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Classification of Crop Yield Variability in Irrigated Production Fields

^a Dep. of Agron. and Hortic., Univ. of Nebraska, P.O. Box 830915, Lincoln, NE 68583-0915
^b Dep. of Biol. Syst. Eng., Univ. of Nebraska, P.O. Box 830726, Lincoln, NE 68583-0726

View larger version (117K): [in a new window]   Fig. 1. Maps of mean, standard deviation (SD), and coefficient of variation (CV) of relative yield at Sites A (Clay Center) and B (Cairo). Light colors show high-yielding areas; dark colors show low-yielding areas with high yield variability among years.

View larger version (86K): [in a new window]   Fig. 2. Maps of three crop yield classes formed by empirical classification procedures: (i) based on mean and CV of yield (MCV-3, Blackmore, 2000), (ii) based on mean and standard deviation of yield (MSD-3), (iii) using t test at 90% probability based on mean and standard deviation of yield difference (T90-3), and (iv) using t test at 60% probability based on mean and standard deviation of yield difference (T60-3). Light colors show high-yielding areas; dark colors show low-yielding areas with high yield variability among years.

View larger version (26K): [in a new window]   Fig. 3. Average yield variance accounted for by the classification (RVc) as a function of data sources used and the number of classes selected: (i) hierarchical cluster analysis using Ward's methods (WAR), (ii) nonhierarchical cluster analysis using k means (KME), (iii) unsupervised nonhierarchical ISODATA clustering method (ISO), and (iv) nonhierarchical fuzzy-k-means cluster analysis (FUZ).

View larger version (26K): [in a new window]   Fig. 4. Ranges (min.–max. bars) and means (circles) of yield variance accounted for by the classification at Sites A and B. The width of the bars indicates how well a particular classification method performed in accounting for the yield variation in each of the five (Site A) or six (Site B) different years. Methods shown: hierarchical cluster analysis using Ward's methods (WAR), nonhierarchical cluster analysis using k means (KME), unsupervised nonhierarchical Iterative Self-Organizing Data Analysis (ISODATA) clustering method (ISO), and nonhierarchical fuzzy-k-means cluster analysis (FUZ). For each method, values are shown for three different data sources [mean yield (MY), mean and standard deviation of yield (MS), and yields in all years (AY)] and five to seven classes.

View larger version (116K): [in a new window]   Fig. 5. Maps of yield classes at Site A (Clay Center) formed by hierarchical and nonhierarchical clustering procedures as affected by the choice of input data: (i) hierarchical cluster analysis using Ward's methods and six classes (WAR-6), (ii) nonhierarchical cluster analysis using k means and six classes (KME-6), (iii) unsupervised nonhierarchical Iterative Self-Organizing Data Analysis (ISODATA) clustering method using six classes (ISO-6), and (iv) nonhierarchical fuzzy-k-means cluster analysis using six classes (FUZ-6). For each method, maps are shown for three different data sources [mean yield (MY), mean and standard deviation of yield (MS), and yields in all years (AY)]. Light colors show high-yielding areas; dark colors show low-yielding areas with high yield variability among years.

View larger version (101K): [in a new window]   Fig. 6. Maps of yield classes at Site B (Cairo) formed by hierarchical and nonhierarchical clustering procedures as affected by the choice of input data: (i) hierarchical cluster analysis using Ward's methods and six classes (WAR-6), (ii) nonhierarchical cluster analysis using k means and six classes (KME-6), (iii) unsupervised nonhierarchical Iterative Self-Organizing Data Analysis (ISODATA) clustering method using six classes (ISO-6), and (iv) nonhierarchical fuzzy-k-means cluster analysis using six classes (FUZ-6). For each method, maps are shown for three different data sources [mean yield (MY), mean and standard deviation of yield (MS), and yields in all years (AY)]. Light colors show high-yielding areas; dark colors show low-yielding areas with high yield variability among years.