Oil palm cultivation on peatlands causes severe environmental damage, yet its greenhouse gas (GHG) footprint and underlying biogeochemical drivers remain poorly quantified. This study assessed how nitrogen fertilizer application impacts soil respiration and CH4 emissions across spatio-temporal scales in a fertilization trial with three application rates: 0 (N0), 154.5 (N1) and 309 (N2) kg N ha−1 yr−1. Fluxes and environmental variables were monitored monthly over a year and daily for a month after two fertilizations. Measurements were taken close to palms (CP), where fertilizer was applied, and farther from palm (FP). Soil respiration (Mg CO2-C ha−1 yr−1) was high compared to the literature, with N0 and N2 (27.3 ± 2.8 and 28.5 ± 3.0) exceeding N1 (18.8 ± 2.5). Fertilization triggered short-lived respiration increases, without affecting annual rates. Treatment and spatial differences (FP > CP) aligned with soil temperature and soil N2O emission patterns, suggesting links to peat mineralization. CH4 emissions (kg CH4-C ha−1 yr−1) were greater in N0 (3.9±1.5) compared to N1 (0.7 ± 0.6) and N2 (1.0 ± 1.3), inversely mirroring soil nitrate distribution under unfertilized conditions. A transient fertilizer effect occurred, without affecting annual rates. Our findings highlight strong interactions between soil respiration, CH4 emissions and N cycling in drained tropical peatlands, warranting further investigation to constrain GHG process-based modelling.
Tag: Biogeochemistry
Evaluation and improvement of the E3SM land model for simulating energy and carbon fluxes in an Amazonian peatland
Tropical peatlands are one of the largest natural sources of atmospheric methane (CH4) and play a significant role in regional and global carbon budgets. However, large uncertainties persist regarding their feedbacks to climate variations. The Energy Exascale Earth System Model (E3SM) Land Model (ELM) is an ongoing state-of-the-science model, which has developed new representations of soil hydrology and biogeochemistry and includes a new microbial-functional-group-based CH4 module. This model has been tested in boreal forest peatlands, but has not yet been evaluated for simulating energy and carbon exchange for tropical peatlands. Here, we evaluated the ELM performance in simulating energy, carbon dioxide (CO2) and CH4 fluxes of an Amazonian palm swamp peatland in Iquitos, Peru. ELM simulations using default parameter values resulted in poor performance of seasonal carbon dynamics. Several algorithms were improved according to site-specific characteristics and key parameters were optimized using an objective surrogate-assisted Bayesian approach. The modified algorithms included the soil water retention curve, a water coverage scalar function for CH4 processes, and a seasonally varying leaf carbon-to-nitrogen ratio function. The revised tropics-specific model better simulated the diel and seasonal patterns of energy and carbon fluxes of the palm swamp peatland. Global sensitivity analyses indicated that the strong controls on energy and carbon fluxes were mainly attributed to the parameters associated with vegetation activities, such as plant carbon distribution, stomatal regulation, photosynthetic capacity, and leaf phenology. Parameter relative importance depended on biogeochemical processes and shifted significantly between wet and dry seasons. This modeling study advanced the understanding of biotic controls on the energy and carbon exchange in Amazonian palm swamp peatlands and identified knowledge gaps that need to be addressed for better prediction of carbon cycle processes and budgets for tropical peatlands.
Strong priming of soil organic matter induced by frequent input of labile carbon
Labile carbon (C) inputs to soil (e.g., litter and root exudation) can prime soil organic matter (SOM) decomposition, and strongly influence SOM dynamics. The direction and intensity of priming, as well as the net C balance in soil, depend on the amount and frequency of labile C inputs. Most recent priming studies are based on single C additions, which are not truly representative of common litter inputs or root exudation in terrestrial ecosystems. Here, we evaluated the effects of 14C-labeled glucose addition to soil in the same final amounts (360 μg C g−1) split into two temporal patterns: seldom (20% of microbial biomass every two months) and frequent addition (4% of microbial biomass every 10 days) on the dynamics of CO2 production and SOM priming over a 200-day incubation. For the first time, we combined enzyme kinetics with substrate-induced growth respiration and fungal diversity to monitor microbially mediated SOM mineralization in response to the labile C input frequency. Frequent glucose addition decreased 14C incorporation into microbial biomass and almost doubled cumulative priming compared to seldom addition, resulting in a net loss of SOM for seldom and frequent C additions of −94 and −367 μg C g−1 respectively. Larger priming loss of SOM with frequent C inputs was accompanied by increased activities of β-glucosidase, chitinase, and acid phosphatase, and by a shift in fungal community towards increased abundance of K-strategist fungal species (mainly Mortierellales sp. and Trichoderma sp.) capable of SOM mineralization. In conclusion, frequent labile C inputs (e.g., rhizodeposits in rhizosphere or litterfall in disturephere) to soil will stimulate a shift in fungal community structure and functions, resulting in intensive priming of SOM decomposition and CO2 losses. © 2020 Elsevier Ltd
Global maps of twenty-first century forest carbon fluxes
Managing forests for climate change mitigation requires action by diverse stakeholders undertaking different activities with overlapping objectives and spatial impacts. To date, several forest carbon monitoring systems have been developed for different regions using various data, methods and assumptions, making it difficult to evaluate mitigation performance consistently across scales. Here, we integrate ground and Earth observation data to map annual forest-related greenhouse gas emissions and removals globally at a spatial resolution of 30 m over the years 2001–2019. We estimate that global forests were a net carbon sink of −7.6 ± 49 GtCO2e yr−1, reflecting a balance between gross carbon removals (−15.6 ± 49 GtCO2e yr−1) and gross emissions from deforestation and other disturbances (8.1 ± 2.5 GtCO2e yr−1). The geospatial monitoring framework introduced here supports climate policy development by promoting alignment and transparency in setting priorities and tracking collective progress towards forest-specific climate mitigation goals with both local detail and global consistency.
Tropical tree growth driven by dry-season climate variability
Interannual variability in the global land carbon sink is strongly related to variations in tropical temperature and rainfall. This association suggests an important role for moisture-driven fluctuations in tropical vegetation productivity, but empirical evidence to quantify the responsible ecological processes is missing. Such evidence can be obtained from tree-ring data that quantify variability in a major vegetation productivity component: woody biomass growth. Here we compile a pantropical tree-ring network to show that annual woody biomass growth increases primarily with dry-season precipitation and decreases with dry-season maximum temperature. The strength of these dry-season climate responses varies among sites, as reflected in four robust and distinct climate response groups of tropical tree growth derived from clustering. Using cluster and regression analyses, we find that dry-season climate responses are amplified in regions that are drier, hotter and more climatically variable. These amplification patterns suggest that projected global warming will probably aggravate drought-induced declines in annual tropical vegetation productivity. Our study reveals a previously underappreciated role of dry-season climate variability in driving the dynamics of tropical vegetation productivity and consequently in influencing the land carbon sink.