Spatial variations of nitrogen trace gas emissions from tropical mountain forests in Nyungwe, Rwanda

Globally, tropical forest soils represent the second largest source of N2O and NO. However, there is still considerable uncertainty on the spatial variability and soil properties controlling N trace gas emission. Therefore, we carried out an incubation experiment with soils from 31 locations in the Nyungwe tropical mountain forest in southwestern Rwanda. All soils were incubated at three different moisture levels (50, 70 and 90 % water filled pore space (WFPS)) at 17 °C. Nitrous oxide emission varied between 4.5 and 400 μg N m−2 h−1, while NO emission varied from 6.6 to 265 μg N m−2 h−1. Mean N2O emission at different moisture levels was 46.5 ± 11.1 (50 %WFPS), 71.7 ± 11.5 (70 %WFPS) and 98.8 ± 16.4 (90 %WFPS) μg N m−2 h−1, while mean NO emission was 69.3 ± 9.3 (50 %WFPS), 47.1 ± 5.8 (70 %WFPS) and 36.1 ± 4.2 (90 %WFPS) μg N m−2 h−1. The latter suggests that climate (i.e. dry vs. wet season) controls N2O and NO emissions. Positive correlations with soil carbon and nitrogen indicate a biological control over N2O and NO production. But interestingly N2O and NO emissions also showed a positive correlation with free iron and a negative correlation with soil pH (only N2O). The latter suggest that chemo-denitrification might, at least for N2O, be an important production pathway. In conclusion improved understanding and process based modeling of N trace gas emission from tropical forests will benefit from spatially explicit trace gas emission estimates linked to basic soil property data and differentiating between biological and chemical pathways for N trace gas formation

Detailed regional predictions of N2O and NO emissions from a tropical highland rainforest [Discussion paper]

Tropical forest soils are a significant source for the greenhouse gas N2O as well as for NO, a precursor of tropospheric ozone. However, current estimates are uncertain due to the limited number of field measurements. Furthermore, there is considerable spatial and temporal variability of N2O and NO emissions due to the variation of environmental conditions such as soil properties, vegetation characteristics and meteorology. In this study we used a process-based model (ForestDNDC-tropica) to estimate N2O and NO emissions from tropical highland forest (Nyungwe) soils in southwestern Rwanda. To extend the model inputs to regional scale, ForestDNDC-tropica was linked to an exceptionally large legacy soil dataset. There was agreement between N2O and NO measurements and the model predictions though the ForestDNDC-tropica resulted in considerable lower emissions for few sites. Low similarity was specifically found for acidic soil with high clay content and reduced metals, indicating that chemo-denitrification processes on acidic soils might be under-represented in the current ForestDNDC-tropica model. The results showed that soil bulk density and pH are the most influential factors driving spatial variations in soil N2O and NO emissions for tropical forest soils. The area investigated (1113 km2) was estimated to emit ca. 439 ± 50 t N2O-N yr−1 (2.8–5.5 kg N2O-N ha−1 yr−1) and 244 ± 16 t NO-N yr−1 (0.8–5.1 kg N ha−1 yr−1). Consistent with less detailed studies, we confirm that tropical highland rainforest soils are a major source of atmospheric N2O and NO

Spatial variations of nitrogen trace gas emissions from tropical mountain forests in Nyungwe, Rwanda [Discussion paper]

Globally, tropical forest soils represent the second largest source of N2O and NO. However, there is still considerable uncertainty on the spatial variability and soil properties controlling N trace gas emission. To investigate how soil properties affect N2O and NO emission, we carried out an incubation experiment with soils from 31 locations in the Nyungwe tropical mountain forest in southwestern Rwanda. All soils were incubated at three different moisture levels (50, 70 and 90% water filled pore space (WFPS)) at 17 °C. Nitrous oxide emission varied between 4.5 and 400 μg N m−2 h−1, while NO emission varied from 6.6 to 265 μg N m−2 h−1. Mean N2O emission at different moisture levels was 46.5 ± 11.1 (50% WFPS), 71.7 ± 11.5 (70% WFPS) and 98.8 ± 16.4 (90% WFPS) μg N m−2 h−1, while mean NO emission was 69.3 ± 9.3 (50% WFPS), 47.1 ± 5.8 (70% WFPS) and 36.1 ± 4.2 (90% WFPS) μg N m−2 h−1. The latter suggests that climate (i.e. dry vs. wet season) controls N2O and NO emissions. Positive correlations with soil carbon and nitrogen indicate a biological control over N2O and NO production. But interestingly N2O and NO emissions also showed a negative correlation (only N2O) with soil pH and a positive correlation with free iron. The latter suggest that chemo-denitrification might, at least for N2O, be an important production pathway. In conclusion improved understanding and process based modeling of N trace gas emission from tropical forests will not only benefit from better spatial explicit trace gas emission and basic soil property monitoring, but also by differentiating between biological and chemical pathways for N trace gas formation

Prediction of water retention of soils from the humid tropics by the nonparametric k-nearest neighbor approach

Nonparametric approaches such as the k-nearest neighbor (k-NN) approach are considered attractive for pedotransfer modeling in hydrology; however, they have not been applied to predict water retention of highly weathered soils in the humid tropics. Therefore, the objectives of this study were: to apply the k-NN approach to predict soil water retention in a humid tropical region; to test its ability to predict soil water content at eight different matric potentials; to test the benefit of using more input attributes than most previous studies and their combinations; to discuss the importance of particular input attributes in the prediction of soil water retention at low, intermediate, and high matric potentials; and to compare this approach with two published tropical pedotransfer functions (PTFs) based on multiple linear regression (MLR). The overall estimation error ranges generated by the k-NN approach were statistically different but comparable to the two examined MLR PTFs. When the best combination of input variables (sand + silt + clay + bulk density + cation exchange capacity) was used, the overall error was remarkably low: 0.0360 to 0.0390 m(3) m(-3) in the dry and very wet ranges and 0.0490 to 0.0510 m(3) m(-3) in the intermediate range (i.e., -3 to -50 kPa) of the soil water retention curve. This k-NN variant can be considered as a competitive alternative to more classical, equation-based PTFs due to the accuracy of the water retention estimation and, as an added benefit, its flexibility to incorporate new data without the need to redevelop new equations. This is highly beneficial in developing countries where soil databases for agricultural planning are at present sparse, though slowly developing

Estimation of N₂O and NO emission from a tropical highland forest in Rwanda

Despite the fact that tropical rainforest soils are considered the strongest natural terrestrial source of N2O and NO, only a relatively small number of detailed studies investigating the temporal and spatial variability of the N2O and NO soil-atmosphere exchange are available, particularly for Africa. The objective of this research was to improve N2O and NO emission predictions from tropical rainforest. The research has been carried out in a specific tropical highland forest in Africa (Nyungwe, Rwanda). In this study N2O and NO emission estimates were determined for the Nyungwe forest using both an experimental and process-based model (ForestDNDC-tropica) approach. The results of this work led to (i) a better understanding of the N2O and NO source strength of the central African highland tropical forest, (ii) improved NO and N2O emission estimates from the central African tropical highland forest and (iii) a better insight in parameters that preferentially should be monitored to allow better global simulations of N2O and NO emissions from tropical rainforests

Nonparametric techniques for predicting soil bulk density of tropical rainforest topsoils in Rwanda

Nonparametric techniques are of interest for soil and environmental sciences because they enable to effectively predict soil data from basic soil properties without the need of a priori selected equations. We applied two nonparametric techniques, k-nearest neighbor (k-NN) and boosted regression tree (BRT), on data of an existing soil survey database to predict topsoil bulk density (BD) of a tropical mountain forest (Nyungwe) in Rwanda. Soil particle size distribution, organic carbon (OC) content, pH, and cation exchange capacity (CEC) were used as input data and soil depth (topsoil or subsoil), land use (forest or nonforest) and soil horizon notation were tested as possible grouping and limiting factors for model training. The k-NN and BRT techniques showed a comparable performance and predicted BD for an independent data set equally well as the Adams-Minasny-Hartemink and Adams-Rawls-Brakensiek pedotransfer function (PTF) but significantly better than the Adams-De Vos-et al. PTF developed for tropical nonforest soils, nontropical (United States) nonforest soils and soils in the tropics, and nontropical (Belgian) forest soils, respectively. Adding particle size distribution, pH, and cation exchange capacity (CEC) as input variables or grouping samples by different limiting factors did not enhance the predictive capacity significantly compared to a model that used OC content as the sole input. Thus, it appears that robust soil OC data is essential for successfully predicting soil BD in African tropical forests, which in turn is an essential parameter for soil fertility assessments and drives many biogeochemical models. Despite this, OC levels still remain largely unknown for such areas. High throughput analyses based on infrared (IR) spectroscopy might help in collecting OC data for data poor areas

N2O and NO emission from the Nyungwe tropical highland rainforest in Rwanda

Tropical forest soils are a significant source for N2O and NO. Current estimates of N2O and NO emissions areuncertain due to the limited number of fieldmeasurements and model input data. Furthermore, considerable spatialand temporal variability exists due to variation of soil properties, vegetation characteristics and meteorology.Weused a process-based model (ForestDNDC-tropica) to estimate N2O and NO emissions from the entire (970 km2)tropical highland forest (Nyungwe) in southwestern Rwanda. Scaling these results to that regional level using legacysoil, meteorological and simulated vegetation data we found in most cases agreement between N2O and NOmeasurements and model predictions. Limited agreement was found for acid soils with high clay content and reducedmetals, indicating that abiotic N2O and NO forming processes in acidic soils might be under-represented inthe current ForestDNDC-tropica model. The Nyungwe forest was estimated to emit 439 t N2O-N year?1 (2.8–5.5 kg N2O-N ha?1 year?1) and 244 t NO-N year?1 (0.8–5.1 kg N ha?1 year?1), corroborating previous studies intropical forests and highlighting that also tropical highland rainforest soils are a major source of atmospheric N2Oand NO. The uncertainty for the N2O and NO emission estimates was 153 and 50 t N2O-N year?1 and 36 and 16 tNO-N year?1 considering uncertainty in model input data and annual variability, respectively. The results showedthat soil bulk density and pH were the most influential factors driving spatial variation and model uncertainty. Toimprove global model-based estimates of N2O and NO emission from tropical forest focus should therefore alsobe oriented in delivering more detailed soil and vegetation data