Uncertainty in estimating land use and management impacts on soil organic carbon storage for US agricultural lands between 1982 and 1997

Abstract

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.

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

“…When partitioning the large region under consideration into spatially adjacent divisions, the within-cell variation must be accounted for first (King et al, 1991;van Bodegom et al, 2000;Ogle et al, 2003Ogle et al, , 2010). The baseline model estimate is usually established by running the model once in a cell.…”

“…It is preferable to partition the region so that the model inputs in the cells are as statistically independent of each other as possible (King et al, 1991;Ogle et al, 2003Ogle et al, , 2010. When data are scarce, however, the criterion of intercell independence may result in the partition of large cells, leading to a reduced level of spatial details.…”

“…However, a finer spatial resolution requires sufficient model input data; otherwise, data must be shared among cells for at least some, if not all, the model inputs. This type of intercell nonindependence among the cells (resulting from data scarcity and requiring data sharing) complicates the uncertainty analysis (Ogle et al, 2003) when finer spatial resolutions are adopted.…”

“…To analyze the uncertainty due to errors in model inputs in each cell, the Monte Carlo simulation has been recognized as an effective method (IPCC, 2000), and it has been applied in many studies (Ogle et al, 2003(Ogle et al, , 2010Yu et al, 2012). Based on the probability distribution functions (PDFs) derived from measurements and/or a priori knowledge of the model inputs, the Monte Carlo method involves randomly and repeatedly drawing values from the PDFs to drive the model and produce varying model estimates.…”

Agricultural land abandonment in Mediterranean environment provides ecosystem services via soil carbon sequestration

“…In the original IPCC method, the SOCref is based on values from undisturbed native ecosystems. This approach was modified, according to Ogle and Breidt (2003), to use conventionally managed cropland as the reference (SOCref) instead of native conditions. In fact, SOC stock measurements are more reliable in soils under cultivated than natural conditions and agriculture management must be considered the reference SOC (SOCref).…”