The Effects of Management and Plant Diversity on Carbon Storage in Coffee Agroforestry Systems in Costa Rica

Agroforest Syst (2012) 86:159–174 DOI 10.1007/s10457-012-9545-1

The effects of management and plant diversity on carbon storage in coffee agroforestry systems in Costa Rica

Achim Ha¨ger

Received: 3 September 2011 / Accepted: 28 June 2012 / Published online: 11 July 2012 Ó Springer Science+Business Media B.V. 2012

Abstract Agroforestry systems can mitigate green- found that the combined effect of farm type, species house gas (GHG) emissions, conserve biodiversity and richness, species composition and slope explained generate income. Whereas the provision of ecosystem 83 % of the variation in total C-storage across all services by agroforestry is well documented, the farms (P \ 0.001). Coffee agroforestry in general and functional relationships between species composition, organic farms in particular may contribute to GHG diversity and carbon (C)-storage remain uncertain. mitigation and biodiversity conservation in a syner- This study aimed to analyze the effects of management gistic manner which has implications for the effective (conventional vs. organic), woody plant diversity and allocation of resources for conservation and climate plant composition on aboveground and belowground change mitigation strategies in the agricultural sector. C-storage in coffee agroforestry systems. It was expected that organic farms would store more C, and Keywords Agrobiodiversity Functional diversity that an increase in plant diversity would enhance Greenhouse gas mitigation Organic coffee C-storage due to complementarity effects. Addition- Payment for environmental services ally, it was expected that steep slopes decrease C-storage as a result of topsoil erosion. Woody plants were identified on 1 ha plots within 14 coffee farms Introduction (7 conventional and 7 organic). C-stocks in trees, coffee plants and roots were estimated from allometric Agriculture is directly responsible for 10–12 % of equations. C-stocks in litter and topsoil (0–25 cm) anthropogenic greenhouse gas (GHG) emissions and were estimated by sampling. On average, farms stored annual emissions are expected to increase further 93 ± 29 Mg C ha-1. Soil organic carbon accounted during the next decades (Smith et al. 2007). Cropland for 69 % of total C. Total C-stocks were 43 % higher and pastures currently cover between 40 and 50 % of on organic farms than on conventional farms (P \ the terrestrial surface area (FAO 2007; Smith et al. 0.05). Conventional and organic farms differed in 2007) and agriculture is a main driver for deforestation vegetation structure, but not in species diversity. It was and habitat destruction in the tropics (Wright and Muller-Landau 2006). However, Smith et al. (2008) estimated that agriculture has a mitigation potential of A. Ha¨ger (&) -1 5.5–6.0 Gt year CO2-equivalent by 2030, with as Center for Sustainable Development Studies, much as 89 % of this from soil organic carbon (SOC) School for Field Studies, P.O. Box 150-4013, Atenas, Costa Rica storage. One strategy for combining GHG mitigation e-mail: ahaeger@fieldstudies.org with economic benefits and food production in the 123 160 Agroforest Syst (2012) 86:159–174 tropics is agroforestry (Watson et al. 2000). Research- contribute more to ecosystem functions, than the effect ers have increasingly found evidence that agroforestry of overall diversity (sampling or selection effect). In provides carbon sequestration and other ecosystem the case of tropical agroforestry, positive relationships services, such as biodiversity protection and soil between plant species are well established. Vander- conservation (Jose 2009). meer (1995) pointed out that intercropping can increase Coffee is one of the most important export com- productivity by reducing interspecific competition (com- modities for many developing countries and represents petitive production principle), or as a result of positive a source of income for millions of producers, mostly effects that some plant species have on the growing smallholders (Donald 2004). Intensified coffee pro- environment of other species (facilitation). duction in monocultures has expanded during the last The objective of this study was to analyze the effect decades, resulting in habitat destruction, biodiversity of farm management (conventional vs. organic), loss and soil degradation (Donald 2004). At the same woody plant diversity and species composition on time, it has also been established that traditional coffee the carbon storage potential of 14 coffee agroforestry agroforestry represents a viable strategy for sustain- systems in the Rio Grande watershed in the Central able agriculture in the tropics. For example, Dossa Valley of Costa Rica. The study area has a steep, et al. (2008) found that shaded coffee has an important broken topography. It is known that slope affects potential for GHG mitigation, while, e.g. Perfecto et al. erosion processes and soil carbon storage in agricul- (2003) and Philpott et al. (2008) emphasized the high tural systems (e.g. Smith et al. 2007; Martinez-Torres importance of shade grown coffee for biodiversity 2008). Consequently, the effect of slope on carbon conservation. Organic coffee farming has the potential storage was also taken into consideration. to further enhance the environmental benefits from It was expected that total carbon storage would be sustainable agriculture, because it eliminates agro- higher under organic management, because organic chemicals, decreases fossil fuel dependency, controls farmers rely on the integration of trees and SOM for the erosion and accumulates soil organic matter (SOM) maintenance of soil fertility. It was further predicted (Pimentel et al. 2005; Martinez-Torres 2008). that carbon storage would increase with woody plant Uncertainties remain about the fundamental relation- diversity across farm types, due to complementary ships between management and agroecosystem func- relationships and the presence of functional groups that tioning. Few studies have explored the interactions potentially enhance the accumulation of biomass and between species composition, biological diversity, and SOM. Steep slopes were expected to limit carbon carbon storage potential in tropical agroforestry systems storage across farms types, due to increased topsoil (Kirby and Potvin 2007; Henry et al. 2009;Sahaetal. erosion. 2009). These interactions are particularly intriguing, because they may indicate potential synergies between biodiversity conservation and GHG mitigation. Methods Evidence for the positive effects of species diversity and composition on ecosystem function (e.g. Study site and sampling design productivity) has been accumulated mostly for relatively simple experimental assemblages and temperate Fourteen (seven conventional and seven certified grasslands (e.g. Tilman et al. 1997, 2001; Spehn et al. organic) coffee farms were assessed within the Rıó 2005). Tscharntke et al. (2005) reviewed different Grande watershed near Atenas (9.98°N, 84.38°W), in mechanisms that can explain relationships between the Central Valley of Costa Rica between November species diversity and the function of agroecosystems. 2008 and April 2011. Farms sizes varied between 1.4 For example, species complementarity may increase and 24.5 ha, and elevation ranged from 800 to the efficiency of resource exploitation in diverse 1,250 m a.s.l. According to the classification by the communities and species redundancy stabilizes eco- Unites States Department of Agriculture (USDA), the system functioning in the face of environmental dominant soil order in this area is Alfisols. Twelve of change (insurance hypothesis). Finally, higher species the farms were located on Haplustalfs and the richness increases the chance that an assemblage remaining two farms were located on small patches contains disproportionately important species which of Ultisols and Inceptisols (ITCR 2008). Precipitation 123 Agroforest Syst (2012) 86:159–174 161 at the study site ranges from 2,000 to 2,500 mm. All of each subplot were pooled into a single sample, farms were located either in the premontane wet resulting in four samples per farm, which were sent to forest, or in the tropical moist forest (transition to the University of Costa Rica for analysis of SOC premontane) Holdridge Life Zones (ITCR 2008). content (see below). Additionally, one soil core As the number of suitable certified organic farms sample of 510 cm3 was taken at the center of each was limited, seven organic farms with a coffee crop subplot (four samples per farm) and dried at 105 °C area [1 ha were selected first within the Rio Grande for 48 h to determine bulk density. Watershed. All farms had been managed organically for The slope of the terrain was determined by at least 7 years at the time of the study. The organic overlaying the GPS coordinates for the 1 ha plots on farms were then paired with seven conventional farms geo-referenced maps (scale 1:10,000; IGNCR 2008), which were selected based on highest possible bio- with 5 m contour lines in ArcGIS 9.2 (ESRI 2006). physical similarity (micro-watershed, elevation, pre- The slope varied considerably across farms, however, cipitation, soil type). All 14 farms can be classified as average slope was similar for conventional (27 ± 8°) agroforestry systems as shade trees were incorporated. and organic farms (24 ± 15°). Structured interviews were conducted with all Data collection farmers between November 2008 and April 2011, as well as in February 2012 to collect information about A 1 ha plot (100 m 9 100 m) was established in the farm history (previous land use, age of the plantations, approximate center of each farm and the respective year of conversion to organic practices) and farm GPS coordinates were recorded. In some cases the management (fertilizer applications, herbicide use, shape of the plot varied or had to be divided due to the soil conservation practices, coffee plant pruning and topography and shape of the farm. Within the 1 ha plot coffee yield). all woody plant species with a diameter at breast height (DBH, measured 1.3 m above the ground) Plant diversity and similarity [5 cm were identified in the field. If a plant could not be readily identified, samples were taken to a botanist As the number of woody plants varied greatly (84–641 at the University of Costa Rica. When a plant could not individuals per 1 ha plot), individual-based rarefac- be identified at all, it was treated as a morpho-species. tion techniques were applied to obtain comparable Across all farms nine woody plants could neither be values of species richness across all farms. According identified, nor be unequivocally distinguished as a to Gotelli and Colwell (2011) rarefaction allows for morpho-species; these plants were omitted from the the comparison of communities, based on the number species richness estimates. of individuals (n) from the smallest sample: At each farm 4 subplots (20 9 25 m) were estab- Es*ðÞ¼jn* s; lished within the 1 ha plot, constituting a total area of 0.2 ha per farm. For the estimation of aboveground where n* = a random subsample of n individuals carbon (AGC), the DBH and height for all woody (n = the size of the smallest sample) out of a larger plants with a DBH [ 5 cm were measured within the sample with N individuals and S different species, subplots. Furthermore, the height and diameter (at s* = the number of species found in the subsample 15 cm above the ground) of 25 coffee plants were with n* individuals, and s* = the mean number of recorded in the center of each subplot, totaling 100 species from repeated subsamples, which estimates plants per farm. Leaf litter, including branches less E(s*|n*), the expected number of species found in a than 10 cm in diameter, was collected from an area of subsample with n* individuals out of a larger sample 30 cm 9 30 cm in the corners of each subplot, with a size of N. For this study rarefied species totaling 16 samples per farm. Litter samples were richness (s* ± standard deviation) was calculated dried in the laboratory of the Center for Sustainable based on 1000 iterations of randomly drawn subsam- Development Studies, in Atenas, at 80 °C for 48 h and ples of n* = 84 for the 13 farms with N [ 84 weighed. individuals per hectare. A total of 16 soil cores were taken, one in the corner Evenness was calculated using Simpson’s Measure of each subplot to a depth of 25 cm. The four samples (E1/D), following Krebs (1999): 123 162 Agroforest Syst (2012) 86:159–174

X 2 Log10ðÞ¼ AGB 1:113 þ 1:578 Log10ðÞ D15 E1=D ¼ 1= pi =s þ 0:581 Log10ðÞ H ; where p = the proportion of the species i of the total i where AGB = aboveground biomass (kg), D = number of individuals per 1 ha plot and s = the total 15 diameter in 15 cm above the ground (cm) and H = number of species per plot. plant height (m). Species composition between all farms was com- Following Kirby and Potvin (2007), the factors 0.47 pared by calculating pairwise Bray-Curtis similarity and 0.45 were used for the conversion of AGB into measures (1 - B) for all farms. The Bray Curtis Index AGC in trees and organic litter, respectively. AGB of takes into account the abundance for each species in coffee plants was converted into AGC by applying a the compared samples (Krebs 1999): X X factor of 0.5, according to Medina-Fernandez et al. B ¼ Xij Xik = Xij þ Xik (2006). Root biomass was estimated from total AGB in where B = Bray Curtis measure of dissimilarity and trees and coffee plants, using the allometric equation Xij,Xik = number of individuals of species i in the for tropical forests developed by Cairns et al. (1997): two different samples j and k. RB ¼ exp½1:0587 þ 0:8863 LnðÞ AGB/1000 ; where RB = root biomass (Mg ha-1), AGB = total Estimation of aboveground and belowground aboveground biomass from trees and coffee plants carbon stocks (kg ha-1). Carbon content in roots was assumed to be 50 %, according to Penman et al. (2003). Aboveground biomass (AGB) of shrubs and shade SOC content (%) of soil samples was determined by trees was estimated by applying the allometric equa- dry combustion (Elementar vario EL Cube) at the tion for tropical moist forest, proposed by Chave et al. University of Costa Rica. Total estimated SOC (Mg ha-1) (2005): was projected by multiplying average SOC % per farm (n = 4) with the sample depth (25 cm), the area (1 ha) AGB ¼ 0:0509 q D2 H; and the respective average bulk density per farm (n = 4). where AGB = aboveground biomass (kg), q = wood density (g cm-3), D = diameter at breast height (cm) Statistical analysis and H = tree height (m). Specific wood densities were compiled from dif- The number of woody plants per hectare, species ferent sources (Brown 1997; Fearnside 1997; Cordero richness and evenness indices were compared between and Boshier 2003; Flores-Vindas and Obando-Vargas conventional and certified organic farms by applying 2003; Penman et al. 2003; Chave et al. 2006;Orwa non-parametric Mann–Whitney tests. The similarity et al. 2009). If a species was included in different structure of woody plant species composition across databases, then the lowest published wood density was farms was explored by feeding the matrix of pairwise applied to achieve conservative AGC estimates. A Bray-Curtis measures for all farms into a Principal total of 76 different species was found on the subplots Components Analysis (PCA). The first two principal of all farms. Of those, 49 could be identified to species, components (PC), which explained 43.5 % of the 8 to genus and 6 to family; 13 morpho-species could variance in the data were plotted to determine if farms not be identified at all. Wood densities were found for segregate into groups with distinctive species assem- 40 species and 5 genera in the literature. The average blages according to management system (conven- wood density of these was 0.46 ± 0.16 g cm-3.This tional vs. organic). To compare the primary use of value was assigned for the remaining 31 species that trees in different assemblages, information was com- could either not be identified or were not found in any piled from Cordero and Boshier (2003), Flores-Vindas database. and Obando-Vargas (2003), and Orwa et al. (2009). For the estimation of AGB stored in coffee plants an DBH, height and wood density were not normally allometric equation from Segura et al. (2006) was distributed and could not be transformed. Consequently applied: these variables were compared between conventional 123 Agroforest Syst (2012) 86:159–174 163 and organic farms using univariate, non-parametric Table 1 Information on the history of the 14 coffee farms Mann–Whitney tests. Mann–Whitney tests were further assessed in the Rio Grande watershed between November 2008 used to analyze differences in the individual carbon and April 2011 storage components between conventional and organic Farm Area Previous Coffee Organic farms. (ha) land use production management since since The univariate effects of species richness (rarefied number of species, s*), species composition (the first CON 1 5.6 No Before – component from the PCA on the Bray-Curtis similar- information 1989a ity matrix, explaining 24.2 % of variance) and the CON 2 2.5 Pasture 2005 – slope of the terrain on total C-storage were analyzed CON 3 4.0 Maize, beans 1982 – by simple linear regression. The independent variables CON 4 1.5 Pasture 1992 – showed significant relationships among each other, CON 5 2.1 Maize, beans 1996 – as slope was correlated to both species richness (r = CON 6 24.5 Pasture 1994 – -0.63, P = 0.02) and species composition (r = 0.63, CON 7 18.9 Pasture 1992 – P = 0.02). To eliminate collinearity, the variables for ORG 1 2.4 Pasture 1985 2002 species richness, species composition and slope were ORG 2 2.8 Coffee, 1952 2002 fed into a PCA. The resulting first PC accounted for forest 71.6 % of the variance in the data. This PC provided a ORG 3 2.5 Maize, 1962 1998 consolidated variable, allowing to examine the com- beans, tobacco bined effect of farm type (conventional vs. organic), ORG 4 2.8 No Before 1999 species richness, species composition and slope on information 1989a total C-storage across farms by using an ANCOVA ORG 5 1.8 No Before 2002 without violating the assumption of no collinearity information 1989a among covariates. ORG 6 7.7 Coffee, 1912 2002 All statistical tests were performed in JMP 7.0 (SAS maize, 2007). Rarefied species richness was estimated using beans ORG 7 1.4 Plantains, Before 2002 EcoSim 7.0 (Gotelli and Entsminger 2001). The Bray a Curtis indices were calculated with PAST 1.94b coffee 1989 (Hammer et al. 2001). CON conventional, ORG certified organic farms a Historical land use of the year 1989 obtained from IGNCR (1991) Results mix of land uses (coffee and plantains, coffee and Farm history and management forest), before switching entirely to coffee production (Table 1). The organic farms had converted from Farm sizes ranged between 1.4 and 24.5 ha and on conventional management between 1998 and 2002 average the organic farms were smaller (3.1 ± 2.1 ha, (Table 1). At the time of the study each farm had been average ± standard deviation) than the conventional managed organically for at least 7 years. farms (8.4 ± 9.3 ha). The year of conversion into Average coffee plant density was 6,054 ± 1,563 coffee ranged from 1912 to 2005. Four of the farmers plants per ha, most farmers pruned their plants regularly indicated that coffee had been grown for ‘‘a long time’’ at a height of 0.3–0.5 m, and all farmers left the organic on their farms, and three farmers did not know what material from the pruning on their farm to decompose. the previous land had been (Table 1). The most The conventional farmers applied between 600 and common land use before conversion into coffee was 3,300 kg ha-1 of fertilizer per year, all of them used pasture. Four of the conventional and one of the synthetic NPK fertilizers, and two farmers comple- organic farms had been covered by cattle pastures in mented synthetic products with organic material the past. Four farms (two conventional, two organic) (Table 2). The organic farmers applied between 0 and had grown annual crops like maize, beans or tobacco, 10,500 kg of organic soil fertilizers ha-1 annually, and two of the organic farms had been covered by a which included manure, compost, coffee pulp and 123 164 Agroforest Syst (2012) 86:159–174

Table 2 Information on the management of the 14 coffee farms assessed in the Rio Grande watershed Farm Coffee Coffee Pruning Soil fertilizer Fertilizer Average plant pruning frequency application coffee density height (years) (kg ha-1 a-1) yield (n ha-1) (m) (kg ha-1 a-1)

CON 1 3,880 0.3 4 NPK 18-5-15 1,400 7,300 CON 2 6,440 0.3 4 NPK 18-2-30, chicken manure, lime 2,900 12,300 CON 3 9,170 0.3 8–10 NPK-Mg 18-5-15-6.2 800 4,500 CON 4 6,830 0.5 4 NPK-Mg 18-5-15-6.2, NPK 20-3-20, Urea 1,800 9,100 CON 5 5,140 0.4 4 NPK 18-2-14 3,300 19,200 CON 6 6,770 0.3 5 NPK-Mg 18-5-15-6.2, Magnesamon 600 13,900 CON 7 6,460 0.8 5–6 NPK 15-3-31, coffee pulp 700 9,100 ORG 1 5,330 0.3 Irregular, None (herbs and grasses as green manure) 0 3,800 selective ORG 2 7,110 0.3 6 Compost 800 1,400 ORG 3 7,310 0.5 10 Chicken and cow manure, coffee pulp 10,500 3,600 ORG 4 7,370 0.3 4–5 Fruit pulp, molasses, zinc 400 2,700 ORG 5 4,910 0.3 Irregular, None (herbs and grasses as green manure) 0 3,800 selective ORG 6 3,500 0.3 3–4 None (herbs and grasses as green manure) 0 1,700 ORG 7 4,540 0.3 3 None (herbs and grasses as green manure) 0 1,800 Coffee yield refers to the average during the last 5 years. All data were obtained by structured interviews in February 2012 (CON conventional, ORG certified organic farms) molasses. Organic matter from green manure (herbs, The largest single carbon pool was SOC with 63.1 ± grasses) was used on four organic farms as the only 21.4 Mg C ha-1, which represented an average of means of improving soil fertility (Table 2). Six conven- 69 ± 12 % of total C. On average, BGC (SOC and C tional farmers used herbicides to eliminate weeds stored in roots) was more than 2.5 times higher than extensively on the entire plantation and one conven- AGC (trees, coffee plants and litter). Within the tional farmer (CON 6) indicated to use herbicides only carbon-subplots of all farms a total of 1,337 woody very localized, because the relatively dense shade plants were measured, representing 76 different spe- canopy avoided the extensive growth of weeds on his cies. Trees and non-coffee shrubs represented the farm. Four conventional and four organic farmers used second largest carbon pool after SOC (Table 3). There living fences as erosion barriers. There was a higher was a high variability in individual carbon pools density of living fence species (Dracaena fragrans and across farms, especially for C stored in trees and Yucca guatemalensis) on the organic farms (95 ± 117 coffee plants (Table 3). plants ha-1) than on the conventional farms (48 ± 68 On average the amount of total C stored on organic plants ha-1). Additionally, three of the organic farmers farms was 43 % higher compared to the conventional used ditches or holestotraporganic litter on steep slopes. farms (Mann–Whitney-test, Z = 2.04, P = 0.04), Coffee yield (cherries, approximate average during although no statistically significant differences were the last 5 years) on the conventional farms was found between individual carbon pools (P = 0.06 and 10,771 ± 4,833 kg ha-1. The organic farmers pro- P = 0.07 for AGC and BCG, respectively) (Table 3). duced 2,686 ± 1,059 kg ha-1, roughly 25 % com- Vegetation structure differed clearly between man- pared to conventional production (Table 2). agement systems. Whereas only 9 % of the trees measured across all conventional farms exceeded Aboveground and belowground carbon stocks 10 m in height, 23 % of the trees measured on organic farms ranged between 10 and 30 m. Consequently, The average total C (AGC ? BGC) stored across trees were significantly shorter under conventional farm types was 92.6 ± 29.0 Mg C ha-1 (Table 3). management (6.2 ± 4.6 m, compared to 7.2 ± 4.4 m, 123 Agroforest Syst (2012) 86:159–174 165

Table 3 Estimated carbon stored (mean ± standard devia- 87 species were found on all 7 organic farms and 65 tion) in different pools of aboveground (AGC) and below- were found across all conventional farms (Table 4). ground carbon (BGC) on 14 coffee farms in the Rio Grande The number of individuals per ha ranged between watershed 84 and 641 and on average was almost twice as high on Carbon All farms Conventional Organic farms the organic farms than on the conventional farms pool (Mg ha-1) farms (Mg ha-1) (Mg ha-1) (Mann–Whitney test Z = 2.43, P = 0.02) (Table 4). The total number of species per ha ranged between 5 Trees 17.9 ± 12.1 12.6 ± 12.4 ns 23.2 ± 10.1 ns and 37, and the rarefied number of species s* varied Coffee 2.8 ± 2.0 3.4 ± 2.2 ns 2.3 ± 1.7 ns between 5 and 21.6 ± 1.0 (average from 1,000 plants iterations ± standard deviation). The average number Litter 4.1 ± 1.6 3.3 ± 0.9 ns 4.8 ± 1.8 ns of rarefied species was not significantly higher on the Total 24.8 ± 12.0 19.3 ± 12.1 ns 30.2 ± 9.7 ns organic farms, compared to the conventional farms AGC (Mann–Whitney test Z = 0.64, P = 0.52) (Table 4). Soil 63.1 ± 21.4 53.1 ± 11.6 ns 73.0 ± 25.1 ns organic C Evenness values were overall relatively low (average Simpson’s Measure E = 0.24 ± 0.10), reflecting Roots 4.8 ± 2.5 3.8 ± 2.6 ns 5.8 ± 2.1 ns 1/D that the coffee farms were generally dominated by Total 67.8 ± 22.1 56.9 ± 11.7 ns 78.8 ± 25.3 ns BGC a few woody plant species. There were no signifi- Total C 92.6 ± 29.0 76.1 ± 18.4 a 109.1 ± 29.1 b cant differences in evenness between management systems (Mann–Whitney tests Z =-0.26, P = 0.78) Different letters a, b indicate statistical difference between (Table 4). conventional and organic farms, Mann–Whitney test P \ 0.05 The first 5 PC that were extracted from a PCA on a ns not significant (P [ 0.05) Bray Curtis similarity matrix, presented eigenvalues [1 and together accounted for 77.5 % of the variance Mann–Whitney test, Z =-3.99, P \ 0.0001). Aver- in the data across all farms (Table 5). The loadings of age wood density was significantly lower under the eigenvectors show that the similarity indices for conventional management as well, with 0.37 ± the farms CON 3, CON 5, ORG 6 and ORG 7 had the 0.12 g cm-3, compared to 0.41 ± 0.13 cm-3 under most important influence on the first PC. The indices organic management (Mann–Whitney test, Z =-5.72, for the farms CON 2, ORG 3 and ORG 4 had the P \ 0.0001). highest leverage on the second PC. ORG 4 and CON 2 had the highest average Bray-Curtis similarity indices Woody plant species diversity and similarity from pairwise comparisons with all other farms (0.30 between farm systems and 0.28, respectively). The loadings of the eigenvectors for the farms CON 1, CON 7 and ORG 2 had the In total 5,091 woody plants (DBH C 5 cm) were highest influence on the third PC (Table 5). counted on the 1 ha plots of all 14 farms. These plants By plotting the scores of the first two PC (43.5 % of belonged to 108 different species. Of these, a total of variance), farms were separated in three distinctive

Table 4 Average number of woody plants (±standard devia- seven certified organic coffee farms in the Rio Grande tion) with a DBH [ 5 cm, rarefied number of species per ha and watershed, Costa Rica evenness (Simpson’s Measure, E1/D) for seven conventional and Farm type Individuals (n ha-1) Total species Rarefied Evenness (n) species (n ha-1)

Conventional 249 ± 151 a 65 13.0 ± 5.0 ns 0.27 ± 0.13 ns Organic 479 ± 131 b 87 13.9 ± 4.4 ns 0.22 ± 0.05 ns Overall 364 ± 181 108 13.4 ± 4.6 0.24 ± 0.10 Different letters a, b indicate statistical difference between conventional and organic farms, Mann–Whitney test P \ 0.05 ns not signiﬁcant (P [ 0.05) 123 166 Agroforest Syst (2012) 86:159–174

Table 5 Coefficients of the first five principal components, based on pair-wise Bray-Curtis similarity indices for woody plant species composition across 14 coffee farms in the Rio Grande watershed Farm PC 1 PC 2 PC 3 PC 4 PC 5 Eigenvectors Eigenvectors Eigenvectors Eigenvectors Eigenvectors

CON 1 0.173 0.156 0.435 -0.194 0.196 CON 2 0.238 0.494 -0.066 0.041 0.039 CON 3 20.351 0.324 0.065 0.105 -0.064 CON 4 0.331 0.245 0.104 -0.099 20.505 CON 5 0.386 0.091 0.330 -0.109 -0.011 CON 6 0.084 -0.140 0.300 20.455 0.362 CON 7 0.044 -0.177 20.456 -0.257 0.322 ORG 1 0.250 -0.177 0.151 0.535 0.108 ORG 2 0.100 -0.080 20.417 -0.267 20.388 ORG 3 -0.278 0.373 -0.108 0.164 0.295 ORG 4 0.139 0.518 -0.219 0.079 0.140 ORG 5 0.284 -0.244 -0.062 0.510 0.047 ORG 6 20.365 -0.053 0.273 0.026 20.432 ORG 7 20.373 -0.018 0.221 0.061 0.097 Eigenvalue: 3.39 2.70 2.15 1.60 1.01 Cumulative percentage 24.2 43.5 58.8 70.3 77.5 of variance: CON conventional, ORG certiﬁed organic farms. Bold numbers indicate eigenvectors that contribute most to the differences between farms

Species composition and C-storage

The farms within the three groups segregated by the PCA, based on species composition also differed in the primary function of dominant tree species and total C-storage. The first group consisted of 3 organic (ORG 3, ORG 6, ORG 7) and 1 conventional farm (CON 3) (Fig. 1). This group was characterized by high total C-storage (126 ± 28 Mg ha-1) and contained the three farms with the highest BGC and total C-storage across all sampled farms (ORG 3, ORG 6 and ORG 7). Woody plant species diversity was high; the average Fig. 1 Principal component analysis, based on pair-wise Bray- number of rarefied species across these four farms was Curtis similarity indices for woody plant species composition 15.4 ± 4.8. In terms of species composition the farms across 14 coffee farms in the Rio Grande watershed, assessed were dominated by living fence species, mainly D. between November 2008 and April 2011. The first two fragrans (27 % relative abundance), timber species components explain 24.2 and 19.3 % of the variation in the data, respectively. Filled circles correspond to conventional such as Cedrela odorata, Cordia alliodora and Diphysa coffee farms, white circles to certified organic farms. Identical americana (altogether 24 % relative abundance) and a numbers indicate paired farms; letters indicate the micro- broad variety of fruiting trees (22 %), such as Citrus watershed where each farm is located spp., Mangifera indica, Spondias purpurea, Annona muricata and Byrsonima crassifolia (Fig. 2). Nitrogen groups, based on the similarity of woody plant species fixing shade trees (legumes that tolerate frequent composition. These groups did not consistently corre- pruning such as Erythrina berteroana, Inga vera, Inga spond to farm type (conventional vs. organic) (Fig. 1). densiflora and Albizia adinocephala) comprised 19 % 123 Agroforest Syst (2012) 86:159–174 167

mainly by Inga spp. (19 %) and E. fusca (16 %). Timber species (Juglans olanchana, C. odorata, D. americana, Tababuia rosea and others) made up 30 % of the individuals, followed by trees with multiple purposes (Bursera simarouba, 14 %), living fence species (Y. guatemalensis and D. fragrans, 11 %) and fruit trees (S. purpurea, Citrus spp., 10 %). Forest species made up only 3 % of the species assemblage across these ﬁve farms (Fig. 2).

Contribution of individual tree species and species composition to C-storage on farms Fig. 2 Relative abundance of woody plants with different primary functions across three distinctive groups of coffee farms. The groups have been segregated by principal component In terms of total AGC-storage across all farms, analysis (Fig. 1), based on species composition (pairwise Bray E. poeppigiana was the most important species, due Curtis similarity of the studied 14 farms) to its high abundance, high absolute frequency (it occurred on 9 out of 14 farms) and to relatively large of the relative abundance across this group. The tree diameters (Table 6). Together with two other proportion of species that mostly regenerate spontane- species, the genus Erythrina accounted for almost a ously (‘‘forest species’’ such as Cecropia spp., Tecoma quarter of the total individuals counted on the 1 ha stans and Anacardium excelsum) was 8 %. On these plots across all farms, and for a third of the total four farms woody plants were distributed relatively AGC from woody plants. Tree heights of Erythrina evenly across different functional groups (Fig. 2). spp. were usually maintained below 5 m by pruning. The second group of farms included 4 conventional E. berteroana has a slightly higher wood density, and farms (CON 1, CON 2, CON 4, CON 5) and 1 organic on average was maintained at a larger stature than the farm (ORG 4) (Fig. 1). Average storage of total C was other species of this genus. C. odorata, a native timber low (73 ± 13 Mg ha-1). The three farms with the tree, was the second most important species in terms of lowest AGC-storage (CON 1, CON 4 and CON 5) and its contribution to total AGC-storage across farms. the two farms with the lowest total C-storage (CON 4 The genus Inga was represented by three different and CON 5) were part of this group. Woody plant species which altogether accounted for 15 % of AGC species diversity was low as well. The average number from woody plants across all farms. I. densiflora and of rarefied species was 11.2 ± 3.9. These farms were I. vera are characterized by relatively high wood clearly dominated by nitrogen fixing shade trees densities. Other species accumulated high sums of (Erythrina poeppigiana, Erythrina fusca and Inga AGC primarily due to high abundance (D. fragrans, spp., altogether 59 % relative abundance). E. poepp- or Citrus aurantium), high specific wood densities igiana comprised 38 % of all trees across these five (P. guajava, D. americana), or relatively large stature farms. Living fence species, such as D. fragrans and (Guazuma ulmifolia, Ficus jimenezii). D. americana Y. guatemalensis made up 26 % of woody plants, and G. ulmifolia also stored the highest amounts of followed by fruit trees (Citrus spp., S. purpurea and AGC per tree, besides the one exceptionally large Psidium guajava, 8 % relative abundance). Five individual of F. jimenezii that was estimated to store percent of all individuals could be classified primarily more than 2.5 Mg of AGC on one of the conventional as timber trees and only 1 % were forest species farms (Table 6). (Fig. 2). The third group of farms consisted of three organic The effects of management, topography, farms (ORG 1, ORG 2, ORG 5) and two conventional plant diversity and species composition on total farms (CON 6 and CON 7) (Fig. 1). This cluster was carbon stocks intermediate in terms of total C-storage (87 ± 18 Mg ha-1). These farms were also dominated by Individually, all of the examined independent variables nitrogen fixing legumes (45 % relative abundance), farm type (Mann–Whitney-test, Z = 2.04, P = 0.04, 123 168 123

Table 6 The 20 most important woody plant species, in terms of their contribution to estimated AGC stored on seven conventional and seven certiﬁed organic coffee farms in the Rio Grande watershed, Costa Rica, according to measurements taken between November 2008 and April 2011 (measurements from four 0.05 ha subplots per farm) Species Wood DBH Height (m) Total Frequency Total C Share Average C per species density (cm) abundance (n farms) (Mg) of AGC C per tree (Mg ha-1 ) (g cm-3 ) (n) woody (Mg) plants (%)

Erythrina poeppigiana 0.20 26 ± 14 7.5 ± 7.1 162 9 10.304 20.6 0.064 ± 0.196 5.7 ± 7.9 Cedrela odorata 0.33 17 ± 13 8.7 ± 4.8 99 8 5.678 11.3 0.057 ± 0.175 3.5 ± 4.1 Inga densiﬂora 0.58 21 ± 12 8.6 ± 4.0 48 5 4.172 8.3 0.087 ± 0.161 4.2 ± 3.8 Erythrina berteroana 0.25 28 ± 18 8.8 ± 5.7 41 2 3.934 7.9 0.096 ± 0.230 9.8 ± 2.9 Inga vera 0.56 21 ± 6 7.8 ± 2.8 58 3 2.984 6 0.052 ± 0.037 5.0 ± 6.4 Ficus jimenezii 0.32 135 18.9 1 1 2.636 5.3 2.636 13.2 Juglands olanchana 0.42 13 ± 4 11.4 ± 3.4 94 2 2.286 4.6 0.024 ± 0.032 5.7 ± 2.5 Erythrina fusca 0.22 18 ± 11 5.0 ± 3.5 107 5 2.270 4.5 0.021 ± 0.050 2.2 ± 3.1 Spondias purpurea 0.31 20 ± 12 7.0 ± 2.6 62 8 2.151 4.3 0.035 ± 0.044 1.3 ± 1.8 Cordia alliodora 0.33 21 ± 14 10.5 ± 4.9 25 6 1.939 3.9 0.078 ± 0.131 1.6 ± 1.3 Diphysa americana 0.60 20 ± 19 7.9 ± 4.0 15 5 1.868 3.7 0.125 ± 0.207 1.9 ± 3.0 Citrus aurantium 0.46 11 ± 6 4.7 ± 1.8 112 6 1.130 2.3 0.010 ± 0.015 0.9 ± 1.1 Guazuma ulmifolia 0.45 35 ± 14 12.3 ± 3.1 5 2 0.899 1.8 0.180 ± 0.108 2.2 ± 0.1 Gliricidia sepium 0.50 17 ± 11 8.8 ± 7.5 14 2 0.864 1.7 0.062 ± 0.096 2.2 ± 2.4 Albizia adinocephala 0.46 14 ± 8 6.8 ± 5.6 21 5 0.745 1.5 0.036 ± 0.070 0.7 ± 1.0 Tecoma stans 0.46 12 ± 10 5.8 ± 3.0 22 5 0.539 1.1 0.025 ± 0.084 0.5 ± 0.9 gooetSs 21)86:159–174 (2012) Syst Agroforest Dracaena fragrans 0.46 7 ± 2 4.7 ± 1.1 175 6 0.469 0.9 0.003 ± 0.003 0.4 ± 0.4 Inga sp. 0.49 10 ± 6 3.6 ± 1.5 75 3 0.444 0.9 0.006 ± 0.011 0.7 ± 0.5 Psidium guajava 0.63 13 ± 12 5.0 ± 3.8 8 3 0.428 0.9 0.054 ± 0.139 0.7 ± 1.1 Albizia saman 0.42 45 19.9 1 1 0.405 0.8 0.404 2.0 Agroforest Syst (2012) 86:159–174 169

regression R2 = 0.61, P = 0.001) and the slope of the terrain (linear regression R2 = 0.46, P = 0.007) had a significant effect on total C-storage (Fig. 3). The first PC extracted from a PCA on the three continuous independent variables for species richness, species composition and slope explained 71.6 % of the variance in the data, with an eigenvalue of 2.15. The remaining two components showed eigenvalues \1. The combined effect of farm type and the consolidated PC based on species richness, species composition and slope on total C-storage was highly significant (whole 2 model ANCOVA, adjusted R = 0.83, F2,11 = 33.08, P \ 0.0001). The individual effects of farm type, and the PC for species richness, species composition and slope both were highly significant within the ANCOVA model (P = 0.004 and P \ 0.0001, respectively).

Discussion

C-storage in organic and conventional coffee agroforestry systems

Average total C-storage estimates from this study (93 ± 29 Mg ha-1) fall within or below ranges reported for comparable carbon pools in tropical agroforestry systems. For example, Albrecht and Kandji (2003) indicated a carbon storage potential between 39 and 102 Mg ha-1 for agroforestry in the humid tropics of South America. Soto-Pinto et al. (2010)foundthat coffee farms in Mexico stored between 122 and 150 Mg C ha-1 in living biomass and soils (0–20 cm). Studies from Costa Rica estimated between 93 and 195 Mg ha-1 of total C-storage for shaded coffee farms (Avila et al. 2001; Mena-Mosquera 2008). These two studies included SOC to a depth of 25 and 30 cm, Fig. 3 Univariate effects of a the rarified number of woody respectively. plant species (s* ± standard deviation) b species composition With an average of 24.8 ± 12.0 Mg ha-1 the (first component from a PCA on Bray Curtis similarities across estimates for AGC fall into the lower range of values all farms) and c) the slope of the terrain on total carbon storage reported for shade grown coffee in Costa Rica. Avila on seven conventional and seven certified organic coffee farms in the Rio Grande watershed, Costa Rica. Filled circles et al. (2001), Mena-Mosquera (2008) and Salgado- correspond to conventional coffee farms, white circles to Vasquez (2010) gave total AGC estimates between 6 certified organic farms and 70 Mg ha-1, whereas De Melo and Abarca-Monge (2008) considered a wider range (9–154 Mg ha-1) for AGC stored only in the trees on coffee farms. One Table 3), estimated species richness from rarefac- reason for the relatively low AGC values obtained by tion analysis (linear regression R2 = 0.41, P = 0.01), this study could be that the lowest wood density found species composition (the first component from the in the literature was applied in the allometric equation PCA on the Bray-Curtis similarity matrix) (linear from Chave et al. (2005) for a given tree species. 123 170 Agroforest Syst (2012) 86:159–174

The average SOC-storage found in this study farmers incorporated more erosion barriers, which was (63.1 ± 21.4 Mg ha-1) fell within or below the range reflected by a higher average density of living fence reported by similar studies. Avila et al. (2001) and species (D. fragrans, Y. guatemalensis) per hectare. Mena-Mosquera (2008) reported high SOC values for Three organic farmers applied additional erosion coffee farms in Costa Rica (81–161 Mg ha-1, repre- control techniques, such as ditches to retain SOM on senting between 63 % and over 90 % of total C). Soto- steep slopes. Soil erosion may be further decreased by Pinto et al. (2010) estimated SOC-storage between 83 the absence of herbicides. All conventional farmers and 108 Mg C ha-1 for coffee agroforestry in Mexico applied herbicides, whereas most of the organic (0–20 cm soil depth), which represented over 70 % of farmers indicated to use the herb layer as green total C found above 1,000 m a.s.l. manure (Table 2). The application of herbicides leads As expected, total carbon storage was significantly to soil exposure (Hartemink 2006), and thus may higher on organic farms than on conventional farms, contribute to soil erosion and SOC loss. According to although the differences between individual carbon USDA (2000) and Payan et al. (2009) C is directly pools were only nearly statistically significant (Table 3). added to the soil through inputs of manure and organic The effect of farm type also was highly significant within amendments under organic management. In the pres- the combined ANCOVA model that included a consol- ent study three organic farmers and two conventional idated covariate for species diversity, composition and farmers applied organic fertilizers to their soils, slope. Results from other studies that compare C-storage however, with the exception of one farm (ORG 3), in organic and conventional coffee farms appear to vary. soil fertilizer inputs on organic farms were very low For example, Salgado-Vasquez (2010) did not find a (Table 2). significant effect of management type on the AGC of There are some factors that might confound the true coffee farms in Costa Rica and Nicaragua, whereas effect of farm type on current SOC-storage. Sander- Payan et al. (2009) reported that SOC concentrations man and Baldock (2010) emphasized that SOC- were higher on organic coffee farms in Costa Rica, storage between farming systems cannot be properly probably due to the higher input of organic matter from compared without knowing their baseline SOC status. shade trees. In this study the SOC levels at the time of the In the present study there were several differences conversion to organic management were unknown, in management between farm types that could have which leads to uncertainties about the effect of farm contributed to the significant differences in total management on SOC. Conventional farms on average C-storage. Average tree density per hectare, tree were converted into coffee more recently, and four out height and wood density were significantly higher on of seven farms had previously been covered by cattle the organic farms, which potentially enhanced AGC- pasture, which could potentially affect baseline SOC storage. Accordingly, Muschler (2000) argued that levels in comparison to the organic farms (Table 1). there are numerous motivations for organic coffee Furthermore, Jobbagy and Jackson (2000) pointed out farmers to incorporate trees. Besides erosion control, that high clay contents stabilize SOC. There is no benefits include N-fixation, enhanced nutrient cycling, detailed information on soil texture for the sampled as well as natural pest control. farms, although the paired sample design aimed to The organic farms were certified according to minimize physical differences between management criteria from USDA National Organic Program systems. Finally, there is no information on the (USDA 2000) and the national legislation (MAG vertical distribution of SOC on the studied farms. 2001), which both prescribe specific soil conservation Consequently the SOC measurements to a depth of practices, such as erosion control. It has been shown 25 cm might substantially underestimate this C-pool. that slope is strongly correlated with soil erosion in SOC represents potentially one of the largest C-pools coffee agroforestry systems (e.g. Martinez-Torres in tropical agroforestry systems. For future studies 2008). As the present study found that slope had a it is recommendable to compare time series between significant effect on total C-storage across manage- management systems, to analyze SOC in different ment systems, it appears that preventing erosion depths across the plant rooting zone and to control for can contribute to maintaining SOC-storage. Organic physical variables such as texture.

123 Agroforest Syst (2012) 86:159–174 171

Relationships between woody plant diversity SOC than plantations of non N-fixing trees. Species and C-storage in coffee agroforestry systems complementarity can further be achieved by combining plants of different stature and shade tolerance into Plant species diversity and species composition did multilayered agroforestry. For example, tall timber significantly affect total C-storage across all sampled trees with relatively small crowns such as C. alliodora farms. The available results on relationships between and Tabebuia rosea do not seem to have a pronounced plant diversity and carbon storage in tropical agrofor- negative effect on coffee yield, (Muschler 2000; estry systems so far are mixed. Kirby and Potvin (2007) Cordero and Boshier 2003; Haggar et al. 2011). Niche did not find a correlation between tree species diversity differentiation by rooting depths may be another and C-storage in Panamanian home gardens. Similarly, important factor (Fridley 2001). Jobbagy and Jackson Henry et al. (2009) did not find a relationship between (2000) found that plant functional types (grass, shrubs, perennial plant diversity and AGC-storage in home trees) control the vertical distribution of SOC in soils gardens and other agricultural systems in Kenya. On on a global scale, due to different rooting zones and the other hand Saha et al. (2009) did report a positive shoot/root allocations. These findings could be rele- relationship between plant diversity and SOC-storage vant for maximizing SOC-storage in agroforestry in Indian home gardens. systems, as well. According to Tilman et al. (1997), both species According to Tilman et al. (1997) the number of composition and species diversity determine the vari- functional traits within an assemblage might have a ation in resource use efficiency and resource require- stronger effect on ecosystem processes than the number ments within a community. Diverse communities may of species. In this study, three distinctive clusters of enhance ecosystem functions through niche differenti- farms were determined, based on species composition ation and facilitation (complementarity effects). Addi- (Figs. 1, 2). The farms which were strongly dominated tionally, they are more likely to include highly efficient, by pruning-tolerant, N-fixing shade trees (mostly competitive species that may increase overall produc- Erythrina spp.) seemed to store less carbon than those tivity of the system (sampling effects) (Tscharntke et al. that showed a more even distribution of trees with 2005; Kirby and Potvin 2007). different primary functions (living fence, timber, Fridley (2001) argued that species composition is N-fixing shade trees, fruit trees and forest species). It not random in managed systems and that stochastic can be assumed that the diverse tree functions utilized immigration and extinction events are limited. In the by farmers imply a broader variety of structural and case of coffee agroforestry systems, we are not likely physiological traits, which in turn may enhance to observe primarily the effects of plant diversity on agroecosystem function and C-storage. C-storage, but the effects of widely intentional plant assemblages. These assemblages exploit and enhance functional traits which are in accordance with the Conclusions management goals of the farmer. Coffee farmers may take advantage of well-known complementarity Coffee agroforestry systems provide considerable car- mechanisms that potentially increase overall produc- bon storage potential and SOC seems to be of partic- tivity and thus C-storage. ularly high importance. Organic management may One of the best documented facilitating mecha- further enhance C-storage. Organic systems incorpo- nisms in plant communities is nitrogen fixation by rated more trees per hectare that significantly differed legumes (Vandermeer 1995; Tilman et al. 1997; in stature and average wood density from those in Fridley 2001). Shade trees like Erythrina spp. and conventional farms. Furthermore, organic management Inga spp. fix nitrogen and offer desirable functional relies on soil improvement by incorporating vegetation traits, such as fast growth and tolerance to pruning. elements, the application of organic amendments, green Leaf litter and mulch from pruning may improve manure and erosion barriers. Increasing slopes nega- physical soil properties and increase SOC (Muschler tively affected total C-storage across management 2000; Cordero and Boshier 2003; Youkhana and Idol types, which emphasizes the importance of erosion 2009). Resh et al. (2002) found that tropical forest control as a strategy for increasing C-storage in tropical plantations with N-fixing trees accumulated more agriculture. 123 172 Agroforest Syst (2012) 86:159–174

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