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China initiated the "Grain for Green Project" in 1999 to mitigate soil erosion. The vegetation cover of the Chinese Loess Plateau, one of the most erosive regions in the world, has been greatly increased. However, studies on quantitatively investigating the climate change and human activities on vegetation coverage change were rare. In this study, spatio-temporal changes in vegetation coverage were investigated using MODIS normalized difference vegetation index (NDVI) data over 2000-2016. And a new method was introduced using Net Primary Productivity (NPP) model and relationship between NPP and NDVI to quantitatively and spatially distinguish the NDVI affected by climate change and human activities. Results showed that mean NDVI value over 2009-2016 were 14.46% greater than that over 2000-2007. In order to quantify the contribution of climate change and human activities to vegetation change, an NPP model suitable for the grassland of the Chinese Loess Plateau was identified using biomass observations from field survey and literature. The NDVI affected by climate change (NDVI climate ) was estimated by the NPP model and the relationship between NPP and NDVI. And the NDVI affected by human activities (NDVI human ) was calculated by actual NDVI minus NDVI climate . Comparison of the two stages showed that human activities and climate change contributed 42.35% and 57.65% respectively to the ΔNDVI on grassland in the Loess Plateau. After analysis of numerous NDVI human related factors, the slopes restored by the "Grain for Green Project" was considered the main influence factor of human activities., (Copyright © 2019. Published by Elsevier B.V.)

The Tibetan Plateau (TP) is experiencing high rates of climatic change. We present a novel combined mechanistic-bioclimatic modeling approach to determine how changes in precipitation and temperature on the TP may impact net primary production (NPP) in four major biomes (forest, shrub, grass, desert) and if there exists a maximum rain use efficiency (RUE(MAX)) that represents Huxman et al.'s "boundary that constrain[s] site-level productivity and efficiency." We used a daily mechanistic ecosystem model to generate 40-yr outputs using observed climatic data for scenarios of decreased precipitation (25-100%); increased air temperature (1 degrees - 6 degrees C); simultaneous changes in both precipitation (+/- 50%, +/- 25%) and air temperature (+1 to +6 degrees C) and increased interannual variability (IAV) of precipitation (+1 sigma to +3 sigma, with fixed means, where sigma is SD). We fitted model output from these scenarios to Huxman et al.'s RUE(MAX) bioclimatic model, NPP = alpha + RUE x PPT (where alpha is the intercept, RUE is rain use efficiency, and PPT is annual precipitation). Based on these analyses, we conclude that there is strong support (when not explicit, then trend-wise) for Huxman et al.'s assertion that biomes converge to a common RUE(MAX) during the driest years at a site, thus representing the boundary for highest rain use efficiency; the interactive effects of simultaneously decreasing precipitation and increasing temperature on NPP for the TP is smaller than might be expected from additive, single-factor changes in these drivers; and that increasing IAV of precipitation may ultimately have a larger impact on biomes of the Tibetan Plateau than changing amounts of rainfall and air temperature alone.

Soil microbial communities regulate soil carbon feedbacks to climate warming through microbial respiration (i.e., metabolic rate). A thorough understanding of the responses of composition, biomass, and metabolic rate of soil microbial community to warming is crucial to predict soil carbon stocks in a future warmer climate. Therefore, we conducted a field manipulative experiment in a semiarid grassland on the Loess Plateau of China to evaluate the responses of the soil microbial community to increased temperature from April 2015 to December 2017. Soil temperature was 2.0°C higher relative to the ambient when open‐top chambers (OTCs) were used. Warming did not affect microbial biomass or the composition of microbial functional groups. However, warming significantly decreased microbial respiration, directly resulting from soil pH decrease driven by the comediation of aboveground biomass increase, inorganic nitrogen increase, and moisture decrease. These findings highlight that the soil microbial community structure of semiarid grasslands resisted the short‐term warming by 2°C, although its metabolic rate declined. [ABSTRACT FROM AUTHOR]

Past vegetation and climatic conditions are known to influence current biodiversity patterns. However, whether their legacy effects affect the provision of multiple ecosystem functions, that is, multifunctionality, remains largely unknown. Here we analyzed soil nutrient stocks and their transformation rates in 236 drylands from six continents to evaluate the associations between current levels of multifunctionality and legacy effects of the Last Glacial Maximum (LGM) desert biome distribution and climate. We found that past desert distribution and temperature legacy, defined as increasing temperature from LGM, were negatively correlated with contemporary multifunctionality even after accounting for predictors such as current climate, soil texture, plant species richness, and site topography. Ecosystems that have been deserts since the LGM had up to 30% lower contemporary multifunctionality compared with those that were nondeserts during the LGM. In addition, ecosystems that experienced higher warming rates since the LGM had lower contemporary multifunctionality than those suffering lower warming rates, with a ~9% reduction per extra degree Celsius. Past desert distribution and temperature legacies had direct negative effects, while temperature legacy also had indirect (via soil sand content) negative effects on multifunctionality. Our results indicate that past biome and climatic conditions have left a strong "functionality debt" in global drylands. They also suggest that ongoing warming and expansion of desert areas may leave a strong fingerprint in the future functioning of dryland ecosystems worldwide that needs to be considered when establishing management actions aiming to combat land degradation and desertification. [ABSTRACT FROM AUTHOR]

Understanding under which climate and soil conditions the plant productivity–precipitation relationship is linear or nonlinear is useful for accurately predicting the response of ecosystem function to global environmental change. Using long-term (2000–2016) net primary productivity (NPP)–precipitation datasets derived from satellite observations, we identify > 5600 pixels in the North Hemisphere landmass that fit either linear or nonlinear temporal NPP–precipitation relationships. Differences in climate (precipitation, radiation, ratio of actual to potential evapotranspiration, temperature) and soil factors (nitrogen, phosphorous, organic carbon, field capacity) between the linear and nonlinear types are evaluated. Our analysis shows that both linear and nonlinear types exhibit similar interannual precipitation variabilities and occurrences of extreme precipitation. Permutational multivariate analysis of variance suggests that linear and nonlinear types differ significantly regarding to radiation, ratio of actual to potential evapotranspiration, and soil factors. The nonlinear type possesses lower radiation and/or less soil nutrients than the linear type, thereby suggesting that nonlinear type features higher degree of limitation from resources other than precipitation. This study suggests several factors limiting the responses of plant productivity to changes in precipitation, thus causing nonlinear NPP–precipitation pattern. Precipitation manipulation and modeling experiments should combine with changes in other climate and soil factors to better predict the response of plant productivity under future climate. [ABSTRACT FROM AUTHOR]

Many uncertainties exist in our quest to understand and predict how terrestrial ecosystems will respond to climate change. A particularly challenging issue is how increases in extreme precipitation regimes, which are characterized by larger but fewer individual precipitation events, will impact ecosystems. Based on a wide-ranging review of empirical studies of both hydrological and ecological processes, Knapp et al. (2008) generated a suite of hypotheses positing how these processes would respond to an increase in extreme precipitation regimes and, from this, concluded that mesic ecosystems would be more detrimentally impacted than xeric ones. In this study we present the first thorough test of these hypotheses by examining how forest, shrubland, grassland and desert ecosystems of the Tibetan Plateau, having very different vegetation and climate characteristics, respond to more extreme rainfall regimes. We accomplished this by using a simulation model (Biome-BGC) to examine the integrated behavior of these ecosystems based on the simultaneous responses and interactions of 10 hydrological and ecological processes: runoff, canopy evaporation, soil evaporation, soil water storage, transpiration, net primary productivity, soil respiration, net ecosystem exchange, nitrogen [N] mineralization, and N leaching. We ran forty-year simulations (1986–2008) where we manipulated mean growing season precipitation to create more extreme intra-annual precipitation regimes characterized by lower precipitation frequencies, longer dry periods, and larger individual (daily) precipitation events. When compared to ambient conditions, our simulations showed that increases in extreme rainfall regimes (1) impacted all hydrological processes in mesic ecosystems, resulting in a reduction of soil mineral N due to increased leaching; and (2) enhanced plant growth in xeric ecosystems, leading to larger and denser canopies and higher light interception. The responses of hydrological processes tended to follow Knapp et al.’s hypotheses more so than ecological responses. Overall, responses of mesic ecosystems closely followed the hypotheses but xeric ecosystems were highly variable and only weakly consistent with them. Our findings provide new insights as to how more extreme rainfall regimes may potentially affect the functioning of terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Human activities are critical factors influencing ecosystem sustainability. However, knowledge on regarding the mechanisms underlying the response of vegetation dynamics to human activities remains limited. To detect the driving factors and their individual contribution to the grassland vegetation dynamics in China's Loess Plateau, a structural equation model (SEM) and a principal component regression model were built. The SEM showed that population change and urbanization, temperature and humidity, and agriculture and economy accounted for 62.5%, 31.2%, and 7.7%, respectively, of the overall impact directly affecting grassland vegetation dynamics. Furthermore, the principal component regression model demonstrated that the effects of the urbanization rate on the grassland above-ground biomass exceeded those of the other factors. The agriculture population had the maximum negative effect on grassland area. The higher the urbanization rate means the higher the number of residents migrates from rural to urban areas. Following this argument, the disturbances of human activities to grassland vegetation were expected to gradually decrease in rural areas, where the vast majority of the Loess Plateau is located. The migration of rural residents to urban areas promoted the increase in biomass and areas of grassland vegetation. Our findings suggest that the effect of urbanization should be considered when assessing vegetation change. [ABSTRACT FROM AUTHOR]

Ridge-furrow mulching can reduce soil evaporation and conserve rainfall, thereby increasing crop yield and water use efficiency (WUE) in dryland cropping systems. In this study, we collected 837 observations from 50 published papers and used meta-analysis to investigate whether ridge-furrow and mulching practices are equally effective on maize yield, WUE and evapotranspiration (ET) across a range of precipitation and temperature gradients and soil types in China. Five practices were included: (i) straw mulch on flat plots (SMF), (ii) straw mulch on ridge-furrow plots (SMR), (iii) plastic mulch on flat plots (PMF), (iv) plastic mulch on ridge-furrow plots (PMR), and (v) flat plots without mulch, which was used as a control (CK). The meta-analysis showed that both straw mulch and plastic mulch significantly increased maize yields and WUE (except for SMR), and that plastic mulch was more effective than straw mulch in increasing yields, particularly in cold and dry environments. PMR has the highest yields and is more effective in clay loam than in silt loam soils. Straw mulch, but particularly plastic mulch, increased the soil moisture compared to the CK, while plastic mulch increased soil temperature, mainly in spring. However, the positive effect of plastic mulch on maize yield diminished with increasing mean growing-season temperature and precipitation, reaching zero (similar to the CK of no mulch and flat plots) when the growing-season precipitation was greater than 770 mm and the mean growing-season air temperature exceeded 24 °C. The small benefit of straw mulch (on average about 12%) was similar across the precipitation and temperature gradients, including when the benefits of plastic mulch reached zero. While our analysis has shown the benefits of ridge-furrow plastic mulch on yield and water-use efficiency, it has also highlighted the limitations of the benefits. The results provide a guide to the regions where plastic-film mulch and ridge-furrow planting are likely to improve maize yields and regions where the benefits are likely to be limited. [ABSTRACT FROM AUTHOR]