Greenhouse gas emissions and carbon sequestration by agroforestry systems in southeastern Brazil

Agrosilvopastoral and silvopastoral systems can increase carbon sequestration, offset greenhouse gas (GHG) emissions and reduce the carbon footprint generated by animal production. The objective of this study was to estimate GHG emissions, the tree and grass aboveground biomass production and carbon storage in different agrosilvopastoral and silvopastoral systems in southeastern Brazil. The number of trees required to offset these emissions were also estimated. The GHG emissions were calculated based on pre-farm (e.g. agrochemical production, storage, and transportation), and on-farm activities (e.g. fertilization and machinery operation). Aboveground tree grass biomass and carbon storage in all systems was estimated with allometric equations. GHG emissions from the agroforestry systems ranged from 2.81 to 7.98 t CO2e ha −1 . Carbon storage in the aboveground trees and grass biomass were 54.6, 11.4, 25.7 and 5.9 t C ha −1 , and 3.3, 3.6, 3.8 and 3.3 t C ha −1 for systems 1, 2, 3 and 4, respectively. The number of trees necessary to offset the emissions ranged from 17 to 44 trees ha −1 , which was lower than the total planted in the systems. Agroforestry systems sequester CO2 from the atmosphere and can help the GHG emission-reduction policy of the Brazilian government.

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Introduction

The Paris Agreement, adopted in the 21st session of the Conference of the Parties (COP 21) for the United Nations Framework Convention on Climate Change (UNFCCC), aims to maintain the global average temperature below 2 °C of pre-industrial levels 1 . The signatory countries stipulate their Intended Nationally Determined Contributions (INDCs), which are the main commitments and contributions of that country for the fulfillment of the agreement 2,3 .

The Brazilian INDC proposed to reduce the greenhouse gases (GHG) emission by 37% in 2025, based on 2005 levels 4 . Agriculture is the main emission source with enteric fermentation being responsible for 90% of CH4 and animal manure on pasture for 33% of N2O emissions in Brazil in 2014 5 . The Brazilian government established a “low-carbon agriculture plan” to promote sustainable practices in agriculture by reducing greenhouse gas (GHG) emissions while maintaining profitability 6 .

This plan is based on practices such as restoration of degraded pastures, crop-livestock-forest integration, no-till farming, biological nitrogen fixation and forestry and agroforestry systems 6 . The agroforestry system is a land use management system combining trees and/or woody perennial plants, pasture and livestock benefiting from ecological and economic interactions between its component parts due to production diversification 7 . Food production 8 and carbon sequestration by tree planting 9 in these systems can help to reduce deforestation in tropical countries 10,11 .

Agrosilvopastoral and silvopastoral systems are agroforestry system types that can reduce and offset GHG emissions from the Brazilian agricultural sector, mainly using cattle and forest integration 12,13,14 . These systems lower animal emission levels 12 by improving grass quality, which can reduce CH4 emissions from enteric fermentation 15 and digestion efficiency 16 . Furthermore, these systems may mitigate GHG emissions by enhancing carbon sequestration through increasing above and belowground biomass 17,18,19 .

The objective of this study was to estimate GHG emissions, tree and grass aboveground biomass and carbon storage in silvopastoral and agrosilvopastoral systems in southeastern Brazil, and the number of trees required to offset these emissions.

Results

GHG emissions

The pre-farm GHG emissions were 0.37, 0.15, 0.12 and 0.10 t CO2e ha-1 in systems 1, 2, 3 and 4, respectively. Nitrogen production was the main emission source for pre-farm activities (Fig. 1). On-farm GHG emissions were 7.61, 4.10, 3.92 and 2.71 t CO2e ha −1 in systems 1, 2, 3 and 4, respectively. Enteric fermentation and manure produced by livestock were the main emission sources for on-farm activity (Fig. 1). Total GHG emissions were 7.98, 4.25, 4.04 and 2.80 t CO2e ha −1 , on systems 1, 2, 3 and 4, respectively (Table 1).

figure 1

figure 2

Pre-farm emissions were calculated using emission factors (Table 5) 51 and the following equation: emAgr = agrochemical *EF*(44/12), EmAgr = annual emissions resulting from production, packaging, storage and distribution of agrochemicals, kg CO2 year −1 ; agrochemical = agrochemical applied, kg year −1 ; EF = emission factor, kg carbon equivalent kg −1 ; 44/12 = C to CO2 conversion factor.

Table 5 Carbon emissions (mean ± SD) for the production, transportation, storage, and transfer of agrochemicals. Values according to a previous study 51 .

On-farm emissions were calculated based on the “Guidelines for National Greenhouse Gas Inventories” 52 . GHG sources included nitrogen fertilization, farm machinery, enteric fermentation, and manure management.

Input emissions from synthetic fertilizers were calculated via two pathways: direct and indirect. The direct emissions refer to mineral fertilizer applications 52 . Direct emissions are the product of the nitrogen applied by the emission factor (0.01) 52 using the 44/28 factor to convert N2 to N2O, and N2O global warming potential (298 units of CO2e) 53 . The equation used to estimate direct emissions was: EmDiF = FSN/FRP*EF1*(44/28)*GWP; EmDiF = direct CO2e emissions from N inputs to managed soils, kg CO2 ha −1 ; FSN = annual amount of synthetic fertilizer N applied to soils, kg N ha −1 ; FPRP = annual amount of dung and urine N deposited on soils, kg N −1 ; EF1 = emission factor developed for N2O emissions from synthetic fertilizer, kg N2O–N (kg N) −1 ; 44/28 = N2 to N2O conversion factor; GWP = global warming potential.

Indirect emissions result from volatilization, atmospheric deposition of NH3 and NOx, and nitrogen leaching and runoff from the fertilizers 54,55 . Indirect emissions were calculated using annual amount of fertilizer N applied to soils and the nitrogen fraction lost by volatilization, leaching and/or runoff 56 . The emission factor was 0.01 for volatilization and 0.0075 for leaching/runoff. The nitrogen fraction lost due to volatilization and leaching/runoff was fixed as 0.1 and 0.2, respectively 52 . The equation used to estimate indirect on-farm N2O emissions per system was EmLnL = FSN*FracLEACH-(H)*EF3*(44/28)*GWP, where EmLnL = amount of CO2e produced from additions to managed soils, kg CO2 ha −1 ; FSN = amount of synthetic fertilizer N applied to soils, kg N ha −1 ; EF3 = emission factor for N2O emissions from N leaching and runoff, kg N2O–N (kg N leached and runoff) −1 ; FracLEACH-(H) = fraction of all N added to/mineralized in managed soils in regions where leaching/runoff occurs that is lost through leaching and runoff, kg N (kg of N additions) −1 ; GWP = global warming potential.

NO2 emissions from urea were calculated with the same equations used for the other nitrogen fertilizers. CO2 emissions were the product of the urea applied to the soil by its emission factor, 0.20 52 . The equation used to estimate on-farm CO2 emissions was EmUrea = M*EF4, where EmUrea = amount of CO2e produced from urea application, t CO2 ha −1 ; M = amount of urea applied to soils, t N ha −1 ; EF4 = emission factor for applied urea, t of C (ton of urea) −1 .

CO2 emissions from agricultural machinery were those generated by fuel consumption during eucalypt planting due to its emission factor (EF5), 2.327 kg CO2 −1 52 . The equation used in each system was EmD = F*EF5, where EmD = amount of CO2e produced from fuel consumed, kg CO2 ha −1 ; F = fuel consumed, L ha −1 ; EF5 = emission factor, kg C (L fuel) −1 .

The CH4 emissions by enteric fermentation from cattle were calculated using the factor of 39 kg CH4 year −1 animal unit −1 57 . The equation used was: EmFE = N* EF6* GWP, where EmFE = emissions from enteric fermentation, kg CO2 ha −1 ; N = number of animals, head ha −1 ; EF6 = emission factor for enteric fermentation (kg CH4) head −1 ; GWP = CH4 global warming potential. N2O emissions due to manure deposition were calculated with the same equations as those for nitrogen fertilizer.

Carbon storage in aboveground biomass

Ten pasture grass samples (1 m 2 ) between tree rows were collected, per season, from June 2012 to October 2013. Their fresh weight was obtained and the fresh:dry weight ratio calculated with 25 g from each sample. These samples were dried at approximately 65 °C in an oven until weight stabilization.

The diameter at breast height (DBH), total height, and commercial height (stem height up to 3-cm diameter) of trees per system were measured between July and August 2012. Trees were grouped into DBH classes, and three individuals per class were selected and felled to determine their total volume, biomass and carbon levels in their stem, branches and leaves.

The trees selected were cut at ground level, and the stem diameters measured at 0.3, 0.7, and 1.3 m from their base, and thereafter at every 2 m until the diameter reached 3 cm. The volume of these stem sections was calculated using the Smalian’s formula 58 . The stems per sample were weighed and 2.5 cm thick stem discs were collected at the base, 25, 50, 75, and 100% of the commercial height to calculate the aboveground biomass. An additional stem disc was cut at breast height (1.3 m). The branches and stem discs were dried at 103 ± 2 °C until dry weight stabilization was reached. The leaf and branch weights per tree sampled were recorded. Fresh leaf and branch samples were weighed in the field, stored in bags and sent to the laboratory to determine their dry/fresh weight ratio 59 . Leaf and branch samples were dried at 65 ± 2 °C until dry weight stabilization.

The stem, leaf and branch carbon content was determined with a LECO TruSpec Micro CHN analyzer (LECO Corp., St. Joseph, MI). The carbon stock was obtained by multiplying the aboveground biomass by the carbon content.

Field data was fitted to allometric equations 60,61 to estimate the tree aboveground biomass, and carbon (stem + branches + leaves) per system as: Y1 = β01*DBH β11 *H β21 *ε1; Y2 = β02*(DBH 2 *H) β12 *ε2, whereYj the biomass or carbon stock (kg) of the j th model; H total height (m); β0i, β1i, and β2i the parameters of the j th model and εi:the random errors.

All statistical analyses were performed with R statistical software 62 . The best equations were based on the criteria: parameter significance (p < 0.05) by Wald test; coherence of the sign associated with a specific parameter; goodness of fit statistics: R 2 adj = 1 − [(n − p − 1)/(n − p)] * (1 − R 2 ); R 2 = 1 − [Σ(y − \(\hat\) ) 2 /Σ(y − \(\bar\) ) 2 ); RMES% = (100/ \(\overline\) ) * \(\sqrt>(y-\hat)2/n>\) ; \(\bar \% \) = (100/ \(\bar\) ) * (Σ(y − \(\hat\) )/n), where, R 2 is the empirical determination coefficient or model efficiency; R 2 adj , an empirical adjusted determination coefficient; \(\bar\) %, a relative bias; RMSE%, the root square error in percentage; n, the observation number; p, the number of explanatory variables; \(\bar\) , the mean of dependent variable (volume, biomass and carbon); yi, the i th observed value; and \(\hat\) , the i th value of the dependent variable.

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Acknowledgements

We acknowledge the financial support from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, productivity grants), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Ph.D. scholarship, Grant No. BEX 10570/12-8) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG, research funding). Thanks also to farmers Francisco de Freitas and Lino Roberto Ferreira for allowing us to work inside their properties. We also thank Lucas Arthur, Breno Loureiro, Gabriel Barros, Henrique Colares, Paulo Villanova, Bruno Schettini, Samuel José and Mateus Castro for laboratory and fieldwork. Dr. Phillip John Villani (The University of Melbourne, Australia) revised and corrected the English language used in this manuscript.

Author information

Authors and Affiliations

  1. Departamento de Engenharia Florestal, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil Carlos Moreira Miquelino Eleto Torres, Laércio Antônio Gonçalves Jacovine, Sílvio Nolasco de Olivera Neto & Carlos Pedro Boechat Soares
  2. Department of Agricultural and Biological Engineering, University of Florida, P.O. Box 110570, Gainesville, FL32611, USA Clyde William Fraisse
  3. TTG Brasil Investimentos Florestais Ltda, 18470-130, Itapeva, São Paulo, Brazil Fernando de Castro Neto
  4. Departamento de Fitotecnia, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil Lino Roberto Ferreira
  5. Departamento de Entomologia/BIOAGRO, Universidade Federal de Viçosa, 36570-900, Viçosa, Minas Gerais, Brazil José Cola Zanuncio
  6. Instituto de Ciências Agrárias, Universidade Federal de Minas Gerais, 39404-547, Montes Claros, Minas Gerais, Brazil Pedro Guilherme Lemes
  1. Carlos Moreira Miquelino Eleto Torres