Comparison of Different Sampling Scales to Estimate Weed Populations in three Soybean Fields

Comparison of Different Sampling Scales to Estimate Weed Populations in three Soybean Fields A study was conducted to evaluate the accuracy of different weed sampling scales to accurately describe populations in soybean. Three soybean fields were sampled at 8 and 6 weeks after planting in 1998 and 1999, respectively. All weed species were counted on a 10 m grid, using a 0.58-m2 quadrat. Data were eliminated from the original 10 m grid sample of weeds for each field to develop 40 m, 60 m, and 80 m independent data sets. Distribution and population maps were interpolated using an inverse distance weighted method. Data were extracted from the interpolated maps at known coordinates so that the observed population and the predicted population could be compared. The 10 m grid served as a standard to which all others were compared. No differences in population accuracies between each scale were detected when results were compared on a per weed basis, except when weed populations were very high, generally exceeding 400 plants ha−1. When the weed density was not at an extreme, the results from these data indicate the ability to describe or account for the weed population fairly accurately, when using coarser grid sizes. These results also suggest that when using a regular grid coordinate system as the sampling structure, an increase from a 10 m scale to an 80 m scale will not cause a significant loss of information when weed populations were not extreme and will provide the necessary information for making suitable weed treatment decisions. However, some small weed patches were not detected with the coarser sampling scales, and the larger sampling scales would not meet their needs if the producer’s objective is complete control of a species. Precision Agriculture Springer Journals

Comparison of Different Sampling Scales to Estimate Weed Populations in three Soybean Fields

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Kluwer Academic Publishers
Copyright © 2005 by Springer Science+Business Media, Inc.
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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