Reconciling carbon quality with availability predicts temperature sensitivity of global soil carbon mineralization.

Soil organic carbon (SOC) mineralization is a key component of the global carbon cycle. Its temperature sensitivity Q10 (which is defined as the factor of change in mineralization with a 10 °C temperature increase) is crucial for understanding the carbon cycle-climate change feedback but remains uncertain. Here, we demonstrate the universal control of carbon quality-availability tradeoffs on Q10. When carbon availability is not limited, Q10 is controlled by carbon quality; otherwise, substrate availability controls Q10. A model driven by such quality-availability tradeoffs explains 97% of the spatiotemporal variability of Q10 in incubations of soils across the globe and predicts a global Q10 of 2.1 ± 0.4 (mean ± one SD) with higher Q10 in northern high-latitude regions. We further reveal that global Q10 is predominantly governed by the mineralization of high-quality carbon. The work provides a foundation for predicting SOC dynamics under climate and land use changes which may alter soil carbon quality and availability.

Spatiotemporal patterns and driving factors of gross primary productivity over the Mongolian Plateau steppe in the past 20 years.

The Eurasian steppe is the largest temperate grassland in the world. The grassland of the Mongolian Plateau (MP) represents an important part of the Eurasian steppe with high climatic sensitivity. Gross primary productivity (GPP) is a key indicator of the grassland's production, status and dynamic on the MP. In this study, we calibrated and evaluated the grassland-specific light use efficiency model (GRASS-LUE) against the observed GPP collected from nine eddy covariance flux sites on the MP, and compared the performance with other four GPP products (MOD17, VPM, GLASS and GOSIF). GRASS-LUE with higher R2 (0.91) and lower root mean square error (RMSE = 0.99 gC m-2 day-1) showed a better performance compared to the four GPP products in terms of model accuracy and dynamic consistency, especially in typical and desert steppe. The parameters of the GRASS-LUE are more suitable for water-limited grassland could be the reason for its outstanding performance in typical and desert steppe. Mean grassland GPP derived from GRASS-LUE was higher in the east and lower in the west of the MP. Grassland GPP was on average 205 gC m-2 over the MP between 2001 and 2020 with mean annual total GPP of 322 TgC yr-1. 30 % of the MP steppe showed a significant GPP increase. Growing season precipitation is the main factor affecting GPP of the MP steppe across regions. Anthropogenic factors (livestock density and population density) had greater effect on GPP than growing season temperature in pastoral counties in IM that take grazing as one of main industries. These findings can inform the status and trend of the productivity of MP steppe and help government and scientific research institutions to understand the drivers for spatial pattern of grassland GPP on the MP.

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.

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.

[Quality evaluation of Fufang Jinqiancao granules based on ultra performance liquid chromatography-ultraviolet detection quantitative fingerprint combined with chemical pattern recognition].

Fufang Jinqiancao granules have a large market demand due to the fact that they contain diuretics, inhibit urinary calculi formation, and exhibit both anti-inflammatory and antioxidant effects. In the current study, a fast and efficient quantitative ultra performance liquid chromatography-ultraviolet detection (UPLC-UV) fingerprinting method was established to analyze the Fufang Jinqiancao granules, while a chemical pattern recognition technology was used to evaluate the quality of the granules over different years. More specifically, the UPLC-UV system consisted of a Waters Acquity UPLC BEH C18 column (100 mm×2.1 mm, 1.7 µm), acetonitrile (mobile phase A), and a 0.1% formic acid aqueous solution (mobile phase B), wherein a gradient elution protocol was followed. Ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS, Agilent Infinity Ⅱ 1290-6545) was used in combination with reference substances and literature comparisons to identify the common peaks present in the quantitative fingerprint. The fingerprints of 35 batches of Fufang Jinqiancao granules were established by means of the quantitative UPLC-UV fingerprinting method, and the fingerprint data obtained for these samples were further analyzed by chemical pattern recognition techniques, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). The quality difference markers, namely mangiferin and isomangiferin, were screened, and their contents were determined. It was found that 12 common peaks existed in the fingerprint of the Fufang Jinqiancao granules, and the similarities of all 35 batches of samples were greater than 0.952. In addition, for the purpose of HCA, the 35 batches were divided into two categories, of which sample years 2018 and 2019 belonged to one category, and sample years 2020 and 2021 belonged to another category. Notably, PCA gave the same clustering trends as HCA. Based on the obtained results, the mangiferin and isomangiferin components responsible for the differences between the 2018, 2019 and 2020, 2021 samples were further screened by orthogonal partial least squares discriminant analysis (OPLS-DA). Moreover, the contents of the 35 batches of samples were determined using the two differential markers mangiferin and isomangiferin as indicators. The obtained results indicated that the chromatographic peaks of all 35 batches had acceptable resolutions, with mangiferin exhibiting a good linear relationship in the range of 5.3291-133.2276 mg/L, and isomangiferin exhibiting a similar linear relationship in the range of 4.1847-104.6170 mg/L. The average recovery of mangiferin was 101.7%-105.6%, with a relative standard deviation (RSD) of 0.63%-1.43%, while that of isomangiferin was 103.4%-105.5%, with an RSD of 0.60%-1.18%. Importantly, all RSD values were less than 1.43%, thereby indicating that our method meets the requirements of the Chinese Pharmacopoeia (2020 Edition). Among the 35 batches of samples, the contents of mangiferin and isomangiferin were higher in the 2020 and 2021 samples, and the content fluctuation range was smaller. Overall, the development of an accurate and reliable quality control method for Fufang Jinqiancao granules, and a reasonable and effective quality evaluation of Fufang Jinqiancao granule samples from different years was realized. We therefore expect that this study will provide a reference for establishing a more systematic and comprehensive quality control system.

Global soil profiles indicate depth-dependent soil carbon losses under a warmer climate.

Soil organic carbon (SOC) changes under future climate warming are difficult to quantify in situ. Here we apply an innovative approach combining space-for-time substitution with meta-analysis to SOC measurements in 113,013 soil profiles across the globe to estimate the effect of future climate warming on steady-state SOC stocks. We find that SOC stock will reduce by 6.0 ± 1.6% (mean±95% confidence interval), 4.8 ± 2.3% and 1.3 ± 4.0% at 0-0.3, 0.3-1 and 1-2 m soil depths, respectively, under 1 °C air warming, with additional 4.2%, 2.2% and 1.4% losses per every additional 1 °C warming, respectively. The largest proportional SOC losses occur in boreal forests. Existing SOC level is the predominant determinant of the spatial variability of SOC changes with higher percentage losses in SOC-rich soils. Our work demonstrates that warming induces more proportional SOC losses in topsoil than in subsoil, particularly from high-latitudinal SOC-rich systems.

Rapid Discrimination and Prediction of Ginsengs from Three Origins Based on UHPLC-Q-TOF-MS Combined with SVM.

Ginseng, which contains abundant ginsenosides, grows mainly in the Jilin, Liaoning, and Heilongjiang in China. It has been reported that the quality and traits of ginsengs from different origins were greatly different. To date, the accurate prediction of the origins of ginseng samples is still a challenge. Here, we integrated ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) with a support vector machine (SVM) for rapid discrimination and prediction of ginseng from the three main regions where it is cultivated in China. Firstly, we develop a stable and reliable UHPLC-Q-TOF-MS method to obtain robust information for 31 batches of ginseng samples after reasonable optimization. Subsequently, a rapid pre-processing method was established for the rapid screening and identification of 69 characteristic ginsenosides in 31 batches ginseng samples from three different origins. The SVM model successfully distinguished ginseng origin, and the accuracy of SVM model was improved from 83% to 100% by optimizing the normalization method. Six crucial quality markers for different origins of ginseng were screened using a permutation importance algorithm in the SVM model. In addition, in order to validate the method, eight batches of test samples were used to predict the regions of cultivation of ginseng using the SVM model based on the six selected quality markers. As a result, the proposed strategy was suitable for the discrimination and prediction of the origin of ginseng samples.

Younger carbon dominates global soil carbon efflux.

Soil carbon (C) is comprised of a continuum of organic compounds with distinct ages (i.e., the time a C atom has experienced in soil since the C atom entered soil). The contribution of different age groups to soil C efflux is critical for understanding soil C stability and persistence, but is poorly understood due to the complexity of soil C pool age structure and potential distinct turnover behaviors of age groups. Here, we build upon the quantification of soil C transit times to infer the age of C atoms in soil C efflux (aefflux ) from seven sequential soil layer depths down to 2 m at a global scale, and compare this age with radiocarbon-inferred ages of C retained in corresponding soil layers (asoil ). In the whole 0-2 m soil profile, the mean aefflux is 194 21 1021 (mean with 5%-95% quantiles) year and is just about one-eighth of asoil ( 1476 717 2547 year), demonstrating that younger C dominates soil C efflux. With increasing soil depth, both aefflux and asoil are increased, but their disparities are markedly narrowed. That is, the proportional contribution of relatively younger soil C to efflux is decreased in deeper layers, demonstrating that C inputs (new and young) stay longer in deeper layers. Across the globe, we find large spatial variability of the contribution of soil C age groups to C efflux. Especially, in deep soil layers of cold regions (e.g., boreal forests and tundra), aefflux may be older than asoil , suggesting that older C dominates C efflux only under a limited range of conditions. These results imply that most C inputs may not contribute to long-term soil C storage, particularly in upper layers that hold the majority of new C inputs.

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Decoupling environmental impact from economic growth to achieve Sustainable Development Goals in China.

Developing countries, such as China, have achieved unprecedented success in a single Sustainable Development Goal (SDG), which usually leads to trade-offs between the three pillars of sustainability, and even destroys sustainability. Quantifying the degrees of coupling among the pillars is essential to support policymakers' systematic actions to minimize trade-offs and maximize co-benefits between the pillars, and simultaneously achieve all SDGs. However, assessing the degrees of coupling among the pillars for the full SDGs is lacking. Here, we evaluate the progress of the pillars towards the SDGs and quantify the degrees of coupling among them at both national and sub-national levels in China from 2000 to 2015. The results indicate that the degrees of coupling among the pillars were almost constant while the degrees of coupling between the pillars and economic growth declined over time. The degrees of coupling between environmental impact and economic growth accounted for 52%-83% of the SDGs' progress. Reducing the degrees of coupling helps achieve simultaneously economic growth and environmental protection. The higher the degrees of coupling, the lower progress. This trend was universal among all provinces (sub-national level) regardless of their development levels. Our study highlights not only the necessity to track the degrees of coupling among the pillars, but also decoupling environmental impact from economic growth to achieve the SDGs.

Identification and risk prediction of potentially contaminated sites in the Yangtze River Delta.

Identification and risk prediction of potentially contaminated sites (PCS) are crucial for the management of contaminated sites. However, the identification and risk prediction methods of PCS are lacking at a regional scale. Here, we established the fuzzy matching algorithm based on the site's name for identifying PCS in the Yangtze River Delta (YRD) from 2000 to 2020. The results showed that PCS in the YRD increased by over ten times, from 336 in 2000 to 4191 in 2020. Socio-economic and physical geography drive the growth of PCS and its spatiotemporal distribution, while the former has a more significant impact than the latter. We also presented a risk probability zoning strategy based on the source-pathway-receptor model, and proposed the patch-generating land-use simulation model to predict the risk probability of PCS in 2030. The results of risk probability zoning from 2000 to 2020 indicated that the local government of the YRD has started to pay attention to PCS management and risk control while developing social and economic. The results of risk prediction demonstrated that the proportion of low-risk probability pixels is 96.1% in 2030. Therefore, the planned indicator in the Action Plan on contaminated sites established by the State Council of China can be achieved in the YRD. Our experience in identifying and predicting PCS can inform how the local government worldwide manages PCS and tackles future challenges of achieving the ambition of site pollution control.

Supply chains for processed potato and tomato products in the United States will have enhanced resilience with planting adaptation strategies.

Food systems are increasingly challenged to meet growing demand for specialty crops due to the effects of climate change and increased competition for resources. Here, we apply an integrated methodology that includes climate, crop, economic and life cycle assessment models to US potato and tomato supply chains. We find that supply chains for two popular processed products in the United States, French fries and pasta sauce, will be remarkably resilient, through planting adaptation strategies that avoid higher temperatures. Land and water footprints will decline over time due to higher yields, and greenhouse gas emissions can be mitigated by waste reduction and process modification. Our integrated methodology can be applied to other crops, health-based consumer scenarios (fresh versus processed) and geographies, thereby informing decision-making throughout supply chains. Employing such methods will be essential as food systems are forced to adapt and transform to become carbon neutral due to the imperatives of climate change.