DEFINING AND MEASURING RESILIENCE OF SMALLHOLDER … · Risico is een zeer belangrijke factor voor kleinschalige landbouwers in Afrika: de onvoorspelbare natuur, die de landbouw kenmerkt,

DEFINING AND MEASURING

RESILIENCE OF SMALLHOLDER

FARM HOUSEHOLDS IN TANZANIA

Bram Van Hecke Student number: 01308095

Promotor: prof. dr. ir. Marijke D’Haese

Tutor: Ir. Eline D’Haene

Master dissertation submitted for obtaining the grade of:

Master of Science in bioscience engineering: agricultural sciences

Academic year: 2017 – 2018

DEFINING AND MEASURING

RESILIENCE OF SMALLHOLDER

FARM HOUSEHOLDS IN TANZANIA

Bram Van Hecke Student number: 01308095

Promotor: prof. dr. ir. Marijke D’Haese

Tutor: Ir. Eline D’Haene

Master dissertation submitted for obtaining the grade of:

Master of Science in bioscience engineering: agricultural sciences

Academic year: 2017 – 2018

De auteur en promotor geven de toelating deze scriptie voor consultatie beschikbaar te stellen en

delen ervan te kopiëren voor persoonlijk gebruik. Elk ander gebruik valt onder de beperkingen van het

auteursrecht, in het bijzonder met betrekking tot de verplichting uitdrukkelijk de bron te vermelden

bij het aanhalen van resultaten uit deze scriptie.

The author and promoter give the permission to use this thesis for consultation and to copy parts of it

for personal use. Every other use is subject to the copyright laws, more specifically the source must be

extensively specified when using from this thesis.

Ghent, June 2018

The promotor: Prof. dr. ir. M. D’Haese

The tutor: ir. E. D’Haene

The author: Bram Van Hecke

Acknowledgements iii

Acknowledgements

A master dissertation has certain similarities to a long journey. You leave, expecting you will be home

again soon. You start with your knapsack full of courage and eagerness to explore. After all, a master

thesis is the last hurdle to obtain a degree in bioscience engineering. Yet the road is tougher than one

could ever expect. Writing a dissertation is an expedition, with stops at the most picturesque places,

but also with nights of silent solitude.

Still, only during the trip, you start to understand the real value of writing a master thesis. Although

not every day is flawless, and the path leads to unexpected obstacles, only pride and satisfaction

remain at the end of the journey.

Even though a dissertation seems like a journey without company, I would not be able to deliver a

thesis without the guidance of certain people. Without them, I would probably still be lost and

wandering. Therefore, I would like to express my gratitude to them.

First of all, I want to thank ir. Eline D’Haene for providing direction. Every doubt, all those questions,

each confusion, it was never an issue. Swiftly and straight-forward all of them were resolved.

Furthermore, instead of just providing answers, you guided me towards solutions for my problems.

You taught me to explore instead of just waiting for an answer. I am very grateful for all the help I

received. Without it, I would not have been able to deliver this work.

Furthermore, I also want to thank prof. dr. ir. Marijke D’Haese for her willingness to be my promotor.

A promotor is someone that watches how the things are going from afar and intervenes if needed. I

am grateful that you accepted me as a thesis student and gave me a wake-up call when needed.

Also, Rikolto deserves my words of appreciation. The thesis topic was initially an idea from Rikolto, and

although the idea changed during the journey, my dissertation would never be the same without their

ideas.

My parents and family deserve more than just a simple “thank you” for their encouraging words and

endless listening capabilities, not only in this dissertation, but in everything I do. For every difficulty on

my path, every project I want to start, every moment I was disheartened, every moment of glory; they

were always there. They have been there since dawn, and I am sure they will be there till dusk.

Finally, the last word of thanks is meant for my friends. Not only for the infinite support, but even more

for the countless moments, which I will remember for life. For all the overtime we did, and I would do

again immediately, if it were with you guys. For all the nights from which the hours ticked away so fast.

For all the laughs and all the magnificent moments we had. I will not forget, these were the days of

our lives (Mercury, 1991).

Bram Van Hecke

Ghent, May 2018

iv Acknowledgements

Abstract (English) v

Abstract (English)

Risk is a very important factor for farmers in Africa the unpredictable nature of farming as well as

common incidents might cause shocks. These shocks will in turn heavily affect farming households.

To cope with these shocks, building resilience might be an answer. Yet, resilience has a very broad

scope with several dimensions. Therefore, we tried to define the concept in the context of Tanzanian

smallholder farmers. Furthermore, we also try to make the “fuzzy” word resilience measurable in the

field. Therefore, an indicator framework was developed.

Resilience is defined as the capacity of a system to absorb disturbance and reorganize while

maintaining basically the same function (Holling, 1973; Walker et al., 2004). From this definition, three

capabilities are found: (1) buffer capability, (2) adaptability and (3) transformative capability.

Consequently, based on the definition of resilience and the literature on factors influencing resilience,

eight indicators were found, including (1) education, (2) health, (3) social capital, (4) natural capital, (5)

economic capital, (6) off-farm diversity, (7) on-farm diversity and (8) innovativeness. For all of these

eight indicators, an estimator was found which resulted in an indicator framework.

This indicator framework was executed on farm households sampled in a Living Standard

Measurement Survey from the Kagera region in Tanzania. For each indicator of the framework, every

household received a score. These scores were then rescaled to values between zero and one, with

zero being the lowest score a household in the sample obtained for the particular indicator and one

the highest. With these indicators, a radar chart was developed to examine the strengths and

weaknesses of each household compared to the average. Furthermore, the distributions of the

particular estimators were examined, and the correlation was researched. Finally, several tests were

executed to check whether the indicator framework was representative for the resilience in this

context.

We conclude that not all distributions of the indicators are equally good. Yet, because of the limitations

of the survey, no better estimators were available. Also, the correlation between the indicators is

overall small. Furthermore, we tested the representativeness of the indicator framework. For this, we

created two groups, one with bad scores for the different estimators and one with good scores. The

number of shocks a household perceived in the period 2004-2010 was for both groups. As well we

assessed their perceptions on these years. The count of shocks was not significantly different between

the high resilience and the low resilience group. The second test revealed that the group with high

resilience perceived the years significantly better than the group with low resilience.

Finally, the framework developed in the master dissertation was compared to alternative frameworks

found in literature. All models have their differences, including contrasting scopes and goals. Each

framework does have some indicators that others do not have. Hence the application of the different

frameworks requires careful attention for the goal these need to serve, the area of application and the

available data.

Keywords: resilience, smallholders, risk, buffer capacity, adaptive capacity, transformative capacity,

capitals, indicator, Tanzania

vi Abstract (English)

Samenvatting (Nederlands) vii

Samenvatting (Nederlands)

Risico is een zeer belangrijke factor voor kleinschalige landbouwers in Afrika: de onvoorspelbare

natuur, die de landbouw kenmerkt, evenals andere incidenten vormen de vele bedreigingen waarmee

landbouwers worden geconfronteerd. Om het hoofd te bieden aan deze schokken, kan veerkracht

(“resilience”) een oplossing bieden. Maar veerkracht is een containerwoord met vele dimensies. Wij

hebben geprobeerd om in de context van de kleinschalige landbouwer in Tanzania veerkracht te

definiëren. Verder poogden we om het eerder vage woord veerkracht meetbaar te maken door middel

van een monitoringskader.

Veerkracht kan omschreven worden als de capaciteit van een systeem om schokken te absorberen en

zich te reorganiseren om zo zijn functie te behouden. (Holling, 1973; Walker et al., 2004). Deze definitie

bestaat dus uit drie delen: (1) bufferen, (2) adaptatievermogen en (3) transformatievermogen. Hierop

volgend werden, gebaseerd op de literatuur, acht indicatoren gevonden die veerkracht kunnen

bepalen: (1) scholing, (2) gezondheid, (3) sociaal kapitaal, (4) natuurlijke hulpbronnen, (5) economisch

vermogen, (6) diversiteit in jobs, (7) diversiteit op het landbouwbedrijf en (8) innovatiekracht. Voor elk

van de indicatoren werd een schatter gezocht.

Het bekomen monitoringskader werd toegepast op landbouwgezinnen die voorkwamen in een “Living

Standard Measurement Survey” in de provincie Kagera in Tanzania. Elk van de gekozen gezinnen in de

steekproef had landbouwgrond ter beschikking. Elk huishouden kon voor elke indicator een score

krijgen en deze scores werden vervolgens herschaald naar scores tussen nul en één. Met deze

indicatoren werd een sterdiagram opgesteld, zodat het mogelijk wordt om de zwaktes en sterktes te

bepalen voor elk landbouwersgezin. Verder werden ook de distributies van de verschillende

indicatoren opgesteld en werd de correlatie van de indicatoren onderzocht. Ten slotte werden

verschillende testen uitgevoerd om de representativiteit van het monitoringskader te onderzoeken.

We kunnen concluderen dat niet alle distributies van de indicatoren voldoen aan de vereisten. Omwille

van de beperkingen van het onderzoek, waren geen betere schatters voor handen. Voorts was de

correlatie tussen de indicatoren klein. Vervolgens werd ook de representativiteit van het

monitoringskader getest. Er werden twee groepen gemaakt: een groep met slechte scores voor de

schatters en een met goede scores. Voor de gezinnen in deze groepen werden het aantal schokken in

de periode 2004-2010 geteld en werd ook gekeken naar hoe zij deze jaren ervaarden. Voor het aantal

schokken werd een verschil gemeten, doch niet significant. De groep met goede scores ervaarde deze

jaren echter wel significant beter dan de groep met de slechte scores.

Ten slotte werd ons model vergeleken met andere modellen in de literatuur. Hieruit werd duidelijk dat

het zeer belangrijk is om de focus en de doelstellingen van een monitoringskader te begrijpen.

Hierdoor heeft elk kader zijn verschillen. Zo heeft elk model bijvoorbeeld enkele indicatoren die bij

andere niet voorkomen. Het kan dus interessant zijn om te testen of deze indicatoren in ons

monitoringskader passen.

Kernwoorden: veerkracht, kleinschalige landbouw, risico, bufferen, adaptatievermogen,

transformatievermogen, kapitaal, indicator, Tanzania

viii Samenvatting (Nederlands)

Contents ix

Contents ACKNOWLEDGEMENTS III

ABSTRACT (ENGLISH) V

SAMENVATTING (NEDERLANDS) VII

CONTENTS IX

ABBREVIATIONS XIII

CHAPTER 1 INTRODUCTION 1

1.1 RESEARCH QUESTION AND OBJECTIVE 1

1.2 DISSERTATION OUTLINE 1

CHAPTER 2 LITERATURE STUDY 3

2.1 AGRICULTURE AND POVERTY: SETTING THE SCENE 3

2.1.1 SMALLHOLDERS AND THEIR CHALLENGES 3

2.1.2 FOOD DEMAND: A CHANGING LANDSCAPE 4

2.1.3 FOOD SUPPLY: A SYSTEM UNDER PRESSURE 4

2.1.4 RISK: A PERPETUAL CHALLENGE IN FARMING 5

2.2 DEVELOPMENT PARADIGMS 6

2.2.1 THE EARLY POVERTY DEBATES (1960-1980) 6

2.2.2 WASHINGTON CONSENSUS (1980 – 1997) 7

2.2.3 POST-WASHINGTON CONSENSUS (1997 – 2001) 9

2.2.4 PRO-POOR POLICIES (2001- 2008) 10

2.2.5 INCLUSIVE GROWTH (2008 – PRESENT) 11

2.2.6 WHAT IS NEXT? 12

2.3 SMALLHOLDER FARMING 12

2.3.1 DEFINITION AND IMPORTANCE 12

2.3.2 UNIQUE FEATURES OF SMALLHOLDER FARMING 13

2.3.2.1 Capital 13

2.3.2.2 Smallholders and productivity 15

2.3.2.3 Off-farm jobs 16

2.3.2.4 Risk in smallholder farming 17

2.4 RESILIENCE 18

2.4.1 DEFINITION AND IMPORTANCE 18

2.4.2 HISTORY OF RESILIENCE 19

2.4.2.1 Ecological systems 19

2.4.2.2 Psychology 20

2.4.2.3 Small and medium enterprises 20

x Contents

2.4.2.4 Differences and similarities 21

2.4.3 THEORETICAL FRAMEWORK 21

2.4.3.1 Buffer capability 22

2.4.3.2 Adaptive capacity 23

2.4.3.3 Transformative capability 24

2.4.3.4 Integration of the three capabilities 25

2.5 INDICATORS FOR RESILIENCE 26

2.5.1 HUMAN CAPITAL 26

2.5.1.2 Education 26

2.5.1.3 Health 27

2.5.2 SOCIAL CAPITAL 27

2.5.3 NATURAL CAPITAL 28

2.5.4 ECONOMIC CAPITAL 29

2.5.5 DIVERSITY 29

2.5.5.1 Off-farm income 29

2.5.5.2 On-farm diversity 29

2.5.6 INNOVATION 30

CHAPTER 3 METHODOLOGY 31

3.1 KAGERA HEALTH AND DEVELOPMENT SURVEY (2010) 31

3.1.1 LSMS SURVEYS AND THE KAGERA HEALTH AND DEVELOPMENT SURVEY 31

3.1.2 KAGERA REGION: CHARACTERISTICS 32

3.1.3 PRELIMINARIES OF THE SAMPLE 33

3.2 INDICATORS AND THEIR MEASUREMENT 33

3.2.1 S.M.A.R.T. PRINCIPLES 33

3.2.2 HUMAN CAPITAL – EDUCATION 34

3.2.3 HUMAN CAPITAL – HEALTH 35




3.2.7 OFF-FARM DIVERSITY 37

3.2.8 ON-FARM DIVERSITY 38

3.2.9 INNOVATIVENESS– PESTICIDE USE 38

3.2.10 CORRELATION TESTS 38

3.3 RESEARCH METHOD 39

3.3.1 GENERAL OUTLINE OF THE RESEARCH 39

3.3.2 R AND THE METHODS OF CODING 40

3.3.3 CREATION OF GROUPS 40

3.3.4 TESTS EXECUTED ON THE GROUPS 41

CHAPTER 4 RESULTS AND DISCUSSION 43

4.1 DESCRIPTIVE STATISTICS 43

4.1.1 ANALYSIS OF INDICATORS 43

4.1.1.1 Human capital – average education level of the household members. 43

4.1.1.2 Human capital – average health status of household members 45

Contents xi

4.1.1.3 Social capital – amount of memberships households have 46

4.1.1.4 Natural capital – soil quality of the household’s plots 47

4.1.1.5 Economic capital - wealth index 48

4.1.1.6 Off-farm diversity – amount of non-agricultural activity 49

4.1.1.7 On-farm diversity – amount of crops and sorts of livestock 51

4.1.1.8 Innovativeness – pesticide use 52

4.1.2 CORRELATION BETWEEN THE INDICATORS 53

4.1.3 CONCLUSION 54

4.2 TESTS EXECUTED ON THE GROUPS 55

4.2.1 AMOUNT OF SHOCKS PERCEIVED 55

4.2.2 REASON AND SOLUTION FOR THE PERCEIVED SHOCKS. 56

4.2.3 AVERAGE OF THE PERCEPTION SCORE FOR EACH YEAR 56

4.3 COMPARISON WITH OTHER MODELS 58

4.3.1 COMPARISON OF DIFFERENT INDICATOR FRAMEWORKS 61

4.3.1.1 Resilience index and measurement – II 61

4.3.1.2 Social-ecological resilience indicator framework 62

4.3.1.3 Behavior-based indicators framework 64

4.3.2 CONCLUSION 66

4.4 IMPLICATIONS FOR STAKEHOLDERS 67

4.4.1 EDUCATION 67

4.4.2 HEALTH 68




4.4.6 OFF-FARM DIVERSITY 69

4.4.7 ON-FARM DIVERSITY 69

4.4.8 INNOVATIVENESS 70

4.4.9 CONCLUSION 70

CHAPTER 5 CONCLUSION 71

CHAPTER 6 BIBLIOGRAPHY 73

ANNEXES 89

ANNEX 1 ADDITIONAL FIGURES AND TABLES 89

ANNEX 2 EXACT OUTLINE OF FUNCTIONS USED 98

ANNEX 3 ADDITIONAL INFORMATION ON THE EDUCATION SYSTEM IN TANZANIA 100

xii Contents

Abbreviations xiii

Abbreviations BBI Behavior-based indicators

DHS Demographic and Health Surveys

e.g. Exempli gratia

FAO Food and Agriculture Organization

GDP Gross Domestic Product

i.e. Id est

IG Inclusive growth

IMF International Monetary Fund

IQR Interquartile range

KHDS Kagera Health and Development Survey

LSMS Living Standards Measurement Study

NGO Non-governmental organizations

PDCA Plan-do-check-act

PSU primary sampling unit

PWC Post-Washington consensus

RIMA-II Resilience Index Measurement and Analysis – II

ROSCA Rotating savings and credit associations

SD Standard deviation

SDG Sustainable Development Goals

SERI Social-ecological resilience indicators

SES Social-ecological systems

SME Small and medium sized enterprises

So. sh. Somali shilling

SSA Sub-Saharan Africa

Ts. sh. Tanzanian shilling

USAID United States Agency for International Development

VB Very bad

VG Very good

WB World Bank

WMW Wilcoxon-Mann-Whitney

xiv Abbreviations

Chapter 1: Introduction 1

Chapter 1 Introduction

“It is time to change the way we think. Farmers are not the cause of Africa’s poverty; they are a

potential solution.”

– Koffi Annan, former UN Secretary General

1.1 Research question and objective Smallholders in Africa face many problems. The unpredictable nature of farming is definitely one of

them. This unpredictability will only grow for African smallholders: the burden of climate change will

be a lot higher for smallholders in developing countries than for other regions in the world. Reasons

are the higher overall vulnerability and the climate of the developing countries (Morton, 2007).

Furthermore, also other shocks, such as low crop prices or health issues could occur. Despite the

challenges those smallholders face, they are crucial to ensure future food security (Tscharntke et al.,

2012). For that reason, the huge challenges smallholders face need an appropriate response.

An emerging answer to the threats smallholders face and the lack of possibility they have to grasp

opportunities is the concept of resilience. In all fields, but definitely in the development-agriculture

nexus, this approach arises. This is proved by the use of the word in the recent Sustainable

Development Goals (SDGs) (United Nations, 2018). Nevertheless, resilience is a word with a very wide

use, which resulted in several definitions with each its own dimensions (Tambo & Wunscher, 2017).

Darnhofer (2014) argues that fuzziness of the word might have an adverse impact on the value of the

concept.

Therefore, we have the goal to define and measure resilience in this master dissertation. The first

research question could thus be the following: “how can resilience be defined in the context of

Tanzanian smallholders?” Furthermore, a second research question emphasizes the measuring of

resilience: “what could be an appropriate indicator network to measure resilience hands-on in the

context of smallholders in Tanzania?” In this dissertation we will thus try to form an appropriate

answer to these questions.

1.2 Dissertation outline Accordingly, the outline of the dissertation will be the following: first, the scene will be set with a

review of the issue of food security and the important role of smallholders in developing countries in

this matter. It is important to see the relevance of this master dissertation as an outstretched hand to

smallholders. After all, the concept of resilience might help them to arm themselves with stronger

tools to withstand shocks. Afterwards an overview of the different development paradigms after the

Second World War will be given. Because the term “resilience” cannot be placed in a bigger context

without the knowledge of the history of those paradigms, it is needed to understand the shifts that

were made in the development world over time. Subsequently, the smallholders will be defined and

characterized. It is obvious that resilience of smallholders cannot be researched, without even knowing

2 Chapter 1: Introduction

what a smallholder farm is and what characteristics are typical. Therefore, the definition will be looked

into and certain features of a smallholder will be examined.

Only after this, resilience will be outlined. First of all, the definition of resilience will be summarized

after which the history and strains of literature about resilience will be discussed. Also, the theoretical

framework shall be outlined. This will give a full overview of the concept. Finally, several indicators for

resilience will be researched. This is the stepping stone to the part where an attempt will be made to

measure resilience.

To measure resilience, the survey used in this dissertation will be delineated and the different

indicators with their respective estimators. After this, the structure of the several tests, done with

those indicators will be explained. In this section, the methodology, the outline of the proposed

measuring tool and tests executed on this measuring tool will thus be given. Afterwards, the analysis

of the results and the discussion will be done simultaneously. This will guarantee that a full critical

analysis is possible. The different indicators will be looked into, and then the tests will be analyzed. In

this section, the validity of the indicators and the model will thus be tested.

Since the relevance of the developed model needs to be discussed, our model will also be compared

with other frameworks that are to be found in the literature. Three other frameworks are discussed

and compared to look what similarities and disparities can be found. Finally, the different implications

this research could have for stakeholders in the Tanzanian agricultural sector are mentioned. For the

household, the community, the government and non-governmental organizations (NGOs), advice is

formulated.

Chapter 2: Literature study 3

Chapter 2 Literature study

2.1 Agriculture and Poverty: setting the scene

2.1.1 Smallholders and their challenges Smallholders are of growing importance to resolve the current food security threats. They are in the

perfect place to help mitigate the effects of these threats. Before the food security threats are

elaborated on, it might be interesting to give a proper definition for smallholders. Also their

importance needs to be clarified. HLPE (2013) defines smallholder farming as agriculture practiced by

households, using mostly family labor. Although this definition is quite clear, it needs to be mentioned,

that a definition for smallholders cannot be rigid, because of the diversity among farms.

The importance of smallholders is easily demonstrated by statistics. Overall, they are a big part of the

agricultural sector worldwide. Indeed, Lowder et al. (2016) estimated that out of 570 million different

farms around the world, 90 per cent are family farms1, with the notion that a family farm and a

smallholder farm are interchangeable terms in most of the world. This is confirmed by the fact that 85

per cent of the family farms are smaller than two hectares. In low-income countries, this number is

even bigger (Lowder et al., 2016). This results in a share of 62 per cent of the labor force working in

agriculture (ILO, 2014). Finally, A research conducted in East Africa found that over 75 per cent of

agricultural outputs are produced by smallholder farmers, although the main production is for home

consumption (Salami et al., 2010). All the above mentioned numbers make clear that smallholders

make a crucial contribution to the world food supply.

Moreover, smallholders have several advantages, which make them indispensable to solve the current

challenges. To begin with, it is often argued that small-scale farms are more productive in terms of

total output per hectare than large commercial farms (De Schutter, 2017; Heltberg, 1998). Sen (1962)

argues that the extra input in terms of family labor would increase the output. Furthermore,

agricultural growth on small farms has proven to be highly effective in slashing poverty. This raises

rural living standards and eradicates hunger out of those regions. As well, the opposite effect applies:

if agricultural development is not invested in, a country remains trapped in poverty (Hazell et al., 2010).

Smallholder are very important for the future food security, yet, smallholders also face a lot of

challenges. The demand and supply of food markets is thoroughly changing the last decades.

Furthermore, risk is a perpetual factor in farming. We will now look into the challenges and risks

smallholders face.

1 Lowder et al. (2016) acknowledges that this is a broad estimation. She took into account 167 countries, which represent 96 per cent of the world’s population and 90 per cent of the agricultural land.

4 Chapter 2: Literature study

2.1.2 Food demand: a changing landscape Both population growth, rise of income and urbanization lead to a shifting diet and a growing demand

for food (Gale et al., 2002). All three effects though have a different effect on demand and pattern of

consumption.

The world’s population is growing vigorously: in 2017, the human population numbered approximately

7.6 billion. Over the next 33 years , the amount of people on this planet will rise with another 2.2 billion

(UN DESA, 2017). This means food demand will continue to increase certainly until 2050 (Godfray et

al., 2010). The population growth will be paired with rise in income and urbanization. Rae (1998) saw

that for the East Asian economies both urbanization and income growth were positive indicators for a

changing food pattern as well.

But why? As income rises, people shift their diet towards more saturated fats, sugar, refined foods and

less fiber (Popkin, 1994). Furthermore, also the consumption of animal products increases heavily:

people in developing countries currently consume one third of meat, compared to the developed

world. Yet, this is changing rapidly in developing countries, while the meat consumption stagnates in

developed countries: in 2020, the meat consumption in developing countries is expected to be 63 per

cent of the world’s total consumption of meat (Delgado, 2003). This higher demand for processed

foods and animal products that comes with higher purchasing power adds pressure to the food supply

system (Godfray et al., 2010). Livestock production for example, is highly dependent on the use of

cereals, in the context of intensive production. In turn, the increasing demand for meat and milk boosts

cereal production. Nevertheless, the percentage of cereals used for livestock is still low in developing

countries, compared to the US. In the US, 40 per cent of cereals are used for feed, while in Africa, this

is only fourteen per cent. Obviously, an increased production of livestock will eventually push the

cereal use for animal products upwards as well (if cereal production remains stagnant) (Speedy, 2003).

Urbanization is a motor for the shift towards a new dietary pattern as well (Rae, 1998). Because better

organization of food markets and the higher opportunity cost of the time of preparing food, processed

and pre-prepared food replaces homemade food (De Haen et al., 2003). Moreover, the above

mentioned shift towards more energy dense diets is also seen for people that migrate to cities. This

also happens because the availability of different sorts of food is more abundant. (De Haen et al.,

2003). As the migration from rural to urban environments is increasing, this should be a major concern.

The United Nations (2008) predicts that in 2050 there will be 600 million rural inhabitants less then

today. Those people will move to cities and thus change their diet, and in this way change demand to

certain foods that need more resources. All three societal changes push the food system to its limits.

This comes directly at the account of farmers and definitely smallholders in developing countries.

2.1.3 Food supply: a system under pressure The food patterns are changing thoroughly. Yet, not only demand of food is under pressure. Also the

supply is under threat. Several reasons such as climate change and the agricultural system can be seen

as threats for food security.

The first threat that should be considered is the agricultural system: the current system is definitely

under discussion. After the Second World War, agricultural policies in developed countries mainly

focused on producing to stay in phase with the population and income growth. As a consequence, the

yields increased massively (De Schutter, 2017). This might seem all positive, but this policy approach

had several negative consequences as well. First of all, it created huge surpluses. The so-called butter

mountains and milk lakes on their turn, distorted trade (Krebs et al., 1999). Also, and maybe even more

important, the system did not take into account the vulnerability of natural resources. Thrupp (2000)


states that soils, water and biodiversity have not gained from the current agricultural system. She

furthermore argues that this trend does not only harm socially and ecologically, but can also

undermine future productivity. There is now a broad consensus that these intensive food system are

indeed not sustainable in the long run (De Schutter, 2017). Smallholders can be an alternative to these

intensive farming systems. They often farm less intensive and often have a much lower impact the on

the social and ecological aspects of a society

Another burden to agricultural productivity is climate change. Jones & Thornton (2003) found that in

Africa and Latin America, the overall reduction in productivity of maize could be ten per cent in 2050.

However, the variability among different areas is enormous. McMichael (2001) suggests that yields will

suffer more in already food insecure regions (e.g. Southeast Asia and Sub-Saharan Africa), because the

climate in these regions is less temperate. It needs to be said that predicting future events always

implies great uncertainty, but the possible negative effects of climate change on future yields are a

main concern for future food security.

The above mentioned challenges to food security take place mostly in developing countries. It is known

that most of the population growth will occur in high-fertility countries, which are all developing

countries (UN DESA, 2017). Also income growth and urbanization, with its effects on demand, will

mostly happen in those countries. This will in turn create a need for developing countries providing

their own food markets (Heinemann et al., 2011). It is clear that the challenges mentioned are for an

important part happening in developing countries. Because the agricultural productivity in these

countries is quite low, closing the yield gap will be one of the main strategies to assure global food

security (Godfray et al., 2010).

2.1.4 Risk: a perpetual challenge in farming For smallholder farms, risk is a critical factor. Shocks may affect the wellbeing of rural society in a very

direct manner. This is even more true in poor rural economies and it’s these people which are less able

to cope with risks as well (Fafchamps, 2003). The reason is that those poor households have less buffer

to fall back upon (Heinemann et al., 2011). Following Heinemann et al. (2011), there are different

sources of risk whereof illness, food prices, the state, weather and climate change are the most

important. The changing climate in particular is an increasing source of risk, because of more recurrent

extreme weather and declining yields (High-Level Expert Forum, 2009).

To cope with risk, farmers found several strategies. An answer to the shocks a farm faces is resilience,

which can be seen as the ability of a system to endure hazardous events in a timely and efficient way

(IPCC, 2012, p. 563)

In the following paragraphs, we will discuss the succession of development paradigms since the Second

World War. This series of paradigms culminated in the recent upswing of inclusive growth. In this light,

the recognition of resilience as a risk mitigation strategy is increasing and more obvious. Although the

usefulness of resilience in this concept is self-evident, the research on this field has been rather limited.

Inclusive growth will be linked with resilience, after which smallholders will be looked into. Once the

scene is set, the concept of resilience can be defined more thoroughly.


2.2 Development paradigms

Since the Second World War, a lot of development paradigms have passed by. All of them started as a

hopeful expression of measures developing countries should take. Nevertheless, all paradigms were

criticized heavily as well. After some time, they were replaced or at least modified. Currently, inclusive

growth (IG) could be called the mainstream development paradigm, but it seems likely that this

paradigm will also be replaced, as the zeitgeist changes.

In the following paragraphs, every paradigm since the 1960’s is discussed, after which also the criticism

is mentioned. As paradigms fade into each other and do not have rigid boundaries, the beginning and

the ends of the paradigms are chosen rather arbitrarily. On Figure 1, a schematic timeline of the

paradigms is given.

2.2.1 The Early Poverty Debates (1960-1980) The pre-Washington consensus period had place mostly during the presidency of Robert McNamara’s

at the World Bank (WB) (1968-1981) (Saad-Filho, 2010). The key believe of this period was that state

intervention was needed to promote the “take-off” of these countries. This so called “big push” would

lead to increased investment and thus to fast growth (Easterly, 2006). The boost would occur in a

limited group of mainly more modern and industrialized sectors (Rostow, 1959). An example of the

“big push” approach would be the first economic plan of Somalia after its independence. In the first

five year plan (1963-1967), 1400 million Somali Shilling (So. Sh.) (196 million US Dollar2) would be

spent. The expenditure was mainly on a handful of big projects such as the expansion of an already

existing sugar factory, the development of the food industry, the construction of roads and the building

of three ports (Mehmet, 1971).

This paradigm has its roots in the theory of Keynes, a leading economic theory in developed countries

in the post-war period (Broad, 2006). John Maynard Keynes (1883 – 1946) is one of the most famous

economists all time. His book “The General Theory of Employment, Interest and Money” (1936)

became the reference point for the economic thought of the years following the second world war

(BBC, 2014). The theory of Keynes puts emphasis on the government to guide the economy, because

he believed markets are not self-regulating. The state intervention was here executed by state

planning or financing of big projects (Broad, 2006).

In the seventies of the twentieth century, this theory was heavily contested, as the growth rate in most

developed countries collapsed (this did not happen in Asia) (Gore, 2000). Reasons for the collapse of

the economic growth of developing countries are the quadrupling of oil prices in 1973, world recession

in 1974 and 1975 and the slow recovery from these events (Balassa, 1985). But also in the years before

this collapse, the rapid economic growth was accompanied by poverty and rising inequality (Saad-

Filho, 2010). Because of the critique on the Keynesian approach, a new idea of development emerged:

the influence of neoliberalism and free markets became more important (Broad, 2006; Gore, 2000).

Thorsen (2010) defines neoliberalism as the believe that the state should be reduced in strength and

size3. This would mean that the “big push” approach, with big emphasis on the state was replaced by

a concept where intervention was seen as undesirable.

2 The exchange rate of the So. Sh. with the US Dollar was 7.14 So. Sh. = 1 $ at the time of writing by Mehmet (1971). 3 Neoliberalism is different from liberalism. Liberalism could be defined rather broadly as political ideology, which is in favor of freedom and democracy, while neoliberalism is a modified form of liberalism that puts emphasis on free markets and minimal state intervention (Thorsen, 2010).


Above mentioned arguments were only the foreplay of the following period, the Washington

consensus, with its focus on trade liberalization and neoliberalism (Broad, 2006).

2.2.2 Washington consensus (1980 – 1997) Due to the criticism on the big push approach, the mainstream development paradigm shifted. A

leading influencer in this shift is John Williamson, who also gave a name to the paradigm. It has to be

mentioned though, that the paradigm was already existent before Williams wrote about it, but he

summarized and characterized it. Williamson was working at the Peterson Institute, when he wrote a

background paper for a conference on the shift of ideas that influenced Latin American economic

policy in 1989 (Williamson, 2009). In this paper, he came up with the name “Washington consensus”

to indicate ten ideas on the topic which “more or less everyone in Washington would agree with

(Williamson, 1990).” It is important to know that this paper initially studied Latin American

development. Nevertheless, this has afterwards become a worldwide paradigm (Gore, 2000). Fiscal

discipline, reordering public expenditures, tax reform, liberalizing interest rates, competitive exchange

rates, trade liberalization, liberalization of foreign investment, privatization, deregulation and property

rights were the ten consensus ideas. We will briefly touch on the most important ideas of this paper,

while not describing others.

The first two factors are described by McCleery & De Paolis (2008) as preconditions for economic

growth:

- Property rights: these unlock the potential of assets, such as land, to serve as capital. Assets

are now often held informally by poor people (De Soto, 2000)

- Fiscal discipline: it is argued that high fiscal deficits might push inflation up, which hits the poor

because prices rise (Williamson, 1990).

As well, McCleery & De Paolis (2008) mentioned four factors that are now known to be quite complex:

- Trade liberalization: Although Williamson (1990) described the costly distortions protecting

domestic industries could create, McCleery & De Paolis (2008) also saw that as importing

country, trade might hurt the employment and income of the poor.

- Liberalization of interest rate was initially meant to avoid resource misallocation as it would

not encourage efficient use of capital (Polak, 1989; Williamson, 1990) but is now also

challenged because it might lead to sustained high interest rates as response to financial crises

in some countries (McCleery & De Paolis, 2008).

- Privatization: it is argued that bringing an enterprise into the competitive market might bring

benefits when done properly, but Williamson (2009) himself also noted that privatization can

also be a highly corrupt process that transfers assets only to the privileged few.

- Deregulation in Latin America was seen as highly necessary, because the countries in this

region where among the most regulated countries at the time, and this created a possibility

for corruption. Furthermore, deregulation would boost competitiveness (Williamson,

1990).Yet, deregulation on safety and environmental issues is undesirable for example.

Generalizing deregulation is thus not appropriate (Williamson, 2009).


Figure 1: timeline of the development paradigms from the 1960's onwards


Since the late 1980’s, the Washington consensus has been the dominant approach of the WB and the

International Monetary Fund (IMF). As can be seen in the ten ideas, the policies embody the believe in

the market oriented and neoliberal attitude (Gore, 2000). The approach could be seen as “trickle-

down”: policies raise the rate of economic growth, and thereby reduce poverty indirectly, because

economic growth increases the average income of people. The possible inflation of inequality is not

considered, because it is argued that inequality does not matter if the poor are ultimately better of

then before (McCleery & De Paolis, 2008).

The Washington consensus has been under criticism from the moment it was launched. First, it was

stated that the East Asian fast growing economies did not follow the policies of the Washington

consensus. They were more protectionist and did not have free markets. Yet they were quite successful

in their catch-up (Saad-Filho, 2010).

Secondly, also the reasoning of Chang & Grabel (2004) should be considered. They argue that the

Washington consensus is at least oblivious of the rising inequality the approach causes. The neoliberal

agenda is too much seen as the single correct approach. The human costs of adjusting countries was

not taken into account. It was argued that the cost of adjusting was too much taken by the poor.

Finally, there is criticism on the demands of the Bretton Woods4 institutions. These institutions wanted

that countries implemented a democracy that takes care of all groups in society. But on the other hand,

the countries also had to carry out economic policies that exclude the majority of the people. Indeed:

the neoliberal Washington consensus wanted to implement policies that achieved development by

economic growth, where the poor would not benefit from directly. This is contradictory as a

democratic state should follow the demands of the majority (Saad-Filho, 2010).

In the middle of the nineties of the twentieth century, the demand for a shift away from the

Washington consensus became clear. It was not much later that Stiglitz (1998) wiped out the

Washington consensus paradigm with his paper “More instruments and broader goals: moving toward

the post-Washington consensus”. A new paradigm was born.

2.2.3 Post-Washington consensus (1997 – 2001) Joseph Stiglitz, chief economist of the WB from 1997 till 2000, leaded the shift from Washington

consensus towards post-Washington consensus (PWC) (Saad-Filho, 2010). He criticizes the focus on

pure economics, improvement in allocation of resources and the technical approach of the

developmental problem in the decades preceding the PWC. Instead, he suggests a broader focus,

where transformation of society is central (Stiglitz, 1998).

Nevertheless, the PWC recognizes the importance of growth. But it sees growth only as part of the

solution. The new paradigm sees poverty and inequality reduction as objectives themselves as well,

rather than having a sole emphasis on economic growth (Öniş & Şenses, 2005; Stiglitz, 1998).

The importance of the state and its institutions to alleviate poverty is another significant feature. By

institutions, the systems that protects rights and enforce contracts, the legislators and police force and

finally other market supporting structures are meant (Rodrik, 2000). These institutions should be

effective, strong and well governed. Although the high emphasis on institutions and the state, open

markets and liberal policy are still important. But it is acknowledged that markets do not function

without a supporting state and governance (Öniş & Şenses, 2005).

4 Bretton Woods institutions are institutions established during the United Nations Monetary and Financial Conference in Bretton Woods, in 1944 in New Hampshire, United States. These institutions are the World Bank and the International Monetary Fund (Voutsa et al., 2014).


The need for a robust financial system is also underlined. This should help mobilize savings and allocate

capital. Leaving the market completely free, will not direct towards wealth, unless the economy is

competitive. Therefore, the government also plays a great role in guiding the economy (Stiglitz, 1998).

Finally, the need for equality, democratic regimes and accountable states is one of the hallmarks of

the PWC. Promotion of these values will augment the likelihood that policies will be sustainable

(Stiglitz, 1998). Following Öniş & Şenses (2005), ignorance of such values leads to illiberal governance

with corruption and state failure. Despite the obvious benefits of promoting these values, this

approach was barely present in the Washington consensus, where development policies were seen as

economic rather than broad societal policies.

Just like the earlier development paradigms, this approach was criticized. A first important critique on

the paradigm was the heavy emphasis on the institutions. The institutional change – the shift towards

stronger institutions, has been exaggerated, according to some authors (Saad-Filho, 2010).

Furthermore, the literature does not find a strong link between stronger institutions and economic

growth. Growth happens when investors feel secure, but the institutional setting that makes investors

feel secure is not defined (Rodrik, 2006).

Moreover, empirical work on the determinants of economic growth shows that institutions may

matter to initiate growth, but that they are not deterministic (Jones & Olken, 2005). Hausmann et al.

(2005) found that most sustained growth accelerations since the 1950s are not an outcome of

institutional change.

From the late 1990s onwards, the idea that poverty reduction was not a spontaneous outcome of

growth emerged (Saad-Filho, 2010). With this came the emergence of proposed policies and

framework that would specifically favor the poor. This approach differed from the PWC, which can be

seen as an extended view on the Washington consensus, given by Stiglitz, because it goes even further

away from the neoliberal Washington consensus approach (Mosley, 2001).

2.2.4 Pro-poor policies (2001- 2008) Since the start of the new millennium, the pro-poor concept has emerged. Both Kakwani & Pernia

(2000) and Ravallion (2004) have a definition of pro-poor growth. The first believes pro-poor growth is

growth that increases the income share of the poor. Thus, in this approach only growth that decreases

inequality is pro-poor (Kakwani & Pernia, 2000). The second concept focusses on improvement of the

living standards of the poor, regardless of changes in inequality. This means all growth that rises the

income share of the poor is considered as pro-poor, even if inequality rises as well (Ravallion, 2004).

In the first definition, growth is pro-poor when the poor gain on a relative level, while in the second

definition, the poor gain on an absolute level (World Bank, 2009).

An example makes the theoretical concepts more straightforward. The definition of Kakwani & Pernia

(2000) would describe average growth of two per cent with poor growth of three per cent as pro-poor

while for Ravallion (2004) average growth of six per cent with the poor only growing four per cent is

also seen as pro-poor growth. For Kakwani & Pernia (2000), this would not be pro-poor growth, as the

poor grow less than the average and the inequality thus rises (World Bank, 2009).

Although both of this visions differ slightly, the reform of the mainstream is clear. The rise of the pro-

poor ideas indicates the turn away from the “trickle-down” approach (Kakwani & Pernia, 2000; Saad-

Filho, 2010). This “trickle-down” approach assumes that economic growth should be boosted, after

which this would trickle down to the poor, by reducing the poverty level (McCleery & De Paolis, 2008).

Yet it doesn’t take into account that not all groups are affected evenly by growth (Ravallion, 2004).


It is important to know that the poor do usually share in the wealth created (Ravallion, 2001). So

growth is typically pro-poor, at least by the definition of Ravallion (2004). Inequality and economic

growth are not related: Ravallion (2003) found a correlation coefficient of approximately zero between

these two factors. So one could say that on average growth is distribution-neutral. Yet, cross-country

evidence suggests that there can be a large variation in poverty reduction for the same growth rate

(Son, 2004).

As the effect of growth on the poor can both be positive or negative, depending on economic and

political context, Eastwood & Lipton (2000) examined several policies that would create pro-poor

growth. He suggest that to achieve growth with maximal poverty reduction, the policies should have

the following properties: growth should be labor intensive, ensure cheaper food and demand for rural

labor and reduce fertility5. Still, it is a challenge for the policymakers to combine growth-promoting

tools with reforms that enforce the capacity of the poor to participate in the created opportunities

(Ravallion, 2004).

Although pro-poor growth meant a turn away in the mainstream from the focus on economic growth,

it is argued that it does not go far enough. Klasen (2010) suggests that the sole focus of pro-poor

growth on people below the poverty line, neglect other parts of society (the near-poor, middle income

groups…). On the other hand, Ali (2007) describes another concern on Pro-Poor Growth: there will

always be some chronically poor who will, for different reasons, not be able to share in the benefits of

growth. Inclusive growth (IG) serves an answer for these problems by trying to give equal opportunity

to all stripes of society (Ali & Son, 2007).

2.2.5 Inclusive growth (2008 – present) McKinley (2009) suggests that since the global crisis in 2008, development gave more attention again

to economic growth, instead of the great emphasis on equity in pro-poor growth. With this shift,

“inclusive growth” was born. IG refers both to the pace and the pattern of growth. Both are important

to achieve high and sustainable growth and durable poverty reduction (World Bank, 2009). IG does

underline the importance of growth and the wide range of possible suitable policy initiatives to achieve

poverty reduction (Saad-Filho, 2010). With IG, a new step in the poverty paradigms was taken,

although McKinley (2009) doubts whether this change is progressive or rather regressive.

IG is in line with pro-poor growth, at least if the definition of Ravallion (2004) for pro-poor growth is

considered. Growth is pro-poor if the impoverished benefit in absolute terms. The relative definition

of Kakwani & Pernia (2000) does not fit in the IG paradigm as it could lead to wrong conclusions of

growth in the IG context. IG values higher average growth with rising inequality more than low average

growth with falling inequality (World Bank, 2009).

IG has a few key features. First of all, growth should have a high pace and be sustained over a long

period of time. To make this growth sustainable, it should be broad based across sectors and the labor

force (World Bank, 2009). Moreover, the main instrument for sustained growth is productive

employment. The policies of this paradigm should therefore look at a way to strengthen the productive

resources and open up opportunities for additional employment (Ianchovichina & Lundström, 2009).

Finally, in several papers, the long-term perspective of IG is stated (Ianchovichina & Lundström, 2009;

World Bank, 2009). Because the approach focusses on productive employment rather than on

immediate redistribution (World Bank, 2009). It is also important to acknowledge the time lag between

action and outcome (Ianchovichina & Lundström, 2009).

5 The fertility would be defined as the number of children that would be born from each woman, if she were to live to the end of her child-bearing years (OECD, 2018).


Yet this new approach also has limitations. Several limitations are proposed by Saad-Filho (2010). First

of all, the fact that IG assumes that economic growth is the dominant factor for eliminating poverty is

criticized. Growth can also be a burden for the poor, because technological changes, property rights

and labor markets can also impoverish lots of people, if not well managed.

Additionally, IG assumes that countries fail through lack of good policies. But it can equally be true that

it is not possible to implement such policies because of several constraints as e.g. payment crises, lack

of aid, lack of market access.

Finally, Saad-Filho (2010) expresses that the social safety net in IG is rather a means to an end than an

end in itself. Distribution of growth is not seen as critical. The focus is too much on the growth in itself

and the welfare gains that will derive from this, he suggests.

2.2.6 What is next? With IG, the last paradigm in a long row has emerged. But this is certainly not the end of the sequence.

Just as all the previous approaches, IG has disadvantages as well. Yet, it is significant that there has

been a shift from a sole focus on growth to an approach where the poor are not only seen as a part of

the society, but also as key partners to alleviate a country out of poverty and to achieve sustainable

growth.

The concept of resilience fits the most in the latest paradigms. This comes together with the rising

interest in the theory of resilience. Also the SDGs play a very important role lately. In both SDG 2 (end

hunger) and SDG 13 (climate action), resilience is mentioned as a key component (United Nations,

2018). Resilience is thus obviously of growing importance as a concept. Nevertheless, the research on

the implementation of resilience has been scarce. It is therefore interesting to research the potential

of resilience to help farmers cope with shocks and thus to help them to sustain a decent livelihood.

Before we can examine the concept of resilience thoroughly, it is important to review smallholder

farming first. As we want to measure the resilience of these people against shocks, it is important to

know who they are, what they do and how they can be perceived. Because the word “smallholders”

can be interpreted in various ways, the definition needs to be narrowed down for this dissertation. In

the following section, smallholders and their importance will be defined, and certain typical features

of smallholder farming will be characterized.

2.3 Smallholder farming

2.3.1 Definition and importance As there are very much smallholders in the world, the literature suggests a wide variation of

interpretations. A working paper of the Food and Agriculture Organization (FAO), by Garner & de la

Campos (2014) found 36 different definitions. Although all are slightly different, there were some

similarities. For example, family labor is one of the main aspects of a smallholder farm. This is the

reason that the words “family farm” and “smallholder farm” are often used interchangeably (Lowder

et al., 2016). From all of the above mentioned characterizations, the broadest definition for

smallholders would be “agriculture practiced by families using only or mostly family labor.” As well,

the smallholder family relies on agriculture for at least a part of the food consumed (through self-

provision or market exchange). It also needs to be mentioned that “small” in smallholders comes from

the scarcity of resources. Especially land is scarce for smallholders (HLPE, 2013).

The importance of smallholder farming should not be underestimated. Much of the world’s farms are

managed by smallholders. From the 570 million farms worldwide, 500 million are estimated to be

smaller than two hectares. Furthermore, as already stated in chapter 1, smallholders are of importance


for food security. Nevertheless, the response to the growing food demand has been through increased

production of mainly large-scale commercial farms (Heinemann et al., 2011). Meanwhile, the yields of

smallholders lack behind. The so-called “yield gap”, which denotes the difference between the actual

and best possible yield, is big in most parts of Sub-Saharan Africa (SSA) (Godfray et al., 2010). For

example, Rwanda has an average wheat yield of 0.37 tons/ha, while the worldwide average is 2.67

tons/ha. This means the yield is seven times lower in Rwanda, compared to the world average (Oerke

& Dehne, 2004). The potential of African agriculture to increase production is thus enormous. Small

farms in developing countries will need to play a greater role in securing food supply. Yet, they are only

able to do this if they overcome their current production constraints. Above, a smallholder and his

importance was defined. To deepen out this definition, we will now discuss certain remarkable

features in smallholder agriculture.

2.3.2 Unique features of smallholder farming As mentioned, numerous aspects of small-scale farming are typical (although not general). Only certain

features that help us to understand the context of smallholders better will be considered. More in

detail, we will talk about the different sorts of capital, agricultural productivity, off-farm jobs and risk

in farming. All of these features are key aspects of smallholder farmers and are of more importance

for them, compared to other farmers.

2.3.2.1 Capital The first feature is capital, as this is a crucial factor for smallholder farms. The Department for

International Development of the United Kingdom defined five different sorts of capital: human, social,

natural, physical and financial capital. This was done in the livelihoods framework, which is a tool to

improve the understanding of livelihoods of the poor. Rather than trying to model reality, its aim is to

provide a perspective for the assets6 poor households have. The capital is divided into five forms,

because it is argued that no single category of assets on its own is sufficient to provide a decent living

standard.

Because a distinction between physical and financial assets is often hard to make, especially when the

resource is easily traded, the approach of Scoones (1998), who pools financial and physical capital into

one integrated capital, economic capital, will be followed (Keil et al., 2008). Thus for us, there are four

sorts of capital, which are all of importance for small-scale farmers.

Human capital

Human capital is defined by the OECD (1998) as “the knowledge, skills, competences and other

attributes embodied in individuals that are relevant to economic activity.” This rather abstract

definition becomes clearer as we distinguish the different forms of human capital. These would be

schooling, training, health and nutrition (Becker, 1994; Ram & Schultz, 1979). It is clear that schooling

is an investment in future earnings: primary school as well as high school and university studies raise

a person’s income, even if the cost of schooling and the background of the family are taken into

account (Becker, 1994). But also investments in health and nutrition are considered important when

promoting human capital. Poor health and nutrition can indeed lead to the inability to work, and if it

does not lead to this, it can still lead to lower labor productivity. Thus investing in health and nutrition

adds up to the human capital of person (O'Mahony & Samek, 2016).This sort of capital can be a

stepping stone towards a decent livelihood, but it can also be an end in itself. Many people do indeed

6 A distinction between capital and assets must be made. In this master dissertation, we will use the word “capital” for the different categories of resources, while an asset will be used for the actual means of these resources. For example, a car is an asset that belongs to the category of economic capital.


see a bad health and low education as a dimension of poverty. Overcoming these, will leave people

less impoverished. Human capital is thus important, both intrinsically and as a part of the livelihood

framework, to achieve positive livelihood outcomes (DfID, 1999).

Social capital

Secondly, it is broadly known that social capital is an important asset. The common saying “it’s not

what you know, it’s who you know” approves this. In the literature, social capital is defined as

networks, memberships and connections with family, friends and acquaintances that can be leveraged

at harsh times (Woolcock, 2001). All these sorts of social capital are interrelated. Wilson (1996) sees

the lack of this as a defining feature of the poor, as they are excluded from networks and institutions

that could help them with a secure job and good housing.

Moser (1997) found that communities with more social networks will be more able to cope with

vulnerability. Furthermore, it is argued that mutual trust lowers the costs of working together. This

means social capital has an immediate impact on other types of capital. Just as for the human capital

above, it is argued that social capital should not only be seen as a means of becoming something but

also as a value in itself. This asset can make a significant contribution to people’s sense of well-being

(DfID, 1999).Yet, it needs to be stated that social capital is not per se positive. Exemplary would be

networks that are strictly hierarchical and that thus limit the possibilities and even prevent people

from escaping poverty (DfID, 1999).

Natural capital

Also natural capital is of value for smallholder farmers. This sort of capital is given by local resource

endowment, but it is an outcome of human action as well (HLPE, 2013). It consists of the parts of the

local environment that give benefits in the short and long term. An example of these benefits would

be better yields, but also the resilience against drought, as the land is more able to cope with this

shock. It could thus be said that these benefits include both goods and services. This natural capital

can in the end be transposed into economic value for communities and people (Costanza et al., 1997).

In particular, between natural capital and vulnerability there is a close connection. Many of the shocks

smallholders perceive are natural hazards, but a household will also possibly only feel the lack of this

sort of capital when shocks arrive (DfID, 1999).

Natural capital is of the highest important for those who derive a big part of their income from natural

resources, such as farmers. Although the understanding of ecosystems is not complete, it is clear that

natural capital is not only crucial in a direct manner, but also indirectly because of the effects on health

and well-being.

Economic capital

Finally, the most obvious form of capital comes in. Economic capital is build up out of two parts.

Following Scoones (1998), both physical and financial capital are part of economic capital. Physical

capital includes all resources for production and processing, as well as the personal assets and services.

This means farm equipment and stock are part of physical capital, but also assets such as cars and

radio. Even services like a toilet and running water are considered part of physical capital (Scoones,

1998). Lack of infrastructure increase the cost of other sorts of capital. For example, if there is lack of

transportation equipment, it is harder to fertilize properly and to get large amounts of produce to the

markets. This can imply a cost on all other sorts of capital, as returns are lower (DfID, 1999).


Financial capital includes cash, credit and savings. This capital base is essential for any livelihood

strategy as it is a key component for the production of goods and services (Scoones, 1998). It is very

useful because it can be easily converted into other types of capital. Besides, it can also be used to

escape poverty directly (DfID, 1999).

Nevertheless, this sort of capital tends to be the sort that is the least available to the poor. It is because

they lack financial capital that other sorts of capital are so important for them.

Integration of the different kinds of capital

First, all four capitals are important to have a good resource base. Abundant possession of one sort of

capital will therefore not lead to well-endowed households. For example, a household that is very well

educated will not necessarily have a lot of the other sorts of capital.

Furthermore, the above mentioned capitals emphasize all of the assets farmers have to ensure a stable

income. The size of these capitals is important for the ability of a farmer to cope with shocks. In

addition, all four of the capitals have to be integrated. Only then, it can be understood how people

combine and transform these assets to their needs. Furthermore, the integration of all capitals is also

important to comprehend how they are able to expand their asset base (Bebbington, 1999).

The different capitals are in particular crucial for smallholders, because they often have limited access

to certain types of capital (DfID, 1999). This lack of capital is a typical contrast between smallholder

farms and large farms. Large farms typically have more capital that can be interchanged and used to

leverage certain outcomes, while smallholders have difficulties doing this.

From these sorts of capital, other features can be derived. Both agricultural productivity and off-farm

labor can be seen as outcomes of proper use of these capitals. Risk can be seen as a threat for the

deterioration of these capital assets.

2.3.2.2 Smallholders and productivity Productivity can be defined as the rate of output per unit of input. The more output is obtained per

input, the more productive something is (Oxford Dictionaries, 2018). Agricultural productivity is then

characterized as the productivity of the agricultural sectors.

Agricultural productivity is positively linked to welfare growth of rural neighborhoods. The explanation

is the fact that productivity growth will increase farm income, because more is produced with the same

inputs (Jayne et al., 2010). Lipton (2005) argues that this increase in output would also create a demand

for more farm labor and thus increase the wages and thereby also income of the rural workers at a

farm. Gains in productivity thus keep workers at the rural countryside (McGowan & Vasilakis, 2015).

Although productivity growth in agriculture is very important, crop yields only slightly increased since

1960 in Africa, while more than doubling throughout the rest of the world. Maize would be a good

example to prove this: The yield in Africa grew from one ton/ha in 1961 to 1.6 tons/ha in 20037, while

the average world yield was 1.9 tons in 1961 and grew to 5.6 tons in 2003 (FAOSTAT, 2016). Yet, there

is large variability across countries and regions in SSA. The main explanation for the low productivity

in SSA is that there has not been an African green revolution so far (Jayne et al., 2010). One of the

principal reasons for the low output is the low amount of inputs used. Mainly the lack of fertilizer is

mentioned as a concern (Jayne et al., 2010; Pretty, 1999). Organic and inorganic fertilizers are crucial

to maintain high yields and prevent nutrient depletion of the soil, but the cost of fertilizers brings a

financial risk with. Furthermore, the availability of the fertilizer and access to credit to purchase it often

lacks (Kelly et al., 2003).

7 This number accounts for the whole of Africa. It might be expected that the numbers for SSA will be even worse, as for example South Africa, Egypt and Ethiopia are all above this average (VIB, 2017)


It would be expected that a conversion towards large-scale farms might be beneficial for the overall

yield. Yet, a lot of authors argue that often more is produced per hectare on small farms than on large-

scale farms (De Schutter, 2017; Heltberg, 1998; Sen, 1962; Wiggins et al., 2010). This phenomenon is

described as the “inverse relationship” indicating the inverse ratio between farm size and output (on

a hectare base) (Heltberg, 1998). One explanation for this is the higher input smaller farms get in the

form of family labor (Sen, 1962). Wiggins et al. (2010) suggests that labor from the household is indeed

important: households are self-supervising, have more incentive to work with care and are flexible

towards the unpredictable timing of farming activities. While on large commercial farms, the hired

labor tends to be less flexible and feels less responsible to work with care. Nevertheless, it needs to be

said that other authors do doubt this relationship as well (Ahmad et al., 1999; Pol, 1984)

The relationship between smallholder farms and productivity is odd. Although their productivity is low,

the efficiency of these farmers is very high. This inverse relationship is unique for smallholder farmers.

It is also important because productivity and efficiency determine the income of smallholders.

2.3.2.3 Off-farm jobs Another interesting feature of small-scale farms is the fact that the households often hold several other

jobs. In the literature it is found that as much as twenty to 50 per cent of the household income is

derived from off-farm jobs, although these numbers are very diverse among regions (Barrett et al.,

2001; Haggblade et al., 1989)8. Furthermore, for a lot of small-scale farmers it is often a source of cash

income, more than farming, as crops are often used for own needs as well (Haggblade et al., 1989).

There is great variation in the different sort of jobs that are taken up when diversifying towards

nonfarm labor. The most prominent one is definitely food processing, but also non-agricultural sectors

as the textile, wood, and mining industry are of importance (Haggblade et al., 1989).

Barrett et al. (2001) found a number of reasons to explain that farmers search for off-farm activities.

He divides these motives into two sets: push and pull factors. Push factors include risk reduction,

surplus labor and reaction to a crisis. These reasons make farmers search for an off-farm income

because of need. They push smallholders out of the agricultural sector: farmers search for off-farm

activities because they are in search for alternative income. On the other hand, pull factors include

strategic complementarities between activities and comparative advantages, which can be seen as

opportunities that “pull” people out of the farm sector. In this case, farmers choose to increase off-

farm labor not because they need to, but because of an opportunity to increase income. From the

numerous reasons that farmers have to attain an off-farm income, only discuss three motivations that

are of high importance for us will be discussed: labor surplus, self-insurance and shocks.

A smallholder household with relatively little land and a lot of labor (i.e. a lot of people to work) often

offers some labor for non-farm employment (Barrett et al., 2001). For example, female-headed

households may have more difficulties to access land and may for this reason have surplus labor

compared to the land they have. In this case it is interesting to rent out the excess labor of the

household (Lay et al., 2009). Yet, if the household is forced to diversify towards other activities because

the land cannot sustain subsistence, the return of the off-farm labor is often low. This is also called

desperation-led diversification (Barrett et al., 2001; Lay et al., 2009).

The second incentive is the self-insurance is a concept of ex-ante risk mitigation (Barrett et al., 2001).

Indeed, the variability of income will be reduced with a diverse portfolio of occupations

(Alderman & Paxson, 1992). It is known that farming generates an income that fluctuates because of

8 There are nevertheless outliers where more than 70 per cent of the income is derived from off-farm labor. These numbers are found in parts of Ghana and Zambia, and mentioned by Haggblade et al. (1989).


several influences such as weather and market prices. It can thus be interesting to diversify into off-

farm labor, to maintain a certain amount of income. It needs to be mentioned that this sort of risk

mitigation is ex-ante and thus a proactive approach to withstand fluctuations better.

It is also possible that off-farm labor is a response to shocks. Indeed, coping with shocks is a reason to

diversify into non-agricultural activities. As we speak about a response to shocks, this will be named

ex-post coping with risk. When crops and livestock do not yield as expected, households must

reallocate labor to off-farm activities (Barrett et al., 2001). An example would be the fact that wildlife

poaching in Tanzania increases after catastrophic events whereby productivity decreases (Barrett &

Arcese, 1998). The poaching can be seen as a diversification of the income, as the animals will be a

part of food intake of the household. Because it is likely that poorer people are less able to self-insure

themselves ex-ante against shocks they are more likely to diversify ex-post (Barrett et al., 2001).

2.3.2.4 Risk in smallholder farming Above the term risk came up already several times. Because smallholders face risk in their daily

occupations, it is an essential feature. Risk is also inherent to farming worldwide. Therefore, the

concept of risk will first be defined and after which different mechanisms to cope with risk will be

looked into.

Defining risk

Risk is a rather broad term. Therefore, several definitions are found in the literature. Brooks (2003)

looked into ten of them and saw that the consensus of those definitions says that risk is “the probability

of occurrence of a hazard.” A hazard is subsequently characterized as threatening event that could

potentially cause damage such as droughts, floods and storms (Brooks, 2003; Downing et al., 2001).

Thus, a hazard could be defined as the source of danger that can induce a risk (HLPE, 2013).

Furthermore, different households will encounter a different degree of loss. The degree of loss is

defined by the vulnerability of a household. The more vulnerable a family is, the more damage will

occur (Downing et al., 2001). An example would be the flooding of a field. Gilard (2016) stated that the

potential water intrusion from a river overflow would be considered as a hazard. This hazard can in

effect be quantified by parameters as water level, duration, velocity of the water and duration of the

flooding. Furthermore, the land is characterized by a vulnerability. The susceptibility of the land

depends on the use (crops, management…). Risk can then be defined as the combination of hazard

and vulnerability (Gilard, 2016). The risk is then described as the expected losses due to a hazard and

the vulnerability of a certain household.

Although risk affects everyone in the society, smallholder farmers are more severely affected.

Smallholders live in more precarious conditions which make them more vulnerable (Harvey et al.,

2014). They have less buffer to fall back upon (Heinemann et al., 2011). Furthermore, Harvey et al.

(2014) found that small-scale farmers lack safety nets they could fall back upon. For example, most

smallholders do not have access to a formal banking system and are not able to access credit or loans.

These institutions could nevertheless be helpful in times of need. Instead of formal safety nets,

smallholders now rely on informal supportive systems such as borrowing money and food from friends

and family.


How do smallholder farmers handle risk

Farmers are heavily affected by risks. But they are able to endure those risks. Therefore, several

strategies can be identified. First there are the coping strategies. These allow farmers to react to

shocks. But there are also adaption strategies. These strategies reduce the possibility that shocks

heavily affect the household.

Coping strategies are typical to smallholders are diversification, buffering and social relationships

(Harvey et al., 2014; Heinemann et al., 2011; Woolcock, 2001) . These are all of higher importance for

small-scale farms than for large farms. The reason for this is the lack of capital those smallholders have.

As adaption strategy, diversification is also very important. Furthermore, management of natural

capital and increasing off-farm income are mentioned (Tazeze et al.). Both the coping and the adaption

strategies are clearly important for smallholders to handle risk.

2.4 Resilience

2.4.1 Definition and importance Resilience is derived from the Latin word “resilire”, which could be translated as bouncing back, or

recovering (Darnhofer, 2014). Holling (1973) first described resilience as “a measure of the ability […]

to absorb changes of state variables, driving variables, and parameters, and still persist.” While this

definition was intended for the field of ecology, it is seen as the first definition of the broader concept

of resilience as well. More recently, Walker et al. (2004) defined the concept as “the capacity of a

system to absorb disturbance and reorganize while undergoing change so as to still retain essentially

the same function, structure, identity, and feedbacks.” Although the difference between both

definitions is limited, the emphasis shifted from absorbing towards changing capacity as a feature of

the system. Indeed, while Holling (1973) says that absorbing shocks will lead a system to persist,

Walker et al. (2004) adds the word “reorganize”, to state that adapting to a changing environment is

of importance as well.

Resilience should not be seen as a strict theory, but rather as a conceptual framework. This framework

would allow us to reflect on processes in new, more holistic and more dynamic ways (Darnhofer, 2014;

Davoudi et al., 2012). Also Fraser et al. (1999) argues that resilience cannot be seen as a trait but rather

should it be understood as set of conditions inherent to the system combined with the local properties

of the environment.

Because the emphasis on both adsorption of adverse effects and adaptive renewal (Walker et al.,

2004), resilience can be seen as a part of sustainability that does not focus on the equilibrium and

steady state processes as a goal (Darnhofer et al., 2010b). Holling (1973) furthermore claims that a

very resilient system can still be very unstable. This is indeed correct strictu sensu, as for example for

ecosystems, it is possible that an insect population declines heavily during unfavorable conditions, but

that it is also highly capable of absorbing these extreme fluctuations as a species: the insect population

will not become extinct (Holling, 1973; Watt, 1968).

The usefulness of the concept of resilience is evident. The new insights this concept brings can add to

the research on human-environment interactions and the stresses these cause (Adger, 2006).

Furthermore, resilience thinking introduced a new mindset that focusses on coping and adapting to

change rather than aspiring that systems are stable (Adger et al., 2005b; Folke, 2006). This approach

reveals the shortcomings of the traditional focus on stability and control (Milestad & Darnhofer, 2003).

In this light, Folke (2006) mentioned that resilience gives the opportunity to open up new paths and

processes. Resilience implicates innovation, because this is a prerequisite for adaptive capacity.

Innovation will foster new sources of income, and enhance current sources. In this sense, resilience


provides support in continual development. Yet it is important to know that resilience is not always

desirable. Some unwelcome ecological systems may indeed be very resistant and resilient (Walker et

al., 2004). For example, invasive species are very unwelcome for ecological and agricultural systems.

Yet, once they are established, it is often very hard to eradicate them again.

2.4.2 History of resilience 2.4.2.1 Ecological systems The concept of resilience is recently receiving a lot of attention. But already in the early 70’s, Holling

(1973) was the first to describe the term in the field of ecology. He argued that for ecological systems,

a view on stability had a lot of shortcomings. It is of more importance to measure the persistence of a

system and its ability to change then its competence to return to a steady state. Certainly in the light

of survival of organisms, this approach is useful: in Slobodkin (1964)‘s words, “evolution can be seen

as a game, of which the only gain is to stay in the game.” In this sense, persistence in the moves to

make, by maintaining flexibility, is a key feature. In this sense, resilience is more important than

stability.

Hollings’ paper, “Resilience and stability of ecological systems”, can be seen as departure from the

stability-based thinking. The paper challenged the notion that nature was organized around a unique

equilibrium and instead the multiple stable attractors approach was defended (Zebrowski, 2013). Since

an ecosystem is not organized around one equilibrium, the aim of protection should not be to stabilize

it, but to make it more able to cope with and adapt to changes (Holling & Meffe, 1996).

As stated above, ecosystems do usually not have one stable equilibrium. Rather do they have several

stable regimes and will they always be pushed away from these stable regimes by perturbations.

Ecosystems will then tend to return to these initial equilibria, but never will constancy be reached

(Gallopin, 2006; Holling, 1996). Resilience can thus be defined as the ability to remain in one system,

and to not flip into an undesired regime (Gunderson & Holling, 2002).

If an ecosystem suffers too much disturbance, it will switch to another stable equilibrium (Walker et

al., 2006). This is called a regime shift, a word also used in oceanography and first adopted by Scheffer

& Carpenter (2003) for ecosystems. These regime shifts change the function and structure of the

system as it was known before and may be reversible or irreversible (Walker et al., 2006). Often, there

are several possible regime shifts, depending on the different ecosystems, but in practice only a few

regimes will be feasable (Anderies et al., 2006). These shifts may thus both be desirable or undesirable

also depending on economic and ecological terms it is measured with (Walker et al., 2006). It is better

to avoid these regime shifts, as the new stable equilibrium is uncertain and might be highly resilient

and undesirable (Anderies et al., 2006).

From the new millenium onwards, it became increasingly evident that ecosystems are heavily

influenced by human activity (Berkes & Folke, 1998; Darnhofer, 2014). Therefore the focus on social-

ecological systems (SES) increased. The literature focused more on the interactions, feedbacks and

coevolution between the social and ecological aspects of a system (Darnhofer, 2014; Liu et al., 2007).

Anderies et al. (2006) defines SES as systems with interactions among three different factors. These

are (1) agents as plants, humans and microbes, (2) possible actions the agents can undertake in the

framework of their characteristics and (3) a physical environment wherein the interactions happen

(Anderies et al., 2006). In the literature, the complexity of these SES is described.


Because a combination of different fields of study is necessary, research on SES offer a unique

interdisciplinary vision on the topic (Liu et al., 2007). It was also the stepping stone for other fields such

as agriculture and development to use the word, as a SES can be interpreted very broadly.

2.4.2.2 Psychology The concept of resilience, which was found in the field of ecology, was later adapted to the field of

psychology. Both on the individual and the community level, the literature has used resilience as a

model (Darnhofer, 2014). The definition of the word is consistent with the description by Holling and

Walker above, but has slightly different nuances.

Individual resilience is defined by Luthar & Cicchetti (2000) as “a dynamic process wherein individuals

display positive adaptation despite experiences of significant adversity or trauma.” Thus, these words

define resilience as the capacity to stand up after a shock and to manifest positive outcomes. This

definition highlights, the dynamic and multidimensional scale of resilience (Brown & Westaway, 2011).

Also on community level, there has been research on resilience. This has been separated into two

strands: One that focusses on capacity of a collective to respond to slow change, and one that looks at

communities facing shocks (Darnhofer, 2014). In both strands of research, resilience is understood as

the ability to embrace change and to anticipate to unexpected events (McManus et al., 2012; Sorensen

& Epps, 2005).

2.4.2.3 Small and medium enterprises The third part of the literature on resilience is about small and medium sized enterprises (SME) and

farms. An SME is described by Bhamra et al. (2011) as an enterprise that has less than 250 employees

and where the annual turnover is not exceeding 50 million euros. It is to be mentioned that a lot of

research has been done on the way SME and farms cope with change, outside the framework of

resilience, although studying the same phenomena. There is thus substantial overlap between the two

fields (Darnhofer, 2014).

As SME account for a large part of a country’s employment, a country will be very much reliant on the

resilience of these enterprises. Of course, there has been profound research on these SME. Yet, the

literature on resilience puts much value on the theoretical models on SME, but it lacks empirical

evidence on how companies can achieve resilience (Bhamra et al., 2011).

In essence, the theory is to derive from earlier resilience concepts. Accordingly, Kitching et al. (2009a)

suggests that small businesses have the ability to influence their own resilience. The way this resilience

is achieved may nevertheless have an influence in the long term performance of the company (Bhamra

et al., 2011). Another research by Kitching et al. (2009b) argues that the degree of resilience of an SME

is dependent on the available resources and the ability of an enterprise to adapt.

Darnhofer (2010) did quite a bit of research on the impact of resilience on Austrian farms. For farms,

the focus is on slow resilience, that will help a farm cope with ecological, social and economic change

rather than shock resilience that is intended to help farms withstand fast shifts in the environment.

Furthermore, resilience of farms is not only on the scale of the enterprise. The members of the farm

family are all to be involved. Resilience should not only be applied on certain facets of the farm, but to

the farm as a whole. It is important to know that the farm should not only include the crops and

animals, but also the farming family and the cultural values the community has.


2.4.2.4 Differences and similarities One could say, resilience has gone a long way. It was initially used in the literature on ecology, while it

is now adopted to a lot of other disciplines, such as agriculture. While the focus in ecology was more

on buffering, the framework broadened along the way. Psychology added several factors to this initial

approach. First of all, more focus was on adaptation and transformation rather than only the buffering

part. Furthermore, in psychology, the theory was applied on people and communities, while in the

field of ecology, the emphasis was more on ecosystems.

The strand of literature on SME’s added to this the company aspect. This made the concept of

resilience very easily accessible for the field of agriculture. Moreover, in the SME literature, buffering,

adaptation and transformation are all prominent. All three strands have thus clearly added to the

literature of resilience and created a concept that fitted very well for agricultural systems.

Furthermore, it should not be seen as evident that a theory, which has its roots in the ecology literature

is now broadly used in a lot of fields. The concept of resilience has become a theory that can be applied

to nearly all strands of literature. Nevertheless, the concept of resilience is not an empty word. In the

literature, a theoretical framework that characterizes resilience is developed. This is, in contrary to

other frameworks mentioned in the literature9, of value for us, because it makes the concept of

resilience more clear-cut.

2.4.3 Theoretical framework The concept of resilience can be divided into three components: buffer capability, adaptive capability

and transformative capability. In the first definition by Holling (1973), buffer capability, then defined

as “persistence”, was highlighted. Later, the emphasis on adaptability and transforming capacity

became stronger, which culminated in the definition of Walker et al. (2004). Not only “bouncing back”

to an equilibrium, or buffer capacity is now seen as important, but also the two other elements are.

While buffering capacity is described as “the ability of the community to absorb impacts using

predetermined coping responses” (Cutter et al., 2008), adaptive capacity is described by the IPCC

(2001, p. 894) as “The potential or capability of a system to adapt to stimuli or their effects or impacts.”

Transformative capacity is described as “capacity to create a fundamentally new system (Walker et al.,

2004).

Indeed: buffering, adaption and transformative capacity are the main capabilities that should help and

strengthen resilience (Béné et al., 2012; Keck & Sakdapolrak, 2013). But what exactly is meant by

capability? Following Darnhofer (2014), the word denotes the ability to see opportunities instead of

an inherent asset or characteristic of a system. It is the capacity to take action rather than an intrinsic

characteristic of the system. The three capabilities within resilience can be quite confusing. For this

reason, a summarizing table is inserted underneath (Figure 2), to point out the key characteristics of

each capability. Further in the text, each characteristic will be looked deeper into.

9 Concepts such as the adaptive cycle, panarchy and self-organization are mentioned in the literature, but are considered to theoretical and not useful for this master dissertation.


Figure 2: summarizing table of the different capabilities of resilience. Based on sources in the text below

2.4.3.1 Buffer capability The ability to withstand any disturbance without changing the structure of a system is called buffer

capability (Darnhofer, 2014). It is the amount of change a system can undergo before it flips to a

different state, which can be desirable or undesirable. This undesirable state can be seen as a shift

away from the known structure, function and identity of the system (Speranza, 2013). This explanation

is quite abstract, therefore, we will look into an example to make the concept clearer. A case by

Jørgensen & Mejer (1977) from the field of ecology will be used. He wrote about the response of a lake

to increasing phosphorus pollution. The concentration of phosphorus in the water does not change,

because the sediment has a storage capacity. Yet, when the buffering capacity of the sediment is

exceeded, the concentration of phosphorus in the water increases rapidly. It is clear that the lake has

a certain buffer capacity. Yet, once the buffer capacity is exceeded, the shock does affect the lake.

Although this example is very illustrative and not in our field of research, the concept is also applicable

for farmland and livelihoods as well as climate shocks and economic crises.

Buffer capacity is of importance for farmers. It can avoid the flip towards an undesirable system by

steering the system away from the tipping point, whereby the current desirable system is maintained

(Darnhofer, 2014; Darnhofer et al., 2010b). For the household, it is important that a livelihood can

maintain its core functions and cope with disturbance without undergoing declines in production

(Speranza, 2013). Nevertheless, it is probably of higher importance to buffer small shocks than to

withstand greater crises (Darnhofer, 2014).

Several measures for buffer capacity have been found. These will only be mentioned briefly here and

will be discussed more elaborate later in the text.

- Moser (1998) found that a main mean of buffering towards shocks are the capitals and their

entitlements, as well as the financial means one already has. These can be mobilized and used

at time of crisis.

- The second measure could be conservation. Darnhofer (2014) and Rose (2009) describes this

as maintaining production with fewer inputs. Often, it is possible to cut back on certain

avoidable costs. This makes the production more efficient. Yet, this might be unsustainable on

the long run. One might for example decide to cut back on vaccination costs, because these

costs don’t cause trouble in the short run. Nevertheless, they might increase costs in the long

run.


- Furthermore, higher input of family labor is often used as a measure to cope with shocks. More

women and children would join the labor force, while they were not working before the shock.

This proves how important labor is as an asset. (Moser, 1998).

- Finally, there is a social aspect to buffering. Relationships and networks can help people to

buffer shocks in time of need. Trust and cooperation as well as financial help can be provided

by social relations (Moser, 1998).

2.4.3.2 Adaptive capacity That adaptation is a key aspect of resilience seems evident. Rose (2009) even links it to the Darwinian

idea: “a species that cannot adapt is unlikely to survive.” Indeed, adaptive capacity of a system is a key

feature to persist. Following Folke et al. (2010), adaptive capability is to be defined as the capacity of

a system to adjust, while facing changing external forces and internal processes. It is important to know

that although change is embraced, the changes are rather small, and the system still stays in the

current regime (Berkes et al., 2003). The flexibility to experiment, learn and develop appropriate

responses towards shocks are important (Walker et al., 2002).

Resources and entitlements, as well as knowledge, experience and perception are recognized as

prerequisites for adaption to shocks. These traits can make a household more proactive and

anticipatory. In the face of the highly uncertain and dynamic farming environment in particular, this

can be of high interest (Eakin et al., 2016). Examples of these small adjustment that represent the

adaptive capacity are changing technologies, new marketing channels and the pooling of resources in

a cooperative (Darnhofer, 2014). These examples make it clear that the system itself does not change.

A farm will still produce mostly the same, on more or less the same way as it was before.

Experimentation is the keyword in this process. Bricolage, as Senyard et al. (2014) calls it, fosters

innovation. This innovation will create new pathways and opportunities to endure shocks. Also,

flexibility and diversity will promote adaptability. Darnhofer et al. (2010a) stated that very simple

specialized and almost “engineered” farms have a lower adaptive capacity because they are so

specialized. Therefore, smallholders, with their diversified portfolio of activities and often low-tech

equipment, have a great potential for adaptive capability.

Following on this example of Darnhofer, it needs to be mentioned that greater adaptability might lead

to lower efficiency (Papachroni et al., 2016). Maximizing cash flow in the short term might endanger

long-term financial viability of a system (Darnhofer et al., 2010b; Mayumi & Giampietro, 2001). If a

farm would for example only grow the most profitable crop, it does not have any other farm income

to rely on, if the yield is bad. Therefore, it might be interesting to grow multiple crops: while the total

income of a farmer might be lower on average, the volatility of the income will also be lower, because

other crops can buffer bad yield in one crop.

Moreover, an increase in adaptability to cope with one kind of shock may lead to a loss of adaptability

to other sorts of shocks (Walker et al., 2006). An example here would be the potential trade-off

between economic resilience and vulnerability to pests. For example, organic crops that could give

better and more stable returns to the farm, might be more vulnerable to pests, because a lot of

pesticides cannot be used in organic agriculture.

Also for adaptive capability, there are strategies that should be followed. Examples of measures are

diversification and learning. Nevertheless, some characteristics that will create a greater adaptive

capacity are inherent to the farm and cannot be changed in the short term. Examples of these

characteristics are economic well-being, geographical factors and natural resources (Adger et al.,

2005a).


2.4.3.3 Transformative capability Sometimes, shocks might be so significant that former activities and income generation becomes

untenable. At this moment a fundamental transformation is needed (Eakin et al., 2016). Walker et al.

(2006) defined this feature as “the capacity to create a fundamentally new system when ecological,

economic or social conditions make the existing system untenable.” In this new system, different

factors become important. The change is definitely not marginal, and can be called qualitative. It is as

if new rules of the game are applicable (Darnhofer, 2014). This is accompanied by a rediscovery of the

work and a shift in perception of opportunity (Coquil et al., 2014; Folke et al., 2010). An example of a

transformation, mentioned by Cumming (1999), is the conversion of a cattle farm on rangeland to

ecotourism. Very much does then change on the farm. The farm may quit to breed animals, will have

very different labor and a changed income pattern. This big transformation will often (but not always)

be a gradual process over time, rather than a sudden shift (Darnhofer, 2014). Consequently, a clear

break between the previous and the new farming system cannot be observed. During this process, one

can expect uncertainty and upheaval, because a lot of conflicting processes will occur (Reghezza-Zitt

et al., 2012). While the change is ongoing, it might be unclear whether the change will be substantial

or rather marginal (Darnhofer, 2014).

The reason for this transition is often a shock that affected the household, although gradual insight

into the fact that the current system has failed might also be important. Furthermore, the decreasing

social acceptance can also be a trigger (Scoones et al., 2007). A shock usually represents harsh times,

but Folke et al. (2010) also argues that it might open up opportunities for innovating towards a new

and more desirable system. A crisis is also the time when the household agrees that a part of the farm

is dysfunctional. Reasons can be work overload, excessive debt or environmental issues, for example

(Darnhofer, 2014).

Another reason for transformation is the return to normal after a disaster such as hurricanes.

Darnhofer (2014) questions whether a return to normal is desirable. A transition towards a new system

that is more adapted to the challenges facing the system is probably preferable. An example would be

the transition towards a more hurricane resilient community after the disaster.

Walker et al. (2006) found several determinants of transformability. One would be the incentive to

change. For example subsidies from the government will make more smallholders transform their

farm. The willingness to experiment is also mentioned as an important determinant. This is closely

related to the “bricolage” from Senyard et al. (2014), mentioned above. With this “bricolage”, testing

and prototyping are meant.

Another determinant would be the amount of social, human, natural and economic capital a household

has. It might indeed be easier to convert a farming system if the farming household is relatively better

endowed in assets, because investment is often needed to change (Walker et al., 2006). It is a fact that

it will be harder to transform a farm if one is not well endowed. Transformation indeed does demand

the investment of capital. Anderies et al. (2006) added to this that the social memory and the

understanding of the limitations of the current system and the possibilities with another system are

important.

Albeit the difference between adaptive and transformative capability should be clear, Martin-Breen &

Anderies (2011) argued that the a transformation would rather look like an adaptation, if considered

in another time scale. This is also because the different capabilities are rather a conceptualization

made by researchers than a distinct property of the system. In this way, the choice for an adaptation

or a transformation has a lot to do with the analyst’s perspective and for some changes on the farm,

both concepts would fit.


One might wonder why transforming to a different state is desirable, while it is mentioned above that

resilience is all about preventing that the system would shift to an undesirable state. It is crucial to

know that some alternate regimes may be desirable and some may be undesirable. Whether a given

regime is conceived as desirable will depend on the person and his position in society, and on the way

that is looked at the regime. For example, an assessment of on ecological, social or economic terms

will differ highly (Walker et al., 2006). With transformation a proactive approach where one can choose

the direction of the shift is represented. The alternative is letting the parameters of the disaster choose

which direction the shift will take.

We have now explained the different capabilities to a certain extent. Although the three capabilities

might seem distinct, they are much interconnected and there is a lot of interaction. The interactions

between the different capabilities will now be distinguished.

2.4.3.4 Integration of the three capabilities It might be counterintuitive that adaptation and transformation are also essential to maintain

resilience, because change (adaptability and transformation) and persisting (buffering) seem to be

opposing terms (Folke et al., 2010). Yet, resilience depends on a balance of the three capabilities

(Darnhofer, 2014). Shocks come in very diverse ways, intensities and scales and thus require different

levels of resilience. Therefore interventions that strengthen all three components are required (Béné

et al., 2012). An approach where buffer, adaptive and transformative capability are maximized will

indeed generate the most resilient system.

Although the literature agrees that all three capabilities are important, there is ongoing debate on the

synergy or competition between the different dimensions (Béné et al., 2012). An example of a synergy

is the accumulation of marginal changes (adaptation) that ends as a real transformation of the farm

(Darnhofer, 2014). On the other hand, adaptation might also lead to specialization that hinders both

buffer and transformative capacities. Buffering capability might suffer because specialization will lead

to more volatility, as mentioned above. On the other hand transformative capability might suffer

because it is hard to create transformative change starting from a very specialized system.

Allison & Hobbs (2004) examined a case of the so-called “lock-in”. He says that if in agro-ecological

systems nutrients are mined away, the economic returns will diminish, while being mined away even

more to keep the ongoing production. In the end, the system will become impoverished and very

immovable. The system will be resilient in the sense that it will not easily return to the previous state,

because it is poor in resources. This is called a “lock-in”, because a possible flip towards the old state

is not likely.

Although there are certainly trade-offs and tensions between the different capabilities, seeing the

dimensions completely separately neglects the possible synergies between the different capacities.

The separation approach misses the interdependence that exists between the different dimensions

(Béné et al., 2012)

A final remark on the dimensions of resilience is that perception and context play a significant role in

the vision on resilience and the strategy pursued. The strategy that should be pursued is seldom self-

evident, as one has to deal with scarce resources. Moreover, the family members, each with their own

vision, have a big impact on the situation (Darnhofer, 2014). Therefore, the weight given to the

different dimensions is often ambiguous. Also the context is important: both higher and lower scales

will have a meaningful impact on the resilience of a certain scale. The higher scale will influence the

ability of a farm to be resilient. For example policies in a country will have a big impact on farming.

Agricultural policies might greatly affect, but not determine the ability of farms to cope with shocks


(Darnhofer, 2014). Also influence from lower scales might affect the higher scale. The resilience of an

animal towards shocks might for example greatly affect the management of the whole farm.

2.5 Indicators for resilience

In the chapters above, the concept of resilience has been described in depth. Yet, this does not make

us able to measure resilience. As measuring resilience is one of the key outcomes of our research, we

will now look into different indicators that might help us to measure resilience.

There is quite a lot of literature on resilience. Much attention is given to the theoretical concept and

the significance of the approach. Yet, the literature on how resilience can be enhanced is limited

(Tambo & Wunscher, 2017). Still, several sources have made a framework with different indicators to

measure resilience (see Gbetibouo et al. (2010), van Oudenhoven et al. (2011) and Ifejika Speranza et

al. (2014)). Yet Bhamra et al. (2011) also argue that more empirical research is needed to make the

theory of real value. Therefore, several indicators for resilience will be defined. All of them were

already described in the literature. Because resilience is such a broad concept, a list of indicators can

never be complete. Nevertheless, a series of the important characteristics of resilience, found in

different papers and deducted from the theory will be listed.

The first indicators can be found in the “sustainable livelihood framework”, as described by the UK

department for International Development. In this framework, the different sorts of capital are

described. This is already touched upon in the chapter on smallholders, but now the potential of these

capitals to increase resilience will be examined.

2.5.1 Human capital As already mentioned above, human capital can be divided into two parts: knowledge and skills &

health. Both factors can be translated in the amount of labor that is available for a household (DfID,

1999). It might seem weird to reduce these factors to labor, but the approach is very useful. The more

human capital one has, the more labor can be used. For example, a sick household member will reduce

this amount of labor. Also an uneducated person will not be able to create as much value within the

hours he works. In the following, both parts of human capital will be discussed separately.

2.5.1.2 Education The first part of human capital its importance for resilience is straightforward. Moser (1998) confirmed

this with the statement that the education level of household members is immediately linked to the

income level and also to the probability of falling below the poverty line. This is important for their

resilience, as this has an immediate effect on economic capital, which is also a part of resilience.

Yet, not only proper school education but also site-specific knowledge is needed. This can be gained

through experience in the specific environment where the farm is situated in. For example migrant

farmers tend to lack knowledge of the new environment they arrived in. This can be deducted from

the fact that they use seeds and techniques that do not fit the new location’s agro-ecological conditions

(Elbers, 2002). This site-specific knowledge can be seen as a memory of the dynamics of the local

environment. The better this memory, the better a household will be able to cope with shocks (van

Oudenhoven et al., 2011).


In practice, several estimators10 for education can be proposed.

- Gbetibouo et al. (2010) argue that the literacy rate of a household would be a good proxy for

the level of education, willingness to learn, and other skills. Yet, this test can only take two

values for a person, which are “not able to read and write”, and “able to read and write”. This

is a disadvantage. Nevertheless, if the average for all household members would be

considered, a continuous variable could be obtained.

- Also the percentage of children in school was suggested by Glewwe & Kremer (2006), because

of the abundance in the literature and the availability to compare.

- The education level of the household head was also proposed. This seems to be the most

traditional manner to estimate the education level of a household (Asfaw & Admassie, 2004).

- Finally, the average education level of all members in the household should be researched.

From the literature it is also clear that this is a good estimator for education (Jolliffe, 1997,

2004).

2.5.1.3 Health The other part of human capital is health and nutrition. Although the literature on health as a part of

human capital is much more restricted, it is of major importance for our research (Becker, 2007). The

health status of a person is very important because it makes one able to strive towards livelihood goals.

Also for this indicator, certain estimators might be interesting to examine. Although several indicators

have already been developed, compared with education, indicators for health might not be as easy to

assess (DfID, 1999). The following indicators have been found in the literature.

- Because of the high incidence of HIV in South Africa, the prevalence of this disease is also used

as a proxy for health status. In this country, it is assumed that these households are overall

more vulnerable and food insecure (Gbetibouo et al., 2010). Yet, because this estimator does

not acknowledge any other health issues, there might be a certain bias.

- DfID (1999) mentions that the life expectancy rate would be a very good estimator. Yet, they

acknowledge the difficulty to assess this indicator.

It is of course possible to create another indicator, based on the questions in the used survey.

2.5.2 Social capital The second sort of capital, social capital, is possibly the most widely accepted measure to cope with

risk. The social resources one can fall back upon are seen as a major safety net for smallholder

households.

Poor smallholders often have a great deal of social capital. These include mostly informal relations

between families, but also broader networks such as community organizations (Martin-Breen &

Anderies, 2011). These social structures can help families with support (in cash or in kind), access to

loans and insurances (Mausch et al., 2017).

The community around people will as well be an important buffer for households. Moser (1998) calls

social relations “shock absorbers”, as they can reduce one’s vulnerability in the short term. Also, the

dynamics of a community are important, because they impact the influence a shock has.

Social capital might seem as a quick win for smallholder farmers, as social behavior is inherent to

humankind. Nevertheless, social resources cannot be taken for granted. Under harsh economic

conditions, both an erosion and a consolidation of social capital could be observed (Moser, 1998). An

example will clarify this. In Zambia, during harsh times, on the one hand growing relations of trust are

seen among women, because they all experience the same shock. Yet, on the other hand poor women

10 The word “indicator” is used for a measure that tests a certain component of resilience, while the word “estimator” is used for the test to estimate the score for a certain indicator.


in certain communities have ceased to borrow from neighbors, as they are increasingly incapable of

repaying them (Moser, 1998).

The concept of social capital is evident. The implications for resilience are also clear. But the

quantification of the concept remains difficult (Adger, 2003). The amount of networks and reciprocal

bonds are not easy to measure. However, some proxies are proposed.

- Gbetibouo et al. (2010) suggests that the farmer organizations can definitely be used as proxy

for social capital for smallholders. For example the membership in these organizations can be

used. These organizations help to inform farmers, facilitate cooperation and help farmers with

their constraints.

- Ifejika Speranza et al. (2014) and Paxton (1999) argue that the number and the type of groups

in which a household head is member and also the degree of participation can be very useful

as an indicator for the social capital of a household. Narayan & Pritchett (2000) conducted a

survey in Tanzania and asked the number of groups and which groups an individual was

member of. These groups did not have to be farming-related, and could thus also be a church

group, a women’s society or a political party. Furthermore also commitment made to the

group was asked.

2.5.3 Natural capital Natural capital is capital that is provided by natural resource stock. These create streams of resources

and services (DfID, 1999). It might seem as natural capital is only of importance in natural

environments, but also in traditional agricultural systems, there is an ecosystem function. Through a

variety of practices this stock in resources is maintained and only if certain sorts of farming are

executed, this landscape and its ecosystem will be preserved (van Oudenhoven et al., 2011).

Biodiversity is of significant influence on resilience of agro-ecosystems. If different species respond

differently to environmental fluctuations, they can compensate for each other (Adger et al., 2005a).

An example of a degenerated ecosystem that had a big impact on the local households is the

deforestation of mangroves for intensive shrimp production in Asia. Deforestation has reduced the

livelihood options for the local farming communities, as they could not rely anymore on the natural

environment (Adger et al., 2012).

Natural capital is important for all three components of the resilience framework. It helps to buffer

against crises, because nature can absorb shocks to a certain extent (Jørgensen & Mejer, 1977). For

adaptive and transformative capability, natural capital is a prerequisite, as it is seen as inherent to a

farm (Adger et al., 2005a). Furthermore, natural capital can also be seen as an asset, which can be used

to change the farm (Walker et al., 2006).

Although it is clear that natural resources matter for local farmers, the way to measure this is not as

clear. Yet, Cabell & Oelofse (2012) suggest that slow changing variables as soil organic matter and

biodiversity should be enhanced, because they are the foundation of sustainable agro-ecosystems.

Therefore, it might be used as an indicator in our framework as well.


2.5.4 Economic capital Both of these are major means to buffer shocks. Households can manage these assets in the face of

hardship. It is also clear that the more economic assets one has, the less vulnerable towards crises they

are (Moser, 1998). Not only can economic capital help to buffer shocks, economic capital can also be

a stepping stone to change the farm (Walker et al., 2006).

Moser (1998) furthermore describes that the capacity to manage assets and to transform them into

income and basic necessities is of importance to increase resilience. If money is not well spend, you

might as well not have had it.

Economic capital might be easier to measure then the other ones.

- Gbetibouo et al. (2010) suggest that farm income might be used as an indicator to measure

resilience. This does not consider how the income is used (whether it is saved up, or spend all

at once), but can surely be used as a proxy for financial capital.

- As physical capital would not be considered in the case mentioned above, it might be

interesting to measure the amount of infrastructure of a farm as well.

- The last indicator is the wealth index. This index is a quite complicated index, made by the

United States Agency for International Development (USAID) to measure wealth without data

on income or consumption (The DHS Program, 2018b).

2.5.5 Diversity Not only are the sorts of capital important. Other features as diversity and innovating capacity should

be considered. These characteristics cannot really be placed into the different capitals but are very

closely linked to some of them.

Diversity is one of the main strategies to confer resilience at time of crises. If a certain level of diversity

is maintained, there are for example more choices for transformation of the farm (Darnhofer et al.,

2010b). If one source of income fails, one can rely on other channels of income and even spend more

time on other activities to ensure a stable income (Mausch et al., 2017). Diversity will be divided into

two parts: diversifying on farm and diversifying of farm.

2.5.5.1 Off-farm income Off-farm income is a part of diversity and therefore of a more resilient household. Engaging into non-

farm activities can help a farm to buffer, but can also create a cash income that help a farmer to make

investments on the farm (Ellis, 1998). These investments can help to adapt the farm and make it thus

more resilient.

The share of total income that comes from non-agricultural activities can be used as an indicator for

off-farm income. The lower the relative amount of income out of agriculture is, the more off-farm

income is gathered. This could reveal the ability of farmers to respond to reduced income out of

agriculture (Gbetibouo et al., 2010).

2.5.5.2 On-farm diversity On farms, crop diversity enhances the capacity to cope with several risk factors such as pests, yield loss

and low prices. Furthermore, Darnhofer et al. (2010a) argues that diverse farms keep more options

open to change the farm, compared to very specialized farms.

As an estimator for on-farm diversity, the amount of crops planted on the farm could be interesting to

measure. It might seem likely that farmers would choose for several crops on the farm, for reasons as

crop rotation and risk management. Yet, Lin (2011) argues that economic incentives might encourage


production of only a select number of crops, instead of a very diversified portfolio. It can thus be

interesting to measure the differences between farms on this aspect.

2.5.6 Innovation Farms innovate constantly. A farm can apply more efficient milking technologies, test new methods to

more efficiently plan the labor throughout the year or cooperate with other farmers to find new

marketing strategies. All of these can be called innovation as they improve the products and services

a farm delivers. Innovation can be described as the improvement of the outcomes of farms, with

technical, managerial and social means (Woodward et al., 2008).

The importance of innovation for resilience may not be as clear. Yet, there are several reasons why a

farm should innovate. Fan et al. (2014) argues that certainly for resilience-orientated innovations, the

outcome could be less vulnerable households, if implemented well. Furthermore, innovation also

fosters adaptive and transformative capacity. Innovation might indeed increase farm resilience

allocation of resources to innovation at good times might help farmers to overcome temporal setbacks

(Reinmoeller & Van Baardwijk, 2005). Also, if a farms for example decides to apply innovative

techniques at times when no shocks occur, it might add to the adaptive capacity at times of crisis.

For innovation, just as for other indicators, measurement is not straightforward, as innovation can be

very diverse and in several sections of the farm. Iglesias et al. (2009) proposes fertilizer consumption

and use of agricultural machinery as proxies. This does not acknowledge that innovation can also take

place in the non-technological part of the farm, but it can be assumed that if some common

innovations are present, others can be expected as well.

All of the above mentioned indicators will be useful to test the concept of resilience. In the following

chapters, an indicator framework for resilience will be set up. It will be based on the literature study

above, but practical implications ask for a pragmatic approach. Therefore, sometimes not it will not be

possible to do as the theory describes.

Chapter 3: Methodology 31

Chapter 3 Methodology

3.1 Kagera Health and Development Survey (2010)

Now that we have defined the concept of resilience and characterized its framework, it is time to come

to the second part of this master dissertation. An objective of this research is to create a tool to

measure resilience. Open source data from a Livings Standards Measurement Study (LSMS) survey was

used. More specific, we worked with the Kagera Health and Development Survey (KHDS) 2010. This

was the sixth wave of the survey.

In the following, the survey, and its characteristics will briefly be discussed, after which an in depth

view on the method of research and indicators will be given. Finally, the method of analysis of the

results will be explained.

3.1.1 LSMS surveys and the Kagera Health and Development Survey When LSMS surveys were established in 1980, the main goals of this surveys were developing new

methods for monitoring progress in raising levels of living, and improving communications between

different researchers. Therefore, multi-topic questionnaires with an extensive quality control were

created (Grosh & Glewwe, 1995).

The Kagera Health and Development Survey is one of those LSMS surveys. This survey was used

because of several reasons. First of all, the survey is a complete study in Tanzania’s Kagera region. (De

Weerdt et al., 2012). Also, the long record of the survey indicates that there is already quite a bit

experience. Because of this, it can be expected that a lot of shortcomings are resolved. Finally, the fact

that this survey analyses the households on the shocks that are used is very interesting for this

research.

The KHDS was developed from 1991 onwards to measure the economic impact of deaths of adults on

surviving household members. As adult deaths in a household are a quite rare event (seen on a yearly

base), a stratified design11 was chosen (World Bank & University of Dar Es Salaam, 2017). Because it is

chosen to sample in strata, less variance can be expected. Thus, this stratified sampling made it easier

to see the effects of adult deaths in a household.

The initial sampling in the KHDS survey in 1991-1994 was drawn in two stages. In the first stage, 550

primary sampling units (PSU) were classified according to eight strata: four agronomic zones were

used, and within each zone the labels high or low adult mortality were given (World Bank, 2004). PSU

are small villages or neighborhoods. From these 550 PSU, 51 were finally selected. Within each of these

PSU, sixteen households needed to be selected. This was also done by stratified random sampling. It

was asked to the households in the PSU whether somebody in the household has passed away or was

sick. With this information, the strata “sick” and “well” were created (World Bank, 2004). From the

sixteen households to be interviewed, fourteen were selected from the “sick” households, while two

were sampled from the “well” families. In total 816 household were interviewed initially (EDI, 2015).

11 A stratified sampling design samples within strata, so the different strata each are more homogenous (Vermeulen & Thas, 2017).

32 Chapter 3: Methodology

In addition, 24 extra households were surveyed, and respectively 46 and 29 households were added in

the second and third passage of the survey. This made a total of 915 respondents in the initial survey

(World Bank, 2004). As the different household members founded new households over time, the

amount of households that were interviewed grew to 3314 in 2010 (Kofol & Naghsh Nejad, 2017).

The KHDS 2010 survey compromised four questionnaires: the Household Questionnaire, the Wedding

Questionnaire, the Mortality Questionnaire and the Sibling Roster Questionnaire. For this research,

only the household questionnaire was of importance.

The Household Questionnaire is the largest of the four questionnaires. It covers numerous topics. The

survey has ten sectors, each with several subsectors in each sector (De Weerdt et al., 2012). Mostly,

the parts that really characterize the abilities of a household or a household member were important.

These can be translated into indicators, such as education and health. Obviously, the section on the

events a household encounters (shocks) is also of high importance. The survey was not conducted over

all of Tanzania, but only in the Kagera region. Therefore, it might be interesting to outline the area

briefly. This will be done in the following paragraphs

3.1.2 Kagera region: characteristics Kagera is a region in Tanzania, bordering with Rwanda, Burundi and Uganda. Kagera is the furthest

away from Dar es Salaam, which is the administrative center of the country. It is also isolated because

of poor roads and the location right in between neighboring countries and Lake Victoria. The region

nevertheless has a population of two million (Kessy, 2005).

Figure 3: Kagera region situated in Tanzania (Sémhur, 2009)

Although this region has an annual production surplus of 681 000 tons of starchy foods, it also has the

lowest gross domestic product (GDP) per capita in Tanzania. This results in 29 per cent of the

households living below the poverty line. Yet, the GDP per capita has grown significantly in the last

decade: in the year 2000, the GDP per capita was approximately 140 000 Tanzanian shilling (Ts. Sh.) ,

while in 2011 increased to 600 000 Ts. Sh.12 (AfDB, 2015).

The Kagera region has been severely hit by the HIV pandemic. It was one of the first regions to show a

number of cases in the 1980s. In the early years of the new millennium, the overall prevalence of HIV

12 1 EUR is 2733.57 Ts. Sh., therefore 140 000 and 600 000 Ts. Sh are respectively 51.22 EUR and 219.49 EUR (Exchange Rates UK, 2018).


amongst blood donors is still as high as twenty per cent. As a result, a large number of orphans are to

be found in Kagera (Kessy, 2005). It is estimated that 8.3 per cent of the children under eighteen in

Kagera are orphans (NBS, 2012).

3.1.3 Preliminaries of the sample As it is not interesting to sample every household in the survey, we only used a selection of households.

The households were chosen according to certain preliminaries.

The first preliminary was the fact that a household needed to have used land in the last twelve months.

Otherwise, the household could not be considered a farming household and therefore, not a

smallholder. As the scope of this research wants to measure resilience for smallholders, those

households would be useless.

The second preliminary deletes households that do mention the cultivation of crops in the last twelve

months but do not give any information on the plots used. In the creation of the indicators, it will prove

to be necessary to indicate the number of plots that are used by the smallholders. Those households

are thus eliminated from the measurement. From a total of 3314 households interviewed by the KHDS,

a set of 2886 households was finally retained for future analysis.

3.2 Indicators and their measurement

Above, the survey was described. Now we will look into the indicators that are used in this survey. In

this part of the dissertation, the choice of estimators will be discussed. Later, in the results section, the

analysis of the results will take place.

3.2.1 S.M.A.R.T. principles Before the indicators can be described, the criteria that are important, need to be mentioned. The

selection framework by Doran (1981) , who wrote about S.M.A.R.T. indicators was followed. This

expression is an acronym that stands for five words that are crucial to obtain good objectives. It

contains the following words:

- Specific: the indicator needs to be specific, and not broad or fuzzy.

- Measurable: an indicator that is not quantifiable is not a good indicator.

- Assignable: it needs to be clear to whom the indicator or objective can be assigned. This is of

interest in a company, but not in the case of a theoretical exercise such as this.

- Realistic: The indicator or objective needs to be realistic. It makes no sense to create indicators

that are unachievable.

- Time-related: it needs to be specified when the results can be achieved.

Although these criteria are more applicable to objectives than to indicators, the concept can be

extrapolated. These criteria will be used as a guideline for good indicators. Furthermore, also the

criteria relevance and representativeness will be taken into account. The difference between those

two criteria is that relevance means usefulness of an indicator, while representativeness touches upon

the possible bias an indicator has. Obviously, we want an unbiased estimator for the indicator.

Another thing to mention is the fact that sometimes a proxy for a certain indicator might be used,

rather than an indicator itself. This could be seen as “pars pro toto”, because only a certain aspect of

the indicator is measured, and it is expected that this is representative for other aspects of the

indicator as well. In this context, a proxy can thus be seen as an approximation of a normal indicator

(European Commission, 2016).


Finally, it is important to keep in mind that all our indicators are household based. This is important,

because it is not self-evident. Some indicators will be only measured on certain household members

instead of the household itself, but every time, it will be extrapolated to the household level.

3.2.2 Human capital – education The first indicator to take into account is the education of a household. In the chapters above, the

crucial importance of schooling is already mentioned. Yet, the measurement of education is not as

easy. First of all, there are several members in a household, each with their own level of education.

Secondly, it is not easy to give the different levels of education adequate scores. Because the Tanzanian

education system might not be familiar, it is briefly explained in Annex 3.

Several estimators for this indicator that were proposed in the literature study were tested. To begin,

the percentage of children that are in school was analyzed. This estimator was recommended by

Glewwe & Kremer (2006). Yet, because the distribution of this estimator was not good (i.e. nearly all

children were in school), this estimator was withdrawn.

Secondly, the education level of the household head was considered. This is the most traditional way

to measure the education of a household. Yet, Jolliffe (1997) states that this would not be a good proxy

for the education level of an entire family. Furthermore Asfaw & Admassie (2004) suggested to

measure the education level of other household members as well, because there might be

considerable spill-over effects from the other household members on the household’s head decision.

They researched the effect of the household members’ education on the adoption of fertilizers and

found a significant effect of the education of the different household members and therefore argue to

not only measure the education level of the household head. Finally Basu et al. (1999) suggested that

the literacy rate of certain household members will have a positive effect on the illiterate household

members. Yet, it is not specified whether or not this literate person is the household head.

Finally, the average education level of all members of the household was researched. All household

members that are older than eighteen are taken into account, to not have a bias for household

members that did not reach the highest level of education, which is university, yet.

There were 25 different levels of education defined: one within pre-school, seven within primary

school, four for secondary school, two for advanced level education13 and eight for university level

education. Furthermore, also two sorts of informal education were mentioned: adult education and

Koranic school. These different levels of education were all ranked with integer values, in which the

score for preschool is one, and the highest score was equal to 24. Going from the seventh to the eighth

year of university was thus just as valuable as going from primary school to high school. This was done

to maintain simplicity. Koranic school and adult education were placed in between pre-school and

primary school, as Barro & Lee (1996) suggest. In Table A.1 of Annex 1 a detailed overview of the

different scores for the different levels of education is given.

13 Advanced level education is the bridge in between secondary education and university level education.


3.2.3 Human capital – health The second part of human capital is the health of the households. Also for this indicator, defining a

proxy is not easy. Different sorts of sickness or injury should be ranked, or not ranked at all. For this

indicator, two proxies were considered.

The first one was the ranking the household head gave to the different household members’ health. A

score between one (excellent) and five (bad) could be given. This indicator was abandoned because it

was subjective. Therefore, we could not be sure whether a bias would be present.

A second proxy that was looked into was the amount of household members that have encountered

any short-term health problems (illness or injury) the last four weeks and the amount that has been

sick for longer than six months. It was chosen to give short- and long-term health issues the same

value, as no information for the severity of the health problem was taken into account. The long-term

problem might for example not hinder the labor potential of a household member, while the short-

term illness or injury might. Furthermore, it is chosen to count only once if there is both a short-term

and long-term health problem. As one problem might cause the other, this would put a bias on the

results.

This proxy is a dummy for each household member, i.e. a household member can only have the score

zero (no health problems) or one (health problems). Nevertheless, because the weighted average over

all household members is taken, the proxy is continuous.

3.2.4 Social capital In the survey, membership in two sorts of organizations are mentioned: funeral societies and rotating

savings and credit associations (ROSCAs). Those two sorts of organization are not known in Belgium

and thus need further explanation.

A funeral society is an association that ensures payout in cash or in kind when a household member

deceases and the funeral needs to be arranged. This kind of organization can be seen as an insurance

group that help to buffer the expenses of a household at times of need. These funeral societies can

also provide other insurances to households, although this is not general (Dercon et al., 2006). As with

any insurance, benefits of this insurance are not evenly spread among members. It is clear that a

person with age 80 is more likely to benefit than somebody who is only twenty. Yet, there is no age

barrier on funeral societies (De Weerdt et al., 2012). Nonetheless, an age barrier will be set on the age

of eighteen. This is in line with the other age barriers and avoids the bias of children not being part of

a funeral society.

ROSCAs are common informal financial institutions in developing countries. The basic principle is

similar in every country or region. The principle goes as follows: a group of people meet for a series of

meetings. At every meeting, each ROSCA member contributes to a common pot. This pot is then

awarded to only one group member. After this, the member is excluded from receiving the common

pot, although a contribution from this member is still expected. This process is repeated until all

members have received the pot (Ambec & Treich, 2007). As the age limit for ROSCAs in Cameroon is

eighteen, this age will be considered as the minimum age in the Kagera region as well (Etang et al.,

2011).

The sum of the amount of memberships in both funeral societies and ROSCAs is taken as indicator for

social capital. The higher this number, the better. Because one can be member of several funeral

societies and several ROSCAs, the theoretical maximum number of memberships is infinite.


3.2.5 Natural capital For natural capital, the problem was not the choice between the different estimators for an indicator,

but the lack of indicators. Indeed, the amount of questions that could be used to measure natural

capital in the survey is rather low. Nevertheless, it is important to know the natural capital of a

household to measure resilience.

The only question that touches upon the natural capital of a household is a poll on the soil quality of

the plots. More in detail, the household head is asked how he perceives the soil quality. Obviously, this

estimator is subjective. Yet, given the limitations of the survey, we will use this estimator anyway and

analyze it carefully. This question has three possible answers: bad (one), average (two), good (three).

Because this question is answered for every plot a household poses, a continuous indicator is obtained.

The weighted average is taken for every household. As different plots have different sizes, the size of

a plot is taken in account to have a correct picture of the soil quality.

3.2.6 Economic capital For economic capital, a lot of estimators can be thought of. Nevertheless, when these indicators were

tested, most of them turned out to be rather useless. I tested four different estimators for economic

capital. They will each be touched upon briefly.

1. A self-given score for wealth: this was abandoned because it is subjective and the distribution

was not good (i.e. nobody thought he was rich)

2. Economic capital divided into financial capital and physical capital

a. For financial capital, the possession of a bank account and access to credit could be

considered. Yet, most of the people answered positive. Therefore, the distribution was

skewed and not useful.

b. For physical capital an estimator is harder to find. There is a section on durable assets

a household owns in the KHDS, but it is hard to compare a television with a bicycle for

example.

3. The amount of hectares a household owns could be used as a proxy for economic capital.

Although there is considerable bias, as a household that is not well endowed in land might

have other assets that are not taken into account.

4. Therefore, the final indicator is the wealth index. This is an index, made by USAID to help

surveyors measure the wealth of households.

The wealth index is an aggregated index that takes into account different aspects of the household to

measure the wealth of a household. It was made in the framework of The Demographic and Health

Surveys (DHS) of USAID.

As the DHS did not collect any data on income or consumption expenditure data, it was needed to

create an alternative indicator for wealth. Therefore, the data on assets owned was used to predict

wealth (Rutstein, 2008). Furthermore, comparative research made clear that the wealth index and

expenditure data are equally valid to measure (Filmer & Pritchett, 2001).

One problem that immediately occurs is the aggregation of assets is the fact that it is not easy to give

values to assets of a household as these are ambiguous and highly differing. Therefore principal

component analysis (PCA) was used. This is a statistical approach to weigh the different components.

It extracts from a set of variables those combinations that are independent and capture the

information most successfully.

Finally, for every asset, two values are created. One value if the household does not own the certain

asset and one if it does own the asset. The assets used are very wide-ranging: car, water tap, toilets,


roofing, flooring, electricity and many other values are included. In total, 90 different values should be

aggregated after which the sum is to be seen as the total assets a household has. As the total is

standardized, the average household should have a score zero (The DHS Program, 2018b). The full list

of assets that was taken with is given in Table A.6 in Annex 1.

Because it was out of the scope of this research to make a wealth index ourselves, we decided to use

a wealth index created by DHS itself. The survey used was from the year 2010 and is sampled in all

provinces of Tanzania (NBS, 2010). The values to be used for the creation of the wealth index was freely

available online (The DHS Program, 2018a).

To couple the given values to the data in the KHDS, the same questions were searched in our survey.

Although most questions were similar, some disparities could be found. Mostly, these were only small

differences that are inherent to different surveys. To make clear where the differences are situated,

they were all indicated in in Table A.6 in Annex 1.

The data from the year 2010 was chosen, because the KHDS is also conducted in that year.

Furthermore, it is important to know that the wealth index is created for all of Tanzania, instead of the

Kagera region. The wealth data will thus not be fully representative. For this reason, some bias on the

sample might be expected.

3.2.7 Off-farm diversity Diversity is important for the resilience of a household. Above, two sorts of diversity were defined: off-

and on-farm diversity. Both of them will be measured. The first kind of diversity, off-farm diversity, is

the amount of income that could be derived from non-agricultural income sources. Because there was

no easily accessible data available on the amount of income from off-farm activities, it was decided to

take the amount of off-farm jobs and enterprises as a proxy.

This was done as follows: in the survey, respondents could describe two off-farm jobs and two non-

agricultural businesses. For each off-farm job or off-farm business, a point was given. Apart from this,

a farmer got an extra point if he had a non-agricultural enterprise. This was done, because it could be

argued that a person must show entrepreneurship and courage to start an enterprise.

Entrepreneurship fosters growth, because it intensifies competition and increases productivity (Acs,

2006). Furthermore, entrepreneurship is important for resilience: to begin with, it drives innovation

(Reinmoeller & Van Baardwijk, 2005). As well, Williams & Vorley (2014) found that entrepreneurship

is essential to sustain a dynamic economy and diversification plays a key role in this process. The

advantage of entrepreneurs for both the local economy and the household itself, is thus considerable.

Nevertheless, the second enterprise that could possibly be held by a family member does not get an

extra point. It is argued that an extra enterprise would not demand the double of entrepreneurship on

enterprise needs. Although the importance of entrepreneurship should not be underestimated, we

argue that it should not be double counted as well. A score between zero and five can thus be obtained.

The score is explained more in detail in Table 1


Table 1: score given for a certain amount of non-agricultural jobs and enterprises

amount of

off-farm jobs

Amount of

off-farm

enterprises

Score used

0 0 0

1 0 1

2 0 2

0 1 2

1 1 3

0 2 3

2 1 4

1 2 4

2 2 5

3.2.8 On-farm diversity The second kind of diversity is on-farm diversity. To measure this, it was decided to count the amount

of different crops and animals a farmer has. The higher the amount of crops and animals a household

has, the more diversified they are.

In total, there are 45 different crop options and eight different animals. All of these are named in Annex 1 (Table A.5). In theory, a farm can have 53 different options. Obviously, a farmer will not have all the crops and animals.

3.2.9 Innovativeness– pesticide use The last indicator that will be considered is the innovativeness of the household. Finding an estimator

for this indicator was not that easy. Not because of the abundance of estimators, but because of the

scarcity. Only two proxies could be considered.

The first one is the use of modern varieties14 in maize and sorghum. This seems an easy indicator, as

modern varieties are clearly innovative. Yet, not all farmers cultivate maize or sorghum. It is thus not

applicable on all households, which would make this a bad proxy.

The second estimator is the last time pesticides were used to fight a pest. Four options are given: never,

one year or more ago, one month or more ago, one week or more ago, yesterday. This question is

asked to every household, so it is more representative than the previously mentioned proxy. Finally, it

needs to be mentioned that the last two sections (one week or more ago and yesterday) were taken

together, to lower the effect of the time of visit. There are thus four options left.

3.2.10 Correlation tests To look whether there was correlation between the different indicators, the Pearson correlation test

was used. Yet, the Pearson correlation test assumes bivariate normal distribution. Because not all

estimators are normally distributed, it was decided to use a rank-based inverse transformation, to

make the values normal. This approach is suggested by (Bishara & Hittner, 2012), for sample sizes

larger than twenty. Afterwards, correlation matrices were created. Also the significance of the

correlation was tested.

14 Modern varieties are the outcome of scientific breeding and are characterized by a high yield and a high degree of genetic uniformity. In contrast, farmer’s varieties (also known as landraces) are the product of breeding or selection carried out by farmers. They represent higher levels of genetic diversity (FAO, 2018)


3.3 Research method

3.3.1 General outline of the research A part of the research question of this master dissertation was the measurement of resilience on the

dataset. To properly measure resilience, we need to find characteristics of smallholders that might

have a positive effect on the resilience of the household. In the literature, several possible

characteristics that might serve as an indicator were already found. These different indicators have

been transposed into estimators that can measure the components of resilience. All of these indicators

envisage a different facet of resilience. Thus, a fragmented figure is obtained.

To overcome this problem, an aggregated score was created. The sum of all eight indicators was chosen

because it makes households very comparable. This score lies between zero and eight. It needs to be

mentioned that this score should be observed critically. A very high score can for example compensate

for a very low score. The aggregated score does not give information on the different indicators.

Furthermore, the indicators were plotted in a radar chart. For each household one radar chart was

made. An example is showed in Figure 4. These radar charts give a good overview of the performance

of the household, compared to the other households in the dataset.

From these radar charts, certain groups were derived. On these groups, various tests concerning the

amount of shocks were executed. In the following sections, we will go more into detail on the

methodology, but first it is needed to describe the program that was used as well as the methods of

coding.

Figure 4: example of a radar chart with the eight indicators


3.3.2 R and the methods of coding The data was analyzed in R, because of several reasons. R is an easy to use program, which is specifically

made for data analysis. Furthermore it is open source, which might be interesting for later use of the

indicator framework by other stakeholders.

The data is extracted using several functions. The exact outline of a general function is explained in

Annex 2. When this function was executed, the data still needed to be cleaned and rescaled. For this

purpose, outliers were replaced by the mean for the indicator. A value was considered an outlier if it

was three times the interquartile range (IQR) or more above the third quartile or three time the IQR

or more below the first quartile, as Kirkman (1996) described .

Finally, the data is rescaled to a value between zero and one. This made the values comparable and

made it possible to plot them on a radar chart. With the value for the indicator, rescaling is done

according to the following formula:

scaled value = value − minimum

maximum − minimum

3.3.3 Creation of groups The radar charts are and the aggregated score are interesting to analyze on household level, but what

is even more intriguing is the relevance of the indicators to measure resilience. Therefore, several tests

to research whether households that perform better on the indicators can be considered as more

resilient. For this part of the research, the survey section on shocks was used, as households that are

better off, should experience less shocks and do better in general.

The first step to test the indicator framework, was the creation of groups. These groups were based

on the amount of times a household performs above the average for an indicator. A score between

zero and eight can thus be obtained.

Two groups will be formed: a “very bad” (VB) group, that has less than three indicators above the

average and a group that performs “very good” (VG), because more than five indicators are above the

average. This is done because the three outermost options are chosen (respectively zero, one and two

& six, seven and eight). This is illustrated in Figure 5.

These two groups are of different size because there are more households that are below the averages

than above. The groups of VB and VG are respectively the size of 540 and 270. Because the sample is

quite large we did not make the sample sizes for the VB and VG groups equal. Finally, these groups will

be analyzed to look whether there is a difference in amount of shocks.


Figure 5: distribution of the amount of times a household had a score above the mean of an indicator, compared with the

normal distribution. The different groups are denoted with the red lines.

3.3.4 Tests executed on the groups To test the relevance of the indicator framework, several tests were executed on both groups. All of

the tests had to do with the amount, the severity and the reasons a household perceived a shock.

Therefore, the part of the survey on “life events” was used.

Instead of indicating certain shocks, the survey asked how every year was perceived. Nevertheless, this

was not done mechanically. First, the surveyor asked for bad and good events in the past years. After

this, the changes that occurred in the wealth and living conditions a household had were looked into.

With this information, the household head of each household was asked to compare the years and

give as score based on this.

Although it could be said that these questions might thus be a bit subjective, they are objectivized as

much as possible. Moreover, data on shocks and how a year was perceived by a household are hard

to estimate completely. We could thus say that, given the limitations, this is the best test available.

The following tests were executed on both groups:

1. Difference between the amounts of shocks15 perceived from 2004 till 2010.

2. Reasons those shocks were perceived

3. The coping strategy used for a shock

4. The average score over seven years (years 2004 till 2010)

For the second and third test, a full table with the possible answers is given in the Annex 1, specifically

in Table A.7 and Table A.8. The last test might need an extra word of explanation. For every year, a

score going from “very good” (one) to “very bad” (five). For every household, a mean is taken over the

different years (EDI, 2015). When these means are compared between the groups, one might see

whether there is a real difference between the both groups.

15 From now on, a year that was indicated as a “very bad year”, will be described as a shock year, or a shock.


On the first and the last test, a Wilcoxon-Mann-Whitney (WMW) test was executed. The reason is that

the distribution of the groups is not always normal, and thus the primary assumption of a T-test cannot

be complied with. A WMW test is distribution-independent (Vermeulen & Thas, 2017).

Chapter 4: Results and discussion 43

Chapter 4 Results and discussion

4.1 Descriptive statistics

4.1.1 Analysis of indicators In the following section, the different estimators and their distribution will be analyzed together with

descriptive statistics and their meaning. The general statistics for all estimators are summarized in

Table 2.

Table 2: summarizing statistics for all indicators

Estimator Name Minimum Maximum Average Standard deviation

Mean level of education of household members HC 0 18 7.80 3.41

Mean health status of household members HC2 0 1 0.35 0.30

Membership in organizations SC 0 7 1.46 1.37

Soil quality NC 1 3 2.10 0.79

Wealth index WI -0.83 1.51 0.065 0.47

Off-farm diversity OD 0 3 0.77 0.65

On-farm diversity OND 1 13 5.31 2.54

Pesticide use INOV 1 4 2.29 0.96

4.1.1.1 Human capital – average education level of the household members. The first indicator that will be analyzed is education. This estimator can take a theoretical maximum of

24: this is the maximum amount of levels of education one can indicate in the survey. The score of 24

is given for someone who was in university for eight years. Nevertheless, the actual maximum value is

eighteen, which is equal to the second year of university. In this case, most household members have

thus followed university. Nonetheless, most of the respondents of the survey never get this far. The

average score is 7.80, which is the score for the sixth year of primary school. The precise value of each

level of education is given in Annex 1 (Table A.1), while more information on education in Tanzania can

be found in Annex 3.

From the distribution (Figure 6), the fact that most people only finish primary school is clear:

approximately half of the households in the sample had an average score of nine, which indicates that

they have finished the seventh and final grade of primary school. Just after primary school a lot of

people quit, because high school is not obliged and not free as well (World Bank, 2014).

All other levels of education are more or less equally populated, with the notion that more people

seem to be on the left side of the peak. The skewness has a value of -0.38, which confirms the fact that

the focus is more on the left side, and the symmetry is not complete. The Kurtosis has a value of 0.33,

which indicates that the curve is more centered, with relatively small tales. This confirms our

suggestion that more people had less education, and that a lot of people quit education after primary

school.

44 Chapter 4: Results and discussion

For the whole of Tanzania, the results we found are confirmed. Most people do attend primary school:

the enrollment rates are about 79 per cent (World Bank, 2014). Yet, for secondary education, the

participation rate is a lot lower.

Figure 6: distribution of the average education level of the households. The scores each present an education level.

Furthermore, it is interesting to know whether the household head had education and what the

highest level of education was. From this, it can be derived that only eleven per cent of the household

heads did not have education at all. The correlation between the test statistic and the question

whether the household head had education gives a score of 0.57. There is thus correlation, yet not

very big.

Also the highest level of education of the household head is researched. On Figure 7, it can be seen

that more than 50 per cent finished primary education. Overall, this chart is very similar to the

distribution of the estimator. The correlation of 0.81 only confirms this.

In 3.2.2, we advocated the use of the whole household to measure the indicator “education”. Yet, it

could be said that for this case, the sole use of the education level of the household head is sufficient,

as the level of schooling of the household head and the level of the whole household are heavily

correlated.


Figure 7: bar chart of the highest education level of the household head

4.1.1.2 Human capital – average health status of household members The second estimator is the health status of the households. For this estimator, every household

member was categorized as “healthy” (zero) or “sick” (one). The average of all household members

was taken, and the overall score was thus between zero and one.

In Figure 8, the distribution of the test statistic is displayed. The distribution slopes downwards, with

peaks at a score of zero and one. This thus means that a lot of households are overall healthy. On the

other hand, for a considerable amount of households, every household member is considered as “sick”

as well.

The average of the estimator is 0.35, which means that quite a high number of household members

are considered “sick”. Therefore, it could be interesting to look into the amount of people in every

group (short-time illness, injury and long-term illness).

In Table 3, the percentage of illnesses and injuries are displayed. These percentages are household

member based. Only a small part of all short-term issues are injuries. The amount of long-term illness

is also considerable. Most of the problems are short-term illnesses. The most important sorts of illness

are malaria (25 per cent) and flue (48 percent). It nevertheless needs to be mentioned that a lot of

respondents did not answer this question and are thus not indexed. Only one third of the surveyed

people did fill in a certain disease. The long-term diseases are much less diagnosed, and those that are

diagnosed are much more spread out over several diseases. The complete numbers for diseases can

be found in Annex 1 (Table A.4 and Table A.3). Also the long-term illness is looked into. From all

respondents, ten per cent were sick for a long time. Some people might also have both a short-term

and a long-term issue. Although these respondents are only counted once, it is an interesting number.

About three percent of all respondents indeed have a long- and short-term illness


Figure 8: distribution of the score for the average health of the household members

Table 3: amount of household members that had a short or long-term illness

What Percentage (%)

Short-term illness 22.93

Short-term injury 0.73

Long-term illness 10.78

Short-term and long-term-illness 2.84

Total percentage of sick household members

30.92

4.1.1.3 Social capital – amount of memberships households have The second form of capital is social capital. For this estimator, values could theoretically be very high:

in theory, an infinite score could be obtained. This would mean that the household members have an

infinite amount of memberships in ROSCAs and funeral societies. In practice, the maximum is obviously

a lot below this theoretical value. The household with the most memberships is member of seven

associations. The average amount of memberships is a lot lower, with 1.46. It is logical that this

distribution is linearly decreasing, as can be seen in Figure 9. The reason is that it is much more likely

that one is member of one association rather than seven organizations.

When Narayan & Pritchett (1999) looked into the social capital of households in (rural) Tanzania, they

found an amount of memberships of 1.5. The numbers for all of Tanzania and our survey are thus quite

similar. Though, the paper by Narayan & Pritchett (1999) is already quite old.


Figure 9: distribution of the amount of memberships in associations

4.1.1.4 Natural capital – soil quality of the household’s plots The next estimator is the soil quality of a plot. This is a continuous estimator that can take values

between one (good soil quality) and three (bad soil quality). The average is 2.10, which indicates more

households perceive their land as rather bad than the other way round. Nevertheless, the distribution

(Figure 10) shows that most households have a score of one or three, with much less respondents

being in between these values. From this, it can be derived that a lot of respondents filled in the same

score for all of their fields. Anyway, as households answered this question following their own

perception of soil quality, this estimator is not ideal. Another disadvantage of this estimator is the

impossibility to compare this indicator to other data from the whole of Tanzania to set a baseline. The

reason is that “soil quality” is a very broad term that can include several soil parameters. However,

because of the limitations of the survey, no better estimator was available.

Figure 10: distribution of the perceived soil quality of the plots


4.1.1.5 Economic capital - wealth index The next estimator that is looked into is the wealth index. For this test statistic, all values are between

-0.83 and 1.51. This is to be expected, as the wealth index is standardized with mean of zero. In Figure

11, it can be seen that the distribution is not normal, as would be expected, but rather skewed towards

the left side. This is confirmed by the skewness, which is 0.76.

As this indicator is standardized around a mean of zero with a standard deviation of one the average

household in Tanzania would have a wealth index of zero. Thus, it could be concluded that in Kagera,

more people are poor than rich. The description of the region confirms this: Kagera is a region that is

very far from the capital Dar es Salaam, sandwiched in between neighboring countries and lake Victoria

and is very remote overall (Kessy, 2005).

The average wealth index in Kagera region is 0.065, which is little above zero, contrary to the

expectations. The national average is zero (as the estimator is standardized), and we expected that the

average for Kagera would be lower. In Table 4 an explanation is showed. A lot more people are below

the average, yet because the distance to the average is higher for the richer households, the average

is so high. It can thus be said that more people are relatively poor, but most of them are not extremely

poor, compared to the average. On the other side, although less people are above the household, most

of them are relatively seen quite rich, compared to the average.

Figure 11: distribution of the wealth index for the sample

Table 4: amount of households that are below or above the average wealth index in the Kagera region in 2010

Number of households

Percentage of households

Mean distance to average

Above average

1114 38.6 0.46

Below average

1772 61.4 0.34


4.1.1.6 Off-farm diversity – amount of non-agricultural activity Off-farm diversity was determined by the amount of non-agricultural activities that were conducted.

The scores that were given for the off-farm jobs and enterprises a household member had are

displayed in Table 1. Although the theoretical maximum score is five, the highest score that was

observed is three (Figure 12). The average of the estimator is 0.77. As this test statistic is measured for

every household member, after which the average is taken, it is not easy to interpret this number

immediately. Therefore, the distribution of the scores for the household members is displayed in

Figure 13. In this figure, it can be seen that 60 per cent of household members that answered the

question did not have an off-farm job or enterprise. Fifteen per cent of the household members has

score one, which is one off-farm job. Furthermore, a bit more household members had score two. This

means that they could either have two off-farm jobs or one off-farm enterprise. In fact, 93 per cent of

household members that got a score of two, had an enterprise, and thus only seven per cent of

respondents had two off farm jobs.

Figure 12: distribution of the score for the indicator "off-farm diversity”

Finally, the main activity of the household was researched. From Figure 14, it is clear that only a bit

more than 60 per cent of the households sees farming as the main activity, although all of them have

cropland. Off-farm employment and enterprises are thus also very important sources of income for

the households in the survey.

If informal and formal employment are added up, a value of about fourteen per cent is obtained.

Furthermore, 9.5 per cent sees their off-farm enterprise as their most important source of income.

Other important occupations are casual labor and trade with respectively six and 13.5 per cent. It might

be expected that livestock keeping would be quite important as well, as it matches with crop farming.

Nevertheless, less than one per cent sees this as their main activity. Also fishing is not a main source

of income for most of the households in the survey.


It is interesting to compare these results with other regions in Tanzania. For the Njombe district, which

lies in the South of Tanzania, 91 per cent has farming as the main activity. This is 30 per cent more than

the Kagera region. 73 per cent of the household also has non-agricultural activities, besides farming.

This is similar to our survey (Chamicha, 2015). Although Figure 13 indicates that 60 per cent of the

household members do not have an off-farm job or enterprise, only in a bit more than 20 per cent of

the households, nobody has an off farm activity. These numbers are thus very similar.

Another case study for the Dodoma region, which lies central in Tanzania, stated that about 70 per

cent had off-farm activities. Which is comparable to the case study in the Njombe district. Trade and

service providing were the most abundant here. Also in the Kagera region, trade is an important non-

farm activity (Katega & Lifuliro, 2014).

Figure 13: distribution of the scores of individual household members for the indicator "off-farm labor"

Figure 14: bar chart of the main activity (i.e. most income generating activity) of a household.


4.1.1.7 On-farm diversity – amount of crops and sorts of livestock Another estimator for diversity is the diversity in terms of crops and animals. The average score for

this estimator is 5.31, which means most households have on average five different crops and/or

animals. This can be split up in 4.19 different crops and 1.13 sorts of animals. Nevertheless, the

maximum amount of crops and/or animals a farmer has is thirteen. As can be seen in Figure 15, this is

only an extreme. Most households are to be found around the center.

Figure 15: distribution of the amount of different crops and animals households have

Furthermore, it is interesting to look into the crops the farms in the sample have. This can be seen in

Table 5. More than half of the farmers have cassava, and cooking bananas, while maize, beans and

Robusta coffee were also part of the cultivation plan of more than 30 per cent of the farmers.

Crop diversity in Tanzania was also measured by Anderson (2013) He found an average for the Kagera

region of 3.3, which is a bit lower than our average. Reasons could be the sampling method. Another

reason is that some crops were not assigned by Anderson (2013), which make the sample obviously

lower. Furthermore, the survey of Anderson (2013) also proved that the crop diversity in Kagera was

high, compared to other regions in Tanzania. The crop diversity in most regions was only 2.3.

Finally, also the animals should be looked into. Livestock is a provider of income and employment.

Moreover, this is a valuable asset and an insurance for the poor. Additionally, it should not be forgotten

that animal products supply a very rich source of nutrients, for those that need it the most (Herrero et

al., 2013). Therefore, it is definitely interesting to know how much and which animals the households

have. The data, given Table 6, makes clear that chicken and goats are quite popular animals.

Nevertheless, other animals as cows and pigs are also kept by more than ten per cent of the

households. The results that are found here can be coupled with the “livestock ladder”. Some animals

generate more wealth and social status than others do. For example chicken and pigs are lower on the

livestock ladder than goats and cattle. The animals that are lower on the ladder are cheaper. If

households can climb up the livestock ladder, they are thus able to increase their wealth (Maass et al.,

2013). With the knowledge of this concept, the fact that the amount of households that has chicken is

fifteen per cent higher than the amount of households that has goats can be explained. It means that

there are more households that are lower on the livestock ladder.


Overall, 61 per cent of the households do have animals. As it is a prerequisite to have agricultural land

to be in the sample, these households thus have both crops and animals. Mixed farming systems are

clearly abundant. This system has several advantages. It enables the farmer to integrate several

branches on the farm. For example, livestock can be used to cultivate the land, while crop residues can

be recycles as fodder for the animals. Furthermore, the livestock may buffer the low crop prices and

thus serve as an insurance for the fluctuating yields (Herrero et al., 2010).

Table 5: different crops and their abundance among households

Table 6: different animals raised and their abundance among households

4.1.1.8 Innovativeness – pesticide use The last estimator is the pesticide use of the households. A score between one (never uses pesticide)

and four (used pesticides last week) is given. These scores were given in the survey, and were

maintained in the creation of the indicator. The average score is 2.29, which means a large part of the

households uses pesticides frequently. The distribution (Figure 16) shows that an almost uniform

distribution can be seen, although, it is hard to make conclusions, because the distribution is not

continuous.

It needs to be mentioned that this estimator has some disadvantages. First, it only takes one facet of

innovativeness into account. A farmer that uses a lot of pesticides might not be innovative in other

parts of the farm. Moreover, this test statistic is discrete and can only take four values. Very much

distinction between households can thus not be made. These disadvantages make the indicator less

useful, yet because of the limitations of the survey, we were not able to find a better estimator.

Amount of farmers that cultivates crop

Name Absolute Relative (%)

Cassava 1572 54.47

Traditional cooking bananas 1539 53.33

Maize 1316 45.60

Beans 1286 44.56

Robusta coffee 906 31.39

Sweet potatoes 763 26.44

Exotic cooking bananas 649 22.49

Yam 605 20.96

Timber trees 259 8.97

Brewing bananas 225 7.80

Arabica coffee 188 6.51

Firewood trees 109 3.78

Traditional desert bananas 94 3.26

Tomatoes 90 3.12

Sorghum 88 3.05

Bambaranuts 69 2.39

Paddy rice 64 2.22

Amount of farmers that cultivates crop

Name Absolute Relative (%)

Bulls 14 0.49

Cows 368 12.75

Sheep 86 2.98

Goats 914 31.67

Chicken 1353 46.88

Pigs 310 10.74

Duck 157 5.44

Rabbits 61 2.11

All animals 1760 60.98


Figure 16: distribution of the score for the indicator "pesticide use"

4.1.2 Correlation between the indicators To make sure that all the indicators are independent, the correlation between the indicators will now

we be researched. In Table 7, the correlation and the significance of this correlation is displayed.

From the tables, it is clear that nearly all factors are significantly correlated. From the 28 possible

correlations, only eight are not significantly correlated. Yet, only two values have a correlation above

|0.3|. These correlations are between wealth index and health, as well as wealth index and on-farm

diversity. All other values are lower than |0.3|, and can thus be considered to have only small

correlation (Calkins, 2005). An explanation for the fact that such small correlations are significant is the

size of the sample. Because 2886 households are in the sample, even small correlations will be

significant.

Furthermore, it might be interesting to examine the direction of the correlations as well. Between

health and wealth index, a positive correlation can be found. This is logical: the better the health of a

household is, the more a household can work and the lower the health costs are, and therefore the

wealth will be better. On the other hand, wealthier households will also be less vulnerable to diseases,

as they are much more able to do prevention. Between on-farm diversity and wealth index, there is a

negative correlation. This can be explained by the loss in efficiency diversification causes. Because not

all of the resources are invested in the most profitable crop, naturally a loss of efficiency occurs

(Papachroni et al., 2016).

We conclude from this that for future research, it might be interesting to consider that in this case ,

wealth index is correlated with both health and on-farm diversity. Therefore, it could be interesting to

look into only one of the values. Nevertheless, as the correlation is low, the results are still considered

as representative.


Table 7: correlation matrix with the indicators, combined with the major statistics for the indicators. The significance is given by the stars, where one, two and three stars respectively mean a p-value ≤0.05, ≤0.01, ≤0.001 on an α=0.05 level.

4.1.3 Conclusion All of the indicators have now been examined separately. Nevertheless, to make proper results for

households, it is important to look into an aggregated score. To do this, the sum of the scaled values

for each indicator was taken. From Figure 17, it is clear that the distribution is quite normal. Yet,

although the theoretical maximum is eight, the actual maximum is only 5.79. The average sum of the

indicators for the households is 3.16 and the minimum is 0.31. It is thus important to indicate although

the distribution is normal, it is certainly skewed to the left side. This illustrates the fact that a lot of

people have relatively low resilience.

Figure 17: sum of the scaled indicators for each household, in a histogram

Variable Mean SD Min Max 1. 2. 3. 4. 5. 6. 7. 8.

1. health 7.80 3.41 0 18 1

2. Education 0.35 0.30 0 1 0.14*** 1

3. Membership 1.46 1.37 0 7 0.15*** -0.01 1

4. Soil quality 2.10 0.79 1 3 -0.03 0.06** -0.06** 1

5. Wealth index 0.07 0.47 -0.8 1.5 0.49*** 0.09*** -0.01 -0.02 1

6. Off-farm diversity

0.77 0.65 0 3 0.14*** -0.06** 0.09*** 0.00 0.06** 1

7. On-farm diversity

5.31 2.54 1 13 -0.1*** 0.05* 0.22*** 0.07*** -0.3*** - 0.12** 1

8. Innovation 2.29 0.96 1 1 0.06** 0.01 0.08*** 0.00 0.06*** 0.02 0.17*** 1


Another important remark is that some estimators are not very objective or do not cover the whole

area of the indicator. Yet, because of the limitations of the survey, they are included. This means that

in the future, it might be interesting to replace them, according to the availability of data.

Following up on this, some indicators might not be measured that easily in the field. For example, the

wealth index has 90 different fields that need to be filled in. To obtain results that are more easily

collectable and thus cheaper, a different estimator, such as the amount of hectares or assets, or the

income could be asked. Also for other indicators, it is important to know that they can be measured in

a different way.

Moreover, all of these indicators are relevant for smallholders in the Kagera region in Tanzania. Yet,

for other regions or specific sorts of farmers, other estimators might be more relevant. For example in

the context in Flanders all indicators would be relevant. Yet, some estimators, such as pesticide use,

are not appropriate and should be replaced. We could conclude that the indicators are quite universal,

but it might be interesting to keep in mind that all of these indicators and estimators need to be looked

upon critically before every use.

Finally, the indicators might possibly change over time. Some indicators that are important now, will

not be important in the future, and some other indicators will emerge. A reason is that the needs of

smallholders will probably change over time. Therefore, the plan-do-check-act (PDCA) circle is

proposed (Deming, 2000). Bushell (1992) argues that the PDCA-tool can be very interesting to improve

decision making and management of systems. As the name suggests, four steps can be distinguished.

The first step (“plan”) is all about understanding the needs, collecting data and designing action plans.

The “do”-step implements this action plan and measures the progress that has been made. After this,

the “check”-phase looks into the measured progress and analyzes it. From this, problems and errors

are reviewed. Finally, the “act”-stop comes in: the improvements are standardized and the “current

best approach” is formalized (Gidey et al., 2014). As the PDCA circle is a cyclical process, the “act”-step

is followed up by planning again. This way, this model is a method for continuous improvement

(Bushell, 1992).

4.2 Tests executed on the groups

4.2.1 Amount of shocks perceived Several tests were executed on the households. First of all, the amount of shocks a household

perceived was tested. In the KHDS, a household had to give a score for every year, in the interval 2004-

2010. The households could choose a score between one (very good) and five (very bad). If the score

for “very bad” was given, the year was seen as a shock. Consequently both the VB and the VG group

were tested on the average amount of shocks that were perceived during the surveyed years.

From Figure 18 it is clear that the amount of shocks perceived is similar for both the VG and VB group.

We see that a bit more people did not perceive shocks in the VG group, yet the difference is not more

than three per cent. Also for the amount of households that had two bad years out of seven, there was

only a small difference of four percent.

The average amount of shocks for the VG and VB group are respectively 0.37 and 0.46. This difference

is quite small. The WMW test that was executed on the households is therefore not significant, with a

p-value of 0.26.


Figure 18: pie charts with the distribution of the amount of shocks that were perceived for both the "very bad" and "very

good" group. P-value: 0.26

4.2.2 Reason and solution for the perceived shocks. The reasons for the perceived shock are important as well. Even more important are the differences

between the two groups. The groups have a similar percentage for most of the reasons, yet for some

there is quite some difference. From Figure 19, it can be seen that the most important reasons for

shocks are poor harvest due to weather, a death of a family member and illness. A big difference can

be found in crop prices, where the VG group has more shocks because of low prices, relatively

compared to the VB group. This is contrary to the expectations: as the VG and VB group would each

get the same prices, according to market principles, we could expect that the percentage would be the

same. The difference can be explained by the fact that the reasons are on a relative scale. This would

mean that the importance is the same, but because other reasons are more abundant, the importance

of low crop prices is relatively smaller. On the other hand, death of a household member is far more

often a problem for the VB group.

Also the coping strategies of the households were surveyed. There were in total fourteen options

displayed in Figure 19. In this figure, we can see that for the VG group reduced consumption was ten

per cent more abundant as a coping strategy. On the other hand, 35 per cent of the times that the VB

had to cope with shocks, they relied on their family. This is about twenty percent more than the VG

group. Social capital is thus very important for less resilient households. For all other categories, no big

differences were seen.

4.2.3 Average of the perception score for each year As already mentioned above, the household had to score each year between “very bad”(five) and “very

good” (one). An average of all of these scores was looked into. The average score for the VG and VB

group were respectively 3.19 and 2.93, in which the lower score is better. The difference between both

groups is thus about 0.3. The distribution of the groups is also looked into. In Figure 21, the boxplots

for the VG and the VB group are displayed. From this figure, it is clear that the distribution for the VB

groups is higher than the VG group. Finally, to really test this, WMW test was executed on both groups.

For this test, the p-value was 4.87e-14. This means that the null hypothesis is thus rejected and that

the two distributions do significantly differ.


Figure 19: bar chart for the different reasons for shocks with their respective percentages

Figure 20: bar chart for the different strategies to cope with shocks with their respective percentages of use


Figure 21: boxplots for the mean score given by the households for each year (2004-2010), for both the VG and the VB group

4.3 Comparison with other models

Our indicator framework is not the only measurement for resilience that can be found. Several other

frameworks are described in the literature. The reason that quite a bit of indicator frameworks exists

are the following: first of all, there are several interpretations of resilience. Depending on how

resilience is perceived, the interpretation will obviously be different (Schipper & Langston, 2015).

Furthermore, there are several sorts of indicators. Some indicator frameworks are household-based,

while others are society based or both. This difference has an obvious effect on the measuring and

thus on the outcome.

Another reason for the multitude of systems is the absence of consensus on how to measure resilience.

This is on the one hand an outcome of the different interpretations, but nevertheless, a broad set of

rules could have narrowed the variety of systems down (Winderl, 2014). It would have been useful if

there was more consensus on the measuring of resilience. Now, literally every indicator that can be

properly motivated can be used.

Finally, Panpakdee & Limnirankul (2017) mention that “measuring resilience is like aiming at a moving

target”. Therefore, it is suggested that it is more preferable to develop certain “rules of thumb” instead

of creating a set of very distinct indicators that narrow down the system (Darnhofer et al., 2010a).

Because some systems to measure resilience have already been developed, it is interesting to compare

several of these models to see what the advantages and disadvantages of each system are, compared

to our system. Therefore, three frameworks will be examined and compared to our system. These

frameworks are Resilience Index Measurement and Analysis – II (RIMA-II) by FAO, the social-ecological

resilience indicators (SERIs) by Panpakdee & Limnirankul (2017) and the behavior-based indicators

(BBIs) of Cabell & Oelofse (2012).

These three indicator frameworks were chosen because they are all quite different in definition,

approach and indicators. Furthermore, all of them were influential. The RIMA-II framework was made


by FAO and is used for measuring resilience against climate shocks. The BBI of Cabell & Oelofse (2012)

is more abstract and therefore cited a lot. Furthermore, the framework was published in the respected

journal “Ecology and Society”. Finally, the SERI framework was maybe not as influential, as it is a case

study, yet, it was very interesting to compare this framework to our model, as this framework was very

practical and made to be implemented in the field rather than in just theory.

In Table 8, an overview of the different frameworks is given. More explanation on the characteristics

and dimensions of each framework will be discussed underneath.


Table 8: summary of different frameworks that were examined. The focus, base and definition as well as a comparison of the different dimensions and indicators are given.

Our framework RIMA-II SERI BBI

Focus Smallholders in Tanzania Food security of smallholders; Shocks

Organic rice farming in Thailand Theoretical and abstract rules of thumb

Based on Literature review Literature review Interviews with farmers Literature review

Definition used

“The capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks (Walker et al., 2004).”

“The capacity of a household to bounce back to a previous level of well-being after a shock” (Alinovi et al., 2009).

“A measure of the ability […] to absorb changes of state variables, driving variables, and parameters, and still persist.” (Holling, 1973); Adaptation and self-organization are added

“A measure of the ability […] to absorb changes of state variables, driving variables, and parameters, and still persist.”(Holling, 1973); The adaptive cycle (Holling & Gunderson, 2002)

Matching indicators

☑ education

☑ health

☑ social capital

☑ natural capital

☑ wealth index

☑ off-farm diversity

☑ on-farm diversity

☑ innovativeness

☑ education

☑ health

☑ social capital

☐ natural capital

☑ wealth index



☑ innovativeness

☑ education

☐ health

☑ social capital

☐ natural capital

☑ wealth index



☑ innovativeness

☑ education

☐ health

☑ social capital

☑ natural capital

☑ wealth index



☐ innovativeness

Inicators

Education Access to basic services Adaptive capacity

Learning to live with change; Combining types of knowledge & learning

Human capital; Reflected learning; Honoring legacy;

Health Access to basic services / /

Social capital Social safety nets Creation of opportunities Social capital

Natural capital / / Ecologically self-regulated; Local natural capital

Wealth index Access to basic services; Assets

Nurturing diversity Reasonable profit

Off-farm diversity Adaptive capacity Nurturing diversity Connectedness; Functional and response diversity; Heterogeneity

On-farm diversity Adaptive capacity Nurturing diversity

Innovativeness Adaptive capacity Learning to live with change /

Not mentioned in our framework

Sensitivity Adaptive capacity; Indigenous knowledge; Creation of opportunity;

Autonomy of the system; Exposure to shocks; Redundancy of the system


4.3.1 Comparison of different indicator frameworks 4.3.1.1 Resilience index and measurement – II The first framework that will be discussed and compared with the framework created in this

dissertation is the RIMA-II framework. This framework is the second version of an econometric

framework, created by FAO in 2008. As the FAO has a quite long history in the use of resilience, the

creation of a measurement tool was only logical.

The definition on which this model was built on is of importance. RIMA-II described resilience as “the

capacity of a household to bounce back to a previous level of well-being after a shock” (Alinovi et al.,

2009). Already at this point, a difference can be seen. While the above mentioned definition of

resilience is very much shock-based and looking at returning to the previous state, the definition used

for our framework is more universal. Indeed, the definition by Walker et al. (2004)16 does not focus on

one or another implication of resilience. It also does not see “bouncing back” as a necessity, nor does

it speak about shocks. Furthermore, the approach of FAO wants to measure food security, while our

framework has no specific focus.

The FAO framework is built up out of five main dimensions17: i.e. access to basic services, assets, social

safety nets, sensitivity and adaptive capacity (Table 9Error! Reference source not found.). These

dimensions are measured with several indicators and these indicators represent scores/values at

household level, just as in our approach. Although these are quite different from our eight indicators,

some similarities can be found.

Table 9:dimensions with their indicators of the RIMA-II framework (FAO, 2016)

Dimension Indicator

Access to basic services Distance to schools

Assets Income and assets

Social Safety Nets Formal and informal transfers

Adaptive Capacity Innovativeness and diversity

Sensitivity Amount of shocks

Access to basic services such as schools, health centers, and markets is seen as a fundamental aspect

of resilience. This is measured, amongst others, by the density of the road network in the region.

Access to basic services is important for resilience, as proper access to those services will make one’s

life easier and will in several ways create more resilience. For example, when the distance to a school

is closer, the access is easier and the human capital grows. Yet, this is not incorporated in our indicator

framework. A reason is that a household itself cannot change much of their access to basic services,

apart from migrating. Therefore, it could be argued that this is more an indicator on community level,

rather than on household level. In contrary, all our indicators are household based. The fact that this

indicator is not incorporated is not necessarily a problem, because most of the outcomes are

measured. For example education, health and economic capital can be seen as outcomes of the

“access” indicator.

The second indicator is the assets a household owns. Both the income as well as the productive and

non-productive assets are measured. Although it might seem easy to measure income in theory, it is

hard in practice. Therefore, consumption is proposed as an indicator for income. It is clear that this

dimension is very similar to economic capital.

16 This definition is the following: “the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks.” 17 The word “dimension” is here used for a certain group of indicators. In this dimension, the indicators can be found. These indicators are then measured with estimators.


Moreover, also social safety nets are measured. Although this indicator is narrowed down to the

amount of (formal and informal) transfers, cash and in kind, a household can access, the link with social

capital can be made. The social capital in our framework is more universal, as it also focusses on the

relationships instead of the outcome of those relationships (e.g. transfers). Nevertheless, the both

indicators are comparable. Nonetheless, it needs to be mentioned that a relationship does not

necessarily lead to a transfer.

Another indicator is adaptive capacity. This represents the ability of a household to adapt to a changing

situation. At first sight, it might seem as if this indicator does not occur in our framework. Yet, when

the estimators that are used by the RIMA-II framework are looked into, similarities can be found. Both

innovativeness and knowledge as well as on- and off-farm diversity are mentioned as beneficial. We

can thus say that these indicators are mentioned in both systems, although the weight given is only 1

5⁄ in the RIMA-II framework and 4 8⁄ 18 in our research. Proposed estimators are very similar to our

estimators in several categories: years of education, sources of income and crop diversification are

important in both our framework as RIMA-II

Finally, sensitivity is a dimension. It relates to exposure of risk and resistance to shocks. This can be

seen as the amount of shocks a household encounters. As this is not something a household can

influence, the RIMA-II framework sees this indicator as endogenous. For this indicator, the both

frameworks differ quite much, as in our system, sensitivity towards shocks is seen as an outcome of

low resilience, rather than an inherent part of resilience (FAO, 2016).

The RIMA-framework did start from a different focus on resilience, compared to our system. Therefore,

the definition and approach was different. Nevertheless, a lot of similarities could be found. To begin

with, both indicator frameworks started from the literature. Furthermore, all indicators in our

framework are covered by the RIMA-II framework, except from natural capital. Furthermore, our

framework did not touch upon the “sensitivity” dimension. This can all be seen in Table 8.

4.3.1.2 Social-ecological resilience indicator framework The second framework that is looked into is created by Panpakdee & Limnirankul (2017). It was based

on organic rice farmers in Thailand. 53 different organic farmers were interviewed and the results were

used to set up a framework. In these interviews, the farmers were asked about things that could

increase their resilience.

The goal of these interviews was the creation of several indicators that could describe resilience in the

given context. As a starting point for these indicators the definition, as phrased by Holling (1973) was

taken. The persistence of a system is important, but also adaptation and self-organization (i.e. an order

that arises from spontaneous actions in a system), were added (Yates, 1983).

This definition is thus similar to the definition we stated. It has the same starting point. Yet, we have

never looked into the self-organization of a system, As well, Panpakdee & Limnirankul (2017) group

adaptive and transformative capability.

This framework and the previously mentioned RIMA-II framework also differ in their scope. While this

model does not go into detail that much, the FAO framework has a focus on shocks and food security.

On the other hand, this model is made specifically with and for organic rice farmers in Thailand, while

the RIMA-II framework focusses on all farm households. If our model would be compared with both

frameworks, it could be said that our framework is constituted for wider application, as it is based on

a literature study, rather than practical interviews or experience. Nevertheless, it is applied on the

18 One fifth in the RIMA-II framework, because it is one out the five indicators. Yet, it matches with education, innovativeness and off- and on-farm diversity in our framework. These are four out of the eight indicators and

thus 4 8⁄ .


Kagera region in Tanzania, and some indicators or estimators might change, if households with slightly

different characteristics are considered. For example, using crop diversification as an indicator in

countries with already high differentiated crops will not contribute to the measuring of resilience.

Finally, 47 indicators or SERIs were obtained. These were divided up following the classification of Folke

et al. (2003). The dimensions were (1) learning to live with change, (2) nurturing diversity (3) combining

types of knowledge and learning and (4) creating opportunities. Folke et al. (2003) described these as

four principles that would help to build resilience. These dimensions are used in the model, because it

is an ideal starting point to develop a model. As there are 47 SERIs, each dimension has about ten to

fifteen indicators. These are summarized in Table 10. A full version of the framework is given in Table

A.9 in Annex 1.

Table 10: summarized dimensions and indicators of the SERI-framework (Panpakdee & Limnirankul, 2017)

Dimension Indicator

Learning to live with change Education, experience, investment and willingness to change

Nurturing diversity Diversity in biodiversity, opportunities, resources and relationships

Combining types of knowledge and learning

Indigenous knowledge, informal learning and adaptive capacity

Creating opportunities Dependence on resources on the farm, community and government level

In the first category, learning to live with change, education and experience as well as investments and

willingness to change were incorporated. All of the estimators within this category focus on self-

empowerment, which is the capacity to believe in itself and to do what is best for oneself. Therefore,

it can be closely related to the indicator education, which is incorporated in our model. Yet, it is clear

that the approach is much wider in the SERI-framework than in our system. For example, also the

indicator innovativeness is quite close to this set of estimators, as it can be seen as a proxy for

willingness to change and investments.

The second category is named “nurturing diversity”. It contains estimators that are all focused on

diversity. Diversity of rice species, economic opportunities and resources are incorporated. Mostly,

these estimators encompass the same as the on- and off-farm diversity that are mentioned in our

model. Yet there are slight differences. The “nurturing diversity” dimension has some economic aspect

as well. In this aspect, it is explained that establishing cooperative networks could be beneficial for

diversity and therefore for resilience.

Following, the category “combining types of knowledge and learning” is mentioned. This category

might be the most abstract to understand. Panpakdee & Limnirankul (2017) mentions the indigenous

knowledge, adaptive capacity and willingness to learn as parts of this dimension. These are measured

by the degree of heritage of indigenous knowledge and the amount of adaptations made to the farm.

It is clear that knowledge and education make a household more resilient. That is also the main focus

of this set of estimators. Yet, this is made more universal with the incorporation of indigenous

knowledge and adaptive capacity. Therefore, it is hard to say what indicator of our framework it can

be linked with. Obviously, this category is very close to our indicator education. Though, adaptive

capacity and indigenous knowledge are not mentioned in our framework. These components make a


household more resilient, and it could thus be interesting to add these features to our resilience

framework in the future. Furthermore, one can remark that in the SERI-framework, education is

measured both under this dimension as w under the first dimension (learning to live with change).

There can indeed be double counting because of this reason. Nevertheless, the focus in both

dimensions is slightly different. In the first dimension, the focus is more on self-empowerment, while

in the third dimension the knowledge, experience and willingness to learn are included.

The last category is “creation of opportunity”. Within this category, dependence on resources, the

usage of the assets one has and the networks with governments are mentioned. The creation of

innovative solutions to the occurring problems is suggested as well. This would be measured by the

own production of rice seeds. Furthermore social capital is also named as important. This was

measured by knowledge exchange through networks. It is clear that this category has some similarities,

but maybe even more differences to our indicators. Both innovativeness and social capital would

match with parts of this category, but the overall dependency of a household and the networks with

governments are not encountered at all in our framework. To begin with, dependency is particularly

important for organic farming. An organic farmer is much more reliant on other stakeholders to sustain

his business. For example, if a neighbor would spray, and drift would occur, the rice could be

considered as non-organic. Also, the markets are much more specific, and the practices more difficult.

For what concerns the role of the governments, it could be argued that this is crucial for a farmer’s

resilience. Yet, this is not an indicator that can be changed on household level. Therefore, it should not

be withheld in our framework.

As well as the RIMA-II framework, this framework has a quite different approach compared to our

framework. The focus is much narrower than our framework, yet, the definition is similar. Another big

difference would be the fact that this framework is based on interviews with farmers, while all the

others aren’t. In terms of indicators, both health and natural capital are not mentioned in the SERI

framework. Yet, adaptive capacity and indigenous knowledge are not mentioned in our research. It

could thus be said that both frameworks can learn from each other.

4.3.1.3 Behavior-based indicators framework The last system that will be compared with our framework is the BBI framework by Cabell & Oelofse

(2012). Similar to the RIMA-II framework, this framework is more general and multi-applicable, in

contrast to the SERI-framework that is made with and for rice farmers in Thailand.

The starting point of this indicator framework is the definition of Holling (1973), just as in the SERI-

framework by Panpakdee & Limnirankul (2017). Yet, much emphasis is laid on the concept of adaptive

cycle as well. This concept states that an ecological system goes through four phases: conservation,

disturbance, reorganization and growth (Pelling & Manuel-Navarrete, 2011). In this cycle, a shock thus

occurs, after which the system reorganizes and is able to grow once again. An example would be the

occurrence of a wildfire in an ecosystem. The wildfire is obviously a shock to the ecosystem. This

disturbance will deteriorate the natural capital of the system. Yet, the ecosystem will reorganize and

after a while, a new “order” will arise. Then the “growth” phase will occur: the natural capital of the

ecosystem will increase significantly in this period. After this comes a conservation phase. For every

indicator, the phase in the adaptive capacity cycle where it is important is mentioned.

The indicators that are mentioned are based on the literature. Furthermore, it is noteworthy that all

of the mentioned indicators are clearly much more theoretical and abstract, compared to the other

frameworks discussed above as well as our framework. This is already clear by the fact that no

estimators are formulated, and this model is thus rather theoretical. Finally, it also needs to be

mentioned, that contrary to the previous frameworks, this model is community based rather than

household based.


Table 11: dimensions of the BBI framework (Cabell & Oelofse, 2012)

Dimension

Human Capital Honoring their legacy

Social capital Connectedness

Reasonable profit Functional and response diversity

Ecologically self-regulated Heterogeneity

Local natural capita Redundancy of the system

Reflected learning Exposure to disturbance

Autonomy of the system

Of the thirteen indicators that were formulated (Table 11), some were very similar to the ones that

were defined in this dissertation. For example, both human and social capital are mentioned quite

literally. With human capital, the knowledge, skills and experience that can be mobilized are meant. It

would thus be appropriate to compare this with education, and not with health. The degree of

organization in a community would be seen as a proper indicator for social capital.

Also economic capital is mentioned. Yet, this is less obvious. Economic capital is resembled by the

indicator “reasonable profit”. This indicator argues that it is important to accumulate wealth to sustain

a farm. If an agroecosystem must meet the human needs, farmers should be able to make a living out

of farming. Nevertheless, the exact definition of a decent income is not mentioned in the paper.

Apart from these very similar indicators, also other dimensions of the BBI framework can be compared

with our indicators. For example, natural capital is mentioned in two estimators: both “ecologically

self-regulated” and “local natural capital” focus on the natural capital of an ecological system. Yet,

there is a small difference in nuance between the two. The first indicator focusses on the impact of

farmers on the organization of ecological systems, while the second one has a focus on the natural

capital of a natural good in itself. This emphasizes the ecosystem services a well-functioning natural

environment can generate.

Also education is can be linked to two of the thirteen indicators: “reflected learning” and “honoring

their legacy” both state the importance of learning from the past. Thus, education can be linked to

three indicators in the BBI framework, as human capital also links with the indicator “education”. Yet,

every indicator its focus is different. The focus of “reflected learning” is on the ability to learn from

past experiences and to not make the same mistake twice. In contrary, “honoring legacy” talks about

the importance of the history of a community for the future, in terms of knowledge, institutions,

traditions and even assets (e.g. seed banks). The already mentioned human capital will also focus on

skills and general understanding.

Three indicators can be linked with diversity as well. These are “connectedness”, “functional and

response diversity” and “heterogeneity”. The first of these indicators talks about a preferable

abundancy of “components” that are all interconnected. These components thus represent a certain

diversity to choose from. On the other hand functional and response diversity represent respectively

the amount of elements (e.g. the amount of crops) and amount of available responses to answer

towards shocks. The third indicator focusses on the heterogeneity of landscapes and crops. It is argued

that these should both differ on a temporal and spatial degree. For example, a region with a

monoculture of one crop will likely suffer very hard from a pest, while a heterogenic landscape might

not. Furthermore, monoculture are obviously less resilient than shifting cultivation.

Another indicator is the redundancy of the system. This could also be called the surplus a system has.

It is argued that if a system has some surplus left, it will better be able to cope with shocks. Yet, it is

not mentioned in what sorts of capital this surplus exists. We can thus assume that this surplus is in all


sorts of capital. The indicator itself is thus not mentioned in our framework, but could be divided up

under the sorts of capital.

Furthermore, exposure to disturbance is referred to. Exposure to shocks would push the processes of

evolution and adaptation and this would thus make a system more resilient. This indicator was already

mentioned as “sensitivity” in the RIMA-II framework. As already mentioned then, in our framework

this is seen as a negative outcome of low resilience.

The final indicator is the autonomy of a system. The relative freedom from controls from for example

purchasers and suppliers can definitely make a household more able to move freely and therefore, a

household could be more resilient. This indicator is similar to “dependency”, mentioned in the SERI-

framework. It is already mentioned that this might be interesting.

Although this framework is much more theoretical than our model, it is thus interesting to compare

both systems. Yet, both systems do not mention certain indicators. Indeed, we do not mention

“autonomy of the system”, “exposure to shocks” and “redundancy of the system”, while in the BBI

framework health and innovativeness were not discussed.

4.3.2 Conclusion Now three different systems have been discussed, it is clear that each system is quite different,

although similarities can be found. For example, great differences exist in terms of amount of

dimensions and indicators. The RIMA-II framework has only five dimensions, the SERI framework has

four, but the BBI model has thirteen different dimensions. On the other hand, similarities are also

abundant. For example diversity and education come back in every framework. Often, the dimensions

differ a lot in names and description, but when analyzed, similarities can be found.

A critique on all of the frameworks could be that they are incomplete. Since all models have some

indicators others have not, it might seem as all of the frameworks could be supplemented with new

indicators. Yet, all frameworks are built up from a different viewpoint, and different demands of their

smallholders. Therefore, no indicator can simply be added without a thorough review of it.

Furthermore, addition of certain indicators could have a big effect on the outcomes of the framework.

Nevertheless, it might be interesting to test the different indicators and to try to immerse them in a

different framework, if done with care.

Following up on this, it is important to realize that although all frameworks differ and can be compared,

it is not useful to rank them. Each framework has its own specificities and is built up starting from a

different point of view. All of them are thus valuable. Furthermore, a different framework might be

chosen based on the circumstances, region and needs of the households. For example, if we want to

research the resilience related to shocks, it might be beneficial to use the RIMA-II framework, while

for organic rice farmers, the SERI framework is more interesting.

As all frameworks should be considered valuable but different, it might be interesting to apply all of

the systems to the same set of households. Nevertheless, this would ask a considerable effort, because

none of the models is ready for use on any dataset. It needs to be adapted and specified. For example,

the SERI-framework is specifically made for the Thai farmers, and for the BBI model, indicators and

their estimators need to be specified. Therefore, this is not done in this dissertation. It might

nevertheless be interesting for further research.


4.4 Implications for stakeholders

Above, resilience was defined in the context of smallholder farmers in Tanzania. Furthermore, an

indicator framework was created to measure the resilience of those smallholders. Although it is

interesting to measure resilience, information is only valuable if it leads to improvements. Therefore,

we will try to characterize advice and implications that could improve the resilience of farmers. These

implications can also be seen as the usefulness of the concept of resilience for different stakeholders

in the sector.

We acknowledge that it is easy to give advice on paper, while watching from the sideline. It is obviously

a lot harder to imply these advices in the field. Moreover, they are personal opinions, based on my

experience acquired during this master dissertation. They should thus not be seen as the consensus

thought on these subjects. For each indicator the implications for households, the community, policy

makers and research institutes are suggested. Because NGOs cover a broad range of organizations, the

NGO Rikolto is chosen to make the advice more practical. The implications for the different

stakeholders are all summarized in Table 12.

Table 12: summarizing table on the implications this dissertation could have for different stakeholders in the sector. (X=has an implication; (X)=ambiguous; an empty box means that there is no implication for this stakeholder.)

Edu

cati

on

Hea

lth

Soci

al

cap

ital

Nat

ura

l ca

pit

al

Eco

no

mic

ca

pit

al

Off

-far

m

div

ers

ity

On

-far

m

div

ers

ity

Inn

ova

tive

-ne

ss

Household (X) (X) X X (X) X X (X)

community (X) X X X

Government X X X X

Research institutions X X X

Rikolto X X X (X) X X

4.4.1 Education The first indicator we mentioned for resilience is education. Several actors in the agricultural sector in

Tanzania can influence education. To begin with, farming households themselves can enhance their

education level in several ways. They should realize the importance of good education for all household

members. Not only primary education, also secondary education and even higher education will have

a big impact on households. Yet, it needs to be mentioned that the economic capital of a household is

probably often a limiting factor for the households to invest in education.

Not only households themselves can influence education. Communities could create a stimulating

environment for the schooling of households. If it is seen as usual that children follow secondary

education, this will make a household choose to send their children as well. Also the role of the

government in the education of its residents is obvious. By providing secondary and higher education

that is more attractive, the schooling grade of the citizens will rise. Research institutions on the other

hand do barely have the possibility to rise the schooling grade.

Finally, Rikolto can have a big impact on the education level of households. Although stimulating

households to send their children to school is definitely important, there might be an even bigger role

for the NGO in helping farmers to learn in the field and from each other. Moreover, if farmers would

learn how to learn from what they see, they will be able to acquire knowledge a lot faster themselves.

For Rikolto, there would thus rather be a role in informal training.


4.4.2 Health For the second indicator of resilience, advice for the different stakeholder is not as self-evident as for

education. Obviously, for the households themselves it is important to follow up the health status of

all household members on a regular basis and to prevent diseases with bed nets, vaccinations et

cetera. Yet, just as for education, probably the economic capital households have is the limiting factor.

The most important stakeholder for improving the health status of households is the government.

Although it is easier to write it down on paper than to implement it, a proper and robust framework

for healthcare in rural areas is indispensable to develop resilient farming households.

In addition, the community, research institutes and Rikolto cannot do much. Research institutes can

of course make healthcare cheaper because of research, but this is a slow and worldwide process.

Rikolto could provide in health assistance in some examples, but overall, this is out of the scope of the

NGO. Other NGOs are probably better placed to do this.

4.4.3 Social capital The third indicator is social capital. To improve this capital, both the households and the communities

are of very high importance. Contrary to the first two indicators, households themselves are able to

invest in social relationships, even if they do not have economic capital. Households should really make

time for this and not see the time spent together as a waste, as building social capital will prove very

useful in time of need. Also the communities have an important role: only from a community

perspective, it is possible to notice the household that have difficulties integrating in the community.

Therefore, special attention should be given to them in the form of invites and involvements in social

activities. It is obvious that the difference between a very silent and a very lively community is mostly

made by the members of the community and the atmosphere in the community. The role to improve

social capital of households lies thus mainly in the households and the community, rather than the

government and the research institutions. Nevertheless, for NGOs as Rikolto there might be a role in

making social organizations an associations more robust. By training and proper association building,

the organizations’ capabilities will increase a lot.

4.4.4 Natural capital Following, we will look into the implications of natural capital for the stakeholders. Evidently,

households themselves have a big impact on the natural capital they manage. It is important they

understand the importance of their natural capital to sustain the farm in the long term. Furthermore,

farmers need to recognize that this is an investment that will not yield in the short term, but only in

the long run. Although farmers certainly have a crucial role in sustaining natural capital, it must be

acknowledged that it is very hard to apply good management on the farm, when there is no precedent

to learn from. Therefore, we should not give farmers the full responsibility for this indicator.

Research institutes and NGOs could also play an important role in this. Research institutes are of the

highest importance to examine what the best options are to manage the land properly. As well, these

institutes must play in important role in providing extension that helps the farmers to learn those

techniques. NGOs on the other hand, might be very useful to stimulate farmers to improve their

natural capital. Also test plots and informal education on sustainability can teach farmers how to

manage their natural capital. In my opinion, the government should not play a big role in this.

Only in exceptional occasions it is needed to create appropriate policy that makes sure the natural

environment is protected. For most cases, I do believe much more in the sustaining of environments

because the farmers want to and see the need, rather than because of the obligation.


4.4.5 Economic capital For economic capital, households themselves are also of importance, just as the other cases. To gain

economic capital, capital should be managed responsibly. Although this is the responsibility of the

household, NGOs as Rikolto can play an important role. They can help households in the management

of their assets. Furthermore, they can increase the profitability of farming enterprises by increasing

the cooperation amongst farmers. Nevertheless, there is such a thing as the poverty trap. This concept

states that people cannot escape poverty without a significant capital injection. Households are thus

trapped in poverty. Because lack of possibility to find good work and education, from generation to

generation, households will be in a cyclical pattern of poverty. Only with a planned investment in the

to help and solve the problems the poor in developing countries face, this poverty trap can be

overcome (Banerjee & Duflo, 2011). From this, it can be concluded that for households, it is very hard

to gain assets without support from others. Possibly, there is a much more responsibility for the

government: they should shape a favorable environment for economic growth and prosperity. Also

international institutions such as the World Bank can contribute a lot with their expertise in the field

and worldwide influence. Finally, both the communities as well as research institutions cannot do

much to increase the economic capital of households.

4.4.6 Off-farm diversity The first of both sorts of diversity is off-farm diversity. A household itself can do a lot to enhance its

off-farm diversity. Obviously, they can choose themselves what amount of their income that would be

derived from non-agricultural activities would be. But, as already mentioned in the literature study,

not every off-farm job should be considered the same. Non-agricultural activities that are taken out of

opportunity are more valuable than those taken out of need (Barrett et al., 2001). Therefore, every

off-farming activity should be reviewed critically by the household. Furthermore, the community could

also be very important: if the better jobs would partially be given to those that are the most in need,

this would make those families a lot more resilient. Nevertheless, we acknowledge that this is probably

a lot easier in theory than in practice. Finally, the role of the government should not be

underestimated. With the proper legislation and framework, they could create more jobs that fit farm

workers well. For the research institutes and organizations as Rikolto, it is probably harder to enhance

this indicator.

4.4.7 On-farm diversity Also for on-farm diversity, farmers can do a lot themselves. Cultivating different crops or breeding

different animals is a job that requires courage. But it is definitely achievable, and often it is possible

to peek at other farmers’ management to gain inspiration. In this light, the community can also take

up responsibility. If the community is open about their management and households can learn from

each other, it is much more likely that farmers will adapt more easily. Additionally, research institutes

and NGOs are also crucial to enhance on-farm diversity. Research institutes should do this by reducing

the risk of adopting a new crop. Therefore, research should be aimed at creating new variety, and

optimizing robustness and yield of crops and animals. Also extension is essential to make farmers adopt

new crops and animals. Finally, also NGOs play a role in the enhancement of on-farm diversity. By

stimulating diversity and showing the importance of it, farmers will more easily apply it.


4.4.8 Innovativeness The last indicator is innovativeness. This is clearly the odd man out in the indicator framework. To

begin with, this is the hardest indicator for farmers to enhance themselves. It is very hard for farmers

to be innovative as a farmer if nobody shows how or if no good examples are available. Nevertheless,

it is certainly possible to be innovative without any proper precedent. Also, both research institutions

and NGOs as Rikolto are very important. Research institutions can do the fundamental research on

innovative techniques and practices. This will reduce the risk significantly. Furthermore, extension is

crucial to show farmers the options that are available and easily applicable on the farm. It is clear that

research is indispensable to make farmers innovate. Furthermore, Rikolto can also help farmers to

innovate. To begin with, they can stimulate farmers and show them examples of successful

innovations. Furthermore, an NGO as Rikolto would be placed very well to build a framework to help

and decrease the risk that innovations bring with, by sharing the risk in cooperatives for example.

4.4.9 Conclusion From the advice stated above, it can be concluded that the households themselves have an important

role in enhancing their resilience. This should not be interpreted as if households themselves are only

responsible for their own resilience. The reason that households can influence every indicator is that

all indicators were created to apply on farmers, so a distinction could be made between farmers in the

same region. A result of this is that farmers can influence those indicators largely themselves.

Furthermore, all other stakeholders that are mentioned have their influence on certain field.

Therefore, only with proper cooperation, they will be able to maximize those indicators. Interaction

between the different stakeholders is indeed indispensable to create a favorable environment for

farmers to enhance their resilience. When the settings are encouraging, farmers will naturally become

more resilient.

Chapter 5: Conclusion 71

Chapter 5 Conclusion

The definition of resilience in the context of smallholder farming made clear that it is rather a broad

concept that has many dimensions. Nevertheless, we were able to narrow the word down and to find

eight indicators that could describe resilience in the context of Tanzanian family farmers the best.

These indicators were (1) education, (2) health, (3) social capital, (4) natural capital, (5) economic

capital, (6) off-farm labor, (7) on-farm labor and (8) innovativeness. These made up the indicator

framework, of which the resilience of households could be tested.

Two groups were deduced from the sample. One group that had overall good scores for the indicators

and on with rather bad scores. With these scores, it was tested whether there was a difference in the

amount of shocks that were perceived during the years 2004-2010 and the overall perception of these

years was looked into. On the first test, the VG group did slightly better, but not significantly. On the

second test, there was a significant difference. It can thus be said that our indicator framework does

work, yet it has some limitations. For example not all estimators have a good distribution.

Furthermore, some estimators are only proxies of the respective indicators, because no better

alternative is available.

To critically analyze the indicator framework, it was also compared to three other frameworks. From

this comparison, it became clear that all frameworks are quite different in scope and starting point, as

well as objectives. Therefore, it is only logical that the result will also differ. A second finding is that

the other frameworks have a level that stands above the indicators. These were called dimensions.

The dimensions indicate a very broad overarching theme in which several indicators are placed. In

contrary, our indicator framework does only have indicators and estimators to measure these

indicators. Furthermore, we found that all frameworks do have some indicators others do not have.

We argue that it might be interesting to compare the frameworks more in depth or to apply them on

a certain case study to analyze the similarities and differences more in detail. It might also be

interesting to try to add certain new indicators to our framework.

Also, implications and advice was formulated for several stakeholders. The households themselves,

the community, the government, research institutes and also Rikolto were evaluated and suggestions

for improvements were given. We can conclude that all stakeholders can improve the resilience of a

household by influencing certain indicators. Yet, only the household have an impact on all indicators.

For every indicator, several collaborators influence together. Therefore, cooperation between these

stakeholders is of the highest importance. This cooperation will allow that the indicators advance. In

turn, this will allow that smallholder farmers become much more resilient.

72 Chapter 5: Conclusion

Finally, the importance of a general framework for resilience must be mentioned. At this moment,

resilience has been more an academic debate than a novelty in the field. Because the concept is this

broad and abstract, it is very interesting to research it in theory, but very hard to apply it in the field.

Nevertheless, several frameworks for resilience are already available. We state the need for a general

framework for resilience of smallholders that can be used all over the world. Because the needs and

concerns differ highly across the world, this framework should be rigid in its indicators but flexible in

the estimators. The framework should also be easy accessible and measurable to enhance broad use.

This model will prove to be valuable to measure and enhance the resilience of farmers around the

world.

Chapter 6: Bibliography 73

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Annexes 89

Annexes

Annex 1 Additional figures and tables Table A.1: different scores that can be obtained for the indicator "education"

Code What Number in code

P0 Pre-school 1

KORANIC Koranic School 2

ADULTED Adult education 2

P1 Year of primary school 3








S1 Year of secondary school 11




A1 Year of Advanced level 15

A2 Year of Advanced level 16

U1 Year of University level 17








90 Annexes

Table A.4: full table of short-term diseases with their

respective occurrence (on household member’s base)

Long term absolute percentage

Aids 38 3.69

herpes 9 0.87

urinary infection

1 0.1

gonorrhea 1 0.1

Syphilis 9 0.87

Malaria 46 4.46

Typhoid 1 0.1

measles 21 2.04

Meningitis 5 0.48

Polio 37 3.59

TBC 1 0.1

Tetanus 63 6.11

Asthma 26 2.52

Pneumonia 1 0.1

Bilharzia 15 1.45

Intestinal worms

1 0.1

Diarrhea 1 0.1

Dysentery 1 0.1

Kwashiorkor 4 0.39

Marasmus 20 1.94

Fracture 8 0.78

Diabetes 42 4.07

Pressure 123 11.93

Flu 3 0.29

Witchcraft 7 0.68

Anemia 11 1.07

doctor doesn’t know

69 6.69

Other 5 0.48

other illness 417 40.45

other injury 12 1.16

don't know 33 3.2

Sum (people) 1266

Short term absolute percentage

Syphilis 2 0.16

Malaria 318 25.12

Typhoid 32 2.53

Meningitis 4 0.32

TBC 2 0.16

Asthma 9 0.71

Pneumonia 5 0.39

Bilharzia 1 0.08

Intestinal worms 30 2.37

Diarrhea 34 2.69

Dysentery 2 0.16

Kwashiorkor 1 0.08

Marasmus 1 0.08

Fracture 3 0.24

Poisoning 1 0.08

Pressure 4 0.32

Flu 611 48.26

Witchcraft 2 0.16

Anemia 1 0.08

doctor doesn’t know

1 0.08

Other 1 0.08

illness 193 15.24

other injury 2 0.16

don't know 6 0.47

Sum (people) 1031

Table A.3: full table of short-term diseases with their

respective occurrence (on household member base

Annexes 91

Table A.5: list of the possible choices for crops and animals, given in the KHDS

Crops

Number Crop Number Crop

1 Robusta Coffee 2 Arabica Coffee

3 Trees, nursery 4 Trees, timber

5 Trees, firewood 6 Cooking bananas, traditional

7 Cooking bananas, improved 8 Desert bananas, traditional

9 Desert bananas, improved 10 Roasting bananas

11 Brewing bananas 12 Tea

13 Tobacco 14 Cotton

15 Cassava 16 Yams

17 Sweet potato 18 Irish potato

19 Maize 20 Bullrush Millet

21 Finger Millet 22 Sorghum

23 Vanilla 24 Mushroom

25 Paddy rice 26 Beans

27 Sya Bean 28 Bambaranuts

29 Peas 30 Sunflower

31 Avocado 32 Mangoes

33 Pawpaw 34 Pineapple

35 Citrus fruit 36 Passion fruit

37 Other Fruits 38 Tomatoes

39 Onion/leek 40 Eggplant

41 Cabbage 42 Other vegetables

43 Other crops 44 Groundnuts

88 None

Animals

1 Bull 2 Cows

3 Sheep 4 Goats

5 Chicken 6 Pigs

7 Ducks 8 Rabbits

92 Annexes

Table A.6: different elements in the wealth analysis and their respective weights. Comments on the implementation in the KHDS survey are also given

Asset If has If does not have

Comments

Electricity 0.205 -0.036 We look if there is an electricity bill

Paraffin lamp 0.054 -0.044 Bought paraffin last month

Radio 0.033 -0.052 In “durable assets”

Television 0.211 -0.031 In “durable asset”

Mobile telephone 0.069 -0.062 We take mobile phone, because 99 per cent of phones are mobile (TCRA, 2010) Telephone (non-mobile) 0.272 -0.003

Iron 0.113 -0.037 In “durable asset”

Refrigerator 0.249 -0.018 In “durable asset”

Watch 0.062 -0.030 In “durable asset”

Bicycle 0.003 -0.003 In “durable asset”

Motorcycle or Scooter 0.170 -0.006 In “durable asset”

Car or Truck 0.252 -0.005 In “durable asset”

Bank account 0.172 -0.024

Agricultural land owned for farming 0.000 0.000 function haowned() used

Agricultural land owned for grazing -0.002 0.000 Not mentioned in KHDS, not used.

Agricultural land used for farming 0.000 0.000 All respondents have this

Agricultural land used for grazing 0.001 0.001 Not mentioned in KHDS, not used.

Number of members per sleeping room -0.001

Weighted for household members, afterwards product with score is taken

Piped into dwelling 0.175 -0.012

Piped into yard/plot 0.121 -0.006

Communal tap -0.010 0.002

Neighbors tap 0.071 -0.007

Open well in dwelling 0.000 0.000 Useless

Open well in yard/plot -0.010 0.000

Open well in community -0.051 0.013 Not mentioned in KHDS, not used.

Open well of neighbor 0.000 0.000

covered well in dwelling 0.148 0.000 Not mentioned in KHDS, not used.

covered well in yard/plot 0.065 0.000 Not mentioned in KHDS, not used.

covered well in community -0.029 0.003 Not mentioned in KHDS, not used.

Borehole/covered well of neighbor 0.094 -0.002 Not mentioned in KHDS, not used.

Spring -0.038 0.003 Taken together, because mentioned as one in KHDS

Surface water-river, lake, dam, etc. -0.064 0.011

Water from rain 0.023 0.000

Water from tanker truck 0.095 -0.001

Water from vendor 0.144 -0.001

Water from bottle 0.222 -0.001 Not mentioned in KHDS, not used.

Flush toilet to sewer 0.233 -0.001 All flush toilets taken as one. Mean is used as value

Flush toilet to septic tank 0.253 -0.003

Annexes 93

Flush toilet to pit latrine 0.172 -0.014

Flush toilet to elsewhere 0.172 -0.001

Traditional pit latrine -0.026 0.034 All pit toilets taken as one. Mean is used as value

Pit latrine with slab 0.035 -0.006

VIP latrine 0.155 -0.004

Composting toilet/ecosan 0.069 0.000 Not mentioned in KHDS, not used.

No facility/bush/field -0.073 0.015 Not mentioned in KHDS, not used.

Other type of latrine/toilet -0.059 0.000

toilet with other households 0.042 -0.013 Not mentioned in KHDS, not used.

Earth, sand, dung floor -0.063 0.121

Rudimentary wood plank, bamboo floor -0.044 0.000

Only bamboo is mentioned in KHDS

Cement floor 0.117 -0.056

Vinyl, asphalt strip floor 0.277 0.000 Not mentioned in KHDS, not used.

Ceramic tile floor 0.274 -0.002 Only “tile” is mentioned in KHDS

Carpeted floor 0.224 -0.001 Not mentioned in KHDS, not used.

Polished wood floor 0.069 0.000

Other type of flooring -0.034 0.000

Grass/thatch/mud walls -0.084 0.001 Taken together because second option not available in KHDS. Word “mud brick” used instead of mud walls in KHDS.

Poles and mud walls -0.057 0.027

Sundried brick walls -0.048 0.014 Not mentioned in KHDS, not used.

Baked brick walls 0.018 -0.004 Taken together. One category in KHDS.

Stone walls -0.037 0.000

Timber, wood walls 0.144 -0.031

Cement block walls 0.060 -0.002

Stone walls -0.057 0.001

Other type of walls -0.081 0.053

Grass/thatch/mud roof 0.051 -0.073

Iron sheet roof 0.089 0.000

Asbestos roof 0.143 -0.001

Tile roof 0.286 -0.001

Concrete roof -0.049 0.000

Other type of roof 0.269 -0.002

Electricity for cooking 0.313 -0.001

LPG, natural gas for cooking 0.145 -0.003 Only gas is mentioned in KHDS.

Kerosene for cooking 0.143 -0.031

Charcoal for cooking -0.040 0.147

Wood, straw for cooking 0.085 -0.001 Only wood is mentioned in KHDS

Does not cook 0.098 0.000 Not mentioned in KHDS, not used.

Other fuel for cooking 0.208 -0.036

Electricity for lighting 0.082 0.000

94 Annexes

Solar electricity for lighting 0.283 0.000

Gas for lighting 0.027 -0.008 Gas, paraffin and kerosene are taken together in KHDS. Paraffin-hurricane lamp -0.052 0.000

Paraffin-pressure lamp -0.062 0.070

Wick lamp for lighting 0.073 0.000

Candles for lighting -0.080 0.001

Firewood for lighting -0.062 0.003

Other type of lighting -0.015 0.002 Not mentioned in KHDS, not used.

Rents land for farming 0.042 -0.001 All sorts of rent mentioned in KHDS are pooled.

Sharecrops land for farming -0.010 0.001 Not mentioned in KHDS, not used.

Free use of private land for farming -0.036 0.001

communal land for farming 0.004 -0.012

No unowned land for farming 0.205 -0.036

Annexes 95

Table A.7: all reasons for shocks the households could choose in the KHDS

Table A.8: all coping strategies households could choose in the KHDS

Reasons for shock

1. Low crop prices

2. Poor harvest (because of weather)

3. Poor harvest (because of other reason)

4. Livestock death

5. Loss of age-employment

6. Low income because of due to own business

7. Low income because of remittances

8. Withdrawal of support by organizations

9. Loss of house due to eviction or replacement

10. Death of a family member

11. Serious illness

12. Loss of assets due to crime or violence

88. Other

Coping strategy for shock

1. Reduced consumption

2. Sold livestock

3. Sold land

4. Sold other assets

5. Started selling processed food

6. Started other business

7. Took casual employment

8. Took off-farm wage employment

9. Introduced other crops

10. Relied on support from formal organization

11. Relied on support from informal organization

12. Relied on support from family and friends

13. Migrated to work elsewhere (fishing)

14. Migrated to work elsewhere (other than fishing)

15. Took children from school

16. Migrated to live with relatives or friends

88. Other

96 Annexes

Table A.9: full table of all the principles with their respective SERIs, mentioned in the SERI framework of Panpakdee & Limnirankul (2017)

Resilience principle Component N° SERI

Learning to live with change and uncertainty

Being prepared themselves for unpredictable events

1 Educational level

2 Rice farming experience

3 Occupational skills

4 Gender equality

Reasonable investment to reduce risk

5 Investment in farm assets

6 Investment in basic farm equipment

Know how to use familiar resources

7 Utilization of ecological services

8 Additional exploitation of existing water resources

Being open minded and willing to make changes on the farm

9 Inquisitive mind for lifelong leraning

10 Organically oriented mindset

11 Land tenure

Nurturing diversity for reorganization and renewal

Diversity of bio-diversification

12 Diversity of plant species

13 Diversity of rice varieties for production

Diversity of economic opportunities

14 Diversity of income sources

15 Diversity of marketing channels

16 Ownership of guaranteed price and organic certification

17 Given honorific address

Diversity of resources 18 Diversity of water resources

19 Diversity of credit resources

Diversity of information sources

20 Diversity of information sources

Diversity of parnters and relationship types

21 Diversity of collaborative networks

Combining different types of knowledge for learning

Acquiring knowledge from science and indigenous knowledge

22 Knowledge designed by a bottom-up approach

23 Heritage of indigenous knowledge

24 Existence of dialect and local traditions

Obtaining knowledge from self-efforts

25 A variety of learning approaches

26 Obtaining knowledge through the second form of agricultural employment

27 Effective use of ICT

Adaptive capacity 28 Adaptation

29 Value added products

30 Organizeing fincancial flows with the household account

31 Reasonable farm scale

32 Securing consumer confidence

Time availablity for learning 33 Being full time farmer

34 Marital status and independence of children

35 Number of farming generations

Annexes 97

Living in the environment favorable for learning

36 Number of neighboring organic farmers

Creating opportunity for self organization and cross scale linkages

Farm level: being dependent on available resources

37 Dependence on household resources

38 Self-rice seed production

39 Dependence on rice and dietary materials self-production

40 Dependence on household labor

41 Rice field location

Community level: co-usage of livelihood assets

42 Co-operative farming

43 Knowledge exchange through networks

44 Dependence on locally productive inputs

45 Dependence on local food systems

46 Mutual labor exchange

Cross-scale level: opening up networks with the governments

47 Favorable support from the goverments

98 Annexes

Annex 2 Exact outline of functions used

The functions made, have a typical outlook. Although a lot of functions have been made, the outline of all

of them is similar. We will only touch upon the general outline.

1. The function always starts with importing the data file and the creation of empty arrays.

2. After this, a for-loop19 is generated, with the length of the total amount of rows of data. This is not

necessarily equal to the amount of households in the data: it might as well be a multiple, because

a row is created for each household member, or for the amount of plots a certain household has.

3. Following, an if-function20 is set up. The condition of this if-function is the question whether this

is the first time the loop runs or not. This is important: as the data should not be given per

household member or plot, but per household in total, the i’th row’s household code should be

different than the (i-1)th. Yet, for the first row, i-1 would be 0, which is not accessible.

4. Next, another if-function starts. This one has the fact that the household should be part of the

demanded sample as a condition. Only then, the code for this loop should run, otherwise, a new

loop should be started.

5. Then a while-loop21 is constructed. As we need to obtain a weighted average of all the household

members, or the amount of plots, a while-loop is used. The condition of this while loop is that the

household number is the same. Obviously, for data that is on household base, this while-loop is

not necessary.

6. In this while-loop, the data is collected. The indicator for a certain household member is asked and

added in an array. Also the amount of household members or plots is demanded.

7. Finally, after the loops, the weighted average is obtained by dividing the aggregated sum per

household by the amount of household members or plots.

8. This number is then put into a data frame22 and returned.

These steps are shown graphically in a flow chart on Figure A.1.

19 A for-loop is a loop used in programming to repeat a section of code a given number of times. 20 An if-function is a function that only executes a section of code when a given condition is met. 21 A while-loop is a loop used in programming to repeat a section as long as a certain condition is met. 22 A data frame is a data type that represents all the data as tabular, while the different cells in the table might be of a different data class. I.e. one row can be of data type “character”, which means that it is text, while the other one can be “numeric” or numbers. A data frame thus provides neat data tables that are easy readable and accessible.

Annexes 99

Figure A.1: flow chart of the structure of a general function for indicator creation

100 Annexes

Annex 3 Additional information on the education system in Tanzania

The schooling system in Tanzania important in the margin of this dissertation. Therefore, it is outlined

here. In Tanzanian education, four levels can be distinguished. These are primary, secondary, advanced

level and university education. Pre-school or pre-primary education is available before primary education.

Yet this is not compulsory. In contrary, primary education is compulsory and free as well. Only school

materials should be provided by the household itself (McClure, 2017). Because it is compulsory and free,

the attendance rate is quite high: 79 per cent of children aged seven to fourteen attend school (World

Bank, 2014). Primary education takes about seven years and ends with a leaving school examination. Based

on the results, they can enter secondary education or vocational training (Nuffic, 2015). Vocational training

was defined by Bosch & Charest (2008) as education for jobs that require less than a bachelor’s degree.

Secondary education is for children aged fourteen to seventeen, as mentioned in Table A.10. Secondary

education is not free and most schools are private or boarding schools. Therefore, secondary education is

by far not as popular as primary education. The rate is of participation in secondary education is only 32

per cent as in 2013 (Unesco, 2018).

Advanced level education could be seen as a second part of secondary education. This level takes up two

years (McClure, 2017).

Finally, one can attend university level education. This is the highest level of education that is available in

Tanzania. The length of a diploma at university level depends on field of study, but generally, a bachelor

degree would take up three years. Also at this level, fees have to be paid.

Table A.10: different levels of education in the Tanzanian education system with age of the child and duration

Age of child Amount of years Level of education

5-6 2 Pre-school

7-13 7 Primary school

14-17 4 Secondary school

18-19 2 Advanced level education

20-… 3 or more University level education