Developing pedotransfer functions to estimate the S-index for indicating soil quality |
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| Institution: | 1. Key Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, Hunan, China;2. Huanjiang Observation and Research Station for Karst Ecosystem, Chinese Academy of Sciences, Huanjiang 547100, Guangxi, China;3. University of Chinese Academy of Sciences, Beijing 100049, China;1. University of São Paulo, “Luiz de Queiroz” College of Agriculture, Department of Soil Science, 11 Avenida Pádua Dias, Piracicaba, SP 13418-900, Brazil;2. Federal University of Technology-Paraná, Department of Agronomy, Via do Conhecimento, km 1, Pato Branco, PR 85503-390, Brazil;3. University of São Paulo, Center for Nuclear Energy in Agriculture, 303 Avenida Centenário, Piracicaba, SP 13400-970, Brazil;1. Department of Renewable Resources, University of Alberta, 442 Earth Sciences Building, Edmonton, Alberta, T6G 2E3, Canada;2. Agriculture and Agri-Food Canada, Lethbridge Research and Development Centre, Lethbridge, Alberta, T1J 4B1, Canada;1. Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai, Tsukuba, Ibaraki, 305-8604, Japan;2. Western Region Agricultural Research Center, National Agriculture and Food Research Organization. 6-12-1 Nishifukatsu-cho, Fukuyama-shi, Hiroshima, 721-8514, Japan;1. Department of Chemical and Environmental Engineering, University College of Engineering of Vitoria-Gasteiz, University of the Basque Country UPV/EHU, Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain;2. NEIKER-Tecnalia, Dept. Ecology and Natural Resources, Soil Microbial Ecology Group, Parcel 812, Berreaga 1, E-48160 Derio, Bizkaia, Spain;3. GAIKER-IK4 Technology Centre, Ed. 202, 48.170 Zamudio, Bizkaia, Spain;4. Department of Plant Biology and Ecology, Faculty of Science and Technology, University of the Basque Country UPV/EHU, Barrio Sarriena s/n, 489400 Leioa, Bizkaia, Spain;5. Department of Zoology and Animal Cell Biology, CBET Research Group, Faculty of Science and Technology, University of the Basque Country UPV/EHU, Barrio Sarriena s/n, 489400 Leioa, Bizkaia, Spain;1. Center of Rural Science, Federal University of Santa Maria, Roraima Avenue, Number 1000, CEP: 97.105-900, Santa Maria, Rio Grande do Sul, Brazil;2. Center of Technological Development, Federal University of Pelotas, Campus Porto, Gomes Carneiro Street, Number 1, CEP: 96010-610, Pelotas, Rio Grande do Sul, Brazil;3. Department of Soil, Federal University of Pelotas, Campus Universitário s/n, CEP 96010-900, Capão do Leão, Rio Grande do Sul, Brazil;4. Department of Soil, Federal University of Pelotas, Campus Universitário s/n, CEP 96010-900, Capão do Leão, Rio Grande do Sul, Brazil;5. Department of Vegetation Studies, Landscape Management, Federal Institute of Hydrology, Koblenz, Germany;6. Department of Rural Engineering, Faculty of Agronomy, Federal University of Pelotas, Campus Universitário s/n, CEP: 96010-900, Capão do Leão, Rio Grande do Sul, Brazil |
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| Abstract: | Indicating soil quality usually requires many soil properties of which the measurements are time consuming. Therefore, it is desirable to developing simple and effective indices for reflecting soil quality based on soil properties that can be readily obtained. The soil physical quality index, S-index, derived from the slope at the inflection point of the water retention curve (particularly the Van-Genuchten equation), is a comprehensive index for indicating soil properties. By comparing the S-index with a widely used soil quality index (SQI), this study used 298 samples to determine soil chemical and physical properties for calculating SQI, and found that the correlation coefficient between the S-index and SQI was 0.88, indicating that the S-index can represent soil quality well. An artificial neural network (ANN) model and a linear regression (LR) model were proposed for estimating S-index. Results showed that the ANN model was better than LR model in estimating S-index. Particularly, the ANN model with the soil bulk density and soil organic carbon (scenario A1) as inputs, had the highest R2 of 0.807, while the LR model get the highest R2 (predicted v.s. observed) of 0.75 with the combination of soil organic carbon, soil bulk density, total nitrogen and available nitrogen. This study is helpful for extending the applications of S-index. |
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| Keywords: | Soil quality Soil quality index S-index Artificial neural network |
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