![]() ![]() A way of defining the number of groups on statistical fundaments is described. The advantages of principal components (PC's) in ordering Opuntia accessions are described, and the convenience of using cluster analysis for the same purpose is discussed. This research report presents the case of developing a strategy for an objective ordering and numerical classification in terms of fruit mass and shape (prickly pear, tuna, or nochtli) of the genus Opuntia. SUMMARY Numerical classifications in biology have been based on genetics and phylogenetics. rainfall from April to October) and water-use efficiency. Rainfall and profit were linked with a whole-farm model that estimates crop yield as a function of seasonal rainfall (i.e. To test this hypothesis, we used 40-years rainfall series to (1) investigate rainfall features in 11 locations in the Mallee, (2) test the skill of simple rules to predict seasonal rainfall, as developed by local farmers, and (3) calculate whole-farm profit for conservative, risky and dynamic cropping strategies. We tested the hypothesis that whole-farm profitability could be enhanced by the adoption of a dynamic cropping strategy shifting from a cereal-only, conservative strategy in dry years, to a more risky strategy involving both cereals and canola in wet years. This approach has, however, a substantial opportunity cost by missing the potential profit from more intense cereal cropping and high yielding oilseed crops in more favourable seasons. To reduce risk related to likely water deficits, many growers adopt a conservative cropping strategy based on low-input cereal crops. Low rainfall and soils with low water holding capacity are major features of the Mallee region of south-eastern Australia. This approach may be applicable where large land masses with similar geographical features occur. The validity of the mean rainfall equations was tested on three further sites: the mean prediction error was 6.9%. Differences between regions are accounted for in the analysis, making the equations widely applicable. Nearly all of the equations explain between 80% and 94% of the variation in rainfall. These equations use altitude, longitude and latitude as predictors. The second part of the analysis uses multiple regression to provide a set of equations for rainfall prediction at any location in the region, for a number of rainfall periods. ![]() Based on these two rainfall periods, a partitioning of the study area reveals five distinct regions. The summer rainfall differences account for over two thirds of the variation. Most of the variation in rainfall pattern across the region is accounted for by differences in October-to-March (summer) rainfall and in April-to-September (winter) rainfall. ![]() This analysis examines, firstly, the pattern of rainfall over 2.3 million ha of a high-quality wheat-producing region, and secondly, develops regression equations for rainfall prediction over this region. Rainfall is an important variable in the wheat production areas of Australia. ![]()
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