Carbon Storage Potential of Integrated Forest Patches and Banana (Musa spp.) Agroecosystems in the Agricultural Landscape of Mindanao, Philippines

Carbon Storage Potential of Integrated Forest Patches and Banana (Musa spp.) Agroecosystems in the Agricultural Landscape of Mindanao, Philippines | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Carbon Storage Potential of Integrated Forest Patches and Banana (Musa spp.) Agroecosystems in the Agricultural Landscape of Mindanao, Philippines Adrian M. Tulod, Eric N. Bruno, Angela Grace Toledo-Bruno, Lowell G. Aribal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6299097/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Agroecosystems are known to cause high depletion of terrestrial carbon stocks, and their expansion is seen to reduce natural habitats within agricultural landscapes to smaller patches. Understanding the carbon storage potential of these systems is essential for optimizing ecological benefits and addressing gaps in carbon accounting, which often overlook forest patches in agricultural areas. This study quantified the carbon storage potential of small forest patches and banana agroecosystems within agricultural landscapes in Mindanao, Philippines. Results indicated that forest patches had significantly higher biomass carbon (60.3 ± 12.2 to 206.4 ± 47.2 Mg ha⁻¹) than banana agroecosystems (22.78 to 85.21 Mg ha⁻¹). Carbon storage in forest patches was concentrated in trees (60–70%) and roots (15–20%), while in banana agroecosystems it was primarily in pseudo-stems (45–55%) and litter layer (45–50%). Soil organic carbon was comparable between forest patches (45.45–95.33 Mg ha⁻¹) and banana agroecosystems (47.96 to 80.53 Mg ha⁻¹). The absence of understorey vegetation in banana agroecosystems reflects the impact of intensive management practices. Despite this, banana agroecosystems had higher carbon accumulation rates (86.70 ± 24.92 Mg ha⁻¹ yr⁻¹) than forest patches (41.63 ± 16.31 Mg ha⁻¹ yr⁻¹) but lower CO₂ fixation (5.96 ± 1.48 vs . 8.89 ± 1.48 Mg CO₂-eq ha⁻¹ yr⁻¹). While the rapid growth rates of bananas drive their carbon accumulation, their short harvest cycles limit long-term storage, unlike the woody biomass in forest patches. These findings emphasize the need for agroforestry policies that promote integrated management of forest patches and agricultural lands for optimal carbon storage. Agricultural landscape banana agroecosystems carbon storage carbon accumulation rates carbon dioxide fixation rates forest fragments Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Agricultural expansion and land-use changes, particularly in tropical regions, have increasingly degraded and fragmented forest ecosystems, threatening biodiversity and reducing carbon conservation potential. The Food and Agriculture Organization (FAO) reported that over 80% of global deforestation is attributed to agricultural development and expansion including crop production and livestock grazing (FAO 2020 ). Between 2003 and 2019, global cropland expanded by 9%, with nearly half of this growth came at the expense of natural habitats (Potapov et al. 2022 ). Global food systems contribute about 30% of anthropogenic greenhouse gas emissions, releasing an estimated 9,800–16,900 megatonnes of carbon dioxide equivalent (MtCO2e) annually, with crop production responsible for about 80% of these emissions (Vermeulen et al. 2012 ). As the global population is projected to peak at around 10 billion by the end of this century, emissions from agricultural expansions are expected to rise, particularly in tropical developing nations. This growth will further impact natural habitats in agricultural landscapes, and degrade the ecosystem services they provide, including carbon storage. In the Philippines, past deforestation and expansion of croplands, have led to the loss of at least 100,000 hectares of natural forest per year, resulting in an 8.8 million-ton carbon emission annually (Lasco and Pulhin 1998 ). However, current forest patches within agricultural landscapes in the country - often remnants of secondary growth from past deforestation, which may have lower carbon densities than primary forests - are increasingly at risk of being lost due to agricultural expansion. Previous studies reported that as these patches are reduced in size due to agricultural expansion, their capacity to store carbon diminishes due to edge effects and fragmentation (Ma et al. 2017 ). Despite this, carbon storage potential of natural forest patches in agroecosystems remains poorly documented and often overlooked in both global carbon budgets and national carbon accounting (Zomer et al. 2016 ), possibly due to the common perception that agroecosystems deplete terrestrial carbon pools (Lal 2011 ). However, if properly managed or left to regenerate, forest patches within agroecosystems are crucial for restoring lost carbon pools and other ecosystem services that would otherwise be depleted with complete conversion to monocultures. In areas where agriculture dominates the economy, agricultural ecosystems or agroecosystems proliferate. For instance, banana production is one of the dominant agricultural landscapes in the Southern Philippines. These banana plantations are significant contributors to the local economy in terms of both production volumes and export earnings and play a vital role as a source of rural employment in the Philippines. However, previous studies have shown mixed results on how such agricultural lands affect terrestrial carbon including soil organic carbon – some reported depletion (Magalhães et al. 2024 ), while others observe minimal changes despite agriculture intensification (Bruun et al. 2021 ). These conflicting findings suggest that site-specific management practices may be necessary. This paper reports on a case study on the carbon stock and accumulation potential of small forest patches and banana ( Musa spp.) agroecosystems in Southern Philippines. We propose that when forest patches are managed as integral elements within agricultural matrices in an agroforestry context, the combined effect can significantly increase carbon storage and could potentially contribute to long-term carbon offsets for the agricultural sector. Understanding carbon storage potential of these integrated land-use systems is essential for developing strategies that optimize the ecological benefits and ecosystem services. The study is expected to fill a gap in current carbon accounting systems, which often overlook forest patches within agricultural settings. The specific objectives of this study were to estimate their carbon stocks in different carbon pools ( e.g. , stem, understorey vegetation, litter, roots, and soil) and assess their carbon accumulation and fixation rates. Methodology Sampling Sites The study was conducted in nine locations within forest patches adjacent to banana agroecosystems in Bukidnon Province, Southern Philippines (Fig. 1 ). These forest patches, situated within the province’s agricultural landscape and along the edges of banana agroecosystems, represent narrow strips of second-growth forest regenerating from past deforestation, with a diverse mix of native and exotic species (Table 1 ). The dominant agroecosystems cultivated with bananas (Table 2 ) are large-scale, export-oriented, and privately owned, primarily cultivating the Cavendish variety for fresh export. These sampling sites were specifically selected to reflect direct proximity and potential ecological interactions between forest patches and intensive banana agroecosystems. The province belongs to a Type IV climate under the modified Coronas classification, distinguished by the absence of defined dry season and a relatively consistent rainfall pattern throughout the year. On average, Bukidnon receives about 2,800 millimeters of rainfall annually with temperature typically fluctuate between 20°C and 34°C, while relative humidity remains persistently high, ranging from 90.86–92.85%. These environmental conditions—along with its topographic complexity—make Bukidnon a vital ecological zone, which supports both agricultural productivity and diverse forest ecosystems. Table 1 Description of nine forest patches covered in the study. The sites indicate the name of the Town in the Province of Bukidnon, Mindanao, Philippines. Sites Forest patch cover Description Elevation (m) Area (ha) Pangantucan Exotic-dominated secondary forest Mixed secondary forest characterized by a few dominant exotic tree species, particularly Gmelina arborea , Senna spectabilis , Leucaena leucocephala . 820 8.5 Dagumbaan Bamboo-dominated secondary forest Mixed secondary forest dominated by Dendrocalamus asper and other native trees like Melanolepis multiglandulosa and Artocarpus heterophyllus 686 2.1 Dangcagan Piper aduncum- dominated e arly successional forest Early successional forest characterized by a dense proliferation of the invasive exotic Piper aduncum , alongside pioneers like Trema orientalis and Polyscias nodosa 300 4.6 La Roxas Mixed pioneer forest Transitional regenerating forest, characterized by a dense presence of pioneer species such as Leucosyke capitellata , Ficus septica , and Spathodea campanulata . 800 11.3 Valencia Gmelina-dominated mixed secondary forest Gmelina arborea -dominated mixed secondary forest, with scattered agroforestry species, bamboo, and pockets of native regeneration 864 18.4 Lantapan Guioa koelreuteria- dominated secondary forest Features a mix of evergreen and deciduous native tree species but dominated by Guioa koelreuteria 600 15.0 Dalwangan Falcataria falcata -dominated secondary forest Secondary growth forest dominated by fast-growing Falcataria falcata 896 6.0 Sumilao Acacia mangium -dominated regenerating forest Mix of native and introduced species dominated by Acacia mangium 600 50.3 Baungon Regenerating forest dominated by Artocarpus and Ficus species Regenerating forest characterized by the dominance of Artocarpus spp. and various Ficus species 409 2 Table 2 Description of banana agroecosystems covered in the study. The sites indicate the name of the Town in the Province of Bukidnon, Mindanao, Philippines. Sites Elevation (m) Area (ha) Pangantucan 820 120.99 Dagumbaan 686 38.12 Dangcagan 300 2.56 La Roxas 800 12.97 Valencia 864 80.27 Lantapan 600 65.73 Dalwangan 896 10.65 Sumilao 600 9.98 Baungon 409 5.94 Biomass and Carbon Density Estimation Biomass and carbon density within the forest patches and banana agroecosystems were estimated following the sampling method described by Hairiah et al. ( 2011 ), which is also used commonly in the Philippines (e.g., Tulod 2015 ) (Fig. 2 ). Six (6) 5 m x 40 m or 200 m 2 sampling plots ( i.e. , three within the forest patches and three within the banana agroecosystems) were established in each study site. Sampling of standing plants (species name and dbh) with 5 cm to 30 cm diameter at breast height (dbh) was conducted within the 5 m x 40 m plot. If trees or plants with > 30 cm in dbh are present in the sampling plot, an additional larger plot of 20 m x 100 m (2,000 m 2 ) was established where all plants with dbh of > 30 cm were measured. The biomass and carbon density were computed using the following allometric equations: Y = exp{ -2.134 + 2.53*In*D} by Brown ( 1997 ) for native trees/shrubs and other tree species without available allometric equations Y = 0.153D 2.217 for Gmelina arborea by Kawahara et al. ( 1981 ) Y = 0.022D 2.920 for Swietennia macrophylla by Kawahara et al. ( 1981 ) Y = 0.071563D 1.96 H 0.74 for Leucaena leucocephala by Tandug (1986) Y = 0.0477D 2.6998 for Acacia mangium by Heriansyah ( 2005 ) Y = 0.0581D 2.523 for Tectona grandis by Buvaneswaran et al. ( 2006 ) Y = 10^[− 0.9836 + 1.8036∗log (D) + 0.8702∗log10 (H)] for Falcata falcataria by ERDB (2008) Y = 2.43 + 1.17*(D)-0.70*Ht for Bamboo by Rawat et al. ( 2018 ) Y = 0.0303D2.1345 for Banana or Musa spp. by Hairiah et al. ( 2001 ) Where: Y = tree biomass (kg/tree); D = diameter at breast height (cm) at 1.3 m; H t = Total height (m); ln = natural logarithm C Stored (Mg ha − 1 ) = Tree/Banana biomass density x C content Where: Tree Biomass Density = Tree biomass (Mg) /sample area in hectare; C content = A default value of 45% was used to determine the carbon stored in tree biomass, which is an average carbon content of wood samples collected from secondary forests from several locations in the Philippines (Lasco and Pulhin 2003 ). Biomass density of understorey vegetation was estimated using a destructive sampling technique in four 1 m × 1 m subplots randomly located within each 5 m × 40 m plot. The entire fresh sample of harvested biomass was first weighed on-site, after which around 300 grams were set aside as a sub-sample for later oven drying. The oven-dried weights of sub-samples were measured to calculate the total dry biomass. Samples were dried at 80°C until a constant weight was achieved. A portion of the dried plant material was then used for carbon content analysis. Litter layer was sampled in the 0.5 m x 0.5 m subplots on four random locations within the understorey sample plot. As with the understorey vegetation, approximately 300 grams of the litter were sub-sampled for oven drying and subsequent carbon content analysis. The carbon stored (Mg ha − 1 ) in the biomass was then computed using the following equations: C density (Mg ha − 1 ) = Total Dry Weight x C Content Root biomass was estimated as a function of the above-ground biomass following the equation below by Cairns et al. ( 1997 ). Root Biomass = Exp[− 1.0587 + 0.8836 * ln (AGB)] Where: ln = natural log; AGB = Aboveground biomass Root biomass carbon in woody plants was estimated by multiplying a carbon fraction of 45% to the root biomass. In the study of Lasco and Pulhin ( 2003 ), they estimated that tree root biomass has approximately 45% carbon; hence, they used a constant 45% to determine the carbon stored in root biomass. For banana plantations, actual root biomass samples were collected and subjected to laboratory analysis to determine their carbon content. Soil organic carbon (SOC) stocks in each stand were calculated using values for soil carbon concentration, bulk density, and the depth of sampling. Soil samples were obtained within the understorey plot using a 0.5 m × 0.5 m sampling grid. Bulk density measurements were based on samples extracted with a cylindrical metal core measuring 5 cm in diameter and 30 cm in height. For SOC analysis, approximately 1 kilogram of soil was collected from the 0–30 cm depth layer. Soil organic carbon was determined using the computations below: Carbon density (Mg ha − 1 ) = weight of soil * %SOC Where: Weight of soil (Mg) = bulk density * volume of 1 hectare Bulk density (g/cc) = Oven-dried weight of soil / Volume of canister Volume of canister = π r 2 h Volume of one ha = 100m * 100m * 0.30m Total C stored = C stored (Mg ha − 1 ) * area (ha) Annual C accumulation rates and CO 2 fixation The total amount of carbon stored in the forest patches and banana agroecosystems per site was estimated by multiplying the carbon stocks by the total area covered by the forest fragment or plantation. The accumulation rates of carbon were calculated by dividing the total amount of carbon by the respective age of the forest patch and banana agroecosystem. Because of the presence of several native species, we estimated the age of the forest fragments in the province to be 41 (for Dagumbaan, La Roxas, Dangcagan, Lilingayon, Adtuyon) and 42 (Lantapan, Dalwangan, Sumilao, Baungon) years old at the time of sampling in 2021 and 2022, respectively, as logging in Bukidnon, or in Mindanao more generally, was a profitable business only until 1980 (Tulod 2015 ). For banana agroecosystems, the average life span of 25 years for commercial plantations was considered, as specific establishment dates were unavailable, and measurements included re-sprouted individuals within each plot. The following equations (N’Gbala et al. 2017 ) were used to estimate the accumulation and fixation rates of carbon: Car = (CS x S)/Age (15) Where: Car = Carbon stock accumulation rate (Mg ha − 1 year − 1 ); CS = Carbon stock (total biomass C, Mg ha − 1 ); S = Area covered of the forest patch or banana plantation (ha); A = Age of land-use type. The potential of emission prevented by each land-use type was determined by using the following widely used equation of converting carbon stock into CO 2 equivalents (N’Gbala et al. 2017 ). SeqCO 2 = Cstock x 44/12 Where: SeqCO 2 = quantity of CO 2 potentially fixed in the biomass (MgCO 2 -eq. ha − 1 yr − 1 ); Cstock = total carbon stock in each site; 44/12 = conversion factor of carbon stock into CO 2 . Data analysis Differences in carbon density in the different carbon pools of forest patches and banana agroecosystems were assessed using linear regression models. These models employed sites and tree or pseudo-stem density as predictor variables with adjustments made through log transformation for variables violating homoscedasticity and normality assumptions to improve variance stability and residual normality. The model significance was tested with F-tests, and site-specific differences were evaluated using post hoc analyses. The relationship between carbon density and plant density was further explored through linear and polynomial regression models, selecting the best-fit model based on the lowest Akaike’s Information Criteria (AIC) from the compareLM function. Similarly, differences in the total biomass carbon, soil carbon density, carbon accumulations rates, and CO 2 fixation rates between forest patches and banana plantations were assessed using linear mixed effects models (LMMs). To address violations of homoscedasticity and normality assumptions, log transformations of the dependent variables were applied prior to analysis to stabilize variance and improve the normality of residuals. When there were singularity fit issues with the LMMs, the Bayesian linear mixed-effects analysis was used. Model significance was tested with Wald chi-square tests, and site-specific effects were assessed through post hoc analyses. All statistical analyses were performed using the R software (R Core Team 2022 ). Results Biomass carbon distribution in the small forest patches and banana agroecosystems Biomass carbon distribution in the forest patches was mostly concentrated in standing trees, which had about 60–70% of the total carbon, followed by the root compartment (15–20%), and litter layer (10–15%), with the lowest percentages found in the understory vegetation (below 5%) (Fig. 3 ). In contrast, banana agroecosystems showed more carbon stored in the pseudo-stems and litter layer accounting for roughly 45–55% and 45–50% of the total carbon, respectively, with the root system holding the least or contributing less than 10% across sites. There was no understorey vegetation compartments in banana agroecosystems. Carbon storage of different carbon pools in forest patches and banana plantations Significant differences in carbon density (Mg ha − 1 ) among forest patch sampling sites were observed only in the litter layer ( F [8,17] = 4.821, P 0.05) (Fig. 4 ). There was a strong positive influence of tree density (trees ha − 1 ) on the carbon stocks (Mg ha − 1 ) of the various carbon pools in the forest patches including total biomass carbon (Fig. 5 ). In banana agroecosystems, significant differences in biomass carbon density (Mg ha − 1 ) in different carbon pools were observed among sampling sites ( P < 0.05), with the exception of soil carbon ( F [8,17] = 0.949, P = 0.5043) (Fig. 6 ). Among the sites, the Pangantucan site consistently showed higher carbon stocks across different carbon pools compared to other sites, which had relatively comparable carbon stocks. In contrast to forest patches, the influence of banana pseudo-stem density (stems ha⁻¹) was evident only on stem and root biomass carbon density (Mg ha⁻¹) (Fig. 7 ). No significant relationship was found between pseudo-stem density (stems ha⁻¹) and either litter biomass carbon (Mg ha⁻¹) ( F [1,17] = 1.189, P = 0.290) or soil carbon (Mg ha⁻¹) ( F [1,17] = 0.457, P = 0.508). Total mean biomass carbon and soil organic carbon between forest patches and banana agroecosystems Total mean biomass carbon (Mg ha − 1 ) differed significantly between forest patches and banana agroecosystems ( χ 2 [1] = 13.549, P < 0.001) (Fig. 8 ). Although forest patches occupy smaller areas within the agricultural landscape, they had higher mean biomass carbon across sites (98.57 ± 16.40 Mg ha⁻¹) with values ranging from 35.91 to 192.93 Mg ha⁻¹. In contrast, banana agroecosystems had a lower total mean biomass carbon of 40.65 ± 3.76 Mg ha⁻¹, with values ranging from 22.78 to 85.21 Mg ha⁻¹. Soil organic carbon (SOC) density in forest patches, which ranged from 45.45 to 95.33 Mg ha⁻¹ with an average of 64.84 Mg ha⁻¹, did not differ significantly from that in banana agroecosystems ( χ 2 [1] = 0.857, P = 0.355), where values ranged from 47.96 to 80.53 Mg ha⁻¹ with a mean of 59.80 Mg ha⁻¹ (Fig. 8 ). Carbon accumulation and CO 2 fixation rates between forest patches and banana plantations The carbon accumulation rate (Mg ha -1 yr -1 ) and CO₂ fixation rate (Mg CO₂-eq ha -1 yr -1 ) differed significantly between banana agroecosystems and forest patches (Fig. 9). Banana agroecosystems had a significantly higher mean carbon accumulation rate compared to forest patches (χ2 [1] = 5.829, P=0.016), with approximately 86.70 ± 24.92 Mg ha -1 yr -1 and 41.63 ± 16.31 Mg ha -1 yr -1 , respectively. On the other hand, the CO₂ fixation rate was significantly higher in forest patches (8.89 ± 1.48 Mg CO₂-eq ha -1 yr -1 ) than in banana agroecosystems (5.96 ± 1.48 Mg CO₂-eq ha -1 yr -1 ) (χ2 [1] = 4.186, P=0.041). Discussion The carbon storage potential of natural forest patches in agroecosystems is often overlooked in both global carbon budgets and national carbon accounting (Zomer et al. 2016 ), possibly due to the prevailing perception that agroecosystems deplete terrestrial carbon pools (Lal 2011 ). However, this study's findings suggest that forest patches within agroecosystems, when properly managed or allowed to regenerate, are crucial for restoring lost carbon pools – that would otherwise be depleted with complete conversion to monocultures, such as banana plantations. The forest patches examined in this study, despite their smaller size, demonstrated higher and more stable carbon storage potential compared to banana agroecosystems. The inherent lower lignin (a carbon-rich organic polymer) and higher water content in banana biomass compared to woody plants likely explain these discrepancies, as these properties can result in lower carbon storage in banana plantations. Unlike forest patches, bananas are harvested more frequently, which can prevent long-term carbon buildup and result in a more cyclic carbon storage pattern that may not contribute as effectively to long-term climate mitigation goals. However, the mean biomass carbon in the forest patches (98.57 ± 16.40 Mg ha⁻¹) is substantially lower than those reported in other secondary forests in the country, e.g. , 159.80 ± 52.45 Mg C ha − 1 in Mindanao (Tulod 2015 ) and 305.5 Mg C ha − 1 in Luzon (Lasco and Pulhin 2003 ), and comparable with other disturbed forest ecosystems elsewhere ( e.g. , Kauffman et al. 2009 ), suggesting that the forest fragments in this study are highly disturbed. Disturbances such as fragmentation can reduce tree density and size, and limit woody regeneration, which can diminish biomass carbon (Islam et al. 2017 , Kauffman et al. 2009 ). Forest patches stored carbon primarily in trees and roots, while banana agroecosystems concentrated carbon in faster-decomposing pseudo-stems and litter from crop residues used for mulching. The intensive nature of management practices in banana agroecosystems likely contributed to the absence of understorey vegetation compartments in this agroecosystem. On the other hand, the observed differences in forest litter carbon storage among sites could be due to differences in disturbances across the forest patches, which influence the variation in accumulation of litter on the forest floor (N’Gbala et al. 2017 ). For other carbon pools, the lack of differences in carbon density among forest patches may indicate a relatively uniform vegetation structure especially for trees across sites, as most are second-growth vegetation regenerating from past deforestation. Martínez-Sánchez et al. ( 2015 ) noted that stand structure can strongly influence carbon storage, suggesting that managing tree diameter could further enhance carbon storage in regenerating forests. However, further reductions in size of these forest patches due to agricultural expansion could diminish carbon storage capacity through edge effects and fragmentation (Ma et al. 2017 ). For banana ecosystems, the variations in carbon density among sites was likely due to differences in the size of land area covered ( cf. higher in Pangantucan compared to other sites), which likely contributed to its greater biomass accumulation and carbon storage compared to other sites. This aligns well with earlier findings that total biomass carbon storage is often positively related to the area covered by vegetation (Muluneh and Worku 2022 ). However, substantial variations in carbon stocks among sites may also reflect differences in historical land-use, specific site management practices, intensity of agricultural inputs, or previous disturbance levels (Jose and Bardhan 2012 ; Cardinael et al. 2015 ; Tumwebaze et al. 2021). For instance, sites subjected to prolonged intensive agricultural management might exhibit depleted carbon stocks compared to areas with more recent or less intensive cultivation histories (Lal 2011 ; Tumwebaze et al. 2021). Such differences are likely relevant to the banana agroecosystems in the present study, given that these plantations were established at different times and managed under varying intensities. We found evidence of the strong positive influence of tree density on the carbon stocks of the various carbon pools including total biomass carbon. These findings, however, contrast with Aryal et al. ( 2018 ), who reported negative relationships between biomass carbon and tree density. In our study, although the relationships were non-linear, all models generally indicated higher biomass carbon stocks at greater tree densities, but the relationships became negative when tree density exceeded 400 trees per hectare – perhaps an example of the crowding effect. This means that, at higher tree densities, particularly in such a limited spatial extents of forest patches in the study, the competition for resources outweighs the benefits of increased tree numbers, leading to decreased growth per tree and carbon storage capacity per hectare. In contrast, tree biomass density had no significant influence on the soil carbon density within forest patches which is consistent to earlier report about the uncertainties surrounding the influence of trees on soil organic carbon density (Upson et al. 2016 ). Soil organic carbon is a particularly stable and resilient carbon pool (against changes or disturbances) within ecosystems and has the longest residence time than other organic carbon pools (Lugo and Brown 1992 ). On the other hand, the influence of banana pseudo-stem density was evident only on stem and root biomass carbon density. This lack of correlation may be attributed to the higher water content in banana pseudo-stem biomass, which can lead to highly unstable plant dry biomass and result in variable contributions to carbon storage in litter and soil. Such discrepancy suggests that while the pseudo-stem contributes significant biomass, its inherent moisture content may limit its effectiveness in stabilizing and contributing to carbon storage in litter and soil. This result is surprising as litter biomass carbon stocks in this study were proportionately comparable to the carbon content of the pseudo-stem biomass. This is also likely due to slower decomposition rates of litter in banana plantations, possibly resulting from lower soil faunal activity in this intensively managed system. Nonetheless, the relationships between the pseudo-stem density and stem and root biomass carbon density were also non-linear with all models indicated higher biomass carbon stocks at greater banana pseudo-stem densities, although with no signs of the crowding effect. Meanwhile, similarity in soil organic carbon (SOC) density between forest patches and banana agroecosystems, suggests that banana agroecosystems in this study may be approaching SOC levels close to woody vegetation, although the observed SOC levels in banana agroecosystems might be from residual soil carbon inherited from prior land use or vegetation (Turner et al. 2013 ). However, it is important to note that soil carbon in banana agroecosystems in this study is only approximately 47% higher than the biomass carbon, while in forest patches, soil carbon is around 52% higher than their respective biomass carbon or around 10% less soil carbon in banana agroecosystems than the forest patches. These disparities, albeit modest, compared to the 40% reduction in soil carbon observed in other areas for banana plantations versus woodlands by Walker and Desanker ( 2004 ), may raise concerns regarding possible unsustainability of the current management practices in the banana agroecosystems in this study including potential issues on soil degradation or compaction, which could negatively affect soil carbon storage over time. In Africa, for instance, Magalhães et al. ( 2024 ) reported SOC losses of at least 37% from the upper 20 to 40-cm depth layer following forest conversion to banana plantation and cited the complete removal of the arboreal component and crop residues, the erodibility of the soils on the study area’s steep hillslopes, and the potential for banana plantations to increase throughfall kinetic energy, and splash erosion through canopy dripping as the leading causes of SOC losses. Although soil organic carbon has a longer residence time than other carbon pools, it is expected to decline during land-use changes, particularly along the forest-agroforest-agriculture-pasture continuum (Chatterjee et al. 2018), if sustainable soil conservation practices are not implemented. Moreover, our findings showed significant differences in how forest patches and banana agroecosystems process and store carbon, which are critical in the broader landscape for their distinct ecological functions and contributions to carbon cycling. Banana agroecosystems had more than 100% higher mean carbon accumulation rate compared to forest patches, which can be attributed to the rapid growth rates and substantial biomass accumulation of banana plants. The extensive areas covered by banana agroecosystems in this study also likely enhanced their carbon accumulation capabilities. However, it is important to note that the carbon stored in banana agroecosystems is relatively short-lived, as bananas are typically harvested within one to two years, unlike forest patches that consist of longer-living woody biomass capable of storing carbon over extended periods, especially when protected. In contrast, the nearly 50% higher CO₂ fixation rate observed in forest patches suggests their potential as long-term carbon sinks, despite possible indications of degradation or disturbance from adjacent agricultural activities. This higher fixation rate is likely due to the presence of trees and woody perennials, which are known to sequester more CO₂ over time compared to shorter-lived vegetation types (Kraenzel et al. 2003 ). In the context of the Philippines, agroforestry strategies such as boundary plantings, alley cropping, and maintenance of native tree species within agricultural landscapes were particularly effective in promoting both carbon storage and sustainable productivity (Lasco et al. 2014 ). Conclusions The findings support our hypothesis that by managing forest patches as permanent features within agroecosystems, and considering these collectively as an agroforestry system, the combined effect would significantly increase carbon storage and potentially contribute to long-term carbon offsets for the agricultural sector. Small forest patches in the study demonstrated significant carbon storage potential with biomass carbon stocks substantially higher than those of banana agroecosystems. The necessity of protecting these forest patches from reduction and promoting their expansion is clear, as larger patches can sustain higher tree densities without the negative impacts of crowding effect, thus boosting carbon storage capacity. Moreover, while soil organic carbon levels were similar between the two systems, forest patches demonstrated superior CO₂ fixation rates, which indicate their crucial role in long-term carbon sequestration. Thus, policies should prioritize the conservation and restoration of forest patches as permanent features within agricultural landscapes to optimize carbon storage and mitigate ecological degradation. Declarations Author contributions AMT, ENB, AGTB and LGA contributed to the design and implementation of the study, AMT to the analysis of the results and the initial writing of the manuscript. Funding The study was supported by Central Mindanao University. Data availability No datasets were generated or analysed during the current study. Conflict of interest The authors declare no competing interests. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institution. References Aryal S, Shrestha S, Maraseni T, Wagle P, Gaire N (2018) Carbon stock and its relationships with tree diversity and density in community forests in Nepal. International Forestry Review 20(3):263–273 Brown S (1997) Estimating biomass and biomass change of tropical forests: A Primer (Volume 134). FAO Forestry paper. Food and Agriculture Organization of the United Nations, Rome, Italy. 58p Bruun TB, Ryan CM, De Neergaard A, Berry NJ (2021) Soil organic carbon stocks maintained despite intensification of shifting cultivation. Geoderma 388:114804 Buvaneswaran C, George M, Perez D, Kanninen M (2006) Biomass of teak plantations in Tamil Nadu, India and Costa Rica compared. 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Heriansyah I (2005 Potensi hutan tanaman industri dalam mensequester karbon: Studi kasus di hutan tanaman akasia dan pinus. Inovasi Online 3(17):1–12 Islam M, Deb GP, Rahman M (2017) Forest fragmentation reduced carbon storage in a moist tropical forest in Bangladesh: Implications for policy development. Land Use Policy 65:15–25 Jose S, Bardhan S (2012) Agroforestry for biomass production and carbon sequestration: an overview. Agroforestry Systems 86:105–111 Kauffman JB, Hughes RF, Heider C (2009) Carbon pool and biomass dynamics associated with deforestation, land use, and agricultural abandonment in the neotropics. Ecological Applications 19(5):1211-1222 Kawahara T, Kanazawa Y, Sakurai S (1981) Biomass and net production of man-made forests in the Philippines. Journal of the Japanese Forestry Society 63(9):320–327 Kraenzel M, Castillo A, Moore T, Potvin C (2003) Carbon storage of harvest-age teak ( Tectona grandis ) plantations, Panama. Forest Ecology and Management 173(1-3): 213–225 Lal R (2011) Sequestering carbon in soils of agro-ecosystems. Food policy 36: S33–S39 Lasco RD, Delfino RJP, Espaldon MLO (2014) Agroforestry systems: helping smallholders adapt to climate risks while mitigating climate change. Wiley Interdisciplinary Reviews: Climate Change 5(6):825-833 Lasco R, Pulhin F (1998) Philippine forestry and CO 2 sequestration: opportunities for mitigating climate change. Environmental Forestry Programme (ENFOR), UPLB College of Forestry and Natural Resources, Laguna, Philippines. 24p. Lasco RD, Pulhin FB (2003) Philippine forest ecosystems and climate change: carbon stocks, rate of sequestration and the Kyoto Protocol. Annals of Tropical Research 25(2):37–52 Lugo AE, Brown S (1992) Tropical forests as sinks of atmospheric carbon. Forest Ecology and Management 54(1-4):239–255 Ma L, Shen C, Lou D, Fu S, Guan D (2017) Ecosystem carbon storage in forest fragments of differing patch size. Scientific reports 7(1):13173 Magalhães TM, Cossa ERB, Nhanombe HE, Mugabe ADM (2024) Montane evergreen forest deforestation for banana plantations decreased soil organic carbon and total nitrogen stores to alarming levels. Carbon Balance and Management 19(1):28 Martínez-Sánchez JL, Tigar BJ, Cámara L, Castillo O (2015) Relationship between structural diversity and carbon stocks in humid and sub-humid tropical forest of Mexico. Ecoscience 22(2-4):125–131 Muluneh MG, Worku BB (2022) Carbon storages and sequestration potentials in remnant forests of different patch sizes in northern Ethiopia: an implication for climate change mitigation. Agriculture & Food Security 11(1):57 N’Gbala FNG, Guéi AM, Tondoh JE (2017) Carbon stocks in selected tree plantations, as compared with semi-deciduous forests in centre-west Côte d’Ivoire. Agriculture, Ecosystems & Environment 239:30–37 Potapov P, Turubanova S, Hansen MC, Tyukavina A, Zalles V, Khan A, Song X-P, Pickens A, Shen Q, Cortez J (2022) Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food 3(1):19–28 R Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Rawat R, Arora G, Rawat V, Borah HR, Singson MZ, Chandra G, Nautiyal R, Rawat J (2018) Estimation of biomass and carbon stock of bamboo species through development of allometric equations. Indian Council of Forestry Research and Education. 34p. Tulod AM (2015) Carbon stocks of second growth forest and reforestation stands in Southern Philippines: baseline for carbon sequestration monitoring. Advances in Environmental Sciences 7(3):422–431. Tumwebaze SB, Bevilacqua E, Briggs R, Volk T (2012) Soil organic carbon under a linear simultaneous agroforestry system in Uganda. Agroforestry systems 84:11-23 Turner MG, Donato DC, Romme WH (2013) Consequences of spatial heterogeneity for ecosystem services in changing forest landscapes: priorities for future research. Landscape ecology 28(6):1081–1097 Upson MA, Burgess PJ, Morison J (2016) Soil carbon changes after establishing woodland and agroforestry trees in a grazed pasture. Geoderma 283:10–20 Vermeulen SJ, Campbell BM, Ingram JS (2012) Climate change and food systems. Annual Review of Environment and Resources 37:195–222 Walker SM, Desanker PV (2004) The impact of land use on soil carbon in Miombo Woodlands of Malawi. Forest Ecology and Management 203(1-3):345–360 Zomer RJ, Neufeldt H, Xu J, Ahrends A, Bossio D, Trabucco A, Van Noordwijk M, Wang M (2016) Global Tree Cover and Biomass Carbon on Agricultural Land: The contribution of agroforestry to global and national carbon budgets. Scientific reports 6(1):29987. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6299097","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445128948,"identity":"d2934c06-b40f-4c47-9c3c-567c135d75f8","order_by":0,"name":"Adrian M. Tulod","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYLCCBwzMciD6wAPi1DMzMCQwMBuDtSSQoiWxAcQmSgv/tPPHJBIqrNPnhx1+CLTFTk63gYAWidvJbBIJZ9JzN95OMwBqSTY2O0DIGpCWxLbDuRtnJ4C0HEjcRkiLPFRLuuHs9A/EaTGAakmQl84h0hbD28nGFkC/GG6Qzik4kGBAhF/kbic+vPGhwlpefnb65g8fKuzkCHsf7kKwSgNilYOAfAMpqkfBKBgFo2BEAQBtn0WUnggiTwAAAABJRU5ErkJggg==","orcid":"","institution":"University of the Philippines Los Baños","correspondingAuthor":true,"prefix":"","firstName":"Adrian","middleName":"M.","lastName":"Tulod","suffix":""},{"id":445128949,"identity":"7a2e237c-4328-4e56-abbf-0c41ad4bf20e","order_by":1,"name":"Eric N. Bruno","email":"","orcid":"","institution":"Central Mindanao University","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"N.","lastName":"Bruno","suffix":""},{"id":445128950,"identity":"3d8954d0-7283-47a7-a28f-75f4960ef1d9","order_by":2,"name":"Angela Grace Toledo-Bruno","email":"","orcid":"","institution":"Central Mindanao University","correspondingAuthor":false,"prefix":"","firstName":"Angela","middleName":"Grace","lastName":"Toledo-Bruno","suffix":""},{"id":445128951,"identity":"c6c4d0d7-5cd5-461f-ad88-3f80118411d6","order_by":3,"name":"Lowell G. Aribal","email":"","orcid":"","institution":"Central Mindanao University","correspondingAuthor":false,"prefix":"","firstName":"Lowell","middleName":"G.","lastName":"Aribal","suffix":""}],"badges":[],"createdAt":"2025-03-25 02:08:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6299097/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6299097/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81144889,"identity":"b6437c35-b9a4-4f0b-b481-7e232d198c43","added_by":"auto","created_at":"2025-04-22 17:42:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":500534,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of study sites in the Province of Bukidnon, Mindanao, Philippines.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6299097/v1/07911e1e7ba1cca4140adf05.png"},{"id":81144224,"identity":"c8946ede-900f-4af7-810f-1362db047596","added_by":"auto","created_at":"2025-04-22 17:34:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25950,"visible":true,"origin":"","legend":"\u003cp\u003eSampling plot design for sampling the different biomass carbon pools in the forest patches and banana agroecosystems.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6299097/v1/e7b470c454161466891df584.png"},{"id":81144225,"identity":"609c4e32-8452-4096-a02a-47364b23ec58","added_by":"auto","created_at":"2025-04-22 17:34:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46199,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of biomass carbon stock among carbon pools within forest patches (top) and banana plantations (bottom).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6299097/v1/2e43bee3aad1e318c373fabc.png"},{"id":81144227,"identity":"10c3ec3c-2fa5-4bd8-b2bb-76bfc16bf89d","added_by":"auto","created_at":"2025-04-22 17:34:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":103134,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon storage of different carbon pools in forest patches (untransformed values shown, except for understorey carbon, which was not log-transformed for analysis). Significant effects are indicated by letters, with box-and-whisker plots marked by different letters indicating significant differences at \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05. The black colored point inside the box-and-whisker plot represents the mean value.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6299097/v1/c3493cecc3e491f99eea3a75.png"},{"id":81144229,"identity":"d38ceec2-0147-4f2b-9602-1bd192d69004","added_by":"auto","created_at":"2025-04-22 17:34:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90167,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between tree density (trees ha\u003csup\u003e-1\u003c/sup\u003e) and carbon stock density (Mg ha\u003csup\u003e-1\u003c/sup\u003e) of different carbon pools in forest patches. Only significant relationships are shown. The shaded region around the regression line is the confidence interval (0.95).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6299097/v1/cbc6b1a4face6811d78cc531.png"},{"id":81144230,"identity":"3634582d-29c6-4e1e-8417-bcf13049cb71","added_by":"auto","created_at":"2025-04-22 17:34:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":92320,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon storage of different carbon pools in banana plantations (untransformed values shown, except for root biomass and soil carbon, which were not log-transformed for analysis). Significant effects are indicated by letters, with box-and-whisker plots marked by different letters indicating significant differences at \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05. The black colored point inside the box-and-whisker plot represents the mean value.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6299097/v1/491f576d94a4e527b3a3fe89.png"},{"id":81144231,"identity":"bf3acfa2-1bc3-4ef4-b35e-2398ea5b0f2f","added_by":"auto","created_at":"2025-04-22 17:34:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":65121,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between pseudo-stem density (stem ha\u003csup\u003e-1\u003c/sup\u003e) and carbon stock density (Mg ha\u003csup\u003e-1\u003c/sup\u003e) of different carbon pools in banana plantations. Only significant relationships are shown. The shaded region around the regression line is the confidence interval (0.95).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6299097/v1/00b2112345b62f34c105a076.png"},{"id":81145074,"identity":"634a7da7-77dc-40de-9014-fb7718b48644","added_by":"auto","created_at":"2025-04-22 17:50:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":34204,"visible":true,"origin":"","legend":"\u003cp\u003eTotal biomass carbon and soil organic carbon between forest patches (Mg ha\u003csup\u003e-1\u003c/sup\u003e) and banana agroecosystems (Mg ha\u003csup\u003e-1\u003c/sup\u003e). Significant effects are indicated by letters, with box-and-whisker plots marked by different letters indicating significant differences at \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05. The black colored point inside the box-and-whisker plot represents the mean value.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6299097/v1/8a641799648c6d73f7ea2ab1.png"},{"id":81144242,"identity":"77cb6a70-f988-4808-853e-3c2470bdaf16","added_by":"auto","created_at":"2025-04-22 17:34:04","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":33120,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon accumulation rate (untransformed values shown) and CO\u003csub\u003e2\u003c/sub\u003e fixation rate between forest patches (Mg ha\u003csup\u003e-1\u003c/sup\u003e) and banana agroecosystems (Mg ha\u003csup\u003e-1\u003c/sup\u003e). Significant effects are indicated by letters, with box-and-whisker plots marked by different letters indicating significant differences at \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05. The black colored point inside the box-and-whisker plot represents the mean value.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6299097/v1/9397abbf324cc9869022c554.png"},{"id":86752900,"identity":"52bc1ea8-883a-47fa-9cda-ccd042135d3d","added_by":"auto","created_at":"2025-07-15 08:54:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1560486,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6299097/v1/912055ca-ef6a-43cc-9901-85cb5c6bb022.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Carbon Storage Potential of Integrated Forest Patches and Banana (Musa spp.) Agroecosystems in the Agricultural Landscape of Mindanao, Philippines","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAgricultural expansion and land-use changes, particularly in tropical regions, have increasingly degraded and fragmented forest ecosystems, threatening biodiversity and reducing carbon conservation potential. The Food and Agriculture Organization (FAO) reported that over 80% of global deforestation is attributed to agricultural development and expansion including crop production and livestock grazing (FAO \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Between 2003 and 2019, global cropland expanded by 9%, with nearly half of this growth came at the expense of natural habitats (Potapov et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Global food systems contribute about 30% of anthropogenic greenhouse gas emissions, releasing an estimated 9,800\u0026ndash;16,900 megatonnes of carbon dioxide equivalent (MtCO2e) annually, with crop production responsible for about 80% of these emissions (Vermeulen et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). As the global population is projected to peak at around 10\u0026nbsp;billion by the end of this century, emissions from agricultural expansions are expected to rise, particularly in tropical developing nations. This growth will further impact natural habitats in agricultural landscapes, and degrade the ecosystem services they provide, including carbon storage.\u003c/p\u003e \u003cp\u003eIn the Philippines, past deforestation and expansion of croplands, have led to the loss of at least 100,000 hectares of natural forest per year, resulting in an 8.8\u0026nbsp;million-ton carbon emission annually (Lasco and Pulhin \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). However, current forest patches within agricultural landscapes in the country - often remnants of secondary growth from past deforestation, which may have lower carbon densities than primary forests - are increasingly at risk of being lost due to agricultural expansion. Previous studies reported that as these patches are reduced in size due to agricultural expansion, their capacity to store carbon diminishes due to edge effects and fragmentation (Ma et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite this, carbon storage potential of natural forest patches in agroecosystems remains poorly documented and often overlooked in both global carbon budgets and national carbon accounting (Zomer et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), possibly due to the common perception that agroecosystems deplete terrestrial carbon pools (Lal \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, if properly managed or left to regenerate, forest patches within agroecosystems are crucial for restoring lost carbon pools and other ecosystem services that would otherwise be depleted with complete conversion to monocultures.\u003c/p\u003e \u003cp\u003eIn areas where agriculture dominates the economy, agricultural ecosystems or agroecosystems proliferate. For instance, banana production is one of the dominant agricultural landscapes in the Southern Philippines. These banana plantations are significant contributors to the local economy in terms of both production volumes and export earnings and play a vital role as a source of rural employment in the Philippines. However, previous studies have shown mixed results on how such agricultural lands affect terrestrial carbon including soil organic carbon \u0026ndash; some reported depletion (Magalh\u0026atilde;es et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while others observe minimal changes despite agriculture intensification (Bruun et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These conflicting findings suggest that site-specific management practices may be necessary.\u003c/p\u003e \u003cp\u003eThis paper reports on a case study on the carbon stock and accumulation potential of small forest patches and banana (\u003cem\u003eMusa\u003c/em\u003e spp.) agroecosystems in Southern Philippines. We propose that when forest patches are managed as integral elements within agricultural matrices in an agroforestry context, the combined effect can significantly increase carbon storage and could potentially contribute to long-term carbon offsets for the agricultural sector. Understanding carbon storage potential of these integrated land-use systems is essential for developing strategies that optimize the ecological benefits and ecosystem services. The study is expected to fill a gap in current carbon accounting systems, which often overlook forest patches within agricultural settings. The specific objectives of this study were to estimate their carbon stocks in different carbon pools (\u003cem\u003ee.g.\u003c/em\u003e, stem, understorey vegetation, litter, roots, and soil) and assess their carbon accumulation and fixation rates.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eSampling Sites\u003c/p\u003e \u003cp\u003eThe study was conducted in nine locations within forest patches adjacent to banana agroecosystems in Bukidnon Province, Southern Philippines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These forest patches, situated within the province\u0026rsquo;s agricultural landscape and along the edges of banana agroecosystems, represent narrow strips of second-growth forest regenerating from past deforestation, with a diverse mix of native and exotic species (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The dominant agroecosystems cultivated with bananas (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) are large-scale, export-oriented, and privately owned, primarily cultivating the Cavendish variety for fresh export. These sampling sites were specifically selected to reflect direct proximity and potential ecological interactions between forest patches and intensive banana agroecosystems.\u003c/p\u003e \u003cp\u003eThe province belongs to a Type IV climate under the modified Coronas classification, distinguished by the absence of defined dry season and a relatively consistent rainfall pattern throughout the year. On average, Bukidnon receives about 2,800 millimeters of rainfall annually with temperature typically fluctuate between 20\u0026deg;C and 34\u0026deg;C, while relative humidity remains persistently high, ranging from 90.86\u0026ndash;92.85%. These environmental conditions\u0026mdash;along with its topographic complexity\u0026mdash;make Bukidnon a vital ecological zone, which supports both agricultural productivity and diverse forest ecosystems.\u003c/p\u003e \u003cp\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\u003eDescription of nine forest patches covered in the study. The sites indicate the name of the Town in the Province of Bukidnon, Mindanao, Philippines.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest patch cover\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePangantucan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExotic-dominated secondary forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixed secondary forest characterized by a few dominant exotic tree species, particularly \u003cem\u003eGmelina arborea\u003c/em\u003e, \u003cem\u003eSenna spectabilis\u003c/em\u003e, \u003cem\u003eLeucaena leucocephala\u003c/em\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDagumbaan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBamboo-dominated secondary forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixed secondary forest dominated by \u003cem\u003eDendrocalamus asper\u003c/em\u003e and other native trees like \u003cem\u003eMelanolepis multiglandulosa\u003c/em\u003e and \u003cem\u003eArtocarpus heterophyllus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDangcagan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePiper aduncum-\u003c/em\u003edominated \u003cem\u003ee\u003c/em\u003early successional forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly successional forest characterized by a dense proliferation of the invasive exotic \u003cem\u003ePiper aduncum\u003c/em\u003e, alongside pioneers like \u003cem\u003eTrema orientalis\u003c/em\u003e and \u003cem\u003ePolyscias nodosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLa Roxas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed pioneer forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTransitional regenerating forest, characterized by a dense presence of pioneer species such as \u003cem\u003eLeucosyke capitellata\u003c/em\u003e, \u003cem\u003eFicus septica\u003c/em\u003e, and \u003cem\u003eSpathodea campanulata\u003c/em\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValencia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGmelina-dominated mixed secondary forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGmelina arborea\u003c/em\u003e-dominated mixed secondary forest, with scattered agroforestry species, bamboo, and pockets of native regeneration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLantapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGuioa koelreuteria-\u003c/em\u003edominated secondary forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeatures a mix of evergreen and deciduous native tree species but dominated by \u003cem\u003eGuioa koelreuteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDalwangan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFalcataria falcata\u003c/em\u003e-dominated secondary forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary growth forest dominated by fast-growing \u003cem\u003eFalcataria falcata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSumilao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAcacia mangium\u003c/em\u003e-dominated regenerating forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMix of native and introduced species dominated by \u003cem\u003eAcacia mangium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaungon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegenerating forest dominated by \u003cem\u003eArtocarpus\u003c/em\u003e and \u003cem\u003eFicus\u003c/em\u003e species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegenerating forest characterized by the dominance of \u003cem\u003eArtocarpus\u003c/em\u003e spp. and various \u003cem\u003eFicus\u003c/em\u003e species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\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\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\u003eDescription of banana agroecosystems covered in the study. The sites indicate the name of the Town in the Province of Bukidnon, Mindanao, Philippines.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePangantucan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDagumbaan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDangcagan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLa Roxas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValencia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLantapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDalwangan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSumilao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaungon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.94\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\u003eBiomass and Carbon Density Estimation\u003c/p\u003e \u003cp\u003eBiomass and carbon density within the forest patches and banana agroecosystems were estimated following the sampling method described by Hairiah et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which is also used commonly in the Philippines (e.g., Tulod \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSix (6) 5 m x 40 m or 200 m\u003csup\u003e2\u003c/sup\u003e sampling plots (\u003cem\u003ei.e.\u003c/em\u003e, three within the forest patches and three within the banana agroecosystems) were established in each study site. Sampling of standing plants (species name and dbh) with 5 cm to 30 cm diameter at breast height (dbh) was conducted within the 5 m x 40 m plot. If trees or plants with \u0026gt;\u0026thinsp;30 cm in dbh are present in the sampling plot, an additional larger plot of 20 m x 100 m (2,000 m\u003csup\u003e2\u003c/sup\u003e) was established where all plants with dbh of \u0026gt;\u0026thinsp;30 cm were measured. The biomass and carbon density were computed using the following allometric equations:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;exp{ -2.134\u0026thinsp;+\u0026thinsp;2.53*In*D} by Brown (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) for native trees/shrubs and other tree species without available allometric equations\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.153D\u003csup\u003e2.217\u003c/sup\u003e for \u003cem\u003eGmelina arborea\u003c/em\u003e by Kawahara et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.022D\u003csup\u003e2.920\u003c/sup\u003e for \u003cem\u003eSwietennia macrophylla\u003c/em\u003e by Kawahara et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.071563D\u003csup\u003e1.96\u003c/sup\u003eH\u003csup\u003e0.74\u003c/sup\u003e for \u003cem\u003eLeucaena leucocephala\u003c/em\u003e by Tandug (1986)\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.0477D\u003csup\u003e2.6998\u003c/sup\u003e for \u003cem\u003eAcacia mangium\u003c/em\u003e by Heriansyah (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.0581D\u003csup\u003e2.523\u003c/sup\u003e for \u003cem\u003eTectona grandis\u003c/em\u003e by Buvaneswaran et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;10^[\u0026minus;\u0026thinsp;0.9836\u0026thinsp;+\u0026thinsp;1.8036\u0026lowast;log (D)\u0026thinsp;+\u0026thinsp;0.8702\u0026lowast;log10 (H)] for \u003cem\u003eFalcata falcataria\u003c/em\u003e by ERDB (2008)\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;2.43\u0026thinsp;+\u0026thinsp;1.17*(D)-0.70*Ht for Bamboo by Rawat et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.0303D2.1345 for Banana or \u003cem\u003eMusa\u003c/em\u003e spp. by Hairiah et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWhere: Y\u0026thinsp;=\u0026thinsp;tree biomass (kg/tree); D\u0026thinsp;=\u0026thinsp;diameter at breast height (cm) at 1.3 m; H\u003csub\u003et\u003c/sub\u003e = Total height (m); ln\u0026thinsp;=\u0026thinsp;natural logarithm\u003c/p\u003e \u003cp\u003eC Stored (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;Tree/Banana biomass density x C content\u003c/p\u003e \u003cp\u003eWhere: Tree Biomass Density\u0026thinsp;=\u0026thinsp;Tree biomass (Mg) /sample area in hectare; C content\u0026thinsp;=\u0026thinsp;A default value of 45% was used to determine the carbon stored in tree biomass, which is an average carbon content of wood samples collected from secondary forests from several locations in the Philippines (Lasco and Pulhin \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBiomass density of understorey vegetation was estimated using a destructive sampling technique in four 1 m \u0026times; 1 m subplots randomly located within each 5 m \u0026times; 40 m plot. The entire fresh sample of harvested biomass was first weighed on-site, after which around 300 grams were set aside as a sub-sample for later oven drying. The oven-dried weights of sub-samples were measured to calculate the total dry biomass. Samples were dried at 80\u0026deg;C until a constant weight was achieved. A portion of the dried plant material was then used for carbon content analysis. Litter layer was sampled in the 0.5 m x 0.5 m subplots on four random locations within the understorey sample plot. As with the understorey vegetation, approximately 300 grams of the litter were sub-sampled for oven drying and subsequent carbon content analysis. The carbon stored (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in the biomass was then computed using the following equations:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"40\" width=\"480\"\u003e\u003c/p\u003e\u003cp\u003eC density (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;Total Dry Weight x C Content\u003c/p\u003e \u003cp\u003eRoot biomass was estimated as a function of the above-ground biomass following the equation below by Cairns et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRoot Biomass\u0026thinsp;=\u0026thinsp;Exp[\u0026minus;\u0026thinsp;1.0587\u0026thinsp;+\u0026thinsp;0.8836 * ln (AGB)]\u003c/p\u003e \u003cp\u003eWhere: ln\u0026thinsp;=\u0026thinsp;natural log; AGB\u0026thinsp;=\u0026thinsp;Aboveground biomass\u003c/p\u003e \u003cp\u003eRoot biomass carbon in woody plants was estimated by multiplying a carbon fraction of 45% to the root biomass. In the study of Lasco and Pulhin (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), they estimated that tree root biomass has approximately 45% carbon; hence, they used a constant 45% to determine the carbon stored in root biomass. For banana plantations, actual root biomass samples were collected and subjected to laboratory analysis to determine their carbon content.\u003c/p\u003e \u003cp\u003eSoil organic carbon (SOC) stocks in each stand were calculated using values for soil carbon concentration, bulk density, and the depth of sampling. Soil samples were obtained within the understorey plot using a 0.5 m \u0026times; 0.5 m sampling grid. Bulk density measurements were based on samples extracted with a cylindrical metal core measuring 5 cm in diameter and 30 cm in height. For SOC analysis, approximately 1 kilogram of soil was collected from the 0\u0026ndash;30 cm depth layer. Soil organic carbon was determined using the computations below:\u003c/p\u003e \u003cp\u003eCarbon density (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;weight of soil * %SOC\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eWeight of soil (Mg)\u0026thinsp;=\u0026thinsp;bulk density * volume of 1 hectare\u003c/p\u003e \u003cp\u003eBulk density (g/cc)\u0026thinsp;=\u0026thinsp;Oven-dried weight of soil / Volume of canister\u003c/p\u003e \u003cp\u003eVolume of canister\u0026thinsp;=\u0026thinsp;π r\u003csup\u003e2\u003c/sup\u003e h\u003c/p\u003e \u003cp\u003eVolume of one ha\u0026thinsp;=\u0026thinsp;100m * 100m * 0.30m\u003c/p\u003e \u003cp\u003eTotal C stored\u0026thinsp;=\u0026thinsp;C stored (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) * area (ha)\u003c/p\u003e \u003cp\u003eAnnual C accumulation rates and CO\u003csub\u003e2\u003c/sub\u003e fixation\u003c/p\u003e \u003cp\u003eThe total amount of carbon stored in the forest patches and banana agroecosystems per site was estimated by multiplying the carbon stocks by the total area covered by the forest fragment or plantation. The accumulation rates of carbon were calculated by dividing the total amount of carbon by the respective age of the forest patch and banana agroecosystem. Because of the presence of several native species, we estimated the age of the forest fragments in the province to be 41 (for Dagumbaan, La Roxas, Dangcagan, Lilingayon, Adtuyon) and 42 (Lantapan, Dalwangan, Sumilao, Baungon) years old at the time of sampling in 2021 and 2022, respectively, as logging in Bukidnon, or in Mindanao more generally, was a profitable business only until 1980 (Tulod \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For banana agroecosystems, the average life span of 25 years for commercial plantations was considered, as specific establishment dates were unavailable, and measurements included re-sprouted individuals within each plot.\u003c/p\u003e \u003cp\u003eThe following equations (N\u0026rsquo;Gbala et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were used to estimate the accumulation and fixation rates of carbon:\u003c/p\u003e \u003cp\u003eCar = (CS x S)/Age (15)\u003c/p\u003e \u003cp\u003eWhere: Car\u0026thinsp;=\u0026thinsp;Carbon stock accumulation rate (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); CS\u0026thinsp;=\u0026thinsp;Carbon stock (total biomass C, Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); S\u0026thinsp;=\u0026thinsp;Area covered of the forest patch or banana plantation (ha); A\u0026thinsp;=\u0026thinsp;Age of land-use type.\u003c/p\u003e \u003cp\u003eThe potential of emission prevented by each land-use type was determined by using the following widely used equation of converting carbon stock into CO\u003csub\u003e2\u003c/sub\u003e equivalents (N\u0026rsquo;Gbala et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeqCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Cstock x 44/12\u003c/p\u003e \u003cp\u003eWhere: SeqCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;quantity of CO\u003csub\u003e2\u003c/sub\u003e potentially fixed in the biomass (MgCO\u003csub\u003e2\u003c/sub\u003e-eq.\u0026nbsp;ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); Cstock\u0026thinsp;=\u0026thinsp;total carbon stock in each site; 44/12\u0026thinsp;=\u0026thinsp;conversion factor of carbon stock into CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eDifferences in carbon density in the different carbon pools of forest patches and banana agroecosystems were assessed using linear regression models. These models employed sites and tree or pseudo-stem density as predictor variables with adjustments made through log transformation for variables violating homoscedasticity and normality assumptions to improve variance stability and residual normality. The model significance was tested with F-tests, and site-specific differences were evaluated using post hoc analyses. The relationship between carbon density and plant density was further explored through linear and polynomial regression models, selecting the best-fit model based on the lowest Akaike\u0026rsquo;s Information Criteria (AIC) from the compareLM function.\u003c/p\u003e \u003cp\u003eSimilarly, differences in the total biomass carbon, soil carbon density, carbon accumulations rates, and CO\u003csub\u003e2\u003c/sub\u003e fixation rates between forest patches and banana plantations were assessed using linear mixed effects models (LMMs). To address violations of homoscedasticity and normality assumptions, log transformations of the dependent variables were applied prior to analysis to stabilize variance and improve the normality of residuals. When there were singularity fit issues with the LMMs, the Bayesian linear mixed-effects analysis was used. Model significance was tested with Wald chi-square tests, and site-specific effects were assessed through post hoc analyses. All statistical analyses were performed using the R software (R Core Team \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBiomass carbon distribution in the small forest patches and banana agroecosystems\u003c/p\u003e \u003cp\u003eBiomass carbon distribution in the forest patches was mostly concentrated in standing trees, which had about 60\u0026ndash;70% of the total carbon, followed by the root compartment (15\u0026ndash;20%), and litter layer (10\u0026ndash;15%), with the lowest percentages found in the understory vegetation (below 5%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, banana agroecosystems showed more carbon stored in the pseudo-stems and litter layer accounting for roughly 45\u0026ndash;55% and 45\u0026ndash;50% of the total carbon, respectively, with the root system holding the least or contributing less than 10% across sites. There was no understorey vegetation compartments in banana agroecosystems.\u003c/p\u003e\u003cp\u003eCarbon storage of different carbon pools in forest patches and banana plantations\u003c/p\u003e \u003cp\u003eSignificant differences in carbon density (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) among forest patch sampling sites were observed only in the litter layer (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e[8,17]\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.821, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.01), while other carbon pools (trees, understorey, roots, total biomass carbon, and soil) showed no significant variation among sites (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There was a strong positive influence of tree density (trees ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) on the carbon stocks (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of the various carbon pools in the forest patches including total biomass carbon (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In banana agroecosystems, significant differences in biomass carbon density (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in different carbon pools were observed among sampling sites (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the exception of soil carbon (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e[8,17]\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.949, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.5043) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among the sites, the Pangantucan site consistently showed higher carbon stocks across different carbon pools compared to other sites, which had relatively comparable carbon stocks.\u003c/p\u003e \u003cp\u003eIn contrast to forest patches, the influence of banana pseudo-stem density (stems ha⁻\u0026sup1;) was evident only on stem and root biomass carbon density (Mg ha⁻\u0026sup1;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). No significant relationship was found between pseudo-stem density (stems ha⁻\u0026sup1;) and either litter biomass carbon (Mg ha⁻\u0026sup1;) (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e[1,17]\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.189, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.290) or soil carbon (Mg ha⁻\u0026sup1;) (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e[1,17]\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.457, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.508).\u003c/p\u003e \u003cp\u003eTotal mean biomass carbon and soil organic carbon between forest patches and banana agroecosystems\u003c/p\u003e \u003cp\u003eTotal mean biomass carbon (Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) differed significantly between forest patches and banana agroecosystems (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e[1]\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;13.549, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Although forest patches occupy smaller areas within the agricultural landscape, they had higher mean biomass carbon across sites (98.57\u0026thinsp;\u0026plusmn;\u0026thinsp;16.40 Mg ha⁻\u0026sup1;) with values ranging from 35.91 to 192.93 Mg ha⁻\u0026sup1;. In contrast, banana agroecosystems had a lower total mean biomass carbon of 40.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76 Mg ha⁻\u0026sup1;, with values ranging from 22.78 to 85.21 Mg ha⁻\u0026sup1;.\u003c/p\u003e \u003cp\u003eSoil organic carbon (SOC) density in forest patches, which ranged from 45.45 to 95.33 Mg ha⁻\u0026sup1; with an average of 64.84 Mg ha⁻\u0026sup1;, did not differ significantly from that in banana agroecosystems (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003csub\u003e[1]\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.857, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.355), where values ranged from 47.96 to 80.53 Mg ha⁻\u0026sup1; with a mean of 59.80 Mg ha⁻\u0026sup1; (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCarbon accumulation and CO\u003csub\u003e2\u003c/sub\u003e fixation rates between forest patches and banana plantations\u003c/p\u003e\n\u003cp\u003eThe carbon accumulation rate (Mg ha\u003csup\u003e-1\u003c/sup\u003e yr\u003csup\u003e-1\u003c/sup\u003e) and CO₂ fixation rate (Mg CO₂-eq ha\u003csup\u003e-1\u003c/sup\u003e yr\u003csup\u003e-1\u003c/sup\u003e) differed significantly between banana agroecosystems and forest patches (Fig. 9). Banana agroecosystems had a significantly higher mean carbon accumulation rate compared to forest patches (\u0026chi;2\u003csub\u003e[1]\u003c/sub\u003e = 5.829, P=0.016), with approximately 86.70 \u0026plusmn; 24.92 Mg ha\u003csub\u003e-1\u003c/sub\u003e yr\u003csub\u003e-1\u003c/sub\u003e and 41.63 \u0026plusmn; 16.31 Mg ha\u003csup\u003e-1\u003c/sup\u003e yr\u003csup\u003e-1\u003c/sup\u003e, respectively. On the other hand, the CO₂ fixation rate was significantly higher in forest patches (8.89 \u0026plusmn; 1.48 Mg CO₂-eq ha\u003csup\u003e-1\u003c/sup\u003e yr\u003csup\u003e-1\u003c/sup\u003e) than in banana agroecosystems (5.96 \u0026plusmn; 1.48 Mg CO₂-eq ha\u003csup\u003e-1\u003c/sup\u003e yr\u003csup\u003e-1\u003c/sup\u003e) (\u0026chi;2\u003csub\u003e[1]\u003c/sub\u003e = 4.186, P=0.041).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe carbon storage potential of natural forest patches in agroecosystems is often overlooked in both global carbon budgets and national carbon accounting (Zomer et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), possibly due to the prevailing perception that agroecosystems deplete terrestrial carbon pools (Lal \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, this study's findings suggest that forest patches within agroecosystems, when properly managed or allowed to regenerate, are crucial for restoring lost carbon pools \u0026ndash; that would otherwise be depleted with complete conversion to monocultures, such as banana plantations.\u003c/p\u003e \u003cp\u003eThe forest patches examined in this study, despite their smaller size, demonstrated higher and more stable carbon storage potential compared to banana agroecosystems. The inherent lower lignin (a carbon-rich organic polymer) and higher water content in banana biomass compared to woody plants likely explain these discrepancies, as these properties can result in lower carbon storage in banana plantations. Unlike forest patches, bananas are harvested more frequently, which can prevent long-term carbon buildup and result in a more cyclic carbon storage pattern that may not contribute as effectively to long-term climate mitigation goals. However, the mean biomass carbon in the forest patches (98.57\u0026thinsp;\u0026plusmn;\u0026thinsp;16.40 Mg ha⁻\u0026sup1;) is substantially lower than those reported in other secondary forests in the country, \u003cem\u003ee.g.\u003c/em\u003e, 159.80\u0026thinsp;\u0026plusmn;\u0026thinsp;52.45 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in Mindanao (Tulod \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and 305.5 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in Luzon (Lasco and Pulhin \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and comparable with other disturbed forest ecosystems elsewhere (\u003cem\u003ee.g.\u003c/em\u003e, Kauffman et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), suggesting that the forest fragments in this study are highly disturbed. Disturbances such as fragmentation can reduce tree density and size, and limit woody regeneration, which can diminish biomass carbon (Islam et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Kauffman et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eForest patches stored carbon primarily in trees and roots, while banana agroecosystems concentrated carbon in faster-decomposing pseudo-stems and litter from crop residues used for mulching. The intensive nature of management practices in banana agroecosystems likely contributed to the absence of understorey vegetation compartments in this agroecosystem. On the other hand, the observed differences in forest litter carbon storage among sites could be due to differences in disturbances across the forest patches, which influence the variation in accumulation of litter on the forest floor (N\u0026rsquo;Gbala et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For other carbon pools, the lack of differences in carbon density among forest patches may indicate a relatively uniform vegetation structure especially for trees across sites, as most are second-growth vegetation regenerating from past deforestation. Mart\u0026iacute;nez-S\u0026aacute;nchez et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) noted that stand structure can strongly influence carbon storage, suggesting that managing tree diameter could further enhance carbon storage in regenerating forests. However, further reductions in size of these forest patches due to agricultural expansion could diminish carbon storage capacity through edge effects and fragmentation (Ma et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For banana ecosystems, the variations in carbon density among sites was likely due to differences in the size of land area covered (\u003cem\u003ecf.\u003c/em\u003e higher in Pangantucan compared to other sites), which likely contributed to its greater biomass accumulation and carbon storage compared to other sites. This aligns well with earlier findings that total biomass carbon storage is often positively related to the area covered by vegetation (Muluneh and Worku \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, substantial variations in carbon stocks among sites may also reflect differences in historical land-use, specific site management practices, intensity of agricultural inputs, or previous disturbance levels (Jose and Bardhan \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cardinael et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tumwebaze et al. 2021). For instance, sites subjected to prolonged intensive agricultural management might exhibit depleted carbon stocks compared to areas with more recent or less intensive cultivation histories (Lal \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tumwebaze et al. 2021). Such differences are likely relevant to the banana agroecosystems in the present study, given that these plantations were established at different times and managed under varying intensities.\u003c/p\u003e \u003cp\u003eWe found evidence of the strong positive influence of tree density on the carbon stocks of the various carbon pools including total biomass carbon. These findings, however, contrast with Aryal et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who reported negative relationships between biomass carbon and tree density. In our study, although the relationships were non-linear, all models generally indicated higher biomass carbon stocks at greater tree densities, but the relationships became negative when tree density exceeded 400 trees per hectare \u0026ndash; perhaps an example of the crowding effect. This means that, at higher tree densities, particularly in such a limited spatial extents of forest patches in the study, the competition for resources outweighs the benefits of increased tree numbers, leading to decreased growth per tree and carbon storage capacity per hectare.\u003c/p\u003e \u003cp\u003eIn contrast, tree biomass density had no significant influence on the soil carbon density within forest patches which is consistent to earlier report about the uncertainties surrounding the influence of trees on soil organic carbon density (Upson et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Soil organic carbon is a particularly stable and resilient carbon pool (against changes or disturbances) within ecosystems and has the longest residence time than other organic carbon pools (Lugo and Brown \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, the influence of banana pseudo-stem density was evident only on stem and root biomass carbon density. This lack of correlation may be attributed to the higher water content in banana pseudo-stem biomass, which can lead to highly unstable plant dry biomass and result in variable contributions to carbon storage in litter and soil. Such discrepancy suggests that while the pseudo-stem contributes significant biomass, its inherent moisture content may limit its effectiveness in stabilizing and contributing to carbon storage in litter and soil. This result is surprising as litter biomass carbon stocks in this study were proportionately comparable to the carbon content of the pseudo-stem biomass. This is also likely due to slower decomposition rates of litter in banana plantations, possibly resulting from lower soil faunal activity in this intensively managed system. Nonetheless, the relationships between the pseudo-stem density and stem and root biomass carbon density were also non-linear with all models indicated higher biomass carbon stocks at greater banana pseudo-stem densities, although with no signs of the crowding effect.\u003c/p\u003e \u003cp\u003eMeanwhile, similarity in soil organic carbon (SOC) density between forest patches and banana agroecosystems, suggests that banana agroecosystems in this study may be approaching SOC levels close to woody vegetation, although the observed SOC levels in banana agroecosystems might be from residual soil carbon inherited from prior land use or vegetation (Turner et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, it is important to note that soil carbon in banana agroecosystems in this study is only approximately 47% higher than the biomass carbon, while in forest patches, soil carbon is around 52% higher than their respective biomass carbon or around 10% less soil carbon in banana agroecosystems than the forest patches. These disparities, albeit modest, compared to the 40% reduction in soil carbon observed in other areas for banana plantations versus woodlands by Walker and Desanker (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), may raise concerns regarding possible unsustainability of the current management practices in the banana agroecosystems in this study including potential issues on soil degradation or compaction, which could negatively affect soil carbon storage over time. In Africa, for instance, Magalh\u0026atilde;es et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported SOC losses of at least 37% from the upper 20 to 40-cm depth layer following forest conversion to banana plantation and cited the complete removal of the arboreal component and crop residues, the erodibility of the soils on the study area\u0026rsquo;s steep hillslopes, and the potential for banana plantations to increase throughfall kinetic energy, and splash erosion through canopy dripping as the leading causes of SOC losses. Although soil organic carbon has a longer residence time than other carbon pools, it is expected to decline during land-use changes, particularly along the forest-agroforest-agriculture-pasture continuum (Chatterjee et al. 2018), if sustainable soil conservation practices are not implemented.\u003c/p\u003e \u003cp\u003eMoreover, our findings showed significant differences in how forest patches and banana agroecosystems process and store carbon, which are critical in the broader landscape for their distinct ecological functions and contributions to carbon cycling. Banana agroecosystems had more than 100% higher mean carbon accumulation rate compared to forest patches, which can be attributed to the rapid growth rates and substantial biomass accumulation of banana plants. The extensive areas covered by banana agroecosystems in this study also likely enhanced their carbon accumulation capabilities. However, it is important to note that the carbon stored in banana agroecosystems is relatively short-lived, as bananas are typically harvested within one to two years, unlike forest patches that consist of longer-living woody biomass capable of storing carbon over extended periods, especially when protected.\u003c/p\u003e \u003cp\u003eIn contrast, the nearly 50% higher CO₂ fixation rate observed in forest patches suggests their potential as long-term carbon sinks, despite possible indications of degradation or disturbance from adjacent agricultural activities. This higher fixation rate is likely due to the presence of trees and woody perennials, which are known to sequester more CO₂ over time compared to shorter-lived vegetation types (Kraenzel et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In the context of the Philippines, agroforestry strategies such as boundary plantings, alley cropping, and maintenance of native tree species within agricultural landscapes were particularly effective in promoting both carbon storage and sustainable productivity (Lasco et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe findings support our hypothesis that by managing forest patches as permanent features within agroecosystems, and considering these collectively as an agroforestry system, the combined effect would significantly increase carbon storage and potentially contribute to long-term carbon offsets for the agricultural sector. Small forest patches in the study demonstrated significant carbon storage potential with biomass carbon stocks substantially higher than those of banana agroecosystems. The necessity of protecting these forest patches from reduction and promoting their expansion is clear, as larger patches can sustain higher tree densities without the negative impacts of crowding effect, thus boosting carbon storage capacity. Moreover, while soil organic carbon levels were similar between the two systems, forest patches demonstrated superior CO₂ fixation rates, which indicate their crucial role in long-term carbon sequestration. Thus, policies should prioritize the conservation and restoration of forest patches as permanent features within agricultural landscapes to optimize carbon storage and mitigate ecological degradation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eAMT, ENB, AGTB and LGA contributed to the design and implementation of the study, AMT to the analysis of the results and the initial writing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e The study was supported by Central Mindanao University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e No datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e All procedures performed in studies involving human participants were in accordance with the ethical standards of the institution.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAryal S, Shrestha S, Maraseni T, Wagle P, Gaire N (2018) Carbon stock and its relationships with tree diversity and density in community forests in Nepal. 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Scientific reports\u003cem\u003e \u003c/em\u003e6(1):29987. \u003c/li\u003e\n\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":"Agricultural landscape, banana agroecosystems, carbon storage, carbon accumulation rates, carbon dioxide fixation rates, forest fragments","lastPublishedDoi":"10.21203/rs.3.rs-6299097/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6299097/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgroecosystems are known to cause high depletion of terrestrial carbon stocks, and their expansion is seen to reduce natural habitats within agricultural landscapes to smaller patches. Understanding the carbon storage potential of these systems is essential for optimizing ecological benefits and addressing gaps in carbon accounting, which often overlook forest patches in agricultural areas. This study quantified the carbon storage potential of small forest patches and banana agroecosystems within agricultural landscapes in Mindanao, Philippines. Results indicated that forest patches had significantly higher biomass carbon (60.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2 to 206.4\u0026thinsp;\u0026plusmn;\u0026thinsp;47.2 Mg ha⁻\u0026sup1;) than banana agroecosystems (22.78 to 85.21 Mg ha⁻\u0026sup1;). Carbon storage in forest patches was concentrated in trees (60\u0026ndash;70%) and roots (15\u0026ndash;20%), while in banana agroecosystems it was primarily in pseudo-stems (45\u0026ndash;55%) and litter layer (45\u0026ndash;50%). Soil organic carbon was comparable between forest patches (45.45\u0026ndash;95.33 Mg ha⁻\u0026sup1;) and banana agroecosystems (47.96 to 80.53 Mg ha⁻\u0026sup1;). The absence of understorey vegetation in banana agroecosystems reflects the impact of intensive management practices. Despite this, banana agroecosystems had higher carbon accumulation rates (86.70\u0026thinsp;\u0026plusmn;\u0026thinsp;24.92 Mg ha⁻\u0026sup1; yr⁻\u0026sup1;) than forest patches (41.63\u0026thinsp;\u0026plusmn;\u0026thinsp;16.31 Mg ha⁻\u0026sup1; yr⁻\u0026sup1;) but lower CO₂ fixation (5.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48 \u003cem\u003evs\u003c/em\u003e. 8.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48 Mg CO₂-eq ha⁻\u0026sup1; yr⁻\u0026sup1;). While the rapid growth rates of bananas drive their carbon accumulation, their short harvest cycles limit long-term storage, unlike the woody biomass in forest patches. These findings emphasize the need for agroforestry policies that promote integrated management of forest patches and agricultural lands for optimal carbon storage.\u003c/p\u003e","manuscriptTitle":"Carbon Storage Potential of Integrated Forest Patches and Banana (Musa spp.) Agroecosystems in the Agricultural Landscape of Mindanao, Philippines","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 17:33:59","doi":"10.21203/rs.3.rs-6299097/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":"c7ab06b6-6222-4038-8038-02b304174f31","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-15T08:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-22 17:33:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6299097","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6299097","identity":"rs-6299097","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}