Journal of Agricultural Economics, Environment and Social Science 7(1):115-138: May, 2021 Copy Right © 2015. Printed in . All rights of reproduction in any form is reserved. Department of Agricultural Economics, University of Maiduguri, Nigeria Available on line: http://www.jaeess.com.ng ISSN: 2476 – 8423

LIVELIHOOD VULNERABILITY ASSESSMENT TO SOIL EROSION IN STATE, NIGERIA

M.K.Yahaya,1 A. Mustapha,2 A. Suleiman2 and M.A.Abdullahi1

1Department of Agricultural Economics and Extension, Federal University Dutse, Jigawa State, Nigeria

2Department of Agricultural Economics and Extension, Bayero University Kano, , Nigeria

ABSTRACT

Vulnerability is the capacity to anticipate, cope with, resist and recover from the impact of natural disasters. The study aims to assess the level of household livelihood vulnerability to soil erosion in Kano State. The study was conducted in six (6) purposively selected Local Government Areas of Kano state. Data were collected from 376 randomly selected households. The focusing parameters are: health, food, knowledge and skills, livelihood strategies, land, natural disaster and climate variability, socio demographic conditions, social networks, housing and production means and finance and income. The data were aggregated using a Livelihood Vulnerability Index (LVI) and vulnerability scores were compared. was found to be the least vulnerable with LVI of 0.322 because of better access to basic amenities and livelihood strategies while with LVI score of 0.412 was the most vulnerable. The study also found that adaptive capacities of the households are important in limiting vulnerability and thus promotion of resilience. These results have implications for initiation and implementation of household resilience projects by the government, donor agencies and other related organizations.

Keywords: Livelihoods, Resilience, Soil Erosion, Vulnerability

Email: [email protected]

INTRODUCTION Dryland systems are under threat from a combination of socio-economic and biophysical changes that are culminating in a downward spiral of land degradation and the consequences will be severe not only for the economies of individual countries but for the welfare of millions of households (FAO, 2013). Demand on the land for economic development and pressure from a burgeoning population are leading to unprecedented land use change. Thus, unsustainable

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Yahaya et al JAEESS 7(1) May, 2021 land use is driving land degradation. The result is a loss of land productivity with impacts on livelihoods and the economy (Bai et al., 2008).

Soil erosion is a major challenge to land and water resources and the problem may get worse in the future due to population growth and potential climatic and land use changes (Prosser et al., 2001). Soil erosion is the bodily removal of part or all of the soil from its resident position to other areas. This results in a spiraling decay in productive capacity of the affected soil to provide a suitable medium for crop growth (Ahaneku, 2010). It is a slow insidious problem that is continuous. Indeed, 1mm of soil, easily lost in one rain or wind storm is so minute that its loss goes unnoticed by the farmers yet replenishing this amount of soil under agricultural conditions requires a number of years. This threatens future food security by reducing crop productivity (Pimentel et al., 2009). Soil erosion disrupts the natural balance and leads to decrease in yield per unit of applied inputs, loss of income and profit to the farmer, a drop in the value of the agricultural land, pollution and destruction of water resources and public assets, flooding and silting up of waterways and migration of rural populations to urban areas (Telles et al., 2011)

The vulnerability of farming households in Nigeria can be viewed in terms of the problems encountered by the households that hamper increased production. This can be categorized into shocks and trends. The shocks include soil erosion, desertification, soil pollution, drought, pest and diseases and flood while trends are fluctuation in prices, inconsistencies in policies, inadequate access to credit and arable land, land tenure insecurity and marketing problems (Tsue, 2015). The Intergovernmental Panel on Climate Change, (2001) defines vulnerability as the degree to which a system is susceptible to, or unable to cope with adverse effect of climate change. Vulnerability is a function of the character, magnitude and rate of shock to which a system is exposed, its sensitivity and its adaptive capacity (IPCC, 2007). Adaptive capacity describes the ability of a system to adjust to actual or expected stresses or to cope with the consequences (IPCC, 2007). It is considered as a function of wealth, technology, education, information, skills, infrastructure, and access to resources, stability and management capabilities (IPCC, 2007). The risk is believed to be more acute because of the reliance on climate sensitive sectors such as agriculture and fisheries and have low GDPs, high level of poverty and limited human institutional, economic, technical and financial capacity (UNFCCC, 2007).

Understanding livelihood strategies, capital base (human, natural, financial, social and physical), capabilities, the level of knowledge of communities and also their vulnerability to environmental risk such as soil erosion will shed more light on how to reduce vulnerability by empowering local livelihoods. Livelihood vulnerability in environmentally fragile areas is emerging as a key issue due to its positive feedback to environmental degradation. Assessment of sustainable livelihoods is therefore a prerequisite for targeting interventions for achieving sustainable and resilient livelihood outcomes (Wang, 2016).

The recognition that reducing vulnerability is a legitimate normative goal of sustainable development has become apparent in the context of global change. Erosion clearly threatens the livelihood of farmers but the impact on overall household vulnerability is variable and

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Yahaya et al JAEESS 7(1) May, 2021 conditions faced by households are remarkably diverse. The options available to poor households are more constrained than those available to rich households who have easier access to labour, livestock, land, credit and cash. Many farmers in the study area have been turned into marginal farmers and even landless due to soil erosion. Multi-crop producing land becomes single crop or bare land by huge siltation and the cropping pattern has been changed significantly. This research will seek to address this issue. The information on any strategy to increase agricultural productivity and enhance sustainability by improving livelihoods will be of great importance.

Although it is a widely recognized environmental problem threatening sustained agricultural production in many States in Nigeria very little information is available about the status and effect on livelihoods of households affected. This study aims to assess the level of livelihood vulnerability to soil erosion in Kano State, Nigeria.

METHODOLOGY Study Area Kano State lies between 11030`N and 8030`E and its vegetation falls mostly within the Sudan savannah agro ecological zone of Nigeria. The State has 44 Local Government Areas with a total land area of 42,582.8Km2comprising of 30,684.8 Km2 for agricultural land while 11,898.Km2for forest and grazing land (KNSG, 2004). The climate of the state is tropical dry climate with a mono modal rainfall distribution ranging from 884-1200mm (from north to south of the state). Average temperature is 290C with minimum temperature of 150C occurring from November to February and highest temperature of 390C occurring in March and April (Olafin and Tanko, 2002).

The soils in Kano State as in the other northern region of Nigeria are reddish brown or brown soils and are light or moderately leached. They are known as tropical ferruginous soils with pH level of 6.0-7.0 and bulk densities of about 1.4 g/cm3 and as a result of the dominant kaolinitic type of parent material, the cation exchange capacity is low resulting in reduced buffering capacity (Adamu and Aliyu, 2012). Therefore, the soils are poor in fertility status and structure and are readily susceptible to degradation where the protective cover of vegetation is weakened or removed (Odunze, 2006).

The major crops grown include rice, maize, sorghum, millet, groundnut, soybean and cotton. Pepper, onion and tomatoes are also grown. Though sole cropped fields of crops occur, intercropping is the dominant practice. Livestock production is also an integral part of the farming system as both crops and animals are sources of food and cash income for farmers. The State is the most populous of Nigeria’s 36 states with 44 Local Government Areas and has a projected population of 13,076,892 (NBS, 2018).

Data Collection

Primary data was used for this study. The primary data was obtained using a structured questionnaire. The questionnaire contained information on livelihood strategies, health, food, knowledge and skills, land, natural disaster and climate variability, socio-demographic conditions, social networks, housing and production means and finance and income.

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Sampling Technique

Multi-stage sampling technique was used to select study sites and sample farm households. The first stage involved purposive selection of 6 sites which include , DawakinTofa, , Tofa, Ungogo and based on high level of soil erosion in the areas. The second stage involved balanced spatial sampling of the areas using GIS and remote sensing to stratify and capture the spatial variation of soil erosion. The areas were stratified as low, moderate, high and very high. From the GIS and remote sensing, estimated sample frame was established as 37,176 erosion affected spots on the entire study area. Using RAO-SOFT sample size calculator, proportionate random sampling was used to draw 381 of the erosion affected spots and included as sample size for the study. Farmers cultivating each of the sampled spot (land) were identified for interview.

Table 1: Summary of Sample frame and sample size of the respondents

S. Sample Class % Shanono Bagwai D/ Tofa Tofa Ungogo Makoda Frame Size

Low 12726 34.2 23 23 29 27 18 10 130

Moderate 13441 36.2 28 24 22 35 19 11 139

High 7652 20.6 19 17 14 12 7 9 78

Very 3357 9.0 9 5 4 2 2 12 34 High

Total 37176 100 79 69 69 76 46 42 381

Source: Survey Data, 2020

2.4 Analytical Technique

The Livelihood Vulnerability Index LVI, charts and radar vulnerability diagrams was used. The LVI was based on the IPCC vulnerability definition of adaptive capacity, exposure and sensitivity.

Econometric and indicator approaches are two techniques commonly employed to measure vulnerability (Deressa et al., 2009). The econometric technique employs the use of econometric methods such as regression analysis. The challenge of this approach is the problem of testing

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Yahaya et al JAEESS 7(1) May, 2021 various econometric assumptions regarding standard errors, hypothesis and confidence intervals as well as imputing causality without making stringent assumption. This study therefore adopts the indicator approach in measuring the vulnerability of agricultural communities. According to Deressa et al., (2009), the indicator approach involves selection of indicators that a researcher considers to largely account for vulnerability. Therefore, the weakness of the approach is that there is some level of subjectivity in choosing the various indicators.

LVI was calculated by applying a balanced weighed average approach (Can, Tu and Hoanh, (2013), Hahn, Riederer and Foster, 2009). Each sub component contributes equally to the overall index even though each major component comprises of different number of sub components. A simple method with equal weights was applied for all major components. Because each sub component will be measured on a specific scale, it will therefore be normalized as an index.

Indexsv = Sv–Smin Smax- Smin Where Sv= Value of sub component for household

Smin and Smax= min and max values respectively from data of that sub component in village

After normalizing sub component values, the value of each major component will be calculated by

푛 Mvj= ∑푖=1 indexsvi N Where Mvj= value of major component j for household v Indexsvi= value of sub component s indexed by i of major component Mj

N= number of sub components in major component Mj

The major components were aggregated to 5 values for livelihood assets [H (human capital), N (Natural capital), S (social capital), P (physical capital) and F (financial capital) before use to obtain the weighted average of LVI as shown in the Appendix.

10 LVI= Σ wMjMvj 10 Σ wMj

LVI= wHHv + wNNv + wSSv + wPPv + wFFv wH + WN + wS + wP + WF

Where LVIv= Livelihood vulnerability index of household v

Wmj= weight value of major component j

wH, wN, wS, wP, wF= weight value of asset H, N, S, P, F

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Exposure, sensitivity and adaptive capacity were calculated. The 3 contributing factors were integrated using the following equation:

LVI= (exposure- adaptive capacity) * sensitivity

The LVI is ranged from 0-1; 0 denoting least vulnerable and 1 denoting most vulnerable

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RESULT

The results of the analysis revealed a number of subtle, yet important differences between the study areas. The overall vulnerability indexes of the communities are 0.3227, 0.3513, 0.3657, 0.3233, 0.4152 and 0.3699 for Ungogo, Shanono, Bagwai, Tofa, DawakinTofa and Makoda respectively. This makes Ungogo (0.3227) the least vulnerable while DawakinTofa (0.4152) the most vulnerable as presented in Table 2.

Table 2: Livelihood Vulnerability Index

Capital Ungogo Shanono Bagwai Tofa D/Tofa Makoda Human 0.3004 0.2950 0.2632 0.2957 0.2835 0.3034 Natural 0.5582 0.5749 0.4981 0.5597 0.6460 0.5829 Social 0.3332 0.3607 0.4153 0.4094 0.4203 0.3096 Physical 0.3448 0.4235 0.4220 0.3057 0.3048 0.3871 Finance 0.5795 0.6148 0.6116 0.6327 0.6495 0.5884 LVI 0.3227 0.3513 0.3657 0.3233 0.4152 0.3699 Source: Survey Data, 2020

Ungogo 0.7 0.6 0.5 Makoda 0.4 Shanono Human 0.3 0.2 Natural 0.1 0 Social Physical Dawakin Tofa Bagwai Finance

Tofa

Source: Survey Data, 2020

Figure 1: Distribution of livelihood capital vulnerability index across study area.

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LVI

Ungogo 0.5 0.4 Makoda 0.3 Shanono 0.2 0.1 0 LVI Dawakin Bagwai Tofa

Tofa

Source: Survey Data, 2020

Figure 2: Livelihood Vulnerability Index across study area

Human Capital: This is made up of four (4) major components namely; health, food, knowledge and skills and livelihood strategies. The human capital index of Makoda is the highest (0.3034) while Bagwai (0.2632) is the lowest as presented in Table 2. Makoda has the highest human capital index due to its slightly higher health index (0.1550), knowledge and skills index (0.2800) as shown in Table 3. This is as a result of low proportion of household members’ with illness and high proportion of household members with better access to food (0.1188) and knowledge and skills (0.2533) as shown in Table 3. Although a large proportion of households depend on agriculture as major source of income without much non-farm activities

Natural Capital: This consist of land and natural disaster and climate variability components. DawakinTofa has the highest capital index of 0.6460 while Bagwai has the lowest value of 0.4981as shown in Table 2. DawakinTofa also has the highest land and natural disaster and climate variability index of 0.6026 and 0.6894 respectively as presented in Table 3. In terms of land, the higher index in DawakinTofa is mainly due to higher percentage of households depending on or exploiting natural resources, use bullock for ploughing, slope of the farm and observing decrease in yield as a result of soil erosion. DawakinTofa also has a higher index on natural disaster and climate variability because of higher proportion of households recording loss of properties, land or livestock due to soil erosion. These contributed to making it the most vulnerable local government in the study area. On the other hand, Ungogo has the lowest index (0.5185) for land while Bagwai has the least index (0.4701) as presented in Table 3 for natural disaster and climate variability.

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Social Capital: This comprises of socio-demographic conditions and social networks. Dawakin Tofa has the highest socio demographic index because of higher dependency ratio and average household size (0.5888) as presented in Table 3. Makoda has the least dependency ratio and average household size (0.4648). Although in terms of social networks which comprises of average receive: give ratio, average borrow: lend ratio, percentage of households that are not members of any organization and percentage of households with family members working in a different community, Dawakin Tofa ranked second (0.2519) with Bagwai and Ungogo being the highest and lowest with values of 0.2646 and 0.1133 respectively as presented in Table 3. Furthermore, Ungogo and Makoda have the least value of 0.1133 and 0.4648 for social networks and socio demographic respectively.

Physical Capital: This is made of housing and production means which comprises of percentage of households without concrete houses and those with houses affected by erosion. The cause of household vulnerability could be due to the cultural characteristics of the people in the region of using mud and thatch in construction of housing. This makes it more vulnerable to damage as a result of soil erosion. Housing reinforcement for poor or low income household is necessary to build their resilience, improve their living conditions and help them escape poverty. Table 3 indicates that Shanono has the highest value (0.4235) followed by Bagwai (0.4220) while Dawakin Tofa has the least (0.3048). This also contributes in making the local government area to be the most vulnerable in the study area as a result of its low housing and production means index.

Financial Capital: This is made up of three (3) sub components namely; percentage of household borrowing money, percentage of household without access to financial institutions and average household income. Dawakin Tofa has the highest value (0.495) for financial vulnerability while Ungogo has the least (0.5795) followed by Makoda (0.5884) because of low number of households borrowing money and better access to financial institutions and higher average household income. This would help the households with coping unexpected hazards. Households with low financial capital vulnerability would be able to make productive investments such as in family education or use as buffer during emergencies.

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Table 3: Major components scores in study area

Component Ungogo Shanono Bagwai Tofa DawakinTofa Makoda

Health 0.2849 0.2651 0.2047 0.2028 0.1550 0.2391

Food 0.1848 0.1648 0.1188 0.1931 0.1619 0.1520

Kn&Skills 0.2669 0.2556 0.2533 0.2703 0.2815 0.2800

LStrategies 0.4650 0.4948 0.4761 0.5341 0.5086 0.5327

Land 0.5185 0.5232 0.5260 0.5931 0.6026 0.5229

ND&CV 0.5978 0.6266 0.4701 0.5263 0.6894 0.6429

Snetwork 0.1133 0.2161 0.2646 0.2438 0.2519 0.1545

H&Pmeans 0.3448 0.4235 0.4220 0.3057 0.3048 0.3871

F&Income 0.5795 0.6148 0.6116 0.6327 0.6495 0.5884

Sociodemo 0.5530 0.5053 0.5660 0.5750 0.5888 0.4648

Source: Survey Data, 2020

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Health 0.7 Sociodemo 0.6 Food 0.5 0.4 0.3 Ungogo F&income K&Skills 0.2 Shanono 0.1 Bagwai 0 Tofa Dawakin Tofa H&Pmeans L.Strategies Makoda

Snetworks Land

ND&CV

Figure 3: Major components score

Source: Survey Data, 2020

The Livelihood Vulnerability Index was divided into 3 vulnerability classes; low level (LVI of less than 0.30), moderate level (LVI of 0.31-0.60) and high level (LVI greater than 0.61). Majority of the respondents (53%) had low livelihood vulnerability while 33.7% and 13.3% had moderate and high level of livelihood vulnerability.

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250

200

150 Low Moderate 100 High

50

0 LVI

Figure 4: Distribution of vulnerability Classes

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Table 4: Nature of farming, other source of livelihood and number of livelihood sources

Vulnerability Classes Total Low Moderate High F % F % F % F % Nature of Farming Subsistence Based 138 69.3 91 71.7 35 70 264 70.2 Market Based 47 23.6 24 18.9 15 30 86 22.9 Both 14 7.1 12 9.4 0 0 26 6.9

Other source of livelihood Farm 39 19.6 42 33.1 9 18 90 23.9 Off-farm 60 30.2 28 22 12 24 100 26.6 ;8Non-farm 100 50.2 57 44.9 29 58 186 49.5

No of livelihood sources 1 3 1.5 8 6.3 5 10 16 4.3 2 124 62.3 72 56.7 34 68 230 61.2 3 60 30.2 39 30.7 11 22 110 29.2 4 12 6.0 8 6.3 0 0 20 5.3 Source: Survey Data, 2020

Table 4 shows that majority of the respondents (49.5%) have an alternative income that is non- farm related. These non-farm sources are mostly menial. Although diversification away from agriculture is an important factor in reducing livelihood vulnerability in general on subsistence farmers especially those located in marginal environments to improve their adaptive capacity.

With the challenges associated with dryland agriculture, farmers have developed diverse measures to reduce their livelihood risk and vulnerabilities. Most of the farmers’ (70.2%) practiced subsistence farming with 69.3%, 71.7% and 35% for low, moderate and high vulnerability classes. This result showed how households rely heavily on agriculture thereby increasing their vulnerability. Table 6 also reports that 95.7 of the respondents had 2 or more livelihood sources. The results indicated that increase in number of livelihood sources is important in reducing vulnerability.

DISCUSSION

As defined earlier, livelihood vulnerability is not merely a function of exposure. It is defined by pre-existing conditions which include livelihood diversification, education, etc all of which shape the sensitivity and adaptive capacity of households. Vulnerability is therefore a reflection of the totality of livelihoods rather than simple representation dealing either with causes or outcomes. In

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Yahaya et al JAEESS 7(1) May, 2021 a typical community in Sub-Saharan Africa, livelihood activities at any point in time are a function of the opportunities and constraints they face and the pre-existing conditions (Adger, 2001).

As mentioned, factors that contribute to higher adaptive capacity include education, access to information, finance, strong social networks (Hahn et al., 2009). According to the findings, high natural, social and financial vulnerability contributed in making DawakinTofa the most vulnerable local government area. As observed in the study sites, livelihood strategies, social networks and finance all contributed in reducing the level of livelihood vulnerability. This study assumed that households that possess skills and capacities in addition to farming are less vulnerable and can easily use the additional skills to be absorbed in another profession. This is reflected in the study wherein many of the respondents had 2 or more livelihood sources.

Similarly, social capital is an important pillar in reducing livelihood vulnerability (Qaisrani, 2015). People with strong social networks have been observed to survive calamities and rebuild their lives with the help of social capital (family ties, relatives and community networks) faster than those whose social ties are weak (Adgeret al., 2003). DawakinTofa has highest social capital vulnerability. This indicates that community members are less integrated in terms of reliance on each other for support during times of need (Adger, 2001).

CONCLUSIONS

The livelihood vulnerability to soil erosion in Kano state was assessed using a livelihood vulnerability index (LVI). Six (6) local government areas namely Ungogo, Shanono, Bagwai, Tofa, DawakinTofa and Makoda were considered. LVI was constructed using health, food, knowledge and skills, livelihood strategies, land, natural disaster and climate variability, social networks, housing and production means, finance and income and socio demographic conditions. These were further grouped into human, natural, social, physical and finance.

From the study, DawakinTofa and Ungogo were found to be the most vulnerable and least vulnerable. Stronger social ties, better access to basic amenities and finance made Ungogo the least vulnerable while high natural, social and financial vulnerability contributed in making DawakinTofa the most vulnerable. Although, majority of the respondents had low level of livelihood vulnerability, had non-farm sources of income and more than 2 livelihood sources, most of them practice subsistence farming. Lastly, improving the capitals identified in this study could help reduce the livelihood vulnerability in the study area.

RECOMMENDATION

Based on the findings of this study, the following recommendations are made to strengthen policy intervention:

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i. A result from this study indicates that diversifying livelihoods are important in reducing vulnerability. Therefore, it is recommended to train farmers to diversify livelihood options rather than depending solely on agriculture based income. ii. There is a need to strengthen community networks and local organizations at village level to reduce social capital vulnerability and that livelihood vulnerability can be improved by social programs. iii. Policies and programs aimed at expanding the delivery of credit should incorporate environmental risks farmers face in order to expand livelihood diversification. Also insurance and credit schemes need to take better account of household exposure to shocks and vulnerability.

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References

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Adger, W.N. (2001) Social capital and climate change. Tyndall Centre for Climate Change Research. Working Paper No.8

Adger, W.N., Huq, S, Brown, K, Conwoy, D, Hulme, M (2003) Adaptation to climate change in the developing world. Program Developing Stud 3(3):179-195

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Bhuiyan, M. A. H, Aidar-Ul Islam, S. M and Azam, G (2017) Exploring impacts and livelihood vulnerability of riverbank erosion hazard among rural households along the river Padma of Bangladesh. Environment System Research6: 25

Can, N.D., Tu, V.H. and Hoanh, C.T (2013) Application of Livelihood Vulnerability Index to Assess Risks from Flood Vulnerability and Climate Variability- A case study in the Mekong Delta of Vietnam. Journal of Global Environmental Change (1) 74-88

Etwire, P.M, Al-Hassan R. M., Kuwornu, J.K.M and Osei-Owusu, Y (2013) Application of Livelihood Vulnerability to Climate Change and Variability in Northern Ghana Journal of Environment and Earth Science Vol3: 2

FAO (Food and Agriculture Organization) (2013) Restoring the Land. Food and Agriculture Organization of the United Nations, Rome, Italy http://www.fao.org/docrep/u8480e1480eod.htm accessed on 1 August, 2019

Hahn, M.B., Riederer, A.M. and Foster, S.O. (2008)The livelihood Vulnerability Index: A Pragmatic approach to assessing risks from climate variability and change. A case study in Mozambique. Journal of Global Environmental Change (1) 74-88

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Pimentel, D (2009) Soil Erosion: A food and environmental threat. Environ. Dev. Sustain. 8:119- 137

Prosser, I.P., Rutherford, I.D., Olley, J.M., Young, W.J., Wallbrink., P.J. and Moran, C.J (2001) Large scale patterns of erosion and in river networks with examples from Australia. Marine and Fresh Water Research. 52: 81-99

Sujakhu, N. M., Ranjitkar, S., Niraula, R., Salim, M. A., Nizami, A., Schmidt-Vogt, D and Xu, J (2018) Determinants of livelihood vulnerability in farming communities in two sites in the Asian Highlands, Water International 43(2) 165-182

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Appendix:

Vulnerability components

Exposure Natural Disaster and Climate Variability

Sensitivity Health, Food, Land

Adaptive Capacity Socio demographic conditions, Livelihood strategies, Social networks, Knowledge and skills, Finance and income, Housing and production means

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Capitals, Major Components and Sub components comprising of the Livelihood Vulnerability Index

Major Assumed Livelihood Capital Cmpnt Sub Component Source Quantified as fxnal r/ship

Human Health Share of HH with ill family members Etwireet al., (2013) Numeric, Continous +

Share of HH where a family member has to miss work/school due to illness Etwireet al., (2013) Numeric, Continous +

Average time to nearest health facility Etwireet al.,(2013) Numeric, Continous +

Food HH dependent solely on family farm for food Hahn et al., (2009) Numeric, Continous +

Months HH struggle to find food Etwireet al., (2013) Numeric, Continous +

HH that do not save crops Hahn et al., (2009) Numeric, Continous +

HH that do not save seeds Hahn et al.,( 2009) Numeric, Continous +

Numeric, Continous:[1/ (No of crops grown by HH Crop diversity Index Hahn et al., (2009) + 1) +

Knowledg e &Skills No of children enrolled in school Panthiet al., (2015) Numeric, Continous +

No of children dropped out of primary school Qaisrani, 2018 Numeric, Continous -

No of person in the HH with soil management training Qaisrani, 2018 Numeric, Continous +

Livelihoo d Numeric, Continous[1/ (N Strategies Livelihood diversification Index Hahn et al.,( 2009) of livelihood act. + 1) +

HH depending on farming as major source of income Hahn et al.,( 2009) Numeric, Continous +

Natural Land Change in Yield as a result of soil erosion Suyakhuet al., (2018 Numeric, Ordered 1-3 +

Slope of farm Suyakhuet al., (2018 Numeric, Dummy

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No of farm plots Campbell (2013) Numeric, Continous

Use bullock for ploughing Numeric, Dummy

HH that cultivate 3rd crop Campbell, 2013 Numeric, Continous +

HH depending on or exploiting natural resources Campbell, 2013 Numeric Continous +

Natural Disaster & Climate Variabilit y Loss of properties, land, livestock or due to soil erosion Hahn et al, 2009 Numeric, Dummy -

Experience crop failures on farm during last 3 years Campbell, 2013 Numeric, Continous

Mean standard deviation of monthly temperature Hahn et al, 2009 Numeric, Continous +

Mean standard deviation of monthly precipitation Hahn et al., 2009 Numeric, Continous +

Socio demograp hic Numeric, Continous (no of Social conditions Dependency Ratio Hahn et al., 2009 dependents/HH Size x 100) -

Average HH members Campbell (2013) Numeric, Continous +

Social Networks Average receive: give ratio Hahn et al., (2009) Numeric Continous +

Average borrow: lend ratio Hahn et al., (2009) Numeric, Continous +

HH that are not members of any organization Campbell (2013) Numeric, Continous -

HH with family members working in a different community Campbell (2013) Numeric, Continous +

Housing & Productio Physical n Means HH without concrete house Suyakhuet al (2018) Numeric, Continous -

137

Yahaya et al JAEESS 7(1) May, 2021

HH with house affected by erosion Suyakhuet al.,(2018) Numeric, Continous -

Distance to erosion site Suyakhuet al., (2018) Numeric, Continous +/-

Finance & Financial Income HH borrowing money Bhuiyanet al., (2017) Numeric, Dummy -

HH without access to financial institution Bhuiyanet al., (2017) Numeric, Dummy -

Average HH income Bhuiyanet al., (2017) Numeric, Continous +

138