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Integrating Economics in the Critical Period for Weed Control Concept in Corn

Published online by Cambridge University Press:  20 January 2017

Martina Keller
Affiliation:
Department of Weed Science (360b), University of Hohenheim, 70599 Stuttgart, Germany
Geoffroy Gantoli
Affiliation:
Department of Weed Science (360b), University of Hohenheim, 70599 Stuttgart, Germany
Jens Möhring
Affiliation:
Bioinformatic Unit, Institute of Crop Science, University of Hohenheim, 70599 Stuttgart, Germany
Christoph Gutjahr
Affiliation:
Department of Weed Science (360b), University of Hohenheim, 70599 Stuttgart, Germany
Roland Gerhards
Affiliation:
Department of Weed Science (360b), University of Hohenheim, 70599 Stuttgart, Germany
Victor Rueda-Ayala*
Affiliation:
Department of Weed Science (360b), University of Hohenheim, 70599 Stuttgart, Germany
*
Corresponding author's E-mail: [email protected]
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Abstract

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The effect of weed interference on corn yield and the critical period for weed control (CPWC) were determined in Germany and Benin. Treatments with weed control starting at different crop growth stages and continuously kept weed-free until harvest represented the “weed-infested interval.” Treatments that were kept weed-free from sowing until different crop growth stages represented the “weed-free interval.” Michaelis–Menten, Gompertz, logistic and log–logistic models were employed to model the weed interference on yield. Cross-validation revealed that the log–logistic model fitted the weed-infested interval data equally well as the logistic and slightly better than the Gompertz model fitted the weed-free interval. For Benin, economic calculations considered yield revenue and cost increase due to mechanical weeding operations. Weeding once at the ten-leaf stage of corn resulted already profitable in three out of four cases. One additional weeding operation may optimize and assure profit. Economic calculations for Germany determined a CPWC starting earlier than the four-leaf stage, challenging the decade-long propagated CPWC for corn. Differences between Germany and Benin are probably due to the higher yields and high costs in Germany. This study provides a straightforward method to implement economic data in the determination of the CPWC for chemical and nonchemical weed control strategies.

Type
Weed Management
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits noncommercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited.
Copyright
Copyright © Weed Science Society of America

References

Literature Cited

Anonymous (2014a) LfL-Deckungsbeiträge und Kalkulationsdaten – Körnermais [LfL contribution margins and calculation data for corn]. https://www.stmelf.bayern.de/idb/default.html. Accessed April 2, 2014Google Scholar
Anonymous (2014b) Ernten ausgewählter landwirtschaftlicher Früchte [Yields of selected crops]. http://www.statistik.baden-wuerttemberg.de/Landwirtschaft/Landesdaten/LRt0705.asp?1988_t00. Accessed April 2, 2014Google Scholar
Anonymous (2014c) Körnermais – Preisgraphiken [Corn – price charts]. https://www.landwirtschaft-bw.info/pb/MLR.LEL,Lde/Startseite/Agrarmaerkte+und+Ernaehrung/Koernermais. Accessed April 2, 2014Google Scholar
Anonymous (2014d) Cross-compliance, http://ec.europa.eu/agriculture/envir/cross-compliance/index_en.htm. Accessed May 10, 2014Google Scholar
Anonymous (2014e) Deutscher Wetterdienst [German weather service]. http://www.dwd.de/. Accessed May 10, 2014Google Scholar
Baer, H, Dittrich, R, Ewert, K, Goessner, K, Goetz, R, Kraatz, M, Krueger, B, Kupfer, S, Meinlschmidt, E, Naujok, M, Naumann, E, Pelzer, S, Politz, B, Schroeder, G, Thate, A, Tuemmler, C, Viehweger, G, Weiske, E (2010) Hinweise zum sachkundigen Einsatz von Pflanzenschutzmitteln im Ackerbau und auf dem Grünland 2010 [Recommendations for sound use of plant protection product in fields and meadows 2010]. Landesamt für Verbraucherschutz, Landwirtschaft und Flurneuordnung, Abteilung Pflanzenschutzdienst, Frankfurt, GermanyGoogle Scholar
Cousens, R (1985) A simple model relating yield loss to weed density. Ann App Biol. 107:239252 Google Scholar
Cousens, R (1988) Misinterpretations of results in weed research through inappropriate use of statistics. Weed Res. 28(4):281289 Google Scholar
Dunan, CM, Westra, P, Schweizer, EE, Lybecker, DW, Moor, FD (1995) The concept and application of early economic period threshold: the case of DCPA in onions (Allium cepa). Weed Sci. 43:634639 Google Scholar
Duvick, DN (1997) The contribution of breeding to yield advances in corn (Zea mays L.). Adv Agron. 86:83145 Google Scholar
FAOSTAT Production, Crops. http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#. Accessed April 11, 2014Google Scholar
Gantoli, G, Rueda-Ayala, V, Gerhards, R (2013) Determination of the critical period for weed control in corn (Zea mays L.). Weed Technol. 27:6371 Google Scholar
Hall, MR, Swanton, CJ, Anderson, CJ (1992) The critical period of weed control in grain corn. Weed Sci. 40:441447 Google Scholar
Hastie, TR, Tibshirani, R, Friedman, J (2009) The Elements of Statistical Learning. New York, NY Springer. 745 pGoogle Scholar
Keller, M, Gantoli, G, Kipp, A, Gutjahr, C, Gerhards, R (2012) The effect and dynamics of weed competition on maize in Germany and Benin. Pages 289300 in Proceedings of the 25th German Conference on Weed Biology and Weed Control. Braunschweig, Germany Nordmeyer H, Ulber L, Julius Kühn-Institut Google Scholar
Knezevic, SZ, Evans, SP, Blankenship, EE, Van Acker, RC, Lindquist, JL (2002) Critical period for weed control: the concept and data analysis. Weed Sci. 50:773786 Google Scholar
Koch, W, Kemmer, A (1980) Schadwirkung von Unkräutern gegenüber Mais in Abhängigkeit von Konkurrenzdauer und Unkrautdichte [Negative effect of weeds on corn in dependence of the length of weed competition and weed density]. Med Fac Landbouww Rijksuniv Gent. 45:10991109 Google Scholar
Liu, J, Mahoney, K, Sikkema, P, Swanton, C (2009) The importance of light quality in crop–weed competition. Weed Res. 49:217224 Google Scholar
Mehrtens, J, Schulte, M, Hurle, K (2005) Unkrautflora in Mais Ergebnisse eines Monitorings in Deutschland [Weed flora results from a survey in Germany]. Gesunde Pflanz. 57:206218 Google Scholar
Oerke, EC, Dehne, HW (2004) Safeguarding production: losses in major crops and the role of crop protection. Crop Prot. 23:275285 Google Scholar
Oliver, LR (1988) Principles of weed threshold research. Weed Technol. 2:398403 Google Scholar
[ONASA] Office National D'Appui à la Sécurité Alimentaire (2003) Rapport d'Evaluation de la Production Vivrière en 2002 et les Perspectives Alimentaires pour 2003 au Bénin: Situation par Départment [Evaluation of food production in 2002 and perspectives for 2003 in Benin]. Tech Rep, Vol II, 150 pGoogle Scholar
Page, ER, Tollenaar, M, Lee, EA, Lukens, L, Swanton, CJ (2009) Does the shade avoidance response contribute to the critical period for weed control in maize (Zea mays)? Weed Res. 49:563571 Google Scholar
R Core Team (2013) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0. http://www.R-project.org/. Accessed July 28, 2014Google Scholar
Ritz, C, Streibig, JC (2008) Nonlinear Regression. Use R. Seattle, WA; Wien, Austria; Baltimore, MD Springer. 148 pGoogle Scholar
Ritz, C, Streibig, JC (2014) Package “drc. ” http://cran.r-project.org/web/packages/drc/drc.pdf. Accessed July 2, 2014Google Scholar
Seitz, T, Hoffmann, MG, Kraehmer, H (2003) Herbizide für die Landwirtschaft: Chemische Unkrautbekämpfung [Herbicides for agriculture: Chemical weed control]. Chem Unserer Zeit. 37:112126 Google Scholar
Shiferaw, B, Prasanne, B, Hellin, J, Bänziger, M (2011) Crops that feed the world 6. Past successes and future challenges to the role played by corn in global food security. Food Sec. 3:307327 Google Scholar
Swanton, CJ, Weise, SF (1991) Integrated weed management: the rationale and approach. Weed Technol. 5:657663 Google Scholar
Terry, PJ (1983) Quelques Adventices Banales des Cultures de l'Afrique Occidentale et la Lutte contre Celles-là [Some common weeds in the crops of West Africa and their control]. United States Agency for International Development Regional Food Crop Protection Project 625-0928Google Scholar
Vernon, R, Parer, JMH (1983) Maize/weed competition experiments: implications for tropical small-farm weed control research. Exp Agr. 19:341347 Google Scholar
Vissoh, P, Gbehounou, G, Ahanchede, A, Kuyper, TW, Röling, NG (2004) Weeds as agricultural constraint to farmers in Benin: results of a diagnostic study. NJAS-Wagen J Life Sci. 52:305329 Google Scholar