University of Groningen

Transitions of land requirements for foodIbarrola Rivas, Maria Jose

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ACKNOWLEDGMENTS I want to thank Thomas Kastner for his advice and follow up on the calculations and analysis of the Land Requirements for Food throughout this project. Furthermore, I want to thank Sanderine Nonhebel for her useful input and interesting discussions. Also, I want to thank Annemiek Huizinga for her efficient help. Finally I want to thank Henk Moll for his interesting comments and advice in the final report.

TABLE OF CONTENTS

Summary.......................................................................................................................... 5

1. Introduction ............................................................................................................... 7

1.1 Parameters that influence the Land Requirements for Food (LRF)...................................7 1.2 Previous studies at IVEM...............................................................................................8 1.3 Aim of the project ..............................................................................................................9 1.4 System boundaries and limitations ....................................................................................9

2. Methodology ............................................................................................................ 11

2.1 Regions of study ..............................................................................................................11 2.2 Data..................................................................................................................................14 2.3 Methodology to calculate LRF ........................................................................................15 2.4 Adaptation and limitation of the Methodology................................................................17

3. Development in population, diet and agricultural technology............................ 19

3.1 Population growth............................................................................................................19 3.2 Change in consumption patterns......................................................................................20 3.3 Improvements of agricultural technology........................................................................25

4. Transition of Land Requirements for Food.......................................................... 29

4.1 Global and national scale.................................................................................................29 4.2 Regional scale..................................................................................................................34

5. Conclusions .............................................................................................................. 39

Further research .....................................................................................................................40

References ...................................................................................................................... 41

Annex 1. Food items that were used for the calculations of LRF............................. 43

A1.1 Global and National scale .............................................................................................43 A1.2 Regional scale ...............................................................................................................45

Annex 2. Transition in diet for the global and national scale .................................. 47

Organizations and institutions relevant for this project - CONAVAL: Mexican National Committee for Evaluation of the Social Development Policy (Consejo Nacional de Evaluación de la Política de Desarrollo Social) www.coneval.gob.mx

- ENIGH: Mexican National Survey of Income and Expenditure in Households (Encuesta Nacional de Ingreso y Gasto en los Hogares) http://www.inegi.org.mx/inegi/default.aspx?s=est&c=10656 - FAO: Food and Agriculture Organization of the United Nations. www.fao.org

- INEGI: Mexican Statistics and Geography National Institute (Instituto Nacional de Estadística y Geografía) www.inegi.org.mx

- SAGARPA: Mexican Ministry of Agriculture, Livestock, Rural Development, fisheries and food (Secretaría de Agricultura, Ganadería, Desarrollo Social, Pesca y Alimentación) www.sagarpa.gob.mx

- SIAP: Mexican Agrifood and Fishery Information Service (Servicio de Información Agroalimentaria y Pesquera) www.siap.gob.mx

- SMN: Mexican National Meteorological System (Sistema Meteorológico Nacional) http://smn.cna.gob.mx

- UN : United Nations www.un.org/en/

SUMMARY Land availability for food production is a key to assure food security in the future. One way to evaluate it is to look back to the transitions of arable land required for food (LRF) for the last decades in order to assess future scenarios. LRF depends on population, agricultural technology and consumption patterns. All these parameters change over time in different ways. In one hand, population growth and the change to a more affluent diet increases the LRF; and in the other hand, improvements of agricultural technology, by increasing yields, reduce the LRF. In this project I analyse the transitions of these parameters for the last decades and its impact on the LRF. I do the analysis in different levels of scale. First, I analyse the global pattern; then I compare it with different countries and regions within a country. I use the methodology developed by Kastner & Nonhebel (2009) to calculate LRF. For the global and national analysis, I combine the data of the Food Balance Sheets (FAO, 2010b) with production data (FAO, 2010a) of all food items. Every food item is converted into its crop equivalent and combined with its yield’s value in order to calculate the LRF of every country. For the regional level I analyse different Mexican states using a Mexican database (SIAP, 2010). In this case, I only use 15 food items which represent more than 80% of the total caloric intake. First I analysed and discussed the data of population, diet and yields in the last decades (FAO, 2010a; FAO, 2010b; SIAP, 2010; Martínez Jasso & Villezca Becerra, 2003). This showed large differences in the transition of these patterns for both national and regional scale. Then, by using this data, I calculated the world’s average pattern of LRF’s transition in the last 50 years. During this period the LRF per capita decreased by 20% due to technology improvements. This means that agricultural technology compensated the increase to a more affluent diet. Nevertheless, by including population growth, the total world’s LRF increased 40% in this time span. However, countries deviate from this global pattern. Diets and population have not changed in the same way, consumption patterns vary as well as agricultural technologies. For all these differences, the total LRF in a country level has developed differently. In one hand, developed countries didn’t change considerably its LRF. Cases like France even decreased by 5%. In the other hand, in developing countries like Mexico, The Philippines and Mali, the total LRF almost doubled mainly because of population growth and change to a more affluent diet. Nevertheless, these countries have large differences in population, diet and agricultural patterns which turn out with a different development in the LRF during the last 50 years. This study showed that developed countries followed a similar trend; however the three developing countries that I analysed vary a lot between each other. Even within a country there can be large differences in consumption and agricultural technology which has a direct impact in the LRF. In this project I study seven Mexican states. The results show large differences. For instance, in Sinaloa State the maize yield is 8 times larger than in other regions reducing the LRF per capita by three times. In conclusion, countries and regions have different patterns in population, diet and agricultural technology. Because of this, the transition in LRF of individual countries and regions deviates considerably from the global picture. Therefore, by doing the analysis in a regional and national scale it would be possible to assess the availability of land for food production in different regions in a more accurate way than by doing it in a global scale. Then, by adding up the regional studies, the analysis can be scaled up to a global level to evaluate if there will be enough agricultural area to achieve worldwide food production in the future.

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1. INTRODUCTION Availability of arable land for food production is essential to assure food security in the future. One way to assess this is to study the transitions of Land Requirements for Food (LRF) in the past in order to forecast future trends and evaluate how much arable land is required to feed the world in the coming decades. The transition of LRF depends on the development of population, consumption patterns and agricultural technology (Kastner & Nonhebel, 2009). Firstly, agricultural improvements with higher yields reduce the LRF because more crops are produced in an area. Secondly, the change of consumption patterns from a staple diet to a more affluent diet increases the LRF. According to Gerbens-Leenes and Nonhebel (2002) a diet based on wheat requires six times less land than a more affluent diet with meat. Finally, population growth increases the LRF because more people need to be fed. Several organizations and academics have been studying the transitions of these three parameters (population, diet and agricultural technology) in the past in order to evaluate the prospects for the coming decades. In the following paragraphs I describe the most relevant ones.

1.1 Parameters that influence the Land Requirements for Food (LRF)

Population growth

According to the United Nations (2007), world’s population has doubled since the 1950’s. This increase has been mostly absorbed by the less developed regions. For the coming decades, the population of developed countries is expected to remain unchanged because of a low fertility rate. In contrast, population in the 50 least developed countries is expected to double its value of 2007 (UN, 2007). This population growth will highly depend on the trend of the fertility rate which is uncertain. However, the United Nations states that continued population growth until 2050 is inevitable even if the decline of the fertility rate accelerates.

Change in consumption patterns

There is a relationship between income and diet. Based on the experience of countries that have met economic modernization, there is a universal trend that, by increasing income, the demand for animal products is higher (Caballero & Popkin, 2002). However, Caballero and Popkin state that since the end of World War II there is a quicker change in diet and nutrition status. Nevertheless, there are differences in the trend of food categories in diet between developed and developing countries. For example, since the last decades there has been a large increase in fat’s consumption in low-income nations because of the availability of cheap vegetable oils and fats (Drewnowski & Popkin, 1997). Drewnowski and Popkin found out that this has caused a nutrition transition within a lower level of GDP than previously. Therefore the trends of consumption patterns are changing.

Agricultural improvements

In the last 50 years, yield improvements have been the largest source of world’s crop production growth (FAO, 2002). In the other hand, only 15% of this increase has been by expansion of arable

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land and 7% by increasing cropping intensity. FAO1’s projections for the next 30 years suggest that this trend will continue for developing countries. However, less expansion of arable land will occur. For instance, from 1960-1997, in developing countries, arable land increased by 25%.; and in the next 30 years, it is expected that the increase will only be 13% (FAO, 2002). This decrease will mainly be caused by a slower growth in food demand and by yield’s improvements. FAO’s studies suggest that in the following decades, in a global level, there will be no shortages of suitable agricultural land. However, the unused suitable cropland for the expansion of arable land is unevenly distributed. In one hand, in sub-Sahara Africa and Latin America only 20% of the suitable agricultural land is used. In the other hand, in Near East and North Africa 87% of suitable cropland is already used (FAO, 2002). Therefore the increase of arable land cannot make the same transition in all regions. According to FAO (2002), cereal’s sector is crucial for food supply since they are still the world’s most important source of food. Cereals are used directly for human consumption or indirectly as livestock feed. Wheat is the major cereal crop with a share of 30% of global cereals consumption (FAO, 2002). In developing countries wheat consumption is increasing and most of these countries are dependent on imports. Since FAO’s projections expect an increase of wheat consumption in all regions, this is a threat for food security in developing countries depending on wheat’s imports. Furthermore, other crops are also relevant for future food supply. For instance, oil crops have been the world’s most dynamic sector in the last decades. Their growth has been twice the speed of the whole agriculture in the world (FAO, 2002). In conclusion, population and diet are increasing and will keep growing in the coming decades, especially in developing countries. The growth of these two parameters increases the LRF. However, yield’s improvements will continue at least in the following 30 years which contributes to the decrease of the LRF. Therefore, since the transition of LRF depends on the development of these three parameters, then it is not strait forward to visualize what will be the trend of the LRF. Moreover, according to the previous paragraphs, there have been large differences between regions in the world. Therefore a global trend might not be representative for regional transitions. For this project I want to analyze the transitions of these parameters and their impact on the LRF in a global, national and regional scale. 1.2 Previous studies at IVEM This project was developed at the Centre of Energy and Environmental Science (IVEM) of the University of Groningen. In previous years, several studies about LRF have been done in this Centre. This project continues with this research line. Firstly, LRF was calculated for every food item of the average Dutch diet and then relate it with different consumption patterns (Gerbens-Leenes & Nonhebel, 2002; Gerbens-Leenes et al., 2002). These studies showed that by changing from a staple to a more affluent diet the LRF per capita increases. Then, Kastner & Nonhebel (2009) studied the transitions of consumption patterns, population growth as well as improvements in agricultural technology and their impact in the LRF for the last 100 years in The Philippines. Then, Miedema (2010), using the same methodology, studied these transitions for The Netherlands in the last 200 years.

1 FAO is the Food and Agriculture Organization of the United Nations. For more information see www.fao.org

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These studies show that there are large differences of development between these two countries. Even though, during the period of study, both countries increased their caloric intake in their diets, The Netherlands shows that the share of animal products in their diet remained unchanged. In contrast, The Philippines shows a significant increase. However, since they both improved their agricultural technology, their LRF per capita decreased during the period of study. This shows that there are different transitions between these countries. 1.3 Aim of the project

There are differences between global and regional transitions of diet, population and agriculture as well as between countries and regions (chapter 1.1). For this reason, the aim of this project is to study the transitions of LRF in different levels of scale: global, national and regional as well as the way population, diet and agricultural technology impact the LRF. Then, compare the transitions of LRF between levels of scale as well as between countries and regions. First I describe the developments of population growth, consumption patterns and agricultural technology in the last decades in turn to calculate the LRF. Then I can analyze in which way these three parameters impact the LRF in the different levels of scale. For the national analysis I chose six countries with large differences in population, diet and agricultural technology. For the regional analysis I focus on seven Mexican states with large ecologic, social and economic differences.

Research Questions:

In order to achieve the aim of the project I have two main research questions:

- What are the differences of the population, diet and agricultural technology patterns between countries and regions?

- How have these patterns impact the transition of LRF in last decades? - What are the differences (if any) in the transitions of LRF in the different regions and levels

of scale? 1.4 System boundaries and limitations For this study, only a limited amount of countries and regions within a country were chosen for the analysis in order to find whether there are large variations between them or not. The criterion of choosing them is that the food consumption of their population should mainly be supplied by the production within the country or region. This means that imports and exports represent a small share of the total domestic supply. For this reason, I didn’t include The Netherlands, even though it is the country in which this research line started. The reason is that a large share of Dutch’s food consumption is imported; and also a large share of its food production is exported. Moreover, in order to compare different transitions of population, diet, agriculture and LRF, I choose these countries and regions with extreme values of these parameters. For the calculations of LRF, information from different databases was used. In one hand, for the global and national analysis the FAOSTAT database (FAO, 2010a) was used. This includes a timeframe from 1961 to 2005. Therefore the analysis was done for almost 50 years. For the analysis of regions within a country a Mexican database was used (SIAP, 2010). This database includes a timeframe from 1980-2008. In this case, the analysis was done for almost 30 years.

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2. METHODOLOGY

In this project I study the transitions of Land Requirements for Food (LRF) in the global level, in six countries and in seven regions in Mexico. To calculate the LRF for each region I used the method developed by Kastner & Nonhebel (2009). For these calculations I used different databases for each level of scale. In this chapter, first I describe briefly the regions of study. Secondly, I mention the databases that I used for each region. Then, I describe the methodology. Finally, I discuss the limitations of this methodology and the adaptations that I needed to do for the different levels of scale.

2.1 Regions of study

For this project I chose six countries and seven Mexican states for the analysis of the transitions of LRF. The choice of these regions was based on the fact that the crop’s production of each area represents what is consumed within the region. This means that most of the food produced in the region is consumed in it; and, moreover, the share of imports is relatively small. Furthermore, these countries and regions have large differences in climate as well as in social and economic factors. By comparing a limited amount of regions with these large differences the results give insight about the relevance of these variations in relation to the Land Requirements for Food. In the following paragraphs I describe the relevant characteristics for this project according to each country and region.

National scale

The FAO and the UN show that there are large climate, social and economic differences between Europe, America, Asia and Africa. Each of the countries that I studied is from one of these continents. The countries are Mali, The Philippines, Mexico, France, Austria and the USA. These countries have different climate conditions which influences the agricultural production. Mali has a very arid climate with large problems of droughts. This affects the agricultural production because it depends on rainfall. In the contrary, The Philippines has a tropical climate. This benefits crop’s production because there is enough rainfall for irrigation and it is possible to do more than one harvest in a year (Kastner & Nonhebel, 2009). Mexico is a diverse country. Half of the territory is arid or semiarid and the other half is forest and rain forest (INEGI, 2003). This influences the agricultural production in uneven ways. For instance, in some areas of the country there are large problems of droughts and in other areas there is enough rainfall (see table 1). For this reason I made a further analysis within the Mexican states which are described in the regional scale analysis. Austria and France have temperate climates and are important primary food producers. The USA has a diversity of climates but in the overall they are large agricultural producers and net exporters. Moreover, these countries have different Gross Domestic Product (GDP) per capita, see Figure 1. The GDP per capita of a country is an indicator of its development stage. In general, a country with higher GDP per capita is considered more developed than a country with a lower GDP per capita. Figure 1 shows that there are large differences between the countries. The extreme shows that the USA has a GDP per capita almost 40 times higher than Mali (Conference Board, 2010). This figure shows that USA, Austria and France have the highest GDP per capita and for this reason they are considered developed countries. Also, these countries had a larger change during this period

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in which they tripled their GDP per capita. In contrast, Mexico, The Philippines and Mali are considered developing countries because of their lower GDP. They had lower changes during this period; Mexico doubled their GDP per capita, and The Philippines and Mali only increased by 80%.

Transition of Gross Domestic Product (GDP)

per capita in the second half of the 20th

century

Austria

France

Mexico

MaliThe Philippines

USA

0

10

20

30

40

50

1961 1971 1981 1991 2001

GD

P p

er c

apita

[200

9 t

hou

san

d U

S$

]

Figure 1. Transition of GDP per capita during the second half of the 20th century. The units are in 2009

thousand US$ based on GDPEKS. Source of data: Conference Board (2010)

The relevance of doing this classification is to relate the development stage of each country with its consumption patterns and the type of agricultural technology. In chapter 3 I do this analysis. In conclusion, this brief description shows that there are large differences between the six countries that I studied. These differences influence the consumption patterns and the agricultural production. For this reason, it is important to consider them when analyzing the LRF since the latter is determined by the former.

Regional scale

As I mentioned above, Mexico a country with large diversity and there are large climate differences which affects the agricultural production (INEGI, 2003). For the regional analysis I chose seven Mexican States with different agricultural productivity according to SIAP (2010). In Mexico the main food item in the Mexican diet is maize. For this reason, the criterion for choosing these States is that their maize production fulfils the state’s consumption of maize. In this way, the State’s maize production is representative of what is consumed within the state. This value was calculated based on the SIAP database (see chapter 2.2 for the description of this Mexican entity). These states have climate and economic differences which influence the agricultural technology and the consumption patterns. Table 1 shows the climate differences and table 2 shows the economic differences. Table 1 illustrates that climate varies widely between these regions. This could affect the crop yields. For example, the annual rainfall is an indicator of the availability of water for crop production. With larger available water crop yields generally increase. There are large differences between these regions, for instance while Chihuahua has very few annual rain, Chiapas’ has large annual rainfall (INEGI, 2003; INEGI, 2006). These differences should be considered when analyzing the agricultural production of the region.

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Table 1. Climate and environmental indicators of the seven Mexican states. This table illustrates the differences in climate between the Mexican States and Mexican average. Source of

data: Annual precipitation (INEGI, 2006), Annual average mean temperature (SMN, 2010), Land use (INEGI, 2003)

Land Use

Annual Precipitation

[mm]

Annual average mean temperature

[°C]

Share of forest from

total area [%]

Share of rainforest

from total area [%]

Share of bushes and grasslands from total area

[%]

Share of crop area from total

area [%]

Mexican average 773 21 18 18 44 16

Sinaloa State 774 24 16 39 5 35

Jalisco State 823 21 32 25 17 24

Chihuahua State 423 17 29 2 54 7

Michoacán State 808 22 28 32 10 28

Veracruz State 1487 24 3 18 52 23

Oaxaca State 1517 22 38 32 14 15

Chiapas State 1965 24 26 32 22 18

In addition to this, there are large economic and social differences which also influence the crop yields. For instance, with poor investments in agriculture, crop yields generally are smaller than with larger investments. Table 2 illustrates some economic and social indicators. The first two columns of table 2 show that these seven states have more or less similar population and importance in the agricultural production. For this reason it is relevant to compare them. The first column shows the share of the state’s population from the national population. This demonstrates that these states have similar population size. The second column shows the share of the State’s GDP which participates in the primary sector from the total Mexican primary sector. Agricultural production is part of this primary sector, for this reason, this column shows that these states have similar importance in the agricultural production. The last two columns show some social and economic differences between these states which highly influences the consumption patterns. According to CONEVAL2, food poverty refers to the share of the population which is incapable to obtain the household’s basic food even thought all family’s income is used for acquiring food (CONEVAL, 2005). The third column shows that some states have high poverty problems. For instance, half of the population of Chiapas State does not have enough money to acquire the basic food. In contrast, less than 10% of the population of Chihuahua, Sinaloa and Jalisco has this type of poverty.

Table 2. Economic and social indicators of the seven Mexican states. This table shows the differences in poverty and income between the seven states and the Mexican average value.

Source of data: Population (INEGI, 2005b), participation in the primary sector (INEGI, 2008), Food poverty (CONEVAL, 2005), GDP per capita (INEGI, 2008)

Share of the total Mexican population [%]

Share of GDP participation in the primary sector [%]

Food Poverty [%]

GDP per capita [thousand Mexican Pesos $]

Mexican average 100 100 18 86

Sinaloa State 3 8 14 68

Jalisco State 7 9 11 85

Chihuahua State 3 5 9 89

Michoacán State 4 7 23 54

Veracruz State 7 7 28 56

Oaxaca State 3 4 38 39

Chiapas State 4 5 47 38

2 CONEVAL is the Mexican National Committee for Evaluation of the Social Development Policy. For more information see www.coneval.gob.mx

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The last column, the GDP per capita, shows the same differences between the states. In this case, the Mexican average is higher than the seven Mexican states that I studied. The reason is that the highest GDP per capita in the country is concentrated in Mexico City which population represent one fifth of the country. For this reason the Mexican average is highly influenced by Mexico City’s population and is higher than the rest of the States. To conclude, table 1 and 2 show that there are large differences between the seven Mexican states that will be analyzed in this report. The tables also illustrate that the Mexican average values clearly does not represent most of these Mexican states. For this reason, it is important to study each region in particular.

2.2 Data

The methodology used by Kastner & Nonhebel to calculate the LRF links dietary patterns with crop yields data. Therefore, for this calculation the data that is required are consumption and yield for every food item in the diet. The consumption data is for every food item, however, the yield data is for each crop. It is important to point out that each food item is not necessary a crop. For example, the consumption refers to how much sugar is consumed and the crop’s yield is how much sugar cane is produced in a certain area. Therefore in order to link the food item to the crop’s yield it is necessary to convert the food item into its crop equivalent. In this case the sugar into sugar cane equivalent. This is explained in more detail in chapter 2.3. In this chapter I mention the source for the consumption and yield data. I used different databases depending on the level of scale.

Global and national scale

For the world and country level I used the FAOSTAT database (FAO, 2010a). This is the same database used by Kastner & Nonhebel for the study of The Philippines. The crop’s yield data can be found in the production information of FAOSTAT. It gives the yield’s data in tonnes per hectare of all crops that are produced in each country from 1961 to 2008. For the consumption data I used the Food Balance Sheets (FAO, 2010b) of the FAOSTAT. The Food Balance Sheets (FBS) gives the information of the food supply in daily kilocalories per capita. This caloric supply is the available food per capita in the region. It gives an indication of the food consumption per capita; however, it is not the actual diet. It also includes losses in the food production chain. However, for this analysis this value is used as the consumption per capita in each region. The FBS gives the information of 90 food items from 1961 to 2005. Annex 1 shows the 90 food items that were used for the calculations. Furthermore, I used import and national production data for the 90 food items from the FBS. With this information I estimated the share of each food item that was imported. This data was used for the calculations of LRF. This calculation is described in chapter 2.3.

Regional scale

For the analysis of the seven Mexican states I could not use the FAOSTAT database since there is not regional information. Therefore, for the crop yield’s data, I used the SIAP (2010) which is a Mexican database. The SIAP (Agrifood and Fishery Information Service) is the entity responsible for designing and operating the information service of the Mexican Ministry of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA, 2010).

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The SIAP has yield’s data in tonnes per hectare of 317 crops for every Mexican state from 1980 to 2009. However, the data is difficult to extract because you can only download one crop and one year in a time. For this reason, extracting and merging these data is very time consuming. Therefore, for the analysis of the seven Mexican states I only used 15 food items which represents more than 80% of the calories of the Mexican average diet. This is a very close estimate to including all food items of the Mexican diet. Annex 1 shows the 15 food items that were used for the calculations. For the consumption data I used the study by Martínez Jasso & Villezca Becerra (2003). It is a study based on the Mexican National Survey of Income and Expenditure in Households, called ENIGH by its initials in Spanish (INEGI, 1998). The study shows that there are differences in consumption patterns within the Mexican society. In the ENIGH, the population is divided in 10 equal groups called Decils. Decil I is the group with the lowest income and Decil X is the group with the highest income. Based on the household’s survey of each Decil, the ENIGH reports the expenditure of each food category in every household. Based on this data Martínez Jasso & Villezca Becerra calculated the calories consumed in each Decil’s household. With this information I can compare the different diets within the Mexican society. However, there is not information about the share of the population of each Decil in every Mexican state. For this reason I cannot relate the differences in diet within each region that I study. Nevertheless, I study the differences in diet to have an insight in this issue.

2.3 Methodology to calculate LRF

The methodology that I used to calculate the LRF is described in the article by Kastner & Nonhebel (2009). They link the values of consumption of each food item per capita with the yield’s value of its crop equivalent. Figure 2 illustrates the flow chart of the calculations. This method was used to calculate the LRF for all vegetal products. However, for the calculation of animal products this methodology cannot be used since there is no specific information about the way animals are fed. Therefore, a simple assumption was used to calculate the LRF for the animal products (see below). In the following paragraphs I describe this methodology using as an example the consumption of sugar (raw equivalent) in Mexico in 2005. The starting point of the calculation is the data of consumption for each food item. For example, according to the FBS (FAO, 2010b) in 2005 the sugar (raw equivalent)’s consumption in Mexico was 46,8 kg per capita. This value is converted into its crop equivalent. In order to do this I used the publication Nutritive Factors (FAO, 2010c). This document indicates the amount of kilocalories for each 100 grams of every food commodity. For instance, 100 grams of sugar (raw equivalent) has 373 kilocalories and 100 grams of sugar cane has 30 kilocalories. With these two values I calculated the conversion factor from sugar (raw equivalent) into sugar cane which is 12.4. The conversion factor values for all food items that were used in the calculations are mentioned in Annex 1. The consumption of the food item is multiplied by the conversion factor in order to find the consumption of its primary crop equivalent. In this particular case, the annual consumption of sugar cane equivalent for Mexican consumption in 2005 ends up with 582 kg per capita. Then, the primary crop equivalent is divided by the yield’s crop. By doing this the LRF per capita for this specific food item is calculated. Furthermore, the imports are also considered. The reason is that the crops that were imported were produced with a different yield than the national one. I calculated the share of imports from the total food supply in the country. Then, I calculated the LRF per capita in two steps. First, for each food item that was produced within the country I used the national yield of each crop equivalent. Secondly, for the food items that were imported I used the world’s yield of each crop equivalent. In each case the food item’s crop equivalent was divided by the yield the LRF per capita was calculated. Finally I added up these two values in order to find the LRF per capita of each food item.

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In the case of sugar consumption in Mexico in 2005 only 1% of the total supply of sugar (raw equivalent) in the country was imported. So the LRF of 1% of the sugar consumption was calculated using the world’s sugar cane yield which was 67 tonnes per hectare; and the other 99% was calculated using the Mexican sugar cane yield which was 77 tonnes per hectare. By adding these two values I found the LRF per capita of sugar (raw equivalent) in 2005 which was 75.6 m2 per capita. This value is different for every crop. It depends on the consumption and the yield of each specific year. For instance, in this same year the LRF per capita of soybean oil was 64 m2 per capita, which is very similar to the sugar’s LRF. Nevertheless it represents a smaller share in the average diet. The sugar (raw equivalent)’s consumption represents 14% of the daily caloric intake and the soybean oil’s consumption represents only 4%. The reason for the difference in LRF is that the soybean’s yield is much smaller than the sugar cane’s yield. This shows that the yield has a large influence on the LRF. This procedure was done for every vegetal food item and each year during the period of study. The values of all vegetal food items are added up in order to find the total vegetal LRF per capita for every year. For the animal products another procedure was done. In general, more land is required to produce one calorie of animal product than a calorie of vegetal product. However, this statement strongly depends on the production system and there is not accurate data about this. Nevertheless, the analysis of the animal’s production system is outside the boundaries of this project. For this reason, a simple supposition was used. For the animal products, it was assumed that three calories of vegetal product are required to produce one calorie of animal products (Kastner & Nonhebel, 2009). Then, to calculate the animal product’s LRF per capita, I considered this assumption for every year during the period of study for every region. By adding up the vegetal and animal LRF per capita, I obtained a time series of the development of the total LRF per capita for every region. Finally, this time series is multiplied by the change in population during the same period. As a result, I obtained the transitions of the total LRF for each region. For the particular case of sugar in Mexico in 2005, the population was 104 Million people, so the total LRF of sugar raw equivalent was 0.79 Mha. This is the total area planted of sugar cane which was used for the Mexican consumption in 2005.

Figure 2. Flow chart of the calculations for the LRF. The image shows the Mexican sugar consumption in 2005 as an example. See text for further details. This figure is based on the image by Kastner & Nonhebel (2009) with

additional adaptations for the methodology used in this project.

17

2.4 Adaptation and limitation of the Methodology For this project some adaptations for the methodology developed by Kastner & Nonhebel were required. For their calculation of LRF in The Philippines, they included the cropping intensity for every crop. The cropping intensity refers to the amount of crops that are grown during one year in the same land. In humid tropical countries like The Philippines double cropping is common practice. To explain the cropping intensity I give the following example. In a certain region in the beginning of the year an hectare of land is planted to produce maize and in the second half of the year this same hectare is used to produce beans. If the cropping intensity factor is considered, then the LRF of the harvest of this amount of maize and beans is one hectare since the same land is used to produce both crops. In the contrary, if the cropping intensity factor is not considered, then the LRF of the harvest of maize and beans are two hectares. This shows that by including the cropping intensity the calculation of LRF is less than by not including it. Nevertheless, there is no information available of these factors for all the regions that I studied. For this reason I did the calculation without including the cropping intensity. Furthermore, in this project, the calculations of LRF were done differently depending on the level of scale. For the national and global level I considered all the food items of the diet. In the contrary, for the regional level I only used 15 food items. Nevertheless, since these 11 food items represents more than 80% of the diet’s calories then the difference of including all the food items is not representative. Finally, an important limitation of this methodology is the way that the LRF for animal products was calculated (see chapter 2.3). Firstly, this method does not make difference between each type of animal product. However in reality there are differences (Elferink & Nonhebel, 2007). For instance, one calorie of beef requires more vegetal calories for feeding than one calorie of chicken. Secondly, this method does not consider the different production techniques for animal products are used in each region. This could also affect the calculation of LRF. For instance, the land requirement for one calorie of beef which was feed with grains is different than one calorie of beef which was produced by pasture. Nevertheless, the aim of this project is to analyse the way population, diet and agricultural technology impact the LRF; therefore the analysis of the different animal feeding systems are outside the boundaries of this project. To consider the different feeding systems a more detail analysis must be done. For this reason a simple calculation was used for the animal products.

18

19

3. DEVELOPMENT IN POPULATION, DIET AND AGRICULTURAL

TECHNOLOGY

To calculate the transition of LRF during a specific period of time I used data of population growth, change in consumption patterns and improvements in agricultural technology during the same period. In this chapter, I analyse the transitions of these three parameters in the different levels of scale. The results that I show are only a discussion about the data. Then, in chapter 4, I show and discuss the calculations of LRF based on the data that I analyse in this chapter. 3.1 Population growth

Global and national scale

Population had different growth rates in the different levels of scale. Figure 3 shows the increase of the population in 2005 in relation to 1961, where 1 is the value in 1961. For instance, this figure shows that The Philippines tripled its population during this period. The world’s population doubled since 1961. However, Mexico, Mali and The Philippines increased faster; and, USA, Austria and France had a slower growth. This figure shows a possible relation between the development stage of a country and its population growth. During this period, the more developed countries show a slower population growth than the world’s average, and the less developed countries show a faster rate than the world’s average. However, it is not a linear relationship. For example, The Philippines, which has a higher GDP than Mali, increased faster than the latter. Besides, the USA has a higher GDP than Austria and France and increased faster than the last two countries. Therefore this relationship has deviations when looking at particular cases.

Population growth in the period of 1961-2005

0

1

2

3

Mali

The P

hilip

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s

Mex

ico

Wor

ld

Franc

e

Aus

tria

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Pop

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wth

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19

61

Figure 3 Population growth in a global and national scale. The y axis indicates the growth of population from

1961 to 2005, where 1 is the value in 1961. See text for more details. Source of data: population (FAO, 2010b),

Moreover, population growth rate is not always related to the birth rate. According to the United Nations (2007) the fertility rate in developed countries is currently 1.60 children per woman which would decrease the population. However, in these countries population growth has been mainly due to immigration. In the other hand, in the least developing countries population growth has been related to the high fertility rate which currently is 4.6 children per woman. According to UN, even though

20

developing countries are expected to decrease their fertility rate, in the coming decades world’s population growth will be mainly in these regions.

Regional scale

For the regional scale I analysed population growth of seven Mexican states. Figure 4 relates the GDP per capita of each state with the population growth in the period of 1980 to 2005. The y axis indicates the growth of population in relation to 1980, where 1 is the value in 1980. The figure shows this relationship for each state and for the Mexican average. In this case, rich and poor states show similar population growth since 1980. Only Chiapas had a significant higher growth than the Mexican average and the other states. For this reason, figure 4 does not show the relationship between the GDP per capita and the growth rate that was clear in figure 3. The reason is more complex since in this regional scale other parameters are involved. Firstly mobility is not limited within the country as it is throughout nations. Also there has been large migration from rural to urban areas. These urban centres are not necessary in the same state. However, to find the reasons for this deviation are beyond the boundaries for this research. Therefore, the discussion of the LRF for the Mexican states should not involve the population impact. Then I should focus on the LRF per capita which includes only the impact in diet and agricultural technology.

Population growth in the period of 1980-2005

OaxacaSinaloa

Chiapas

Chihuahua

Jalisco

Michoacán

Veracruz

Mexico avg

1

2

30 40 50 60 70 80 90

GDP per capita [thousand Mexican Pesos $ in 2005]

Pop

ula

tion

gro

wth

in

rel

atio

n to

198

0

Figure 4. Population growth in a regional scale. The GDP per capita is the value in 2005. The y axis indicates the growth of population in relation to 1980, where 1 is the value in 1980. The black diamond shows the growth of the whole Mexican population. See text for more details. Source of data: population (INEGI, 2005c) GDP per

capita (INEGI, 2008)

3.2 Change in consumption patterns

Global and national scale

During the period of 1961 to 2005, the world’s average caloric supply per capita increased as well as for the six countries that I studied (FAO, 2010b). As I mentioned in chapter 2, the caloric supply is the available food per capita in the region. It gives an indication of the food consumption per capita; however, it is not the actual diet because it includes losses in the food production chain. Moreover, it is an average value and the available food in a region could be distributed unequally throughout the

21

population. However, it is a good estimation of the actual diet. In this project I consider the caloric supply as the calories consumed per capita in each region. Figure 5 shows the transition of caloric intake from 1961 to 2005 for each country. The diamond indicates the stage in 2005 for each country. The black and grey bars show the world’s average diet in the beginning and end of the period. This figure illustrates that there is a pattern between the development stage of a country and the total caloric intake. This pattern indicates that by increasing the GDP per capita, the total caloric intake also increases following a fixed trend. For example, in the beginning of the period, France and Austria had an average caloric intake of 3.200 kcal per capita. At the end of the period, Mexico reached the GDP of France and Austria in 1961 and also their caloric intake which was 3.300 kcal per capita. Moreover, the poor countries increased faster their caloric intake than the rich countries. All countries are following this trend.

From 1960 to 2005 the six countries show different development stages. Therefore, in accordance with the relationship between GDP per capita and total caloric intake explained above, they also show different transitions in consumption patterns. Low-income countries increased more their caloric intake than high-income countries. For instance, Mali increased by 54% their caloric intake; in contrast, France only increased 12%. However, it is important to realize that the value of the total calories per capita is different between the six countries. For instance, even though Mali increased more than France, at the end of the period, the average French caloric intake was 1.000 daily calories more than an average Malian diet, see figure 5. Figure 5 shows that all countries, by rising the GDP, increased their caloric intake. However, by reaching higher GDP values the growth rate slows down (possibly) reaching a steady state in which the caloric intake remains constant. However, none of these countries have shown this steady state. It is significant to point out that with this universal pattern the caloric intake does not decline. Therefore, since there is a global trend of increasing economic development; then, diets are expected to increase as well. Nevertheless diets are not changing in the same way. By analysing the transitions for several food categories, the trends of the six countries do not show a clear pattern between GDP per capita and caloric intake for each food category. The complete diet’s transition for the different food categories are shown in Annex 2. For the world's average as well as for the six countries, the calories of cereals, animal products, sugars and vegetable oils increased in the last 50 years (with exception of Austria and France which cereal’s consumption decreased by 5%). But, the share in diet and the value of the calories of each food category do not show a pattern between the development stage of a country and the consumption of each food category. In the next paragraphs I discuss some of these differences.

The share of animal products in diet (Figure 6) shows that Mali, The Philippines and Mexico are not reaching the same trend as France, Austria and USA. For example, at the end of the period, Mexico has a similar GDP as France, Austria and USA in 1961. However, at this point, Mexico is not reaching the 30%-35% share of animal products in diet as these three developed countries had in 1961. In the contrary, Mexico has a share of only 20% of animal products in diet (or almost half the calories of animal products than these 3 countries). Furthermore, Mali shows that the calories of animal products did not increased substantially. And, since other food categories like sugars and vegetable oils increased much more, then the share of animal products in their diet actually decreased in this period.

22

Transition of daily caloric intake from 1961 to 2005

USA

AustriaFrance

Mexico

The Philippines

Mali

1000

2000

3000

4000

0 10 20 30 40 50

GDP per capita [thousand US$ 2009]

To

tal ca

lori

es in

die

t

[kca

l/ca

p/d

ay]

Figure 5. Transition in daily total caloric supply per capita from 1961 to 2005. The colour lines of each country show the transition of GDP per capita and caloric intake from 1961 to 2005; where the diamond show the final stage of this period. The grey and black bars show the average world’s caloric intake in the beginning and the end of this period. See text for details. Source of data: caloric supply (FAO, 2010b), GDP per capita based on

GDPEKS (Conference Board, 2010)

The transition of cereals’ consumption (Figure 6) also shows that all countries are not following the same trend. Cereals in the diet of Mali, The Philippines and Mexico still account for the largest share even though they increased their GDP. Nevertheless, even within these three countries there are differences in cereal’s consumption. Cereal’s consumption in Mexico is based on Maize; in The Philippines is based on rice; and in Mali is based on millet, sorghum and rice evenly. As I mentioned in the introduction, Drewnowski and Popkin (1997) say that there has been an increase in the consumption of vegetable oils within countries with a lower-income than previous trends. In accordance with this state, Figure 6 shows that Mali, with a very low GDP, is increasing very rapidly its vegetable oil’s consumption. However, not all countries show this trend, The Philippines, which also has a low GDP, only increased by 10% its vegetable oil’s consumption and actually decreased its share in their diet (figure 6). At the same time, during this period, all the other countries in this study doubled its vegetable oil's consumption. So to conclude, the six countries that I study are following the global pattern of increasing caloric intake in diets. Figure 5 shows a clear relationship between the GDP and the total calories in diet of the countries. However, by analysing the transitions of different food categories, then, countries do not follow a clear pattern (figure 6).

World 1961

World 2005

23

Share of animal products

USA

Austria

France

Mexico

The

Philippines

Mali

0%

20%

40%

0 10 20 30 40 50GDP per capita [thousand US$ 2009]

Sh

are

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anim

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rod

uct

s in

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t

Share of cereals

USA

AustriaFrance

Mexico

The

Philippines

Mali

0%

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80%

0 10 20 30 40 50

GDP per capita [thousand US$ 2009]

Sh

are

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als

in d

iet

Share of vegetable oils

USA

Austria

France

Mexico

The

Philippines

Mali

0%

5%

10%

15%

20%

25%

0 10 20 30 40 50

GDP per capita [thousand US$ 2009]

Sh

are

of

veg

etab

le o

ils

in d

iet

Figure 6 Transition in the share of different food categories in the daily caloric intake per capita and the GDP per capita from 1961 to 2005. The diamonds show the final stage of this period. The grey and black bars show

the average world’s share of each food category in diet. See text for details. Source of data: caloric supply (FAO, 2010b), GDP per capita based on GDPEKS (Conference Board, 2010)

World 1961 World 2005

World 1961 World 2005

World 1961

World 2005

24

Regional scale

Mexico has large socio-economic diversity within its population. This influences the consumption patterns within the different socio-economic groups. Martínez Jasso & Villezca Becerra (2003) analysed the National Mexican Survey of Households’ Income and Expenditure of 1998, ENIGH, (INEGI, 1998). They calculated the caloric intake in the different social groups called Decils based on the expenditure in each Decil’s household. See section 2.2 for the Decil’s definition. As I explained in chapter 2, the data of this survey is an indicator of what is actually consumed in the household (doesn't include losses in the food production chain), however, again is not the actual diet. It depends on the answers of the households and does not include food consumption outside the house. In this case, the data is different than the one we used for the national scale. For the latter we used the available food in each region reported in the FBS of the FAO. For this regional scale we use data obtained by asking households the food they buy for their consumption.

Consumption patterns in the different Decil groups of the Mexican population

0

1000

2000

3000

Decil I Decil V Decil X

Co

nsu

mp

tion

[kca

l/ca

p/d

ay]

Vegetal products Meat & eggs Milk

Figure 7. Differences in caloric intake between different socio-economic sectors in the Mexican population. According to the ENIGH (INEGI, 1998) the Mexican population is divided in 10 equal groups called Decils,

where Decil I is the group with the lowest income and Decil X is the group with the highest income. The figure shows the differences in consumption patterns between the different Decils. See text and chapter 2.2 for details.

Source of data: caloric intake per Decil (Martínez Jasso & Villezca Becerra, 2003)

Figure 7 shows the results by Martínez Jasso & Villezca Becerra. This figure illustrates the daily caloric intake of vegetal products, meat and eggs as well as milk for the different Decils. The high-income sector (Decil X) consumes an average of 35% more calories than the average low-income sector (Decil I). Moreover there are differences in the type of diet. The rich diet of Decil X includes more animal products (especially milk), fruits, sugars and vegetable oils than the poor diet of Decil I. However, the affluent diet of Decil X is not reaching the same pattern as the diet of developed countries like France, Austria and USA. For Decil X, the share of animal products is still low (~20%) in comparison with France, Austria and USA with a share of animal products of around 35% (see Figure 6). For Decil X cereals are the most important food category in their diet. This means that, even though Decil X is reaching wealthy levels like those of people in developed countries; their consumption patterns are not following diet trend of France, Austria and USA with large share of animal products. The different consumption patterns within the Mexican society might influence the LRF of each social sector. However, since there is no data about the share of each Decil group in every Mexican state, it was not possible to relate different consumption patterns to different Mexicans states in this project. Therefore, for the analysis of the LRF of the different Mexican states the average Mexican caloric intake was used.

25

3.3 Improvements of agricultural technology In the 1950’s the so called Green Revolution started. This is the term that has been given to the large increase of worldwide food production in the second half of the 20th century by using “modern” agricultural technology to increase crop’s yield (McKinney et al., 2007). Special focus was given to cereals since they are the most important source of food (FAO, 2002), see introduction. The data that I analyze in this section shows that in the last decades of the 20th century, cereal’s yields increased in most of the countries and regions that I studied.

Global and national scale

The data source for the yield’s analysis was the FAO crop's production statistics (FAO, 2010a). Figure 8 shows the yields’ development for the most important cereals in each country during the last 50 years. This figure shows that in all countries crop yields improved during this period. However there are large differences between countries and they deviate from the world’s average value. For maize production, figure 8 shows that maize’s yield of France, Austria and the USA are above the world’s average; in contrast, Mexico, The Philippines and Mali are below the world’s average. This shows that there is a relationship between the GDP of the country and the yield’s value. The most developed countries have higher yields than the less developed countries. Furthermore, USA is a large maize producer and exporter. In 2007 its maize production was 330 million tonnes and exported 17% of this amount (FAO, 2010a). Because of this, the world’s maize production is largely influenced by it. This is illustrated in Figure 8, where in 1983 and 1988 there were important declines in USA’s maize yields which are also shown in the world’s average.

For wheat, figure 8 shows that Austria, France and Mexico are above the world’s average and USA has similar yields as the world’s average. In contrast to maize production, wheat’s yield shows that the relationship between yields and the development stage of the country is not always the case. If the relationship of GDP and yield is always followed, it should be expected that the USA would have higher values than the average world’s values and, certainly, higher than Mexico. Nevertheless, this deviation requires a further analysis. Firstly, wheat’s yield should not be considered as a typical Mexican agriculture technology because its production is mainly focused in the north of the country (SIAP, 2010) which is not representative of the whole country’s agricultural technology. Secondly, USA is a large wheat producer. Wheat is the main cereal of the USA’s diet with a share of 73% of the cereals’ consumption and only 7% is imported (FAO, 2010b). In summary, USA is a large producer of both wheat and maize. But, by comparing the yields with other countries, the low value of wheat’s yield and the high value of maize’s yield suggest that different production systems are used for these crops. Firstly, intensive systems are used for maize production to achieve high yield. Secondly, extensive systems are used to produce wheat. In conclusion, it is not always true that rich countries have large crop’s yields by modern agricultural technologies and poor countries have low crop yields by extensive systems. There are deviations between countries and crops. Furthermore, figure 8 shows that there are large variations from year to year that some countries encounter (see the fluctuations during this period for the countries’ yields). However, the global yield's transition does not show these variations. The reasons for these fluctuations are outside the boundaries of this research. However, it is important to keep in mind that the global pattern does not show possible local conditions that countries face with which affect the annual yield of certain crop e.g. droughts and pests. Nevertheless, some countries do not show large changes. For instance, The Philippines and Mexican maize production had low variations during this period.

26

Maize yield

World

USAAustria

France

Mexico

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The Philippines

0

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1961 1970 1979 1988 1997 2006

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/ha]

Wheat yield

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1961 1970 1979 1988 1997 2006

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/ha]

Rice yield

USA

World

The

Philippines

Mali

Mexico

0

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8

1961 1970 1979 1988 1997 2006

Yie

ld [

ton

/ha]

Figure 8. Cereals’ yields development in a global and national scale. The black line shows the average world’s

yields. See text for details. Source of data: cereals’ yield (FAO, 2010b)

27

Finally, figure 8 shows that Mali’s crop yields are the lowest and present large variations from year to year. As I mentioned above, the reason could be related to the development stage of the country. However, not only the development level influences the crop’s yield but also the climate conditions. Mali, from the countries that I studied, has the most marginal climate. It is very arid and has large problems of droughts. Adding the fact that it is a poor country and has low investments in agriculture, then the production systems depend on rain. For this reason, cereal’s yields are very low and vary from year to year. This threatens their food security because their diets largely depend on these cereals which account for 70% of their calories. To conclude, crop yields largely divert throughout the countries and from the world’s average value. In general, the most developed countries have higher yields than the less developed countries. However, this is not always the case like wheat’s yield in the USA. Furthermore, there are large variations from year to year due to local conditions like droughts and pests. However, these variations are not visible in the global pattern.

Regional scale

Maize production is essential for food security in Mexico since one third of the average Mexican caloric intake is maize (FAO, 2010b). In this section I analyse the value of maize yield in the Mexican states that I study and compare it with the Mexican average (SIAP, 2010). There are large annual variations which are outside the scope of this project. For this reason I used average values of five years. Figure 9 illustrates that there are differences in maize yield in the different Mexican states. For example, in 2008 the maize yield in Sinaloa was five times higher than in Chiapas. The cause could be related with the production system that is used. According to INEGI (2005a) in Sinaloa 45% of the agricultural area uses chemical fertilizers and in Chiapas only 10%.

Development of Maize yield in the different Mexican states from 1980 to 2008

Mexico (avg)

Sinaloa

ChihuahuaJalisco

Michoacán

Veracruz

Chiapas

Oaxaca

0

3

6

9

1980 2008

Yie

ld [

ton

/ha]

Figure 9. Development of maize yield in different Mexican states from 1980 to 2008. The black line shows the

average country’s maize yield. Due to large annual variations I used average values of five years. Source of data: maize yield (SIAP, 2010)

28

Furthermore, the development of maize yield in these regions in the last 30 years also diverts. In 1980, Sinaloa’s maize yield was similar as Chiapas’ yield. But, during this period, the maize yield of the former increased almost 6 times reaching similar values of France’s and USA’s maize yields (see figure 10). In contrast, Chiapas even decreased its maize yield during this same period having similar values as Mali.

To conclude, there are large differences in crop yields within a country. Similar to the national scale analysis, crop’s yields are largely affected by the development stage of the region as well as by climate conditions. Rich regions like Sinaloa generally achieve higher yields by using intensive systems. Poor regions like Chiapas and Oaxaca usually have low yields by using extensive systems. However, this is not always the case since there are deviations between regions and crops.

Regional and national comparison of Maize yield in 2005

0

4

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Aust

riaUSA

France

Sinal

oa

Chi

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ua

World

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hilip

pine

s

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acru

z

Chi

apas

Mali

Oax

aca

Yie

ld [

ton

/ha/

yr]

Figure 10. Maize yield comparison between Mexican states and other countries. The yellow bars show the maize yield value for the Mexican states. The grey bars illustrate the average maize yields for other countries for comparison. Source of data: Mexican States’ maize yield (SIAP, 2010), Countries’ maize yield (FAO, 2010a)

29

4. TRANSITION OF LAND REQUIREMENTS FOR FOOD

The transition of LRF is influenced by the change of three parameters: population, consumption patterns and agricultural technology. In the previous chapter, I discussed the changes of these three parameters in the last decades for the world’s average, six countries and seven Mexican states. Large differences were found in these three parameters. In this chapter I show and discuss the overall effect of these parameters on the LRF.

4.1 Global and national scale

Land Requirements for food per capita

Figure 11 shows a comparison between the global and national transitions in LRF per capita for each food category. Each graph of this figure illustrates the area per capita required to produce each food category in a time series of 50 years. These transitions have been different from the global to the national level as well as throughout the countries and food items. For the global average the total LRF per capita declined almost 1.000 m2/cap in the last 50 years (from 2.750 m2/cap in 1961 to 1.750 m2/cap in 2005). Keeping in mind two issues that were discussed in chapter 3: firstly, the world’s average caloric intake increased as well as changed to a more affluent diet (e.g. more animal); secondly, the yields increased due to the Green Revolution; then the fact that the LRF per capita declined means that agricultural improvements compensated the change in consumption patterns. In general, this global pattern was followed by all the countries that I studied except Mali (see Figure 11). For the world’s average, the food categories that impact the most to the LRF are cereals and animal products. During this period, cereal’s LRF decreased from 1.100 m2/cap to 500 m2/cap because of cereals’ yields’ improvements. The LRF for animal products are related to the vegetal products’ LRF (see chapter 2.3 for animal’s LRF calculation). During this period the vegetal LRF decreased due to yield’s improvements. However, the animal products’ LRF did not decrease because its consumption increased. Mexico and The Philippines show a similar trend in which cereals and animal products are the most relevant food categories for the LRF. In both countries cereal’s LRF decreased by almost 60% because yields improvements. In The Philippines the area decreased from 1.200 m2/cap to 550 m2/cap and in Mexico from 1.400 m2/cap to 550 m2/cap. Moreover, the area required for animal products did not change substantially because, even thought vegetal products’ yields improved, in both countries the consumption of animal products increased. However, for animal products, the LRF in Mexico is almost twice than the LRF in The Philippines. The developed countries, USA, Austria and France, do not show this trend. In these countries, the area required for animal products is 50% more of the total LRF per capita, and the cereal’s LRF is not very relevant. The reason is that cereal’s consumption is not as important as in Mexico, The Philippines and Mali. Furthermore, the improvements of LRF were not as large as in Mexico and The Philippines. For Mali, the LRF for cereals is more relevant than in any other country that was studied. It accounts for two thirds of the total LRF per capita. The reason is that 70% of Malian total caloric intake is cereals (see chapter 3.2 and annex 2). In this case the agricultural improvements were not enough to compensate the change in consumption patterns, therefore there were no improvements in the LRF during this period like in the other countries.

30

Transition in the LRF per capita for the different

food categories in a global and national scale

World

LR

F p

er c

apita

[m2/c

ap]

0

1000

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1961 1968 1975 1982 1989 1996 2003

USA Austria

LR

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[m2/c

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1961 1968 1975 1982 1989 1996 2003

France Mexico

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1961 1968 1975 1982 1989 1996 2003

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1961 1968 1975 1982 1989 1996 2003

0

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1961 1968 1975 1982 1989 1996 2003

animal products Cereals Pulsesvegetables oils and oilcrops sugar and sugar crops Fruits and vegetablesother vegetal food items

Figure 11. Transition in LRF per capita from 1961 to 2005 for the world’s average and six countries. This graph shows the change in LRF per capita for the different food categories.

Source of data: calculations from the author

31

Moreover, Mali shows very large variations in its transition of LRF per capita since 1961 due to yields instability (see chapter 3.3). The variations from year to year were as large as 2.000 m2 per capita (figure 11 and 12). Figure 11 shows that the variations are mainly cause by cereals’ LRF. This threatens their food security. The value of the LRF per capita of Mali at the end of this period was 30%-40% higher than countries like France, USA, Austria and Mexico with a higher affluent diet and an average intake of 1.000 more calories than the Malian diet. See figure 5 and annex 2 for the caloric intake comparison and figure 11 and 12 for LRF per capita comparison. Figure 12 illustrates the relationship between the transition of LRF per capita and development of each country in the last 50 years. The different colour lines show the transition for each country. The diamond indicates the value in 2005. The grey and black bars indicate the world’s average value in 1961 and 2005. It shows that all these countries except The Philippines have been above the world’s average value. Like in Figure 11, figure 12 shows that, similar to the global trend, all countries declined their LRF per capita except Mali.

Transition in the LRF per capita in a national scale

Figure 12 Transitions in LRF per capita and GDP per capita from 1961 to 2005 in a national scale. The diamonds show the final stage of this period. The grey and black bars show the average world’s value in the beginning and end of the period. The square box at the upper right shows the zoom in of Mali’s transition of

LRF. See text for details. For the LRF per capita transitions of each food category see Figure 11. Source of data: GDP per capita based on GDPEKS (Conference Board, 2010), LRF per capita: calculations from the author

In contrast to Mali, The Philippines achieved the largest improvements in the LRF per capita since 1961. They reduced it by more than 40%. This was due to the large achievements of crop yields during the Green Revolution (Kastner & Nonhebel, 2009). Moreover, on 2005, their LRF per capita was around 40% lower than the ones of Mexico, USA, Austria and France. The reason is not only the

USA

Austria

France

Mexico

The

Philippines

Mali

0

1000

2000

3000

4000

0 10 20 30 40 50

GDP per capita [thousand US$ 2009]

LR

F p

er c

apita

[m2/c

ap]

Mali

World 1961

World 2005

32

yields’ improvements but also the fact that The Philippines’ total caloric intake has around 800-1200 less kilocalories per capita than the other 4 countries. To conclude, Figure 11 shows that the most relevant food categories for the global LRF are animal products and cereals. This fact varies throughout countries. In general, for developed countries animal products account for more than half the LRF and cereals are not very relevant. In contrast, for developing countries, both cereals and animal products are the most relevant food categories for the LRF. Furthermore, Figure 12 shows that countries with high GDP, like Austria, France and USA, have a similar pattern in the transition of LRF per capita. However, countries with lower GDP, like Mali, Mexico and The Philippines, divert a lot from each other. For this reason, attention should be given to the transitions of LRF in developing countries since there is not a global trend for them.

Total Land Requirements for food

The total LRF for the global and national scale increased during the period of study (Figure 13 and 14). Kastner and Nonhebel (2009), in their study of The Philippines, analysed the individual impact of population, diet and agricultural technology on the total LRF. Using this same methodology, Figure 13 shows the impact of each factor for the development of the LRF in the global and national scale. In order to do this, firstly constant population and diet at 1961 is assumed to visualize the impact of agricultural improvements. In this way, only the impacts of the change in yields are shown (yellow area). Secondly, the actual population growth is added, still assuming constant diet at 1961 (blue area). Finally, the change in the caloric intake is added to visualize the impact of the change in consumption patterns (red area). For the World’s average, the total LRF has increased almost 50% in the last 50 years. The yellow area shows that agricultural improvements (the increase of yield) decreased the LRF. This graph illustrates that if population and consumption pattern would have remained constant at the value in 1961, then the World’s LRF would have reduced by half. However, due to population growth (blue area) and the change to a more affluent diet (red area), the total LRF increased by 50%. This graph shows that the largest contributor to the increase of LRF has been population growth. Nevertheless, the six countries that were studied divert from this pattern. In general, the USA, Austria and France remained more or less constant. The USA had a small impact on the change of consumption patterns and largely improved their agricultural technology. In the same way as the world’s average, if population and consumption patterns would have remained constant at the value in 1961, then the USA’s LRF would have reduced by half. However, overall the USA increased by 10% their LRF mainly by population growth. Austria had small impact due to population growth and change in consumption patterns as well as small agricultural improvements. However, overall Austria declined by 5% their LRF. The total LRF for France remained constant with small improvements in agriculture and small growth in population and diet. In contrast, Mexico, The Philippines and Mali almost doubled their LRF. For these three countries more than two thirds of the total LRF at the end of this period has been due to population growth and the change to a more affluent diet. Mexico and The Philippines have had very large agricultural improvements. If the population and the consumption patterns would have remained constant at the value in 1961, then the Mexican LRF would have reduced by 50% and The Philippines’ LRF by 60%. However, because of population growth and change to a more affluent diet their total LRF doubled. For Mali, the impact of the change in diet is shown until the beginning of the 80’s, in contrast to the other countries where the change in diet started since the 60’s (see red area in figure 13). Furthermore, the agricultural improvements were not as large as in The Philippines and Mexico.

33

Total Land Requirements for food

World

To

tal

LR

F [

Mh

a]

0

700

1961 1968 1975 1982 1989 1996 2003

USA Mexico

To

tal

LR

F [

Mh

a]

0

40

80

1961 1968 1975 1982 1989 1996 2003

0

10

20

1961 1968 1975 1982 1989 1996 2003

Austria The Philippines

To

tal

LR

F [

Mh

a]

0

1

2

1961 1968 1975 1982 1989 1996 2003

0

7

14

1961 1968 1975 1982 1989 1996 2003

,

France Mali

To

tal

LR

F [

Mh

a]

0

9

18

1961 1968 1975 1982 1989 1996 2003

0

2

4

1961 1968 1975 1982 1989 1996 2003

technology population growth diet

Figure 13. Global and national transition in LRF. This graph shows the individual impact of the change in agricultural technology, population growth and consumption patterns in the last 50 years. See text for more

details. Source of data: calculation from the author

34

Figure 14 shows the relationship between the GDP per capita in 2005 of each country and their change in the total LRF from 1961 to 2005, where 1 is the value of the LRF in 1961. The black bar shows the average world’s increase of LRF. It illustrates that the least developed countries increased more than the world’s average, in contrast to the developed countries which LRF was more or less stable. To conclude, in general, the total LRF has increased in the last 50 years. In developed countries it has more or less remained constant and in developing countries it has doubled. Even thought the agricultural improvements have largely reduced the area required for food production, population growth and change in consumption patterns have caused the large increase in the total LRF.

Total Land Requirements for Food in a national scale

Mali

USA

AustriaFrance

Mexico

The

Philippines

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

0 10 20 30 40 50 60

GDP per capita [thousand US$ 2009]

Chan

ge

in t

ota

l L

RF

in r

elat

ion

to

196

1

Figure 14 Transitions in the total LRF in a national scale. The GDP per capita is the value in 2005. The y axis

indicates the change in the total LRF from 1961 to 2005, where 1 is the value in 1961. The black bar shows that the average world’s LRF increase 40% in the period of 1961 to 2005. See text for more details. Source of data:

GDP per capita based on GDPEKS (Conference Board, 2010), LRF: calculations from the author

4.2 Regional scale Similar to the national scale, the seven regions in Mexico show different transitions of LRF per capita. For these regions a similar diet was assumed: the average Mexican diet (FAO, 2010b). For this reason, the differences of LRF illustrate only the different agricultural technologies of the region and not the different consumption patterns. The differences of agricultural technology are especially of maize production because it is the most important food item of the average Mexican diet. It accounts for 30% of the daily caloric intake and 74% of the cereals’ consumption (FAO, 2010b) Figure 15 shows the transitions in LRF per capita for the different food categories for each Mexican state. It is important to point out that since for these calculations only 15 food items were considered; then, the results show lower values than the analysis in the national scale (Figure 11). This figure shows that the food categories more relevant for the LRF are animal products and cereals. The LRF for animal products have remained more or less constant. However, the LRF for cereals have been very dynamic during this period and causes the largest differences throughout the Mexican States.

World

35

Transition in the LRF per capita for the different

food categories in a regional scale

Mexican average Sinaloa

LR

F p

er c

apita

[m2/c

ap]

0

1000

2000

3000

1980 1985 1990 1995 2000 2005

0

1000

2000

3000

1980 1985 1990 1995 2000 2005

Jalisco Michoacán

LR

F p

er c

apita

[m2/c

ap]

0

1000

2000

3000

1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005

Chihuahua Oaxaca

LR

F p

er c

apita

[m2/c

ap]

0

1000

2000

3000

1980 1985 1990 1995 2000 2005

0

1000

2000

3000

1980 1985 1990 1995 2000 2005

Veracruz Chiapas

LR

F p

er c

apita

[m2/c

ap]

0

1000

2000

3000

1980 1985 1990 1995 2000 2005

0

1000

2000

3000

1980 1985 1990 1995 2000 2005

animal products Cereals Pulsesvegetables oils and oilcrops sugar and sugar crops Fruits and vegetablesother vegetal food items

Figure 15. Transition in LRF per capita from 1980 to 2005 for the Mexican average and seven Mexican states. This graph shows the change in LRF per capita for the different food categories.

Source of data: calculations from the author

36

Figure 15 shows that since the 80’s, the average Mexican LRF per capita has decreased 25% mainly by the decrease of cereals’ LRF (specially maize). However this is not the general pattern of the Mexican States. For instance, Sinaloa decreased 60% by the large maize yield improvements (see chapter 3.3). However, other states like Oaxaca, Chiapas and Veracruz remained almost constant. Furthermore, the LRF per capita of these states is much larger than in other states. For example, Oaxaca’s LRF per capita is almost three times larger than the one from Sinaloa and Jalisco, see figure 15. Figure 16 shows the relationship between the value of maize yield and the LRF per capita from 1980 to 2005 in each Mexican state. The colour lines indicate the transition of each state, where the diamonds is the value at the end of the period and the beginning of the line is the start of the period. The black line is the country’s average value. This figure shows that most of these states do not follow the average country’s transition. This figure shows that with higher maize yield, the LRF per capita decreases. During this period, all states improved their maize yield and decreased their LRF per capita except Chiapas (see zoom in of figure 16).

Transition in the LRF per capita in a regional scale

Figure 16. Transitions in maize yield and LRF per capita from 1980 to 2005 in a regional scale. The colour lines show the transition in each state. The diamonds indicate the value at the end of the period (2005) and the

opposite side of the line indicates the value in the beginning of the period (1980). The black line shows the average transition of the country. See text for details. Source of data: LRF per capita (calculations from the

author), Maize yield (SIAP, 2010)

MEXICO

Sinaloa

Chihuahua

Oaxaca

Chiapas

Michoacan

Veracruz

Jalisco

500

1500

2500

0 2 4 6 8 10

Maize yield [ton/ha]

LR

F p

er c

apita

[m2

/cap

]

MEXICO

Chiapas

Veracruz

37

There are large differences between the states. In one extreme, Sinaloa decreased by more than 50%; and in the other extreme, Chiapas even increased by 10%. In conclusion, even regions within a country have different values and transitions of LRF per capita. Since the differences are related to the yields, then it can also be related to the agricultural systems that are used in each state. For instance, according to INEGI (2005a), in Sinaloa 45% of the agricultural area uses chemical fertilizers and in Chiapas only 10%. This could be the reason that Sinaloa reduced by half their LRF per capita and Chiapas even increased.

To conclude, as I mentioned above, the differences of LRF per capita throughout the Mexican regions are mainly caused by the maize’s LRF. This issue illustrates that there are very large differences in the maize production systems throughout the country.

38

39

5. CONCLUSIONS In this project I showed that countries and regions have had different transitions in population growth, change in consumption patterns and agricultural improvements. Since these three parameters determine the Land Requirements for Food (LRF), then the impact of these three patterns is different in each country and region and deviates from the global pattern. For this project I studied the LRF in different levels of scale: for the world’s average, six countries and seven Mexican states. During the last 50 years, the global LRF increased. During this period there were large agricultural improvements which declined the area required to produce a certain amount of food. However, the impact of population growth and the change to a more affluent diet have increased the world’s LRF by 50%. This trend diverts throughout countries. The USA, Austria and France have had a more or less constant LRF during this period due to a small dynamic in their population, consumption diet and agricultural improvements. In contrast, Mali, Mexico and The Philippines doubled their LRF in the last 50 years. Since the 60’s, Mexico and The Philippines had large agricultural improvements. If the population and the consumption patterns would have remained constant at the value in 1961, then the Mexican LRF would have reduced by 50% and The Philippines’ LRF by 60%. However, the LRF doubled due to population growth and change to a more affluent diet since the 60’s. The Philippines tripled their population and Mexico doubled their population. Mali had smaller agricultural improvements, their population doubled since the 60’s and the change to a more affluent diet started in the beginning of the 80’s. In result, Mali also doubled their LRF in the last 50 years. See figure 13. Countries with high GDP per capita like Austria, France and the USA followed similar transition patterns. In contrast, countries with low GDP per capita like Mali, Mexico and The Philippines deviated throughout each other and from the global trend (see figure 12). Population growth, change to a more affluent diet and yield’s improvements are expected to happen in developing countries in the coming decades. For these reasons, it is important to analyse the LRF in a national scale to assess the possible different trends throughout these developing countries since there is not a global trend for them. Moreover, I studied seven regions within Mexico with agriculture and climate differences between each other. This analysis showed that agricultural improvements in these regions have been different in the last 30 years and deviate from the Mexican average leading to different transitions in LRF. For instance, in Sinaloa State the LRF per capita decreased by 50% and, in contrast, Chiapas State even increased by 10% due to the very different types of agricultural production systems (see figure 15). To conclude, by doing the analysis in a regional and national scale it could be possible to assess the availability of land for food production in different regions in a more accurate way than by doing it in a global scale. The results show that the increase of LRF has been mainly in developing countries and developed countries have remained constant. Furthermore, even thought crop yields largely improved since the 60’s, population growth and the change to a more affluent diet have increased the LRF. According to the UN and the FAO, in the coming decades, population growth, change to a more affluent diet and yield improvements are expected to happen in developing countries. For this reason it is important to assess individually the impact of agricultural technology, population and diet in the LRF in a national and regional scale.

40

Further research For this project, we assumed that the area required to produce one kilocalory of animal food product is three times larger than the area required to produce one kilocalory of vegetal food products. This assumption is a limitation for the project. The methodology does not take into account the type of production system in each country and make no distinction throughout the different animal food products (see chapter 2.4). It is important that for further research the LRF of animal products is analyse in more detail. The results of this project show that there are large differences throughout developing countries. Therefore, to assess a possible trend for worldwide food production it is important to include all the countries in the analysis. By doing this, it will be possible to identify similar characteristics and transition patterns throughout specific groups of countries and assess their future trends. Finally, the different agricultural technologies have different environmental impacts. In this project I showed that high yields reduce the LRF; however, to achieve these yields a large amount of inputs are required (e.g. fertilizers, irrigation). These inputs have large environmental impact like soil and water pollution, large indirect energy use, and others. For instance, the fact that Mali has a higher LRF per capita than countries with a much more affluent diet should not be understood as that Malian stable diet has higher environmental impacts than the affluent diet of a rich country with high input agricultural systems. For this reason, in future analysis it is important to consider the environmental impacts of the agricultural production systems related to the LRF.

41

REFERENCES

Caballero, B. & Popkin, B. M. (2002). The Nutrition Transition. Diet and Disease in the Developing

World. San Diego, USA: Academic Press.

CONEVAL (2005). Mapas de Pobreza por Ingresos y Rezago Social 2005. Consejo Nacional de Evaluación de la Política de Desarrollo Social [On-line]. Available: http://www.coneval.gob.mx/mapas/

Conference Board (2010). The Conference Board Total Economy Database, January 2010 [On-line]. Available: http://www.conference-board.org/data/economydatabase/

Drewnowski, A. & Popkin, B. M. (1997). The nutrition transition: new trends in the global diet. Nutr

Rev., 55, 31.

Elferink, E.V. & Nonhebel, S. (2007) Variations in land requirements for meat production. Journal of

Cleaner Production 15,18, 1778-1786.

FAO (2002). World Agriculture: Towards 2015/2030, a FAO perspective Rome.

FAO (2010a). FAOSTAT Statistical Database [On-line]. Available: http://faostat.fao.org

FAO (2010b). Food Balance Sheets (FBS) [On-line]. Available: http://faostat.fao.org/site/368/default.aspx#ancor

FAO (2010c). Nutritive Factors [On-line]. Available: www.fao.org/economic/ess/publications-studies/publications/nutritive-factors/en/

Gerbens-Leenes, P. W. & Nonhebel, S. (2002). Consumption patterns and their effects on land required for food. Ecological Economics, 42, 185-199.

Gerbens-Leenes, P. W., Nonhebel, S., & Ivens, W. P. M. F. (2002). A method to determine land requirements relating to food consumption patterns. Agriculture, Ecosystems & Environment,

1755, 1-12.

INEGI (1998). Encuesta Nacional de Ingreso y Gasto en los Hogares (ENIGH). Información estadística, Encuestas en hogares [On-line]. Available: www.inegi.org.mx

INEGI (2003). Principales usos del suelo y tipos de vegetación por entidad federativa, 2002. SEMARNAT.Compendio de Estadísticas Ambientales, México, D.F. [On-line]. Available: www.inegi.org.mx

INEGI (2005a). Censo Agropecuario 2007. Censos y Conteos [On-line]. Available: www.inegi.org.mx

INEGI (2005b). Conteo de Población y Vivienda 2005. Censos y Conteos de Población y Vivienda [On-line]. Available: www.inegi.org.mx

INEGI (2005c). Conteos de Población y Vivienda 1980, 1990, 1995, 2000, 2005. Censos y Conteos de Población y Vivienda [On-line]. Available: www.inegi.org.mx

42

INEGI (2006). Información Estadística Medio Ambiente. SAGARPA, SMN [On-line]. Available: www.inegi.org.mx

INEGI (2008). Información Estadística Economica. Sistema de Cuentas Nacionales de México [On-line]. Available: www.inegi.org.mx

Kastner, T. & Nonhebel, S. (2009). Changes in land requirements for food in the Philippines: A historical analysis. Land Use Policy, In Press, Corrected Proof.

Martínez Jasso, I. and Villezca Becerra, P. A. (2003). La alimentación en México: un estudio a partir de la Encuesta Nacional de Ingresos y Gastps de los Hogares. Revista de Información y

análisis, 21, 26-37.

Miedema, J. H. (2010). Developments in the Dutch diet:Dynamics on two centuries of land

requirements for food in the Netherlands (1800-2000) Groningen, The Netherlands: Training Thesis for the MSc Energy and Environmental Science (IVEM), University of Groningen.

SAGARPA (2010). Secretaría de Agricultura, Ganadería, Desarrollo Social, Pesca y Alimentación [On-line]. Available: www.sagarpa.gob.mx

SIAP (2010). Sistema de Información Agroalimentaria y Pesquera, SAGARPA [On-line]. Available: www.siap.sagarpa.gob.mx

SMN (2010). Temperatura y Precipitación. Sistema Meteorológico Nacional (SMN); Comisión Nacional del Agua (CONAGUA) [On-line]. Available: http://smn.cna.gob.mx/climatologia/climatologia.html

UN (2007). World Population Prospects. The 2006 Revision New York: United Nations.

43

ANNEX 1.

FOOD ITEMS THAT WERE USED FOR THE CALCULATIONS OF LRF

A1.1 Global and National scale

Food item Crop equivalent Conversion Factor

VEGETAL PRODUCTS

Wheat Wheat 1,00

Rice (Milled Equivalent) Rice, paddy 1,29

Barley Barley 1,00

Maize Maize 1,00

Rye Rye 1,00

Oats Oats 1,00

Millet Millet 1,00

Sorghum Sorghum 1,00

Cereals, Other Cereals, nes 1,00

Cassava Cassava 1,00

Potatoes Potatoes 1,00

Sweet Potatoes Sweet Potatoes 1,00

Yams Fruit Fresh Nes 2,24

Roots, Other Roots and Tubers, nes 1,00

Sugar Cane Sugar Cane 1,00

Sugar Beet Sugar Beet 1,00

Sugar, Non-Centrifugal Sugar Cane 11,70

Sugar (Raw Equivalent) Sugar Cane 12,43

Sweeteners, Other Cereals, nes 1,35

Beans Beans, dry 1,00

Peas Peas, dry 1,00

Pulses, Other Pulses, nes 1,00

Soyabeans Soybeans 1,00

Groundnuts (Shelled Eq) Groundnuts, with shell 1,37

Sunflowerseed Sunflower seed 1,00

Rape and Mustardseed Rapeseed 1,00

Cottonseed Seed cotton 1,00

Coconuts - Incl Copra Coconuts 1,00

Sesameseed Sesame seed 1,00

Palmkernels Oil palm fruit 3,25

Olives Olives 1,00

Oilcrops, Other Mustard seed 1,00

Soyabean Oil Soybeans 2,64

Groundnut Oil Groundnuts, with shell 2,14

Sunflowerseed Oil Sunflower seed 2,87

Rape and Mustard Oil Rapeseed 1,79

Cottonseed Oil Seed cotton 3,49

Palmkernel Oil Oil palm fruit 5,59

Palm Oil Oil palm fruit 5,59

Coconut Oil Coconuts 4,80

Sesameseed Oil Sesame seed 1,54

Olive Oil Olives 5,05

Ricebran Oil Rice, paddy 3,33

44

Food item Crop equivalent Conversion Factor

Maize Germ Oil Maize 2,48

Oilcrops Oil, Other Mustard seed 2,21

Tomatoes Tomatoes 1,00

Onions Onions, dry 1,00

Vegetables, Other Vegetables fresh nes 1,00

Oranges, Mandarines Oranges 1,00

Lemons, Limes Lemons and limes 1,00

Grapefruit Grapefruit (inc. pomelos) 1,00

Citrus, Other Lemons and limes 1,00

Bananas Bananas 1,00

Apples Apples 1,00

Pineapples Pineapples 1,00

Dates Dates 1,00

Grapes Grapes 1,00

Fruits, Other Fruit Fresh Nes 1,00

Coffee Coffee, green 1,00

Cocoa Beans Cocoa beans 1,00

Pepper Pepper (Piper spp.) 1,00

Pimento Pepper (Piper spp.) 1,00

Spices, Other Spices, nes 1,00

Wine Grapes 1,28

Beer Barley 0,15

Beverages, Fermented Sugar cane 0,14

Beverages, Alcoholic Sugar cane 6,56

ANIMAL PRODUCTS Bovine Meat Bovine Meat 1,00

Mutton & Goat Meat Mutton & Goat Meat 1,00

Pigmeat Pigmeat 1,00

Poultry Meat Poultry Meat 1,00

Meat, Other Meat, Other 1,00

Butter, Ghee Butter, Ghee 1,00

Cream Cream 1,00

Fats, Animals, Raw Fats, Animals, Raw 1,00

Fish, Body Oil Fish, Body Oil 1,00

Fish, Liver Oil Fish, Liver Oil 1,00

Freshwater Fish Freshwater Fish 1,00

Demersal Fish Demersal Fish 1,00

Pelagic Fish Pelagic Fish 1,00

Marine Fish, Other Marine Fish, Other 1,00

Crustaceans Crustaceans 1,00

Cephalopods Cephalopods 1,00

Molluscs, Other Molluscs, Other 1,00

Meat, Aquatic Mammals Meat, Aquatic Mammals 1,00

Aquatic Animals, Others Aquatic Animals, Others 1,00

Aquatic Plants Aquatic Plants 1,00

Honey Honey 1,00

Eggs + Eggs + 1,00

Milk - Excluding Butter + Milk - Excluding Butter + 1,00

45

A1.2 Regional scale

Food item Crop equivalent Conversion Factor

VEGETAL PRODUCTS

maize maize 1,0

wheat wheat 1,0

Rice (Milled Equivalent) rice paddy 1,3

soyabean oil soybean 2,6

sugar (raw equivalent) sugar cane 12,4

Potatoes potato 1,0

beans beans 1,0

Onions onion 1,0

tomatoes tomato 1,0

bananas banana 1,0

ANIMAL PRODUCTS milk - excluding butter + milk 1,0

eggs + eggs 1,0

bovine meat bovine 1,0

poultry meat poultry meat 1,0

pigmeat pigmeat 1,0

46

47

ANNEX 2.

TRANSITION IN DIET FOR THE GLOBAL AND NATIONAL SCALE

World

0

1000

2000

3000

4000

1961 1968 1975 1982 1989 1996 2003

Co

nsu

mp

tio

n [

kca

l/ca

p/d

ay]

USA Austria

Consu

mption [

kca

l/ca

p/d

ay]

0

1000

2000

3000

4000

1961 1968 1975 1982 1989 1996 2003

0

1000

2000

3000

4000

1961 1968 1975 1982 1989 1996 2003 France Mexico

Consu

mption [

kca

l/ca

p/d

ay]

0

1000

2000

3000

4000

1961 1968 1975 1982 1989 1996 2003

0

1000

2000

3000

4000

1961 1968 1975 1982 1989 1996 2003

The Philippines Mali

Consu

mption [

kca

l/ca

p/d

ay]

0

1000

2000

3000

4000

1961 1968 1975 1982 1989 1996 2003

0

1000

2000

3000

4000

1961 1968 1975 1982 1989 1996 2003

animal products Cereals Pulsesvegetables oils and oilcrops sugar and sugar crops Fruits and vegetablesother vegetal food items

The different color area in each graph show the total caloric intake transition for each food category from 1961 to 2005.