Diffuse reflectance spectroscopy (DRS) is emerging as a rapid and cost-effective alternative to routine laboratory analysis for many soil properties. However, it has primarily been applied in project-specific contexts. Here, we provide an assessment of DRS spectroscopy at the scale of the continental United States by utilizing the large (n > 50,000) USDA National Soil Survey Center mid-infrared spectral library and associated soil characterization database. We tested and optimized several advanced statistical approaches for providing routine predictions of numerous soil properties relevant to studying carbon cycling. On independent validation sets, the machine learning algorithms Cubist and memory-based learner (MBL) both outperformed random forest (RF) and partial least squares regressions (PLSR) and produced excellent overall models with a mean R² of 0.92 (mean ratio of performance to deviation = 6.5) across all 10 soil properties. We found that the use of root-mean-square error (RMSE) was misleading for understanding the actual uncertainty about any particular prediction; therefore, we developed routines to assess the prediction uncertainty for all models except Cubist. The MBL models produced much more precise predictions compared with global PLSR and RF. Finally, we present several techniques that can be used to flag predictions of new samples that may not be reliable because their spectra fall outside of the calibration set.

Accurate knowledge of¹³C isotopic signature (δ¹³C) of methane from each source is crucial for separating biogenic, fossil fuel and pyrogenic emissions in bottom-up and top-down methane budget. Livestock production is the largest anthropogenic source in the global methane budget, mostly from enteric fermentation of domestic ruminants. However, the global average, geographical distribution and temporal variations of the δ¹³C of enteric emissions are not well understood yet. Here, we provide a new estimation of C3-C4 diet composition of domestic ruminants (cattle, buffaloes, goats and sheep), a revised estimation of yearly enteric CH₄emissions, and a new estimation for the evolution of its δ¹³C during the period 1961–2012. Compared to previous estimates, our results suggest a larger contribution of ruminants’ enteric emissions to the increasing trend in global methane emissions between 2000 and 2012, and also a larger contribution to the observed decrease in the δ¹³C of atmospheric methane.

Quantifying the holistic notion of soil health requires a large suite of measurements spanning the physical, chemical and biological properties of soil. The cost of measuring the full suite of soil health indicators via traditional methods is cost prohibitive for the spatial and temporal monitoring many land managers would like. Here we investigated the ability to make predictions of numerous soil health indicators from the large mid‐infrared (MIR) spectral library developed by the National Soil Survey Center. We developed nationally applicable models with test‐set validation coefficient of determination ( R ² ) > 0.80 for water retention (WT), bulk density (BD), texture (as percentages of sand, silt and clay), cation exchange capacity (CEC), exchangeable bases, base saturation (BS), electrical conductivity (EC), pH, soil organic carbon (SOC) and total nitrogen (TN). Using the memory‐based learning (MBL) model, a locally weighted approach, we found that this database could also predict aggregate stability, plant available P and K with an R ² > 0.70 on independent validation samples. Models for nitrate and micronutrients were less successful but these properties were only sparsely represented in the database. Two test cases were used to demonstrate the utility of MIR spectroscopy in providing a wealth of data for a fraction of the time and cost involved with traditional analyses. While MIR spectroscopy‐based predictions may not always be appropriate for applications requiring very high levels of accuracy, results from this work and elsewhere suggest that this technology can replace, or at least supplement, traditional analyses for many purposes.

Much concern has been raised about the increasing threat to air quality and human health due to ammonia (NH₃) emissions from agricultural systems, which is associated with the enrichment of reactive nitrogen (N) in southern Asia (SA), home of more than 60% the world's population (i.e., the people of West, central, East, South, and Southeast Asia). Southern Asia consumed more than half of the global synthetic N fertilizer and was the dominant region for livestock waste production since 2004. Excessive N application could lead to a rapid increase of NH₃ in the atmosphere, resulting in severe air and water pollution in this region. However, there is still a lack of accurate estimates of NH₃ emissions from agricultural systems. In this study, we simulated the agricultural NH₃ fluxes in SA by coupling the Bidirectional NH₃ exchange module (Bi‐NH₃) from the Community Multi‐scale Air Quality model with the Dynamic Land Ecosystem Model. Our results indicated that NH₃ emissions were 21.3 ± 3.9 Tg N yr⁻¹ from SA agricultural systems with a rapidly increasing rate of ~0.3 Tg N yr⁻² during 1961−2014. Among the emission sources, 10.8 Tg N yr⁻¹ was released from synthetic N fertilizer use, and 10.4 ± 3.9 Tg N yr⁻¹ was released from manure production in 2014. Ammonia emissions from China and India together accounted for 64% of the total amount in SA during 2000−2014. Our results imply that the increased NH₃ emissions associated with high N inputs to croplands would likely be a significant threat to the environment and human health unless mitigation efforts are applied to reduce these emissions.