Soil is the largest carbon reservoir in terrestrial ecosystems, holding more carbon than the atmosphere and all vegetation combined. Yet when forests are converted to intensive agriculture, surface soils can lose between 30 and 75 percent of their organic carbon, releasing carbon dioxide into the atmosphere while stripping the land of the fertility that sustains crops. The Food and Agriculture Organization has identified the rebuilding of soil organic carbon as a cornerstone of climate-smart agriculture, making it essential to understand which land-use systems actually deliver carbon gains under real farm conditions.

Zamora Chinchipe, in southeastern Ecuador, provided an ideal natural laboratory. The province sits within the Ecuadorian Amazon, with a humid to sub-humid climate, mean annual temperatures between 18 and 30 degrees Celsius, and annual precipitation of roughly 1,900 to 2,100 millimeters. Its topography ranges from steep mountain terrain to inter-Andean valleys transitioning into the Amazon basin, producing an extraordinary diversity of soils and ecosystems. Those soils, however, are generally shallow, acidic, and of relatively low fertility, and they face mounting pressure from agricultural expansion and, increasingly, mining.

To capture that diversity, the research team evaluated 187 farms across six land-use types: cacao agroforestry systems, coffee agroforestry systems, cacao monocultures, natural forest, extensive pastures, and silvopastoral systems that combine trees, grass, and cattle. The farms were selected in coordination with local producer associations and spread across the cantons of Zamora, Chinchipe, Palanda, Yantzaza, Paquisha, and Centinela del Cóndor. Between 2023 and 2024, the researchers collected 187 composite soil samples from the top 20 centimeters of the profile, each built from six subsamples mixed together. Sampling locations on different farms were separated by at least five kilometers to reduce spatial dependence, and undisturbed soil cores were taken to measure bulk density, a key indicator of compaction.

The laboratory work was exhaustive. Texture was assessed with the Bouyoucos hydrometer method, pH was measured potentiometrically, and available nitrogen, phosphorus, sulfur, boron, and a suite of cations were quantified using standardized colorimetric, spectrometric, and turbidimetric procedures. Soil organic matter was determined by loss-on-ignition and converted to organic carbon using a conventional factor of 0.58, allowing carbon stocks to be expressed in megagrams per hectare. Because the data did not meet the assumptions of parametric statistics, the team relied on the non-parametric Kruskal-Wallis test with Dunn’s post hoc comparisons and Bonferroni correction, supplemented by Spearman rank correlations and a principal component analysis.

The headline result concerns carbon. Coffee agroforestry systems recorded the highest topsoil carbon stocks, averaging more than 80 megagrams per hectare, and differed significantly from most other land uses. Silvopastoral systems and pastures followed closely, while cacao monocultures registered the lowest values. When the researchers compared paired systems directly, the pattern became striking: cacao grown under shade trees stored roughly 20 percent more carbon than cacao monoculture, silvopastoral systems held about 24 percent more than treeless pastures, and forest soils carried approximately 27 percent more carbon than cacao monocultures. Forest stands themselves averaged around 69.45 megagrams of carbon per hectare in the surface layer, a figure consistent with other Amazonian studies even if somewhat below values reported elsewhere in the region.

Fertility told a parallel story. Coffee agroforestry showed the most favorable nutrient profile for nitrogen and sulfur, while cacao agroforestry stood out for calcium, magnesium, and manganese. Pastures, by contrast, generally exhibited the lowest concentrations of sulfur, calcium, and magnesium, along with lower pH values and elevated iron, a signature of acidification and nutrient depletion associated with continuous grazing, trampling, and the removal of nutrients without replenishment. The correlation analysis reinforced these patterns, revealing strong positive associations between calcium and magnesium and between pH and both bases, while carbon stocks correlated positively with nitrogen. Soil pH remained acidic everywhere, ranging from roughly 4.5 to 6.0, with the highest values in cacao agroforestry and the lowest in forest soils.

The mechanisms behind these differences are well understood in principle. Trees in agroforestry systems continuously deposit litter and organic residues, feeding nutrient cycling and raising the soil’s cation exchange capacity. Their deep root systems pump nutrients upward from lower horizons and return them to the surface, buffering acidity and improving structure. In pastures, dense grass roots do promote aggregation and can accumulate substantial carbon, with species such as Brachiaria humidicola capable of building hundreds of grams of root biomass per square meter in the top 15 centimeters. But the study’s authors caution that carbon accumulation in pastures can coexist with chemical degradation, meaning high carbon numbers alone do not guarantee soil health or long-term sustainability.

The implications extend beyond soil science. Many of the participating farms already hold organic certification, and the researchers note that agroforestry and silvopastoral systems can generate higher returns per unit of labor than full-sun monocultures, with income from timber, fruit, and other tree products helping to offset lower cacao yields. By offering viable production alternatives, tree-based systems may also reduce pressure on remaining natural forests, a critical point in a province where extractive mining is expanding. Previous work in the Ecuadorian Amazon has shown that shaded cacao systems store about 40 percent more carbon in aboveground biomass than monocultures, and the new soil data suggest the belowground benefits run in the same direction.

The authors are careful to frame their findings as associations rather than proof of causation, and they acknowledge limitations: the assessment covered only the top 20 centimeters of soil, leaving deeper carbon dynamics unmeasured, and factors such as elevation, slope, and the conventional carbon conversion factor introduce uncertainty. They call for future sampling at depths of 30 to 60 and 60 to 120 centimeters to capture the full vertical distribution of carbon. Even so, the message from Zamora Chinchipe is clear. In tropical production landscapes, keeping trees on farms is not a compromise between agriculture and the environment; it is one of the most practical strategies available for sustaining fertility, storing carbon, and building resilience in the soils on which both livelihoods and the climate depend.

Subject of Research: Effects of tree-based production systems on topsoil fertility and carbon storage in the tropical Ecuadorian Amazon

Article Title: Tree-based production systems and their relationships with topsoil fertility and carbon storage in tropical regions

Article References: Jiménez, L., Carrera, R., Fierro, N., Lasso, J., Roa, J., Ochoa, P., Loján, C., Donoso, R., & Capa-Mora, D. (2026). Tree-based production systems and their relationships with topsoil fertility and carbon storage in tropical regions. Journal of Agriculture and Food Research, Article 103350. https://doi.org/10.1016/j.jafr.2026.103350

Image Credits: AI Generated

DOI: 10.1016/j.jafr.2026.103350

Keywords: soil organic carbon, agroforestry, silvopastoral systems, cacao, coffee, Ecuadorian Amazon, soil fertility, carbon stocks, land use change, tropical soils, pasture degradation, climate-smart agriculture

The team, led by Romario Oliveira de Santana of the Chico Mendes Institute for Biodiversity Conservation, working with colleagues at the University of São Paulo and the Federal University of Acre, drew on gross primary productivity (GPP) estimates from the MODIS satellite instrument at 500-meter resolution and soil organic carbon (SOC) stocks from Brazil’s MapBiomas Soil initiative, which estimates carbon in the top 30 centimeters of soil at 30-meter resolution using machine learning trained on field observations. After harmonizing the datasets onto a common grid and classifying the landscape into six land use categories — forest, natural nonforest formation, agriculture, pasture, forestry plantations and mixed-use mosaics — the researchers analyzed annual records from 2001 through 2023.

The productivity side of the ledger behaved as expected. Natural forests recorded the highest average GPP at 2.89 kilograms of carbon per square meter per year, followed closely by commercial forestry plantations at 2.78. Agricultural fields and pastures trailed well behind, at roughly 2.0 kilograms per square meter per year. The Kruskal–Wallis test confirmed these differences were highly significant, and the pattern aligns with global findings that forest conversion reduces landscape-level photosynthetic capacity — one international analysis cited in the study estimated that historical agricultural expansion has cut global GPP by approximately 4.4 percent.

The soil carbon side of the ledger, however, defied intuition. The richest stocks, averaging 88.3 tonnes per hectare with enormous variability, occurred not under towering rainforest but in natural nonforest formations — the grasslands, savannas and open ecosystems that conservation assessments often overlook. Forests held intermediate stocks of 56.0 tonnes per hectare, while agriculture, pasture and plantations carried the lightest carbon loads. High aboveground productivity, in other words, did not translate into high belowground carbon storage.

Statistical modeling drove the point home. Simple regressions between GPP and SOC within each land use class explained very little variation — the strongest relationship, in agricultural areas, accounted for only about 12 percent of the variance, and in natural nonforest formations the relationship was statistically indistinguishable from zero. Multiple regression models incorporating soil clay content and land use explained roughly 38 percent of variation in each variable, underscoring that soil texture and management history, not current photosynthesis, largely govern where soil carbon accumulates.

Perhaps the study’s most methodologically significant twist came when the researchers confronted a problem that plagues landscape-scale ecology: spatial autocorrelation. Both GPP and SOC showed pronounced clustering, with Moran’s I averaging 0.578 for productivity and 0.704 for soil carbon. Conventional regression residuals remained strongly spatially structured, particularly for SOC. Fitting spatial error models year by year collapsed the residual autocorrelation to near zero and slashed the Akaike information criterion by thousands of points — but it also dissolved the GPP–SOC relationship. After properly accounting for spatial dependence, the effect of productivity on soil carbon was significant in only 3 of 23 years. Much of the apparent coupling detected by naive models was shared spatial structure, not ecology.

The temporal analysis added a further layer of divergence. Regional GPP showed no significant directional trend across the 23-year record, fluctuating year to year but remaining functionally stable — although natural forests stayed consistently productive while forestry plantations declined significantly. Estimated SOC, by contrast, crept upward at the regional scale until the end of the 2010s before dipping in recent years, and beneath that aggregate trend lay a patchwork: significant declines in agriculture, plantations and natural nonforest formations, a significant rise in pasture, and near-stability in forests. Productivity and soil carbon, the authors conclude, operate on different clocks — one responding within seasons to light, water and canopy structure, the other accumulating and eroding over decades through decomposition, mineral association and land management.

By classifying every pixel against regional median values for both variables, the study produced a map of four functional regimes that reads like a diagnosis of the corridor’s ecological health. Ecological hotspots — areas combining high productivity and high soil carbon — clustered overwhelmingly in forest ecosystems, which accounted for 66.9 percent of their extent. The low-functionality regime, where both variables fall short, was dominated by pasturelands, which made up 82.2 percent of its area and formed the principal landscape matrix across inland sections. Two intermediate regimes — carbon-dominated and productivity-dominated — filled the gaps, each with its own characteristic land use signature. The association between regimes and land use was, statistically speaking, about as strong as associations get.

The implications ripple outward from southern Bahia. For conservation planners, the message is that satellite greenness alone cannot identify a landscape’s most valuable carbon reservoirs; open natural ecosystems with modest tree cover may be quietly safeguarding enormous belowground stocks and should not be dismissed as degraded land. For restoration strategists, the pasture-dominated low-functionality zones emerge as prime candidates for improved management and ecological recovery, though the authors caution that site-specific assessments must guide priorities. And for anyone banking on tropical forests to offset emissions, the study offers a sobering technical reminder: planting something green does not guarantee that carbon will stay in the ground. Carbon persistence depends on organo-mineral stabilization, aggregate protection and the slow choreography of soil chemistry — processes that no satellite can see, but that this integrated spatial framework now brings into focus.

Subject of Research: Spatial relationships between gross primary productivity and soil organic carbon across land use systems in the Atlantic Forest of Brazil

Article Title: Environmental monitoring of soil carbon and ecosystem productivity across land use systems in the Atlantic Forest of Brazil

Article References: de Santana, R. O., Trindade, L. R. S. L. C., & Delgado, R. C. (2026). Environmental monitoring of soil carbon and ecosystem productivity across land use systems in the Atlantic Forest of Brazil. Environmental Monitoring and Assessment, 198(10), Article 1100. https://doi.org/10.1007/s10661-026-15952-4

Image Credits: AI Generated

DOI: 10.1007/s10661-026-15952-4

Keywords: Atlantic Forest, soil organic carbon, gross primary productivity, land use change, remote sensing, MODIS, MapBiomas, spatial autocorrelation, carbon cycle, tropical ecology, ecological restoration, pasture degradation