Building Variable Productivity Ratios for Improving Large Scale Spatially Explicit Pruning Biomass Assessments

Daniel García-Galindo, Arkadiusz Dyjakon and Fernando Cay Villa-Ceballos
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Daniel García-Galindo: Research Centre for Energy Resources and Consumption (CIRCE), 50018 Zaragoza, Spain
Arkadiusz Dyjakon: Institute of Agricultural Engineering, Wroclaw University of Environmental and Life Sciences, 51-630 Wroclaw, Poland
Fernando Cay Villa-Ceballos: Department of Mechanical Engineering, University of Zaragoza, 50018 Zaragoza, Spain

Energies, 2019, vol. 12, issue 5, 1-25

Abstract: Biomass assessments of agro–residues performed at large geographical scales generally base calculations on single constant pruning productivity ratios (RSRs). Reliability of biomass assessments shall be improved if RSRs respond to prevailing regional crop growing conditions. The present paper describes the methodology applied to create geographically varying pruning RSR ratios–tons of dry matter per hectare—for five crop groups: vineyard, olive, fruit species, citrus and dry fruits. A newly created database containing 230 records–from seven EU28 countries—is submitted to statistical analysis. Results reveal that agro-climatic conditions are able to explain a not negligible share of the pruning productivity as dependent variable. Subsequent regression analysis provides two equations—for vineyard and citrus—achieving a reasonable good fitting ( R 2 0.18 and 0.42 respectively) between RSR and the agroclimatic variables. Analysis of olive, fruit species and dry fruits scatter and whisker plots were useful for zoning and inducing ramp functions. A Geographical Information System (GIS) was utilised to apply the functions to the agroclimatic raster coverages in order to obtain RSR raster grids. Zonal statistic procedures applied by European regional units (NUTs0, NUTs2, NUTs3) provide a specific crop RSR ratio per administrative unit as a principal output of the present work.

Keywords: pruning; biomass; resources assessment; regression analysis; geographical information systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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