Geoderma Regional最新文献

{"title":"Soil depth and catchment geomorphology: A field, vegetation and GIS based assessment","authors":"I.P. Senanayake ,&nbsp;G.R. Hancock ,&nbsp;W.D.D.P. Welivitiya","doi":"10.1016/j.geodrs.2024.e00824","DOIUrl":"10.1016/j.geodrs.2024.e00824","url":null,"abstract":"<div><p>Soil depth is closely related to topography and influences vegetation health and landscape productivity at both hillslope and catchment scales. Soil depth also influences land management and ecosystem sustainability. However, comprehensive hillslope and catchment scale soil depth data remain scarce. In response, this study investigates the relationship between soil depth, surface and bedrock topographic metrics (elevation, slope, aspect, and SAGA topographic wetness index - SWI), and pasture response within a semi-arid hillslope ecosystem in south-eastern Australia. The Normalized Difference Vegetation Index (NDVI) was used as an indicator of vegetation health. A dataset of 183 soil depth measurements was collected using a petrol-powered auger across a 6-ha catchment. Our findings reveal that the relationships between soil depth, topography and vegetation response are complex. There was a general increase in soil depth downslope, indicating potential fluvial transport processes involving erosion and deposition. A subtle increase in NDVI was observed upslope. Soil depth showed no strong correlations with aspect or SWI, but a weak inverse relationship was observed with slope angle. Notably, NDVI displayed a positive correlation with SWI while showing an inverse correlation with slope. Furthermore, the study highlights instances of shallower soils and reduced grass cover beneath two isolated trees, potentially attributable to cattle movement. Soil erosion rates for the catchment are known and soil production rates can be estimated. The data suggests that under current land use practices, soil is being lost at a rate faster than it is being produced. These findings provide valuable insights for land management strategies and sustainable ecosystem maintenance, while demonstrating the complexity of the soil-landscape system.</p></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"38 ","pages":"Article e00824"},"PeriodicalIF":4.1,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352009424000713/pdfft?md5=daad24222646a1316264cd5136740632&pid=1-s2.0-S2352009424000713-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141390092","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}

{"title":"Soil textural class modeling using digital soil mapping approaches: Effect of resampling strategies on imbalanced dataset predictions","authors":"Fereshteh Mirzaei ,&nbsp;Alireza Amirian-Chakan ,&nbsp;Ruhollah Taghizadeh-Mehrjardi ,&nbsp;Hamid Reza Matinfar ,&nbsp;Ruth Kerry","doi":"10.1016/j.geodrs.2024.e00821","DOIUrl":"10.1016/j.geodrs.2024.e00821","url":null,"abstract":"<div><p>In a digital soil mapping (DSM) context, machine learning (ML) algorithms are widely used to model soil textural classes (STCs). However, in the real world most soil class datasets exhibit imbalanced distributions. This poses a challenge as ML algorithms are designed to handle balanced classes, leading to a bias towards the majority classes while often overlooking the minority classes. Furthermore, within the DSM framework, two strategies can be employed to model STCs: direct and indirect approaches. In the direct approach, STCs are directly inputted into the model for prediction. In contrast, the indirect approach involves introducing soil texture fractions (i.e., clay, silt, sand) as initial inputs, then STCs are obtained from the outputs. Limited research has been conducted on the impact of data balancing on STC predictions, and there is a lack of comparative analysis between direct and indirect approaches in this context. Therefore, this study aimed to evaluate the efficacy of a resampling technique (SMOTE: synthetic minority oversampling technique) in handling an imbalanced soil texture dataset collected from the Kuhdasht region in western Iran. Additionally, the study sought to compare the performance of direct and indirect modeling approaches. Environmental covariates derived from Landsat 8 and Sentinel 2 images along with a digital elevation model (DEM) were used as input variables to a random forest (RF) model to model STCs and soil texture fractions. The results revealed that terrain attributes and Euclidean distances played a more significant role in modeling both balanced and imbalanced datasets compared to remotely sensed data. Kappa indices for balanced and imbalanced datasets, as well as for the indirect approach were found to be 89%, 68% and 38% respectively. In the same way, the overall accuracies were 91%, 79% and 68%, respectively. Among the imbalanced classes, clay loam and loam which accounted for the majority of observations showed the highest recall values, followed by sandy clay loam, sandy loam and silty clay loam. When employing the indirect approach, the RF model failed to capture the minority classes in terms of validation statistics. Additionally, modeling with the imbalanced dataset resulted in the exclusion of three minority STCs from the final map. Overall, this study showed the importance of balancing STCs prior to modeling to achieve more accurate estimates of STCs, as well as the superiority of employing the direct approach (using balanced data sets) over the indirect approach.</p></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"38 ","pages":"Article e00821"},"PeriodicalIF":4.1,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141400904","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":"Fine root production and turnover rate responses to long-term warming and nitrogen addition in a semi-arid grassland","authors":"Wen-Bin Ke ,&nbsp;Yong-Xian Wei ,&nbsp;Xin Song ,&nbsp;Wei-Tao Liu ,&nbsp;Juan Chen ,&nbsp;Qin-Yue Cai ,&nbsp;Chao Fang ,&nbsp;Jian-Sheng Ye","doi":"10.1016/j.geodrs.2024.e00823","DOIUrl":"10.1016/j.geodrs.2024.e00823","url":null,"abstract":"<div><p>Soil carbon pool is closely linked to fine root production and turnover rate. Lack of knowledge about the effects of nitrogen addition and warming on fine root production and turnover rate limits our ability to accurately predict soil carbon stocks. We studied the responses of fine root production and turnover rate with an 8-year soil warming (ambient +1 °C) and nitrogen addition (ambient + 4.42 g N m⁻² yr⁻¹) experiment in a semi-arid grassland on the Loess Plateau of China. Warming significantly decreased fine root production but increased fine root turnover rate due to warming-induced reduction in soil water content. Conversely, nitrogen addition significantly increased fine root production due to decreased soil phosphorus availability. Combined warming and nitrogen addition had no significant effect on fine root production, but decreased fine root turnover rate due to low phosphorus availability caused by nitrogen addition and low soil moisture induced by warming. Our findings have important implications for more accurately predicting the belowground responses of dryland to global changes.</p></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"38 ","pages":"Article e00823"},"PeriodicalIF":3.1,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141391477","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":"13C evidence for the selective loss of active and inert organic carbon in soil aggregates under rain-induced overland flow erosion","authors":"Jiuqi Qu ,&nbsp;Jinquan Huang ,&nbsp;Lin Liu","doi":"10.1016/j.geodrs.2024.e00822","DOIUrl":"10.1016/j.geodrs.2024.e00822","url":null,"abstract":"<div><p>Due to the hierarchical structure of aggregates, raindrops cause changes in the composition, distribution and state of aggregate-wrapped organic carbon (OC) fractions during erosion, greatly affecting soil carbon turnover and sequestration. To explore these regulatory mechanisms, the selective transport of light (LFoc) and heavy (HFoc) fraction OC within aggregates of varying sizes was traced via the ¹³C/¹²C carbon isotope ratio (δ¹³C) under splash and sheet erosion conditions. A “three-zone” variable-slope soil pan was filled with loess soil with a high aggregate concentration to monitor rainfall erosion on a slope. The OC aggregate composition, aggregate stripping and δ¹³C values of the sediment aggregates of various particle sizes were measured. When the erosion intensity was low, the splash-eroded sediment was mainly enriched in ¹³C-rich HFoc, and as the rainfall intensity increased, the concentrations of ¹²C-rich LFoc and HFoc gradually increased. That is, under heavy rainfall, aggregate fragmentation exposed more large fragments rich in younger HFoc and LFoc, and these fragments underwent saltation. There was no obvious correlation between the trends in splash erosion and OC, so runoff transport was an important factor influencing the correlation between δ¹³C and OC (<em>P</em> &lt; 0.05). Raindrop impact exposed HFoc-rich aggregate fragments of different sizes, e.g., stable mineral-associated OC. Runoff promoted the obvious preferential transport of LFoc and the redistribution of particulate OC (POC) among different sediment particles. Mineral-associated OC and 2–0.05 mm easily decomposable LFoc or POC were preferentially transported, causing OC with the highest and median δ¹³C values to be preferentially transported. Overall, the transport order of aggregate-exposed OC particles was clay + silt particle-bonded OC, POC, POC-bonded aggregate fragments and silt-bonded aggregate fragments. These results verified the selective loss of aggregate-detached OC fragments during erosion, which led to a change in the OC sequestration and reaggregation potential of OC in the eroded and deposited soil.</p></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"38 ","pages":"Article e00822"},"PeriodicalIF":3.1,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141402817","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":"Spatial variability of clay minerals in a semi-arid region of Turkiye","authors":"Hikmet Günal ,&nbsp;Nurullah Acir","doi":"10.1016/j.geodrs.2024.e00820","DOIUrl":"10.1016/j.geodrs.2024.e00820","url":null,"abstract":"<div><p>Clay minerals are the source of many chemical and physical properties that influence the provision of soil-based ecosystem services. This study aimed to identify the most significant soil characteristics contributing to the spatial variability of clay minerals in a semi-arid region of Turkiye. Additionally, the study assessed the predictive capabilities of Classification and Regression Tree (CART), Random Forest Regression (RF) and eXtreme Gradient Boosting Regression (XGBoost) in estimating soil clay mineral content. Smectite+vermiculite (SMVR) was the most abundant clay mineral in the study area, followed by illite and kaolinite. Hyperparameter tuning significantly improved model accuracy, with root mean square error (RMSE) reductions ranging from 2.53% to 97.3%. The machine learning algorithms demonstrated varying performances in spatial prediction accuracy. The RF model achieved the lowest RMSE (8.587%) and the highest R² values (0.796) for predicting SMVR. The XGBoost outperformed other models for kaolinite (RMSE: 4.814%, R²:0.713) and illite (RMSE:7.368%, R²:0.613). Exchangeable cations, particularly magnesium (Mg) and calcium (Ca), were identified as crucial factors influencing the spatial distribution of clay minerals. Among these, M concentration had the strongest influence on predicting both SMVR (38.1%) and illite (26.3%). Conversely, for kaolinite prediction, Ca concentration played the most significant role (38.7%), followed by Mg (19.93%). In conclusion, this study demonstrates the effectiveness of machine learning models, particularly XGBoost which achieved the lowest RMSE for all clay minerals investigated. These models offer a valuable tool for predicting clay mineral content in the Kazova Plain. The findings highlight the importance of parent material, weathering processes, and specific soil properties, such as exchangeable cations, in shaping clay mineral distribution. This knowledge not only contributes to a deeper understanding of soil formation in semi-arid environments but also practical applications. For instance, by predicting the abundance of SVMR, known for its high cation exchange capacity land managers can develop targeted strategies for optimizing fertilizer application in the Kazova Plain.</p></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"38 ","pages":"Article e00820"},"PeriodicalIF":3.1,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141278219","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":"Mapping sub-surface distribution of soil organic carbon stocks in South Africa's arid and semi-arid landscapes: Implications for land management and climate change mitigation","authors":"Omosalewa Odebiri ,&nbsp;Onisimo Mutanga ,&nbsp;John Odindi ,&nbsp;Rob Slotow ,&nbsp;Paramu Mafongoya ,&nbsp;Romano Lottering ,&nbsp;Rowan Naicker ,&nbsp;Trylee Nyasha Matongera ,&nbsp;Mthembeni Mngadi","doi":"10.1016/j.geodrs.2024.e00817","DOIUrl":"10.1016/j.geodrs.2024.e00817","url":null,"abstract":"<div><p>Soil organic carbon (SOC) stocks are critical for land management strategies and climate change mitigation. However, understanding SOC distribution in South Africa's arid and semi-arid regions remains a challenge due to data limitations, and the complex spatial and sub-surface variability in SOC stocks driven by desertification and land degradation. Thus, to support soil and land-use management practices as well as advance climate change mitigation efforts, there is an urgent need to provide more precise SOC stock estimates within South Africa's arid and semi-arid regions. Hence, this study adopted remote-sensing approaches to determine the spatial sub-surface distribution of SOC stocks and the influence of environmental co-variates at four soil depths (i.e., 0-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). Using two regression-based algorithms, i.e., Extreme Gradient Boosting (XGBoost) and Random Forest (RF), the study found the former (RMSE values ranging from 7.12 t/ha to 29.55 t/ha) to be a superior predictor of SOC in comparison to the latter (RMSE values ranging from 7.36 t/ha to 31.10 t/ha). Nonetheless, both models achieved satisfactory accuracy (R² ≥ 0.52) for regional-scale SOC predictions at the studied soil depths. Thereafter, using a variable importance analysis, the study demonstrated the influence of climatic variables like rainfall and temperature on SOC stocks at different depths. Furthermore, the study revealed significant spatial variability in SOC stocks, and an increase in SOC stocks with soil depth. Overall, these findings enhance the understanding of SOC dynamics in South Africa's arid and semi-arid landscapes and emphasizes the importance of considering site specific topo-climatic characteristics for sustainable land management and climate change mitigation. Furthermore, the study offers valuable insights into sub-surface SOC distribution, crucial for informing carbon sequestration strategies, guiding land management practices, and informing environmental policies within arid and semi-arid environments.</p></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"37 ","pages":"Article e00817"},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141142049","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":"Effects of different tillage and cropping systems on water repellency and hydraulic properties in a tropical Alfisol of southwestern Nigeria","authors":"Funke Florence Akinola ,&nbsp;Johnson Toyin Fasinmirin ,&nbsp;Idowu Ezekiel Olorunfemi","doi":"10.1016/j.geodrs.2024.e00815","DOIUrl":"10.1016/j.geodrs.2024.e00815","url":null,"abstract":"<div><p>This study examined the effects of growing intercrops (Sorghum, SOR (<em>Sorghum bicolor</em> L. <em>Moench</em>) in between rows of cowpea, COW (<em>Vigna unguiculata</em>) and soybean, SOY (<em>Glyxine</em> max <em>L.Merr</em>)) under different tillage systems on soil physico-chemical properties, soil water sorptivity (<em>S</em>_<em>w</em>), hydraulic conductivity (K), and soil hydrophobicity (<em>R)</em> in tropical Alfisol of Southwestern Nigeria. Results demonstrated cropping systems significantly impacted soil pH under each conventional tillage (CT), no tillage (NT), and compacted no till systems (NT_C) tillage system in 2019, but not in the 2020 cropping season. Sorghum-cowpea SC) and sorghum-soybean (SS) intercropping systems under the three tillage systems had higher soil organic matter (SOM) contents than the corresponding sole sorghum (SOR) at the end of the study. Soil water sorptivity differed significantly (<em>p</em> &lt; 0.001) among the cropping systems in 2019 and follows the order: SC &gt; SS &gt; SOY &gt; SOR &gt; COW while in 2020, averaged over three tillage systems, mean Sw values showed an increasing trend in the order: SOR (77.67 cm h^-1/2) &lt; COW (82.42 cm h^-1/2) &lt; SOY (88.00 cm h^-1/2) &lt; SS (88.73 cm h^-1/2) &lt; SC (98.29 cm h^-1/2). In general, intercropping had a lower R than sole cropping. NT_C soil was 46.48% and 56.39%; 32.47% and 41.67% more hydrophobic than soils under NT and CT in 2019 and 2020. Intercropping with legumes might have contributed to the higher K levels. Organic matter in soil improves conductivity by improving soil structure. The intercrop may have improved soil water conservation because of their shading and better soil protection. The study demonstrated that conservation tillage with intercropping could effectively enhance soil hydraulic properties.</p></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"37 ","pages":"Article e00815"},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141133636","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":"Utilizing ragweed and oyster shell derived biochar as an effective stabilizer for the restoring Cd and Pb- contaminated soil","authors":"Xueliu Gong ,&nbsp;Wanli Lian ,&nbsp;Shuai Tian ,&nbsp;Qiuyu Yu ,&nbsp;Zilin Guo ,&nbsp;Xin Zhang ,&nbsp;Yuan Yuan ,&nbsp;Yuqing Fan ,&nbsp;Zhiwei Liu ,&nbsp;Jufeng Zheng ,&nbsp;Rongjun Bian ,&nbsp;Lianqing Li ,&nbsp;Genxing Pan","doi":"10.1016/j.geodrs.2024.e00816","DOIUrl":"10.1016/j.geodrs.2024.e00816","url":null,"abstract":"<div><p>This study aimed to compare the effects of limestone and biochar amendments on heavy metal availability, soil fertility, and crop yield in cadmium (Cd) and lead (Pb) contaminated soil. A pot experiment with Chinese cabbage was conducted to evaluate two types of biochar derived from invasive plant (ragweed) and oyster shells for their potential in stabilizing Cd and Pb. The results showed that both biochar amendments significantly reduced the availability and uptake of Cd and Pb in soil and cabbage, comparable to the effects of limestone. Moreover, biochar amendment synergistically enhanced soil fertility, cabbage yield and quality. Application of ragweed-derived biochar at a 0.5% dosage consistently and most effectively promoted leaf biomass by 18% and 25% and increased vitamin C levels by 20% and 30% at harvest 1 and 2, respectively. Additionally, it led to a reduction in leaf content of nitrate by 26% and 37%, as well as a decline in Cd content by 41% and 21% and Pb content by 78% and 58%. Notably, despite the increased application cost, ragweed-derived biochar has far greater financial benefits than lime due to increased crop yield. These findings highlight the potential of employing natural and high-performance biochar with multiple functionalities, offering benefits not only in controlling invasive species and managing waste but also as a promising substitute for lime application, thereby contributing significantly to sustainable soil management.</p></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"37 ","pages":"Article e00816"},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141144601","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":"Texture prediction of natural soils in the Brazilian Amazon through proximal sensors","authors":"Quésia Sá Pavão ,&nbsp;Paula Godinho Ribeiro ,&nbsp;Gutierre Pereira Maciel ,&nbsp;Sérgio Henrique Godinho Silva ,&nbsp;Suzana Romeiro Araújo ,&nbsp;Antonio Rodrigues Fernandes ,&nbsp;José Alexandre Melo Demattê ,&nbsp;Pedro Walfir Martins e Souza Filho ,&nbsp;Silvio Junio Ramos","doi":"10.1016/j.geodrs.2024.e00813","DOIUrl":"10.1016/j.geodrs.2024.e00813","url":null,"abstract":"<div><p>Proximal sensors provide fast, low-cost, environmentally friendly, and reliable analyses for the characterization of soils and other materials. Numerous studies have been conducted on soils in temperate regions, but there are knowledge gaps regarding the use of these devices in tropical soils, especially in the Amazon region. In this regard, this study utilized portable proximal sensors of X-ray fluorescence spectroscopy (pXRF) and diffuse reflectance spectroscopy in the visible to near-infrared region (Vis-NIR) for predicting the texture of natural soils in 61 municipalities in the state of Pará, Amazon region, Brazil. The objectives were: i) to investigate the accuracy of soil texture prediction based on data from sensors separately and sensor fusion (pXRF and Vis-NIR data) using two supervised algorithms (Random Forest, RF, and Support Vector Machine, SVM) and ii) to assess the effect of soil horizon (superficial and subsuperficial horizons, and their combination) in predicting the texture of tropical natural soils. In total, 233 soil samples were collected in the 0–20 cm and 80–100 cm depths, equivalent to superficial and subsuperficial horizons in areas with primary or secondary forest cover with at least 20 years of natural regeneration and approximately 20 ha of coverage area. The hydrometer method was used for soil texture analysis. In parallel, a portion of the soil samples was analyzed by pXRF and Vis-NIR, in triplicate, under laboratory conditions. The predictive models with RF were more robust compared to the models obtained with SVM, according to ratio performance interquartile distance (RPIQ), coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The R² values obtained by pXRF, Vis-NIR, and sensor data combination were, respectively, 0.89, 0.87, and 0.93 for sand; 0.92, 0.90, and 0.93 for clay; and 0.91, 0.67, and 0.93 for silt. Overall, clay prediction models achieved higher R² values compared to sand and silt models. Soil texture prediction using sensor fusion showed lower RMSE values and higher R² and RPIQ values, respectively (sand: 7.79, 0.93, 4.69; clay: 5.58, 0.93, 3.86; and silt: 5.72, 0.92, 2.92) compared to the best-performing sensor individually (Vis-NIR). With regard to the optimal model utilizing individual sensor data, Vis-NIR models exhibited reduced error for clay and sand prediction. The effect of combining horizons to a single and bigger dataset was minimally important for the models. The results demonstrate confidence in the use of proximal sensors for soil texture assessment in natural Amazon soils, aiming to reduce costs and the time required for analyses.</p></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"37 ","pages":"Article e00813"},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141026420","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":"Land use change detection and quantification of prime agricultural lands in Southern Ontario","authors":"Solmaz Fathololoumi,&nbsp;Daniel Saurette,&nbsp;Tegbaru Bellete Gobezie,&nbsp;Asim Biswas","doi":"10.1016/j.geodrs.2024.e00775","DOIUrl":"10.1016/j.geodrs.2024.e00775","url":null,"abstract":"<div><p>Spatiotemporal land use change evaluation and quantification are essential for supporting and enhancing land use policies and for the sustainable management of land resources. To quantify the semi-decadal change in land use particularly prime agricultural lands in Southern Ontario, maps and matrices of spatial and temporal change of agricultural lands to other types land uses including built-up land were prepared from the Agriculture and Agri-Food Canada (AAFC) land use data. Then, the spatial and temporal changes of capable soils for agriculture and built-up land was evaluated. Finally, the amount of capable soil change in different regions and periods was calculated. The area of built-up and agricultural land has increased and decreased by 68% and 4% from 1990 to 2020, respectively. The amount of change in agricultural lands to built-up land in the period of 1990–2005 and 2005–2020 was 1179 and 1640 km², respectively. In these periods, 920 and 1204 km² of highly capable soils for agriculture have been converted into other land uses, respectively. The results showed that the change in agricultural lands to other types, specifically built-up areas and loss of highly capable soils for agriculture has increased over the recent years.</p></div>","PeriodicalId":56001,"journal":{"name":"Geoderma Regional","volume":"37 ","pages":"Article e00775"},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352009424000221/pdfft?md5=3485da0f2a1fa4d35aaf1c6a72ebdd01&pid=1-s2.0-S2352009424000221-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139664218","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}