Deforestation and current management practices reduce soil organic carbon in the semi-arid Chaco, Argentina

Agricultural Systems 178 (2020) 102749

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Deforestation and current management practices reduce soil organic carbon in the semi-arid Chaco, Argentina

T

Pablo Baldassinia, , José María Parueloa,b,c ⁎

a

IFEVA and Departamento de Métodos Cuantitativos y Sistemas de Información, Facultad de Agronomía, Universidad de Buenos Aires and CONICET, Buenos Aires, Argentina b Instituto Nacional de Investigaciones Agropecuarias, INIA La Estanzuela, Ruta 50 km 11, Colonia, Uruguay c IECA, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay

ARTICLE INFO

ABSTRACT

Keywords: Soil organic carbon Net primary production CENTURY Land use change HANPP

The soil is a fundamental component of the C cycle. Land use changes can alter the soil organic carbon (SOC) content, a key determinant of several regulation ecosystem services. Here, we analyzed the effects of land cover (e.g. from forest to cropland) and land use (i.e. management practices) change on SOC in the semi-arid Chaco of Argentina, a global deforestation hotspot. Using the CENTURY model, we analyzed SOC changes over 20 years for two sites with contrasting rainfall (600 and 800 mm). For each site, we evaluated the effect of different combination of management practices (e.g. fertilization, grazing intensity) and land uses (i.e. annual crops and sown pastures). 98.5% of the simulations performed for cropping systems showed a reduction in SOC, with an average reduction of 25% respect to the native forest. Wheat proportion in the crop rotation had the highest relative influence on SOC variation (54%), higher than the proportion of maize (26.4%) and nitrogen fertilization (9.8%). For sown pastures, < 40% of the simulations showed SOC decreases up to 18%. Grazing intensity had the greatest relative influence on SOC variation in both sites (> 60%), followed by burning (19%), temporary exclusion of cattle (12%) and nitrogen fertilization (4.3%). In most cases SOC changes were mainly explained by changes in C inputs (i.e. NPP) rather than by changes in outputs (i.e. respiration and erosion). Therefore, delta SOC showed a strong negative relationship with the Human Appropriation of NPP (R2 = 0.54 and 0.67 in cropping systems and pastures, respectively). Overall, our results suggest that land use change has negative effects on SOC regardless of the management practices implemented. The few combinations that balanced food production and carbon sequestration were maize monocultures with annual N fertilization, and cattle raising with temporary exclusion and burning suppression. Our results may be used to define management practices that allow maintaining soil carbon stocks in the upper soil layer.

1. Introduction The soil is a fundamental component of the C cycle, since it constitutes the largest reservoir of organic C in the biosphere (Batjes, 1996; Lal, 2004). Soil organic carbon (SOC) is a key determinant of multiple regulation ecosystem services (ES) (Millennium Ecosystem Assessment, 2005) from which humans derive benefits (i.e. final ecosystem services, Fisher et al., 2009). SOC is closely related to many soil characteristics that are intimately related to food production, such as cation exchange capacity and soil nutrients (Sitompul et al., 2000), structural aggregate

stability, resistance to erosion (Wischmeier and Smith, 1978) and soil compaction (Soane, 1990), and water holding capacity (Alvarez et al., 2015). The quantification of SOC losses is important not only to assess the sustainability of agroecosystems, but also to measure the addition of greenhouse gases into the atmosphere (Alvarez, 2001). The stock of C in soils is controlled by two fundamental processes: C inputs by net primary production (NPP) and outputs through decomposition (Jobbágy and Jackson, 2000; Wang and Hsieh, 2002). Water availability regulates primary production (Webb et al., 1978) and temperature the rate of organic matter mineralization (Cole et al.,

Abbreviations: AGB, Aboveground biomass; ANPP, Aboveground net primary productivity; BD, Bulk density; BNPP, Belowground net primary productivity; C, Carbon; DM, Dry matter; ES, Ecosystem services; HANPP, Human appropriation of net primary productivity; MODIS, Moderate Resolution Imaging Spectroradiometer; NPP, Net primary production; RMSD, Root mean square deviation; TRMM, Tropical Rainfall Measuring Mission; SOC, Soil organic carbon; SON, Soil organic nitrogen ⁎ Corresponding author. E-mail addresses: [email protected] (P. Baldassini), [email protected] (J.M. Paruelo). https://doi.org/10.1016/j.agsy.2019.102749 Received 10 April 2019; Received in revised form 1 October 2019; Accepted 12 November 2019 0308-521X/ © 2019 Elsevier Ltd. All rights reserved.

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1993) resulting in a positive relationship between the ratio rainfall/ temperature and SOC at a regional scale in temperate and tropical soils (Theng, 1989; Alvarez and Lavado, 1998). At local scale (stand or plot), C dynamics in cropping systems is primarily driven by management practices (e.g. crop rotations, tillage, fertilization, livestock stocking rate) (Viglizzo et al., 2004). It is often difficult to identify how much of the C losses result from a negative C balance or by the removal through erosion. Therefore, a key aspect to assess is the relative importance of C inputs and outputs on the formation of SOC (Eclesia et al., 2016). In this context, simulation models, such as CENTURY, provide an attractive avenue for exploring and anticipating the success or failure of management strategies aimed at maximizing C gains. The impact of land use and management practices on SOC dynamics requires long observation periods, since it may take several years before measurable changes can be detected (Pretty et al., 2006). Plus, the information obtained in experimental plots can rarely be extrapolated to other soils and climates. Hence, simulation models can overcome some of the limitations of experimental approaches and also provide an operative way to analyze the effects of multiple management practices on SOC, such as crop rotations. Among the many simulation models that were developed for studying SOC dynamics, CENTURY has been one of the most widely used on a broad range of ecosystems (Parton et al., 1993; Smith et al., 1997). This model can simulate the temporal dynamics of soil organic matter and carbon for natural ecosystems (grasslands, forests and savannas) and agroecosystems (crops and pastures), and under a wide array of environments and management practices. Therefore, it can be used to characterize different management schemes with regard to their effect on SOC and, consequently, to evaluate the environmental and agronomically sustainability of different systems (Piñeiro et al., 2006; Caride et al., 2012). Carbon gains or NPP have being identified as an integrative descriptor of ecosystem functioning (McNaughton et al., 1989). In fact, many final ES (e.g. C storage or forage production) are positively correlated to NPP (Richmond et al., 2007) and its seasonal dynamics (Viglizzo et al., 2011; Paruelo et al., 2016). Human appropriation of NPP (HANPP) quantifies the fraction of NPP used directly (e.g. biomass harvest) or indirectly (e.g. forage for livestock) by humans (Vitousek et al., 1986), and it reflects a measure of the available energy for the trophic web (Field, 2001). HANPP has been used to describe the magnitude of human impact on agroecosystems (Baeza and Paruelo, 2018) or a measure of their intensification (Haberl, 1997; Krausmann et al., 2013). Thus, HANPP can be used to describe, in an integrative and quantitative form, the intensification of agroecosystems due to management practices. The dry forests of the South American Gran Chaco have been one of the global hotspots of deforestation since the beginning of the century (Hansen et al., 2013). For the Argentinean fraction of these forests, between 1976 and 1997, the recorded deforestation rate was of 84.000 ha.yr−1, which increased 1.7 times for the 2006–2012 period (Vallejos et al., 2015). The rapid conversion of native forest conversion to cropping and pasture affected mean C inputs and outputs (Gasparri et al., 2008; Baumann et al., 2017), their seasonality (Volante et al., 2012) and also the allocation into above and belowground compartments driven by changes in plant functional types. However, since dry forests contain almost twice as much C in the first soil meter (114 T C.ha−1) than in the aboveground biomass (Villarino et al., 2017), a more comprehensive description of the effects of land use change on SOC is critical. Global metaanalysis studies suggest that cropping generally reduces SOC stock, with average losses of 20–52% respect to the original cover (Davidson and Ackerman, 1993; Murty et al., 2002; Smith et al., 2016). However, pastures might have both positive and negative C balances, depending on specific circumstances, such as application of fertilizer or retention or removal of plant residues (Guo and Gifford, 2002; Murty et al., 2002). Recently, several studies have reported decreases of approximately 26–40% in the upper soil layer following the conversion of dry forests to pastures and annual crops,

respectively, in different areas of the Argentinean Dry Chaco (Caruso et al., 2012; Conti et al., 2014; Rojas et al., 2016; Villarino et al., 2017; Osinaga et al., 2018; Lumbreras et al., 2019). Even though deforestation and subsequent cropping reduce SOC up to 1 m depth (Villarino et al., 2017), while pastures showed this decrease only in the surface layer (Osinaga et al., 2018; Lumbreras et al., 2019), the upper soil layer not only contains more C than in depth but it is also where C was most reduced, being the most negatively affected to management practices (Villarino et al., 2017). By means of CENTURY, we analyzed SOC changes over 20 years for two sites with contrasting rainfall (600 and 800 mm). For each site, we evaluated the effect of different combination of management practices (e.g. fertilization, grazing intensity) and land uses (i.e. annual crops and sown pastures). We used HANPP to describe the degree of intensification level due to management practices in order to derive impact functions that relate management practices with SOC. We predicted that SOC is mainly determined by changes in soil C inputs rather than by shifts in output flows (respiration and erosion). Further, we also expected that SOC changes would be greater in the wet portion due to a higher impact of land use changes on C gains or NPP (Volante et al., 2012). Specifically, the questions that guided our study were a) how do management practices affect SOC stocks and which ones have the highest impact? and b) How do SOC stocks change across an intensification gradient defined by HANPP? 2. Materials and methods 2.1. Study area and sampling The semi-arid Chaco of Argentina (Cabrera, 1976) occupies an area of approximately 25 Mha covering a large fraction of the provinces of Salta, Santiago del Estero, Chaco and Formosa (Fig. 1A). The region is topography wide sedimentary plain (average slope is < 0.1% (Jobbagy et al., 2008)) where the predominant soils are Udic Argiustoles, Udic Haplustoles and Entic Haplustoles (Vargas Gil and Vorano, 1988). Our study area was located in the department of Anta in eastern Salta province. This department has an east-west rainfall gradient ranging from 800 mm in the northwest to 600 mm in the southeast (Fig. 1C) which highly determines land use and cover (annual crops in the west and pastures in the east). The climate is subtropical continental, with average annual temperatures ranging from 20 °C to 25 °C. Rainfall has a monsoonal regime and is concentrated between November and April. The natural vegetation of this region encompasses fundamentally xerophytic forests and shrublands, dominated by deciduous and semideciduous species, adapted to major fluctuations in water availability and seasonal variations in temperature (Morello and Adámoli, 1974). The dominant tree species are: quebracho colorado santiagueño (Schinopsis lorentzii), quebracho colorado chaqueño (Schinopsis balansae), quebracho blanco (Aspidosperma quebracho-blanco), guayacán (Caesalpinia paraguariensis), mistol (Ziziphus mistol) and several species of the Prosopis spp. (Vargas Gil and Vorano, 1988). Xerophytic forests are rapidly being converted into annual crops (soybean, maize and wheat) and sown pastures (Tálamo et al., 2009; Hansen et al., 2013; Vallejos et al., 2015). Although summer crops such as maize and soybean are the most important in cultivated area (Paruelo et al., 2005; Fehlenberg et al., 2017; Baumann et al., 2017) the area of wheat has recently increased primarily for controlling erosion (Sobral et al., 2009). Cropping systems is rainfed and mainly under no tillage. Nowadays, forest conversion is mainly driven by pasture expansion (Gasparri et al., 2013; Volante et al., 2016) where Gatton panic (Megathyrsus maximus) is the dominant sown species (Riffel and Elizalde, 2008; Avila et al., 2014) driven by its high productivity and easy implantation (Chiossone et al., 2014). Across the rainfall gradient, 31 sites (13 croplands, 7 pastures and 11 patches of native forest) were sampled using a tubular auger (Fig. 1 2

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P. Baldassini and J.M. Paruelo

Fig. 1. The study area is located in the Department of Anta in the Argentinean Dry Chaco. 31 sites were sampled, of which 13 correspond to croplands (Orange), 7 to sown pastures (Red) and 11 to native dry forests (Yellow) (C). Most of the points were slightly moved to allow a better visualization. The geographical coordinates of each site is indicated in Table 1. The blue lines indicate the average annual rainfall (in mm.year−1). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

and Table 1). At each site, 3 sub-samples were taken from the upper 20 cm of the soil layer. Soil texture, bulk density, %N and %C were determined in laboratory (Table 1) and used for model calibration (see section 2.2.2) and evaluation (see section 2.2.3). The C content of each sample was determined by dry combustion using an Automatic Elemental Analyzer (Carlo Erba NA 1500). Soil texture was determined by Bouyoucos (Bouyoucos, 1962; Elliot et al., 1999) and soil bulk density was estimated by dividing the total dry weight by the total volume (volume of each soil core * number of subsamples in the composite sample).

2.2.2. Model calibration The inputs of the model include information on the biophysical (climate and soil) and management (land use, crop sequence, tillage, fertilizer, stocking rate, burning frequency and intensity) characteristics of each site. Historical climate data (precipitation and temperature) were obtained from IRI Data Library (Blumenthal et al., 2014), which provides monthly information from 1900 to 2002. For the period between 2003 and 2013, monthly average temperature was used while precipitation information was derived from TRMM images (Tropical Rainfall Measuring Mission) (Huffman et al., 2007; Prigent, 2010). Texture and bulk density of soils were obtained from laboratory soil analysis (see section 2.1). A pH equal to 6.48 was used, corresponding to the average of soil samples from forests located in the study area (Amdan et al., 2013). Historical management information was provided by local farmers. The plant growth sub-model is mainly determined by temperature (Parton, 1996) and the parameters of the model were adapted for local genotypes and yields. Atmospheric deposition (dry and wet) is the main input of N, and it was estimated as a linear function of annual rainfall, based on the average wet deposition reported by the National Atmospheric Deposition Program (NADP. NationalAtmosphericDepositionProgram (NRSP-3), 2015) for the only station in Argentina, located in the province of Córdoba. Published and unpublished local data for model parameterization were used and default values were considered if no information was available (see Appendix A for details and references). Simulations were conducted in two steps: (1) carbon dynamics in native forest until SOC stabilization (at least a time extension of

2.2. Modeling the effects of land use, management practices and environmental conditions on SOC content 2.2.1. Century description The effects of land use, management practices and environmental conditions on SOC content in the upper 20 cm of the soil were quantified using CENTURY 4.6 (Parton et al., 1987). This model simulates the monthly dynamic C and other elements in forests, savannas, steppes, pastures and agroecosystems. The flows between different pools are controlled by environmental conditions (temperature, soil water content and soil texture), and by litter quality (lignin content and C/N ratio). The soil organic matter is divided into three different pools: active, slow and passive (recalcitrant organic matter), with turnover times of 1–5, 20–40 and 100–1000 years, respectively. More information on model characteristics is available at: http://www.nrel.colostate. edu/projects/century. 3

Sampling year

2013 2014 2010 2013 2014 2010 2013 2010 2010 2010 2010 2010 2014 2010 2013 2014 2010 2014 2010 2014 2014 2010 2014 2010 2010 2013 2014 2014 2014 2010 2014

Site

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Forest Cropping Cropping Cropping Cropping Cropping Cropping Cropping Cropping Cropping Cropping Cropping Cropping Cropping Pasture Pasture Pasture Pasture Pasture Pasture Pasture

Land use

25°32′13.25′´ 24°25′29.97′´ 24°30′58.19′´ 25°33′16.36′´ 24°27′41.57′´ 24°30′58.36′´ 25°33′19.89′´ 24°30′57.99′´ 24°28′49.82′´ 24°30′58.09′´ 24°30′58.22′´ 24°30′58.11′´ 24°27′43.20′´ 25°30′02.14′´ 25°33′19.87′´ 24°27′43.04′´ 24°30′58.26′´ 24°27′43.80′´ 24°30′58.26′´ 24°27′43.77′´ 24°27′44.01′´ 24°29′53.35′´ 24°27′42.22′´ 24°29′53.36′´ 25°33′16.38′´ 25°32′13.31′´ 24°27′42.82′´ 24°27′43.20′´ 24°27′42.72′´ 24°30′58.02′´ 24°27′42.91′´

Latitude (S) 63°22′11.87′´ 63°24′23.91′´ 64°24′26.47′´ 63°21′08.47′´ 63°24′22.59′´ 64°24′25.04′´ 63°21′03.79′´ 63°22′13.15′´ 63°20′57.35′´ 64°24′26.44′´ 64°24′25.05′´ 64°24′26.45′´ 64°25′27.83′´ 64°24′26.38′´ 63°21′03.85′´ 64°25′27.47′´ 64°24′25.01′´ 64°25′27.15′´ 64°24′25.10′´ 64°25′27.13′´ 64°24′26.63′´ 63°22′14.46′´ 63°24′22.94′´ 63°22′14.37′´ 63°21′08.53′´ 63°22′11.88′´ 64°25′27.98′´ 64°25′27.50′´ 64°25′27.95′´ 63°22′13.15′´ 63°24′23.94′´

Longitude (W)

Geographical coordinates

0.18 0.23 0.17 0.23 0.13 0.22 0.24 0.20 0.23 0.19 0.23 0.11 0.06 0.15 0.13 0.11 0.16 0.07 0.11 0.08 0.06 0.19 0.11 0.19 0.13 0.15 0.14 0.13 0.14 0.14 0.09

%N

1.74 2.35 1.41 2.44 1.47 2.11 2.58 2.20 1.99 1.60 1.98 0.85 0.52 1.07 1.25 1.10 1.14 0.69 0.88 0.82 0.59 1.48 1.07 1.58 1.22 1.41 1.41 1.30 1.42 1.19 0.86

%C

9.61 10.42 8.49 10.49 11.47 9.37 10.85 10.92 8.47 8.58 8.72 7.62 9.22 7.31 9.39 9.94 6.96 9.98 7.92 10.19 9.66 7.80 10.11 8.12 8.03 9.59 10.13 10.11 10.31 8.30 9.57

C:N

0.88 0.83 0.89 0.86 0.95 0.83 0.77 0.78 0.78 0.85 0.83 1.12 1.18 1.01 0.88 1.20 1.01 1.21 1.04 1.21 1.20 0.92 1.15 0.84 0.95 1.00 1.06 1.14 1.12 0.95 1.13

Bulk density (BD) (g/cm3) 26.34 38.73 25.95 33.67 27.92 35.28 31.95 33.42 30.00 27.84 32.36 22.98 12.25 22.42 23.39 20.36 32.19 16.77 23.49 19.87 14.30 30.34 24.60 27.11 23.11 27.19 29.97 29.56 31.78 31.25 24.27

T C.ha-1 (0–20 cm) 15.90 32.60 17.23 32.15 13.40 23.48 21.35 20.00 27.73 15.00 29.15 26.58 5.80 28.12 18.40 13.75 25.00 8.75 34.08 11.25 6.25 26.25 11.25 31.25 27.37 14.30 15.00 12.50 15.00 18.40 10.00

% Clay

50.80 30.80 28.84 37.05 25.00 45.09 53.30 48.75 49.50 32.41 40.50 31.20 11.25 42.86 60.80 33.75 38.57 8.75 44.77 16.25 13.75 47.32 17.50 44.91 46.38 41.25 37.50 26.60 22.40 48.93 29.10

% Silt

33.30 36.60 53.93 30.80 61.60 31.43 25.35 31.25 22.77 52.59 30.35 42.22 82.95 29.02 20.80 52.50 36.43 82.50 21.15 72.50 80.00 26.43 71.25 23.84 26.25 44.45 47.50 60.90 62.60 32.67 60.90

% Sand

28.90 25.27 26.82 28.90 28.97 26.82 29.17 28.97 29.68 26.82 26.82 26.82 26.82 27.82 29.17 26.82 26.82 26.82 26.82 26.82 26.82 28.97 28.97 28.97 27.82 28.90 26.82 26.82 26.82 28.97 28.97

Max Temp (C°) 15.49 11.86 13.47 15.49 15.75 13.47 15.49 15.75 16.27 13.47 13.47 13.47 13.47 14.41 15.49 13.47 13.47 13.47 13.47 13.47 13.47 15.75 15.75 15.75 14.41 15.49 13.47 13.47 13.47 15.75 15.75

Min Temp (C°) 608 788 675 608 786 675 600 668 640 675 675 675 804 592 600 804 675 730 675 810 811 668 787 668 592 608 817 810 817 668 788

Average annual rainfall (mm)

– – – – – – – – – – – 1979 1976 1979 2011 1976 1993 1985 1993 1985 1993 2007 2003 2008 1979 1993 1985 1976 1985 2005 1979

Deforestation year

Table 1 Description and spatial location of the sampled sites. Each soil sample was characterized by %Nitrogen, % Carbon, C/N ratio, Bulk density and soil texture. We estimated the C in the top 20 cm of the soil using %C and bulk density. Maximum temperature, minimum temperature and average annual rainfall were obtained from IRI Data Library (Blumenthal et al., 2014) and TRMM images ((Huffman et al., 2007; Prigent, 2010). For sites under cropping systems or livestock raising the year of forest conversion was determined using a geo-database (Vallejos et al., 2015).

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144 I) Continuous grazing II) Continuous grazing with exclusion every 3 years

272 No tillage I) Monoculture II) With crop rotation

I) 0.1 II) 0.25 III) 0.5 IV) 1.0 V) Intensive grazing VI) No grazing

I) No burning II) Burning every 3 years

N fertilization during maize sowing I) No fertilization II) Average fertilization (43 kg.ha−1) III) High fertilization (2 x average) (86 kg.ha−1) N fertilization during pasture sowing I) No fertilization II) Average fertilization (46 kg.ha−1) III) High fertilization (2 x average) (92 kg.ha−1)

Number of simulations

Sown pasture of gatton panic (Megathyrsus maximus)

2.2.4. Land use scenarios and management practices simulated We analyzed the two main land uses in the region: annual crops and pastures of Gatton panic (Megathyrsus maximus) (Table 2), under two annual rainfall average conditions (600 and 800 mm, respectively). Both land uses were simulated for a 20-year period following deforestation, resulting in a total of 416 simulations. The main crops considered were soybean, maize and wheat (Volante et al., 2005; MINAGRI, and Ministerio de Agricultura, Ganadería y Pesca, 2011) (Table 2). By combing different annual crops in time, several single and double crop sequences were simulated (Table 2). In the driest condition, only single crop sequences were simulated due to limitations in soil water availability (Murray et al., 2016). No tillage was assumed, applying conventional tillage for soil preparation only after land clearing. Only N fertilization was considered, assuming no P and S limitations. Three levels of fertilization were used for maize crops: (i) no fertilization, (ii) the average fertilization reported for the region (43 kg N.ha−1) (Bolsa de Cereales, 2011) and (iii) high fertilization (2 × average = 86 kg N.ha−1). Fertilization was not simulated for wheat neither soybean due to the low application levels observed in the region (Bolsa de Cereales, 2011). Burning after clearing was considered as a possible practice, which has the purpose of eliminating the woody component removed from forest. A total of 272 simulations were performed, 108 and 164 for the dry and wet site respectively. For pastures we simulated the following management practices: stocking rate, N fertilization at planting, grass burning and temporary

I) No burning II) Initial burning of organic residues

It was not necessary to apply this correction to compare the outputs of the different land use and management practices simulated (see section 2.2.4) because the bulk density considered was the same across the entire simulation period, thus the comparisons was made under equivalent soil masses. Comparisons were made by regressing observed vs. predicted values, and multiple statistical criteria were used to evaluate the accuracy of the model: Coefficient of Determination (R2), the Root Mean Square Deviation (RMSD) and the Pearson Correlation Coefficient. We tested the significance of both slope and intercept and also the deviation from the 1:1 relationship (Null Hypothesis: slope = 1, intercept = 0). Additionally, we compared simulated values with estimates reported in the literature for forest aboveground biomass (Gasparri and Baldi, 2013; Baccini et al., 2016), forest NPP (Haberl et al., 2007; del Grosso et al., 2008), pasture above and belowground NPP (Hui and Jackson, 2005), and soybean and maize yields (local farmers, personal communication). A t-test was performed to determine the existence of significant differences between the simulated values and those derived from various sources (see Appendix B for more details).

NS

(1)

Annual crops (wheat, soybean, maize)

(0.95/Bulk density in the sampling)

Management practices

Cmeasured in the sampling

Fertilization

Ccorrected =

Burning

2.2.3. Model evaluation CENTURY performance was evaluated by comparing simulated and field measured SOC. Because it not possible to modify the physical parameters during the same simulated period in the CENTURY model (i.e. the bulk density is the same at the beginning and at the end of each simulation) we used a constant bulk density value of 0.95 g/cm3 which represents the average measured in all samples (Table 1). To express the SOC in an equivalent soil mass and to make both simulated and observed values comparable (Davidson and Ackerman, 1993), the SOC estimated by the model was corrected by the bulk density of each sample using the Eq. (1):

Stocking rate (AU.ha−1)

1970 years), and (2) variable time lapse associated with the year of forest conversion (Table 1). Intense fire events were considered every 300 years, which occur under extreme climatic conditions (Morello, 1970). In cropping systems, we considered that conventional tillage was the main practice until 1993 and afterwards no tillage replaces it (Alapin, 2009).

Land use

Table 2 Land use and management scenarios simulated using CENTURY. We analyzed crop sequence, fertilization, grazing, stocking rate and burning frequency and intensity. The number of simulations indicates the total model runs that were made combining different levels of each management practice (column) in two sites with contrasting average rainfall: 600 and 800 mm. The acronym NS means “Not Simulated”. A total of 416 scenarios were simulated.

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every 300 years, which occur under extreme climatic conditions (Morello, 1970). We evaluated the relative influence of each management practice (i.e. nitrogen fertilization, burn, cropping sequences, grazing and exclusion) and their interactions on SOC changes. In order to calculate main effects and interactions, we used the weighted sums of squares as the test statistics (Konstantopoulos and Hedges, 2009). Also, a linear regression analysis was performed between SOC and quantitative explanatory variables in cropping systems (i.e. proportion of wheat and maize in the rotation proportion), and the slope of the relationship was calculated. 2.3. Relationship between human appropriation of net primary production (HANPP) and changes in SOC

Fig. 2. Estimation of Human Appropriation of Net Primary Productivity (HANPP). NPPO = NPP of the native forest, NPPact = NPP of a pasture under a specific management practice, NPPh = NPP harvested through the herbivore consumption, NPPt = NPP remaining after the harvest. The HANPP considers both change in NPP between the original vegetation (forest) and the new land use replacement (NPPLC) and the productivity harvested (NPPh). The last bar (Maize monoculture) shows a situation where, after harvesting, the remaining NPP of the actual system is higher than the NPP of the original native forest, resulting in a negative HANPP. The values used for estimating each variable were derived from simulations of CENTURY and represent the average values across 20 years (Adapted from Rueda et al., 2013).

We estimated HANPP for each simulation and explored its relationship with changes in SOC (%), calculated as the relative difference between native forest and the particular management condition after 20 years of continuous use. HANPP is a proportion that considers both the direct and indirect appropriation of NPP. Direct appropriation includes the extraction of plant biomass as grains by harvest and the consumption of plant biomass by livestock. Indirect appropriation refers to the difference in NPP between the original vegetation (native forest) and the productive systems that replaces it (land uses and management practices) (Haberl et al., 2007). HANPP was estimated as follows:

exclusion of grazing (Table 2). For stocking rate, different levels of animal units (AU) were considered. An animal unit (AU) is defined as the number of cattle, buffalo, sheep, goats, horses, and camels weighted by their relative size and growth rates [AU = n (cows + buffalo) + 0.2 n (sheep + goats) + 1.2 n (horses + camels)] (Asner et al., 2004). In our case the system was only grazed by cattle. The levels considered were: (i) no grazing, (ii) 0.1 AU.ha−1, (iii) 0.25 AU.ha−1, (iv) 0.5 AU.ha−1, (v) 1 AU.ha−1, (vi) Intensive grazing (summer period from December to March) based on pasture productivity (Rossi, 2007). Continuous grazing throughout the year was assumed (with the exception of intensive grazing), although a higher consumption in summer months was considered, associated with an increase in grass productivity (Baldassini et al., 2018) and higher animal requirements. Livestock consumption as a percentage of ANPP ranged from 0% to 64% and from 0 to 76% for the wet and dry site, respectively (see Appendix A for parameters details). Only N fertilization was considered, assuming no P and S limitations. Three levels of fertilization were used: (i) no fertilization, (ii) the average fertilization reported in the region (46 kg N.ha−1) (Sanchez et al., 2012) and (iii) high fertilization (2 × average = 92 kg N.ha−1). Grass burning before the start of the growth period is a common practice reported by local farmers, however, it is usually applied every 3 years. We simulated two frequencies: (i) no burning and (ii) burning every 3 years. Also, we considered two grazing conditions: (i) continuous grazing without temporary livestock exclusion and (ii) continuous grazing with temporary livestock exclusion every 3 years (Luisoni, 2010). A total of 144 simulated were performed, half of them for the site of the rainfall gradient. The simulations had the following assumptions (see Appendix A for parameters details): (i) no direct effect of grazing on ANPP or root/ shoot partitioning, (ii) constant N deposition through time, but variable among sites in direct relationship with annual precipitation, (iii) in pastures, the N and C that return to soil by livestock urine and dung was calculated as a function of digestibility, which varied along the year according to grass quality (De León et al., 1995), (iv) constant atmospheric CO2 concentration along all the simulated period (300 ppm), (v) constant climate for the simulations before the year 1900; after that climate data from databases and satellite imagery was used, (vi) no irrigation was considered as a management practice, (vii) no rainwater losses due to surface runoff, (viii) intense fire events were considered

HANPP = NPPo –NPPt /NPPo

(2)

NPPt = NPPact –PPNh

(3)

where NPP0 is the original NPP of the native forest, NPPt is the NPP remaining after the harvest (NPPh) and NPPact is the NPP of the current vegetation (crops or pasture) (Haberl et al., 2007) (Fig. 2). HANPP was expressed as a percentage with respect to potential NPP (Eq. (2)). Positive values indicate systems where human activity reduces the actual NPP (NPPact) below the potential natural NPP (NPPo) of natural vegetation (e.g. the conversion of a forest into a pasture). Both NPP losses generated by land use change (NPPLC) and harvest (NPPh) contribute to increase the HANPP (Fig. 2). Negative values indicate systems where NPPact exceeds the NPPo due of human intervention (e.g. double cropping, fertilization, irrigation) (Fig. 2). The values for calculating the Eqs. (2) and (3) are the average of model simulations across 20 years. For pastures, we considered the appropriation of NPP by herbivore consumption, but the NPP that returns to the soil by excrete of animals was discounted. Thus, given an average digestibility of 54% for Gatton panic (De León et al., 1995), we considered that 46% of the consumed forage was excreted. 3. Results 3.1. Performance of CENTURY model The model showed a good performance based on the comparison of measured and simulated SOC values (Fig. 3 and Table 3). The analysis of both land uses and the pooled data showed similar results regardless of the statistical criteria (Table 3). R2 ranged between 0.62 and 0.69, while Pearson coefficient varied between 0.79 and 0.83, being in both cases higher for the pooled data (Fig. 3 and Table 3). The Root mean square deviation (RMSD) was higher for the pooled data and Forests, while pastures showed the lowest value (Table 3). Although all regressions presented a significant relationship between the simulated and measured data (B1 ≠ 0, p value < .05) (Fig. 3 and Table 3), pooled data and Forests presented a slope significantly smaller than the 1:1 line (Table 3). Thus, SOC was overestimated in forests which in turn affected the pooled estimation. An additional analysis was done by exploring the relationship between delta SOC (difference between 6

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Fig. 3. Comparison of simulated and measured SOC in the top 20 cm of the soil (in T C.ha−1). The left panel indicates the general adjustment (including all land uses), and right panels correspond to each land uses. Forest (n]11), cropping systems (n]13) and pastures (n = 7) are indicated by points, squares and triangles, respectively.

measured and simulated data) and the soil clay content. We observed that SOC contents were overestimated by CENTURY at low and high clay contents (lower than 10% and above 30% approximately) (results are discussed in more detail in Appendix C). Thus, we used a clay content value (18.71%) derived from the intersection between the regression lines of measured and simulated SOC as response variables and clay content as predictor.

Table 3 Multiple statistical criteria used to evaluate the performance of the simulation model CENTURY. The p value < .05 indicates the significance of the slope for a lineal model that has measured and simulated data, as response and predictor variables, respectively. For the same model, the intercept and the slope of the 1:1 line indicate the significance of the deviation of the model with respect to the 1:1 line. The R2, root mean square deviation (RMSD) and Pearson coefficient indicate the correlation between the measured and simulated data. The statistical criteria was calculated for the pooled data and for each land uses.

3.2. Effect of land use and management practices in SOC

SOC

Intercept line 1:1 Slope line 1:1 p value R2 RMSD Pearson coefficient

Pooled

Forest

Cropping

Pasture

< 0.0001 < 0.0001 < 0.0001 0.69 4.5 0.83

< 0.0001 < 0.0001 < 0.01 0.63 6.02 0.79

0.18 0.19 < 0.01 0.62 3.96 0.79

0.55 0.88 0.026 0.66 1.91 0.81

In the top 20 cm, SOC was 29.1 and 32.8 T C.ha−1 in the dry and wet site, respectively (Fig. 4). After 20 years of continuous cropping SOC decreased, respect to native forest, 25% (7.30 T C.ha−1) and 24% (7.68 T C.ha−1) in the dry and wet site, respectively (Fig. 4). In the wet site and under double cropping sequences, the highest losses of SOC occurred in the wheat/soybean-maize rotation without burning or fertilization, being 10.97 T C.ha−1 (33.45%). In single cropping sequences, the highest losses occurred under wheat monocultures, being 13.28 T C.ha−1 (45.64%) and 14.52 T C.ha−1 (44.29%) in the dry and

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Fig. 4. Simulated soil organic carbon (SOC) changes with respect to native forest for cropping systems and livestock (pastures) derived from the simulation model CENTURY. The bars indicate the average value under different levels of management practices. The dotted black line indicates the average SOC in the native forest and the vertical lines indicate the standard deviation. Cropping systems: Fertilization (L: low, M: Medium, H: High), Burn (NB: No burn, B: Burn), cropping sequences (S: Single cropping, D: Double cropping). Pastures: Grazing (NG: No grazing, 1: 0.1 AU.ha−1, 2: 0.25 AU.ha−1, 3: 0.5 AU.ha−1, 4: 1 AU.ha−1, IG: Intensive grazing), Fert = Fertilization (L: low, M: Medium, H: High), Excl = Exclusion (NE: No exclusion, E: With temporal exclosion), Burn (NB: No burn, B: Burn).

proportion of wheat cultivation and nitrogen fertilization had a greater impact under dry conditions, being 54% and 9.8%, respectively (Table 4). In both sites, the wheat proportion in the crop rotation had a negative effect on SOC variation (Table 4). An increase between 0.68 and 1.69 T C.ha−1 in SOC was observed when nitrogen was annually applied as fertilization under monoculture of maize, in the dry and wet site, respectively. After 20 years, the conversion of the native forests into pastures was followed by both increases (up to 12%) and decreases (up to 18%) in SOC depending on management practices (Fig. 4). Grazing intensity had the greatest relative influence on SOC variation in both sites (60.8% and 74.4% in the dry and wet site respectively) (Fig. 4 and Table 4). To a lesser extent, burning every 3 years (19% and 14.3%), grazing management (temporary exclusion of herbivores) (12% and 4.5%) and nitrogen fertilization (4.3% and 3.1%) contributed to SOC variation in the dry and wet site, respectively (Fig. 4 and Table 4). In systems with a high stocking rate (1 AU.ha−1) and under intensive grazing in summer, we observed an average decrease of 5.4% in SOC in the wet site (with a maximum of 11.7%) and an average decrease in 3.72% in the driest site (with a maximum 10.11%). Under high stocking rates, the only alternative to maintain SOC is through the suppression of burning, the application of medium or high doses of nitrogen at sowing, and through the temporary exclusion of cattle.

Table 4 Relative importance (RI in %) of each management practices on the change of SOC respect to native forest. Management practices included were: Grazing, nitrogen fertilization, temporal exclusion of cattle, cropping sequence, burn, maize and wheat proportion in the cropping rotation. The SS column indicates the sum of squares, while the column B1 indicates the value of the slope between SOC and the quantitative variable (wheat and maize proportion).

Cropping systems

Pasture

Management pra ctices

600 mm

800 mm

SS

RI (%)

B1

SS

RI (%)

B1

Nitrogen Fertilization Burn Cropping sequences Interactions Maize proportion Wheat proportion Nitrogen Fertilization Burn Grazing Exclusion Interactions

86.6

9.8

–

89.2

7.8

–

21.8 –

2.5 –

– –

53.9 52.9

4.7 4.6

– –

0.0 68.7

0.0 8.9

– 4.1

0.6 244.3

0.0 26.4

– 5.6

417.9

54.0

−9.4

320.8

34.6

−5.3

5.6

4.3

–

7.0

3.1

–

24.5 78.6 15.5 5.0

19.0 60.8 12.0 3.9

– – – –

32.0 67.2 10.0 8.4

14.3 74.4 4.5 3.7

– – – –

3.3. Carbon balance of the soil

wet site, respectively. In the wet site, wheat proportion in the crop rotation had the greatest relative influence on SOC variation (34.6%), higher than the proportion of maize (26.4%), nitrogen fertilization (7.8%), post-clearance burning (4.7%) or the cropping sequences (single or double) (4.6%) (Table 4). The relative influence of the management practices on SOC was similar in both sites, but the

Around 98.5% of the simulations performed for crop systems showed SOC decreases. For pastures < 40% of the simulations generated SOC losses (Fig. 5A). The effect of management practices on C practices was similar in both sites (Fig. 5A). For cropping systems, 105 and 163 management practices simulations showed a reduction in SOC 8

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Fig. 5. A) Relative frequency of simulations with negative and positive delta SOC respect to the native forest in both sites. The I (input) and O (output) indicate which one varied relatively more to explain the observed changes in the delta SOC. The values on the bars indicate the total number of simulations. B) Carbon input (difference between total NPP and harvested NPP) and output (respiration + erosion) for both land uses at both sites. For each bar, vertical lines indicate the standard deviation and represent the variability associated with different management practices. The positive dotted black line indicates the average NPP of native forest, and the negative dotted black line indicates the average output C by total respiration of native forests.

in the dry and wet site, respectively (Fig. 5A). Conversely, for pastures 55 out of 144 simulations showed negative values of delta SOC (Fig. 5A). In most cases SOC changes (either positive or negative) were mainly determined by changes in C inputs rather than in variations in outputs (Fig. 5A). Although crops and pastures showed a reduction in losses due to respiration with respect to the native forest, most simulations showed a greater reduction in NPP (Fig. 5A and B). The balance between inputs (total NPP - harvested NPP) and outputs (soil respiration + erosion) was negative in both land use at the two sites, although cropping systems's balance was more negative. Although the driest site showed lower values with respect to the wet site in both input and output C flows, the two sites were similar with respect to the effects of land use change on C gains and losses. On average, total NPP was highest in cropping systems (5.8 and 6.9 T C.ha−1.yr−1 in the dry and wet site, respectively) (Fig. 5B). In the wet site, double cropping sequences showed a higher productivity than single crops (7.38 and 6.68 T C.ha−1.yr−1, respectively), due to the presence of maize in the rotations. However, around 35% of the NPP was harvested as grains, so the remaining NPP was 16.6% and 27.5% lower than in native forests in the dry and wet site, respectively (Fig. 5B). Losses by respiration were also reduced compared to forests (with average values of 5.2 and 5.9 T C.ha−1.yr−1 in the dry and humid site, respectively) though to a lesser extent (11 and 8%, respectively) (Fig. 5B). The erosion losses were on average 0.44 and 0.63 T C.ha−1.yr−1 in the dry and humid site, respectively, and therefore represented a low proportion of total outputs (respiration + erosion), being between 8.4 and 11.5% in the cropping sites. However, these were very important when analyzed with respect to the C balance (remnant NPP - respiration). The average C balance was −1.21 and − 1.48 T C.ha−1.yr−1 for the dry and humid site, respectively. C losses associated to the balance varied between 63 and 68% while erosion losses varied between 32 and 37%. Regardless of management practices, pastures presented a NPP between 13 and 18% lower than forests, showing an average value of 4.3 and 5.04 T C.ha−1.yr−1 in the dry and wet site, respectively (Fig. 5B). However, when considering the livestock consumption, the remaining NPP was reduced on average between 19 and 25.2% (Fig. 5B). Respiration losses were also reduced with respect to forests between 19.3 and 23.6%, in the wet and dry site, respectively (Fig. 5B). Erosion losses were on average 0.24 and 0.23 T C.ha−1.yr−1 in the dry and humid site, respectively (Fig. 5B). As well as for cropping systems, erosion represented a low proportion of the total outputs (respiration +

erosion), being between 4 and 5%, although they were very important when analyzed with respect to the C balance (remnant NPP- respiration). The average C balance was −0.26 and − 0.32 T C.ha−1.yr−1 for the dry and humid site, respectively. However, both C balances and the average losses due to erosion were substantially lower than those recorded in the cropping systems. 3.4. Relationship between human appropriation of NPP and SOC HANPP and the delta SOC (%) showed a negative relationship for both cropping systems and livestock rising. That is, the greater the HANPP the more negative the delta SOC. There was no difference in the regression parameters between sites, so it was possible to adjust a unique linear regression by land use (Fig. 6). In cropping systems, HANPP explained 54% of the variation in the delta SOC (%) and it was located in the range from −0.25 to 0.75 (Fig. 6). As the proportion of

Fig. 6. Relationship between changes in in content of soil organic carbon (delta SOC) and human appropriation net primary productivity (HANPP) in both sites for multiples management practices in cropping systems (squares) and pastures (triangles) systems. The dry and wet site are indicating in red and blue, respectively. The delta SOC was calculated as the percentage difference between the native forest and each land use and management practice combination. The HANPP was calculated as the relative difference between the original and remaining NPP for the productive system after harvest. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 9

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appropriated NPP in cropping systems was fixed, since it corresponds to the harvest index, changes in HANPP were mainly due to changes in NPP between forests and croplands. Although the total NPP of cropping systems largely exceeded that of forests, the remaining NPP after harvest was lower than that of the forests in most cases (Fig. 5B). Almost all the cropping systems presented reductions in SOC (Figs. 5A and 6), except for a few cases where management practices increase it, mainly related to maize monoculture under high doses of nitrogen fertilization (Fig. 4). For pastures, HANPP explained a greater percentage of the variability in delta SOC (%), being 67%, and it was located in the range from 0.05 to 0.42 in both sites (Fig. 6). Also, the regression presented a significant difference in the interception respect to cropping systems regression. That is, a same value of HANPP had a different impact on SOC, depending of the land use type. The average NPP varied depending on the stocking rate, with a linear reduction close to 7% at the maximum simulated stocking rate (1 AU.ha−1) (data not shown). In this way, an increase in HANPP under high stocking rates was observed due to a reduction in total productivity and an increase in livestock consumption.

Others, however, associated SOC losses with changes in N inputs and outputs promoted by herbivores (Piñeiro et al., 2006). We observed that the increase in N losses could not be compensated by the faster return of N through urine and feces resulting in a reduction in SON and N in general. In such a way, C accumulation in the soil could be limited by N availability, which would suggest the need to add N to increase the C reserves (Murty et al., 2002). Maize cultivation had a positive impact on SOC, due to the high remnant biomass that returns to the soil (Studdert and Echeverría, 2000). In fact, other studies have reported that the proportion of maize in crop rotations was positively associated with SOC increases in other areas of the Argentine Chaco (Villarino et al., 2017). The simulations showed an average difference in maize's NPP of 33% and 80% with respect to soybean and wheat, respectively which is mainly determined by its higher radiation use efficiency. The high biomass production and grain yield of maize suggests that the replacement of current soybeans monocultures by rotations that include maize could be a sustainable land use transition (Murray et al., 2016). Nitrogen fertilization was the management practices that had the highest relative effect on SOC variability, due to its positive impact on biomass production. Soil nitrogen is a key factor in organic matter formation and their balance in the soil showed a higher effect than C inputs on C losses (Caride et al., 2012). Large amounts of N are exported when grains are harvested, so the replenishment of this nutrient becomes essential. In cropping systems of the Argentinean Pampas, the rotations that included legume pastures restored the lost nitrogen and showed SOC losses three times lower than sequences of continuous grain crops (Caride et al., 2012). However, the former study only considered variations in the aboveground NPP, and it is possible that changes in SOC were due to shifts in the ratio between aboveground and belowground NPP. Pastures, unlike annual crops, could increase C inputs due to a higher root biomass production. Thus, under pastures increases in SOC could be observed even when NPP remains unchanged (Guo and Gifford, 2002). Contrary than expected, the double cropping sequences had a lower total NPP than monocultures, and therefore, had a negative effect on SOC in the wetter site. Thus, the monocultures of maize and soybean showed an average yield of 43% and 26% higher than sequences that included wheat as a previous winter crop, respectively. Hence, the total average productivity was 26% and 23% lower in the double cropping sequences, respectively. Similarly, wheat monocultures showed an average productivity 82% higher than sequences that included summer crops. These differences can be explained by the amount of water stored in the soil profile at the beginning of the critical period in each crop. While for a summer monocultures water is generally not limiting in normal years, the inclusion of a previous winter crop would reduce the soil water content and therefore affect yield (Giménez et al., 2016). A similar effect would take place in winter crop monocultures. A larger proportion of the wheat growing period occurs before the start of the rainy season. The lack of rains during winter and early spring determines that the level of water available for the crop depends of the water loss during the previous summer, which depends in turn on summer crops evapotranspiration (Giménez et al., 2016). Thus, our results agree with those reported for the semi-arid Pampean region, where the water consumption from the winter crop interfered on the normal supply of water for the successor summer crop (Duarte, 2002; Scianca et al., 2008). Fernández et al. (2012) observed a decrease in soil water availability for the summer crop between 30 and 100 mm while Unger and Vigil (1998), at the same way, recorded lower yields in maize (between 500 and 900 kg.ha−1) than without a predecessor winter crop.

4. Discussion Forest conversion into crops and pastures had, in general, a negative effect on SOC of the upper 20 cm of the soil (Villarino et al., 2017; Osinaga et al., 2018). Under cropping systems, only few management practices allowed to maintain or increase SOC, such as annual N fertilization in schemes of monoculture of maize. For pastures, the maintenance or increase in SOC was due to a decrease in grazing intensity, to the suppression of burning, to the application of medium or high doses of nitrogen at sowing, and to the temporary exclusion of livestock. Overall, the negative C balance was mainly determined by decreases in C inputs (i.e. remnant NPP) than by changes in outputs (i.e. respiration losses and erosion). SOC level was highly sensitive to changes in NPP, showing a strong relationship with HANPP. So, HANPP is a key indicator to evaluate the effects of land uses changes and management practices on biogeochemical cycles (Haberl et al., 2014) and, probably, on ES supply. CENTURY model had a good performance in simulating SOC dynamics in the semi-arid Chaco. Although, some differences were detected between observed and simulated values which were primarily associated with soil texture (see Appendix C for a detailed discussion). Besides, the lack of fit between observed and simulated values could also be due to the absence of detailed information on land use history of sampled sites, especially sowing and harvest dates. 4.1. Effects of land use change and management practices on SOC As expected, SOC changes were greater in the wetter site in absolute terms, although the relative variations respect to the native forest were very similar in both sites. For croplands, SOC changes were in the range reported by several global meta-analyzes (Guo and Gifford, 2002; Murty, 2002; Smith et al., 2016) and local studies (Rojas et al., 2016; Villarino et al., 2017; Osinaga et al., 2018), which showed reductions between 20 and 45% with respect to the original land cover. For pastures, local studies showed a significant reduction on SOC only in the surface layer (Osinaga et al., 2018), while global meta-analyzes suggest mixed changes in SOC. While Murty et al. (2002) observed both gains and losses of SOC when forests are converted into pastures, Guo and Gifford (2002) and Eclesia et al. (2012) observed increases in SOC in sites with annual average rainfall between 1500 and 3000 mm, and non-significant effects above or below this range. For livestock systems, stocking rate was the management practices that had the highest relative effect on SOC variability. Some authors suggest that high stocking rates could have negative effects on SOC due to a decrease in aboveground biomass, and that low or moderate stocking rate may not affect it (Milchunas and Lauenroth, 1993).

4.2. SOC is more affected by C inputs than outputs In croplands the reduction in C gains was between 2 and 3 times greater than the decrease in C outputs by soil respiration. More subtle differences were observed in sown pastures. These results were 10

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consistent with those observed in humid subtropical forests, where the observed changes in SOC were controlled mainly by C inputs and their retention in the soil (Eclesia et al., 2016). The reduction in the mineralization rate under different land uses can be explained by the amount C inputs to the soil, regardless of its quality (Naldini, 2016), and the immobilization of nutrients in the soil biota (Albanesi et al., 2003). In cropping systems, the negative C balance was the main cause of SOC decrease, being C losses by erosion less important. Recently, Villarino et al. (2017) associated the negative C balance in cropping systems with a lower ANPP of crops with respect to the forest (Volante et al., 2012). Similar results were obtained by Alvarez and Steinbach (2006); Alvarez et al., 2015) for the Pampean Region, where an average C balance of −1.7 T.ha−1.yr−1 was observed. These authors assigned 58% of the loss to a negative C balance and 42% to soil erosion.

Despite of a 13% reduction in total NPP, SOC increased in pastures. This result might be explained by differences in the amount of roots produced between pastures and forest, and also by the turnover rate of roots which may constitute a major contribution to belowground carbon cycle (Lukac, 2012). Root turnover rate is usually defined as the number of times fine root biomass is replaced each year and is equal to the inverse of root longevity. Grasses such as pastures, generally have higher fine root biomass, root turnover, and decomposition rate compared to forests (Solly et al., 2014). Thus, while forest showed an average root turnover rate (coarse and fine roots) close to 13%, pastures showed an average of 59%, similar to the values observed by Gill and Jackson (2000) at global scale. It has been suggested that root chemical composition is the primary control of root decomposition, while climate and environmental factors play secondary roles (Silver and Miya, 2001; Solly et al., 2014; Zhang and Wang, 2015). The roots of grasses often contain larger amounts of more labile forms of C (carbohydrates, sugars, amino acids) and smaller fractions of structural materials (lignin, cellulose, hemicellulose) than trees (Seastedt et al., 1992). Similar results were observed in humid subtropical systems of Argentina where increase on SOC in pastures was associated with a higher belowground C input than the original forest (Eclesia et al., 2016). This would favor the retention and stabilization of the new C input (Eclesia et al., 2016).

4.3. HANPP and SOC SOC changes were highly associated with HANPP. Previous studies described patterns of HANPP at global (Vitousek et al., 1986; Haberl et al., 2007; Krausmann et al., 2013), regional (Rueda et al., 2013; Baeza and Paruelo, 2018) and local scales (Haberl et al., 2004) and even their historical trajectory at country scale (e.g. Krausmann et al., 2013). However most of these studies focus on the magnitude of the appropriation, while the relationship between HANPP, used as a proxy for land use intensity, and its ecological consequences was rarely explored (e.g. Haberl et al., 2005). Haberl et al. (2014) suggest that HANPP is a useful indicator of land-use intensity but it remains unclear which level of HANPP can be considered sustainable at which spatial scale and for different ecosystem services. Here, the HANPP under different land uses and management practices and their relationship with SOC stocks was assessed. To our knowledge, this was not done before. The relationship between SOC and HANPP differed between land use covers (croplands or pastures) (Fig. 6). Depending on land use, the same HANPP level could result in either positive (under pastures) or negative (under cropping systems) changes in SOC. Additionally our HANPP estimates did not consider the dissipative appropriation associated with the land clearing process, which could consume a biomass amount equivalent to the accumulated ANPP for a decade in natural vegetation (Rueda et al., 2013). The range of HANPP values estimated in this study were higher than those reported in others. At global scale, some authors reported HANPP values between 10 and 63% (Rojstaczer et al., 2001; Haberl et al., 2007) and practically they have not been reported negative values. The simulation approach of this study suggest that HANPP ranged between −19 and 78% for cropping and livestock systems of the Dry Chaco. Therefore, negative values of HANPP where common under multiple management practices of crop systems. In almost all cropping systems, regardless of the management practice, HANPP was positive, which indicates that the remaining NPP was lower than that of the forest. However, some management practices determined negative HANPP values and had less SOC than the forest. These schemes comprised high N fertilization and the inclusion of maize at least once every 2 years. This result may be associated with changes in the allocation of above and belowground C by the plants. Thus, while the remaining NPP of cropping systems was in average only 6% higher than the forest, an increase of 21% in ANPP and a decrease of 29% in BNPP were observed. It has been suggested that roots contribute, on average, 2.4 times more to soil C than aboveground plant litter (Rasse et al., 2005; Kätterer et al., 2011). Something similar occurred in pastures but the opposite pattern was observed. The average ANPP in pastures was 47% lower than that of the native forest, but an average increase of 56% in BNPP was observed.

5. Conclusion The two land uses simulated (croplands and sown pastures) generated both reductions and increases on SOC in the upper soil layer depending on the management practice considered. These practices generally affected more soil C inputs than outputs. Under current management practices the effects of land use change on SOC were negative. However, other simulated management practices allowed to identify scenarios were food production and C sequestration are balanced. For cropping systems, the management practices that enabled an increase of SOC were related to maize monocultures and annual N fertilization. For livestock systems, SOC increased or was maintained with a reduction in grazing intensity, the suppression of burning, the application of medium or high doses of N at sowing and the temporary exclusion of livestock. The simulation approach enabled to assess the potential impacts of land uses change, and management practices, in a fast, relatively simple and reproducible manner for different areas and time frames. Thus it may be used as a tool for supporting decision making aimed at evaluating the effects of different management practices and public policies on SOC stocks and fluxes. However, as soil organic C losses are not restricted to the first few centimeters of the soil, it is necessary to develop new models that allow simulating the impact of land use change on SOC at a greater depth. HAPPN showed a negative relationship with SOC and was different for cropping and livestock systems. HANPP, an indicator of land use intensity, may be used as a quick and effective tool to evaluate the impact of different management practices on SOC. Acknowledgments This work was carried out with the aid of a grant from the InterAmerican Institute for Global Change Research (IAI) CRN3095, which is supported by the US National Science Foundation (Grant GEO1128040). This research was also supported by UBA, CONICET and FONCyT-PICT 2199. We thank C. Bagnato, S. Aguiar, L. Amdan, L Ciuffoli for their assistance with field work and samples processing. We thank Agropecuaria Andorrana and Anta del Dorado for allowing us carry our fieldwork in their farms. We are grateful to F. Mónico and the staff of INTA AER J.V. Gonzalez for their logistic support.

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Appendix A The CENTURY 4.6 model is composed of twelve data files that contain specific parameters, both for the site and for the multiples management practices options. Each of these files has a “0.100” extension and they are incorporated (in case of is required by the model) into a “.sch” file, which describes the temporal sequence of the different events that will occur during the simulation. The output file “.bin” contains all the desirable output variables. Next, we describe the parameters that were modified in each “0.100” file and that were used for the simulation of different events I) Parameters to simulate the plants growth. The productivity of the different land use covers (crops, pastures and forests) was simulated through modifying temperature parameters that define plant growth (Table A.1). In the case of cropping systems, we adjusted different parameters to describe the average yield of maize and soybean reported by MINAGRI, and Ministerio de Agricultura, Ganadería y Pesca (2011) for the region. Thus, we differentiate between cultivars with lower and higher yield according to the decade of production (Table A.1). In the 80´s decade maize had an average yield of 2505 kg.ha−1 and it increased to 4606 kg.ha−1 in the last 15 years. At the same time, soybean registered a yield of 2031 kg.ha−1 in the past while currently are around 2600 kg.ha−1. Contrary, no clear differences in time were observed in wheat, where the average yield during the whole period was between 1400 and 2000 kg.ha−1. At the same time, we simulated the productivity of weeds that could grow after the harvest where conventional tillage was practiced (see section VI). II) Parameters to simulate grazing in sowed pastures. CENTURY model allows the simulation on both direct effects of grazing on ANPP (decreases or increases) and stem/root ratio. However, in our work they were not simulated (parameter GRZEFF = 0). In the sown pastures of Gatton panic simulated, the effects of grazing on ANPP occurred due to other mechanisms such as nitrogen availability, shading, changes in soil temperature, etc. The stem/root ratio changed only by temperature effect (Parton et al., 1993). The proportion of C excreted by herbivores as feces and urine (parameter GFCRET) was calculated as the inverse of the digestibility, which varied according to the year season (De León et al., 1995). The proportion of N consumed by herbivores that returns to the soil as feces and urine was considered constant throughout the year and was independent of the stocking rate (parameter GRET). We assumed that half of the total N that returned to soil was contributed by feces and the other half by urine (parameter FECF). These values are only shown in the top table because present the same values in all tables that indicate different stocking rates (Table A.2). The consumption parameters associated with green and dry forage availability (parameters FLGREM and FDGREM, respectively) was calculated with a monthly temporal resolution, considering a senescence rate of 15% (parameter FALLRT from crop.100 table). Also, we assumed a constant animal consumption of 80% and 20% of green and dry forage, respectively. The proportion of green and dry forage was variable throughout the year, being completely green at the beginning of the growing season and completely dry at the end, associated with the growth period of the grass (Baldassini et al., 2018). A total of four stocking rates under continuous grazing were considered: 0.1, 0.25, 0.5 and 1 AU.ha−1 (an animal unit (AU) is defined as the number of cattle, buffalo, sheep, goats, horses, and camels weighted by their relative size and growth rates [AU = n (cows + buffalo) + 0.2 n (sheep + goats) + 1.2 n (horses + camels)] (Asner et al., 2004)). Also, intensive grazing was simulated with higher stocking rates during the summer. The stocking rate varied throughout the year, being higher during the summer months, when more forage is produced (Baldassini et al., 2018). This information was provided by local farmers. Also, the consumption was variable, considering that an AU consumes 8 kg of DM daily in autumn/winter and 12 kg DM daily in spring/summer. The consumption by herbivores is defined as a percentage of the forage production (Table A.3). III) Parameters to simulate deposition and nitrogen fixation. Nitrogen deposition was defined based on the average values estimated by NADP. NationalAtmosphericDepositionProgram (NRSP-3) (2015), which was 0.07 kg N.ha−1.cm−1 for the period 2011–2016. The deposition parameters of CENTURY model present in site.100 table were modified to the following values: EPNFA (1): 0.05 and EPNFA (2): 0.007. These parameters represent the intercept and the slope of the linear relationship with precipitation, respectively. The N input by deposition was considered constant during the whole simulation and only variable according to the average annual precipitation. There are no available data of the nonsymbiotic N fixation in the region, so the default parameters of the CENTURY model have been used (EPNFS (1): 30 and EPNFS (2): 0.01). The symbiotic N fixation from the dry forest was considered (SNFXMX (2): 0.0001) and the soybean crops (SNFXMX (1): 0.003), while the default parameters present in tables tree.100 and crop.100 were used. IV) Parameters to simulate disturbances in the forest. The occurrence of an intense disturbance every 300 years associated with fire events in the native forest was considered. The burning of hard wood forests would only occur under extreme climatic conditions and the dry forest of the Chaco would have a very low frequency of fire (Morello, 1970). We considered that fire removed 90% of live leaves and branches and 70% of live and dead woods (REMF parameters). Also, we assumed that 90% of the fine roots and 70% of the coarse roots were removed by this event (FD parameters). We assumed a low return of C, intermediate of N and total of P to the soil after fire (RETF parameters) (Table A.4). The events of land clearing with and without fire simulated the release of forest for cropping and livestock production (Albanesi, 2008). We assumed that both practices remove completely the forest cover and a portion of N and P return to soil after the burning of the organic material removed. V) Parameters to simulate cropping-livestock management practices. The sampling of cropping systems and pastures sites that were used to evaluate the CENTURY model included sites which land have been cleared in the 70 and 80 decades. In these cases, the use of conventional soil tillage until the mid-90s was considered, where no-tillage takes place (Alapin, 2009). Also, we assumed conventional soil tillage as a primary practice after land clearing in order to prepare the soil for the new land uses (Table A.5). The tillage increases the soil aeration and incorporates plant residues on the surface. This modifies the environmental site conditions that control the activity of microorganism (Liu et al., 2006) and, as consequences; an increase in emission of C-CO2 could be observed (Alvarez et al., 2015). In the region, an increase in microbial activity and soil respiration attributable to the effect of tillage has been observed, causing a rapid mineralization of the carbon substrates (Abril et al., 1995, 2013). Different types of soil tillage were simulated before sowing the crops, which differed in the proportion of surface residues incorporated into the soil (CULTRA (1–7) parameters) (Table A.5) and in the increase in the decomposition of the organic material (CLTEFF (1–4) parameters). In pastures, the use of fire as a complementary management is a frequency practice between local farmers (Anriquez et al., 2008). The objective is to control the shrub formations of the genera Acacia, Celtis and Schinus, which have susceptibility to fire, and facilitate the regrowth of lignified grasses (Albanesi, 2008). We simulated an intense burn during the month of August every 3 years in accordance with the reported by local farmers (Table A.6).

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Table A.1

Parameters used to simulate maximum productivity and optimum plant growth temperatures for each land use type, present in crop.100 and tree.100 tables of the CENTURY model. For a detailed description of each parameter and units, see www.nrel.colostate.edu/projects/century/. Parameter

Maize 80´

Soybean 80´

Weeds

Maize 00´

Soybean 00´

Wheat

Gatton panic

Forest

prdx(1) prdx(2) ppdf(1) ppdf(2) ppdf(3) ppdf(4)

0.2 – 30 45 1 2.5

0.2 – 27 40 1 2.5

0.6 – 27 45 1 3

0.8 – 30 45 1 2.5

0.5 – 27 40 1 2.5

1 – 5 35 0.5 5

0.8 – 30 45 1 2.5

– 0.5 30 45 1 2.5

Table A.2

Parameters used to simulate grazing under different stocking rates in pastures, present in graz.100 table of the CENTURY model. The average values to both sites are shown. For a detailed description of each parameter and units, see www.nrel.colostate.edu/projects/century/. Average parameters for a stocking rate of 0.1 AU.ha−1in Gatton panic pastures Month

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sept

Oct

Nov

Dec

FLGREM FDGREM GFCRET GRET GREZEFF FECF FECLIG

0.04 0.10 0.40 0.80 0.00 0.50 0.25

0.02 0.02 0.40 0.80 0.00 0.50 0.25

0.02 0.01 0.45 0.80 0.00 0.50 0.25

0.04 0.00 0.49 0.80 0.00 0.50 0.25

0.04 0.00 0.54 0.80 0.00 0.50 0.25

0.00 0.01 0.59 0.80 0.00 0.50 0.25

0.00 0.01 0.59 0.80 0.00 0.50 0.25

0.00 0.02 0.51 0.80 0.00 0.50 0.25

0.00 0.02 0.43 0.80 0.00 0.50 0.25

0.00 0.02 0.35 0.80 0.00 0.50 0.25

0.02 0.00 0.38 0.80 0.00 0.50 0.25

0.02 0.06 0.40 0.80 0.00 0.50 0.25

Average parameters for a stocking rate of 0.25 AU.ha−1in Gatton panic pastures Month

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sept

Oct

Nov

Dec

FLGREM FDGREM

0.11 0.24

0.06 0.05

0.04 0.03

0.10 0.01

0.10 0.01

0.00 0.02

0.00 0.03

0.00 0.05

0.00 0.07

0.00 0.07

0.07 0.00

0.05 0.17

Average parameters for a stocking rate of 0.5 AU.ha−1in Gatton panic pastures Month

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sept

Oct

Nov

Dec

FLGREM FDGREM

0.21 0.48

0.12 0.12

0.09 0.07

0.22 0.02

0.22 0.02

0.00 0.05

0.00 0.09

0.00 0.13

0.00 0.19

0.00 0.22

0.19 0.00

0.13 0.44

Average parameters for a stocking rate of 1 AU.ha−1in Gatton panic pastures Month

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sept

Oct

Nov

Dec

FLGREM FDGREM

0.43 0.96

0.28 0.27

0.21 0.18

0.56 0.05

0.64 0.05

0.00 0.18

0.00 0.36

0.00 0.68

0.00 0.91

0.00 1.00

1.00 0.00

0.71 1.00

Average parameters for an intensive grazing in Gatton panic pastures Month

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sept

Oct

Nov

Dec

FLGREM FDGREM

0.44 0.98

0.98 0.93

0.98 0.82

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

0.30 1.00

Table A.3

Proportion of the average annual ANPP that is consumed by herbivores in pastures under different stocking rates. AU.ha−1

600 mm 800 mm

0.1

0.25

0.5

1

Intensive grazing

0.08 0.06

0.19 0.16

0.38 0.32

0.76 0.64

0.58 0.56

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Table A.4

Parameters used to simulate fire events in the native forest, present in treem.100 table of the CENTURY model. For a detailed description of the parameters and units, see www.nrel.colostate.edu/projects/century/. Parameters

Forest burn

Total clearing

Total clearing with burning

EVNTYP REMF(1–2) REMF(3–5) FD(1) FD(2) RETF(1) RETF(2) RETF(3) RETF(4)

1.00 0.90 0.70 0.90 0.70 0.00 0.30 1.00 0.00

0.00 1.00 1.00 1.00 1.00 0.00 0.00 0.00 0.00

1.00 1.00 1.00 1.00 1.00 0.00 0.30 1.00 0.00

Table A.5

Parameters used to simulate soil tillage in sites under cropping systems and livestock production, present in table cult.100 of the CENTURY model. For a detailed description of the parameters and units, see www.nrel.colostate.edu/projects/century/. Parameters

CULTRA(1) CULTRA(2) CULTRA(3) CULTRA(4) CULTRA(5) CULTRA(6) CULTRA(7) CLTEFF(1–4)

Conventional tillage

No tillage

Tillage 1

Tillage 2

Tillage 3

0.00 0.10 0.90 0.10 0.90 0.90 1.00 4.00

0.00 0.50 0.50 0.50 0.50 0.50 1.00 1.60

0.40 0.40 0.20 0.20 0.20 0.20 1.00 1.30

0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00

Table A.6

Parameters used to simulate the burning of pastures present in fire.100 table of the CENTURY model. For a detailed description of the parameters and units, see www.nrel.colostate.edu/projects/century/. Parameters

Pasture burning

FLFREM FDFREM(1) FDFREM(2) FRET(1) FRET(2) FRET(3) ‘FRTSH’ ‘FNUE(1)’ ‘FNUE(2)’

0.80 0.80 0.40 0.40 0.20 0.20 0.20 10.00 30.00

Appendix B The CENTURY model was also evaluated by comparing simulated and estimated variables as productivity and aboveground biomass obtained from several information sources. In the case of pastures, the output variables agbacc and bgbacc were used, which represent the annual accumulated above and belowground net primary production, respectively. The variables rlvacc, fbracc and rlwacc, on the one hand, and frtacc and crtacc, on the other hand, describe above and belowground net primary production of woody components in the native forest, respectively. Also, the output variables rleavc, rlwodc and fbrchc were used due to indicate the total aboveground biomass. In cropping systems the variable cgrain was used that represents the crop yield We used t-tests for comparing simulated and estimated means. In pastures, the simulated ANPP was compared with estimations based on MODIS images using the Monteith's model (Monteith, 1972). This model proposes that aboveground net primary production (ANPP) is proportional to the fraction of the photosynthetically active radiation intercepted by green vegetation (FPAR), the incoming photosynthetically active radiation (PARi) and the radiation use efficiency (RUE). The FPAR was estimated through a linear regression with NDVI (Normalized Difference Vegetation Index) obtained from MODIS images for the period 2000–2013 for all sites sampled. Incoming photosynthetically active radiation data were obtained from total incoming solar radiation data base for South America, estimated by high resolution GOES images (DSA/CPTEC/INPE, http://satelite.cptec.inpe. br/radiacao/). We used monthly average products with a spatial resolution of 0.04° × 0.04° for the same period. The RUE values were obtained from Baldassini et al. (2018) who estimate this variable through a field work for the study region. Also, we compared the BNPP simulated and the values estimated using the average BNPP/NPP ratio obtained from Hui and Jackson (2005) for different C4 species. No significant differences were detected in any comparison of means (Fig. B.1 and Table B.1). In the case of the native forest, total NPP simulated by CENTURY was compared with productivity estimated by Haberl et al. (2007) and Del 14

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Grosso et al. (2008) at global scale. The first one estimation was based on a biogeochemical vegetation model while the second one explored the relationships between the NPP and the precipitation and temperature. There were no significant differences between means values of CENTURY model and the first source of information (Fig. B.1 and Table B.1), although the simulations were significantly higher than the estimates made by Del Grosso et al. (2008). However, the simulated values of NPP were within the range reported by Murphy and Lugo (1986) for dry forests (cited by Martínez-Yrízar, 2009), which was between 8 and 21 T DM.ha−1.yr−1. The aboveground biomass of the native forest simulated by the CENTURY model was compared with two biomass map generated by Gasparri and Baldi (2013) and Baccini et al. (2016). The first one was developed for the Argentinean Chaco region and was based on 50 field data of diameter at breast height, allometric equations and NDVI data from MODIS images. The second one corresponds to an estimation based on field measurements, radar based altimetric records (LiDAR), Random Forest models and reflectance values of Landsat 7 ETM for the year 2000 (available at climate. globalforestwatch.org). The results showed differences between the simulated data and those from the first source of information, but did not show significant differences with the last one (Fig. B.1 and Table B.1). In the case of cropping systems, simulated yields by CENTURY model were compared to those reported by local farmers for soybean and maize crops for the period 2001–2010. The wheat is planted mainly for cover the soil and it is rarely harvested, so we did not have enough information available for the comparison of yields. No significant differences were detected in any comparison of means (Fig. B.1 and Table B.1).

Fig. B.1. Mean comparison between simulated values by the CENTURY model for the sampled sites and estimated values provided by several information sources. A) NPP (Kg DM.ha−1.year−1) simulated by CENTURY and estimated by Haberl et al. (2007) and by Del Grosso et al. (2008). B) Aboveground biomass (AGB in T C.ha−1) simulated by CENTURY and estimated by Gasparri and Baldi (2013) and Baccini et al. (2016). C) Crop yield (qq.ha−1) simulated by CENTURY and reported by local farmers for soybean and maize. D) ANPP and BNPP simulated by CENTURY and estimated by MODIS images and BNPP/NPP ratio obtained from Hui and Jackson (2005).

Table B.1

P-values corresponding to the t-tests for the comparison of means between values simulated by the CENTURY model and those obtained from different sources of information. The results are separated for land use and type of variable. Land use

Cropping systems Pasture Forest

Differences between means Variable

Observed/Estimated (Source)

p-value

Soybean yield Maize yield ANPP BNPP Aboveground biomass (AGB) Aboveground biomass (AGB) NPP NPP

Observed Observed MODIS MODIS Gasparri and Baldi (2013) Baccini et al. (2016) Haberl et al. (2007) Del Grosso et al. (2008)

0.4824 0.1426 0.6843 0.0689 0.0004 0.9828 0.14 < 0.0001

Appendix C The delta SOC (T C.ha−1), calculated as the difference between simulated and observed data, showed a significant quadratic relationship with the clay content in the soil (%), both for the pooled data and for each land use type (B1 ≠ 0, p value < .05, and R2 between 0.59 and 0.82) (Fig. C.1). The 15

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adjustments showed that the SOC were overestimated by the CENTURY model at low and high clay contents (lower than 10% and above 30% approximately), with the lowest delta SOC values under intermediate contents (Fig. C.1). Thus, the simulations of different land use and management practices were performed using a clay content of 18.71%, corresponding to the intersection of the adjustment lines between the observed and simulated SOC with the clay content. The linear association between SOC and clay content, associated mainly with the protective effect of clays on organic compounds, has been widely documented in the scientific literature (Alvarez et al., 2015). In our work, this relationship has been observed (data not shown); however, as in other studies, differences between the observed and estimated values by the CENTURY model related to soil texture have been observed. The sensitivity of the model to this factor has been widely documented in the scientific literature considering different uses and climatic characteristics. For example, Parton et al. (1987) have observed that the CENTURY model tended to overestimate SOC on fine-textured soils and underestimate SOC on coarse-textured soils in Great Plains pastures. Bricklemyer et al. (2007) noted that SOC estimates by the CENTURY model also increased linearly with the increase in the clay content in five different sites of Montana, EEUU. Similarly, Galdos et al. (2009) observed an inverse relationship between the SOC and the sand content of the soil. Silver et al. (2000) concluded that CENTURY underestimated the C in the soil under coarsetextured soils and overestimated them in very clayey Oxisols in forests of the Brazilian Amazon. Tornquist et al. (2009) observed that the model performed well in very clayey soils, but underestimated the SOC contents in silty and sandy soils, originally under forests and pastures. In the CENTURY model there are two mechanisms that lead to increasing the SOC and to linearly scale with the clay content. On the one hand, the C flows from the slow compartment to the passive compartment of the soil organic matter is scaled or weighted according to its sand content. In sites with high clay content, more passive organic matter is formed that reflects the protective role of the soil. On the other hand, the efficiency of the C transfers between both compartments is affected by the soil texture, losing more CO2 in the soils with a thicker texture. In this way, fine textures favor slow decomposition rates and greater stability of organic matter (Parton et al., 1994). However, in view of the results obtained in our work, the model seems to be too sensitive to the effect of the texture on the organic matter stabilization. The results show that the default parameters of the CENTURY model could overweight the effect of the texture on the passive organic matter formation and on the efficiencies of C transfers between compartments (Silver et al., 2000).

Fig. C.1. Adjustment between delta SOC (T.ha−1), calculated as the difference between the simulated and observed data, and the clay content (%) of the samples. The panel on the left indicates the pooled adjustment that includes all land uses, while the panels on the right correspond to each land uses type. Forests are indicated as points, cropping systems using squares and pastures through black triangles.

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