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State-Space Approach to Understand Soil-Plant-Atmosphere Relationships

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Abstract

This chapter presents two different state-space approaches to evaluate the relation between soil and plant properties using examples of sugarcane, coffee and forage. These state-space approaches take into account sampling positions and allow a better interpretation of the data in relation to the field. Concepts of autocorrelation and crosscorrelation functions are first introduced, followed by theoretical aspects of both state-space approaches. More emphasis is given to the last one based on the Bayesian formulation, which gives more attention to the evolution of the estimated observations. It is concluded that the use of these dynamic regression models improve data analyses, being therefore recommended for several studies involving time and space data series, related to the performance of a given soil-plant-atmosphere system.

Keywords

Time series Regression models Soil variability Spatial variability Temporal variability Dynamic models State-space models 
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Notes

Acknowledgments

We wish to express thanks to the Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy, to the Brazilian Research Council (CNPq) and to the Brazilian Federal Agency for Improvement of Graduate Education (CAPES) for the scholarships and funding.

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Rural Engineering Department, Faculty of AgronomyFederal University of PelotasCapão do LeãoBrazil
  2. 2.Soil Physics LaboratoryCENA/USPPiracicabaBrazil
  3. 3.Soil Science Department, Faculty of AgronomyFederal University of PelotasCapão do LeãoBrazil
  4. 4.Agronomy Post-Graduate Program, Faculty of AgronomyFederal University of PelotasCapão do LeãoBrazil
  5. 5.Soil and Water Management and Conservation Post-Graduate Program, Faculty of AgronomyFederal University of PelotasCapão do LeãoBrazil
  6. 6.Crop Production DepartmentESALQ/USPPiracicabaBrazil

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