W. Troy Baisden

Soil N and 15N variation with time in a California annual grassland ecosystem

The %N and δ 15N values of soils and plants were measured along a chronosequence spanning 3 to 3000 Ky in a California annual grassland. Total soil N decreased with increasing soil age (1.1 to 0.4 kg N m−2) while the mean δ 15N values of the soil N increased by several ‰ from the youngest to oldest sites (+3.5 to +6.2 ‰). The δ 15N values of plants varied along the gradient, reflecting changing soil N pools and differences in the form of N uptake. The decline in total N storage with time is hypothesized to be due to a shift from N to P limitation with increasing soil age. The general increase in δ 15N values with time is interpreted using a N mass balance model, and appears to reflect a shift toward an increasing proportional losses of inorganic mineral forms of N (vs. organic forms) with increasing soil age. We develop a quantitative index of this trend (mineral vs. organic forms of N loss) using mass balance considerations and parameters. The %N and δ 15N values along the California age gradient were compared to the published data for a comparably aged chronosequence in Hawaii. Most striking in this comparison is the observation that the California soil and plant δ 15N values are several ‰ greater than those on comparably aged Hawaiian sites. Multiple explanations are plausible, but assuming the sites have a similar range in δ 15N values of atmospheric inputs, the isotopic differences suggest that N may be, at least seasonally, in greater excess in the strongly seasonal, semi-arid, California grassland.

AN ANALYTICAL APPROACH TO ECOSYSTEM BIOGEOCHEMISTRY MODELING

Models of terrestrial ecosystem carbon and nutrient biogeochemistry commonly utilize several compartments of organic matter (OM) to simulate the storage and turnover of OM in plant–soil systems. While much work has focused on numerical simulations of ecosystems to understand responses to perturbations, relatively little work explores the analytical properties of the mathematical systems used and their implications for the understanding of terrestrial ecosystems. We explore carbon and nutrient dynamics using a 4 × 4 transfer matrix, which is similar to the mathematical core of commonly used simulation models (e.g., Century). The C and N vectors contain one pool for photosynthesizing biomass and several pools for soil detritis. The model yields steady-state solutions for the C pool size of each compartment dependent solely on the amount of the nutrient (nitrogen) stored within the compartment and the C:nutrient ratio. Furthermore, the C and nutrient storage in the model compartments depends on the inputs and losses of nutrients to and from the system as well as the coefficients for turnover from and transfer between compartments. When examining the transient behavior of the system, the eigenvalues for the system provide a useful tool for predicting and understanding the characteristic time scales with which plant–soil systems respond to perturbations. We use the model eigenvalues to examine ecosystem development from freshly exposed parent material as well as the response to a “catastrophic” disturbance event. Ultimately, the models analytical tools can be extended to characterize soil organic matter (SOM) responses to several major land-use changes occurring in agroecosystems—including the initiation of tillage, the application of industrial fertilizers, and the introduction of reduced-tillage practices. Analysis of the eigenvalues for these cases suggests that agroecosystems tend to lose SOM more rapidly following the initiation of tillage than they can regain SOM after the initiation of conservation-tillage practices. Additionally, the amount of N export (harvesting or loss) places an important control on the storage and future productivity of agricultural systems. Overall, we suggest that the model—with straightforward and accessible steady-state solutions and eigenvalues—offers a complementary perspective to more complex numerical simulations. Corresponding Editor: D. S. Schimel

Influence of soils on oxygen isotope ratio of atmospheric CO2

The soil component of atmospheric CO 2 oxygen isotope budget is evaluated in light of the recent recognition of abiotic oxygen isotope exchange of atmospheric CO 2 and soil water in excess of soil respiratory CO 2 flux. By using variations of published analytical models, we estimate that the amount of atmospheric CO 2 that undergoes oxygen isotope exchange with soil water, exclusive of soil-respired CO 2 , to be approximately 0.2-0.7 μmol m -2 s -1 for representative unsaturated soils from a range of biomes. Globally, the amount of atmospheric CO 2 that undergoes oxygen isotope exchange with soil water through purely nonbiological processes is probably significantly larger than the current annual fossil fuel combustion, yet this process has been neglected in all recent global 18 O-CO 2 budgets. Furthermore, abiotic oxygen isotope exchange with soils will occur roughly equally in soils with low and high respiration rates, suggesting that soils with low respiration rates are currently under-represented disproportionately in the existing global CO 2 oxygen isotope budgets. Because soils with low respiration rates tend to have the most extreme soil water δ 18 O values, their underrepresentation may have a large and heretofore unsuspected impact on the global atmospheric C 18 O 16 O budget. Finally, soil carbon is no longer in steady state due to land use practices, and this additional source of CO 2 to the atmosphere may contribute to the decreasing trend in atmospheric CO 2 -δ 18 O values through time.

Stable Isotope Tracers and Mathematical Models in Soil Organic Matter Studies

Light stable isotopes have become widely used bio-geochemical tracers in earth science and ecology research during the last half of this century. With some exceptions, these applications have originated as a result of pioneering geochemical research at the University of Chicago and the California Institute of Technology in the 1950s and 1960s (Taylor et al. 1991). The breadth of isotopic tracer studies in present-day ecosystem sciences is now so great that it eludes even the most ambitious review article or book.

Agricultural and forest productivity for modelling policy scenarios: Evaluating approaches for New Zealand greenhouse gas mitigation

Abstract The New Zealand Government is currently reviewing policies for the Kyoto Protocols first commitment period, and entering international negotiations that will define the rules for the second commitment period. Policy makers and stakeholders require robust tools to evaluate national scale policy scenarios. Models for robust scenario analysis must combine the best available biophysical and economic information, and include understanding of uncertainties. I evaluate three different datasets as candidates for estimating the biological net primary production (NPP) of pastoral and forest land, as well as C sequestration potential under exotic or indigenous forest. For scenario analysis of land‐use change, estimating NPP is critical to represent the flow of agricultural and forest production into the economy, while estimating C sequestration potential in forests is required to estimate the quantity of C available for credits and liabilities. First, a 4‐year average of NASAs 1 km annual NPP product from the MODIS satellite sensor was verified using published New Zealand data, and used to test and calibrate the following indices. The MODIS NPP data were compared to average stocking indices for pasture and site indices for Pinus radiata forests derived from 10‐ to 3 0‐year‐old mapping in the Land Resources Inventory (LRI). Third, a “Storie Index” approach was developed using factors representing climate and soil properties that co‐limit potential productivity, and calibrated to MODIS NPP. Each approach represents the apparent variation in NPP at the national scale, but may risk serious errors at a more local scale. The calibrated Storie Index approach offers the best predictions for pastoral and tussock land (R2= 0.71) and indigenous forest (R2 = 0.38), while the LRI‐derived site index is most predictive for exotic forests (R2 = 0.16). While both the LRI‐derived indices and the “Storie Index” are recommended for modelling policy scenarios, the “Storie Index” approach shows the greatest versatility because it does not depend on current or past land use for estimation, provides the highest level of spatial detail, and can easily be improved.