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An Integrated Approach to Assessing the Soil Quality and Nutritional Status of Large and Long-Term Cultivated Rice Agro-Ecosystems

Authors :
Vassilis Aschonitis
Christos G. Karydas
Miltos Iatrou
Spiros Mourelatos
Irini Metaxa
Panagiotis Tziachris
George Iatrou
Source :
Agriculture, Vol 9, Iss 4, p 80 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

Abstract

The aim of this study is to develop an integrated approach to soil quality and fertility assessment in high-yielding rice agro-ecosystems threatened due to overexploitation of soil resources by intensive agriculture. The proposed approach is implemented considering representative pilot fields allocated throughout a study area based on the assumption that soils of similar general properties present a similar nutritional status due to common long-term management practices. The analysis includes (a) object-based image analysis for land zonation, (b) hot-spot analysis for sampling scheme evaluation, (c) setting of critical thresholds in soil parameters for detecting nutrient deficiencies and soil quality problems, and (d) Redundancy Analysis, TITAN analysis, and multiple regression for identifying individual or combined effects of general soil properties (e.g., organic matter, soil texture, pH, salinity) or non-soil parameters (e.g., topographic parameters) on soil nutrients. The approach was applied using as a case study the large rice agro-ecosystem of Thessaloniki plain in Greece considering some site specificities (e.g., high rice yields, calcareous soils) when setting the critical thresholds in soil parameters. The results showed that (a) 62.5% of the pilot fields’ coverage has a simultaneous deficiency in Zn, Mn, and B, (b) organic matter (OM) was the most significant descriptor of nutrients’ variance, and its cold spots (clustered regions of low OM values) showed important overlapping with the cold spots of K, Mg, Zn, Mn, Cu, and B, (c) a higher rate of availability increase in P, K, Mg, Mn, Zn, Fe, Cu, and B was observed when the OM ranged between 2 and 3%, and (d) the multiple regression models that assess K and P concentrations based on general soil properties showed an adequate performance, allowing their use for general assessment of their soil concentrations in the fields of the whole agro-ecosystem.

Details

Language :
English
ISSN :
20770472
Volume :
9
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Agriculture
Publication Type :
Academic Journal
Accession number :
edsdoj.7b65acf53a554bd19910b41cd9beefa3
Document Type :
article
Full Text :
https://doi.org/10.3390/agriculture9040080