Soil macrofauna as an indicator for evaluating soil based ecosystem services in agricultural landscapes

Acta Oecologica 100 (2019) 103446

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Soil macrofauna as an indicator for evaluating soil based ecosystem services in agricultural landscapes

T

E. Velasqueza,∗, P. Lavelleb a b

Universidad Nacional de Colombia Sede Palmira, Colombia Université Paris Sorbonne (IEES-IRD), Paris, France

ARTICLE INFO

ABSTRACT

Keywords: Indicators Soil quality Morphology Biodiversity

Soils participate in the provision of numerous ecosystem services of great importance for the maintenance of ecosystems and human societies. Physical and chemical soils properties sustain supporting ecosystem services like plant production and the infiltration and provision of clean water. Carbon sequestration is a regulating ecosystem service important for climate regulation. Soils are also home of a large biodiversity whose participation in soil processes is critical for their sustainable use. Evaluating these functions to assess the provision of ecosystem services is an important challenge for which simple and accurate tools and methodologies are requested. In this work, the relationship of the soil macro-fauna with some soil-based ecosystem services was evaluated in three different regions of Colombia, Brazil and Nicaragua with rather contrasted conditions of soils and management options. In each country, we calculated a set of sub-indicators of physical quality, chemical fertility, organic matter accumulation, soil macro aggregation and macro-invertebrate communities abundance and diversity. The combination of these sub indicators in a General Indicator of Soil Quality (GISQ) provided an overall assessment of the quality of the soil and associated ecosystem services. The strong relationship observed between macrofauna indicators and soil physical, chemical and organic matter characteristics makes it a powerful tool for evaluating and monitoring soil quality.

1. Introduction Ecosystems provide a wide range of benefits to humanity, from food, drinking water and protection against floods to cultural heritage. However, many of these benefits, known as ‘ecosystem services', are under severe threat due to human pressures (Science for Environment Policy, 2015; Sukhdev et al., 2014). Changes in biodiversity and plant covers due to human activities have been faster in the last 50 years than at any other time in the history of mankind. The drivers of change that led to the loss of biodiversity either do not show any signs of decline over time, or increase in intensity (Millennium Ecosystem Assessment. MEA, 2005). The Earth planet has already lost a large part of its biodiversity and a simple look at the rising prices of raw materials and food shows the consequences of this loss for society. The extinction of species and the deterioration of ecosystems are inevitably linked to human well-being and, therefore, it is urgent that corrective actions be taken (TEEB, 2008). Population and economic growth and the conversion of natural ecosystems for agricultural exploitation will continue and this significantly affects ecosystem services, especially the ones that depend on soil quality. Soils are deteriorating rapidly due to erosion, indiscriminate use of fertilizers and pesticides, natural depletion of nutrients, loss of organic carbon

∗

and biodiversity, compaction and other threats. The increase in soil erosion, desertification, salinity and the expansion of urban areas over natural areas are increasing this problem. Soils provide ecosystems with key services such as climate regulation through C sequestration nutrient cycling operated via decomposition of soil organic matter (SOM), flood and erosion control (Brussaard et al., 1997; Swift et al., 2004). Most of these services derive from closely related ecological processes. However, although several frameworks have been proposed for their evaluation, they are generally not sufficient and clear in their interpretation. Several authors affirm that these services are still poorly recognized by policy-makers or by societies in general (Sukhdev et al., 2014) and that tools are needed to evaluate in a holistic way the contribution of soils to ecosystem services, to limit their use and management within acceptable ecological limits (Costanza et al., 1997; Dominati, 2013; De Groot et al., 2012). The soil hosts a quarter of the planet's biodiversity and the invertebrate communities of the soil are highly sensitive to changes in the ecosystems. Their effects on soil processes - for example, decomposition of organic matter, formation of biogenic structures, nutrient cycling and disease control - have been extensively reviewed (Lee and Foster, 1991;

Corresponding author. Carrera 32 Chapinero, vía Candelaria Palmira, Colombia. E-mail address: [email protected] (E. Velasquez).

https://doi.org/10.1016/j.actao.2019.103446 Received 21 July 2018; Received in revised form 2 February 2019; Accepted 1 July 2019 Available online 20 September 2019 1146-609X/ © 2019 Published by Elsevier Masson SAS.

Acta Oecologica 100 (2019) 103446

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Fig. 1. Different steps in calculation of sub indicators of soil quality (used as indicators of associated ecosystem services) and the general indicator of soil quality GISQ. (after Velasquez et al., 2007).

Pankhurst et al., 1995; Edwards and Bolhen, 1996; Paoletti, 1999; Lavelle y Spain, 2001; Lavelle et al., 2006), but they are also very susceptible to the various factors of change. In this sense, it is vitally important to generate tools such as soil quality indicators that allow evaluating the favourable or unfavourable changes that occur in soils at the plot, landscape and regional levels (Ruiz-Camacho, 2004). In this work, we evaluated the relationship among soil macrofauna communities and some soil-based ecosystem services assessed in a set of sub indicators further combined into a General Indicator of Soil Quality. This indicator was generated in regions of three different countries: (i) the Wibuse microbasin in Nicaraguan hilly regions; (ii) three Amazonian landscape windows in the Brazilian Para state, located in the municipalities of Maçaranduba, Palmares and Pacajá and (iii) the Potrerillo micro-basin in Colombia. At all sites, different types of land use were investigated, among them the natural forests, agroforestry and conventional perennial or annual crops. We tested the hypothesis that macroinvertebrate communities are accurate indicators of soil based ecosystem services as assessed by a set of sub indicators further combined in a synthetic indicator. Our prediction was that changes in soil quality associated with land use systems would determine changes in macroinvertebrate communities. We also expected to find significant correlations among soil macroinvertebrate data and corresponding descriptors of the chemical, and physical states of soils in different types of landuse, soil macroaggregation and other elements of its morphology and soil organic matter. If demonstrated, this statement would justify using macroinvertebrate communities as surrogates of soil quality in monitoring changes in soil quality associated to management practices and restoration programs.

800–1600 mm annual rainfall and is normally covered with tropical savanna; temperatures vary between 22.5 and 25.0 °C, with a dry season between November and April (Velasquez, et al. 2007a). Five different soil types can be found: (i) Lithic Haplaquoll (Mollisol with minimum horizon development and aquic conditions); (ii) Typic Usthorthent (an sthorthent refers to a common Entisol with ustic moisture regime), (iii) Vertic Tropudalf (a Tropudalf is an Alfisol with udic moisture regime in the Tropics); (iv) Ultic Tropudalf (Ultic refers to properties typical for an Ultisol); (v) Aquic Argiustolls (an Argiustoll is a Mollisol with ustic moisture regime that has an argillic horizon) (Velasquez, et al. 2007a). Sampling in Nicaragua was conducted during 4 weeks, in May 2001. Sampling was done at 61 points regularly distributed across the landscape every 200 m on a regular grid covering in total 2 km2. Eighteen points were located on pastures (PAS), 17 in maize crops (MC), eight in coffee plantations (CP), eight in fallows (FW), four in secondary forests (SF), three in mixed cultures (MIX), two in house yards (HY) and one on an eroded land (Velasquez, et al. 2007a). The methodology used and original data can be found in Velasquez, et al. 2007a and Fig. 1. 2.2. Brazil The sampling was carried out between February and May 2008, in three landscape windows located in the municipalities of Maçaranduba, Palmares and Pacaja, in the state of Para), with different degrees of deforestation (30–90%), colonization histories and types of exploitation. The three landscape windows are located between 3°0 ′- 6°0′ South and 49°0′- 52°0′ W. At Maçaranduba, annual average temperature is 27 °C, annual rainfall 2118 mm and average altitude, 99 m above sea level. Soils have a sandy loam texture in most places. Palmares has an average temperature around 26 °C, annual precipitation of 1870 mm and an average altitude of 179 mm, with predominantly clayey and clay sandy loam soils. Pacaja, has an average temperature of 26 °C, annual precipitation of 211 1 mm, an average elevation of 165 m and soils generally with sandy loam texture. The zone has a unimodal precipitation pattern, with maximum rainfall in December–April, and a dry season from August to October. Soils are mostly Ferralsols (FAO classification), in a landscape mainly composed of pastures, forests, crops and fallows. We selected 27 farms (9 in each landscape window) with representative socioeconomic conditions of the area. The sampling was carried out during the rainy season at all the sites. In each farm, 5 points

2. Materials and methods Part of the data included in this article have already been published (Velasquez et al., 2005; Velasquez, et al. 2007a; Grimaldi et al., 2014). 2.1. Study sites Nicaragua: The Wibuse watershed is a 170 km2 area located in the San Dionisio municipality (Matagalpa Department), 125 km North East to Managua, in the central mountain range of Nicaragua. It belongs to the watershed of the Calico river, located at 12°45′N and 85°49N at elevations ranging between 350 and 1100 m. The site has an 2

Acta Oecologica 100 (2019) 103446

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were taken every 200 m along a 1 km transect located on the largest dimension of the farm. A total of 135 points were thus sampled representing 11 different types of land use: Crop (14), Fallow (17), Burned Forest (10), Forest exploited (24), Conserved forest (15), Clean pasture (18), Overgrown pasture (17), Mixed pasture (3), Perennial (4), Old secondary forest (7) and Young secondary forest (6). Variables evaluated were grouped into five categories: Macrofauna; Physical properties; Chemical fertility; Morphology and Organic Matter.

2.8. Colombia Samples were taken in february 2001 from eight land use systems in the Potrerillo micro watershed (Cauca Department, Colombia), located at an altitude of 1500 m, with a 19.3 8 °C average temperature and 1900 mm annual average rainfall with a bimodal pattern. Soils are derived from volcanic ash and classified as Oxic Dystropepts (Inceptisols) in the USDA soil classification system. We sampled three types of agroforestry systems: (i) trees associated with perennial crops such as fruits and coffee plantations; (ii) mixed home gardens; and (iii) agro sylvopastoral systems. In the micro watershed 51% of the soil is under cultivation, 31% in pastures, 14% in secondary forests and 4% in fallow (Velasquez et al., 2005). Sampling was completed in 20 days in February 2001. Eight land use systems were evaluated in an area of 1 km2: a coffee plantation (CP), a pasture (PAS), a secondary forest (SF), an eroded plot without vegetation (ERO), a 1-year fallow after an intensive cassava crop (FAC), a coffee plantation under shade (CPS), fallow in a water-logged zone (FWL) and a degraded PAS with grazing (DPG) (Velasquez, et al. 2007a). At each of the eight sites, ten samples were taken every 5 m on two parallel transects of five points separated by a 1 0 m distance. The soil variables evaluated were grouped into five categories: Macrofauna, Morphology, Chemical fertility, Physical properties and Organic matter.

2.3. Macroinvertebrate community The macrofauna was determined using the standard ISO 23611–5 (Lavelle, 1988; Anderson & Ingram, 1993). At each of the 5 sampling points in each farm, we took 3 blocks of soil 25 × 25cm and 30 cm deep. Macroinvertebrates visible at the naked eye were taken from 4 successive soil layers: surface leaf litter, 0–10, 10–20 and 20–30 cm. They were conserved in 75% alcohol and the worms in formaldehyde at 4%. They were identified and 8 variables were generated: Oligocheta; Pontoscolex corethrurus; Formicidae; Isoptera; Coleoptera; Myriapoda; Taxonomic diversity and others (leaf litter fauna). All are expressed as individuals per square meter (Table Annex 1). 2.4. Physical quality Ten variables were measured at each point using classical methodologies: bulk density (Bd), total porosity (P), moisture content percentage of sand, silt and clay, vertical resistance (VR), shear strength (SS), Infiltration speed (INFIL), macroporosity, plant available water at 0–10 cm depth (AW010) and maximum water content at 0–10 cm (MW010) (Table Annex 2).

2.9. Macroinvertebrate community Macrofauna was sampled using the standard ISO 23611-5 TSBF method. Soil monoliths 25 × 25cm and 30 cm of depth were taken, the macro invertebrates were separated from the 4 successive soil samples: litter, 0–10, 10–20 and 20–30 cm; later they were placed in alcohol at 75% and the worms in formaldehyde at 4%. Finally, they were identified at the level of taxonomic units (Table Annex 6).

2.5. Chemical fertility All physical and chemical variables were measured in soil taken from each monolith used for the macrofauna sampling. Samples were air dried and passed through a 2 mm sieve. Nine variables described soil chemical fertility: available phosphorus, exchangeable K, Ca, Mg, Na, pH, CEC, sum of bases and CEC/total bases. Cation concentrations were quantified by atomic absorption. The available phosphorus was determined with fluorine ammonium and hydrochloric acid. Soil pH was measured in 02:01 water/suspended soil (Table Annex 3).

2.10. Morphology Soil morphology was evaluated according to the methodology developed by Velasquez et al. (2007a., 2007b), At each point a monolith 10 × 10cm and 5 cm deep was taken and separated among (1) Biogenic aggregates, produced by “ecosystem engineers” such as earthworms, termites, ants and some coleopterous larvae; (2) Physical aggregates, produced by physical-chemical processes; (3) Aggregates produced by the roots, (4) Organic matter (fragments of stems, leaves, wood, seeds, flowers), (5) roots, (6) stones and (7) residual material from the separation of the previous ones components was sieved (2.00 mm) and considered as non-macro aggregated soil. All the components were dried for 15 days in the shade and weighed later.

2.6. Morphology Soil morphology was described according to Velasquez et al. (2007a. 2007b). At each point a 10 × 10cm and 5 cm deep monolith was taken and gently broken apart into (1) Biogenic aggregates, produced by “ecosystem engineers” such as earthworms, termites, ants and some larvae of coleoptera. These macroaggregates can be differentiated by their dark colours (earthworm casts), circular shapes, galleries and internal macropores, (2) Physical aggregates, produced by animal trampling and drying/ rewetting sequences, differentiated by their flat and angular forms, (3) Aggregates produced by roots, seen as small millimetric structures stuck to the roots (4) Organic residues (fragments of stems, leaves, wood, seeds, flowers), (5) Soil non macroaggregated, that passed through the sieve (6), roots and (7) stones. All the components of the soil block were dried for 15 days in the shade and weighed later (Table Annex 4).

2.11. Chemical fertility Physical and chemical variables were measured from soil taken in the monoliths collected for faunal assessment or from undisturbed cores immediately adjacent to them, when necessary. Two composite soil samples 1 kg each were obtained, in each one of the eight land use units by mixing soil from the upper 10 cm of five of the monoliths on each transect. Chemical fertility was assessed through eight variables: extractable P (P-Bray II), total P-total, exchangeable K, Ca, Mg, Al. Cation contents were quantified by atomic absorption. Total phosphorus was measured by a colorimetric method with ammonium molybdate after acid digestion; the available P (P-Bray II), with ammonium fluoride and hydrochloric acid (Velasquez et al., 2005). (Table Annex 7).

2.7. Organic matter The ten variables evaluated were: total nitrogen at three depths (0–10; 10–20; 20–30 cm); mineral N (NH4+), Carbon at two depths (0–10 cm and 0–30 cm), Carbon in tree and shrub biomass (TreeCS), Carbon in total biomass (VegCS), Carbon in the plant necromass (NecCS) and total Carbon (Cstocktot) (Table Annex 5).

2.12. Physical state Six variables were measured in the central zone of each plot using established methodologies: Bulk density (BD), real density (RD), 3

Acta Oecologica 100 (2019) 103446

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Table 1 Proportion of variance explained by axes 1 and 2 of PCAs of macrofauna, physical, morphological, chemical and OM sub indicators and separation of landuse types by Monte Carlo tests (p value). Brazil

Colombia

Axes (%)

Macrofauna Morphology Chemical Physical OM

p-value

1

2

33.2 22.9 59.4 26.8 40.1

17.3 20.4 15.9 26.5 28.8

P P P P P

< < < < <

0.001 0.001 0.001 0.001 0.001

Nicaragua

Axes (%)

p-value

1

2

33.4 45.6 53.9 57.5 47.6

24.9 23.6 27.2 25.1 20.2

Porosity (P) derived from the last two variables, moisture content (M) (% dry soil), Shear strength (SS) and penetration resistance with a hand penetrometer Eijkelkamp (PR). (Table Annex 8).

P P P P P

< < < < <

0.001 0.001 0.001 0.001 0.001

Axes (%)

p-value

1

2

21.6 43.2 32.8 36.4 43.0

11.6 12.0 23.2 23.6 21.9

P P P P P

< < < < <

0.001 0.001 0.001 0.001 0.001

history of land use would be comprised in the part of variance that remains unexplained. 3. Results

2.13. Organic matter

A significant proportion of variance was explained by the variables in all cases and land uses were significantly separated, for all the sub indicators, with no exception (Table 1). Coinertia analysis performed on the same tables showed significant covariations among all the groups of variables, with only two exceptions in Nicaragua. In all cases, the first two factors explained more than 50% of the total variance. (Table 2). For each country, a General Index of Soil Quality (GISQ) was generated from the five sub-indicators. The formula was further applied to each sampling site (Velasquez et al., 2007a). Brazil:

Total C, total N and mineral N (N–NH4+ and N–NO3-) and Ludox organic matter fractions (OMF) were evaluated in the Colombian sites. Mineralization of soil organic carbon was measured by the method of the dynamic closed chambers. The Ludox method, was used to determine the OMF. This method distinguishes three fractions depending on their density: LL—light (> 150 μm, < 1.13 g cm-3), IL—intermediate (> 150 μm, 1.13–1.37 g cm-3), and HL—heavy (> 150 μm, > 1.37 g cm-3). In brief, an air-dried soil sample (250 g) was gradually wetted, then flooded with 2:1 of water, thoroughly mixed, and sieved through two superimposed sieves (top, 250 mm; bottom, 150 mm). The Ludox light (LL), intermediate (LM) and heavy (HL) fractions were obtained by density fractionation of the 150 mm size fraction in a colloidal silica suspension gravimetrically adjusted to 1.13 and 1.37 g cm-3. Ludox fractions were washed three times with 100 ml deionised water before drying to constant weight at 40 °C Velasquez et al., 2005. (Table Annex 9).

GISQ = 0.90 SIOM + 0.30 SIPhysical + 0.30 SIFauna +0.7 SIMorphology + 1.30 SIChemistry Colombia: GISQ = 1.90 SIOM + 1.10 SIPhysical + 1.70 SIFauna + 1.50 SIMorphology + 1.00 SIChemistry Nicaragua:

2.14. Data treatment

GISQ = 1.51 SIOM + 1.13 SIPhysical + 1.11 SIFauna + 1.10 SIMorphology + 0.35 SIChemistry

Five groups of soil variables were constituted according to the ecosystem services that they indicate: (organic matte for climate regulation, chemical fertility for primary production, soil physical quality and morphology for hydrological services). Macroinvertebrate communities were a principal component analysis (PCA) and a discriminant analysis were run using the R software and the ade4 package for multivariate analysis. Coinertia analyses were carried out to describe and test the significance of covariation among the five data sets: (i) macrofauna; (ii) chemical fertility; (iii) SOM variables; (iv) physical parameters; (v) morphology variables. Formulation of the general indicator of soil quality (GISQ) followed four different steps: (i) PCA analysis of each of the five sets of variables allowed testing the significance of their variation among land use types; (ii) identification of the variables that best differentiate the sites according to soil quality; (iii) creation of sub indicators of soil physical quality, chemical fertility, organic matter, morphology and soil macrofauna, with values ranging from 0.10 to 1.00; (iv) combination of all five sub indicators into a general indicator GISQ (Velasquez, et al. 2007a) (Fig. 1). For each country, a PCA was made with each group of variables and the ability of the variables tested (Macrofauna, Morphology, Organic matter, Chemical fertility, Physical state) to separate land uses was tested with a Monte Carlo test on PCA coordinates. Test done with the ade4 library allow determining the proportion of variance explained by currently observed land uses and indicate to what extent this effect is significant. Other effects linked to possibly different soil types and

While the organic matter sub indicator had the highest weight in the GISQ of Colombia and Nicaragua, Chemistry has the greatest influence in the Brazilian GISQ. Macrofauna, second in importance in Colombia, had less weight in the other two countries. In all cases, however, the soil macro-fauna significantly separated the different types of land use with similar patterns as the GISQ (Table 1). In Brazil, the lowest values of GISQ were observed in annual crops, “clean” and mixed pastures, followed by fallows and young secondary forests. The highest values were calculated in the forests (Table 3) (Fig. 2). Crop and pasture systems that exhibited higher soil chemical quality, due to ash or fertilizer additions, generally had relatively low accumulations of organic matter. They also had low values of the morphology sub indicator as a result of low proportions of biogenic macro aggregates and high abundance of non-aggregated or physically aggregated soil. The PCA of the macrofauna data set ranked land use systems in a comparable order as the PCA done on all sub indicators (Fig. 3). Forests were associated with high density and taxonomic richness, opposite to pastures and annual crops. Fallows and secondary forests represented intermediate situations. In Colombia the highest values of the subindicators and of GISQ were recorded in the old secondary forest (GISQ = 1.0) and in an ungrazed pasture (GISQ = 0.9) (Table 4) (Fig. 4). F1 that classified sites according to values of all indicators explained a very high (76.7%) 4

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Table 2 Co-inertia analysis among groups of variables. Axes: Percentage variance explained by axes 1 and 2; p-value: significance of differences observed among different land uses. Coinercia

Brazil

Colombia

Axes (%)

Macrofauna-Morphology Macrofauna-Phisic Macrofauna-Chemical Macrofauna-OM Morphology-Phisic Morphology-Chemical Morphology-OM Phisic-OM Phisic-Chemical

p-value

1

2

59.2 54.4 50.4 63.0 55.6 70.9 74.9 89.3 77.7

20.7 37.1 32.0 19.5 24.6 18.3 13.4 8.4 15.9

P P P P P P P P P

< < < < < < < < <

0.005 0.001 0.001 0.019 0.001 0.001 0.001 0.001 0.002

Nicaragua

Axes (%)

p-value

1

2

68.2 65.4 80.8 91.6 93.0 88.2 84.1 85.5 87.0

26.4 21.3 13.8 3.8 6.2 9.9 11.5 12.2 11.5

P P P P P P P P P

< < < < < < < < <

0.054 0.009 0.001 0.001 0.028 0.240 0.005 0.037 0.001

Axes (%)

p-value

1

2

73.9 68.3 45.6 98.0 67.0 54.9 72.9 87.5 62.0

8.5 14.4 19.2 1.32 21.0 28.3 10.4 6.3 25.3

P P P P P P P P P

< < < < < < < < <

0.001 0.013 0.004 0.001 0.008 0.140 0.130 0.009 0.047

p-valor♠ = Montecarlo test.

proportion of variance. F2 that stressed the positive effect of secondary forests on organic matter accumulation had a much lower discriminating power. Analysis of macroinvertebrate communities provided rather similar results, ranking sites according to the abundance and diversity of macrofauna along axis 1 (Fig. 5). Secondary forests and ungrazed pastures that had the highest abundance and diversity of

macroinvertebrate projected on the left end side of the plane, opposite to eroded soils, and fallows. In Nicaragua, the land use systems with the highest values of the sub indicators and the GISQ were secondary forests, coffee plantations and fallows (Table 5); and the systems with the lowest soil quality were eroded soil, house yards and maize crops (Table 5, Fig. 6). Like in Brazil and Colombia, a similar separation was observed with the soil macro-

Table 3 Brazil. Average values of the sub-indicator in each land use system. M: mean value; R: range; Std: Standard Deviation. N: number of sites represented; OM: Organic matter sub indicator; GISQ: General Indicator of Soil Quality. Landuse Annual crops (n = 14) Fallows (n = 17) Burned forests (n = 10) Exploited forests (n = 24) Preserved forests (n = 15) Cleaned pastures (n = 18) Invaded pastures (n = 17) Mixed pastures (n = 3) Perennial crops (n = 4) Secondary old forests (n = 7) Secondary young forests (n = 6)

M R Std M R Std M R Std M R Std M R Std M R Std M R Std M R Std M R Std M R Std M R Std

Macrofauna

Morphology

Chemical

Phisical

OM

GISQ

0.39 0.22–0.55 0.11 0.60 0.38–0.93 0.14 0.47 0.31–0.65 0.11 0.56 0.28–0.80 0.14 0.58 0.30–0.85 0.14 0.46 0.10–0.74 0.16 0.57 0.13–0.94 0.21 0.42 0.35–0.47 0.05 0.57 0.45–0.69 0.10 0.54 0.44–0.86 0.13 0.65 0.37–1.00 0.20

0.51 0.25–0.81 0.18 0.45 0.23–0.82 0.15 0.55 0.38–0.87 0.13 0.55 0.17–0.96 0.20 0.57 0.31–0.90 0.15 0.51 0.10–0.86 0.18 0.57 0.27–1.00 0.19 0.58 0.14–0.91 0.32 0.50 0.22–0.61 0.16 0.51 0.26–0.69 0.17 0.53 0.38–0.79 0.14

0.53 0.28–.77 0.15 0.41 0.18–0.82 0.20 0.26 0.14–0.37 0.07 0.33 0.18–1.00 0.19 0.25 0.10–0.57 0.10 0.43 0.21–0.78 0.15 0.37 0.24–0.58 0.09 0.48 0.29–0.81 0.24 0.48 0.35–0.75 0.16 0.32 0.15–0.66 0.16 0.47 0.26–0.74 0.18

0.74 0.41–0.95 0.12 0.73 0.56–0.99 0.12 0.86 0.65–1.00 0.10 0.66 0.43–0.90 0.13 0.68 0.51–0.81 0.08 0.44 0.21–0.90 0.17 0.46 0.23–0.74 0.15 0.34 0.10–0.58 0.20 0.54 0.45–0.62 0.06 0.69 0.47–0.84 0.12 0.65 0.51–0.75 0.10

0.27 0.16–0.36 0.06 0.34 0.22–0.47 0.07 0.22 0.10–0.32 0.07 0.40 0.18–1.00 0.16 0.43 0.23–0.67 0.11 0.27 0.12–0.40 0.08 0.32 0.18–0.41 0.07 0.33 0.29–0.39 0.04 0.39 0.33–0.43 0.04 0.30 0.12–0.45 0.14 0.26 0.20–0.38 0.06

0.49 0.27–0.67 0.11 0.55 0.27–0.77 0.15 0.67 0.54–0.79 0.07 0.65 0.10–1.00 0.18 0.73 0.55–0.90 0.09 0.51 0.12–0.74 0.14 0.57 0.34–0.72 0.09 0.51 0.10–0.74 0.29 0.52 0.27–0.63 0.15 0.61 0.41–0.80 0.12 0.48 0.23–0.66 0.16

Ma = Mean. Rb = range. Rb = range. Stdc = standard deviation. ne = Sites with equal land use. OMd = organic matter. GISQf = General Indicator of Soil Quality. 5

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Fig. 2. Projection of sites in factorial space defined by subindicators of soil quality and the GISQ in Brazilian sites. (a) Variables of subindicators and GISQ with factors 1 and 2 of PCA. (b) Projection of sites in the plane defined by factors 1 and 2. (c) Boxplot representation of GISQ values in different types of landuse.

Fig. 3. Projection of sites in F1F2 factorial space defined by Macrofauna variables in Brazilian sites. (a) Macrofauna variables. (b) Projection of sites and barycenters of land use categories.

Table 4 Subindicators and general indicator (GISQ) values according to landuse system in Colombia. Sub-indicators

Coffee Plantations

Secondary Forest

Ungrazed pasture

Eroded soil

Fallow after intensive crop

Coffee plantation under shade

Fallow in water logged zone

Pasture with grazing

Macrofauna Morphology Chemical Physical OM GISQ

0.3 0.7 0.2 0.4 0.2 0.3

0.8 1.0 0.5 0.5 0.9 1.0

1.0 0.9 0.5 0.9 0.6 0.9

0.2 0.1 0.1 0.3 0.2 0.1

0.2 0.2 0.3 0.4 0.3 0.2

0.7 0.3 0.7 0.4 0.5 0.5

0.7 0.9 0.9 0.8 0.5 0.8

0.6 0.4 0.2 0.4 0.3 0.4

aOM = organic matter. bGISQ = value average of the general indicator of soil quality in each soil-use system. 6

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Fig. 4. Projection of Colombian sites in factorial space F1F2 defined by subindicators and GISQ. (a) Subindicator and GISQ variables. (b) Projection of sites in the plane defined by factors 1 and 2.

fauna data set (Fig. 7).

variables to properly describe the different aspects of soil qualities. This has led some authors to propose a minimum set of variables to measure (Bispo et al., 2009; Lawton., et al., 1998; Ritz., et al., 2009). In our studies, we have identified such minimum sets. Macroinvertebrate sampling and soil morphology provide the same set of variables everywhere. In contrast, chemical and physical sets of variables varied slightly depending on sites, mainly due to variables facilities and available resources. At the Brazilian sites, variables like infiltration rate or maximum water content in the 0–10 cm strata were measured at the Brazilian site and not at the other two. Some chemical parameters were also measured at different depths in soil while in others, only the top 10 cm as analyzed. These variables, however, tend to covary, and our studies show that, locally, the number of variables required to describe some aspect of soil quality, may be largely reduced after an initial study. We show, for example, that in the regional context considered in this study, there is no need to measure bulk density at different depth in the soil since there is a very high correlation

4. Discussion Due to the growing state of environmental degradation on a global scale, the use of tools such as quality indicators is required to assess each situation, forecast trends and implement activities that lead to prevention, correction, mitigation or recovery. The generation of this type of indicators requires a lot of basic information that is obtained through the evaluation of multiple variables (linked to the characteristics of each region) highly correlated and whose analysis is translated in synthetic form. The evaluation of soil quality has been, and still is, a matter for much debate and controversies. A large number of definitions and indicators of different kinds have been proposed, as cently reviewed by Bunemann et al. (2018). A general lesson learnt from this synthesis and previous ones as well (Turbé et al., 2010) is the need to consider a large number of

Fig. 5. Colombia: Projection of macrofauna variables (a) and sites according to land uses (b) in the F1F2 factorial space of a PCA analysis of the Colombian data set. 7

Acta Oecologica 100 (2019) 103446

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Table 5 Nicaragua. Average values of the sub-indicator in each land use system. M: mean value; R: range; Std: Standard Deviation. N: number of sites represented. MO: Organic matter sub indicator; GISQ: General Indicator of Soil Quality. Range

Coffee Plantations

Secondary Forests

Soil-Use System Fallows

Pastures

Maize Crops

House Yards

Mixed Crops

Eroded soil

Average

nc=(8)

n=(4)

n=(8)

n=(18)

n=(17)

n=(2)

n=(3)

n=(1)

M R M R M R M R M R M R

0.78–0.42 0.56 1.00–0.54 0.72 0.39–0.10 0.26 0.72–0.29 0.45 1.00–0.69 0.89 1.00–0.69 0.81

0.73–0.11 0.51 0.84–0.54 0.33 0.51–0.25 0.51 0.73–0.19 0.69 0.80–0.14 0.57 0.77–0.20 0.57

1.00–0.38 0.44 0.56–0.16 0.66 0.68–0.25 0.37 0.90–0.42 0.40 0.84–0.31 0.54 0.80–0.42 0.62

0.57–0.20 0.38 0.61–0.22 0.41 0.79–0.21 0.42 1.00–0.28 0.64 0.82–0.23 0.49 0.78–0.31 0.54

0.57–0.31 0.45 0.52–0.10 0.35 1.00–0.10 0.71 0.78–0.15 0.52 0.79–0.27 0.37 0.76–0.19 0.45

0.39–0.10 0.44 0.24–0.21 0.26 0.36–0.18 0.38 0.33–0.10 0.49 0.58–0.51 0.50 0.32–0.25 0.28

0.58–0.38 0.24 0.48–0.10 0.22 0.88–0.48 0.27 0.54–0.50 0.21 0.47–0.32 0.55 0.58–0.36 0.47

0.33–0.33 0.33 0.31–0.31 0.31 0.17–0.17 0.17 0.15–0.15 0.15 0.10–0.10 0.10 0.10–0.10 0.10

Sub-indicators

Macrofauna Morphology Chemical Phisic MO* GISQd

Ma = Mean. Rb = range. nc = Sites with equal land use. GISQd = General Indicator of Soil Quality.

among these different variables. It will be enough in future studies to assess this attribute in the 0–10 cm layer where the time and cost dedicated to this analysis will be minimized. Most chemical variables describing the nutrient status of our soils were equally highly correlated, to the point that in most cases changes in pH might be sufficient to follow losses or gains in basic cations and subsequent changes in Al saturation. In each of the regions of the three countries included in this study, different groups of representative variables of the genesis of each soil were evaluated and highly correlated with each other. The high values of the coinercia analysis show the correlation between all the variables. Each principal component analysis (PCA) significantly separated the systems of land use, according to the variables evaluated and the ecosystem services measured through them.

Variables describing a given aspect of soil quality, and their respective synthetic sub indicators covaried significantly in all our sites. This covariation among ecosystem services, widely observed, with either negative (considered as trade offs) or positive (synergies) relationships. has generated the concept of “multiple” or “bundles of” ecosystem services (Cardinale et al., 2007; Bennet et al., 2009; Raudsepp-Hearne et al., 2010). The relationship among soil based ecosystem services and soil macroinvertebrate communities has also been observed in several other case (Lavelle et al., 2014, 2016) allowing to conclude that it is a general pattern, at least across mixed landscapes that associate productive and natural ecosystems. The proportion of variance explained by these relationships among soil based ES and macroinvertebrate communities, the RV Matrix correlation

Fig. 6. Projection of Nicaraguan sites in factorial space F1F2 defined by sub-indicators and GISQ. (a) Sub-indicator and GISQ variables. (b) Projection of sites in the plane defined by factors 1 and 2. 8

Acta Oecologica 100 (2019) 103446

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Fig. 7. Nicaragua: Projection of macrofauna variables (a) and sites according to land uses (b) in the F1F2 factorial space of a PCA analysis of the Colombian data set. C: Boxplot representation of variations of the macrofauna sub indicator across different types of land use.

coefficient, however, may be relatively low (Table 2). Previous studies indeed have shown that changes in soil indicators after the change in land use was implemented, may occur at different speeds, depending on the processes involved. In land use systems derived from degraded natural savannas old Colombia, macroinvertebrate communities would change first, followed by physical and morphological attributes. Chemical parameters would vary very little except if massive soil amendments were practiced (Lavelle et al., 2014). In our study, macroinvertebrate communities significantly varied with all the other sub indicators. Since this indicator can be assessed at a very low cost and with no specific equipment, it should be widely used. The growing interest in soil restoration to establish sustainable environmental friendly practices will require indicators to monitor progresses and adapt practices to local conditions. A whole set of studies, mainly conducted in tropical areas, show the accuracy of indicators of invertebrate communities as predictors of indicators of soil based ecosystem services (Lavelle et al., 2014; Marichal et al., 2014., Sanabria et al., 2014). A great advantage of indicators based on macroinvertebrate communities is their simplicity and low cost which facilitates their use by technicians and farmers. With the help of basic documentsand a minimum training in the field, farmers become able to evaluate soil macroinvertebrate communities and get an overall estimation of soil based ecosystem services. Preliminary published studies and on-going research have shown the potential of species of ants, termites and other macroinvertebrates to indicate determined ranges of values for soil based ecosystem services. On farm collaborative research and the organization of training activities and appropriate documents are activities that will allow developing such a promising perspective. They will considerably help developing new sustainable agriculture practices that will replace the current conventional practices and help restore soil quality that they severely impaired in most cases. Soil macrofauna can be used as an indicator of soil quality, of fast, easy and economic management. Its relationship with the different physical-chemical and organic matter characteristics, make it a powerful tool for evaluation and monitoring of soil quality.

Each of the sub-indicators of soil quality generated in this work allows to evaluate the status and production of different ecosystem services. Supportive ecosystem services, necessary for the production of other services, such as the nutrient cycle can be measured through the chemical fertility subindicator and their values can facilitate decision making in the application of various types of fertilizers. The physical subindicator can be used to measure hydraulic properties such as water infiltration and storage, two very important ecosystem services that are linked to regulatory services such as flood control and protection against erosion and floods. The subindicators of organic matter and soil morphology allow the evaluation of regulatory ecosystem services linked to climate regulation. Through the morphology it is possible to evaluate the state of aggregation of the soil that determines important properties of this, such as structure, infiltration, water retention and carbon storage. The structure of the soil affects plant growth by influencing the distribution of the root and the ability to drink water and nutrients. The increased transfer of water through the soil can reduce the retention of fertilizer in the soil matrix and the effectiveness of fertilizer use in plants. The morphology of the soil also allows to evaluate the activity of some groups of soil macrofauna through the bio-structures that these create and that are directly related to the hydraulic properties (infiltration and storage of water) and carbon sequestration. The presence of biogenic aggregates of different sizes, as well as invertebrates and roots, shows a high biological activity, which probably indicates a high quality in the soil processes and an optimal biological regulation in the functioning of the soil. All these ecosystem services are intimately related and can be integrated into a soil quality indicator that will allow the accurate detection of any problem in soil function. This will facilitate the identification of sustainable agricultural practices and the measurement of ecosystem services in the soil. These indicators will also help in the design of soil restoration projects and facilitate the implementation of monitoring mechanisms, once the appropriate interventions have been carried out. It will also be very useful in evaluating general policies aimed at protecting or improving soil quality. 9

Acta Oecologica 100 (2019) 103446

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Table Annex 1

Macrofauna communities in 27 farms Brazil (Pará state) Landuse

Oligocheta

P. corethrurus

Formicidae

Isoptera

Coleoptera

Myriapoda

Otherse

(ind m2)

(ind m2)

(ind m2)

(ind m2)

(ind m2)

(ind m2)

(ind m2)

Annual crops (nd = 14)

Ma Rb Stdc

35.43 0.0–192 49.9

13.71 0.00–176.00 46.9

541.71 128.00–1968.00 558.0

195.43 0.00–1264.00 332.0

200.00 32.00–368.00 110.3

137.14 0.00–288.00 100.0

108.57 16.00–208.00 56.2

Fallows (n = 17)

Ma Rb Stdc

145.9 0.00–960.00 222.9

64.0 0.00–336.00 86.0

993.9 48.00–3792.00 913.0

1372.2 0.00–5904.00 1843.1

212.7 96.00–464.00 91.0

161.9 0.00–544.00 129.8

116.7 32.00–304.00 71.5

Burned forests (n = 10)

Ma Rb Stdc

86.40 0.00–352.00 109.35

1.60 0.00–16.00 5.06

377.60 48.00–1152.00 357.71

724.80 0.00–4624.00 1421.91

169.60 16.00–464.00 122.83

230.40 64.00–544.00 148.61

161.60 48.00–336.00 84.48

Exploited forests (n = 24)

Ma Rb Stdc

108.00 0.00–384.00 96.61

31.33 0.00–192.00 47.53

1355.33 0.00–10016.00 2265.92

877.33 16.00–7968.00 1647.89

164.67 32 .00–352.00 101.68

196.67 0.00–656.00 168.83

171.33 16.00–736.00 182.88

Preserved forests (n = 15)

Ma Rb Stdc

108.80 0.00–464.00 127.17

46.93 0.00–160.00 52.88

1484.80 0.00–13312.00 3307.17

707.20 0.00–4352.00 1167.80

142.93 32.00–320.00 75.16

201.60 0.00–960.00 232.71

195.20 64.00–848.00 213.65

Cleaned pastures (n = 18)

Ma Rb Stdc

83.56 0.00–432.00 128.9

64.89 0.00–512.00 129.1

542.22 48.00–3008.00 818.3

479.11 0.00–1680.00 518.1

263.11 16.00–960.00 207.0

109.33 0.00–544.00 156.3

74.67 16.00–192.00 50.9

Invaded pastures (n = 17)

Ma Rb Stdc

150.59 0.00–432.00 156.16

132.71 0.00–704.00 202.53

588.24 0.00–2592.00 731.69

1158.59 0.00–4432.00 1518.01

143.06 0.00–448.00 106.05

123.29 0.00–368.00 124.29

117.65 0.00–576.00 145.65

Mixed pastures (n = 3)

Ma Rb Stdc

74.67 0.00–144.00 72.15

32.00 0.00–80.00 42.33

581.33 288.00–832.00 274.50

549.33 0 .00–1568.00 883.10

229.33 112.00–448.00 189.54

69.33 0.00–144.00 72.15

21.33 16.00–32.00 9.24

Perennial crops (n = 4)

Ma Rb Stdc

104.00 0.00–224.00 105.7

32.00 0.00–80.00 39.2

396.00 96.00–592.00 214.8

324.00 128.00–528.00 165.4

64.00 16 .00–96.00 34.6

256.00 96.00–464.00 152.9

224.00 96.00–560.00 224.4

Secondary old forests (n = 7)

Ma Rb Stdc

80.0 32.00–192.00 55.4

38.9 0 .00–256.00 95.9

628.6 48.00–3296.00 1180.6

1842.3 32 .00–6336.00 2240.5

246.9 32 .00–544.00 155.1

182.9 48.00–352.00 118.6

128.0 80.00–288.00 72.1

Secondary young forests (n = 6)

Ma Rb Stdc

408.00 0.00–1072.00 432.0

40.00 0.00–240.00 98.0

666.67 208.00–1184.00 390.2

1461.33 48.00–5424.00 2016.0

218.67 112.00–432.00 124.2

232.00 96.00–304.00 73.5

293.33 144.00–400.00 101.0

Ma = Mean. Rb = range. Stdc = standard deviation. nd = Sites with equal land use. Otherse = other litter invertebrates. Diversityf = taxon richness.

10

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Table Annex 2

Ranges and average values of physical variables in each land use system. Brazil (Pará state) Landuse

BD10e

Vr

SSg

Clay

Silt

Sand

INFILh

Macpor010i

AW010j

MWk10

(g cm3-1)

(kgf cm-2)

(kPa)

(%)

(%)

(%)

(mm h-1)

(cm)

(cm)

(cm)

Annual crops (nd = 14)

Ma Rb Stdc

1.33 1.09–1.52 0.1

52.04 21.50–134.00 31.0

17.68 11.50–29.00 4.6

24.95 7.53–56.93 13.0

13.90 4.58–27.87 6.6

56.63 36.95–82.71 10.3

2189.32 52.65–6364.64 1935.3

1.69 0.64–2.21 0.5

1.24 0.58–2.28 0.5

3.50 2.58–4.27 0.4

Fallows (n = 17)

Ma Rb Stdc

1.3 1.09–1.55 0.1

42.7 19.00–100.50 20.2

23.1 12.50.43.00 8.6

17.8 2.37–45.84 10.5

15.7 5.42–56.74 12.0

61.8 19.10–90.52 18.5

1738.8 19.00–6366.18 1585.4

1.8 0.58–2.39 0.5

1.1 0.22–2.18 0.5

3.6 3.09–4.14 0.3

Burned forests (n = 10)

Ma Rb Stdc

1.16 1.00–1.25 0.08

53.45 12.50–134.00 38.21

19.55 10.50–31.00 6.07

54.07 37.94–71.18 11.00

7.63 4.82–10.45 1.84

34.71 19.86–51.09 9.28

3433.83 715.52–8228.57 2115.49

1.82 1.161–2.39 0.48

1.40 0.58–1.976 0.47

3.16 2.63–3.88 0.34

Exploited forests (n = 24)

Ma Rb Stdc

1.37 1.15–1.57 0.11

63.33 25.00–134.00 25.44

23.80 10.00–38.25 6.70

19.23 8.67–56.13 13.57

15.95 4.29–33.23 6.48

56.16 10.74–74.02 15.50

1857.87 219.10–8861.53 1874.15

1.70 0.95–2.20 0.30

1.15 0.611–3.10 0.52

3.41 2.97–4.14 0.32

Preserved forests (n = 15)

Ma Rb Stdc

1.36 1.21–1.47 0.09

58.05 31.50–103.00 17.76

23.53 11.00–38.50 8.37

16.60 4.80–48.94 11.28

12.95 6.51–38.49 8.34

61.00 28.42–77.66 12.96

2810.19 285.14–7200.00 2024.14

1.78 1.13–2.31 0.30

1.03 0.46–1.89 0.38

3.40 3.06–3.77 0.20

Cleaned pastures (n = 18)

Ma Rb Stdc

1.44 1.14–1.62 0.1

93.08 47.00–134.00 29.1

53.97 13.50–75.50 17.0

23.94 1.98–69.52 17.8

14.60 2.86–33.46 7.9

50.32 6.49–76.99 20.5

548.33 16.64–3200.00 746.6

1.38 0.93–1.93 0.3

1.19 0.50–2.63 0.5

3.04 2.36–3.93 0.4

Invaded pastures (n = 17)

Ma Rb Stdc

1.45 1.18–1.67 0.11

92.76 29.50–134.00 29.48

48.40 7.50–91.50 24.12

23.57 5.85–65.31 18.88

10.37 3.65–19.44 4.89

57.36 29.78–77.91 16.26

589.90 17.48–3113.51 787.81

1.55 1.16–2.32 0.33

1.11 0.02–2.21 0.58

3.14 2.45–4.09 0.40

Mixed pastures (n = 3)

Ma Rb Stdc

1.47 1.35–1.57 0.11

122.17 98.50–134.00 20.50

57.50 28.50–96.00 34.74

8.69 2.82–13.67 5.48

9.47 8.03–11.37 1.72

68.21 44.02–83.11 21.13

753.94 326.34–1238.70 458.86

1.32 1.13–1.57 0.23

1.50 1.43–1.54 0.06

3.38 2.97–3.70 0.38

Perennial crops (n = 4)

Ma Rb Stdc

1.50 1.43–1.61 0.1

65.25 34.5.0–88.00 25.8

27.75 13.5–35.5 9.9

14.31 10.25–16.46 2.9

17.55 11.13–29.61 8.5

60.15 52.78–69.23 7.0

2216.73 530.51–6031.12 2609.3

1.59 1.27–1.70 0.2

1.06 0.67–1.34 0.3

3.33 3.02–3.71 0.3

Secondary old forests (n = 7)

Ma Rb Stdc

1.3 1.05–1.53 0.1

65.7 33.50–119.50 31.1

27.6 16.00–51.50 11.9

26.3 8.44–52.76 17.2

20.8 8.31–63.59 20.0

46.4 10.28–76.52 24.0

1494.3 420.43–4800.00 1546.1

1.6 1.20–2.05 0.3

1.4 1.05–1.59 0.2

3.5 2.96–4.03 0.4

Secondary young forests (n = 6)

Ma Rb Stdc

1.43 1.29–1.62 0.1

48.25 32.75–63.00 14.2

24.63 15.50–33.50 6.7

25.98 7.49–38.37 12.6

12.07 7.72–17.39 4.0

59.22 40.31–74.52 12.5

2091.69 603.14–7200.00 2534.2

1.96 1.43–2.43 0.4

0.83 0.52–1.11 0.2

3.37 3.22–3.72 0.2

Ma = Mean. Rb = range. Stdc = standard deviation. nd = Sites with equal land use. BD10e = bulk density 0–10 cm. VRf = vertical resistance. SSg= Shear strength. INFILh = Infiltration rate into the soil. Macpor010i = macroporosity 0–10 cm. AW010j = Soil storage capacity of plant available water 0–10 cm. MW010k = Soil storage capacity 0–10 cm.

11

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Table Annex 3

Ranges and average values of variables related to soil chemistry in each type of landuse. Brazil (Pará state) Landuse

pH

Al

Ca

Mg

K

SBe

CECf

Vg

P

H2O

cmolc kg-1

cmolc kg-1

cmolc kg-1

cmolc kg-1

cmolc kg-1

cmolc kg-1

%

mg kg-1

Annual crops (nd = 14)

Ma Rb Stdc

6.56 4.63–8.47 1.17

0.08 0–0.46 0.14

3.98 1.31–7.38 2.02

0.83 0.28–1.54 0.37

0.24 0.09–0.57 0.12

5.06 1.84–8.26 2.11

5.13 2.13–8.26 2.04

0.97 0.85–1.0 0.05

11.39 3.17–31.74 9.53

Fallows (n = 17)

Ma Rb Stdc

5.57 4.13–7.51 0.92

0.39 0–2.40 0.61

2.28 0.26–6.47 2.02

0.96 0.17–2.06 0.64

0.17 0.06–0.32 0.08

3.43 0.58–8.61 2.58

3.82 1.53–8.61 2.34

0.84 0.30–1.0 0.22

6.72 1.93–24.83 5.10

Burned forests (n = 10)

Ma Rb Stdc

4.47 3.88–4.95 0.36

1.21 0.31–2.45 0.74

1.05 0.27–2.01 0.57

0.61 0.22–0.89 0.22

0.16 0.09–0.30 0.06

1.84 0.6–3.22 0.81

3.05 2.2–3.59 0.41

0.60 0.20–0.90 0.24

6.39 1.07–12.92 3.28

Exploited forests (n = 24)

Ma Rb Stdc

5.09 4.02–6.39 0.74

0.62 0.05–2.26 0.67

1.51 0.16–9.51 2.08

0.74 0.25–2.67 0.66

0.17 0.07–0.59 0.11

2.44 0.51–12.02 2.61

3.06 1.02–12.25 2.46

0.72 0.28–0.99 0.24

8.24 2.03–39.29 7.31

Preserved forests (n = 15)

Ma Rb Stdc

4.70 3.89–5.76 0.46

0.84 0.15–3.29 0.82

0.73 0–3.97 0.97

0.49 0.11–1.45 0.36

0.12 0.08–0.22 0.04

1.35 0.44–5.64 1.32

2.19 1–5.90 1.30

0.59 0.18–0.96 0.26

5.25 3.26–6.98 1.25

Cleaned pastures (n = 18)

Ma Rb Stdc

5.84 4.25–7.41 0.80

0.18 0–1.52 0.35

2.64 0.61–7.53 1.92

0.82 0.17–1.57 0.40

0.23 0.04–0.57 0.15

3.70 1.18–8.70 2.13

3.88 1.26–8.81 2.04

0.94 0.48–1.00 0.12

5.49 1.9–13.71 3.05

Invaded pastures (n = 17)

Ma Rb Stdc

5.68 5.68 0.68

0.21 0–0.97 0.32

1.89 0.83–3.32 0.87

0.74 0.23–1.89 0.37

0.16 0.07–0.29 0.07

2.80 1.15–5.40 1.18

3.01 1.23–5.51 1.05

0.91 0.60–1.00 0.13

6.45 1.76–22.60 4.64

Mixed pastures (n = 3)

Ma Rb Stdc

5.77 6.10 5.77

0.03 0–0.09 0.05

2.60 1.32–5.11 2.17

1.32 0.4–2.87 1.35

0.20 0.11–0.37 0.14

4.14 1.92–8.36 3.66

4.17 2.01–8.36 3.63

0.99 0.95–1.00 0.03

8.77 4.29–14.08 4.95

Perennial crops (n = 4)

Ma Rb Stdc

6.50 5.75–8.13 1.10

0.06 0–0.90 0.04

3.42 2.15–6.61 2.14

0.77 0.31–1.31 0.46

0.13 0.09–0.18 0.04

4.35 2.7–7.78 2.32

4.40 2.79–7.78 2.28

0.98 0.96–1.00 0.01

35.81 5.85–111.58 50.87

Secondary old forests (n = 7)

Ma Rb Stdc

18.70 4.43–6.05 0.60

19.42 0.12–2.30 0.88

20.55 0.2–3.44 1.10

21.62 0.12–2.26 0.74

22.91 0.08–1.21 0.40

24.46 0.59–6.93 2.19

25.81 1.37–7.17 1.92

27.36 0.20–0.97 0.31

29.76 2.38–12.84 3.61

Secondary young forests (n = 6)

Ma Rb Stdc

5.78 4.83–6.46 0.60

0.18 0–0.69 0.23

3.24 0.96–5.99 1.99

0.91 0.35–1.60 0.40

0.18 0.07–0.29 0.07

4.35 1.41–7.82 2.39

4.52 1.52–7.82 2.26

0.93 0.73–1.00 0.09

7.23 2.83–11.49 3.31

Ma = Mean. Rb = range. Stdc = standard deviation. nd = Sites with equal land use. SBe = total base exchange. CECf = cation exchange capacity. Vg = CEC/total base.

12

Acta Oecologica 100 (2019) 103446

E. Velasquez and P. Lavelle

Table Annex 4

Ranges and average values of variables related to soil morphology. Brazil (Pará state) Landuse

ABe

APf

ARg

Soilh

OMi

Roots

Stones

(g)

(g)

(g)

(g)

(g)

(g)

(g)

Annual crops (nd = 14)

Ma Rb Stdc

264.43 79.80–460.00 130.3

10.94 0.00–153.10 40.9

5.76 0.0–15.40 5.0

165.83 66.10–324.60 82.0

3.32 0.00–10.00 3.0

0.75 0.00–2.80 0.9

1.43 0.00–15.00 4.1

Fallows (n = 17)

Ma Rb Stdc

200.5 57.60–372.80 94.4

57.7 0.00–271.50 88.0

7.2 0.00–21.30 6.0

113.2 14.70–328.50 79.5

6.4 0.50–13.30 4.2

1.5 0.00–5.60 1.4

14.1 0.00–160.50 40.8

Burned forests (n = 10)

Ma Rb Stdc

258.35 206.79–361.29 49.91

13.27 0.00–112.20 35.35

14.23 0.80–53.60 16.12

95.56 16.80–135.20 39.78

6.68 2.40–15.40 5.01

1.33 0.20–2.30 0.72

0.00 0.00–0.00 0.00

Exploited forests (n = 24)

Ma Rb Stdc

220.19 25.60–390.10 106.77

20.30 0.00–250.40 55.61

11.91 1.40–50.10 11.07

164.00 70.30–394.30 77.87

7.32 0.00–30.00 6.67

2.31 1.00–10.00 1.91

28.22 0.00–467.00 100.11

Preserved forests (n = 15)

Ma Rb Stdc

232.65 61.54–374.20 93.62

6.27 0.00–51.40 16.62

10.41 0.80–16.60 4.55

192.96 93.60–345.60 76.06

5.34 1.60–12.20 3.09

2.52 1.10–6.70 1.62

9.41 0.00–76.80 22.11

Cleaned pastures (n = 18)

Ma Rb Stdc

224.33 28.40–381.00 91.3

91.99 0.00–365.80 128.8

9.13 0.00–49.50 11.1

118.32 0.00–265.70 79.7

3.55 0 .00–16.10 5.0

1.17 0.00–3.50 1.0

28.88 0.00–440.00 103.2

Invaded pastures (n = 17)

Ma Rb Stdc

257.92 53.20–446.60 116.29

89.28 0.00–421.40 132.81

10.98 0.7 0–38.30 10.59

135.29 41.00–330.40 77.63

4.70 0.00–21.40 5.56

0.75 0.07–1.90 0.65

14.14 0.00–78.30 25.25

Mixed pastures (n = 3)

Ma Rb Stdc

156.92 23.37–298.10 137.52

0.00 0.00–0.00 0.00

20.72 3.26–41.10 19.09

181.60 78.00–296.40 109.63

2.50 0.30–4.70 2.20

3.60 0.40–5.90 2.86

116.70 0 .00–350.10 202.13

Perennial crops (n = 4)

Ma Rb Stdc

248.70 54.70–327.90 130.3

66.80 0.00–267.20 133.6

3.40 1.30–5.20 1.7

124.68 73.00–183.10 47.0

6.93 3.5.0–9.60 2.9

1.43 0.20–3.00 1.2

10.63 0.00–42.50 21.3

Secondary old forests (n = 7)

Ma Rb Stdc

186.9 26.80–333.50 114.1

12.5 0.00–87.40 33.0

12.5 2.00–24.30 8.9

113.6 52.40–280.20 84.0

3.8 0.60–7.00 2.3

3.2 0.50–8.00 3.0

10.4 0.00–51.70 19.8

Secondary young forests (n = 6)

Ma Rb Stdc

263.09 96.96–406.00 105.8

8.73 0.00–52.40 21.4

6.88 0.40–16.50 6.4

154.58 31.20–324.30 110.5

4.45 0.80–9.30 3.6

1.38 0.20–2.70 1.1

0.00 0.00–0.00 0.0

Ma = Mean. Rb = range. Stdc = standard deviation. nd = Sites with equal land use. ABe = aggregates of faunal origin. APf = aggregates of physical origin. ARg = aggregates produced by the root. Soilh = soil without adding. OMi = litter organic matter.

13

Acta Oecologica 100 (2019) 103446

E. Velasquez and P. Lavelle

Table Annex 5

Ranges and average values of variables related to soil organic matter status. Brazil (Pará state) Landuse

Ne10

Ne20

Ne30

NH4+

Cf 0-1

Cg 0-3

TreeCSh

VegCSi

NecCSj

Cstocktotk

(g kg-1)

(g kg-1)

(g kg-1)

(mg kg-1)

(Mg ha-1)

(Mg ha-1)

(Mg ha-1)

(Mg ha-1)

(Mg ha-1)

(Mg ha-1) 44.95 34.64–65.17 9.47

Annual crops

Ma Rb

1.53 0.8628–2.78

1.01 0.5988–1.52

0.74 0.4671–1.23

22.12 12.02–51.51

19.67 13.07–30.08

43.88 33.46–63.12

1.07 0–6.47

1.44 0.10–6.56

4.42 0.92–12.20

(nd = 14)

Stdc

0.55

0.24

0.18

12.76

4.38

9.12

1.77

1.72

3.38

Fallows (n = 17)

Ma Rb

1.22 0.60–2.78

0.77 0.49–.08

0.62 0.26–0.95

23.50 9.8–72.59

18.62 11.75–31.88

41.64 27.56–61.18

48.78 0–246.85

49.26 0.18–247.18

6.35 2.08–20.35

Stdc

0.53

0.19

0.18

16.24

5.83

9.40

60.85

60.90

4.25

Burned forests

Ma Rb

1.97 1.32–2.79

1.29 0.64–1.86

1.01 0.60–1.60

17.25 14.13–19.52

23.20 19.66–31.65

53.41 42.04–70.65

46.30 23.12–97.53

8.07 4.70–11.40

(n = 10)

Stdc

0.46

0.38

0.28

1.90

3.67

8.51

46.18 22.34–97.51 21.97

21.88

2.41

Exploited forests

Ma Rb

1.23 0.41–2.42

0.76 0.24–1.19

0.64 0.20–1.39

28.65 8.34–91.82

20.96 11.66–49.21

43.57 32.95–72.94

168.67 37.76–1163.32

5.17 0.61–15.43

(n = 24)

Stdc

0.58

0.28

0.28

20.96

8.47

10.55

225.49

3.01

Preserved forests

Ma Rb

1.20 0.45–2.43

0.70 0.21–1.33

0.59 0.05–1.03

24.19 12.51–45.20

20.15 12.068–35.30

41.84 29.08–64.34

(n = 15)

Stdc

0.59

0.34

0.30

10.20

6.66

Cleaned pastures

Ma Rb

1.31 0.38–2.08

0.86 0.28–1.34

0.76 0.29–1.06

21.28 12.33–51.48

(n = 18)

Stdc

0.49

0.33

0.26

Invaded pastures

Ma Rb

1.13 0.50–2.18

0.69 0.31–1.32

(n = 17)

Stdc

0.52

Mixed pastures

Ma Rb

(n = 3)

167.98 36.78–1162.95 225.60

212.50 49.32–557.83

7.77 3.20–13.71

10.58

211.96 48.79–557.27 139.38

139.35

3.61

22.58 13.80–46.41

48.29 30.00–86.58

0.71 0–6.17

1.89 0.12–7.63

3.84 0.48–18.00

11.43

7.72

14.56

1.64

1.91

4.05

0.62 0.24–1.03

24.50 11.86–68.32

19.26 13.34–27.18

40.42 31.15–51.48

3.60 0–44.03

5.44 0.11–44.10

4.24 0.57–24.74

0.31

0.24

16.14

4.06

7.29

10.53

10.11

5.75

0.98 0.76–1.10

0.70 0.31–0.90

0.66 0.39–0.80

14.82 11.29–18.42

17.90 15.89–20.24

38.67 30.82–45.24

0.74 0–2.22

2.58 1.88–3.61

1.15 0.40–2.28

Stdc

0.19

0.33

0.23

3.56

2.19

7.29

1.28

0.91

1.00

Perennial crops

Ma Rb

0.83 0.56–1.28

0.54 0.31–0.76

0.41 0.22–0.57

28.44 14.30–56.05

17.42 12.40–21.60

39.84 29.25–48.13

60.50 9.06–182.69

7.19 4.15–14.89

(n = 4)

Stdc

0.34

0.19

0.18

19.06

3.94

7.83

59.95 8.10–182.25 82.01

81.91

5.15

Secondary old forests

Ma Rb

32.08 0.57–2.22

35.41 0.31–1.78

39.63 0.22–1.41

45.12 16.68–34.04

49.34 10.65–28.78

53.70 25.09–66.23

53.14 3.31–347.65

56.39 3.40–7.10

(n = 7)

Stdc

0.71

0.57

0.44

7.35

6.78

17.06

51.50 2.33–347.75 121.36

121.53

1.29

Secondary young forests

Ma Rb

1.71 0.87–2.55

0.90 0.621–1.07

0.75 0.38–0.97

24.12 9.33–58.88

22.86 13.17–25.96

47.51 31.87–53.67

10.38 2.63–20.40

3.58 1.62–6.82

(n = 6)

Stdc

0.59

0.19

0.20

16.07

4.59

7.49

5.48

1.64

Ma = Mean. Rb = range. Stdc = standard deviation. nd = Sites with equal land use. Ne10 = nitrogen 0–10 cm. Ne20 = nitrogen 10–20 cm. Ne30 = nitrogen 20–30 cm. Cf 0–1 = carbon 0–10 cm. Cg 0–3 = carbon 0–30 cm. TreeCSh = carbon in arboreal and shrub biomass. VegCSi = carbon in total biomass. NecCSj = carbon in the vegetal necromas Cstocktotk = C stock in soil.

14

10.17 2.57–20.01 5.43

90.41 33.79–286.49 61.63 99.59 77.98–144.96 19.82 211.56 91.54–1200.22 223.47 253.80 106.06–590.59 136.44 49.00 30.00–86.58 14.70 44.02 32.22–89.60 14.08 39.41 33.04–45.24 6.12 99.79 49.00–223.69 83.07 109.07 30.74–393.82 121.83 57.68 37.83–72.47 11.24

Acta Oecologica 100 (2019) 103446

E. Velasquez and P. Lavelle

Table Annex 6

Macrofauna communities in eight land use systems in the each soil-use system in the microwatershed of Potrerillo, Cauca Province, Colombia Landuse

Oligocheta

Formicidae

Isoptera

Coleoptera

Myriapoda

Diversityf

Otherse

(ind m2)

(ind m2)

(ind m2)

(ind m2)

(ind m2)

(ind m2)

(ind m2)

Coffee Plantations (nd = 10)

Ma Rb Stdc

3.5 0–14 4.4

103.8 14–500 140.9

0.0 0 0.0

2.2 0–8 3.2

1.2 0–4 1.7

5.6 2–9 2.5

11.6 1–33 10.7

Pasture with grazing (nd = 10)

Ma Rb Stdc

8.6 0–36 13.4

80.1 1–165 60.1

0.0 0 0.0

2.9 0–8 2.7

0.1 0–1 0.3

5.1 3–8 1.4

19.6 7–48 12.5

Secondary Forest (nd = 10)

Ma Rb Stdc

10.1 2–20 12.3

167.9 4–658 241.3

25.5 0–79 30.0

3.3 0–7 2.4

14.9 7–30 7.8

9.5 7–13 1.7

20.8 6–34 10.4

Eroded soil (nd = 10)

Ma Rb Stdc

3.4 0–22 6.9

237.9 0–1525 475.2

0.1 0–1 0.3

0.6 0–4 1.3

1.7 0–10 3.7

3.6 0–10 3.7

7.7 0–30 11.3

Coffee plantation under shade (nd = 10)

Ma Rb Stdc

22.1 1–44 13.4

77.9 3–374 119.8

0.1 0–1 0.3

1.2 0–4 1.2

4.4 0–9 2.8

8.1 5–11 2.2

11.3 5–23 6.5

Fallow in water logged zone (nd = 10)

Ma Rb Stdc

29.0 4–78 24.9

43.2 0–148 61.0

0.0 0 0.0

2.8 0–9 2.6

0.8 0–5 1.5

6.6 4–10 1.9

8.5 2–14 4.3

Ungrazed pasture (nd = 10)

Ma Rb Stdc

70.9 1–137 60.3

113.9 2–494 152.8

0.2 0–1 0.4

9.0 3–33 9.5

2.8 0–8 2.7

7.0 5–8 1.2

17.6 8–42 11.3

Fallow after (nd = 10)

Ma Rb Stdc

4.8 0–15 4.8

281.9 20–1539 449.0

0.0 0 0.0

1.6 0–4 2.3

0.7 0–3 1.3

5.9 3–7 1.2

11.0 4–25 6.2

Ma = Mean. Rb = range. Stdc = standard deviation. nd = Sites with equal land use. Otherse = other litter invertebrates. Diversityf = taxon richness.

Table Annex 7

Chemical variables in each soil-use system in the microwatershed of Potrerillo, Cauca Province, Colombia Variables

Depth

Soil mixa

cm P total (mg kg-1)

P Bray II (mg kg-1)

Ca (meq/100 g)

Mg (meq/100 g)

0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30

M1 M2 M1 M2 M1 M2 M1

Coffee

Secondary

Ungrazed

Eroded

Fallow after

Coffee plantation

Fallow in water

Pasture with

Plantations

Forest

pasture

soil

intensive crop

under shade

logged zone

grazing

677.4 572.7 455.6 661.0 578.7 441.2 3.3 1.5 1.2 2.7 1.6 1.1 2.0 1.1 0.8 2.2 1.3 1.0 0.6 0.3 0.2

425.9 386.9 369.0 380.9 338.5 374.1 4.1 5.3 1.5 7.2 3.0 1.3 1.9 0.3 0.1 2.1 0.6 0.3 0.9 0.3 0.3

722.1 471.7 229.0 830.7 607.5 284.2 3.4 1.2 1.1 3.8 2.5 1.4 2.8 1.2 0.6 3.1 2.2 0.8 1.7 0.5 0.2

180.6 140.7 132.3 213.7 175.5 151.8 2.1 2.6 1.3 1.9 1.5 1.6 0.7 0.4 0.3 0.5 0.3 0.3 0.4 0.3 0.2

731.4 637.2 378.4 790.9 620.3 328.3 2.9 1.7 2.3 3.4 2.3 1.0 2.7 2.6 2.1 3.0 2.6 2.1 0.6 0.4 0.4

625.4 467.5 407.2 567.6 506.5 466.7 4.3 3.8 4.1 5.1 3.4 3.3 4.3 2.2 1.4 3.4 1.4 0.9 1.9 1.0 0.6

891.9 673.7 580.4 918.2 615.2 543.9 10.0 5.8 6.3 8.4 4.1 5.5 3.3 1.8 1.7 3.1 1.8 1.7 1.8 0.8 0.7

445.4 349.5 211.2 439.5 306.2 200.2 2.1 1.0 1.2 2.4 1.7 1.2 2.9 1.7 0.9 2.3 1.2 0.9 1.0 0.4 0.2

(continued on next page) 15

Acta Oecologica 100 (2019) 103446

E. Velasquez and P. Lavelle

Table Annex 7 (continued) Variables

Depth

Soil mixa

cm

K (meq/100 g)

Al (meq/100 g)

0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30

M2 M1 M2 M1 M2

Coffee

Secondary

Ungrazed

Eroded

Fallow after

Coffee plantation

Fallow in water

Pasture with

Plantations

Forest

pasture

soil

intensive crop

under shade

logged zone

grazing

0.6 0.3 0.2 0.4 0.2 0.2 0.4 0.2 0.1 1.0 1.1 0.5 1.0 1.3 0.6

1.3 0.5 0.3 0.1 0.1 0.1 0.2 0.1 0.1 3.3 2.8 1.1 2.0 2.8 2.0

1.8 1.0 0.3 1.1 0.7 0.5 1.3 1.3 0.9 0.5 0.5 0.3 0.5 0.5 0.2

0.3 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1 4.0 3.5 3.3 3.1 3.3 4.0

0.7 0.5 0.4 0.6 0.2 0.2 0.6 0.4 0.3 0.6 0.5 0.3 0.5 0.8 0.3

1.9 0.7 0.3 0.2 0.1 0.2 0.2 0.1 0.1 1.8 0.3 3.3 2.5 3.5 4.0

1.6 0.8 0.7 0.6 0.2 0.1 0.6 0.1 0.1 2.8 3.8 4.8 2.8 4.3 4.8

1.0 0.4 0.3 0.5 0.4 0.2 0.4 0.2 0.1 0.8 0.6 0.4 0.5 0.8 0.3

Soil mixa = soil mix of five monoliths.

Table Annex 8

Ranges and average values of physical variables in each land use system in the microwatershed of Potrerillo, Cauca Province, Colombia Variables

Porosity (%)

Depth

Coffee

Secondary

Ungrazed

Eroded

Fallow after

Coffee plantation

Fallow in water

Pasture with

cm

Plantations

Forest

pasture

soil

intensive crop

under shade

logged zone

grazing

61.3–79.3 67.5 60.6–74.0 68.29 63.4–75.2 70.34 2.2–2.4 2.33 2.2–2.5 2.38 2.4–2.6 2.49 0.49–0.92 0.76 0.6–1.0 0.76 0.6–0.8 0.74 12–38.7 24.9 29.3–40.0 34.2 16.9–39.3 29.63 13.8–17.9 15.89 18.5–23.9 20.43 16.2–24.3 24.8 64.9–67.6 66.43 60.3–74.7 68.73 71.3–88.6 78.89

67.6–70.7 69.37 64.6–71.8 67.81 68.7–81.4 75.83 2.23–2.31 2.27 2.2–2.5 2.39 2.4–2.6 2.5 0.69–0.72 0.7 0.7–0.9 0.77 0.5–0.8 0.61 36.7–44.0 40 39.3–58.0 50 16.7–70.0 48.23 13.8–21.0 18.36 22.3–24.7 22.9 13.8–23.5 22.4 0.69–0.72 58.23 57.2–67.9 61.18 59.6–64.3 62.25

71.3–74.2 72.5 75.3–80.8 77.39 73.3–79.0 75.67 2.24–2.27 2.25 2.0–2.4 2.21 2.3–2.5 2.41 0.58–0.64 0.62 0.4–0.6 0.5 0.4–0.6 0.59 55.3–64.7 59.57 35.3–46.7 40.43 38.0–54.7 45.57 31.1–35.3 20.6 34.0–36.5 24.8 21.0–44.4 27.9 93.3–110.6 102.18 88.47–104.4 99.1 96.0–109.0 101.2

60.5–66.5 62.83 47.7–57.3 53.39 40.9–65.0 53.86 2.63–2.64 2.64 2.4–2.7 2.57 2.6–2.7 2.64 0.89–1.0 0.98 1.1–1.3 1.2 0.9–1.5 1.22 48.0–81.3 69.53 84.0–96.7 90.9 90.0–109.3 97.33 26.3–33.6 30.28 31.9–40.6 36.31 35.0–41.0 39.2 36.2–57.6 46.19 33.8–60.2 47.71 33.2–61.8 49.23

63.2–73.5 67.78 46.8–75.5 62.56 70.1–76.6 72.37 2.3–2.4 2.36 2.4–2.5 2.46 2.4–2.6 2.48 0.6–0.8 0.76 0.6–1.4 0.93 0.6–0.8 0.69 19.3–28.0 24.87 36.7–48.0 41.33 44.7–66.0 57.57 7.9–15.8 10.6 9.9–17.8 14.3 16.4–21.3 19.3 52.5–69.5 59.25 54.4–80.5 66.93 56.0–85.4 66.23

65.9–74.5 70.14 68.0–70.0 69.1 63.7–71.0 68.27 2.3–2.4 2.41 2.4–2.5 2.46 2.4–2.5 2.48 0.6–0.8 0.72 0.7–0.8 0.76 0.7–0.9 0.78 28.0–38.7 34.9 30.0–35.0 31.9 32.0–33.0 33.67 11.2–16.2 14.03 20.4–23.0 23.3 26.9–35.6 30.5 62.2–62.8 63.12 57.1–63.5 59.39 52.6–74.8 61.82

64.2–78.9 69.63 72.9–83.6 77.05 51.4–85.6 64.18 1.9–2.4 2.2 2.1–2.5 2.22 1.8–2.4 2.02 0.5–0.9 0.67 0.4–0.6 0.5 0.3–0.8 0.68 38.7–52.7 45.37 22.7–39.3 28.43 14.7–29.3 19.77 6.2–11.1 8.4 6.0–10.4 8.23 6.2–10.7 8.39 76.4–102.3 92.55 70.6–108.7 93.41 98.3–101.3 99.75

44.9–63.4 56.22 58.3–73.3 65.02 39.7–58.4 47.28 2.4–2.5 2.45 2.5–2.6 2.54 2.5–2.6 2.61 0.91–1.3 1.07 0.7–1.1 0.89 1.1–1.6 1.38 2.3–2.5 73.1 44.0–56.7 51.8 48.0–77.3 65.77 14.8–24.2 33.1 17.0–28.3 35.2 18.7–35.9 38.3 46.1–53.7 50.39 47.9–59.8 53.06 49.1–52.9 50.57

0–10 10–20 20–30

Real density (g/cm3)

0–10 10–20 20–30

Bulk density (g cm-3)

0–10 10–20 20–30

Shear strength (kPa)

0–10 10–20 20–30

Vertical resistance (kgf/cm2)

0–10 10–20 20–30

Moisture (% dry weight)

0–10 10–20 20–30

Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb Ma Rb

Ma = Mean. Rb = range.

16

Acta Oecologica 100 (2019) 103446

E. Velasquez and P. Lavelle

Table Annex 9

Variables related to organic matter in each soil-use system in the microwatershed of Potrerillo, Cauca Province, Colombia Variables

Depth

Soil mix

cm C (%)

N (mg kg-1)

NH4+ (mg kg-1)

NO3(mg kg-1)

LL ‡

LM ‡

LH ‡

0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30 0–10 10–20 20–30

M1 M2 M1 M2 M1 M2 M1 M2 M1 M2 M1 M2 M1 M2

Coffee

Secondary

Ungrazed

Eroded

Fallow after

Coffee plantation

Fallow in water

Pasture with

Plantations

Forest

pasture

soil

intensive crop

under shade

logged zone

grazing

9.2 7.6 4.2 8.5 8.2 4.8 7061.5 6435.0 2340.0 6465.0 5651.0 2481.7 14.2 16.4 13.8 19.9 22.0 17.6 13.0 8.1 11.3 14.3 8.1 6.5 0.2 0.1 0.0 0.2 0.1 0.0 0.1 0.1 0.0 0.1 0.1 0.0 0.1 0.0 0.0 0.1 0.0 0.0

11.8 6.2 5.1 12.0 5.7 5.4 8307.0 3045.6 2418.9 6324.0 2588.8 2450.0 108.6 22.5 18.3 93.6 31.7 11.0 3.8 2.8 3.9 2.8 2.9 1.9 0.2 0.2 0.1 0.7 0.1 0.1 0.1 0.1 0.1 0.5 0.1 0.1 0.1 0.0 0.0 0.1 0.0 0.0

10.0 7.0 3.0 9.6 7.6 4.3 6351.0 4662.0 1639.3 6351.0 4662.0 1639.3 58.7 24.8 8.9 46.1 42.5 10.2 7.2 4.8 3.6 10.7 8.0 4.2 15789.0 0.1 0.6 119165.0 0.5 0.1 0.2 0.1 0.1 0.2 0.2 0.0 0.0 0.1 0.0 0.1 0.0 0.0

2.6 0.8 0.4 2.4 1.1 0.8 821.3 323.8 254.5 1151.4 511.6 302.3 5.2 3.6 4.1 8.1 3.3 3.9 3.3 2.8 3.5 10.2 6.4 6.0 0.9 0.2 0.1 0.3 0.1 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0

7.4 6.6 3.6 4.0 6.6 3.6 4923.0 4671.0 2167.8 5919.0 4636.0 1950.2 11.4 10.7 19.5 11.2 24.1 15.3 2.8 4.1 3.8 4.1 3.6 4.1 0.3 0.1 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

7.4 5.6 4.4 8.6 6.4 5.6 5223.0 2509.8 1913.1 5043.0 2911.6 2551.7 16.2 25.6 37.9 20.1 14.8 15.6 5.0 3.4 3.2 7.3 5.4 5.0 0.6 0.1 0.1 0.7 0.1 0.1 0.1 0.0 0.0 0.1 0.0 0.0 0.1 0.0 0.1 0.1 0.0 0.0

8.0 5.2 4.8 7.4 5.0 4.6 5277.0 2752.6 2288.6 5295.0 2429.7 2050.6 39.8 17.4 16.9 28.9 18.6 18.0 6.9 5.9 2.7 2.7 4.0 2.7 0.4 0.1 0.1 0.6 0.1 0.1 0.1 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0

5.1 4.2 2.5 4.9 3.6 2.2 2520.6 2234.8 1529.2 2433.3 1843.7 1132.2 26.1 11.3 20.4 16.2 18.3 20.2 4.8 5.2 5.4 4.4 9.2 4.0 0.4 0.2 0.1 0.1 0.2 0.2 0.2 0.1 0.0 0.0 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.0

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Acta Oecologica 100 (2019) 103446

E. Velasquez and P. Lavelle

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