We aimed to quantify the variation of leaf δ¹³C along an arid and semi-arid grassland transect in northern China. We also evaluated the effects of environmental factors (i.e. precipitation, temperature and altitude) on the spatial variation of leaf δ¹³C in northern grasslands and Tibetan Plateau, China. We sampled leaves of plant species belonging to three herb genera (Stipa spp., Leymus spp. and Cleistogenes spp.) and three shrub genera (Caragana spp., Reaumuria spp. and Nitraria spp.) for carbon isotope analysis from 50 locations along a 3200-km arid and semi-arid grassland transect in northern China. Leaf δ¹³C data in Tibetan Plateau and northern grasslands in China were also compiled from studies in literature. Along the transect, leaf δ¹³C for C₃ plants ranged from −28.0‰ to −23.3‰, and from −16.3‰ to −13.8‰ for C₄ plant Cleistogenes spp.. The change in leaf δ¹³C ranged from −0.26‰ to −3.51‰ with every 100mm increase of annual precipitation, and leaf δ¹³C of shrubs (Nitraria spp., Reaumuria spp. and Caragana spp.) responded more markedly to climatic factors (precipitation and temperature) than that of herbs (Stipa spp., Leymus spp. and Cleistogenes spp.), indicating higher sensitivity of shrub δ¹³C to climatic changes. The most important factor regulating spatial variations of leaf δ¹³C in Tibetan Plateau was altitude, while it was precipitation in northern grasslands. Our results suggested that shrubs are more adapted to increasing drought in arid and semi-arid grassland. Controls of environmental factors on leaf δ¹³C depended on the most limiting factors in arid grassland (precipitation) and Tibetan grasslands (atmospheric CO₂ concentration).

The relative contribution of roots versus shoots to soil organic carbon (SOC) is a long‐debated topic related to soil carbon formation and dynamics. However, the variation of root‐ and shoot‐derived carbon in natural soils remains poorly constrained due to limited tools to separate them. Here we overcome the difficulty by analyzing biomarkers for root (suberin) and shoot (cutin) in grassland soils spanning >7,000 km on the Qinghai‐Tibetan and Mongolian Plateaus. Coupled with a comprehensive list of climatic, edaphic, and vegetation variables, we assess their inputs, distribution, and preservation patterns. We find that while suberin was a root‐specific tracer, cutin biomarkers that occurred only in trace amount in roots also had a minor but nonnegligible contribution from belowground sources due to the massive root mass in the alpine grasslands. Both components had higher SOC‐normalized concentrations in the alpine than temperate grasslands, along with a higher preservation percentage against net primary productivity and a lower degradation parameter, suggesting a better preservation of these plant‐derived lipid components in the alpine region. Moreover, while cutin and suberin abundances were affected by plant inputs and other SOC constituents, the root mass normalized incorporation efficiency of suberin was only positively correlated to SOC in both regions with a steeper slope in the alpine grasslands and also negatively correlated to root mass in the alpine soils. These results imply that root carbon incorporation is sensitive to environmental variations in the cold region, suggesting potentially more dynamic alterations to root carbon sequestration with environmental changes.

Previous studies have shown that it is possible to accurately predict wheat grain quality and yields using microbial indicators. However, it is uncertain what the best timing for sampling is. For optimal usefulness of this modeling approach, microbial indicators from samples taken early in the season should have the best predictive power. Here, we sampled a field every two weeks across a single growing season and measured a wide array of microbial parameters (amplicon sequencing, abundance of N-cycle related functional genes, and microbial carbon usage) to find the moment when the microbial predictive power for wheat grain baking quality is highest. We found that the highest predictive power for wheat grain quality was for microbial data derived from samples taken early in the season (May–June), which coincides roughly with the seedling and tillering growth stages, that are important for wheat N nutrition. Our models based on LASSO regression also highlighted a set of microbial parameters highly coherent with our previous surveys, including alpha- and beta-diversity indices and N-cycle genes. Taken together, our results suggest that measuring microbial parameters early in the wheat growing season could help farmers better predict wheat grain quality.

Long time series and large-scale rice calendar datasets provide valuable information for agricultural planning and field management in rice-based cropping systems. However, current regional-level rice calendar datasets do not accurately distinguish between rice seasons in China, causing uncertainty in crop model simulation and climate change impact analysis. Based on satellite remote sensing data, we extracted transplanting, heading, and maturity dates of early-, middle-, and late-season rice across China from 2003 to 2022 and established a multi-season rice calendar dataset named ChinaRiceCalendar (https://doi.org/10.7910/DVN/EUP8EY, Liu et al., 2023). Overall, the ChinaRiceCalendar dataset shows good agreement with field-observed phenological dates of early-, middle-, and late-season rice in Chinese agricultural meteorological stations (AMSs). According to the calendar data from 2003 to 2022 in China, the transplanting dates for early-, middle-, and late-season rice shifted by +0.7, −0.7, and −5.1 DOY (day of year) per decade, respectively; the heading dates for early-, middle-, and late-season rice shifted by −0.5, +2.7, and −0.6 DOY per decade, respectively; the maturity dates for early-, middle-, and late-season rice shifted by −0.7, +3.8, and −1.6 DOY per decade, respectively. ChinaRiceCalendar can be utilized to investigate and optimize the spatiotemporal structure of rice cultivation in China under climate and land use change.

In terrestrial ecosystems, leaves are aggregated into different spatial structures and their spatial distribution is non-random. Clumping index (CI) is a key canopy structural parameter, characterizing the extent to which leaf deviates from the random distribution. To assess leaf clumping effects on global terrestrial ET, we used a global leaf area index (LAI) map and the latest version of global CI product derived from MODIS BRDF data as well as the Boreal Ecosystem Productivity Simulator (BEPS) to estimate global terrestrial ET. The results show that global terrestrial ET in 2015 was 511.9 ± 70.1 mm yr⁻¹ for Case I, where the true LAI and CI are used. Compared to this baseline case, (1) global terrestrial ET is overestimated by 4.7% for Case II where true LAI is used ignoring clumping; (2) global terrestrial ET is underestimated by 13.0% for Case III where effective LAI is used ignoring clumping. Among all plant functional types (PFTs), evergreen needleleaf forests were most affected by foliage clumping for ET estimation in Case II, because they are most clumped with the lowest CI. Deciduous broadleaf forests are affected by leaf clumping most in Case III because they have both high LAI and low CI compared to other PFTs. The leaf clumping effects on ET estimation in both Case II and Case III is robust to the errors in major input parameters. Thus, it is necessary to consider clumping effects in the simulation of global terrestrial ET, which has considerable implications for global water cycle research.