Soil responses to management, increased precipitation, and added nitrogen in ponderosa pine forests

Forest management, climatic change, and atmospheric N deposition can affect soil biogeochemistry, but their combined effects are not well understood. We examined the effects of water and N amendments and forest thinning and burning on soil N pools and fluxes in ponderosa pine forests near Flagstaff, Arizona (USA). Using a ¹⁵N-depleted fertilizer, we also documented the distribution of added N into soil N pools. Because thinning and burning can increase soil water content and N availability, we hypothesized that these changes would alleviate water and N limitation of soil processes, causing smaller responses to added N and water in the restored stand. We found little support for this hypothesis. Responses of fine root biomass, potential net N mineralization, and the soil microbial N to water and N amendments were mostly unaffected by stand management. Most of the soil processes we examined were limited by N and water, and the increased N and soil water availability caused by forest restoration was insufficient to alleviate these limitations. For example, N addition caused a larger increase in potential net nitrification in the restored stand, and at a given level of soil N availability, N addition had a larger effect on soil microbial N in the restored stand. Possibly, forest restoration increased the availability of some other limiting resource, amplifying responses to added N and water. Tracer N recoveries in roots and in the forest floor were lower in the restored stand. Natural abundance δ¹⁵N of labile soil N pools were higher in the restored stand, consistent with a more open N cycle. We conclude that thinning and burning open up the N cycle, at least in the short term, and that these changes are amplified by enhanced precipitation and N additions. Our results suggest that thinning and burning in ponderosa pine forests will not increase their resistance to changes in soil N dynamics resulting from increased atmospheric N deposition or increased precipitation due to climatic change. Restoration plans should consider the potential impact on long-term forest productivity of greater N losses from a more open N cycle, especially during the period immediately after thinning and burning.

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Authors:Hungate BA Hart SC Selmants PC Boyle SI Gehring CA

Understanding drivers of straw decomposition is essential for adopting appropriate management practice to improve soil fertility and promote carbon (C) sequestration in agricultural systems. However, predicting straw decomposition and characteristics is difficult because of the interactions between many factors related to straw properties, soil properties, and climate, especially under future climate change conditions. This study investigated the driving factors of straw decomposition of six types of crop straw including wheat, maize, rice, soybean, rape, and other straw by synthesizing 1642 paired data from 98 published papers at spatial and temporal scales across China. All the data derived from the field experiments using little bags over twelve years. Overall, despite large differences in climatic and soil properties, the remaining straw carbon (C, %) could be accurately represented by a three-exponent equation with thermal time (accumulative temperature). The lignin/nitrogen and lignin/phosphorus ratios of straw can be used to define the size of labile, intermediate, and recalcitrant C pool. The remaining C for an individual type of straw in the mild-temperature zone was higher than that in the warm-temperature and subtropical zone within one calendar year. The remaining straw C after one thermal year was 40.28%, 37.97%, 37.77%, 34.71%, 30.87%, and 27.99% for rice, soybean, rape, wheat, maize, and other straw, respectively. Soil available nitrogen and phosphorus influenced the remaining straw C at different decomposition stages. For one calendar year, the total amount of remaining straw C was estimated to be 29.41 Tg and future temperature increase of 2 °C could reduce the remaining straw C by 1.78 Tg. These findings confirmed the long-term straw decomposition could be mainly driven by temperature and straw quality, and quantitatively predicted by thermal time with the three-exponent equation for a wide array of straw types at spatial and temporal scales in agro-ecosystems of China.

Runoff in the United States is changing, and this study finds that the measured change is dependent on the geographic region and varies seasonally. Specifically, observed annual total runoff had an insignificant increasing trend in the US between 1950 and 2010, but this insignificance was due to regional heterogeneity with both significant and insignificant increases in the eastern, northern, and southern US, and a greater significant decrease in the western US. Trends for seasonal mean runoff also differed across regions. By region, the season with the largest observed trend was autumn for the east (positive), spring for the north (positive), winter for the south (positive), winter for the west (negative), and autumn for the US as a whole (positive). Based on the detection and attribution analysis using gridded WaterWatch runoff observations along with semi-factorial land surface model simulations from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), we found that while the roles of CO 2 concentration, nitrogen deposition, and land use and land cover were inconsistent regionally and seasonally, the effect of climatic variations was detected for all regions and seasons, and the change in runoff could be attributed to climate change in summer and autumn in the south and in autumn in the west. We also found that the climate-only and historical transient simulations consistently underestimated the runoff trends, possibly due to precipitation bias in the MsTMIP driver or within the models themselves.

Abstract Phenology is a first-order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system. Here, we present the PHENOR R package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network (USA-NPN), the Pan European Phenology Project (PEP725), MODIS phenology (MCD12Q2) combined with (global) retrospective and projected climate data. We show an example analysis, using the phenor modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared (RMSE) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework. In conclusion, we hope the phenor phenology modelling framework in the r language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products.