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    Mapping the spatial and temporal stability of production in mixed farming systems: an index that integrates crop and pasture productivity to assist in the management of variability

    76194.pdf (1.674Mb)
    Access Status
    Open access
    Authors
    McEntee, P.J.
    Bennett, Sarita
    Belford, R.K.
    Date
    2019
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    McEntee, P.J. and Bennett, S.J. and Belford, R.K. 2019. Mapping the spatial and temporal stability of production in mixed farming systems: an index that integrates crop and pasture productivity to assist in the management of variability. Precision Agriculture.
    Source Title
    Precision Agriculture
    DOI
    10.1007/s11119-019-09658-6
    ISSN
    1385-2256
    Faculty
    Faculty of Science and Engineering
    School
    School of Molecular and Life Sciences (MLS)
    Remarks

    The final publication is available at Springer via http://dx.doi.org/10.1007/s11119-019-09658-6

    URI
    http://hdl.handle.net/20.500.11937/75972
    Collection
    • Curtin Research Publications
    Abstract

    While precision agriculture (PA) technologies are widely used in cropping systems, these technologies have received less attention in mixed farming systems. Little is known about the nature, extent, and temporal stability of spatial variability of pastures in mixed farming systems and the feasibility of managing this variability. This paper describes a technique to create a Stability Index based on both crop grain yield and pasture total green dry matter (TGDM) production over time, using high resolution spatial data in two climatic zones of Australia. Four productivity zones were used to characterise the Stability Index: high and stable, high and unstable, low and stable, and low and unstable. Mapping the indices shows the location and size of the spatial and temporal features of each paddock. The features of the stability zones generally corresponded with soil texture classes. Testing the Stability Indices with a Kruskal–Wallis one-way ANOVA showed significantly different medians for high and low production categories for both grain yield and pasture TGDM (p < 0.01). Crop grain yield stability showed significant differences between medians. In pasture TGDM, the differences between stability medians were not significant, but the technique still separated medians into stable and unstable groupings. This production Stability Index has the potential to be used by farmers to manage spatial variability in mixed farming systems by identifying homogenous areas within a paddock for investigation/amelioration and can also separate out areas of either spatial and/or temporal instability for specific management strategies.

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