Field sprayer for inter and intra-row weed control: performance and labor savings

J. Carballido, A. Rodríguez-Lizana, J. Agüera, M. Pérez-Ruiz

Abstract


Studies of new tools and methods for weed control have been motivated by increased consumer demand for organic produce, consumer and regulatory demands for a reduction in environmentally harmful herbicide use, and the decreased availability of farm workers willing to perform manual tasks, such as hand weeding. This study describes the performance of a new sprayer system for commercial production that integrates two herbicide applications in a single pass, selective herbicide (SH) application in narrow bands over the crop row, and a non-selective herbicide (NSH) application between crop rows. A real-time kinematic (RTK) global positioning system (GPS) was used for auto-guidance in seeding and spraying operations. Conventional broadcast SHs and experimental treatments were applied at a constant nominal speed of 5.5 km h-1 for comparison. Trials in commercial sugar beet fields demonstrated the following: (i) average hand-weeding time can be reduced by 53% (ii) the new sprayer system reduced SH use by 76%, and (iii) sugar beet density did not change significantly during treatment. These results demonstrate the feasibility of using the new RTK-GPS controller sprayer system for differential and efficient herbicide application in inter- and intra-row zones in row crop production.

Keywords


hooded sprayer; precision farming; herbicide application; site-specific management

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References


Abidine AZ, Heidman BC, Upadhyaya SK, Hills D J, 2002. Application of RTK GPS based auto-guidance system in agricultural production. ASAE Paper No. O21152. ASAE, St. Joseph, MI, USA.

Abidine AZ, Heidman BC, Upadhyaya SK, Hills DJ, 2004. Autoguidance system operated at high speed causes almost no tomato damage. Calif Agric 58(1): 44-47. http://dx.doi.org/10.3733/ca.v058n01p44

Ascard J, 1998. Comparison of flaming and infrared radiation techniques for thermal weed control. Weed Res 38: 69-76. http://dx.doi.org/10.1046/j.1365-3180.1998.00073.x

Ascard J, Fogelberg F, Hansson D, Svensson SE, 2011. Weed control in vegetable-Report from a round table discussion. 9th EWRS Workshop on Physical and Cultural Weed Control. Samsun, Turkey, 28-30 March 2011.

Bainer R, Kepner RA, Barger EL, 1963. Principles of farm machinery. Wiley, NY.

Bermejo JL, Martínez JJ, Morillo-Velarde R, 2008. Memoria técnica. Plan de competitividad 2008 (Technical Report-Competitiveness Plan 2008). Research Association for the Improvement of Sugar Beet Crop of Spain (AIMCRA). Valladolid, Spain.

Dedousis AP, Godwin R J, O'Dogherty M J, Tillett ND, Grundy AC, 2007. Inter and intra-row mechanical weed control with rotating discs. Proc. 6th Eur Conf in Precision Agriculture, Skiathos, Greece. pp: 493-498.

Desplanque B, Boudry P, Broomberg K, Saumitou-Laprade P, Guguen J, Van Dijk H, 1999. Genetic diversity and gene flow between wild, cultivated and weedy forms of Beta vulgaris L. (Chenopodiaceae), assessed by RFLP and microsatellite markers. Theor Appl Genet 98: 1194-1201. http://dx.doi.org/10.1007/s001220051184

Forcella F, 2012. Air-propelled abrasive grit for postemergence in-row weed control in field corn. Weed Technol 26: 161-164. http://dx.doi.org/10.1614/WT-D-11-00051.1

Goldberg KM, Iglewicz B, 1992. Bivariate extensions of the boxplot. Technometrics 34: 307-320. http://dx.doi.org/10.2307/1270037

Gopinath KA, Kumar N, Mina BL, Srivastva AK, Gupta HS, 2009. Evaluation of mulching, stale seedbed, hand weeding and hoeing for weed control in organic garden pea (Pisum sativum subsp. Hortens L.). Arch Agron Soil Sci 55(1): 115-123. http://dx.doi.org/10.1080/03650340802287026

Griepentrog H W, Nørremark M, Nielsen H, Blackmore BS, 2003. Individual plant care in cropping systems. Proc 4th Eur Conf on Precision Agriculture ECPA. Berlin, Wageningen Acad. Press, Wageningen, NL, pp: 247-251.

Griepentrog HW, Norremark M, Nielsen J, Soriano-Ibarra J, 2006. Autonomous inter-row hoeing using GPS based side-shift control. Proc. Automation Technology for Off-Road, Bonn, Germany, 1–2 September; pp. 117–124.

Griepentrog HW, Norremark M, Nielsen J, Soriano Ibarra J, 2007. Autonomous inter-row hoeing using GPS based side-shift control. Proc. Automation Technology for Off-Road Equipment (ATOE). Bonn, Germany. September 1-2, pp: 117-124.

Harunur M, Murshedul M, Rao AN, Ladha JK, 2012. Comparative efficacy of pretilachlor and hand weeding in managing weeds and improving the productivity and net income of wet-seeded rice in Bangladesh. Field Crop Res 128: 17-26. http://dx.doi.org/10.1016/j.fcr.2011.11.024

Kaya R, Buzluk S, 2006. Integrated weed control in sugar beet through combinations of tractor hoeing and reduced dosages of a herbicide mixture. Turkish J Agric Forest 30: 137-144.

Leandro RF, Santos MC, Langley RB, 2011. Analyzing GNSS data in precise point positioning software. GPS Solutions 30(1): 1-13. http://dx.doi.org/10.1007/s10291-010-0173-9

Leer S, Lowenberg-DeBoer J, 2004. Purdue study drives home benefits of GPS auto guidance. Available in http://news.uns.purdue.edu/UNS/html4ever/2004/040413.Lowenberg.gps.html [May 16, 2011].

Melander B, 1997. Optimization of the adjustment of a vertical axis rotary brush weeder for intra-row weed control in row crops. J Agric Eng Res 68: 39–50. http://dx.doi.org/10.1006/jaer.1997.0178

Melander B, Rasmussen IA, Barberi P, 2005. Integrating physical and cultural methods of weed control: examples from European research. Weed Sci 53: 369-381. http://dx.doi.org/10.1614/WS-04-136R

Mee RW, 1990. Confidence intervals for probabilities and tolerance regions based on a generalization of the Mann-Whitney statistic. J Am Stat Assoc 85: 793-800. http://dx.doi.org/10.1080/01621459.1990.10474942

Misra P, Enge P, 2006. Global positioning system: signals, measurements, and performance, 2nd ed. Gamba-Jamuna Press, Lincoln, MA, USA.

Miyama S, 1999. Competition between tomato and barnyardgrass in relation to nitrogen fertilizer source. M.S. Thesis. Vegetable Crops Department. University of California, Davis, CA, USA.

Morales-Payan JP, Santos BM, Bewick TA, 1996. Purple nutsedge (Cyperus rotundus L.) interference on lettuce under different nitrogen levels. Proc South Weed Sci Soc 49: 201.

Morillo-Velarde R, 2012. Recommendations for sugar beet production. Revista de la Asociación de Investigación para la Mejora del Cultivo de la Remolacha Azucarera (AIMCRA) 112: 26-31.

Perez-Ruiz M, Slaughter DC, Gliever CJ, Upadhyaya SK, 2012. Automatic GPS-based intra-row weed knife control system for transplanted row crops. Comput Electron Agr 80: 41-49. http://dx.doi.org/10.1016/j.compag.2011.10.006

R Development Core Team, 2011. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available in http://www.R-project.org/ [June 10, 2013].

Rousseeuw PJ, 1984. Least median of squares regression. J Am Stat Assoc 79: 871-880. http://dx.doi.org/10.1080/01621459.1984.10477105

Rueda-Ayala V, Rasmussen J, Gerhards R, 2010. Mechanical weed control. In: Precision crop protection-the challenge and use of heterogeneity (Oerke EC, Gerhards R, Menz G, & Sikora RA, eds.). Springer, Dordrecht, Netherlands. pp: 279-294. http://dx.doi.org/10.1007/978-90-481-9277-9_17

Schroers JO, Gergards R, Kunisch M, 2010. Economic evaluation of precision crop protection measures. In: Precision crop protection-the challenge and use of heterogeneity (Oerke EC, Gerhards R, Menz G, & Sikora RA, eds.). Springer, Dordrecht, Netherlands. pp: 417-426. http://dx.doi.org/10.1007/978-90-481-9277-9_26

Scott RK, Wilcockson SJ, 1976. Weed biology and the growth of sugar beet. Ann Appl Biol 83(2): 331-335. http://dx.doi.org/10.1111/j.1744-7348.1976.tb00619.x

Shrestha A, Browne GT, Lampinen BD, Schneider SM, Simon L, Trout TJ, 2008. Perennial crop nurseries treated with methyl bromide and alternative fumigants: effects on weed seed viability, weed densities, and time required for hand weeding. Weed Technol 22: 267–274. http://dx.doi.org/10.1614/WT-07-122.1

Slaughter DC, Giles DK, Fennimore SA, Smith RF, 2008. Multispectral machine vision identification of lettuce and weed seedlings for automated weed control. Weed Technol 22: 378-384. http://dx.doi.org/10.1614/WT-07-104.1

Slaughter DC, Perez-Ruiz M, Fathallah F, Upadhayaya S, Gliever CJ, Miller B, 2012. GPS-based intra-row weed control system: performance and labor savings. Proc. Int Conf of Agricultural Engineering CIGR-AgEng 2012. Valencia, Spain. July 8-12.

Tillett ND, Hague T, Miles SJ, 2002. Inter-row vision guidance for mechanical weed control in sugar beet. Comput Electron Agr 33: 163–177. http://dx.doi.org/10.1016/S0168-1699(02)00005-4

Tillett ND, Hague T, Grundy AC, Dedousis AP, 2008. Mechanical within-row weed control for transplanted crops using computer vision. Biosyst Eng 99: 171-178. http://dx.doi.org/10.1016/j.biosystemseng.2007.09.026

Viard F, Bernard J, Desplanque B, 2002. Crop-weed interactions in the Beta vulgaris complex at a local scale: allelic diversity and gene flow within sugar beet fields. Theor Appl Genet 104: 688-697. http://dx.doi.org/10.1007/s001220100737 PMid:12582675

Vidotto F, Letey M, De Palo F, Mancuso F, 2011. Cost comparison between soil steaming and conventional methods for weed control. 9th EWRS Workshop on Physical and Cultural Weed Control. Samsun, Turkey, 28-30 March 2011, pp: 83-84.

Wellmann A, 1999. Konkurrenzbeziehungen und Schadensprognose in Zuckerrüben bei variiertem zeitlichen Auftreten von Chenopodium album L. und Chamomilla recutita (L.) [Competition and yield prediction in sugar beet by occurrence of Chenopodium album L. and Chamomilla recutita (L.)]. PhD Thesis, University Göttingen, Germany.

William RD, Warren GF, 1975. Competition between purple nutsedge and vegetables. Weed Sci 23: 317-323.




DOI: 10.5424/sjar/2013113-3812