Pub Date : 2024-12-13DOI: 10.1016/j.geoderma.2024.117137
Fernanda Magno Silva, Anita Fernanda dos Santos Teixeira, Marcelo Mancini, Giovana Clarice Poggere, Alberto Vasconcellos Inda, Luiz Roberto Guimarães Guilherme, Nilton Curi, David C. Weindorf, Sérgio Henrique Godinho Silva
In tropical regions, pedogenesis studies are challenging since most soils are polygenetic and studies on this approach are still lacking. Thus, complementary data is needed to understand their formation, which has been possible through proximal sensing tools. The objective of this study was to assess the efficiency of proximal sensing data to investigate the presence of lithological discontinuities and the within-profile variation of polygenetic soils formed from different parent materials and with varying weathering degrees. Soil morphology, texture, fertility, mineralogy, and reflectance analyses were conducted to characterize soil samples collected per horizon from five studied profiles. Additional samples were collected following a 15 x 15 cm grid and analyzed via portable X-ray fluorescence (pXRF) spectrometry. Parent material discontinuities were investigated through the ratios Ti/Zr, Si/Al, fine sand/coarse sand (FS/CS), and differences in the mineralogy of the sand, silt, and clay fractions. The five studied profiles were classified as: Fluvic Cambisol (CY), Sideralic Cambisol (CX), Xanthic Gibbsic Ferralsol (LA), Xanthic Ferralsol (LVA), and Rhodic Gibbsic Ferralsol (LV) per the World Reference Base (WRB/FAO) for Soil Resources. pXRF data revealed within-horizon variation of elemental contents. Chemical traits of anthropic and pedogenetic origin were successfully identified. The Ti/Zr ratio and mineralogical analysis of the sand, silt, and clay fractions were able to identify parent material discontinuities in LVA. By interpreting Vis-NIR spectra, it was possible to separate soils based on texture and mineralogy. Proximal sensor data, especially from pXRF, allowed for the detection of parent material discontinuities that were unapparent during field morphology analysis, contributing to improved details on soil genesis assessment and comprehension of previous soil formation events.
{"title":"Proximal sensing characterization of polygenetic soils variability in Brazil","authors":"Fernanda Magno Silva, Anita Fernanda dos Santos Teixeira, Marcelo Mancini, Giovana Clarice Poggere, Alberto Vasconcellos Inda, Luiz Roberto Guimarães Guilherme, Nilton Curi, David C. Weindorf, Sérgio Henrique Godinho Silva","doi":"10.1016/j.geoderma.2024.117137","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117137","url":null,"abstract":"In tropical regions, pedogenesis studies are challenging since most soils are polygenetic and studies on this approach are still lacking. Thus, complementary data is needed to understand their formation, which has been possible through proximal sensing tools. The objective of this study was to assess the efficiency of proximal sensing data to investigate the presence of lithological discontinuities and the within-profile variation of polygenetic soils formed from different parent materials and with varying weathering degrees. Soil morphology, texture, fertility, mineralogy, and reflectance analyses were conducted to characterize soil samples collected per horizon from five studied profiles. Additional samples were collected following a 15 x 15 cm grid and analyzed via portable X-ray fluorescence (pXRF) spectrometry. Parent material discontinuities were investigated through the ratios Ti/Zr, Si/Al, fine sand/coarse sand (FS/CS), and differences in the mineralogy of the sand, silt, and clay fractions. The five studied profiles were classified as: Fluvic Cambisol (CY), Sideralic Cambisol (CX), Xanthic Gibbsic Ferralsol (LA), Xanthic Ferralsol (LVA), and Rhodic Gibbsic Ferralsol (LV) per the World Reference Base (WRB/FAO) for Soil Resources. pXRF data revealed within-horizon variation of elemental contents. Chemical traits of anthropic and pedogenetic origin were successfully identified. The Ti/Zr ratio and mineralogical analysis of the sand, silt, and clay fractions were able to identify parent material discontinuities in LVA. By interpreting Vis-NIR spectra, it was possible to separate soils based on texture and mineralogy. Proximal sensor data, especially from pXRF, allowed for the detection of parent material discontinuities that were unapparent during field morphology analysis, contributing to improved details on soil genesis assessment and comprehension of previous soil formation events.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"38 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1016/j.geoderma.2024.117131
Katelyn G. Gobbie, Nicole Pietrasiak, Brian M. Jusko, Rebecca E. Drenovsky
Biological soil crust communities (biocrusts) establishing on gypsum soils have been well-documented for their prolific appearance and rich diversity of lichens and bryophytes. However, studies characterizing gypsum biocrusts have occurred primarily outside of the U.S., most of which lack comparisons to other soil types. We conducted intensive field surveys to evaluate the ground cover and frequency of biocrust functional groups and moss species on gypsum and non-gypsum soils in the U.S. regions with the most extensive gypsum outcrops, the northern Chihuahuan and eastern Mojave Deserts. Study sites were stratified by geomorphology and paired, so that every gypsum site was matched with a non-gypsum site in the same region. We employed canonical correspondence analysis (CCA) to relate the observed differences in biocrust abundance and composition across soil types to distinct environmental variables. Additionally, we assessed species richness of biocrust mosses on gypsum versus non-gypsum soils, as well as in the Chihuahuan versus Mojave Deserts. Our results indicate that differences in biocrust communities on gypsum and non-gypsum soils are predominantly due to gypsum’s profuse dark algal (mostly cyanobacteria-formed) rather than lichen and moss biocrusts in these two hot desert biomes. Biocrust functional groups did not exhibit distinct associations with environmental variables. However, moss species appear to be strongly influenced by environmental variables and exhibited differential preferences for substrate parent material. Moss species richness was greater on gypsum soils and, surprisingly, in the hottest and driest North American Desert, the Mojave. Differences in species richness across deserts were strongly correlated to mean annual and seasonal temperatures, as well as mean winter precipitation. Overall, our data suggest that environmental and climate conditions all play important roles in the ecology of biocrusts, specifically moss diversity and distribution, in the northern Chihuahuan and eastern Mojave Deserts of the U.S. More importantly, we emphasize that gypsum soils of the U.S. are unique refugia for moss-forming biocrusts.
{"title":"Climate and gypsum parent material shape biocrust communities and moss ecology in the Chihuahuan and Mojave Deserts","authors":"Katelyn G. Gobbie, Nicole Pietrasiak, Brian M. Jusko, Rebecca E. Drenovsky","doi":"10.1016/j.geoderma.2024.117131","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117131","url":null,"abstract":"Biological soil crust communities (biocrusts) establishing on gypsum soils have been well-documented for their prolific appearance and rich diversity of lichens and bryophytes. However, studies characterizing gypsum biocrusts have occurred primarily outside of the U.S., most of which lack comparisons to other soil types. We conducted intensive field surveys to evaluate the ground cover and frequency of biocrust functional groups and moss species on gypsum and non-gypsum soils in the U.S. regions with the most extensive gypsum outcrops, the northern Chihuahuan and eastern Mojave Deserts. Study sites were stratified by geomorphology and paired, so that every gypsum site was matched with a non-gypsum site in the same region. We employed canonical correspondence analysis (CCA) to relate the observed differences in biocrust abundance and composition across soil types to distinct environmental variables. Additionally, we assessed species richness of biocrust mosses on gypsum versus non-gypsum soils, as well as in the Chihuahuan versus Mojave Deserts. Our results indicate that differences in biocrust communities on gypsum and non-gypsum soils are predominantly due to gypsum’s profuse dark algal (mostly cyanobacteria-formed) rather than lichen and moss biocrusts in these two hot desert biomes. Biocrust functional groups did not exhibit distinct associations with environmental variables. However, moss species appear to be strongly influenced by environmental variables and exhibited differential preferences for substrate parent material. Moss species richness was greater on gypsum soils and, surprisingly, in the hottest and driest North American Desert, the Mojave. Differences in species richness across deserts were strongly correlated to mean annual and seasonal temperatures, as well as mean winter precipitation. Overall, our data suggest that environmental and climate conditions all play important roles in the ecology of biocrusts, specifically moss diversity and distribution, in the northern Chihuahuan and eastern Mojave Deserts of the U.S. More importantly, we emphasize that gypsum soils of the U.S. are unique refugia for moss-forming biocrusts.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"30 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1016/j.geoderma.2024.117138
Samuel Pizarro, Narcisa G. Pricope, Jesús Vera, Juancarlos Cruz, Sphyros Lastra, Richard Solórzano-Acosta, Patricia Verástegui Martínez
The quality and safety of soil are crucial for ensuring social and economic development and providing contaminant-free food. The availability and quality of soil data, particularly for multiple metals and metalloids, are often insufficient for comprehensive analysis. Soil formation and the distribution of metals are shaped by various factors such as geology, climate, topography, and human activities, making accurate modeling highly challenging. Additionally, agricultural intensification, urban expansion, road construction, and mining activities frequently result in soil pollution, posing serious risks to ecosystems and human health. This study aims to integrate diverse geospatial datasets with machine learning for high resolution soil contamination mapping (10 m spatial resolution) in a major agricultural region of Peruvian highlands. This study mapped 25 elements (Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn, V) in the Peruvian Mantaro Valley using a training dataset of 109 topsoil samples combined with various geospatial datasets (remote sensing, climate, topography, soil data, and distance). The model provided satisfactory results in predicting the spatial distribution of the selected elements, with R2 values ranging from 0.6 to 0.9 for most elements. Edaphic, climate, and topographic covariates were the most significant predictors, particularly for croplands near rivers, whereas spectral variables were less important. The results reveal As, Pb, and Cd concentrations significantly above permissible limits, highlighting urgent health risks. These findings suggest that it is feasible to identify polluted soils and improve regulations based on widely available geospatial datasets with minimal training data. The study contributes to the development of models to assess the impact of pollutants on environmental and human health in the short-to-medium term, emphasizing the need for further research on the translocation of toxic metals into food crops and the implications for public health.
{"title":"Comprehensive spatial mapping of metals and metalloids in the Peruvian Mantaro Valley using advanced geospatial data Integration","authors":"Samuel Pizarro, Narcisa G. Pricope, Jesús Vera, Juancarlos Cruz, Sphyros Lastra, Richard Solórzano-Acosta, Patricia Verástegui Martínez","doi":"10.1016/j.geoderma.2024.117138","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117138","url":null,"abstract":"The quality and safety of soil are crucial for ensuring social and economic development and providing contaminant-free food. The availability and quality of soil data, particularly for multiple metals and metalloids, are often insufficient for comprehensive analysis. Soil formation and the distribution of metals are shaped by various factors such as geology, climate, topography, and human activities, making accurate modeling highly challenging. Additionally, agricultural intensification, urban expansion, road construction, and mining activities frequently result in soil pollution, posing serious risks to ecosystems and human health. This study aims to integrate diverse geospatial datasets with machine learning for high resolution soil contamination mapping (10 m spatial resolution) in a major agricultural region of Peruvian highlands. This study mapped 25 elements (Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn, V) in the Peruvian Mantaro Valley using a training dataset of 109 topsoil samples combined with various geospatial datasets (remote sensing, climate, topography, soil data, and distance). The model provided satisfactory results in predicting the spatial distribution of the selected elements, with R<ce:sup loc=\"post\">2</ce:sup> values ranging from 0.6 to 0.9 for most elements. Edaphic, climate, and topographic covariates were the most significant predictors, particularly for croplands near rivers, whereas spectral variables were less important. The results reveal As, Pb, and Cd concentrations significantly above permissible limits, highlighting urgent health risks. These findings suggest that it is feasible to identify polluted soils and improve regulations based on widely available geospatial datasets with minimal training data. The study contributes to the development of models to assess the impact of pollutants on environmental and human health in the short-to-medium term, emphasizing the need for further research on the translocation of toxic metals into food crops and the implications for public health.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"5 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Simultaneous estimation of multiple soil properties from vis-NIR hyperspectra presents a cost-effective and time-efficient approach. Previous studies have utilized multi-task convolutional neural network (multi-CNN) with share-bottom structures based on the hard parameter sharing. However, multi-CNN often ignores the differential characteristics of correlations between soil properties, limiting the accuracy of soil property estimation. The multi-gate mixture-of-experts network (MMoE) offers a solution by extracting both common features across all soil properties and unique features specific to each soil property, which probably could provide better estimation outcomes than the conventional shared-bottom multi-CNN. In the present study, a MMoE was built based on a total of 17,272 mineral soil samples from the Land Use/Cover Area Frame Survey (LUCAS) topsoil database that includes vis-NIR spectra with ten physicochemical properties, i.e., clay, silt, sand, pH (in water), organic content (OC), calcium carbonate (CaCO3), nitrogen (N), phosphorous (P), potassium (K), and cation exchange capacity (CEC). To evaluate the performance of MMoE, a series of other models were also built, i.e., partial least square regression (PLSR), single-task convolutional neural network (single-CNN), multi-task convolutional neural network (multi-CNN) and multi-task long short-term memory (multi-LSTM). Furthermore, performance of feature-spectrum selected by competitive adaptive reweighted sampling (CARS) on the accuracy of the MMoE was also explored, as well as a data augmentation method of stacking raw spectra with five preprocessed spectra data. The results demonstrated that MMoE had higher accuracy than PLSR, single-CNN, and multi-LSTM models, with RMSE reduction of 5 %–48 %, R2 improvement of 1 %–119 %, and CCC improvement of 0 %–74 %. Compared with multi-CNN, MMoE showed better accuracy for all properties except pH, with RMSE reduction of 3 %–8 %, R2 improvement of 1 %–12 %, and CCC improvement of 0 %–5 %. However, the feature-spectrum selected by CARS did not improve the accuracy of MMoE compared to full-band spectrum, whereas the data augmentation method was effective in improving the estimation accuracy of MMoE compared to raw spectra, with RMSE reduction of 14 %–28 %, R2 improvement of 3 %–88 %, and CCC improvement of 1 %–63 %. Consequently, this study proves that MMoE based on data augmentation is an efficient and accurate method for the simultaneous estimation of multiple soil properties from vis-NIR spectra.
{"title":"Simultaneous estimation of multiple soil properties from vis-NIR spectra using a multi-gate mixture-of-experts with data augmentation","authors":"Xiaoqing Wang, Mei-Wei Zhang, Ya-Nan Zhou, Lingli Wang, Ling-Tao Zeng, Yu-Pei Cui, Xiao-Lin Sun","doi":"10.1016/j.geoderma.2024.117127","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117127","url":null,"abstract":"Simultaneous estimation of multiple soil properties from vis-NIR hyperspectra presents a cost-effective and time-efficient approach. Previous studies have utilized multi-task convolutional neural network (multi-CNN) with share-bottom structures based on the hard parameter sharing. However, multi-CNN often ignores the differential characteristics of correlations between soil properties, limiting the accuracy of soil property estimation. The multi-gate mixture-of-experts network (MMoE) offers a solution by extracting both common features across all soil properties and unique features specific to each soil property, which probably could provide better estimation outcomes than the conventional shared-bottom multi-CNN. In the present study, a MMoE was built based on a total of 17,272 mineral soil samples from the Land Use/Cover Area Frame Survey (LUCAS) topsoil database that includes vis-NIR spectra with ten physicochemical properties, i.e., clay, silt, sand, pH (in water), organic content (OC), calcium carbonate (CaCO<ce:inf loc=\"post\">3</ce:inf>), nitrogen (N), phosphorous (P), potassium (K), and cation exchange capacity (CEC). To evaluate the performance of MMoE, a series of other models were also built, i.e., partial least square regression (PLSR), single-task convolutional neural network (single-CNN), multi-task convolutional neural network (multi-CNN) and multi-task long short-term memory (multi-LSTM). Furthermore, performance of feature-spectrum selected by competitive adaptive reweighted sampling (CARS) on the accuracy of the MMoE was also explored, as well as a data augmentation method of stacking raw spectra with five preprocessed spectra data. The results demonstrated that MMoE had higher accuracy than PLSR, single-CNN, and multi-LSTM models, with RMSE reduction of 5 %–48 %, R<ce:sup loc=\"post\">2</ce:sup> improvement of 1 %–119 %, and CCC improvement of 0 %–74 %. Compared with multi-CNN, MMoE showed better accuracy for all properties except pH, with RMSE reduction of 3 %–8 %, R<ce:sup loc=\"post\">2</ce:sup> improvement of 1 %–12 %, and CCC improvement of 0 %–5 %. However, the feature-spectrum selected by CARS did not improve the accuracy of MMoE compared to full-band spectrum, whereas the data augmentation method was effective in improving the estimation accuracy of MMoE compared to raw spectra, with RMSE reduction of 14 %–28 %, R<ce:sup loc=\"post\">2</ce:sup> improvement of 3 %–88 %, and CCC improvement of 1 %–63 %. Consequently, this study proves that MMoE based on data augmentation is an efficient and accurate method for the simultaneous estimation of multiple soil properties from vis-NIR spectra.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"21 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-10DOI: 10.1016/j.geoderma.2024.117135
Jian Chen, Enze Xie, Yuxuan Peng, Guojing Yan, Jun Jiang, Wenyou Hu, Yuguo Zhao, Khalid Saifullah Khan, Yongcun Zhao
The degradation of fertile Mollisols due to unsustainable management practices poses serious threats to climate change mitigation and food security. Yet, the lack of four-dimensional (4D) dynamic information (i.e., space, depth, and time) on cropland soil pH hinders sustainable soil management. To fill this knowledge gap, over 17,000 soil pH samples were first collected from the Mollisols region in Northeast China. Then, an automatic machine learning model coupled with space-for-time substitution (AutoMLst) was developed for mapping the 4D dynamics of cropland soil pH during 1980–2023. Results showed that AutoMLst performed well in 4D modelling of cropland soil pH, with a coefficient of determination of 0.88. The topsoil (0–30 cm) pH significantly declined from 6.83 in 1980 to 6.43 in 2023 in Northeast China, with an average decline rate of 0.0038 units yr−1 (0.0014–0.0063 units yr−1). The pH declines in the deeper soil layers (30–60 and 60–100 cm) were slight and statistically insignificant. The excessive use of chemical nitrogen (N) fertilizers and N deposition jointly contributed to the decline of cropland soil pH, but the impact of N deposition has increased over time. Although implementing China’s zero-growth policy for chemical fertilizer application will increase soil pH under the shared socioeconomic pathway (SSP) 1–2.6 and 5–8.5 scenarios, the current decline in cropland soil pH should not be overlooked to ensure the health of Mollisols. These findings suggest that the sustainable management of Mollisols resources requires strict monitoring of soil pH dynamics to mitigate potential soil acidification risks.
不可持续的管理做法导致肥沃的软土退化，对减缓气候变化和粮食安全构成严重威胁。然而，缺乏耕地土壤pH的四维动态信息（即空间、深度和时间）阻碍了土壤的可持续管理。为了填补这一知识空白，首先从东北Mollisols地区收集了17,000多个土壤pH样品。在此基础上，建立了基于时空替换的自动机器学习模型（AutoMLst），用于绘制1980-2023年农田土壤pH的四维动态。结果表明，AutoMLst在农田土壤pH的4D模拟中表现良好，其决定系数为0.88。东北地区表层土壤（0 ~ 30 cm） pH由1980年的6.83显著下降至2023年的6.43，平均下降幅度为0.0038 units yr - 1 （0.0014 ~ 0.0063 units yr - 1）。深层土壤（30 ~ 60 cm和60 ~ 100 cm） pH值下降幅度较小，且无统计学意义。化学氮肥的过量施用和N沉降共同导致了农田土壤pH的下降，但随着时间的推移，N沉降的影响越来越大。尽管在共享社会经济路径（SSP） 1-2.6和5-8.5情景下，实施中国化肥施用零增长政策将增加土壤pH值，但为了确保Mollisols的健康，目前农田土壤pH值的下降不应被忽视。这些发现表明，要实现软土资源的可持续管理，需要严格监测土壤pH动态，以减轻潜在的土壤酸化风险。
{"title":"Four-dimensional modelling reveals decline in cropland soil pH during last four decades in China’s Mollisols region","authors":"Jian Chen, Enze Xie, Yuxuan Peng, Guojing Yan, Jun Jiang, Wenyou Hu, Yuguo Zhao, Khalid Saifullah Khan, Yongcun Zhao","doi":"10.1016/j.geoderma.2024.117135","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117135","url":null,"abstract":"The degradation of fertile Mollisols due to unsustainable management practices poses serious threats to climate change mitigation and food security. Yet, the lack of four-dimensional (4D) dynamic information (i.e., space, depth, and time) on cropland soil pH hinders sustainable soil management. To fill this knowledge gap, over 17,000 soil pH samples were first collected from the Mollisols region in Northeast China. Then, an automatic machine learning model coupled with space-for-time substitution (AutoML<ce:inf loc=\"post\">st</ce:inf>) was developed for mapping the 4D dynamics of cropland soil pH during 1980–2023. Results showed that AutoML<ce:inf loc=\"post\">st</ce:inf> performed well in 4D modelling of cropland soil pH, with a coefficient of determination of 0.88. The topsoil (0–30 cm) pH significantly declined from 6.83 in 1980 to 6.43 in 2023 in Northeast China, with an average decline rate of 0.0038 units yr<ce:sup loc=\"post\">−1</ce:sup> (0.0014–0.0063 units yr<ce:sup loc=\"post\">−1</ce:sup>). The pH declines in the deeper soil layers (30–60 and 60–100 cm) were slight and statistically insignificant. The excessive use of chemical nitrogen (N) fertilizers and N deposition jointly contributed to the decline of cropland soil pH, but the impact of N deposition has increased over time. Although implementing China’s zero-growth policy for chemical fertilizer application will increase soil pH under the shared socioeconomic pathway (SSP) 1–2.6 and 5–8.5 scenarios, the current decline in cropland soil pH should not be overlooked to ensure the health of Mollisols. These findings suggest that the sustainable management of Mollisols resources requires strict monitoring of soil pH dynamics to mitigate potential soil acidification risks.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"200 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-09DOI: 10.1016/j.geoderma.2024.117130
Jiao Yang, Huan Ma, Rongfei Zhang, Wei Ji
Soil hydrologic functions are important for landscapes where soil water loss is significant. The largest ecological restoration engineering project, namely the “Grain for Green” program (GGP), has being implemented since 1999 in China. However, the general patterns of the effects of GGP on soil hydraulic properties under different conditions remain unclear. Aiming to understand the influence of soil hydrologic functions by GGP, a literature review and subsequent meta-analysis were conducted based on the evaluated literature data. The results revealed that GGP significantly increased soil saturated hydraulic conductivity (Ks) by 33.51 %, 23.53 %, and 22.58 % in the surface (0–20 cm), subsurface (20–40 cm) and deep soil layer (>40 cm), respectively. The soil Ks increased over time since GGP implementation. The response changes of Ks showed significant difference among different plantation types, the conversion of cropland to orchards may lead to soil Ks decrease. And the natural restoration approach of grass may lead to greater increase in Ks than artificial restoration approach. In addition, the GLM (General Linear Model) model explained about 97.22 % of the total variables. Among the input variables, soil organic matter content (SOM) explained the largest proportion (39.55 %), followed by plantation type (36.97 %), bulk density (BD) (17.77 %), sand content (2.19 %) and it can be implied that the GGP plays an important positive role in soil hydraulic properties probably because soil organic matter content increased in GGP. This study suggests the GGP implementation caused soil hydrologic functions improving, and provide useful knowledge for ecological restoration practices and management.
{"title":"Effects of “Grain for Green” program on soil hydraulic properties: A meta-analysis","authors":"Jiao Yang, Huan Ma, Rongfei Zhang, Wei Ji","doi":"10.1016/j.geoderma.2024.117130","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117130","url":null,"abstract":"Soil hydrologic functions are important for landscapes where soil water loss is significant. The largest ecological restoration engineering project, namely the “Grain for Green” program (GGP), has being implemented since 1999 in China. However, the general patterns of the effects of GGP on soil hydraulic properties under different conditions remain unclear. Aiming to understand the influence of soil hydrologic functions by GGP, a literature review and subsequent <ce:italic>meta</ce:italic>-analysis were conducted based on the evaluated literature data. The results revealed that GGP significantly increased soil saturated hydraulic conductivity (<ce:italic>K<ce:inf loc=\"post\">s</ce:inf></ce:italic>) by 33.51 %, 23.53 %, and 22.58 % in the surface (0–20 cm), subsurface (20–40 cm) and deep soil layer (>40 cm), respectively. The soil <ce:italic>K<ce:inf loc=\"post\">s</ce:inf></ce:italic> increased over time since GGP implementation. The response changes of <ce:italic>K<ce:inf loc=\"post\">s</ce:inf></ce:italic> showed significant difference among different plantation types, the conversion of cropland to orchards may lead to soil <ce:italic>K<ce:inf loc=\"post\">s</ce:inf></ce:italic> decrease. And the natural restoration approach of grass may lead to greater increase in <ce:italic>K<ce:inf loc=\"post\">s</ce:inf></ce:italic> than artificial restoration approach. In addition, the GLM (General Linear Model) model explained about 97.22 % of the total variables. Among the input variables, soil organic matter content (SOM) explained the largest proportion (39.55 %), followed by plantation type (36.97 %), bulk density (BD) (17.77 %), sand content (2.19 %) and it can be implied that the GGP plays an important positive role in soil hydraulic properties probably because soil organic matter content increased in GGP. This study suggests the GGP implementation caused soil hydrologic functions improving, and provide useful knowledge for ecological restoration practices and management.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"19 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-07DOI: 10.1016/j.geoderma.2024.117132
Maria Gabriela de Oliveira Andrade, Carlos Felipe dos Santos Cordeiro, Amanda Ferraresi Roberto, Juliano Carlos Calonego, Ciro Antonio Rosolem
The synergistic effects of soil acidity alleviation and nitrogen (N) fertilization on soil physical attributes and their impacts on crop yields in highly weathered soils have not been assessed. The study was carried out in southeastern Brazil, in a tropical climate environment, in a sandy clay textured Oxisol. In total there were 12 treatments, that investigated the effects of surface application of lime, phosphogypsum, and N fertilization on soil chemical and physical attributes and soybean and maize nutrition and yield in two seasons (2020–2022). The treatments consisted of a control (no lime or, phosphogypsum), lime (2.9 Mg ha−1), and lime + phosphogypsum (2.0 Mg ha−1) combined with four different N rates applied to maize (0, 80, 160 and 240 kg ha−1). Lime and gypsum increased maize and soybean yields, especially under low N input. Lime and gypsum enhanced soil aggregate stability up to a depth of 60 cm and increased aggregate size up to a depth of 40 cm at N rates of up to 160 kg ha−1. Lime and gypsum also increased soil surface and subsurface pH and soil calcium and magnesium levels up to a depth of 60 cm, particularly under low N input. Moreover, lime and gypsum increased soil organic matter content in both the surface and subsurface layers, particularly under high N input. Overall, our findings emphasize the benefits of combining lime and gypsum with moderate input of N-fertilizer for improving crop yields through enhanced soil physical and chemical properties.
在高风化土壤中，土壤酸化和氮肥对土壤物理属性的协同效应及其对作物产量的影响尚未得到评价。这项研究是在巴西东南部的热带气候环境中进行的，在砂质粘土质地的Oxisol中进行的。试验共12个处理，研究了两个季节（2020-2022年）施用石灰、磷石膏和氮肥对土壤理化属性和大豆、玉米营养及产量的影响。这些处理包括对照（不施用石灰或磷石膏）、石灰（2.9 Mg ha - 1）和石灰+磷石膏（2.0 Mg ha - 1），外加4种不同施氮量（0、80、160和240 kg ha - 1）。石灰和石膏提高了玉米和大豆产量，特别是在低氮投入下。石灰和石膏在施氮量为160 kg ha - 1的情况下，可增强深度达60 cm的土壤团聚体稳定性，并增加深度达40 cm的团聚体尺寸。石灰和石膏还增加了60厘米深度的土壤表面和地下pH值以及土壤钙和镁水平，特别是在低氮输入下。此外，石灰和石膏增加了土壤表层和次表层有机质含量，特别是在高氮输入下。总的来说，我们的研究结果强调了石灰和石膏与适量氮肥的结合通过改善土壤的物理和化学性质来提高作物产量的好处。
{"title":"Lime and gypsum reduce N-fertilizer requirements and improve soil physics, fertility and crop yields in a double-cropped system","authors":"Maria Gabriela de Oliveira Andrade, Carlos Felipe dos Santos Cordeiro, Amanda Ferraresi Roberto, Juliano Carlos Calonego, Ciro Antonio Rosolem","doi":"10.1016/j.geoderma.2024.117132","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117132","url":null,"abstract":"The synergistic effects of soil acidity alleviation and nitrogen (N) fertilization on soil physical attributes and their impacts on crop yields in highly weathered soils have not been assessed. The study was carried out in southeastern Brazil, in a tropical climate environment, in a sandy clay textured Oxisol. In total there were 12 treatments, that investigated the effects of surface application of lime, phosphogypsum, and N fertilization on soil chemical and physical attributes and soybean and maize nutrition and yield in two seasons (2020–2022). The treatments consisted of a control (no lime or, phosphogypsum), lime (2.9 Mg ha<ce:sup loc=\"post\">−1</ce:sup>), and lime + phosphogypsum (2.0 Mg ha<ce:sup loc=\"post\">−1</ce:sup>) combined with four different N rates applied to maize (0, 80, 160 and 240 kg ha<ce:sup loc=\"post\">−1</ce:sup>). Lime and gypsum increased maize and soybean yields, especially under low N input. Lime and gypsum enhanced soil aggregate stability up to a depth of 60 cm and increased aggregate size up to a depth of 40 cm at N rates of up to 160 kg ha<ce:sup loc=\"post\">−1</ce:sup>. Lime and gypsum also increased soil surface and subsurface pH and soil calcium and magnesium levels up to a depth of 60 cm, particularly under low N input. Moreover, lime and gypsum increased soil organic matter content in both the surface and subsurface layers, particularly under high N input. Overall, our findings emphasize the benefits of combining lime and gypsum with moderate input of N-fertilizer for improving crop yields through enhanced soil physical and chemical properties.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"23 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-07DOI: 10.1016/j.geoderma.2024.117123
Amanda Sengeløv, Giacomo Capuzzo, Sarah Dalle, Hannah F. James, Charlotte Sabaux, Elisavet Stamataki, Marta Hlad, Carina T. Gerritzen, Emma M. Legrand, Barbara Veselka, Guy De Mulder, Rica Annaert, Mathieu Boudin, Kevin Salesse, Eugène Warmenbol, Nadine Mattielli, Christophe Snoeck, Martine Vercauteren
Understanding the spatial distribution of strontium isotopes in plants or other archives within a region is crucial for various fields, including archaeology, environmental studies, food sciences and forensic science. This study aims to create a detailed dynamic strontium isoscape for Belgium through high-density plant sampling, presented in a web application (IsoBel) that serves the mentioned research fields. A total of 540 plant samples (199 locations), representing various species of grass, shrubs, and trees across Belgium were collected and were analysed for their strontium isotope ratios (87Sr/86Sr) to create a first biologically available strontium map. Sampling sites were selected to cover diverse lithological formations and soil types, ensuring representative coverage of the region’s geological heterogeneity, by using a novel high density grid mapping method. Sixty-four previously published plants from 21 locations are also included in this study, bringing the total amount of plant samples used to 604 from 220 locations. The results reveal significant variations in 87Sr/86Sr across Belgium (ranging from 0.7054 to 0.7259), which reflect the underlying lithology and geological processes (tectonics, weathering,…) which shaped the landscape. Although overlapping 87Sr/86Sr is seen across the majority of lithologies, there is a statistically significant difference between the distribution of 87Sr/86Sr values across all different lithological units in Belgium (Kruskal-Wallis test; p < 0.0001). Distinct regional patterns were observed, with higher 87Sr/86Sr in the older geological south-eastern part of Belgium, compared to the younger north-western parts. The high-density plant sampling approach employed in this study allowed for enhanced spatial resolution and improved accuracy in the predictive surfaces for bioavailable 87Sr/86Sr created by Empirical Bayesian Kriging (EBK). These findings provide valuable insights into the geographic distribution of strontium isotopes within Belgium and offer a foundation for future studies in archaeology, ecology, environmental studies, food sciences and forensics. Furthermore, the extensive coverage of various plant species provided a robust representation of the local ecosystems and their strontium sources. Overall, this study contributes to the growing body of knowledge on regional strontium isoscapes, enhancing our understanding of the complex interplay between litho- and biosphere in shaping the strontium isotope compositions of ecosystems.
{"title":"From plants to patterns: Constructing a comprehensive online strontium isoscape for Belgium (IsoBel) using high density grid mapping","authors":"Amanda Sengeløv, Giacomo Capuzzo, Sarah Dalle, Hannah F. James, Charlotte Sabaux, Elisavet Stamataki, Marta Hlad, Carina T. Gerritzen, Emma M. Legrand, Barbara Veselka, Guy De Mulder, Rica Annaert, Mathieu Boudin, Kevin Salesse, Eugène Warmenbol, Nadine Mattielli, Christophe Snoeck, Martine Vercauteren","doi":"10.1016/j.geoderma.2024.117123","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117123","url":null,"abstract":"Understanding the spatial distribution of strontium isotopes in plants or other archives within a region is crucial for various fields, including archaeology, environmental studies, food sciences and forensic science. This study aims to create a detailed dynamic strontium isoscape for Belgium through high-density plant sampling, presented in a web application (IsoBel) that serves the mentioned research fields. A total of 540 plant samples (199 locations), representing various species of grass, shrubs, and trees across Belgium were collected and were analysed for their strontium isotope ratios (<ce:sup loc=\"post\">87</ce:sup>Sr/<ce:sup loc=\"post\">86</ce:sup>Sr) to create a first biologically available strontium map. Sampling sites were selected to cover diverse lithological formations and soil types, ensuring representative coverage of the region’s geological heterogeneity, by using a novel high density grid mapping method. Sixty-four previously published plants from 21 locations are also included in this study, bringing the total amount of plant samples used to 604 from 220 locations. The results reveal significant variations in <ce:sup loc=\"post\">87</ce:sup>Sr/<ce:sup loc=\"post\">86</ce:sup>Sr across Belgium (ranging from 0.7054 to 0.7259), which reflect the underlying lithology and geological processes (tectonics, weathering,…) which shaped the landscape. Although overlapping <ce:sup loc=\"post\">87</ce:sup>Sr/<ce:sup loc=\"post\">86</ce:sup>Sr is seen across the majority of lithologies, there is a statistically significant difference between the distribution of <ce:sup loc=\"post\">87</ce:sup>Sr/<ce:sup loc=\"post\">86</ce:sup>Sr values across all different lithological units in Belgium (Kruskal-Wallis test; p < 0.0001). Distinct regional patterns were observed, with higher <ce:sup loc=\"post\">87</ce:sup>Sr/<ce:sup loc=\"post\">86</ce:sup>Sr in the older geological south-eastern part of Belgium, compared to the younger north-western parts. The high-density plant sampling approach employed in this study allowed for enhanced spatial resolution and improved accuracy in the predictive surfaces for bioavailable <ce:sup loc=\"post\">87</ce:sup>Sr/<ce:sup loc=\"post\">86</ce:sup>Sr created by Empirical Bayesian Kriging (EBK). These findings provide valuable insights into the geographic distribution of strontium isotopes within Belgium and offer a foundation for future studies in archaeology, ecology, environmental studies, food sciences and forensics. Furthermore, the extensive coverage of various plant species provided a robust representation of the local ecosystems and their strontium sources. Overall, this study contributes to the growing body of knowledge on regional strontium isoscapes, enhancing our understanding of the complex interplay between litho- and biosphere in shaping the strontium isotope compositions of ecosystems.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"9 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/j.geoderma.2024.117129
Dave O’Leary, Colin Brown, Jim Hodgson, John Connolly, Louis Gilet, Patrick Tuohy, Owen Fenton, Eve Daly
Peat soils are high in soil organic matter (SOM) and are recognised stores of carbon. Knowledge of the spatial distribution of peat soils is becoming the focus of many studies and is related closely to peatland mapping. Accurate maps of peat soils have many applications of international importance e.g., gaseous emission inventory reporting or soil organic carbon stock accounting. Traditional mapping methods include in-situ soil auger sampling or peat probing (for depth) while modern methods also incorporate satellite data (optical and radar). However, both methods have limitations. Traditional sampling often omits boundaries and transition zones between peat and mineral soils, while satellite data only measure the surface and may not be able to penetrate landcover, potentially omitting areas of peat under, for example, grassland or forestry. Radiometrics is a measurement of naturally occurring gamma radiation. Peat soils attenuate this radiation through high soil moisture content. For the present study in Ireland, the supervised classification of gridded airborne radiometric data, acquired over multiple years, is performed using neural network pattern recognition to identify areas of peat and non-peat soils. Classification confidence values are used to identify the transition zone between these soil types, providing a simplified visualisation of this transition. Validation is performed using Loss on Ignition (LOI %) point data and several different (blanket bog, raised bog, transition zone) sites in Ireland, showing classified data can detect the presence of peat soils from broad to local scales. Airborne geophysical methods, in particular airborne radiometrics, can bridge the gap between the accuracy of point measurement and the spatial coverage of satellite data to identify peat soils by providing uniform data and objective analysis. The resulting map is a step towards understanding the true spatial distribution of peat soils in Ireland, including transition zones.
{"title":"Airborne radiometric data for digital soil mapping of peat at broad and local scales","authors":"Dave O’Leary, Colin Brown, Jim Hodgson, John Connolly, Louis Gilet, Patrick Tuohy, Owen Fenton, Eve Daly","doi":"10.1016/j.geoderma.2024.117129","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117129","url":null,"abstract":"Peat soils are high in soil organic matter (SOM) and are recognised stores of carbon. Knowledge of the spatial distribution of peat soils is becoming the focus of many studies and is related closely to peatland mapping. Accurate maps of peat soils have many applications of international importance e.g., gaseous emission inventory reporting or soil organic carbon stock accounting. Traditional mapping methods include in-situ soil auger sampling or peat probing (for depth) while modern methods also incorporate satellite data (optical and radar). However, both methods have limitations. Traditional sampling often omits boundaries and transition zones between peat and mineral soils, while satellite data only measure the surface and may not be able to penetrate landcover, potentially omitting areas of peat under, for example, grassland or forestry. Radiometrics is a measurement of naturally occurring gamma radiation. Peat soils attenuate this radiation through high soil moisture content. For the present study in Ireland, the supervised classification of gridded airborne radiometric data, acquired over multiple years, is performed using neural network pattern recognition to identify areas of peat and non-peat soils. Classification confidence values are used to identify the transition zone between these soil types, providing a simplified visualisation of this transition. Validation is performed using Loss on Ignition (LOI %) point data and several different (blanket bog, raised bog, transition zone) sites in Ireland, showing classified data can detect the presence of peat soils from broad to local scales. Airborne geophysical methods, in particular airborne radiometrics, can bridge the gap between the accuracy of point measurement and the spatial coverage of satellite data to identify peat soils by providing uniform data and objective analysis. The resulting map is a step towards understanding the true spatial distribution of peat soils in Ireland, including transition zones.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"28 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-05DOI: 10.1016/j.geoderma.2024.117128
Eduardo Vázquez, Marie Spohn
Northern forests are characterized by low temperatures that play a key role in the whole ecosystem functioning. However, Northern forests are expected to experience the largest temperature increase of all forest biomes in the next decades, which could affect central ecosystem processes, such as carbon (C) mineralization and N2 fixation. Aiming to clarify the temperature-dependence of non-symbiotic N2 fixation and C mineralization in Northern forest soils, we quantified the rates of both processes in soils of Scots Pine (Pinus sylvestris) forests located along a temperature gradient in Sweden in laboratory incubations at different temperatures (5, 12 and 20 °C). Our results show that N2 fixation by free-living bacteria in the organic layer of these forest soils ranges between 2 and 10 kg N ha−1 yr−1 which highlights the importance of non-symbiotic N2 fixation in Northern forest soils. We found a positive correlation between non-symbiotic N2 fixation per area and mean annual temperature (MAT). This relationship was caused by the positive relationship between the organic layer stock and MAT rather than by the direct effect of temperature on the process rate. In contrast, C mineralization per g of soil was negatively related to MAT. Furthermore, our results show that C mineralization is more sensitive to changes in incubation temperature (it increased by a factor of 2.2 from 5 to 12 °C as well as from 12 to 20 °C) than non-symbiotic N2 fixation that was not significantly affected by incubation temperature. Taken together, while N2 fixation responded little to changes in incubation temperature, our results suggest that the higher N2 fixation rate per area at sites with higher MAT is beneficial for primary production and organic matter inputs to soil leading to larger organic layer stocks. Hence, there is a positive, temperature-dependent feedback among non-symbiotic N2 fixation, primary production, and the organic layer formation in Northern forests.
北方森林的特点是低温，这对整个生态系统的功能起着关键作用。然而，在未来几十年，北方森林将经历所有森林生物群系中最大的温度升高，这可能会影响中心生态系统过程，如碳(C)矿化和N2固定。为了阐明北方森林土壤中非共生的N2固定和C矿化的温度依赖性，我们量化了位于瑞典沿温度梯度的苏格兰松林（Pinus sylvestris）土壤在不同温度（5、12和20°C）的实验室孵化过程中这两个过程的速率。我们的研究结果表明，在这些森林土壤的有机层中，自由生活的细菌固定N2的范围在2到10 kg N ha - 1 yr - 1之间，这突出了北方森林土壤非共生固定N2的重要性。研究发现，非共生固氮面积与年平均气温呈正相关。这种关系是由有机层料与MAT之间的正相关关系引起的，而不是由温度对过程速率的直接影响引起的。相比之下，每克土壤C矿化与MAT呈负相关。此外，我们的研究结果表明，C矿化对孵育温度的变化更为敏感（从5°C到12°C以及从12°C到20°C增加了2.2倍），而非共生N2固定则不受孵育温度的显著影响。综上所述，尽管氮固定对孵育温度变化的响应不大，但研究结果表明，在高MAT的地点，较高的单位面积氮固定率有利于初级生产和土壤有机质输入，从而导致更大的有机层储量。因此，在北方森林中，非共生固氮、初级生产和有机层形成之间存在正的、温度依赖的反馈。
{"title":"Non-symbiotic N2 fixation is less sensitive to changes in temperature than carbon mineralization in Northern forest soils","authors":"Eduardo Vázquez, Marie Spohn","doi":"10.1016/j.geoderma.2024.117128","DOIUrl":"https://doi.org/10.1016/j.geoderma.2024.117128","url":null,"abstract":"Northern forests are characterized by low temperatures that play a key role in the whole ecosystem functioning. However, Northern forests are expected to experience the largest temperature increase of all forest biomes in the next decades, which could affect central ecosystem processes, such as carbon (C) mineralization and N<ce:inf loc=\"post\">2</ce:inf> fixation. Aiming to clarify the temperature-dependence of non-symbiotic N<ce:inf loc=\"post\">2</ce:inf> fixation and C mineralization in Northern forest soils, we quantified the rates of both processes in soils of Scots Pine (<ce:italic>Pinus sylvestris</ce:italic>) forests located along a temperature gradient in Sweden in laboratory incubations at different temperatures (5, 12 and 20 °C). Our results show that N<ce:inf loc=\"post\">2</ce:inf> fixation by free-living bacteria in the organic layer of these forest soils ranges between 2 and 10 kg N ha<ce:sup loc=\"post\">−1</ce:sup> yr<ce:sup loc=\"post\">−1</ce:sup> which highlights the importance of non-symbiotic N<ce:inf loc=\"post\">2</ce:inf> fixation in Northern forest soils. We found a positive correlation between non-symbiotic N<ce:inf loc=\"post\">2</ce:inf> fixation per area and mean annual temperature (MAT). This relationship was caused by the positive relationship between the organic layer stock and MAT rather than by the direct effect of temperature on the process rate. In contrast, C mineralization per g of soil was negatively related to MAT. Furthermore, our results show that C mineralization is more sensitive to changes in incubation temperature (it increased by a factor of 2.2 from 5 to 12 °C as well as from 12 to 20 °C) than non-symbiotic N<ce:inf loc=\"post\">2</ce:inf> fixation that was not significantly affected by incubation temperature. Taken together, while N<ce:inf loc=\"post\">2</ce:inf> fixation responded little to changes in incubation temperature, our results suggest that the higher N<ce:inf loc=\"post\">2</ce:inf> fixation rate per area at sites with higher MAT is beneficial for primary production and organic matter inputs to soil leading to larger organic layer stocks. Hence, there is a positive, temperature-dependent feedback among non-symbiotic N<ce:inf loc=\"post\">2</ce:inf> fixation, primary production, and the organic layer formation in Northern forests.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"21 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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