Fractal Analysis of Temporal Yield Variability of Crop Sequences: Implications for Site-Specific Management
- Bahman Eghball and
- Gary E. Varvel
Characterizing spatial and temporal variability is important in site-specific or long-term studies to evaluate the effects of different management systems on crop performance. Long-term experiments offerunique possibilities to study the effects of management practices on crops and soils over time. The objective of this study was to characterize temporal grain yield variability of seven crop sequences using fractal analysis and to determine whether temporal or spatial variability dominated the grain yield variability. Three crops of corn (Zea mays L.), soybean [Glycine max (L.) Merr.], and sorghum [Sorghum bicolor (L.) Moench] were studied from 1975 to 1995 in various sequences. Semivariograms were estimated for the standardized crop yield. The slopes of the regression lines of log semivariogram vs. log lag (year) were used to estimate and compare fractal dimensions, which are indications of variability patterns. The intercepts of the log-log lines, which indicate extent of yield variability, were also compared between crop sequences. A small D-value indicates dominance of long-term variation, while a large D-value (near 2) indicates dominance of short-term (year-to-year) variation. Corn had significantly less temporal yield variability than soybean or sorghum. Continuous corn had less yield variability than corn following soybean. Soybean had the greatest yield variability, regardless of crop sequence. Temporal variability was much more dominant than spatial variability in this study. Temporal variability may greatly influence how spatial variability is expressed in a given field. Yield maps, which are used as an indication of past management in site-specific cases, may not be useful in making future management decisions when temporal variability is great. In a less productive year, spatial variability of any nutrient may not make much difference in crop yield of a given field.
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