Selection of ancillary data to derive production management units in sweet corn (<Emphasis Type="Italic">Zea Mays</Emphasis> var. rugosa) using MANOVA and an information criterion |
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Authors: | J A Taylor B M Whelan |
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Institution: | (1) INRA, UMR LISAH, Batiment 21, 2 Place Pierre Viala, Montpellier, 3460, France;(2) Australian Centre for Precision Agriculture, The University of Sydney, John Woolley Building A20, Sydney, 2006, Australia;; |
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Abstract: | In production systems where high-resolution harvest data are unavailable there is often a reliance on ancillary information
to generate potential management units. In these situations correct identification of relevant sources of data is important
to minimize cost to the grower. For three fields in a sweet corn production system in central NSW, Australia, several sets
of high-resolution data were obtained using soil and crop canopy sensors. Management units were derived by k-means classification for 2–5 classes using three approaches: (1) with soil data, (2) with crop data and (3) a combination
of both soil and crop data. Crop quantity and quality were sampled manually, and the sample data were related to the different
management units using multivariate analysis of variance (MANOVA). The corrected Akaike information criterion (AICc) was then
used to rank the different sources of data and the different orders of management units. For irrigated, short-season sweet
corn production the management units derived from the crop canopy sensor data explained more variation in key harvest variables
than management units derived from an apparent soil electrical conductivity (ECa) survey or a mixture of crop and soil sensor data. Management units derived from crop data recorded just prior to side-dressing
outperformed management units derived from data recorded earlier in the season. However, multi-temporal classification of
early and mid-season crop data gave better results than single layer classification at any time. For all three fields in this
study, a 3- or 4-unit classification gave the best results according to the information criterion (AICc). For growers interested
in adopting differential management in irrigated sweet corn, investment in a crop canopy sensor will provide more useful high-resolution
information than that in a high-resolution ECa survey. |
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