Template-Type: ReDIF-Article 1.0 Author-Name: Editorial Department Title: List of Reviewers 2022 Journal: Soil and Water Research Pages: I Volume: 18 Issue: 1 Year: 2023 File-URL: http://swr.agriculturejournals.cz/artkey/swr-202301-0001_list-of-reviewers-2022.php File-Format: text/html Handle: RePEc:caa:jnlswr:v:18:y:2023:i:1:id:swr-202301-0001 Template-Type: ReDIF-Article 1.0 Author-Name: Daniel Toth Author-Workplace-Name: Department of Economics, University College of Business in Prague, Prague, Czech Republic Author-Name: Jaroslava Janků Author-Workplace-Name: Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Adéla Marie Marhoul Author-Workplace-Name: Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Josef Kozák Author-Workplace-Name: Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Mansoor Maitah Author-Workplace-Name: Department of Economics, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Jan Jehlička Author-Workplace-Name: Department of Environmental Geosciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, the Czech Republic Author-Name: Lukáš Řeháček Author-Workplace-Name: Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Richard Přikryl Author-Workplace-Name: Department of Soil Science and Soil Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Tomáš Herza Author-Workplace-Name: Hydrosoft Veleslavín, Limited Liability Company, Prague, Czech Republic Author-Name: Jan Vopravil Author-Workplace-Name: Research Institute for Soil and Water Conservation, Prague, Czech Republic Author-Name: David Kincl Author-Workplace-Name: Research Institute for Soil and Water Conservation, Prague, Czech Republic Author-Name: Tomáš Khel Author-Workplace-Name: Research Institute for Soil and Water Conservation, Prague, Czech Republic Title: Soil quality assessment using SAS (Soil Assessment System) Abstract: The paper proposes a new soil evaluation system using the principle of the Saaty method. The Saaty method has been modified and named Soil Assessment System (SAS). Significance weights are assigned to individual soil characteristics (indicators). This provides a more detailed differentiation of the significance of the indicator on soil quality and a more accurate assessment, especially in marginal cases where the assessment by the methods used so far has not been fully conclusive. In addition to physico-chemical properties, other criteria are taken into account to assess not only productional but also non-productional functions. The possibility of using indicators referring to a broader context (e.g., soil sealing value) is also important, thus enabling a comprehensive assessment of the quality of the land. This results in points for individual sampling locations. Soils are categorized according to the number of points and results are shown on maps. Keywords: Saaty method, SAS - Soil Assessment System, soil ecosystem services, soil protection, soil quality indicators, soil quality scoring Journal: Soil and Water Research Pages: 1-15 Volume: 18 Issue: 1 Year: 2023 DOI: 10.17221/141/2022-SWR File-URL: http://swr.agriculturejournals.cz/doi/10.17221/141/2022-SWR.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/swr-202301-0002.txt Handle: RePEc:caa:jnlswr:v:18:y:2023:i:1:id:141-2022-SWR Template-Type: ReDIF-Article 1.0 Author-Name: Tran Van Dung Author-Workplace-Name: Soil Science Department, College of Agriculture, Can Tho University, Can Tho, Vietnam Author-Name: Kim Thu Nguyen Author-Workplace-Name: Cuu Long Delta Rice Research Institute, Can Tho, Vietnam Author-Name: Nguyen Hoang Phuc Ho Author-Workplace-Name: Cuu Long Delta Rice Research Institute, Can Tho, Vietnam Author-Name: Nguyen Thanh Lich Duong Author-Workplace-Name: Cuu Long Delta Rice Research Institute, Can Tho, Vietnam Author-Name: Ngoc Minh Tam Vu Author-Workplace-Name: Cuu Long Delta Rice Research Institute, Can Tho, Vietnam Author-Name: Thi Phong Lan Nguyen Author-Workplace-Name: Cuu Long Delta Rice Research Institute, Can Tho, Vietnam Author-Name: Long Vu Van Author-Workplace-Name: Faculty of Natural Resources-Environment, Kien Giang University, Kien Giang, Vietnam Author-Name: Ben MacDonald Author-Workplace-Name: CSIRO Agriculture and Food, Black Mountain, Canberra, Australia Title: Reducing greenhouse gas emission by alternation of the upland crop rotation in the Mekong Delta, Vietnam Abstract: Agricultural production is one of the main sources of anthropogenic greenhouse gas (GHG) emissions, contributing 50% and 60% of CH4 and N2O emissions, respectively. This study evaluated the rice yield and components, the CH4 and N2O emissions and the global warming potential between the triple rice (R-R-R) and sesame-rice rotation (S-R-R) systems in Can Tho city, Vietnam. The experiments were conducted in 3 cropping seasons: Spring-Summer 2016, Summer-Autumn 2016, and Winter-Spring 2016-2017. The results showed that there was no significant difference in yield components and grain yield between triple rice and rotation systems. The application of sesame rotation in rice-based could reduce the CH4 and N2O emission by 30.5% and 18.7%, respectively. Global warming potential in the S-R-R rotation was 9860 kg CO2e/ha, significantly lower than the R-R-R rotation (12410 kg CO2e/ha) by 20.6%. These results show that the S-R-R rotation has the potential to mitigate GHG emissions, especially CH4, which contributes to a large amount of emissions in rice cultivation. Keywords: global warming potential, methane, nitrous oxide, Oryza sativa L., rotation, triple rice Journal: Soil and Water Research Pages: 16-24 Volume: 18 Issue: 1 Year: 2023 DOI: 10.17221/44/2022-SWR File-URL: http://swr.agriculturejournals.cz/doi/10.17221/44/2022-SWR.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/swr-202301-0003.txt Handle: RePEc:caa:jnlswr:v:18:y:2023:i:1:id:44-2022-SWR Template-Type: ReDIF-Article 1.0 Author-Name: Kamila Báťková Author-Workplace-Name: Department of Water Resources, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Svatopluk Matula Author-Workplace-Name: Department of Water Resources, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Markéta Miháliková Author-Workplace-Name: Department of Water Resources, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Eva Hrúzová Author-Workplace-Name: Department of Water Resources, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: David Kwesi Abebrese Author-Workplace-Name: Department of Water Resources, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Recep Serdar Kara Author-Workplace-Name: Department of Water Resources, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Cansu Almaz Author-Workplace-Name: Department of Water Resources, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic Title: Prediction of saturated hydraulic conductivity Ks of agricultural soil using pedotransfer functions Abstract: The determination of the saturated hydraulic conductivity Ks on a field scale presents a challenge in which several variables have to be considered. As there is no benchmark or reference method for the Ks determination, the suitability of each available method has to be evaluated. This study is aimed at the functional evaluation of three publicly available types of pedotransfer functions (PTFs) with different levels of utilised predictors. In total, ten PTF models were applied to the 56 data sets including the measured Ks value and the required predictors (% sand, silt and clay particles, dry bulk density, and organic matter/organic carbon content). A single agricultural field with a relatively homogenous particle size distribution was selected for the study to evaluate the ability of the PTF to reflect the variability of Ks. The correlation coefficient, coefficient of determination, mean error, and root mean square error were determined to evaluate the Ks prediction quality. The results showed a high variability in Ks within the field; the measured Ks values ranged between 10 and 1261 cm/day. Although the tested PTF models are based on a robust background of soil databases, they could not provide estimates with satisfactory accuracy unless local soil data were incorporated into the PTF development. Keywords: functional evaluation, machine learning, neural network, non-linear regression, soil hydraulic properties Journal: Soil and Water Research Pages: 25-32 Volume: 18 Issue: 1 Year: 2023 DOI: 10.17221/130/2022-SWR File-URL: http://swr.agriculturejournals.cz/doi/10.17221/130/2022-SWR.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/swr-202301-0004.txt Handle: RePEc:caa:jnlswr:v:18:y:2023:i:1:id:130-2022-SWR Template-Type: ReDIF-Article 1.0 Author-Name: Abdelkader Laribi Author-Name: Charles Shand Author-Workplace-Name: The James Hutton Institute, Craigiebuckler, Aberdeen, United Kingdom Author-Name: Renate Wendler Author-Workplace-Name: The James Hutton Institute, Craigiebuckler, Aberdeen, United Kingdom Author-Name: Brahim Mouhouche Author-Workplace-Name: Département de Génie Rural, Laboratoire de Maîtrise de l'eau en Agriculture (LMEA), Ecole Nationale Supérieure Agronomique (ENSA), El Harrach, Algiers, Algeria Author-Name: Stephen Hillier Author-Workplace-Name: The James Hutton Institute, Craigiebuckler, Aberdeen, United Kingdom Author-Workplace-Name: Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden Author-Name: Gilles Colinet Author-Workplace-Name: University of Liège, Gembloux Agro Bio-Tech, BIOSE Dpt, Gembloux, Belgium Title: Ambient background and quality reference values for trace metals in soils from Algeria Abstract: The establishment of the reference ambient background concentrations (ABCs) and quality reference values (QRVs) for trace metal (TM) concentrations in soils are required for the environmental assessment and any implementation of a protective action. This information is lacking for soils of the eastern Mitidja plain, which is an important agricultural production area in Algeria. Data for the aqua regia extractable Cd, Cr, Cu, Fe, Ni, Pb and Zn concentrations from 180 composite topsoil samples taken across the Mitidja plain in a stratified random pattern were statistically analysed. Descriptive statistical methods and linear regression equations were applied to determine the upper limit of the ABCs for the TMs. After removal of outliers, the derived QRVs were: Cd 0.24, Cr 62.1, Cu 99.3, Fe 45 590, Ni 47.7, Pb 33 and Zn 115 mg/kg. Iron is a macro element in the soils, but is included as its concentration can be used to normalise the concentrations of the other elements. The derived QRVs are similar or less than those reported for other regions of the world, apart from Cu, where a wide range (36 to 206 mg/kg) is reported. These reference values can be used to identify areas that may require follow-up surveys or to identify priority sites for decision making. Keywords: environmental assessment, Mitidja plain, potentially toxic elements, soil contamination, threshold value Journal: Soil and Water Research Pages: 33-42 Volume: 18 Issue: 1 Year: 2023 DOI: 10.17221/143/2021-SWR File-URL: http://swr.agriculturejournals.cz/doi/10.17221/143/2021-SWR.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/swr-202301-0005.txt Handle: RePEc:caa:jnlswr:v:18:y:2023:i:1:id:143-2021-SWR Template-Type: ReDIF-Article 1.0 Author-Name: Baoyang Liu Author-Workplace-Name: School of Automation, Hangzhou Dianzi University, Hangzhou, P.R. China Author-Name: Baofeng Guo Author-Workplace-Name: School of Automation, Hangzhou Dianzi University, Hangzhou, P.R. China Author-Name: Renxiong Zhuo Author-Workplace-Name: School of Automation, Hangzhou Dianzi University, Hangzhou, P.R. China Author-Name: Fan Dai Author-Workplace-Name: School of Automation, Hangzhou Dianzi University, Hangzhou, P.R. China Author-Name: Haoyu Chi Author-Workplace-Name: School of Automation, Hangzhou Dianzi University, Hangzhou, P.R. China Title: Prediction of the soil organic carbon in the LUCAS soil database based on spectral clustering Abstract: The estimation of the level of the soil organic carbon (SOC) content plays an important role in assessing the soil health state. Visible and Near Infrared Diffuse Reflectance Spectroscopy (Vis-NIR DRS) is a fast and cheap tool for measuring the SOC. However, when this technology is applied on a larger area, the soil prediction accuracy decreases due to the heterogeneity of the samples. In this paper, we first investigate the global model performance in the LUCAS EU-wide topsoil database. Then, different clustering strategies were tested, including the k-means clustering based on the principal component analysis (PCA) and hierarchical clustering, combined with the partial least squares regression (PLSR) models, and a clustering based on a local PLSR approach. The best validation results were obtained for the local PLSR approach with R2 = 0.75, root mean squared error of prediction (RMSEP) = 13.38 g/kg and ratio of performance to interquartile range (RPIQ) = 2.846, but the algorithm running time was 30.05 s. Similar results were obtained for the k-means clustering method with R2 = 0.75, RMSEP = 14.61 g/kg and RPIQ = 2.844, at only 4.52 s. This study demonstrates that the PLSR approach based on k-means clustering is able to achieve similar prediction accuracy as the local PLSR approach, while significantly improving the algorithm speed. This provides the theoretical basis for adapting the spectral soil model to the needs of real-time SOC quantification. Keywords: cluster analysis, regression analysis, retrieve, soil properties, Vis-NIR spectroscopy Journal: Soil and Water Research Pages: 43-54 Volume: 18 Issue: 1 Year: 2023 DOI: 10.17221/97/2022-SWR File-URL: http://swr.agriculturejournals.cz/doi/10.17221/97/2022-SWR.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/swr-202301-0006.txt Handle: RePEc:caa:jnlswr:v:18:y:2023:i:1:id:97-2022-SWR Template-Type: ReDIF-Article 1.0 Author-Name: Mojtaba Zangeneh Author-Workplace-Name: Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran Author-Name: Mahdi Sarai Tabrizi Author-Workplace-Name: Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran Author-Name: Amir Khosrojerdi Author-Workplace-Name: Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran Author-Name: Ali Saremi Author-Workplace-Name: Department of Water Engineering and Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran Title: Developing a decision-making model for improving the groundwater balance to control land subsidence Abstract: This study aimed to control land subsidence by improving the groundwater balance in the Varamin plain using the Groundwater Modelling System software and a multi-criteria decision model. For this purpose, aquifer level quantification and subsidence rate simulation were performed with the MODFLOW model and SUB package, respectively. The results showed a 6 m decrease in the aquifer level over a 5-year period and the subsidence rate in the central parts was 37 cm. Accordingly, the aquifer was evaluated by considering eight different restoration strategies based on reduced exploitation and artificial feeding. The results showed that the environmental criterion related to the subsidence adjustment index had the highest weight (0.27) and was introduced as the most important decision-making criterion. The evaluation of the results and priorities using the Complex Proportional Assessment (COPRAS) method showed that a 30% reduction in exploitation with artificial feeding is the best restoration strategy and can improve the subsidence rate and aquifer level by 36% and 76%, respectively, over a 5-year period (2024). Keywords: COPRAS method, GMS software, MODFLOW, SUB package, SWARA method Journal: Soil and Water Research Pages: 55-65 Volume: 18 Issue: 1 Year: 2023 DOI: 10.17221/57/2022-SWR File-URL: http://swr.agriculturejournals.cz/doi/10.17221/57/2022-SWR.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/swr-202301-0007.txt Handle: RePEc:caa:jnlswr:v:18:y:2023:i:1:id:57-2022-SWR