climate mainly result from human activities, but there were many people who believe that agricultural production has no or very little impact on climate change. This problem causes farmers to use too much chemical fertilizers, and not really pay attention enough to sustainable farming methods. The finding suggests that there is a demand to boost knowledge on the causes of climate change to encourage farmers to counteract global warming. This corresponds to the findings of Azadia et al. (2019) that understanding the causes of global climate change encourages people to act. A plenty number of farmers confirmed that the local agricultural production in general and their families’ production in particular have been affected by climate change. The most recorded damages include destroying irrigation systems, lowering productivity, income, and working conditions causing illness in crops and livestock. Thus, 78.5% of the farmers in Lai Chau have implemented seven adaptation techniques to mitigate climate change's effects on their livelihoods. They were more likely to use a combination of strategies to manage climate risks rather than only an individual strategy. The most common adaptation strategies reported include Y2_Intercropping, Y4_Using improved varieties, Y1_Crop/livestock switching, and Y5_Adjusting production techniques. Because these measures are quite effective and have an average cost that is appropriate for most farmers’ economic conditions. Farmers tend to increase apply Y7_diversify their income sources away from agricultural activities. Because of a lack of qualifications, farmers can only do hard work with low income. Therefore, it is necessary to support and guide farmers to develop agritourism. Agritourism activities not only can offer the required extra revenue to allow small and mid-scale farms as well as rural communities to be preserved but also help to protect the environment that includes productive agriculture (Sustainable Agriculture Research & Education Program, 2017).

Besides, this study identified the key factors influencing farmers’ adaptation decision to climate change. The analysis results indicated that farm experience, farm size, farmer association membership, credit access, extension access, distance to market, and risk perception have statistically significant impact on strategies selection. Along with our finding that farmland size is a factor positively affecting farmers’ adoption of adaptations, the program of “land accumulation” brings efficiency in agricultural, rural development and directly benefits farmers. Therefore, the government should encourage farmers to increase output and promote the implementation of the “land accumulation” program. The implementation of this program may assist Vietnam in avoiding land fragmentation and abandonment to ensure farmers’ livelihoods in facing climate change. Access to extension and membership of farmers association positively impact climate change adaptation. This recommends that the government should encourage farmers to join social organizations, as well as farmers association. Besides, there is a need to increase the intensive capacity of trainings on adaptation measures, introduce climate-smart varieties, promote soil conservation practices, and adjust towards the extension service which may become more relevant and accessible to farmers. Another vital factor that helps farmers in adapting to climate change is access to loan facilities. Thus, supporting both formal and informal financial institutions operating to make loans available and increased institutional support to accessible is necessary. Distance/Time to the market reduces the uptake of climate adaptation practices of farmers. This hints that expanding agricultural production markets, creating favorable conditions for economic sectors to provide inputs, and signing contracts for cooperative production, consumption, and processing. It is also necessary to develop and repair rural roads to improve farmers’ access to the market. Risk perception positively influences the voluntary adoption of mitigation measures. It is therefore important for the government to develop a risk communication strategy for climate change. Government agencies should provide information regarding climate change scenarios, seasonal variability, and the impacts of climate change so that farmers can make decisions instructed about the best mitigation and adaptation methods available.

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ACKNOWLEDGEMENT

The authors would like to express their gratitude to colleagues at Faculty of Economics and Rural Development, VNUA for their assistance in collecting primary data.

AUTHORS’ CONTRIBUTIONS

Vu Thi My Hue contributed to the design and implementation of the research. Vu Thi My Hue and Hio-Jung Shin contributed to the analysis of the results. Vu Thi My Hue, Hio-Jung Shin, and Nguyen Tien Da jointly participated in the writing of the manuscript.

COMPETING INTEREST

All the authors declare that there is no competing interest.

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