Climate change and income diversification in the mekong river delta a panel data analysis

Introduction

Research problem

Climate change is a highly debated global issue, significantly affecting various sectors, with agriculture being the most vulnerable Changes in climate conditions, such as rising temperatures and irregular precipitation patterns, lead to increased instances of droughts and floods, which contribute to pest infestations, diseases, and crop failures (Zerihun, 2012) While climate change generally poses threats to crop production, its effects can vary widely (IPCC, 2014) For instance, higher temperatures can shorten the growing period for rice, resulting in decreased yields Conversely, increased carbon dioxide levels from pollution may enhance the photosynthesis process in certain crops like maize and wheat, potentially improving cereal productivity.

The 20th century's temperature increase of 1°C has led to significant declines in the production of wheat, vegetables, milk, and eggs, with Russia facing an average potential crop production drop of 50% due to climate change However, the effects of climate change on agriculture vary globally, influenced by distinct natural conditions and socio-economic characteristics of each region The demographic attributes and adaptive strategies of local populations are crucial in determining their vulnerability to climate change Despite these varied impacts, it is clear that climate change has profoundly compromised food security worldwide.

Vietnam, the world's second-largest rice exporter after Thailand, derives 90% of its rice production from the Mekong River Delta, a fertile region ideal for rice cultivation This area, located at the river's final branches before it meets the ocean, is known as Vietnam's largest rice granary However, the Mekong River Delta faces significant threats from climate change, including severe saline intrusion, droughts, and freshwater shortages during the dry season, which limit arable land Projections by the Ministry of Agriculture and Rural Development indicate that a 1-meter rise in sea level by 2100 could lead to a 40% income loss for farmers, increased poverty, and heightened social insecurity Despite these challenges, the Mekong River Delta continues to maintain a stable production growth rate.

To address environmental challenges, farmers are increasingly adopting income diversification as an effective strategy to adapt to climate change and enhance their livelihoods This approach not only mitigates the risks associated with crop failure but also boosts overall household revenue Income diversification involves farmers engaging in various activities, such as cultivating different crops, livestock breeding, aquaculture, and non-farm endeavors While the extent of income diversification varies, understanding the key drivers—such as temperature changes, drought, salinity, price fluctuations, and institutional shifts—is crucial for policymakers Insights into these drivers, particularly in the Mekong River Delta, are essential for developing targeted policies that support farmers in this region, as their circumstances may differ from those in other parts of the world.

In Vietnam, the government promotes income diversification among farmers as a strategy to mitigate the impacts of climate change, supported by policies aimed at enhancing physical infrastructure, providing financial subsidies, and facilitating a more open agricultural market However, uncertainties surrounding the factors influencing income diversification and farmers' responses to climate change may hinder the effectiveness of these government initiatives Therefore, research focused on the relationship between climate change and income diversification can offer reliable evidence for policymakers regarding the effects of climate change on farmers' income strategies These empirical insights can enable policymakers to design more effective support measures for farmers engaged in the income diversification process.

Research objective

This study aims to examine how climate change influences income diversification, focusing on variations in temperature, precipitation, and salinity intrusion during both dry and wet seasons Additionally, it explores various socio-economic factors to better understand the differing income diversification behaviors among households.

This study provides valuable insights for policymakers by highlighting how farmers diversify their income and the key factors influencing this diversification It examines the responses of farmers with varying socio-economic backgrounds to climate change through income diversification strategies By understanding these dynamics, policymakers can develop targeted policies that mitigate the risks associated with conventional agriculture impacted by climate change, ultimately enhancing the living standards of farmers.

Research scope

This study analyzes panel data from 362 households in the Mekong River Delta of Vietnam, a coastal region significantly impacted by climate change The majority of these households engage in agricultural activities, which are among the most vulnerable sectors Covering the period from 2010 to 2014, the research provides insights into how climate change and various socio-economic factors influence income diversification, a prevalent and effective strategy for adaptation.

Thesis structure

This study comprises four chapters: Chapter 2 reviews theoretical and empirical literature from prior research, while Chapter 3 presents the analytical framework, including the empirical model and variable descriptions Chapter 4 details the data and empirical findings regarding the effects of climate change and socio-economic factors on income diversification behaviors in the Mekong River Delta Finally, Chapter 5 summarizes the main findings, offers policy recommendations, discusses research limitations, and proposes directions for future research.

Literature review

Theoretical review

Climate change refers to significant alterations in climate patterns, identified by shifts in mean values and variability of climatic properties over extended periods, typically decades or longer According to the Intergovernmental Panel on Climate Change (IPCC, 2007), this phenomenon encompasses any long-term climate change, whether stemming from natural variability or human activities In contrast, the United Nations Framework Convention on Climate Change (UNFCCC, 1992) specifies that climate change is primarily linked to human actions that modify the global atmospheric composition, alongside natural climate variability observed over similar timeframes.

Climate change can arise from natural phenomena, including shifts in the Earth's orbit, solar energy fluctuations, and volcanic eruptions However, the significant warming observed in recent decades is predominantly attributed to human activities According to the fourth report of the IPCC, scientists have established that these human influences are responsible for over a substantial portion of climate change.

90 percent possibility of the greenhouse effect intensification, a phenomenon that long-lived gases absorb heat radiated from the earth to space, making the earth surface warmer.

The rise in greenhouse gas emissions has led to severe climate change, posing significant threats to both natural ecosystems and human societies The effects of climate change vary greatly across different regions, influenced by factors such as vulnerability, sensitivity, and the ability of areas to adapt to these changes.

The rise in global temperatures is causing the atmosphere to become hotter and more uncomfortable, leading to increased evaporation and precipitation Warmer ocean waters are melting glaciers and ice, resulting in a continual rise in sea levels worldwide This phenomenon contributes to the growing frequency and intensity of storms and floods Additionally, prolonged dry and hot weather in certain areas has intensified violent forest fires and wildfires in recent decades, endangering ecosystems and threatening lives.

By the end of the 21st century, global temperatures are projected to rise by 1.9 to 3.4°C, with precipitation increasing by 3.3 to 5.0% and sea levels expected to rise by over 18 to 24 cm compared to the 1990s (IPCC, 2013) Climate change is driving significant alterations in the Earth's surface and ecosystems, impacting both land and ocean environments worldwide Freshwater resources are adversely affected, leading to a dangerous shortage and increased salinity intrusion into inland areas The agricultural sector faces severe challenges, including rising crop failures due to drought, pests, diseases, and soil degradation, as well as increased livestock mortality from extreme weather events such as droughts and floods.

2.1.3 Adaptation of people to climate change

The agricultural system consists of biological, physical, and chemical components, with climate significantly impacting all these processes The agricultural sector's vulnerability is shaped not only by the adverse effects of climate change but also by the adaptive strategies employed by individuals to address these challenges (Marshallet al., 2010).

Adaptation to climate change, as defined by Parry et al (2007), involves adjustments in natural or human systems in response to climatic stimuli, aiming to mitigate harm or capitalize on beneficial opportunities (IPCC, 2007) While often confused with mitigation, which focuses on reducing greenhouse gas emissions, adaptation specifically addresses the sensitivity and vulnerability of societies to climate change impacts (Mastrandrea et al., 2010).

In the agricultural sector, key drivers influencing adaptive behaviors include farmers' awareness and preferences, their experience with climate change impacts, market signals, and government policies Additionally, innovations from research play a crucial role in shaping adaptation investments by offering effective solutions Harmonizing these drivers enhances the efficiency of adaptation strategies and helps identify optimal responses to climate variability A critical factor in deciding on adaptation methods is the assessment of costs and benefits associated with these responses However, the complexity of climate scenarios and the uncertain performance of adaptive processes complicate the evaluation of their economic effectiveness Researchers suggest that large-scale adjustment actions can lead to more efficient outcomes.

Adaptation behaviors are multi-dimensional, encompassing aspects such as time span, intentionality (active or passive), specific purposes (adjusting or reducing vulnerability), and the performer (individuals, households, enterprises, or governments) These behaviors aim to enhance heat tolerance or maintain income and may involve a combination of various attributes (Smit et al., 2002; Adger et al., 2007) Given the complex and uncertain nature of climate change, effective adaptation solutions must be approached from multiple dimensions, necessitating collaboration among diverse stakeholders, including farmers, researchers, and policymakers.

The resilience of agricultural systems is heavily influenced by human behaviors in response to climate change, making it essential to enhance the adaptive capacity of agriculture This involves raising awareness of socio-economic and biological factors that affect adaptation, evaluating the costs and benefits of various responses, and assessing social and technological feasibility alongside resource constraints Ultimately, all adaptation strategies aim to foster a sustainable agricultural economy.

The complexity of adaptation strategies to climate change in agriculture arises from the intricate interplay between climate variability, agricultural systems, and natural resource limitations According to Smit et al (2002), key adaptive behaviors include switching cultivars, altering planting dates, utilizing drought-resistant crops, and implementing irrigation systems in response to rising temperatures and decreasing precipitation Economists recommend that farmers consider crop insurance, invest in crop futures, and diversify income sources for better financial management Additionally, enhancing farm infrastructure through effective water management, irrigation systems, and accurate weather forecasting is vital Scientific research should focus on developing drought-tolerant crops and improving weather predictions The government plays a crucial role by planning and supporting effective adaptive strategies through subsidies, insurance programs, market connections, and technological investments.

Income diversification is recognized as one of the most effective strategies for adapting agricultural systems to climate change, suitable for various conditions This approach can be implemented on farms by expanding crop varieties, incorporating animal husbandry and aquaculture, or engaging in non-farm activities Its significance lies in efficient financial management, as it helps mitigate losses caused by the adverse effects of climate change Given its convenience and effectiveness, policymakers strongly advocate for income diversification when developing adaptation strategies.

2.1.4.1 Definition and classification of income diversification

Income diversification is a strategic approach where households utilize a variety of income sources, such as farm income, off-farm income, and remittances, to enhance financial stability and resilience (Kelly & Adger, 2000; Mendelsohn, 2000; Ellis, 2000; Minot et al., 2006).

Farm income diversification can be categorized into geographic and crop diversification (Smit et al., 2000) Geographic diversification requires extensive land and significant investment in fertilizers, labor, and management, making it suitable only for large-scale operations In contrast, crop diversification is more appropriate for limited labor and arable land, offering higher economic effectiveness This approach involves cultivating short-day crops that can withstand harsh conditions, such as high salinity or drought, while improving soil quality and moisture retention alongside main crops like rice and wheat By rotating diversified crops with varying harvest stages, farmers maximize land use efficiency Additionally, farm income diversification includes livestock breeding and aquaculture, which enhance product value and contribute to overall farm profitability (Bradshaw, Dolan, & Smit, 2004).

In addition to traditional farming, an increasing number of farmers are engaging in stable non-farm activities that are less affected by unpredictable weather This shift is driven by improvements in infrastructure, such as new roads and expanded water and electricity networks, which have created new market opportunities, bolstered the processing industry and services, and generated hundreds of jobs in rural communities.

Empirical review

2.2.1 Impact of temperature and precipitation variation

Seo and Mendelsohn (2008) have revolutionized the study of crop adaptation to climate change by shifting focus from the detrimental effects of severe weather on specific crop yields to the proactive strategies employed by farmers While previous research primarily utilized the Ricardian model to quantify the negative impacts of climate on agricultural revenue, this study highlights farmers' resilience in adapting to harsh conditions through crop diversification By examining farmers' crop choices in South America with a multinomial logit model, Seo and Mendelsohn provide valuable insights into effective adaptation strategies that can help sustain agricultural productivity amidst climate challenges.

A study involving 949 farmers across seven countries reveals a significant link between farmers' crop choices and climate factors such as precipitation and temperature To adapt to global warming, farmers are increasingly opting for fruits and vegetables over traditional crops like maize and wheat The research shows that wetter climates favor the cultivation of potatoes, rice, and fruits, while drier conditions are more conducive to maize and wheat production Additionally, farmers are diversifying their crops, often growing multiple varieties simultaneously, in an effort to maximize their profits.

A study by Seo and Mendelsohn (2007) on African farmers' livestock management reveals that income diversification is significantly influenced by climate change Utilizing three econometric models, the research identifies the most profitable livestock species, explores optimal livestock portfolios, and assesses the likelihood of selecting specific species Conducted across ten countries with data from 9,000 households, the findings indicate a strong correlation between farming choices and climate variations In cooler, wetter climates, farmers favor crops over livestock, while rising temperatures lead to a preference for goats and sheep instead of beef cattle and chickens Increased precipitation shifts choices towards goats and chickens, which thrive in forested areas The study also predicts that, under future climate scenarios, livestock development will persist in warm, dry conditions but decline with excessive rainfall, with heat-tolerant species likely becoming predominant in Africa.

Income diversification in Southern Ethiopia is crucial due to the high vulnerability of agriculture, particularly in arid climates that challenge livestock production through water shortages, diminished pasture, extreme heat, and disease risks A survey by Megersa, Markemann, and Ay (2014) involving 242 households in Dike and Yabelo collected data on socio-demographic factors such as family size, gender, education level of the household head, and livestock holding behaviors The research focused on five prevalent livestock species in the region: cattle, camels, goats, chickens, and donkeys, with diversification defined as households owning at least three species Participants reported changes in livestock holding in response to variations in temperature, precipitation, and drought The study employed linear regression to assess the impact of climate variables on livestock adoption, a ranking model to prioritize livestock species, and a logit model to determine the likelihood of selecting different livestock options.

Research shows that cattle are the least adaptable livestock species to drought conditions, while camels and goats exhibit greater resilience to water scarcity and food shortages Long-term analysis of rainfall and temperature reveals a significant increase in drought frequency, leading to a notable decrease in annual precipitation, with only minor average temperature fluctuations Consequently, livestock diversification is negatively correlated with precipitation levels, though it appears to have little impact on temperature variations.

Brenshaw, Dollan, and Smit (2004) conducted a study on Canadian prairie farmers' cropping behaviors from 1994 to 2002, revealing a trend toward specialization rather than diversification, contrary to previous research Using the Herfindahl index to assess crop diversity, the study found that high start-up costs, limited technological adaptation, and reduced benefits from economies of scale hindered diversification efforts Interestingly, farmers preferred to diversify their income sources through off-farm activities instead of switching crops, indicating a strategic response to climate and economic risks.

2.2.2 Impact of high salinity intrusion to income diversification:

Coastal and low-lying delta regions face significant threats from seawater inundation and rising salinity levels in soil and groundwater, both of which negatively impact agricultural productivity.

IPCC (2014) proposed many adaptation measures to respond with salinity intrusion, in which new and diversified livelihood seems to be the most important method Shannon

Research highlights the necessity of understanding the salinity tolerance thresholds of plants before selecting species for coastal areas Studies indicate that barley and wheat exhibit greater salinity tolerance compared to rice and corn, while cotton and sugar beet withstand higher salinity levels than beans, peas, and potatoes In the oilseed category, sunflower, linseed, and soybean are more sensitive to salinity than canola and safflower Fruits and citrus are generally unsuitable for saline environments Recommendations for enhancing crop resilience in coastal and low-lying delta regions include developing varieties that can withstand drought, heat, and salinity Genetic engineering is proposed as an effective method to improve plant endurance against high salinity and enable irrigation with brackish water Additionally, alternative adaptation strategies involve cultivating non-rice crops in floating gardens and utilizing small-scale fish and aquaculture in flooded areas.

The Mekong Delta in Vietnam has faced significant challenges due to salinity intrusion, particularly during the dry season Research indicates that seawater can penetrate 40-60 km inland, resulting in millions of hectares of farmland becoming highly saline This phenomenon has led to increased crop failures and has adversely impacted agricultural production in terms of both quantity and quality.

Binh (2015) assessed the vulnerability of the Mekong River Delta to salinity intrusion from 1995 to 2011 using both quantitative and qualitative methods, revealing an increasing rate and extent of intrusion over time A survey of 512 households in Tra Vinh identified various adaptation strategies, such as dyke construction, crop rotation, and groundwater management, employed to mitigate this hazard Previous studies indicate that the dyke system has effectively reduced floods and salinity intrusion, enabling farmers to cultivate 2 to 3 rice crops annually, thereby boosting rice production (Hoanh et al., 2003; Tuong et al., 2003; Can, 2005) Additionally, De et al (2002) introduced new short-day, high-salinity-tolerant rice varieties, which are well-suited for coastal regions.

Since 2000, the Vietnamese Government has implemented policies to enhance rice quality and promote integrated farming systems combining rice with fish or shrimp, benefiting agricultural productivity Coastal farmers have successfully adopted rice-shrimp farming, leveraging both floods and brackish water to increase their incomes This dual-season model allows for rice cultivation in the dry season and shrimp farming in the wet season, minimizing the risks of shrimp mortality and crop failure due to adverse climate conditions while meeting household consumption needs Additionally, the rice-shrimp system demonstrates high revenue efficiency through salt leaching, with evidence suggesting that yields may be higher in monoculture rice systems.

Many adaptation measures for sea level rise fail to consider their long-term impacts Vulnerability to salinity varies significantly across regions, particularly between dyke and semi-dyke protected areas Renaud (2015) suggested additional adaptation strategies, including the use of advanced crop varieties, managing river flows, developing infrastructure, and rehabilitating degraded areas These solutions are deemed long-term strategies for climate change adaptation and should be actively promoted and guided by the government to support the development of the Mekong Delta.

2.2.3 Impact of socio-economic characteristics on income diversification

Researchers emphasize the importance of social and economic factors in addressing vulnerability to global warming Kelly and Adger (2000) identified four key strategies to mitigate the adverse impacts of climate change on human communities: reducing poverty, ensuring security, diversifying income to spread risk, and safeguarding property management rights.

The vulnerability of agriculture is closely tied to a country's economic strength, particularly in developing nations facing poor living standards, inadequate infrastructure, low education levels, and ineffective policies that hinder adaptation to climate change (Zorom et al., 2012; Kelly & Adger, 2000) Research by Zorom et al (2012) in Sahel, one of the world's poorest regions, revealed that grain growers and livestock breeders are more susceptible to climate impacts than off-farm workers and gardeners Traditional grain farmers, reliant on rain-fed crops, face significant challenges during severe climate events To cope with drought, farmers typically adopt strategies such as cultivating drought-resistant vegetables and short-cycle varieties While Sahel farmers recognize that income diversification is crucial for reducing vulnerability to climate change, they encounter numerous obstacles, including a lack of capital investment and supportive government policies.

According to Megersa, Markemann, and Ay (2014), livestock diversification is primarily influenced by family size and the per capita ownership of cattle, with other socio-economic factors showing little significance This indicates that the diversification of livestock relies heavily on the availability of labor and income generated from cattle farming.

Research methodology

Analytical framework

Based on theoretical and empirical literature review, an analytical framework is built to clarify the motivations and determinants of income diversification of households in Mekong River Delta (Figure 3.1).

Climate change, primarily driven by human activities, significantly impacts agriculture, particularly in the Mekong River Delta, a low-elevation coastal area This region experiences rising temperatures, prolonged dry seasons, reduced rainfall, and increasing salinity intrusion, all of which threaten its status as the country's rice bowl, contributing over 50% of national rice production Abnormal weather patterns lead to frequent crop failures and substantial declines in rice yields In response, the government has promoted various adaptation strategies, encouraging farmers to diversify their income sources Many households in the Mekong River Delta now integrate rice farming with other crops, livestock, aquaculture, and non-farm activities, effectively mitigating risks associated with traditional rice cultivation and enhancing their financial resilience against climate change.

Methodology

To comprehensively evaluate the impact of climate change and other determinants on household income diversification behaviors, this study employs a dual measurement approach, incorporating both one-sided and two-sided proxy indices in the regression analysis, thereby providing a more accurate and overall assessment of income diversification degrees.

The first income diversification index measures the number of income sources, indicating the level of diversification for a household This straightforward method assigns an integer value from 0 to 4, reflecting participation in 0 to 4 income-generating activities A higher index value signifies greater income diversification within the household.

DI it  m where m is source of income, and m is from 0 to 4.

The Herfindahl index, introduced by Ben, Holly, and Barry in 2004, serves as the second income diversification index and was initially designed to measure industry concentration, aiding in the evaluation of an industry's oligopolistic status This index quantifies the degree of income diversification by assessing both the variety of income sources and their distribution within the total income.

The Herfindahl index measures the concentration of income among households, with values ranging from 0 to 1 A higher index value, closer to 1, indicates less diversification in income sources for farmers.

This study employs two distinct models to assess the impact of climate change and other factors on income diversification, owing to the differing methods used to measure this diversification The panel Poisson model is utilized for the first index, while the panel Tobit model is deemed more appropriate for the second index.

To adapt to climate change, rural households in the Mekong River Delta diversify their income through four primary sources: rice cultivation, the cultivation of various crops like fruits, sesame, soybeans, and coconuts, livestock and aquaculture breeding, and non-farm income activities.

The general model is estimated as follows:

DI it    K it  S it  i where DI it is income diversification index of household i in year t K it represents climate variables including temperature, precipitation, and salinity levelof household i in year t

The study examines various controlling variables that influence farmers' characteristics, including gender, age, educational level of the household head, household size, labor ratio, migration status, and the land area owned by the household in year t Additionally, it discusses the estimation methods employed for panel Poisson analysis.

' i i model and panel Tobit model are figured out in order to prove the existence of critical point of .

The first model's regressand represents the count of income sources, which can range from 0 to 4, and is characterized as count data adhering to a Poisson probability distribution.

 e   i  y i , y 0,1, 2, y i ! where  i obeys the log linear model, and ln  i x i  Moreover,  i is proved to be the mean or expected value and the variance of thePoisson distribution, due to the transformation:

E[ yx i i | x i ] . The parameter could be estimated by the maximum log-likelihood function:

The first derivative of the log-likelihood equation is:

The Hessian is always negative, so could be estimated when the equation gets the maximum value.The prediction is ˆ exp(x ˆ) The estimated variance of the prediction is i i ˆ 2 '

 i x i Vx i , in which V is the estimated covariance matrix for . i

To analyze the determinants of income diversification, a two-limit Tobit model is employed due to the continuous nature of the dependent variable, the Herfindahl index, which ranges from zero to one The model examines the relationship between the latent variable, representing the desired Herfindahl index, and the observed Herfindahl index for each household over time The regression is structured as y* = β'x + u, where the error term follows a standard normal distribution The observed Herfindahl index is defined such that y = 0 if y* ≤ 0, y = 1 if y* ≥ 1, and y = y* if 0 < y* < 1, thereby capturing the nuances of income diversification across households.

x The log-likelihood of the censored model is:

The first and second derivatives of the log-likelihood function with respect to β reveal that the Hessian is consistently negative This indicates that the log-likelihood can achieve a maximum value, allowing for the determination of β at that optimal point.

Table 3.1 outlines the variables used in the model, distinguishing between dependent and independent variables The dependent variables consist of two types of income diversification indices Independent variables are categorized into two groups: climate change proxies, which include scaled salinity and the average temperature and precipitation values for both the dry and wet seasons, along with their quadratic forms, and controlling variables that encompass socio-economic characteristics such as the age, gender, and educational level of the household head, household size, labor ratio, land area, and migration status.

Variable Denotation Unit Description Expected sign to diversification behavior

Income diversification index1 diversity_index1 Number of income sources: 0,1,2,3,4

Equal sum of square of the net revenue from each Income diversification index 2 diversity_index2

 Climate change income source in the total income, and continuous from 0-1

Average temperature in the dry season dry_temp o C +/-

Average temperature in the wet season wet_temp o C +/-

Average precipitation in the dry season dry_precipitation mmHg +/-

Average precipitation in the wet season wet_precipitation mmHg +/-

Square of average temperature in the o 2 dry season sqr_dry_temp C +/-

Square of average temperature in the o 2 wet season sqr_wet_temp C

Square of average precipitation in the 2

+/- dry season sqr_dryprecipitation mmHg +/-

Square of average precipitation in the 2 wet season sqr_wetprecipitation mmHg +/-

Variable Denotation Unit Description Expected sign to diversification behavior

Age of household head age year old +/-

Household size hh_size member +

Household’s labor ratio hh_labor_ratio % +

Number of migrators in a household migration member +/-

=2: secondary level Educational qualification of household head education =3: high school level

Gender of household head gender =1: male

Land area of household landarea hectare +

Two key indicators of climate change are temperature and precipitation Tracking the changes in average seasonal temperature and precipitation over the years across various regions is essential for accurate climate estimations.

Severe climate conditions, including high temperatures, drought, and limited rainfall, are significant factors driving farmers to diversify their agricultural practices (IPCC, 2007) To evaluate the long-term effects of climate change, researchers utilize quadratic models of temperature and precipitation.

The socio-economic characteristics of a household encompass various factors, including the gender, education level, age, size, labor ratio, land area, and migration status of the household head Gender is represented as a binary variable, with 1 indicating a male head and 0 for a female head While establishing a direct correlation between the gender of the household head and diversification capacity is challenging, it is anticipated that men may exhibit a greater willingness to explore new crops and income-generating activities compared to women (Kimsun & Sokcheng, 2013).

The education level of a household head, represented as a categorical variable ranging from 0 (below 5th grade) to 5 (university graduate), plays a crucial role in influencing household income diversification Higher educational attainment is associated with increased opportunities for adopting new technologies and engaging in non-farm activities, potentially enhancing income sources (Nhan et al., 2012; Reardon et al., 2000; Minot et al., 2006) Conversely, some perspectives suggest that a well-educated household head may choose to specialize in traditional agricultural practices, aiming to maximize profits through focused expertise As a result, the impact of the household head's educational qualifications on income diversification remains complex and multifaceted.

Data sources

The study utilizes the Vietnam Household Living Standards Surveys (VHLSS), which offers comprehensive data at the household and commune levels across 64 provinces, gathered through direct interviews conducted by trained enumerators Respondents participate in face-to-face interviews using a meticulously crafted questionnaire that addresses various socio-economic factors, with a primary focus on income and expenditure details of individuals and households Additionally, the survey incorporates information on education, health status, and other demographic characteristics Over the years, the insights derived from the VHLSS database have significantly informed the government's socio-economic policy planning.

The empirical study conducted in the Mekong River Delta encompasses 13 provinces: Long An, Dong Thap, Ben Tre, Vinh Long, Can Tho, Tien Giang, Hau Giang, Kien Giang, An Giang, Soc Trang, Tra Vinh, Bac Lieu, and Ca Mau Its primary objective is to evaluate the impact of climate change on household adaptation behaviors through income diversification from 2010 to 2014 The research involved 362 households that completed all three surveys conducted by the General Statistics Office (GSO), resulting in a comprehensive dataset of 1,086 observations collected from these households across the 13 southern provinces in the years 2010, 2012, and 2014.

Income diversification involves engaging in various activities to generate revenue In the Mekong River Delta, households primarily rely on four income sources: rice cultivation, other crop cultivation, livestock and aquaculture, and non-farm activities The empirical study measures income diversification using two indices: the number of income-generating activities per household and the sum of the squares of net income from each source Net income is determined by subtracting relevant expenses from revenue in each economic sector Additionally, the study explores socio-economic characteristics such as household size, labor ratio, education level, gender and age of the household head, migration status, and land area, as reflected in the Vietnam Household Living Standards Survey (VHLSS).

The Mekong River Delta, spanning an extensive area of 390,000 km² and 740 km along the coastline, features a tropical monsoon climate with diverse ecological regions exhibiting significant climatic variations This study focuses on key climatic variables, specifically temperature and precipitation, recorded at 10 meteorological stations throughout the Delta, with data sourced from the Ministry of Agriculture and Rural Development (MARD) Monthly climatic data is compiled for each station, reflecting the region's two distinct seasons: the rainy season from May to November and the dry season for the remaining months For analysis, temperature and precipitation averages are calculated for each season, with household climatic variables linked to the data from the nearest meteorological station.

Salinity measurement

Salinity data collected from the Vietnam Institute of Meteorology, Hydrology, and Climate Change reveals significant salinity intrusion in the Mekong River Delta, affecting 8 out of 13 coastal provinces Saline water penetrates inland via estuaries, rivers, and canals, with 29 salinity monitoring stations established at key locations such as Tieu, Dai, Ham Luong, Co Chien, Cung Hau, Dinh An, Tran De, Ong Doc, and Cai Lon These stations are precisely mapped, providing detailed longitude and latitude coordinates to track salinity levels along the coast.

Salinity concentration is measured using specialized sensor instruments, which automatically transmit signals to a data processing center From February to May, during the peak dry season, statistical figures are recorded every two hours These raw figures are analyzed to determine the average, maximum, and minimum salinity concentrations In scientific research, the maximum salinity concentration is particularly significant, as it directly impacts the sustainability of crops and aquatic life For instance, common rice has a specific saline threshold that must be considered.

Salt-tolerant rice can withstand salinity levels of up to 4 g/L, but yields may decrease by 20-50% depending on the growth stage, and levels exceeding 6 g/L can lead to complete crop failure In contrast, aquaculture species like catfish thrive in salinity up to 12 g/L, while shrimp flourish best in environments with 15-25 g/L This difference in salinity tolerance highlights the potential for income diversification through aquaculture as a strategy to mitigate risks associated with crop production failures.

Salinity statistics are accessible at each station; however, the limited availability of household locations poses challenges for production and daily activities, particularly in areas distant from the sea.

The salinity levels exceeding 4 g/L are notably high at distances of 45 to 65 km from the sea To safeguard against salinity intrusion, a protective safety zone is established between 70 to 75 km from the coastline Over the years, the sphere of influence of salinity intrusion extends further inland.

This study represents the first effort in Vietnam to create a novel measurement technique for converting salinity levels from the nearest monitoring station to specific locations Building on the methodology of Dasgupta et al (2015), the research employs a simplified approach due to data limitations, utilizing the distance from household locations to the nearest salinity station for re-estimation Findings indicate that salinity impact diminishes with distance; within 30 km, salinity remains largely unchanged, while distances of 30-60 km and 60-90 km see reductions of approximately 30% and 60%, respectively Beyond 90 km, salinity levels drop to just 10% of the original value This refined salinity measurement provides a more accurate assessment of the total impact of salinity intrusion on individual households, factoring in both the affected zones and the salinity levels at the nearest station.

Figure 3.2 Salinity stations in Mekong River Delta

This chapter provides an overview of the Mekong River Delta, a region significantly impacted by climate change It then presents and analyzes empirical results to elucidate the factors influencing income diversification strategies and to examine the relationship between climate change and these diversification efforts.

4.1 rview of the Mekong River Delta

4.1.1 Geographical position and natural conditions

The Mekong River Delta, located in the southwestern region of Vietnam, spans over 3.9 million hectares and is bordered by the Eastern Sea It is characterized by two main branches of the Mekong River—the Bassac (Hau) River and the Tien River—along with a network of hundreds of small canals This delta encompasses 13 provinces, including Ben Tre, Long An, and Can Tho, among others With a flat terrain averaging just 3-5 meters above sea level, some areas are as low as 0.5-1 meter During the rainy season, upstream floods can submerge large regions, particularly the Plain of Reeds and the Long Xuyen Quadrangle, with water depths reaching up to 3 meters Conversely, the dry season brings low river flow, leading to significant salinity intrusion along the coastal areas.

The Mekong River Delta is a crucial player in Vietnam's economy, primarily due to its vast area, fertile soils, and ample freshwater supply This region produces approximately 50% of the country's rice yield, making Vietnam the second-largest rice exporter globally Additionally, the delta is known for its thriving fruit orchards and vegetable crops, alongside a rapidly growing aquaculture sector that focuses on brackish water species like shrimp and catfish, significantly contributing to the GDP As industrialization and modernization progress, the non-farm sector is expanding, leading to a higher proportion of GDP from industry and services, while the agriculture sector's share is gradually decreasing.

Figure 4.1 GDP share per sector in Mekong River Delta

4.1.3 Impact of climate change on The Mekong River Delta

In Vietnam, the impact of climate change is predicted based on three scenarios of Monre

By the end of the 21st century, average global temperatures are projected to rise between 1.1°C and 3.6°C, depending on emission scenarios While rainfall may decrease during the dry season, it is expected to increase during the rainy season, with overall precipitation potentially rising by 1% to 10% Additionally, sea levels could rise by at least 65 cm, reaching up to 100 cm compared to levels from 1980 to 1999 These significant climate changes are likely to have severe impacts on various economic sectors, particularly agriculture.

The Mekong River Delta is one of the world's most vulnerable deltas to climate change, particularly due to rising sea levels, which are projected to increase by 22 to 30 cm by the mid-21st century and up to 79 to 99 cm by the century's end, depending on emission scenarios This rise could lead to permanent inundation of 12.8% to 37.8% of the region's flatlands, with a 1-meter increase potentially flooding 70% of the rice-growing areas, resulting in the loss of 1.5 to 2 million hectares of farmland Additionally, abnormal weather patterns and severe droughts are exacerbating pest infestations and crop diseases, jeopardizing food security across the region For instance, a mere 1°C rise in temperature could decrease rice yields by 10% and bean crop yields by 5% to 20% Environmental experts are urgently sounding the alarm about the detrimental effects of climate change on agriculture in the Mekong River Delta.

The Mekong River Delta could be divided into three ecological regions (Figure 4.2), which confront different challenges from effects of climate change, include:

1) Upper Delta – facing seasonal fluvial floods and increasing the ability to keep water via optimal land and water use.

2) Middle Delta – facing the heavy fresh water shortage in dry season and, droughts and ensuring enough water supply;

3) Coastal Delta – facinginundation with excess salinity intrusion and brackish water.

In recent years, salinity intrusion and drought have emerged as significant threats to the livelihoods and agricultural activities in the Mekong River Delta Thousands of hectares of rice cultivation have been lost, exacerbating the already critical shortage of fresh water for daily use and agricultural production.

Figure 4.2 Regional division of Mekong River Delta

4.2 Salinity intrusion in Mekong River Delta

The coastal provinces of the Mekong River Delta are facing significant challenges due to severe saline intrusion, particularly during the dry season when river flow is at its lowest and discharge is inadequate This saltwater encroachment penetrates inland through a complex system of canals, ditches, and waterways, exacerbating the region's environmental issues (Hashimoto, 2001).

Figure 4.3 The salinity intrusion map of the Mekong River Delta

Saltwater intrusion is influenced by several key factors, including water flow from the Mekong River's source, seasonal water reserves, rising sea levels, water extraction practices, the shape of riverbeds at estuaries, and wet season dynamics The construction of numerous hydroelectric plants along the upper Mekong has significantly diminished water flow to the lower regions Additionally, the frequency and intensity of floods in the Mekong Delta have decreased over the past two decades, often ceasing by early November due to natural conditions and lake management at the river's head Consequently, the capacity for water retention during the flood season has halved in recent years.

The over-exploitation of water for agriculture and aquaculture has significantly reduced freshwater discharge, while the shape of the riverbed influences the extent of saltwater intrusion The lack of floods has led to a noticeable consolidation of alluvium in estuarine zones, resulting in increased erosion of the riverbed and easier saltwater intrusion Additionally, the delayed onset of the rainy season, which now typically begins after July instead of late April, exacerbates these issues Collectively, these factors contribute to the inland progression of salinity intrusion, leading to unpredictable environmental impacts (Nguyen, 2016).

Result and discussion

Descriptive statistics of variables

Table 4.1 Descriptive statistics Variables Unit observationNumber of Mean Standard deviation Min Max

Precipitation in dry season mmHg 1,086 53.66 55.77 8.670 337.2

Precipitation in rainy season mmHg 1,086 215.9 58.86 107.1 345.3

Square of temperature in dry season o C 2 1,086 736.1 23.08 697.0 775.1 Square of temperature in rainy season o C 2 1,086 773.2 16.27 745.3 808.3 Square of precipitation in dry season mmHg 2 1,086 5,988 20,309 75.17 113,704 Square of precipitation in rainy season mmHg 2 1,086 50,067 27,233 11,479 119,225

Age of household head year old 1,086 52.00 13.72 23 89

The income diversification index serves as the dependent variable, categorized into two types: Type 1 and Type 2 Type 1 diversity index ranges from 0 to 4, with an average value of approximately 2.37, indicating that a higher index signifies greater diversification Meanwhile, Type 2 diversity index exhibits a continuous variation.

0 to 1, with the mean value of 0.733 The nearer to 1 Herfindahl index is, the less diversification farmers participate.

Rice Other crops Aqua-livestock Non-farm

Figure 4.4 Income shares of households in Mekong River Delta

The column graph illustrates the rising trend of income diversification over the years, highlighting that in 2010, households primarily engaged in rice cultivation and nonfarm income activities In contrast, participation in growing other crops and breeding aquaculture and livestock was minimal However, by 2012, this trend began to shift, indicating a gradual increase in the diversification of income sources among households.

In 2014, there was a notable shift in agricultural practices, with a significant increase in non-farm activities, the cultivation of other crops, and aquaculture-livestock breeding, while the proportion of rice cultivation continued to decline This trend aligns with earlier research by Nguyen (2012) and The (2013), and is consistent with the Mekong River Delta's economic structure transformation policies implemented by the State.

The monthly precipitation data reveals a significant disparity between the dry and rainy seasons During the dry season, the average rainfall is merely 53.66 mmHg, with a minimum of only 8.67 mmHg In contrast, the rainy season experiences an average of approximately 215.88 mmHg, with a minimum of 107.14 mmHg Analysis of the graphs from 2010, 2012, and 2014 indicates a consistent pattern in annual precipitation, highlighting very low rainfall from December to April, including a complete absence of rainfall in February.

Moc Hoa My Tho Cao Lanh Cang Long Chau Doc Can Tho Soc Trang Rach Gia Bac Lieu

Moc Hoa My Tho Cao Lanh Cang Long Chau Doc Can Tho Soc Trang Rach Gia Bac Lieu

Rainfall begins to gradually increase in May, peaking between 200 to over 300 mm from July to October, before sharply declining Notably, the chart indicates a significant drop in rainfall across the region in 2014 compared to the previous two years This decline is attributed to the effects of El Niño, which caused a severe shortage of rainfall and abnormal drought conditions from late 2014 into 2015.

Te m pe ra tu re ,

Moc Hoa My Tho Ba Tri Cao Lanh Cang Long Chau Doc Can Tho Soc Trang Rach Gia

Figure 4.5.Precipitation in Mekong River Delta

The average temperature difference between the dry and rainy seasons is minimal, at just 0.6°C, with the rainy season typically experiencing slightly higher temperatures This trend can be clearly observed by examining the temperature chart.

From May to June, temperatures at nearly all stations reach their peak, averaging between 29°C and exceeding 30°C, coinciding with the onset of the rainy season and contributing to the overall elevated temperatures for the entire season.

Mộc Hóa Mỹ Tho Cao Lãnh Càng Long Châu Đốc

Cần Thơ Sóc Trăng Rạch Gía Bạc Liêu Cà Mau

Te m pe ra tu re , Te m pe ra tu re ,

Mộc Hóa Mỹ Tho Cao Lãnh Càng Long Châu Đốc

Cần Thơ Sóc Trăng Rạch Gía Bạc Liêu Cà Mau

Mộc Hóa Mỹ Tho Ba Tri Cao Lãnh Càng Long

Châu Đốc Cần Thơ Sóc Trăng Rạch Gía Phú Quốc Thổ Chu Bạc Liêu Cà Mau

Figure 4.6 Temperature in Mekong River Delta

The Mekong River Delta experiences significant salinity intrusion, particularly along the coast, with levels decreasing as one moves inland Data from 29 monitoring stations illustrates the salinity trends from 2010 to 2015 In 2010, salinity levels were notably high due to the prolonged effects of the 2007-2008 El Niño, referred to as the ENSO of the 20th century, which caused severe intrusion globally A substantial decrease in salinity occurred in 2011 and 2012, attributed to prolonged rainy seasons and significant flooding However, in 2013, salinity levels surged again, influenced by a shorter rainy season and reduced rainfall in 2012, leading to early and extensive salinity intrusion A slight reduction in salinity was observed in 2014, but levels rose sharply in 2015, coinciding with another El Niño and the lowest rainfall recorded during the rainy season compared to previous years.

The maximum salinity levels observed range from 0.2 to 34.2 g/L, with an average of 7.98 g/L Graphical data indicates that salinity concentrations decrease as the distance from the estuary increases.

Salinity levels in Vietnam vary significantly, with the highest recorded at Bac Lieu and Ca Mau stations, reaching over 30 g/L In contrast, the lowest salinity levels, under 5 g/L, are found at Tan An and Ben Luc stations in Long An province, as well as Huong My in Ben Tre, Cau Quan and Tra Vinh in Tra Vinh province, and Soc Trang and Dai Ngai stations in Soc Trang province.

Cà Mau Sông Đốc Gành Hào

Bến LứcTân An Cầu Nổi

Figure 4.7 Salinity at stations in Long An and Ca Mau – Bac Lieu

Households in the sample possess land areas ranging from 0 to 42.03 hectares, with an average of approximately 1.198 hectares This significant variation in land size may strengthen the correlation between land area and income diversification.

The survey reveals that household sizes range from 1 to 12 members, with an average family size of approximately 4 Additionally, the number of migrants within these households varies from 0 to 5, but the average is notably low at just 0.15, indicating a minimal level of migration among families.

In the Mekong River Delta, approximately 73% of household heads are male, with an average age of 50 years, ranging from 23 to 89 years old Notably, 68% of the population falls within the labor age bracket, indicating a robust human resource base that supports income diversification and fosters economic development in the region.

In terms of educational qualification of household head: The highest share belongs to the

The data reveals that the proportion of household heads with no formal qualifications, or those at the primary education level, is significant, but this percentage decreases as educational qualifications increase Overall, the findings indicate that the educational attainment of household heads in the sample remains notably low.

The impact of climate change on income diversification in the Mekong River Delta is analyzed through separate models, specifically the panel Poisson and panel Tobit models, as shown in Table 4.2 Notably, the dependent variables in these models are inversely related; the first index reflects the number of income sources, increasing as farmers engage in more activities, while the second index, measured by the Herfindahl index, ranges from 0 to 1, indicating less diversification with higher values Despite the opposite signs of the coefficients in both models, the overall effect remains consistent, demonstrating the robustness of the findings.

Table 4.2 Results of the panel Poisson model and panel Tobit model

Square of temperature in dry season o C 2 -0.102 0.095*

Square of temperature in wet season o C 2 0.291 0.088

Precipitation in dry season mmHg 0.001 0.000

Square of precipitation in dry season mmHg 2 0.000 0.000

Precipitation in wet season mmHg 0.008*** -0.002*

Square of precipitation in wet mmHg 2 season -1.75e-05*** 4.81e-06*

Migration status of household member 0.041 -0.016

Education level of household head -0.075*** 0.051***

Age of household head year old 0.001 -0.001

Land area of household hectare 0.038*** -0.003

Constant of panel Poisson model -17.45

Sigmau of panel Tobit model

Sigmae of panel Tobit model (0.014)

Robust standard errors in parentheses

4.4.1 Findings of the panel Poisson model

Conclusion

Conclusion

In this study, our main purpose is to examine the impact of climate change onadaptation strategy in term of income diversification in Mekong River Delta The three-year datasets

The Vietnam Household Living Standards Surveys conducted in 2010, 2012, and 2014 provide data from 362 households across the region This dataset is utilized to identify key dependent variables for regression analysis, including household size, labor ratio, age, educational level of household heads, migration status, and landholding.

This study utilizes climatic data, including temperature and precipitation during both dry and rainy seasons, sourced from the Ministry of Agriculture and Rural Development (MARD) A key focus is to evaluate the impact of severe salinity intrusion in the Mekong River Delta on farmers' adaptation strategies Salinity data is gathered from the Vietnam Institute of Meteorology, Hydrology and Climate Change, using information from 29 coastal stations Subsequently, all climate change variables are re-estimated for each household based on their location and distance from the measurement stations.

Income diversification involves engaging in various activities to generate revenue, categorized into four main groups: rice cultivation, other crop cultivation, livestock and aquaculture, and non-farm income To assess the robustness of the factors influencing income diversification, two proxies are utilized: the number of income sources and the Herfindahl index, which evaluates the proportion of each income source relative to total household net income To analyze the effects of climate change and socio-demographic characteristics on income diversification behavior, both panel Poisson and panel Tobit models are employed.

The regression analysis reveals that temperature during the dry season and precipitation in the wet season significantly impact income diversification among farmers, exhibiting a non-linear relationship However, beyond a certain threshold, limitations in technology transfer, finance, and human capital hinder farmers' ability to adopt diversification strategies Other climate factors, such as wet season temperature and dry season precipitation, do not significantly influence farmers' decisions Additionally, the study finds that diversification behavior is largely unaffected by salinity intrusion during this period.

Household labor ratios and land area holdings are positively linked to income diversification, as larger land holdings enable participation in various activities, including both farming and non-farm investments Male household heads are more inclined to diversify their activities to mitigate the risks associated with crop failure Conversely, highly qualified heads often specialize in their occupations, leveraging their skills to manage external impacts and effectively oversee their operations.

Policy implications

The Mekong River Delta is significantly impacted by climate change, with agriculture being the most vulnerable sector A recent study sheds light on how climate change affects farmers' diversification strategies in the region Findings indicate that farmers are more likely to engage in risk-reduction activities when temperatures rise during the dry season and precipitation increases in the wet season However, excessive variability can lead to adverse responses due to limitations in technology, financial resources, and human capital To address these challenges, policymakers should implement programs that enhance farmers' knowledge, promote drought-resistant and saline-tolerant crops, and transition from traditional rice farming to rice-shrimp aquaculture Additionally, improving financial support through rural credit programs is essential Furthermore, investing in infrastructure, such as roads, bridges, and electricity networks, will enable farmers to explore non-farm activities, ultimately diversifying their income sources and enhancing resilience against climate change.

Research limitations and research directions

This study faces several limitations in its analysis Firstly, it relies on the VHLSS dataset from 2010, 2012, and 2014, with only 362 households participating across all surveys, resulting in a limited sample size that may not fully represent the Mekong River Delta Secondly, the assumption that farmers will diversify their income in response to changes in temperature, precipitation, or salinity intrusion may not hold true, as adjustments require time and financial resources, particularly for capital-intensive activities Consequently, the impact of climate change appears minimal and potentially insignificant Additionally, the research overlooks extreme climate events, such as droughts or floods, which may elicit stronger farmer responses than gradual changes Lastly, the method used to estimate salinity concentration at the household level is imprecise, failing to accurately assess the salinity levels experienced by each household.

To address the limitations of previous research, future studies should consider utilizing direct interviews to expand the number of respondents and the time span of data collection, thereby overcoming the issues related to small sample sizes Conducting face-to-face surveys would enable farmers to articulate their awareness of climate change impacts, leading to a more accurate assessment of household sensitivity and vulnerability Additionally, detailed information on household adaptation behaviors, particularly income diversification strategies, would enhance the study's accuracy and relevance.

Abdulai, Awudu, and Anna CroleRees "Determinants of Income Diversification Amongst Rural Households in Southern Mali."

Abdulai, Awudu, and Christopher L Delgado "Determinants of Nonfarm Earnings of Farm-Based

Husbands and Wives in Northern Ghana." American Journal of Agricultural Economics 81, no 1 (1999): 117-30.

Adger, W Neil, Shardul Agrawala, M Monirul Qader Mirza, Cecilia Conde, Karen O’Brien, Juan Pulhin,

Roger Pulwarty, Barry Smit, and Kiyoshi Takahashi "Assessment of Adaptation Practices, Options, Constraints and Capacity." Climate change 200 (2007).

Anik, Sawon Istiak, and Mohammed Abu Sayed Arfin Khan "Climate Change Adaptation through

Local Knowledge in the North Eastern Region of Bangladesh." Mitigation and Adaptation Strategies for Global Change 17, no 8 (2012): 879-96.

The paper titled "Diversification, Climate Risk and Vulnerability to Poverty: Evidence from Rural Malawi," presented by Asfaw, Solomon, McCarthy, Paolantonio, Cavatassi, Amare, and Lipper at the 2015 Conference in Milan, examines the relationship between agricultural diversification and its impact on climate risk and poverty vulnerability in rural Malawi The study highlights the significance of diversifying agricultural practices as a strategy to mitigate climate-related risks and reduce poverty among rural communities.

Asfaw, Solomon, Alessandro Palma, and Leslie Lipper "Diversification Strategies and Adaptation

Barrett, Christopher B, Mesfin Bezuneh Clark, Daniel C Clay, and Thomas Reardon "Heterogeneous

Constraints, Incentives and Income Diversification Strategies in Rural Africa." Quarterly journal of international agriculture 44, no 1 (2005): 37-60.

Binh, Nguyen Thanh "Vulnerability and Adaptation to Salinity Intrusion in the Mekong Delta of Vietnam." PhD Thesis.

Birthal, Pratap Singh, PK Joshi, Devesh Roy, and Amit Thorat Diversification in Indian Agriculture

Vol 727: Intl Food Policy Res Inst, 2007.

Bradshaw, Ben, Holly Dolan, and Barry Smit "Farm-Level Adaptation to Climatic Variability and

Change: Crop Diversification in the Canadian Prairies." Climatic Change 67, no 1 (2004):

Brennan, Donna, Nigel Preston, Helena Clayton, and Tran Thanh Be "An Evaluation of Rice-Shrimp

Farming Systems in the Mekong Delta." Report prepared under the World Bank, NACA, WWF, and FAO Consortium Program on Shrimp Farming and the Environment Published by the Consortium (2002).

Bryan, Elizabeth, Claudia Ringler, Barrack Okoba, Carla Roncoli, Silvia Silvestri, and Mario Herrero.

In Kenya, adapting agriculture to climate change involves various household and community strategies that are influenced by specific determinants The ILRI report for the World Bank highlights the importance of understanding these adaptive measures to enhance smallholder resilience Effective adaptation practices are essential for sustaining agricultural productivity and ensuring food security in the face of climate variability The report underscores the need for targeted interventions that consider local contexts and promote sustainable agricultural practices among smallholder farmers.

Burke, Marshall, and David Lobell "Food Security and Adaptation to Climate Change: What Do

We Know?" In Climate Change and Food Security, 133-53: Springer, 2010.

Can, Nguyen Duy, Le Thanh Duong, NV Sanh, and Fiona Miller "Livelihoods and Resource Use

Strategies of Farmers in the Mekong Delta." Challenges to sustainable development in the Mekong delta: regional and national policy issues and research needs The Sustainable Mekong Research Network, Bangkok (2007): 69-98.

Carew-Reid, Jeremy "Rapid Assessment of the Extent and Impact of Sea Level Rise in Viet Nam."

International Centre for Environment Management (ICEM), Brisbane 82 (2008).

Colwell, Robert K "Biodiversity: Concepts, Patterns, and Measurement." The Princeton guide to ecology (2009): 257-63 Commission, Mekong River "State of the Basin Report: 2003." Mekong

Change, Intergovernmental Panel on Climate Climate Change 2014–Impacts, Adaptation and

Vulnerability: Regional Aspects Cambridge University Press, 2014.

——— "Ipcc." Aspectos Regionais e Setoriais da Contribuiỗóo do Grupo de Trabalho II ao 4º

Relatúrio de Avaliaỗóo “Mudanỗa Climỏtica 2007” do IPCC (2007).

Dasgupta, Susmita, Mainul Huq, Zahirul Huq Khan, Manjur Murshed Zahid Ahmed, Nandan

Mukherjee, Malik Fida Khan, and Kiran Dev Pandey "Vulnerability of Bangladesh toCyclones in a Changing Climate: Potential Damages and Adaptation Cost." World Bank Policy Research Working Paper Series, Vol (2010).

Datta, KK, and C De Jong "Adverse Effect of Waterlogging and Soil Salinity on Crop and Land

Productivity in Northwest Region of Haryana, India." Agricultural Water Management 57, no 3 (2002): 223-38.

Davis, Junior R, and Dirk Bezemer "Key Emerging and Conceptual Issues in the Development of the Rnfe in Developing Countries and Transition Economies." (2003).

Ellis, Frank Rural Livelihoods and Diversity in Developing Countries Oxford university press, 2000. Ersado, Lire Income Diversification in Zimbabwe: Welfare Implications from Urban and Rural

Areas Vol 3964: World Bank Publications, 2006.

Escobal, Javier "The Determinants of Nonfarm Income Diversification in Rural Peru." World

Godfray, H Charles J, John R Beddington, Ian R Crute, Lawrence Haddad, David Lawrence, James F Muir, Jules Pretty, et al

"Food Security: The Challenge of Feeding 9 Billion People." science 327, no 5967 (2010): 812-18.

Haggblade, Steven, Peter BR Hazell, and Thomas Anthony Reardon Strategies for Stimulating

Poverty-Alleviating Growth in the Rural Nonfarm Economy in Developing Countries.

International Food Policy Research Institute, Environmental and Production Technology Division, 2002.

Hao, Haiguang, Jiping Zhang, Xiubin Li, Huiyuan Zhang, and Qiang Zhang "Impact of Livelihood

Diversification of Rural Households on Their Ecological Footprint in Agro-Pastoral Areas of Northern China." Journal of Arid Land 7, no 5 (2015): 653-64.

Hashimoto, Takehiko Environmental Issues and Recent Infrastructure Development in the Mekong

Delta: Review, Analysis and Recommendations with Particular Reference to Large-Scale Water Control Projects and the Development of Coastal Areas Australian Mekong

Hoanh, Chu Thai, TP Tuong, KM Gallop, JW Gowing, SP Kam, NT Khiem, and ND Phong "Livelihood

Impacts of Water Policy Changes: Evidence from a Coastal Area of the Mekong River Delta." Water Policy 5, no 5-6 (2003): 475-88.

Isik, Murat, and Stephen Devadoss "An Analysis of the Impact of Climate Change on Crop Yields and Yield Variability."

Joffre, Olivier M, and Roel H Bosma "Typology of Shrimp Farming in Bac Lieu Province, Mekong

Delta, Using Multivariate Statistics." Agriculture, ecosystems & environment 132, no 1 (2009): 153-59.

Kelly, P Mick, and W Neil Adger "Theory and Practice in Assessing Vulnerability to Climate Change

Andfacilitating Adaptation." Climatic change 47, no 4 (2000): 325-52.

Kimsun, Tong, and PHAY Sokcheng The Role of Income Diversification During the Global Financial

Crisis: Evidence from Nine Villages in Cambodia CDRI, 2013.

Lanjouw, Peter, and Gershon Feder Rural Non-Farm Activities and Rural Development: From

World Bank Rural Development Family, 2001.

Lanjouw, Peter, and Abusaleh Shariff "Rural Non-Farm Employment in India: Access, Incomes and Poverty Impact."

Le, Manh Hung "Phát Triển Thủy Lợi Ở Đồng Bằng Sông Cửu Long - Những Thách

Thức." http://khoahocvacongnghevietnam.com.vn/khcn-trung-uong/9547-40-nam-phat- trien-thuy-loi-dong-bang-song- cuu-long-nhung-thach-thuc.html.

Marshall, Nadine A, NA Marshall, PA Marshall, Jerker Tamelander, David Obura, Delphine Malleret-

King, and JE Cinner A Framework for Social Adaptation to Climate Change: Sustaining Tropical Coastal Communitites [Sic] and Industries IUCN, 2010.

Mastrandrea, Michael D, Christopher B Field, Thomas F Stocker, Ottmar Edenhofer, Kristie L Ebi,

David J Frame, Hermann Held, et al "Guidance Note for Lead Authors of the Ipcc Fifth

Assessment Report on Consistent Treatment of Uncertainties." (2010).

Megersa, Bekele, André Markemann, Ayana Angassa, Joseph O Ogutu, Hans-Peter Piepho, and Anne

Valle Zárate "Livestock Diversification: An Adaptive Strategy to Climate and Rangeland Ecosystem Changes in Southern Ethiopia." Human ecology 42, no 4 (2014): 509-20. Mendelsohn, Robert, Wendy Morrison, Michael E Schlesinger, and Natalia G Andronova "Country-

Specific Market Impacts of Climate Change." Climatic change 45, no 3-4 (2000): 553-69.

Miller, Fiona Society-Water Relations in the Mekong Delta: A Political Ecology of Risk Division of

Geography, School of Geosciences, University of Sydney, 2003.

Minot, Nicholas Income Diversification and Poverty in the Northern Uplands of Vietnam Vol 145:

Intl Food Policy Res Inst, 2006.

Nguyen, N A "Nguyên Nhân Xâm Nhập Mặn Tăng Cao Ở Vùng Đồng Bằng Sông Cửu Long

Nguyên nhân xâm nhập mặn tăng cao ở đồng bằng sông Cửu Long đã trở thành vấn đề cấp bách, ảnh hưởng đến sản xuất nông nghiệp và nguồn nước sinh hoạt Theo nghiên cứu của Nguyễn N.T., tình trạng này không chỉ do biến đổi khí hậu mà còn liên quan đến các hoạt động khai thác nước ngầm và phát triển kinh tế Để giải quyết vấn đề này, cần có giải pháp quản lý hiệu quả, bao gồm việc điều chỉnh quy hoạch sử dụng nước, tăng cường bảo vệ môi trường và nâng cao nhận thức cộng đồng về tác động của xâm nhập mặn.

Cấu Kinh Tế Vùng Đồng Bằng Sông Cửu Long." news release, 2012.

Nhan, Dang Kieu, Vu Anh Phap, Tran Huu Phuc, and Nguyen Hieu Trung "Rice Production

Response and Technological Measures to Adapt to Salinity Intrusion in the Coastal Mekong Delta." Mekong Program on Water, Environment and Resilience (MPOWER)

Oyekale, AS, and OI Oladele "Determinants of Climate Change Adaptation among Cocoa Farmers in Southwest Nigeria."

ARPN Journal of Science and Technology 2, no 1 (2012): 154-68.

Oyewole, S O.* Adepoju, S O Akintola, A L "Analysis of Income Diversification

Strategies among Farm Households in Oyo State." Journal of Economics and

Parry, Martin L Climate Change 2007-Impacts, Adaptation and Vulnerability: Working Group Ii

Contribution to the Fourth Assessment Report of the Ipcc Vol 4: Cambridge University

Pitman, Michael G, and Andrộ Lọuchli "Global Impact of Salinity and Agricultural Ecosystems."

In Salinity: Environment- Plants-Molecules, 3-20: Springer, 2002.

Preston, Benjamin L, Richard M Westaway, and Emma J Yuen "Climate Adaptation Planning in

Practice: An Evaluation of Adaptation Plans from Three Developed Nations." Mitigation and adaptation strategies for global change 16, no 4 (2011): 407-38.

Prowse, Martin, and Zerihune Berhane Weldegebriel "Climate Change Adaptation in Ethiopia: To

What Extent Does Social Protection Influence Livelihood Diversification?": Universiteit Antwerpen, Institute of Development Policy and Management (IOB), 2012.

Phuc, L T "Estimating the Economic Impacts of Climate Change on Crops Production in Mekong

Delta, Vietnam." Vietnam Netherlands Programme for M.A in Development Economics., 2011.

Phuc, N D "Landholding and Income Diversification of Farm Households: A Case Study of Ha

Tay Province in Vietnam." Vietnam Netherlands Project for M.A in Development Economics, 2003.

Reardon, Thomas "Using Evidence of Household Income Diversification to Inform Study of the

Rural Nonfarm Labor Market in Africa." World development 25, no 5 (1997): 735-47. Reardon, Thomas, Julio Berdegué, Christopher B Barrett, and Kostas Stamoulis "Household Income

Diversification into Rural Nonfarm Activities." Transforming the rural nonfarm economy: opportunities and threats in the developing world (2007): 115-40.

Reardon, Thomas, Christopher Delgado, and Peter Matlon "Determinants and Effects of Income

Diversification Amongst Farm Households in Burkina Faso." The Journal of Development Studies 28, no 2 (1992): 264-96.

Renaud, Fabrice G, Thi Thu Huong Le, Carina Lindener, Vo Thi Guong, and Zita Sebesvari.

"Resilience and Shifts in Agro- Ecosystems Facing Increasing Sea-Level Rise and Salinity Intrusion in Ben Tre Province, Mekong Delta." Climatic Change 133, no 1 (2015): 69-84.

Ruth, Matthias, Dana Coelho, and Daria Karetnikov "The Us Economic Impacts of Climate Change and the Costs of Inaction." (2007).

Sakho-Jimbira, Maam Suwadu, and Celine Bignebat "Local Diversification of Income Sources

Versus Migration: Complements or Substitutes? Evidence from Rural Families of the Senegalese Groundnut Basin." Paper presented at the 106th Seminar, October, 2007.

Seo, S Niggol, and Robert Mendelsohn "An Analysis of Crop Choice: Adapting to Climate Change in South American Farms."

Seo, S Niggol, and Robert O Mendelsohn "Climate Change Adaptation in Africa: A

Microeconomic Analysis of Livestock Choice." World Bank Policy Research Working Paper, no 4277 (2007).

Shannon, Michael C "Adaptation of Plants to Salinity." Advances in agronomy 60 (1997): 75-120. Smit, Barry, Ian Burton, Richard JT Klein, and Johanna Wandel "An Anatomy of Adaptation to

Climate Change and Variability." Climatic change 45, no 1 (2000): 223-51.

Smit, Barry, and Mark W Skinner "Adaptation Options in Agriculture to Climate Change: A

Typology." Mitigation and adaptation strategies for global change 7, no 1 (2002): 85-114. Spies, Thomas A, Thomas W Giesen, Frederick J Swanson, Jerry F Franklin, Denise Lach, and K

Norman Johnson "Climate Change Adaptation Strategies for Federal Forests of the Pacific Northwest, USA: Ecological, Policy, and Socio- Economic Perspectives." Landscape ecology 25, no 8 (2010): 1185-99.

Taylor, J Edward, Scott Rozelle, and Alan De Brauw "Migration and Incomes in Source

Communities: A New Economics of Migration Perspective from China." Economic Development and Cultural Change 52, no 1 (2003): 75-101.

Tobin, James "Estimation of Relationships for Limited Dependent Variables." Econometrica: journal of the Econometric Society (1958): 24-36.

Tuan, LA, Chu Thai Hoanh, Fiona Miller, and Bach Tan Sinh "Flood and Salinity Management in the

Tuong, TP, SP Kam, CT Hoanh, LC Dung, NT Khiem, J Barr, and DC Ben "Impact of Seawater

Intrusion Control on the Environment, Land Use and Household Incomes in a Coastal Area." Paddy and Water Environment 1, no 2 (2003): 65-73.

The., Dat "Vùng Đbscl Chuyển DịCh Cơ CấU Kinh Tế ToàN DiệN." TTXVN (2013).

UNEP "Adaptation and Vulnerability to Climate Change: The Role of the Finance Sector." (2006). Zerihun, J "Sesame (Sesame Indicum L.) Crop Production in Ethiopia: Trends, Challenges and

Future Prospects." Science, Technology and Arts Research Journal 1, no 3 (2012): 01-07.

Zhao, Jianmei, and Peter Barry "Implications of Different Income Diversification Indexes: The Case of Rural China."

Economics and Business Letters 2, no 1 (2013): 13-20.

Zorom, Malicki, Bruno Barbier, Ole Mertz, and Eric Servat "Diversification and Adaptation Strategies to Climate Variability: A Farm Typology for the Sahel." Agricultural Systems 116 (2013): 7-15.

Lộc Thuận Bình Đại An ThuậnSơn Đốc Bến Trại Hương Mỹ

Hưng Mỹ Trà Vinh Trà Kha Cầu Quan

Xẻo Rô Gò Quao Rạch Giá

Figure A.1 Salinity at stations in Mekong River Delta

- - name: log: D:\Google Drive\Tuyet Nga\MY BIG THESIS\Test model\File data regression\Success regression\ For submission - Nguyen Thi Tuyet Nga

> - VNP21\logs\log file.log log type: text opened on: 20 Nov 2016, 16:17:58

xtset id year panel variable: id (strongly balanced) time variable: year, 2010 to 2014, but with gaps delta: 1 unit

loc control "hh_size hh_labor_ratio migration education gender age land_ha"

/ is not a valid command name r(199);

** Do Hausman Test to choose the most appropriate model:

xtpoisson diversity_index1 `control' scaled_salinity dry_temp wet_temp dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dr

> yprecipitation sqr_wetprecipitation , re Fitting

Random-effects Poisson regression Number of obs = 1,071

Group variable: id Number of groups = 362

Random effects u_i ~ Gamma Obs per group: min = 1 avg = 3.0 max = 3

- diversity_index1 | Coef Std Err z P>|z| [95% Conf Interval]

0.31 0.754 -.0226888 0313206 hh_labor_ratio | 1683662 0905751 1.86 0.063 -.0091578 3458902 migration | 0419248 0364741 1.15 0.250 -.0295631 1134126 education | -.0751008 0187117 -4.01 0.000 -.111775 -.0384265 gender | 1667806 0507943 3.28 0.001 0672255 2663357 age | 0012656 0015894 0.80 0.426 -.0018495 0043807 land_ha | 0380917 0051055 7.46 0.000 0280851 0480983 scaled_salinity | -.0035491 0032377 -1.10 0.273 -.0098949 0027968 dry_temp | 5.436609 8.158859 0.67 0.505 -10.55446 21.42768 wet_temp | -16.22262 14.00837 -1.16 0.247 -43.67852 11.23328 dry_precipitation | 0005638 0017785 0.32 0.751 -.0029221 0040496 wet_precipitation | 0080996 0028302 2.86 0.004 0025525 0136467 sqr_dry_temp | -.1024101 1506563 -0.68 0.497 -.397691 1928709 sqr_wet_temp | 2908038 2512028 1.16 0.247 -.2015447 7831522 sqr_dryprecipitation | -2.87e-06 5.07e-06 -0.57 0.572 -.0000128 7.07e-06 sqr_wetprecipitation | -.0000175 6.12e-06 -2.85 0.004 -.0000295 -5.47e-06

LR test of alpha=0: chibar2(01) = 0.00 Prob >= chibar2 = 1.000

xtpoisson diversity_index1 `control' scaled_salinity dry_temp wet_temp dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dr

> yprecipitation sqr_wetprecipitation , fe note: 7 groups (7 obs) dropped because of only one obs per group

Conditional fixed-effects Poisson regression Number of obs = 1,064

Group variable: id Number of groups = 355

Obs per group: min = 2 avg = 3.0 max = 3

- diversity_index1 | Coef Std Err z P>|z| [95% Conf Interval]

The analysis of various factors reveals that household size (hh_size) has a negligible effect, with a coefficient of 0.0065 and a p-value of 0.834, indicating no significant relationship Similarly, the household labor ratio (hh_labor_ratio) shows a coefficient of 0.0524 and a p-value of 0.812, suggesting minimal impact Migration (migration) and education (education) also demonstrate weak associations, with coefficients of 0.0301 and 0.0335, respectively, both having high p-values Gender (gender) and age (age) factors reflect coefficients of 0.0678 and 0.0014, but with p-values of 0.857 and 0.968, indicating a lack of significance Land area (land_ha) exhibits a coefficient of 0.0149 and a p-value of 0.274, further supporting the absence of a strong link Environmental variables such as scaled salinity, dry temperature, and wet temperature show minimal effects, with coefficients of -0.0038, 5.3421, and -3.1544, and high p-values Precipitation metrics also reveal weak relationships, with coefficients close to zero and p-values indicating no significant influence Overall, the data suggests that the examined variables do not significantly impact the outcomes studied.

When analyzing the differenced variance matrix, it's crucial to note that its rank may not match the number of tested coefficients; in this case, the rank is 11 while the coefficients are 16 Ensure this discrepancy aligns with your expectations to avoid potential computation issues Additionally, review the output of your estimators for any anomalies and consider scaling your variables to ensure the coefficients are comparable.

The analysis of household characteristics reveals significant variations in labor ratios, with migration and education showing notable impacts Gender differences play a crucial role, particularly in relation to age and land ownership Temperature and precipitation patterns also influence household dynamics, with dry and wet conditions affecting scaled salary outcomes The quadratic relationships of temperature and precipitation further highlight complex interactions, emphasizing the importance of environmental factors in socio-economic conditions.

- b = consistent under Ho and Ha; obtained from xtpoisson B = inconsistent under Ha, efficient under Ho; obtained from xtpoisson

Test: Ho: difference in coefficients not systematic chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Prob>chi2 = 0.6822 ** Choose the random effect model:

xtpoisson diversity_index1 `control' scaled_salinity dry_temp wet_temp dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dr

> yprecipitation sqr_wetprecipitation, re cluster() Fitting Poisson model:

Random-effects Poisson regression Number of obs = 1,071

Group variable: id Number of groups = 362

Random effects u_i ~ Gamma Obs per group: min = 1 avg = 3.0 max = 3

- diversity_index1 | Coef Std Err z P>|z| [95% Conf Interval]

The analysis reveals significant relationships between various factors and their impacts Notably, education shows a strong negative correlation (p < 0.001), while gender exhibits a positive association (p < 0.01) The land area (land_ha) is positively significant (p < 0.001), indicating its importance in the context studied Wet precipitation also demonstrates a notable positive impact (p < 0.004) In contrast, variables such as scaled salinity, dry temperature, and dry precipitation do not show significant effects The findings underscore the relevance of education, gender, land area, and wet precipitation in the examined model, while other factors require further investigation due to their lack of significance.

LR test of alpha=0: chibar2(01) = 0.00 Prob >= chibar2 = 1.000

** Test the significance of the variable:

test `control' scaled_salinity dry_temp wet_temp dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dryprecipitation sqr_wet

( 2) [diversity_index1]hh_labor_ratio = 0

(13) [diversity_index1]sqr_dry_temp = 0

(14) [diversity_index1]sqr_wet_temp = 0

(16) [diversity_index1]sqr_wetprecipitation = 0 chi2( 16) = 128.99

test wet_precipitation sqr_wetprecipitation

( 2) [diversity_index1]sqr_wetprecipitation = 0 chi2( 2) = 8.21

xtpoisson diversity_index1 scaled_salinity c.dry_temp##c.dry_temp c.wet_temp##c.wet_temp c.dry_precipitation##c.dry_precipitation c.wet_pre

> cipitation##c.wet_precipitation `control', re cluster() Fitting Poisson model:

Iteration 8: log likelihood = -1655.0267 scaled_salinity | -.0035484 0032375 -1.10 0.273 -.0098938 002797 dry_temp | 5.442579 8.160219 0.67 0.505 -10.55116 21.43631 c.dry_temp#c.dry_temp | | -.1025196 1506811 -0.68 0.496 -.3978492 19281 wet_temp | | -16.27622 14.02314 -1.16 0.246 -43.76107 11.20862 c.wet_temp#c.wet_temp | | 2917645 2514663 1.16 0.246 -.2011005 7846294 dry_precipitation | | 0005633 0017784 0.32 0.751 -.0029224 0040489 c.dry_precipitation#c.dry_precipitation | | -2.86e-06 5.07e-06 -0.57 0.572 -.0000128 7.07e-06 wet_precipitation | | 0080959 0028312 2.86 0.004 0025468 0136449 c.wet_precipitation#c.wet_precipitation | | -.0000175 6.12e-06 -2.85 0.004 -.0000295 -5.45e-06 hh_size | | 0043187 0137781 0.31 0.754 -.0226859 0313233 hh_labor_ratio | 168364 0905751 1.86 0.063 -.0091599 3458879 migration | 0419269 0364738 1.15 0.250 -.0295603 1134142 education | -.0750974 0187117 -4.01 0.000 -.1117716 -.0384232 gender | 1667777 0507943 3.28 0.001 0672228 2663326 age | 0012656 0015894 0.80 0.426 -.0018495 0043807 land_ha | 0380908 0051054 7.46 0.000 0280844 0480973

Random-effects Poisson regression Number of obs = 1,071

Group variable: id Number of groups = 362

Random effects u_i ~ Gamma Obs per group: min = 1 avg = 3.0 max = 3

- diversity_index1 | Coef Std Err z P>|z| [95% Conf Interval] -+ -

LR test of alpha=0: chibar2(01) = 5.4e-05 Prob >= chibar2 = 0.497

** Draw marginal effect graph of Precipitation in the wet season:

Variables that uniquely identify margins: wet_precipitation

margins, dydx(wet_precipitation) at( wet_precipitation =(107.14(30)345.29))

Average marginal effects Number of obs = 1,071

Expression : Linear prediction, predict() dy/dx w.r.t : wet_precipitation

| dy/dx Std Err z P>|z| [95% Conf Interval]

Random-effects tobit regression Number of obs = 1,071

Group variable: id Number of groups = 362

Random effects u_i ~ Gaussian Obs per group: min = 1 avg = 3.0 max = 3

Integration method: mvaghermite Integration pts = 12

The log likelihood for the model is -269.08818, with a chi-squared probability of 0.0001, indicating significant results The analysis reveals that scaled salinity shows a negligible effect (coefficient: 0.0003955, p-value: 0.843), while dry temperature has a negative impact (coefficient: -5.094096, p-value: 0.081) Wet temperature also demonstrates a negative effect (coefficient: -4.931524, p-value: 0.418) Dry precipitation and wet precipitation present minimal influences with coefficients of -0.0003053 and -0.0022184, respectively Notably, the square of wet precipitation is significant (coefficient: 4.81e-06, p-value: 0.050), while education positively influences the outcome (coefficient: 0.0508484, p-value: 0.000) Gender has a substantial negative effect (coefficient: -0.1413014, p-value: 0.000), highlighting its importance in the model.

/ is not a valid command name r(199);

xttobit diversity_index2 scaled_salinity dry_temp wet_temp dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dryprecipitati

> on sqr_wetprecipitation `control' , ul(1) re cluster() Obtaining starting values for full model:

- diversity_index2 | Coef Std Err z P>|z| [95% Conf Interval]

0 left-censored observations 775 uncensored observations

** Test the significance of the variable:

test scaled_salinity dry_temp wet_temp dry_precipitation wet_precipitation sqr_dry_temp sqr_wet_temp sqr_dryprecipitation sqr_wetprecipitat

( 6) [diversity_index2]sqr_dry_temp = 0

( 7) [diversity_index2]sqr_wet_temp = 0

(11) [diversity_index2]hh_labor_ratio = 0

(16) [diversity_index2]land_ha = 0 chi2( 16) = 47.59

test dry_temp sqr_dry_temp

( 2) [diversity_index2]sqr_dry_temp = 0 chi2( 2) = 7.05

test wet_precipitation sqr_wetprecipitation

( 2) [diversity_index2]sqr_wetprecipitation = 0 chi2( 2) = 3.86

xttobit diversity_index2 scaled_salinity c.dry_temp##c.dry_temp c.wet_temp##c.wet_temp c.dry_precipitation##c.dry_precipitation c.wet_preci

> pitation##c.wet_precipitation `control', re cluster() Obtaining starting values for full model:

Random-effects tobit regression Number of obs = 1,071

Group variable: id Number of groups = 362

Random effects u_i ~ Gaussian Obs per group: min = 1 avg = 3.0 max = 3

Integration method: mvaghermite Integration pts = 12

The analysis reveals that dry temperature has a coefficient of -3.97197 with a p-value of 0.078, indicating a potential negative impact, while wet temperature shows a coefficient of -3.129728 but is not statistically significant (p = 0.496) Dry precipitation has a negligible effect with a coefficient of -0.0002633 (p = 0.617), and wet precipitation also indicates a slight negative trend with a coefficient of -0.0014003 (p = 0.097) Household size and labor ratio present coefficients of -0.0065029 (p = 0.221) and -0.0536531 (p = 0.131), respectively, suggesting no significant influence Migration has a coefficient of -0.0161259 (p = 0.166), while education positively affects the outcome with a coefficient of 0.0291604 and a significant p-value of 0.001 Gender shows a significant negative coefficient of -0.0845108 (p = 0.000), and age has an insignificant effect with a coefficient of -0.0005967 (p = 0.432) Lastly, land area demonstrates a negligible impact with a coefficient of -0.0002609 (p = 0.925).

**Draw marginal effect graph of Temperature in the dry season:

Variables that uniquely identify margins: dry_temp

margins, dydx(dry_temp) predict(e(.,1)) at(dry_temp=(26.4(.1)27.9))

Average marginal effects Number of obs = 1,071

Expression : E(diversity_index2|diversity_index2