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Abstract: In this paper we present a segmentation algorithm, inspired from an image-processing region-merging algorithm, for the delineation of discrete contiguous management zones in agriculture. The algorithm is unique in that it is applicable to high- or low-density irregular data sets, such as yield data. The algorithm is described and a brief example presented using unprocessed sensor-derived grain yield data. A comparison between the segmentation algorithm and a common classification algorithm (k-means clustering) was done using an aerial normalised differences vegetation index (NDVI) image collected on a 200ha vineyard in Olite, Southern Navarre, Spain. Classification was performed as a univariate (NDVI) analysis and a spatially constrained analysis. Segmentation and classification were run to find 2, 4, 6, …, 24 levels and the effectiveness of the outputs determined by how well it explained the variance in vine trunk circumference, a correlated but independent measurement. The results obtained demonstrated that for a given number of manageable (effective) zones the segmentation outputs were equivalent or superior to the classification outputs for partitioning vine circumference variance. The segmentation output also generated more coherent management units that should facilitate differential management. The algorithm presented is a first generation segmentation algorithm and several aspects still need to be developed, in particular methods for eliminating edge effects and converting management zones into management (treatment) classes. The results of the segmentation algorithm presented here would indicate that with further development, segmentation might provide an alternative and possibly preferable approach to delineating management zones. [Copyright &y& Elsevier]

Abstract: Since 2002, the Galician Administration (NW Spain) has been working in the development of a decision support system for Agricultural Production Planning. To this end, an initial decision tool, Gestop®, was developed. However, the tool had a number of limitations for practical use because of the lack of differentiated data about the real situation of farms. With a view to improving the initial tool, a combinatorial method (CM) was developed. Such a combinatorial method used official census data to classify farms based on the different production processes, sizes and geographical locations of farms, among other factors. The combinatorial method proved very useful as a source of differentiated data for the decision support system. Yet, validating the results using robust statistical methods was considered essential in order to guarantee the suitability of the model for use. This paper uses discriminant analysis (DA) techniques to validate the types of dairy farms obtained from the combinatorial method for Galicia. Results clearly validate the combinatorial method and its use as a data source for decision support systems in Agricultural Production Planning. [Copyright &y& Elsevier]