Precision Agriculture

, Volume 14, Issue 4, pp 376–391

An integrated framework for software to provide yield data cleaning and estimation of an opportunity index for site-specific crop management

Authors

    • State Key Laboratory of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences
  • Brett Whelan
    • Precision Agriculture LaboratoryFaculty of Agriculture and Environment, The University of Sydney
  • Alex B. McBratney
    • Precision Agriculture LaboratoryFaculty of Agriculture and Environment, The University of Sydney
  • Budiman Minasny
    • Precision Agriculture LaboratoryFaculty of Agriculture and Environment, The University of Sydney
Article

DOI: 10.1007/s11119-012-9300-7

Cite this article as:
Sun, W., Whelan, B., McBratney, A.B. et al. Precision Agric (2013) 14: 376. doi:10.1007/s11119-012-9300-7

Abstract

This paper proposes an integrated framework for software that provides yield data cleaning and yield opportunity index (Yi) calculation for site-specific crop management (SSCM). The artifacts in many yield data sets, which inevitably occur, can pose a significant effect on the validity of Yi. Automated and standardised yield correction procedures were designed to improve the data quality by removing: (1) unreasonable outliers; (2) distribution outliers (globally and locally); and (3) position errors. The calculation of Yi uses two aspects of crop yield assessment, the magnitude of yield variation and the spatial structure of the variation. The cleaning algorithms were applied to four yield data sets with known integrity issues to demonstrate effectiveness. Approximately 13–20 % of the original yield data were removed, and this resulted in an increased mean yield of 0.13 t/ha (average). The semivariograms of cleaned data were shown to possess smaller nugget values compared with the original data. The opportunity index calculation algorithm was demonstrated on a field with nine seasons of yield data. The results demonstrated that using a ranking of Yi provides a rational, agronomic assessment of the opportunity for SSCM based on the quantity and pattern of production variability displayed in yield data sets. This provides farm managers with a rapid way to assess whether the observed variability deserves further investigation and eventual investment in SSCM operations.

Keywords

Precision agricultureYield variabilitySpatial variationYield mapsYield data trimming

Copyright information

© Springer Science+Business Media New York 2012