Soil bulk electrical resistivity and forage ground cover: nonlinear models in an alfalfa (Medicago sativa L.) case study

  • Roberta Rossi Research Unit for the Extensive Animal Husbandry, Council for Agricultural Research and Analysis of Agricultural Economics, Bella (PZ), Italy.
  • Alessio Pollice Department of Economics and Mathematics, University of Bari Aldo Moro, Bari, Italy.
  • Gianfranco Bitella | gianfranco.bitella@gmail.com School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy.
  • Rocco Bochicchio School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy.
  • Amedeo D'Antonio Department of Agricultural, Food and Forestry Policies, Campania Region, Naples, Italy.
  • Alaa Aldin Alromeed School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy.
  • Anna Maria Stellacci Research Unit for Cropping Systems in Dry Environments, Council for Agricultural Research and Analysis of Agricultural Economics, Bari, Italy.
  • Rosanna Labella School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy.
  • Mariana Amato School of Agricultural, Forestry, Food and Environmental Sciences, University of Basilicata, Potenza, Italy.

Abstract

Alfalfa is a highly productive and fertility-building forage crop; its performance, can be highly variable as influenced by within-field soil spatial variability. Characterising the relations between soil and forage- variation is important for optimal management. The aim of this work was to model the relationship between soil electrical resistivity (ER) and plant productivity in an alfalfa (Medicago sativa L.) field in Southern Italy. ER mapping was accomplished by a multi-depth automatic resistivity profiler. Plant productivity was assessed through normalised difference vegetation index (NDVI) at 2 dates. A non-linear relationship between NDVI and deep soil ER was modelled within the framework of generalised additive models. The best model explained 70% of the total variability. Soil profiles at six locations selected along a gradient of ER showed differences related to texture (ranging from clay to sandy-clay loam), gravel content (0 to 55%) and to the presence of a petrocalcic horizon. Our results prove that multi-depth ER can be used to localise permanent soil features that drive plant productivity.

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Published
2015-12-09
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Short communications
Keywords:
Geophysical mapping, normalised difference vegetation index, alfalfa, soil spatial variability, generalised additive models.
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How to Cite
Rossi, R., Pollice, A., Bitella, G., Bochicchio, R., D’Antonio, A., Alromeed, A. A., Stellacci, A. M., Labella, R., & Amato, M. (2015). Soil bulk electrical resistivity and forage ground cover: nonlinear models in an alfalfa (Medicago sativa L.) case study. Italian Journal of Agronomy, 10(4), 215-219. https://doi.org/10.4081/ija.2015.647