Greenhouse gas balance related to conventional and sustainable fruit production systems in the Highlands region of Pasto, Colombia Received for publication: 27 January, 2016. Accepted for publication: 30 June, 2016. Doi: 10.15446/agron.colomb.v34n2.55417 1 Grupo de Investigación Producción de Frutales Andinos, Faculty of Agricultural Sciences, Universidad de Nariño. Pasto (Colombia). 2 Grupo de Investigación Innovacion en Sistemas Agricolas y Forestales (ISAF), Faculty of Agricultural Sciences and Natural Resources, Universidad de Los Llanos (Unillanos). Villavicencio (Colombia). asilvap@unillanos.edu.co Agronomía Colombiana 34(2), 277-284, 2016 Greenhouse gas balance related to conventional and sustainable fruit production systems in the Highlands region of Pasto, Colombia Balance de gases de efecto invernadero relacionado a sistemas convencionales y sostenibles de producción de frutas en la región del Altiplano de Pasto, Colombia Hernando Criollo E.1, Amanda Silva P.2, and Hernando Delgado H.2 ABSTRACT RESUMEN This research focused on the greenhouse gas (GHG) emissions and potential sinks associated with conventional and sustain- able fruit production systems in the Highlands region of Pasto, Nariño, Colombia. Based on the IPCC (2006) methodologies, the annual emission balance for a 6-year production cycle in- cluded agricultural sources and gasoline consumption related to the main agricultural activities and the potential for soil C ac- cumulation and biomass C fixation in all of the studied systems. The multivariate analysis showed that positive GHG balance emissions would be achieved in all sustainable fruit production systems, as compared to conventional fruit production systems with greater impact on (SS1): Rubus glaucus Benth. associated with Acacia decurrens trees and live coverage of kikuyu Pen- nisetum clandestinum grass. According to the results of this study, (SS1) showed the beneficial total GHG balance emission accounting for -21,079 kg of atmospheric CO2eq ha-1 yr-1 divided into -4,587 kg CO2eq ha-1 yr-1 and -17,102 kg CO2eq ha-1 yr-1 due an annual soil and biomass C sequestration potential that could help offset its emissions (610 kg CO2eq ha-1 yr-1). Este trabajo se enfoca en las emisiones de gases de efecto inver- nadero (GEI) y los sumideros potenciales asociados a sistemas convencionales y sostenibles de producción de frutas en la región del Altiplano de Pasto, Nariño, Colombia. Basados en las metodologías del IPCC (2006), el balance de las emisiones anuales para un ciclo de producción media de 6 años incluyó las fuentes agrícolas y el consumo de gasolina relacionado con las principales actividades agrícolas y el potencial para acumu- lar C en el suelo y fijar C en la biomasa en todos los sistemas estudiados. El análisis multivariado mostró que un positivo balance de emisiones de GEI puede ser alcanzada con todos los sistemas sostenibles de producción de frutales comparados con los sistemas convencionales de producción con gran impacto en (SS1): Rubus glaucus Benth. asociado con árboles de Acacia decurrens y cobertura viva de pasto kikuyo Pennisetum clan- destinum. Basado en los resultados de este estudio, el sistema (SS1) mostró benéfico balance del total de las emisiones de GEI contabilizando -21,079 kg CO2eq atmosférico por ha-1 por año, dividido en -4,587 kg CO2eq ha-1 año-1 y -17,102 kg CO2eq ha-1 año-1, debido al potencial de secuestro anual de C en el suelo y la biomasa que puede contrarrestar en parte las emisiones del sistema (610 kg CO2eq ha-1año-1). Key words: biomass C, climate change, mitigation practices, GHG emissions, potential sinks, soil C. Palabras clave: C de la biomasa, cambio climático, prácticas mitigadoras de GEI, potenciales sumideros, C del suelo. for CO2, 30.1% for CH4 and 19.1% for N2O, being those two last gases mainly related to agricultural sources (Pedraza et al., 2009). Worldwide, the systems that are exposed to intensive uses has higher utilization of agricultural inputs as soluble fertil- izers, mainly nitrogen and pesticides (Smith et al., 1997; Lal, 2004; Tubiello et al., 2013), practices that results in direct and indirect GHG emissions (Lal, 2004). Pesticide manufacturing represents about 9% of the en- ergy used for arable crops (IPCC, 2006; Lal, 2004). It is assumed that due course all the carbon included in the Introduction The agricultural sector represents a significant source of greenhouse gas (GHG) worldwide due to direct and indirect emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Estimates have shown that agriculture contributes to the enhanced GHG effect by emitting around 7.1 Gt of CO2eq, or ~18% of total global anthropogenic GHG emissions (Gerber et al., 2013). Agricu ltura l sources a re responsible for 38.09% of Colombiá s GHG total emission (62 million t of CO2 per year). The main partition of emission of GHG are 49.8% http://dx.doi.org/10.15446/agron.colomb.v34n2.55417 278 Agron. Colomb. 34(2) 2016 pesticide will be broken down and emitted to the atmo- sphere as carbon dioxide (IPCC, 2006). Agroforestry systems can have a major effect on the pro- ductivity of fruit production systems, where nitrogen is often a limiting factor in production systems (Nair et al., 2009). Agroforestry systems can substantially reduce the use of synthetic fertilizers through the biological nitrogen fixation (BNF) of leguminous tress (Nair et al., 2009; Naranjo et al., 2012). Agroforestry is based on principles of sustainable produc- tion and diversification (Nair et al., 2009; Naranjo et al., 2012); an agroforestry system retains soil and biomass carbon stocks (Lal, 2011). Moreover, the soil carbon stock is related to management soil factors as land use, residue inputs of soil C and soil tillage practices (Albrecht and Kandji, 2003; IPCC, 2006). Lal (2011) estimated that 89% of the agriculture sector’s total GHG mitigation potential is from soil organic carbon (SOC) sequestration. Carbon sequestration has the poten- tial to offset fossil fuel emissions by 0.4 to 1.2 gigatons of carbon per year (Lal, 2011). The objective of this study is to estimate the GHG balance related to conventional and sustainable fruit production systems in the Highlands region of Pasto, Colombia, in order to identify the fruit production system that emits less GHG emissions and has greater potential to mitiga- tion GHG emissions (soil and biomass C sequestration). Materials and methods Location and production systems The systems considered in our study refer to the fruit pro- duction located in the highlands region of Pasto, state of Nariño, South-West Colombia. Geographical coordinates of 0°37’ to 2º47’N and 79°03’ to 76º47’W. It is one of the highest plateaus of the country located between 2,400 to 2,800 m a.s.l. Three types of conventional fruit production systems were found in the various degraded areas of the Highlands re- gion of Pasto, Colombia, were as follows: Monoculture of Rubus glaucus Benth. (S1); monoculture of Physalis peruviana (S2) and monoculture of Solanum quitoense Lam. (S3). The cultivation of these systems had used the conventional type. On the other hand, three types of sustainable fruit production systems were found in the various areas of the highlands region of Pasto, were as follows: Agroforestry system of Rubus glaucus Benth. (SS1); agroforestry system of Physalis peruviana (SS2); agroforestry system of Solanum quitoense Lam (SS3). The cultivation of these systems had been associated with Acacia decurrens trees as living fence and live coverage of kikuyu Pennisetum clandestinum grass. In this study, data was collected from 20 actual farms for each one of the systems considered. Tab. 1 presents the details associated with the agronomic parameters of the fruit production systems considered in this study. All of the systems were run in a 06-year cycle (Tab. 1). Characterization of production systems Emission sources and sinks and amount of supplies Table 2 presents the sequence of sources and potential sinks related to the main greenhouse gases (CO2, CH4 and N2O) associated to each of the production systems under analysis in this study. Table 3 and Figure 1 present the agricultural supplies and fuel consumption due to agricultural activities conducted in each of the studied systems corresponding to variables from 1 to 8 and analysis de continuous variables in mul- tivariate analysis. Emission factors N2O from soil management and CO2 from agricultural activities The direct plus indirect emissions from N fertilizer appli- cations and above ground residues were estimated by using the IPCC (2006) methodology. Emission factor regarding lime was assumed as 0.477 kg CO2eq kg-1 (dolomite) (IPCC, 2006). Emission factors associated with the manufacturing, transport and storage of potassium and phosphate fertili- zers were 0.2 kg CO2eq kg-1 for P and 0.15 kg CO2eq kg-1 for K, as proposed by Lal (2004). For pesticides, the emission factor EF depends on the type of pesticide applied (Helsel, 1992) to control pests and diseases in all systems. An emission factor considered as 2.33 kg CO2eq L-1 of gasoline, under tropical conditions (IPCC, 2006). Greenhouse gas emissions variables were expressed in CO2 equivalent units to account for global warming potential of each gas in accordance with IPCC (2006), assuming a 100-year time horizon (298 for N2O and 1 for CO2) (Table 4, variables from 9 to 19). 279Criollo E., Silva P., and Delgado H.: Greenhouse gas balance related to conventional and sustainable fruit production systems in the Highlands region of Pasto... TABLE 1. Land use historical of areas dedicated to fruit production systems in the Highlands region of Pasto, Colombia. Agricultural characterization of the systems1 Crop sequence of management in each year run in a 06-year cycle previous crop 1 2 3 4 5 6 S1 Pasture of kikuyu grass Crop planting Permanent crop of Rubus glaucus S2 Pasture of kikuyu grass Crop planting Monoculture crop Crop planting Monoculture crop S3 Pasture of kikuyu grass Crop planting Monoculture crop Crop planting Monoculture crop SS1, SS2, SS3 Introduction of sustainable fruits production systems each year similarly as explained above in fence permanent of Acacia decurrens and pasture Agronomic characterization Conventional systems Sustainable systems Land use (FLU) Continuous crop Agroforestry2 Type of management system Intensive Sustainable Tillage practices (FMG) Conventional Minimum Residue inputs (FI) Zero High3 Living mulches Zero High Practices for pesticides reduction Biological control Plant pruning (S1) = Monoculture of Rubus glaucus Benth.; (S2) = Monoculture of Physalis peruviana; (S3) = Monoculture of Solanum quitoense Lam.; (SS1) = Agroforestry system of Rubus glaucus Benth.; (SS2) = Agroforestry system of Physalis peruviana; (SS3) = Agroforestry system of Solanum quitoense Lam. 1The agronomic characterization was obtained directly in the production sites. 2Agroforestry system combination of fruit production systems with shrubs of Acacia decurrens at a density of 400 trees per hectare as a living fence. 3According to Giraldo et al. (1995), litter production of 407 trees per ha of Acacia decurrens is of 367 kg DM ha-1 yr-1. TABLE 2. Emission sources, greenhouse gases and sinks considered in each of the practices conducted in fruit production systems in the Highlands region of Pasto, Colombia. Sources Conventional fruit production systems Sustainable fruit production systems Emissions from soil management N2O from N syntethic fertilizer N2O from N organic fertilizer N2O from N syntethic fertilizer N2O from N organic fertilizer N2O from pasture residues during soil tillage N2O from tree Acacia decurrens residues Emissions from agricultural sources CO2 from lime use CO2 from lime use CO2 from P and K use CO2 from P and K use CO2 from pesticides use CO2 from pesticides use CO2 from fossil fuel (Gasoline) CO2 from fossil fuel (Gasoline) Potential to soil C sequestration Biomass C Soil C sequestration --------- Soil C sequestration Biomass C of permanent pasture and Acacia decurrens TABLE 3. Annual amount of applied agricultural supplies, fossil fuel use (medium values for a 6 years cycle) for each fruit production systems in the Highlands region of Pasto, Colombia. Farm variables Supplies Amount (kg ha-1 yr-1) Stadystical variables Conventional systems Sustainable systems SD7 Min8 Max9 S1 S2 S3 SS1 SS2 SS3 V1 N synthetic fertilizer 14 692 100 ---- 372 50 33.36 0 100 V2 N organic fertilizer 561 ---- ---- 561 233 75 32.68 0 100 V3 N from crop residues 30 59 59 22 22 22 16.74 22 59 V4 Lime 83 185 250 83 93 125 61.89 4 131 V5 P fertilizers 20 30 131 5 4 71 45.07 83 250 V6 K fertilizers 49 150 83 49 20 106 42.85 4 131 V7 Pesticides 18 8 44 ----- 6 21 14.33 20 150 V8 Gasoline 61 78 78 45 45 45 --- --- --- (S1) = Monoculture of Rubus glaucus Benth.; (S2) = Monoculture of Physalis peruviana; (S3) = Monoculture of Solanum quitoense Lam.; (SS1) = Agroforestry system of Rubus glaucus Benth.; (SS2) = Agroforestry system of Physalis peruviana; (SS3) = Agroforestry system of Solanum quitoense Lam. The amounts present were obtained directly in the production sites and supported by 1Angulo (2006); 2Silva et al. (2015) and 3Angulo (2011). 280 Agron. Colomb. 34(2) 2016 FIGURE 1. Diagram of annual amount of applied agricultural supplies and fossil fuel use (medium values for a 6-years cycle) for each fruit production systems in the Highlands region of Pasto, Colombia. (S1) = Monoculture of Rubus glaucus Benth.; (S2) = Monoculture of Physalis peruviana; (S3) = Monoculture of Solanum quitoense Lam.; (SS1) = Agroforestry system of Rubus glaucus Benth.; (SS2) = Agroforestry system of Physalis peruviana; (SS3) = Agroforestry system of Solanum quitoense Lam. SS3 N synthetic fertilizer 47% N organic fertilizer 29% N from crop residues 3% Lime 12% P fertilizers 0% Pesticides 1% K fertilizers 2% Gasoline 6% SS2 Gasoline 9% Pesticides 4% K fertilizers 21% P fertilizers 14% Lime 24% N from crop residues 4% N organic fertilizer 14% N synthetic fertilizer 10% SS1N synthetic fertilizer 13% N organic fertilizer 0% N from crop residues 8% Lime 34% P fertilizers 18% K fertilizers 11% Pesticides 6% Gasoline 10% S3 Gasoline 6% Pesticides 0% K fertilizers 6% P fertilizers 1% Lime 11% N from crop residues 3% N organic fertilizer 73% N synthetic fertilizer 0% N synthetic fertilizer 2% N organic fertilizer 67% N from crop residues 4% Lime 10% P fertilizers 2% K fertilizers 6% Pesticides 2% Gasoline 7% S1 Gasoline 6% Pesticides 1% K fertilizers 12% P fertilizers 3% Lime 15% N from crop residues 5% N organic fertilizer 0% N synthetic fertilizer 58% S2 Soil and biomass C pools The estimative of potential sinks either in soil or in biomass is presented in Tab. 5 (kg CO2eq ha-1 yr-1) considering the 6-year cycle. Reference values for the soil C stock in conventional S1, S2 and S3 fruit production systems of Highlands region of Pasto, Colombia, was based on soil analysis, being these 156.76, 51.84, 169.8 t C ha-1 in the top 30 cm layer. These values were used for estimating changes in final soil carbon stocks (V21) (Tab. 5) by converting from conventional to sustainable fruit production systems. Ratios of gains/losses of soil C (V22) (Tab. 5) in the studied fruit production systems were estimated by using specific methodology proposed by IPCC (2006), which takes into account factors related to soil management practices: land use (FLU), tillage practices (FMG) and residue inputs (FI) for a time-period of 20 years (IPCC, 2006). In addition to the intensity of management adopted (for instance, high, medium and low inputs) those factors take into account climate and soil type in the specific region. Rates of gains/ losses of soil C were multiplied by 3.66 to convert it from C to CO2eq (V23) (Tab. 5). 281Criollo E., Silva P., and Delgado H.: Greenhouse gas balance related to conventional and sustainable fruit production systems in the Highlands region of Pasto... TABLE 4. Farm variables related to total GHG emissions, considering each emission source (kg CO2eq ha -1 yr-1) in fruit production systems of Highlands region of Pasto, Colombia. Farm variables Supplies GHG emissions kg CO2eq ha -1 yr-1 Statistical variables Conventional systems Sustainable systems SD1 Min2 Max3 S1 S2 S3 SS1 SS2 SS3 V9 N2O from N synthetic fertilizer 79 404 585 ---- 215 293 195.70 0 585 V10 N2O from N organic Fertilizer 329 ---- ---- 329 130 439 170.87 0 439 V11 N2O from N crop residues 173 345 345 129 129 130 97.75 129 345 V12 N2O from soil management 581 749 930 458 474 862 --- --- --- V13 CO2 from Lime 40 88 120 39 44 59 29.81 39 120 V14 CO2 from P fertilizers 4 6 26 1 1 14 8.90 1 26 V15 CO2 from K fertilizers 7 22 13 7 3 16 6.39 3 22 V16 CO2 from pesticides 75 58 200 ------ 33 86 62.48 0 200 V17 CO2 from gasoline 141 181 181 105 105 105 --- --- --- V18 CO2 from agricultural activities 267 355 540 152 186 280 126.96 152 540 V19 Total GHG emissions 848 1,104 1.470 610 660 1,142 299.47 610 1,470 (S1) = Monoculture of Rubus glaucus Benth.; (S2) = Monoculture of Physalis peruviana; (S3) = Monoculture of Solanum quitoense Lam.; (SS1) = Agroforestry system of Rubus glaucus Benth.; (SS2) = Agroforestry system of Physalis peruviana; (SS3) = Agroforestry system of Solanum quitoense Lam. 1Standard deviation, 2Minimum 3Maximum values of continuous variables. TABLE 5. Total GHG balance emission of fruit production systems in the Highlands region of Pasto, Colombia. Variables Farm Components Conventional Sustainable Stadystical variables S1 S2 S3 SS1 SS2 SS3 SD3 Min4 Max5 20 Total GHG emission (kgCO2eq ha -1-1yr-1) 848 1,104 1,470 610 660 1,142 299.47 610 1,470 21 Soil C final stock (t C ha-1) 138.33 44.58 130.85 180.51 74.64 188.25 52.10 44.58 188.23 22 Rate soil C gain/loss (t C ha-1yr-1) -0.97 -1.21 -2.05 1.25 1.20 0.97 1.32 -1.05 1.25 23 Soil C sequestration1 (kgCO2eq ha -1yr-1) 3,559 4,440 7,523 -4,587 -4,404 -3,559 1,337 3,559 7,523 24 Biomass C fixation2 (kgCO2eq ha -1 yr-1) ---- ---- ----- -17,102 -17,102 -17,102 8,551 0 17,102 25 Balance GHG emissions 4,407 5,544 8,993 -21,079 -20,846 -19.519 13,477 8,993 21,079 (S1) = Monoculture of Rubus glaucus Benth.; (S2) = Monoculture of Physalis peruviana; (S3) = Monoculture of Solanum quitoense Lam.; (SS1) = Agroforestry system of Rubus glaucus Benth.; (SS2) = Agroforestry system of Physalis peruviana; (SS3) = Agroforestry system of Solanum quitoense Lam. 2Negative values refer to gain in biomass C stock; 3Standard deviation; 4Minimum 5Maximum values of continuous variables. Accumulation rate of C in biomass was estimated only in sustainable fruit production system. The increase in biomass C stock was assumed as 4.5 t C ha-1 year-1, con- sidering the wood component, based on the IPCC (2006) methodology for Acacia ssp in South America. The accu- mulation rate of C in biomass of Pennisetum clandestinum grass of 0.16 t C ha-1 year-1 was calculated of Giraldo et al. (2008); these rates were expressed in kgCO2eq and related to (V24) variable (Tab. 5). Total GHG balance The results of total GHG balance emission (V25) consi- dering the potential for soil C gain/loss and biomass C fixation are reported on Tab. 5. Statistical analysis A Principal Components Multivariate Analysis was perfor- med in order to reduce the number of explanatory variables, using variables that were not collinear. A numerical classi- fication of farms was then performed using cluster analysis method with the same variables identificated. All analyses were conducted with SAS® software. Results and discussion Correlation between variables The correlation matrix description of emission sources and total GHG emissions variables under different fruit production systems of Highlands region of Pasto, Colombia indicated that the variables (V12) (CO2 from agricultural activities), (V20) (total GHG emissions) and (V25) (GHG balance emissions) considered independently, demons- trated positive correlation with others variables. The (V12) variable (CO2 from agricultural activities) was best explained by the (V4) variable (lime) (r= 0.95), the (V13) 282 Agron. Colomb. 34(2) 2016 variable (CO2 from lime) (r=0.95) and the (V14) variable (CO2 from P) (r=0.90). According to the Intergovernmental Panel on Climate Change IPCC (2006), CO2 emissions from all lime added in the year of application although the effect of liming usu- ally lasts for a few years (after the new addition of lime), depending on climate, soil and cultivation practices (IPCC, 2006). For instance, all C in lime is eventually released as CO2 to the atmosphere (IPCC, 2006). Emission factors of phosphates and potassic fertilizers are associated with manufacturing, transportation, storage and application. On agroforestry systems, nutrient recycling is higher, reducing dependence on lime, phosphate and potassic fertilizers (Nair et al., 2009). On the other hand, the (V20) variable (total GHG emis- sions) was increased when increased the (V3) variable (N from crop residues) (r=-0.89) and the (V11) variable due to N2O from N crop residues emissions (r=-0.88). Soil C losses in terms of CO2 emissions can be as high as the an- nual C sequestration rates due to N2O from N crop residues emissions occasioned by conventional tillage (La Scala et al., 2008). N2O is a gaseous by-product of nitrification that is ultimately released into the atmosphere (IPCC, 2006). The variables that were highly correlated with the (V25) variable (total GHG balance emissions) were the (V22) vari- able (rates of gains/losses soil C) (r=-0.99) and the (V24) variable (biomass C fixation) (r=0.95). Soil carbon sequestration is a process in which CO2 is re- moved from the atmosphere and stored in the soil carbon pool, primarily mediated by plants through photosynthesis (Lal, 2011). Sustainable fruit production systems showed a large potential of sequestering carbon in soil and biomass, as observed by Giraldo et al. (2008) in an agroforestry system located in an Andean region of Colombia; which suggests the importance of the agroforestry fruit systems evaluated through on GHG mitigation. Multivariate analyses To make a distinction between the systems analyzed, prin- cipal components were generated (Factor 1 and Factor 2). The PCA considered the first two factors with a cumulative value of 66.84 % for the variables analyzed (Table 6) was negatively associated with the variables V1 (N synthetic fertilizers) (r=-0.93), V4 (lime) (r=-0.97), V9 (N2O from N synthetic fertilizers) (r=-0.93), V13 (CO2 from Lime) (r=- 0.98), V18 (CO2 from agricultural activities) (r=-0.93), and the V20 (total GHG emissions) (r=-0.95), being the most sensitive variables in these analyses (Tab. 6). However, these variables can also be observed in the vector diagram, where the variables are closer to the axis of this factor to demonstrate that most can inf luence the distinc- tion between the types of fruit systems evaluated (Fig. 3). Diagram generated for the projection vectors demonstrated that the (V22) variable (rate soil C gains/losses) (r=0.85) was that most positivity inf luence the distinction between the types of fruit production systems (Fig. 3). Despite the huge potential for mitigation of GHG emis- sions, especially in sustainable fruit production systems, it is important to point that soil C accumulation could be lost rapidly, depending on the soil management decisions made at those sites. For instance, Conant et al. (2001) re- viewed about 115 studies in 17 countries on the effects by conversion from agricultural crops to agroforestry system on soil C accumulation. This author considered values of soil C sequestration rates ranged from -0.2 to 3.0 t C ha-1 yr-1 respectively. It could be concluded that better soil management is pos- sible through the use of agroforestry systems as demon- strated also by Nair et al. (2009) and Giraldo et al. (2008). TABLE 6. Correlation coefficients of the principal components analysis (factors 1 and 2) for the variables associated to GHG balance emissions related to conventional and sustainable fruit production systems in the Highlands region of Pasto, Colombia. Variables Factor 1 Factor 2 V1 V2 V3 V4 V5 V6 V7 V9 V10 V11 V13 V14 V15 V16 V18 V20 V21 V22 V23 V24 V25 -0.93 -0.35 -0.89 -0.97 -0.87 -0.58 -0.33 -0.93 0.65 -0.90 -0.98 -0.87 -0.61 -0.90 -0.99 -0.95 0.25 0.85 -0.75 0.71 0.78 -0.03 0.91 -0.41 -0.04 0.49 -0.35 0.50 -0.03 0.05 -0.41 -0.04 0.49 -0.31 0.37 0.09 0.20 0.86 0.22 0.19 0.37 -0.31 V1 = N synthetic fertilizer; V2 = N organic fertilizer; V3 = N from crop residues; V4 = Lime; V5 = P fertilizers; V6 =K fertilizers; V7 = pesticides; V8 = gasoline ; V9, V10, V11, V12 = N2O emissions from N synthetic fertilizer, N organic fertilizer, N from crop residues, N from soil management; V13, V14, V15, V16, V17, V18 = CO2 from lime, P fertilizers, K fertilizers, pesticides, gasoline, agricultural activities; V19, V20, V21, V22, V23, V24, V25 = Total GHG emission, Soil C final stock, Rate soil C gain/loss, Soil C sequestration, Biomass C fixation, Total balance GHG emissions. 283Criollo E., Silva P., and Delgado H.: Greenhouse gas balance related to conventional and sustainable fruit production systems in the Highlands region of Pasto... FIGURE 3. Diagram of the vectors projection of the variables associated to factor 1 and 2 of GHG balance related to conventional and sustainable fruit production systems in the Highlands region of Pasto, Colombia. V1 = N synthetic fertilizer; V2 = N organic fertilizer; V3 = N from crop residues; V4 = Lime; V5= P fertilizers; V6 = K fertilizers; V7 = pesticides; V9, V10, V11 = N2O emissions from N synthetic fertilizer, N organic fertilizer, N from crop residues; V13, V14, V15, V16, V18 = CO2 emissions from lime, P fertilizers, K fertilizers, pesticides, agricultural activities; V19, V20, V21, V22, V23, V24, V25 = Total GHG emission, Soil C final stock, Rate soil C gain/loss, Soil C sequestration, Biomass C fixation, Total balance GHG emissions. For factor 2, the variables V2 (N organic fertilizer) (r=0.91) and V21 (Soil final C stock) (r=0.86) explained 16.9 % of the variation (Tab. 6). The increase in soil C stock is subject to greater amounts of crop residues returned to the soil (Albrecht and Kandji, 2003) and minimal soil disturbance (Johnson et al., 2010) (Tab. 6). The cluster analysis performed with the same variables as the principal components analysis identified three groups (Fig. 4). The cluster analysis showed that the first cluster consisted of conventional (S3) and (S2) fruit systems, with no sta- tistical differences; in the (S3) system, characterization with continuous variables of cluster or categories showed that the variables that had greater weight was total GHG balance emissions (V25) (4,407 kg CO2eq ha-1 yr-1) (P= 0.046) (Fig. 4), due to that has on its favor the highest soil C losses (V23) (P= 0.048) (7,523 kg CO2eq ha-1 yr-1) and an additional potential for GHG emissions (V20) (P= 0.037) equivalent to 1,470 kg CO2eq ha-1 yr-1, statistically equating to (S2) system (Figure 4) with total GHG balance emis- sions (V25) of 5,544 kg CO2eq ha-1 yr-1, further emissions would be expected according to our estimations due also to potential of soil C losses (V23) and total GHG emissions (4,404 and 1,104 kg CO2eq ha-1 yr-1) (V20). FIGURE 4. Hierarchical cluster analysis GHG balance related to fruit pro- duction systems in the Highlands region of Pasto, Colombia. (S1) = Monoculture of Rubus glaucus Benth.; (S2) = Monoculture of Physalis peruviana; (S3) = Monoculture of Solanum quitoense Lam.; (SS1) = Agroforestry system of Rubus glaucus Benth.; (SS2) = Agroforestry system of Physalis peruviana; (SS3) = Agroforestry system of Sola- num quitoense Lam. The multivariate analyses also showed that N from the synthetic fertilizers (V10) significantly inf luenced the formation of this cluster (P= 0.029). The use of N synthetic fertilizers in agriculture in Colombia is 137 kg ha-1, almost double the intensity in South American, with an average of 74 kg ha-1 (World Bank, 2008), resulting in higher direct and indirect N2O emissions (Smith et al, 1997). The formation of the cluster 2 as showed in Fig. 5, agrofor- estry system of Rubus glaucus Benth. (SS1) can neutralize higher emissions (-21,079 kg CO2eq ha-1 yr-1) (V25), as according to our results has, in addition to the higher po- tential soil C accumulation (-4,587 kg CO2eq ha-1 yr-1) (V23) and lower total GHG emissions (610 kg CO2eq ha-1 yr-1) (V20), results from this study are compared with relevant studies of Naranjo et al. (2012). It was statistically similar to agroforestry system of Physalis peruviana (SS2) (Fig. 4) accounting for total GHG balance emission reduction of -20,846 kg CO2eq ha-1 yr-1 (V25). But in turn the agroforestry system of Physalis peruviana (SS2) was statistically equals to monoculture of Rubus glaucus Benth. (S1) (Fig. 4), although it is a system that does not neutralize GHG emissions but if it emits less GHG to the atmosphere (4,407 kg CO2eq ha-1 yr-1) than the other two conventional (S2) and (S3) fruit production systems The cluster analysis in Fig. 4 showed an intermediate cluster to the agroforestry system of Solanum quitoense Factor 2 Factor 1 -0.8 -0.4 0 0.4 0.8 -0.8 -0.4 0 0.4 0.8 v6 v15 v11 v25 v9 v4 v1 v23 v13 v20 v16 v14 v7 v2 v21 v10 v24 v22 S2 SS3 SS1 SS2 S1 S3 284 Agron. Colomb. 34(2) 2016 Lam. (SS3) system, accounting for a total GHG balance emissions reduction of -19,519 kg CO2eq ha-1 yr-1 (V25); statistically it may be equal to sustainable fruit production (SS1) and (SS2) systems, but also to the conventional fruit production (S1) system. It is important to point that differences in management practices by adoption from conventional (S1) monoculture of Rubus glaucus Benth. to sustainable (SS1) agroforestry system of Rubus glaucus Benth. could significantly affect subsequent trends in increases of soil carbon accumulation and potential for mitigation of the GHG emissions. Conclusion Sustenaible fruit production systems (agroforestry) have the potential to offset GHG emissions, representing an important alternative to the recovery of degraded areas of conventional fruit production systems in Highlands region of Pasto, Colombia because they are able to maintain biomass C and soil organic matter through the addition of litter and crop residues in the soil. Literature cited Albrecht, A. and S.T. Kandji. 2003. Carbon sequestration in tropical agroforestry systems. Agric. Ecosyst. Environ. 99, 15-27. Doi: 10.1016/S0167-8809(03)00138-5 Angulo, R. 2006. Lulo el cultivo: Solanum quitoense Lam. 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