Responses of plant leaf stomatal conductance and photosynthesis to water deficit have been extensively reported; however, little is known concerning the relationships of stomatal density with regard to water status and gas exchange. The responses of stomatal density to leaf water status were determined, and correlation with specific leaf area (SLA) in a photosynthetic study of a perennial grass, Leymus chinensis, subjected to different soil moisture contents. Moderate water deficits had positive effects on stomatal number, but more severe deficits led to a reduction, described in a quadratic parabolic curve. The stomatal size obviously decreased with water deficit, and stomatal density was positively correlated with stomatal conductance (gs), net CO2 assimilation rate (An), and water use efficiency (WUE). A significantly negative correlation of SLA with stomatal density was also observed, suggesting that the balance between leaf area and its matter may be associated with the guard cell number. The present results indicate that high flexibilities in stomatal density and guard cell size will change in response to water status, and this process may be closely associated with photosynthesis and water use efficiency.

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Warming, watering and elevated atmospheric CO₂-concentration effects have been extensively studied separately; however, their combined impact on plants is not well understood. In the current research, we examined plant growth and physiological responses of three dominant species from the Eurasian Steppe with different functional traits to a combination of elevated CO₂, high temperature, and four simulated precipitation patterns. Elevated CO₂ stimulated plant growth by 10.8-41.7 % for a C₃ leguminous shrub, Caragana microphylla, and by 33.2-52.3 % for a C₃ grass, Stipa grandis, across all temperature and watering treatments. Elevated CO₂, however, did not affect plant biomass of a C₄ grass, Cleistogenes squarrosa, under normal or increased precipitation, whereas a 20.0-69.7 % stimulation of growth occurred with elevated CO₂ under drought conditions. Plant growth was enhanced in the C₃ shrub and the C₄ grass by warming under normal precipitation, but declined drastically with severe drought. The effects of elevated CO₂ on leaf traits, biomass allocation and photosynthetic potential were remarkably species-dependent. Suppression of photosynthetic activity, and enhancement of cell peroxidation by a combination of warming and severe drought, were partly alleviated by elevated CO₂. The relationships between plant functional traits and physiological activities and their responses to climate change were discussed. The present results suggested that the response to CO₂ enrichment may strongly depend on the response of specific species under varying patterns of precipitation, with or without warming, highlighting that individual species and multifactor dependencies must be considered in a projection of terrestrial ecosystem response to climatic change.

The uncertainties of China's gross primary productivity (GPP) estimates by global data-oriented products and ecosystem models justify a development of high-resolution data-oriented GPP dataset over China. We applied a machine learning algorithm

BackgroundGrazing is one of the main grassland disturbances in China, and it is essential to quantitatively evaluate the effects of different grazing intensities on grassland production for grassland carbon budget and sustainable use.MethodsA meta-analysis was conducted to reveal general response patterns of grassland production to grazing in China. We used weighted log response ratio to assess the effect size, and 95% confidence intervals to give a sense of the precision of the estimate. Grazing effects were estimated as a percentage change relative to control (%).ResultsA total of 48 studies, including 251 data sets, were included in the meta-analysis. Grazing significantly decreased total biomass by 58.34% (95% CI: −72.04%∼−37.94%, CI: Confidence Interval), increased root/shoot ratio by 30.58% and decreased litter by 51.41% (95% CI: −63.31%∼−35.64%). Aboveground biomass and belowground biomass decreased significantly by 42.77% (95% CI: −48.88%∼−35.93%) and 23.13% (95% CI: −39.61%∼−2.17%), respectively. However, biomass responses were dependent on grazing intensity and environmental conditions. Percentage changes in aboveground biomass to grazing showed a quadratic relationship with precipitation in light grazing intensity treatment and a linear relationship in moderate and heavy grazing intensity treatment, but did not change with temperature. Grazing effects on belowground biomass did not change with precipitation or temperature. Compared to the global average value, grazing had greater negative effects on grassland production in China.ConclusionsGrazing has negative effects on grassland biomass and the grazing effects change with environmental conditions and grazing intensity, therefore flexible rangeland management tactics that suit local circumstances are necessary to take into consideration for balancing the demand of grassland utilization and conservation.

Background Vegetation water content is one of the important biophysical features of vegetation health, and its remote estimation can be utilized to real-timely monitor vegetation water stress. Here, we compared the responses of canopy water content (CWC), leaf equivalent water thickness (EWT), and live fuel moisture content (LFMC) to different water treatments and their estimations using spectral vegetation indices (VIs) based on water stress experiments for summer maize during three consecutive growing seasons 2013–2015 in North Plain China. Results Results showed that CWC was sensitive to different water treatments and exhibited an obvious single-peak seasonal variation. EWT and LFMC were less sensitive to water variation and EWT stayed relatively stable while LFMC showed a decreasing trend. Among ten hyperspectral VIs, green chlorophyll index (CI green ), red edge normalized ratio (NR red edge ), and red-edge chlorophyll index (CI red edge ) were the most sensitive VIs responding to water variation, and they were optimal VIs in the prediction of CWC and EWT. Conclusions Compared to EWT and LFMC, CWC obtained the best predictive power of crop water status using VIs. This study demonstrated that CWC was an optimal indicator to monitor maize water stress using optical hyperspectral remote sensing techniques.

Croplands are important in land‐atmosphere interactions and in the modification of local and regional weather and climate; however, they are poorly represented in the current version of the coupled Weather Research and Forecasting/Noah with multiparameterization (Noah‐MP) land surface modeling system. This study introduced dynamic corn (Zea mays) and soybean (Glycine max) growth simulations and field management (e.g., planting date) into Noah‐MP and evaluated the enhanced model (Noah‐MP‐Crop) at field scales using crop biomass data sets, surface heat fluxes, and soil moisture observations. Compared to the generic dynamic vegetation and prescribed‐leaf area index (LAI)‐driven methods in Noah‐MP, the Noah‐MP‐Crop showed improved performance in simulating leaf area index (LAI) and crop biomass. This model is able to capture the seasonal and annual variability of LAI and to differentiate corn and soybean in peak values of LAI as well as the length of growing seasons. Improved simulations of crop phenology in Noah‐MP‐Crop led to better surface heat flux simulations, especially in the early period of growing season where current Noah‐MP significantly overestimated LAI. The addition of crop yields as model outputs expand the application of Noah‐MP‐Crop to regional agriculture studies. There are limitations in the use of current growing degree days (GDD) criteria to predict growth stages, and it is necessary to develop a new method that combines GDD with other environmental factors, to more accurately define crop growth stages. The capability introduced in Noah‐MP allows further crop‐related studies and development.

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