No‐tillage (NT) management has been promoted as a practice capable of offsetting greenhouse gas (GHG) emissions because of its ability to sequester carbon in soils. However, true mitigation is only possible if the overall impact of NT adoption reduces the net global warming potential (GWP) determined by fluxes of the three major biogenic GHGs (i.e. CO2, N2O, and CH4). We compiled all available data of soil‐derived GHG emission comparisons between conventional tilled (CT) and NT systems for humid and dry temperate climates. Newly converted NT systems increase GWP relative to CT practices, in both humid and dry climate regimes, and longer‐term adoption (>10 years) only significantly reduces GWP in humid climates. Mean cumulative GWP over a 20‐year period is also reduced under continuous NT in dry areas, but with a high degree of uncertainty. Emissions of N2O drive much of the trend in net GWP, suggesting improved nitrogen management is essential to realize the full benefit from carbon storage in the soil for purposes of global warming mitigation. Our results indicate a strong time dependency in the GHG mitigation potential of NT agriculture, demonstrating that GHG mitigation by adoption of NT is much more variable and complex than previously considered, and policy plans to reduce global warming through this land management practice need further scrutiny to ensure success.

We propose a new time series representation of persistence in conditional variance called a long memory stochastic volatility (LMSV) model. The LMSV model is constructed by incorporating an ARFIMA process in a standard stochastic volatility scheme. Strongly consistent estimators of the parameters of the model are obtained by maximizing the spectral approximation to the Gaussian likelihood. The finite sample properties of the spectral likelihood estimator are analyzed by means of a Monte Carlo study. An empirical example with a long time series of stock prices demonstrates the superiority of the LMSV model over existing (short-memory) volatility models, ~ 1998 Elsevier Science S.A.

We conducted a meta-analysis to quantify the impact of changing agricultural land use and management on soil organic carbon (SOC) storage under moist and dry climatic conditions of temperate and tropical regions. We derived estimates of management impacts for a carbon accounting approach developed by the Intergovernmental Panel on Climate Change, addressing the impact of long-term cultivation, setting-aside land from crop production, changing tillage management, and modifying C input to the soil by varying cropping practices. We found 126 articles that met our criteria and analyzed the data in linear mixed-effect models. In general, management impacts were sensitive to climate in the following order from largest to smallest changes in SOC: tropical moist>tropical dry>temperate moist>temperate dry. For example, long-term cultivation caused the greatest loss of SOC in tropical moist climates, with cultivated soils having 0.58 AE 0.12, or 58% of the amount found under native vegetation, followed by tropical dry climates with 0.69 AE 0.13, temperate moist with 0.71 AE 0.04, and temperate dry with 0.82 AE 0.04. Similarly, converting from conventional tillage to no-till increased SOC storage over 20 years by a factor of 1.23 AE 0.05 in tropical moist climates, which is a 23% increase in SOC, while the corresponding change in tropical dry climates was 1.17 AE 0.05, temperate moist was 1.16 AE 0.02, and temperate dry was 1.10 AE 0.03. These results demonstrate that agricultural management impacts on SOC storage will vary depending on climatic conditions that influence the plant and soil processes driving soil organic matter dynamics.

Uncertainty was quantified for an inventory estimating change in soil organic carbon (SOC) storage resulting from modifications in land use and management across US agricultural lands between 1982 and 1997. This inventory was conducted using a modified version of a carbon (C) accounting method developed by the Intergovernmental Panel on Climate Change (IPCC). Probability density functions (PDFs) were derived for each input to the IPCC model, including reference SOC stocks, land use/management activity data, and management factors. Change in C storage was estimated using a Monte‐Carlo approach with 50 000 iterations, by randomly selecting values from the PDFs after accounting for dependencies in the model inputs. Over the inventory period, mineral soils had a net gain of 10.8 Tg C yr−1, with a 95% confidence interval ranging from 6.5 to 15.3 Tg C yr−1. Most of this gain was due to setting‐aside lands in the Conservation Reserve Program. In contrast, managed organic soils lost 9.4 Tg C yr−1, with a 95% confidence interval ranging from 6.4 to 13.3 Tg C yr−1. Combining these gains and losses in SOC, US agricultural soils accrued 1.3 Tg C yr−1 due to land use and management change, with a 95% confidence interval ranging from a loss of 4.4 Tg C yr−1 to a gain of 6.9 Tg C yr−1. Most of the uncertainty was attributed to management factors for tillage, land use change between cultivated and uncultivated conditions, and C loss rates from managed organic soils. Based on the uncertainty, we are not able to conclude with 95% confidence that change in US agricultural land use and management between 1982 and 1997 created a net C sink for atmospheric CO2.

Process-based model analyses are often used to estimate changes in soil organic carbon (SOC), particularly at regional to continental scales. However, uncertainties are rarely evaluated, and so it is difficult to determine how much confidence can be placed in the results. Our objective was to quantify uncertainties across multiple scales in a processbased model analysis, and provide 95% confidence intervals for the estimates. Specifically, we used the Century ecosystem model to estimate changes in SOC stocks for US croplands during the 1990s, addressing uncertainties in model inputs, structure and scaling of results from point locations to regions and the entire country. Overall, SOC stocks increased in US croplands by 14.6 Tg C yr À1 from 1990 to 1995 and 17.5 Tg C yr À1 during 1995 to 2000, and uncertainties were AE 22% and AE 16% for the two time periods, respectively. Uncertainties were inversely related to spatial scale, with median uncertainties at the regional scale estimated at AE 118% and AE 114% during the early and latter part of 1990s, and even higher at the site scale with estimates at AE 739% and AE 674% for the time periods, respectively. This relationship appeared to be driven by the amount of the SOC stock change; changes in stocks that exceeded 200 Gg C yr À1 represented a threshold where uncertainties were always lower than AE 100%. Consequently, the amount of uncertainty in estimates derived from process-based models will partly depend on the level of SOC accumulation or loss. In general, the majority of uncertainty was associated with model structure in this application, and so attaining higher levels of precision in the estimates will largely depend on improving the model algorithms and parameterization, as well as increasing the number of measurement sites used to evaluate the structural uncertainty.