Soil最新文献

{"title":"Contribution of soil Microbial Necromass Carbon to Soil Organic Carbon fractions and its influencing factors in different grassland types","authors":"Shenggang Chen, Yaqi Zhang, Jun Ma, Mingyue Bai, Jinxiao Long, Ming Liu, Yinglong Chen, Jianbin Guo, Lin Chen","doi":"10.5194/egusphere-2025-1122","DOIUrl":"https://doi.org/10.5194/egusphere-2025-1122","url":null,"abstract":"<strong>Abstract.</strong> Microbial necromass carbon(MNC) is a significant source of soil organic carbon (SOC), the quantitative contribution of MNC to distinct SOC fractions and its regulatory mechanisms across various grassland types remain largely unexplored. This study through a comprehensive investigation of soil profiles (0–20 cm, 20–40 cm, and 40–100 cm) across four grassland types in Ningxia, China, encompassing meadow steppe (MS), typical steppe (TS), desert steppe (DS), and steppe desert (SD). We quantified mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and their respective microbial necromass components, including total microbial necromass carbon (TNC), fungal necromass carbon (FNC), and bacterial necromass carbon (BNC), and analyzed the contributions to SOC fractions and influencing factors. Our findings reveal three key insights. First, the contents of MAOC and POC in the 0–100 cm soil layer were in the following order of magnitude: Meadow steppe (MS) &gt;Typical steppe (TS) &gt; Desert steppe (DS) &gt; Steppe desert (SD), with the average content of POC was 9.3 g/kg, which was higher than the average content of MAOC (8.73 g/kg). Second, the content of microbial TNC in MAOC and POC decreased with the depth of the soil layer, the average content of FNC was 3.02 g/kg and 3.85 g/kg, which was higher than the average content of BNC (1.64 g/kg and 2.08 g/kg). FNC dominated both MAOC and POC, and its contribution was higher than the contribution of BNC. Thid, through regression analysis and random forest modeling, we identified key environmental drivers of MNC dynamics: mean annual rainfall (MAP), electrical conductance (EC), and soil total nitrogen(TN) emerged as primary regulators in surface soils (0–20cm), while available potassium(AK), SOC, and mean annual temperature (MAT) dominated deeper soil layers (20–100 cm). This research by: 1) establishing the vertical distribution patterns of MNC and SOC fractions in soil profiles; 2) quantifying the relative contributions of MNC to SOC fractions across different grassland ecosystems soil profiles and elucidating their environmental controls, offering a deeper understanding of the mechanisms driving MNC to soc fractions accumulation in diverse grassland ecosystems, and provide data support for further research on the microbiological mechanisms of soil organic carbon formation and accumulation in arid and semi-arid regions.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"37 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of trachyte and basalt powder on the growth and yield of maize (Zea Mays L.) in the Sudano-Sahelian zone of Cameroon (Central Africa)","authors":"Bienvenu Sidsi, Claudine Vounba, Simon Djakba Basga, Aubin Nzeugang Nzeukou, Merlin Dedzo Gountie, Désiré Tsozué","doi":"10.5194/egusphere-2025-930","DOIUrl":"https://doi.org/10.5194/egusphere-2025-930","url":null,"abstract":"<strong>Abstract.</strong> The Sudano-Sahelian zone of Cameroon, characterized by a low annual rainfall, faces challenges in soil fertility preservation due to agricultural intensification and unsustainable practices. This study aims to evaluate the effect of trachyte and basalt powders inputs on soil and maize yield in Guiring experimental farm. Fieldwork involved collecting and describing samples of trachyte, basalt, and soil and setting up the experimental design. In the laboratory, the ground rock samples underwent geochemical analysis, and the soil samples were analysed for their mineralogical and physicochemical properties. The experiment followed a completely randomized block design with three repetitions and six treatments (T0, T1, T2, T3, T4 and T5). The soil consists of kaolinite, smectite, sepiolite, and quartz. Its texture is dominated by sand fraction, with a neutral pH (6.98). The organic matter (1.30 to 3.17 %) and total nitrogen contents (0.11 to 0.13 %) are relatively low. The concentrations of potassium, magnesium, sodium, and calcium vary from 0.10 to 0.40 cmol_c kg^-1, 0.72 to 5.44 cmol_c kg^-1, 0.13 to 0.56 cmol_c kg^-1, and 2.64 to 6 cmol_c kg^-1, respectively. The cation exchange capacity is moderate to high, ranging from 18.70 to 25 cmol_c kg^-1, while the available phosphorus content is high, ranging from 12.60 to 30.30 mg kg^-1. The studied soils are moderately suitable for maize cultivation. Fertilization trials showed a significant improvement in maize growth and yield, within plots treated with basalt powder yielding higher (2558.64 kg ha^-1 and 2931.16 kg ha^-1) than those treated with trachyte powder (2362.87 kg ha^-1and 2763.91 kg ha^-1) and the control plots (645.83 kg ha^-1). Plots treated with NPK fertilizer recorded the highest yield (3164.45 kg ha^-1). Although the treatment with conventional fertiliser resulted in a relative higher yield, the advantage of using rock powders lies in their environmental benefits, long-term effectiveness, and more affordable cost.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"56 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Organic matter-mediated leaching of alkalinity in limed acid soils is affected by dissolved organic carbon adsorption and soil structure","authors":"Hannah Van Ryckel, Lynn Van Aelst, Toon van Dael, Erik Smolders","doi":"10.5194/egusphere-2025-1012","DOIUrl":"https://doi.org/10.5194/egusphere-2025-1012","url":null,"abstract":"<strong>Abstract.</strong> Subsurface soil acidity severely limits crop growth and is challenging to adjust by surface liming. There have been several proposals for subsurface liming using the combination of lime and an organic amendment, as organic anions may migrate deeper in acid subsoil than carbonates. This study aimed to identify mechanisms of subsurface liming, postulating that it is hindered by dissolved organic carbon (DOC) adsorption but enhanced in structured compared to sieved soils due to preferential flow in macropores. Column leaching experiments were set up using three sieved acid soils with contrasting properties, of which one was additionally sampled as undisturbed soil cores. The upper layer of each soil was treated with lime, compost, or a combination of both, in addition to an untreated control and columns were leached with artificial rainwater. Deeper subsurface liming in the lime+compost treatment than in the lime treatment was detected in only one of the three soils. The effect of compost on the migration of alkalinity was explained by differences in DOC sorption among soils, the lowest sorption leading to deepest subsurface liming. Imaging of in situ pH using a planar optode showed evidence of preferential alkalinity flow in the structured soil, however destructive sampling of bulk soil layers did not confirm this. We conclude that combining lime with an organic amendment can effectively ameliorate subsoil acidity but this requires weakly DOC adsorbing subsoils. The role of soil structure on this process needs to be corroborated with plant responses to identify benefits of liming the macropores.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"92 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying spatial uncertainty to improve soil predictions in data-sparse regions","authors":"Kerstin Rau, Katharina Eggensperger, Frank Schneider, Michael Blaschek, Philipp Hennig, Thomas Scholten","doi":"10.5194/egusphere-2025-166","DOIUrl":"https://doi.org/10.5194/egusphere-2025-166","url":null,"abstract":"<strong>Abstract.</strong> Artificial Neural Networks (ANNs) are valuable tools for predicting soil properties using large datasets. However, a common challenge in soil sciences is the uneven distribution of soil samples, which often results from past sampling projects that heavily sample certain areas while leaving similar yet geographically distant regions under-sampled. One potential solution to this problem is to transfer an already trained model to other similar regions. Robust spatial uncertainty quantification is crucial for this purpose, yet often overlooked in current research. We address this issue by using a Bayesian deep learning technique, Laplace Approximations, to quantify spatial uncertainty. This produces a probability measure encoding where the model’s prediction is deemed reliable, and where a lack of data should lead to a high uncertainty. We train such an ANN on a soil landscape dataset from a specific region in southern Germany and then transfer the trained model to another unseen but to some extend similar region, without any further model training. The model effectively generalized alluvial patterns, demonstrating its ability to recognize repetitive features of river systems. However, the model showed a tendency to favor overrepresented soil units, underscoring the importance of balancing training datasets to reduce overconfidence in dominant classes. Quantifying uncertainty in this way allows stakeholders to better identify regions and settings in need of further data collection, enhancing decision-making and prioritizing efforts in data collection. Our approach is computationally lightweight and can be added post-hoc to existing deep learning solutions for soil prediction, thus offering a practical tool to improve soil property predictions in under-sampled areas, as well as optimizing future sampling strategies, ensuring resources are allocated efficiently for maximum data coverage and accuracy.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"18 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of soil storage on microbial parameters","authors":"Nathalie Fromin","doi":"10.5194/soil-11-247-2025","DOIUrl":"https://doi.org/10.5194/soil-11-247-2025","url":null,"abstract":"Abstract not available","PeriodicalId":48610,"journal":{"name":"Soil","volume":"54 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional synthesis and mapping of soil organic carbon and nitrogen stocks at the Canadian Beaufort coast","authors":"Julia Wagner, Juliane Wolter, Justine Ramage, Victoria Martin, Andreas Richter, Niek Jesse Speetjens, Jorien E. Vonk, Rachele Lodi, Annett Bartsch, Michael Fritz, Hugues Lantuit, Gustaf Hugelius","doi":"10.5194/egusphere-2025-1052","DOIUrl":"https://doi.org/10.5194/egusphere-2025-1052","url":null,"abstract":"<strong>Abstract.</strong> Permafrost soils are particularly vulnerable to climate change. To assess and improve estimations of carbon (C) and nitrogen (N) budgets it is necessary to accurately map soil carbon and nitrogen in the permafrost region. In particular, soil organic carbon (SOC) stocks have been predicted and mapped by many studies from local to pan-Arctic scales. Several studies have been carried out at the Canadian Beaufort Sea coast, though no regional synthesis of terrestrial carbon stocks based on spatial modelling has been conducted yet. This study synthesises available field data from the Canadian coastal plain and uses it to map regional SOC and N stocks using the machine learning algorithm random forest and environmental variables based on remote sensing data. We explore local differences in soil properties and how soil data distribution across the region affects the accuracy of the predictions of SOC and N stocks. We mapped SOC and N stocks for the entire region and provide separate models for the coastal mainland area and Qikiqtaruk Herschel Island. We assessed performance of different random forest models by using the Area of Applicability (AOA) method. We further applied the quantile regression forest method to the mainland and Qikiqtaruk Herschel Island models for SOC stocks and compared the results with the AOA method. Our results indicate that not only the selection of data is crucial for the resulting maps, but also the chosen covariates, which were picked by the models as most important. The estimated SOC stock for the upper metre is 56.7 ± 5.6 kg m⁻²and the N stock 2.19 ± 0.51 kg m⁻². The average SOC stocks vary significantly when including or excluding data in the predictive models. Qikiqtaruk Herschel Island is geologically different from the coastal mainland and has lower SOC stocks. Including Qikiqtaruk Herschel Island soil data to predict SOC stocks at the mainland has large impact on the results. Differences in N stocks were not as dependent on the location as SOC stocks and rather differences between individual studies occurred. The results of the separate models show 36.2 ± 5.7 kg C m⁻²and 2.66 ± 0.39 kg N m⁻²for Qikiqtaruk Herschel Island and 57.2 ± 4.5 kg C m⁻²and 2.17 ± 0.50 kg N m⁻²for the mainland. Our results diverge from previous studies of lower resolution, showing the added regional-scale accuracy and precision that can be achieved at intermediate resolution and with sufficient field data.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"10 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Living cover crops reduce pesticide residues in agricultural soil","authors":"Noé Vandevoorde, Igor Turine, Alodie Blondel, Yannick Agnan","doi":"10.5194/egusphere-2025-943","DOIUrl":"https://doi.org/10.5194/egusphere-2025-943","url":null,"abstract":"<strong>Abstract.</strong> Living cover crops play a key role in reducing nitrogen leaching to groundwater during fallow periods. They also enhance soil microbial activity through root exudates, improving soil structure and increasing organic matter content. While the degradation of pesticides in soil relies primarily on microbial biodegradation, the extent to which cover crops influence this degradation remains poorly quantified. In this paper we (1) monitored pesticide residue levels in soil and soil solution under two different cover crop densities and (2) correlated the observed reductions with physicochemical properties of the active substances. Our results show that thin cover crops (0.4 t_DMha^-1) reduce pesticide leaching 80 days after sowing compared to bare soil, retaining the residues in the microbiologically active topsoil. In addition, well-developed cover crops (1 t_DM ha^-1) reduce soil pesticide contents by more than 33 % for compounds with low to high water solubility (s ≤ 1400 mg L^-1) and low to moderate soil mobility (K_oc ≥ 160 mL g^-1). This effect is probably due to enhanced pesticide degradation of the retained pesticide in the rhizosphere. These results confirm previous studies on individual compounds, individual cover crop types and individual soil compartments, while providing new thresholds for physicochemical properties associated with significant pesticide degradation. By directly enhancing pesticide degradation within the soil compartment where pesticides are applied, cover crops limit their transfer to other environmental compartments, particularly groundwater.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"56 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In silico analysis of carbon stabilisation by plant and soil microbes for different weather scenarios","authors":"Mona Giraud, Ahmet Kürşad Sırcan, Thilo Streck, Daniel Leitner, Guillaume Lobet, Holger Pagel, Andrea Schnepf","doi":"10.5194/egusphere-2025-572","DOIUrl":"https://doi.org/10.5194/egusphere-2025-572","url":null,"abstract":"<strong>Abstract.</strong> A plant's development is strongly linked to the water and carbon (C) flows in the soil-plant-atmosphere continuum. Ongoing climate shifts will alter the water and C cycles and affect plant phenotypes. Comprehensive models that simulate mechanistically and dynamically the feedback loops between water and C fluxes in the soil-plant system are useful tools to evaluate the sustainability of genotype-environment-management combinations that do not yet exist. In this study, we present the equations and implementation of a rhizosphere-soil model within the CPlantBox framework, a functional-structural plant model that represents plant processes and plant-soil interactions. The multi-scale plant-rhizosphere-soil coupling scheme previously used for CPlantBox was likewise updated, among others to include an implicit time-stepping. The model was implemented to simulate the effect of dry spells occurring at different plant development stages, and for different soil biokinetic parametrisations of microbial dynamics in soil. We could observe diverging results according to the date of occurrence of the dry spells and soil parametrisations. For instance, an earlier dry spell led to a lower cumulative plant C release, while later dry spells led to higher C input to the soil. For more reactive microbial communities, this higher C input caused a strong increase in CO₂ emissions, while, for the same weather scenario, we observed a lasting stabilisation of soil C with less reactive communities. This model can be used to gain insight into C and water flows at the plant scale, and the influence of soil-plant interactions on C cycling in soil.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"30 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143569646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining electromagnetic induction and remote sensing data for improved determination of management zones for sustainable crop production","authors":"Salar Saeed Dogar, Cosimo Brogi, Dave O'Leary, Ixchel Hernández-Ochoa, Marco Donat, Harry Vereecken, Johan Alexander Huisman","doi":"10.5194/egusphere-2025-827","DOIUrl":"https://doi.org/10.5194/egusphere-2025-827","url":null,"abstract":"<strong>Abstract.</strong> Accurate delineation of management zones is essential for optimizing resource use and improving yield in precision agriculture. Electromagnetic induction (EMI) provides a rapid, non-invasive method to map soil variability, while the Normalized Difference Vegetation Index (NDVI) obtained with remote sensing captures above-ground crop dynamics. Integrating these datasets may enhance management zone delineation but presents challenges in data harmonization and analysis. This study presents a workflow combining unsupervised classification (clustering) and statistical validation to delineate management zones using EMI and NDVI data in a single 70 ha field of the patchCROP experiment in Tempelberg, Germany. Three datasets were investigated: (1) EMI maps, (2) NDVI maps, and (3) a combined EMI-NDVI dataset. Historical yield data and soil samples were used to refine the clusters through statistical analysis. The results demonstrate that four EMI-based zones effectively captured subsurface soil heterogeneity, while three NDVI-based zones better represented yield variability. A combination of EMI and NDVI data resulted in three zones that provided a balanced representation of both subsurface and above-ground variability. The final EMI-NDVI derived map demonstrates the potential of integrating multi-source datasets for field management. It provides actionable insights for precision agriculture, including optimized fertilization, irrigation, and targeted interventions, while also serving as a valuable resource for environmental modelling and soil surveying.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"23 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aeration and mineral composition of soil determine microbial CUE","authors":"Jolanta Niedźwiecka, Roey Angel, Petr Čapek, Ana Catalina Lara, Stanislav Jabinski, Travis B. Meador, Hana Šantrůčková","doi":"10.5194/egusphere-2025-481","DOIUrl":"https://doi.org/10.5194/egusphere-2025-481","url":null,"abstract":"<strong>Abstract.</strong> Microbial carbon use efficiency (CUE) in soils is used to estimate the balance of CO₂ respired by heterotrophs versus the accumulation of organic carbon (C). While most CUE studies assume that aerobic respiration is the predominant degradation process of organic C, anoxic microniches are common inside soil aggregates. Microorganisms in these microniches carry out fermentation and anaerobic respiration using alternative electron acceptors, e.g. NO^3-, Fe, SO₄^2-. Extracellular metabolites are also not traditionally accounted for but may represent a significant C flux. Moreover, climate change may modulate soil microbial activity by altering soil aeration status on a local level due to warming and elevated frequency of extreme precipitation events. Therefore, CUE should be measured under more realistic assumptions regarding soil aeration. This study focused on the effect of oxygen and Fe on C mineralisation in forest soils and quantified C distribution between biomass and different extracellular metabolites. Forest soils were collected from two Bohemian Forest (Czechia) sites with low and high Fe content and incubated under oxic and anoxic conditions. A solution of 13C-labelled glucose was used to track stable isotope incorporation into the biomass, respired CO₂, and extracellular metabolites. We estimated CUE based on microbial respiration, glucose consumption, biomass growth, and extracellular metabolites. RNA-SIP was used to identify the active bacteria under each treatment. As expected, the oxic incubation showed a rapid utilisation and immediate production of biomass and CO₂. Under anoxic conditions, 90 % of the added glucose was still present after 72 h, and anoxic soils showed significantly lower microbial activity. The low-Fe soil samples were more active under oxic conditions, while the high-Fe samples were more active under anoxia. Our findings confirm that anoxia in soils enhances short-term C preservation. Accordingly, excluding exudates in mass flux calculations would underestimate apparent CUE values.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"50 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}