Regional mapping of perennial weeds in cotton with the use of geostatistics

dc.contributor.author	Kalivas, DP	en
dc.contributor.author	Vlachos, CE	en
dc.contributor.author	Economou, G	en
dc.contributor.author	Dimou, P	en
dc.date.accessioned	2014-06-06T06:52:01Z
dc.date.available	2014-06-06T06:52:01Z
dc.date.issued	2012	en
dc.identifier.issn	00431745	en
dc.identifier.uri	http://dx.doi.org/10.1614/WS-D-11-00121.1	en
dc.identifier.uri	http://62.217.125.90/xmlui/handle/123456789/5816
dc.subject	abiotic factors	en
dc.subject	abundance index	en
dc.subject	co-kriging	en
dc.subject	Spatial outliers	en
dc.subject.other	abundance	en
dc.subject.other	autocorrelation	en
dc.subject.other	cotton	en
dc.subject.other	decision making	en
dc.subject.other	environmental factor	en
dc.subject.other	geostatistics	en
dc.subject.other	grass	en
dc.subject.other	growing season	en
dc.subject.other	interpolation	en
dc.subject.other	kriging	en
dc.subject.other	outlier	en
dc.subject.other	perennial plant	en
dc.subject.other	sedge	en
dc.subject.other	soil property	en
dc.subject.other	spatial distribution	en
dc.subject.other	weed	en
dc.subject.other	weed control	en
dc.subject.other	yield response	en
dc.subject.other	Greece	en
dc.subject.other	Convolvulus	en
dc.subject.other	Cynodon (angiosperm)	en
dc.subject.other	Cyperus rotundus	en
dc.subject.other	Gossypium hirsutum	en
dc.subject.other	Sorghum halepense	en
dc.title	Regional mapping of perennial weeds in cotton with the use of geostatistics	en
heal.type	journalArticle	en
heal.identifier.primary	10.1614/WS-D-11-00121.1	en
heal.publicationDate	2012	en
heal.abstract	Perennial weeds constitute a serious problem in Greek cotton-growing areas, as they strongly competing against the crop and downgrade the final product. Monitoring weeds at a regional scale and relating their occurrence with abiotic factors will assist in the control of these species. Purple nutsedge, field bindweed, bermudagrass, and johnsongrass were studied in cotton crops for three consecutive growing seasons (2007 through 2009) in a large area of central Greece. Weed densities and uniformities per sampling site were assessed in relation to soil and climatic data. Abundance index (AI), which is highly dependent on abiotic factors, was also estimated, and revealed purple nutsedge to the most persistent and damaging species among the recorded weeds. Field bindweed showed the highest correlation with soil properties and especially with clay content. Furthermore, correlation analysis was used over the sampling years in order to assess the stability of weed occurrence in the sampling sites. Purple nutsedge, field bindweed, and bermudagrass proved to be stable in location and intensity. The weed density spatial distribution was evaluated by using local indicators of spatial autocorrelation (LISA) statistics, and was mapped by ordinary kriging and co-kriging interpolation methods. Only 1 to 3 spatial outliers were identified in each 1 of the 3 yr. Between the two interpolation methods co-kriging delivered better results for field bindweed and purple nutsedge, indicating that soil data could improve the estimation of weed occurrence. These co-kriging interpolated weed maps would be a very useful tool for decision makers in taking appropriate weed control measures. © 2012 Weed Science Society of America.	en
heal.journalName	Weed Science	en
dc.identifier.issue	2	en
dc.identifier.volume	60	en
dc.identifier.doi	10.1614/WS-D-11-00121.1	en
dc.identifier.spage	233	en
dc.identifier.epage	243	en