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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Notes
The soil sensors and canopy reflectance sensors used were chosen based on their availability to the researchers. Their use does not imply a specific endorsement for these sensors from the authors who acknowledge there are several other sensors capable of providing equivalent ECa and canopy reflectance data.
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Acknowledgments
The project data used was facilitated by HAL in partnership with AUSVEG. The Australian Government provides matched funding for all HAL’s R&D activities. The authors would like to thank Mr J McSpedden for assistance and access to the production system. Dr Taylor’s position at INRA is funded by the Agropolis Foundation, France.
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Taylor, J.A., Whelan, B.M. Selection of ancillary data to derive production management units in sweet corn (Zea Mays var. rugosa) using MANOVA and an information criterion. Precision Agric 12, 519–533 (2011). https://doi.org/10.1007/s11119-010-9195-0
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DOI: https://doi.org/10.1007/s11119-010-9195-0