Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders

Multivariate time series (MTS) imputation is a widely studied problem in recent years. Existing methods can be divided into two main groups, including (1) deep recurrent or generative models that primarily focus on time series features, and (2) graph neural networks (GNNs) based models that utilize the topological information from the inherent graph structure of MTS as relational inductive bias for imputation. Nevertheless, these methods either neglect topological information or assume the graph structure is fixed and accurately known. Thus, they fail to fully utilize the graph dynamics for precise imputation in more challenging MTS data such as networked time series (NTS), where the underlying graph is constantly changing and might have missing edges. In this paper, we propose a novel approach to overcome these limitations. First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures. Then, we design a new model named PoGeVon which leverages variational autoencoder (VAE) to predict missing values over both node time series features and graph structures. In particular, we propose a new node position embedding based on random walk with restart (RWR) in the encoder with provable higher expressive power compared with message-passing based graph neural networks (GNNs). We further design a decoder with 3-stage predictions from the perspective of multi-task learning to impute missing values in both time series and graph structures reciprocally. Experiment results demonstrate the effectiveness of our model over baselines.Comment: KDD 202

Soil carbon under current and improved land management in Kenya, Ethiopia and India: Dynamics and sequestration potentials

Agriculture is a major contributor to climate change, emitting the three major greenhouse gases (GHGs) – carbon dioxide (CO2), methane and nitrous oxide – into the atmosphere. According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), the Agriculture, Forestry and Other Land Use sector “is responsible for just under a quarter (~10–12 Gt CO2eq/yr) of [all] anthropogenic GHG emissions mainly from deforestation and agricultural emissions from livestock, soil and nutrient management”. Land use change – often associated with deforestation – contributes about 11.2% to this share, while agricultural production is responsible for 11.8% (IPCC, 2014). To reduce emissions from agriculture, while providing and maintaining global food security, there is a growing interest to develop and promote low-emission greengrowth pathways for future agricultural production systems. Sub-Saharan Africa (SSA) faces two concerns in that respect: a) agriculture is the major emitter of GHGs on this sub-continent, and b) agriculture is largely underperforming. To feed a growing population, productivity and total production need to increase significantly. To achieve this while reducing emissions from agriculture at the same time is a major challenge. Climate-smart agriculture (CSA) sets out to address this challenge by transforming agricultural systems affected by the vagaries of climate change. CSA aims at improving food security and system’s resilience while addressing climate change mitigation

Simulating soil organic carbon in maize-based systems under improved agronomic management in Western Kenya

Improved management practices should be implemented in croplands in sub-Saharan Africa to enhance soil organic carbon (SOC) storage and/or reduce losses associated with land-use change, thereby addressing the challenge of ongoing soil degradation. DayCent, a process-based biogeochemical model, provides a useful tool for evaluating which management practices are most effective for SOC sequestration. Here, we used the DayCent model to simulate SOC using experimental data from two long-term field sites in western Kenya comprising of two widely promoted sustainable agricultural management practices: integrated nutrient management (i.e. mineral fertilizer and crop residues/farmyard manure incorporation) and conservation agriculture (i.e. minimum tillage and crop residue retention). At both sites, correlations between measured and simulated SOC were low to moderate (R2 of 0.25−0.55), and in most cases, the model produced fairly accurate prediction of the SOC trends with a low relative root mean squared error (RRMSE < 7%). Consistent with field measurements, simulated SOC declined under all improved management practices. The model projected annual SOC loss rates of between 0.32 to 0.35 Mg C ha-1 yr-1 in continuously tilled maize (Zea mays) systems without fertilizer or organic matter application over the period 2003–2050. The most effective practices in reducing the losses were the combined application of 4 Mg ha-1 of farmyard manure and 2 Mg ha-1 of maize residue retention (reducing losses up to 0.22 Mg C ha-1 yr-1), minimum tillage in combination with maize residue retention (0.21 Mg C ha-1 yr-1), and rotation of maize with soybean (Glycine max) under minimum tillage (0.17 Mg C ha-1 yr-1). Model results suggest that response of the passive SOC pool to the different management practices is a key driver of the long-term SOC trends at the two study sites. This study demonstrates the strength of the DayCent model in simulating SOC in maize systems under different agronomic management practices that are typical for western Kenya

Soil biogeochemistry across Central and South American tropical dry forests

The availability of nitrogen (N) and phosphorus (P) controls the flow of carbon (C) among plants, soils, and the atmosphere, thereby shaping terrestrial ecosystem responses to global change. Soil C, N, and P cycles are linked by drivers operating at multiple spatial and temporal scales: landscape-level variation in macroclimate and soil geochemistry, stand-scale heterogeneity in forest composition, and microbial community dynamics at the soil pore scale. Yet in many biomes, we do not know at which scales most of the biogeochemical variation emerges, nor which processes drive cross-scale feedbacks. Here, we examined the drivers and spatial/temporal scales of variation in soil biogeochemistry across four tropical dry forests spanning steep environmental gradients. To do so, we quantified soil C, N, and P pools, extracellular enzyme activities, and microbial community structure across wet and dry seasons in 16 plots located in Colombia, Costa Rica, Mexico, and Puerto Rico. Soil biogeochemistry exhibited marked heterogeneity across the 16 plots, with total organic C, N, and P pools varying fourfold, and inorganic nutrient pools by an order of magnitude. Most soil characteristics changed more across space (i.e., among sites and plots) than over time (between dry and wet season samplings). We observed stoichiometric decoupling among C, N, and P cycles, which may reflect their divergent biogeochemical drivers. Organic C and N pool sizes were positively correlated with the relative abundance of ectomycorrhizal trees and legumes. By contrast, the distribution of soil P pools was driven by soil geochemistry, with larger inorganic P pools in soils with P-rich parent material. Most earth system models assume that soils within a texture class operate similarly, and ignore subgrid cell variation in soil properties. Here we reveal that soil nutrient pools and fluxes exhibit as much variation among four Neotropical dry forests as is observed across terrestrial ecosystems at the global scale. Soil biogeochemical patterns are driven not only by regional differences in soil parent material and climate, but also by local-scale variation in plant and microbial communities. Thus, the biogeochemical patterns we observed across the Neotropical dry forest biome challenge representation of soil processes in ecosystem models

Inorganic carbon is overlooked in global soil carbon research: A bibliometric analysis

Soils are a major player in the global carbon (C) cycle and climate change by functioning as a sink or a source of atmospheric carbon dioxide (CO2). The largest terrestrial C reservoir in soils comprises two main pools: organic (SOC) and inorganic C (SIC), each having distinct fates and functions but with a large disparity in global research attention. This study quantified global soil C research trends and the proportional focus on SOC and SIC pools based on a bibliometric analysis and raise the importance of SIC pools fully underrepresented in research, applications, and modeling. Studies on soil C pools started in 1905 and has produced over 47,000 publications (>1.7 million citations). Although the global C stocks down to 2 m depth are nearly the same for SOC and SIC, the research has dominantly examined SOC (>96 % of publications and citations) with a minimal share on SIC (<4%). Approximately 40 % of the soil C research was related to climate change. Despite poor coverage and publications, the climate change-related research impact (citations per document) of SIC studies was higher than that of SOC. Mineral associated organic carbon, machine learning, soil health, and biochar were the recent top trend topics for SOC research (2020–2023), whereas digital soil mapping, soil properties, soil acidification, and calcite were recent top trend topics for SIC. SOC research was contributed by 151 countries compared to 88 for SIC. As assessed by publications, soil C research was mainly concentrated in a few countries, with only 9 countries accounting for 70 % of the research. China and the USA were the major producers (45 %), collaborators (37 %), and funders of soil C research. SIC is a long-lived soil C pool with a turnover rate (leaching and recrystallization) of more than 1000 years in natural ecosystems, but intensive agricultural practices have accelerated SIC losses, making SIC an important player in global C cycle and climate change. The lack of attention and investment towards SIC research could jeopardize the ongoing efforts to mitigate climate change impacts to meet the 1.5–2.0 °C targets under the Paris Climate Agreement of 2015. This bibliographic study calls to expand the research focus on SIC and including SIC fluxes in C budgets and models, without which the representation of the global C cycle is incomplete