Weed vegetation of sugarcane cropping systems of northern argentina data mining methods for assessing the environmental and management effects on species composition
Weed composition may vary because of natural environment, management practices, and their interactions. In this study we presented a systematic approach for analyzing the relative importance of environmental and management factors on weed composition of the most conspicuous species in sugarcane. A d...
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| Lenguaje: | Inglés |
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| Acceso en línea: | http://ri.agro.uba.ar/files/intranet/articulo/2012Ferraro2.pdf LINK AL EDITOR |
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| 100 | 1 | |9 11063 |a Ferraro, Diego Omar | |
| 245 | 0 | 0 | |a Weed vegetation of sugarcane cropping systems of northern argentina |b data mining methods for assessing the environmental and management effects on species composition |
| 520 | |a Weed composition may vary because of natural environment, management practices, and their interactions. In this study we presented a systematic approach for analyzing the relative importance of environmental and management factors on weed composition of the most conspicuous species in sugarcane. A data-mining approach represented by k-means cluster and classification and regression trees [CART] were used for analyzing the 11 most frequent weeds recorded in sugarcane cropping systems of northern Argentina. Data of weed abundance and explanatory factors contained records from 1976 sugarcane fields over 2 consecutive years. The k-means method selected five different weed clusters. One cluster contained 44 percent of the data and exhibited the lowest overall weed abundance. The other four clusters were dominated by three perennial species, bermudagrass, johnsongrass, and purple nutsedge, and the annual itchgrass. The CART model was able to explain 44 percent of the sugarcane's weed composition variability. Four of the five clusters were represented in the terminal nodes of the final CART model. Sugarcane burning before harvesting was the first factor selected in the CART, and all nodes resulting from this split were characterized by low abundance of weeds. Regarding the predictive power of the variables, rainfall and the genotype identity were the most important predictors. These results have management implications as they indicate that the genotype identity would be a more important factor than crop age when designing sugarcane weed management. Moreover, the abiotic control of cropweed interaction would be more related to rainfall than the environmental heterogeneity related to soil type, for example soil fertility. Although all these exploratory patterns resulting from the CART data-mining procedure should be refined, it became clear that this information may be used to develop an experimental framework to study the factors driving weed assembly. Nomenclature: Bermudagrass, Cynodon dactylon Pers. [CYNDA]; johnsongrass, Sorghum halepense [L.] Pers. [SORHA]; purple nutsedge, Cyperus rotundus L. [CYPRO]; itchgrass, Rottboellia exaltata [L.] L.f.[ROOEX]. | ||
| 653 | 0 | |a CLASSIFICATION AND REGRESSION TREES | |
| 653 | 0 | |a STATISTICS | |
| 653 | 0 | |a SUGARCANE | |
| 653 | 0 | |a WEED COMPOSITION | |
| 653 | 0 | |a ABUNDANCE | |
| 653 | 0 | |a CLUSTER ANALYSIS | |
| 653 | 0 | |a CROPPING PRACTICE | |
| 653 | 0 | |a DATA MINING | |
| 653 | 0 | |a ENVIRONMENTAL FACTOR | |
| 653 | 0 | |a GRASS | |
| 653 | 0 | |a INTEGRATED APPROACH | |
| 653 | 0 | |a INTEGRATED PEST MANAGEMENT | |
| 653 | 0 | |a MANAGEMENT PRACTICE | |
| 653 | 0 | |a RAINFALL | |
| 653 | 0 | |a SOIL FERTILITY | |
| 653 | 0 | |a SOIL TYPE | |
| 653 | 0 | |a SUGAR CANE | |
| 653 | 0 | |a WEED CONTROL | |
| 653 | 0 | |a ARGENTINA | |
| 653 | 0 | |a CYNODON [ANGIOSPERM] | |
| 653 | 0 | |a CYNODON DACTYLON | |
| 653 | 0 | |a CYPERUS ROTUNDUS | |
| 653 | 0 | |a ROTTBOELLIA | |
| 653 | 0 | |a ROTTBOELLIA COCHINCHINENSIS | |
| 653 | 0 | |a SORGHUM HALEPENSE | |
| 700 | 1 | |9 7549 |a Ghersa, Claudio Marco | |
| 700 | |a Rivero, Darío Ernesto |9 13021 | ||
| 773 | |t Weed Science |g Vol.60, no.1 (2012), p.27-33 | ||
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| 900 | |a ^aFerraro^bD.O.^tIFEVA, Ctedra de Cerealicultura, Facultad de Agronoma, Universidad de Buenos Aires/CONICET, Av. San Martn 4453, Buenos Aires [1417DSE], Argentina | ||
| 900 | |a ^aGhersa^bC.M.^tIFEVA, Ctedra de Ecologa, Facultad de Agronoma, Universidad de Buenos Aires/CONICET, Argentina | ||
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| 900 | |a Vol. 60, no. 1 | ||
| 900 | |a 33 | ||
| 900 | |a CLASSIFICATION AND REGRESSION TREES | ||
| 900 | |a STATISTICS | ||
| 900 | |a SUGARCANE | ||
| 900 | |a WEED COMPOSITION | ||
| 900 | |a ABUNDANCE | ||
| 900 | |a CLUSTER ANALYSIS | ||
| 900 | |a CROPPING PRACTICE | ||
| 900 | |a DATA MINING | ||
| 900 | |a ENVIRONMENTAL FACTOR | ||
| 900 | |a GRASS | ||
| 900 | |a INTEGRATED APPROACH | ||
| 900 | |a INTEGRATED PEST MANAGEMENT | ||
| 900 | |a MANAGEMENT PRACTICE | ||
| 900 | |a RAINFALL | ||
| 900 | |a SOIL FERTILITY | ||
| 900 | |a SOIL TYPE | ||
| 900 | |a SUGAR CANE | ||
| 900 | |a WEED CONTROL | ||
| 900 | |a ARGENTINA | ||
| 900 | |a CYNODON [ANGIOSPERM] | ||
| 900 | |a CYNODON DACTYLON | ||
| 900 | |a CYPERUS ROTUNDUS | ||
| 900 | |a ROTTBOELLIA | ||
| 900 | |a ROTTBOELLIA COCHINCHINENSIS | ||
| 900 | |a SORGHUM HALEPENSE | ||
| 900 | |a Weed composition may vary because of natural environment, management practices, and their interactions. In this study we presented a systematic approach for analyzing the relative importance of environmental and management factors on weed composition of the most conspicuous species in sugarcane. A data-mining approach represented by k-means cluster and classification and regression trees [CART] were used for analyzing the 11 most frequent weeds recorded in sugarcane cropping systems of northern Argentina. Data of weed abundance and explanatory factors contained records from 1976 sugarcane fields over 2 consecutive years. The k-means method selected five different weed clusters. One cluster contained 44 percent of the data and exhibited the lowest overall weed abundance. The other four clusters were dominated by three perennial species, bermudagrass, johnsongrass, and purple nutsedge, and the annual itchgrass. The CART model was able to explain 44 percent of the sugarcane's weed composition variability. Four of the five clusters were represented in the terminal nodes of the final CART model. Sugarcane burning before harvesting was the first factor selected in the CART, and all nodes resulting from this split were characterized by low abundance of weeds. Regarding the predictive power of the variables, rainfall and the genotype identity were the most important predictors. These results have management implications as they indicate that the genotype identity would be a more important factor than crop age when designing sugarcane weed management. Moreover, the abiotic control of cropweed interaction would be more related to rainfall than the environmental heterogeneity related to soil type, for example soil fertility. Although all these exploratory patterns resulting from the CART data-mining procedure should be refined, it became clear that this information may be used to develop an experimental framework to study the factors driving weed assembly. Nomenclature: Bermudagrass, Cynodon dactylon Pers. [CYNDA]; johnsongrass, Sorghum halepense [L.] Pers. [SORHA]; purple nutsedge, Cyperus rotundus L. [CYPRO]; itchgrass, Rottboellia exaltata [L.] L.f.[ROOEX]. | ||
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