Pub Date : 2024-12-10DOI: 10.1016/j.geodrs.2024.e00905
Yiqi Lin , William Lidberg , Cecilia Karlsson , Gustav Sohlenius , Florian Westphal , Johannes Larson , Anneli M. Ågren
Reliable information on soil-forming parent materials is crucial for informed decision-making in infrastructure planning, land-use management, environmental assessments, and geohazard mitigation. In the northern landscapes previously affected by glacial processes, these parent materials are predominantly Quaternary deposits. This study explored the potential of machine learning to expedite soil parent material mapping in Sweden. Two Extreme Gradient Boosting models were trained, one using terrain and hydrological indices derived from Light Detection and Ranging data, and the other incorporating additional ancillary map data. Both models were trained on 29,588 soil observations and evaluated against a separate hold-out set of 3500 observations. As a baseline, the existing most detailed maps achieved a Matthews Correlation Coefficient of 0.36. The Extreme Gradient Boosting models achieved higher MCC values of 0.45 and 0.56, respectively. To understand spatial variations in model performance, the second model was evaluated across 28 physiographic regions in Sweden. The results revealed that model performance varied across regions and deposit types, with till and peat exhibiting better performance than sorted sediments. These findings underscore the need for region-specific analyses to optimize the application of machine learning in digital soil mapping.
{"title":"Mapping soil parent materials in a previously glaciated landscape: Potential for a machine learning approach for detailed nationwide mapping","authors":"Yiqi Lin , William Lidberg , Cecilia Karlsson , Gustav Sohlenius , Florian Westphal , Johannes Larson , Anneli M. Ågren","doi":"10.1016/j.geodrs.2024.e00905","DOIUrl":"10.1016/j.geodrs.2024.e00905","url":null,"abstract":"<div><div>Reliable information on soil-forming parent materials is crucial for informed decision-making in infrastructure planning, land-use management, environmental assessments, and geohazard mitigation. In the northern landscapes previously affected by glacial processes, these parent materials are predominantly Quaternary deposits. This study explored the potential of machine learning to expedite soil parent material mapping in Sweden. Two Extreme Gradient Boosting models were trained, one using terrain and hydrological indices derived from Light Detection and Ranging data, and the other incorporating additional ancillary map data. Both models were trained on 29,588 soil observations and evaluated against a separate hold-out set of 3500 observations. As a baseline, the existing most detailed maps achieved a Matthews Correlation Coefficient of 0.36. The Extreme Gradient Boosting models achieved higher MCC values of 0.45 and 0.56, respectively. To understand spatial variations in model performance, the second model was evaluated across 28 physiographic regions in Sweden. The results revealed that model performance varied across regions and deposit types, with till and peat exhibiting better performance than sorted sediments. These findings underscore the need for region-specific analyses to optimize the application of machine learning in digital soil mapping.</div></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"40 ","pages":"Article e00905"},"PeriodicalIF":3.1,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-08DOI: 10.1016/j.geodrs.2024.e00904
M. Tähtikarhu, T.A. Räsänen, J. Uusi-Kämppä, J. Hyväluoma
Spatial patterns of land-cover and agricultural operations have clear impacts on soil erosion. Allocating a portion of cultivated area for grass is a widely applied strategy to control erosion. However, it is still unclear how much and where grassed area should be spatially targeted in different landscapes to control erosion efficiently. To address this challenge, we estimate the potential of high-resolution RUSLE-based spatial targeting of grassed areas to improve erosion mitigation in two topographically different catchments in southern Finland. Erosion reductions of 1) policy-based targeting (buffer strips along main streams according to current CAP strategy) were compared with 2) RUSLE-targeted grassed areas (based on the highest computed erosion values within field parcels and sub-catchments). Furthermore, we computationally explored 3) how different rates of optimally located grass areas affected erosion and 4) how the areas could be computationally processed to continuous entities. The erosion reductions were estimated with 2 × 2 m2 resolution RUSLE computations in all the scenarios. The RUSLE-targeted grassed areas demonstrated greater erosion reductions compared to the policy-based siting of grass areas along riparian fields. With optimal targeting, erosion risks could potentially be reduced up to 24 percentage points (up to 46 % erosion reduction), compared to the buffer strips. Increasing optimally targeted grassed area gradually from 0 to 100 % decreased erosion non-linearly. The largest share of erosion was generated in disproportionally small land areas (∼20 % of the land area). The location of the hotspots in relation to the streams varied between the sub-catchments and field parcels. These quantifications demonstrate the potential value of models for targeted landscape scale spatial erosion management. A more comprehensive assessment of erosion mitigation could benefit of improved empirical validation and consideration of other aspects of erosion and sediment transport, such as local drainage efficiency and reduction of erosion during flooding of rivers.
{"title":"How to site grassed areas to reduce agricultural erosion efficiently? A computational analysis in Finland","authors":"M. Tähtikarhu, T.A. Räsänen, J. Uusi-Kämppä, J. Hyväluoma","doi":"10.1016/j.geodrs.2024.e00904","DOIUrl":"10.1016/j.geodrs.2024.e00904","url":null,"abstract":"<div><div>Spatial patterns of land-cover and agricultural operations have clear impacts on soil erosion. Allocating a portion of cultivated area for grass is a widely applied strategy to control erosion. However, it is still unclear how much and where grassed area should be spatially targeted in different landscapes to control erosion efficiently. To address this challenge, we estimate the potential of high-resolution RUSLE-based spatial targeting of grassed areas to improve erosion mitigation in two topographically different catchments in southern Finland. Erosion reductions of 1) policy-based targeting (buffer strips along main streams according to current CAP strategy) were compared with 2) RUSLE-targeted grassed areas (based on the highest computed erosion values within field parcels and sub-catchments). Furthermore, we computationally explored 3) how different rates of optimally located grass areas affected erosion and 4) how the areas could be computationally processed to continuous entities. The erosion reductions were estimated with 2 × 2 m<sup>2</sup> resolution RUSLE computations in all the scenarios. The RUSLE-targeted grassed areas demonstrated greater erosion reductions compared to the policy-based siting of grass areas along riparian fields. With optimal targeting, erosion risks could potentially be reduced up to 24 percentage points (up to 46 % erosion reduction), compared to the buffer strips. Increasing optimally targeted grassed area gradually from 0 to 100 % decreased erosion non-linearly. The largest share of erosion was generated in disproportionally small land areas (∼20 % of the land area). The location of the hotspots in relation to the streams varied between the sub-catchments and field parcels. These quantifications demonstrate the potential value of models for targeted landscape scale spatial erosion management. A more comprehensive assessment of erosion mitigation could benefit of improved empirical validation and consideration of other aspects of erosion and sediment transport, such as local drainage efficiency and reduction of erosion during flooding of rivers.</div></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"40 ","pages":"Article e00904"},"PeriodicalIF":3.1,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-06DOI: 10.1016/j.geodrs.2024.e00903
Yunduo Zhao , Dongsheng Li , Jinxing Zhou
Microbial necromass plays a significant role in soil carbon storage under climate warming, as it is considered a crucial component of the stable carbon pool in soils. However, how the warming, including various warming patterns, affects microbial necromass and its contribution to the organic carbon pool in alpine regions remains largely unexplored. A meta-analysis was conducted utilizing data from ten publications to assess the effects of warming on microbial necromass on the Qinghai-Tibet Plateau. The findings indicated that the soil organic carbon (SOC) content did not exhibit significant changes after warming; however, microbial necromass carbon (MNC) and its ratio to SOC experienced significantly increases of 17.7 % and 52.0 %, respectively. The effect size of warming on fungal necromass carbon (FNC; +19.5 %) was larger than that of bacterial necromass carbon (BNC; +9.2 %). Furthermore, the warming patterns influenced the accumulation of microbial necromass and its ratio to SOC. The accumulation of microbial necromass and its ratio to SOC were increased (19.8 % and 63.9 %) under the low-magnitude warming and slowed down (14.0 % and 20.3 %) under the high-magnitude warming. The MNC and FNC were increased under both long-term warming (1.61 g/kg and 0.86 g/kg) and short-term warming (0.96 g/kg and 0.50 g/kg), but there was no significant change in BNC under long-term warming patterns. The effect sizes of warming on BNC, FNC and MNC were larger in the subsoil (16.3 %, 25.1 % and 24.2 %) than that in the topsoil (7.8 %, 19.1 % and 17.5 %). These results highlight the importance of warming patterns as predictors of microbial necromass. Nonetheless, these conclusions may be restricted by the insufficient sample size, and future researches should expand the sample size to reveal the threshold and mechanism underlying the effect of warming patterns on microbial necromass.
{"title":"Microbial necromass as a critical driver of soil organic carbon accumulation in Qinghai-Tibet Plateau under climate warming: A meta-analysis","authors":"Yunduo Zhao , Dongsheng Li , Jinxing Zhou","doi":"10.1016/j.geodrs.2024.e00903","DOIUrl":"10.1016/j.geodrs.2024.e00903","url":null,"abstract":"<div><div>Microbial necromass plays a significant role in soil carbon storage under climate warming, as it is considered a crucial component of the stable carbon pool in soils. However, how the warming, including various warming patterns, affects microbial necromass and its contribution to the organic carbon pool in alpine regions remains largely unexplored. A meta-analysis was conducted utilizing data from ten publications to assess the effects of warming on microbial necromass on the Qinghai-Tibet Plateau. The findings indicated that the soil organic carbon (SOC) content did not exhibit significant changes after warming; however, microbial necromass carbon (MNC) and its ratio to SOC experienced significantly increases of 17.7 % and 52.0 %, respectively. The effect size of warming on fungal necromass carbon (FNC; +19.5 %) was larger than that of bacterial necromass carbon (BNC; +9.2 %). Furthermore, the warming patterns influenced the accumulation of microbial necromass and its ratio to SOC. The accumulation of microbial necromass and its ratio to SOC were increased (19.8 % and 63.9 %) under the low-magnitude warming and slowed down (14.0 % and 20.3 %) under the high-magnitude warming. The MNC and FNC were increased under both long-term warming (1.61 g/kg and 0.86 g/kg) and short-term warming (0.96 g/kg and 0.50 g/kg), but there was no significant change in BNC under long-term warming patterns. The effect sizes of warming on BNC, FNC and MNC were larger in the subsoil (16.3 %, 25.1 % and 24.2 %) than that in the topsoil (7.8 %, 19.1 % and 17.5 %). These results highlight the importance of warming patterns as predictors of microbial necromass. Nonetheless, these conclusions may be restricted by the insufficient sample size, and future researches should expand the sample size to reveal the threshold and mechanism underlying the effect of warming patterns on microbial necromass.</div></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"40 ","pages":"Article e00903"},"PeriodicalIF":3.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144987","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}
Traditional soil classification systems are designed to communicate information; however, surveyor biases and tacit knowledge can lead to subjective soil class designations. Consequently, different soil scientists may classify the same soil differently. This becomes a critical issue when mapping soil classes, as there could be multiple interpretations for the same observation. To address this problem, numerical soil classification systems have been developed. However, little is known about how well they compare to taxonomic systems when spatially predicted on a national scale. This study aimed to compare a previously developed, unsupervised numeric classification system and South Africa's taxonomic soil classification system in terms of their spatial predictions across the country. The taxonomic system of South Africa has 19 defined subsoil horizons, which were aggregated into eight horizons and compared to a nine horizon numeric classification as well as South Africa's profile (soil form) classification comprising of 73 different soil groupings, which was used as a control. The comparison was conducted from predictions through gradient tree boosting in Google Earth Engine at a 30 m resolution. The numerical system (kappa = 0.30, accuracy = 0.57) exhibited poor spatial predictions, with a kappa 22% lower and accuracy 2% lower than the control (kappa = 0.52, accuracy = 59%). On the other hand, the taxonomic system performed well, with a kappa of 0.57 and an accuracy of 67%, exhibiting a 5% increase in kappa and an 8% increase in accuracy compared to the control. It was hypothesized that the overpredictions of the predominant horizon contributed to the numeric system's poor performance. Nevertheless, both systems showed the highest maximum entropy in arid regions of the Karoo and savannah biomes, albeit in spatially distinct ecoregions. It was thought that the divergence in the two systems' maximum entropy was due to their association with precipitation differences (amount and seasonality) as well as vegetation type and cover (woodlands vs. shrublands). To map the country in more detail, further soil sampling should be conducted in arid regions and optimisation of the predictive algorithm for each soil category should be performed.
{"title":"A comparison of country-scale subsoil predictions between a numeric and a taxonomic soil classification system","authors":"Trevan Flynn , Catherine Clarke , Rosana Kostecki , Ansa Rebi","doi":"10.1016/j.geodrs.2024.e00902","DOIUrl":"10.1016/j.geodrs.2024.e00902","url":null,"abstract":"<div><div>Traditional soil classification systems are designed to communicate information; however, surveyor biases and tacit knowledge can lead to subjective soil class designations. Consequently, different soil scientists may classify the same soil differently. This becomes a critical issue when mapping soil classes, as there could be multiple interpretations for the same observation. To address this problem, numerical soil classification systems have been developed. However, little is known about how well they compare to taxonomic systems when spatially predicted on a national scale. This study aimed to compare a previously developed, unsupervised numeric classification system and South Africa's taxonomic soil classification system in terms of their spatial predictions across the country. The taxonomic system of South Africa has 19 defined subsoil horizons, which were aggregated into eight horizons and compared to a nine horizon numeric classification as well as South Africa's profile (soil form) classification comprising of 73 different soil groupings, which was used as a control. The comparison was conducted from predictions through gradient tree boosting in Google Earth Engine at a 30 m resolution. The numerical system (kappa = 0.30, accuracy = 0.57) exhibited poor spatial predictions, with a kappa 22% lower and accuracy 2% lower than the control (kappa = 0.52, accuracy = 59%). On the other hand, the taxonomic system performed well, with a kappa of 0.57 and an accuracy of 67%, exhibiting a 5% increase in kappa and an 8% increase in accuracy compared to the control. It was hypothesized that the overpredictions of the predominant horizon contributed to the numeric system's poor performance. Nevertheless, both systems showed the highest maximum entropy in arid regions of the Karoo and savannah biomes, albeit in spatially distinct ecoregions. It was thought that the divergence in the two systems' maximum entropy was due to their association with precipitation differences (amount and seasonality) as well as vegetation type and cover (woodlands vs. shrublands). To map the country in more detail, further soil sampling should be conducted in arid regions and optimisation of the predictive algorithm for each soil category should be performed.</div></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"40 ","pages":"Article e00902"},"PeriodicalIF":3.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.geodrs.2024.e00900
Fouzieh Beigmohammadi , Eisa Solgi , Ali A. Besalatpour , Mohsen Soleimani
Biochar derived from agricultural waste is recognized as an environmentally sustainable method for immobilizing potentially toxic elements (PTEs) in contaminated soils. This study investigated the efficacy of biochar produced from grape residues in immobilizing PTEs such as Pb, Ni, Mn, Cu, and Co in contaminated soils. A total of 110 surface soil samples were collected from three land-use types (agricultural, urban, and industrial) in Arak, Iran. The bioavailable fractions of PTEs were analyzed using the diethylenetriamine penta acetic acid (DTPA) extraction method. The properties of biochar were characterized through X-ray diffraction, field emission scanning electron microscopy, Fourier-transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, and Brunauer-Emmett-Teller (BET) analysis. Biochar was incorporated into the contaminated soils at a rate of 5 % (w/w) and incubated for two months. The results indicated that the biochar application enhanced soil properties, including pH, electrical conductivity, cation exchange capacity, organic matter, and soil microbial respiration. Simultaneously, the DTPA-extractable concentrations of Cu, Ni, Pb, and Co decreased from 7.26, 1.83, 5.82, and 0.25 mg/kg, to 5.54, 0.86, 4.06, and 0.18 mg/kg, respectively, corresponding to reductions of 24 % to 79 % in bioavailability. The reductions were attributed to the functional groups with negative charges and the high specific surface area of the biochar, as identified by FTIR and BET analyses. A random forest analysis further revealed that organic matter and soil microbial respiration were the most influential factors in in reducing the bioavailability of PTEs following biochar amendment. These findings underscore the potential of grape residue-derived biochar as an effective amendment for mitigating PTE contamination in soils.
{"title":"Immobilization of potentially toxic elements by grape waste biochar in contaminated soils","authors":"Fouzieh Beigmohammadi , Eisa Solgi , Ali A. Besalatpour , Mohsen Soleimani","doi":"10.1016/j.geodrs.2024.e00900","DOIUrl":"10.1016/j.geodrs.2024.e00900","url":null,"abstract":"<div><div>Biochar derived from agricultural waste is recognized as an environmentally sustainable method for immobilizing potentially toxic elements (PTEs) in contaminated soils. This study investigated the efficacy of biochar produced from grape residues in immobilizing PTEs such as Pb, Ni, Mn, Cu, and Co in contaminated soils. A total of 110 surface soil samples were collected from three land-use types (agricultural, urban, and industrial) in Arak, Iran. The bioavailable fractions of PTEs were analyzed using the diethylenetriamine penta acetic acid (DTPA) extraction method. The properties of biochar were characterized through X-ray diffraction, field emission scanning electron microscopy, Fourier-transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, and Brunauer-Emmett-Teller (BET) analysis. Biochar was incorporated into the contaminated soils at a rate of 5 % (<em>w</em>/w) and incubated for two months. The results indicated that the biochar application enhanced soil properties, including pH, electrical conductivity, cation exchange capacity, organic matter, and soil microbial respiration. Simultaneously, the DTPA-extractable concentrations of Cu, Ni, Pb, and Co decreased from 7.26, 1.83, 5.82, and 0.25 mg/kg, to 5.54, 0.86, 4.06, and 0.18 mg/kg, respectively, corresponding to reductions of 24 % to 79 % in bioavailability. The reductions were attributed to the functional groups with negative charges and the high specific surface area of the biochar, as identified by FTIR and BET analyses. A random forest analysis further revealed that organic matter and soil microbial respiration were the most influential factors in in reducing the bioavailability of PTEs following biochar amendment. These findings underscore the potential of grape residue-derived biochar as an effective amendment for mitigating PTE contamination in soils.</div></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"39 ","pages":"Article e00900"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179280","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}
Pub Date : 2024-12-01DOI: 10.1016/j.geodrs.2024.e00898
Georges K. Kome , Roger K. Enang , Bernard P.K. Yerima , Eric Van Ranst
Soil organic carbon (SOC) is a very important parameter for assessing the quality of agricultural soils. However, the availability and use of such data by resource-poor farmers, especially in Sub-Saharan Africa, remains a major challenge due to the financial and time constrains involved. Thus, there is need to assess and adopt reliable methods for the rapid estimation of soil organic carbon content by indigenous farmers and field users. The objective of this study was to evaluate the quantitative relationships between soil organic carbon and Munsell colour attributes (value and chroma) in highly weathered tropical soils of the Northwestern Highlands of Cameroon. Forty-six soil profiles (28 Acrisols and 18 Ferralsols), including 46 surface (A) horizons and 181 subsurface horizons (Bo, Bt) were used. Soil organic carbon data and Munsell colour attributes, obtained through standard procedures, were subjected to descriptive statistical, correlation, regression and principal components analyses, in order to evaluate the relationships existing between SOC and Munsell colour attributes. In general, there were negative and significant (p < 0.001) correlations between SOC and all Munsell colour attributes (chroma, value, value + chroma, and value +0.5 chroma). The best models relating SOC and Munsell colour attributes were logarithmic models, with soil colour explaining >70 % of the variance. The results indicate that SOC in highly weathered tropical soils can be conveniently estimated using Munsell soil colour attributes (value + chroma). Better estimates were obtained using logarithmic models for surface (A horizon) soil samples having a sand content >50 %.
{"title":"Quantitative relationships between Munsell colour attributes and organic carbon in highly weathered tropical soils","authors":"Georges K. Kome , Roger K. Enang , Bernard P.K. Yerima , Eric Van Ranst","doi":"10.1016/j.geodrs.2024.e00898","DOIUrl":"10.1016/j.geodrs.2024.e00898","url":null,"abstract":"<div><div>Soil organic carbon (SOC) is a very important parameter for assessing the quality of agricultural soils. However, the availability and use of such data by resource-poor farmers, especially in Sub-Saharan Africa, remains a major challenge due to the financial and time constrains involved. Thus, there is need to assess and adopt reliable methods for the rapid estimation of soil organic carbon content by indigenous farmers and field users. The objective of this study was to evaluate the quantitative relationships between soil organic carbon and Munsell colour attributes (value and chroma) in highly weathered tropical soils of the Northwestern Highlands of Cameroon. Forty-six soil profiles (28 Acrisols and 18 Ferralsols), including 46 surface (A) horizons and 181 subsurface horizons (Bo, Bt) were used. Soil organic carbon data and Munsell colour attributes, obtained through standard procedures, were subjected to descriptive statistical, correlation, regression and principal components analyses, in order to evaluate the relationships existing between SOC and Munsell colour attributes. In general, there were negative and significant (<em>p</em> < 0.001) correlations between SOC and all Munsell colour attributes (chroma, value, value + chroma, and value +0.5 chroma). The best models relating SOC and Munsell colour attributes were logarithmic models, with soil colour explaining >70 % of the variance. The results indicate that SOC in highly weathered tropical soils can be conveniently estimated using Munsell soil colour attributes (value + chroma). Better estimates were obtained using logarithmic models for surface (A horizon) soil samples having a sand content >50 %.</div></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"39 ","pages":"Article e00898"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743685","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}
Forest tree species composition influences soil aggregate stability (SAS) and labile organic carbon (LOC) components, which may affect soil organic carbon (SOC) sequestration. Despite the general belief that mixed forests enhance SOC storage, evidence suggests that certain monocultures may outperform mixed forests. Therefore, information on the specific impact of tree species mixing on SOC through SAS and LOC remains limited. This study aimed to investigate the effects of tree species composition on SAS and LOC in 49-year-old monoculture (Larix gmelinii (Lg), Pinus koraiensis (Pk)) and mixed conifer-broadleaf (Larix gmelinii - Fraxinus mandshurica (LF), and Pinus koraiensis - Populus ussuriensis (PP)) stands, and its impact on SOC sequestration. We measured SAS indices (including mean weight diameter (MWD), geometric mean diameter (GMD), and > 0.25 mm water stable aggregate proportion (WSA)), SOC storage, and LOC components (easily oxidized organic carbon (EOC), microbial biomass carbon (MBC), and dissolved organic carbon (DOC)) in 0–40 cm soil horizons during the growing season (June, August, and October). Our results showed that tree species composition significantly influenced SAS and SOC components, with the highest SAS found in the 0–20 cm soil horizon in Pk and PP forests (p < 0.05) (Jun.: MWD Pk: 0.98, GMD Pk: 1.88, WSA Pk: 1.79, MWD PP: 0.98, GMD PP: 1.90, WSA PP: 1.81; Aug.: MWD Pk: 0.99, GMD Pk: 1.85, WSA Pk: 1.77, MWD PP: 0.99, GMD PP: 1.89, WSA PP: 1.81; Oct.: MWD Pk: 0.98, GMD Pk: 1.86, WSA Pk: 1.76, MWD PP: 0.97, GMD PP: 1.83, WSA PP: 1.73) and the highest SOC components in the LF stand (p < 0.05) (soil horizon 0–20 cm: Jun.: SOC: 110.23 g/kg, EOC: 92.81 g/kg, MBC: 1418.39 mg/kg, DOC: 1090.01 mg/kg; Aug.: SOC: 108.46 g/kg, EOC: 79.57 g/kg, MBC: 1369.91 mg/kg, DOC: 1316.11 mg/kg; Oct.: SOC: 109.78 g/kg, EOC: 44.37 g/kg, MBC: 1782.6 mg/kg, DOC: 671.05 mg/kg). Correlation analysis revealed significant relationships between SOC and LOC components (p < 0.05, r EOC = 0.43, r MBC = 0.46, r DOC = 0.17) but not associated with SAS (p > 0.05, r MWD = −0.07, r GMD = −0.07). Tree species composition in plantation stands significantly affects SAS and SOC pools. In conclusion, the positive effect of mixed coniferous and broad-leaved forests on SAS and SOC pools is also contingent upon the tree species identity. The results suggest that targeted selection of tree species could better enhance SAS and SOC pools in plantation than a mere increase in species richness.
{"title":"Effects of tree species composition in plantation forest on soil aggregate stability and organic carbon pools in northeastern China","authors":"Changzhun Li, Qingcheng Wang, Huirong Wu, Yong Zhang, Shuangjiao Ma, Liqing Xu","doi":"10.1016/j.geodrs.2024.e00899","DOIUrl":"10.1016/j.geodrs.2024.e00899","url":null,"abstract":"<div><div>Forest tree species composition influences soil aggregate stability (SAS) and labile organic carbon (LOC) components, which may affect soil organic carbon (SOC) sequestration. Despite the general belief that mixed forests enhance SOC storage, evidence suggests that certain monocultures may outperform mixed forests. Therefore, information on the specific impact of tree species mixing on SOC through SAS and LOC remains limited. This study aimed to investigate the effects of tree species composition on SAS and LOC in 49-year-old monoculture (<em>Larix gmelinii</em> (Lg), <em>Pinus koraiensis</em> (Pk)) and mixed conifer-broadleaf (<em>Larix gmelinii - Fraxinus mandshurica</em> (LF), and <em>Pinus koraiensis - Populus ussuriensis</em> (PP)) stands, and its impact on SOC sequestration. We measured SAS indices (including mean weight diameter (MWD), geometric mean diameter (GMD), and > 0.25 mm water stable aggregate proportion (WSA)), SOC storage, and LOC components (easily oxidized organic carbon (EOC), microbial biomass carbon (MBC), and dissolved organic carbon (DOC)) in 0–40 cm soil horizons during the growing season (June, August, and October). Our results showed that tree species composition significantly influenced SAS and SOC components, with the highest SAS found in the 0–20 cm soil horizon in Pk and PP forests (<em>p</em> < 0.05) (Jun.: MWD <sub>Pk</sub>: 0.98, GMD <sub>Pk</sub>: 1.88, WSA <sub>Pk</sub>: 1.79, MWD <sub>PP</sub>: 0.98, GMD <sub>PP</sub>: 1.90, WSA <sub>PP</sub>: 1.81; Aug.: MWD <sub>Pk</sub>: 0.99, GMD <sub>Pk</sub>: 1.85, WSA <sub>Pk</sub>: 1.77, MWD <sub>PP</sub>: 0.99, GMD <sub>PP</sub>: 1.89, WSA <sub>PP</sub>: 1.81; Oct.: MWD <sub>Pk</sub>: 0.98, GMD <sub>Pk</sub>: 1.86, WSA <sub>Pk</sub>: 1.76, MWD <sub>PP</sub>: 0.97, GMD <sub>PP</sub>: 1.83, WSA <sub>PP</sub>: 1.73) and the highest SOC components in the LF stand (<em>p</em> < 0.05) (soil horizon 0–20 cm: Jun.: SOC: 110.23 g/kg, EOC: 92.81 g/kg, MBC: 1418.39 mg/kg, DOC: 1090.01 mg/kg; Aug.: SOC: 108.46 g/kg, EOC: 79.57 g/kg, MBC: 1369.91 mg/kg, DOC: 1316.11 mg/kg; Oct.: SOC: 109.78 g/kg, EOC: 44.37 g/kg, MBC: 1782.6 mg/kg, DOC: 671.05 mg/kg). Correlation analysis revealed significant relationships between SOC and LOC components (<em>p</em> < 0.05, r <sub>EOC</sub> = 0.43, r <sub>MBC</sub> = 0.46, r <sub>DOC</sub> = 0.17) but not associated with SAS (<em>p</em> > 0.05, r <sub>MWD</sub> = −0.07, r <sub>GMD</sub> = −0.07). Tree species composition in plantation stands significantly affects SAS and SOC pools. In conclusion, the positive effect of mixed coniferous and broad-leaved forests on SAS and SOC pools is also contingent upon the tree species identity. The results suggest that targeted selection of tree species could better enhance SAS and SOC pools in plantation than a mere increase in species richness.</div></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"39 ","pages":"Article e00899"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180103","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}
Pub Date : 2024-12-01DOI: 10.1016/j.geodrs.2024.e00895
M. Oelbermann , S.X. Chang , M. Pulleman , J.K. Whalen
{"title":"Transitioning to soil health and carbon sequestration with agroforestry and perennial crop systems","authors":"M. Oelbermann , S.X. Chang , M. Pulleman , J.K. Whalen","doi":"10.1016/j.geodrs.2024.e00895","DOIUrl":"10.1016/j.geodrs.2024.e00895","url":null,"abstract":"","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"39 ","pages":"Article e00895"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180856","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}
Pub Date : 2024-12-01DOI: 10.1016/j.geodrs.2024.e00901
Sang Ho Jeon , Ho Jun Jang , Wartini Ng , Budiman Minasny , Seong Heon Kim , Jay Hong Shim , Ahnsung Roh , Soon ik Kwon , Jin-Ju Yun
The national fertiliser policies in South Korea aim to provide guidance to farmers for efficient fertiliser application and thus rely on the annual collection and analysis of soil samples. Providing timely soil analysis results remains a challenge, as wet laboratory analysis is time-consuming and expensive. This study represents a pioneering effort in South Korea, by investigating mid-infrared (MIR) spectroscopy for accurate soil properties prediction and its application in developing fertiliser recommendations for several crop types. Additionally, we examined the time efficiency of MIR spectroscopy compared to conventional analytical methods. A total of 567 soil samples from diverse soil and land use types (paddy, upland, orchard, and greenhouse fields) in South Korea (0–20 cm depth) were collected and scanned using an MIR spectrometer. Four machine learning algorithms (partial least squares regression, support vector machine, cubist, and random forest) were trialled and compared for their prediction accuracies using 15-fold cross-validation for eight essential soil properties: organic matter, total nitrogen (N), available phosphorus (P), pH, exchangeable calcium (Ca), potassium (K), magnesium (Mg), and available silica. Results demonstrated robust predictive performance (R2 > 0.70) across the selected soil properties, with organic matter and total nitrogen exhibiting excellent accuracy (R2 > 0.9). Compared with conventional analysis, the average difference in fertiliser application recommendation for seven crops using MIR prediction was 3.8 % for N, 13.9 % for P and 8.1 % for K. Based on the measurement of 11 soil properties, analysis using MIR spectroscopy was about 12 times faster than conventional methods. The study demonstrates the potential of this approach to revolutionise soil analysis protocols, offering a more efficient and cost-effective solution for sustainable agricultural practices in South Korea.
{"title":"Predicting soil properties for fertiliser recommendation in South Korea using MIR spectroscopy","authors":"Sang Ho Jeon , Ho Jun Jang , Wartini Ng , Budiman Minasny , Seong Heon Kim , Jay Hong Shim , Ahnsung Roh , Soon ik Kwon , Jin-Ju Yun","doi":"10.1016/j.geodrs.2024.e00901","DOIUrl":"10.1016/j.geodrs.2024.e00901","url":null,"abstract":"<div><div>The national fertiliser policies in South Korea aim to provide guidance to farmers for efficient fertiliser application and thus rely on the annual collection and analysis of soil samples. Providing timely soil analysis results remains a challenge, as wet laboratory analysis is time-consuming and expensive. This study represents a pioneering effort in South Korea, by investigating mid-infrared (MIR) spectroscopy for accurate soil properties prediction and its application in developing fertiliser recommendations for several crop types. Additionally, we examined the time efficiency of MIR spectroscopy compared to conventional analytical methods. A total of 567 soil samples from diverse soil and land use types (paddy, upland, orchard, and greenhouse fields) in South Korea (0–20 cm depth) were collected and scanned using an MIR spectrometer. Four machine learning algorithms (partial least squares regression, support vector machine, cubist, and random forest) were trialled and compared for their prediction accuracies using 15-fold cross-validation for eight essential soil properties: organic matter, total nitrogen (N), available phosphorus (P), pH, exchangeable calcium (Ca), potassium (K), magnesium (Mg), and available silica. Results demonstrated robust predictive performance (R<sup>2</sup> > 0.70) across the selected soil properties, with organic matter and total nitrogen exhibiting excellent accuracy (R<sup>2</sup> > 0.9). Compared with conventional analysis, the average difference in fertiliser application recommendation for seven crops using MIR prediction was 3.8 % for N, 13.9 % for P and 8.1 % for K. Based on the measurement of 11 soil properties, analysis using MIR spectroscopy was about 12 times faster than conventional methods. The study demonstrates the potential of this approach to revolutionise soil analysis protocols, offering a more efficient and cost-effective solution for sustainable agricultural practices in South Korea.</div></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"39 ","pages":"Article e00901"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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