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Microbial biomass, extracellular enzyme activity, and their stoichiometry in soil play an important role in ecosystem dynamics and functioning. To better understand the improvement of sand soil quality and the limitation of soil nutrients after adding feldspathic sandstone, we investigated changes in soil microbial activity after 10 months of mixing feldspathic sandstone and sand, and compared the dynamics with soil properties. We used fumigation extraction to determine soil microbial biomass carbon (MBC), nitrogen (MBN), phosphorus (MBP), and microplate fluorometric techniques to measure soil β-1,4-glucosidase (BG), β-1,4-xylosidase (BX), β-D-cellobiohydrolase (CBH), N-acetyl-β-glucosaminidase (NAG), and Alkaline phosphatase (AKP). We also measured soil organic carbon (SOC), pH, electrical conductivity (EC), soil inorganic carbon (SIC), and soil water content (SWC). Our results showed that the soil microbial biomass C, N, P, and individual extracellular enzyme activities significantly increased in mixed soil. Similarly, the soil microbial biomass C:N, C:P, N:P, MBC:SOC, and BG:NAG significantly increased by 54.3%, 106.3%, 33.1%, 23.0%, and 65.4%, respectively. However, BG:AKP and NAG:AKP decreased by 19.0% and 50.3%, respectively. Additionally, redundancy analysis (RDA) and Pearson's correlation analysis showed that SWC, SOC, porosity and field capacity were significantly associated with soil microbial biomass indices (i.e., C, N, P, C:N, C:P, N:P in microbial biomass, and MBC:SOC) and extracellular enzyme activity metrics (i.e., individual enzyme activity, ecoenzymatic stoichiometry, and vector characteristics of enzyme activity), while pH, EC, and SIC had no correlation with these indices and metrics. These results indicated that mixing feldspathic sandstone and sand is highly susceptible to changes in soil microbial activity, and the soil N limitation decreased while P became more limited. In summary, our research showed that adding feldspathic sandstone into sand can significantly improve soil quality and provide a theoretical basis for the development of desertified land resources. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP), known as "The Roof of World", has expansive alpine grasslands and is a hotspot for climate change studies. However, cropland expansion and increasing anthropogenic activities have been poorly documented, let alone the effects of agricultural activities on food security and environmental change in the TP. The existing cropland mapping products do not depict the spatiotemporal characteristics of the TP due to low accuracies and inconsistent cropland distribution, which is affected by complicated topography and impedes our understanding of cropland expansion and its associated environmental impacts. One of the biggest challenges of cropland mapping in the TP is the diverse crop phenology across a wide range of elevations. To decrease the classification errors due to elevational differences in crop phenology, we developed two pixel- and phenology-based algorithms to map croplands using Landsat imagery and the Google Earth Engine platform along the Brahmaputra River and its two tributaries (BRTT) in the Tibet Autonomous Region, also known as the granary of TP, in 2015–2019. Our first phenology-based cropland mapping algorithm (PCM1) used different thresholds of land surface water index (LSWI) by considering varied crop phenology along different elevations. The second algorithm (PCM2) further offsets the phenological discrepancy along elevational gradients by considering the length and peak of the growing season. We found that PCM2 had a higher accuracy with fewer images compared with PCM1. The number of images for PCM2 was 279 less than PCM1, and the Matthews correlation coefficient for PCM2 was 0.036 higher than PCM1. We also found that the cropland area in BRTT was estimated to be 1979 ± 52 km2 in the late 2010s. Croplands were mainly distributed in the BRTT basins with elevations of 3800–4000 m asl. Our phenology-based methods were effective for mapping croplands in mountainous areas. The spatially explicit information on cropland area and distribution in the TP aid future research into the effects of cropland expansion on food security and environmental change in the TP. [ABSTRACT FROM AUTHOR]

The leaching of soil nitrogen (N) has become one of the most concerning environmental threats to ecosystems. Arbuscular mycorrhizal (AM) fungi have important ecological functions, however, their influence on soil N leaching and the mechanism of action remain unclear. We conducted a two-factor (N application level × AM inoculation) experiment on poplar, and for the first time, comprehensively analyzed the mechanism by which AM fungi influence soil N leaching. The results showed that, under optimum (7.5 mM) and high (20 mM) N levels, the nitrate (NO3−) and ammonium (NH4+) concentrations of leachate in the AM inoculated treatment (+AM) were lower than in the non-inoculated treatment (−AM), with significant reductions of 20.0% and 67.5%, respectively, under high N level, indicating that AM inoculation can reduce soil N leaching and that it is more effective for NH4+. The arbuscular and total colonization rates gradually increased, and the morphology of spores and vesicles changed as the N level increased. Under optimum and high N levels, +AM treatment increased the root N concentration by 11.7% and 50.7%, respectively; the increase was significant (p < 0.05) at the high N level, which was associated with slightly increased transpiration and root activity despite reductions in root surface area and root length. Additionally, the +AM treatment increased soil cation exchange capacity (CEC), soil organic carbon (SOC), and significantly (p < 0.05) increased the proportions of macroaggregates (but without significant change in microaggregates), causing soil total nitrogen (TN) to increase by 7.2% and 4.7% under optimum and high N levels, respectively. As the N levels increased, the relative contributions of AM inoculation on N leaching increased, however, the contributions of plant physiological and soil variables decreased. Among all of the variables, SOC had important contributions to NH4+ and total N in the leachate, while root N concentration had a higher contribution to NO3−. In conclusion, AM fungi can mitigate soil N leaching and lower the risk of environmental pollution via enhancing N interception by the inoculated fungi, increasing N sequestration in plant roots, and by improving soil N retention. [ABSTRACT FROM AUTHOR]

As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPPMOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPPEC) from a global network of eddy covariance towers (FluxNet), we compared GPPMOD and GPPEC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPPMOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R2), and root mean square errors (RMSE). We found that GPPMOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (R2 = 0.66 (CI, 0.63–0.69), RMSE = 2.46 (2.33–2.58) g Cm−2 day−1) for the three grassland biomes. GPPMOD had varied performances with R2 values of 0.72 (0.68–0.75) (temperate), 0.64 (0.59–0.68) (alpine), and 0.40 (0.27–0.52) (tropical). Thus, GPPMOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPPMOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (εmax) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in εmax, FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes. [ABSTRACT FROM AUTHOR]

Knowing the stand age of rubber tree (Hevea brasiliensis) plantations is vitally important for best management practices, estimations of rubber latex yields, and carbon cycle studies (e.g., biomass, carbon pools, and fluxes). However, the stand age (as estimated from the establishment year of rubber plantation) is not available across large regions. In this study, we analyzed Landsat time series images from 1987–2015 and developed algorithms to identify (1) the establishment year of rubber plantations; and (2) the pre-conversion land cover types, such as old rubber plantations, evergreen forests, and cropland. Exposed soil during plantation establishment and linear increases in canopy closure during non-production periods (rubber seedling to mature plantation) were used to identify the establishment year of rubber plantations. Based on the rubber plantation map for 2015 (overall accuracy = 97%), and 1981 Landsat images since 1987, we mapped the establishment year of rubber plantations on Hainan Island (R2 = 0.85/0.99, and RMSE = 2.34/0.54 years at pixel/plantation scale). The results show that: (1) significant conversion of croplands and old rubber plantations to new rubber plantations has occurred substantially in the northwest and northern regions of Hainan Island since 2000, while old rubber plantations were mainly distributed in the southeastern inland strip; (2) the pattern of rubber plantation expansion since 1987 consisted of fragmented plantations from smallholders, and there was no tendency to expand towards a higher altitude and steep slope regions; (3) the largest land source for new rubber plantations since 1988 was old rubber plantations (1.26 × 105 ha), followed by cropland (0.95 × 105 ha), and evergreen forests (0.68 × 105 ha). The resultant algorithms and maps of establishment year and pre-conversion land cover types are likely to be useful in plantation management, and ecological assessments of rubber plantation expansion in China. [ABSTRACT FROM AUTHOR]

Soil respiration (SR) is an important process in the carbon cycle. However, the means by which changes in understory plant community traits affect this ecosystem process is still poorly understood. In this study, plant species surveys were conducted and soil samples were collected from forests dominated by black locust (Robinia pseudoacacia L.), with a chronosequence of 15, 25, and 40 years (RP15, RP25, and RP40, respectively), and farmland (FL). Understory plant coverage, evenness, diversity, and richness were determined. We investigated soil microbial biomass carbon (MBC), nitrogen (MBN), phosphorus (MBP), and stoichiometry (MBC:MBN, MBC:MBP, and MBN:MBP). Soil enzyme assays (catalase, saccharase, urease, and alkaline phosphatase), heterotrophic respiration (HR), and autotrophic respiration (AR) were measured. The results showed that plant coverage, plant richness index (R), evenness, and Shannon-Wiener diversity were higher in RP25 and RP40 than in RP15. SR, HR, and AR were significantly higher in the forested sites than in farmland, especially for SR, which was on average 360.7%, 249.6%, and 248.2% higher in RP40, RP25, and RP15, respectively. Meanwhile, catalase, saccharase, urease, and alkaline phosphatase activities and soil microbial C, N, P, and its stoichiometry were also higher after afforestation. Moreover, significant Pearson linear correlations between understory plants (coverage, evenness, diversity, and richness) and SR, HR, and AR were observed, with the strongest correlation observed between plant coverage and SR. This correlation largely depended on soil enzymes (i.e., catalase, saccharase, urease, and alkaline phosphatase), and soil microbial biomass C, N, and P contents and its stoichiometry, particularly urease activity and the MBC:MBP ratio. Therefore, we conclude that plant communities are drivers of soil respiration, and that changes in soil respiration are associated with shifts in soil enzyme activities and nutrient stoichiometry. [ABSTRACT FROM AUTHOR]