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Regional Environmental Change

, Volume 18, Issue 1, pp 63–75 | Cite as

Seeing the forest not for the carbon: why concentrating on land-use-induced carbon stock changes of soils in Brazil can be climate-unfriendly

  • Jens Boy
  • Simone Strey
  • Regine Schönenberg
  • Robert Strey
  • Oscarlina Weber-Santos
  • Claas Nendel
  • Michael Klingler
  • Charlotte Schumann
  • Korbinian Hartberger
  • Georg Guggenberger
Original Article

Abstract

Soil carbon stocks of 29 plots along a transect through tropical Brazil showed only minor soil carbon losses after land use shift, although replacement of forest-derived carbon was detectable in subsoil and topsoil, indicating that new equilibria in soil carbon stocks might not have been reached after deforestation. The proportion of carbon lost from soils was negligible as compared to the emissions from biomass reduction by deforestation itself. Industrial agriculture had the best ratio between food production and carbon loss, pointing toward a potential reduction of deforestation pressure by further agricultural intensification, which is not achieved in practice due to institutional obstacles and uneven benefit sharing. In contrast, farmers at the agricultural frontier were identified as change agents if alternative sustainable land uses, taking advantage of biodiversity-related ecosystem services, are fostered by better access to credit lines and extension management. Thus, constraining the climate change debate in agriculture to sole management of carbon stock changes in soil is misleading and draws the attention from the most urgent problems: deforestation caused by wrong incentives.

Keywords

Soil carbon Alternative land uses Climate change mitigation Food production Brazil 

Introduction

Carbon stocks of tropical rainforests are an important global sink for CO2 (e.g., Stockmann et al. 2013) and without a doubt, the best possible solution for mitigating global climate change is to protect them (Fearnside, this issue). Nevertheless, all tropical forests worldwide are under severe threat or already largely destroyed (Lewis et al. 2015) for a multitude of direct and indirect causes (Lambin et al. 2003), which are often a result of unsustainable agricultural practices, global consumption patterns, unsolved land tenure issues, and institutional weaknesses (Schönenberg et al. 2015; Klingler et al., this issue). No matter whether the ecologically unsustainable human behavior of deforestation is understandable from a moral perspective if being about to feed one’s own family or, contrastingly just a symptom of an unregulated and little controlled market which imposes the costs of the greed of a few to the bill paid by all, a common point is the production of agricultural goods. Agricultural goods which are in any case increasingly needed to feed an ever-growing human population (Godfray et al. 2010; Anderson and Alexandratos 1996). The clash of interests between desired ideal solutions and undesired realities of forest preservation is mirrored in the scientific discussion on tropical forests, agriculture, and climate change. Ecologically oriented research highlights the need to preserve and conserve natural forests for the sake of biodiversity and ecosystem functioning, often not taking the human dimension causing impacts on ecosystems into account. Land-use-based research investigates into the imposed changes by forest destruction on environmental factors like, e.g., erosion, greenhouse gas (GHG) emissions, or biodiversity (Phalan et al. 2013), whereas applied and agriculturally focused research evaluates sustainable productivity and yield increase (Smilovic et al. 2015; da Silva et al. 2012; Franchini et al. 2012) mostly regardless of its impact on natural ecosystems. Research on human impacts finally elicits the role of forest conversion for society (Kammerbauer et al. 2001; Fankhauser et al. 2015), often without consideration of the feedback loops of society alterations on ‘green’ parameters.

Without a doubt, a trans-disciplinary approach is urgently needed (Laurance et al. 2012; Schönenberg et al., this issue). Such an approach forces researchers to precise the ‘desired’ scenarios of successful forest preservation and carbon optimized agriculture beyond the disciplinary level and to evaluate the possibilities of deforestation-pressure alleviation by identifying best-practice recommendations for already deforested sites. Under the realistic scenario of an (in the end) limited area for food production and the reality of encroachment of intact forest areas by low-productive but highly destructive agricultural practices, it is therefore necessary to ask for the best trade-off between greenhouse gas (GHG) emissions, food provision, and local ownership adapted on land where the ‘original sin’ of forest destruction already happened. Such a trade-off is the best chance to lower future pressure on still pristine forest areas, as it satisfies human needs on smaller areas, by this leaving space for forest preservation, given that political will and law enforcement guarantees the establishment of forest reserves.

A thorough monitoring of C stock changes seems thereby crucial for addressing the resulting risk for climate change induced by deforestation and different land use types. The loss of the standing biomass by deforestation leads to an instantaneous emission of GHG to the atmosphere and comprises already roughly the half of the C potentially lost by the conversion of a tropical forest (DeFries et al. 2002; Houghton et al. 2012). In contrast, the evaluation of the contribution of soil organic carbon stock alterations to climate change is complicated by various edaphic factors, resulting in differing turn-over rates of soil organic carbon for comparable land uses (Ahlstrom et al. 2015; Ashagrie et al. 2005; von Lutzow et al. 2008). Further complications result from soil heterogeneity and a non-standardized depth to which the C stocks in soil are recognized (Schrumpf et al. 2013). Therefore, it is difficult to disentangle whether land-use-induced soil organic carbon (SOC) changes are indeed a result of the respective land use or rather of differing soil type, microclimatic variance or an insufficiently shallow sampling depths, especially if data from crop fields or grasslands are compared to baseline forest sites (Don et al. 2011). Forests root deeper than crops, thus spread their C-input, either by root biomass or rhizodeposition of photoassimilates, over comparably larger soil depths (Stahl et al. 2013; Schmidt et al. 2011). As microbiological activity is likewise reduced with soil depth, changes to C stocks are likely to show a time-lag to shallower soil depths after forest conversion, which complicates final conclusions about new equilibria of C stocks in subsoil, as conversion of tropical forest often happened during the last four decades or earlier in vast areas of Amazonia or the old-world tropics. As an example, literature on C stock changes in Amazonia indeed reveals contrasting results. Don et al. (2011) found declining OC stocks in soils of all types of land uses after deforestation, ranging from 32.0 ± 3.5 Mg ha−1 under cropland to 12.06 ± 3.0 Mg ha−1 under pasture, whereas Braz et al. (2013) detected between 6 and 47 Mg OC ha−1 additionally stored in 0–20 cm soil depth after forest conversion to pasture. Several other studies in tropical Brazil also identified increasing SOC stocks after the conversion of native forests to cropland, particularly under no-till management (Bayer et al. 2006; Marchao et al. 2009; Roscoe and Buurman 2003). The few studies dealing with subsoil changes after forest conversion highlighted the need to address land-use imposed changes to subsoil C stocks (e.g., Rumpel 2014).

In the evaluation of the impact of land use on climate change in Amazonia (Brienen et al. 2015; Zhang et al. 2015; Exbrayat and Williams 2015; Barni et al. 2015), the question of productivity per area stays largely untouched. Nevertheless, it becomes directly apparent that this is of major importance for climate change mitigation if considering that an unconverted area firstly spares the emission of the standing biomass and secondly a highly productive but relatively C stock-friendly management of already deforested sites likely lowers the pressure on unconverted areas (White et al. 2001).

The idea of considering the potential area of conversion as limited emerged in the discussion of land sparing versus land sharing (e.g., Paul and Knoke 2015; Lee et al. 2014), i.e., the question whether it would be better, from an (mostly biodiversity oriented, conservational) point of view, to promote a mosaic of strictly protected forest reserves and agriculturally intensified areas or rather low-impact management options such as agroforestry or silvopasture over larger areas (Hohnwald et al. 2006; Franchini et al. 2014; Gil et al. 2015). In the latter management schemes, agricultural pressure on natural environment is minimized, while the land consumption as weighted by its feeding potential by agricultural production per area is lowered. On an endless area, where low-intensity management options never rival primary forest sites, they appear an ideal solution, whereas on a limited area they lead to conversion of forest for the sake of food production (Fischer et al. 2014). Transferring this concept to C emissions caused by land use shift, a relatively higher emitting land use type which is highly productive might have a comparably lower impact on the climate as compared to a less productive, but better carbon stock preserving option, simply by the fact that it ‘feeds more mouths’ per area used.

In order to integrate these aspects of drivers of climate change into a holistic approach, we conducted a regional case study along a large gradient of different agricultural intensities after clearance along a 1000 km transect through Southern Amazonia, Brazil, to compare the impact of different land uses on soil carbon stocks in topsoil and subsoil by comparison to pristine forest baselines. We furthermore weighted the C stock changes of the land use types to their yield expressed as caloric production to identify agricultural options with the best trade-off between C emission avoidance and food production, hence potential lowering of pressure to pristine forest sites. In a mixed field-based and institutional approach, we identified the state of local livelihood, and the potential financial, cultural and legislative challenges for the identified best-practice management options.

Our hypotheses were that for Acrisols and Ferralsols in Amazonia (1) Different land use types alter carbon stocks in soil differently, with more depleted carbon stocks under intensive than under extensive agriculture, (2) substantial amounts of carbon are stored in the subsoil and already react to land use shift after relatively short time spans along the comparably young agricultural transitional gradient in Brazil, (3) intensive agricultural land use types have a better carbon food-print if C stock change per food-unit is considered, and, as objectives, (4) to evaluate the most important socio-economical mechanisms fostering climate-unfriendly solutions and to identify change agents.

Materials and methods

Research area

We chose a South-North transect in Central Brazil comprising the vegetation gradient from tropical savannah (Cerrado) to moist evergreen Amazonian rainforest for our study (Fig. 1). This 1000 km transect follows the translocation of the agricultural frontier in the region from Mato Grosso to the state of Pará, a translocation which was facilitated by the construction of the highway BR163 in 1972 and its successive paving during the last decades. The conversion into agricultural land along the BR163 thereby followed similar patterns, starting with timber extraction followed by forest clearing, changing the use of land either over pasture to cropland or directly to cropland (Fearnside 2007).
Fig. 1

Map of research area along the BR163 with the three study regions: R1—Novo Progresso; R2—Sinop; R3—Cuiaba

In three different regions along the transect, we evaluated the shifts in soil organic carbon (SOC) stocks as imposed by different forms of land use change (Fig. 1). In region 1 (R1; southern Pará around the city of Novo Progresso), the native vegetation is tropical rainforest—’Floresta Ombrófila Densa/Aberta Submontana’ (IBGE—Instituto Brasileiro de Geografia e Estatistica 2012). With a dry period between June and September, a mean annual temperature of 25 °C and a mean annual precipitation of 2450 mm (temperature and precipitation means for the regions: 30 years mean 1981–2010 from reanalysis-data, Institute of Geography, Hamburg, personal communication), R1 is climatically situated at the outer rim of moist tropical rainforests in Amazonia. R1 is the spearhead of the contemporary agricultural development. Forest encroachment is followed up by, often illegal, deforestation for the establishment of extensively used pastures, with typically exotic fodder grass species (Brachiaria spec. or Panicum maximum) in monoculture (Coy and Klingler 2014). The installation of crop fields is mostly initiated by removing trees and roots which remained from the former forest, followed by tillage (20–30 cm deep), fertilization and liming.

Region 2 (R2; central Mato Grosso close to the city of Sinop) receives less precipitation than R1 (1974 mm; Mota et al. 2013) due to a prolonged dry period of 4–5 months, but has comparable mean annual temperatures. Consequently, the dominating vegetation is semi-deciduous forest (‘Floresta Estacional Sempre-Verde’ (IBGE—Instituto Brasileiro de Geografia e Estatistica 2012). The agricultural land use is based on equally distributed cattle and soybean/corn farming, but the ongoing development shifts land use toward mechanized soybean/corn crop including fertilization (NPK), revitalization by subsoiler, and liming (Fearnside 2007; Brando et al. 2013).

In the Cuiabá region (R3; southern Mato Grosso), the native vegetation is Cerrado, a tropical savanna, owed to a lower annual precipitation of 1500 mm and a dry period from April to September with only a slightly lower mean annual temperature of 24 °C as compared to the other regions. There is a variety of Cerrado subtypes, spanning from dense forest savanna (Savana Florestada—Cerradão) to open savanna (Savana Gramíneo-Lenhosa, Campo-Limpo-de-Cerrado) (IBGE—Instituto Brasileiro de Geografia e Estatistica, 2012). Our baseline vegetation was ‘Savana Arborizada’ as it is the most common Cerrado type in R3. The region around Cuiaba has the most advanced, i.e., highest mechanized agriculture with intensified soybean/corn/cotton/millet crop rotation. The arable soils are managed under no-till, but also at R3 harrowing is necessary every 3–5 years to avoid soil compaction (information from cooperating farmers).

Ferralsols and Acrisols are the dominant soil types for all three regions, with a tendency of less Acrisols (25 % of the total share) toward the South.

Experimental design and soil sampling

In this study, the investigated land use types were native vegetation, pasture, and crop fields. We regarded the forest type replaced by any of the other land uses as the native vegetation and used it as baseline. In case of R1 and R2, the native vegetation was rainforest and in R3 Cerrado. Sites under use were classified as ‘young’ if they were transformed <10 years ago, and ‘old’ if land use change from native vegetation occurred after >10 years. Due to the dynamic and recent movement of the agricultural frontier along the BR 163, ‘old’ plots had been converted around 30 years ago, on average. As far as possible, we sampled every land use type on both reference soil groups, Ferralsol and Acrisol, and maintained close proximity between native vegetation and transformed land uses to ensure comparability.

In total 29 plots of a size of 100 m × 100 m were selected (Table 1) and 996 soil samples were taken. We used a grid system setting the distance between the sample points to 25 m each (9 samples per plot) in order to cope with soil heterogeneity. At each sample site, 100 cm deep soil cores were taken by an Edelmann-Auger, and cores were divided into the increments of 0–10, 10–30, 30–60, and 60–100 cm. In R2 we took only five cores per sample site due to farmer arrangements.
Table 1

Site description with coordinates, soil type and land use

LUT

Research region

Soil type

Coordinates

Year of deforestation

Municipal

Samples (n)

Native Vegetation—Ferralsol

Cerrado

R3

Ferralsol (Oxyaquic)

15°23′38.8″S/54°50′13.23″W

Campo Verde

36

Cerrado

R3

Haplic Ferralsol

15°30′13.70″S/54°4′4.11″W

Primavera do Leste

36

Rainforest

R1

Haplic Ferralsol

7°10′41.47″S/55°22′59.90″W

Novo Progresso

36

Rainforest

R1

Haplic Ferralsol

8°22′52.83″S/54°32′57.45″W

Altamira

36

Rainforest

R2

Haplic Ferralsol

12°4′30.99″S/55°20′3.47″W

Santa Carmen

25

Young Pasture—Ferralsol

Pasture

R1

Haplic Ferralsol

7°10′38.25″S/55°23′2.11″W

2003

Novo Progresso

36

Pasture

R1

Haplic Ferralsol

7°10′35.64″S/55°22′55.98″W

2003

Novo Progresso

36

Old Pasture—Ferralsol

Pasture

R1

Ferralsol (Sombric)

7°10′33.08″S/55°22′19.00″W

1988

Novo Progresso

36

Pasture

R1

Haplic Ferralsol

7°10′31.89″S/55°22′9.96″W

1987

Novo Progresso

36

Pasture

R1

Haplic Ferralsol

6°44′59.97″S/55°28′51.05″W

1986

Novo Progresso

36

Pasture

R2

Haplic Ferralsol

12°4′50.57″S/55°20′7.97″W

1990

Santa Carmen

25

Pasture

R3

Ferralsol (Oxyaquic)

15°23′38.40″S/54°51′8.28″W

1990

Campo Verde

36

Pasture

R3

Haplic Ferralsol

15°29′37.09″S/54°7′29.09″W

1972

Primavera do Leste

36

Young Crop—Ferralsol

Croplanda

R1

Umbric Ferralsol

7° 8′50.18″S/55°24′9.39″W

1999/2009

Novo Progresso

36

Croplandb

R1

Umbric Ferralsol

7° 7′59.75″S/55°26′15.75″W

2002/2009

Novo Progresso

36

Croplandc

R3

Haplic Ferralsol

15°29′46.80″S/54°7′39.24″W

1972/2006

Primavera do Leste

36

Plantationd

R1

Haplic Ferralsol

6°56′11.29″S/55°26′14.43″W

1976/2006

Novo Progresso

36

Old Crop—Ferralsol

Cropland

R2

Haplic Ferralsol

11°59′0.18″S/55°30′25.22″W

1987

Sinop

25

Cropland

R3

Haplic Ferralsol

15°22′6.21″S/54°50′23.19″W

1986

Campo Verde

36

Croplande

R3

Haplic Ferralsol

15°22′7.35″S/54°50′18.38″W

1986

Campo Verde

36

Cropland

R3

Haplic Ferralsol

15°29′46.65″S/54°8′25.46″W

1972

Primavera do Leste

36

Native Vegetation—Acrisol

Rainforest

R1

Haplic Acrisol

7° 0′47.31″S/55°23′7.21″W

Novo Progresso

36

Rainforest

R1

Stacnic Acrisol

8°22′41.63″S/54°31′15.43″W

Altamira

36

Rainforest

R1

Haplic Acrisol

8°22′38.55″S/54°36′1.50″W

Altamira

36

Young Pasture—Acrisol

Pasture

R1

Haplic Acrisol

7° 0′52.57″S/55°21′57.20″W

2004

Novo Progresso

36

Old Pasture—Acrisol

Pasture

R1

Stagnic Acrisol

7° 1′33.02″S/55°22′33.76″W

1984

Novo Progresso

36

Pasture

R1

Haplic Acrisol

7° 0′56.31″S/55°21′39.01″W

1995

Novo Progresso

36

Pasture

R1

Gleyic Acrisol

6°50′1.44″S/55°28′15.03″W

1979

Novo Progresso

36

Young Crop—Acrisol

Croplandf

R1

Haplic Acrisol

7° 0′56.20″S/55°23′3.81″W

1993

Novo Progresso

36

R1 Novo Progresso, R2 central Mato Grosso, R3 southern Mato Grosso

aSince 1999 pasture, transformed to cropland in 2009; b since 2002 pasture, transformed to cropland in 2009

cSince 1972 pasture, transformed to cropland in 2006; d since 1970 pasture, transformed to Açai-Plantation in 2006

eSince 1986 cropland –soy/maize, since 2008 cotton; f since 1993 pasture, reformed to cropland in 2010

Additionally, in R1 we dug two deep soil pits under pasture and rainforest down to a depth of 5 m (the rainforest pit to 10.5 m) to estimate deep-subsoil carbon stocks exemplarily. Here, we took soil samples in four strains (every 90° around the circle profile) in 20 cm increments.

All samples were immediately air-dried followed by an additional drying in the laboratory at 50 °C until weight constancy. All samples were sieved <2 mm for further analysis. For calculating bulk density, a soil pit was opened (1 m × 1 m × 1.1 m) on each plot for taking undisturbed core samples (100 cm3) in increments of 10 cm in three replicates. Soils were classified according to IUSS Working Group WRB (2014).

Soil and data analyses

Soil OC concentrations were measured on an elemental analyzer (ISOTOPE CUBE, Elementar GmbH, Hanau, Germany) on samples previously ground with a ball mill (Retsch MM200, Haan, Germany). Bulk density was calculated gravimetrically after drying the 100 cm3 samples for 48 h by 105 °C. In order to calculate OC stocks for topsoils (0–30 cm), subsoils (30–100 cm), and the whole profile (0–100 cm), we first extrapolated OC concentrations along the depth profile using an exponential model (Bernoux et al. 1998):
$$y = yO + A^{{\left( {\frac{ - x}{t}} \right)}}$$
(1)
where yO describes the offset from y axis (stands for the lowest OC value measured in the profile), A is the amplitude (representing the largest OC value measured in the profile), x indicates the depth in cm, and t describes the curves fading. All plots showed an r 2 > 0.98.
As land use change is accompanied by changes in the soil bulk density (Roscoe and Buurman 2003), we assumed similar initial conditions (space-for-time substitution) of the plots under agriculture and their reference plots and corrected the soil mass after land use change to the corresponding soil mass on the reference plots under native vegetation (Ellert and Bettany 1995; Poeplau and Don 2013):
$${\text{OCSTOCK}}_{\text{corr}} = {\text{OCSTOCK}}_{i} \left( {\frac{{{\text{SM}}_{i} + {\text{SM}}_{\text{nativest}} }}{{{\text{CV}}_{i} }} \times C_{\text{deepest}} } \right)$$
(2)
where OCSTOCKcorr is the corrected OC stock in Mg ha−1, OCSTOCK i defines the original calculated OC stock in Mg ha−1, SM i is the soil mass of the individual sample in g, and SMnativet the soil mass of the corresponding native vegetation in g, whereas CV i defines the volume in cm−3, and C deepest is the C concentration in % of the deepest considered soil layer. Due to mass correction for single soil depths (0–30 cm and 30–100 cm), the corrected OC stocks to 100 cm do not equal the sum of topsoil plus subsoil stocks. We compared each sample to the mean of its corresponding native vegetation for topsoil and subsoil, as well as for the total soil depth, to analyze the effects of land use change for every individual sampling point. We used the harmonic mean to calculate average values, in order to avoid overestimation due to OC concentration outliers produced by hotspots of charcoal which remained from burning the forest for clearing (Murage et al. 2007).

One-way analysis of variance (ANOVA) with a subsequent Tukey HSD test was used for pairwise comparisons of the differences between means of land use types (p < 0.05). The data (OC contents and stocks) were log-transformed to fulfill the assumption of ANOVA. All statistic calculations have been done using the free statistical software Rstudio, Version 0.98.1102 (Rstudio, Inc., Boston, USA).

Results and discussion

Modification of OC stocks by land use change

Surprisingly, land use change had little impact on OC stocks of soils along the transect (Fig. 2). Neither were the SOC stocks significantly higher under native vegetation nor were they significantly altered in any direction under the investigated land uses except for a slight decrease of SOC stock under young crop both in Ferralsols for the topsoil and Acrisols for all soil depths. Thus, our first hypothesis of an increased C stock depletion in soils under intensive agricultural use had to be rejected. This contradicts the results of other studies on land use change in the Amazon or on sites formerly occupied by Cerrado, either reporting an increase (e.g., Braz et al. 2013; Koutika et al. 1997; de Moraes et al. 1996; Miranda et al. 2016) or a decrease after forest conversion (e.g., da Silva et al. 2004; Marchao et al. 2009). We identified as a possible reason an unequal inclusion of different soil types into the same study, as Ferralsols and Acrisols stored different amounts of OC (Fig. 2), most likely due to texture differences between the soil types leading to differing sequestration capacities in organo-mineral complexes or influencing microbial activity by altering hydrology and ventilation in soils (discussed in detail in Strey et al. 2016). Other factors leading to contrasting results on land use shift are scale issues and size of the study. Indeed, we observed a relatively high heterogeneity in our data set on individual plot level, ranging from losses of up to −20 Mg ha−1 to gains of +20 Mg ha1 as compared to OC stocks under native vegetation. This suggests that identified losses or gains in smaller studies might be explained by insufficient repetition numbers, a known phenomenon already described by other authors (e.g., Batlle-Bayer et al. 2010; Murty et al. 2002; Fujisaki et al. 2015).
Fig. 2

Mass corrected OC stocks (Mg ha−1) for different land use types in Ferralsols and Acrisols. Stocks are calculated for topsoil (0–30 cm), subsoil (30–100 cm) and total sampled soil depth (0–100 cm) with standard error. Different letters show significant differences (p < 0.05) between land use types in the same soil depth. n the amount of individual sampling points

Likewise to the topsoil (0–30 cm), the subsoil (30–100 cm) showed only minor changes between the land use types (Fig. 2). Also our exploratory deep-subsoil pits under forest and pasture did not suggest considerable changes in the amount of SOC sequestration potential between the land uses, as both sum up to around 190 Mg ha−1 C until the depth of 5 m (Fig. 3). In a delta-13C approach taking advantage from C3 (forest) to C4 (pasture) transition, we found only 5–15 % of forest-derived C in subsoil of Acrisols replaced during a period of up to 50 years by pasture or crop derived C (Strey et al. 2016), indicating that transition times of land use are too short in the region to judge on the fate of subsoil carbon on the long term.
Fig. 3

Organic carbon concentrations in two deep-subsoil profiles under forest and pasture in R1

From today’s perspective, all land uses applied are largely able to replace the lost forest-derived soil carbon. This might nevertheless change in the future, although it could be doubted if this risk is of any interest for decision makers who typically think in shorter intervals.

Our second hypothesis is partially supported by considerable amounts of C stored in (deep) subsoil, and although first changes of carbon stocks induced by land use change can be observed on a mechanistic level, their amount did not change over the transition times realized in the region.

Our results show that the scope of action in climate change mitigation by soil management in tropical Brazil either is depressingly small or already very well put into practice. Indeed, intensive agricultures in R3 already apply industrial no-till agriculture, which certainly has a significantly negative impact on the environment due to the tremendous amounts of pesticides applied, but is at least known for reducing carbon losses in soil (Powlson et al. 2014; Lal et al. 2011). In our study region, other ecosystem services provided by OC in soil, especially water and nutrient retention, seem more promising targets for agricultural management optimization than carbon sequestration taken alone.

Reduction potential by merging the climate change and the food security discussion

As soil management fails as a quickly applicable climate change mitigation measure for agricultural areas in Amazonia, deforestation as such comes into focus. Figure 4 shows that the range of carbon stock changes in soil represents less than the tenth part of the carbon lost by destruction of the above-ground biomass (data corresponding to Fearnside, this issue).
Fig. 4

Soil OC alterations following land use change for topsoil (0–30 cm) in black bars and total soil depth (0–100 cm) in gray bars. Green bars indicate soil OC (0–100 cm) changes combined with aboveground biomass losses after deforestation (color figure online)

Thus, forest protection has to be the first priority in avoidance of GHG emissions. This insight is not new, but is worth to be stated in times where the climate change discussion increasingly loses grounds for the sake of minor adjustments instead of taking measures to avoid the most unnecessary losses of carbon sinks (although REDD and PES certainly contribute). Given the little impact of intensive agriculture on carbon stock changes in soil along our transect, the ratio of C changes and potential food production seems crucial, as an area becomes ‘uninteresting’ for applying climate change mitigation measures directly after deforestation. Indeed, crop fields (soy beans in the plotted case) produce up to tenfold the amount of calories per Mg C-lost as pasture (Fig. 5, upper panel). Thereby, it is insignificant whether the topsoil (0–30 cm) or the soil column to a depth of 1 m is taken into account. Differences between the two soil depths slightly decrease if production periods of 10 or 20 years are assumed at the plot level (Fig. 5, lower panel), or even more if two harvests per year are assumed. The negative budget for pastures is furthermore strengthened by a rapid degradation of pastures in the area after roughly 20 years, leading to erosion or the occurrence of Phytophtora spec. infections, locally called ‘morte súbita’ (quick death), of the fodder grass species. The result of this process is either wasteland or a secondary shrub community, which both neither are productive nor likely to regenerate to forest.
Fig. 5

Average SOC changes in topsoil and subsoil combined with aboveground biomass losses in main land use types (LUT) compared to produced energy useable for human or animal diets. The upper panel shows an idealized difference in production on a yearly base, the lower panel attributes the observed soil-C changes as compared to the baseline over the realized production on plot level

Thus, our third hypothesis of a better carbon footprint of intensive agriculture is supported, if food potential is considered.

Under an idealized perspective, ten times the food produced per Mg C emitted means reducing the pressure of deforestation by a factor of ten without imposing additional risk for global warming, if the area for both, food production and forest protection, is limited. Under the contemporary circumstances, this is a naïve fallacy. Industrial farming mainly tailored to meet the global protein demand, especially by providing fodder for local meat production (Bowman et al. 2012) or exportation (Grenz et al. 2007; Stoll-Kleemann and O’Riordan 2015), contributes little to regional food security but imposes additional environmental pressure in terms of, e.g., nutrient export from the production sites (Gill et al. 2015; Richards et al. 2014; Gollnow and Lakes 2014; Lathuilliere et al. 2014). Additionally, industrial farming is an unreachable management option for peasant farmers due to the large investments required (Gil et al. 2015).

Socioeconomic mechanisms hampering the implementation of climate-friendly agriculture

On grounds of the whole set of agrarian and environmental law and the related societal practice to apply the latter (Pacheco 2009; Pacheco and Benatti 2015; Benatti 2011), we identified in own interviews at the highway BR 163 between 2012 and 2014 (Schönenberg et al. 2015, Klingler et al., this issue; Schumann et al. 2015) two important complexes of mechanisms hampering the implementation of climate-friendly agriculture and ranching: (1) the relation between disputed land tenure, environmental embargos and the resulting limited access to agricultural credit financing, and (2) the availability of agricultural consultation and plot-based farming system training and the concomitant consultation within the credit-departments of local banks. The most targeted clients for governmentally supported land use intensification programs are large-scale farming or ranching systems (Galford et al. 2013). The discussion on agricultural intensification refers to farms where extensive cattle ranching is being practiced or to monocultures where crop rotation would contribute to the regeneration of soil. Whereas in the former case of degraded pastures (southern Pará), there are hardly any definitive land titles in place, in the case of agro-business in Mato Grosso, land titles are not the problem but indebtedness. Both problems decrease agricultural productivity.

Until recently, decline in productivity has been solved by further deforestation (a practice increasingly sanctioned today), which again represents a strong incentive for establishing secure land tenure. For anybody without a recognized land title, there is no access to land-related credit lines which is needed for pasture regeneration and crop rotation; furthermore, customers who are already indebted are hardy eligible for a further credit. Consequently, the need for adequate credit lines and the respective agricultural consultation on the plot and within the credit-departments of local banks becomes vital. We found generally very few agricultural consultations undertaken on the plot, and local banks regularly failed to advice the application of such credit lines on an economically solid basis. As an example, in a MAPA (Ministry of Agriculture) interview in 2013, it was admitted that the capacity-building measures regarding ‘ABC’ credit lines (Agricultura de Baixo Carbono, the most recent credit program in Brazil) for local banks had not even been started. Consistently, we identified very limited access to ABC credit lines within our research area until today, as access to these credit lines is often impeded by missing communication to the potential receivers (Gil et al. 2015). Consequently, farmers requiring further financial services are confronted with a race between tackling the bureaucratic hurdles and bankruptcy. This situation might lead to additional illegal land acquisition at unconsolidated agricultural frontiers.

On the other end of the scale, the implementation of comparably low-productive but resilient farming practices like agroforestry and silvopasture, farmers tended to perceive agroforestry measures as unproductive and due to missing commercialization economically not feasible. Nevertheless, some farmers stated to perform silvopasture by hosting remnant brazil-nut (Bertholletia excelsa) trees on their ground. Others tried to label selectively logged forests as silvopasture, showing that the discussion on alternative land uses is partly noticed, but misinterpreted or technically out of reach. Additionally, farmers practiced ideas of land sparing in an intuitive way, as they were proudly conserving their mandatory forest portion on their land instead of allowing for slow deforestation by selective logging, as it is otherwise common practice in the region.

Taken together, the best entry point for change therefore is the linkage between technically evolved extension management which reduces the risk of environmental embargos and defines the necessary access to credit lines, thereby securing the successful implementation of sustainable agricultural practice. Besides, the large obstacles to be overcome in communication with the farmers and training of extension managers and bankers, there are also scientific shortcomings. Trade-offs between productivity, financial and ecological resilience, sustainability, conservational aspects and carbon management measures have to be addressed. The challenge here is the abstraction to a generally applicable process, understanding in a deterministic way, as case studies are often biased by a quickly changing institutional and societal setting.

Future research needs

In order to merge the interest of climate change mitigation and food production and security, it is crucial to understand the trade-offs between the productivity of an applied land use, its carbon sequestration potential, opportunity costs and resulting land consumption imposed. Special attention is needed for examining the relation between ecosystem services provided by biodiversity for resilience, climate mitigation and sustainable land use, latter in the sense of both economically and ecologically sustainability. There are several studies stating a positive effect of a slightly increased biodiversity (as compared to intensified agriculture) on all aspects in schemes of agroforestry (Tremblay et al. 2015; Kirby and Potvin 2007; Redondo-Brenes and Montagnini 2006; Lorenz and Lal 2014) or silvopasture (Lindgren and Sullivan 2014; Mosquera-Losada et al. 2009). But on a process-based level, it stays largely unclear, how big the contribution of increased biodiversity on, e.g., nutritional sustainability (Lagerstrom et al. 2013; Wilcke et al. 2009), translocation (Boy et al. 2008b), scavenging (Boy et al. 2008a; Boy and Wilcke 2008) or nutrient mining from formerly inaccessible sources (van Schöll et al. 2008) is, and if this effects can be still achieved under the economical restrictions of an global market. The list of processes can easily be broadened to nutrient uplift or base-pump effect (Porder and Chadwick 2009), interspecies nourishing (Beiler et al. 2010), or pathogen control (Henri et al. 2015).

Although it seems unlikely that alternative land use practices taking advantages of the described processes will be applied by mechanized ranching systems due to automation obstacles, it has to be kept in mind that not industrial agriculture but small scale producers on the agrofrontiers are the aim of such. Intensification of already deforested sites is one possible way to follow in terms of climate change mitigation, as long as this intensification only takes place on already deforested sites. But excluding industrial agriculture from pristine carbon sinks like tropical forest is a political and legislative, not a technical, problem. In contrast, biodiversity-related alternative land use practices as agroforestry or silvopasture are promising tools to slow down encroachment and deforestation induced by pasture or cropland degradation, as they may provide income for the overwhelmingly large proportion of the population who does not benefit from industrial agriculture.

Conclusion

  1. 1.

    Changes of soil carbon stocks by land use shift in the Amazon are small after forest conversion in terms of total amount, at least on Ferralsols and Acrisols. Thus they are a weak argument in the climate change debate and likely distract the attention from the real threat of deforestation.

     
  2. 2.

    Although carbon stocks did not change in amount, the origin of incorporated carbon into soil stemming from newly adapted land uses was detectable in topsoil and subsoil in all cases, indicating that new equilibria in carbon storage might not have been reached.

     
  3. 3.

    Agricultural intensification provides a better ratio between food production and soil carbon loss from Acrisols and Ferralsols than extensive agricultural practices as realized at the agricultural frontier in Amazonia, which potentially lowers deforestation pressure on tropical forests. Nevertheless, this positive effect is contradicted by unequal benefit sharing and institutional shortcomings.

     
  4. 4.

    Alternative land uses combining increased production and degradation avoidance by taking advantage of biodiversity-related ecosystem services have the best potential to effectively lower carbon losses in Amazonia, if accessibility to credit lines and extension management is secured for all actor types of farmers.

     

Notes

Acknowledgments

This study was carried out in the framework of the interdisciplinary project CarBioCial funded by the German Ministry of Education and Research (BMBF) in the FONA-line, under the grant number 01LL0902F. We want to thank the Brazilian counterpart project Carbioma (UFMT, UFPA-NAEA, Embrapa Arroz e Feijão) for collaboration, all involved farmers, stakeholders, and Brazilian scientific colleagues for their creative contributions, support and their patience during the sampling campaign. We express our gratitude to the Kayapó people that allowed us on their territory and accompanied our research activities with interest and understanding. Without the cooperation of their Institute Kabu, important data presented here could not have been collected. Our gratitude also belongs to the anonymous reviewers for their support to improve the manuscript, and Silke Bokeloh and Steffen Söffker for their valuable technical support.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jens Boy
    • 1
  • Simone Strey
    • 1
  • Regine Schönenberg
    • 2
  • Robert Strey
    • 1
  • Oscarlina Weber-Santos
    • 3
  • Claas Nendel
    • 4
  • Michael Klingler
    • 5
  • Charlotte Schumann
    • 2
  • Korbinian Hartberger
    • 2
  • Georg Guggenberger
    • 1
  1. 1.Institute of Soil ScienceLeibniz Universität HannoverHanoverGermany
  2. 2.Lateinamerika Institut (LAI)Freie Universität BerlinBerlinGermany
  3. 3.Departamento de Solos e Engenharia RuralUniversidade Federal do Mato Grosso – UFMT/FAMEVCuiabáBrazil
  4. 4.Institut für LandschaftssystemanalyseLeibniz-Zentrum für Agrarlandschaftsforschung (ZALF)MünchebergGermany
  5. 5.Geographisches InstitutUniversität InnsbruckInnsbruckAustria

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