Predicting Weed Species Occurrence Based on Site Properties and Previous Year's Weed Presence

Predicting Weed Species Occurrence Based on Site Properties and Previous Year's Weed Presence Probabilities of Setaria spp., Solanum ptycanthum, Helianthus annuus and Abutilon theophrasti occurrence were predicted based on two site property factors and weed species presence in a previous year using logistic regression models. Weed seedling surveys were conducted just prior to post-emergence weed management in two grower-managed fields in the central Platte River Valley of Nebraska, USA at Alda in 1995 and 1996 and at Shelton in 1994, 1995 and 1997. Weed species density data were re-classified as present or absent at each pair of points on the sampling grid, representing quadrat locations either in the pre-emergence herbicide band or between the crop rows. Site property data were collected in March 1995 at Alda and March 1994 at Shelton. Using factor analysis, two independent factors were derived from correlated attributes of relative elevation, percent organic carbon, pH, nitrate, phosphate, and soil texture measured at Alda. Logistic regression models were estimated and parameterized for each weed species at Alda in 1996 based on the two factors (“topography and soil type” and “soil fertility status”) and weed species presence in 1995. Performance of these models for each weed species was evaluated using the independent data set from Shelton. Between and on crop row Setaria spp. and Solanum ptycanthum models described these populations at Alda. At Shelton, on row Setaria spp. occurrence and between row Solanum ptycanthum occurrence were adequately predicted. Helianthus annuus or Abutilon theophrasti occurrence was not well predicted even with knowledge of their presence in the previous year, probably as a result of low actual occurrence within a given year. Maps of predicted occurrence have value in directing weed scouting to field locations where the species is most likely to occur. Precision Agriculture Springer Journals

Predicting Weed Species Occurrence Based on Site Properties and Previous Year's Weed Presence

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Kluwer Academic Publishers
Copyright © 2002 by Kluwer Academic Publishers
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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