A field incubation approach to evaluate the depth dependence of soil biogeochemical responses to climate change.

Soil biogeochemical processes may present depth-dependent responses to climate change, due to vertical environmental gradients (e.g., thermal and moisture regimes, and the quantity and quality of soil organic matter) along soil profile. However, it is a grand challenge to distinguish such depth dependence under field conditions. Here we present an innovative, cost-effective and simple approach of field incubation of intact soil cores to explore such depth dependence. The approach adopts field incubation of two sets of intact soil cores: one incubated right-side up (i.e., non-inverted), and another upside down (i.e., inverted). This inversion keeps soil intact but changes the depth of the soil layer of same depth origin. Combining reciprocal translocation experiments to generate natural climate shift, we applied this incubation approach along a 2200 m elevational mountainous transect in southeast Tibetan Plateau. We measured soil respiration (Rs) from non-inverted and inverted cores of 1 m deep, respectively, which were exchanged among and incubated at different elevations. The results indicated that Rs responds significantly (p < .05) to translocation-induced climate shifts, but this response is depth-independent. As the incubation proceeds, Rs from both non-inverted and inverted cores become more sensitive to climate shifts, indicating higher vulnerability of persistent soil organic matter (SOM) to climate change than labile components, if labile substrates are assumed to be depleted with the proceeding of incubation. These results show in situ evidence that whole-profile SOM mineralization is sensitive to climate change regardless of the depth location. Together with measurements of vertical physiochemical conditions, the inversion experiment can serve as an experimental platform to elucidate the depth dependence of the response of soil biogeochemical processes to climate change.

Spatiotemporal co-optimization of agricultural management practices towards climate-smart crop production.

Co-optimization of multiple management practices may facilitate climate-smart agriculture, but is challenged by complex climate-crop-soil management interconnections across space and over time. Here we develop a hybrid approach combining agricultural system modelling, machine learning and life cycle assessment to spatiotemporally co-optimize fertilizer application, irrigation and residue management to achieve yield potential of wheat and maize and minimize greenhouse gas emissions in the North China Plain. We found that the optimal fertilizer application rate and irrigation for the historical period (1995-2014) are lower than local farmers' practices as well as trial-derived recommendations. With the optimized practices, the projected annual requirement of fertilizer, irrigation water and residue inputs across the North China Plain in the period 2051-2070 is reduced by 16% (14-21%) (mean with 95% confidence interval), 19% (7-32%) and 20% (16-26%), respectively, compared with the current supposed optimal management in the historical reference period, with substantial greenhouse gas emission reductions. We demonstrate the potential of spatiotemporal co-optimization of multiple management practices and present digital mapping of management practices as a benchmark for site-specific management across the region.