The research, published in Discover Forests by Niamjit Das of Shahjalal University of Science and Technology and Md. Qumruzzaman Chowdhury, examined three of the most widely planted timber taxa in South and Southeast Asia: Acacia auriculiformis, Acacia mangium, and the interspecific hybrid between them. Working across ten plantation sites in Sylhet Division, a humid monsoon landscape receiving roughly 5,000 millimeters of rain each year, the team measured 1,050 trees spanning ages of 8 to 15 years and stand densities of 1,100 to 1,400 trees per hectare. Crucially, they did not stop at tape measurements. They felled 75 trees, 25 of each taxon, cut the stems into sections, and dried them in ovens at 105 degrees Celsius until their weight stopped changing, producing the kind of ground-truth biomass data that allometric modeling depends on.

The central finding is that stump diameter, measured just 30 centimeters above the ground, predicts breast-height diameter almost perfectly. A simple log-log regression achieved adjusted R-squared values exceeding 0.98 across all three taxa, with root mean square errors as low as 0.048. In practical terms, this means a forester walking through a recently harvested compartment can measure the remaining stumps and reconstruct the diameter distribution of the trees that once stood there with negligible error. The relationship makes intuitive biological sense: the base of the stem and the breast-height section are connected by the tree’s taper, and within a given species and growth environment that taper follows a consistent geometric pattern.

Biomass prediction proved nearly as strong. Stump-based models performed comparably to the conventional DBH-based equations, with the parsimonious log-log stump model reaching adjusted R-squared values between 0.945 and 0.968 for stem biomass. The authors were careful to emphasize validation statistics rather than goodness-of-fit alone, a discipline that forest scientists have repeatedly called for after decades of unreliable generic equations being applied across unrelated ecosystems. Observed and predicted values clustered tightly around the one-to-one line, paired t-tests found no significant bias, and residuals were balanced across the size spectrum. For the hybrid Acacia, mixed-effects models that accounted for random variation among species and sites reduced prediction bias even further.

The study also put machine learning to the test, and the results offer a cautionary tale for the current enthusiasm surrounding artificial intelligence in environmental science. Random Forest, an ensemble method that averages hundreds of decision trees, consistently identified stump diameter as the strongest predictor of stem biomass and produced stable, generalizable predictions under five-fold cross-validation. Gradient Boosting, by contrast, assigned the highest relative importance to DBH and showed a telltale 12 percent gap between training and validation accuracy, the classic signature of overfitting. The authors are refreshingly candid about what this means: stump diameter’s dominance in the Random Forest reflects its strong correlation with overall tree size, not some newly discovered biological law, and variable importance rankings can shift with model settings and data structure.

Converting biomass into carbon introduced a species-specific dimension that generic accounting often misses. Rather than applying the default IPCC carbon fraction, the researchers used conversion factors drawn from published Acacia studies: 0.475 for A. auriculiformis, 0.480 for A. mangium, and 0.485 for the hybrid, reflecting real differences in wood chemistry among the taxa. The resulting carbon stock estimates ranked the hybrid Acacia highest, followed by A. mangium and then A. auriculiformis, a pattern consistent with the hybrid’s reputation for superior growth and wood quality. The authors stress that these figures cover stem carbon only, excluding branches, foliage, and roots, and therefore should not be read as total ecosystem carbon stocks.

The methodological rigor underlying these numbers deserves attention. Measurements were restricted to the dry season to minimize moisture-related variability, instruments were calibrated daily, and any readings differing by more than 0.5 centimeters in diameter or 0.2 meters in height triggered re-measurement. Oven-drying continued for 48 to 72 hours with weights checked at 12-hour intervals, and data entry was cross-checked by two independent researchers. The validation design was two-tiered: 975 trees held back from model development served as an independent test set, and five-fold cross-validation stratified by species and site confirmed that the models were not merely memorizing the quirks of particular plantations. This is precisely the kind of quality assurance that earlier reviews of forest biomass estimation have found lacking in much of the published literature.

Why does this matter beyond Bangladesh? Tropical forests hold nearly a third of terrestrial carbon, and programs such as REDD+ depend on credible, verifiable measurements of how much carbon is stored and, crucially, how much is removed when trees are harvested. Under current practice, post-harvest carbon accounting is notoriously difficult because the trees that would provide the measurements are gone. Stump-based equations change that calculus entirely: the stumps remain in the ground, they are easy to locate and measure, and they now carry enough information to reconstruct the stand’s biomass and carbon content. The authors note that this capability could inform measurement, reporting, and verification frameworks under REDD+ and support IPCC Tier 2 and Tier 3 approaches, though they are careful to say that broader policy applications require independent validation.

The limitations are stated with unusual honesty. The study covered healthy trees in even-aged Acacia plantations on fertile alluvial loams in a single region; damaged or suppressed trees were excluded, which may limit applicability in operational forestry where such trees are common. Buttressed stems and mixed-species stands remain challenging, and the models were calibrated on trees aged 8 to 15 years, so extrapolation to older rotations or different environments is untested. The improvements offered by mixed-effects and machine learning approaches, while statistically significant, were described as modest in operational terms, and the authors insist that DBH remains the conventional standard with stump diameter serving as a complementary surrogate rather than a replacement.

Even with those caveats, the study delivers something tropical forestry has needed for a long time: a cheap, fast, and demonstrably accurate way to audit carbon in the places where conventional measurement fails. As plantation forestry expands across the tropics to meet timber demand and climate goals under SDG 13 and SDG 15, the ability to turn a field of cut stumps into a credible carbon ledger could reshape how harvest impacts are monitored and reported. The next step, the authors argue, is testing these equations across wider environmental gradients, diverse plantation ages, and mixed-species stands, and incorporating belowground biomass into the accounting. Until then, the message to foresters and carbon accountants is clear: do not overlook the stump, because it remembers everything the tree knew.

Subject of Research: Stump diameter as a predictor of biomass and carbon stocks in tropical Acacia plantations in Bangladesh

Article Title: Stump diameter is a robust predictor of biomass and carbon stocks in tropical Acacia plantations

Article References: Das, N., & Chowdhury, M. Q. (2026). Stump diameter is a robust predictor of biomass and carbon stocks in tropical Acacia plantations. Discover Forests, 2(1), Article 73. https://doi.org/10.1007/s44415-026-00137-1

Image Credits: AI Generated

DOI: 10.1007/s44415-026-00137-1

Keywords: stump diameter, biomass estimation, carbon stocks, Acacia plantations, allometric models, REDD+, Bangladesh, machine learning, mixed-effects models, forest mensuration, destructive sampling, carbon accounting

The research, led by Binayak Poudel of the Institute of Forestry at Tribhuvan University, together with colleagues in Canada and Nepal, quantified both vegetation carbon and soil organic carbon across three dominant forest types: Shorea robusta (sal), Pinus roxburghii (chir pine), and mixed broadleaved forests. Working in two community forests in Kaski District—the 162-hectare Sityum Kasyari and Simsar Patleswara Jukepani Community Forest and the 14-hectare Banpale Community Forest—the team established nineteen nested circular plots of 500 square meters each, measuring every tree with a diameter at breast height of at least five centimeters. Soil samples were collected from four depth intervals reaching down to 80 centimeters, with five subsamples composited at each depth to smooth out local heterogeneity.

The results were striking. Shorea robusta forests emerged as the undisputed carbon champions, storing 113.5 plus or minus 7.1 megagrams of carbon per hectare in vegetation and 102.0 plus or minus 2.3 megagrams per hectare in soil—substantially more than chir pine forests, which held 96.5 and 85.5 megagrams respectively, and far ahead of mixed broadleaved forests at just 64.2 and 84.9 megagrams. Together, the sal forests banked roughly 215 megagrams of carbon per hectare across vegetation and soil combined, placing them at the upper end of the range reported for South Asian subtropical forests.

But the real analytical payoff came from teasing apart why these differences exist. The researchers evaluated fourteen predictors spanning stand structure, anthropogenic disturbance, soil properties, topography, and climate, using Pearson correlation analysis and principal component analysis to identify the dominant drivers. For tree carbon, two structural variables towered above the rest: basal area, with a correlation coefficient of 0.641, and canopy cover, at 0.635, both highly significant. Tree height and stem density, by contrast, showed only weak, non-significant positive relationships—a reminder that a forest crowded with small stems stores far less carbon than one anchored by a few massive trunks.

Equally important was what the analysis revealed about human pressure. The team constructed an Anthropogenic Disturbance Index combining stem cutting, leaf litter collection, lopping intensity, and fire influence, each weighted equally. This index correlated negatively with tree carbon at r equals minus 0.60, a statistically significant signal that where people extract biomass, carbon stocks suffer. Mixed broadleaved forests, which local communities depend on most heavily for fuelwood and cattle fodder, showed the highest disturbance levels and the lowest carbon stocks. Chir pine forests, whose needles are unsuitable for composting or fodder, experienced the least disturbance.

Soil organic carbon told a different story entirely. Here, the strongest correlate was not a property of the trees but a property of the ground itself: soil texture. Silt content correlated positively with SOC at r equals 0.587, while sand content showed a significant negative correlation at r equals minus 0.581. This pattern reflects a well-established mechanism in soil science—fine mineral particles form stable organo-mineral complexes that physically shield organic matter from microbial decomposition, whereas sandy soils, with poor aggregate structure and low water retention, accelerate mineralization. Canopy cover was the only structural variable significantly associated with soil carbon, at r equals 0.560, suggesting that dense canopies moderate soil temperature and moisture in ways that favor organic matter accumulation.

Principal component analysis confirmed this division of labor. For tree carbon, the first two principal components explained 53.2 percent of total variance, with carbon clustering alongside basal area, canopy cover, and stem density. For soil carbon, the first two components accounted for 52.8 percent of variation, dominated by textural and site variables. Permutational multivariate analysis of variance showed that forest type explained 45 to 46 percent of the variation in both pools—statistically significant for each. Intriguingly, elevation correlated negatively with soil carbon while aspect correlated positively with tree carbon, and mean annual temperature showed a positive relationship with SOC that the authors attribute to the co-distribution of warmer, more productive lower-elevation sal forests rather than a direct climatic effect.

The comparison with earlier Nepalese and Himalayan studies adds valuable context. Previous work in Makawanpur district reported sal forest biomass as high as 313.69 megagrams per hectare in well-managed community forests, while degraded stands in Dang district stored barely 99 megagrams. Protected-area studies in Shuklaphanta National Park found core-zone carbon stocks of 258.56 megagrams per hectare versus 193.3 in buffer zones—a protected-area effect that mirrors the disturbance gradient documented in the new study. The authors also note that Nepal’s total forest topsoil SOC has been estimated at 494 million tons, a figure that local-scale studies like this one help refine for national inventories.

The study is candid about its limitations. Nineteen plots distributed unevenly across three forest types constrain statistical power, and the geographic scope of two community forests in a single district limits extrapolation. The analysis relies on correlation and PCA, which identify associations without establishing causality, and the disturbance index applies equal weights to pressures that likely differ in carbon impact. Climate data at 1-kilometer resolution from WorldClim varied too little across plots to serve as strong predictors, and the fieldwork captured only a single post-monsoon season. The authors recommend denser plot networks, structural equation modeling, soil carbon fractionation, and longer observation windows in future work.

Yet the management implications are clear and actionable. Because basal area, canopy cover, and disturbance intensity are readily measurable in routine forest inventories, they can be embedded directly into community forest operational plans and carbon monitoring frameworks. Protecting large-diameter trees, maintaining canopy closure, and strictly regulating cutting, lopping, litter removal, and fire emerge as the highest-leverage interventions for maximizing ecosystem carbon. For Nepal’s REDD+ program and national carbon accounting, the message is that structural integrity—rather than forest type alone—should anchor carbon-oriented planning in the country’s community forests, which cover nearly 45 percent of the national land area and represent one of the developing world’s most celebrated conservation success stories.

Subject of Research: Carbon storage drivers in Nepalese mid-hill community forests

Article Title: Stand structure drives vegetation carbon storage while soil texture regulates soil organic carbon in Nepalese mid-hill community forests

Article References: Poudel, B., Bhattarai, S., Koirala, S., Chapagain, J., & Timilsina, S. (2026). Stand structure drives vegetation carbon storage while soil texture regulates soil organic carbon in Nepalese mid-hill community forests. Discover Forests, 2(1), Article 69. https://doi.org/10.1007/s44415-026-00130-8

Image Credits: AI Generated

DOI: 10.1007/s44415-026-00130-8

Keywords: forest carbon, soil organic carbon, stand structure, basal area, canopy cover, Shorea robusta, anthropogenic disturbance, soil texture, community forestry, Nepal, REDD+, carbon stocks

The research team, led by Anaelle Brunda Djiaha and Marlène Ngansop Tounkam of the University of Douala together with Philippes Mbevo Fendoung of the National Advanced School of Public Works in Yaoundé, built their analysis on Landsat satellite scenes captured in the dry-season months of February 2000, March 2010, December 2014, and January 2022. Choosing dry-season imagery was a deliberate technical decision: cloud cover in Cameroon’s wet season can obscure the ground and corrupt classification algorithms, so the researchers favored dates when visibility was near optimal. Each scene was assembled into multispectral composites, radiometrically corrected to convert raw digital numbers into surface reflectance, and classified using the Maximum Likelihood algorithm, a supervised method that assigns each pixel to the land cover class it statistically most resembles. Five classes emerged: mature highland forest, medium-aged secondary forest, shrub savannah, agrosystems, and built-up areas.

The rigor of this classification was validated on two fronts. Confusion matrices built from thousands of reference points, ranging from 3,215 samples in 2000 to more than 31,000 in 2022, yielded Kappa coefficients between roughly 0.93 and 0.99 across all four dates, well above the 0.85 threshold generally accepted as indicating strong agreement between classified and reference data. On the ground, the team surveyed more than 110 GPS-truth points over 20 days of fieldwork, correcting misclassified pixel blocks and confirming which land cover categories were genuinely present. This dual validation matters because the entire downstream analysis, from change-detection matrices to carbon accounting, rests on the accuracy of those classifications.

The temporal story that emerges is dramatic. In 2000, high-altitude forest covered 66 percent of the reserve, roughly 6,199 hectares, with secondary forest at 13 percent, savannah at 15 percent, and agriculture and settlement each at 3 percent. By 2010 the mature forest share had slipped to 63 percent, but the steepest decline came in just four years: between 2010 and 2014, highland forest plummeted from 5,932 to 3,693 hectares, an annual loss of more than 570 hectares, while medium-aged secondary forest nearly doubled as degraded and regenerating land replaced old-growth canopy. By 2022, mature forest covered only 38 percent of the reserve, agrosystems had surged from 6 to 14 percent, and built-up areas had climbed to 5 percent, more than doubling from 136 to 456 hectares in just eight years. Transition matrices confirm the scale of conversion: over the full 22-year period, more than 6,000 hectares of land shifted out of secondary forest trajectories and over 1,250 hectares of mature forest were converted to other uses.

To understand what is driving this transformation, the researchers combined remote sensing with field observation and interviews. The verdict was unambiguous: agriculture accounts for 46 percent of degradation, human occupation for 34 percent, illegal logging for 14 percent, and bushfires for the remaining 6 percent. The reserve is surrounded by villages inhabited primarily by Mbo’o and Bamiléké communities whose livelihoods depend on cash crops such as cocoa, coffee, and oil palm as well as food crops like maize and cassava. A 20-kilometer secondary road linking Foumban to Bale has intensified access and anthropization, while selective artisanal sawmilling and slash-and-burn clearing accelerate the fragmentation. Elephants and panthers have already vanished from the reserve; the remaining wildlife, including primates, pangolins, aulacodes, and monitor lizards, now survives in an increasingly perforated habitat.

Beyond mapping change, the study quantified what this forest still stores in climate terms. Using established allometric equations for Cameroon’s tropical moist forests, the team measured every tree in 30 square plots of 30 by 30 meters distributed across the vegetation types, converting diameter and wood density into aboveground biomass, then into carbon using the standard 0.47 conversion fraction. The reserve’s overall carbon rate came to 0.874 tonnes of carbon per hectare, with medium-aged secondary forests standing out as the strongest carbon sinks at 0.534 tC/ha, followed by highland forest at 0.280 tC/ha. Agrosystems stored a negligible 0.002 tC/ha. The authors note candidly that these values are well below the 30 to 50 tC/ha typically reported for tropical secondary forests, attributing the gap to advanced degradation, plot representativeness, and extrapolation uncertainty, and they cross-validated their spatial carbon maps with an R-squared of 0.78 and a margin of error of roughly plus or minus 15 percent.

Perhaps the most consequential result is spatial: by overlaying the 2000 and 2022 forest classifications and subtracting what remained, the team identified 3,703 hectares, 53 percent of the entire reserve, as suitable for reforestation. These are zones where forest existed at the start of the century and no longer does, prime candidates for planting native species or assisted natural regeneration. If restored, the researchers estimate, this area could hold a carbon storage capacity of about 0.83 tC/ha, a meaningful contribution to both biodiversity recovery and climate mitigation in a region where protected areas are increasingly recognized as critical carbon reservoirs.

Looking forward, the team ran a CA-Markov cellular automata model in Idrisi Selva software, using transition probabilities derived from the historical record to simulate land cover in 2050. The projections, which the authors carefully frame as exploratory scenarios rather than predictions, suggest agricultural land expanding by 30 percent, built-up areas by 40 percent, and agroecosystems by 25 percent, primarily at the expense of remaining primary forest and savannah. The model rests on a stationarity assumption, that past trends will persist, which the authors acknowledge is vulnerable to disruption by policy shifts, land reform, or economic shocks, and they call for sensitivity analyses and hindcasting validation in future work. Even with those caveats, the trajectory is clear: without intervention, demographic and economic pressure will continue converting the reserve into farmland and settlements.

The policy implications extend well beyond one Cameroonian reserve. The authors argue that reversing these trends requires a comprehensive package: promoting sustainable agricultural practices, launching reforestation in the 3,703 hectares of priority zones, and, critically, involving local communities through training and participatory natural resource management. They point to evidence that REDD+ initiatives succeed best when payments for ecosystem services are coupled with local development programs such as agroforestry, and they stress the need for stricter land use regulations and the integration of conservation objectives into public policy. For a reserve whose very existence as a protected area is being tested by the communities that surround it, the path forward depends on aligning economic incentives with ecological survival, ensuring that Santchou’s remaining forests, and the carbon and biodiversity they harbor, are still standing in 2050.

Subject of Research: Forest cover dynamics, carbon stock assessment, and 2050 land use projections for the Santchou Wildlife Reserve in western Cameroon

Article Title: Forest dynamics and carbon stocks in the Santchou wildlife reserve from 2000 to 2022 and projections for 2050

Article References: Djiaha, A. B., Tounkam, M. N., & Fendoung, P. M. (2026). Forest dynamics and carbon stocks in the Santchou wildlife reserve from 2000 to 2022 and projections for 2050. Discover Conservation, 3(1), Article 28. https://doi.org/10.1007/s44353-026-00097-9

Image Credits: AI Generated

DOI: 10.1007/s44353-026-00097-9

Keywords: deforestation, forest degradation, carbon stocks, remote sensing, Landsat, Cameroon, protected areas, CA-Markov model, reforestation, REDD+, biodiversity conservation, land use change

The review, led by Shishir Chandrakar and Krishna Kumar Chandra of Guru Ghasidas Vishwavidyalaya together with Bhavana Dixit of Chhattisgarh Rajya Niti Aayog, is deliberately framed as a critical synthesis rather than a new empirical study. The authors screened more than 90,000 records from Web of Science, Scopus and Google Scholar, narrowing these through duplicate removal, title and abstract screening, and full-text assessment to a final pool of 223 publications meeting rigorous methodological criteria. The twenty-year window they define as long-term is not arbitrary: it spans four assessment cycles of the India State of Forest Report, covers the entire post-2005 period against which India’s Nationally Determined Contributions and forest-carbon pledges are tracked, and is long enough to capture slow processes such as soil organic carbon turnover and stand-level biomass accumulation that shorter studies simply cannot resolve.

The technical picture that emerges is one of enormous variability and genuine alarm. Aboveground biomass carbon in Central Indian forests fluctuated between 26.4 and 131.1 megagrams of carbon per hectare across the reviewed studies, while soil organic carbon stocks ranged from 24.6 to 50.2 megagrams per hectare. Total biomass in some landscapes reached 338.3 megagrams per hectare, and closed natural forests held considerably more carbon, around 208.22 megagrams per hectare, than open forests at roughly 95.11. Intact stands of mixed sal and teak performed best, and carbon stock densities across various forest types spanned roughly 50 to 180 megagrams per hectare, depending on tree density, species diversity, age structure and management history. These ranges matter because carbon accounting schemes, carbon markets and national inventories all depend on knowing how much carbon a given hectare actually stores.

What makes the findings striking is the disconnect they reveal between national aggregates and regional reality. Nationally, Indian forests remain recognised as vital carbon sinks, holding an estimated 7,124 million tonnes of carbon, and national reports recorded a net increase of 377 million tonnes of carbon between 1995 and 2005. Yet the granular, site-level evidence compiled in this review shows that vulnerable ecosystems in Central India are actively degrading and releasing carbon even as headline statistics improve. In one disturbed tropical forest landscape, approximately 1,851.8 hectares were lost between 2000 and 2020, largely to agricultural conversion, producing a net loss of 0.065 teragrams of biomass and roughly 59 gigagrams of carbon, equivalent to about 216 gigagrams of carbon dioxide. National averages, the authors argue, may be masking precisely the localised declines that matter most for conservation and climate policy.

The drivers of this carbon loss are neither mysterious nor singular. Deforestation driven by agricultural expansion, mining, urbanisation and infrastructure development remains the dominant force, but the review highlights a web of interacting pressures that together erode carbon stocks. Overgrazing suppresses regeneration and shifts species composition toward lower-biomass stands. Repeated low-intensity fires, many of them human-ignited, kill trees and reduce both biomass and soil carbon availability. Linear infrastructure such as roads and transmission lines fragments habitat and creates edge effects that elevate tree mortality and fire risk. Fuelwood extraction has a long history in the region, with an estimated deficit of 86 million tonnes recorded as far back as 1996, and excessive livestock grazing has stunted forest floor regeneration in around 67 percent of national parks and 83 percent of wildlife sanctuaries. Timber extraction alone accounted for roughly half of degradation in some studied landscapes, followed by fuelwood collection, fires and grazing.

Climate change is compounding, rather than replacing, these anthropogenic pressures. The Intergovernmental Panel on Climate Change projects global temperature rises of 1.5 to 4.5 degrees Celsius alongside doubled atmospheric carbon dioxide by the end of the century, and Central India sits squarely in the crosshairs. Vulnerability assessments rank Chhattisgarh, Madhya Pradesh and Odisha among the most climate-exposed regions in the country. Projections summarised in the review suggest that by 2050, large areas of tropical moist deciduous and semi-evergreen forest, covering some 520,280 square kilometres of India’s most dominant forest types, will fall within climatic hotspots, facing altered precipitation, elevated temperatures and increased drought and fire. Conflicting model scenarios add to the uncertainty: greenhouse gas forcing models predict warmer and wetter conditions that could boost productivity, while aerosol-inclusive models foresee drier, water-stressed futures that would accelerate the shift from moist to drier forest types.

Perhaps most unsettling is the evidence that extreme climate events can temporarily flip the sign of the regional carbon budget. During drought episodes, plant respiration in Central India’s deciduous forests has been documented to exceed primary productivity, releasing an estimated 210 million tonnes of carbon annually under those conditions. Drought-linked tree mortality accelerates decay-driven carbon release and raises fire probability, and El Niño-associated droughts globally amplify exactly this pattern. Soils, which hold the largest share of India’s forest carbon at over 50 percent of total stocks, are particularly exposed: decomposition rates respond directly to warming and shifting moisture, and the review notes a clear decline of soil organic carbon with depth, meaning topsoil degradation translates disproportionately into carbon loss.

The synthesis does not end on a purely pessimistic note, and its prescriptions are unusually concrete. Sustainable forest management, assisted natural regeneration of degraded lands, mixed-species planting with native species, soil moisture conservation, fire-line maintenance and invasive species control all emerge as proven levers for restoring carbon storage. India’s Nationally Determined Contributions target the sequestration of 2.5 to 3 billion tonnes of additional carbon dioxide and the restoration of 26 million hectares of degraded land by 2030, and the rehabilitation of degraded forests is estimated to offer a further 1,008.49 teragrams of carbon mitigation potential over 75 years. Species diversity itself matters: taxonomic richness and structural diversity correlate strongly with aboveground biomass through niche complementarity, and dominant regional species such as Diospyros melanoxylon, Butea monosperma and Shorea robusta are key carbon contributors. Monoculture plantations, by contrast, typically store less carbon and are more vulnerable to pests and climate extremes, a nuance the authors stress against simplistic area-based afforestation targets.

Community governance emerges as perhaps the decisive variable. Madhya Pradesh holds India’s largest forest area and second-largest carbon stock at 608 million tonnes, with Chhattisgarh close behind at 505 million tonnes, and both states sit at the heart of the Green India Mission and World Bank-supported Ecosystem Services Improvement Project. Chhattisgarh has been actively financing Community Forest Resource management plans developed by gram sabhas, the village assemblies empowered under the Forest Rights Act. Yet the review documents persistent friction: village councils often struggle to access funds because of complex administrative prerequisites and lingering resistance from state forest departments, while growing investment interest in forest lands for mining, carbon schemes and ecotourism raises concerns about pressure on Adivasi territories. The authors argue that genuine carbon mitigation in Central India hinges on authentic devolution of rights and finance to forest communities rather than on the scale of announced programmes.

Ultimately, the review positions Central India as both a warning and a test bed. The evidence for a region-wide shift to net carbon source status still rests on a limited number of regional studies rather than a dense monitoring network, and the authors are candid that belowground carbon dynamics remain under-sampled and methodological differences across studies complicate comparisons. But the direction of travel is clear enough that the distinction between national gains and regional losses cannot be ignored. Meeting India’s forest-carbon commitments, the synthesis concludes, will depend less on planting trees across aggregate areas and more on safeguarding the quality, species composition and soil integrity of existing natural forests in Chhattisgarh, Madhya Pradesh and Odisha. The authors call for harmonised long-term monitoring networks that integrate field inventories, remote sensing and soil carbon measurement, calibrated to Forest Survey of India assessment cycles. In a region where ecological sensitivity and socio-economic dependence on forests converge at their most intense, the next two decades will determine whether these landscapes slip further into carbon deficit or recover their role as durable climate allies.

Subject of Research: Twenty-year synthesis of forest carbon dynamics, stocks and sink-to-source transition in Central Indian tropical forests

Article Title: A critical synthesis of forest carbon dynamics in central india over two decades

Article References: Chandrakar, S., Chandra, K. K., & Dixit, B. (2026). A critical synthesis of forest carbon dynamics in central india over two decades. Discover Forests, 2(1), Article 64. https://doi.org/10.1007/s44415-026-00123-7

Image Credits: AI Generated

DOI: 10.1007/s44415-026-00123-7

Keywords: forest carbon dynamics, Central India, carbon sequestration, soil organic carbon, deforestation, REDD+, sustainable forest management, climate change, systematic review, India State of Forest Report, community forest rights, dry deciduous forests