Management intensity controls soil N2O fluxes in an Afromontane ecosystem

Machine learning reveals dynamic controls of soil nitrous oxide emissions from diverse long-term cropping systems

Soil nitrous oxide (N2O) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003-2019) of measurements at the Kellogg Biological Station Long-Term Ecological Research (LTER)/Long-Term Agroecosystem Research (LTAR) site to better understand the controls of N2O emissions in four corn-soybean-winter wheat rotations employing conventional, no-till, reduced input, and biologically based/organic inputs. We used a random forest machine learning model to predict daily N2O fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2-day precipitation, water-filled pore space, and soil nitrate and ammonium concentrations. The model explained 29%-42% of daily N2O flux variability in the test data, with greater predictability for the corn phase in each system. The long-term rotations showed different controlling factors and threshold conditions influencing N2O emissions. In the conventional system, the model identified ammonium (>15 kg N ha-1) and daily air temperature (>23°C) as the most influential variables; in the no-till system, climate variables such as precipitation and air temperature were important variables. In low-input and organic systems, where red clover (Trifolium repens L.; before corn) and cereal rye (Secale cereale L.; before soybean) cover crops were integrated, nitrate was the predominant predictor of N2O emissions, followed by precipitation and air temperature. In low-input and biologically based systems, red clover residues increased soil nitrogen availability to influence N2O emissions. Long-term data facilitated machine learning for predicting N2O emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers.

Soil N2 O emissions from specialty crop systems: A global estimation and meta-analysis

Nitrous oxide (N2 O) exacerbates the greenhouse effect and thus global warming. Agricultural management practices, especially the use of nitrogen (N) fertilizers and irrigation, increase soil N2 O emissions. As a vital sector of global agriculture, specialty crop systems usually require intensive input and management. However, soil N2 O emissions from global specialty crop systems have not been comprehensively evaluated. Here, we synthesized 1137 observations from 114 published studies, conducted a meta-analysis to evaluate the effects of agricultural management and environmental factors on soil N2 O emissions, and estimated global soil N2 O emissions from specialty crop systems. The estimated global N2 O emission from specialty crop soils was 1.5 Tg N2 O-N year-1 , ranging from 0.5 to 4.5 Tg N2 O-N year-1 . Globally, soil N2 O emissions exponentially increased with N fertilizer rates. The effect size of N fertilizer on soil N2 O emissions generally increased with mean annual temperature, mean annual precipitation, and soil organic carbon concentration but decreased with soil pH. Global climate change will further intensify the effect of N fertilizer on soil N2 O emissions. Drip irrigation, fertigation, and reduced tillage can be used as essential strategies to reduce soil N2 O emissions and increase crop yields. Deficit irrigation and non-legume cover crop can reduce soil N2 O emissions but may also lower crop yields. Biochar may have a relatively limited effect on reducing soil N2 O emissions but be effective in increasing crop yields. Our study points toward effective management strategies that have substantial potential for reducing N2 O emissions from global agricultural soils.

Deep decarbonization options for the agriculture, forestry, and other land use (AFOLU) sector in Africa: a systematic literature review

Greenhouse gases (GHG) emanating from agriculture, forestry, and other land use (AFOLU) sector are among top contributors to anthropogenic climate change in Africa and globally. Minimizing AFOLU sector GHG emissions in Africa is notoriously hard because of difficulties in emission estimation, the disperse nature of AFOLU emissions, and the complex links between AFOLU activities and poverty reduction. Yet, there are very few systematic reviews dealing with decarbonization pathways for the AFOLU sector in Africa. This article explores the options for achieving deep decarbonization of AFOLU sector in Africa, through a systematic review. Using the method of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA Statement), 46 studies of interest were selected from the databases of Scopus, Google Scholar, and Web of Science. Four sub-themes were identified from the critical review of the selected studies covering key decarbonization approaches used in AFOLU sector. The literature suggests that while forest management and reforestation reduction of GHG in animal production and climate-smart practices in agriculture hold great promises for AFOLU sector decarbonization in Africa, there appears to be very limited coherent policy in the continent addressing any of these AFOLU sub-sectors.

Deciphering nitrous oxide emissions from tropical soils of different land uses

Tropical regions are hotspots of increasing greenhouse gas emissions associated with land-use change. Although many field studies have quantified soil fluxes of nitrous oxide (N₂O; a potent greenhouse gas) from various land uses, the driving mechanisms remain uncertain. Here, we used tropical soils of diverse land uses and actively manipulated the soil moisture (35%, 60%, and 95% water-filled pore space [WFPS]) and substrate supply (control, nitrate, and nitrate plus glucose) to investigate the responses of N₂O emissions with short-term incubations. We then identified key factors regulating N₂O emissions out of a series of soil physicochemical and biological factors and explored how these factors interacted to drive N₂O emissions. Land-use changes from primary forest to oil palm or Acacia plantation risks emitting more N₂O, whereas low emissions could be maintained by conversion to Macaranga forest or Imperata grassland; these laboratory observations were corroborated by a literature synthesis of field N₂O measurements across tropical regions. Soil redox potential (Eh) and labile organic nitrogen (LON; amino acid mixture, arginine, and urea) mineralization were among the factors with greatest influence on N₂O emissions. In contrast to common understandings, the control of WFPS over N₂O emissions was largely indirect, and acted through Eh. The mineralization of LON, particularly arginine, potentially played multiple roles in N₂O production (e.g., bottlenecks of nitrifier-denitrification or simultaneous nitrification-denitrification versus substrate competition for co-denitrification). Structural equation models suggest that soil-environmental factors of different levels (from distal including land use, soil moisture, and pH to proximal such as LON mineralization) drive N₂O emissions through cascading interactions. Overall, we show that, despite identical initial soil conditions, land conversion can substantially alter the N₂O emission potential. Also, collectively considering soil-environmental regulators and their interactions associated with land conversion is crucial to predict and design mitigation strategies for N₂O emissions from land-use change.

Integrating major agricultural practices into the TRIPLEX-GHG model v2.0 for simulating global cropland nitrous oxide emissions: Development, sensitivity analysis and site evaluation

Nitrous oxide (N₂O) emissions from croplands are one of the most important greenhouse gas sources while the estimation of which remains large uncertainties globally. To simulate N₂O emissions from global croplands, the process-based TRIPLEX-GHG model v2.0 was improved by coupling the major agricultural activities. Sensitivity experiment was used to measure the impact of the integrated processes to modeled N₂O emission found chemical N fertilization have the highest relative effect sizes. While the coefficient of the NO₃^- consumption rate for denitrification (COE_dNO3), controlling the first step of the denitrification process was identified to be the most sensitive parameter based on sensitivity analysis of model parameters. The model performed well when simulating the magnitude of the daily N₂O emissions for 39 calibration sites and the continental mean of the parameters were used to producing reasonable estimations for the means of the measured daily N₂O fluxes (R² = 0.87, slope = 1.07) and emission factors (EFs, R² = 0.70, slope = 0.72) during the experiment periods. The model reliability was further confirmed by model validation. General trend of modeled daily N₂O emissions were reasonably consistent with the observations of selected validated sites. In addition, high correlations between the results of modeled and observed mean N₂O emissions (R² = 0.86, slope = 0.82) and EFs (R² = 0.66, slope = 0.83) from 68 validation sites were obtained. Further improvement on more detailed estimations for the variation of the environmental factors, management effects as well as accurate model input model driving data are required to reduce the uncertainties of model simulations. Consequently, our simulation results demonstrate that the TRIPLEX-GHG model v2.0 can reliably estimate N₂O emissions from various croplands at the global scale, which contributes to closing global N₂O budget and sustainable development of agriculture.

Low N2O and variable CH4 fluxes from tropical forest soils of the Congo Basin

Globally, tropical forests are assumed to be an important source of atmospheric nitrous oxide (N₂O) and sink for methane (CH₄). Yet, although the Congo Basin comprises the second largest tropical forest and is considered the most pristine large basin left on Earth, in situ N₂O and CH₄ flux measurements are scarce. Here, we provide multi-year data derived from on-ground soil flux (n = 1558) and riverine dissolved gas concentration (n = 332) measurements spanning montane, swamp, and lowland forests. Each forest type core monitoring site was sampled at least for one hydrological year between 2016 - 2020 at a frequency of 7-14 days. We estimate a terrestrial CH₄ uptake (in kg CH₄-C ha⁻¹ yr⁻¹) for montane (−4.28) and lowland forests (−3.52) and a massive CH₄ release from swamp forests (non-inundated 2.68; inundated 341). All investigated forest types were a N₂O source (except for inundated swamp forest) with 0.93, 1.56, 3.5, and −0.19 kg N₂O-N ha⁻¹ yr⁻¹ for montane, lowland, non-inundated swamp, and inundated swamp forests, respectively.

The Congo Basin is home to the second largest stretch of continuous tropical forest, but the magnitude of greenhouse fluxes are poorly understood. Here the authors analyze gas samples and find the region is not actually a hotspot of N2O emissions.