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UNIVERSITY OF CAMPINAS
SCHOOL OF AGRICULTURAL ENGINEERING
NARIÊ RINKE DIAS DE SOUZA
TECHNO-ECONOMIC AND ENVIRONMENTAL EVALUATION OF BEEF PASTURE
INTENSIFICATION WITH SUGARCANE ETHANOL
AVALIAÇÃO TECNO-ECONÔMICA E AMBIENTAL DA INTENSIFICAÇÃO DE
PASTAGENS E ETANOL DE CANA-DE-AÇÚCAR
CAMPINAS
2017
NARIÊ RINKE DIAS DE SOUZA
TECHNO-ECONOMIC AND ENVIRONMENTAL EVALUATION OF BEEF PASTURE
INTENSIFICATION WITH SUGARCANE ETHANOL
AVALIAÇÃO TECNO-ECONÔMICA E AMBIENTAL DA INTENSIFICAÇÃO DE
PASTAGENS E ETANOL DE CANA-DE-AÇÚCAR
Dissertation presented to the School of Agricultural
Engineering of the University of Campinas in partial
fulfillment of the requirements for the degree of Master
in Agricultural Engineering in the Area of Rural
Constructions and Environment.
Dissertação apresentada à Faculdade de Engenharia
Agrícola da Universidade Estadual de Campinas como
parte dos requisitos exigidos para obtenção do título de
Mestra em Engenharia Agrícola na Área de Construções
Rurais e Ambiência.
Supervisor: Luis Augusto Barbosa Cortez
Co-supervisor: Juliana Aparecida Fracarolli
This exemplary corresponds to the final version of
the Master’s thesis defended by the student Nariê
Rinke Dias de Souza, with Prof. Dr. Luis Augusto
Barbosa Cortez as supervisor and Prof. Dr. Juliana
Aparecida Fracarolli as co-supervisor.
CAMPINAS
2017
3
DEDICATION
I dedicate my dissertation and all the hard work I have had so far to my beloved father (in
memoriam) who has taught me everything I know including how to be as strong as I was to fulfill
my Master’s obligations while I lost my world and a piece of my heart.
ACKNOWLEDGMENT
I would like to express my deepest gratitude to my advisor Professor Dr. Luis Cortez for the
opportunity and patience to teach me important lessons;
To my co-advisor Professor Dr. Juliana Fracarolli for her dedication;
To the Brazilian Bioethanol Science and Technology Laboratory (CTBE) Biorefinery team:
Antonio Bonomi, Tássia Junqueira, Mateus F. Chagas, Terezinha F. Cardoso, Marcos D. B.
Watanabe, Otávio Cavalett, Isabelle L. M. Sampaio, Vera Gouveia and Bruno C. Klein for the
uncountable help;
To Solismar Venzke from Rotar, Antony Sewell from Boviplan, Sergio R. de Medeiros and Luis
G. Barioni from EMBRAPA, professor Manoel Regis Lima Verde Leal from NIPE, professor
Bruce Dale from Michigan State University and Ricardo Baldassin for all the attention and
meetings;
To professor Dr. Galen Erickson from University of Nebraska and Prof. Dr. Dan Loy from Iowa
State University, for all knowledge shared and for having received me so well;
To the Zootechnical team from Vale do Rosário for having received me and having shared their
experience in sugarcane-livestock integration;
To FEAGRI for having accepted me as a student and to supply me with incredible moments and
friends;
To Coordination for the Improvement of Higher Education Personnel - CAPES for my master
scholarship;
To my family and friends that have always supported and loved me, specially my mother Marilda,
whom I love so much;
And finally, to God for letting me experience this, learn a lot and meet all these amazing people.
ABSTRACT
There is a world concern about greenhouse gases (GHG) emission and it was established on the
21th Climate Conference in Paris that the global temperature cannot increase above 2°C. Due to
climate change concern, there is a growing demand for bioenergy to replace fossil fuels. However,
the advantages of bioenergy decrease if it leads to deforestation and/or indirect land use change
(ILUC). Considering that, new models for renewable energy production are needed to
simultaneously decrease GHG emissions, use land more efficiently and replace large amounts of
fossil fuel. Ethanol and livestock integration as happens in the United States (USA) might be a
possible solution for a new model of production. Brazil, the second largest ethanol and beef cattle
producer in the world, can modify the USA model of corn ethanol and cattle integration for its own
circumstances. Brazil uses about 168 million hectares as extensive pasture and about 9 million
hectares for sugarcane production. These two products are pillars of the country’s economy. This
work evaluates the techno-economic and environmental feasibility of sugarcane ethanol and cattle
integration, thereby avoiding pasture displacement by sugarcane expansion and the ILUC. Cattle
before finished in pasture land can be finished in feedlots fed with sugarcane ethanol by-products
(in natura bagasse, hydrolyzed bagasse, molasses, wet yeast plus grains). Intensification of cattle
production by integration with sugarcane releases pasture area to produce more sugarcane, without
needing more land for cattle production. For this study, six different scenarios were assessed
varying relation between sugarcane and cattle area from 0 to 1. System-wide simulations were
performed using the Virtual Sugarcane Biorefinery (VSB) model developed by the Brazilian
Bioethanol Science and Technology Laboratory (CTBE). Data for the models were obtained from
literature and current actual cases of beef cattle/sugarcane integration. The calculated economic
parameters are internal rate of return (IRR), net present value (NPV) and payback time (PT). GHG
emissions were assessed via Life Cycle Assessment using ReCiPe Midpoint (H) method and the
Climate Change impact category. As result, integration decreases overall GHG emissions
compared to non-integrated systems and techno-economic feasibility is achieved considering
revenues from rental of released pasture area and from carbon credit.
Key-words: integration, sugarcane-cattle integration, ILUC, pasture intensification, sustainability,
life cycle assessment
RESUMO
Há uma preocupação mundial com a emissão de gases de efeito estufa (GEE) e durante a 21ª
Conferência do Clima em Paris, foi acordado que o aumento da temperatura global não pode
crescer mais que 2°C. Devido à preocupação com a mudança climática, há um crescimento na
demanda de bioenergia para substituir os combustíveis fósseis. No entanto, as vantagens da
bioenergia podem ser reduzidas se levar ao desmatamento e/ou à mudança indireta do uso da terra
(ILUC). Nesse contexto, novos modelos de produção são necessários para diminuir as emissões de
GEE, usar a terra mais eficientemente e substituir as grandes quantidades de combustíveis fósseis.
A integração de etanol e gado como acontece nos Estados Unidos pode ser uma resposta viável
para um novo modelo de produção. O Brasil, segundo maior produtor mundial de etanol e bovino
de corte, pode adaptar esse modelo Estadunidense de integração para a realidade brasileira. O Brasil
tem cerca de 168 milhões de hectares utilizados como pastagens extensivas e cerca de 9 milhões
de hectares para a produção de cana-de-açúcar. Estes dois produtos são pilares para a economia do
país. Este trabalho avalia a viabilidade tecno-econômica e ambiental da integração de etanol de
cana-de-açúcar com gado de corte, assim evitando o deslocamento de pastagens pela expansão da
cana-de-açúcar e o ILUC. O gado antes terminado em pastagens pode ser terminado em
confinamentos com ração contendo bagaço in natura, bagaço hidrolisado, melaço, levedura, grãos
de milho e farelo de soja. A área liberada pela intensificação das pastagens é usada para produzir
mais cana-de-açúcar, sem precisar de mais terra para a produção de gado. Para este trabalho foram
avaliados seis cenários diferentes variando a relação de área de cana-de-açúcar e de pastagem de 0
a 1. As simulações foram realizadas na Biorefinaria Virtual de Cana-de-Açúcar (BVC) do
Laboratório Nacional de Ciência e Tecnologia de Bioetanol (CTBE). Os dados para simulações
foram obtidos em literatura e casos reais de integração. Para a avaliação econômica os parâmetros
são taxa interna de retorno (TIR), valor presente líquido (VPL) e tempo de retorno do investimento.
As emissões de gases de efeito estufa são avaliadas com Avaliação do Ciclo de Vida usando o
método ReCiPe Midpoint (H) e a categoria de impacto Mudanças Climáticas. Como resultado, a
integração diminui as emissões de GEE em relação aos sistemas não integrados e a viabilidade
tecno-econômica é atingida quando consideradas as receitas com aluguel da área de pastagem
liberada e também das receitas geradas pelos créditos de carbono.
Palavras-Chave: integração, integração cana-pecuária, mudança indireta de uso da terra,
intensificação de pastagens, sustentabilidade, avaliação de ciclo de vida
LIST OF FIGURES
Figure 1: World Agricultural Land in 2013 in billion hectares .................................................... 22
Figure 2: Land use in Brazil ......................................................................................................... 23
Figure 3: Brazilian CO2 eq emission by sector ............................................................................ 24
Figure 4: Representation of possible ILUC caused by sugarcane expansion on pasture land .... 26
Figure 5: Sugarcane production in Brazilian Center-South region and in São Paulo State ......... 29
Figure 6: Brazilian annexed sugarcane plant diagram flow ......................................................... 30
Figure 7: Brazilian ethanol and sugar production ........................................................................ 31
Figure 8: Brazil and the world’s largest cattle producers in 2016 (thousand heads) ................... 32
Figure 9: Largest states in Brazil considering cattle herd (million heads), pasture land (million
hectares) and slaughter (million heads) in 2015 ............................................................................ 33
Figure 10: Corn ethanol and livestock integrated production in the US ...................................... 37
Figure 11: US corn ethanol and DGS production along years ..................................................... 38
Figure 12: Dry-grind process for corn ethanol production ........................................................... 40
Figure 13: Wet-grind process for corn ethanol production .......................................................... 41
Figure 14: Production model of sugarcane ethanol and cattle integration ................................... 44
Figure 15: Location of Amazon forest, cattle herd and sugarcane plants in Brazil ..................... 47
Figure 16: São Paulo State mapping of sugarcane plants............................................................. 48
Figure 17: São Paulo State mapping of cattle herd ...................................................................... 48
Figure 18: Sugarcane agricultural steps diagram ......................................................................... 50
Figure 19: Ethanol plant industrial steps diagram ........................................................................ 50
Figure 20: Scenario 0.................................................................................................................... 52
Figure 21: Scenario 1.................................................................................................................... 52
Figure 22: Scenario 2.................................................................................................................... 52
Figure 23: Scenario 3.................................................................................................................... 53
Figure 24: Scenario 4.................................................................................................................... 53
Figure 25: Scenario 5.................................................................................................................... 53
Figure 26: Life Cycle Assessment Methodology ......................................................................... 59
Figure 27: Sugarcane ethanol and cattle integrated chain for LCA ............................................. 60
Figure 28: Total tCO2 eq emissions per scenario assessed........................................................... 70
Figure 29: Comparison of CO2 eq emissions per kilogram of meat produced in pasture and in
feedlot ............................................................................................................................................ 70
Figure 30: Sensitivity of gCO2 eq per MJ of ethanol ................................................................... 71
Figure 31: Total CO2 eq emissions (Mt) for the ethanol plant per scenario with “avoided ILUC”
sensitivity ....................................................................................................................................... 72
LIST OF EQUATIONS
Equation 1: Relation between cattle area and sugarcane area ....................................................................51
r = Ac/Api
Equation 2: Net Present Value ....................................................................................................................56
NPV = ∑Cn
(1 + r)n
n=N
n=0
Equation 3: Internal Rate of Return ............................................................................................................56
0 = ∑𝐶𝑛
(1+𝐼𝑅𝑅)𝑛𝑛=𝑁𝑛=0
LIST OF ACRONYMS AND ABBREVIATIONS
1G First generation ethanol
2G Second Generation Ethanol
ABIEC Brazilian Association of Meat Exporting Industries/Associação Brasileira das
Indústrias Exportadoras de Carne
ABNT Brazilian Association for Technical Standards/Associação Brasileira de
Normas Técnicas
ADF Acid Detergent Fibers
ADG Average Daily Gain
BNDES The National Bank for Economic and Social Development/ Banco Nacional de
Desenvolvimento Econômico e Social
CGEE Center for Strategic Studies and Management Science, Technology and
Innovation/Centro de Gestão e Estudos Estratégicos
CTBE Brazilian Bioethanol Science and Technology Laboratory/ Laboratório
Nacional de Ciência e Tecnologia de Bioetanol
CO2 Carbon Dioxide
CO2 eq Carbon Dioxide equivalent
CONAB National Supply Company/Companhia Nacional de Abastecimento
COP 21 21th Conference of Parties
CP Crude Protein
CTM Clean Trade Mechanism
CWE Carcass Weight Equivalent
DGS Distillers Grains with Solubles
DDGS Dried Distillers Grains with Solubles
DM Dry Matter
EMBRAPA Brazilian Agricultural Research Corporation/ Empresa Brasileira de Pesquisa
Agropecuária
EPA Environmental Protection Agency
EPE Energy Research Company/Empresa de Pesquisa Energética
FAO Food and Agriculture Organization of the United Nations
GHG Greenhouse Gases
GPD Gross Domestic Product
GTPS Brazilian Roundtable On Sustainable Livestock/Grupo de Trabalho da
Pecuária Sustentável
GWh Giga Watt hour
ha Hectare
hd/ha Heads Per Hectare
IBC Iowa Beef Center
IBGE Brazilian Institute of Geography and Statistics/Instituto Brasileiro de
Geografia e Estatística
IGPM General Price Index – Market/ Índice Geral de Preços – Mercado
ILUC Indirect Land Use Change
IPAM Amazon Environmental Research Institute/Instituto de Pesquisa Ambiental
da Amazônia
IPCA Broad National Consumer Price Index/Índice Nacional de Preços ao
Consumidor Amplo
IRR Internal Rate of Return
ISO International Organization for Standardization
kg/hd.day-1 Kilograms per head per day
kg/hd.y-1 Kilograms per head per year
LCA Life Cycle Assessment
LUC Land Use Change
LW Live Weight
MAPA Ministry of Agriculture, Livestock and Supply/ Ministério da Agricultura,
Pecuária e Abastecimento
MDGS Modified Distillers Grains with Solubles
Mha Million Hectares
MJ Mega Joule
MOG Material Other than Grain
MR$ Million R$
Mt Million tons
NDF Neutral Detergent Fibers
NPV Net Present Value
PT Payback Time
RFA Renewable Fuel Association
SEEG System for Greenhouse Gas Emissions and Removals Estimates/Sistema de
Estimativas de Emissões de Gases de Efeito Estufa
t/y Tonne per Year
tc Tonne of Sugarcane
t/ha.y-1 Tonne per Hectare per Year
TDN Total Digestible Nutrients
UNICA Brazilian Sugarcane Industry Association/União da Indústria de Cana-de-
Açúcar
USDA United States Department of Agriculture
USGC United States Grain Council
VSB Virtual Sugarcane Biorefinery
WDGS Wet Distillers Grains with Solubles
LIST OF TABLES
Table 1: Brazilian cattle management .........................................................................................................35
Table 2: Sugarcane and dry-grind corn ethanol by-products ......................................................................45
Table 3: Scenarios definition .......................................................................................................................54
Table 4: Ethanol plant parameters ...............................................................................................................54
Table 5: Feed formulation for feedlot managements ..................................................................................55
Table 6: Assumptions for economic evaluation ..........................................................................................57
Table 7: Estimate of feed final price ...........................................................................................................58
Table 8: Production results of scenarios simulation ....................................................................................61
Table 9: Total investments for each scenario ..............................................................................................62
Table 10: Investments for ethanol plant ......................................................................................................62
Table 11: Economic results – ethanol plant ................................................................................................63
Table 12: Products participation on total revenue of ethanol plant .............................................................63
Table 13: Allocation costs for ethanol plant products .................................................................................64
Table 14: Economic results – cattle production considering land rental revenue of released pasture area
for sugarcane production ..............................................................................................................................64
Table 15: Economic results – cattle production considering land rental revenue of released pasture area
for sugarcane production and C credits revenue ..........................................................................................65
Table 16: Economic results - cattle production considering the cattle manager owns the land plus revenue
from land rental for sugarcane production ...................................................................................................66
Table 17: Economic results - cattle production considering the cattle manager owns the land and revenue
from land rental for sugarcane production plus carbon credits ....................................................................66
Table 18: Participation on cattle production total cost ................................................................................67
Table 19: Economic sensitivity for cattle production (IRR) .......................................................................68
Table 20: Results of Climate Change emissions .........................................................................................69
Table 21: Economic and environmental results for sugarcane ethanol and pasture intensification ............73
SUMMARY
1 INTRODUCTION .....................................................................................................................................17
2 OBJECTIVE .............................................................................................................................................19
2.1 Specific Objectives .............................................................................................................................19
3 LAND USE AS A MAJOR ROLE IN THE FUTURE SCENARIOS OF FOOD AND ENERGY
PRODUCTION IN THE WORLD ..............................................................................................................20
3.1 Environmental Aspects of Biofuel Production ...................................................................................21
3.2 Possible ILUC Caused by Sugarcane Expansion ...............................................................................25
4 BRAZILIAN MODEL OF SUGARCANE ETHANOL PRODUCTION ................................................28
5 BRAZILIAN BEEF CATTLE PRODUCTION........................................................................................32
5.1 Other Cattle Producing Countries ......................................................................................................36
6 INTEGRATION MODEL: THE UNITED STATES ...............................................................................37
6.1 Corn Ethanol Production in the US ....................................................................................................38
7 REAL CASES OF ETHANOL-CATTLE INTEGRATION IN BRAZIL ................................................42
7.1 Potential of Sugarcane Ethanol By-Products as Cattle Feed ..............................................................44
7.2 Sugarcane Ethanol and Beef Cattle Integration in Brazil ...................................................................45
8 METHODOLOGIES .................................................................................................................................49
8.1 Data Collection ...................................................................................................................................49
8.2 Scenarios Definition ...........................................................................................................................51
8.3 Economic Evaluation .........................................................................................................................55
8.4 Life Cycle Assessment .......................................................................................................................58
9 RESULTS AND DISCUSSION ...............................................................................................................61
10 CONCLUSIONS .....................................................................................................................................75
REFERENCES .............................................................................................................................................77
ANNEX 1 – Cattle Production and Economic Evaluation ...........................................................................86
17
1 INTRODUCTION
The world is worried about Climate Change mainly due to greenhouse gases emissions
(GHG) from fossil fuel energy, agriculture-livestock production and land use change (LUC).
Countries have signed the COP 21 agreements to decrease GHG emission, replace fossil fuel
energy and established goals and mandates to achieve these purposes.
Bioenergy production is increasing mainly due to the environmental concern;
according to Food and Agriculture Organization of the United Nations (FAO, 2012), not only the
world energy demand will increase until 2050, but also the demand for food, feed and fiber.
However, international environmental and climate change agencies started accounting
indirect land use change (ILUC) emissions for biofuels, which can cancel bioenergy advantages if
considering it leads to deforestation. Sugarcane expansion is constantly blamed of been one of the
reasons for Amazon forest deforestation due to ILUC from pasture land displacement.
Many authors have different perspectives about ILUC and biofuels leading to
deforestation. Regions as São Paulo State had large cattle herds before sugarcane expansion and
due to land price increase, the herd were pushed to cheaper areas, such as Amazon forest areas.
But, nowadays there are many and complex factors responsible for Amazon forest deforestation
such as illegal logging, lack of land tenure, indigenous and rural settlements and agricultural
expansion (GTPS, 2016).
Regarding world demand increase, land use plays a major role in this situation. The
world future trend is to intensify pasture land use to release area for crops production and then
achieve future demand of food and bioenergy sustainably. Currently, 26% of the global land use is
dedicated to pastures and meadows and only 1% used for crops. This is also observed in Brazil,
which has around 168 million hectares of pasture, 20% to 25% of the country’s total area, while
sugarcane occupies only 1% (9 Million hectares).
In order to dismiss the arguments that Brazilian sugarcane ethanol cause ILUC, its
production needs to expand without increasing area. For that, there are genetics improvement, 2G
ethanol production from bagasse and straw and also adaptation of the integrated model of the
United States.
18
In the United States the corn, ethanol and livestock sector have cooperated with each
other in a well-succeed integration for more than one decade. Most cattle are fed with corn ethanol
by-products and finished in feedlots.
Regarding Brazil, the country is currently the second largest ethanol and beef producer
in the world, with sugarcane and cattle sectors representing important economic roles. Ethanol is
mostly produced annexed to sugar mills and cattle are mostly produced in extensive pastures using
a huge amount of land with low costs of production. Although sugarcane ethanol represents a
considerable decrease of GHG emissions compared to gasoline, this difference can be reduced due
to ILUC emissions. In addition, livestock production is the largest source of GHG emissions inside
the agricultural sector.
Sugarcane area should expand on degraded pasture, with improvement in cattle
management. New models of food and fuel production must meet both demand increase and the
COP 21 agreements, without causing deforestation. Sugarcane ethanol and cattle integration can
help decrease GHG emissions and avoid ILUC. This integrated model can sustainably increase
bioenergy production through intensification of cattle production.
19
2 OBJECTIVE
The objective of this project is to assess the techno-economic and environmental
feasibility of sugarcane ethanol and cattle integration.
2.1 Specific Objectives
• Scenarios definition of sugarcane ethanol and beef cattle integrated systems
• Virtual simulation of sugarcane agricultural steps
• Virtual simulation of cattle production
• Virtual simulation of ethanol plant industrial steps
• Techno-economic evaluation of integrated scenarios
• Life Cycle Assessment of integrated scenarios to assess GHG emissions
20
3 LAND USE AS A MAJOR ROLE IN THE FUTURE SCENARIOS OF FOOD AND
ENERGY PRODUCTION IN THE WORLD
Global warming is a worldwide discussed issue and during the 21st Climate Conference
– COP 21, which happened in Paris, 2015, representatives from all over the world committed to
decrease GHG emissions until 2050 to slow down temperature increase. Each country established
domestic goals to achieve the greater cause.
World population will achieve 9 billion people in a few decades, mainly in developing
countries with higher population density. This population growth will be concentrated in urban
areas (70% of population) with higher income levels, which helps increase consumption patterns
of food and energy (FAO, 2009). The global demand increase of food, fiber, feed and energy will
pressure land use and consequently environmental issues (POPP et al., 2016).
Part of the world’s action to decrease GHG emissions is replacing fossil fuel by
renewable energy and bioenergy. However, the world still is most dependent on fossil fuel energy
sources as coal, oil and natural gas. Bioenergy from biological sources is growing and nowadays
bioenergy represents the largest share in the renewable energy matrix (IEA; FAO, 2017).
Bioenergy plays a major role on the decarbonization needed to achieve COP 21
agreements. Its growth is a trend and will keep increasing to replace fossil fuels. Regarding
bioethanol production, the United States are the largest ethanol producer, followed by Brazil
(CORTEZ, 2010). In 2016, Brazil produced 28 billion liters of ethanol, behind 58 billion liters
produced by the United States (RFA, 2016). Most of this ethanol is blended to gasoline in Otto
cycles vehicles (CORTEZ, 2010).
According to the Center for Strategic Studies and Management Science, Technology
and Innovation (CGEE, 2009b) gasoline demand worldwide will increase around 48% until 2025
from 1.2 trillion liters to 1.7 trillion liters. Adopting the 5% of Brazilian sugarcane ethanol blend
in gasoline in global scale, considered in CGEE (2012), there would be a 102 billion liters demand
of ethanol. To meet this demand, additional 17 million hectares would be necessary to produce
sugarcane in Brazil (CGEE, 2012). Adopting 10% mandatory ethanol blend for global gasoline,
there would be the necessity to increase sugarcane additional area to 24 million hectares
considering currently technology and yields of production (CGEE, 2009b).
21
Other studies estimate ethanol demand increase considering integrated production
and/or pasture intensification as potential alternatives. Cortez (2016) states bioenergy production
is increasing in order to meet COP 21 agreements and pasture land use intensification is releasing
area for food and energy production, through cattle stocking rate intensification.
3.1 Environmental Aspects of Biofuel Production
Land use management can play a major role in the GHG mitigation and an established
carbon market could decrease LUC (BERNDES et al., 2016). Currently land use and land use
change are responsible for around 25% of global GHG emissions (POPP et al., 2016).
Biofuels are expected to contribute to decrease GHG emissions and replace fossil fuel.
However, its production can lead to LUC and ILUC due deforestation (VALIN et al., 2015).
According to the author, this ILUC conversion can release CO2 to the atmosphere since each crop
has a different carbon stock on the soil, and these emissions started to be included in GHG balance
of biofuels.
According to the European Commission (2015a), biofuels must decrease GHG
emission in 35% to be considered sustainable; this target rises to 50% in 2017 and rises again to
60% in 2018. This sustainability includes life cycle since cultivation, processing and transport; in
addition, biofuels cannot be planted in areas with high carbon stock, with high biodiversity land
and cannot be produced from raw materials obtained from land with high biodiversity such as
primary forests or highly biodiverse grasslands (EUROPEAN COMMISSION, 2015a).
The Environmental Protection Agency – EPA affirmed sugarcane decreases 61% of
GHG emission compared to gasoline (CORTEZ et al., 2016) against 90% reduction reported by
Sousa and Macedo (2010). This difference is because EPA accounts LUC and ILUC emissions.
However, even if ILUC factor is calculated, Brazilian sugarcane ethanol still is the only one which
meets RFS criteria of “Advanced Biofuel” (CORTEZ et al., 2016).
Regarding land use trends, some studies under development funded by FAPESP (São
Paulo Research Foundation) are evaluating potential and available areas to produce biofuels
without compromising food production: LACAf (potential areas in Latin America, Caribbean and
Africa to produce sugarcane ethanol) coordinated by Luis Augusto Barbosa Cortez, from School
of Agriculture Engineering – FEAGRI/UNICAMP; and Global Sustainable Bioenergy Initiative:
22
Geospatial & environmental analysis of pastureland intensification for bioenergy (pasture
intensification) coordinated by John Sheehan from Colorado State University. Most of available
land to produce biofuels are in degraded pastures or low stock rates pastures and, in Brazil, most
of the sugarcane expansion had already happened in pasture lands (CORTEZ, 2010).
In general, the future trend is to recover pasture land and intensify cattle stocking rate
to release area to crops production (GTPS, 2016). From the 13 billion hectares of land in the world,
around 26% are pasture and meadows and only 1% are crops. Total agriculture area represents 4.9
billion hectares divided as Figure 1.
Figure 1: World Agricultural Land in 2013 in billion hectares
Source: WORLD BIOENERGY ASSOCIATION (2016)
Berndes et al. (2016) state that the released pasture area from intensification could
accommodate expansion of crops production and decrease deforestation. According to the authors,
pasture intensification is possible with improvement in land productivity; the improvement in meat
and dairy production is essential to release area for bioenergy crops since most of available land
for bioenergy production is currently used as extensive pasture.
Using pasture land to plant bioenergy crops has the advantage of emitting less GHG
emissions than forest conversion for crops, however it can lead to more ILUC if no pasture
improvement occurs to support higher cattle stocking rates capacity (BERNDES et al., 2016).
23
In Brazil, the Energy Research Company (EPE, 2016) estimates food production will
increase more than area increase due to new models of production, crop productivity and cattle
stocking rate increase; pasture area will decrease, releasing land to food and bioethanol production.
According to Vale (2014) and Latawiec et al. (2014), pasture intensification in Brazil is a viable
way to increase agriculture production and spare land, and consequently causing no deforestation
or ILUC.
Brazil is the 5th largest country considering territorial extension, with 851 million
hectares or 8,515,767 km2 (IBGE, 2013). The area is divided in forests, legal reserves, permanent
preservation, urban and agriculture areas (Figure 2).
Figure 2: Land use in Brazil
Source: EMBRAPA (2016)
After forests, pasture area represents the largest amount of land used in Brazil. Close
to 20% of Brazilian territory is used as pasture and only 1% for sugarcane. Sugarcane is the third
largest crop area in Brazil, with around 9 million hectares (CONAB, 2017).
According to Alkimim; Sparovek; Clarke (2015), Brazil has nearly 112 million
hectares of cultivated pastures, from which 24 million hectares are conditionally suitable, 37
million hectares has low suitability, 14 million hectares are moderately suitable and 36 million
24
hectares are highly suitable for sugarcane production. These moderately and highly suitable areas
are located in Goias, Mato Grosso, Pará, Paraná, São Paulo, Mato Grosso do Sul and Minas Gerais
States and represent 50 million hectares, about five times more than the current sugarcane area.
Sugarcane expansion on pasture land has a huge role in GHG mitigation in Brazil. If
the country had an improvement in cattle stocking rate, a lot of pasture land would be available to
additional crop production without causing deforestation and ILUC emissions (ALKIMIM;
SPAROVEK; CLARKE, 2015).
Regarding GHG emissions, Brazil emitted around 2.3 billion tons of CO2 eq in 2016
(SEEG, 2017). The highest emissions come from forest and land use change (Figure 3), followed
by agriculture emissions, both led by Mato Grosso State, and energy sector emissions led by São
Paulo State.
Figure 3: Brazilian CO2 eq emission by sector
Source: SEEG (2017)
Energy emissions due to fuel combustion represented 17.5% of total emissions, while
livestock emissions, due to enteric fermentation plus manure management emissions, represented
15.2% of total emissions (SEEG, 2017). Considering that, sugarcane-ethanol and cattle integration
can increase biofuel production without pasture displacement, without ILUC while also
contributing with Brazilian obligations in the COP 21 agreements.
25
In the COP 21 agreements, Brazil established a 43% decrease of GHG by 2030 and
incorporating 45% of renewable fuels in the energy matrix (year 2005 as baseline); also, to decrease
illegal deforestation in the Amazon forest; recover 12 million hectares of deforested areas; recover
15 million hectares of degraded pasture and insert 5 million hectares of livestock-crop-forest
integration.
Taking this into account, sugarcane ethanol and beef cattle integration can help
decrease emissions from the 3 largest sectors of GHG emissions in Brazil (forest and land use
change, agriculture and energy). With integration, less land is used to finish cattle (in feedlots) and
sugarcane production can increase over pasture land without compromising beef production. Also,
the integration can decrease enteric fermentation emissions, due to the reduction of finishing cattle
cycle from around 12 months to 3 months; and as already stated, bioethanol decreases GHG
emission compared to gasoline.
3.2 Possible ILUC Caused by Sugarcane Expansion
According to Marin et al. (2016), 88% of the sugarcane expansion has occurred due to
frontier expansion and only 12% is related to yield increase. This expansion has occurred mainly
in pasture land (CORTEZ, 2010). However, using the Sugarcane Agroecological Zoning, it was
concluded Brazil has 64.7 hectares available to produce sugarcane and 37.2 million hectares to
expand on current pasture lands, without compromising native vegetation and forests areas
(MAPA, 2009). Sugarcane production increased land price, mainly in São Paulo State and pushed
cattle to other areas with lower land prices such as North region (CEDEBERG; MEYER; FLYSJO,
2009). When cattle production changes to Amazon forest areas, the so called ILUC occur (Figure
4).
The ILUC is a complex and highly debate topic, which is driven by different
approaches and methodologies that are not well established and totally accepted yet. ILUC
explanation relies on European Commission definition:
When biofuels are produced on existing agricultural land, the demand for food
and feed crops remains, and may lead to someone producing more food and feed
somewhere else. This can imply land use change (by changing e.g. forest into
agricultural land), which implies that a substantial amount of CO2 emissions are
released into the atmosphere (EUROPEAN COMISSION, 2012a, p.1).
26
Figure 4: Representation of possible ILUC caused by sugarcane expansion on pasture land
Source: Elaborated by the author based on European Commission (2012b)
Cattle expansion in Brazil happened mainly in North and Midwest region, been one of
the causes of Amazon forest deforestation (IPAM, 2016). However, according to Martha Jr; Alves;
Contini (2012), frontier expansion to increase beef production happened only from 1950 to 1975;
expansion of extensive pasture occurred due to low opportunity costs and Governmental incentives
to develop the Cerrado biome and parts of Amazon forest, plus the need to secure land property.
Since 1975, the cattle production has increased due to productivity and genetic improvement, better
animal health and forage quality, better animal management practices and improved nutrition, but
without causing pasture land expansion. Currently, Amazon forest deforestation is driven by illegal
logging, lack of land tenure, deforestation of small area plots and deforestation due to indigenous
and rural settlements, with only relatively small part due to livestock and agricultural expansion
(GTPS, 2016).
Amazon forest deforestation is decreasing and compared to 1996-2005 period it
decreased in 75% due to surveillance, networking of civil society and government plus actions of
27
stakeholders in agriculture (BERNDES et al., 2016). These stakeholders’ action in agriculture has
the demand certification for sustainable agriculture as incentive, which is growing in Brazil
(BERNDES et al., 2016). The increase in cattle productivity saved 525 million hectares from 1950
to 2006 (MARTHA JR; ALVES; CONTINI, 2012).
Brazil still has great potential to improve beef production even more adapting the
ethanol-livestock integration that happens successfully in the United States.
28
4 BRAZILIAN MODEL OF SUGARCANE ETHANOL PRODUCTION
Sugarcane has been cultivated in Brazil since the colonial period, for over five
centuries. It was introduced in São Paulo State, but developed in the Brazilian Northeast region to
export sugar to Europe (CORTEZ et al., 2016). After coffee crisis in 1929, sugarcane was back to
São Paulo State.
Brazil was the first country to use ethanol in large scale. Primarily, gasoline was
blended to 2% of ethanol; after 1930 until 1975 this blend was 5% to 7%; and with the Proálcool
program (Brazilian program to decrease oil dependence in the 70s), the mandatory blend increased
to 10% plus hydrous ethanol vehicles development (CORTEZ et al., 2016). Nowadays ethanol
mandatory blend in gasoline is 27% according to Portaria nº 75 from March 5th 2015 from Ministry
of Agriculture, Livestock and Supply (BRASIL, 2015a).
Until 2007 Brazil was the world’s largest ethanol producer, being overpast by the
United States and its corn ethanol and livestock integrated way of production (RFA, 2016a).
Currently, Brazil is world reference in sugarcane technology (EPE, 2016). The country
is the largest sugarcane producer (657.18 million tons), the largest sugar producer (38.69 million
tons), second largest ethanol producer (27.9 billion liters) and exports about 50% of all sugar
consumed in the world (CONAB, 2017). Sugarcane is produced in majority in Center-South region
(São Paulo, Rio de Janeiro, Espírito Santo, Minas Gerais, Mato Grosso, Mato Grosso do Sul, Goiás,
Paraná States) (Figure 5). In Center-South region, sugarcane is harvested without burning (green
cane) and mechanically, straw is mostly left in field and bagasse is used mainly to produce
bioelectricity (CARDOSO et al., 2018; CAVALETT et al., 2012). Before Law N° 11.241, from
September 12th, 2002 (ASSEMBLEIA LEGISLATIVA DE SÃO PAULO, 2002), which prohibits
sugarcane burning, straw was burned.
São Paulo State is responsible for 56.3% of the Brazilian production of sugarcane,
62.4% of sugar and 49.5% of ethanol (CONAB, 2017). Furthermore, the State stands out in terms
of technology and investments in sugarcane-ethanol sector (CORTEZ, 2010).
29
Figure 5: Sugarcane production in Brazilian Center-South region and in São Paulo State
Source: CONAB (2017)
Brazilian model produces both sugar and ethanol in annexed mills (Figure 6) in the
majority, with one of the lowest production costs in the world (CAVALETT et al., 2012; CORTEZ,
2016). According to Cortez (2010), 65-68% of costs come from sugarcane agricultural steps, 20-
25% from industrial steps and the other part comes from administrative costs.
9.0
72.6
657.2
38.7 27.85.7
76.5
436.0
28.1 16.54.8
77.5
369.9
24.1 13.7
Brazil Brazilian Center-South São Paulo State
Area (Mha) Productivity (t/ha) Sugarcane (Mt) Sugar (Mt) Ethanol (bi L)
30
Figure 6: Brazilian annexed sugarcane plant diagram flow
Source: Adapted from Bonomi et al. (2016)
This model diverts 50-60% of sugarcane to sugar and 40-50% to ethanol production
(SALLES-FILHO, 2015), having 64% of mills annexed (produces ethanol and sugar) and only
38% producing only ethanol, which makes ethanol being generally dependent on sugar production
(CORTEZ, 2010). Ethanol and sugar prices are connected and varies according to international
market of sugar and gasoline; in 62% of annexed mills cases, sugar is more economically attractive
than ethanol (SALLES-FILHO, 2015).
Still according to Cortez (2010), a rapid expansion in ethanol production would need
to overcome this dependent sugar-ethanol model, which have worked up until now to meet
domestic market. Until now, sugar and ethanol production are growing together (Figure 7),
however if future demand of ethanol production achieves the predicted in CGEE (2009b) and sugar
31
demand keeps growing only 2-3% a year (CORTEZ, 2010), how will ethanol production increase
without sugar following the same growth rate? What would be the new model of ethanol
production?
To meet global demand, a new model of ethanol production must be developed that
grows more than 2-3% a year that does not necessarily increase sugar production. The ethanol –
cattle integration could diversify the mixed model (sugar-ethanol) which has a trend to grow
together (Figure 7) and decrease the huge gap between the future prediction of sugar and ethanol
demand.
Figure 7: Brazilian ethanol and sugar production
Source: Adapted from UNICA (2016), CGEE (2009b) and Cortez (2010)
0
50
100
150
200
250
Ethanol (billion liters) Sugar (million tons)
Future prediction
32
5 BRAZILIAN BEEF CATTLE PRODUCTION
Cattle were first introduced in Brazil in the 16th century. Later it started being raised to
produce beef, which continues until nowadays. Brazil is the second largest beef producer in the
world (Figure 8 ) (USDA, 2016a).
Currently, according to the Ministry of Agriculture, Livestock and Supply Brazil has
the second largest cattle herd in the world with 213 million heads (BRASIL, 2015b) and cattle
stocking rate of around 1.2 heads per hectare. The cattle sector is one of the main pillar of Brazilian
economy and has important economic role in exportation. In 2015, Brazilian Gross Domestic
Product (GDP) reached R$5.9 trillion, agribusiness represented 21% of GDP and livestock
represented 30% of agribusiness (BEEFPOINT, 2016).
Figure 8: Brazil and the world’s largest cattle producers in 2016 (thousand heads)
Source: USDA (2016a)
*India includes buffalo
In Brazil, Nelore breed is mostly used for beef production and the management system
varies among the regions (EMBRAPA, 2005). But it is usually predominantly extensive pasture
grazed all year long with a small fraction of beef finished in feedlots (FAO, 2006). Due to extensive
management with no advanced technology or proper management, Brazilian beef production has
one of the lowest costs (FERRAZ; FELICIO, 2010), but also lowest productivity rates per hectare
9284
11389
69007850
4250
26002075
Brazil US China Europe Union India Argentina Australia
33
in the world (BOVIPLAN, 2015). Moreover, EMBRAPA (2014) estimates 50% of pasture land in
Brazil is hardly degraded, which means around 85 million hectares needs intervention.
Brazilian Midwest region (Mato Grosso, Mato Grosso do Sul and Goiás States) has the
largest pasture areas, cattle herds and slaughter rates (Figure 9). However, São Paulo State is also
important, being responsible for 13.4% of slaughtered heads in 2015 (IBGE, 2016b). The State also
has 4.8% of the Brazilian total herd (MAPA, 2015), and 3.1% of total Brazilian pasture area
(BEEFPOINT, 2016). Most of cattle is São Paulo is raised in semi-intensive systems and finished
in feedlots (EMBRAPA, 2011a).
Figure 9: Largest States in Brazil considering cattle herd (million heads), pasture land (million
hectares) and slaughter (million heads) in 2015
Source: BRASIL (2015b), IBGE (2016) and BeefPoint (2016)
There are 3 types of cattle enterprises in Brazil: cow-calf, backgrounding and stocker,
and finishing. It is possible to have the complete cycle that includes those 3 enterprises. Usually
calves and heifers are raised in pasture and fattening can occur in pasture or feedlots. In complete
cycle, cows represent 35% of the herd and calves 20% of the herd. The higher herd productivity,
the higher calves representation will be (EMBRAPA, 2011a).
Brazil has different types of climate and soil and the 3 enterprises mentioned before
can be managed in different systems: extensive, semi-intensive and intensive, which can also be
intensive-irrigated.
29.2
22.4
4.5
20.4
14.4
3.4
23.9
14.7
2.8
21.8
15.1
3.0
20.8 20.7
2.6 3.0
Cattle Herd (Mhds) Pasture Land (Mha) Slaughter (Mhds)
Mato Grosso Mato Grosso do Sul Minas Gerais Goiás Pará São Paulo
34
One important point to consider about the decision for the management system is the
forage stocking capacity. Forage is a grassy, which can be native or planted in pasture land to feed
cattle. Forage ingestion per animal per day is around 2.5 to 3.0% of their live weight (EMBRAPA,
2011a). The forage quality impacts cattle stocking rate and it also varies significantly between wet
and dry seasons. Tropical forages produce 75% of their total stocking capacity during wet season
(November until April) and 25% during dry season (May until October) (EMBRAPA, 2011a).
Andropogon gayanys cv. Planaltina forage can handle 1.6 animals per hectare during wet season
and 0.5 per hectare during dry season. In the soils of the Cerrado biome, during dry season,
Brachiaria humidicola, Brachiaria decumbes and Brachiaria brizhanta cv. Marandu species of
forage handle 1 animal per hectare (EMBRAPA, 2011a).
Usually, in São Paulo State, forage can handle 2.8 animal units (450 kg of live weight)
per hectare during the wet season with an average daily gain (ADG) of 0.8 kg/hd; and 1:1 during
the dry season, with ADG of 0.4 kg/hd (EMBRAPA, 2011a). During dry season, cattle need feed
supplementation to avoid losing weight, or can be finished in feedlots.
Extensive management is characterized by native or cultivated pastures as exclusive
feed; almost no management; natural reproduction; low birth rate (SALOMONI, 1983). Semi-
Intensive management has a better productivity per hectare compared to extensive system. And
intensive management has technologies application; higher productivity and usually finish cattle
in feedlots.
According to EMBRAPA (2011a), in feedlots, the feed costs must be economically
feasible and feed formulation must respect 1.5 kg of fat or 60% grain maximum, due to Zebu breed
restriction to high grain diet formulation. Feedlots average size varies from 100 to 3,000 animal,
but “cattle hotels” can feed 70,000 heads per year (EMBRAPA, 2005b). “Cattle hotel” is a feedlot
business that charges daily rates according to initial weight and animal sex, in exchange of cattle
fattening.
In feedlots, finishing time varies from 60 to 110 days (240 days in premature system)
(EMBRAPA, 2005b). Average initial weight is around 350 kg (220kg premature system) and final
weight around 470 kg, aging 24 to 36 months (only 12 months in premature system) (EMBRAPA,
2011a). Cattle finished in feedlots are increasing year after year; from 2001 to 2015, there was an
35
increase from 2.06 to 5.05 million heads (ABIEC, 2016), which represent 13% of total heads
slaughtered (BEEFPOINT, 2016). In Table 1 there is a summary of Brazilian cattle management.
Table 1: Brazilian cattle management
Parameters Extensive Semi-Intensive Intensive Finished
Feedlot
Daily Intake (% LW) 2.0 – 2.5 2.0 – 2.5 2.0 – 2.5 2.0 – 2.5
Slaughter Age (months) 42 32 – 36 14 –24 12 – 24*
Daily Gain (kg/hd) 0.2 – 0.4 0.4 – 0.6 0.6 – 1.0 1.0 – 1.8
Stocking Rate (hd/ha) 0.3 – 1.0 1.0 – 2.0 2.0 < 4.0 –
Carcass Yield (%) 52.3 – 55.0 52.3 – 55.0 52.3 – 55.0 52.3 – 55.0
Productivity (kg/ha.y-1) 60 –120 240 – 600 600 –1200 <1200
Pasture Management Almost none Fertilization Fertilization,
irrigation –
Feed Pasture, Mineral
Salt Pasture, Feed Pasture, Feed Feed
Source: BOVIPLAN (2015), BeefPoint (2016) and EMBRAPA (2005b).
*Depending on initial age.
According to EMBRAPA (2011a), the majority of Brazilian cattle is born during dry
season, mainly in August and September and weaning happens from 6 to 7 months beginning in
March and April. The reproduction season is during rainy months from November to December,
ensuring best forage qualities for pregnant and weaning cows. Reproduction is mainly natural,
however there isn’t the genetic improvement provided by artificial insemination. Being born during
dry season avoids diseases as pneumonia and parasites such as infestation of ticks, flies and worms.
Brazilian slaughter age is decreasing. In 1997, 52.18% of cattle were slaughtered after
36 months, in 2007 this number decreased for 11.30% and in 2015, it was only 6.94% (ABIEC,
2016). Most of cattle have been slaughtered from 15 to 36 months, weighting 250kg of carcass
weight equivalent (CWE) (IBGE, 2016b).
According to BeefPoint (2000), until the year 2000, cattle production was losing space
for sugarcane due to lack of profitability, low stocking rate (around 0.7 animal units per hectare)
and low land remuneration mainly in São Paulo State.
36
5.1 Other Cattle Producing Countries
The United States, Argentina and Australia are among the largest beef producers in the
world and have integrated production systems with grain fed diets.
The United States has the largest beef and veal production in the world (USDA, 2016a).
There are around 48.7 million hectares used as pasture (USDA, 2013), which is always the primary
feed (FAO, 2011). Cattle is raised in extensive pastures in South and integrated to corn-ethanol
chain and finished in feedlots in the Midwest. The majority breed is British breeds (Bos Taurus)
and beef production is divided into three main enterprises: cow-calf, back grounding and stocker
and finishing (FAO, 2011a). The country production meets domestic consumption, with only 10%
of total production to exportation (USDA, 2016c). Among the largest beef producers, the country
has the highest slaughter weight: about 620 kg of live weight or 379 kg of CWE (USDA, 2017).
Australian beef production is divided in extensive pastures in the North, with Brahman
and derived breeds; and more intensified pastures in the South, using British breeds (USDA,
2016c). About 2/3 of Australian cattle is finished in pasture, because it is more economically
feasible than feedlots with grains. However, finishing in feedlots is a growing trend to meet Asian
demand for marbled meat (USDA, 2016b). Only 1/3 of cattle production is for domestic demand
and the rest is exported (USDA, 2016b). Slaughter weight is about 320-350 kg CWE; in the South
(intensive) it takes 2.0 to 2.5 years to achieve the slaughter weight and in the North (extensive) 4.0
to 4.5 years (FAO, 2009b).
In Argentina, both grass fed (pasture) and grain fed (feedlots) management are
common, however, finishing in feedlots is becoming more popular, representing about 70 to 80%
of slaughtered heads in 2015 (USDA, 2015). British breeds are common in the Central area of
Argentina and Brahman breeds in warmer areas (USDA, 2016b). Slaughter weight is around 320-
380 kg and production costs are 15% higher than Brazilian costs (USDA, 2016b). In Argentina,
85% of beef production is to meet the domestic demand. The country has one of the highest red
meat consumption of the world, 56 kg per capta per year (USDA, 2016b).
37
6 INTEGRATION MODEL: THE UNITED STATES
There is an integrated system working in synergy among corn, ethanol and cattle
producers (Figure 10) in the United States. The three markets are independent but have been
cooperating for more than a decade (CONROY et al., 2016a).
Figure 10: Corn ethanol and livestock integrated production in the US
Source: Elaborated by the author
The US meet their local demand of animal feed, the Distillers Grains with Solubles
(DGS), and also export. About 50% of DGS are produced for local demand, 25% exported to China,
Japan, South Korea and others and the other 25% to feed poultry and hog (IBC, 2014). Corn ethanol
and DGS production are connected and have experienced a significant growth since 2000.
However, the growth rate decreased after 2011 (Figure 11) maybe due to livestock saturation for
feed demand. Considering a future prediction, what happens if livestock sector gets completely
saturated of corn-ethanol by-products? What will be the new model for the United States?
38
Figure 11: US corn ethanol and DGS production along years
Source: RFA (2016a)
The integration with cattle production is possible because of corn ethanol by-products
nutritional value as animal feed, which have energy value 30% higher than corn grain (ERICKSON
et al., 2005).
DGS represent up to 40% (DM) of feed composition and provides higher daily gains
than diets with only corn (CONROY et al, 2016b). The proportion of DGS on cattle feed is defined
based on its price, which is influenced by corn price, transportation costs, domestic and
international demand (WATSON; LUEBBE; ERICKSON, 2016).
6.1 Corn Ethanol Production in the US
Ethanol market started in 1990 in the United States because of the Clean Air Act
Amendment (when ethanol should be blended to gasoline), plus tax reduction and credits to
producers (SPAROVEK, 2009).
Nowadays, the US is the largest ethanol producer in the world with 57.9 million liters
produced in 2016 (RFA, 2016a) and exports 5.4% to more than 50 countries (RFA, 2016b). The
ethanol industry is responsible for a great part of global animal feed, producing around 50 million
tons of feed annually (RFA, 2016b).
0
10
20
30
40
50
60
70
80
DGS (million tons) Ethanol (billion liters)
Future prediction
39
Ethanol and by-products are produced from corn through dry-grind and wet-grind
processes, being the first one more commonly used (USGC, 2012). In dry-grind, each corn bushel
(25.4 kg) produces about 10.5 liters of ethanol, 7.7 kg of DGS and carbon dioxide (IBC, 2014).
The whole corn grain is ground, ethanol is the main product and DGS are co-products. Corn goes
through moisture and contaminant analysis at the plant, grinding, slurry and liquefaction (Figure
12). The fermentation can happen through batch or continuous process. The two products of
distillation are anhydrous ethanol and whole stillage, that is centrifuged. In centrifugation, the
solids, fibers, proteins and fats are separated resulting in coarse solids and thin stillage. Coarse
solids are dried in different percentages according to market demand resulting in Distillers Grains;
the thin stillage is evaporated resulting in the Solubles. Mixing both Distillers Grains and Soluble
it is obtained the Distillers Grains with Solubles DGS, thereby used as animal feed (USGC, 2012).
According to the University of Nebraska and the Iowa State University Animal Science
Departments, DGS from dry-grind are divided in three types, varying moisture content:
Wet Distillers Grains with Solubles (WDGS): 30-35% DM, 130% energy content
compared to corn. The best co-product to feed cattle and it has the highest energy content among
the others. Mostly used in the United States, having better animal performance. However, it is too
perishable, hard to handle and store and it is not feasible to export.
Dried Distillers Grains with Solubles (DDGS): 90% DM, used mainly to feed dairy
cattle, poultry, swine and to exportation. It has more protein compared to Wet Distillers Grains
with Solubles (WDGS).
Modified Distillers Grains with Solubles (MDGS): 45-55% DM, less expensive than
DDGS, less perishable than WDGS.
Other by-products from corn such as stover, MOG (material other than grain) are used
as fiber source to animal feed during growth period.
40
Figure 12: Dry-grind process for corn ethanol production
Source: Adapted from Erickson et al. (2005)
In wet-grind (Figure 13), corn grain is separated in different fractions and there are
other products besides ethanol, such as corn oil and corn sugar. Co-products from wet-grind are
also used as animal feed, however in different composition and fat content than DGS (IBC, 2014).
41
Figure 13: Wet-grind process for corn ethanol production
Source: Adapted from Erickson et al. (2005)
According to IBC (2014), corn oil was not extracted some years ago, nowadays, about
85% of ethanol plants are extracting oil for the production of biodiesel and for the food industry.
This oil extraction decreases energy and fat content of corn ethanol by-products, which decreases
cattle performance.
42
7 REAL CASES OF ETHANOL-CATTLE INTEGRATION IN BRAZIL
Sugarcane ethanol and cattle integration using ethanol by-products as cattle feed was
common in Brazil during the 1980s and 1990s, primarily in São Paulo State, usually with feedlots
annexed to ethanol plants; there were around 120 cases (SPAROVEK; MAULE; BURGI, 2008).
Nowadays, due to competition for bagasse to bioelectricity production, this integrated system is
not so common as it used to be, however there are successful cases of integration still operating.
Vale do Rosário Mill and Usina Estiva mill are two real cases of integration still functioning in São
Paulo State.
The Vale do Rosário mill belongs to BIOSEV group, one of the largest sugarcane
processing plants in Brazil, and it is located in Morro Agudo city in the State of São Paulo. The
plant has an annual processing capacity of 6.5 million tons of sugarcane (BIOSEV, 2015). Their
feedlot functions as a hotel to finish cattle from other properties around the plant, properties which
are rented to produce more sugarcane. In 2010, 163 cattle producers finished about 20,000 heads
in this feedlot and, in 2008, it had been better: 30,000 heads (PORTALDBO, 2011). The feed using
80% of sugarcane by-products (in natura bagasse, hydrolyzed bagasse, molasses and wet yeast) is
about R$ 100 cheaper (per tonne) and more efficient than traditional ones, with an ADG of 1.6 kg
(BIOSEV, 2014). According to technical visits to Vale do Rosário plant, they have been doing this
integration for more than 25 years.
Usina Estiva mill has been integrating for about 18 years (USINA ESTIVA, 2015a).
The plant has an annual processing capacity of 2.8 million tons of sugarcane (USINA ESTIVA,
2015b) and the feedlot created in 1999 has 11,000 heads of static capacity and about 20,000 heads
per year. Integration happens by using feed to finish cattle composed by 50% of sugarcane by-
products: in natura bagasse, hydrolyzed bagasse, molasses and wet yeast, and using the manure as
crop fertilizer. Through this production model they have a better cattle performance (better average
daily gain), which is 15% higher than the national average, less 20% of the total cost and avoid
expanding 12,000 hectares per year necessary to produce the same amount of cattle without
integration. This extra area is used to produce more sugarcane (USINA ESTIVA, 2015a).
Besides real cases of integration, there are some studies regarding this integrated model
using ethanol by-products (molasses, dry or wet yeast, in natura and hydrolyzed bagasse) as cattle
feed.
43
Taube-Netto et al. (2012) optimized integrated production using mathematic models
based on data from real cases of integration and considering sugarcane ethanol by-products as cattle
feed. The study considers a 2-million-tonne capacity mil which used 10% of bagasse (in natura
and hydrolyzed) plus soybean and corn to feed cattle. There were 28,000 hectares to sugarcane
production used to restore degraded extensive pastures, with corn and soybean crop rotation and
29,988 hectares as pasture. The integration increased 51% meat production compared to non-
integrated system and improved profitability. In their study, 18 million hectares of degraded pasture
could be released to agriculture.
Sparovek (2009) considered different managements (beef and dairy cattle) and
different feed composition, mainly composed by sugarcane ethanol by-products (hydrolyzed
bagasse, in natura bagasse, vinasse, yeast, molasses, filter cake, molasses) and grains. In his study,
even considering a 25% net revenue of the sugarcane plant, the feed production was economically
feasible. In the Sparovek (2009) study the models considered a plant with 1 million tonne capacity
and 15,000 hectares of sugarcane. The investments to produce cattle feed were two bagasse
hydrolyzers and one feed mixer. The plant produces 40,000 tons of feed per year, around US$ 38
per tonne. According to his study, using bagasse to produce feed is more viable economically than
to produce energy. The limitations are feed market, plant capacity and feed perishability, having a
viable distance around 30 and 40km radius from ethanol plant to feedlot.
In the sugarcane ethanol-cattle integration model, not only biofuel is produced
(ethanol) but also feed (by-products for cattle), food (sugar for humans) and energy (electricity)
(Figure 14).
44
Figure 14: Production model of sugarcane ethanol and cattle integration
Source: Elaborated by the author
7.1 Potential of Sugarcane Ethanol By-Products as Cattle Feed
As in the United States, the sugarcane ethanol-livestock integration is only possible
due to ethanol by-products nutritional value and capacity to digest cellulose and hemicellulose of
ruminant animals. Bovine can digest and convert not only forage into nutrient, but also crop
residues, by-products from food, fiber and fuel production (NATIONAL ACADEMIES OF
SCIENCES, ENGINEERING AND MEDICINE, 2016). Considering this, cattle can be fed with
sugarcane-ethanol by-products.
Among potential sugarcane ethanol by-products to compose cattle feed are: bagasse,
yeast and molasses. Bagasse is a fiber source and has a proportion of 280 kg per tons of sugarcane
(CHIEPPE JR., 2012). In Brazil, sugarcane bagasse is burnt to produce electricity and it started
being used to produce second generation ethanol (BNDES; CGEE, 2008). To compose cattle feed,
bagasse needs to be pretreated to increase digestibility (BNDES; CGEE, 2008). Yeast is a protein
source, composed by 62% of protein; and molasses, which has high energy content, is produced in
a proportion of 40 to 60 kg per tonne of sugarcane (CHIEPPE JR., 2012). Cattle producers have
been using sugarcane, sugarcane silage and ethanol by-products to supplement cattle during dry
season for a long time (EMBRAPA, 2002). Their nutritional value have been studied for many
authors: Sparovek; Maule; Burgi (2008), Lacorte; Bose; Ripol (1989), Magalhães; Vasquez; Silva
(1999); Ezequiel; Galati; Mendes (2006) and EMBRAPA (2002). In Table 2 the average nutritional
values of ethanol by-products from sugarcane and corn are presented.
45
Table 2: Sugarcane and dry-grind corn ethanol by-products
Source: CQBAL (2017) database, National Academies Of Sciences, Engineering And Medicine
(2016) and EMBRAPA (2011b)
7.2 Sugarcane Ethanol and Beef Cattle Integration in Brazil
Brazil can follow and adapt the United States integration model regarding ethanol and
cattle production. According to Sparovek; Maule; Burgi (2008), the integration is possible with
small adaptation in plants to hydrolyze bagasse and to fractionate part of sugarcane ethanol by-
products (bagasse, yeast, molasses) to compose the animal feed . The author also states this model
needs public intervention to support ethanol plants adaptation and collaboration, and acceptance
from cattle and sugarcane parts. The integration could provide feed during the dry season, thereby
increasing the stocking rate and land use intensification (SPAROVEK, 2009).
Sugarcane ethanol and cattle integration can avoid ILUC emissions linked to biofuels
production because of its potential as cattle feed. The integration allows feeding cattle heads in
feedlots, thereby avoiding pasture displacement to other areas. The released pasture area is used to
Feed DM
(%)
TDN
(%)
FAT
(%)
CP
(%)
NDF
(%)
ADF
(%)
Lignin
(%)
Sugarcane Ethanol By-Products
Hydrolyzed
Bagasse 40.32 54.62 0.61 1.37 60.48 55.53 12.88
In Natura Bagasse 57.17 42.89 1.19 2.14 85.22 58.02 12.65
Dry Yeast 89.00 80.00 1.50 34.00 1.00 - -
Wet Yeast 23.00 80.00 1.50 34.00 1.00 - -
Molasses 74.00 5.00 72.00 - 80.00 - -
Corn Ethanol By-Products
MDGS 47.83 93.0 10.22 29.08 28.73 14.81 -
WDGS 31.44 98.0 10.84 30.63 31.52 15.27 4.70
DDGS 89.99 89.0 10.73 30.79 33.66 16.17 -
Solubles 30.89 98.0 16.85 18.94 4.71 3.81 -
46
produce additional sugarcane. Integration can also be a new option for the sugar industry,
diversifying its product portfolio.
Considering its distance from the Amazon, São Paulo State in particular is well-
positioned to apply this integrated model (Figure 15) to decrease pressure on the forest from cattle
production. In addition, the State is the largest sugarcane producer in the country, 52.3% of the
Brazilian production in 4.5 million hectares (CONAB, 2017); and already has an improved cattle
management with 10.3 million cattle heads in 5.2 million hectares (BEEFPOINT, 2016), so it is
possible to integrate both systems in a sustainable way. Both sugarcane plants and cattle herd
location are feasible for the integration (Figures 16 and 17). In Figure 17, the cattle herd location
is divided in EDAs (Office of Agricultural Defense of São Paulo) and not per municipality,
according to the methodology used by the Institute of Agricultural Economics (IEA).
According to Cortez (2016), this integrated model could also participate of a Clean
Trade Mechanism with Low Carbon Footprint, where products with higher GHG emission would
have a higher tax.
47
Figure 15: Location of Amazon forest, cattle herd and sugarcane plants in Brazil
Source: Made by the author based on CTBE (2017) and IBGE (2015)
48
Figure 16: São Paulo State mapping of sugarcane plants
Source: (BRASIL, 2015c)
Figure 17: São Paulo State mapping of cattle herd
Source: (IEA, 2016a)
49
8 METHODOLOGIES
RESEARCH SUPPORTED BY CTBE – BRAZILIAN BIOETHANOL SCIENCE
AND TECHNOLOGY LABORATORY, CNPEM/MCTIC THROUGH UTILIZATION OF THE
VIRTUAL SUGARCANE BIOREFINERY (VSB) FACILITY.
To analyze possible integrated scenarios it is necessary to simulate them using models
(POPP et al., 2016). For the sugarcane ethanol and beef cattle integrated scenarios studied here,
simulations were performed with the Virtual Sugarcane Biorefinery (VSB) (BONOMI et al., 2016).
The VSB was developed by Brazilian Bioethanol Science and Technology Laboratory (CTBE), a
framework to simulate the agricultural, industrial and use phases of sugarcane production chain as
well as to perform economic, environmental and social assessment of biorefinery alternatives
(BONOMI et al., 2016).
The VSB was used to generate data from agricultural and industrial steps. The
agricultural steps include types of harvesting and planting, transport stages, agricultural operations,
machinery, implements, labor, agrochemicals, and fertilizers, among others. The industrial steps
include mass and energy balances to evaluate different technologies inside the biorefinery.
8.1 Data Collection
Data were collected from literature and from real cases of integration in São Paulo State
to define the scenarios simulated.
For the agricultural (Figure 18) and industrial steps (Figure 19) of sugarcane
production, data are from VSB database (BONOMI et al., 2016). For the cattle production, data
are from Anualpec 2015 (2015) and BOVIPLAN (2015). The cattle emissions data are from IPCC
(2006), Figueredo et al. (2016) and Ecoinvent database. More details of economic and
environmental inputs for cattle production are in Annex 1.
50
Figure 18: Sugarcane agricultural steps diagram
Source: Bonomi et al. (2016)
Figure 19: Ethanol plant industrial steps diagram
Source: Bonomi et al. (2016)
51
8.2 Scenarios Definition
Boundary Conditions:
All scenarios consider the same 200,000 hectares of production in São Paulo State
(legal reserve and sugarcane seedlings area were not included).
➢ Sugarcane: all scenarios consider 4Mt sugarcane milling capacity and 1G ethanol production
in annexed plants, inside 50,000 hectares. The plants use 50% of sugarcane juice for sugar
production and 50% of sugarcane juice plus molasses for ethanol production. The industrial
and agricultural steps are the same, varying only by-product’s splits in the plants with feed
production. The agricultural scenarios consider 50% of straw recovery (CARDOSO et al.,
2018) that is burnt together with bagasse to produce electricity. Sugarcane productivity
considered is 80 t/ha.y-1 and 200 days of season. The plant without feed production produces
53.5 l ethanol/tc and 51.4 kg sugar/tc. The plant with feed production produces 53.2 l
ethanol/tc and 51.4 kg sugar/tc. For feed production, hydrolysis of bagasse is steam
explosion, carried out at 200°C and 10 minutes of residence time.
➢ Cattle: Nelore breed, considering only fattening management. Initial weight is 360kg and
final weight is 480kg. When in pasture, the same stocking rate of 1 animal per hectare is
considered, ADG is less than 0.4 kg/hd.day-1 (BOVIPLAN, 2015) and slaughter time of 365
days. When in feedlots, it is considered 500 heads per hectare, with ADG around 1.0
kg/hd.day-1, ADI of 21.9 kg/hd.day-1 (CGEE, 2009a) and slaughter time of 120 days. The
feed is composed by ethanol by-products and others according to CGEE (2009a). Stocking
rate capacity is represented by “d” in Figures 20, 21, 22, 23, 24 and 25. Pasture area after
intensification is given by “Ap”. The relation between cattle area and sugarcane area is given
by the equation 1:
Equation 1: Relation between cattle area and sugarcane area
r = Ac/Api (1)
where:
Ac = Sugarcane Area
Api = Initial Pasture Area
52
Scenario 0 (Without sugarcane): It is defined as basis for comparison. Cattle is raised on an
extensive management, in 100% of the area (Figure 20), grazing all year, with mineral salt
supplementation.
Figure 20: Scenario 0
Scenario 1 (With sugarcane and ILUC): Sugarcane is introduced in 25% of area (Figure 21), and
25% of cattle heads are displaced for forest area. Considers ILUC emissions for 50,000 hectares,
accounted for ethanol plant. Cattle management keeps the same as in Scenario 0.
Figure 21: Scenario 1
Scenario 2 (1st step of integration, without ILUC): Those 50,000 heads displaced in Scenario 1
are back inside the 200,000 hectares (Figure 22) and finished in feedlot with feed composed by
ethanol by-products. Feedlot area represents only 0.05% of total area.
Figure 22: Scenario 2
Scenario 3: 50% of the total area is for sugarcane (Figure 23), now there are two ethanol plants
with 4Mt milling capacity each. The number of heads that would be displaced (100,000 heads) are
finished in feedlots with feed composed by ethanol by-products. Feedlot still is relatively small,
representing only 0.10% of total area.
53
Figure 23: Scenario 3
Scenario 4: 75% of the total area is for sugarcane (Figure 24) and there are three ethanol plants
with 4Mt of milling capacity inside the 100,000 hectares. The 150,000 heads that would be
displaced are finished in feedlots with ethanol by-products feed. Feedlot area represents 0.15% of
total area.
Figure 24: Scenario 4
Scenario 5: 100% of pasture area is used for sugarcane production (Figure 25). All cattle heads
are finished in feedlots with feed composed by ethanol by-products. There is no more pasture land.
Even with all cattle heads finished in feedlots, feedlot area represents only 0.20% of total area.
Figure 25: Scenario 5
Table 3 presents a summary of the 6 scenarios defined according to the specification
reported above.
54
Table 3: Scenarios definition
Scenario
0
Scenario
1
Scenario
2
Scenario
3
Scenario
4
Scenario
5
Number of plants - 1 1 2 3 4
Milling capacity (Mt) - 4.0 4.0 4.0 4.0 4.0
Straw Recovery (%) - 50 50 50 50 50
Total Sugarcane
Area (10³ha) - 50 50 100 150 200
Pasture heads 200,000 200,000 150,000 100,000 50,000 -
Stocking Rate (hd/ha) 1.0 1.0 1.0 1.0 1.0 1.0
Pasture Slaughter
time (days) 365 365 365 365 365 -
Mineral Salt (%LW) - 0.1 - - - -
Feedlot heads - - 50,000 100,000 150,000 200,000
Feedlot slaughter
time - - 120 120 120 120
Feed (kg/hd.day-1) - - 21.91 21.91 21.91 21.91
Carcass Yield (%) 53 53 53 53 53 53
LW: Live Weight
The ethanol plants of this work are based on an optimized distillery used as a baseline
in the VSB (Table 4) and adapted to separate a portion of the by-products (bagasse, molasses and
bleed yeast) to produce animal feed in scenarios 2, 3, 4 and 5. Hydrolyzed bagasse is obtained
using steam explosion with 200°C and 10 minutes of residence time.
Table 4: Ethanol plant parameters
Parameter Optimized Distillery (VSB base)
Operation (days) 200
Processing capacity (106 TC. y -1) 4
Sugarcane cleaning Dry
Mill engines Electric
Surplus bagasse use Burnt for electricity
Straw recovery (bale) (%) 50
Fermentation efficiency (%) 90
Wine ethanol content (g.L-1) 80
Dehydration Molecular sieves
Source: Bonomi et al. (2016)
55
Cattle feed composition considered in this study is presented in Table 5. This feed is
used to finish cattle in the scenarios with feedlots.
Table 5: Feed formulation for feedlot managements
Ingredients %DM Daily Consumption* (kg/hd)
Hydrolyzed bagasse 50.47 11.93
In Natura bagasse 4.23 0.90
Wet yeast 10.58 4.89
Molasses 2.75 0.39
Corn grain 20.95 2.53
Soybean bran 7.61 0.91
Urea 0.77 0.08
Mineral salt 2.64 0.28
Rumensin 0.00 0.00
Total 100.00 21.91
Source: (CGEE, 2009a)
*wet basis
8.3 Economic Evaluation
The economic evaluation considers a greenfield project (when the project starts from
scratch, with no previous constructions, buildings, investments) using as reference data December
2016, considering a depreciation of 10 years linear, 25 years of expected lifetime and discount rate
of 12% per year (Table 6). Products costs and prices are calculated based on IPCA of December
2016, considering moving average of the market prices from the last 10 years; equipment costs are
updated based on IGPM. The employees are an estimative based on VSB database, real cases of
integration and on the Anualpec 2015 (2015) approach. The capital and operating costs are based
on VSB approaches for ethanol production; based on the Anualpec 2015 (2015) for cattle
production and based on Sparovek; Maule; Burgi (2008) data for feed production. Pasture land
rental cost was estimate based on CONAB (2010) approach for rental, which considers 3% of land
price. Sugarcane land rental cost was estimate based on IEA (2017).
The economic parameters of evaluation are the internal rate of return (IRR), net present
value (NPV) and payback time (PT). These approaches rely on cash flow analysis, which depends
on the data collection on capital expenditures (investment in buildings, equipment, land, herd,
working capital, etc.); on revenues (prices of main outputs such as ethanol, sugar, electricity, beef
56
cattle, milk and others); and on operating costs (expenditures associated with feedstock, labor,
maintenance, chemicals, utilities, feed, etc).
Net present value (Equation 2) is the difference between cash inflows and the present
value of cash outflows. It indicates how much the investment adds to the business, if the NPV is
negative, the project is not feasible (BONOMI et al., 2016).
Equation 2: Net Present Value
NPV = ∑𝐶𝑛
(1+𝑟)𝑛𝑛=𝑁𝑛=0 (2)
Where:
n = number of time periods
r = discount rate
Cn = net cash inflow during the period n
Payback time is the period which is necessary for the profits to ‘pay’ the investment
costs (BONOMI et al., 2016). And the IRR is calculated with the Equation 3 and it is defined as:
IRR is the average interest rate paid per year by the evaluated project. The IRR of
an investment is the discount rate at which the NPV of costs (negative cash flows)
of the investment equals the NPV of the benefits (positive cash flows) of the
investment. In other words, IRR can be found when NPV equals zero (BONOMI
et al., 2016, p.159).
Equation 3: Internal Rate of Return
0 = ∑𝐶𝑛
(1+𝐼𝑅𝑅)𝑛𝑛=𝑁𝑛=0 (3)
Where:
n = number of time periods
r = discount rate
Cn = net cash inflow during the period n
57
Table 6: Assumptions for economic evaluation
Feed prices are estimated based on costs of external ingredients (corn grain, soybean
bran, mineral salt, urea and rumensin) and ethanol by-products. These costs rely on historical
market prices of the ingredients; for the by-products costs it was assumed the same cost of
sugarcane (Table 7).
Item Value Reference
Expected plant lifetime 25 years Assumption
Discount rate 12% per year Assumption
Reference date December 2016 Assumption
Depreciation 10 years, linear Assumption
Anhydrous ethanol price 1.696 R$ per L (CEPEA, 2017a)
Sugar price 1.263 R$ per kg (CEPEA, 2017b)
Electricity price 193.95 R$ per MWh (BRASIL, 2016) (CGEE, 2017)
Feed price 225 R$ per tonne Assumption
Unfinished meat price 4.73 R$ per kg (AGROLINK, 2017a)
Finished meat price 4.40 R$ per kg (AGROLINK, 2017b)
Sugarcane land rental
price 998.50 R$ per ha (IEA, 2017)
Pasture land rental price 514.37 R$ per ha (ANUALPEC, 2015)
1 US$ (December 2016) 3.38 R$ (UOL Economy, 2017)
58
Table 7: Estimate of feed final price
Ingredients kg/season R$/kg Description Reference
Total Cost (R$) % of total
price
Hydrolyzed
Bagasse 71,580,000.00 0.08
Sugarcane
cost
Assumption 5,726,400.00 19.36
In Natura
Bagasse 5,400,000,00 0.08
Sugarcane
cost
Assumption 432,000.00 1.46
Wet Yeast 29,340,000.0 0.08 Sugarcane
cost
Assumption 2,347,200.00 7.93
Molasses 2,340,000.00 0.08 Sugarcane
cost
Assumption 187,200.00 0.63
Corn
Grain 15,180,000.00 0.60
Historical
price
(MERCADO,
2017a) 9,108,000.00 30.79
Soybean
Bran 5,460,000.00 1.18
Historical
price
(MERCADO,
2017b) 6,442,800.00 21.78
Urea 480,000.00 3.34 Historical
price
(IEA, 2016a) 1,603,200.00 5.42
Mineral
Salt 1,680,000.00 2.21
Historical
price
(IEA, 2016b) 3,712,800.00 12.55
Rumensin 17,400.00 1.30 Historical
price
Assumption 22,620.00 0.08
Total 131,477,400.00 0.22 - 29,582,220.00 100.00
8.4 Life Cycle Assessment
The Life Cycle Assessment is a methodology to evaluate environmental impacts of a
production chain or a product using the pre-defined steps of production (Figure 26). More details
are found in the 14040 and 14044 ISO standards (ISO, 2006). Moreover, in Brazil, there is also
ABNT standards for LCA: ABNT NBR ISO 14040 (ABNT, 2009) and ABNT NBR ISO 14044
(ABNT, 2009b).
59
Figure 26: Life Cycle Assessment Methodology
The goal of this assessment is to evaluate the CO2 eq emissions of the integrated
scenarios using MJ of ethanol as functional unit, considering agricultural and industrial steps and
emissions (Figure 27). The system has a cradle to gate approach. Economic allocation was
considered for the products of the sugarcane plant based on market prices of ethanol, sugar,
electricity and opportunity costs of feed ingredients. Meat is the only product of cattle production
chain, so there was no need of economic allocation. The Climate Change category from ReCiPe
Midpoint (H) method was assessed for all outputs.
60
Figure 27: Sugarcane ethanol and cattle integrated chain for LCA
SOURCE: (BONOMI et al., 2016)
LCA Details
Sugarcane and ethanol plant inputs and emissions are from VSB database. Cattle
emissions were from IPCC (2006) divided as following: enteric fermentation: 56 kg/hd.y-1;
manure: 1 kg/hd.y-1; manure management: 0.59 kg/hd.y-1. Dolomite emissions were included
according to IPCC (2006) for lime and urea application.
The results for sugarcane production considers LUC emissions of 100% expansion on
pasture land, using 0.17 tCO2 eq/ha.y-1 (CHAGAS et al., 2016). Scenario 1 considers an addition
of 11.8 gCO2 eq/MJ ethanol, accounted as ILUC emissions based on European Commission
(2015b) methodology.
61
9 RESULTS AND DISCUSSION
The results for the scenarios production of meat, ethanol, sugar, electricity and feed are
summarized in Table 8. Scenario 5 presents the highest production values for ethanol, meat, sugar,
and bioelectricity all inside 100,000 hectares. The ethanol plants from Scenario 1 and from
Scenario 2 are different. This difference is due to feed production in Scenario 2; this scenario uses
7.3% of bagasse, plus steam for hydrolysis, thereby decreasing bioelectricity production 45.84
GWh per year, or 6.2% of the annual bioelectricity production in Scenario 1. Also, 1.8% of
molasses is used in Scenario 2 to produce cattle feed, which decreases annual ethanol production
by 806.4 tons. In Scenario 3, Scenario 4 and Scenario 5 the ethanol plants are the same from
Scenario 2, the only difference is the number of plants in each scenario.
Cattle finished in feedlots emit less CO2 eq compared to extensive management. This
difference in CO2 eq emissions between extensive management and feedlots generates carbon
credits for the cattle sector.
Table 8: Production results of scenarios simulation
Description Meat LW
(t/y)
Ethanol
(t/y)
Sugar
(t/y)
Electricity
(GWh/y)
Feed
(t/y)
Carbon
Credits (t/y)
SCENARIO 0 96,000 - - - - -
SCENARIO 1 96,000 169,176 205,536 741 - -
SCENARIO 2 96,000 168,370 205,536 696 137,134 45,676
SCENARIO 3 96,000 336,739 411,072 1391 274,268 91,352
SCENARIO 4 96,000 505,109 616,608 2087 411,403 137,028
SCENARIO 5 96,000 673,478 822,144 2783 548,537 182,704
Total capital investments for each scenario are in Table 9. For cattle production, the
necessary investments are fences, concrete troughs and other infrastructure (Table 9). Investment
in cattle production decreases from Scenario 0 to Scenario 5 because there are less land area in
feedlots, consequently fewer fences and concrete trough.
62
Capital investments for ethanol plant with integrated feed production (Scenarios 2 to
5) and without animal feed production (only Scenario 1) are detailed in Table 10. The difference
between capital investments for ethanol plant with and without animal feed production are due to
higher investments costs with generation and distribution of steam and electrical energy in the one
without animal feed production. When 7.3% of bagasse is split for feed production, less energy and
steam is produced, consequently there are less investment costs. Furthermore, investment for feed
production is only 0.26% of the total ethanol plant investment.
Table 9: Total investments for each scenario
Description Ethanol Plant
(MR$)
Feed
Production
(MR$)
Pasture
(MR$)
Feedlot
(MR$)
SCENARIO 0 - - 104 -
SCENARIO 1 1,185 - 104 -
SCENARIO 2 1,163 3 78 11
SCENARIO 3 2,326 6 52 22
SCENARIO 4 3,489 9 26 33
SCENARIO 5 4,652 12 - 44
Table 10: Investments for ethanol plant
Description With feed – MR$ Without feed – MR$
Auxiliary buildings, urbanization and general 156 156
Reception and preparation of sugarcane 42 42
Juice extraction 83 83
Juice treatment and concentration 70 70
Sugar production 60 60
Fermentation (C12 / C6) 44 44
Ethanol production 106 107
Generation and distribution of steam 323 336
Generation and distribution of electrical energy 214 223
Water and compressed air system 64 64
Animal Feed 3 -
Total 1,166 1,185
63
For economic evaluation, the results are presented separately for the ethanol plant and the
cattle production operation.
For the ethanol plants, the plant with integrated feed production (Scenario 2) gave similar
results compared to the plant without feed production (Scenario 1). For better economic results,
more attractive feed prices than those considered in this study (R$ 225.0 per tonne of feed) would
be required. Total NPV and total annual profit for Scenarios 2 to 5 increases proportionally to the
number of plants in each scenario (Table 11), considering the ethanol plants are the same.
Table 11: Economic results – ethanol plant
Description NPV (MR$) IRR (%) PT (Years) Annual Profit (MR$)
Scenario 1 544.3 17.6 8.5 369.5
Scenario 2 537.6 17.7 8.3 369.0
Scenario 3 1,075.2 17.7 8.3 738.0
Scenario 4 1,612.8 17.7 8.3 1,107.1
Scenario 5 2,150.4 17.7 8.3 1,476.1
The revenue from ethanol plant with integrated feed production (MR$ 785.7) is higher
than the revenue from the plant without animal feed production (MR$ 766.8). Feed is an additional
product and has a participation of 3.8% of total ethanol plant revenue (Table 12). This participation
could increase if considering the production of feed for more than 50 thousand heads (number of
cattle heads considered in this study).
Table 12: Products participation on total revenue of ethanol plant
Description Without Feed Production With Feed Production
Sugar (%) 33.9 33.0
Feed (%) - 3.8
Ethanol (%) 47.4 46.0
Electricity (%) 18.8 17.2
64
In Table 13 it is presented the allocated costs of production for each ethanol plant’s
products. The integrated feed production keeps the costs of ethanol and sugar constant and
increases the costs of electricity production compared to the plant without animal feed production.
Table 13: Allocation costs for ethanol plant products
Description Ethanol (R$/kg) Sugar
(R$/kg) Electricity
(R$/MWh) Feed
(R$/kg)
Scenario 0 - - - -
Scenario 1 1.25 0.93 142.52 -
Scenario 2 1.25 0.93 143.12 119.34
Scenario 3 1.25 0.93 143.12 119.34
Scenario 4 1.25 0.93 143.12 119.34
Scenario 5 1.25 0.93 143.12 119.34
Economic results for cattle production are presented in Table 14. The area released
from pasture intensification can generate extra revenue for cattle sector. In this study, land rental
price considered is R$ 484.13/ha. This value is the difference paid by the cattle sector for land
rental and the price sugarcane plants pay for released area, considering the land owner is a third
individual. The NPV is only economic feasible in Scenario 4 and Scenario 5. The last one is totally
integrated and presents the best economic feasibility.
Table 14: Economic results – cattle production considering land rental revenue of released
pasture area for sugarcane production
Description NPV
(MR$)
IRR
(%)
Payback
(Years)
Annual
Profit
(MR$)
Annual
Revenue
(MR$)
Annual
Costs
(MR$)
Meat
(R$/kg)
Scenario 0
and 1 -390.49 * > 100.00 -46.11 422.40 468.51 5.03
Scenario 2 -224.57 * > 100.00 -21.94 446.61 468.55 5.00
Scenario 3 -58.64 -1.59** > 100.00 2.23 470.81 468.58 4.99
Scenario 4 74.47 24.51 5.28 26.40 495.02 468.62 4.97
Scenario 5 199.30 47.59 1.83 50.57 519.23 468.66 4.95
*The IRR is not calculated because the cash flow starts negative (due to investments costs) and remains
negative in the following years of the project (due to costs higher than revenues).
65
**The IRR is calculated and it is negative because the cash flow starts negative and changes to positive
during the following years of the project. However, the positive flows are small and do not pay the initial
investments.
With integration, it is also possible to have extra revenue from carbon credits generated
by cattle finished in feedlots. The carbon credits revenue is assumed to be US$ 90/tonne of CO2 eq
according to Low Carbon Fuel Standard for the State of California for December 2016
(CALIFORNIA ENVIRONMENTAL PROTECTION AGENCY, 2017). The carbon credits
revenue increases the economic feasibility of integration (Table 15).
Table 15: Economic results – cattle production considering land rental revenue of released
pasture area for sugarcane production and C credits revenue
Description NPV
(MR$)
IRR
(%)
Payback
(Years)
Annual
Profit
(MR$)
Annual
Revenue
(MR$)
Annual
Costs
(MR$)
Meat
(R$/kg)
Scenario 0
and 1 -390.49 * > 100 -46.11 422.40 468.51 5.03
Scenario 2 -137.19 * > 100 -7.96 460.58 468.55 5.03
Scenario 3 79.68 22.89 5.93 30.18 498.77 468.58 5.00
Scenario 4 269.54 47.75 1.82 68.33 536.95 468.62 4.99
Scenario 5 459.40 77.48 0.84 106.47 575.13 468.66 4.97
*The IRR is not calculated because the cash flow starts negative (due to investments costs) and remains
negative in the following years of the project (due to costs higher than revenues).
After these economic evaluation, it was noticed that scenarios with higher pasture area
are not feasible (Scenario 0, Scenario 1 and Scenario 2). That happens due to land rental costs
which are too high in São Paulo State and due to relatively low carbon credit revenues.
In Anualpec 2015 (2015) and in Barbieri; Carvalho; Sabbag (2016) pasture land rental
is not considered a cost. In this approach, the cattle owner can also own the land. With pasture
intensification the area released can be rented for the sugarcane plant and, then, generate extra
revenue for the cattle manager. Considering this approach of no land rental costs plus the revenue
of land rental for sugarcane plant, thereafter, better results were obtained for cattle production
(Table 16), when compared to the results considering land rental costs.
66
Table 16: Economic results - cattle production considering the cattle manager owns the land plus
revenue from land rental for sugarcane production
Description NPV
(MR$) IRR (%) PT (Years)
Annual
Profit
(MR$)
Annual
Revenue
(MR$)
Annual
Costs
(MR$)
Meat Cost
(R$/kg)
Scenario 0
and 1 178.57 28.19 4.18 56.76 422.40 365.64 3.96
Scenario 2 263.28 37.33 2.67 472.33 400.02 72.31 4.30
Scenario 3 396.43 52.31 1.58 522.25 423.99 98.26 4.52
Scenario 4 529.58 70.83 0.97 572.18 447.95 124.22 4.75
Scenario 5 662.72 95.62 0.60 622.10 471.92 150.18 4.98
In Table 17 the results for the economic evaluation considers the same approach from
Table 16, but including carbon credits revenue. The considered carbon credits revenue is US$
90/tonne of CO2 eq.
Table 17: Economic results - cattle production considering the cattle manager owns the land and
revenue from land rental for sugarcane production plus carbon credits
Description NPV
(MR$) IRR (%) PT (Years)
Annual
Profit
(MR$)
Annual
Revenue
(MR$)
Annual
Costs
(MR$)
Meat Cost
(R$/kg)
Scenario 0
and 1 178.57 28.19 4.18 56.76 422.40 365.64 3.96
Scenario 2 328.31 42.20 2.21 486.30 400.02 86.29 4.30
Scenario 3 526.48 61.75 1.22 550.20 423.99 126.22 4.52
Scenario 4 724.65 84.96 0.73 614.11 447.95 166.15 4.75
Scenario 5 922.82 115.04 0.44 678.01 471.92 206.08 4.98
Nonetheless, the costs of meat production in Scenarios 3 to 5 are higher than the meat
selling price (R$ 4.40), which turns the integrated system economically unfeasible if there is no
extra revenue from rental of released land area for additional sugarcane production or extra revenue
provided by carbon credits.
67
After purchase of unfinished heads, land and feed costs (mainly corn and soybean bran)
are important factors for economic feasibility of cattle production in São Paulo State. In Table 18
the results are based on the approach of pasture land rental as a cost (R$ 514.37/ha).
Table 18: Participation on cattle production total cost
Description % unfinished heads % land price % feed
Scenario 0 and 1 70.5 21.3 -
Scenario 2 70.9 16.1 6.2
Scenario 3 71.1 10.3 12.3
Scenario 4 71.4 5.4 18.6
Scenario 5 71.7 0.1 24.9
Concerning land rental and feed costs participation on the total cost of cattle
production, some sensitivity analyses were performed considering the minimum land rental, corn
and soybean bran prices in the last 20 years as they were the currently price.
Land use and climate change concern have major role in the world’s future.
Furthermore, the trend for land price is to keep increasing due to competitivity in agricultural
production and land use intensification. Therefore, another sensitivity was done considering future
land price, future carbon credit revenue and future feed prices. Future land price is an estimate
based on the growth rate of the last 20 years; feed estimate price is based on the highest corn and
soybean bran prices on the last 20 years; and carbon credits price is an assumption.
Another analysis was performed without considering extra revenue from rental of
released pasture are for sugarcane production, in order to analyze if without this extra revenue
integration is economically feasible. This sensitivity considers the cattle producer owns the land
(no pasture land rental costs) as explained above.
The detailed results are in Annex 1. In Table 19 there is the IRR of each scenario
considering each sensitivity detailed above.
68
Table 19: Economic sensitivity for cattle production (IRR)
Description < rental price¹ < rental and
feed price² 20 years future³ Owns the land4
Scenario 0 and 1 21.5% 21.5% * 28.19%
Scenario 2 7.2% 14.3% * 15.94%
Scenario 3 * 9.7% * *
Scenario 4 -416.4%** -4.2%** -0.6% *
Scenario 5 * * 41.7% *
¹minimum pasture land rental price considers R$ 88.26/ha;
²minimum pasture land rental and feed price consider R$ 88.26/ha and R$ 146.42/t respectively;
³maximum pasture land rental and feed price and future carbon credits assumption, considers R$ 88.26/ha,
287.14/t and US$ 200.00/t respectively; 4no land rental costs, because the cattle manager owns the land, but there is no revenue from land rental for
sugarcane production
*The IRR is not calculated because the cash flow starts negative (due to investments costs) and remains
negative in the following years of the project (due to costs higher than revenues).
**The IRR is calculated and it is negative because the cash flow starts negative and changes to positive
during the following years of the project. However, the positive flows are small and do not pay the initial
investments.
Lower rental prices turn extensive management economically feasible, however the
scenarios with integrated management aren’t infeasible due to high feed costs. Considering future
predictions and land rental revenue for sugarcane production, integration is feasible and extensive
management is not. In short, for sensitivity results for current economic scenario, integration is
not feasible compared to extensive management due to high feed costs. For a future model of
production, integration can play a major role to meet world’s demand of both food and energy.
In regard to CO2 eq emissions, all scenarios include LUC emissions for ethanol
production with sugarcane expansion on 100% pasture land. The emission considered is 0.17 tCO2
eq/ha.y-1 (CHAGAS et al., 2016). From Scenario 0 to Scenario 1 emissions are higher because
there isn’t ethanol, sugar and electricity production in Scenario 0 (Table 20). The difference
between the emissions from Scenario 1 to 2 are due to ILUC emissions and also due to higher
amount of ashes from bagasse and straw burnt that are applied in sugarcane field, so there is more
diesel being burned and machinery being used. The ILUC emissions were calculated based on the
European Commission (2012b) and European Commission (2015b) data.
69
Their provisional estimate for ILUC emissions from sugar biofuels is 13 gCO2 eq/MJ.
This value considers a sugarcane yield of 96.7 tons per hectares and sugarcane only for ethanol
production. This value was adjusted for this work assumption of 80 tons of sugarcane per hectare
and 50% of sugarcane to produce ethanol. The adjusted ILUC emissions are 11.8 gCO2/MJ of
ethanol, or 119.142 tons of CO2 eq per hectare of sugarcane produced.
Total emissions are higher in Scenario 5 (Figure 28); however, in that Scenario there
are four times more ethanol, sugar, electricity and feed been produced compared to Scenario 2, all
in the same area, without land displacement, deforestation or compromising cattle production.
Which means it is possible to increase ethanol production without pasture displacement.
Table 20: Results of Climate Change emissions
Description Ethanol Plant (tCO2) Cattle production (tCO
2)
Scenario 0 - 1,291,599.1
Scenario 1 332,861.7 1,291,599.1
Scenario 2 198,674.7 1,245,923.0
Scenario 3 397,349.5 1,200,247.0
Scenario 4 596,024.2 1,154,570.9
Scenario 5 794,698.9 1,108,894.9
70
Figure 28: Total tCO2 eq emissions per scenario assessed
As the level of integration increases, emissions of cattle production decreases. It
happens because when finished in feedlots, cattle have lower emissions due to a shorter finishing
cycle and also to no fertilizers application (Figure 29). In pasture, the emission is 13.5 kgCO2 eq
per kg of meat (LW) produced; in feedlots this number decreases to 11.6 kgCO2 eq. In Ecoinvent
database, the emissions for global cattle for slaughter is 13.6 kgCO2eq, so the value calculated in
this work is inside literature average. Emissions for 1 hectare of sugarcane production are 3,416
kgCO2 eq against 6,458 kgCO2 eq from 1 hectare of pasture land.
Figure 29: Comparison of CO2 eq emissions per kilogram of meat produced in pasture and in
feedlot
13.5
11.6
Pasture Meat Feedlot Meat
71
In Figure 30 there is the comparison of CO2 emissions per MJ of ethanol produced.
The difference between the scenarios with and without integration was reported above. When
including ILUC emissions from the displaced pasture in Scenario 1, emissions for MJ of ethanol
produced increases in 11.8 gCO2 (Figure 30, ILUC).
Integration proposal is exactly not to have pasture displacement and ILUC emissions.
What if carbon credits generated by pasture intensification were applied to reduce ethanol
emissions from the integrated plant? The result is that emissions per MJ of ethanol produced
decrease by 9.6 gCO2 eq/MJ (Figure 30, “Integrated + avoided ILUC”). This decrease in ethanol
emissions is called “avoided” ILUC in this study.
Figure 30: Sensitivity of gCO2 eq per MJ of ethanol
▪ Non-Integrated: Emissions per MJ of ethanol produced in a plant without feed production
▪ Integrated: Emissions per MJ of ethanol produced in a plant with feed production
▪ Non-Integrated + ILUC: addition of 11.8 gCO2 eq/MJ of sugarcane bioethanol produced
on the non-Integrated plant
20.8 20.0
32.6
10.4
non-Integrated Integrated non-Integrated + ILUC Integrated + "avoided"ILUC
Ethanol Emissions (gCO2eq/MJ)
72
▪ Integrated + avoid ILUC: Carbon credits generated by pasture intensification are applied to
reduce ethanol emissions for the Integrated plant
The results of LCA are similar to the ones in literature, when considering LUC
emissions, the results from European Commission (2012b) are 20.2 gCO2 eq/MJ for sugarcane
ethanol. And when considering ILUC emissions, the results from European Commission (2012b)
are 33.0 gCO2 eq/MJ for biofuels.
Comparison of total emissions of ethanol plant per scenario simulated with the
sensitivities reported above (Figure 30) is presented in Figure 31.
Figure 31: Total CO2 eq emissions (Mt) for the ethanol plant per scenario with “avoided ILUC”
sensitivity
*Scenario 1 isn’t integrated and there is no carbon credits generation
In Table 21 there is a summary of this work results for economic and environmental
evaluation. The results for cattle production considers the approach of no land costs, plus revenues
from the land rental for sugarcane production and revenues from carbon credits. For ethanol plant
emissions, it is considered the addition of ILUC emissions for the ethanol plant without feed
production (Scenario 1) and the avoided ILUC emissions are not accounted for Scenarios 2 to 5.
332.9
198.7
397.3
596.0
794.7
-
153.0
306.0
459.0
612.0
SCENARIO 1 SCENARIO 2 SCENARIO 3 SCENARIO 4 SCENARIO 5
Baseline* "avoided ILUC"
73
Table 21: Economic and environmental results for sugarcane ethanol and pasture intensification
Meat
LW
(t/y)
Ethanol
(t/y)
IRR
(%)
Cattle
IRR
(%)
Ethanol
Ethanol
Plant*
(tCO2)
Cattle
production
(tCO2)
Carbon
Credits
(t/y)
gCO2/MJ
ethanol**
Scenario
0 96,000.0 - 28.2 - - 1,291.6 - -
Scenario
1 96,000.0 - 28.2 17.6 332.9 1,291.6 - 32.6
Scenario
2 96,000.0 169,176.0 42.2 17.7 198.7 1,291.6 45,676.0 20.0
Scenario
3 96,000.0 168,370.0 61.7 17.7 397.3 1,245.9 91,352.0 20.0
Scenario
4 96,000.0 336,739.0 85.0 17.7 596.0 1,200.2 137,028.0 20.0
Scenario
5 96,000.0 505,109.0 115.0 17.7 794.7 1,154.6 182,704.0 20.0
*considers ethanol, sugar and electricity production
** Scenario 1 considers ILUC emissions, Scenarios 2 to 5 considers Integrated plant emissions
The United States’ model for corn ethanol and cattle integration can be adapted to
Brazilian’s conditions and applied to sugarcane ethanol and cattle production chains. Integration
can increase ethanol production without expanding agricultural frontiers or pasture displacement
due to cattle stocking rate increase, pasture land use intensification and sugarcane ethanol by-
products as cattle feed ingredients.
The ethanol plant must be modified to direct part of the molasses, yeast and bagasse to
animal feed, but this is certainly technically feasible.
Due to sugarcane expansion on released pasture land, production of ethanol, sugar,
electricity and by-products production increases, without compromising cattle production. It is
worthwhile to mention that straw recovery, increasingly adopted by the Brazilian sugar-ethanol
sector (CARDOSO et al., 2018), plays an important role for compensating bagasse split, increasing
surplus electricity at the same time.
As in the United States, the by-products of sugarcane ethanol production along with
other feed components can replace forage (grazing), due to its nutritional value. Including by-
products in cattle diets enables increased cattle stocking rates, stabilizing the herd number and meat
production even during the dry season, when forages are only able to support fewer head.
74
Cattle feed composed of by-products has higher energetic and protein feed
characteristics, which provides a higher average daily weight gain and shorter cattle production
cycle compared to pasture fattening systems.
Currently, integration is economically feasible due to the revenue from rental of
released pasture area for sugarcane production. Feed and land rental costs are comparatively high
in São Paulo State, which makes the cattle production system economically unfeasible if no
revenue from land rental is considered. Integration has the extra advantage of potential revenue
from carbon credits. This extra revenue further increases the economic feasibility of integration.
However, commercialization of carbon credits is not yet widespread.
Environmentally, integration is desirable and avoids ILUC emissions estimated for
ethanol production by International Agencies. In our study, total ethanol plant CO2 eq emissions
are 40.3% lower in the integrated model compared to the non-integrated model. Furthermore, the
“avoided ILUC” approach suggested here decreases the total emissions per MJ of ethanol produced
due to pasture intensification. Considering avoided ILUC, the total ethanol plant CO2 emissions
are about 23.0% lower than the emissions of the integrated plant without avoided ILUC.
75
10 CONCLUSIONS
Sugarcane ethanol and beef cattle integration can be a new model of ethanol production
which avoid ILUC emissions generally linked to biofuels. The model can increase ethanol
production without compromising cattle production. This integration model also can avoid pasture
displacement or deforestation due to pasture land use intensification, where more animals are
finished inside smaller areas.
Besides, pasture intensification reduces emissions per kg of meat produced and these
can become carbon credits, a new source of revenue for the cattle sector. The carbon credits
generated by integration plus avoided ILUC, are called in this work “avoided ILUC”. This concept
can become a new method to reduce even more Brazilian emissions due to biofuels production and
use.
Integration is economically feasible for the cattle sector, only considering the revenue
from rental of released pasture area. On the ethanol sector feed production increases sugarcane
ethanol plant economic feasibility. In the future, with an established Carbon Market, integration
can become the new model of beef and ethanol production.
Integration has potential to become Brazil’s new model of ethanol production and it
will allow ethanol expansion without depending on sugar production. The country has strong
potential to integrate sugarcane ethanol and beef cattle, mainly in São Paulo State, where most of
the sugarcane production is located. Integration is possible due to sugarcane ethanol by-products
nutritional value as cattle feed, that can replace grazing. Considering world Climate Change
concern and reduction of GHG emissions, integration has huge potential as new model of ethanol
production
However, more studies need to be done for a better understanding of the boundaries
of this new model of production.
76
SUGGESTIONS FOR FUTURE WORKS
One possible limit for the expansion of the integrated model is the saturation of beef
market, regarding the current experience of integration in United States. For future studies, it is
suggested to consider future predictions of meat consumption and demand.
Enteric fermentation can change according to the type of feed. It is suggested to
consider effects of sugarcane by-products on the cattle enteric fermentation emissions and also on
the manure emissions.
77
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ANNEX 1 – Cattle Production and Economic Evaluation
The detailed inventory of economic inputs for 50,000 heads finished in feedlots and in pasture are
presented in Table 1.
Table 1: Economic inputs for cattle production
Pasture Feedlot Reference
Employees 125 125 (ANUALPEC, 2015)
INPUTS (R$)
Unfinished Cattle 85,140,000.00 85,140,000.00 (AGROLINK, 2017a)
Mineral Salt 39,780.00 - (INSTITUTO DE ECONOMIA
AGRÍCOLA - IEA, 2016)
Feed - 29,565,000.00
(MERCADO,2017a),
(MERCADO,2017b), (IEA,
2016) and VSB database
Vaccines 298,785.62 298,785.62 (ANUALPEC, 2015)
Deworming 199,386.47 199,386.47 (ANUALPEC, 2015)
Other 505,230.02 505,230.02 (ANUALPEC, 2015)
Pasture Cleanning 121,161.10 - (ANUALPEC, 2015)
Fertilizers/Lime 2,133,000.00 -
VSB database, (INSTITUTO DE
ECONOMIA AGRÍCOLA -
IEA, 2016) and (BOVIPLAN,
2015)
Land rental 25718500.00 51,437.00 (IEA, 2017)
MACHINERY (R$)
Diesel 404,880.00 9,638.11 (BOVIPLAN, 2015) and VSB
database
Machinery Services 295,256.66 - (ANUALPEC, 2015)
Administrative 768,137.84 - (ANUALPEC, 2015)
Others 721,673.15 1,066,923.47 (ANUALPEC, 2015)
Investments (Fences+
Concrete – troughs 26,653,663.02 10,950,851.43
(BOVIPLAN, 2015) and
(BARBIERI; CARVALHO;
SABBAG, 2016)
The detailed inventory of environmental inputs for 50,000 heads finished in feedlots and in pasture
are presented in Table 2.
87
Table 2: Environmental inputs for cattle production
Pasture Feedlot Reference
Dolomite (kg/ha) 60.00 - (BOVIPLAN, 2015)
SSP (kg/ha) 40.00 - (BOVIPLAN, 2015)
Stainless Steel (g/ha) 10.26 0.50 (BOVIPLAN, 2015)
Concrete (m³/ha) 0.00 2.75 (BOVIPLAN, 2015)
Mineral Salt (kg/hd) 0.36 - Assumption
Unfinished cattle (hd/ha) 360 180,000 Ecoinvent database
Feed (kg/ha) - 1,314,000.00 VSB simulation
Diesel (l/ha) 2.39 17.04 (BOVIPLAN, 2015) and
Assumption
CH4 - Enteric (kg/ha) 56.00 9205.48 (IPCC, 2006)
CH4 - Manure (kg/ha) 1.00 164.38 (IPCC, 2006)
N2O (kg/ha) 0.59 96.99 (FIGUEREDO et al., 2016)
Inputs (km) 200 200 VSB simulation
Feed (km) - 50 (SPAROVEK, 2009)
The detailed results for economic evaluation and sensitivities for cattle production are presented in
Table 3.
Table 3: Economic details for cattle production evaluation
Sensitivity NPV (MR$) IRR (%) PT (years)
ANNUAL
PROFIT
(MR$)
Scenario
0 and 1
2016¹ -390.49 * > 100 -46.11
< rental price² 96.45 21.5% 6.61 39.11
20 years prediction³ - - - -
Without rental4 178.57 28.19% 4.18 56.76
Scenario
2
2016 -430.16 * > 100 -54.82
< rental price -30.62 7.2% > 100 9.14
< rental price and
higher feed price5 -81.31 -5.6% > 100 0.97
< rental and feed
price6 17.42 14.3% 14.52 19.46
C 90 US$/tonne7 -189.46 * > 100 -16.33
C 200 US$/tonne8 -235.96 * > 100 -23.76
88
20 years prediction -569.12 * > 100 -77.05
Rental profit9 -278.81 * > 100 -30.62
Rental profit + C
Credits10 -137.18 * > 100 -7.96
Without rental 31.02 15.94% 11.58 22.38
Scenario
3
2016 -404.74 * > 100 -53.12
< rental price -137.78 * > 100 -10.43
< rental and feed
price -13.17 9.7% > 100 10.22
C 90 US$/tonne -229.96 * > 100 -25.17
C 200 US$/tonne -18.89 8.6% > 100 51.73
20 years prediction -306.76 * > 100 -37.46
Rental profit -102.04 * > 100 -4.71
Rental profit + C
Credits 47.40 18.9% 8.43 23.24
Without rental -82.48 * > 100 -1.59
Scenario
4
2016 -379.31 * > 100 -51.43
< rental price -245.30 -416.4%** > 100 -29.99
< rental and feed
price -51.62 -4.2%** > 100 0.98
C 90 US$/tonne -117.14 * > 100 -9.50
C 200 US$/tonne 145.90 34.0% 3.10 41.75
20 years prediction -44.41 -0.6%** > 100 2.14
Rental profit 50.25 20.9% 6.98 21.19
Rental profit + C
Credits 245.33 45.2% 1.97 63.12
Without rental -217.54 * > 100 -25.55
Scenario
5
2016 -353.89 * > 100 -49.73
< rental price -95.65 * > 100 -8.43
Feed 120 R$/tonne -25.26 3.3% > 100 2.83
C 90 US$/tonne -7.20 9.9% > 100 6.18
C 200 US$/tonne 310.70 61.6% 1.22 74.51
20 years prediction 158.19 41.7% 2.25 41.73
Rental profit 183.16 45.3% 1.96 47.10
89
Rental profit + C
Credits 443.26 75.9% 0.87 103.00
Without rental -352.60 * > 100 -49.52
¹prices and costs for December 2016
²minimum land rental price considers R$ 88.26/ha;
³maximum land rental and feed price and future carbon credits assumption, considers R$ 88.26/ha, R$
287.14/t and US$ 200.00/t respectively; 4no land rental costs; 5minimum land rental and higher feed price consider R$ 88.26/ha and R$ 287.14/t respectively; 6minimum land rental and feed price consider R$ 88.26/ha and R$ 146.42/t respectively; 7 carbon credits assumption considers prices and costs for 2016 and US$ 90.00/t; 8 carbon credits assumption considers prices and costs for 2016 and US$ 200.00/t; 9land rental revenue, considers R$ 484.13/ha; 10land rental and C credits revenue, considers R$ 484.13/ha and US$ 90.00/t respectively.
*The IRR is not calculated because the cash flow starts negative (due to investments costs) and remains
negative in the following years of the project (due to costs higher than revenues).
**The IRR is calculated and it is negative because the cash flow starts negative and changes to positive
during the following years of the project. However, the positive flows are small and do not pay the initial
investments.