| 1. |
- He, Liyuan, et al.
(author)
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Global biogeography of fungal and bacterial biomass carbon in topsoil
- 2020
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In: Soil Biology and Biochemistry. - : Elsevier BV. - 0038-0717. ; 151
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Journal article (peer-reviewed)abstract
- Bacteria and fungi, representing two major soil microorganism groups, play an important role in global nutrient biogeochemistry. Biogeographic patterns of bacterial and fungal biomass are of fundamental importance for mechanistically understanding nutrient cycling. We synthesized 1323 data points of phospholipid fatty acid-derived fungal biomass C (FBC), bacterial biomass C (BBC), and fungi:bacteria (F:B) ratio in topsoil, spanning 11 major biomes. The FBC, BBC, and F:B ratio display clear biogeographic patterns along latitude and environmental gradients including mean annual temperature, mean annual precipitation, net primary productivity, root C density, soil temperature, soil moisture, and edaphic factors. At the biome level, tundra has the highest FBC and BBC densities at 3684 (95% confidence interval: 1678–8084) mg kg−1 and 428 (237–774) mg kg−1, respectively; desert has the lowest FBC and BBC densities at 16.92 (14.4–19.89) mg kg−1 and 6.83 (6.1–7.65) mg kg−1, respectively. The F:B ratio varies dramatically, ranging from 1.8 (1.6–2.1) in savanna to 8.6 (6.7–11.0) in tundra. An empirical model was developed for the F:B ratio and it is combined with a global dataset of soil microbial biomass C to produce global maps for FBC and BBC in 0–30 cm topsoil. Across the globe, the highest FBC is found in boreal forest and tundra while the highest BBC is in boreal forest and tropical/subtropical forest, the lowest FBC and BBC are in shrub and desert. Global stocks of living microbial biomass C were estimated to be 12.6 (6.6–16.4) Pg C for FBC and 4.3 (0.5–10.3) Pg C for BBC in topsoil. These findings advance our understanding of the global distribution of fungal and bacterial biomass, which facilitates the incorporation of fungi and bacteria into Earth system models. The global maps of bacterial and fungal biomass serve as a benchmark for validating microbial models in simulating the global C cycle under a changing climate.
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| 2. |
- Li, Huidong, et al.
(author)
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Attributing the impacts of ecological engineering and climate change on carbon uptake in Northeastern China
- 2023
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In: Landscape Ecology. - 0921-2973 .- 1572-9761. ; 38:12, s. 3945-3960
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Journal article (peer-reviewed)abstract
- Context: In the past decades, several ecological engineering (eco-engineering) programs have been conducted in China, leading to a significant increase in regional carbon sink. However, the contribution of different eco-engineering programs to carbon uptake is still not clear, as the location of different programs is difficult to identify, and their impacts are concurrent with climate change. Objectives: We aim to detect the location of eco-engineering programs and attribute the impacts of eco-engineering and climate change on vegetation dynamics and carbon uptake in Northeastern China during 2000–2020. Methods: We developed a new framework to detect the location of eco-engineering programs by combining a temporal pattern analysis method and Markov model, and to attribute the impacts of eco-engineering and climate change on vegetation greenness and carbon uptake by combining a neighbor contrast method within a sliding window and trend analysis on the normalized difference vegetation index (NDVI) and gross primary production (GPP). Results: We identified four main forestry eco-engineering programs: croplands to forest (CtoF), grasslands to forest (GtoF), savannas to forest (StoF), and natural forest conservation (NFC) programs, whose areas accounted for 2.11%, 1.89%, 3.41%, and 1.72% of the total study area, respectively. Both eco-engineering and climate change contributed to the increase in greenness and carbon uptake. Compared to climate change effect, eco-engineering increased NDVI and GPP by 121% and 21.43% on average, respectively. Specifically, the eco-engineering-induced increases in GPP were 54.1%, 9.46%, 8.13%, and 24.20% for CtoF, GtoF, StoF, and NFC, respectively. Conclusions: These findings highlight the important and direct contribution of eco-engineering on vegetation greening with positive effects on carbon sequestration at a fine scale, providing an important implication for eco-engineering planning and management towards a carbon-neutral future.
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| 3. |
- Liu, Yage, et al.
(author)
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Estimating the impact of shelterbelt structure on corn yield at a large scale using Google Earth and Sentinel 2 data
- 2022
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In: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 17:4
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Journal article (peer-reviewed)abstract
- A shelterbelt is an important measure to protect farmland and increase crop yield. However, how a shelterbelt structure affects crop yield is still unclear due to the difficulties accessing sufficient data from traditional field observations. To address this problem, we developed an innovative framework to estimate the shelterbelt structure and crop yield profile at a regional scale based on Google Earth and Sentinel-2 data. Using this method, we quantified the impact of the shelterbelt structure on the corn yield at 302 shelterbelts in the Northeast Plain of China. Generally, the corn yield increased (by 2.41% on average) within a distance of 1.2-15 times the tree height from the shelterbelt. Such an effect was particularly prominent within a distance of two to five times the tree height, where the corn yield was significantly increased by up to 4.63%. The structure of the shelterbelt has a significant effect on the magnitude of increase in yield of the surrounding corn. The increment of corn yields with high-, medium-high-, medium- and low-width-gap grade shelterbelt were 2.01%, 2.21%, 1.99%, and 0.91%, respectively. The medium-high grade shelterbelt achieved the largest yield increase effect. The location of the farmland relative to the shelterbelt also affected the yield, with a yield increase of 2.39% on the leeward side and 1.89% on the windward side, but it did not change the relationship between the yield increase effect and the shelterbelt structure. Our findings highlight the optimal shelterbelt structure for increasing corn yield, providing practical guidance on the design and management of farmland shelterbelts for maximizing yield.
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