caa a22 4500 445864621 CHVBK 20180317145454.0 cr unu---uuuuu 170323e20110801xx s 000 0 eng 10.1007/s11119-010-9195-0 doi (NATIONALLICENCE)springer-10.1007/s11119-010-9195-0 Selection of ancillary data to derive production management units in sweet corn ( Zea Mays var. rugosa) using MANOVA and an information criterion [Elektronische Daten] [J. Taylor, B. Whelan] 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. Springer Science+Business Media, LLC, 2010 Akaike information criterion (AIC) nationallicence Canopy sensors nationallicence Apparent soil electrical conductivity (ECa) nationallicence Plant density nationallicence Cob diameter nationallicence Yield nationallicence k -Means clustering nationallicence Precision horticulture nationallicence Taylor J. INRA, UMR LISAH, Batiment 21, 2 Place Pierre Viala, 3460, Montpellier, France aut Whelan B. Australian Centre for Precision Agriculture, The University of Sydney, John Woolley Building A20, 2006, Sydney, Australia aut Precision Agriculture Springer US; http://www.springer-ny.com 12/4(2011-08-01), 519-533 1385-2256 12:4<519 2011 12 11119 https://doi.org/10.1007/s11119-010-9195-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11119-010-9195-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Taylor J. INRA, UMR LISAH, Batiment 21, 2 Place Pierre Viala, 3460, Montpellier, France aut NATIONALLICENCE 700 1- Whelan B. Australian Centre for Precision Agriculture, The University of Sydney, John Woolley Building A20, 2006, Sydney, Australia aut NATIONALLICENCE 773 0- Precision Agriculture Springer US; http://www.springer-ny.com 12/4(2011-08-01), 519-533 1385-2256 12:4<519 2011 12 11119 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer