Soil respiration is affected by distributions of roots and soil carbon substrates and by temperature and soil water content, all of which vary spatially and temporally. The objective of this paper was to compare a manual system for measuring soil respiration in a temperate forest, which had a greater spatial distribution of measurements (n=12), but poorer temporal resolution (once per week), with an automated system which had poorer spatial distribution (n=3) but superior temporal frequency of measurements (hourly). Soil respiration was measured between 18 June and 21 August, 2002, at the Harvard Forest in central Massachusetts, USA. The fluxes measured within 1 h of each other by these systems were not significantly different. However, extrapolations of the mid‐morning manual measurements to daily flux values were consistently lower (averaging 13% lower) than the daily estimates obtained from summing the 24 hourly measurements of the automated system. On the other hand, seasonal flux estimates obtained by interpolating between weekly manual sampling dates or by summing the hourly automated measurements were nearly identical. Underestimates by interpolated weekly manual measurements during some periods were cancelled by overestimates during other periods. Hence, a weekly sampling schedule may be sufficient to capture the most important variation of seasonal efflux of CO₂ from the soil. The larger number of chambers that could be measured with the manual system (larger n) resulted in a smaller 95% confidence interval for characterizing spatial variability within the study area on most dates. However, the greater sampling frequency of the automated system revealed rapid responses of soil respiration to wetting events, which permitted better empirical modelling of the effects of soil temperature and moisture on soil respiration than could have been achieved with the manual sampling system. Most of the positive residuals of a function that predicts soil respiration based on temperature were from fluxes measured within 12 h of a rain event, and the residuals were positively correlated with water content of the O horizon. The automated system also demonstrated that Q₁₀ values calculated for diel variation in soil temperature over a few days were not significantly different than Q₁₀ values for the entire 3 month summer sampling period. In summary, a manual system of numerous, spatially well‐distributed flux chambers measured on a weekly basis may be adequate for measuring seasonal fluxes and may maximize confidence in the characterization of spatial variance. The high temporal frequency of measurements afforded by automation greatly improves the ability to measure and model the effects of rapidly varying water content and temperature. When the two approaches can be combined, the temporal representativeness of the manual measurements can be tested with the automated measurements and the spatial representativeness of the automated measurements can be tested by the manual measurements.

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.

1 Understanding the mechanisms regulating the efflux of carbon dioxide (CO₂) from the soil to the atmosphere via soil respiration (SR) is a critical component of understanding terrestrial carbon (C) cycle responses to climate change, but requires high-quality measurements of SR fluxes. Thus, measurements of SR have become one of the primary tools used in terrestrial C cycling research. 2 When developing a sampling strategy for SR measurements, researchers must consider the ultimate use of the data set. A weekly or bi-weekly manual sampling strategy is likely sufficient if the desired outcome is an annual estimate of CO₂ efflux. However, if modelling SR on time scales from minutes to days is the purpose of the study, automated SR measurements are advantageous. 3 Automated SR systems produce large volumes of data that present new challenges for quality assurance and quality control. A relatively efficient protocol to analyse large SR data sets is proposed here. 4 Analysis of two large data sets provides information about systematic sampling uncertainties as well as random measurement errors. These must be taken into account when using automated SR measurements in any data–model fusion context.

Production and consumption of nitrous oxide (N₂O), methane (CH₄), and carbon dioxide (CO₂) are affected by complex interactions of temperature, moisture, and substrate supply, which are further complicated by spatial heterogeneity of the soil matrix. This microsite heterogeneity is often invoked to explain non‐normal distributions of greenhouse gas (GHG) fluxes, also known as hot spots and hot moments. To advance numerical simulation of these belowground processes, we expanded the Dual Arrhenius and Michaelis–Menten model, to apply it consistently for all three GHGs with respect to the biophysical processes of production, consumption, and diffusion within the soil, including the contrasting effects of oxygen (O₂) as substrate or inhibitor for each process. High‐frequency chamber‐based measurements of all three GHGs at the Howland Forest (ME, USA) were used to parameterize the model using a multiple constraint approach. The area under a soil chamber is partitioned according to a bivariate log‐normal probability distribution function (PDF) of carbon and water content across a range of microsites, which leads to a PDF of heterotrophic respiration and O₂ consumption among microsites. Linking microsite consumption of O₂ with a diffusion model generates a broad range of microsite concentrations of O₂, which then determines the PDF of microsites that produce or consume CH₄ and N₂O, such that a range of microsites occurs with both positive and negative signs for net CH₄ and N₂O flux. Results demonstrate that it is numerically feasible for microsites of N₂O reduction and CH₄ oxidation to co‐occur under a single chamber, thus explaining occasional measurement of simultaneous uptake of both gases. Simultaneous simulation of all three GHGs in a parsimonious modeling framework is challenging, but it increases confidence that agreement between simulations and measurements is based on skillful numerical representation of processes across a heterogeneous environment.