Quantifying the Effects of Herbicide Dose and Wild Mustard (Sinapis arvensis L.) Density on Wheat and Weed Biomass Production

Document Type : Research Article

Authors

• Heshmatollah Zarinjoub ¹
• Mohammad Hossain Gharineh ¹
• Javid Gherekhloo ²
• Elham Elahi-Fard ¹

¹ Ramin Agriculture and Natural Resources University of Khuzestan

² Gorgan University of Agricultural Sciences and Natural Resources

10.22067/jpp.v31i4.60501

Abstract

Introduction: The effect of weeds on crop yield has been widely studied and models predicting the relationship between weed abundance and crop yield are useful for simulating yield loss and assisting with developing management guidelines. The herbicide doses recommended by manufacturers are selected to give reliable weed control without crop damage. There is a good potential to apply lower herbicide doses within competitive cropping systems. Wild mustard (Sinapis arvensis L.) is one of the most problematic annual weed in wheat fields of Iran. Herbicides especially the sulfonylurea family are widely used for weed control in wheat. “Iodosulfuron-methyl sodium + mesosulfuron methyl + mefenpyr-diethyl” is a new sulfonylurea herbicide being developed for postemergence application on weeds in wheat fields of Iran. Environmental and economic costs now require the optimisation of herbicide effects. This study was therefore conducted to evaluate S. arvensis competitive ability with wheat under sprayed conditions.
Materials and Methods: A field experiment was carried out at Research Station of Ramin Agriculture and Natural Resources University of Khuzestan in 2015-16. The experiment consisted of four replicates of a split-plot factorial design, with two levels of wheat cultivar (Chamran and Verinac) as the main plot treatments. Other two factors including doses of herbicide (“Iodosulfuron-methyl sodium + mesosulfuron methyl + mefenpyr-diethyl”) in five levels of 0.2, 0.4, 0.6, 0.8 and 1 of recommended doses and densities of S. arvensis in four levels of 0, 12, 24 and 36 Plants m-2 were implemented in subplots. The competitive effect of the different densities of S. arvensis decreased by increasing doses of “Iodosulfuron-methyl sodium + mesosulfuron methyl + mefenpyr-diethyl”. Further, same interaction was observed in the standard dose–response curve. The combination of the rectangular hyperbolic model and the standard dose–response curve adequately described the complex effects of herbicide dose and weed competition on wheat biomass. Parametric estimates were used with the model to predict wheat biomass and estimate the doses of “Iodosulfuron-methyl sodium + mesosulfuron methyl + mefenpyr-diethyl” required to restrict wheat yield loss caused by S. arvensis to an acceptable level.
Results and Discussion: The results showed that weed competitivity (β) at no-herbicide treatment was smaller in cv. Chamran than in cv. Verinac, indicating that Chamran was more competitive than Verinac. Herbicide performance, as a result of crop competitivity, was also greater in Chamran with smaller LD50 than Verinac. Using the estimated parameters and the combined model, weed biomass was predicted separately in cvs Chamran and Verinac. The predictions showed that S. arvensis grows better in cv. Verinac than in cv. Chamran. For instance, the model predicted that S. arvensis biomass at 36 S. arvensis plants m-2 with no herbicide treatment was equal to 189.19 g m-2 in cv. Verinac and 171.76 g m-2 in cv. Chamran at selected assessment date, whereas at the same weed density but using half of the recommended dose of “Iodosulfuron-methyl sodium + mesosulfuron methyl + mefenpyr-diethyl” herbicide, weed biomass was predicted to be approximately 82.74 g m-2 in cv. Verinac and 39.91 g m-2 in cv. Chamran. Using the final model and estimated parameters, crop biomass was predicted. Biomass production of wheat with the utilization only half of the recommended dose of herbicide at the highest assessed density of S. arvensis were predicted to be 569.31 g m-2 in cv. Verinac and 720.49 g m-2 in cv. Chamran. . It is speculated that improved crop competitivity may help to minimize herbicide use. Many studies have found that improvement in crop competitivity was achieved by selecting competitive cultivars. A main aim of the modelling approach to crop:weed competition is to predict crop yield production. Incorporating other factors, i.e. herbicide dose, considerably complicates the prediction process. However, the model presented here provides a valuable tool for predicting the effect of these factors.
Conclusion: Results indicates that the standard dose-response model can be modified to a combined model by replacing parameter W0 (the weed biomass at no-herbicide treatment) with the rectangular hyperbolic model. The theoretical outputs of the combined model appear robust and indicate that there are opportunities for reduced herbicide use in the field. Increased crop competitivity by selecting competitive cultivars or increasing crop density may achieve better herbicide performance for crop yield.

Keywords

• Modelling
• Weed competition

20.1001.1.20084749.1396.31.4.9.2

---

References

1- Brain P., Wilson B.J., Wright K.J., Seavers G.P., and Caseley J.C. 1999. Modelling the effect of crop and weed on herbicide efficacy in wheat. Weed Research, 39: 21-35.

2- Christensen S. 1994. Crop: weed competition and herbicide performance in cereal species and varieties. Weed Research, 34: 29–36.

3- Cousens R. 1985. A simple model relating yield loss to weed density. Annals of Applied Biology, 107: 239-252.

4- Derakhshan A., and Gherekhloo J. 2012. Investigating cross-resistance of resistant-Phalaris minor to ACCase herbicides. Weed Research Journal, 4: 15-25.

5- Derakhshan A., Gherekhloo J., and Bagherani N. 2015. Effect of row spacing and herbicide application on the growth indices, yield and yield components of rice in direct seeding. Electronic Journal of Plant Production, 8: 31-49.

6- Derakhshan A., Najari Kalantari N., Gherekhloo J., and Kamkar B. 2015. Resistance of Wild mustard (Sinapis arvensis) and Turnipweed (Rapistrum rugosum) to Tribenuron-methyl Herbicide in Aq Qala. Journal of plant protection, 2: 199-205.

7- Heap I.M. 2016. International Survey of Herbicide Resistant Weeds. Available at: http://www.weedscience.org/summary/MOASummary.asp.

8- Kim D.S., Brain P., Marshall E.J.P., and Caseley J.C. 2002. Modelling herbicide dose and weed density effects on crop: weed competition. Weed Research, 42: 1-13.

9- Kim D.S., Marshall E.J.P., Caseley J.C., and Brain P. 2006. Modelling interactions between herbicide dose and multiple weed species interference in crop–weed competition. Weed Research, 46: 175–184.

10- Lemerle D., Verbeek B., Cousens R.D., and Coombes N.E. 1996. The potential for selecting wheat varieties strongly competitive against weeds. Weed Research, 36: 505-513.

11- Moon B.C., Kim J.W., Cho S.H., Park J.E., Song J.S., and Kim D.S. 2014. Modelling the effects of herbicide dose and weed density on rice-weed competition. Weed Research, 54: 484–491.

12- Richards M.C., and Whytock G.P. 1993. Varietal competitiveness with weeds. Aspects of Applied Biology 34, Physiology of varieties, 345-354.

13- Siadat S.A., Modhej A., and Esfahani M. Cereals. Jihad-e- Daneshgahi Press. Mashhad.

14- Stoate C., Boatman N.D., Borralho R.J., arvalho C.R., de Snoo G.R., and Eden P. 2001. Ecological impacts of arable intensification in Europe. Journal of Environmental Management, 63: 337–365.

15- Streibig J.C. 1980. Models for curve fitting herbicide dose response data. Acta Agriculturae Scandinavica, 30: 59-64.

16- Waggoner J.K., Henneberger P.K., Kullman G.J., Umbach D.M., Kamel F., Beane Freeman L.E., Alavanja M.C., Sandler D.P., and Hoppin J.A. 2012. International Archives of Occupational and Environmental Health, http://dx.doi.org/10.1007/s00420-012-0752-x

17- Wilson B.J., Wright K.J., Brain P., Clements M., and Stephens E. 1995. Predicting the competitive effects of weed and crop density on weed biomass, weed seed production and crop yield in wheat. Weed Research, 35: 265-278.

18- Yousefi A.R., Gonzalez-Andujar J.L., Alizadeh H., Baghestani M.A., RAhimian Mashhadi H., and Karimmojeni H. 2012. Interactions between reduced rate of imazethapyr and multiple weed species–soyabean interference in a semi-arid environment. Weed Research, 52: 242–251.

19- Zand E., Baghestani M.A., Bitarafan M., and Shimi P. 2007. A guideline for herbicide in Iran. Jihad-e- Daneshgahi Press. Mashhad.