Centre for Geo-Information

Thesis Report GIRS-2004-034

Agricultural Indicators of :

a GIS case study in the highlands of

June 2004

By: Uxue Iragui Yoldi Reg. nr: 800229383070

Agricultural Indicators of Poverty:

a GIS case study in the highlands of Guatemala

Uxue Iragui Yoldi

Thesis submitted in partial fulfilment of the degree of Master of Science at Wageningen University and Research Centre, The

Supervisors:

Prof. Dr. Ir. A.K. Bregt

Ing. J. Stuiver Ir. J. van Etten

June 2004, Wageningen, The Netherlands

Thesis code number: GRS-80326 Thesis report GIRS-2004-034 Laboratory of Geo-Information Science and Remote Sensing

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Acknowledgements

Thanks to my supervisors for all the professional support, suggestions and ideas that they have always given me during all this period: Arnold, John, Jacob (and Laura).

Thanks to ALL the students in Alterra with whom I have shared so much during these last two years (you still owe Judith and me 1 euro for the results of the African Cup….!!!, you thought I forgot already?).

Thanks to Mark for all his care and patience every time I started to talk about my thesis.

Finally thanks to my family, even though they are far away, they always make me feel as if they were standing next to me.

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Abstract

When the civil war ended in Guatemala in 1996, new doors for development were opened. The principal problem to solve since then is poverty, and in order to reduce the gap between rich and poor, it is essential to know what causes it. However, information in Guatemala about the variables causing poverty and their distribution are only found in a national scale, but local data is almost non-existent. This thesis tried to study some of the variables that could be causing poverty, at a local scale. Agriculture is the largest sector in Guatemala, employing more people than all other sectors combined. Therefore the study focussed on the relation that agricultural variables have with poverty, in the highlands of Guatemala. The aim of the research was to identify those variables that indicate poverty, and study their distribution around the area. With this purpose, a model was built using different variables and relating these to a real poverty map. The Land Use type, Farm Strategy, Market Development and Accessibility to markets were compared to the poverty level of the study area. The Land Use type seemed to have no correlation with poverty. However, the rest of the variables showed high positive correlations with the later. These variables seem to have a direct relation to the income of the area, making it possible to use them as indicators of poverty. A combination of all the variables of study and poverty were compared and showed an even higher correlation with the presence of poverty, indicating an interaction between the different variables used. The combination also helped to detect those areas that were not explained by the variables of study. The results were proven as robust after checking the sensitivity of the model. Further research should be done to test these results and to compare the variables used in the model to other agricultural variables, to test the overall applicability of it.

Keywords: Guatemalan highlands, Poverty, Land Use, Farm Strategy, Market Development, Accessibility of markets, robustness.

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Table of Contents

ACKNOWLEDGEMENTS . ii ABSTRACT iii TABLE OF ACRONYMS vi TABLE OF TABLES vi TABLE OF FIGURE vi CONTENT OF APPENDIX vii TABLE OF FIGURES IN APPENDIX vii 1 INTRODUCTION ...... 1 1.1 CONTEXT ...... 1 1.2 OBJECTIVES ...... 2 1.3 THESIS PROCEDURE ...... 2 2 BACKGROUND...... 3 2.1 THE STUDY AREA ...... 3 2.1.1 Guatemala ...... 3 2.1.2 Departments of Chimaltenango and Sacatepequez ...... 4 2.2 AGRICULTURAL VARIABLES OF POVERTY ...... 5 2.2.1 Land Use...... 5 2.2.2 Farm Strategy...... 8 2.2.3 Market Development...... 9 2.2.4 Accessibility to markets ...... 10 2.3 POVERTY IN THE STUDY AREA ...... 10 3 MATERIALS AND PRE-PROCESSING ...... 13 3.1 CREATING THE LAND USE MAP...... 13 3.2 CREATING THE FARM STRATEGY MAP ...... 14 3.3 CREATING THE MARKET DEVELOPMENT MAP ...... 16 3.4 CREATING THE MAP OF ACCESSIBILITY TO MARKETS...... 17 3.5 POVERTY DATA OF GUATEMALA ...... 19 4 METHODOLOGY ...... 20 4.1 PROCEDURE ...... 20 4.2 BUILDING THE MODEL ...... 20 4.2.1 Creating the poverty maps...... 20 4.2.2 Relating the created maps with the real poverty data ...... 22 4.3 SENSITIVITY ANALYSIS ...... 23 4.3.1 Order analysis ...... 24 4.3.2 Weight values analysis...... 24 5 RESULTS AND DISCUSSION ...... 25 5.1 RELATION OF EACH AGRICULTURAL VARIABLE WITH POVERTY...... 25 5.1.1 Results and discussion of the Land Use variable...... 25 5.1.2 Results and discussion of the Farm Strategy variable...... 28 5.1.3 Results and discussion of the Market Development variable...... 30 5.1.4 Results and discussion of Accessibility...... 32 5.2 COMBINATION AMONG ALL THE AGRICULTURAL VARIABLES ...... 34 5.3 RESULTS OF THE SENSITIVITY ANALYSIS...... 36 5.3.1 Results and discussion of the order analysis ...... 36 5.3.2 Results and discussion of the weight values analysis ...... 37 5.4 CRITICAL REFLECTION ...... 38 6 CONCLUSIONS AND RECOMMENDATIONS...... 40 6.1 CONCLUSIONS...... 40 6.2 RECOMMENDATIONS ...... 41 REFERENCES 44 APPENDIX I

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Table of Acronyms

2D: Two-Dimensional AGEXPRONT: Asociación Gremial de Exportadores de Productos No Tradicionales ANACAFE: Asociación Nacional del Café ASTER: Advanced Spaceborn Thermal Emission and Reflection Radiometer COVERCO: Comisión para la Verificación de Códigos de Conducta DEM: Digital Elevation Model FAO: Food and Agriculture Organization of the GDP: GIS: Geographic Information Systems GNP: Gross National Product GPS: Global Positioning System INE: Instituto Nacional de Estadística de Guatemala MAGA: Ministerio de Agricultura, Ganadería y Alimentación de Guatemala NGO: Non-Governmental Organization SEGEPLAN: Secretaría de Planificación y Programación de la Presidencia de la República de Guatemala UTM: Universal Transverse Mercator

Table of Tables

Table 1: Classification accuracy assessment results 14 Table 2: Data about the quality of the road in the study area 18 Table 3: Poverty weights entered in the model for each of the classes within each variable 21 Table 4: Correlations between each individual spatial agricultural variable and the real poverty data 25 Table 5: Correlations between the combination of all the spatial agricultural variables and the real poverty data 35 Table 6: Poverty order of each class within each agricultural variable according to the model and to literature review 37 Table 7: Descriptive statistics of the 10 different weight values applied to each variable and level or work in the sensitivity analysis 39

Table of Figures

Fig. 1: Map of , Guatemala, and the area of study (departments of Chimaltenango and Sacatepequez) 3 Fig. 2: Images of the study area. 12 Fig. 3: Steps followed during the pre-processing to create the Land Use map 13 Fig. 4: Steps followed during the pro-processing to create the Farm Strategy map 15 Fig. 5: Steps followed during the pre-processing to create the Market Development map 17 Fig. 6: Steps followed during the pre-processing to create the Accessibility map 18 Fig. 7: Diagram of the model, in which the poverty maps are created using the outputs of the pre- processing part as input data 20 Fig. 8: Steps followed to compare the real poverty map of the government of Guatemala, with the poverty maps created in the model 22 Fig. 9: Diagram followed for the sensitivity analysis 23 Fig. 10: Map relating the land use poverty map created in the model, with the real poverty data, at a municipal and 30x30m resolution 28

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Fig. 11: Map relating the farm strategy poverty map created in the model, with the real poverty data, at a municipal and 30x30m resolution 30 Fig. 12: Map relating the market development poverty map created in the model, with the real poverty data, at a municipal and 30x30m resolution 32 Fig. 13: Map relating the accessibility poverty map created in the model, with the real poverty data, at a municipal and 30x30m resolution 34 Fig. 14: Map relating the poverty map of the combination of all the spatial agricultural variables created in the model, with the real poverty data, at a municipal and 30x30m resolution 36 Fig. 15: Correlation values gotten for 10 different poverty weights applied to each agricultural variable and level of work in the sensitivity analysis 38

Content of Appendix

Appendix 1: Poverty map of the study area II Appendix 2: Interviews III Appendix 3: Digital data of the study area VI Appendix 4: Outputs of the Pre-Processing X

Table of Figures in Appendix

Fig. A1: Map representing % of poverty in the municipalities of the departments of Chimaltenango and Sacatepequez II Fig. A2: Map representing the departments of the study area and the shape of the cities, villages and farms VI Fig. A3: Map representing the municipalities of the study area, and the trajectory of the rivers VII Fig. A4: Map representing the location of the rural markets and the path and quality of the roads of the study area VIII Fig. A5: Map representing the Digital Elevation Model in the municipalities of the study area IX Fig. A6: Map representing the Land Use of the study area created with the pre-processing steps X Fig. A7: Map representing the Farm Strategy of the study area created with the pre-processing steps XI Fig. A8: Map representing the Market Development of the study area created with the pre- processing steps XII Fig. A9: Map representing the Accessibility to markets of the study area created with the pre- processing steps XIII

vii ______Introduction

1 Introduction

1.1 Context On December 29, 1996, the final Peace Accords were signed in Guatemala, bringing to an end the 36-year civil war that had so long wrecked the nation. The Accords have opened the door to massive civilian reconstruction and long needed socio-economic reforms. Despite the era of peace however, the situation has not changed as the population had hoped. The main reason is that unfortunately, information on the evolution of poverty is scarce in this country and the analysis of its causes are almost nonexistent. This has allowed the economic elites and governments to ignore this serious reality (SEGEPLAN, 2001). In Guatemala the main source of employment for the poor is agriculture. This sector accounts for one fourth of GDP and two thirds of exports and employs more than half of the labour force (, 2004). Thus, it is evident that it’s essential to accelerate agricultural growth if poverty is to decline rapidly (Mellor, 2000). If leaders are seriously concerned with the welfare of their people, one of the most effective ways that they can readily improve the welfare for the majority is by helping to raise the farmer’s income (World Bank, 1999). But what is actually happening in rural areas? The problem of agricultural rural poverty has traditionally been explained with the “minifundio-latifundio” structure. Here the capital flows to a few entrepreneurs rather than being dispersed in the local systems, where local specialization and industries are not developed (Dosselaere, 2003). This theory explains that due to the huge inequality of the capital and the gap between rich and poor, the local market for goods are restricted to an elite. However, these previous statements are based on the statistics from the 1950, 1967 and 1979 agricultural census (no new census has taken place since then) (Cabrera del Valle, 2002). Today, it is thought that this idea is not entirely true because a vigorous informal marketing system has developed during the 20th century in the highlands. Also, in the last 20 years new products started to be grown for exportation (such as , strawberries or snow peas), bringing opportunities for farmers to increase their incomes by planting these crops. Nowadays there is a need to investigate the new rural reality, and update the interpretation of the agrarian problematic, always explained by the so-called traditional dualism of “minifundio- latifundio” (Avansco, 2001). Only with an understanding of the immediate causes of poverty is it possible to find a variety of explanations and solutions to the development problem.

This thesis aims to update the rural situation of Guatemala, by studying the relations that different agricultural variables have with the poverty level. The variables to be studied were:

• Land use type (to detect which products are grown), • Farm strategy (to know where do farmers sell their products), • Market type (to detect its development), • Accessibility (to calculate travel time to markets).

The hypothesis is that there are strong relations between the different variables and the level of poverty, making it possible to use them as indicators of poverty. These indicators, when identified, can help the government and policy makers design their strategies in order to alleviate poverty, fulfilling a commitment they promised when the Peace Accords were signed.

______1 ______Introduction

1.2 Objectives The aim of this report is to study the influence that the different agricultural variables (land use distribution, farm strategy, market type and infrastructure) have on the poverty level of the study area, and examine whether there is a significant relation between poverty and such spatial variables. Existing relations can offer the government of Guatemala a comprehensive assessment of agricultural poverty and policies that affect the prospects for poverty reduction in the country. Therefore, the research questions of this thesis are:

• What are the relations between individual spatial agricultural variables studied (land use type, farm strategy, market development, and accessibility), and the level of poverty?

• What are the relations of combined spatial agricultural variables and the poverty level?

• How robust are the identified relations?

1.3 Thesis Procedure The thesis is structured as follows. Chapter 2 provides a description of the area of study in Guatemala, and explains what are the agricultural variables that, potentially, have a relation with poverty. A description of each of them is given, as well as their characteristics, and why they are considered relevant for this thesis. Chapter 3 explains the raw material that was needed in order to carry out the research. Much of this material was not originally found in a digital form. Therefore, an important pre-processing part was done to prepare the data needed to build a model in which the variables of study and poverty could be related. Chapter 3 explains how this input data for the model was created. Chapter 4 presents the methodology followed to compare the variables of study with the poverty level. To do this, a model was created. The different steps followed in the model, as well as the statistics analysis done are discussed in this section. In the last part of the Chapter a sensitivity analysis is explained. This was done in order to check the robustness of the model. Chapter 5 outlines the results of the model. It also discusses the output values and maps, trying to explain why things happen. The results of the sensitivity analysis are also discussed in this section. Chapter 6 answers the research questions, and gives some recommendations for further research.

NOTE: All the pre-processing steps followed in Chapter 3, as well as the steps and scripts done in the model and the sensitivity analysis of Chapter 4, were done in ArcInfo and put together on a CD found at the end of the Appendixes.

______2 ______Background

2 Background

2.1 The study area

2.1.1 Guatemala Guatemala is situated in the northwest of Central America and borders to , Belize, and . It has access to both the Pacific Ocean and the , which both are important trade ways. It has an area of 108,890 km2.

Fig. 1: Map of Central America, Guatemala, and the area of study (departments of Chimaltenango and Sacatepequez) Geologically, Guatemala is at the confluence of three tectonic plates. In the southwest of the country is the Cocos plate, which is boarded by the North American plate. The most southern part of Guatemala lies on top of the Caribbean plate, and when any or all of these plates get frisky, earthquakes and volcanic eruptions occur. The western

______3 ______Background highlands, linked by the Interamericana, are the continuation of Chiapa’s Sierra Madre and include 30 volcanoes reaching heights of more than 4,000 m, some of which are still active. The whole country of Guatemala is in the tropics but due to the altitude differences, the climate differs per regions. By the coasts and the lowlands in the north, it is hot and humid, in the highlands, situated in the centre of the country, the temperature can drop to freezing. Rain and dry periods are clearly divided. The rainy season is from May to November. By July 2003, Guatemala’s population reached 13,909,384. About 40% of live in cities, the biggest of which are , Quetzaltenango and Escuintla (INE, 2004). Spanish is the official language, but there are 19 Mayan languages spoken plus Xinka and Garifuna. Official census statistics show 56% of the population as mestizo or ladino and 44% as indigenous. Guatemala’s Mayans form the largest percentage Ameridian group of any country in North and Central America. The Mayan civilization flourished throughout much of Guatemala and the surrounding region long before the Spanish arrived, but it was already in decline when the Mayans were defeated by Pedro de Alvarado in 1523-1524. During the Spanish colonial rule, most of Central America came under control of the captaincy General of Guatemala. In the year 1821 Guatemala got their independence from . The history of the independent country has been everything but calm. Dictators, democracy and civil wars have affected the nation. Today the society is very divided. Two percent of the population owns 80% of the land, and three quarters of Guatemalans live in poverty with nearly 60% of the population unable to meet minimal nutrition needs (FAO, 2002). Guatemala’s economy is dominated by the private sector, which generates about 85% of GDP. Agriculture contributes to 25% of GDP and accounts for 75% of the export (World Bank 2004). The leading export products are: , , , medicaments and crude petroleum. Fruits, vegetables, and flowers are also grown for export. The distribution of income and wealth remains highly skewed. The wealthiest 10% of the population receives almost one-half of all income; the top 20% receives two-thirds of all income. As a result, approximately 80% of the population lives in poverty, and two-thirds of that number live in extreme poverty. For this reason, reducing poverty is one of the main goals of policy makers in Guatemala.

2.1.2 Departments of Chimaltenango and Sacatepequez Guatemala is a republic divided into 22 administrative regions called departments. Chimaltenango and Sacatepequez are two of those 22 departments, and is the study area of this thesis. They are located in the western highlands of the country (see Fig. 1). Sacatepequez is situated to the west of the department of Guatemala City, where the capital is located. It has an extension of 465 km2 (it is the smallest department of the country), and has a population of 226,181. Chimaltenango is to the west of Sacatepequez, it has an extension of 1,979 km2 and a population of 432,563 (INE, 2004). The relief of the departments is varied, because Sacatepequez is over the Sierra Madre, and Chimaltenango is situated in the Andes Mountain Chain. Therefore they have high plateaus and deep ravines where the rivers, volcanoes, and small valleys are found. Agriculture is the main sector of these two regions, and most of the poor get their incomes from it. The most important grown products are: milpa (a combination of and ), vegetables, coffee, apples, and strawberries (Urrea, 1999).

______4 ______Background

This area was chosen to carry out this research because it is considered as typical for the whole western highlands as it “represents the fate of most Indian communities in western Guatemala” (Dosselaere, 2003). The underdevelopment problems faced by this area are the same as the ones that the rest of the Guatemalan highlands have to cope with. Therefore, a study of the rural reality of these departments can be used to know what is going on in other parts of the country.

2.2 Agricultural variables of poverty As mentioned in Chapter 1, this thesis aims to study the relation that different agricultural variables have with poverty. In this respect, “agricultural variables” are variables that are directly connected to the incomes of people working in agriculture, and that can be regulated by the government or policy makers. There are variables that influence poverty but cannot be regulated by legislation. If direct relation is found between poverty and slope, there is hardly anything a government could do to solve this problem. In this case, low slopes would be just an extra-bonus for the people living there. These “non-influential variables” are usually ecological. However, other variables can be regulated. If it was found that poverty had a direct relation with the products grown in an area, the government could act on this by promoting those crops that give highest incomes. Therefore, only variables that can be changed by policy makers were studied in this thesis. The first variable studied was the land use in the area, which products are grown. The second was the strategy of the farms, where they sell their products, export companies, or in local markets. The third variable considered was the market development, local markets are very important because it is where farmers sell and buy their products. The fourth variable was the accessibility of the area, fundamental to reach the markets. The way these variables can influence poverty is explained in the next paragraphs.

2.2.1 Land Use Land Use is the first variable that was compared with poverty distribution. To find a possible relation, the actual land use situation can be understood by some study of the history of the region. Since the pre-colonial times the Mayans have always lived from agriculture, which is still the most common activity in the region. Milpa, which is an intercrop consisting of maize and beans, has always been found throughout the whole landscape, since it is the base of the Guatemalan diet. During and after the Colonial period, Guatemala exported products that had been originally processed by the Mayan people, such as cacao. At the end of the 19th century, numerous German immigrants came to Guatemala and planted large areas with coffee for exportation. This is how coffee “fincas” or farms originated and today they are still producing some of the finest in the world. In the meantime, in the Guatemalan highlands most farmers were still growing products for self-consumption, the so-called “subsistence agriculture”. Selling the surplus crops in the local market was the only possibility to get an income from working the land. In the 1970’s, the agribusiness of exporting non-traditional products was introduced. The Government of Guatemala promoted the export of vegetables, bringing new opportunities to grow different crops. In this period, Guatemala deliberately started to switch their production pattern away from their dependence on coffee. In 1986, coffee alone contributed for 50% of the total export (Klefbom, 2002). Ten years later coffee answered for 23% of the total export value. Snow peas and broccoli were the new pioneering vegetables. Within 10 years, production of these perish commodities had shifted entirely to smallholders. Despite little

______5 ______Background if any external assistance, by 1996 these crops had grown to support more than 21.000 indigenous families through an estimated US$33 million in additional annual gross income (Hamilton, 2002). Expansion is continuing, as new non-traditional crops such as raspberries gain importance. Agriculture, unlike most other forms of economic activity that benefits from geographic concentration, is tied to a natural resource base that is spatially dispersed and highly variable (Pinstrup, 2001). The spatial distribution of crops can give information about the resources of a certain area and the economical situation of such a place. The land use of the study area has changed in the last years. There has been a decrease in milpa production, for example, and an increase in vegetables. Growing one or another agricultural product has different economical consequences. By means of analysing the land use type of the study area, differences in landscape can be found, and thus possible relations with poverty. In this thesis six land use types were classified: urban areas, vegetables, coffee, maize, forest and bare areas. Each of these classes has different characteristics, and their presence denounce the existence of one or another level of poverty.

• Urban areas: rural poverty is higher than urban. Developing countries offer more opportunities in cities than in the rest of the landscape, and Guatemala is not an exception. This is also prominent in the migration that often occurs from small to big urban centres. Rural areas have a surplus of labour due to the high birth rate. Since the agricultural sector generally grows at a slow rate, this access labour leads to a high level of unemployment and low wages in the rural regions. In urban areas there are more job opportunities, like in the manufacturing sector among others that generally grow at a faster rate than the agricultural sector, thus it can be expected that wages are higher (Perkins, 2001). That is why urban areas are considered to have a lower poverty value than the rest of the land use features.

• Vegetables: from the agricultural products, vegetables are the group that seems to offer the best opportunity for a farmer to raise his income. This group is important because of: its high participation in agricultural GNP (in 1998 vegetables were 4.1% of GNP) and dynamics to create jobs. In 1998 it created 95,300 permanent jobs, 5.4% from the rural employment. 56.3% of the crops is created for the use inside of Guatemala and Central America, and 43.7% is grown to export it outside this area (Hamilton, 2002). Vegetables are the most successful group of export produce for small and medium size farms. The quality of the products is generally excellent due to the fertility of the volcanic lands. The location of Guatemala also offers one more advantage: 75% of the export products go to the , which is easily accessible by plane or boat. Guatemala has specialized in products that are produced efficiently relative to the rest of the world. Therefore, the competitiveness of Guatemalan vegetables is high, and so far farmers who work in this activity are generally well paid. For farmers who do not export, it is also profitable to produce vegetables. Not only is there a wide variety of products to grow, but rotation of vegetable types can be done and prices in local markets are better than for other crops. That is why this land use feature is the second best after urban areas.

• Coffee: coffee is the developing world’s second largest export commodity after oil. It is one of the most important agricultural exports in Central America and also shapes the economy of the region. In Guatemala, it is the biggest and most

______6 ______Background

important crop of the agricultural sector. Over the last twenty years, it has represented an average of 6.6% of GDP and a third of exports (Anzueto, 2002). The coffee industry continually employs 11% of the active population, and this proportion rises to 20% during the harvest period. The problem with this crop is that the coffee price is unstable and that Guatemala is dependent on its coffee export. This combination is not favourable because if the coffee industry is cutting down it affects the population in the form of unemployment, decrease in GDP and a slower economic growth. Coffee is mainly traded in New York and London and one problem with its trading is that the price tends to vary a lot. During 2001 the world experienced the lowest coffee price in nearly a century. In December 2001 the price was down at US$42,50 for hundred pounds coffee, this can be compared with May 1997 when the price was US$318 for hundred pounds of coffee (Klefbom, 2002). Since Guatemala’s economy is more or less dependent on the coffee export revenues, this situation is causing problems. The huge price bust is causing unemployment, poverty, budget deficit and coffee farmers are abandoning coffee or quit farming altogether (Anacafe, 2001). It is surprising at how little research and serious analysis is available concerning the role of coffee in Guatemala’s economy for more than a century (Coverco, 2000). It is the most important export crop of the country, and therefore its economical value should be considered high. But in this thesis it’s considered lower than vegetables, because only a small number of people benefit from the production of coffee while the majority of the labour force earn below average wages.

• Milpa: milpa is a combination of maize and beans. It is the most important food crop in Guatemala, and today its consumption is especially high in low-income population groups, mainly used for self-consumption (Founier Fauchere, 2000). It is grown when a farmer has “nothing better” to plant. When farmers have a chance to grow something else, they do it. Before the export of non-traditional products started, it used to be grown by almost every farmer. But nowadays other crops give higher incomes and milpa has been left out. Yields tend to be low, and extensive poverty is present throughout the area where this crop is grown, reaching levels as high as 80% in the Guatemalan departments of Huehuetenango and Quiche (FAO). Where no alternative sources of cash exist, they are forced to sell output that would otherwise be consumed within the household, hence creating secondary malnutrition. These aspects are a sign of low economical value. For this reason farmers who produce milpa are expected to be poorer than those who produce something else.

• Forest: forests are threatened by colonization, which leads to agriculture and fuel wood collection and commercial logging activities. The loss of forest causes soil erosion and loss of suitable drinking water. That is why today, they are protected by governments, and a higher environmental as well as economical value is given to them. Making a living in the forestry sector can nowadays be more profitable than growing crops. However, this activity only takes places in forest areas close to roads (the rest of the forest is not used for anything). Also only few people benefit from this activity and therefore its economical value is considered low.

• Bare areas: bare areas are infertile. These are volcanic areas composed by rocks, where no plants can be grown and no people live. They only have scientific

______7 ______Background

interest. Since almost nobody makes a profit out of these areas, these are considered to have the lowest economical value.

2.2.2 Farm Strategy Not only which crops are grown is important, but also where these products are sold. If the same product, with the same quality, is sold in the local market, or sold to an export company, the amount of money paid for it is different in both cases. In other words, the income depends on who the consumer of the product is. Thus, to whom the farmer sell his product to must be taken into account. In this case, it is referred to as “farm strategy”. There are many possibilities a farmer has to sell his products. Associations such as NGO’s exist, or local co-operatives, as well as national and international companies that have a wide type of contracts with the farmer. Each group has its own advantages and disadvantages:

• Small and Medium-scale production for exportation: • Producers with a long-term contract: they have the certainty that their products will be sold, and they receive credit (seeds and other inputs). However, the rejection of the product is high (30 or 40%), and the producer does not have other alternatives to sell the products. • Producers with no long-term contract: they can sell small amounts of their products. However, the intermediary is very selective when buying the product, the prices paid here are low, and there is the added difficulty to move the products from the farm to the local market. • Producers belonging to co-operatives: they get credit to buy inputs (such as fertilizers….), they have technical assistance, and they are better prepared for price instability. These producers have a high rejection percentage.

• Big-scale production for exportation: • Producers to local companies: almost no transport costs must be paid, and the export risks are lower. However, prices are significantly lower than in the world market. • Direct export: they obtain better prices, but the products might not be bought (high risk) and payments usually occur when the products have been sold.

• Production for internal consumption: • Selling the products in the farm: it is not necessary to transport the products, so no transportation costs exist. However, the prices are very low. • Producers to local markets: there are no contracts so all the income goes to the producers. Transportation costs can be high, and there is no guarantee that the products will be sold. • Producers to local companies: they have permanent contracts. There are big fluctuations in price.

Some studies suggest that differences among the production groups exist. However, there is a general thought that no clear differences have been found among the different producers. For example, in the 1980’s, co-operative organization allowed the decreasing of risks to some small farmers, increase of access to information and the ability of negotiation (Barham, 1990). In this period co-operative members increased their incomes more than members that did not belong to these associations. However, between 1988 and 1992, this pattern disappeared, and no clear differences have been found among the different groups (Urrea, 1999).

______8 ______Background

Nowadays the difference among different producers seems to be found in whether they have sold their products to agro-businesses or not. During the early years of small-scale production of non-traditional agricultural export, adopters of these crops were able to increase family income. Also within this group of farmers, a difference is seen depending on the quantity of crops sold to the companies or associations. Those farmers who sell 100% of their production to the agro-business they have the contract with have less income than those who save some products for themselves. According to these facts, there are several types of farm strategies: producers who sell in the local market, farmers who sell 100% of their products to agro-businesses Generally to be exported), farmers who sell only 50% of the production to agro-businesses, and farmers who sell between 50 and 100% of their crops. If a relationship exists between the farm type and poverty, then governments can make policies in order to motivate farmers to behave one way or another.

2.2.3 Market Development The role of marketing is an important source of increasing returns and externalities in a market economy, especially at an early stage of development (World Bank, 2001). Many people are dependent on the marketing system for most of their basic provisions. Development of rural markets is a process that allows farmers to adopt production choices that reflect their comparative advantages more closely, contributing to productivity improvements at the aggregate level evaluated at common, market prices. As rural markets develop, production diversity at the farm level goes down quickly, leading to crop specialization. Development of agricultural products markets enables farmers to increase the area allocated to crops for which they have comparative advantages and to depend on markets for food crops for which they do not have a comparative advantage. An identical farm household is expected to get, on average, lower prices for its products if located in a relatively low income, low surplus and hence, less commercialised region (Perkins, 2001). If a farmer is located close to more than one market, not only is it possible to sell the products but the farmer also has the opportunity to choose in which of the markets he or she will get the highest profit. According to this, Christaller studied three marketing principles that imply a different locus of exchange control for an economy (Smith, 1975). Each of these principles has its own economic consequences:

• The competitive principle: here location is not deterministic of the underdevelopment level of the area. A farmer will choose where to sell his or her products according to the prices found in each of the markets, the potential costumers found in them, or other personal reasons. The development level here is high because the competitively among these markets is high. Prices are better in these markets, and the quality of the products tends to be good also.

• The transport principle: in this case, farmers do not always go to those markets where they would like to, but to those where they are able. Transport here is an important element of economic control. The development level of regions where this principle is found is lower, because farmers have restrictions to sell their products, and the incomes might not always be as good as expected.

• The administrative “solar system” principle: each marketing group is poorly articulated with others in the same region. Marketing exists because the elite are tied to the domestic economy for their subsistence, but poor market articulation occurs because the requirement of force overrides market efficiency. These are the areas of the lowest development level.

______9 ______Background

Economic development usually is accompanied by the increasing size and sophistication of this rural marketing network, and in turn that improved network has an important impact on productivity in agriculture. Following Christaller’s principles, three types of market systems can be detected in developing countries. One more concept should be included in the marketing principles: the “subsistence agriculture”, or non-marketing. Subsistence agriculture is carried out for survival, simply for lack of money to reach areas where the products can be sold. The market development in such areas is none. Its presence is a sign of extreme poverty, and it is still often found in the highlands of Guatemala. If relationships between the market development and poverty exist, the government can try to follow different ways to stimulate the marketing network of the region.

2.2.4 Accessibility to markets Road infrastructure is used to calculate the access to markets, which is critical for determining the comparative advantage of a particular location. Market access is a multi- dimensional and dynamic concept (distance to roads, condition of roads, distance to urban centres, degree of competition, access to transport facilities….), but it will be treated as a single predetermined variable. Infrastructure is essential in making places more accessible. Despite small geographic distances, the time spent in reaching the market seems to be an important deterrent to integration. And integration to the market occurs when the gains in productivity from specialization and trade overcome the associated transaction costs. When poor conditions of the infrastructure occur, even small differences in distance have a big impact (Perkins, 2001). High transaction costs of reaching the market places and of accessing opportunities in those markets offset potential gains from specialization and trade. Therefore, it is very important to develop and maintain rural roads. Many communities in Guatemalan highlands are still relatively isolated due to lack of good quality roads and mountainous landscape. Those areas are expected to have a higher poverty level than areas close to markets, because the transportation costs are higher, and the products reach it in worse quality conditions. Different studies have demonstrated the effects of isolation on opportunities, productivity, vulnerability, and access to services. If the accessibility to markets is directly related to poverty, once again the government can take several actions. These include focusing on improving and expanding the network of paved roads in rural areas that are viable all yearlong, or promoting public transport.

2.3 Poverty in the study area What is poverty? Poverty is hunger, is lack of shelter, and is not being able to go to school and not knowing how to read. Poverty is not having a job, is fear for the future, living one day at a time. It is powerlessness, lack of representation and freedom (Worldbank, 2004). Poverty has many faces, changing from place to place and across time, and has been described in many ways. Most often, poverty is a situation people want to escape. To know what helps to alleviate poverty, first this term needs to be defined. Poverty is a situation in which the means to fulfil most basic necessities are lacking, generally material things but also social, cultural and even political (SEGEPLAN, 2002). The most commonly used way to measure poverty is based on incomes or consumption levels. A person is considered poor if his or her consumption or income level falls below some minimum level necessary to meet basic needs. This minimum level is usually called the "poverty line". What is necessary to satisfy basic needs varies across time and societies.

______10 ______Background

Therefore, poverty lines vary in time and place, and each country uses lines that are appropriate to its level of development, societal norms and values. In Guatemala, six over twelve million of people have an insufficient income to satisfy the minimum amount of calories, as well as other non-food related necessities, such as transport, and health (SEGEPLAN, 2002). 80% of the Guatemalans live in poverty, and 23% in extreme poverty. 12% of the population have no sufficient access to drinking water, and 22% lack a sanitary service. Rural areas are also poorer: 75.3% against 28.4% in urban area. Poverty is also higher among indigenous people: 40% against 16% (Dosselaere, 2003). As poverty depends on countless variables, it must also be looked at through a variety of indicators: income levels, social indicators, and now increasingly indicators of vulnerability to risks and of socio-political access). In addition to expanding the range of indicators of poverty, work is needed to integrate data coming from sample surveys with information obtained through more participatory techniques, which usually offer rich insights into why programs work or do not. Because the economy of Guatemala depends to a big extent on agriculture, several variables related with this sector, and therefore called agricultural variables, were studied in this thesis. All these variables interact with each other in complex ways. Market access tends to be better where there is higher population density (urban areas inside land use), since the per capita costs of building roads are lower and the benefits higher in such circumstances. Market access also tends to be better where agricultural potential is higher, since the returns to developing infrastructure are greater. The existence of export farms also affects the increase of vegetables. For farmers that grow vegetables the distance to the market is more determinant since these are perishable goods. And more complex interactions exits. Despite these interrelationships, the relation of each agricultural variable with the others was not studied, but only the relation between the variables and poverty.

______11 ______Background

Fig. 2: Images of the study area. Up to the left, an area with different crops. To its right, some children carrying maize harvest through a small dirt road. Down to the left, indigenous people selling their crops in the local market, and down to the right, shadow grown coffee is shown (coffee grown inside the forests, typical of the study area)

______12 ______Materials and Pre-Processing

3 Materials and Pre-Processing The input data of the model used in this thesis was not originally available in a digital form. Therefore several pre-processing steps needed to be taken to create files that contained:

• The Land Use map of the area of study; • The Farm Strategy; • The Market Development level in the region; • The Accessibility to the markets.

This chapter shows how the initial materials (including digital data, as well as maps, fieldwork activities, interviews and others) were used, transformed and pre-processed in order to prepare the input data of the model.

3.1 Creating the Land Use map In order to get the land use map, different data were used:

Data for Land Use Characteristics Aster image 15 m resolution (June 2002) High cloud coverage, does not cover all the are of study Landsat-7 image 30 m resolution (December 2000) It covers all the area of study 90 GPS points Contain information about land use in each point Measured during fieldwork

These data were combined as follows:

Aster Image Landsat Image GPS points (15 m) (30 m)

Geographic correction Geographic correction UTM Projection UTM Projection

Supervised Supervised classification classification (15 m) (30 m)

Classification Land Use map Accuracy

LEGEND

Input pre-processing

Intermediate steps Output pre-processing (Input for model)

Fig. 3: Steps followed during the pre-processing to create the Land Use map

First the images were pre-processed in Erdas Imagine 8.5 (geometrically corrected and the UTM projection was assigned to them). Afterwards a supervised classification was applied to both of them, using the same computer program. The Aster image had two main advantages over the Landsat-7: it was taken in June, and the spatial resolution was

______13 ______Materials and Pre-Processing four times higher. The combination of these two advantages made it possible to differentiate the crops better. The classification of coffee is difficult because in Guatemala shade-grown coffee is produced, this is coffee planted among forest trees. This means that when looking at a satellite image, only trees can be seen and not the coffee under the forest canopy. However, from March to June forest trees where coffee is inter-planted are pruned. During this time the biomass of the forests that contain coffee is slightly lower than the biomass of forests without coffee. Differences between the reflectance of both types of trees, with and without coffee, could be detected and classified. In the Landsat-7 image this crop was not so easily detectable because by December the pruned trees already have leaves and it is very difficult to differentiate them with such a low resolution. The Landsat-7 image was used to replace the pixels missing in the first image. Not only did the Aster image not cover all the area of study, but it also had a high percentage of cloud cover. The final Land Use map was a combination between the Aster and the Landsat-7 images (the latter was used to replace the missing and cloudy pixels of the first image). Afterwards the classification accuracy assessment was made. It was calculated using the 90 GPS land use reference points. The results were:

Table 1: Classification accuracy assessment results

Image Number of GPS points Accuracy (%) Aster 23 73.91 Landsat-7 90 66.67 Land Use (combination) 90 68.89

The number of GPS reference points to calculate the accuracy was low in the Aster image because, as stated before, the cloud coverage was very high. From 90 reference points, only 23 were not located in cloudy areas. The accuracy of the combined map was almost of 70%. This combination from both images was the Land Use input for the model (see Appendix 4, Fig. A6).

3.2 Creating the Farm Strategy map The original data needed in order to find out the characteristics of each farm in the area of study was:

Data for Farm Strategy Characteristics Village polygon shape file Contains all the cities, villages and farms of the study area Roads line shape file Contains all the roads and their quality of the region Rivers line shape file Contains all the rivers of the study area DEM 1 km resolution Interviews to agro-business To agro-businesses and co-operatives working in the area Made during fieldwork The data to find out the commercialisation strategy of each farm was combined:

______14 ______Materials and Pre-Processing

Villages Interview to agro-business

Road infrastructure Which farms do you work with? Farm areas

Company B: Company n: Rivers Company A: Farm x Farm 1 Farm 4 Farm y Farm 2 Farm 5 Farm z Farm 3 Farm w DEM Slope

Farm attribute table

Farm Strategy map

LEGEND

Input pre-processing

Intermediate steps Output pre-processing (Input for model)

Fig. 4: Steps followed during the pro-processing to create the Farm Strategy map

The cities, villages and farms polygon shape file (called “village file”) contained urban data, that is to say, where farm buildings or villages were located. However, it did not contain any information about the city limits or of each urban area. This information was needed to divide the area into sub-areas, showing to which farm or village the land belonged. This made it possible to work in a farm level rather than being limited to municipalities. To create these boundaries, different variables were taken into account. It was assumed that the boarders of farms in real life have a relation with natural aspects such as the slope of the landscape or the rivers path, and also with human ones such as the road infrastructure. From these three aspects, rivers were considered the most relevant one, then infrastructure, and finally the slope of the area. • Humans use rivers as boundaries to mark property. For example, in the South of Africa some countries share rivers as borders; and the Rio Grande is the natural boarder between Texas and Mexico. This is also visible in the study area. The smallest official boundaries are those dividing municipalities, where the administrative borders often overlap with the trajectory of rivers. For this reason, and to make the boundaries more realistic, river trajectories were considered as natural boarders between two farms: the land to the left side of a river belongs to a farmer, and land to the right side belongs to a different one. • When making a road, the government must buy the land that the road is going to pass by, and pay those farmers that own such land. If the government makes a road pass through the middle of a field, the price to pay to the farmer is very high to compensate for the inconvenience caused to that farmer. Therefore, it is always more practical to build roads in the boarder between two farms, because it is cheaper. Roads also use “old tracks” or trading routs and often farms would

______15 ______Materials and Pre-Processing

be located on the sides of these routs. This way, road infrastructure paths behave like a farm boundary. • Last, another natural variable was considered to divide the area of study: slope. This variable is relevant in mountainous places: it is hard to believe that the area to the left side of a cliff belongs to the same landowner than the area to the other side of the cliff. In this case, the influence of slope is clear. A mountain peak can be a natural turning point as well. The area of study is very mountainous so this variable was used to divide the region for the different farms. However, it was considered the variable with the lowest weight. The resolution of the DEM of the area was rather low (only 1 km per pixel, but no better DEM exists for this part of the world), and the slope was calculated from the DEM. That means that slope values were created but should not be considered as particularly relevant due to the low resolution. This is why slope was in the last place.

The combination of these aspects was done in ArcInfo. This made it possible to assign a small area to each of the farms in the village file, and create boarders among the farms. Afterwards an attribute table was created for this polygon file in order to know what kind of commercialisation strategy each farm followed. The table used the data from the interviews done as part of the fieldwork. During that period all the agricultural companies, associations, agricultural NGO’s, and co-operatives (collectively called “agro- business”) that work with products grown in the study area were contacted. An interview was made with each of those agro-businesses that worked with farms located in the study area. The purpose of this was finding out from which farms in the region they bought the products they commercialised. For example in the interview made to a company called “Maya Pac S.A.”, located in the capital city, it was known that they export products grown in three farms from the study area: Chipiacul, Chuchuca, and Chinimachicaj. This was represented in Fig.4: in this case Company A would be “Maya Pac S.A.”, and farms 1, 2 and 3 “Chipiacul”, “Chuchuca” and “Chinimachicaj”. During the interview to the company several questions were made about the type of contract they have with those farms and characteristics of their business. Some agro-businesses had contracts with only one or two farms of the study area, and some others like “Cooperativa agricola integral Union 4 Pinos”, with more than twenty. The interviews made possible to know which companies have contracts with which farms, or in other words, to which companies the farmer sells his products. The type of company a farmer sells his products to and the type of contract he has with this company has a direct relation to his income. This way, the answers of the interviews were analysed and put in a table. Afterwards the information of this table was linked with the farm areas (as shown in Fig. 4) and the file of Farm Strategies was created (see Appendix 4, Fig.A7).

3.3 Creating the Market Development map In order to locate the markets in the map, these data were needed:

Data for Market Dev. Characteristics Markets point shape file Contains the location of all the rural markets of the area Population data Contains the population of cities and villages Economical data Contains information about prices or variety and quality of agricultural products sold in the market

The data was used as follows:

______16 ______Materials and Pre-Processing

Markets Population Economical data

Market characteristics

Buffer

Market Development map

LEGEND

Input pre-processing

Intermediate steps Output pre-processing (Input for model)

Fig. 5: Steps followed during the pre-processing to create the Market Development map

For the rural markets that were located in the map, a point shape file was created in ArcMap. The population data was needed in order to know how many potential buyers are found in a particular market. The economical data was also used to give a more detailed idea about the characteristics of each market. These two data sets were used to assign a development value to each of the markets. This value was a reflection of the influence of a market in the study area: a farmer would prefer to sell the products in the market where most costumers and opportunities can be found. So even if a farmer is closer to a very primitive market than to a more commercial one, he is willing to travel a larger distance to reach the developed market. In this sense, an area of influence was created for each market in ArcInfo, by making a buffer for each point. The buffer distance was calculated with the development value assigned to each market. This way the Market Development map was created, where the competitiveness and development of a region was reflected according to the number of areas of influence of markets that it belong to (based on the central place theory, explained in Chapter 2) (see Appendix 4, Fig. A8).

3.4 Creating the map of Accessibility to markets The data needed in order to have the input of the accessibility ready was:

Data for Accessibility Characteristics Markets point shape file Contains the location of all the rural markets of the area Roads line shape file Contains all the roads of the region and their quality Rivers line shape file Contains all the rivers of the study area DEM 1 km resolution

The data was combined as shown in the next diagram:

______17 ______Materials and Pre-Processing

Markets

Road infrastructure

Map of Accessiblity Accessibility cost to markets

Rivers

DEM Slope

LEGEND

Input pre-processing

Intermediate steps Output pre-processing (Input for model)

Fig. 6: Steps followed during the pre-processing to create the Accessibility map

The road infrastructure shape file was needed for, not only the path, but also the quality of each road. Areas far from roads are less accessible than areas close to roads. And areas close to good roads are more accessible than areas close to roads that are not passable during the wet seasons:

Table 2: Data about the quality of the road infrastructure in the study area

Road quality Viability Average speed with a motor vehicle (km/h) 1 Good 90 2 Reasonably good 75 3 Intermediate 50 4 Bad 25 5 Very bad 10 6 Almost unviable 4 No road - 1.5

Accessibility does not only depend on infrastructure. When an area was not located on a road, then walking speed depended on the slope of the area. The DEM data was used to calculate the slope with ArcInfo, so that areas with higher slope were said to be less accessible than areas with a low gradient. The rivers file was used as representative of a natural barrier. Rivers were considered to be difficult to cross, so where a river is found the accessibility was lower, unless there were bridges crossing them. A combination of these inputs was done in order to calculate the accessibility cost of each pixel in the study area. Afterwards, the target points (rural markets in this case) were introduced so that the accessibility to them could be calculated. It was calculated by means of time: how long does it take to reach a market from a given point. The Accessibility map was then ready to be used (see Apendix 4, Fig. A9).

______18 ______Materials and Pre-Processing

3.5 Poverty data of Guatemala The poverty map used is an official map made by the government of Guatemala (updated by SEGEPLAN in 2003). It assigned a poverty level to each of the 32 municipalities in the area of study (see Appendix 1, Fig. A1). The poverty levels reflected the quality of life of the different regions in the study area. They were calculated by the Hentschel methodology. The World Bank considers this method relevant because it calculates poverty not only from national census data but also by household sample survey information (Worldbank, 1998). For the poverty map of Guatemala, information provided by the “Tenth Population Census” from 1994, and the “National Inquest of Familiar Incomes and Expenses” from 1999 were used together with sample surveys. Information on consumption and income was obtained through sample surveys, during which households were asked to answer detailed questions on their spending habits and sources of income. The survey also included indicators for education, health, access to services and infrastructure, risk, vulnerability, and social exclusion (SEGEPLAN, 2003). The result was a map representing the expendable income per person in a household. This poverty map was considered as “reliable”, “real” or “true” in order to make comparisons throughout this research.

______19 ______Methodology

4 Methodology

4.1 Procedure The methodology followed contains the following different steps:

• The required data was collected and pre-processed in order to make the variables of study available in a digital form. • The model was built and run. Here all the variables of study, both separately and combined, were correlated with the existing poverty data to find relations among them. • A sensitivity analysis was done. In this step the robustness of the model was studied.

The first step, the way the data was collected and analysed to create the input data for the model, has already been discussed in Chapter 3. The other steps followed in the methodology are explained in the rest of this Chapter.

4.2 Building the model The purpose of building the model was to create poverty maps, derived from the agricultural variables, in order to compare them with the real existing data. How these maps were made and their statistical comparison with the real poverty maps are discussed in the next paragraphs.

4.2.1 Creating the poverty maps

Farm Strategy Market Land Use map Accessibility map map Development map

Poverty weights Poverty weights Poverty weights Poverty weights Land Use Farm Strategy Market Development Accessibility

Land Use Farm Strategy Market Development Accessibility poverty map poverty map poverty map poverty map

Combination of all agricultural factors

LU-F-M-A poverty map LEGEND

Inputs Intermediate steps

Poverty maps

Fig. 7: Diagram of the model, in which the poverty maps are created using the outputs of the pre- processing part as input data The inputs of the model (which are the variables of study, prepared as explained in Chapter 3) had thematic data. The pixel values of these maps were just a nominal description of classes. To create poverty data, these inputs needed to be converted to

______20 ______Methodology maps where the pixel values had an ordinal meaning of poverty. This was done by assigning a poverty weight to each of the classes within each variable. By making a reclassification of the values with the poverty weights, every pixel of the new image had a value that was related to poverty. The weights assigned to each class of the different agricultural variables were based on an extensive literature review. For each of the classes within the variables, the potential to generate income was taken into account. High weights were assigned to classes where poverty was more likely to be found, and low weights were given to classes where more wealth was likely to be present. The poverty weights entered in the model ranged from 100 to 1000, and were:

Table 3: Poverty weights entered in the model for each of the classes within each variable

Input Classes Poverty weights Output Urban 100 Vegetables 400 Coffee 500 Land Use Land Use Maize 600 poverty map Forest 700 Bare 1000 0% export 1000 50% export 700 Farm Strategy Farm Strategy 100% export 600 poverty map 75% export (risk) 400 75% export (stable) 100 0 areas of influence 1000 Market 1 areas of influence 600 Market Development 2 areas of influence 400 Development 3 areas of influence 200 poverty map +4 areas of influence 100 0-40 min 100 40-80 min 200 Accessibility 80-120 min 400 Accessibility 120-160 min 600 Poverty map 160-200 min 800 > 200 min 1000

By assigning the weights to each of the classes, five poverty maps were created: a Land Use poverty map, a Farm Strategy poverty map, a Market Development poverty map, an Accessibility poverty map and a map in which all the variables were combined (called LU- F-M-A poverty map, as seen in Fig. 7). The existing map from the government had high values for poor areas and low values in richer regions. Due to the values of the poverty weights assigned, the created poverty maps also showed this tendency: high values in the five created maps express areas with a higher poverty level and the low values show richer zones. The combined poverty map was calculated by summing the values of all the variables within each pixel. The five poverty maps were compared with the existing poverty data.

______21 ______Methodology

4.2.2 Relating the created maps with the real poverty data Two techniques were used to relate the variables of study: a spatial correlation and a map representing a 2D-relation between the created poverty maps and the real data. The steps followed were:

Land Use Farm Strategy Market Accessibility LU-F-M-A Development poverty map poverty map poverty map poverty map poverty map

Zonal mean for Zonal mean for Zonal mean for Zonal mean for Zonal mean for each municipality each municipality each municipality each municipality each municipality

Correlation LU Correlation F Correlation M Correlation A Correlation (30x30m) (30x30m) (30x30m) (30x30m) (30x30m)

Correlation LU Correlation F Correlation M Correlation A Correlation (municipality) (municipality) (municipality) (municipality) (municipality)

Relation map Relation map Relation map Relation map Relation map LU (30x30m) F (30x30m) M (30x30m) A (30x30m) comb.(30x30m)

Relation map Relation map Relation map Relation map Relation map LU (muni) F (muni) M (muni) A (muni) comb.(muni)

LEGEND

Real poverty map Poverty maps Intermediate steps

Results

Fig. 8: Steps followed to compare the real poverty map of the government of Guatemala, with the poverty maps created in the model.

The correlation coefficient is the statistic that is most commonly used to summarize the relationship between two variables (Isaaks, 1989). In ArcInfo, a spatial correlation was calculated, which is based not only in the comparison of two numbers but also on the specified x-, y- offset. Spatial autocorrelation is a measure of the similarity of objects within an area. It measures the relationship between the differences of the aspatial attributes of objects with the distance between the objects (Ersi, 2001). The correlation coefficient varies from –1 to 1. If the two inputs at the given offset are highly cross correlated the coefficient will equal one, if they are independent, zero, and if there is a strong negative correlation the output value will equal –1. If the hypothesis that there is a relationship between the selected variables and poverty is true, then the correlation between each of the poverty maps (the ones created in the model and the existing one) should be high and close to 1. When the influence that a variable has on poverty is high, it could be considered as an indicator of poverty, and more important is for policy makers to take that variable into consideration when making political decisions. The relation maps result in a visualisation of the spatial variations in the relations between the two variables entered. This way, spatially oriented results were shown. This GIS oriented outputs helped to detect which areas were best explained by the model and which ones weren’t. Two correlations were calculated every time. On one hand using the highest possible resolution (poverty per 30x30m) and on the other hand using values in a municipal level. In the first case a pixel-to-pixel correlation was directly done with ArcInfo. To calculate

______22 ______Methodology the municipal level values, a zonal mean was calculated for each of the municipalities. Later the means were compared with the existing poverty data. The 2D-relation between the created poverty maps and the real data were also done for the two levels of work. The relation maps to the municipal level showed which municipalities were best related to the poverty data of the Guatemalan government. Maps in the 30x30m resolution were useful to find poverty distribution differences within each municipality, and for each of the variables of study. From a GIS point of view, it is more interesting to work with the highest possible resolutions. That is why the 30x30m calculations were done. However, the original data of the existing poverty map was found at a municipal level, and for that reason, this unit was also studied.

4.3 Sensitivity analysis A sensitivity analysis was done to check the robustness of the model. It focused on the poverty weights assigned to each of the agricultural variables. These were based on an extensive literature review and knowledge of the region. However, even if there was a theoretical background for the assignation of the weights, it was still a subjective decision. The analysis was done as follows:

Agricultural variable: LU,F,M,A

Poverty weights 1 Poverty weights 2 Poverty weights 3 .... Poverty weights n

Agricultural variable Agricultural variable Agricultural variable Agricultural variable .... Real poverty map map 1 map 2 map 3 map n

Correlation 1 Correlation 2 Correlation 3 .... Correlation n

LEGEND

Inputs

Sensitivity weights

Intermediate steps

Ouput correlations

Fig. 9: Diagram followed for the sensitivity analysis The correlation between the variables of study and the poverty map depended on the poverty weights assigned. When these values changed, the correlations, and therefore, the results of the model, also varied. The aim of the sensitivity analysis was to study how much the correlation values varied when the poverty weights changed. The more stable the correlation values are, the more robust the model is. There were two analyses to be done:

• Changing the order; • Once the order of the classes was fixed, changing the weight values.

______23 ______Methodology

4.3.1 Order analysis Before the poverty weights were assigned to the classes, first it had to be determined which of the classes is more likely to be found in poorer areas, and which ones are more typical from rich zones. In other words, the order of the classes needed to be concerted. In the case of Land Use, for example, growing milpa is less economically interesting for a farmer than growing vegetables. Therefore, it was considered that milpa was more likely to be found in areas with a higher poverty level, and therefore the poverty weight was higher than for vegetables. In the case of Farm Strategy, farms that export their products had a lower poverty weight then farmers who sell their crops in the local markets. As stated earlier, these decisions were done based on literature. However, what if really isolated areas were considered to be wealthier than those close to markets? Would the correlations of the model be different? This section aimed to prove that the chosen order of the classes was coherent for each agricultural variables. Following the diagram (Fig. 9), different poverty weights were given, by shifting the order of importance of the classes each time. Every time a correlation was calculated a very high weight was given to one of the classes within a variable, and an insignificant value to the rest. This way the relation of that the class had with poverty could be interpreted. A correlation was calculated giving a very high values to each of the classes of each variable. After applying the different weights, correlations were done with the real poverty map. The correlations were expected to show which was the order of importance that best related the variables with poverty. In this sense, the class order suggested in the sensitivity analysis should be similar to the one found in literature.

4.3.2 Weight values analysis Once the class order was coherent, the value analysis needed to be done. The process was similar to the order analysis. With a fixed order of importance, different poverty weights were assigned to the input variables and different correlations were created, as shown in Figure 9. Twenty different poverty weights were applied in the model, each of which gave a different correlation value. Ten from the twenty weights kept the same interval between each class, and the scale of the values applied was changing. For the other ten weights, the scale was fixed (minimum and maximum values were the same every time), and the interval of the classes in between was changed. If the variability of the correlations was high, it would mean that the model was not robust, and the results of the thesis would not be applicable. If the correlation values had an insignificant variation, it would mean that the model is robust and the results obtained from the created poverty maps would be of value.

______24 ______Results and Discussion

5 Results and Discussion The results are structured as follows: first the effect of each spatial variable versus poverty is shown, and then the relation between a combination of all the variables and poverty is studied. The robustness of the model is discussed in a sensitivity analysis. This is followed by a critical review of both the model and the results.

5.1 Relation of each agricultural variable with poverty The correlations between each agricultural variable and poverty were:

Table 4: Correlations between each individual spatial agricultural variable and the real poverty data

Correlation (30x30m) Correlation (municipality) Land Use 0.11524 0.37544 Farm Strategy 0.29242 0.7176 Market Development 0.4629 0.6256 Accessibility 0.40281 0.6678

The correlations at a municipal level were always higher than those in the 30x30m resolution, as shown in the table. This is because the data they were being compared to was defined at a municipal level. The correlation coefficient is inversely dependent on the standard deviation of the values, that is to say, the spread of the data. The 30x30m resolution maps had both high and low values in each municipality, while the municipal level maps represented each municipality with a single value: the zonal mean. The 30x30m maps had a wide data spread, which means the standard deviations were higher, and consequently the correlations were lower. This can be easily understood using a simple example. Suppose an imaginary case where a municipality contained 2 pixels with a real poverty value of 4.5%. What in a 30x30m map was 1 pixel with a value 0 and 1 pixel with a value 10, the municipal map would show it as two pixels with a value of 5 (the mean). If the real poverty level of 4.5% was compared with the municipal resolution map (5 to 4.5), the correlation of those variables would be high. If it was compared with the 30x30m resolution map, the correlation would be lower (0 to 4.5, and 10 to 4.5), because the deviation of the data was higher. The 30x30m resolution maps showed a high range of values for each municipality, giving more details about the unequal distribution of poverty within each zone. This is very common in developing countries, where very rich areas are neighbouring poor ones. Working with average values as it happens in a municipal level, blurs the distribution of poverty. But because the data used by the government was at municipal level, correlations using the same units were higher. The results for each variable will be discussed in the next paragraphs.

5.1.1 Results and discussion of the Land Use variable From the four variables, Land Use was the one that had the lowest correlation with poverty in both scales used. The correlation values at either the 30x30m resolution or the municipal level were not high enough to be considered as positively correlated with the real poverty map. Even if the initial hypothesis stated that the type of land use found in the area affects poverty, the results of the model did not reflect this. According to the model the distribution of land use is random and has little to do with what the distribution of poverty is.

______25 ______Results and Discussion

Dark pixels in the maps of Fig. 12 showed a poor relation between the land use poverty maps created in this thesis and the real poverty data. In contrast, light pixels showed a good relation between the two maps compared. Dark green pixels are those that the model classified as rich but are poor in reality; and dark blue pixels are those that the model classified as poor but are in fact rich. In the first of the two maps, the municipalities located in the northeast and northwest seemed to be satisfactorily related with land use. This fact was not found in the map below, where the relation was rather weak. This is because the spread of the data is very important when correlating two variables, as explained in the first paragraphs of section 5.1. The 30x30m resolution map showed a relation between land use and poverty in the southwestern part, which is the coffee growing area. The pixel values had light tones, which meant a good relation between both variables. However, this pattern was not found in the municipality map. Because land use and poverty were uncorrelated, the interpretation of the maps was rather difficult: different tendencies were found depending on the resolution used. Therefore the maps in Fig. 12 are not applicable. The results seemed to suggest that the local governments in the study area do not need to consider promoting different types of land use in an effort to alleviate poverty, something that was a surprise. Literature showed that there were positive effects for the income of the farmers when they changed from the more traditional crops, like milpa, to growing a more varied range of produce, like vegetables. One important reason that could explain these results is, first of all, the input data. The accuracy of the land use map was not as high as other studies propose. Only two images were available to make a land use classification of the area, one of them with high cloud coverage, and the other one with a rather low pixel resolution. Therefore, the classification accuracy was not as high as it would have been by using more appropriate input data. Another reason could be that two from the six land use features studied had an ecological related presence. Coffee is not distributed around the whole area but is only found in the south, because of climatic conditions and historical tradition. Bare areas are only found around the tops of the volcanoes in the region. They are an ecological land use class that cannot be changed or removed by humans. Therefore the fact that this class is missing in the rest of the study area and has an influence on the areas where they are present caused somewhat skewed comparisons. The Land Use variable looked at the correlation between potential incomes generated from the way land is used and the real poverty map. By looking at the types of land use, such as the crops grown, only the potential income of a resource was studied, and not the number of people depending on this resource for their income. The wealth distribution of the generated income was also not considered, explaining perhaps why the correlation between the Land Use and the real poverty map was low. An example of this can also be seen in forested areas, given a low value because this resource can support only a limited number of people. There are however examples of families earning up to US$500 a month, a high income in Guatemala, working with eco-tourism and other environmental projects within forested areas. Another variable not taken into account was the size of farms. So although milpa was valued lower then vegetables this does not say anything about an individual milpa farmer, since he could own a big farm and only needs to support a small number of people. In general the values used for the different classes were the best possible ones given the available data, describing the potential income of land use given an constant average population density. The weakness of this can be seen when looking at the bare lands. These areas do not produce any income, but at the same time there are no people living here, so there can be no poverty since poverty is a human based index.

______26 ______Results and Discussion

Corr. = 0.37544

Corr. = 0.11524

0 10 20

Kilometers Real poverty level (%)

66 -80

52 -66

± 38- 52

24 -38

10 -24

0 20 40 60 80 100 Poverty level from Land Use (%) Fig. 10: Map relating the land use poverty maps created in the model, with the real poverty data, at a municipal and 30x30m resolution

______27 ______Results and Discussion

5.1.2 Results and discussion of the Farm Strategy variable The Farm Strategy was the variable that showed a highest correlation with poverty in a municipal level. This can be seen in the municipal resolution map of Fig. 13, where no dark colours are found, meaning the average values were good because the created poverty data was similar to the real one. In general both maps of the figure show the same relations, blue areas in the municipal level map correspond to blue areas in the 30x30m resolution map, and the same occurs for the rest of the colours. The sudden changes in the values of the 30x30m map explain why the correlation was so low compared to the one obtained from the municipal scale. In the 30x30m resolution map clearly marked areas were easily seen. Farms of high-income values were located next to farms with low values, creating a spatial distribution difference that was not detected nor in the real poverty data nor in the farm strategy map to a municipal level created by the model. These differences appeared in the map as sharp colour contrast, and made the correlation to be lower. In this case, the significant correlation between the comparisons of the created poverty map at a municipal level and the real existing data, was useful to know that the variable could be used as a poverty indicator. Based on this, the map with a higher resolution can be used to locate target areas for poverty reduction. The municipalities of the northwest and northeast showed the highest level of poverty. A high poverty level was found not only in the farm strategy map created with the model, but also in the real data. One reason to explain the high correlation that this variable had, at least at a municipal scale, could be that here the economical aspect was incorporated into the model. The strategy a farmer chooses has a direct relation to his or her income and is therefore a purely economical value. Also the existence of agro-business, and especially the export activity, has a direct consequence in creating off-farm employment: packing plants and other operations. These activities are important in the region and stimulate the local economy. Therefore, in general terms, the farm strategy could be considered as an indicator of poverty, where the existence of agro-businesses had a positive effect in the local poverty reduction. Finding that farmer’s income is strongly related to the commercialisation of his products, as the high correlation from the municipal level map seems to show, is a very interesting fact. Studies about the influence of agro-businesses have only been done in a national scale. The model seemed to show that these national influences are also present at a local scale. Based on the model it appears that policy aimed at changing the strategies of farmers could be very beneficial. Promoting different activities to stimulate the agro- business activity is then something local government should take into consideration when trying to develop their region.

______28 ______Results and Discussion

Corr. = 0.7176

Corr. = 0.29242

0 10 20

Kilometers Real poverty level (%)

66 -80

52 -66

± 38- 52

24 -38

10 -24

Poverty level from Farm Strategy (%) 0 20 40 60 80 100

Fig. 11: Map relating the farm strategy poverty map created in the model, with the real poverty data, at a municipal and 30x30m resolution

______29 ______Results and Discussion

5.1.3 Results and discussion of the Market Development variable The correlation of the Market Development with the poverty level was positive and significant for the municipality level map, and once again not significant for the 30x30m map. In this case the difference between the correlations in a high a low resolutions was not as big as in the previous variables. This could be explained because the values in the Market Development map were rather continuous, where almost no big jumps were found between two neighbouring pixels. However the data slowly changed from one class to the other. Poorest areas were situated outside the economical influence of any market, and overlapping markets occurred in rich areas. The dark green areas are those zones that have a location advantage, and yet are poor. They were situated around very competitive rural markets, with a considered number of potential buyers and an economical advantage. Maybe the competitiveness of the markets of those areas was overestimated when calculating the input market development map in the pre-processing step. It could also be that a more complex analysis rather than simple buffers should be done with this variable. Apart from those regions, the correlation between the market development and poverty was well explained because rural markets are the place where farmers sell their local products, and thus, their source of income. Market development could be used as a poverty indicator due to the significant correlation. It is not the quantity of markets only but their quality and competitiveness that can show the development of a region. The Market Development variable considered the number of people using the market, either as sellers or as buyers, and the prices of the different products, among other aspects. This value again took one of the most important economical variables into account when giving it a nominal value within the model. This economical data is however somewhat diluted because the model uses buffer zones, showing the area of influence the market should have. The buffer zones do not however consider the accessibility and merely look at the number of people located within the buffer boundaries. This can be one of the reasons for a lowered correlation between the real poverty map and the Market Development maps. Some areas assigned a higher nominal value because they are located within the buffer of an important market centre could be cut off from this market due to a lack of transport. The area therefore has a high value on the real poverty map, while receiving a low value within the model. The positive effect that markets have on the surrounding areas seem to indicate that developing this variable could improve the income of local farmers. Local governments could try to exploit this through promoting markets in their municipalities, or by improving exciting market structures. Especially the northern regions would benefit from better access to market areas.

______30 ______Results and Discussion

Corr. = 0.6256

Corr. = 0.4629

0 10 20

Kilometers Real poverty level (%)

66 -80

52 -66

± 38- 52

24 -38

10 -24

Poverty level from Market Development (%) 0 20 40 60 80 100

Fig. 12: Map relating the market development poverty map created in the model, with the real poverty data, at a municipal and 30x30m resolution

______31 ______Results and Discussion

5.1.4 Results and discussion of Accessibility The effect of accessibility had similar impact as the markets development variable. The correlations were both high, and in the case of the municipal scale, also positively correlated. The values were similar because both market development and accessibility had the location of the different markets as a central target point. The spatial distribution of the map showed also a similar tendency as in the market development map: weakly related areas were situated in the central, southwest and small parts of the southeast. In those cases the poverty calculated with the accessibility map was very low, but the real poverty index, high. Those badly related areas have a location advantage over other parts of the region, but the poverty level is still high. The case of the central parts can be explained. The main roads that cross the study area were built during the 1970’s, to connect the capital city with the second most important urban area of the country, Quetzaltenango, situated in the department to the north of the study area. The target cities were not within the western part of the study area, and the presence of this main road seemed to have little impact in the communities located in its vicinity. In the case of the southwest, the low relation could be because the roads located there were built specifically to make the coffee grown areas more accessible. This seems to have a historical reason because until the end of the twentieth century coffee had been the most important export product of Guatemala. When the most important roads were built, coffee plantations were considered as important targets of accessibility. However, the situation of this crop is very different in the present time. The fact that a better road infrastructure can be found there and not in other zones does not mean that the southwest is more economically developed. In that case the accessibility is more a historical fact than a reflectance of current wealth. In the non-coffee growing areas, however, isolated regions had a higher poverty level than parts where good roads to the markets are found. In those cases the relations were satisfactory. Accessibility is a recognized ingredient for the development of an area. The high correlation also showed this, and according to the model, the degree to which accessibility is developed in an area could be seen as a poverty indicator. So it is to be expected that areas with good roads will have a higher standard of living. One reason this is not seen better in the model could be that roads are only one part of accessibility. If there are roads, but people cannot afford to use a car, then they are merely nice places to walk on and only slightly speed up the transport of products to the market. This could explain why some areas located close to major roads, such as the central north, still have a high level of poverty. Access to public transport or the number of cars owned in an area are also relevant variables not considered in this model and adding this information could raise the correlation. Local governments should, therefore, not only improve the road infrastructure but also the overall transport system. The lack of correlation between roads and improved living standards seem to indicate that the availability of transport is also an important factor. Improving public transport could be a good way to develop an area.

______32 ______Results and Discussion

Corr. = 0.6678

Corr. = 0.40281

0 10 20

Kilometers Real poverty level (%)

66 -80

52 -66

± 38- 52

24 -38

10 -24

Poverty level from Accessibility (%) 0 20 40 60 80 100

Fig. 13: Map relating the accessibility poverty map created in the model, with the real poverty data, at a municipal and 30x30m resolution

______33 ______Results and Discussion

5.2 Combination among all the agricultural variables The combination of the four agricultural variables of study had the following correlations with the poverty level of the study area:

Table 5: Correlations between the combination of all the spatial agricultural variables and the real poverty data

Correlation (30x30m) Correlation (municipality) Combination of all the variables 0.52699 0.7719

Both correlations were positive which means that according to the model the variables of study were directly related with the poverty level. This was expected because agriculture is the sector that creates the most jobs in Guatemala, and any aspect that has to do with it affects the population as a whole. The municipal level correlation was especially high, of almost 0.8. The fact that according to the model there seemed to be a strong correlation among the variables of study and poverty makes it interesting to make more specific studies for these variables, and find actions to alleviate poverty. Even the correlation to a 30x30m resolution was on the edge of being significant, and this hadn’t occurred before due to the inequality distribution effect. The relation maps of Fig. 16 showed which areas were best explained by the agricultural variables, and those areas not explained by these variables according to the model. In general terms, all the area of study seemed to be well explained by the agricultural variables, except for two parts: the southeast and the central north (the dark green pixels.). These are areas that in the model appeared as rich, but in reality they are poor. That is to say, they have some advantages over other poor zones: they are very accessible, close to competitive markets, and their neighbouring farmers make a good profit by selling their crops to different agro-businesses. The fact that the variables of study had a good correlation in most of Chimaltenango and Sacatepequez, suggests that the two areas that behave differently may have other indicators of poverty, probably natural disadvantages such as the fertility of the land. Considering that the model was applicable, the target areas for policy makers should be the ones with light green colour, that is to say, areas that could be successfully explained by the agricultural variables and that both in the model and in reality were very poor. These are mainly the northern and western parts of the study area. The blu-toned zones are those where the agricultural variables are already in well use, and activities there should be maintained. It would have been interesting to make a more detailed combination of the agricultural variables of study, showing interactions among them: for example farmers who export their products are less influenced by the market development than those who make their incomes from the local market. Or accessibility is more crucial for vegetables than for milpa, because they are more perishable. These relations were not taken into account when putting all the variables together, due to time restrictions.

______34 ______Results and Discussion

Corr. = 0.7719

Corr. = 0.52699

0 10 20

Kilometers Real poverty level (%)

66 -80

52 -66

± 38- 52

24 -38

10 -24

0 20 40 60 80 100 Poverty from the combination of all the factors (%)

Fig. 14: Map relating the poverty map of the combination of all the spatial agricultural variables created in the model, with the real poverty data, at a municipal and 30x30m resolution

______35 ______Results and Discussion

5.3 Results of the sensitivity analysis The sensitivity analysis was done to show the robustness of the model. Different economical weights were entered to see how much the correlations varied when the input values were changed. First, the poverty order of each class in each agricultural variable was studied. Second, once the order was assigned, the input values given to it were changed.

5.3.1 Results and discussion of the order analysis The importance of each class within each variable was decided by making considerate literature review. Also interviews with the local people during the fieldwork period helped to get a clearer picture of life in the Guatemalan highlands. In the sensitivity analysis different input orders were introduced, to see whether the order decided according to literature was adequate. The results found by the numerical analysis were compared to the ones decided by literature. In the next table, the order results from the sensitivity analysis are shown next to the values used in the model (obtained from literature).

Table 6: Poverty order of each class within each agricultural variable according to the model and to literature review

Correlation when the poverty Poverty order of Agricultural Class weight was highest for the class…. importance Variable 30x30m Municipality Sensitivity Literature Urban -0.0955 -0.7437 6 6 Vegetables -0.0797 -0.2937 5 5 Coffee -0.0404 -0.1309 4 4 Land Use Maize 0.0737 0.1745 2 3 Forest 0.0479 0.2179 1 2 Bare -0.0058 -0.0344 3 1 0% export 0.2642 0.6838 1 1 50% export 0.0127 0.058 2 2 Farm 100% export 0.0052 0.0112 3 3 Strategy 75% export (risk) -0.1546 -0.4019 4 4 75% export (stable) -0.1653 -0.426 5 5 0 areas of influence 0.2983 0.5303 1 1 Market 1 areas of influence 0.1511 0.4203 2 2 Development 2 areas of influence 0.1457 0.3116 3 3 3 areas of influence 0.1144 0.2693 4 4 +4 areas of influence -0.6171 -0.7535 5 5 0-40 min -0.318 -0.6497 6 6 40-80 min -0.0744 -0.2833 5 5 Accessibility 80-120 min 0.0752 0.2849 4 4 120-160 min 0.1466 0.4706 3 3 160-200 min 0.1737 0.536 2 2 > 200 min 0.2082 0.6127 1 1

High correlation values show a strong relation between poverty and the class for which the poverty weight was high. Negative correlations show an inverse relation between poverty and such classes. The lowest correlation values appeared generally in those classes that were considered to be found in rich areas. And high correlation values were found for those classes that seem to be predominantly found in poor areas. The order of classes suggested by the sensitivity analysis and shown in the second to last column of Table 6 is the consequence of ordering the correlation values from high to low. The order of the sensitivity analysis was the same for the 30x30m and the municipal level results. This order was almost the same as the one found in literature. All the classes, except for some cases of land use, coincide in the order of poverty. Land Use was the variable where the order of the classes was slightly different from the one found in literature. This could be because ordering classes according to something they

______36 ______Results and Discussion are not correlated to, in this case poverty, was rather futile. But the fact that land use and poverty were not correlated was not known a priori, so weights were assigned thinking that the hypothesis that a relation existed between them was true. The class of land use that did not follow the order of literature was bare areas (situated in the third place, and making the rest of the classes be one order below the literature review). It was not particularly important for the model that this class behaved abnormally, since it has already been mentioned that it is more an environmental feature than a consequence of human behaviour. Its location cannot be predicted economically, and the percentage of bare areas was very low. In the case that the order suggested by the sensitivity analysis was different than the one of literature, the later one was the one used in the model. Therefore the order analysis was considered very successful and corroborated the information found in literature.

5.3.2 Results and discussion of the weight values analysis Once an economical order for the classes was decided, 20 different weights were given for each of the variables, to see how much the correlations varied. From those 20 different weights, 10 kept the same interval and the only thing varying was the scale. In the other 10, the scale was fixed and the interval among the numbers was changed. For the scale variation the correlations were equal every time, so only the interval difference correlations are shown in the next figure, for which a wider variety of results were obtained:

0.8

0.7

0.6

each factor 0.5

0.4

0.3 lation value for

e 0.2 r

Cor 0.1

0 12345678910 Number of different weights applied

LU-Municipal F-Municpal M-Municipal A-Municipal LU-30m F-30m M-30m A-30m

Fig. 15: Correlation values gotten for 10 different poverty weights applied to each agricultural variable and level of work in the sensitivity analysis The correlation values were both calculated for municipal and 30x30m levels. Continuous lines, that is to say, the ones defining the municipal results, should be studied separately from the dashed lines that represent the 30x30m correlations. In the municipal scale the land use and farm strategy results did not overlap any other variable. In the market development and the accessibility cases, an overlapping of the correlations might occur depending on the poverty weights assigned. It was mentioned before that the results of these variables were quite similar, because both the poverty maps (market development and accessibility) were buffers created around the local

______37 ______Results and Discussion markets of the study area. However, the way the buffers were made have a different set-up and reasoning, and therefore they should be kept as two different variables. In the 30x30m level results, the lines did not overlap at any moment, so there would be no specifically significant change in the results of the model if the weights were changed a little bit. The next descriptive statistics were calculated:

Table 7: Descriptive statistics of the 10 different weight values applied to each variable and level or work in the sensitivity analysis

Land Use Farm Strategy Market Development Accessibility Level Municipal 30x30m Municipal 30x30m Municipal 30x30m Municipal 30x30m Range 0.1526 0.0165 0.0371 0.0208 0.0711 0.0989 0.0423 0.0620 Min 0.3442 0.1063 0.6936 0.2853 0.6131 0.4365 0.6410 0.3462 Max 0.4968 0.1228 0.7307 0.3061 0.6842 0.5354 0.6833 0.4082 Mean 0.4215 0.1163 0.7173 0.2989 0.6404 0.4869 0.6674 0.3952 Mean 0.0167 0.0017 0.0037 0.0019 0.0075 0.0086 0.0036 0.0060 error Std. 0.0530 0.0053 0.0117 0.0062 0.0237 0.0272 0.0114 0.0191 Deviation Variance 0.0028 .00002 0.0001 .00003 0.0005 0.0007 0.0001 0.0003

The correlations varied depending on the input values assigned, but in no case the existing patterns in the model were going to be significantly different. The ones of land use seemed to vary the most. This is because land use was the variable that according to the model had the weakest relation with poverty (they were uncorrelated). When no relation exists, anything can happen with the variation of the results when the weights change. From all these statistics, the standard deviation seems to be the most interesting coefficient in this analysis, because it showed how wide the spread or distribution of the observations are around the mean. That is to say: for example in the case of farm strategy to a municipal level, the values are found between 0.7173 ± 0.0118 with a 95% confidence level. All the deviations were very low. This meant that once the right order of importance was given to the classes within each variable, the values assigned to them could vary a little bit because the results of the study were not going to be significantly different. This proved the model robust.

5.4 Critical reflection A study of poverty will inevitably deal with economic variables. This study, although it focused on agricultural variables, had to take into account the economical values for these variables. Most of the values used came from literature; however since this model was the first of its kind in the region, many different economical elements had to be combined. The sensitivity analysis has shown that the values of the classes were well chosen, but tell only part of the story. It can be said that this thesis looks more at the potential of an area rather than to explain the actual poverty. Comparing the results created in the model to the real poverty map showed which agricultural variables were the most productive and can explain why certain areas have a higher standard of living. Changing the Farm Strategy or changing the Market Development variable can increase the productivity of an area, so given a stable population, the level of poverty in the area should decrease. The results of Land Use were lower than expected. Agricultural variables were being study, and here classes such as forest or urban areas were being mixed with crops. The

______38 ______Results and Discussion analysis should possibly have been limited to purely agricultural classes, such as milpa and vegetables, but in this case higher resolution images should be used to detect different types of vegetables, since there are many varieties within these groups. The Market Development was analysed by means of buffering the markets. This created sharp contrasts in the relation maps, such as the ones in Fig. 14. To create more realistic results perhaps rings within the buffer should have been used (like it was done for the Accessibility to markets), showing the spatial strength of the markets influence. However, to create rings, more input data would have been needed, and this was not available. The model assumed the area of study as isolated. For the first two factors that was not a vital constraint, but in case of the Market Development and Accessibility it could be important. The analysis of both variables were market location oriented, and markets or more roads might be found in other departments close to the boundaries of the study area. So at least the markets closest to the boundaries should have been considered. The combination of the factors of study could have been done in a different way. A simple sum of the variables was done, giving the same importance to all of them. In reality, they have a different impact on society: not everybody benefits equally from the existence of roads. Vegetable farmers would find fast access to a market more important than a milpa farmer, since vegetables perish quicker. Also interactions among the variables occur: for example, farmers that export are less affected about the market locations that those who have to sell their products in the closest market. So when combining the variables, several conditions could have been added to make it more real. However, this was not done because of time restrictions, and it would have made the model much more complex. Due to the lack of data and time in some cases it was necessary to make assumptions. Reality was made simple, though coherent. The model is easily adaptable if additional input data want to be entered, and it is fast to run. The results are clear, and the sensitivity analysis helps to know if robust.

Poverty is an attack on human dignity. Trying to find what can be done in order to decrease the alarming levels reached in some parts of the highlands of Guatemala meant entering a broad and complex field where a lot of data was needed, and not always available. Recognizing the conceptual and empirical problems that confound such measures does not mean that they should be ignored. Instead, this thesis pointed to the need for supplementary measures to capture the things that were missing. This created a first local approach that hopefully will help local governments to have a better view of what is the reality of their people.

______39 ______Conclusions and Recommendations

6 Conclusions and Recommendations

6.1 Conclusions

• What are the relations between individual spatial agricultural variables studied (land use type, farm strategy, market development, and accessibility), and the level of poverty?

All the agricultural variables studied, with the exception of Land Use, showed a positive significant correlation to the real poverty data, suggesting that they could be used as indicators of poverty in the study area. Land Use seemed to give no direct indication of the level of poverty in the study area, suggesting that the distribution of the different types of land use studied do not influence the income of the people. The Market Development and Accessibility variables showed a similar relation with poverty. This could be explained by the fact that these two variables were closely related to each other. Most markets have developed along road systems or road systems were built to connect existing markets. They seemed to have a good correlation for most of the study area. Farm Strategy showed the highest correlation of all the studied variables. There are studies done about the influence of agro-businesses in the economy of the country, but only found in a national scale. Results from the model seemed to suggest that these national influences are also present on a local scale. The results on a municipal level would suggest that there are clear relations between the different variables and the level of poverty in the study area. The results of the more detailed 30x30m resolution were not as clearly correlated, but given the results from the resolution at the municipal scale could help identify areas of poverty more clearly. Since the variables were correlated on the municipal level, it could be expected to correlate on the 30x30m results if the real poverty data had been higher in resolution. In this sense, policy makers not only know what they can do to tackle poverty but also what is the need of every region within each municipality.

• What are the relations of combined spatial agricultural variables and the poverty level?

The correlation between the combination of the four variables and poverty was very high. The results from the 30x30m resolution were almost correlated, something that did not happen when the variables were analysed separately. Poverty is the consequence of complex interactions among many variables, and the fact that the combination of variables in this thesis increased the overall accuracy also indicated this. The variables complemented each other, meaning that combinations of different variables interact. In the central part and the southeast or Chimaltenango and Sacatepequez, areas that according to the results have a high resource potential showed an unexpected level of poverty, as it could be seen in Fig. 16. The fact that they showed to have a different pattern to the rest of the departments might indicate that these areas have natural or other type of disadvantages that do not let them benefit from the development that is taking place in their neighbouring areas.

______40 ______Conclusions and Recommendations

• How robust are the identified relations?

According to the sensitivity analysis, which focused on the poverty weights assigned in the model, the identified relations were very robust. The order of importance based on literature and chosen for the model were almost the same as the one suggested by the sensitivity analysis. By varying the significance of the different classes it was possible to show that the order of importance chosen was suitable. Also the correlations appeared not to be significantly different when the poverty weights changed in value, as seen in Fig. 17. The analysis showed that the model is robust because as long as the order of the classes within each variable was correct, the results did not really change when the values of the classes varied.

6.2 Recommendations The model created seemed to have a good theoretical approach to find poverty indicators. However, it would be recommendable to introduce more solid input data, since it had a big impact in the output results. The lack of data is a general problem faced when doing a study in a . Not all the needed data exists, and if it does, it is not always available. In order to get data it would be good to make the fieldwork period longer.

Poverty depends on the interaction of many variables, and the fact that the correlation of all variables combined was the highest seemed to confirm this. In this thesis the way the variables were combined was kept simple, but interactions among the variables, creating different conditions and relations among them should be included in further studies.

The Farm Strategy variable was highly correlated with poverty. Because of the important economical impact existing behind this variable, it would be interesting to make a more detailed study about the Farm Strategy in further research.

After combining all the variables of study, some parts were still uncorrelated with poverty. This is not explainable a priori, since the historical, as well as cultural and economical background of those regions were the same as the rest of the study area. Further study is suggested to find the type of variables that affect poverty in those parts, in order to know how to tackle the problem of poverty there.

______41 ______References

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______46 ______Appendix

APPENDIXES

______II ______Appendix

Appendix 1: Poverty map of the study area

Percentage of poverty in the departments of Chimaltenango and Scatepequez

Poverty level 10-20 % 20-30 % 0 10 20

20-30% Kilometers ± 30-40 % 40-50 %

50-60 % 60-70 %

70-80 %

Fig. A1: Map representing % of poverty in the municipalities of the departments of Chimaltenango and Sacatepequez

______II ______Appendix

Appendix 2: Interviews

In order to know what the strategy of each farmer was, different types of interviews were done to companies, farmers and several other people of the study area. The first step was finding out what are the agricultural companies that work with farmers located in the region. After contacting all the companies that work with products grown in the departments of Chimaltenango and Sacatepequez, the next interview was made:

1. Full name of the company / business: 2. Address: 3. Number of members/ partners / producers (size of the business) 4. What kind of arrangement is there with the producers (contract, member of co-operative….)? 5. Total sale capacity (what were the sales last year, for example?): 6. Which products for you work with? (put an x when the answer is positive, if not, leave the box in blank):

Product Yes (X) Raspberry Strawberry Mango Melon Blackberry Fruits Papaya hawaiana Pitahaya Peer Swiss chard Artichoke Chinese peas Sweet peas Celery Broccoli Cebollines Chipilin Vegetables Sprout French string Sweet string bean Elotín Asparagus Spinach Black beans Güicoy Macuy Mini-vegetables Ocra Quilete Radish Radicio Sugar beet

______III ______Appendix

Cabbage Suchini Nuts Macadamia nuts Marañón seed Anatto Cacao Species Ginger Pepper Coffee Coriander Others Peach tree Flowers White maize ¿OTHER PRODUCTS?

7. Farms or villages where the products are grown, and quantity of the products:

Department Municipality Location (village, Products Quantity of farm name, finca) grown products Acatenango Chimaltenango El Tejar Parramos Patzicía Patzún Pochuta San Andrés Itzapa Chimaltenango San José Poaquíl San Juan Comalapa San Martín Jilotepeque Santa Apolonia Santa Cruz Balanyá Tecpán Guatemala Yepocapa Zaragoza Alotenango Antigua Guatemala Ciudad Vieja Jocotenango Magdalena Milpas Altas Sacatepéquez Pastores San Antonio Aguas Calientes San Bartolomé Milpas Altas San Lucas Sacatepéquez San Miguel Dueñas Santa Catarina Barahona Santa Lucía Milpas Altas Santa María de Jesús

______IV ______Appendix

Santiago Sacatepéquez Santo Domingo Xenacoj Sumpango

8. Sales: In case of local or municipal sale, in which markets do you sell your products?: In case of national sale, in which markets do you sell?: In case of international sale, to which countries do you sell your products?:

9. To whom do you sell directly your products and with which kind of agreements (supermarket companies, intermediaries, fix demands, direct export….)?: 10. How old is the business: 11. Why did you choose that location?: 12. Why not other locations?: 13. Why did you choose to work with these products?: 14. Why not other products?:

15. Do you know other companies in the same area (departments of Chimaltenango and Sacatepequez) that work in this sector? What are their names?:

Any other comments to emphasize?

______V ______Appendix

Appendix 3: Digital data of the study area

Map of the departments of Chimaltenango and Sacatepequez: administrative boundaries of the departments, and location of urban areas

0 10 20

Kilometers

Cities, villages and farms ± Departments Chimaltenango Sacatepéquez

Fig. A2: Map representing the departments of the study area and the shape of the cities, villages and farms

______VI ______Appendix

Map of the departments of Chimaltenango and Sacatepequez: administrative boundaries of the municipalities, and river paths

River path Municipalities Acatenango Alotenango Antigua Guatemala Chimaltenango Ciudad Vieja Comalapa El Tejar Jocotenango Magdalena Milpas Altas Parramos Pastores Patzicía Patzún Pochuta San Andrés Iztapa San Antonio Aguas Calientes San Bartolomé Millpas Altas San José Poaquil San Lucas Sacatepéquez San Martín Jilotepeque San Miguel Dueñas Santa Apolonia Santa Catarina Barahona Santa Cruz Balanyá 0 10 20 Santa Lucia Milpas Altas Kilometers Santa María de Jesús. Santiago Sacatepéquez Sto. Domingo Xenacoj ± Sumpango Tecpán Guatemala Yepocaca Zaragoza

Fig. A3: Map representing the municipalities of the study area, and the trajectory of the rivers

______VII ______Appendix

Map of the departments of Chimaltenango and Sacatepequez: road infrastructure and rural location of the rural markets

Rural markets Viability of roads 0 10 20 Good Kilometers Reasonably good Intermediate Bad ± Very bad Almost unviable Department boarders

Fig. A4: Map representing the location of the rural markets and the path and quality of the roads of the study area

______VIII ______Appendix

Map of the departments of Chimaltenango and Satepequez: Digital Elevation Model

Municipalities

Height (m) 400 - 670 m 670 - 870 m 870 - 1,080 m 1,080 - 1,294 m 1,294 - 1,506 m 1,506 - 1,680 m 1,680 - 1,844 m 0 10 20 1,844 - 2,006 m Kilometers 2,006- 2,156 m 2,156 - 2,294 m 2,294 - 2,444 m 2,444 - 2,631 m 2,631 - 2,856 m 2,856 - 3,143 m ±

3,143 m - 3,606 m

Fig. A5: Map representing the Digital Elevation Model in the municipalities of the study area

______IX ______Appendix

Appendix 4: Outputs of the Pre-Processing

Land Use map of the study area

Land Use classes 0 7.5 15 Coffee Kilometers Forest ± Milpa Urban areas Vegetables Bare areas

Fig. A6: Map representing the Land Use of the study area created with the pre-processing steps

______X ______Appendix

Farm Strategy distribution in the study area

0 10 20 Farm Strategy classes Kilometers Products to local markets ± 50 % Export 100 % Export 75 % Export (risk) 75% Export (stable)

Fig. A7: Map representing the Farm Strategy of the study area created with the pre-processing steps

______XI ______Appendix

Market Development map expressed by the area of influence of each market

Market Development classes Inside no market influence Inside 1 market influence 0 10 20 Inside 2 market influence Kilometers Inside 3 market influence ± Inside 4 market influence Inside 5 market influence Inside 6 or more market influence

Fig. A8: Map representing the Market Development of the study area created with the pre-processing steps

______XII ______Appendix

Accessibility map considering the transport on foot to the nearest road followed by monitorized to the nearest

Accessibility (minutes) 0-10 min 10-20 min 20-30 min 30-40 min 40-50 min 50-60 min 60-70 min 60-70 min 70-80 min 80-90 min 90-100 min 100-110 min 110-120 min 120-130 min 130-140 min 140-150 min 0 10 20 Kilometers 150-160 min 160-170 min 170-180 min ± 180-190 min 190-200 min >200 min

Fig. A9: Map representing the Accessibility to markets of the study area created with the pre-processing steps

______XIII