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.


Discussion
The average annual GHG emissions ranged from 0.93 to 1.60 t CO 2 e ha −1 yr −1 , which may be considered low when compared to other systems 20,21 , probably due to the use of no-till farming and the adoption of agroforestry systems with reduced machinery use, fuel inputs and CO 2 emissions 22 . No-till farming in these systems may increase organic carbon and nitrogen content in the soil, and the microbial biomass, mitigating GHG emissions [23][24][25][26] . Usual management practices in agroforestry systems, such as no-till farming and optimal fertilization/manure regimes can increase carbon sequestration while reducing GHG emissions 27 . Such a combination provides additional environmental benefits such as soil erosion reduction and prevention 28,29 , more efficient water-use 30 , and improvement in biodiversity 31 .
The difference in the mean annual aboveground carbon increment (MAI-AGB) on the four systems indicates that the amount of this element sequestered may depend on tree species, age, geographic location, environmental factors, and system management 32,33 . System 1 presented the largest MAI-AGB (11.19 t ha −1 yr −1 ) due to its older age and the fertilization carried out to enhance maize production indirectly increasing tree biomass 34 . System 3 presented the second largest IMA due to its greater plant density (9 × 1 spacing), however competition between   35,36 and may increase future mortality 37 . All systems were important in carbon sequestration and had environmental benefits such as soil fertility and water quality improvement and erosion reduction 18,[38][39][40] . The estimated MAI-AGB found was higher than the 1.43 t ha −1 yr −1 in a silvopastoral system with 105 trees per hectare (eucalypt and acacia) in Minas Gerais, Brazil 41 . The MAI-AGB of 7.67 t ha −1 yr −1 of an agrosilvopastoral system with eucalyptus spaced 10 × 4 m and rice in Paracatu, Minas Gerais, Brazil 42 was similar to that observed in the system 2 of this research. The estimated aboveground grass carbon sequestration was similar to the 3.71 kg C ha −1 of an agrosilvopastoral system with eucalypt in Minas Gerais, Brazil 42 , and the 3.29 kg C ha −1 of a silvopastoral system with 200 pine trees ha −1 in São Paulo, Brazil 43 . These systems had a similar production due to the wide spacing of the trees,  Table 3. Estimated regression coefficients and adjusted standard errors (±SE), adjusted coefficient of determination (R 2 adj ), model bias (E ), and root mean square error (±RMSE) of carbon equations.
allowing sufficient radiation transmittance 18 and improving the microclimate for the forage 15,44 . This shows that agroforestry systems are an alternative to recover degraded pasture land by improving chemical, physical and biological soil conditions and enhancing carbon sequestration 12,18,[45][46][47] .
The number of trees required to offset GHG emissions was lower than that planted in the systems studied, demonstrating their great potential to sequester carbon and to reduce GHG emissions. 12,48,49 . Agroforestry systems are important for the "Low-Carbon Agriculture Plan" of the Brazilian government to achieve GHG emission-reduction targets. These systems decrease the pressure on forests 48 , and improve animal welfare and crop production 12 . Furthermore, the remaining sequestered carbon can be sold in voluntary markets with a higher price for technologies that bring social and environmental benefits including higher farmer income 50 .
The systems had a positive carbon balance and a tree surplus ranging from 232 to 987. The number of trees was higher than necessary to offset GHG emissions in all systems. Therefore, the agroforestry systems can effectively mitigate GHG emissions.

Methods
Study systems. The study was conducted in silvopastoral and agrosilvopastoral systems in Viçosa, Minas Gerais, Brazil. The climate in this region is humid subtropical with dry winters and hot summers, classified as Cwa (Köppen classification). The average annual temperature and rainfall are 19.4 °C and 1,200 mm, respectively. The soil is classified as red-yellow latosol and the topography ranges from strongly undulated to mountainous with an average altitude of 689.7 m.
The agrosilvopastoral systems were composed of maize (Zeya mays) and Eucalyptus saligna (system 1), and bean (Phaseolus vulgaris) and E. urophylla x E. grandis (system 2) during the first year, and the crops were replaced by pasture (Brachiaria decumbens) with livestock grazing in the second year ( Table 4). The silvopastoral systems (3 and 4) had pasture (Brachiaria decumbens) + E. urophylla x E. grandis (Table 4). No-till farming was used in all systems. Beef cattle were reared in all systems (one animal/ha).
System 1 was fertilized after soil analysis. In December 2007, a posthole digger machine was used and 0.2 kg of N-P-K (06-30-06) applied per tree hole. Additional fertilization of 0.16 kg of N-P-K (20-05-20) pit −1 was carried out three months after tree planting. Weeds and leaf-cutting ants were controlled before, during and after tree planting. Animal traction was used to apply 500 kg of N-P-K (08-24-12) ha −1 on maize before planting, and another 500 kg of N-P-K (30-00-10) ha GHG emissions. GHG emission calculations per system were based on pre-farm activities, such as production, storage, and transportation of agrochemicals, and on-farm activities such as fertilization and machinery use (Fig. 2). The data were estimated from personal interviews with farmers. They were asked to report on the use of machine fuel, agrochemicals and estimated crop yield.
Input emissions from synthetic fertilizers were calculated via two pathways: direct and indirect. The direct emissions refer to mineral fertilizer applications 52  Indirect emissions result from volatilization, atmospheric deposition of NH 3 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 N 2 O emissions per system was Em LnL = F SN *Frac LEACH-(H) *EF 3 *(44/28)*GWP, where Em LnL = amount of CO 2 e produced from additions to managed soils, kg CO 2 ha −1 ; F SN = amount of synthetic fertilizer N applied to soils, kg N ha −1 ; EF 3 = emission factor for N 2 O emissions from N leaching and runoff, kg N 2 O-N (kg N leached and runoff) −1 ; Frac LEACH-(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. NO 2 emissions from urea were calculated with the same equations used for the other nitrogen fertilizers. CO 2 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 CO 2 emissions was Em Urea = M*EF 4, where Em Urea = amount of CO 2 e produced from urea application, t CO 2 ha −1 ; M = amount of urea applied to soils, t N ha −1 ; EF 4 = emission factor for applied urea, t of C (ton of urea) −1 . CO 2 emissions from agricultural machinery were those generated by fuel consumption during eucalypt planting due to its emission factor (EF 5 ), 2.327 kg CO 2 −1 52 . The equation used in each system was Em D = F*EF 5 , where Em D = amount of CO 2 e produced from fuel consumed, kg CO 2 ha −1 ; F = fuel consumed, L ha −1 ; EF 5 = emission factor, kg C (L fuel) −1 .
The CH 4 emissions by enteric fermentation from cattle were calculated using the factor of 39 kg CH 4 year −1 animal unit −1 57 . The equation used was: Em FE = N* EF 6 * GWP, where Em FE = emissions from enteric fermentation, kg CO 2 ha −1 ; N = number of animals, head ha −1 ; EF 6 = emission factor for enteric fermentation (kg CH 4 ) head −1 ; GWP = CH 4 global warming potential. N 2 O 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.