Impact of silvopastoral and traditional grazing systems on soil carbon stocks in the highland tropics of northern Antioquia, Colombia

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In dairy and beef farms, silvopastoral systems have become increasingly important for their contributions to animal welfare, pasture biodiversity, soil recovery, and the mitigation of their environmental impact. This study aimed to evaluate the impact of traditional and silvopastoral grazing systems on soil carbon stocks in dairy farms located in the highland tropics of Antioquia, Colombia. Soil samples were collected bimonthly from five depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). Carbon content was analyzed using a CN Leco 828 analyzer. Likewise, soil carbon deposition from cattle manure was estimated. Variance analysis was performed considering grazing systems, temporal sampling, and farm-specific effects as variables. Results showed no significant differences in carbon storage between traditional and silvopastoral systems at the evaluated depths. However, carbon content decreased significantly with increasing soil depth. Livestock manure was identified as a substantial source of soil carbon, and contributions from animal feces, along with the presence of kikuyu grass and other plant species were recognized as key factors in mitigating the environmental impact of livestock production in the highland tropics. cattle manure climate change grasslands greenhouse gases livestock organic carbon Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Colombia holds a priority position in ecosystem conservation efforts due to its rich biodiversity, which is at risk due to population growth and harmful human practices. In many regions of Colombia, the expansion of livestock farming, especially for grazing, has led to negative environmental impacts. This expansion is often linked to pasture degradation, reduced soil fertility, and large-scale deforestation, which negatively affect soil quality, microclimate, and water cycles. Traditional livestock practices often involve deforestation through forest burning, leading to reduced tree cover and soil exposure. This is further exacerbated by the excessive use of agrochemicals, which leads to environmental and production problems (Mandal et al. 2020 ). Deforestation transforms native forests into grass-dominated savannas, which negatively impact soil fertility and require large water inputs. Both forests and agricultural activities can play a crucial role in capturing atmospheric carbon (C) and subsequently retaining it as biomass and soil deposits (Lal et al. 2015 ; Erb et al. 2013 ; Pan et al. 2011 ; Le Quéré et al. 2009 ). However, it is estimated that over one-third of pastures in Latin America are degraded, which reduces their resilience to climate change. In the future, effective monitoring systems will be essential for ensuring adequate land use management in grasslands (Tolimir et al. 2020 ; Stanimirova et al. 2019 ). Inappropriate practices in extensive livestock farming are frequently associated with increased greenhouse gas (GHG) emissions, including carbon dioxide (CO₂), nitrous oxide (N₂O), and methane (CH₄). Increased atmospheric GHG concentration causes fluctuations in global temperatures and has contributed to climate change over time (Costantini et al. 2018 ). The environmental impact of the livestock sector continues to grow alongside the rising demand for meat, milk, and eggs (Steinfeld et al. 2009 ). Thus, adopting alternative production approaches is essential, as traditional livestock practices can evolve toward methods that prioritize social and environmental sustainability. A silvopastoral system (SPS) is an integrated forage system combining grasses, shrubs, and trees to improve livestock nutrition and welfare (Moreno-Galván et al. 2023 ). Additionally, its adoption as an alternative to monoculture pastures serves as a strategy to promote the sustainability of livestock farming and the conservation of tropical soils (Rivera et al. 2023 ). In general, the implementation of agroforestry practices can enhance biomass production, thereby increasing C storage, while also promoting biodiversity conservation (Aryal et al. 2022 ). These systems contribute to GHG mitigation and exhibit a high potential for atmospheric carbon (C) sequestration (Contreras-Santos et al. 2020 ; Veldkamp 1994 ), with a portion of this C being partially stabilized within the soil matrix. A large proportion of the Earth's total biosphere carbon is stored in soil organic matter. In fact, soil organic carbon (SOC) can be three times higher than the amount stored in aboveground biomass, indicating that the cycling and dynamics of C in the biosphere are predominantly governed by soil processes (Moreno et al. 2017 ; Chará et al. 2015 ). SOC content influences the chemical, physical, and biological properties of soil, thereby affecting its fertility and productivity (Zamora-Morales et al. 2019 ). Soil organic carbon (SOC) is a vital component of the global carbon cycle, accounting for approximately 69.8% of the organic carbon stored in the biosphere. Alongside soil pH, it serves as a key indicator of soil health. Although vegetation and grazing animals store less C than soil organic matter (SOM), these aboveground components play essential roles in carbon cycling within grazing systems-through processes such as plant residue deposition and decomposition, excreta return, and methane emissions (Dubeux et al. 2007 ). The objective of this study was to assess the effects of silvopastoral and traditional grazing dairy systems on SOC stocks within the soil profile and to evaluate the contribution of C from animal feces in these livestock production systems. Materials and methods Study area Three farms located in the municipality of San Pedro de los Milagros, a region specialized in dairy and beef cattle farming in Antioquia, Colombia, were evaluated. The farms included: “Hacienda La Montaña”, owned by the University of Antioquia, situated in the Monterredondo district (6°26'59.606"N; 75°32'37.088"W); “El Balcón”, located in the Cerezales district (6°11'13.974"N; 75°36'56.896"W); and “Montenegro”, situated in the San Francisco district (6°28'18.84"N; 75°36'2.363"W). In both traditional and silvopastoral systems, kikuyu grass ( Cenchrus clandestinus ) predominated. In silvopastoral systems, shrub and tree species such as alder ( Alnus acuminata ), elder ( Sambucus peruviana ), and Mexican sunflower ( Tithonia diversifolia ), among others, were prevalent. The selected farms exhibited similar climatic conditions and vegetation composition (Fig. 1 ). Field Sampling Four visits were conducted every two months to collect soil samples for wet chemistry analysis, according to protocols established by the laboratories. A homogeneous area was selected on each farm, considering factors such as topography, productive activity, and soil characteristics. Following a zigzag sampling pattern, 20 to 30 subsamples were obtained using an auger at 20 cm intervals up to a depth of one meter. The subsamples were mixed and homogenized, and a 1 kg composite sample was sent to the laboratory for chemical analysis. Sampling for C measurement Soil pits measuring 1 m × 1 m × 1 m were excavated in both production systems on each farm. Soil samples were collected at different depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm) as shown in (Fig. 2 ). Laboratory analysis Samples were crushed with a wooden mallet, air-dried for 48 hours, and sieved using a 2 mm mesh sieve followed by a 150 mm mesh sieve. C determination was performed using the automated Dumas combustion method (Leco CN 828 series equipment). A 0.1 g sample was weighed into tin foil and placed in a carousel inside the equipment, which analyses C using a non-dispersive infrared (NDIR) cell (Fig. 3 ). Using carbon content and soil mass in a given sample volume, soil organic carbon (SOC) was estimated following FAO (Food and Agriculture Organization of the United Nations) (2019). SOC (t/ha) = CC * Bd * t * 0.1 Where: SOC: Soil organic carbon (t/ha). CC: carbon content mg C/g soil. Bd: Dry soil weight (g) / cylinder volume (cm³). t: Sample depth (cm). 0.1: Conversion factor to estimate SOC per hectare. Carbon added to soils from manure deposition The carbon addition to soils originating from cattle manure was estimated in the three farms. To do this, production information was collected on each farm, including that associated with animal factors (live weight, milk production, milk composition, reproductive performance, etc.), environmental (temperature, relative humidity, grazing system) and nutritional (type of pasture, concentrate, concentrate intake), among others. With this information, the CNCPS model (The Net Carbohydrate and Protein System for Evaluating Herd Nutrition and Nutrient Excretion version 5.0, Department of Animal Science at Cornell University), was used to estimate the nutritional balance and manure production of each animal according to its intake, its physiological condition and its level of production, as well as the chemical composition of its diet. This determination was carried out for both lactating and dry cows. Statistical analysis Several mixed linear models were fitted to evaluate how the fixed variables of system type (τ) and depth (ρ) affect C content, considering random effects such as sampling (s) and farm (f). To improve data distribution, Box-Cox transformations were applied. Adjusted means were compared using transformed values, with results presented on the original scale following the procedure of Marimuthu et al. ( 2022 ). The best model was selected based on the lowest Bayesian Information Criterion (BIC) and compliance with assumptions of normality and homogeneity of residuals. The final model used was: $$\:\left(\frac{{SOC}^{0.626}-1}{0.626}\right)=\:\alpha\:+\:{\tau\:}_{i}\:+\:\rho\:j\:+Nt\:+\:{f}_{s}+{e}_{ijsl}$$ Where: 𝛼: fixed intercept effect, \(\:{\tau\:}_{i}\:\) : fixed effect of production system type (TS and SPS), \(\:{\rho\:}_{j}\:\) : fixed effect of soil depth (20, 40, 60, 80, 100 cm), \(\:Nt\:\) Nitrogen effect, \(\:\:{f}_{m}\) : random effect of the farm with \(\:{\sigma\:}_{s}^{2}\) and \(\:{e}_{ijsl}\) : residual effect, The analyses were conducted using the "nlme" library (Pinheiro and Bates, 2024 ) in the statistical package "R-project" (R Core Team, 2024 ). Results Analysis of soils Results obtained in the soil analyses showed that soils predominantly had sandy loam textures, moderately to slightly acidic pHs, and moderate to high cation exchange capacity (Table 1 ). Table 1 Physicochemical parameters of soils at 0–20 cm depth in the evaluated dairy farms in northern Antioquia. Parameters (methods)* Units La Montaña El Balcón Montenegro SPS TS SPS TS SPS TS Sand g/100g 66.38 70.18 48.3 76.87 60.13 54.03 Clay g/100g 9.71 9.93 14.25 9.99 11.88 11.65 Silt g/100g 23.91 19.89 37.45 13.14 27.99 34.32 Textural class (Bouyoucos) Sandy Loam Sandy Loam Loam Sandy Loam Sandy Loam Sandy Loam pH 5.6 5.83 5.46 5.45 5.51 5.62 EC (B method) dS/m 1.48 1.6 1.34 3.68 0.71 0.97 OC g/100g 14.56 14.04 12.37 18.31 9.96 10.42 SOM (Walkey and Black) g/100g 25.1 24.2 21.33 31.57 17.17 17.96 P mg/kg 204.5 259.5 149.3 237.5 77.9 107.1 S (Calcium phosphate) mg/kg 27.65 31.45 30.02 44.62 20.11 22.28 CEC (Sum of cations) cmol(+)/kg 23.53 32.3 18.42 26.51 15.04 28.74 B (Calcium phosphate) mg/kg 0.66 0.78 0.49 0.71 0.23 0.39 (Al + H) (KCl) cmol(+)/kg NA NA 0.37 0.57 NA NA Al (KCI) cmol(+)/kg NA NA 0.13 0.36 NA NA Ca (A. acetate 1N pH 7.0) cmol(+)/kg 18.11 23.13 13.8 17.23 12.16 22.44 Mg (A. acetate 1N pH 7.0) cmol(+)/kg 4.35 5.52 3.19 5.51 2.71 5.18 K (A. acetate 1N pH 7.0) cmol(+)/kg 0.77 3 0.87 2.77 < 0.09 0.83 Na (A. acetate 1N pH 7.0) cmol(+)/kg 0.3 0.65 0.19 0.43 0.17 0.29 Fe (modified Olsen method) mg/kg 271.9 236.8 443.2 419.2 503.4 647.2 Cu (modified Olsen method) mg/kg 15.78 10.21 7.41 9.56 2.7 3.46 Mn (modified Olsen method) mg/kg 11.05 7.41 11.11 17.87 5.99 7.17 Zn (modified Olsen method) mg/kg 74.83 81.54 74.06 123.7 22.71 30.04 *Parameters. EC = electrical conductivity, OC = organic carbon, SOM = soil organic matter, P = available phosphorus, S = available sulfur, CEC = cation exchange capacity, B = available boron, Al = exchangeable aluminum, Ca = available calcium, A. acetate = Ammonium acetate, Mg = available magnesium, K = available potassium, Na = available sodium, Fe = available iron, Cu = available cooper, Mn = available manganese, Zn = available zinc. SPS = silvopastoral system. TS = traditional system. NA not available. The pH values in the soil profile varied between 4.9 and 6.7, the bulk density was between 0.51 and 0.64 t/m3 for 0–20 cm depth and between 0.86 and 1.27 t/m3 for 80–100 cm. The porosity presented variable values, tending to decrease with sampling depth (Table 2 ). Table 2 Selected physicochemical parameters of the evaluated systems up to a soil depth of one meter. Farm System Parameter Soil sampling depth (cm) 0–20 20–40 40–60 60–80 80–100 La Montaña SPS pH 5.5 5.8 6.5 6.7 6.4 Porosity (%) 56.84 75.1 65.86 58.7 43.4 Bulk density (t/m 3 ) 0.57 0.42 0.7 1.03 1.27 TS pH 5.39 6 5.8 6.3 5.8 Porosity (%) 67.54 69.6 51.67 50.14 47.03 Bulk density (t/m 3 ) 0.53 0.5 0.9 1.09 1.11 El Balcón SPS pH 5.0 5.7 5.3 6.6 6.1 Porosity (%) 59.94 66.38 71.98 65.04 41.67 Bulk density (t/m 3 ) 0.64 0.56 0.48 0.64 1.2 TS pH 5.35 5.59 5.86 5.9 5.9 Porosity (%) 64.5 68.94 71.01 66.03 48.61 Bulk density (t/m 3 ) 0.56 0.55 0.54 0.64 1.1 Montenegro SPS pH 5.87 5.92 5.58 5.28 4.85 Porosity (%) 66.14 58.53 48.75 47.87 48.11 Bulk density (t/m 3 ) 0.59 0.71 0.45 1.08 1.16 TS pH 5.7 5.35 6.02 5.75 5.26 Porosity (%) 69.23 64.88 69.15 61.17 52.48 Bulk density (t/m 3 ) 0.51 0.58 0.58 0.67 0.86 SPS = silvopastoral system. TS = traditional system. Variance analysis This analysis was used to evaluate the influence of different factors on carbon stocks in the soil in the dairy farms evaluated, considering the factors corresponding to the farm, the production system, the sampling, the sampling depth and interactions between depth and sampling. The analysis carried out showed significant differences in carbon stocks in response to sampling depth ( p < 0.0001) (Fig. 4 ), as well as nitrogen ( p 0.05), which indicates that there was not a significant participation in the variation of carbon stocks. The adjusted R 2 indicates that the model explained 87% of the variability in soil carbon stocks, suggesting that the model adjustment was adequate to describe the variability observed in the data. Estimation of carbon addition to soil from cow manure on high-altitude tropical dairy farms Results from this estimation are shown in Table 3 . Table 3 Dry matter intake, manure production and carbon excreted in manure by lactating and dry cows in the three highland tropical dairy farms. Parameter La Montaña El Balcón Montenegro Individual lactating cow Forage DM* intake (kg/d) 11.50 12.00 12.30 Concentrate DM intake (kg kg/d) 3.80 3.20 3.00 Total DM intake (kg/d) 15.30 15.20 15.30 Feces produced in DM (kg/d) 5.58 5.48 5.51 Carbon excreted in DM (kg/d) 2.37 2.32 2.33 Total carbon excreted in DM (kg) in a 305-d lactation 722.9 707.6 712.0 Individual dry cow Forage DM intake (kg/d) 11.90 12.00 12.85 Fecal DM produced (kg/d) 4.52 4.55 4.89 Carbon DM excreted (kg/d) 1.92 1.93 2.07 Total carbon excreted in a 60-d dry period (kg) 115.0 115.8 104.6 Annual per cow or hectare values Annual forage DM intake (t/cow/yr) 4.22 4.38 4.52 Concentrate DM intake (t/cow/yr) 1.16 0.98 1.10 Total DM intake (t/cow/yr) 5.38 5.36 5.62 Manure DM excreted (t/cow/yr) 1.97 1.94 1.97 Carbon excreted (t/cow/yr) 0.84 0.82 0.84 Stocking rate (cows/ha) 6.34 4.00 3.20 Excreted carbon (t/ha/yr) 5.29 3.28 2.69 * DM: dry matter Discussion Conditions of dairy farms and analysis of soils The specific area evaluated exhibited better soil fertility conditions, such as high cation exchange capacity (CEC), elevated phosphorus content, and organic matter levels higher than those reported in the northern subregion of Antioquia, measured at 20 cm depth (Medina-Sierra et al. 2023 ), which could indicate proper agronomic management of the production systems in these soils. The elevated phosphorus content could be explained by the application of swine manure as organic fertilizer, which is a very common practice in this subregion (Ruiz et al. 2017 ). The application of swine manure can improve organic matter content, microbial biomass, and soil health (Yost et al. 2022 ). More broadly, the use of organic fertilizers derived from animal waste is recognized for its potential to enrich soil by providing nitrogen and carbon inputs, and improve pH conditions and CEC (Yost et al. 2022 ; Diacono and Montemurro 2010 ). Some authors suggest the use of swine manure serves as an alternative to nitrogen-based fertilizers in rotational grazing systems (Baron et al. 2023 ), highlighting the positive effects of these organic fertilizers on certain soil chemical properties when used rationally and appropriately. The bulk density values observed showed no significant differences among the evaluated systems, consistent with findings reported for similar systems dominated by the same plant species in a comparable area in Colombia (Benavides et al. 2021 ). This consistency highlights the positive effects of kikuyu grass on soil properties. However, in some regions of Mexico the average soil bulk density tends to be higher in conventional pastures compared to other land uses (Aryal et al. 2022 ). Carbon at different soil depths According to our results, there was significant difference in carbon content among soil sampling depths, with greater carbon content in the first three soil layers (Fig. 4 ), which correspond to the following depths: 0–20 with a value of 119.17 + 2.21 t/ha SOC (untransformed 131.94 + 2.32 t/ha SOC), 20-40cm with 89.87 + 2.16 t/ha C (untransformed 93.10 + 1.74 t/ha SOC) and 40-60cm, with 66.88 + 2.12 t/ha C (untransformed 67.12 + 1.90 t/ha SOC). The above indicates that around 33% of total SOC is in the first 20 cm of the soil and 25% in the following 20–40 cm of soil depth. Different authors have also reported that soil carbon dynamics are highly depth-dependent (Zhao et al. 2017 ), one of the reasons being the lower microbial activity in deeper layers (Wang et al. 2021 ). It is estimated that around 45% of the carbon stored in soils up to one meter deep in pasture and forest soils is found in the top 20 cm (Jobbágy and Jackson 2000 ). In this work, there were no significant differences in the production systems (SPS and TS), which could be attributed to the predominant pasture in the evaluated farms. Kikuyu grass is a perennial grass that produces stolons and rhizomes, has a C4 photosynthetic pathway, is fast growing and dense (Western Australian Herbarium 1998 ). This grass is considered an excellent soil colonizer and stabilizer, with a high capacity for recovery and competition with other plants and has a high growth rate (Muscolo et al. 2013 ). In addition, it could be a useful species to reduce GHG emissions in dairy farming, under proper management (Pérez et al. 2019 ). Other works in the same study area reported that the change in land use from forests to mainly kikuyu pastures did not show significant effects on carbon stocks at the same sampling depth (Medina-Sierra et al. 2022 ). It has been reported that other similar grasses, perennial and with high biomass production, such as Cenchrus ciliaris , also increase the accumulation of organic carbon in soil eco-restoration programs after several years, which may be due to the high production of roots and the contribution of leaves and residues (Ghosh et al. 2021 ). Regarding nitrogen, a highly significant relation to soil carbon was observed ( p < 0.0001), indicating a significant impact on the dependent variable (carbon). This interaction is explained because as nitrogen levels increase, an increase in carbon is expected, since they are considered essential elements for plant growth and on which plant growth in natural ecosystems depends, as it is necessary for the microbial activity and degradation of organic matter. When there is sufficient substrate, the mineralization rate increases, satisfying the system N requirements. The opposite occurs when N content is low, leading to a low mineralization rate, with carbon mineralization rate depending on the addition of nitrogen sources (Cerrato and Alarcón 2001 ). Nitrogen is assimilated in a specific way determined by the presence of microbial biomass that depends on the C/N ratio. The amount of N required by microorganisms is lower than their C requirements. If carbon sources are scarce and nitrogen (N) exceeds the requirements of microbial biomass, N mineralization will occur, making inorganic N available for plants. Likewise, the C/N ratio is a parameter that indicates when the degradation of organic matter is stable (Cantú and Yáñez 2018; Isaza-Arias et al. 2009 ). Estimation of carbon addition to soil from cow manure on high-altitude tropical dairy farms Manure deposition on pastures contributes to soil fertility and carbon accumulation. However, this contribution is poorly quantified, although reports of soil carbon content under different grazing systems are becoming more frequent (Stanley et al. 2024 ). In quantifying manure deposition, it is convenient to estimate DM intake and its origin (i.e. whether animal feeds are purchased or are produced directly on the farm). On average, lactating animals consumed 1.25 times more DM than dry cows, which was represented in the 3.3 kg of concentrate that lactating cows received (Table 3 ). Thus, most of the carbon consumed originated from feeds produced directly on the farm and, therefore, originated from the photosynthesis process, although concentrate intake (and along with this, the import of nutrients, including carbon and nitrogen) represented more than one t/animal/year (Table 3 ). On farms in the same region, Benavides-Patiño et al. ( 2025 ) reported intakes of 1.6 to 5.0 t of concentrate/ha/year (this includes the intake of all animals on the farm, which translates into concentrate intakes of 1.38 to 1.74 t/animal/year). In turn, the DM intake of lactating cows represented 2.55% of their live weight, while that of dry cows represented only 1.8% of their live weight. Both values agree with those reported in the literature in response to the characteristics of the diet characteristics, animal live weight, physiological stage, and milk production level (McDonald et al. 2010 ). In Table 3 , to calculate the total fecal excretion per animal, a duration of 305 d for lactation and 60 d for the dry period (16.4% of the total cycle) was used. However, for that same region, it was reported that, on average for 60 dairy farms evaluated, there was a lactation duration of 328 d and a dry period of 73 d (18.2% of the total cycle) (Ruiz et al. 2019 ). These differences in the duration of the different productive periods become important when fecal production differs greatly between different categories of animals. In this case, lactating cows excreted only 0.86 kg more feces/d than the corresponding dry cows. To calculate carbon excretion from feces, a carbon content of 42.4% in the DM of feces was used (Van Horn et al. 1994 ). The final calculations showed that the annual addition of carbon from feces was around 0.83 t/ha/cow/ year. Evidently, not all of this carbon will remain associated with soil, since it can be lost through runoff or as a result of microbial degradation that occurs in the soil. However, much of this carbon is recalcitrant, so the addition of carbon from feces to the soil is potentially very high, and future research should be carried out to quantify the impact of livestock manure deposition on both carbon capture in soil and soil fertility. No significant differences were observed in soil carbon storage between the two evaluated systems, which may be attributed to the recent establishment of silvopastoral systems in the sampled farms. In both grazing systems located in the high tropics, soil carbon content decreased with depth, with an average of 120 t/ha in the 0–20 cm layer, and only 56% and 32% of this value at 40–60 cm and 80–100 cm depths, respectively. In addition to nitrogen and other nutrients, livestock manure represents a substantial source of soil carbon. Quantifying this contribution is essential for accurately assessing the environmental and productive impacts of livestock activity. Declarations Acknowledgments The authors would like to express their heartfelt gratitude to the local producers Alba Tamayo, Hernán Arango, Noé Arboleda, and Fabio Serna for their valuable contributions to this research. Funding This work was funded by the Comité Para El Desarrollo De La Investigación (CODI) through the project titled ‘Impact of pastures and silvopastoral systems on soil carbon stocks in the profiles of farms in the Southwest and North of the Antioquia Department, Colombia’ (Act No. 2022-55595). The project is part of the 2022 Thematic Call, Act 868: ‘Science and innovation in response to the challenges associated with climate change.’ Authors' Contributions Conceptualization, MM., M.C. and R.B; fieldwork with producers, M.M, I.M.; methodology M.M, I.M. and D.S.; formal analysis, M.C., I.M. and D.S.; investigation, M.M, M.C., I.M. and D.S.; funding acquisition M.M. and M.C.; writing-original draft preparation, M.M, M.C. and R.B. All authors have read and agreed to the final version of the manuscript. Data availability The datasets generated or analyzed during this study are accessible by contacting the corresponding author and are provided upon reasonable request. Ethics approval Experimental procedures for this study was granted by the Ethics Committee of the National Faculty of Public Health, University of Antioquia (Approval Code: 21030002-00190-2024), in accordance with established ethical standards. 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06:05:59","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144029,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7512691/v1/65370cca93a662dd6ce3607a.html"},{"id":91814184,"identity":"80eb5bf8-b3d7-4b66-a221-53344a95eb0e","added_by":"auto","created_at":"2025-09-22 06:05:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201870,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of dairy farms and general conditions of the silvopastoral and traditional systems evaluated.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7512691/v1/8640f13678ba623f64762275.png"},{"id":91814724,"identity":"4755682f-1fa5-4c18-8e35-fd3889b3ba5b","added_by":"auto","created_at":"2025-09-22 06:13:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":262491,"visible":true,"origin":"","legend":"\u003cp\u003eImage of the soil pits excavated to collect soil samples.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7512691/v1/0b89b4472b26f74992e736b9.png"},{"id":91814189,"identity":"33c60574-b60f-4124-93ca-c0024921c5f0","added_by":"auto","created_at":"2025-09-22 06:05:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":295787,"visible":true,"origin":"","legend":"\u003cp\u003eSample sieving and carbon measurement in CN Leco equipment.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7512691/v1/7d464aad592d083f968fec36.png"},{"id":91814722,"identity":"569dc2b6-d9cb-447f-b5d7-36294e8f5a44","added_by":"auto","created_at":"2025-09-22 06:13:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41990,"visible":true,"origin":"","legend":"\u003cp\u003eTotal soil carbon by depth for dairy systems in the highland tropics. Different letters above the bars indicate statistically significant difference at \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7512691/v1/636b02b5e3ffcda7b0828a1f.png"},{"id":94598572,"identity":"18ada2be-c24f-43c6-89b8-8ad62d2bf989","added_by":"auto","created_at":"2025-10-28 18:54:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1991454,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7512691/v1/fccfd888-5d51-407f-a567-a0ebd225a01c.pdf"}],"financialInterests":"","formattedTitle":"Impact of silvopastoral and traditional grazing systems on soil carbon stocks in the highland tropics of northern Antioquia, Colombia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColombia holds a priority position in ecosystem conservation efforts due to its rich biodiversity, which is at risk due to population growth and harmful human practices. In many regions of Colombia, the expansion of livestock farming, especially for grazing, has led to negative environmental impacts. This expansion is often linked to pasture degradation, reduced soil fertility, and large-scale deforestation, which negatively affect soil quality, microclimate, and water cycles. Traditional livestock practices often involve deforestation through forest burning, leading to reduced tree cover and soil exposure. This is further exacerbated by the excessive use of agrochemicals, which leads to environmental and production problems (Mandal et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Deforestation transforms native forests into grass-dominated savannas, which negatively impact soil fertility and require large water inputs. Both forests and agricultural activities can play a crucial role in capturing atmospheric carbon (C) and subsequently retaining it as biomass and soil deposits (Lal et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Erb et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Le Qu\u0026eacute;r\u0026eacute; et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, it is estimated that over one-third of pastures in Latin America are degraded, which reduces their resilience to climate change. In the future, effective monitoring systems will be essential for ensuring adequate land use management in grasslands (Tolimir et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Stanimirova et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInappropriate practices in extensive livestock farming are frequently associated with increased greenhouse gas (GHG) emissions, including carbon dioxide (CO₂), nitrous oxide (N₂O), and methane (CH₄). Increased atmospheric GHG concentration causes fluctuations in global temperatures and has contributed to climate change over time (Costantini et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The environmental impact of the livestock sector continues to grow alongside the rising demand for meat, milk, and eggs (Steinfeld et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThus, adopting alternative production approaches is essential, as traditional livestock practices can evolve toward methods that prioritize social and environmental sustainability. A silvopastoral system (SPS) is an integrated forage system combining grasses, shrubs, and trees to improve livestock nutrition and welfare (Moreno-Galv\u0026aacute;n et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, its adoption as an alternative to monoculture pastures serves as a strategy to promote the sustainability of livestock farming and the conservation of tropical soils (Rivera et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In general, the implementation of agroforestry practices can enhance biomass production, thereby increasing C storage, while also promoting biodiversity conservation (Aryal et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese systems contribute to GHG mitigation and exhibit a high potential for atmospheric carbon (C) sequestration (Contreras-Santos et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Veldkamp \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), with a portion of this C being partially stabilized within the soil matrix. A large proportion of the Earth's total biosphere carbon is stored in soil organic matter. In fact, soil organic carbon (SOC) can be three times higher than the amount stored in aboveground biomass, indicating that the cycling and dynamics of C in the biosphere are predominantly governed by soil processes (Moreno et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Char\u0026aacute; et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSOC content influences the chemical, physical, and biological properties of soil, thereby affecting its fertility and productivity (Zamora-Morales et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Soil organic carbon (SOC) is a vital component of the global carbon cycle, accounting for approximately 69.8% of the organic carbon stored in the biosphere. Alongside soil pH, it serves as a key indicator of soil health. Although vegetation and grazing animals store less C than soil organic matter (SOM), these aboveground components play essential roles in carbon cycling within grazing systems-through processes such as plant residue deposition and decomposition, excreta return, and methane emissions (Dubeux et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The objective of this study was to assess the effects of silvopastoral and traditional grazing dairy systems on SOC stocks within the soil profile and to evaluate the contribution of C from animal feces in these livestock production systems.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eThree farms located in the municipality of San Pedro de los Milagros, a region specialized in dairy and beef cattle farming in Antioquia, Colombia, were evaluated. The farms included: \u0026ldquo;Hacienda La Monta\u0026ntilde;a\u0026rdquo;, owned by the University of Antioquia, situated in the Monterredondo district (6\u0026deg;26'59.606\"N; 75\u0026deg;32'37.088\"W); \u0026ldquo;El Balc\u0026oacute;n\u0026rdquo;, located in the Cerezales district (6\u0026deg;11'13.974\"N; 75\u0026deg;36'56.896\"W); and \u0026ldquo;Montenegro\u0026rdquo;, situated in the San Francisco district (6\u0026deg;28'18.84\"N; 75\u0026deg;36'2.363\"W). In both traditional and silvopastoral systems, kikuyu grass (\u003cem\u003eCenchrus clandestinus\u003c/em\u003e) predominated. In silvopastoral systems, shrub and tree species such as alder (\u003cem\u003eAlnus acuminata\u003c/em\u003e), elder (\u003cem\u003eSambucus peruviana\u003c/em\u003e), and Mexican sunflower (\u003cem\u003eTithonia diversifolia\u003c/em\u003e), among others, were prevalent. The selected farms exhibited similar climatic conditions and vegetation composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eField Sampling\u003c/h3\u003e\n\u003cp\u003eFour visits were conducted every two months to collect soil samples for wet chemistry analysis, according to protocols established by the laboratories. A homogeneous area was selected on each farm, considering factors such as topography, productive activity, and soil characteristics. Following a zigzag sampling pattern, 20 to 30 subsamples were obtained using an auger at 20 cm intervals up to a depth of one meter. The subsamples were mixed and homogenized, and a 1 kg composite sample was sent to the laboratory for chemical analysis.\u003c/p\u003e\n\u003ch3\u003eSampling for C measurement\u003c/h3\u003e\n\u003cp\u003eSoil pits measuring 1 m \u0026times; 1 m \u0026times; 1 m were excavated in both production systems on each farm. Soil samples were collected at different depths (0\u0026ndash;20, 20\u0026ndash;40, 40\u0026ndash;60, 60\u0026ndash;80, and 80\u0026ndash;100 cm) as shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLaboratory analysis\u003c/h3\u003e\n\u003cp\u003eSamples were crushed with a wooden mallet, air-dried for 48 hours, and sieved using a 2 mm mesh sieve followed by a 150 mm mesh sieve. C determination was performed using the automated Dumas combustion method (Leco CN 828 series equipment). A 0.1 g sample was weighed into tin foil and placed in a carousel inside the equipment, which analyses C using a non-dispersive infrared (NDIR) cell (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUsing carbon content and soil mass in a given sample volume, soil organic carbon (SOC) was estimated following FAO (Food and Agriculture Organization of the United Nations) (2019).\u003c/p\u003e\u003cp\u003eSOC (t/ha)\u0026thinsp;=\u0026thinsp;CC * Bd * t * 0.1\u003c/p\u003e\u003cp\u003eWhere: SOC: Soil organic carbon (t/ha). CC: carbon content mg C/g soil. Bd: Dry soil weight (g) / cylinder volume (cm\u0026sup3;). t: Sample depth (cm). 0.1: Conversion factor to estimate SOC per hectare.\u003c/p\u003e\n\u003ch3\u003eCarbon added to soils from manure deposition\u003c/h3\u003e\n\u003cp\u003eThe carbon addition to soils originating from cattle manure was estimated in the three farms. To do this, production information was collected on each farm, including that associated with animal factors (live weight, milk production, milk composition, reproductive performance, etc.), environmental (temperature, relative humidity, grazing system) and nutritional (type of pasture, concentrate, concentrate intake), among others. With this information, the CNCPS model (The Net Carbohydrate and Protein System for Evaluating Herd Nutrition and Nutrient Excretion version 5.0, Department of Animal Science at Cornell University), was used to estimate the nutritional balance and manure production of each animal according to its intake, its physiological condition and its level of production, as well as the chemical composition of its diet. This determination was carried out for both lactating and dry cows.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eSeveral mixed linear models were fitted to evaluate how the fixed variables of system type (τ) and depth (ρ) affect C content, considering random effects such as sampling (s) and farm (f). To improve data distribution, Box-Cox transformations were applied. Adjusted means were compared using transformed values, with results presented on the original scale following the procedure of Marimuthu et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The best model was selected based on the lowest Bayesian Information Criterion (BIC) and compliance with assumptions of normality and homogeneity of residuals. The final model used was:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\left(\\frac{{SOC}^{0.626}-1}{0.626}\\right)=\\:\\alpha\\:+\\:{\\tau\\:}_{i}\\:+\\:\\rho\\:j\\:+Nt\\:+\\:{f}_{s}+{e}_{ijsl}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere: \u0026#120572;: fixed intercept effect, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003e: fixed effect of production system type (TS and SPS), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\rho\\:}_{j}\\:\\)\u003c/span\u003e\u003c/span\u003e: fixed effect of soil depth (20, 40, 60, 80, 100 cm), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Nt\\:\\)\u003c/span\u003e\u003c/span\u003eNitrogen effect,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{f}_{m}\\)\u003c/span\u003e\u003c/span\u003e : random effect of the farm with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{s}^{2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{e}_{ijsl}\\)\u003c/span\u003e\u003c/span\u003e : residual effect, \u003cimg src=\"data:image/png;base64,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\" width=\"66\" height=\"18\"\u003e\u003c/p\u003e\u003cp\u003eThe analyses were conducted using the \"nlme\" library (Pinheiro and Bates, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in the statistical package \"R-project\" (R Core Team, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis of soils\u003c/h2\u003e\u003cp\u003eResults obtained in the soil analyses showed that soils predominantly had sandy loam textures, moderately to slightly acidic pHs, and moderate to high cation exchange capacity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePhysicochemical parameters of soils at 0\u0026ndash;20 cm depth in the evaluated dairy farms in northern Antioquia.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eParameters (methods)*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUnits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eLa Monta\u0026ntilde;a\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eEl Balc\u0026oacute;n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eMontenegro\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSPS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSPS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSPS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg/100g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e54.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg/100g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSilt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg/100g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTextural class (Bouyoucos)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSandy Loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSandy Loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLoam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSandy Loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSandy Loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSandy Loam\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC (B method)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edS/m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg/100g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOM (Walkey and Black)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eg/100g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e204.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e259.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e149.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e237.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e107.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS (Calcium phosphate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEC (Sum of cations)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecmol(+)/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB (Calcium phosphate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Al\u0026thinsp;+\u0026thinsp;H) (KCl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecmol(+)/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAl (KCI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecmol(+)/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCa (A. acetate 1N pH 7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecmol(+)/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMg (A. acetate 1N pH 7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecmol(+)/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK (A. acetate 1N pH 7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecmol(+)/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNa (A. acetate 1N pH 7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecmol(+)/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFe (modified Olsen method)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e271.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e236.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e443.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e419.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e503.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e647.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCu (modified Olsen method)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMn (modified Olsen method)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZn (modified Olsen method)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e123.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e*Parameters. EC\u0026thinsp;=\u0026thinsp;electrical conductivity, OC\u0026thinsp;=\u0026thinsp;organic carbon, SOM\u0026thinsp;=\u0026thinsp;soil organic matter, P\u0026thinsp;=\u0026thinsp;available phosphorus, S\u0026thinsp;=\u0026thinsp;available sulfur, CEC\u0026thinsp;=\u0026thinsp;cation exchange capacity, B\u0026thinsp;=\u0026thinsp;available boron, Al\u0026thinsp;=\u0026thinsp;exchangeable aluminum, Ca\u0026thinsp;=\u0026thinsp;available calcium, A. acetate\u0026thinsp;=\u0026thinsp;Ammonium acetate, Mg\u0026thinsp;=\u0026thinsp;available magnesium, K\u0026thinsp;=\u0026thinsp;available potassium, Na\u0026thinsp;=\u0026thinsp;available sodium, Fe\u0026thinsp;=\u0026thinsp;available iron, Cu\u0026thinsp;=\u0026thinsp;available cooper, Mn\u0026thinsp;=\u0026thinsp;available manganese, Zn\u0026thinsp;=\u0026thinsp;available zinc. SPS\u0026thinsp;=\u0026thinsp;silvopastoral system. TS\u0026thinsp;=\u0026thinsp;traditional system. NA not available.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe pH values in the soil profile varied between 4.9 and 6.7, the bulk density was between 0.51 and 0.64 t/m3 for 0\u0026ndash;20 cm depth and between 0.86 and 1.27 t/m3 for 80\u0026ndash;100 cm. The porosity presented variable values, tending to decrease with sampling depth (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSelected physicochemical parameters of the evaluated systems up to a soil depth of one meter.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFarm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSystem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e\u003cp\u003eSoil sampling depth (cm)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026ndash;20\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20\u0026ndash;40\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40\u0026ndash;60\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60\u0026ndash;80\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e80\u0026ndash;100\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eLa Monta\u0026ntilde;a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePorosity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e65.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e43.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBulk density (t/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePorosity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e51.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e47.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBulk density (t/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eEl Balc\u0026oacute;n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePorosity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e41.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBulk density (t/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePorosity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e71.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e48.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBulk density (t/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eMontenegro\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePorosity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e48.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e47.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e48.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBulk density (t/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eTS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePorosity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e61.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e52.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBulk density (t/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eSPS\u0026thinsp;=\u0026thinsp;silvopastoral system. TS\u0026thinsp;=\u0026thinsp;traditional system.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eVariance analysis\u003c/h2\u003e\u003cp\u003eThis analysis was used to evaluate the influence of different factors on carbon stocks in the soil in the dairy farms evaluated, considering the factors corresponding to the farm, the production system, the sampling, the sampling depth and interactions between depth and sampling.\u003c/p\u003e\u003cp\u003eThe analysis carried out showed significant differences in carbon stocks in response to sampling depth (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), as well as nitrogen (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). No other factor or interaction showed statistical differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which indicates that there was not a significant participation in the variation of carbon stocks. The adjusted R\u003csup\u003e2\u003c/sup\u003e indicates that the model explained 87% of the variability in soil carbon stocks, suggesting that the model adjustment was adequate to describe the variability observed in the data.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEstimation of carbon addition to soil from cow manure on high-altitude tropical dairy farms\u003c/h2\u003e\u003cp\u003eResults from this estimation are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDry matter intake, manure production and carbon excreted in manure by lactating and dry cows in the three highland tropical dairy farms.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLa Monta\u0026ntilde;a\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEl Balc\u0026oacute;n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMontenegro\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eIndividual lactating cow\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForage DM* intake (kg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcentrate DM intake (kg kg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal DM intake (kg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeces produced in DM (kg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbon excreted in DM (kg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal carbon excreted in DM (kg) in a 305-d lactation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e722.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e707.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e712.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndividual dry cow\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForage DM intake (kg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFecal DM produced (kg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbon DM excreted (kg/d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal carbon excreted in a 60-d dry period (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e104.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual per cow or hectare values\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual forage DM intake (t/cow/yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcentrate DM intake (t/cow/yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal DM intake (t/cow/yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManure DM excreted (t/cow/yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbon excreted (t/cow/yr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStocking rate (cows/ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExcreted carbon (t/ha/yr)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e5.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e* DM: dry matter\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eConditions of dairy farms and analysis of soils\u003c/h2\u003e\u003cp\u003eThe specific area evaluated exhibited better soil fertility conditions, such as high cation exchange capacity (CEC), elevated phosphorus content, and organic matter levels higher than those reported in the northern subregion of Antioquia, measured at 20 cm depth (Medina-Sierra et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which could indicate proper agronomic management of the production systems in these soils. The elevated phosphorus content could be explained by the application of swine manure as organic fertilizer, which is a very common practice in this subregion (Ruiz et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The application of swine manure can improve organic matter content, microbial biomass, and soil health (Yost et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). More broadly, the use of organic fertilizers derived from animal waste is recognized for its potential to enrich soil by providing nitrogen and carbon inputs, and improve pH conditions and CEC (Yost et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Diacono and Montemurro \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Some authors suggest the use of swine manure serves as an alternative to nitrogen-based fertilizers in rotational grazing systems (Baron et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), highlighting the positive effects of these organic fertilizers on certain soil chemical properties when used rationally and appropriately.\u003c/p\u003e\u003cp\u003eThe bulk density values observed showed no significant differences among the evaluated systems, consistent with findings reported for similar systems dominated by the same plant species in a comparable area in Colombia (Benavides et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This consistency highlights the positive effects of kikuyu grass on soil properties. However, in some regions of Mexico the average soil bulk density tends to be higher in conventional pastures compared to other land uses (Aryal et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eCarbon at different soil depths\u003c/h2\u003e\u003cp\u003eAccording to our results, there was significant difference in carbon content among soil sampling depths, with greater carbon content in the first three soil layers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which correspond to the following depths: 0\u0026ndash;20 with a value of 119.17\u0026thinsp;+\u0026thinsp;2.21 t/ha SOC (untransformed 131.94\u0026thinsp;+\u0026thinsp;2.32 t/ha SOC), 20-40cm with 89.87\u0026thinsp;+\u0026thinsp;2.16 t/ha C (untransformed 93.10\u0026thinsp;+\u0026thinsp;1.74 t/ha SOC) and 40-60cm, with 66.88\u0026thinsp;+\u0026thinsp;2.12 t/ha C (untransformed 67.12\u0026thinsp;+\u0026thinsp;1.90 t/ha SOC). The above indicates that around 33% of total SOC is in the first 20 cm of the soil and 25% in the following 20\u0026ndash;40 cm of soil depth. Different authors have also reported that soil carbon dynamics are highly depth-dependent (Zhao et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), one of the reasons being the lower microbial activity in deeper layers (Wang et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is estimated that around 45% of the carbon stored in soils up to one meter deep in pasture and forest soils is found in the top 20 cm (Jobb\u0026aacute;gy and Jackson \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this work, there were no significant differences in the production systems (SPS and TS), which could be attributed to the predominant pasture in the evaluated farms. Kikuyu grass is a perennial grass that produces stolons and rhizomes, has a C4 photosynthetic pathway, is fast growing and dense (Western Australian Herbarium \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This grass is considered an excellent soil colonizer and stabilizer, with a high capacity for recovery and competition with other plants and has a high growth rate (Muscolo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, it could be a useful species to reduce GHG emissions in dairy farming, under proper management (P\u0026eacute;rez et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Other works in the same study area reported that the change in land use from forests to mainly kikuyu pastures did not show significant effects on carbon stocks at the same sampling depth (Medina-Sierra et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has been reported that other similar grasses, perennial and with high biomass production, such as \u003cem\u003eCenchrus ciliaris\u003c/em\u003e, also increase the accumulation of organic carbon in soil eco-restoration programs after several years, which may be due to the high production of roots and the contribution of leaves and residues (Ghosh et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegarding nitrogen, a highly significant relation to soil carbon was observed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating a significant impact on the dependent variable (carbon). This interaction is explained because as nitrogen levels increase, an increase in carbon is expected, since they are considered essential elements for plant growth and on which plant growth in natural ecosystems depends, as it is necessary for the microbial activity and degradation of organic matter. When there is sufficient substrate, the mineralization rate increases, satisfying the system N requirements. The opposite occurs when N content is low, leading to a low mineralization rate, with carbon mineralization rate depending on the addition of nitrogen sources (Cerrato and Alarc\u0026oacute;n \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNitrogen is assimilated in a specific way determined by the presence of microbial biomass that depends on the C/N ratio. The amount of N required by microorganisms is lower than their C requirements. If carbon sources are scarce and nitrogen (N) exceeds the requirements of microbial biomass, N mineralization will occur, making inorganic N available for plants. Likewise, the C/N ratio is a parameter that indicates when the degradation of organic matter is stable (Cant\u0026uacute; and Y\u0026aacute;\u0026ntilde;ez 2018; Isaza-Arias et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eEstimation of carbon addition to soil from cow manure on high-altitude tropical dairy farms\u003c/h2\u003e\u003cp\u003eManure deposition on pastures contributes to soil fertility and carbon accumulation. However, this contribution is poorly quantified, although reports of soil carbon content under different grazing systems are becoming more frequent (Stanley et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In quantifying manure deposition, it is convenient to estimate DM intake and its origin (i.e. whether animal feeds are purchased or are produced directly on the farm). On average, lactating animals consumed 1.25 times more DM than dry cows, which was represented in the 3.3 kg of concentrate that lactating cows received (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Thus, most of the carbon consumed originated from feeds produced directly on the farm and, therefore, originated from the photosynthesis process, although concentrate intake (and along with this, the import of nutrients, including carbon and nitrogen) represented more than one t/animal/year (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On farms in the same region, Benavides-Pati\u0026ntilde;o et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) reported intakes of 1.6 to 5.0 t of concentrate/ha/year (this includes the intake of all animals on the farm, which translates into concentrate intakes of 1.38 to 1.74 t/animal/year). In turn, the DM intake of lactating cows represented 2.55% of their live weight, while that of dry cows represented only 1.8% of their live weight. Both values agree with those reported in the literature in response to the characteristics of the diet characteristics, animal live weight, physiological stage, and milk production level (McDonald et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, to calculate the total fecal excretion per animal, a duration of 305 d for lactation and 60 d for the dry period (16.4% of the total cycle) was used. However, for that same region, it was reported that, on average for 60 dairy farms evaluated, there was a lactation duration of 328 d and a dry period of 73 d (18.2% of the total cycle) (Ruiz et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These differences in the duration of the different productive periods become important when fecal production differs greatly between different categories of animals. In this case, lactating cows excreted only 0.86 kg more feces/d than the corresponding dry cows. To calculate carbon excretion from feces, a carbon content of 42.4% in the DM of feces was used (Van Horn et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The final calculations showed that the annual addition of carbon from feces was around 0.83 t/ha/cow/ year. Evidently, not all of this carbon will remain associated with soil, since it can be lost through runoff or as a result of microbial degradation that occurs in the soil. However, much of this carbon is recalcitrant, so the addition of carbon from feces to the soil is potentially very high, and future research should be carried out to quantify the impact of livestock manure deposition on both carbon capture in soil and soil fertility.\u003c/p\u003e\u003cp\u003eNo significant differences were observed in soil carbon storage between the two evaluated systems, which may be attributed to the recent establishment of silvopastoral systems in the sampled farms. In both grazing systems located in the high tropics, soil carbon content decreased with depth, with an average of 120 t/ha in the 0\u0026ndash;20 cm layer, and only 56% and 32% of this value at 40\u0026ndash;60 cm and 80\u0026ndash;100 cm depths, respectively. In addition to nitrogen and other nutrients, livestock manure represents a substantial source of soil carbon. Quantifying this contribution is essential for accurately assessing the environmental and productive impacts of livestock activity.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eThe authors would like to express their heartfelt gratitude to the local producers Alba Tamayo, Hern\u0026aacute;n Arango, No\u0026eacute; Arboleda, and Fabio Serna for their valuable contributions to this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was funded by the \u003cem\u003eComit\u0026eacute; Para El Desarrollo De La Investigaci\u0026oacute;n\u0026nbsp;\u003c/em\u003e(CODI) through the project titled \u0026lsquo;Impact of pastures and silvopastoral systems on soil carbon stocks in the profiles of farms in the Southwest and North of the Antioquia Department,\u0026nbsp;Colombia\u0026rsquo; (Act No. 2022-55595). The project is part of the 2022 Thematic Call, Act 868: \u0026lsquo;Science and innovation in response to the challenges associated with climate change.\u0026rsquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u0026nbsp;\u003c/strong\u003eConceptualization, MM., M.C. and R.B; fieldwork with producers, M.M, I.M.; methodology M.M, I.M. and D.S.; formal analysis, M.C., I.M. and D.S.; investigation, M.M, M.C., I.M. and D.S.; funding acquisition M.M. and M.C.; writing-original draft preparation, M.M, M.C. and R.B. All authors have read and agreed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The datasets generated or analyzed during this study are accessible by contacting the corresponding author and are provided upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003eExperimental procedures for this study was granted by the Ethics Committee of the National Faculty of Public Health, University of Antioquia (Approval Code: 21030002-00190-2024), in accordance with established ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for participation and publication\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAryal DR, Morales-Ruiz DE, L\u0026oacute;pez-Cruz S, Tondop\u0026oacute;-Marroqu\u0026iacute;n CN, Lara-Nucamendi A, Jim\u0026eacute;nez-Trujillo JA, P\u0026eacute;rez-S\u0026aacute;nchez E, Betanzos-Simon JE, Casasola-Coto F, Mart\u0026iacute;nez-Salinas A, Sep\u0026uacute;lveda-L\u0026oacute;pez CJ, Ram\u0026iacute;rez-D\u0026iacute;az R, La O Arias MA, Guevara-Hern\u0026aacute;ndez F, Pinto-Ruiz R, Ibrahim M (2022) Silvopastoral systems and remnant forests enhance carbon storage in livestock-dominated landscapes in Mexico. 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Soil Sci Soc Am J 86(2):450\u0026ndash;486. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/saj2.20359\u003c/span\u003e\u003cspan address=\"10.1002/saj2.20359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZamora-Morales BP, Mendoza-Cari\u0026ntilde;o M, Sangerman-Jarqu\u0026iacute;n DM, Quevedo Nolasco A, Navarro Bravo A (2019) La investigaci\u0026oacute;n cient\u0026iacute;fica en M\u0026eacute;xico: secuestro de carbono org\u0026aacute;nico en suelos agr\u0026iacute;colas y de agostadero. Rev Mex Cien Agr\u0026iacute;c 10(1):155\u0026ndash;164\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao W, Hu Z, Li S, Guo Q, Yang H, Zhang T (2017) Impact of land use conversion on soil organic carbon stocks in an agro-pastoral ecotone of Inner Mongolia. J Geog Sci 27(8):999\u0026ndash;1010. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11442-017-1417-1\u003c/span\u003e\u003cspan address=\"10.1007/s11442-017-1417-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cattle manure, climate change, grasslands, greenhouse gases, livestock, organic carbon","lastPublishedDoi":"10.21203/rs.3.rs-7512691/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7512691/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe current climate crisis demands a reevaluation of society's relationship with natural environments. In dairy and beef farms, silvopastoral systems have become increasingly important for their contributions to animal welfare, pasture biodiversity, soil recovery, and the mitigation of their environmental impact. This study aimed to evaluate the impact of traditional and silvopastoral grazing systems on soil carbon stocks in dairy farms located in the highland tropics of Antioquia, Colombia. Soil samples were collected bimonthly from five depths (0\u0026ndash;20, 20\u0026ndash;40, 40\u0026ndash;60, 60\u0026ndash;80, and 80\u0026ndash;100 cm). Carbon content was analyzed using a CN Leco 828 analyzer. Likewise, soil carbon deposition from cattle manure was estimated. Variance analysis was performed considering grazing systems, temporal sampling, and farm-specific effects as variables. Results showed no significant differences in carbon storage between traditional and silvopastoral systems at the evaluated depths. However, carbon content decreased significantly with increasing soil depth. Livestock manure was identified as a substantial source of soil carbon, and contributions from animal feces, along with the presence of kikuyu grass and other plant species were recognized as key factors in mitigating the environmental impact of livestock production in the highland tropics.\u003c/p\u003e","manuscriptTitle":"Impact of silvopastoral and traditional grazing systems on soil carbon stocks in the highland tropics of northern Antioquia, Colombia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 05:49:54","doi":"10.21203/rs.3.rs-7512691/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bba69034-ea41-4d75-97c8-df56b2371415","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-28T18:22:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 05:49:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7512691","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7512691","identity":"rs-7512691","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}