Skip to main content Accessibility help
×
Home

Weed Vegetation of Sugarcane Cropping Systems of Northern Argentina: Data-Mining Methods for Assessing the Environmental and Management Effects on Species Composition

  • D. O. Ferraro (a1), C. M. Ghersa (a2) and D. E. Rivero (a2)

Abstract

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% 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% 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 crop–weed 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.

Copyright

Corresponding author

Corresponding author's E-mail: ferraro@agro.edu.ar

References

Hide All
Ali, A. D., Reagan, T. E., Kitchen, L. M., and Flynn, J. L. 1986. Effects of johnsongrass (Sorghum halepense) density on sugarcane (Saccharum officinarum) yield. Weed Sci. 34:381383.
Bariuan, J. V., Reddy, K. N., and Wills, G. D. 1999. Glyphosate injury, rainfastness, absorption, and translocation in purple nutsedge (Cyperus rotundus). Weed Technol. 13:112119.
Basanta, M. V., Dourado-Neto, D., Reichardt, K., et al. 2003. Management effects on nitrogen recovery in a sugarcane crop grown in Brazil. Geoderma. 116:235248.
Booth, B. D. and Swanton, C. J. 2002. Assembly theory applied to weed communities. Weed Sci. 50:213.
Braunbeck, O., Bauen, A., Rosillo-Calle, F., and Cortez, L. 1999. Prospects for green cane harvesting and cane residue use in Brazil. Biomass Bioenergy. 17:495506.
Breiman, L., Friedman, R., Olshen, R., and Stone, C. 1984. Classification and Regression Trees. Boca Raton, FL CRC Press. 368 p.
Christoffoleti, P. J., de Carvalho, S.J.P., López-Ovejero, R. F., Nicolai, M., Hidalgo, E., and da Silva, J. E. 2007. Conservation of natural resources in Brazilian agriculture: implications on weed biology and management. Crop Prot. 26:383389.
Debeljak, M., Squire, G. R., Demsar, D., Young, M. W., and Dzeroski, S. 2008. Relations between the oilseed rape volunteer seedbank, and soil factors, weed functional groups and geographical location in the UK. Ecol. Model. 212:138146.
Ellis, R. N., Basford, K. E., Cooper, M., Leslie, J. K., and Byth, D. E. 2001. A methodology for analysis of sugarcane productivity trends. I. Analysis across districts. Aust. J. Agric. Res. 52:10011009.
Evenson, C. I., Muchow, R. C., El-Swaify, S. A., and Osgood, R. V. 1987. Yield accumulation in irrigated sugarcane. I. Effect of crop age and cultivar. Agron. J. 89:638646.
Ferraro, D. O., Rivero, D. E., and Ghersa, C. M. 2009. An analysis of the factors that influence sugarcane yield in Northern Argentina using classification and regression trees. Field Crop. Res. 112:149157.
Firehun, Y. and Tamado, T. 2006. Weed flora in the Rift Valley sugarcane plantations of Ethiopia as influenced by soil types and agronomic practises. Weed Biol. Manag. 6:139150.
Galdos, M., Cerri, C., Cerri, C., Paustian, K., and Van Antwerpen, R. 2010. Simulation of sugarcane residue decomposition and aboveground growth. Plant Soil. 326:243259.
Garside, A. L., Smith, M. A., Chapman, L. S., Hurney, A. P., and Magarey, R. C. 1997. The yield plateau in the Australian sugar industry: 1970–1990. Pages 103124 in Keating, B. A. and Wilson, J. R., eds. Intensive Sugarcane Production: Meeting the Challenges Beyond 2000. Wallingford, UK CAB International.
Garzón, M. B., Blazek, R., Neteler, M., Dios, R. S. d., Ollero, H. S., and Furlanello, C. 2006. Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian Peninsula. Ecol. Model. 197:383393.
Gonzalez-Andujar, J. L., Fernandez-Quintanilla, C., Izquierdo, J., and Urbano, J. M. 2006. SIMCE: an expert system for seedling weed identification in cereals. Comp. Electron. Agr. 54:115123.
Holm, L. G., Plucknett, D. L., Pancho, J. V., and Herberger, J. P. 1977. The World's Worst Weeds: Distribution and Biology. Honolulu, HI University Press of Hawaii. 609 p.
Jain, A. K. 2010. Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31:651666.
Jain, A. K. and Dubes, R. C. 1988. Algorithms for Clustering Data. New Jersey Prentice-Hall. 320 p.
Kang, M. S., Miller, J. D., Tai, P.Y.P., Dean, J. L., and Glaz, B. 1987. Implications of confounding of genotype × year and genotype × crop effects in sugarcane. Field Crop. Res. 15:349355.
Kenkel, N. C., Derksen, D. A., Thomas, A. G., and Watson, P. R. 2002. Multivariate analysis in weed science research. Weed Sci. 50:281292.
Koziol, J. A. G. 1990. Cluster analysis of antigenic profiles of tumours: selection of number of clusters using Akaike's information criterion. Method. Inform. Med. 29:200204.
Kuva, M., Christoffoleti, P., and Pitelli, P. 1999. Critical period of competition between sugarcane and weeds in Brazil. Weed Sci.Soc. Am. Abstr. 25 p.
Kuva, M. A., Pitelli, R. A., Salgado, T. P., and Alaves, P. L. C. A. 2007. Fitossociologia de comunidades de plantas daninhas em agroecossistema cana-crua. Planta Daninha. 25:501511.
Lawes, R. A., Lawn, R. J., Wegener, M. K., and Basford, K. E. 2004. The evaluation of the spatial and temporal stability of sugarcane farm performance based on yield and commercial cane sugar. Aust. J. Agric. Res. 55:335344.
Liu, D. L., Kingston, G., and Bull, T. A. 1998. A new technique for determining the thermal parameters of phenological development in sugarcane, including suboptimum and supra-optimum temperature regimes. Agr. Forest Meteorol. 90:119139.
Magarey, R. C., Yip, H. Y., Bull, J. I., and Johnson, E. J. 1997. Effect of the fungicide mancozeb on fungi associated with sugarcane yield decline in Queensland. Mycol. Res. 101:858862.
Magurran, A. E. 1988. Ecological Diversity and its Measurement. London Croom Helm. 179 p.
Martínez-Ghersa, M. A., Ghersa, C. M., and Satorre, E. H. 2000. Coevolution of agricultural systems and their weed companions: implications for research. Field Crop. Res. 67:181190.
McCune, B. and Mefford, M. J. 1995. PC-ORD: multivariate analysis of ecological data. Gleneden Beach, OR MjM Software Design.
McMahon, G. 1989. Weeds reduce cane yield in early growth stages. Brisbane, Australia Bureau of Sugar Experiment Station. Sugar Exp. Stn. Bull., 27 (July). Pages 2132
Muchow, R. C., Robertson, M. J., and Wood, A. W. 1996. Growth of sugarcane under high input conditions in tropical Australia. II. Sucrose accumulation and commercial yield. Field Crop. Res. 48:2736.
Mueller-Dombois, D. and Ellenberg, H. 1974. Causal analytical inquiries into the origin of plant communities. Pages 335370 in Aims and Methods of Vegetation Ecology. New York Wiley.
Pankhurst, C. E., Magarey, R. C., Stirling, G. R., Blair, B. L., Bell, M. J., and Garside, A. L. 2003. Management practices to improve soil health and reduce the effects of detrimental soil biota associated with yield decline of sugarcane in Queensland, Australia. Soil Till. Res. 72:125.
Pankhurst, C. E., Stirling, G. R., Magarey, R. C., Blair, B. L., Holt, J. A., Bell, M. J., and Garside, A. L. 2005. Quantification of the effects of rotation breaks on soil biological properties and their impact on yield decline in sugarcane. Soil Biol. BioChem. 37:11211130.
Peltzer, D. A., Ferriss, S., and FitzJohn, R. G. 2008. Predicting weed distribution at the landscape scale: using naturalized Brassica as a model system. J. Appl. Ecol. 45:467475.
Peng, S. Y. 1984. The Biology and Control of Weeds in Sugarcane. New York Elsevier Science. 336 p.
Richard, E. P. Jr. 1995. Bermudagrass interference during a three year sugarcane crop cycle. Proc. Int. Soc. Sugar Cane Technol. 21:3139.
Roel, A., Firpo, H., and Plant, R. E. 2007. Why do some farmers get higher yields? Multivariate analysis of a group of Uruguayan rice farmers. Comp. Electron. Agric. 58:7892.
Russell, J. S., Wegener, M. K., and Valentine, T. R. 1991. Effect of weather variables on C.C.S. at Tully simulated by the AUSCANE model. Proc. Aust. Soc. Sugar Cane Technol. 13:157163.
Sampietro, D. A., Vattuone, M. A., and Isla, M. I. 2006. Plant growth inhibitors isolated from sugarcane (Saccharum officinarum) straw. J. Plant Physiol. 163:837846.
Shaw, P. J. 2003. Multivariate Statistics for the Environmental Sciences. New York. 233 p.
Smith, D. M., Inman-Bamber, N. G., and Thorburn, P. J. 2005. Growth and function of the sugarcane root system. Field Crop. Res. 92:169183.
Smith, D. T. 1998. Weed Control in US Sugarcane. Technical Report 98-03. Texas: USDA Department of Soil and Crop Science, CollegeStation, TX: TexaS A&M University. Rep. 98-03.25 p.
Steinberg, D. and Colla, P. 1995. CART: Tree-Structured Non-Parametric Data Analysis. San Diego, CA Salford Systems. 336 p.
Ter Braak, C.J.F. and Prentice, C. 1988. A theory of gradient analysis. Adv. Ecol. Res. 18:271317.
[USDA] U.S. Department of Agriculture. 1989. The Second RCA Appraisal: Soil, Water and Related Resources on Nonfederal Land in the United States. U.S. Department of Agriculture, Soil Conservation Service. 280 p.
Vallis, I., Parton, W. J., Keating, B. A., and Wood, A. W. 1996. Simulation of the effects of trash and N fertilizer management on soil organic matter levels and yields of sugarcane. Soil Till. Res. 38:115132.
Waheed, T., Bonnell, R. B., Prasher, S. O., and Paulet, E. 2006. Measuring performance in precision agriculture: CART—a decision tree approach. Agric. Water Manag. 84:173185.
Wiles, L. and Brodahl, M. 2004. Exploratory data analysis to identify factors influencing spatial distributions of weed seed banks. Weed Sci. 52:936947.
Wood, W. 1991. Management of crop residues following green harvesting of sugarcane in North Queensland. Soil Till. Res. 20:6985.

Keywords

Related content

Powered by UNSILO

Weed Vegetation of Sugarcane Cropping Systems of Northern Argentina: Data-Mining Methods for Assessing the Environmental and Management Effects on Species Composition

  • D. O. Ferraro (a1), C. M. Ghersa (a2) and D. E. Rivero (a2)

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.