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Brasília • DFSeptember 2014

Ministry of Agriculture, Livestock and Food SupplyStrategic Management Offi ce

Minister`s Offi ce

PROJECTIONS OFAGRIBUSINESS

Brazil 2013/14 to 2023/24Long-Term Projections

© 2014 Ministério da Agricultura, Pecuária e Abastecimento.All rights reserved. Reproduction permitted provided the source is acknowledged. Responsibility for copyright texts and images of this work is the author. 5th edition. year 2014 Circulation: 1.000 copies

Preparation, distribution, information: MINISTRY OF AGRICULTURE, FISHERIES AND FOOD SUPPLY Strategic Management Office General Coordination of Strategic Planning Block D, 7th floor, room 752 CEP: 70043-900 Brasília / DF .: Tel (61) 3218 2644 .: Fax (61) 3321 2792 www.agricultura.gov.br email: [email protected]

Customer Service: 0800 704 1995

Editorial coordination: AGE / Mapa

Impresso no Brasil / Printed in Brazil

Biblioteca Nacional de Agricultura - BINAGRI

Catalogação na Fonte

Brazil. Ministry of Agriculture, Livestock and Food Supply.Projections of agribusiness : Brazil 2013/14 to 2019/20 Long-

term Projections / Ministry of Agriculture, Livestock and Food Supply. Strategic Management Advisory Board. – Brasília :

MAPA/ACS, 2014.98 p.

ISBN 978-85-7991-087-6

1. Agronegócio- Brasil. 2. Desenvolvimento Econômico. I. Título. II. Título : Brazil 2013/14 to 2019/20 Long-term Projections.

AGRIS E71CDU 339.56

TEAM:

AGE/Mapa

João Cruz Reis Filho

Renato de Oliveira Brito

José Garcia Gasques

Eliana Teles Bastos

Marco Antonio A. Tubino

TECHNICAL PARTNERS:

Alcido Elenor Wander (Embrapa)

Aroldo Antônio O. Neto (Conab)

Carlos Martins Santiago (Embrapa)

Cid Jorge Caldas (Agroenergia/Mapa)

Daniel Furlan Amaral (Abiove)

Dirceu Talamini (Embrapa)

Djalma F. de Aquino (Conab)

Eledon Oliveira (Conab)

Elieser Barros Correia (Ceplac)

Erly Cardoso Teixeira (UFV)

Fabio Trigueirinho (Abiove)

Francisco Braz Saliba (Bracelpa)

SGE/Embrapa

Geraldo da Silva e Souza

Eliane Gonçalves Gomes

Francisco Olavo B. Sousa (Conab)

Glauco Carvalho (Embrapa)

Gustavo Firmo (Mapa)

Joaquim Bento S. Ferreira (Esalq)

Kennya B. Siqueira (Embrapa)

Leonardo Botelho Zilio (Abiove)

Lucilio Rogério Aparecido Alves (Esalq)

Luis Carlos Job (Mapa)

Luiz Antônio Pinazza (Abag)

Milton Bosco Jr. (Bracelpa)

Olavo Sousa (Conab)

Tiago Quintela Giuliani (Mapa)

Wander Sousa (Conab)

SUMMARY

1. INTRODUCTION

2. SCENARIOS OF PROJECTIONS

3. METHODOLOGY

4. RESULTS FOR BRAZIL

a. Grains

b. Coton Lint

c. Rice

d. Bean

e. Corn

f. Wheat

g. Soybean Complex

h. Coffee

i. Milk

j. Sugar

k. Orange and Orange Juice

l. Meat

m. Pulp and Paper

n. Tobacco

o. Fruits

5. RESULTS OF REGIONAL PROJECTIONS

6. SUMMARY

7. BIBLIOGRAPHY

ANNEX 1 - Methodological Note

ANNEX 2 - Results Tables

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48

84

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31

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52

91

8

34

75

17

55

94

101

10

46

79

21

58

LIST OF ACRONYMS

ABIOVE - Associação Brasileira da Indústria de Óleos Vegetais

ABRAF- Associação Brasileira de Produtores de Florestas Plantadas

AGE - Assessoria de Gestão Estratégica

BRACELPA- Associação Brasileira de Celulose e Papel

CECAT - Centro de Estudos Estratégicos e Capacitação em Agricultura Tropical

CNA - Confederação da Agricultura e Pecuária do Brasil

CONAB - Companhia Nacional de Abastecimento

CEPLAC - Comissão Executiva de Planejamento da Lavoura Cacaueira

EMBRAPA Gado de Leite - Empresa Brasileira de Pesquisa Agropecuária

FAO - Food and Agriculture Organization of the United Nations

FAPRI - Food and Agricultural Policy Research Institute

FGV - Fundação Getúlio Vargas

IBGE - Instituto Brasileiro de Geografia e Estatística

ICONE - Instituto de Estudos do Comércio e Negociações Internacionais

IFPRI - International Food Policy Research Institute

IPEA - Instituto de Pesquisa Econômica Aplicada

MAPA - Ministério da Agricultura, Pecuária e Abastecimento

OECD - Organization for Economic Co-Operation and Development

ONU - Organização das Nações Unidas

SGE- Secretaria de Gestão Estratégica

UFV - Universidade Federal de Viçosa

UNICA - União da Indústria de Cana-de-açúcar

USDA - United States Department of Agriculture

1. INTRODUCTION

This report is an update and revision of the report Projections of Agribusiness - Brazil 2012/13 to 2022/23, Brasília - DF, June 2013, published by the Strategic Management Office of Ministry of Agriculture, Livestock and Food Supply.

The study aims to indicate possible directions of development and provide support to policy makers about the trends of the major agribusiness products. The results also seek to answer to a large number of users in various sectors of national and international economy for which the information now disclosed are of enormous importance. The trends indicated will identify possible trajectories, as well as to structure future vision of agribusiness in the global context for the country keep growing and conquering new markets.

Projections of Agribusiness - Brazil 2013/14 to 2023/24 is a prospective view of the sector, the basis for strategic planning of MAPA - Ministry of Agriculture, Livestock and Supply. For their preparation the work of brazilian and international organizations were consulted, some of them based on models projections.

Among the surveyed institutions highlight the work of the Food and Agriculture Organization of the United Nations (FAO), Food and Agricultural Policy Research Institute (FAPRI), International Food Policy Research Institute (IFPRI), Organization for Economic Co-Operation and Development ( OECD), United Nations (UN), United States Department of Agriculture (USDA), Policy Research Institute / Ministry of Agriculture, Forestry and Fisheries, Japan (PRIMAFF), Confederation of Agriculture and Livestock of Brazil (CNA), Fundação Getulio Vargas (FGV), Brazilian Institute of Geography and Statistics (IBGE), Institute for International Trade Negotiations (ICONE), Institute of Applied Economic Research (IPEA), National Supply Company (Conab), Embrapa Dairy Cattle, Energy Research Company (EPE), the Sugar Cane Industry Union (UNICA), Brazilian Association of Planted Forest Producers (ABRAF), Federation of Industries of São Paulo (FIESP), STCP Consulting, Engineering and Management, Brazilian Association of Pulp and Paper (BRACELPA), Brazilian Association of Vegetable Oil Industries (ABIOVE) and the Brazilian Agribusiness Association (ABAG).

The study was conducted by a group of experts from the Ministry of Agriculture and Embrapa, which cooperated in various stages of preparation. Benefited also from the valuable contribution of people / institutions who analyzed the preliminary results and reported their

6

7

comments, views and ideas on the results of the projections. Observations related to these collaborations were included in the

Report, without nominate partners, but the institutions to which they belong.

2. SCENARIOS OF PROJECTIONS

The scenario of rising prices should remain in 2014. Figure 1 shows the quarterly prices received by U.S. farmers for crops and livestock. Des-pite the relative price fl uctuations, the trend since 2005 has been lifting. Note that the prices of livestock products in 2014 have higher growth rates than crops.

Fig. 1 - Prices Received by Farmers in the United Sta-tes

Source: NASS/USDA, 2014.

80

133

57

98

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40

60

80

100

120

140

160

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3 07

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3 10

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3 01

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4 04

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4

Inde

x

livestock Crops

Domestic prices in Brazil have also shown a tendency to increase in some products as shown in Table 1. For some products, such as soybeans, corn, cattle, rice and cotton prices have shown a trend of growth in 2014. The prices for these products in 2014 are higher than the historical rates and also the prices of 2013.

Table 1 – Prices received by Farmers in Brazil

Brazil expects a record grains harvest in 2014, estimated at 193.6 million tons.

3. METHODOLOGY

The projections cover the period 2013/14 to 2023/24. In general, the basic period of the projections cover 20 years. Taking into account the last year experience, we decided to use, this year as a basic reference period information after 1994. Between 1994 and today, as we know, entered a phase of economic stabilization and this allowed a reduction of uncertainty in variables.

Source: Cepea/Usp. Position at 17/04/2014

8

Product UnitHistorical

price2013 2014

Wheat R$/t 460.3 686.8 606.98

Soybean R$/SC 60kg 37.5 65.4 67.7

Corn R$/SC 60kg 23.9 26.9 30.2

Bovine R$/@ 64.5 105 122.5

Rice R$/SC 50kg 26.8 33.8 35.3

Cotton Cent./libra peso 136.12 202.14 219.9

9

The projections were performed using specific econometric models. They are time series models that have great use in forecasting series. The use of these models in Brazil, for the purpose of this report is unprecedented. We are not aware of published studies in the country who have worked with these models.

Three statistical models were used: exponential smoothing, Box-Jenkins (Arima) and State-Space Model. There is a methodological note (Annex 1) which presents the main characteristics of the three models.

The projections were performed for 26 agribusiness products: corn, soybeans, wheat, orange, orange juice, chicken, beef, pork, sugar cane, sugar, cotton, soybean meal, soybean oil, fresh milk, beans, rice, potatoes, cassava, tobacco, coffee, cocoa, grape, apple, banana, pulp and paper.

The report, however, not discussed all products, but their data are shown in the tables that are part of the Annexes of the study.

The choice of the most likely model was made as follows:

1 Consistency of results;

2 International comparisons of data production, consumption, export, import and trade in the country and the world.;

3 last trend of our data;

4 Growth Potential;

5. Consultations with experts.

The projections were generally for production, consumption, export, import and planted area. Some tests with productivity of some crops were conducted. The tendency was to choose more conservative models and not those indicated bolder growth rates. This procedure was used for selecting the most selected results.

The projections presented in this report are national, where the number of products studied is comprehensive; and regional, where the number of analyzed products is restricted and has specific interest.

10

The projections are accompanied by prediction intervals which become wider with time. The greatest breadth of these ranges reflects the greater uncertainty associated with more distant the last year of the series used as the basis of the projection forecasts.

4. RESULTS FORECASTS FOR BRAZIL

a. Grains

Projections of grains refers to the 15 products surveyed monthly by CONAB as part of their harvest surveys. This set of products is called grains by Conab.

As of this update projections already has the data to the eighth survey of harvest (May survey) for the soy complex products, corn and other products, was used for the 2013/2014 harvest data released by Conab( 2014 ): soybean, soybean oil, soybean meal, corn, beans, meat (beef, chicken, pork), and sugar cane. Thus, the data from 2013/2014 are projections Conab. The projections in this report for these products starting in 2014/2015.

The estimates of grain production point to a crop of 193.6 million tons in 2013/14, and a planted area of 56.4 million hectares (Conab 2014). These two variables are the largest that have been achieved in Brazil over the years.

11

Table 2 – Planted area and Production of Grains

Source: AGE/Mapa and SGE/Embrapa with Conab information.* Models used: Space states.

Projection Up limit. Projection Up limit

2013/14 193,566 - 56,861 -

2014/15 199,656 217,428 58,553 61,469

2015/16 205,411 226,469 59,741 65,172

2016/17 211,315 236,349 60,729 68,068

2017/18 217,176 245,257 61,654 70,621

2018/19 223,056 254,002 62,555 72,917

2019/20 228,930 262,458 63,448 75,051

2020/21 234,807 270,744 64,338 77,063

2021/22 240,684 278,874 65,227 78,985

2022/23 246,560 286,879 66,115 80,834

2023/24 252,437 294,778 67,004 82,624

Production (thousand tons)

Planted Area (thousand hectares)Year

Production 30.4%Planted Area 17.8%

Variation % 2013/14 to 2023/24

12

Fig. 2 – Planted area and Production of Grains

Source: AGE/Mapa and SGE/Embrapa

For 2014/2015 the production expected to be between 199.7 million and 217.4 million tons of grains. This range of variation is a safety for the occurrence of changes over which one has or little control such as climate change droughts and rains.

Projections for 2023/2024 are a crop around 252.4 million tonnes, representing an increase of 30.4% over the current crop. At the upper end projection indicates a production of up to 294.8 million tons in 2023/24. The grain area should increase 17.8% between 2013/14 and 2023/24, from 56.9 million in 2013/2014 to 67.0 million in 2023/2024, which corresponds to an annual increase of 1.6 %.

56,861 67,004

193,566 252,437

0

50,000

100,000

150,000

200,000

250,000

300,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

Planted Area (thousand hectares) Produc>on (thousand tons)

13

Table 3 – Brazil: Planted Area with Five Main Grains


2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14

Rice 3,916 3,018 2,967 2,875 2,909 2,765 2,820 2,427 2,400 2,417

Bean 3,949 4,224 4,088 3,993 4,148 3,609 3,990 3,262 3,075 3,359

Corn 12,208 12,964 14,055 14,766 14,172 12,994 13,806 15,178 15,829 15,726

Soybean 23,301 22,749 20,687 21,313 21,743 23,468 24,181 25,042 27,736 30,105

Wheat 2,756 2,362 1,758 1,852 2,396 2,428 2,150 2,166 2,210 2,617

Total 46,131 45,317 43,554 44,799 45,368 45,263 46,947 48,075 51,250 54,225

2014/15 2015/16 2016/17 2017/18 2018/19 2019/20 2020/21 2021/22 2022/23 2023/24

Rice 2,318 2,220 2,121 2,022 1,924 1,825 1,726 1,627 1,529 1,430

Bean 3,245 3,131 3,016 2,902 2,788 2,674 2,559 2,445 2,331 2,217

Corn 15,659 15,874 15,993 16,080 16,188 16,303 16,412 16,520 16,630 16,739

Soybean 31,598 32,764 33,785 34,751 35,697 36,633 37,565 38,496 39,427 40,357

Wheat 2,676 2,734 2,793 2,851 2,910 2,968 3,027 3,085 3,144 3,203

Total 55,495 56,722 57,708 58,606 59,506 60,402 61,289 62,174 63,060 63,945

14

b. Cotton lint

Cotton production is concentrated in the states of Mato Grosso, Goiás and Bahia, which account for in 2013/14 90.7% of the country’s production. Mato Grosso has the lead with 56.2% of the national production been folloed by state of Bahia, with 29.8% of the Brazilian production, and Goiás, with 4.9%.

The projections for cotton lint production indicate 1.67 million tons in 2013/2014 and 2.35 million tons in 2023/24. This expansion corresponds to a growth rate of 3.1% per year over the projection period and an increase of 40.5% in production. Some analysts noted that the projected production is quite high. What has been argued is that with the emergence of new technologies is possible to obtain higher yields. However, what we have checked is that the research has reached a stage where progress in productivity levels is proving slow or stagnant. It was also observed that

BA

MT

498.3

1,672.3 100.0

29.8

Major producing states

Source: Conab - survey june/2014

National Production

COTTON LINT

Harvest Year2013/2014

(Thousand tons)%

939.4 56.2

GO 79.9 4.8

56.2

4.8

29.8

MATO GROSSO

GOIÁS

BAHIA

Total 1,517.6 90.7

15

the projection for 2014/15, 2,143 thousand tons may not occur and that the tendency is to fall short, close to 2013/14 production of 2013/14, 1,672 thousand tons of cotton lint.

The consumption of this product in Brazil should grow at an annual rate lower than 1.0% in the next ten years, reaching a total of 939 thousand tons consumed in 2023/24. Exports are also forecast strong growth, 55.4% between 2013/14 a 2023/24

The report from the U.S. Department of Agriculture (USDA, 2014) indicates that Brazilian exports between 2013/14 and 2023/24 will more than double, with the country that should increase its exports in the next 10 years. Also according to this source, in a few years Brazil will overtake Central Asia as the third largest source of cotton for export. Brazil has exported to large number of countries, but the main importers in 2013 were South Korea, Indonesia, China, Argentina and Vietnam.

16

Source: AGE/Mapa and SGE/Embrapa with Conab information.* Models used: Production- Space states. Consumption and exports – PRP

Table 4 –Production, Consumption and Export of Cot-ton Lint (thousand tons)

Projection Up limit. Projection Up limit. Projection Up limit.2013/14 1,672 - 900 - 575 -2014/15 2,143 2,517 904 1,000 607 9232015/16 1,900 2,322 908 1,044 639 1,0852016/17 1,719 2,148 912 1,078 671 1,2182017/18 2,099 2,558 916 1,108 702 1,3342018/19 2,271 2,813 920 1,134 734 1,4402019/20 2,072 2,622 924 1,159 766 1,5402020/21 2,135 2,689 928 1,182 798 1,6342021/22 2,411 3,004 932 1,203 830 1,7232022/23 2,426 3,051 936 1,223 862 1,8092023/24 2,350 2,981 939 1,243 893 1,892

Production Consumption ExportsYear

Production 40.5%Consumption 4.4%Exports 55.4%

Variation %

2013/14 to 2023/24

17

c. Rice

Although the rice is a common culture in most of the country, most of the production occurs in 5 states - Rio Grande do Sul, with predominantly irrigated rice concentrates 65.8% of production in 2013/14, Santa Catarina , 8.7% of production, Mato Grosso, 5.2%, Maranhão,5.4 % and Tocantins ,4.4% of national production. In the Northeast, especially in the state of Ceará rice is irrigated and concentrated on irrigation projects. A small amount is also produced in the states crossed bythe São Francisco river pass, as Bahia, Sergipe, Alagoas and Pernambuco and these areas also receive irrigation.

Fig. 3 – Production, Consumption and Exports of Cot-ton Lint


1,672 2,350

900 939

575 893

0

500

1,000

1,500

2,000

2,500

3,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Produc4on Consump4on

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The projected production for 2023/24 is 13.6 million tons, and consumption of 12.2 million tons. We projected to increase 11.3% in rice production over the next 10 years. This increased production is expected to occur mainly through the growth of irrigated areas. The projected increase in production is apparently low, but it is equivalent to the projection of consumption over the next 10 years.

The relative stabilization of the projected consumption of rice is consistent with the data supply Conab in recent years, around 12 million tons in 2013/14 (Conab, 2014).

The estimates for the projection of rice planted area show that the area reduction will occur in the coming years. According to the projections it may fall of 2.4 million hectares in 2013/14 to 1.40 million hectares in 2023/24. According Conab technicians consulted, the area reduction is not likely to occur. The same is shared by researchers at

MARANHÃO

RS

SC

8.059.0

12,250.7 100.0

65.8



National Production

RICEHarvest Year

2013/2014(Thousand tons)

%

1.067.2 8.7

MA 658.4 5.4

MT 639.5 5.2

TO 543.7 4.4

5.2

65.8

8.7

4.4

5.4

RIO GRANDEDO SUL

SANTACATARINA

MATO GROSSO TOCANTINS

Total 10,967.8 89.5

19

Embrapa Rice and Beans. In Rio Grande do Sul, which is now at 1.0 million hectares should remain in that number or even decrease because rice has had to compete with soybean and corn.

The new Brazilian Forest Code limits the incorporation of new areas and the opportunity for Highlands Rice for years to come is in the crop rotation, renovation, rehabilitation or renovation of degraded or even livestock grazing in the transition to agriculture (Santiago, Carlo . Embrapa, 2013).

The productivity should be the main variable in the behavior of the product in the coming years. The projection indicates a productivity of 5.5 tonnes per hectare, about 300 kg more than the current productivity of 5.2 tonnes per hectare. But rice is concentrated in areas of Rio Grande do Sul where the current yield is 7.5 tons per hectare (Conab, 2014).

The consumption of rice in the coming years is expected to grow at 0.2% per year. According to technicians of Embrapa, the projected consumption seems appropriate to the current reality, even if the calculations of apparent per capita consumption have shown declines in recent years. To change this long-term trend, only if Brazil can develop new ways to use and consumption of rice (made from grains of rice products, which depends on R & D and, especially industry, became interested in the subject, which did not can be seen today).

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Table 5 –Production, Consumption and Rice Imports (thousand tons)

Source: AGE/Mapa and SGE/Embrapa with CONAB information* Models used: Production, Consumption and Imports, PRP

Production 8.2%Consumption 2.0%Imports -32.9%

Variation % 2013/14 to 2023/24

Projection Up limit. Projection Up limit. Projection Up limit.2013/14 12,251 - 12,000 - 1,000 -2014/15 12,703 15,285 12,023 12,557 967 1,7692015/16 12,807 16,459 12,047 12,801 934 2,0692016/17 12,910 17,383 12,070 12,994 901 2,2912017/18 13,014 18,179 12,094 13,161 868 2,4732018/19 13,118 18,892 12,117 13,310 836 2,6292019/20 13,222 19,547 12,141 13,447 803 2,7682020/21 13,326 20,158 12,164 13,575 770 2,8922021/22 13,429 20,734 12,188 13,696 737 3,0062022/23 13,533 21,280 12,211 13,811 704 3,1112023/24 13,637 21,803 12,235 13,921 671 3,208

Production Consumption ImportsYear

11.3%

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d. Bean

The geographical distribution of the main producers of beans in the country can be seen on the map. The product is fairly distributed across several states, although the main are Paraná, Minas Gerais and Mato Grosso, which currently produce 74.9% of national production.

Such as rice, beans are part of the basic diet of Brazilians. It is the product that more has the production , a trend that should continue in the next years production. Imports are always to fi ll a small gap between production and consumption (Santiago, C. Embrapa, 2013, and Conab, 2014).

Fig. 4 - Production, Consumption and Rice Imports


12,251 13,637

12,000 12,235

1,000

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Produc4on Consump4on Imports

671

22

PARANÁ

MATO GROSSO

GOIÁS

MINASGERAIS

PR

MG

871.2

3,713.9 100.0

23.5



National Production

BEANHarvest Year


%

596.0 16.0

MT 563.5 15.2

BA 301.3 8.1

GO 255.4 6.9

CE 194.8 5.2

16.0

8.1

5.2

23.5

15.2

6.9

BAHIA

CEARÁ

Total 2,782.2 74.9

23

According to technicians of Embrapa Rice and Beans, each year increases the discussions on production focused exclusively on the domestic market.There are some varieties of beans that can be used for export. If this new opportunity consolidates the projection of production will have to be adjusted upward.

The variation designed for consumption is 3.6%, which is higher than the production variation. Annual average consumption has been 3.5 million tonnes, requiring small amounts of imports. If confirmed projections of production, should be no need to import beans in the coming years. Over the past five years, Brazil has imported annually between 180 000 and 300 000 tonnes of beans (Conab, 2014).

The opinions of Conab and Embrapa technicians is that there may be major changes in the beans in the coming years. Productivity is expected to increase from current levels as producers of soybeans and corn are producing beans destined for export to China, India and some African countries. The Northeast, although a large producer of this product has imported beans from other states in periods of drought. Mato Grosso has produced beans for export.

Some states such as São Paulo and Minas Gerais has been having problems with regards to pests and diseases that attack crops of this product and so far have struggled to adequately control these attacks.

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Projection Up limit. Projection Up limit. Projection Up limit.

2013/14 3,714 3,450 300

2014/15 3,179 3,835 3,463 3,897 307 436

2015/16 2,928 3,644 3,475 4,090 314 497

2016/17 3,268 3,990 3,488 4,240 322 545

2017/18 3,227 4,066 3,500 4,369 329 587

2018/19 3,036 3,949 3,513 4,484 336 625

2019/20 3,164 4,096 3,525 4,589 343 659

2020/21 3,205 4,193 3,538 4,687 350 692

2021/22 3,099 4,149 3,550 4,779 358 723

2022/23 3,129 4,209 3,563 4,866 365 752

2023/24 3,173 4,292 3,575 4,949 372 780


Table 6 – Production, Consumption and Bean Imports (thousand tons)

Source: AGE/Mapa and SGE/Embrapa with CONAB information*Models used: To Production, ARMA Models , to Consumption and Imports, PRP

- - -

Production -14.6%Consumption 3.6%Imports 24.0%

Variation % 2013/14 to 2023/24

25

Fig. 5 – Production, Consumption and Bean Imports

e. Corn

The national maize production in the country is relatively sparse. The main producing states, Mato Grosso, Paraná, Minas Gerais, Goiás, Mato Grosso do Sul and Rio Grande do Sul should answer in 2013/14 by 70.0% of national production. But the major producing regions are South, the with 31.5% of the national production and Midwest with 42.0%. In South leadership is of Paraná, and in the Midwest, Mato Grosso. These are currently the main producers of corn in the country. But Minas Gerais, Goias and Rio Grande do Sul Minas also account for an important part of national production as shown on the map


3,714

3,173 3,450

3,575

300 372

0 500

1,000 1,500 2,000 2,500 3,000 3,500 4,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons


26

The forecast for corn production in Brazil for 2013/14 is estimated at 77.9 million tonnes (Conab, 2014). For 2014/15 the projected production is between 80.7 and 93.9 million tons as the upper limit of the projection. But the tendency is the production lie nearest the projection. For 2023/24 production is projected 103.1 million tons.

As is well known, in Paraná and Mato Grosso, the biggest producers, soybean areas release space for planting corn. In Mato Grosso it is usual to plant soybeans around 15 September and harvest in January to then start the second maize crop. The limit for this planting is February because the risk of loss due the dry season are great if this period is exceeded.

The corn area will increase by 6.4% between 2013/14 and 2023/24, from 15.7 million hectares in 2013/14 to 16.7 million, reaching 22,1 million

MT

PR

16,839.3

77,887.1 100.0

21.6



National Production

CORNHarvest Year


%

15,295.4 19.6

MS 7,530.5 9.7

MG 6,956.5 8.9

RS 5,773.7 7.4

GO 7,489.2 9.6

SP 3,699.7 4.8

SC 3,485.0 4.5

BA 3,283.0 4.2

21.6

9.6

4.2

9.7

8.9

4.8

4.5

19.6

7.4RIO GRANDE

DO SUL

PARANÁ

MATO GROSSO

GOIÁS

MINASGERAIS

BAHIA

SÃO PAULO

MATO GROSSODO SUL

SANTACATARINA

Total 70,352.3 90.3

27

hectares in 2023/24. There will be no need for new areas to expand this activity as soybean areas release the majority of the areas required by corn. The increase in projected area 6.4% is below the growing rate of the past 10 years, that was 25.5%. But the corn had in recent years high productivity gains resulting in less need for additional areas.

The domestic consumption of corn in 2013/14 represents 69.0 % of production should decrease to 62.2 %. Corn exports must pass 21 million tons in 2013/14 to 33.7 million tons in 2023/24. To maintain domestic consumption projected of 64.0 million tons and ensure a reasonable volume level of ending stocks and exports projected, the projected production shoult be of 103.0 million tons, sufficient to meet the demand in 2024. According to technicians working with this culture area should increase more than is being projected and perhaps get closer to its upper limit of growth (See Figure 8)

28


2013/14 77,887 53,818 21,0002014/15 80,717 93,896 54,876 56,652 22,806 30,2642015/16 83,462 100,811 55,868 58,892 25,001 35,1172016/17 86,773 107,583 56,868 60,927 25,910 37,1442017/18 88,118 110,940 57,899 62,859 26,790 39,2642018/19 91,516 117,488 58,936 64,675 28,018 41,7482019/20 93,193 120,947 59,967 66,396 29,192 44,0162020/21 96,528 126,846 61,000 68,055 30,298 46,1212021/22 98,138 129,980 62,034 69,665 31,425 48,2012022/23 101,497 135,617 63,068 71,234 32,565 50,2472023/24 103,121 138,603 64,102 72,770 33,698 52,237


Table 7 – Production Consumption and Corn Export (thousand tons)

Source: AGE/Mapa and SGE/Embrapa with CONAB information* Models used: To production, Consumption and Exports, State – Space Models.

- - -


2013/14 to 2023/24Variation %

29

Fig. 6 – Corn Production

Fig. 7 – Corn Consumption



77,887

103,121

138,603

0 20,000 40,000 60,000 80,000

100,000 120,000 140,000 160,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Projec4on Up limit.

53,818 64,102

72,770

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Projec4on Up limit.

30

Fig. 8 – Planted Area of Corn


22,149

15,726 16,739

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Up limit. ProjecAons

Projection Variation(%) 20013/14 a 2023/24

6,4 a 40,8% 6,4 to 40,8%

31

f. Wheat

Wheat production in the country is concentrated in the South, and Rio Grande do Sul and Paraná are the major producers. In 2013/14 harvest, the forecast indicates that Paraná are responsible for 51.9% of the country’s production and Rio Grande do Sul by 40.4%. The participation of other states, is of the order of 7.7%. This participation is distributed between Santa Catarina, São Paulo, Minas Gerais and Mato Grosso do Sul.

Wheat production in 2013/14 crop is being estimated by Conab in 7.4 million tons; this is the largest crop that Brazil already had. The projected production for 2023/24 is 10.0 million tons, and consumption of 14.3 million tons in the same year. The domestic consumption of wheat in the country is expected to grow 17.4% between 2013/14 and 2023/2024.

RIO GRANDEDO SUL

PARANÁ

7,373.1 100,0

RS 2,978.9 40.4

PR 3,824.6 51.951.9

40.4



National Production

WHEATHarvest Year


%

Total 6,803.5 92.3

32


2013/14 7,373 12,192 5,5002014/15 7,635 10,519 12,405 13,443 5,478 7,2012015/16 7,897 11,975 12,617 14,086 5,456 7,8932016/17 8,158 13,154 12,830 14,628 5,433 8,4182017/18 8,420 14,188 13,042 15,119 5,411 8,8582018/19 8,682 15,131 13,255 15,577 5,389 9,2432019/20 8,944 16,008 13,468 16,011 5,367 9,5882020/21 9,205 16,836 13,680 16,428 5,345 9,9042021/22 9,467 17,625 13,893 16,830 5,322 10,1972022/23 9,729 18,381 14,105 17,221 5,300 10,4702023/24 9,991 19,111 14,318 17,602 5,278 10,728


Table 8 – Production, Consumption and Imports of Wheat (thousand tons)

Source: AGE/Mapa and SGE/Embrapa with CONAB information.* Models used: To Production and Consiumptiion , State – Space model, and to Export, PRP model.

The domestic supply will require imports of 5.3 million tonnes in 2023/24. In recent years, imports has been set between 5.8 and 7.0 million tons, and the most frequent import volume has been 6 million tonnes with an outflow in nearly 2.4 billion dollars in 2013.

Although the increase in wheat production in coming years by more than 30%, Brazil should remain as one of the world’s largest importer. The USDA report estimated Brazilian wheat imports of 8 million tons in 2023/24 (USDA, 2014).

- - -

Production 35.5%Consumption 17.4%Imports -4.0%

2013/14 to 2023/24Variation %

33

Fig. 9 - Production, Consumption and Import of Wheat


7,373 9,991

12,192 14,318

5,500 5,278

0 2,000 4,000 6,000 8,000

10,000 12,000 14,000 16,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons


34

Soybean production expected in the country in 2013/14 is 86.1 million tons Soybean production in Brazil is led by the states of Mato Grosso, with 31.4% of national production; Paraná with 17.1%, Rio Grande do Sul with 14.8%, and Goiás, 10.0%. But, as shown on the map, soybean production is also evolving into new areas in Maranhão, Tocantins, Piauí and Bahia, which in 2013/14 accounted for 10.1% of Brazilian production which corresponds to a production of 8.7 million tons of soybean. This is a region located in the center northeast of the country, which has shown strong potential for grain production, called Matopiba. Despite its deficiencies infrastructure, still attractive price land, the climate, the possibility of deploying large areas and favorable relief, have been several factors that have motivated investments in the region.

RIO GRANDEDO SUL

PARANÁ

BAHIAMATO GROSSO

MATO GROSSODO SUL

Goiás

MT

PR

27,001.6

86,052.2 100.0

31.4



National Production

SOYBEANHarvest Year


%

14,740.8 17.1

RS 12,734.3 14.8

GO 8,636.6 10.0

MS 6,148.0 7.1

BA 3,229.2 3.8

3.831.4

7.1

10.0

17.1

14.8

Total 72,490.5 84.2

g. Soybean Complex

Soybean

35

The projection of soybeans for 2023/24 is 117.8 million tonnes. This number represents an increase of 36.9% over the production of 2013/14. But it is a percentage that is lower than the growth recorded in the last 10 years in Brazil, which was 64.5% (Conab, 2014).

Consumption projections indicate that there must be a large increase in demand for soybean in the international and domestic market. In this market, besides the demand for animal feed, is expected a strong increase in consumption of soybean for bio diesel production, estimated in 2014 by ABIOVE between 10.4 and 12 million tons. This variation depends on the scenario regarding the participation of soybean oil for biodiesel production (ABIOVE matching 05.19.14).

Domestic consumption of soybean is expected to reach 50.4 million tonnes by the end of the projection. Consumption is projected to increase 25.8% by 2023/24. This estimate is close to the growth observed in recent years by Conab of 23.0% within 6 years. There should be an additional consumption of soybean in relation to 2013/14 of around 10.0 million tonnes. As is well known, soybean is an essential component in the manufacture of animal feeds and is gaining importance in human nutrition.

The soybean area should increase 10.3 million hectares over the next 10 years, arriving in 2024 to 40.4 million hectares. It is a crop that will more expand area over the next decade. It represents an increase of 34.1% over the area with soybeans in 2013/14.

In the new areas of the Center Northeast of Brazil, comprising the region of Matopiba, the soybean area should expand greatly according to Conab technicians. This information goes in the same direction as the results obtained in this work. In the present work, the area of grains in this region should expand by 16.3% over the next 10 years. This equates to reach the region area of 8.4 million hectares, which at its upper limit can reach 10.9 million hectares.

In Paraná state, area can grow in the coming years taking areas of other cultures. In Mato Grosso expansion should occur over degraded pastures and new areas, but mostly the first areas. But the trend in Brazil is that the expansion of the area occurs mainly on natural pasture lands.

Exports of soybeans designed for 2023/2024 is 65.2 million tonnes, Representing an increase of 19.9 million tonnes for the quantity exported by Brazil in 2013/14.

36

The expected change in 2024 relative to 2013/14 is an increase in volume of soybeans exports in the order of 44.0% . The soybean export projections in this report are very similar to USDA projections, released in February this year. They design 66.5 million of exports for soybeans at the end of the next decade. This estimate is almost the same as that of this report, 65.2 million tons in 2024.

37

Table 9 – Production, Consumption and Soybean Export (thousand tons)

Source: AGE/Mapa and SGE/Embrapa with CONAB information.* Models used: To Production and Consiumptiion , State – Space model, and to Export, PRP model.


2013/14 86,052 40,080 45,297

2014/15 89,831 98,215 41,233 45,698 47,292 52,768

2015/16 93,254 103,825 42,358 47,988 49,286 57,032

2016/17 96,377 108,549 43,391 49,739 51,281 60,767

2017/18 99,479 113,376 44,401 51,612 53,276 64,229

2018/19 102,555 117,921 45,414 53,329 55,270 67,517

2019/20 105,606 122,309 46,417 54,969 57,265 70,680

2020/21 108,660 126,624 47,420 56,583 59,260 73,750

2021/22 111,712 130,846 48,423 58,152 61,254 76,745

2022/23 114,761 134,999 49,425 59,688 63,249 79,679

2023/24 117,811 139,097 50,427 61,200 65,244 82,563



Variation % 2013/14 to 2023/24

- - -

38

Fig. 10 – Soybean Production

Fig. 11 – Soybean Consumption



86,052

117,811

139,097

0 20,000 40,000 60,000 80,000

100,000 120,000 140,000 160,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Projec4on Up limit.

40,080 50,427

61,200

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Projec4on Up limit.

39

The expansion of soybean production in the country will give by the combination of area expansion and productivity. As production increases planned over the next 10 years is 36.9%, the expansion of the area is 34.1%. In recent years soybean productivity yield has remained stable at 2.7 tons per hectare, and that number is projected to be 3.0 tonnes per hectare in the next 10 years.

Soybean should expand through a combination of frontier expansion in regions where there is still available land, pasture land occupation and substituting orther crops where there is no land available for incorporation. But the trend in Brazil is that the expansion occurs mainly on natural pasture lands.

Figure 13 illustrates the projected area expansion in sugar cane and soybean, which are two activities that compete for the area in Brazil.

Together these two activities in the coming years should presentan expansion area of 12.6 million hectares, 10.3 million hectares of soybean and 2.3 million hectares of cane sugar.

The other crops should have little variation in area in the coming

Fig. 12 – Soybean Export


45,297

65,244

82,563

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Projec4on Up limit.

40

years. However, it is estimated that expansion should occur in areas of great productive potential, as areas of cerrado understood in what is now called Matopiba for understanding land located in the states of Maranhão, Tocantins, Piauí and Bahia. Mato Grosso will lose strenghts in this process of expansion of new areas, mainly due to the price of land in this state that are more than double the price of crop land in the states of Matopiba (FGV-FGVDados). Because these new ventures regions include areas of great extent, the price of land is a decisive factor.

Fig. 13 – Area of Soybean and Sugar Cane

Source: AGE/Mapa and SGE/Embrapa * Area with soybean and cane will grouth 12.6 million hectare**refers to sugar - cane intended to production of alcohol and sugar.

8,811 11.123 -‐ 13.838

30,105

40.357 -‐ 51.915

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

hectare

Soybean Sugar cane**

Soybean- Variation - 34,1 %

Sugar cane - Variation - 26,2 %

,

,

34.1 %

26.2 %

,

,

41

Meal and Soybean Oil

Meal and soybean oil showed moderate dynamism of production in the coming years. The soybean meal production should increase by 25.1% and 25.9% oil. These percentages are slightly higher than what has been observed in the last decade for both products. However, consumption of meal will have stronger growth than soybean oil, 35.2% and 23.1%, respectively.

Exports of meal should increase 15.6% between 2014 and 2024 and 18.4% oil. Exports are presented in the coming years more dynamic domestic consumption in the case of soybean oil.

42

Table 10 – Production, Consumption and Soybean Meal Export (thousand tons)

Source: AGE/Mapa and SGE/Embrapa with CONAB information* Models used: To Production, Consumption and Export, Space – state models.


2013/14 28,105 - 14,100 - 13,579 -

2014/15 28,676 31,078 14,529 15,234 14,166 15,926

2015/16 30,079 33,173 15,046 16,085 14,389 17,103

2016/17 30,534 33,935 15,548 16,793 14,715 18,154

2017/18 31,041 34,918 16,019 17,463 14,783 18,821

2018/19 31,910 36,218 16,538 18,181 14,939 19,545

2019/20 32,562 37,158 17,049 18,851 15,128 20,230

2020/21 33,135 38,043 17,543 19,492 15,257 20,801

2021/22 33,856 39,082 18,050 20,143 15,394 21,358

2022/23 34,539 40,031 18,559 20,783 15,557 21,912

2023/24 35,168 40,919 19,061 21,407 15,701 22,422



2013/14 to 2023/24Variation %

43

Source: AGE/Mapa and SGE/Embrapa with CONAB information* Models Used: To Production, Consumption and Exports, State- Space Models.

Table 11 – Production, Consumption and Soybean Oil Export (thousand tons)


2013/14 7,118 - 5,500 - 1,374 -

2014/15 7,353 8,125 5,566 5,911 1,530 2,119

2015/16 7,510 8,481 5,642 6,225 1,562 2,422

2016/17 7,706 8,827 5,755 6,564 1,598 2,686

2017/18 7,886 9,164 5,880 6,913 1,622 2,945

2018/19 8,066 9,472 6,016 7,258 1,631 3,164

2019/20 8,247 9,773 6,161 7,597 1,637 3,369

2020/21 8,425 10,064 6,309 7,928 1,637 3,556

2021/22 8,604 10,347 6,461 8,250 1,635 3,727

2022/23 8,783 10,624 6,616 8,563 1,631 3,887

2023/24 8,961 10,896 6,772 8,869 1,626 4,036



2013/14 to 2023/24Variation %

44



Fig. 14 – Production, Consumption and Export of Soy-bean Meal

Fig. 15 – Production, Consumption and Exports of Soybean Oil

bean Meal

28,105 35,168

14,100 19,061

13,579 15,701

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Produc4on Consump4on Exports

7,118 8,961

5,500 6,772

1,374 1,626

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000

10,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons


45

The domestic consumption of soybean oil forecast for 2023/24 is estimated at 6.8 million tons. Represents around 75.6% of projected production. Most of the oil is intended for human consumption and another part has been used to produce Biodiesel. According to ABIOVE in 2014, the average use of soybean oil for biodiesel should be between 2.0 and 2.3 million tons. This represents between 28.0 and 32.3% of soybean oil in 2013/14 harvest.

For soybean meal, in the next decade, about 54.0% should be directed to domestic consumption, and 44.6% for exports.

We analyzed the data sent by ABIOVE (2014), at our request, in the form of comments to these projections, and it generally converge toward the results presented in this report.

46

h. Coffee

Coffee production has been showing unusual behavior in2014. Though a period called the High, the expected production this year is supposed to be lower than last year. This crop has a cycle called “ bienalidade “ where years there has been a high production and low production the next. Due to weather problems that occurred earlier this year affecting the main producing regions, the harvest expected in 2014 should be equal to or less than last year. Estimates for 2014 indicate a harvest of 46.9 million 60-kg bags, while last year was 49.2 million bags (DCAF-CONAB-ABIC-MDI / SECEX-OIC-CEPEA / ESALQ, BM & F, 2014 )

MG

ES

1,511.8

2,818.2 100.0

53.6


Source: IBGE - survey - june/2014

Produção Nacional

COFFEEHarvest Year


%

733.3 26.0

53.6

26.0

MINASGERAIS

ESPIRITOSANTO

Total 2,245.1 79.7

47

The projections show that the related production in 2023/24 should rise 30.6% compared to 2013/14. This change is equivalent to an annual growth rate of 2.5%. Consumption is estimated to grow 28.9% by 2023/24, the result of an annual growth rate of 2.4%. The consumption in Brazil has grown to an average annual rate of 4.8% according to the International Coffee Organization, OIC, while the world average has been 2.7% per year. The latest estimates of the Ministry of Agriculture indicate an average annual rate of per capita consumption in Brazil of 5.7% per year in the period 2003-2014 (MAPA / DCAF, ABIC, Conab, 2014).

Coffee exports are projected for 2023/24 at 40.0 million bags of 60 kg. This projected volume represents an increase of 24.0% compared to the exports of 2013/14, representing an average annual rate of 2.2%. It is expected that the country will continue as the world’s largest producer and leading exporter as well as keep the usual buyers and valued partners in 129 countries in 2013. U.S., Germany, Japan and Italy imported 62.7% of the volume exported by Brazil in 2013.

48

i. Milk

Milk was considered a product that has high growth potential. The production is expected to grow at an annual rate between 2.6% and 3.4%. This corresponds to a production of 44.7 billion liters of raw milk at the end of the period of the projections, 29.8% higher than the production year 2013/14.

Table 12 – Production, Consumption and Exports of Coffee (million bags)

Source: AGE/Mapa e and SGE/Embrapa with Mapa/SPAE/DCAF and CONAB Information* Models Used: To production, consumption and Exports, State- Space Models.


2013/14 47 - 20 - 32 -2014/15 48 48 21 21 33 392015/16 51 62 21 22 33 412016/17 53 67 22 23 34 422017/18 54 68 22 24 35 442018/19 55 72 23 24 36 452019/20 56 73 24 25 37 472020/21 58 76 24 26 37 482021/22 59 77 25 27 38 502022/23 60 80 25 27 39 512023/24 61 82 26 28 40 52



Variation % 2013/14 to 2023/24

49

Tabela 13 - Production, Consumption and Exports of Milk (million liters)

Source: AGE/Mapa e and SGE/Embrapa with IBGE/MDIC/Embrapa Gado de Leite information.* Models used: To Production and Consumption, ARMA model, to Imports and Exports, PRP models.

According to technicians of Embrapa Dairy Cattle, the projected growth rates for production should be slightly above the projected in this report. According to them the milk production in Brazil rose more than 4.0% per year in recent years.

- - -

Projection Up limit. Projection Up limit. Projection Up limit. Projection Up limit.

2013/14 34,408 36,298 1,057 1382014/15 36,322 37,897 37,310 40,069 1,047 2,820 142 6572015/16 36,473 38,885 38,302 41,810 1,037 3,209 147 7772016/17 38,377 41,016 39,290 43,420 1,028 3,535 152 8792017/18 38,523 41,826 40,278 44,948 1,018 3,821 157 9702018/19 40,425 43,927 41,265 46,419 1,008 4,079 161 1,0522019/20 40,569 44,623 42,253 47,849 999 4,316 166 1,1282020/21 42,470 46,696 43,240 49,246 989 4,535 171 1,2002021/22 42,613 47,315 44,228 50,617 979 4,740 176 1,2672022/23 44,514 49,368 45,215 51,967 970 4,934 180 1,3302023/24 44,657 49,933 46,203 53,297 960 5,118 185 1,391

ExportsProduction ConsumptionYear

Imports

Production 29.8%Consumption 27.3%Imports -9.2%Exports 34.7%

Variation % 2013/14 to 2023/24

- - - -

50

Fig. 16 – Milk Production

Fig. 17 – Production and Consumption of Milk.



34,408

44,657

49,933

0

10,000

20,000

30,000

40,000

50,000

60,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Projec4on Up limit.

34,408

44,657 36,298

46,203

0

10,000

20,000

30,000

40,000

50,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Produc4on Consump4on

51

Fig. 18 – Import and Export of Milk


1,057 960

138 185

0

200

400

600

800

1,000

1,200

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Imports Exports

52

j. Sugar

The estimates obtained by AGE and SGE for Brazilian sugar production indicate an average annual growth rate of 3.3% in the 2013/2014 to 2023/2024 period. This rate should lead to a production of 52.9 million tons in 2024. Such production corresponds to an increase of 39.7% compared to 2013/14. These projections may be affected if the current situation is maintened where the prospects of the sugar and alcohol sector are not favorable.

Investments have not been made in new units, several production units have paralyzed its activities over the past 3 seasons and many companies are indebted (Mapa / Agroenergia, 2014).

Consumption is expected to grow at an annual rate between 2.4 and 3.3%, thus following the production of the country, but putting the consuption at a level slightly above the national production, it will require some import.

53

Source: AGE/Mapa and SGE/Embrapa with Mapa /SPAE/DCAA; Mapa /SRI and CONAB. information* Models used: To Production and Exports, Space – State model, and to Consumption, ARMA model.

Table14 – Production, Consumption and Sugar Exports (thousand tons)

The projected rates for exports and domestic consumption for the next 10 years are, respectively, 3.7% and 2.3% per year. For exports, the forecast for 2023/2024 is a volume of 38.8 million tonnes.

- - -


2013/14 37,878 12,233 27,154

2014/15 40,330 44,074 12,261 13,640 27,824 32,552

2015/16 41,265 45,774 12,694 14,300 29,207 34,896

2016/17 42,937 48,304 12,963 14,881 30,352 37,128

2017/18 44,264 50,305 13,299 15,442 31,577 39,208

2018/19 45,749 52,415 13,607 15,970 32,775 41,198

2019/20 47,163 54,394 13,927 16,485 33,982 43,122

2020/21 48,608 56,365 14,242 16,983 35,186 44,993

2021/22 50,040 58,288 14,559 17,471 36,391 46,821

2022/23 51,478 60,190 14,875 17,949 37,596 48,614

2023/24 52,913 62,066 15,192 18,419 38,801 50,378


Production 39.7%

Consumption 24.2%

Exports 42.9%

2013/14 to 2023/24Variation %

- - -

54



Fig. 19 – Production, Consumption and Sugar Exports

Fig. 20 – Sugar Production

37,878 52,913

12,233 15,192

27,154

0

10,000

20,000

30,000

40,000

50,000

60,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons


37,878

52,913

62,066

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Projec4on Up limit.

38.801

55

k. Orange and Orange Juice

The orange production should increase from 16.3 million tons in 2013/14 crop to 17.5 million tonnes in 2023/24. This variation corresponds to an annual growth rate of 0.7%.

The area planted with orange should be reduced in the coming years. It should move from the current 717 thousand hectares hectares to 627 thousand. This indicates an annual reduction in the growth rate of 1.3% per year.

Brazil is expected to export 2.6 million tonnes of orange juice at the end of the projection period. But that number may reach, at its upper limit, to 3.2 million tonnes of juice. Trade restrictions in the form of barriers to trade are the main limiting factor for the expansion of the orange juice.

Fig. 21 – Sugar Exports


27,154

38,801

50,378

0

10,000

20,000

30,000

40,000

50,000

60,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Projec4on Up limit.

56

Table 15- Production of Orange and Exports of Orange Juice (thousand tons)

Source: AGE/Mapa and SGE/Embrapa with IBGE and SECEX/MDIC information* Models used: To Production, PRP model, and to Exports, Space – States model.

Projection Up limit. Projection Up limit.

2013/14 16,333 - 2,094 -2014/15 16,452 19,051 2,179 2,4482015/16 16,571 20,247 2,215 2,5372016/17 16,689 21,191 2,272 2,6312017/18 16,808 22,007 2,320 2,7152018/19 16,927 22,739 2,372 2,7992019/20 17,046 23,413 2,423 2,8802020/21 17,165 24,042 2,474 2,9592021/22 17,283 24,635 2,525 3,0362022/23 17,402 25,200 2,575 3,1122023/24 17,521 25,741 2,626 3,187

Production ExportsYear

Production 7.3%Exports 25.4%

Variation % 2013/14 to 2023/24

57

Fig. 22 – Orange Production and Orange Juice Export


16,333 17,521

2,094 2,626

0

4,000

8,000

12,000

16,000

20,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Produc4on Exports

58

i. Meat

Before presenting the projections of meat, we seek to illustrate the current distribution of cattle in Brazil, with respect to the number of animals slaughtered in 2013. Slaughtered this year were 34.4 million head across the country, and Mato Grosso, Mato Grosso do Sul, São Paulo, Minas Gerais, Goiás, Para and Rondonia, leading the slaughter, with 72.0% of slaughters in the country.

MT

MS

5,837,857

34,411,857 100.0

17.0


Source: IBGE - quarterly survey of slaughtered animals - march/2014

National Production

BOVINES

Slaughtered Animals 2013/14(head)

%

4,120,813 12.0

SP 3,548,939 10.3

GO 3,466,231 10.1

PA 2,447,439 7.1

MG 3,032,618 8.8

RO 2,289,653 6.7

RS 1,920,455 5.6

PR 1,424,743 4.1

BA 1,309,373 3.8

TO 1,195,180 3.5

17.0

6.7

7.1

10.1

3.5 3.8

12.0

8.8

10.3

4.1

5.6RIO GRANDE

DO SUL

RONDÔNIA

MATO GROSSO

PARÁ

GOIÁS

MINASGERAIS

BAHIA

SÃO PAULO

MATO GROSSODO SUL

TOCANTINS

Total 30,593,301 88.9

59

Projections of meat for Brazil show that this sector should present strong growth in the coming years. Among meat, the projecting higher growth rates of production in the period 2014-2024 are chicken, which is expected to grow annually at 3.1%, and swine, whose projected growth for this period is 2.8% per year. The beef production has a projected growth of 1.9% per year, which also represents a relatively high value because it can supply domestic consumption and exports.

The total meat production will increase from 26.0 million tons in 2014 to 33.8 million in 2024, an increase of 30.0%.

60


2014 9,753 3,553 12,691

2015 9,762 10,799 3,666 4,067 13,081 14,122

2016 10,309 11,921 3,778 4,346 13,519 14,620

2017 10,632 12,573 3,891 4,586 13,972 15,571

2018 10,451 12,661 4,004 4,806 14,432 16,090

2019 10,589 13,091 4,116 5,013 14,894 16,931

2020 11,027 13,600 4,229 5,212 15,358 17,445

2021 11,105 13,699 4,342 5,403 15,822 18,225

2022 11,159 13,799 4,454 5,589 16,286 18,734

2023 11,615 14,314 4,567 5,771 16,751 19,474

2024 11,975 14,707 4,680 5,948 17,216 19,979

BEEF PORK CHICKEN Year

Table 16– Meat Production (thousand tons)

Source:a AGE/Mapa and SGE/Embrapa with CONAB. information* Models used: To Beef, ARMA model, to Pork, PRP models and to Chicken, State - Space.

- - -

Beef 22.8%Pork 31.7%Chicken 35.7%

Variation % 2014 to 2024

61

Fig. 23- Beef Production

Fig. 24 – Pork Production



9,753 11,975

14,707

0 2,000 4,000 6,000 8,000

10,000 12,000 14,000 16,000

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

thou

sand

tons

Projec3on Up limit.

3,553 4,680

5,948

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

thou

sand

tons

Projec3on Up limit.

62

Projections show the consumption preferences of Brazilian consumers for chicken. The projected annual growth for the consumption of chicken is 2.9% in the period 2014-2024. This is an increase of 33.1% in consumption for the next 10 years. Pork takes second place in consumption growth at an annual rate of 2.6% in the coming years. In the lower level of growth is located if the projection of beef consumption 1.5% per year next ten years.

Fig. 25- Chicken Production


12,691

17,216

19,979

0

5,000

10,000

15,000

20,000

25,000

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

thou

sand

tons

Projec3on Up limit.

63

Source: AGE/Mapa and SGE/Embrapa with CONAB Information* Models: To Beef, ARMA Model, Pork and Chicken, PRP

Table 17 – Meat Consumption (thousand tons)

Beef 15.6%Pork 29.1%Chicken 33.1%

2014 to 2024

Variation %

Projection Up limit. Projection Up limit. Projection Up limit.2014 7,744 - 3,032 - 8,689 -2015 7,615 8,332 3,120 4,750 8,976 9,6152016 7,866 8,880 3,209 5,513 9,263 10,1662017 8,089 9,198 3,297 6,119 9,551 10,6562018 7,992 9,189 3,385 6,644 9,838 11,1152019 8,082 9,399 3,474 7,117 10,125 11,5532020 8,421 9,752 3,562 7,553 10,412 11,9762021 8,501 9,841 3,650 7,961 10,699 12,3892022 8,502 9,906 3,738 8,347 10,987 12,7922023 8,759 10,230 3,827 8,715 11,274 13,1892024 8,953 10,451 3,915 9,068 11,561 13,580

BEEF PORK CHICKENYear

64

Regarding exports, the projections indicate high growth rates for the three types of meat analyzed. Estimates project a favorable environment for Brazilian exports. The chicken and pork lead the annual growth rates of exports in the coming years - the annual rate provided for chicken is 3.8% and for pork 3.9%.

Exports of beef should be located on an annual average of 3.4%. Meat exports has led to numerous countries. In 2013 the beef was destinated to 143 countries, with the main Hong Kong; chicken was destinated for 144 countries, with Saudi Arabia the main buyer and fi nally the pork had 72 destination countries, whose main Russia. The expectation is that these markets are increasingly consolidate so that the projections are feasible.


Fig. 26 – Meat Consumption

7,744 8,953

3,032 3,915

8,689

11,561

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

thou

sand

tons

Beef Pork Chicken

65


2014 2,068 534 4,0022015 2,143 2,515 559 700 4,181 4,6742016 2,223 2,861 584 783 4,323 4,8872017 2,305 3,165 609 853 4,527 5,3842018 2,388 3,435 634 916 4,680 5,6132019 2,471 3,682 659 974 4,890 6,0542020 2,555 3,910 684 1,029 5,046 6,2762021 2,638 4,125 709 1,082 5,258 6,6792022 2,722 4,330 734 1,133 5,415 6,8932023 2,805 4,526 759 1,182 5,627 7,2712024 2,889 4,715 784 1,230 5,784 7,478

BEEF PORK POLTRYYear

Table 18 – Meat Export (thousand tons)

Source: AGE/Mapa and SGE/Embrapa with CONAB information.*Models used: To Beef and Chicken meat, State – Space models, to Pork, PRP

- - -

Beef 39.7%Pork 46.9%Poltry 44.5%

Variation %

2014 to 2024

66

Fig. 27 – Beef Export

Fig. 28 – Export of Pork



2,068 2,889

4,715

0 500

1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

thou

sand

tons

Projec3on Up limit.

534 784

1,230

0 200 400 600 800

1,000 1,200 1,400

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

thou

sand

tons

Projec3on Up limit.

67

m. Pulp and Paper

Forest products represent the fourth rank in the value of exports of brazilian agribusiness, below the soybean complex, meat and sugar and alcohol complex. In 2013 the value of exports of forest products was $ 9.64 billion, and pulp and paper accounted for 74.3% of export value (Mapa / Agrostat, 2014). Pulp and paper and wood and articles thereof comprise this segment of agribusiness.

Fig. 29 – Export of Chicken


4,002

5,784

7,478

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

thou

sand

tons

Projec3on Up limit.

68


2013/14 15,736 6,327 9,853

2014/15 16,173 17,106 6,392 6,862 10,240 11,233

2015/16 16,675 17,952 6,531 7,034 10,621 11,916

2016/17 17,183 18,681 6,654 7,224 11,022 12,527

2017/18 17,651 19,410 6,759 7,356 11,403 13,117

2018/19 18,156 20,116 6,889 7,531 11,794 13,694

2019/20 18,640 20,789 7,001 7,678 12,183 14,248

2020/21 19,128 21,459 7,120 7,829 12,569 14,794

2021/22 19,622 22,113 7,241 7,984 12,959 15,329

2022/23 20,108 22,755 7,356 8,129 13,347 15,855

2023/24 20,599 23,392 7,476 8,279 13,735 16,375


Table 19 – Production, Consumption and Export of Pulp (thousand tons)

Source: AGE/Mapa and SGE/Embrapa with BRACELPA information.*Models used: Production, Consumption amd Exports, Space – States model.

- - -


Variation % 2013/14 to 2023/24

69

Fig. 31 - Production, Consumption and Pulp Export

Fig. 30 - Pulp Production



15,736

20,599

23,392

0

5,000

10,000

15,000

20,000

25,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Projec4on Up limit.

15,736

20,599

6,327 7,476

9,853 13,735

0

5,000

10,000

15,000

20,000

25,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons


70


2013/14 10,759 10,125 1,937

2014/15 10,992 11,237 10,333 10,820 1,995 2,257

2015/16 11,267 11,565 10,598 11,243 2,055 2,470

2016/17 11,516 11,848 10,863 11,595 2,079 2,576

2017/18 11,776 12,136 11,102 11,923 2,122 2,712

2018/19 12,035 12,429 11,377 12,267 2,142 2,782

2019/20 12,289 12,704 11,601 12,562 2,190 2,897

2020/21 12,553 13,000 11,881 12,905 2,213 2,961

2021/22 12,805 13,269 12,102 13,188 2,261 3,068

2022/23 13,070 13,564 12,385 13,529 2,284 3,128

2023/24 13,320 13,830 12,605 13,805 2,332 3,229


Table 20 – Production, Consumption and Paper Export (thousand tons)

Source: AGE/Mapa and SGE/Embrapa with BRACELPA Information* Models used:To Production, Consumption and Export, State – Space Models.

- - -


2013/14 to 2023/24

Variation %

71

Fig. 32 – Paper Production


10,759

13,320

13,830

0 2,000 4,000 6,000 8,000

10,000 12,000 14,000 16,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons

Projec4on Up limit.

72

With regard to the paper, to supply domestic consumption growth of 2.2% annually over the next 10 years, and 1.8% of exports, it will be necessary to expand production faster than the projected rate, which is 2.2 % per year until 2023/2024. According to Bracelpa technicians production and paper consumption have historically accompanied the growth of GDP. Although the paper can fi nd some demand problem, the projected growth in this report for the production seems small. For cellulose, the projection indicates that would be possible production can meet the growth in domestic consumption and exports of the sector.

Fig. 33 – Production, Consumption and Paper Export


10,759

13,320

10,125 12,605

1,937 2,332

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2013

/14

2014

/15

2015

/16

2016

/17

2017

/18

2018

/19

2019

/20

2020

/21

2021

/22

2022

/23

2023

/24

thou

sand

tons


73

n. Tobacco

The inclusion of the projections of some variables related to Tobacco is justified by the importance of the product in the Brazilian trade balance and income formation in the producing regions.

Its production occurs mainly in Rio Grande do Sul, Santa Catarina and Paraná. In 2014, these three states have planted an area of 392 thousand hectares, a total of 417 thousand hectares of land. In Northeast Brazil, there is some production in Alagoas and Bahia . In 2013, tobacco and its products have generated export revenues of $ 3.27 billion.

The projected production for 2023/2024 is 1,060 tons. The projected area is 472 thousand hectares, obtained through an annual growth of 1.2% from 2013/14 until the end of the projections

74

Table 21- Tobacco Production

Source: AGE/Mapa and SGE/Embrapa with IBGE information* Models used: To production, State - Space.

.

Projection Up limit.

2013/14 8652014/15 890 1,0792015/16 904 1,0932016/17 929 1,1972017/18 943 1,2112018/19 968 1,2962019/20 982 1,3102020/21 1,007 1,3852021/22 1,021 1,3992022/23 1,046 1,4692023/24 1,060 1,483

ProductionYear

-

Production 22.6%

Variation %

2013/14 to 2023/24

75

o. Fruits

Among the fruits analyzed in this study, banana is the most widespread throughout the country. But 67.8% of production is in the states of São Paulo, Bahia, Minas Gerais, Santa Catarina, Ceará and Pará. Apple has its production located in Rio Grande do Sul and Santa Catarina and grape in Rio Grande do Sul, Pernambuco and Sao Paulo.

The fruits have been growing in importance in the country, both domestically and internationally. In 2013, the export value of fresh fruit was U.S. $ 878.0 million, slightly below the value exported in 2012, of $ 910 million (Agrostat / Mapa, 2014). Grapes, mangoes and melons are the fastest growing exports in terms of value. As can be seen in the maps of location, banana is the most widespread in the country, while apples and grapes have their more restricted to South and Northeast regions of production.

76

RS

SC

687,448

1,271,014 100.0

54.1

Source: IBGE - Systematic Survey of Agricultural Production - March / 2014

APPLE %

530,601 41.7 41.7

54.1

SANTACATARINA

RIO GRANDEDO SUL

Total 1,218,049 95.8


National Production


(Thousand tons)

RIO GRANDEDO SUL

RS

PE

759,942

1,360.608 100.0

55.9


GRAPE %

236,767 17.4

SP 158,781 11.7

PR 79,052 5.8

SC 52,083 3.8

BA 56,944 4.2

4.2

17.4

11.7

5.8

3.8

55.9

BAHIA

PARANÁ

PERNAMBUCO

SANTACATARINA

SÃO PAULO

Total 1,343,569 98.7


National Production


(Thousand tons)

77

Due to limited data, the projections were restricted to changing production and planted are of grape, apple and banana area. Unlike the orange area which is relatively significant, these fruits have much more restricted areas, and, as is the case of the grape which are cultivated under irrigation and high technological level. Among the three fruits, bananas are the one with the largest area.

The projections of production until 2023/2024, show that the largest expansion will occur in apple production, 2.6% growth per year, followed by grapes, 1.9% per year for the banana, 0.9% per year . A joint production of apples, grapes and bananas should represent 4.0 million tons in 2023/24, representing an increase of 21.7% over 2014.

SP

BA

1,191,547

7,146,788 100.0

16.7


BANANA %

1,160,854 16.2

MG 764,030 10.7

SC 649,609 9.1

CE 501,857 7.0

PA 576,154 8.1

PB 389,337 5.4

PR 269,075 3.8

ES 262,711 3.7

8.1

16.2

10.7

3.7

7.0

5.4

16.7

3.8

9.1

PARÁ

MINASGERAIS

BAHIA

PARANÁ

ESPÍRITOSANTO

PERNAMBUCO

CEARÁ

SANTACATARINA

SÃO PAULO

Total 5,765,174 80.7


National Production


(Thousand tons)

78

Table 22- Fruit Production (thousand tons)

Fig. 34- Fruit Production (thousand tons)

Source: AGE/Mapa and SGE/Embrapa with IBGE information.* Models used: Banana, PRP and to Apple and Grapes, State Space model

Fonte: AGE/Mapa e SGE/Embrapa


2014 701 - 1,271 - 1,361 -2015 707 764 1,306 1,488 1,413 1,6052016 714 793 1,344 1,560 1,424 1,6472017 720 818 1,381 1,638 1,459 1,7332018 726 839 1,418 1,707 1,483 1,7882019 733 859 1,456 1,774 1,513 1,8522020 739 878 1,493 1,838 1,539 1,9072021 746 895 1,530 1,900 1,567 1,9632022 752 912 1,568 1,961 1,595 2,0152023 758 928 1,605 2,020 1,622 2,0672024 765 944 1,642 2,078 1,650 2,117

BANANAS (thousand bunche)

APPLE GRAPE

Year

701 765

1,271

1,642 1,361

1,650

0 200 400 600 800

1,000 1,200 1,400 1,600 1,800

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

thou

sand

tons

BANANAS (thousand bunche) APPLE GRAPE

79

5. RESULTS OF REGIONAL OUTLOOK

Regional projections were made with the objective of identifying possible trends of selected products in major producing regions, and also show the predictions of a slightly more disaggregated. They are divided into two parts: regional projections of consolidated areas, and areas of recent expansion, located in central Brazil, and part of the Northeast. They are: Rice in Rio Grande do Sul; Corn in Mato Grosso, Paraná, Minas Gerais; Soybean in Mato Grosso, Rio Grande do Sul and Paraná; Wheat, Paraná and Rio Grande do Sul; and sugar cane in São Paulo, Paraná, Mato Grosso, Minas Gerais and Goiás. Was included, the area and production projections for the states of Maranhão, Tocantins, Piauí and Bahia, called MATOPIBA.

The projections of these regions were also made to some municipalities in these localities, selected according to their importance in the production of grains.

Regional projections were only for production and planted area because there aren`t more detailed information such as for the national projections.

80

Source: AGE/Mapa and SGE/Embrapa * Located in the Center – Northeast of Brazil and formed by the states of Maranhão, Tocantins, Piauí, Bahia.

Table 23 – Regional Projections - 2013/2014 to 2023/2024 Selected States

2013/14 2023/24 Var. % 2013/14 2023/24 Var. %

RS 8,434 10.540 25.0 1,114 1,178 5.8

2013/14 2023/24 Var. % 2013/14 2023/24 Var. %

GO 69,307 96,918 39.8 859 1,195 39.1

MG 76,741 109,035 42.1 953 1,322 38.7

MT 19,153 25.080 30.9 280 383 36.7

PR 49,227 65,742 33.5 658 878 33.4

SP 404,680 504,406 24.6 5,046 6,395 26.7

2013/14 2023/24 Var. % 2013/14 2023/24 Var. %

MG 6,957 9,154 31.6 1,325 1,234 -6.9

MT 16,839 27,316 62.2 3.250 4,848 49.2

PR 15,295 19,652 28.5 2,575 2,631 2.2

2013/14 2023/24 Var. % 2013/14 2023/24 Var. %

BA 3,229 4,388 35.9 1,313 1,789 36.2

MT 27,002 38,035 40.9 8,616 12,204 41.6

PR 14,741 19,756 34.0 5,019 6,527 30.0

RS 12,734 16,256 27.7 4.940 5,609 13.6

2013/14 2023/24 Var. % 2013/14 2023/24 Var. %

PR 3,825 5,137 30.3 1,323 1,497 13.1

RS 2,979 3,755 26.1 1,103 1,341 21.6

2013/14 2023/24 Var. % 2013/14 2023/24 Var. %

RS 760 922 21.3 50 56 11.1

2013/14 2023/24 Var. % 2013/14 2023/24 Var. %

MATOPIBA* 18,623 22,607 21.4 7,259 8.440 16.3

Production (Thousand tons) Planted Area (Thousand ha)

Wheat - Thousand Tons Thousand hectares

Grapes - Thousand Tons Thousand hectares

Grains - Thousand Tons Thousand hectares

RICE - Thousand Tons Thousand hectares

Sugar cane - Thousand Tons Thousand hectares

Corn - Thousand Tons Thousand hectares

Soybean - Thousand Tons Thousand hectares

,

81

Regional projections show that Rio Grande do Sul should continue leading the production and expansion of rice in Brazil in the coming years. The production of the state that is in 2013/2014, 65.8% of the national rice production must increase production in the coming years in 25.0% and 5.8% in area.

The production of sugar cane must present expansion in all states considered. The greatest expansion of production must occur in Minas Gerais, Goiás and Paraná. In these states sugar cane should expand by reducing area of other crops and also in pastures. Sao Paulo, the leader of national production, should have a production increase of approximately 24.6% over the next decade. To meet this growing area in the state should increase by 26.7% at the end of the period of projections. Projections indicate that only in Minas Gerais production the increase will occur by gains in productivity. In the other the expected growth in production will be done mainly by the increase in area.

Mato Grosso should lead in the coming years the growth of corn production. The projected increase for the next decade is 62.2%, while the area is expected to increase 49.2%. The available information indicates that increased corn production should occur primarily through the second corn crop that has achieved amazing results

Corn must suffer in the coming years 6.9% decrease in the area in Minas Gerais. It is possible that this should occur due to the expansion of sugar cane in the state and also the soybean.

Mato Grosso and Bahia should lead the increase in soybean production in the coming years, increasing by 40.9% and 35.9%, respectively, soybean should increase production, without any reduction of area in any of the analyzed states.

The projections show that the wheat production increases should be similar in Paraná and Rio Grande do Sul, about 30.0% over the next 10 years and 26.1% for Paraná and Rio Grande do Sul, respectively. No reduction in wheat area is expected to occur, and the largest increase should occur in Rio Grande do Sul

The region formed by the states of Maranhão, Tocantins, Piauí and Bahia, known as MATOPIBA has a different growth dynamics. Hence the interest in presenting the results of the main projections. Its growth has been extraordinary.

The latest survey of IBGE (2011) on the municipal GDP shows that these municipalities have pulled the growth of the states where they are located. Its growth has been much higher than the growth of the state

82

and national average.These four states must reach a grain production of 22.6 million

tons over the next 10 years in a planted area of 8.4 million hectares in 2023/2024 but which could reach 10.9 million hectares at its upper bound to the end of the next decade.

The areas that have been settled in these states have some essential features for modern agriculture. Are fl at and extensive, potentially productive soils, water availability, and climate conducive to long days and high intensity of sunshine. The major limitation, however are precarious logistics, especially inland transport, port, communication, and some areas lack of fi nancial services.

Fig. 35 – Grains Projections - MaToPiBa


18,623 16,647

Produc0on (thousand tonnes)

22,607

7,259 7,245

Planted Area (thousand hectares)

8,440

0

5,000

10,000

15,000

20,000

25,000

30,000

2013/14 2014/15 2015/16 2016/17 2017/18 2018/19 2019/20 2020/21 2021/22 2022/23 2023/24

83

Table 24 – Projections of MATOPIBA (*) 2013/2014 to 2023/2024

Source: AGE/Mapa and SGE/Embrapa * Located in the Center – Northeast of Brazil and formed by the states of Maranhão, Tocantins, Piauí, Bahia.

MA

TO

PI

BA

Balsas

Urucuí

Bom Jesus

Formosa doRio Preto

Luiz EduardoMagalhães

Barreiras

Pedro Afonso

Campos Lindos

CerradoBrazilianSavannah

2013/14 2023/24 Var. % 2013/14 2023/24 Var. %

18,623 22,607 21.4 7,259 8,440 16.3

Balsas - MA 464 682 46.9 150 217 45.0

Campos Lindos - TO 185 275 48.9 58 85 48.5

Uruçuí - PI 273 372 36.3 99 147 48.1

Barreiras - BA 353 363 2.9 127 149 17.3

Formosa do Rio Preto - BA 1,532 3,767 145.8 309 466 50.9

São Desidério - BA 829 1,200 44.7 264 275 4.1

Soybean – Selected Municipalities- Thousand tons thousand hectares

Grains

Production (thousand tons) Planted Area (thousand hectares)

84

6. SUMMARY OF MAIN RESULTS

The most dynamic products in agribusiness should be cotton lint, chicken, cellulose, sugar, soybean, pork, wheat and sugarcane. These products are those that indicate greater potential for production growth in the coming years.

85

Table 25 – Brazil - Production Results 2013/14 to 2023/24

Source: AGE/Mapa and SGE/Embrapa Note: Sugar Cane refers to the sugar cane intended to alcohol and sugar production

Rice thousand tons 12,251 13,637 to 21,803 11.3 to 78.0

Bean thousand tons 3,714 3,173 to 4,292 -14.6 to 15.6

Corn thousand tons 77,887 103,121 to 138,603 32.4 to 78.0

Soybean thousand tons 86,052 117,811 to 139,097 36.9 to 61.6

Soybean Meal thousand tons 28,105 35,168 to 40,919 25.1 to 45.6

Soybean Oil thousand tons 7,118 8,961 to 10,896 25.9 to 53.1

Wheat thousand tons 7,373 9,991 to 19,111 35.5 to 159.2

Chicken thousand tons 12,691 17,216 to 19,979 35.7 to 57.4

Beef thousand tons 9,753 11,975 to 14,707 22.8 to 50.8

Pork thousand tons 3,553 4,680 to 5,948 31.7 to 67.4

Coffee million sc 47 61 to 82 29.8 to 74.5

Milk Million liters 34,408 44,657 to 49,933 29.8 to 45.1

Manioc Thousand tons 22,655 21,770 to 32,431 -3.9 to 43.2

Potatoes Thousand tons 3,711 4,406 to 4,831 18.7 to 30.2

Cotton lint Thousand tons 1,672 2,350 to 2,981 40.5 to 78.3

Sugar Cane Thousand tons 658,823 869,777 to 1,053,984 32.0 to 60.0

Tobacco Thousand tons 865 1,060 to 1,483 22.6 to 71.5

Sugar Thousand tons 37,878 52,913 to 62,066 39.7 to 63.9

Orange Thousand tons 16,333 17,521 to 25,741 7.3 to 57.6

Paper Thousand tons 10,759 13,320 to 13,830 23.8 to 28.5

Pulp Thousand tons 15,736 20,599 to 23,392 30.9 to 48.7

Cocoa Thousand tons 256 216 to 392 -15.7 to 52.9

Grape Thousand tons 1,361 1,650 to 2,117 21.3 to 55.6

Apple Thousand tons 1,271 1,642 to 2,078 29.2 to 63.5

Banana Thousand tons 701 765 to 944 9.1 to 34.7

Variation %Products UnitEstimates to 2013/14 Projection 2023/24

86

Grain production should increase from 193.6 million tonnes in 2013/2014 to 252.4 million tons in 2024. This indicates an increase of 58.9 million tons to the current production in Brazil, and relative values 30.4%. This, however, will require an effort of growth that should consist of infrastructure, investment in research and funding. These estimates are compatible with the expansion of grain production in the last ten years where production grew 69.0 % (Conab, 2014). This result indicates that there is growth potential to achieve the designed values.

The production of meat (beef, pork and chicken) will increase by 7.9 million tons. Represents an increase of 30.3% in relation to meat production for 2013/2014. The chicken is the one to present the highest growth, 35.7% over 2014 production. Then pork, which is expected to grow 31.7% and then the beef, 22.8%.

Source: AGE/Mapa and SGE/Embrapa*Grains: refers to crops raised by Conab in their surveys of crops (cotton, peanuts, rice, oats, canola, rye, barley, beans, sunflower, castor, corn, soybean, sorghum, wheat and triticale).

Table 26 – Brazil Production –Projections of Grains and Meat 2013/14 to 2023/24

2014/15 UP Limit. 2023/24

Production Thousand tons 193,566 199,656 to 217,428 252,437 30.4

Planted Area Thousand hectares 56,861 58,553 to 61,469 67,004 17.8

2014/15 Lsup. 2023/24

Chicken Thousand tons 12,691 13,081 to 14,122 17,216 35.7

Beef Thousand tons 9,753 9,762 to 10,799 11,975 22.8

Pork Thousand tons 3,553 3,666 to 4,067 4,680 31.7

Total Thousand tons 25,997 26,509 to 28,987 33,871 30.3

Unit Projection

Projection

2013/14

Increase of 7.9 million tons of meat

variation% 2013/14 to

2023/24

Increase of 58.9 million tons of grains and 10.1 million hectares

Meat Unit 2013/14variation% 2013/14 to

2023/24

Grains

87

The growth of agricultural production in Brazil should continue happening based on productivity. Strong growth of total factor productivity should be maintained, as recent studies have shown, (Fuglie, K., Wang, Sun, Ball, V., 2012 and Gasques, et.al. 2014). These studies show that total factor productivity has grown over 4.0% per year over the past few years. The global average of the last years was 1.84%.

The results show a greater increase in agricultural production that increases in area. Between 2014 and 2024 grain production can grow between 30.4% and 52.3%, while the area should expand by between 17,8 and 45.3%. This projection shows a typical example of growth based on productivity. We do not believe that the grain area expands the upper limit of the projection, because the potential productivity is high, especially in products such as soybeans and corn.

Estimates made until 2023/2024 are that the total planted area with crops must pass the 70,2 million hectares in 2014 to 82.0 million in 2024. An increase of 11.8 million hectares. This area expansion is concentrated in soybean, more than 10.3 million hectares, and cane sugar, more than 2.3 million.

The expansion of soybean area and sugarcane should occur by the incorporation of new areas, areas of natural pastures and also for replacing other crops that will give area. Corn must have an expansion area around 1.0 million hectares (15.7 to 16.7 million hectares between 2014 and 2024) and other crops analyzed mostly tend to lose area.

The domestic market with exports and productivity gains, should be the main factors for growth in the next decade. In 2023/2024, 42.8% of soybean production should be aimed at the domestic market, and corn, 62.2% of production should be consumed internally. Thus there will be a double pressure on increasing domestic production, due to the growth of the domestic market and exports. Currently, 46.6% of the soybean produced is for domestic consumption, and corn, 69.0%.

In meat, there will be strong pressure of the internal market. The expected increase in the production of chicken, 67.2% of output in 2023/2024 will be for the domestic market; of beef produced, 74.8% will go to the internal market, and pork, 83.7% will be for the domestic market. Thus, although Brazil is generally a major exporter of many of these products, domestic consumption is prevalent in the destination of production.

88


Table 27- Brazil: Exports Projections 2013/14 to 2023/24

Cotton lint Thousand t 575 893 to 1,892 55.4 to 229.1

Corn Thousand t 21,000 33,698 to 52,237 60.5 to 148.7

Soybean Thousand t 45,297 65,244 to 82,563 44.0 to 82.3

Soybean meal Thousand t 13,579 15,701 to 22,422 15.6 to 65.1

Soybean oil Thousand t 1,374 1,626 to 4,036 18.4 to 193.9

Chicken Thousand t 4,002 5,784 to 7,478 44.5 to 86.9

Beef Thousand t 2,068 2,889 to 4,715 39.7 to 128.1

Pork Thousand t 534 784 to 1,230 46.9 to 130.4

Coffee Million sacs 32 40 to 52 24.0 to 63.7

Sugar Thousand t 27,154 38,801 to 50,378 42.9 to 85.5

Orange juice Thousand t 2,094 2,626 to 3,187 25.4 to 52.2

Milk Million litters 138 185 to 1,391 34.7 to 912

Paper Thousand t 1,937 2,332 to 3,229 20.4 to 66.7

Pulp Thousand t 9,853 13,735 to 16,375 39.4 to 66.2

Products Unit 2013/14 Projection 2023/24 Variation %

89

Source: USDA,2014 and AGE/Mapa and SGE/Embrapa

Table 28 – Leadind Exporters of Agricultural Products in 2023/24

Million Tons

Share in the World Market (%)

United States 57.2 39.4Brazil 31.9 22.0Argentina 24.1 16.6Former Soviet Union 25.7 17.7Total Exports 145 100.0

Brazil 65.2 43.0United States 48.7 32.1Argentina 16.3 10.7Other South Americans12.5 8.2Total Exports 151.7 100.0

Brazil 2.9 28.9Índia 2.6 25.6United States 1.5 15.5Austrália 1.5 15.1Others 1.5 9.1New Zeland 0.6 5.8Total Exports 10.0 100.0

Brazill 5.8 48.9United States 4.3 36.1Sovietic Union 1.2 9.9Thailand 1.0 8.1China 0.6 4.7Total Exports 11.8 100.0

United States 2.9 36.9Union European 2.4 30.8Canada 1.4 17.2Brazil 0.8 10.0China 0.4 4.9

Total Exports 7.9 100.0

Pork

Corn

Soybean

Beef

Chicken

90

The five complexes shown in the table represent the main food consumed in the world and considered essential by almost all the world’s population.

Should continue expressive and with a tendency to increase the participation of Brazil in world trade in soybeans, beef and chicken. As noted, the Brazilian soybean should have in 2023/2024 a share in world exports of 43.0%, beef 28.9%, and chicken, 48.9%. Besides its importance in relation to those goods Brazil will maintain leadership in world trade in coffee, and sugar.

Finally, the regional projections are indicating that the largest increases in production and area of cane sugar, must occur in the state of Goiás, although this is still a state of small production. But São Paulo as major national producer, also projected high growth of production and area of the product.

Mato Grosso should continue to lead the expansion of maize production in the country with higher expected increases in production to 62.2%. The region called MATOPIBA, to be situated in the Brazilian states of Maranhão, Tocantins, Piauí and Bahia, should present sharp increase in grain production as well as its area must also present significant increase. Projections indicate this region is expected to produce around 22.6 million tons of grain in 2024 (up 21.4%) and an area planted with grains between 8.4 and 10.9 million hectares at the end of the period of the projections.

91

7. REFERENCES

ABIOVE – Associação Brasileira das Indústrias de Óleos Vegetais. Informações obtidas por solicitação, 2014

ABRAF - Associação Brasileira de Produtores de Florestas Plantadas, Anuário Estatístico da ABRAF, Brasília, 2009, 127 p.

AGROSTAT - (Banco de dados sobre comércio exterior). Ministério da Agricultura, Pecuária e Abastecimento, 2014. www.agricultura.gov.br/internacional

BOWERMAN, Bruce L.; O'CONNEL, Richard T. e KOEHLER, Anne B. Forecasting Time Series and Regression, Thomson, 2005.

BOX, George E. P.; JENKINS, Gwilym M. Time Series Analysis: Forecasting and Control, Holden Day.

Bradesco, Boletim Diário Matinal. Disponível em: <http://www.economiaemdia.com.br/>. Acesso em: 15/01/2013

Brasil. Ministério da Agricultura, Pecuária e Abastecimento. Anuário Estatístico da Agroenergia 2012 - Secretaria de Produção e Agroenergia. Brasília 2013, 282 p.

Brasil. Ministério da Agricultura, Pecuária e Abastecimento. Disponível em: <http://www.agricultura.gov.br>. Acesso em maio de 2014.

Brasil. Ministério da Agricultura, Pecuária e Abastecimento. Projeções do Agronegócio: Brasil 2012/2013 a 2022/2023, Assessoria de Gestão Estratégica. Brasília, 2013, 95 p.

BRESSAN FILHO, Ângelo. O etanol como um novo combustível universal. Análise estatística e projeção do consumo doméstico e exportação de álcool etílico brasileiro no período de 2006 a 2011. Conab, agosto de 2008.

BROCKLEBANK, John C.; DICKEY, David A. SAS for Forecasting Time Series - SAS Institute Inc., Cary, NC: SAS Institute Inc., 2003

92

CONAB. [Site oficial] Disponível em: <http://www.conab.gov.br>. Acesso em: maio e junho de 2014

EPE - Empresa de Pesquisa Energética. Perspectivas para o Etanol no Brasil. Cadernos de Energia EPE, (2008).

FAPRI. World agricultural outlook 2008. Center for Agricultural and Rural Development - Iowa State University, 2008. Disponível em: <http://www.fapri.iastate.edu/publications>. Acesso em: julho/2012

FGV - FGVDados. Disponível em: <www.fgvdados.fgv.br>. Acesso em maio de 2014 (banco de dados mediante assinatura)

Cepea/Esalq/USP. Disponível em: <www.cepea.esalq.usp.br>. Acesso em junho de 2014

Foresight. The Future of Food and Farming (2011). Final Project Report. The Government Office for Science. London.

HOFFMANN, R. Elasticidades Renda das Despesas e do Consumo de Alimentos no Brasil em 2002-2003. In: Silveira, F. G.; Servo, L. M. S.; Menezes, F. e Sergio. F. P. (Orgs). Gasto e Consumo das Famílias Brasileiras Contemporâneas. IPEA, V.2, Brasília, 2007, 551p.

HOMEM DE MELO, F. "A comercialização agrícola em 2012 : depreciação cambial deverá compensar a queda de preços internacionais - dados atualizados", publicado no boletim BIF da FIPE do mês de janeiro de 2012.

IBGE – PIB Municipal de 2011. www.ibge.gov.br/sidra . Acesso em junho de 2014

IBGE. Levantamento sistemático da produção agrícola (LSPA). Disponível em: <http://www.ibge.gov.br>. Acesso junho de 2014.IBGE/Cepagro - Ata de 06 de janeiro de 2011

IFPRI. Food Security, farming, and Climate Change to 2050. Scenarios, results, policy options. 2010.

93

Fuglie Keith O., Wang S. Ling and Ball V. Eldon. Productivity growth in agriculture: an international perspective. USA, 2012

Keith, F. Productivity Growth in the Global Agricultural Economy .Pittsburg, 2011

Mapa- Ministério da Agricultura, Pecuária e Abastecimento. Diretoria de Agroenergia. Informações obtidas por solicitação, 2014.

Mapa- Ministério da Agricultura , Pecuária e Abastecimento. Departamento do Café – DECAF. 2014

MORETTIN, Pedro A.; TOLOI, Clelia M. C. Análise de Séries Temporais. ABE - Projeto Fisher e Ed. Blucher, 2004.

OIC – Organização Internacional do Café. Disponível em: <www.ico.org/coffee/statistics>. Acesso em maio e junho de 2014.

Santiago, C. M. Embrapa - Centro Nacional de Pesquisa de Arroz e Feijão, 2013

SAS Institute Inc., SAS / ETS User's Guide, Version 8, Cary, NC: SAS Institute Inc., 1999.

SAS, Institute Inc., Manuais do software versão 9.2, Cary, NC: SAS Institute Inc., 2010.

SOUZA, G. S.; GAZOLLA, R.; COELHO, C. H. M.; MARRA, R.; OLIVEIRA, A. J. DE. Mercado de Carnes: Aspectos Descritivos e Experiências com o uso de Modelos de Equilíbrio Parcial e de Espaço de Estados. Embrapa - SGE, Revista de Política Agrícola, ano XV n. 1, 2006, Brasília.

UNICA - União da Indústria de Cana-de-açúcar - Sugarcane Industry in Brazil, Ethanol, Sugar, Bioelectricity, 2010 (folheto).

USDA. USDA Agricultural Projections. Disponível em: <http://www.ers.usda.gov/publications/oce081>. Acesso em: fevereiro 2008, 2009, 2010, 2011, 2012 e 2013, 2014.

94

Ministério da Agricultura Pecuária e Abastecimento - Assessoria de Gestão Estratégica

Projections of Agribusiness Brazil 2013/2014 a 2023/2024 56

ATTACHMENT 1 – Methodological Note 1. Introduction

The study of the national agribusiness projections consists on the analysis of

historical series with the use of statistical techniques for analysis of time series classified

as Exponential Smoothing, Box and Jenkins (ARIMA) and State-Space. Below, there is a

brief description of the models, methods and some concepts which were used in this

study. As general reference it is suggested Morettin and Toloi, 2004). Other specific

references are given throughout the text.

1.1 Stationary Process: A process is stationary (weakly) when its mean and its

variance are constant through the time and when the value of the co-variance between two

periods of time depends only on the difference between the two periods of time, and not

on the time itself where the covariance is calculated. We have:

Mean: E(Zt) = µ ;

Variance: VAR (Zt) = E(Zt – µ)2 = σ2

Covariance: ψκ = E[(Zt – µ)(Zt+κ – µ) ]

Where ψκ , is the covariance between the values of Zt and Zt+κ that is, between two values of

the time series separated by κ periods.

1.2 Purely Random Process or White Noise: A process (et) is purely random

when its mean is zero, its variance is σ2 and the variables et are not correlated.

1.3 Integrated Process: If a time series (non-stationary) has to be differenced d

times to become stationary, it is said that this series is integrated of order d. An integrated

time series Zt of order d is denoted as: Zt ~ I(d).

2. Exponential Smoothing Models

The Double Exponential Smoothing or Linear Smoothing is adequate to time series

Zt which evolve showing linear trend for which the linear and angular coefficients can also

vary in time. It is possible to demonstrate that optimal representations of the exponential

smoothing models are obtained from the ARIMA models and of State-Space models

described below. In the double exponential smoothing approach (the only one we are

dealing here) the linear coefficient tµ (level) of the series in period t and its growth rate tβ

Annex 1 - Methodological Note

95



in the same period are given by the smoothing equations (see Bowerman, O’ Connel and

Koehler, 2005)

( )1

1 1

(1 )( )(1 )

t t t t

t t t t

Zµ α α µ β

β γ µ µ γ β−

− −

= + − +

= − + −

where α and γ are constants in the interval (0,1) and t=1,2,...,N. The predictor of the

series in period N τ+ based on period N is given by ˆN N NZ τ µ τβ+ = + .

The exponential smoothing, simple, double (discussed here) or even triple can be

obtained from PROC FORECAST (SAS, 2010), but the standard errors of the predictors

may also be computed from state-space methods.

3. ARIMA Models

The Autoregressive Integrated Moving Average (ARIMA) model fits data

generated by a univariate time series, transformed to stationarity through calculations of

differences, using a class of models known as autoregressive processes, moving average

processes or mixed autoregressive-moving average processes

3.1. Autoregressive Process (AR)

Let Zt be a stationary time series. If we model Zt as

(Zt - µ) = α1(Zt -1 - µ) + et ,

where µ is the mean of Zt and et is a white noise, we say that Zt follows an autoregressive

process of first order, or AR(1). In this case, the value of Zt in period t depends on its value

in the previous period and on a random term; the values of Zt are expressed as deviations

of its mean value. So, this model says that the forecasted value of Zt in period t is simply a

proportion (= α1) of its value in the period (t-1) plus a random shock in period t. Stationarity is

achieved imposing 1 1.α <

In general, it is possible to have:

(Zt - µ) = α1(Zt -1 - µ) + α2(Zt -2 - µ) + ... + αp(Zt -p - µ) + et

In this case Zt follows an autoregressive process of order p, or AR(p) if the coefficients iα

satisfy appropriate conditions.

3.2. Moving Average Process (MA)

96



Let Zt be a stationary time series. If we model Zt as

1t t tZ e eµ β −= + −

Where µ and β are constants with 1β < , and the error term et is a white noise, it is

said that the time series defines the MA(1) - moving average process of order 1.

In general, if the time series satisfies

1 1 2 2t t t t q t qZ e e e eµ β β β− − −= + − − − −L

where the coefficients iβ satisfy additional conditions of invertibility, it is said that Zt follows

a moving average process of order q, or MA(q). In summary a moving average process is a

linear combination of terms of a white noise process.

3.3. Autoregressive Moving Average Process (ARMA)

If a stationary time series (Zt) has characteristics of AR with errors following a

process MA, it will be an ARMA process. The series Zt will follow an ARMA process (1,1),

for example, if it can be represented by

1 1t t t tZ Z e eµ α β− −= + + −

In general, for an ARMA process (p,q) there will be p autoregressive terms and q

moving average terms.

3.4. Autoregressive Integrated Moving Average Process (ARIMA)

If a time series is not stationary, but when differenced d times it becomes

stationary, and it is an AR with errors MA, we say that the time series is an ARIMA (p, d,

q), that is, an integrated autoregressive-moving averages time series, where p denotes

the number of autoregressive terms, d is the number of times that we must difference the

series to make it stationary, and q, is the number of moving average terms. It is important

to emphasize ARMA models can be fit only to stationary and invertible time series. These

properties are achieved through differencing. This approach was proposed by Box and

Jenkins (1976). The fit and computation of forecasts of a given time series with the use of

Box and Jenkins techniques were performed here using PROC ARIMA (SAS, 2010).

3.5. Deterministic Trends with ARMA Errors

In one instance (consumption of cellulose) a satisfactory model was not possible

with the use of integrated models. In this case it was used the regression model Zt=F(t)+Ut

97



where Ut is an ARMA error and F(t) a linear function in time. The PROC ARIMA (SAS,

2010) produces statistics for these models using generalized least squares.

4. State-Space Models

The state-space model is a statistical model for a multivariate time series. It

represents the multivariate time series through auxiliary variables, some of which are not

observable directly. These auxiliary variables are denominated state-space variables. The

state-space vector summarizes all the information of values from the present and from the

past on the relevant time series for the prediction of future values for the series. The

observed time series are expressed as linear combination of the state variables. The state-

space model is called a Markovian representation or canonical representation of a

multivariate time series.

Let Zt be a q dimensional time series. Its representation in state-space, relate the

observations vector Zt to the state vector Xt , of dimension k, through the linear system

t t t t t tZ A X d S ε= + + (observation equation),

1t t t t t tX G X c Rη−= + + (state or system equation)

where t=1,..., N ; Αt is the matrix of the system of order (q x k); tε is the noise vector of the

observation of order (q x 1), not correlated in time, with mean vector zero and matrix of

variance tW of order (q x q), ; Gt is the transition matrix of order (k x k) ; tη is a noise vector

not correlated in time, of order (k x 1), with mean vector zero and matrix of variance tQ of

order (k x k); td has order (q x 1) ; tc has order (k x 1); tR has order (k x k).

In the state-space models it is supposed additionally that the initial state X0 has

mean µ0 and matrix of variance Σ0; the noise vectors tε and tη are not correlated with each

other and not correlated with the initial state, that is,

E(εtηs’) = 0, every t , s= 1,...,N; and

E(εt X0’) = 0 and E(ηt X0’) = 0, t= 1,...,N;

It is said that the state-space model is Gaussian when the noise vectors are

normally distributed. The matrixes Αt and Gt are non-stochastic; in this way if there is any

variation in time it will be pre-determined.

98



In this work it was used a particular form of the general representation described

above, which is the stationary representation described in SOUZA, et al, 2006 and

Brocklebank and Dickey, 2004.

It is important to notice here that every ARMA process has a state-space

representation.

The parameters of the state-space representation are estimated by maximum

likelihood supposing that the residual shocks vector are normally (multivariate) distributed.

The fit and forecasts of time series performed via state-space models were

performed using PROC STATESPACE (SAS, 2010).

5. AIC and SBC Information Criterion

The information criteria are very useful to assist in choosing the best model

among those which are potentially adequate. These criteria consider not only the quality of

the fit but also penalize the inclusion of extra parameters. Therefore, a model with more

parameters can have a better fit, however not necessarily it will be preferable in terms of

the information criterion. It is considered the best model by the information criteria the one

which presents the lowest values of AIC or SBC.

The information criterions known as Akaike Information Criterion (AIC) and the

Schwartz Bayesian Criterion (SBC) can be described as follows:

AIC = T ln (estimator of maximum verisimilitude) + 2n,

SBC = T ln (estimator of maximum verisimilitude) + n ln(T)

Where, T is the number of observations used in the computations and n the

number of parameters estimated.

It is interesting to highlight that these information criteria analyzed individually do

not have any meaning considering only one model. Comparison of alternative models (or

competing) is to be done in the same sampling period, in other words, with the same

quantity of information. In this work the use of the information criteria was used in the

choice of the order of some ARMA models and restricted to the Akaike criterion in the

context of the use of the state-space modeling.

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Pu

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6,3

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6,5

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6,6

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6,7

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6,8

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So

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6,7

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6,8

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1,48

91,

521

1,54

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1,58

61,

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1,61

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1,64

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MT

3,6

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3,6

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4,0

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3,9

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4,3

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4,2

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4,6

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3,02

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5,74

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6,32

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6,84

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1

PR

2,4

54

2,3

89

2,7

12

2,7

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2,6

15

2,5

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2,5

37

2,6

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2,6

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2

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2,14

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2,37

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125

-17

lo

wer

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it2,

763

2,70

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3,21

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2,99

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3,11

43,

166

3,13

722

BA

1,4

23

1,4

26

1,5

23

1,5

20

1,6

15

1,6

10

1,7

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1,7

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36

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mit

1,29

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1,31

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1,34

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lo

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8,51

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6

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5,1

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5,2

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5,4

65

5,6

19

5,7

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5,9

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6,0

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6,2

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3

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5,0

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4,9

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4,9

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5,0

67

5,1

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5,3

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5,3

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5,4

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5,5

27

5,6

09

14

U

p li

mit

4,71

74,

380

4,17

94,

155

4,21

54,

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4,28

64,

281

4,28

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304

-13

lo

wer

lim

it5,

330

5,59

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5,97

86,

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6,33

66,

488

6,62

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6,91

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1,3

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1,3

45

1,3

56

1,3

81

1,3

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1,4

19

1,4

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1,4

58

1,4

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1,4

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13

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mit

665

570

465

392

316

254

194

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lo

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2,11

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246

2,37

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2,58

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680

2,77

42,

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2,94

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3

RS

1,0

53

1,1

19

1,0

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1,1

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1,1

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1,2

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1,1

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1,2

88

1,2

55

1,3

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22

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p li

mit

753

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629

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623

669

588

653

582

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lo

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lim

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352

1,52

51,

547

1,66

51,

681

1,80

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810

1,92

31,

928

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RS

51

51

52

52

53

53

54

54

55

56

11

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p li

mit

4847

4645

4544

4444

4444

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lo

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5557

5961

6264

6566

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8,6

16

5,0

19

4,9

40

1,3

23

1,1

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50

1,1

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85

9

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3

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0

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Note

Note

Information Center0800 704 1995

www.agricultura.gov.br

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