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Title: Hazard assessment of pasture soil fertility from an objective and probabilistic approach
Authors: Moral, F.
Rebollo, F.
Serrano, João
Keywords: Management zones
Soil fertility
Rasch model
Issue Date: Nov-2020
Publisher: CRAES Group, University College Dublin and Prudence College Dublin, Ireland. 24
Citation: Moral, F., Rebollo, F., Serrano, J. (2020). Hazard assessment of pasture soil fertility from an objective and probabilistic approach. Book of Abstract Series 1, Published in 2020 by the CRAES Group, University College Dublin and Prudence College Dublin, Ireland. 24, 1st International Symposium on Climate-Resilient Agri-Environmental Systems (ISCRAES 2020), Edited by M.I. Khalil and B.A. Osborne, Virtual, 04-06 November 2020, Pgs 23-24..
Abstract: Although different algorithms have been used to delineate areas with similar properties (e.g., texture or levels of macronutrients) within agricultural fields, there are few applications in pasture systems. A new approach based on the formulation of an objective and probabilistic model, the Rasch model, integrates a number of key soil properties, providing measures of pasture soil fertility that can be used to analyse spatial pattern in the field. To illustrate the proposed approach, a study was performed in a pasture field. The study comprised 76 soil samples of different sampling locations along a gradient of soil fertility, in a depth range of 0–0.30 m, considering the maximum depth of the roots in the pasture, approximately 0.2–0.3 m. Samples were analysed for ten key soil properties: sand, silt, and clay contents, moisture content, pH, organic matter, nitrogen, phosphorus, potassium, and soil apparent electrical conductivity. These data were processed according to the Rasch model and, as the main results, all sampling locations were classified according to their soil fertility, as the Rasch measure, and it was highlighted the influence of each soil property on the pasture soil fertility. Thereafter, a geostatistical algorithm was used to generate probability maps in order to delineate management zones. Doing so, we could identify zones where inputs might be changed (e.g., decrease of inputs in less fertile and less productive areas) and where input costs (e.g., chemical substances) can be minimised, allowing to obtain more cost effective field management, which additionally provides environmental, economic, and energetic benefits.
Type: lecture
Appears in Collections:ERU - Comunicações - Em Congressos Científicos Internacionais
ERU - Comunicações - Em Congressos Científicos Internacionais

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