Economic and environmental feasibility of beef production in different feed management systems in the Pampa biome, southern Brazil

Ecological Indicators 60 (2016) 930–939

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Economic and environmental feasibility of beef production in different feed management systems in the Pampa biome, southern Brazil Clandio Favarini Ruviaro ∗ , Jaqueline Severino da Costa, Thiago José Florindo, Whanderson Rodrigues, Giovanna Isabelle Bom de Medeiros, Paulo Sérgio Vasconcelos Administration, Accounting Sciences and Economics College, Federal University of Grande Dourados, Brazil

a r t i c l e

i n f o

Article history: Received 16 April 2015 Received in revised form 22 August 2015 Accepted 23 August 2015 Keywords: Net present value Intern return rate Annualized profitability index

a b s t r a c t The economic and environmental sustainability of beef cattle from pasture use and preservation in specific biomes is still not well evaluated. In this context, the study of the feasibility of beef production in the Pampa biome stands out because of its relevance in southern Brazil. Thus, this paper aims not only to know the amount of greenhouse gases emitted in different feeding management systems of beef cattle, but also to evaluate the economic and environmental feasibility of that production. Seven typical production systems in the region were considered, and it was aimed to determine which one would be the most viable in the environmental and economic perspective. To achieve this aim, the paper was developed in two stages: the first considers greenhouse gases emissions calculation in all systems and; the second uses some investment analysis tools, such as the net present value (NPV), the internal rate of return (IRR) and the annualized profitability index (API). According to the results obtained from system production VII it is possible to optimize low greenhouse gases emission of beef production with a significant economic return, under certain feed conditions. Furthermore, the results verified from system production II it is possible to obtain beef production increases without the need of new livestock areas, and contribute to the proper use and preservation of the Pampa biome. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction The Brazilian beef industry has been under pressure from national and international organizations because the global warming issues. Through estimates, it is argued that beef production is responsible for more than half of GHG emissions of the national agricultural sector (Ruviaro et al., 2014a). Besides, the Brazilian beef cattle industry can become one of the key sources of GHG mitigation and improve the Brazilian economy. (Latawiec et al., 2014). In 2013, Brazil had 211 million heads of cattle distributed on 160 million hectares of pastures. In the same year, 34 million heads were slaughtered, what corresponds to almost 27% of Brazilian agribusiness Gross Domestic Product (GDP). Furthermore, the country was the largest beef exporter, transacting approximately US$ 5359 billion, exceeding 2012, when the amount was of US$ 4495 billion, with an increase of 19.2% (USDA, 2014).

∗ Corresponding author at: Rodovia Dourados-Itahum KM 12, Caixa Postal 533, CEP 79825-070 Dourados, MS, Brazil. E-mail address: [email protected] (C.F. Ruviaro). http://dx.doi.org/10.1016/j.ecolind.2015.08.042 1470-160X/© 2015 Elsevier Ltd. All rights reserved.

Taking into account cattle importance to Brazil and the rest of the world, a relevant discussion on the environmental impacts of greenhouse gas emissions (GHG) by this sector emerges. GHG global emissions generated by livestock are equivalent to 7.1 gigatons of CO2 per year, what corresponds to 14.5% of all GHG generated by anthropic sources. From cattle total amount, 41% of GHG emissions are from beef cattle responsibility, indicating this activity importance for economy and its role in global climate changes (Gerber et al., 2013). Much of the research on GHG has as information source a methodology called product Life Cycle Assessment (LCA). Kramer et al. (1999) highlights how LCA methodology can contribute measuring agriculture impacts, considering an entire agricultural production chain. Ruviaro et al. (2012) used this method to measure beef cattle environmental impacts in the state of Rio Grande do Sul. However, solely to check which systems types emit more GHG is not sufficient because according to Nabinger et al. (2009), the farmer should receive some remuneration since the production is based on natural pastures which can be considered as an environmental service or a natural resources preservation. Wirsenius et al. (2011) points out that especially in developing countries, there is still

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substantial room for profitable agricultural activity improvement, thus contributing to GHG mitigation. Currently, due to global warming, it is necessary to examine ways to evaluate and compensate the farmer who opts for a more sustainable beef production management. Veysset et al. (2010) point out that economic and environmental assessments are inseparable in today’s farming. In this sense, the objective of this study is to assess seven beef cattle production systems in Southern Brazil, and point the system with the best economic viability, considering its GHG emissions. 1.1. Background The Pampa biome consists of very old grasslands, covering an area of 178,243 km2 and covering the whole Uruguay territory, a part of Argentina’s and about two-thirds of Rio Grande do Sul state in southern Brazil (Suertegaray and Pires Da Silva, 2009; Overbeck et al., 2007; Hasenack et al., 2007). A meadow mosaic, with small scrub vegetation areas and forests (Overbeck et al., 2009; Behling et al., 2009), characterizes its natural vegetation. Pampa biome main developed economic activity is extensive livestock farming, since its low fertility soils render agricultural production (Ribaski et al., 2010; Santos and Trevisan, 2009; Thurow et al., 2009). Livestock production is based mainly in Bos taurus breeds, such as Hereford, Aberdeen Angus, Simmental and Charolais (Latawiec et al., 2014), in native grassland with continuous and extensive animal grazing, consisting of more than 500 grasses species and 250 legume species (Carvalho et al., 2009; Boldrini, 2006). From the socioeconomic point of view, low extensive livestock productivity rates result in local economy minimal returns, causing low population density and lower development compared to other regions of the state, especially when comparing indexes, such as GDP per capita and job generation (Reis, 2009; Santos and Trevisan, 2009). Cattle grazing native pasture is seen as the main conservation tool, as it keeps Pampa biome flora and fauna diversity when preventing agricultural frontier advances (Brandão et al., 2012; Soussana, 2009). However, inappropriate livestock management in this region due to excessive grazing stocking rate during the winter, generates animal weight loss, as well as soil cover negative consequences, contributing to grassland degradation (Overbeck et al., 2009; Carvalho et al., 2009; Soussana, 2009). Brazilian beef industry has suffered pressure from national and international agencies concerned with global warming, through estimates that beef production is responsible for more than half of the national agricultural sector GHG emissions (Ruviaro et al., 2014a; Gianezini et al., 2014; Ruviaro et al., 2014b). According to Beauchemin et al. (2008), among greenhouse gases, the most important is methane (CH4 ). Livestock GHG emissions come from several processes, such as ruminants enteric fermentation (CH4 ) and animal waste (CH4 and N2 O – nitrous oxide) (Balbino et al., 2011). Enteric fermentation occurs through CH4 release during animal breathing or belching, due to microbial fermentation occurred in digestion (Cuéllar and Webber, 2008). Methane emissions by enteric fermentation are correlated with animal feed, and may considerably differ between animals subjected to the same feed (Wirsenius et al., 2011). In Brazil, approximately 70% of CH4 emissions come from cattle, resulting from energy contained in feed inefficient capture physiological process during their digestive process (Ruviaro et al., 2014b; MCT, 2010). Pastures proper management and animal feed quality improvement in Pampa biome native grasslands allow its conservation, farmers’ higher economic returns and GHG production reduce due ˜ et al., 2014; Balbino to decrease in animals’ grazing period (Becona

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et al., 2011; Carvalho et al., 2009). Wirsenius et al. (2011) point out that, in many parts of Europe, cattle and sheep grazing are extremely important for landscape and biodiversity conservation. However, the remuneration received by the farmer is based on natural pastures production and results only from animals’ sale, without any financial compensation for natural resources preservation (Nabinger et al., 2009). According to Latawiec et al. (2014), benefits implementation to producers that use sustainable and intensified grazing systems can generate a strong economic incentive to transition from an extensive livestock to intensive activity. According to Picasso et al. (2014), GHG emission estimations in Uruguay Pampa native grassland production systems are very high, with great potential for emission reduction. The combination of technologies, appropriate management and forage offer optimization can increase animals’ productivity and reduce allocated ˜ et al., 2014; Modernel et al., emissions per beef pound (Becona 2013; Soussana, 2009; Bencke, 2009). Consequently, it is expected that feed efficiency would correspond in reduced methane emissions, or vice versa (Åby et al., 2013; Beauchemin et al., 2008). In a recent study on cattle diet optimization in the United States, White et al. (2014) concluded that pasture production improvement through intensification and management reduced GHG emissions per produced unit. The implementation of rotational grazing techniques with varying intensities, strategic supplemental feeding, soil fertilization and correction, pastures fertilization with nitrogen and animals’ genetic improvement generate benefits from different perspectives (Wall et al., 2010; Bustamante et al., 2012; Bencke, 2009; Nabinger et al., 2009; Maraschin, 2009). According to the authors, these practices reduce animals’ fattening time, increase farmers’ financial return, reduce GHG emissions and preserve the Pampa biome. Thus, Strassburg et al. (2014) points out that sustainable intensification can combine increased agricultural production with natural environments conservation and restoration. This nature services practice by farmers, especially immediate need practices, such as GHG emissions reduction, open ways for a compensation process called Payment for Environmental Services (PES) (Martinkoski et al., 2013; Tornquist and Bayer, 2009). GHG emissions reduce or mitigation are actions that fall under the PES, generating carbon credits and allowing to remunerate who directly or indirectly preserves the environment (Peixoto, 2011; Tornquist and Bayer, 2009). Considering that this is a non-spontaneous market, first there is the need to identify and quantify what is the generated externality, who produced it and who benefits from such nature conservation practices (Martinkoski et al., 2013). Environmental services provided by livestock in native pastures in terms of atmospheric CO2 retention can be estimated by comparing soils organic C storage (Tornquist and Bayer, 2009). Cattle GHG emissions can be quantified and compared between different production systems by its production life cycle assessment (LCA) (Sanders and Webber, 2014; Beauchemin et al., 2010). However, LCA requires more complex appraisement, requiring the analysis to be extended at least to the farm’s gate (O’brien et al., 2014). LCA is an important methodology to assess potential impacts over a product life cycle, from raw materials acquisition to production, use and disposal, including raw materials extraction and processing analysis, as well as product manufacturing, transportation, distribution, use, reuse, maintenance, recycling and waste disposal (ISO, 2006; Guinée, 2001; Finnveden, 1999). LCA enables the identification of critical points to reduce environmental impacts within the supply chain, resource use forms comparison and different production technologies emissions (Pelletier et al., 2010). The LCA is regulated by ISO standards (ISO 14040:1997, ISO 14041:1999, ISO 14042:2000, ISO 14043:2000), where principles

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Fig. 1. Native Pasture location in southern Brazil: (a) South America overview (b) Brazil and Brazilian biomes official classification, according to (IBGE, 2004) and (c) Native Pasture distribution in southern Brazil (abbreviation of states: RS: Rio Grande do Sul; SC: Santa Catarina, PR: Paraná). Source: Overbeck et al. (2009).

and structure, scope definition and inventory analysis, life cycle impact assessment and interpretation are shown, respectively (ISO, 2006).

According to Table 1, seven feed systems were considered for beef cattle production.

2. Methodology

2.2. Investment analysis

2.1. Study area and environmental data source

Discussions about some compensation for beef cattle producers that try to reduce GHG emissions are still very incipient. According to Nabinger et al. (2009), remuneration received by the farmer is based only on natural pastures production and results only from animals’ sale, without any financial compensation from natural resources preservation. Taking into account the paradigm that arises between the international scientific community pressure for reducing GHG in beef cattle production and the lack of an instrument that would not only remunerate economically, but also reduce emissions, this study implementation was necessary. As it was highlighted, this study aim is to assess beef cattle production economic feasibility, in an attempt to compensate the producer that is concerned about reducing GHG emissions. The analysis was performed per animal and per hectare in order to better compare the systems.

In Brazil, there are six biome types: Amazon, Caatinga, Cerrado, Atlantic Forest, Pampa and Wetlands. The Pampa biome is part of Rio Grande do Sul state and occupies 63% of its territory (Fig. 1). Livestock is rearing in the extensive system, with natural and cultivated pastures, besides using variable stocking rates, accounting for the largest meat production in the region. Specific information used in this study come from results of research performed on GHG emissions in seven beef production systems at Rio Grande do Sul Pampa biome, which were carried out by Ruviaro et al. (2014a). The animal GHG emission data were calculated for 60 animals for each production system, totaling 420 observations collected from Angus animals during six years in Rio Grande do Sul western region - Brazil-Uruguay border (Table 1). Table 1 Beef production systems analyzed in the Rio Grande do Sul Pampa biome. Production systems

PS I PS II PS III PS IV PS V PS VI PS VII

Description

Native Pasture Improved Native Pasture + Ryegrass Native Pasture + Ryegrass Improved Native Pasture + Sorghum Ryegrass + Sorghum Native Pasture + Protein Supplement Native Pasture + Protein-energetic Supplement

Source: Ruviaro et al. (2014a). CF = calf; RE I = rearing I; RE II = rearing II; FT I = fattening I; FT II = fattening II. * AU – animal unit reference (weight = 450 kg).

Number of animals per hectare (AU* )

0.70 1.53 1.11 1.53 1.61 0.73 0.94

Interval from calving to fattening, days

CF

RE I

RE II

FT I

FT II

Total

180 180 180 180 180 180 180

150 180 150 180 180 150 150

180 150 180 125 142 180 180

150 – 159 – – 180 –

180 – – – – – –

840 510 669 485 502 660 510

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The calculation per hectare always used animals’ maximum capacity at each production systems stage, respecting pasture animal support capacity. This was reflected in a change in the numbers of animals per hectare among a same system stages. Thus, it would be necessary to account for each production stage separately, what may cause distortions in financial assessments. In order to meet the goal, animals’ acquisition cost was disregarded at all production systems stages. In the analysis per hectare, activity begins on day 0 of cow pregnancy, with costs related to land use (lease), feed and GHG emissions of each phase being assigned. The recipe consisted of multiplying the live weight gain per hectare (LWGH) in each stage by live weight trading price. To calculate production costs per animal, the value for the acquisition of a six months old weaned calf (end of the growing period) was considered at the beginning of Rearing I stage. The acquisition cost was calculated from the multiplication of animal weight at six months of age with the live kilogram price on the market, US$ 1.87 (CTPEC, 2014). This procedure was adopted due to properties being specialized in growing or fattening systems, which are usually held in different properties, in order to have a better regional representation. Costs were considered related to animal nutrition and land use. Land use costs (lease) were assigned according to the market price (September 2014) in the region, which was of US$ 107.76 per hectare/year (US$ 0.295/day) to native pasture lease (ANUALPEC, 2014). Market values of animals for slaughter (430 kg) differ in accordance with the animal age, being of US$ 2 per live weight kilogram for animals under 24 months old and of US$ 1.87 per live weight kilogram for animals that are over 24 months old. Emissions valuation was based on the GHG emission volume of each system, and its cost was appraised using the carbon credit price in the market. The valuation in this study was of US$ 8.83 per CO2 eq ton. GHG emissions monetary value comes as a cost in the production process. It was used the PS I as a parameter for calculating the benefits or penalties on emissions, because this is the production system most widely adopted in the biome pampa. b = (m − e)

(1)

where b = GHG benefit our penalization value; m = GHG valuation of system PS I; e = GHG valuation of the current system. For investment analysis, the net present value (NPV), internal rate of return (IRR) and the annualized profitability index (API) were used for each production system, in order to find the most viable system in the environmental and economic perspective. The net present value indicator, also called as NPV, is used to determine the present value of future payments discounted at an appropriate rate (Gitman et al., 2010).

 NPVt0 =

BVt0 +

 BFVt n (1 + i)

  n

−

CVt0 +

 CFVt n (1 + i)

 n

=0

(2)

where NPVt0 = net present value at the initial time. BVt0 = benefits value at the beginning of each scenario. In this case, there will be no benefit, only at the end, when the animal gets slaughter weight and is marketed. BFVtn = benefits future value, which would be represented by the obtained revenue value from the animal sale at the end of the production cycle. n = production system periods, represented by pregnancy, calf, rearing I, rearing II, fattening I and fattening II. CVt0 = production costs value at the beginning of each scenario. CFVtn = production costs future value, represented by the costs of each stage to slaughter. i = discount rate representing opportunity costs. In this study, the savings rate of 0.50% per month was used.

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The Internal Rate of Return (IRR) is a hypothetical discount rate that, when applied to a cash flow, causes costs and investments values to be taken to the present value, so that the net present value would be equal to zero (Gitman et al., 2010).

 NPVt0 =

BVt0 +



BFVtn

(1 + IRR)n

  −

CVt0 +



CFVtn

(1 + IRR)n

 =0 (3)

where NPVt0 = net present value at the initial time. BVt0 = benefits value at the beginning of each scenario. In this case, there will be no benefit, only at the end, when the animal gets slaughter weight and is marketed. BFVtn = benefits future value, which would be represented by the obtained revenue value from the animal sale at the end of the production cycle. n = production system periods, represented by pregnancy, calf, rearing I, rearing II, fattening I and fattening II. CVt0 = production costs value at the beginning of each scenario. CFVtn = production costs future value, represented by the costs of each stage to slaughter. IRR = internal rate of return. Regarding the annualized profitability index (API), this consists in separating the NPV value per project investment unit, throughout its lifespan, that is, it is a second-order derivative of the net present value, and aims to simultaneously solve the two NPV limitations: projects with different investments and terms (Franco and Galli, 2007). Its formula ⎡ is (Ross et al., ⎤ 2015):

n Rt −Dt  n t=1 (1+i)t ⎣   ⎦ . i(1+)n where (4)API =  i(1+) −1  n Dt   t=1 (1+i)t 

API = annualized

profitability index; n = number of periods, represented by each production scenario duration days (lifespan). Rt = cash inflows (revenues) expected during the t period; Dt = cash outflows (revenues) expected during the t period; i = interest rate or discount rate. To identify the best system among the seven beef cattle production systems, it was first necessary to calculate the weight gain per production system, always using the maximum productivity of each system, according to the phases of pregnancy, calf, rearing and fattening. Then, total GHG emissions by production system were presented. Finally, estimates were made through investment analysis technique application (NPV, IRR and API) to draw up a ranking between production systems on those that are more economically and environmentally viable. 3. Results 3.1. Weight gain by production system Beef cattle production systems productivity was compared per animal and per hectare. Systems had results variation due to alteration in feed quality provided to the animals in the evaluated systems, causing differences in weight gain per animal and animals’ support capacity per hectare. Table 2 shows data related to live weight gain per animal (LWGA) and live weight gain per hectare (LWGH). In the seven production systems, pregnancy phase was standardized in a grazing system on native pasture, which lasts 281 days, with LWGA of 32 kg and LWGH of 185.37 kg. This phase LWGA refers to the calf birth weight in the late pregnancy phase and the LWGH refers to 150 kg cows LWGA during pregnancy, which is multiplied by animals’ average capacity per hectare. Average capacity was calculated by dividing the animal support per hectare (414 kg) by cows’ average weight (335 kg), resulting in 1.24 animals/ha.

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Table 2 Live weight gain per animal and per hectare in each production system. Production systems I II III IV V VI VII

Pregnancy

Calf

LHGA

LWGH

LHGA

LWGH

Rearing I LHGA

LWGH

Rearing II LHGA

LWGH

Fattening I

32.00 32.00 32.00 32.00 32.00 32.00 32.00

185.37 185.37 185.37 185.37 185.37 185.37 185.37

133.00 158.00 133.00 158.00 148.00 188.00 188.00

141.60 235.27 141.60 235.27 255.84 200.15 200.15

30.00 140.00 30.00 140.00 130.00 40.00 40.00

65.25 415.56 65.25 415.56 409.50 65.25 65.25

85.00 100.00 85.00 100.00 120.00 100.00 170.00

156.22 180.59 156.22 180.59 222.57 140.81 215.09

LHGA

LWGH

45.00

58.24

150.00

316.58

70.00

69.38

Fattening II

Total

Total

LHGA

LWGH

LHGA

LWGH

105.00

121.41

430.00 430.00 430.00 430.00 430.00 430.00 430.00

728.09 1016.79 865.03 1016.79 1073.28 660.96 665.86

Source: adapted from Ruviaro et al. (2014a) data.

Among the seven systems, the production system I (Native Pasture), due to using only native pasture, which has low nutritional quality, has a longer period (1121 days) for an animal to be ready to be marketed with 430 kg (Table 2). During the calf period, pastures improvement by nitrogen fertilization and sorghum and ryegrass intercropping resulted in a higher animal rate per hectare in the PS II, PS IV and PS V systems, getting the best LWGH results. However, the low animal rate per hectare supported by natural pasture conditioned the worst results for PS I and PS III, which were related to both LWGA and LWGH. PS VI and PS VII systems extra supplementation had little influence on the increase in supported animal rate per hectare, although obtaining the best LWGA results. II, IV and V production systems had the best LWGH results during the necessary period for an animal to reach 430 kg, being higher than 1000 kg/hectare in all cases. It is also noteworthy that in the three mentioned systems along with PS VII, the animal got the slaughter weight still in the Rearing II phase, reducing the time required for slaughter. PS VII extra supplementation (proteinenergetic) obtained a 70% higher result than PS VI (protein) in the Rearing II phase, although having the worst LWGH results, due to low animal support per hectare. To calculate production costs per animal, the value for the acquisition of a six-month-old weaned calf (end of the growing period) was considered at the beginning of Rearing I phase (Table 3). The acquisition cost was calculated from the multiplication of animal weight at six months of age with the live kilogram price on the market (US$ 1.87). This procedure was adopted due to properties being specialized in growing or fattening systems, which are usually held in different properties, in order to have a better regional representation. Costs were considered related to animal nutrition and land use. PS VI and PS VII, with had the highest calf weaning weight at the end of the raising phase (6 months), had the highest acquisition costs, which were approximately 33% higher than PS I and PS III, with lower acquisition costs. The higher production intensification by pastures fertilization and intercropping with other forages (PS II, PS III, PS IV and PS V) caused higher production costs per animal when compared, due to higher animal feedstock investment. However, slaughter period

was reduced in more than one year when compared to PS I, which used natural pasture only. On the total production cost, PS I got the lowest production cost per animal (US$ 450.97), due to the low investment required for feed. The main cost in this production stage is related to land cost, which is attributed to the low animal capacity per hectare. This reflected in 840 days period for animal slaughter with 430 kg, up by approximately 73% compared to PS IV, with the shortest slaughter time, 485 days. Table 4 shows production costs per hectare between the respective systems stages, with costs related to land use (lease) and feed being accounted, using the stocking rate maximum capacity per hectare in each phase as a parameter. The little need of supplies for PS I production resulted in lower costs for animal feed. However, the longer period needed to slaughter an animal with 430 kg caused higher costs per hectare than the PS VI and PS VII. PS VII obtained higher costs at Rearing I and Rearing II stages compared to PS VII, but due to the higher weight gain per animal, it required a shorter period for slaughter, resulting in lower land use costs. PS II, PS III, PS IV and PS V systems highest yield per hectare, originated from feed with higher quality and/or quantity, reflected in higher production costs. Table 5 describes total GHG emissions by scenario and feeding phase, demonstrating each adopted management system environmental impact. To get the data in Table 5, weight gain per hectare collection was carried out (Table 2) in each production scenario during each phase, and multiplied by the total GHG emissions for each produced kilogram. Systems had the same emission per animal and hectare during pregnancy due to this phase standardization for all systems. Animals’ early slaughter at 17 months (Rearing II) provided lower GHG emissions per animal due to less need for land use, waste emissions and enteric fermentation, as shown in the PS II, PS IV, PS V and PS VII systems. PS V had the lowest emission to produce an animal with 430 kg that is ready for slaughter, 7.94 CO2 eq Ton per animal. Taking into account the animal weight of 430 kg, this is equal to 18.47 kg CO2 eq per kg of animal weight. When comparing emissions per animal and hectare of each system, systems are noted with both low animal and hectare emissions

Table 3 Production cost per animal (US$). Production systems

6 months calf weight kg

Price kg

Calf acquisition

Rearing I

Rearing II

I II III IV V VI VII

165 190 165 190 180 220 220

1.875 1.875 1.875 1.875 1.875 1.875 1.875

309.38 356.25 309.38 356.25 337.50 412.50 412.50

21.74 158.19 21.74 158.19 186.36 32.47 37.74

32.81 124.73 32.81 129.81 187.60 51.05 87.02

Source: Prepared by the authors from Ruviaro et al. (2014a).

Fatten. I 36.28 228.49

54.00

Fatten. II

Total

50.77

450.97 639.17 592.41 644.25 711.46 550.01 537.27

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Table 4 Production cost per hectare (US$). Production systems

Pregnancy

Calf

Rearing I

Rearing II

Fatten. I

Fatten. II

Total

I II III IV V VI VII

105.82 105.82 105.82 105.82 105.82 105.82 105.82

59.63 170.77 59.63 170.77 123.71 62.31 62.31

47.27 469.56 44.29 469.56 587.03 52.97 61.57

60.30 225.25 60.30 234.43 347.95 71.88 110.11

46.95 – 482.25 – – 53.52 –

58.71 – – – – – –

364.32 957.05 737.92 966.22 1150.15 332.13 325.44

Source: Prepared by the authors from Ruviaro et al. (2014a).

(PS VII), low emission per animal and high emission per hectare (PS II, PS III, PS IV and PS V) and high emission both per animal and hectare (PS I and PS VI). Systems that had low emissions in both evaluations were systems with low emissions per animal and low animal average capacity per hectare during production. Systems with low animal emission and high emission per hectare had a higher average animal capacity per hectare, with higher meat production and emission. Finally, systems that had high emissions in both assessments had low efficiency in livestock production and low animal capacity per hectare. 3.2. Greenhouse gases financial valuation GHG emissions currency valuation calculation for each production system was carried out by multiplying the result of Table 5 by carbon credit quotation value in the financial market (Table 6). For example, to get to the calculation of the System I, Pregnancy phase per animal value, was 2.09 tons of emitted CO2 eq (Table 5) was multiplied by US$ 8.83 per ton, coming to the value of US$ 18.46. Calculation was repeated for all scenarios. For PS II benefit calculation per animal, it was subtracted the emission of the production system of reference (PS I), which was 15.99 ton. CO2 eq (Table 5). The result was multiplied by US$ 8.83 per ton, reaching US$ 70.30. The same calculation was performed for the other scenarios. To calculate the commercial revenue, the total weight gain per hectare in each production system was considered, and sales value was calculated according to each system time variation, resulting in distinguished sale values on the market (Table 6). Considered values were of US$ 2 for animals aged below 24 months and US$ 1.87 for animals over 24 months. PS I got the lowest production cost per animal, US$ 450.97. However, it had the lowest total revenue between systems, due to its higher GHG emissions per produced beef kilogram, not getting benefits on reduced emissions and lowest price per sold beef kilogram. By analyzing costs per hectare, PS VI and PS VII had lower costs than PS I due to soil use lower costs (smaller grazing period for slaughter) and low feedstock use. In relation to total revenue per hectare, systems with higher production intensity had better results, resulting in a higher production per hectare during the necessary period for an animal to reach 430 kg. PS V had the best revenues both per

animal and hectare, what was attributed to fewer necessary grazing days for animal slaughter, high meat production per hectare and benefit values for reducing emissions. Systems profitability involves factors related to production costs and GHG emissions during production stages, emissions benefits and animal sales revenue. The difference in days required for production, costs and disbursement dates hinder the comparison between systems, requiring the use combined profitability assessment tools for better understanding. Table 7 shows NPV, IRR and API results divided between animal and hectare, besides the respective systems position. It is noteworthy that the annualized profitability index (API) allows an annual distribution of the NPV value for project investment unit in its whole lifespan. API is considered a second-order NPV, as it allows a design analysis with different investments and deadlines, and this allows solving at the same time NPV and IRR limitations. API allows, therefore, classifying projects and maintaining its integrity, thus being the most appropriate in this study. The analysis result, when compared to systems classification between used tools showed little variability. This variability is due to the structure analysis of each indicator. NPV systems analysis showed differences in the comparison between animal and hectare. PS II, PS III, PS IV and PS V higher yield per hectare reflected in higher revenue values. However, the higher production stages feedstock cost decreased profitability. Analyzing by animal, PS VII had the best result, which is explained by the low production cost, early animal slaughter and benefits on reduced emissions. However, when compared per hectare, the PS II higher LWGH positively reflected, getting a better result than PS VII. IRR analysis also showed differences in the comparison between animal and hectare. However, PS VII result is highlighted, with the best return rate in the two analysis forms. When compared per animal, PS VII obtained a 63% higher result than the second best rate of return (PS II) and, assessed per hectare, obtained a result that was about 35% higher than the second best rate of return. This is explained by the low feedstock need, resulting in low production investments, obtaining better return on invested capital. Analyzing API again, PS VII stands out with the best result when compared by animal (Fig. 2) and per hectare (Fig. 3), again reflecting the low feedstock need for production.

Table 5 CO2 eq emissions by production system (CO2 eq X 1.000 kg). Production Systems

Pregnancy

Calf

Rearing I

Animal

Ha

Animal

Ha

Animal

Ha

Animal

Ha

Animal

Ha

Animal

I II III IV V VI VII

2.09 2.09 2.09 2.09 2.09 2.09 2.09

2.59 2.59 2.59 2.59 2.59 2.59 2.59

1.51 1.44 1.51 1.44 1.50 0.95 0.95

1.61 2.14 1.61 2.14 2.59 1.01 1.01

3.47 1.65 3.47 3.47 1.63 3.46 3.46

7.55 4.89 7.55 10.30 5.12 5.65 5.65

2.37 2.85 2.37 2.79 2.73 2.59 2.23

4.36 5.15 4.36 5.04 5.06 3.65 2.82

3.68 – 1.63 – – 3.35 –

4.76 – 3.45 – – 3.32 –

2.86 – – – – – –

Source: Prepared by the authors from Ruviaro et al. (2014a).

Rearing II

Fatten. I

Fatten. II

Total

Total

Ha

Animal

Ha

3.31 – – – – – –

15.99 8.03 11.08 9.79 7.94 12.44 8.73

24.17 14.76 19.55 20.07 15.35 16.21 12.06

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Table 6 Production systems financial results (US$). Prod. Systems

I II III IV V VI VII

Total production costs

GHG emissions cost

GHG benefit

Commercial Revenue

Total Revenue

Animal

Ha

Animal

Ha

Animal

Ha

Animal

Ha

Animal

Ha

450.97 639.18 592.41 644.25 711.46 550.02 537.27

364.32 957.05 737.92 966.22 1150.15 332.13 325.44

141.22 70.92 97.88 86.49 70.15 109.89 77.10

213.54 130.41 172.72 177.25 135.60 143.18 106.53

0.00 70.30 43.34 54.73 71.07 31.33 64.12

0.00 83.13 40.82 36.28 77.94 70.36 107.01

806.25 860.00 860.00 860.00 860.00 860.00 860.00

1365.17 2033.58 1730.05 2033.58 2146.56 1321.92 1331.72

806.25 930.30 903.34 914.73 931.07 891.33 924.12

1365.17 2116.70 1770.87 2069.86 2224.50 1392.28 1438.73

Source: Prepared by the authors from Ruviaro et al. (2014a).

Table 7 Production systems profitability (US$) comparison through net present value (NPV), internal rate of return (IRR) and annualized profitability index (API). NPV

IRR

Animal

I II III IV V VI VII

Hectare

API

Animal

Hectare

Animal

Hectare

Position

Value

Position

Value

Position

Value

Position

Value

Position

Value

Position

Value

6 2 4 7 5 3 1

137.64 212.02 191.72 128.70 144.31 204.30 300.08

7 1 6 2 4 5 3

653.30 1011.07 739.75 927.29 888.28 801.84 912.90

7 2 4 5 6 3 1

1.14% 2.81% 1.82% 1.79% 1.79% 2.04% 4.59%

4 3 6 5 7 2 1

3.23% 3.33% 2.78% 2.94% 1.43% 5.39% 7.27%

7 2 3 6 5 4 1

22.51% 47.20% 35.97% 32.67% 33.28% 34.31% 71.11%

6 3 5 4 7 2 1

67.32% 107.94% 92.56% 103.28% 54.53% 113.24% 179.87%

Source: Prepared by the authors from Ruviaro et al. (2014a).

I

VII

In systems profitability assessment, PS VII achieved better results in almost all analyzes. However, it is necessary to note that PS II achieved good results, especially when analyzing NPV per hectare, with a LWGH approximately 65% higher than PS VII.

II

4. Discussion

NPV Emissions VI

III

V

IRR API

IV

Fig. 2. NPV, Emissions (kg CO2 eq), IRR and API per hectare. Source: Prepared by the authors.

I

VII

II

NPV Emissions VI

III

IRR API

V

IV

Fig. 3. NPV, Emissions (kg CO2 eq), IRR and API per animal. Source: Prepared by the authors.

For a sustainable production, it is necessary to include not only environmental aspects. Thus, it was attempted to incorporate GHG emissions burden in beef cattle production costs. For this purpose, an economic benefit was assigned for systems that mitigate emissions, when compared to systems with more emission. In this context, a study conducted by Åby et al. (2013) in Norway assigned GHG emissions values as beef production taxes, suggesting that these would be paid by the producer. At first, it seems clear that the polluter (farmers) should pay emission costs. However, as food production is necessary to meet society demands, these costs would likely be passed on to consumers. Differential taxes application to foods according to GHG emissions would result in a change in the population average diet, resulting in GHG emissions agricultural mitigation (Wirsenius et al., 2011). On the other hand, analyzing Pampa biome native pastures use for animal grazing, it appears that this meat production system is considered as a sponsor activity of the region biodiversity (Overbeck et al., 2009), with no farmers’ compensation for this provided environmental service. Similarly, Veysset et al. (2010) points out that French cattle culture calf stage focuses on low fertility mountainous pasture areas, being thus a fundamental activity for regional development, maintaining the landscape and some of the systems biodiversity level. Thus, in order to provide incentives for producers to reduce GHG emissions in the Pampa biome, their emissions burden in the production costs of each system was included, but creating incentives according to the reduction in relation to the highest emission system. In the assessed systems, PS I, based only in native grass use for feed, was the highest GHG emissions system, both when measured per animal as per hectare. This higher emission reflects the native pasture management, supply and nutritional quality of pasture, the

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animal rate per hectare and the need for more grazing days for an animal to reach its slaughter weight of 430 kg. The results are similar to those found by Pelletier et al. (2010) and White et al. (2014), where pasture production improvement and intensification decreased the land use time required to slaughter an animal, and consequently decreased GHG emissions. Åby et al. (2013) found different data, where emissions costs were higher cost for more intensive systems, what can be explained by grains use and emissions related to its production. The assessed production systems consist of usual models in properties of the Pampa biome. However, they showed variations in relation to production per hectare, grazing days for animal finishing, production costs, profitability and emissions. Factors such as genetics, management and animal feeding generate differences in emissions per produced unit in animal products, even if these are produced in the same region (Wirsenius et al., 2011). By analyzing evaluated systems profitability, PS VII, based on animal grazing on native grasslands and protein-energetic supplementation, achieved the best results. Animals’ Protein-energetic supplementation allowed a higher calves weaning weight, weight maintenance during the Rearing I phase (dry season/winter) and great LWGA during Rearing II phase (rainy season/summer). These results are also supported by climate in the Pampa biome, which is controlled by a semi-permanent high-pressure system, carrying moist tropical air masses from the ocean to the mainland, coupled with the Intertropical Convergence Zone annual variation. This special weather condition bring abundant rainfall between the months from October to March (rainy season/spring-summer) and low rainfall during the months from April to September (dry season/fall-winter) (Behling et al., 2009). However, protein-energetic supplementation provided low animal rate per hectare compared to other systems. This system main feature is the reduced production cost provided by the low feed cost and GHG emissions, although productivity per hectare is not high. This low productivity per hectare increases the dependence on land use costs per produced unit and creates higher need for production areas. Therefore, changes in the land cost would negatively influence in higher proportions of this system, affecting its results. PS II production intensification through native grasslands fertilization and intercropping with ryegrass required higher production investments, but obtained a production of approximately 63%, which is higher than PS VII. According to Bustamante et al. (2012), intercropping with other forage can improve animals’ digestibility and help mitigate enteric methane emissions. When analyzing the NPV per hectare, PS II got the best financial results and achieved satisfactory results when analyzed by invested capital (IRR and API). In order to compensate for PS VII lower productivity per hectare, it would be necessary to use other areas for animal production, which would directly compete with agricultural products cultivation. Thus, system intensification increases units per area production and reduces agriculture advancement potential in Brazilian natural areas (Latawiec et al., 2014). From this perspective, this system large-scale adoption could enhance preserved areas use. On the other hand, intensification through more concentrated diets from grains and cereals may cause future conflicts with society, due to competition with food production for human consumption. Nevertheless, Latawiec et al. (2014) points out that legumes intercropping can reduce or completely reduce the need for synthetic fertilizers, which in addition to increasing productivity per hectare, would reduce production costs and animals’ carbon footprint. Thus, PS II legumes intercropping could reduce the need for fertilizers, increase productivity and improve its profitability from invested capital.

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It noted that animal slaughter period reduction decreased GHG emissions per produced unit when comparing production systems emissions. This is related to an intrinsic cattle characteristic, where ruminant’s digestive system inevitably involves methane production in significant levels (Wirsenius et al., 2011). Thus, higher animal retention on pasture will affect in higher enteric fermentation emissions, in addition to emissions related to waste production. In addition, animal methane emissions are energy waste, so it is expected that a reduction in emissions mean betterfeed efficiency (Beauchemin et al., 2008). In Brazilian beef cattle production systems, animal slaughter period reduction could mitigate livestock emissions to higher levels when compared to other countries, given that almost all Brazilian production systems have low GHG emissions related to production inputs (Bustamante et al., 2014). Furthermore, few investments are necessary for the implementation of measures to reduce emissions between evaluated systems. When PS VII (Native pasture grazing and protein-energetic supplementation) emissions and I (Native pasture grazing) are compared, animal supplementation allowed emission reductions of approximately 50%, besides improving profitability. This allows to say that only protein-energetic supplementation implementation in PS I would have short-term impacts on emissions and system profitability. According to O’brien et al. (2014), the reasons why certain producers have not implemented measures to mitigate emissions and increase system profitability are diverse, and may include risk aversion, their behavior on a new technology or management difficulty in maximizing profit. Farmers’ economic incentives could favor measures adhesion that would promote better system efficiency, resulting from carbon credits sale (O’brien et al., 2014). However, emissions monitoring would require a constant measurement of methane and nitrous oxide levels in rural properties, what would involve high transaction costs (Wirsenius et al., 2011). In addition, the need to reduce agricultural GHG emissions, mainly on livestock, has caused several proposals on emissions taxation. However, there are questions about implementation ways and its consequences. Sanders and Webber (2014) point out that if agricultural entities are incorporated in future GHG policies, since assigned emission price in this sector could result in significant emission reduction, it may result in economy changes, due to the lack of consumer behavior. Therefore, taxation should be differentiated according to food GHG emission levels, penalizing the highest emitting products (Wirsenius et al., 2011). It is important to highlight that pastures carbon sequestration potential were not considered. Soil carbon sequestration has great potential for pastures carbon draining (Del Prado et al., 2013), and this is deducted from each system GHG emission levels (Veysset et al., 2010). If carbon sequestration were included in this study, the amount related to emissions would be lower, resulting in lower carbon footprint per animal. Despite the large area covered by pasture in Brazil, there is still no clear understanding of soil organic carbon stock changes (Bustamante et al., 2014). As noted, production intensification by pastures improvement (PS II, PS IV and PS V) and intercropping with other forage (PS II, PS IV and PS V) allow higher beef productivity per hectare, what may reduce grazing areas and affect deforestation rates (Bustamante et al., 2012). However, an inadequate high stocking rate management per hectare can increase soil density due to animal trampling, what can adversely affect pastures performance, because of a smaller root penetration and reduced soil aeration (Latawiec et al., 2014). Latawiec et al. (2014) also shows that there is evidence that animal intensification strategies can harm animals’ welfare in different ways, requiring animal constant monitoring in these systems.

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In this work, the productivity, emissions and profitability calculation of each system were assigned using the maximum potential animal rate per hectare, i.e., 100% of its potential. According to Latawiec et al. (2014), Brazilian pasture current productivity is between 32% and 34% of its potential. Thus, it is possible to assume that Brazilian production systems average emissions exceed the levels assessed in this study. According to Strassburg et al. (2014), the increase in pasture productivity for 49%–52% of its potential would meet the beef demand according to projections for the year of 2040, without natural ecosystems conversion. The definition of GHG emission limits per produced unit can provide a reduction in emission rates per animal, once that there are viable alternatives, economically and environmentally. According to O’brien et al. (2014), the implementation of a national compensation system for agriculture needs low cost transactions and high mitigation potential for emissions. Measures such as a higher taxation to animals with slaughter after 24 months could influence producers’ migration to more intensive production systems in short term because of financial profitability, reducing GHG emissions. 5. Conclusion The growing worldwide demand for animal protein has raised concerns among consumers about environmental depletions. Therefore, beef increased production conditioned to carbon reduction is a major challenge for managers, research institutions and government agencies due to trade-off competitions between economic and environmental objectives. When taking into account the analyzed data, and considering that part of producers have low financial capital increase capacity, protein-energetic supplementation (PS VII) was shown as a viable alternative, because it requires little investment to raise activity profitability and simultaneously reduce greenhouse gases emissions from beef production. Moreover, it should be considered that the limited knowledge by the producer on the environmental and economic benefits of protein-energetic supplementation is one of the resistance reasons to this production system implementation. On the other hand, PS II was shown as an interesting alternative for those producers with sufficient knowledge about the forage adequate management and who have financial capital injection capacity for investment in the activity. Although this system has higher production costs, it provides a higher yield per hectare. Moreover, both PS II and PS VII adoption allows an increase in beef production without requiring new livestock areas, besides contributing to Pampa biome proper use and preservation. Analyzes showed that, in certain feed conditions, it is possible to optimize low CO2 emission meat production with attractive economic returns. Improvements in pastures quality, in animal genetic selection with better-feed conversion rates, pasture management or supplements use are factors to mitigate greenhouse gases with consequent economic gains. The joint efforts of the productive sectors, institutes and research agencies are essential to identify and implement in different regions of Brazil those of meat production systems that are optimized environmentally and economically. References Åby, B.A., et al., 2013. Effect of incorporating greenhouse gas emission costs into economic values of traits for intensive and extensive beef cattle breeds. Livest. Sci. 158 (1), 1–11. ANUALPEC, 2014. Arrendamento de terras, Available in: http://www.anualpec.com. br/secao/terras (accessed August 2015). Balbino, L.C., et al., 2011. Contribuic¸ões dos Sistemas de Integrac¸ão LavouraPecuária-Floresta (iLPF) para uma Agricultura de Baixa Emissão de Carbono. Rev. Bras. Geografia Física, 1163–1175. Beauchemin, K.A., et al., 2008. Nutritional management for enteric methane abatement: a review. Aust. J. Exp. Agric. 48 (1–2), 21–27.

Beauchemin, K.A., et al., 2010. Life cycle assessment of greenhouse gas emissions from beef production in western Canada: a case study. Agric. Syst. Essex 103 (6), 371–379. ˜ G., Astigarraga, L., Picasso, V.D., 2014. Greenhouse gas emissions of beef cow Becona, – calf grazing systems in Uruguay. Sust. Agric. Res. 3 (2), 89–105. Behling, H., et al., 2009. Dinâmica dos campos no sul do Brasil durante o Quaternário Tardio. In: Campos Sulinos: Conservac¸ão e uso sustentável da biodiversidade. MMA, Brasília, pp. 13. Bencke, G.A., 2009. Diversidade e conservac¸ão da fauna dos Campos do Sul do Brasil. In: Campos Sulinos: Conservac¸ão e uso sustentável da biodiversidade. MMA, Brasília, pp. 101. Boldrini, I.I., 2006. Biodiversidade dos Campos Sulinos. In: Dal’Agnol, M., Nabinger, C., Rosa, L.M.G., Silva, J.L.S., Santos, D.T., Santos, R.J. (Eds.), I Simpósio de Forrageiras e Produc¸ão Animal – Ênfase: Importância e Potencial Produtivo da Pastagem Nativa. UFRGS, Depto. Forrageiras e Agrometeorologia/UFRGS, Porto Alegre. Brandão, F.S., et al., 2012. O papel do agronegócio brasileiro na reduc¸ão de emissão de gases de efeito estufa (GEES). Rev. Agroambiente 6 (1), 84–90. Bustamante, M.M.C., et al., 2012. Estimating greenhouse gas emissions from cattle raising in Brazil. Clim. Change 115 (3–4), 559–577. Bustamante, M.M.C., Martinelli, L.A., Ometto, J.P.H.B., Do Carmo, J.B., Jaramillo, V., Gavito, M.E., Araujo, P.I., Austin, A.T., Pérez, T.Y., Marquina, S., 2014. Innovations for a sustainable future: rising to the challenge of nitrogen greenhouse gas management in Latin America. Curr. Opin. Environ. Sustain. 9–10, 73–81. Carvalho, P.C.de F., 2009. Lotac¸ão animal em pastagens naturais: políticas, pesquisas, preservac¸ão e produtividade. In: Campos Sulinos: Conservac¸ão e uso sustentável da biodiversidade. MMA, Brasília, pp. 214–228. CTPEC – Centro de Tecnologia em Pecuária Unipampa, 2014. Boletim da pecuária, 6th ed, Available in: www.porteiras.s.unipampa.edu.br/ctpec/files/2014/11/BP6.pdf (accessed August 2015). Cuéllar, A.D., Webber, M.E., 2008. Cow power: the energy and emissions benefits of converting manure to biogas. Environ. Res. Lett. 3 (3), 034002. Del Prado, A., et al., 2013. Modelling the interactions between C and N farm balances and GHG emissions from confinement dairy farms in northern Spain. Sci. Total Environ. 465, 156–165. Finnveden, G., 1999. Methodological aspects of life cycle assessment of integrated solid waste management systems. Resour. Conserv. Recycl. 26, 173–187. Franco, A.L., Galli, O.C., 2007. Método para análise de investimentos: alternativa para classificac¸ão de projetos com prazo e volume de recursos diferentes. In: XXVII Encontro Nacional de Engenharia de Produc¸ão. Gerber, P.J., et al., 2013. Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO). Gianezini, M., Barcellos, J.O.J., Ruviaro, C.F., Oliveira, T.E., Dewes, H., 2014. Sustainability and market orientation in the Brazilian Beef Chain. J. Agric. Sci. Technol. (USA) 4, 249–260. Gitman, L.J., Juchau, R., Flanagan, J., 2010. Principles of managerial finance. Pearson Higher Education AU. Guinée, J.B., 2001. Handbook on life cycle assessment – operational Guide to the ISO Standards. Int. J. LCA 6 (5), 255. Hasenack, H., Cordeiro, J.L.P., Costa, B.S.C., 2007. In: Dall’agnol, M., Nabinger, C., Sant’anna, D.M., Santos, R.J. (Eds.), Cobertura vegetal atual do Rio Grande do Sul. IBGE - Instituto Brasileiro de Geografia e Estatística, 2004. Mapa da vegetac¸ão do Brasil e Mapa de Biomas do Brasil, Available in: http://www.ibge.gov.br (accessed January 2015). International Organization for Standardization – ISO, 2006. ISO 14040: Life Cycle Assessment – Principles and Framework. International Organization for Standardization, Geneva, Switzerland. Kramer, K.J., et al., 1999. Total greenhouse gas emissions related to the Dutch crop production system. Agric. Ecosyst. Environ. 72 (1), 9–16. Latawiec, A.E., et al., 2014. Intensification of cattle ranching production systems: socioeconomic and environmental synergies and risks in Brazil. Animal 8 (08), 1255–1263. Maraschin, G.E., 2009. Manejo do campo nativo, produtividade animal, dinâmica da vegetac¸ão e adubac¸ão de pastagens nativas do sul do Brasil. In: Campos Sulinos: Conservac¸ão e uso sustentável da biodiversidade. MMA, Brasília, pp. 248–259. Martinkoski, L., Vogel, G.F., Martins, P.J., 2013. Iniciativas e mercado no pagamento por servic¸os ambientais. Rev. Verde Agroecologia Desenvolvimento Sustentável 7 (5), 46–52. Ministério da Ciência e Tecnologia – MCT, 2010. Inventário de emissões e remoc¸ões antrópicas de gases de efeito estufa não controlados pelo Protocolo de Montreal: comunicac¸ão inicial do Brasil, Available in: http://www.mct.gov.br/upd blob/ 0004/4199.pdf (accessed August 2014). Modernel, P., Astigarraga, L., Picasso, V., 2013. Global versus local environmental impacts of grazing and confined beef production systems. Environ. Res. Lett. 8 (3), 035052, http://dx.doi.org/10.1088/1748-9326/8/3/035052. Nabinger, C., et al., 2009. Produc¸ão animal com base no campo nativo: aplicac¸ões de resultados de pesquisa. In: Campos Sulinos: Conservac¸ão e uso sustentável da biodiversidade. MMA, Brasília, pp. 175–198. O’brien, D., et al., 2014. An evaluation of the effect of greenhouse gas accounting methods on a marginal abatement cost curve for Irish agricultural greenhouse gas emissions. Environ. Sci. Policy 39, 107–118. Overbeck, G.E., Müller, S.C., Fidelis, A., Pfadenhauer, J., Pillar, V.D., Blanco, C.C., Boldrini, I.I., Both, R., Forneck, 2007. Brazil’s neglected biome: 325 The South Brazilian Campos. Plant Ecol. Evol. System. E.D. 9, 101–116.

C.F. Ruviaro et al. / Ecological Indicators 60 (2016) 930–939 Overbeck, G.E., et al., 2009. Os Campos Sulinos: um bioma negligenciado. In: Campos Sulinos – conservac¸ão e uso sustentável da biodiversidade. Ministério do Meio Ambiente, Brasil, Brasília, pp. 24–41. Peixoto, M., 2011. Pagamento por Servic¸os Ambientais – Aspectos teóricos e proposic¸ões legislativas. In: Brasília, D.F. (Ed.), Núcleo de Estudos e Pesquisa do Senado. , pp. 1–31 (Discussion papers, 105). Available in: http://www.senado. gov.br/senado/conleg/nepsf1.html. Accessed (August 2014). Pelletier, N., Pirog, R., Rasmussen, R., 2010. Comparative life cycle environmental impacts of three beef production strategies in the Upper Midwestern United States. Agric. Syst. 103 (6), 380–389. Picasso, V.D., et al., 2014. Sustainability of meat production beyond carbon footprint: a synthesis of case studies from grazing systems in Uruguay. Meat Sci. Reis, J.C.L., 2009. O uso de herbicidas para a introduc¸ão de forrageiras nos campos e seus efeitos na flora campestre. In: Campos sulinos-conservac¸ão e uso sustentável da biodiversidade. MMA, Brasília, pp. 266–273. Ribaski, S.A.G., Hoeflich, V.A., Ribaski, J., 2010. Sistemas Silvipastoris como Apoio ao Desenvolvimento Rural para a Região Sudoeste do Rio Grande do Sul. Pesqui. Florestal Bras. 60, 27. Ross, S.A., et al., 2015. Administrac¸ão financeira. McGraw Hill Brasil. Ruviaro, C.F., De Léis, C.M., Lampert, V.N., Barcellos, J.O.J., Dewes, H., 2014a. Carbon footprint in different beef production systems on a southern Brazilian farm: a case study. J. Clean. Prod., http://dx.doi.org/10.1016/j.jclepro.2014.01.037. Ruviaro, C.F., Barcellos, J.O.J., Dewes, H., 2014b. Market-oriented cattle traceability in the Brazilian Legal Amazon. Land Use Policy 38, 104–110. Ruviaro, C.F., Gianezini, M., Brandão, F.S., Winck, C.A., Dewes, H., 2012. Life cycle assessment in Brazilian agriculture facing worldwide trends. J. Clean. Prod. 28, 9–24. Sanders, K.T., Webber, M.E., 2014. A comparative analysis of the greenhouse gas emissions intensity of wheat and beef in the United States. Environ. Res. Lett. 9 (4), 044011. Santos, T.G., Trevisan, R., 2009. Eucaliptos versus Bioma Pampa: compreendendo as diferenc¸as entre lavouras de arbóreas e o campo nativo. In: Lavouras de

939

Destruic¸ão: a (im) posic¸ão do consenso. produc¸ão independente, Brasil, Pelotas, RS, pp. 299–332. Soussana, J.-F., 2009. Os desafios da ciência das pastagens européias são relevantes para os Campos Sulinos? In: Campos Sulinos: Conservac¸ão e uso sustentável da biodiversidade. MMA, Brasília, pp. 331–344. Strassburg, B.B.N., et al., 2014. When enough should be enough: improving the use of current agricultural lands could meet production demands and spare natural habitats in Brazil. Global Environ. Chang. 28, 84–97. Suertegaray, D.M.A., Pires Da Silva, L.A., 2009. Tchê Pampa: histórias da natureza gaúcha. In: Campos Sulinos: Conservac¸ão e uso sustentável da biodiversidade. MMA, Brasília. Thurow, J.M., et al., 2009. Estrutura da vegetac¸ão e comportamento ingestivo de novilhos em pastagem natural do Rio Grande do Sul. Rev. Bras. Zootecnia 38 (5), 818–826. Tornquist, C.G., Bayer, C., 2009. Servic¸os ambientais: oportunidades para a conservac¸ão dos Campos Sulinos. In: Campos Sulinos: Conservac¸ão e uso sustentável da biodiversidade. MMA, Brasília, pp. 122. USDA – United States Department of Agriculture, 2014. Livestock and Poultry: World Markets and Trade. Foreigner Agricultural Service of USDA. Veysset, P., Lherm, M., Bébin, D., 2010. Energy consumption, greenhouse gas emissions and economic performance assessments in French Charolais suckler cattle farms: model-based analysis and forecasts. Agric. Sys. 103 (1), 41–50. Wall, E., Simm, G., Moran, D., 2010. Developing breeding schemes to assist mitigation of greenhouse gas emissions. Animal 4 (03), 366–376. White, R.R., et al., 2014. Optimizing diet and pasture management to improve sustainability of US beef production. Agric. Syst. 130, 1–12. Wirsenius, S., Hedenus, F., Mohlin, K., 2011. Greenhouse gas taxes on animal food products: rationale, tax scheme and climate mitigation effects. Clim. Change 108 (1-2), 159–184.