Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area |
| |
| Authors: | Udayakantha W.A. Vitharana Marc Van Meirvenne David Simpson Liesbet Cockx Josse De Baerdemaeker |
| |
| Affiliation: | 1. Research Group Soil Spatial Inventory Techniques, Department of Soil Management and Soil Care, Ghent University, Coupure 653, 9000 Gent, Belgium;2. Laboratory for Agricultural Machinery and Processing, K.U. Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium;1. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, China;2. USDA-ARS, Pasture Systems & Watershed Management Research Unit, Building 3702, Curtin Road, University Park, PA 16802, USA;3. Pioneer Hi-Bred International Inc., Champaign Research Center, 985 County Road 300 E, Ivesdale, IL 61851, USA;1. Departamento de Ingeniería, Universidad de Almería, Almería, Spain;2. Campus de Excelencia Internacional Agroalimentaria ceiA3, Spain;3. Campus Universitario, Escuela Superior de Ingeniería, 04120, Almería, Spain;4. Departamento de Producción Vegetal, Instituto de Agricultura Sostenible, Córdoba, Spain;1. Forest & Nature Lab, Ghent University, Geraardsbergsesteenweg 267, 9090 Gontrode, Belgium;2. Community and Conservation Ecology Group, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands;3. Afdeling maatschappij en Leefomgeving, Inagro vzw, Ieperseweg 87, 8800 Rumbeke-Beitem, Belgium;4. Laboratory of Plant Ecology, Department of Applied Ecology and Environmental Biology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium;5. Remote Sensing, University of Technology, Sydney (UTS), 745 Harris Street, Broadway, 2007 NSW, Australia;1. Faculty of Information Systems and Technologies, University of Donja Gorica, Oktoih 1, 81000 Podgorica, Montenegro;2. Biotechnical Faculty, University of Montenegro, Mihaila Lali?a 1, 81000 Podgorica, Montenegro;3. Institute of Marine Biology, University of Montenegro, P. Fah 69, 85330 Kotor, Montenegro;4. Faculty of Electrical Engineering, University of Montenegro, D?ord?a Va?ingtona bb, 81000 Podgorica, Montenegro;1. Department of Biosystems Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium;2. Department of Telecommunications and Information Processing, IMEC-TELIN-Ghent University, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium;3. Technology and Food Science Unit, ILVO, Burg. Van Gansberghelaan 115, 9820 Merelbeke, Belgium;4. Plant Sciences Unit, ILVO, Caritasstraat 39, 9090 Melle, Belgium;5. College of Biosystems Engineering and Food Science, Zhejiang University, Yuhangtang Road 866, 310058 Hangzhou, China |
| |
| Abstract: | Recent advances in on-the-go soil sensing, terrain modelling and yield mapping have made available large quantities of information about the within-field variability of soil and crop properties. But the selection of the key variables for an identification of management zones, required for precision agriculture, is not straightforward. To investigate a procedure for this selection, an 8 ha agricultural field in the Loess belt of Belgium was considered for this study. The available information consisted of: (i) top- and subsoil samples taken at 110 locations, on which soil properties: textural fractions, organic carbon (OC), CaCO3 and pH were analysed, (ii) soil apparent electrical conductivity (ECa) obtained through an electromagnetic induction based sensor, and (iii) wetness index, stream power index and steepest slope angle derived from a detailed digital elevation model (DEM). A principal component analysis, involving 12 soil and topographic properties and two ECa variables, identified three components explaining 67.4% of the total variability. These three components were best represented by pH, ECa that strongly associated with texture and OC. However, OC was closely related to some more readily obtainable topographic properties, and therefore elevation was preferred. A fuzzy k-means classification of these three variables produced four potential management classes. Three-year average standardized yield maps of grain and straw showed productivity differences across these classes, but mainly linked to their landscape position. In the loess area with complex soil-landscape interactions pH, ECa and elevation can be considered as key properties to delineate potential management classes. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
