Reservas de carbono en tres sistemas silvopastoriles de la Amazonía peruana

Carbon stocks in three silvopastoral systems in the Peruvian Amazon

María Elena Díaz Pablo^1*; Julio Cesar Alegre Orihuela²; Carlos Alfredo Gómez Bravo³;

Pedro Mendoza Tamani⁴; César O. Arévalo-Hernández^4,5

1 Facultad de Zootecnia, Universidad Nacional de la Amazonía Peruana (UNAP). Yurimaguas, Perú.

2 Facultad de Agronomía, Universidad Nacional Agraria La Molina (UNALM). Lima, Perú.

3 Facultad de Zootecnia, Universidad Nacional Agraria La Molina (UNALM). Lima, Perú.

4 Facultad de Ingeniería, Universidad Nacional Autónoma de Alto Amazonas (UNAAA). Yurimaguas, Perú.

5 Departamento de Suelos, Instituto de Cultivos Tropicales (ICT). Tarapoto, Perú.

* Autor corresponsal: maria.diaz@unapiquitos.edu.pe (M. E. Díaz Pablo).

ORCID de los autores:

M. E. Díaz Pablo: https://orcid.org/0000-0002-2026-3559 J. C. Alegre Orihuela: https://orcid.org/0000-0002-7282-045X

C. A. Gómez Bravo: https://orcid.org/0000-0001-9021-5838 P. Mendoza Tamani: https://orcid.org/0000-0001-8798-3174

C. O. Arévalo-Hernández: https://orcid.org/0000-0002-1390-3503

RESUMEN

Los sistemas silvopastoriles (SS) combinan productividad con mitigación del cambio climático y mejoran las reservas de carbono (C). El objetivo de la investigación fue estimar las existencias de C en tres sistemas silvopastoriles de la selva alta. Para el estudio se seleccionaron tres sistemas silvopastoriles: Cedrelinga cateniformis (Tornillo), Eucalyptus spp. (Eucalipto) e Inga edulis (Inga). Se recolectaron y analizaron muestras de suelo de 0-15 cm y 15-30 cm para determinar sus características físicas y químicas. La estimación de las reservas de C se realizó a partir de biomasa con ecuaciones alométricas. El análisis estadístico se realizó mediante análisis descriptivo (medias y desviación estándar) y ANOVA y uso de la prueba de Scott Knott. Las propiedades del suelo no mostraron diferencias significativas entre SS, a excepción de P, Al y CIC. Las reservas de carbono fueron mayores en el sistema tornillo. En general, las reservas de C en el SS fueron mayores en el suelo y representaron la principal reserva de C, superando a la vegetación. Los resultados resaltan la importancia del suelo como sumidero de carbono y su papel en la mitigación del cambio climático.

Palabras clave: Sistema agroforestal silvopastoril; árboles forestales; pastos; carbono del suelo; hojarasca.

ABSTRACT

Silvopastoral systems (SS) combine productivity with climate change mitigation and improve carbon (C) stocks. The objective of the research was to estimate C stocks in three silvopastoral systems of the high jungle. Three silvopastoral systems were selected for the study: Cedrelinga cateniformis (Tornillo), Eucalyptus spp. (Eucalyptus) and Inga edulis (Inga). Soil samples of 0-15 cm and 15-30 cm were collected and analyzed to determine their physical and chemical characteristics. The estimation of C reserves was carried out from biomass with allometric equations. Statistical analysis was performed using descriptive analysis (means and standard deviation) and ANOVA and use of the Scott Knott test. Soil properties did not show significant differences between SS, except for P, Al and CEC. Carbon reserves were higher in the screw system and lower in the inga system. In general, C reserves in the SS were greater in the soil and represented the main C reserve, surpassing the vegetation. The results highlight the importance of soil as a carbon sink and its role in mitigating climate change.

Keywords: Silvopastoral agroforestry system; forest trees; pasture; soil carbon; litter.

Recibido: 27-05-2024.

Aceptado: 12-08-2024

The Amazon, considered the lungs of the world, is a vast and biodiverse ecosystem that supports a great variety of flora and fauna species (Walker et al., 2020), as well as being one of the world's largest carbon reservoirs due to its extensive forest biomass. Amazonian trees play a fundamental role in climate change mitigation by capturing and fixing atmospheric carbon dioxide (Sharrow and Ismail, 2004; Wright et al., 2001). However, in recent decades, various human activities such as indiscriminate logging, illegal mining, human settlements and natural resource exploitation have led to the deforestation of extensive areas of virgin forests in this region (Assunção et al., 2017; Coomes et al., 2021; Dávalos et al., 2011; Garate-Quispe et al., 2021). While these activities are widely known, it is crucial to highlight the role that extensive cattle ranching and shifting agriculture have played in contributing to the massive deforestation of vast forested areas in the Latin American Amazon, causing the loss of biodiversity-rich ecosystems, the extinction of species and contributing to the increase of greenhouse gas emissions, carbon to the atmosphere, as forests capture about 30% of annual anthropogenic emissions (Gallo Aponte & Sanabria Rodelo, 2019; Kaimowitz & Angelsen, 2008; Le Quéré et al., 2015).

In the Peruvian Amazon, both activities have caused the disappearance of large areas of tropical forests, perpetuating a cycle of environmental degradation where areas deforested by shifting agriculture are subsequently used for extensive grazing, impeding forest regeneration (Dourojeanni, 1987; Velarde et al., 2010). Shifting agriculture, practiced by resource-poor farmers, consists of cultivating forest soils for 2 to 3 years until their fertility is exhausted and then leaving them fallow for periods of 5 to 20 years, driving the constant search for new land due to severe Amazonian soil limitations (Dourojeanni, 1987).

Although extensive livestock farming accounts for 40% of the gross value of national agricultural production (MINAGRI, 2017), its negative impacts are undeniable. Deforestation and land use change are responsible for 47% of greenhouse gas emissions in Peru (MINAM, 2009). In addition, these systems imply low efficiency in land use, erosion, loss of biodiversity and social inequality, factors that have caused cattle ranching to be seen as a productive sector that threatens ecological sustainability (Mahecha, 2003). It is therefore urgent to implement sustainable livestock production systems that reconcile economic, sociocultural and ecological needs and contribute to climate change mitigation.

In this context, silvopastoral systems (SS) have emerged as a promising sustainable alternative. These agroforestry systems integrate trees, shrubs and grasses for the production of livestock, timber, fruits and other goods, increasing pasture quality, improving animal productivity, providing shade for livestock, restoring degraded soils, sequestering carbon, conserving biodiversity and mitigating adverse environmental impacts of conventional livestock systems (Broom, 2017; Buitrago, et al., 2018; Montagnini et al., 2013; Murgueitio et al., 2015); Although there are various SS modalities adapted to different purposes, in the Peruvian Amazon their full adoption has been limited due to the socioeconomic situation of agricultural producers, opting for local adaptations such as pastures with scattered trees and basic management of improved pastures in extensive breeding systems, with some exceptions in dairy production systems (Ríos et al., 2002; Robiglio et al., 2015). However, in some towns in the San Martin region, at the beginning of the 21st century, producers installed improved pastures and adopted pasture management practices, allowing to increase the stocking rate to 3-4 animals per hectare (Ríos et al., 2001). Past research has shown that improved pasture, tree and SS are land uses with high carbon sequestration potential are sustainable for farmers (Anguiano et al., 2013; Ibrahim et al., 2007; McGroddy et al., 2015; Oliva et al., 2017) and may be a climate change mitigation strategy and the sustainable use of Amazonian territory.

Given the evidence of the high carbon sequestration potential of SS and their role as sustainable livestock production systems, the present study aimed to estimate carbon stocks in three SS of the Selva Alta Peruana, in the district of Soritor, province of Moyobamba.

Soil sampling and analysis

For soil sampling, 5 pits were made for each experimental plot and soil samples were collected at 2 different depths: 0-15 cm and 15-30 cm. The soil was air-dried and taken to the laboratory for grinding, sieved at 2 mm and stored for subsequent physical and chemical analysis.

The analyses performed on the soil samples followed the procedures of Anderson and Ingram (1992) and Arévalo-Hernández et al. (2021), where soil texture (Bouyucos), soil bulk density (cylinder method), pH (1:2. 5 in water), organic matter (Walkey and Black, 1934), CEC (1M ammonium acetate, pH 7.0), P (Olsen), Ca, Mg, K (1 M ammonium acetate, pH 7.0) and Al (1 M KCl).

Evaluation of fresh aerial biomass of silvopasto-ral systems

For tree biomass, a 50 x 50 m plot was established and the trees within this area were evaluated and geo-referenced with a GPS navigator. Tree height measurements were taken with a Leica Geosystems laser distance meter. Diameter at breast height (DBH) was measured at 1.30 cm with a tape measure. The diameter of the tree crown was measured in three repetitions with a meter to finally obtain an average, then the vertical projection of the crown to the ground was estimated, after which calculations were made to obtain the radius of the tree. The tree and pasture litter were evaluated in a 0.50 x 0.50 m wooden quadrat at the base of the tree. Fresh litter samples were collected inside the quadrat and weighed. This procedure was repeated 5 times. Subsequently, all the material collected by each SS was sent to the laboratory to determine the dry matter content. For the live biomass of the pastures, seven replicates were randomly cut in a wooden quadrat of 0.50 x 0.50 m in a zigzag pattern along the entire area of the SS. These fresh samples after weighing were taken to the laboratory for dry matter analysis.

Determination of the dry weight of samples

From all samples collected for biomass deter-mination, 100 g of material (leaf and grass) were dried at 105 °C in a circulating air oven and, after 24 hours, the dry weight of the samples was determined.

Then, based on the area harvested and the percentage of dry weight obtained, the biomass in t ha-1 was calculated with the following formula:

Estimation of tree biomass

For biomass estimation, the following formulas were used for each tree species:

For Inga edulis (Terán-Ramírez et al., 2018):

For Eucalyptus sp (Arévalo et al., 2003) :

For Cedrelinga cateniformis (Núñez Silvestre, 2018) :

B: Total biomass (t ha^-1)

DBH: Diameter at breast height (1.3 m).

Finally, the results were transformed into t ha^-1, considering the measured plot (2500 m²) and the transformation from kg to tons.

Estimation of C in vegetation and soil

To estimate C, the conversion factor of 0.45 in plant species was used, following the formula below:

The following formula was used to calculate soil

carbon stocks

OC= Organic carbón in %

BD = Bulk density in t/m³

Depth = Soil Depth in m.

Finally, vegetation C and soil C were added together to calculate total carbon.

Statistical analysis

All statistical analyses were performed in the R statistical package. Descriptive statistics of mean and standard deviation were performed, and analysis of variance -ANOVA at a significance level of 0.05 was also performed. In case of significant differences, the Scott-Knott mean comparison test was performed at 0.05%.

Soil attributes

The results of the soil analysis at 0-15 cm depth in the silvopastoral systems (SS) studied are presented in Table 2 and were only presented at the 0-15 cm depth, since from 15-30 cm it was not significantly different. In general, there were no significant differences (p > 0.05) between the physical properties of the soil, being all of them clay loam, likewise, it was decided to present only the bulk density (DA) of the soil, since it is used in the carbon calculations presented later.

However, for chemical properties, significant differen-ces (p ≤ 0.05) were observed in P, CIC, Ca⁺², Mg⁺² and Al⁺³. The SS with inga obtained the lowest values of P, but the highest average values of CIC and Al³⁺. The high CEC may be associated with the presence of 2:1 phyllosilicates and kaolinite clays type 1.1, which leads to high exchangeable Al values and at the same time is conditioned by the lower value of available P in the SS. However, the p values obtained in all systems are considered very low (< 4 mg/kg) since it is the most limiting element in all acid soils of the Peruvian Amazon.

In the case of Ca and Mg, these values were similar in both the system with Eucalyptus and Inga, being the system with Cedrelinga the one that presented the lowest concentrations of these elements. This could be related to the high nutritional requirements of this species compared to the others (Alvarado, 2015).

Traditional pasture management with excessive ani-mal stocking produces overgrazing and degradation in the physical quality of the soil with increases in the BD on the soil surface and increased mechanical resistance, which hinders root penetration, affecting pore distribution and soil structural stability (Salazar et al. 2024), on the other hand, SS recover the physical properties of the soil improving structural stability by increasing the C content in the soil, since with SS there is a diversified production with higher aerial biomass content and higher levels of soil organic matter as well as better animal/pasture management (Polanía-Hincapié et al. 2021). However, in the present study no such significant changes (p > 0.05) in the 'physical properties' were evidenced. Regarding soil chemical properties, Rodriguez et al. (2018), in their research observed that phosphorus levels increased after the establishment of a SS of Tithonia diversifolia (Hemsl.) A. Grey (Boton de oro) and associated grasses in areas dedicated to cattle ranching. However, they appre-ciated that in areas where the SS had been established for three years, the level of available P in the soil was the same as before the establishment of the SS. Agboola & Joseph (2014) also obtained an increase in soil P in degraded areas invaded by boton de oro.

Table 2

Soil physical and chemical attributes at 0-15 cm depth, in Cedrelinga, Eucalyptus and Inga silvopastoral systems studied in the Peruvian amazon

Silvopastoral Systems	BD*	pH	OM	P	CEC	Ca²⁺	Mg²⁺	K⁺	Al³⁺
Silvopastoral Systems	g cm^-3	--	%	mg kg^-1	cmol+ kg^-1
Cedrelinga	1.33±0.18	4.24±0.50	3.12±0.18	3.43±0.21A	9.07±0.56B	0.36±0.18C	0.25±0.03B	0.14±0.02	0.83±0.40B
Eucalyiptus	1.38±0.04	4.85±0.10	3.76±0.26	3.20±0.95A	9.01±5.05B	7.61±4.01B	1.81±0.97ª	0.35±0.35	0.00±0,00B
Inga	1.22±0.10	4.99±0.13	2.88±0.85	0.93±0.25B	25.49±3.05A	4.67±2.18A	1.77±0.62A	0.64±0.54	6.87±3.87A
PV	0.3205	0.0581	0.1622	<0,01	<0,01	0.0496	0.02874	0.1822	<0.01

*BD= Soil bulk density, OM= Organic matter, CIC= Cation Exchange Capacity.

Different letters in the columns mean significant differences between SS at 0.05 according to the Scott-Knott test.

Aboveground biomass

The results of biomass (dry weight) for each plant fraction (trees, grasses and litter) are presented in Figure 2. The biomass produced was in the order (mean ± standard deviation) of 6.64 ± 0.97 t ha^-1 (trees), 1.01 ± 0.31 t ha^-1 (grass) and 3.14 ± 0.73 t ha^-1 (litter) for the system with Cedrelinga. Meanwhile, the system with Eucalyptus achieved 0.94 ± 0.94 t ha^-1 (trees), 0.59 ± 0.20 t ha^-1 (pasture) and 5.06 ± 1.89 t ha^-1 (litter). Finally, the system with Inga obtained: 0.81 ± 0.71 t ha^-1 (trees), 0.69 ± 0.07 t ha^-1 (pasture) and 2.83 ± 0.79 t ha^-1 (litter).

It can be observed that both the biomass produced by trees (Figure 2A) and grasses (Figure 2B) were significantly (p < 0.05) higher in the system with Cedrelinga. However, in the case of litter production (Figure 2C), production was higher in the system with Eucalyptus.

In the case of overall biomass production (Figure 2D), in all cases the litterfall was higher except for the Cedrelinga system, where the biomass produced by the trees was higher than the dry mass produced by the litterfall.

Oliva et al. (2017) found that in a silvopastoral system with Pinus patula contents of 19.37 t ha^-1 of aerial biomass, and which were higher than those found in the present research; the biomass of herbaceous shrubs (3.50 t ha^-1) was also higher; As for litter biomass, the values were similar to those of the system with Cedrelinga and the system with Inga edulis, except with that of the system with Eucalyptus which was higher (Figure 2D). Likewise, Zavala Solórzano et al. (2018) found higher values in terms of total biomass in an agroforestry system with coffee associated with Inga edulis and other species.

The results of carbon stocks for each fraction (vegetation and soil) are presented in Figure 3. The carbon stocks produced in both aboveground vegetation and soil were of the order (mean ± standard deviation) of 10.8 ± 1.6 t ha^-1 (vegetation) and 66.0 ± 10.9 t ha^-1 (soil) for the system with tornillo, while the Eucalyptus system achieved 6.6 ± 1.9 t ha^-1 (vegetation) and 62.9 ± 15.4 t ha^-1 (soil). Finally, in the system with inga, 4.3 ± 0.1 t ha^-1 (vegetation) and 61.7 ± 42.4 t ha^-1 (soil) were obtained. It was observed that soil carbon stocks were relatively uniform in the three SS, with the highest average accumulated value in the SS with Cedrelinga and the SS with Inga the one that stored the least carbon, probably due to the age of the trees.

In the overall carbon production, in all cases the soil stored most of the carbon in the SS, much more than in the aerial vegetation. These results highlight the importance of soil for carbon storage and its role in climate change mitigation by reducing CO₂ concentrations in the environment.

In the research of Oliva et al. (2017), it was found that in a SS composed of Pinus patula and native herbaceous plants there were 10.9 t ha^-1 in aboveground biomass and 81.2 t ha^-1 in the top 30 centimeters of soil, values higher than those reported here.

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SS	Species	Arrangement	Predominant pasture	Location of the plot
1	Inga (Inga edulis)	Dispersed in pastures	Brachiaria decumbens	1. Nueva Esperanza 2. Nueva Esperanza 3. Pomalca
2	Eucalyptus (Eucalyptus torrelliana)	Life fences	Brachiaria decumbens	1. San José 2. Los claveles 3. Atun Rumi
3	Tornillo (Cedrelinga catenaeformis Ducke)	Dispersed in pastures	Brachiaria decumbens	1. San Marcos 2. San Marcos 3. Nueva Esperanza