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Revealing how plant architecture affects the responses of canopy temperature depression (CTD), water use (WU), and grain yield to population density (PD) in wheat (Triticum aestivumL.) would help explore a water‐saving pathway related to managing population. This study was conducted over three consecutive years under rainfed and supplemental irrigation conditions. The flat‐leafed Jinmai 47 and upright‐leafed Jing 411 genotypes were grown at high population density (HD), medium population density (MD), and low population density (LD); seeding rates were 450, 675, and 900 seeds m−2, respectively). CTD peaked at MD in Jinmai 47 but reached the highest at HD in Jing 411. WU generally increased with increasing PD but remained unchanged from LD to MD in Jinmai 47 under the two water conditions, while it remained unchanged from MD to HD under supplemental irrigation condition in Jing 411. Yield increased with increasing density in both genotypes under the two water conditions. It is thus clear that Jinmai 47 gained higher yield but did not consume more water at MD than at LD under the two water conditions; Jing 411 obtained higher yield at the expense of similar amount of water at HD than at MD under supplemental irrigation condition. The grain yield and WU responses of wheat crops to PD implied that through appropriately adjusting PD in combination with plant architecture and water condition, the dual goals of optimizing yield and effectively saving irrigation water could be realized synchronously. Plant architecture influences canopy temperature and water use responses to population density in wheat.Canopy temperature depression reached its peak and then declined with increasing population density in wheat.Water use and grain yield generally increased as sowing density increased from 450 to 900 seeds m−2.Water use did not change at sowing density of 450–675 seeds m−2for Jinmai 47 and 675–900 seeds m−2for Jing 411.

Multiple cropping index (MCI) is the ratio of total sown area and cropland area in a region, which represents the regional time intensity of planting crops. Multiple cropping systems have effectively improved the utilization efficiency and production of cropland by increasing cropping frequency in one year. Meanwhile, it has also significantly altered biogeochemical cycles. Therefore, exploring the spatio-temporal dynamics of multiple cropping intensity is of great significance for ensuring food and ecological security. In this study, MCI of Huang-Huai-Hai agricultural region with intensive cropping practices was extracted based on a cropping intensity mapping algorithm using MODIS Enhanced Vegetation Index (EVI) time series at 500-m spatial resolution and 8-day time intervals. Then the physical characteristics and landscape pattern of MCI trends were analyzed from 2000–2012. Results showed that MCI in Huang-Huai-Hai agricultural region has increased from 152% to 156% in the 12 years. Topography is a primary factor in determining the spatial pattern dynamics of MCI, which is more stable in hilly area than in plain area. An increase from 158% to 164% of MCI occurred in plain area while there was almost no change in hilly area with single cropping. The most active region of MCI change was the intersection zone between the hilly area and plain area. In spatial patterns, landscape of multiple cropping systems tended to be homogenized reflected by a reduction in the degree of fragmentation and an increase in the degree of concentration of cropland with the same cropping system.

Using the flux data in a rainfed spring maize (Zea maysL.) field on the Loess Plateau during the growth seasons in 2013 and 2014, we analyzed variation in light use efficiency (LUE) and water use efficiency (WUE) in relation to clouds and/or aerosols. The peak values of monthly average diurnal LUE and WUE were found at sunset and at ∼7:00 AM, respectively. During the period of rapid growth, LUE and WUE decreased linearly with increasing clearness index (Kt). Total photosynthetically active radiation (PARt) and evapotranspiration (ET) increased linearly with increasing Kt. Gross primary productivity (GPP) increased rapidly with increasing Ktwhen Kt< 0.4 but was less responsive to Ktwhen Kt> 0.4. The slope of the linear equation between ET and available energy was 0.33 kg H2O m−2d−1in both years, and the slope of the linear equation between GPP and diffuse PAR (PARf) was 0.86 and 0.83 g C m−2d−1in 2013 and 2014, respectively. However, GPP only increased sharply with increasing direct PAR (PARr) when PARr< 5 mol PARrm−2d−1. When Ktdecreased, the ratio of PARfto PARtincreased, producing a more uniform irradiance of the canopy and resulting in increases in LUE and WUE. These findings highlight the fact that clouds and/or aerosols have important effects on the carbon and water cycle in rainfed spring maize fields on the Loess Plateau.

There is a lack of simple ways to predict the vegetation responses to the East Asian Monsoon (EAM) variability in China due to the complexity of the monsoon system. In this study, we found the variation of the Western Pacific Subtropical High (WPSH), which is one of the major components of the EAM, has a profound influence on the vegetation growth in China. When the WPSH is located more to the west of its climate average, the eastern and northwestern parts experience increased yearly-averaged normalized difference vegetation index (NDVI) and gross primary productivity (GPP) by 0.3%–2.2%, and 0.2%–2.2%, respectively. In contrast, when the WPSH is located more to the east of its climate average, the above areas experience decreased yearly-averaged NDVI and GPP by 0.4% to 1.6%, and 1.3% to 4.5%, respectively. The WPSH serves as a major circulation index to predict the response of vegetation to monsoon.

Abstract: The uncertainty in the predicted values of a process-based terrestrial ecosystem model is as important as the predicted values themselves. However, few studies integrate uncertainty analysis into their modeling of carbon dynamics. In this paper, we conducted a local sensitivity analysis of the model parameters of a process-based ecosystem model at the Chaibaishan broad-leaved Korean pine mixed forest site in 2003–2005. Sixteen parameters were found to affect the annual net ecosystem exchange of CO2 (NEE) in each of the three years. We combined a Monte Carlo uncertainty analysis with a standardized multiple regression method to distinguish the contributions of the parameters and the initial variables to the output variance. Our results showed that the uncertainties in the modeled annual gross primary production and ecosystem respiration were 5–8% of their mean values, while the uncertainty in the annual NEE was up to 23–37% of the mean value in 2003–2005. Five parameters yielded about 92% of the uncertainty in the modeled annual net ecosystem exchange. Finally, we analyzed the sensitivity of the meteorological data and compared two types of meteorological data and their effects on the estimation of carbon fluxes. Overestimating the relative humidity at a spatial resolution of 10 km × 10 km had a larger effect on the annual gross primary production, ecosystem respiration, and net ecosystem exchange than underestimating precipitation. More attention should be paid to the accurate estimation of sensitive model parameters, driving meteorological data, and the responses of ecosystem processes to environmental variables in the context of global change.

AbstractThe uncertainty in the predicted values of a process-based terrestrial ecosystem model is as important as the predicted values themselves. However, few studies integrate uncertainty analysis into their modeling of carbon dynamics. In this paper, we conducted a local sensitivity analysis of the model parameters of a process-based ecosystem model at the Chaibaishan broad-leaved Korean pine mixed forest site in 2003–2005. Sixteen parameters were found to affect the annual net ecosystem exchange of CO2(NEE) in each of the three years. We combined a Monte Carlo uncertainty analysis with a standardized multiple regression method to distinguish the contributions of the parameters and the initial variables to the output variance. Our results showed that the uncertainties in the modeled annual gross primary production and ecosystem respiration were 5–8% of their mean values, while the uncertainty in the annual NEE was up to 23–37% of the mean value in 2003–2005. Five parameters yielded about 92% of the uncertainty in the modeled annual net ecosystem exchange. Finally, we analyzed the sensitivity of the meteorological data and compared two types of meteorological data and their effects on the estimation of carbon fluxes. Overestimating the relative humidity at a spatial resolution of 10 km × 10 km had a larger effect on the annual gross primary production, ecosystem respiration, and net ecosystem exchange than underestimating precipitation. More attention should be paid to the accurate estimation of sensitive model parameters, driving meteorological data, and the responses of ecosystem processes to environmental variables in the context of global change.