Soil最新文献

{"title":"Soil erosion in Mediterranean olive groves: a review","authors":"Andres Peñuela, Filippo Milazzo, Emilio Jesús González-Sánchez","doi":"10.5194/egusphere-2025-3542","DOIUrl":"https://doi.org/10.5194/egusphere-2025-3542","url":null,"abstract":"<strong>Abstract.</strong> Olive groves are a defining feature of the Mediterranean landscape, economy, and culture. However, this keystone agroecosystem is under severe threat from soil erosion, a problem exacerbated by the region's unique topographic, climatic conditions and agricultural practices. Although soil erosion in olive groves has been extensively studied, significant uncertainties remain due to the high variability of scales and measurement methods. Knowledge gaps persist regarding the average soil loss rates and runoff coefficients as well as the effects of different management approaches and the influence of triggering factors on soil erosion rates. So far, an effort to quantify this effect on Mediterranean olive cultivation has not been made comprehensively. Therefore, the aim of this literature review is to discern clearer patterns and trends that are often obscured by the overall heterogeneity of the available data. By systematically analysing the data according to measurement methodology, this review provides clear answers to these knowledge gaps and reveals a consistent narrative about the primary drivers of soil loss. While natural factors like topography, rainfall intensity and soil properties establish a baseline risk, this review shows that agricultural management, particularly the presence of groundcovers, is the pivotal factor controlling soil degradation. The long-standing debate on erosion severity is largely reconciled by the finding that reported rates are highly dependent on the measurement methodology, and hence on the spatial and temporal scale. Conservation practices consistently reduce soil loss by more than half, an effect far more pronounced for sediment control than for runoff reduction. Ultimately, the path to sustainability requires a shift away from conventional tillage and bare-soil management towards the widespread adoption of vegetation/groundcover, driven by effective policies and a commitment to multi-scale and multi-proxy research to improve predictive models.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"25 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144898638","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":"Portable X-ray fluorescence as a tool for urban soil contamination analysis: accuracy, precision, and practicality","authors":"Eriell M. Jenkins, John Galbraith, Anna A. Paltseva","doi":"10.5194/soil-11-565-2025","DOIUrl":"https://doi.org/10.5194/soil-11-565-2025","url":null,"abstract":"Abstract. Urban agriculture has become an essential component of urban sustainability, but it often faces the challenge of soil contamination with heavy metal(loid)s like lead (Pb), arsenic (As), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), and zinc (Zn). Traditional laboratory methods for detecting these contaminants, such as atomic absorption spectroscopy and other inductively coupled plasma techniques, are accurate but can be costly and time-consuming and require extensive sample preparation. Portable X-ray fluorescence (PXRF) presents a promising alternative, offering rapid, in situ analysis with minimal sample preparation. The study reviews literature on PXRF analyzers to determine their accuracy and precision in analyzing heavy metal(loid)s in urban soils, with the goal of optimizing sampling, reducing laboratory costs and time, and identifying priority metal contamination hotspots. A literature review was conducted using Web of Science and Google Scholar, focusing on studies that validated PXRF measurements with alternate laboratory methods or certified reference materials (CRMs). This study reviews 84 publications to evaluate the accuracy and precision of PXRF in analyzing heavy metal(loid)s in urban soils. The review covers instrument types, action methods, testing conditions, and sample preparation techniques. Results show that, when properly calibrated, particularly with CRMs, PXRF can achieve reliable accuracy. Ex situ measurements tend to be more precise due to controlled conditions, although in situ measurements offer practical advantages in urban settings. Portable XRF emerges as a viable method for assessing urban soil contamination by balancing accuracy and practicality. Future research should focus on optimizing sample preparation and calibration to further enhance PXRF reliability in urban environments, ultimately strengthening PXRF methodologies and supporting extension efforts through improved, accessible soil-testing tools, facilitating healthier urban soils, safer urban food production, and enhanced community well-being.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"8 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144825295","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 rock powders on maize (Zea mays L.) growth and yield on Fluvisols in Cameroon’s Sudano-Sahelian zone (Central Africa)","authors":"Bienvenu Sidsi, Claudine Vounba, Simon Djakba Basga, Aubin Nzeugang Nzeukou, Merlin Gountié Dedzo, Désiré Tsozué","doi":"10.5194/egusphere-2025-3474","DOIUrl":"https://doi.org/10.5194/egusphere-2025-3474","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 six treatments (T₀, T₁, T₂, T₃, T₄, and T₅) and four replications. Growth and yield parameters of maize, include germination rate, plant height, number of leaves per plant, stem diameter, ear length, ear diameter, ear weight, 100-grain weight, and grain yield (kg ha^-1). The soil consists of kaolinite, smectite, sepiolite, and quartz. Its texture is dominated by sand fraction, with a neutral pH (7.0). The organic matter (2.6±0.67 %) and total nitrogen contents (0.1±0.0 %) are relatively low. The concentrations of potassium, magnesium, sodium, and calcium are 0.2±0.1 cmol_c kg^-1, 2.5±1.6 cmol_c kg^-1, 0.3±0.2 cmol_c kg^-1, and 3.9±1.5 cmol_c kg^-1, respectively. The cation exchange capacity is moderate to high (22.1±2.5 cmol_c kg^-1), while the available phosphorus content is high (19±7.0 mg kg^-1). This soil is classified as Ochric Dystric Fluvisols according to the WRB. These soil characteristics 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 (2558.6 kg ha^-1 and 2931.2 kg ha^-1) than those treated with trachyte powder (2362.9 kg ha^-1 and 2763.9 kg ha^-1) and the control plots (645.8 kg ha^-1). Plots treated with NPK fertilizer recorded the highest yield (3164.5 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":"160 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144796725","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":"Assessing the potential of complex artificial neural networks for modelling small-scale soil erosion by water","authors":"Nils Barthel, Simone Ott, Benjamin Burkhard, Bastian Steinhoff-Knopp","doi":"10.5194/egusphere-2025-3583","DOIUrl":"https://doi.org/10.5194/egusphere-2025-3583","url":null,"abstract":"<strong>Abstract.</strong> Accurately modelling soil erosion by water is essential for developing effective mitigation strategies and preventing on- and off-site damages in agricultural areas. So far, complex artificial neural networks have rarely been applied in small-scale soil erosion modelling, and their potential still remains unclear. This study compares the performance of different neural network architectures for modelling soil erosion by water at a small spatial scale in agricultural cropland. The analysis is based on erosion rate data at a 5 m × 5 m resolution, derived from a 20-year monitoring programme, and covers 458 hectares of cropland across six investigation areas in northern Germany. Nineteen predictor variables related to topography, climate, management and soil properties were selected as inputs to assess their interrelationships with observed erosion patterns and to predict continuous soil erosion rates. A single-layer neural network (SNN), a deep neural network (DNN), and a convolutional neural network (CNN) were applied and evaluated against a random forest (RF) model used as a benchmark. All machine learning models have successfully captured spatial patterns of soil erosion, with the CNN consistently outperforming the others across all evaluation metrics. The CNN achieves the lowest root mean squared error (RMSE: 1.05) and mean absolute error (MAE: 0.41), outperforming the RF (RMSE: 1.31, MAE: 0.58) and the SNN (RMSE: 1.48, MAE: 0.63), while the DNN performs similarly to the CNN with a slightly higher RMSE (1.1) and MAE (0.45). The CNN notably outperforms the other three approaches when evaluating their capability to accurately predict soil erosion within given classes, achieving a weighted mean F1 score of 0.7. A permutation importance analysis identified the digital elevation model as the most influential predictor variable across all models, contributing between 15 % and 18.3 %, while USLE C and R factors also had significant importance. Overall, these findings highlight the potential of complex neural networks for predicting spatially explicit rates of soil erosion by water.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"17 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144802708","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":"Soil stoichiometric characteristics and influencing factors in karst forests under micro-topography and microhabitat scales","authors":"Yi Dang, Hua Zhou, Wenjun Zhao, Yingchun Cui, Chengjiang Tan, Fangjun Ding, Yukun Wang, Run Liu, Peng Wu","doi":"10.5194/egusphere-2025-3510","DOIUrl":"https://doi.org/10.5194/egusphere-2025-3510","url":null,"abstract":"<strong>Abstract.</strong> To quantitatively evaluate the stoichiometric characteristics of karst forest soils and their response mechanisms to complex microenvironments, the study systematically investigated soil stoichiometric traits and influencing factors across micro-topography and microhabitat scales in the Maolan karst forest. Key findings include: (1) Soil nutrients (organic carbon, total nitrogen, hydrolyzable nitrogen, available phosphorus, available potassium, total calcium, exchangeable calcium, and exchangeable magnesium) exhibited strong variability with significant spatial heterogeneity; (2) Microhabitat factors significantly influenced nutrient accumulation, though different elements showed distinct response patterns to microhabitat variations; (3) Micro-topographic parameters (slope gradient, aspect, and position) exerted indirect effects through gravity, light exposure, and erosion, driving the formation of gradient patterns in soil stoichiometry; (4) Differential response mechanisms of nutrients to abiotic factors, combined with the differential nutrient regulation and absorption strategies of various plant life forms, collectively shaped the complex stoichiometric characteristics. Synergistic interactions were observed among microhabitat-micro-topography-plant life form factors, with geomorphological abiotic factors playing predominant roles at this scale. Although biotic factors like plant life forms showed relatively weaker direct influences, their regulatory effects were closely interrelated with microhabitat-topographic factors. This multi-dimensional feedback mechanism between biotic and abiotic factors reflects the complexity of nutrient cycling in karst ecosystems.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"27 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719707","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":"Mineral-bound organic carbon exposed by hillslope thermokarst terrain: case study in Cape Bounty, Canadian High Arctic","authors":"Maxime Thomas, Julien Fouché, Hugues Titeux, Charlotte Morelle, Nathan Bemelmans, Melissa J. Lafrenière, Joanne K. Heslop, Sophie Opfergelt","doi":"10.5194/egusphere-2025-3428","DOIUrl":"https://doi.org/10.5194/egusphere-2025-3428","url":null,"abstract":"<strong>Abstract.</strong> Arctic landscapes could add 55–230 Pg of carbon (in CO₂ equivalent) to the atmosphere, through CO₂ and CH₄ emissions, by the end of this century. These estimates could be quantified more accurately by constraining the contribution of rapid thawing processes such as thermokarst landscapes to permafrost carbon loss, and by investigating the exposed organic carbon (OC) interacting with mineral surfaces or metallic cations, i.e., the nature of these interactions and what controls their relative abundance. Here, we investigate two contrasted types of hillslope thermokarst landscapes: an Active Layer Detachment (ALD) which is a one-time event, and a Retrogressive Thaw Slump (RTS) which repeats annually during summer months in the Cape Bounty Arctic Watershed Observatory (Melville Island, Canada). We analyzed mineralogy, total and soluble element concentrations, total OC and mineral-OC interactions within the headwalls of both disturbances, and within corresponding undisturbed profiles. Our results show that OC stabilized by chemical bonds account for 13 ± 5 % of total OC in the form of organo-metallic complexes and up to 6 ± 2 % associated with poorly crystalline iron oxides. If we add the mechanisms of physical protection of particulate organic matter in aggregates and larger molecules stabilized by chemical bonds, we reach 64 ± 10 % of the total OC being stabilized. Importantly, we observe a decrease in the proportion of mineral-bound OC in the deeper layers exposed by the retrogressive thaw slump: the proportion of organo-metallic complexes drops from ~18 % in surface samples to ~1 % in the deepest samples. These results therefore suggest that the OC exposed by thermokarst disturbances at Cape Bounty is protected by interactions with minerals to a certain extent, but that deep thaw features could expose OC more readily accessible to degradation.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"51 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677491","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":"Using Monte Carlo conformal prediction to evaluate the uncertainty of deep-learning soil spectral models","authors":"Yin-Chung Huang, José Padarian, Budiman Minasny, Alex B. McBratney","doi":"10.5194/soil-11-553-2025","DOIUrl":"https://doi.org/10.5194/soil-11-553-2025","url":null,"abstract":"Abstract. Uncertainty quantification is a crucial step in the practical application of soil spectral models, particularly in supporting real-world decision making and risk assessment. While machine learning has made remarkable strides in predicting various physiochemical properties of soils using spectroscopy, its practical utility in decision making remains limited without quantified uncertainty. Despite its importance, uncertainty quantification is rarely incorporated into soil spectral models, with existing methods facing significant limitations. Existing methods are either computationally demanding, fail to achieve the desired coverage of observed data, or struggle to handle out-of-domain uncertainty. This study introduces an innovative application of Monte Carlo conformal prediction (MC-CP) to quantify uncertainty in deep-learning models for predicting clay content from mid-infrared spectroscopy. We compared MC-CP with two established methods: (1) Monte Carlo dropout and (2) conformal prediction. Monte Carlo dropout generates prediction intervals for each sample and can address larger uncertainties associated with out-of-domain data. Conformal prediction, on the other hand, guarantees ideal coverage of true values but generates unnecessarily wide prediction intervals, making it overly conservative for many practical applications. Using 39 177 samples from the mid-infrared spectral library of the Kellogg Soil Survey Laboratory to build convolutional neural networks, we found that Monte Carlo dropout itself falls short in achieving the desired coverage – its 90 % prediction intervals only covered the observed values in 74 % of the cases, well below the expected 90 % coverage. In contrast, MC-CP successfully combines the strengths of both methods. It achieved a prediction interval coverage probability of 91 %, closely matching the expected 90 % coverage and far surpassing the performance of the Monte Carlo dropout. Additionally, the mean prediction interval width for MC-CP was 9.05 %, narrower than the conformal prediction's 11.11 %. The success of MC-CP enhances the real-world applicability of soil spectral models, paving the way for their integration into large-scale machine learning models, such as soil inference systems, and further transforming decision making and risk assessment in soil science.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"282 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677499","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":"Restorative Mitigation of Contaminated Soil for Ecosystem Services: Influences from Research Enterprise and Sustainable Development Goals","authors":"Isak Rajjak Shaikh, Parveen Rajjak Shaikh","doi":"10.5194/egusphere-2025-271","DOIUrl":"https://doi.org/10.5194/egusphere-2025-271","url":null,"abstract":"<strong>Abstract.</strong> Soil is a vital component of the ecosystem, as it provides nutrients needed for the growth of plants and supports all terrestrial life on the planet. The global agricultural sector underwent enormous change after the World Wars, thanks to some important developments in technology transfer that saw increased crop production during the Green Revolution of the 1960s; the initiatives included the use of high yielding variety seeds and also the application of synthetic agrochemicals as nutrient inputs and crop protection agents. This was meant secure food grains for growing human population. Despite all the achievements, the initiatives taken during the Green Revolution are meeting with some harsh criticism now. Soil is under constant pressure due to irresponsible land use and resource exploitation, erosion, escalating climate change, and also the indiscriminate usage of synthetic pesticides and fertilizers. Synthetic pesticides are contaminating soil, and the contaminants are making serious alterations to the content and most importantly to the chemical quality, properties and functions of soil, requiring an immediate risk assessment owing to the hazard and scientific uncertainty surrounding it. Soil pollution is one of the most serious concerns of our time, which not only limits the sustainability of community livelihood but also compromises ecosystem services, causing depletion in its fertility and risks to the environmental and human health. So, the environmentalists, economists, and social scientists have begun advocating more organic amendments to farming and restoration of ecosystems services of soil. Researchers explore physico-chemical and biological methods to mitigate the soil contamination. Research enterprise, local policy making, and globalized discourses on environment at the highest decision-making authority of intergovernmental organizations are being directed towards sustainable future of socio-ecological system.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"36 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645633","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":"Interplay of coprecipitation and adsorption processes: deciphering amorphous mineral–organic associations under both forest and cropland conditions","authors":"Floriane Jamoteau, Emmanuel Doelsch, Nithavong Cam, Clément Levard, Thierry Woignier, Adrien Boulineau, Francois Saint-Antonin, Sufal Swaraj, Ghislain Gassier, Adrien Duvivier, Daniel Borschneck, Marie-Laure Pons, Perrine Chaurand, Vladimir Vidal, Nicolas Brouilly, Isabelle Basile-Doelsch","doi":"10.5194/soil-11-535-2025","DOIUrl":"https://doi.org/10.5194/soil-11-535-2025","url":null,"abstract":"Abstract. Mineral–organic associations are crucial carbon and nutrient reservoirs in soils. However, conversion from forest to agricultural systems disrupts these associations, leading to carbon loss and reduced soil fertility in croplands. Identifying the types of mineral–organic associations within a single soil is already challenging, and detecting those susceptible to disruption during forest-to-crop conversion is even more complex. Yet, addressing this identification challenge is essential for devising strategies to preserve organic matter in croplands. Here, we aimed to identify the predominant mineral–organic associations within an Andosol (developed on Fe-poor parent material) under both forest and cropland conditions. To achieve this, we collected Andosol samples from both a forested and a cultivated area, located 300 m apart. We then analyzed differences between the two soil profiles in soil physicochemical parameters and characterized mineral–organic associations using an array of spectro-microscopic techniques for comprehensive structural and compositional analysis. At microscale and nanoscale spatial resolution, we observed mineral–organic associations in the form of amorphous coprecipitates, composed of a mix of C+Al+Si and C+Al+Fe+Si nanoCLICs (inorganic oligomers with organics), proto-imogolites and organic matter, some Fe nanophases associated with organic matter, and some metal–organic complexes. This challenges prior conceptions of mineral–organic associations in Andosols by demonstrating the presence of amorphous coprecipitates rather than solely organic matter associated with short-range-order minerals (i.e., imogolite and allophanes). Moreover, chemical mappings suggested that these amorphous coprecipitates may adhere to mineral surfaces (i.e., phyllosilicates and imogolites), revealing secondary interactions of mineral–organic associations in soils. While the presence of similar amorphous coprecipitates in both the forest and crop Andosols was confirmed, the crop soil had 75 % less C in mineral–organic associations (in the 0–30 cm depth). Although the sample size for comparing land use types is limited, these results suggest that the nature of mineral–organic associations remains identical despite quantitative differences. This study highlights the crucial role of amorphous coprecipitates in C stabilization in Andosols and also suggests their vulnerability to disruption after 30 years of a forest-to-crop conversion, thereby challenging our understanding of the persistence of mineral–organic associations in Andosols.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"24 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645632","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":"Drivers and CO2 flux budgets in a Sahelian Faidherbia albida agro-silvo-pastoral parkland: Insights from continuous high-frequency soil chamber measurements and Eddy Covariance","authors":"Seydina Mohamad Ba, Olivier Roupsard, Lydie Chapuis-Lardy, Frédéric Bouvery, Yélognissè Agbohessou, Maxime Duthoit, Aleksander Wieckowski, Torbern Tagesson, Mohamed Habibou Assouma, Espoir Koudjo Gaglo, Claire Delon, Bienvenu Sambou, Dominique Serça","doi":"10.5194/egusphere-2025-2660","DOIUrl":"https://doi.org/10.5194/egusphere-2025-2660","url":null,"abstract":"<strong>Abstract.</strong> Agroforestry systems — combining trees with crops and/or livestock — are increasingly promoted as sustainable and climate-resilient land-use strategies. Despite their widespread presence in the Sahel, experimental data on their potential as carbon sinks are scarce. This study presents a full-year, high-frequency dataset of CO₂ fluxes in a Sahelian agro-silvo-pastoral parkland dominated by <em>F. albida</em>, located in Senegal’s groundnut basin. CO₂ fluxes were continuously measured using automated static chambers, allowing the quantification of soil and crop respiration (Rch), gross primary production (GPPch), and net carbon exchange (FCO₂ch) under both full sun and shaded (under tree canopies) environments. Seasonal patterns of CO₂ fluxes were similar in both environments, with peaks during the rainy season. Rch and GPPch were significantly higher under tree canopies, indicating a ‘fertile island’ effect. CO₂ flux variability was primarily driven by soil moisture and leaf area index. Chamber-based GPP estimates closely matched those from Eddy Covariance measurements. On an annual scale, <em>F. albida</em> trees contributed approximately 50 % of total ecosystem GPP, with a carbon use efficiency of 0.48. Net annual CO₂ exchange was estimated at −1.4 ± 0.02 and −1.8 ± 0.01 Mg C-CO₂ ha⁻¹ using chamber and Eddy Covariance methods, respectively. These findings underscore the role of <em>F. albida</em>-based agroforestry systems as effective carbon sinks in Sahelian landscapes, supporting their potential contribution to climate change mitigation.","PeriodicalId":48610,"journal":{"name":"Soil","volume":"14 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144645268","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}