Predicting Emergence of the Most Important Weed Species in Soybean (Glycine max L.) under Different Management Operation

پژوهش های حفاظت گیاهان ایران

Article 9, Volume 31, Issue 2 - Serial Number 36, June 2017, Pages 322-336   

Document Type: Research Article

DOI: 10.22067/jpp.v0i0.59992

Authors

R. Khakzad¹; M.T. Al-e-Ebrahim^* ; A. Tobeh²; M. Oviesi³; R. Valiolahpor⁴

¹Department of Agronomy and Plant Breeding, Faculty of Agriculture, University of Mohaghegh Ardabili, Iran

²Mohaghegh Ardabili

³Assistant Professor, Department of Plant Pests and Diseases, Agriculture and Natural Resources research center of Mazandaran, Iran

⁴sari university

Abstract

Introduction: Summer annual weeds typically germinate in spring and early summer, grow throughout the summer, and set seeds by fall. Summer annual weeds are a persistent problem in summer annual row crops, competing directly for water, light, and nutrients, causing yield losses in quantity and quality.
Although agriculture is increasingly relying on modern technology, knowledge of the biological systems in which these technologies are used is still critical for implementation of management strategies. Biological information about weeds is valuable and necessary for developing management strategies to minimize their impact. Scouting fields for pest problems are essential in any cropping system and knowledge of the timing and sequence of weed species emergence could increase the effectiveness of weed scouting trips and subsequent management practices.
The success of any annual plant is directly correlated to its time of seedling emergence because it determines the ability of a plant to compete with its neighbors, survive biotic and abiotic stresses, and reproduce. The period and pattern of emergence of the weed community depend on the species present in the seed bank and their interaction with the environment. Therefore, knowledge of the weed species present in the soil seed bank and when these species are most likely to emerge is important in planning effective weed control programs.
Temperature has been reported to be the main environmental factor regulating germination and emergence of weed species. Scientists have developed TT models to predict the emergence of weed species based on a daily accumulation of heat units or growing degree days (GDD) above a minimum base threshold value (Tbase). The predictive models for weed emergence based on the accumulation of TT appear to be accurate enough for projections of weed emergence time (Grundy 2003). Moreover, soil temperature data are easily accessible, making this type of model practical and useful to farmers. Many studies of weed growth, and thus predicting models for areas outside of Mazandaran is performed as a particular study. Because the differences in soil conditions, climatic, geographic and weed species there is a possibility that these models are not appropriate to predict weed species in Mazandaran province. Furthermore, the purpose of this experiment is investigation growth of weeds and develops an empirical model based on GDD to predicting the growth of several species of summer weeds in soybean.
Materials and methods: The experiment was conducted as split split-plot in a randomized complete block design with three replications in the summer of 2016 in Dasht-e-Naz Company Sari-Iran with geographical coordinates 36º 39´ N 53º 11´ E, and 1 meters above sea level. The treatments included two tillage system (No Tillage, Tillage), three densities of 20, 30 and 40 plants per square meter of soybeans and Pursuit-doses (imazethapyr) (0, 50%, 75%, standard dose and 25% of the standard dose, respectively).
To predict the growth pattern in each plot a fixed 50 × 50 cm quadrat fixed in the center of each plot and since the beginning of the season and after the first irrigation, counting of new grown seedlings was began based on weeds species. The Counting was performed weekly and then counted seedlings were eliminated after in any stage as long as new emergence was not seen.
Non-linear regression (Sigma Plot 12.5) was used for the expression pattern of cumulative emergence of seedlings. The 3 parameter logistic function was fitted to the data.

where y represents the predicted cumulative percent emergence, X0, GDD to reach the %50 cumulative emergence, a is the upper asymptote (theoretical maximum percent emergence), b is the slope of the curve.
We considered that soil water was not a limiting factor for weed emergence, using soil temperature (growing degree days, GDD) as the only independent variable for predicting cumulative emergence. Thus, GDD were calculated with the soil temperatures by using the formula:

where Tmax and Tmin are the daily maximum and minimum temperature, respectively, and Tb is the base temperature. Base temperatures used in the calculations of GDD were: 9.0ºC for A. theophrasti, 12.0ºC for S. halepense, 22.3ºC for A. retroflexus, 8.1ºC for E. maculate, 7.5ºC for P. oleracea, 4.0ºC for B. napus.
From the emergence count data, mean emergence time (MET) and emergence rate index (ERI) were calculated as follows:


where N1, ..., Nn is the number of newly emerged seedlings since the time of the previous count, t1, ..., tn are the GDD after sowing, and n is the number of sampling occasions. These two indices give us a simple indication of the emergence process, providing a useful tool to compare the progress of seedling emergence of each species in the two sites. However, they cannot provide more detailed information on emergence duration and speed.
Results and Discussion: The results showed that except sorghum that in tillage treatment had the lowest cumulative emergence, other species in no-tillage treatment had the lowest cumulative emergence. At the end of the sampling patterns of emergence has been specified, all species of weeds, in the density of 40 plants per square meter of soybean and dose of 1.25 liter per hectare of herbicide Pursuit had the lowest cumulative emergence and in the density of 20 plants per square meter of soybean and dose of 0 liters per hectare of herbicide Pursuit had the maximum cumulative emergence. Among other species, Amaranthus retroflexus needed the lowest mean emergence time (MET) and the lowest growing degree days (GDD) to reach 50% emergence. Whereas, among the species, Abutilon theophrasti needed maximum mean emergence time (MET) and maximum growing degree days (GDD) to reach 50% emergence. On this basis, growth stage suitable for controlling pigweed, when the main wave of seedlings of other species still have not found growing. The best management practice used to manage weeds will depend upon the weed species present in the soil seed bank, and diversity of management tactics (e.g., planting dates) will result in fewer shifts in species composition.

Keywords

Emergence pattern; Tillage; Planting density; Herbicide dose

References

Alm D.M., Pike D.R., Hesketh J.D., and Stoller E.W. 1988. Leaf area development in some crop and weed species. Biotronics, 17:29–39.

2- Anderson R.L. 1994. Characterizing weed community seedling emergence for a semiarid site in Colorado. Weed Technology, 8:245–249.

3- Arnold R.N., Murray M.W., Gregory E.J., and Smeal D. 1993. Weed control in pinto beans (Phaseolus vulgaris) with imzethapyr combinations. Weed Technology, 7:361-364.

4- Baskin C.C., and Baskin J.M. 1988. Germination ecophysiology of herbaceous plant species in a temperature region. American Journal of Botany, 75:286–305.

5- Benech-Arnold R.L., Sanchez R.A., Forcella F., Kruk B.C., and Ghersa C.M., 2000. Environmental control of dormancy in weed seed banks in soil. Field Crops Research, 67:105–122.

6- Bilbro J.D., and Wanjura D.F. 1982. Soil crust and cotton emergence relationship. Transactions of American Society of Agricultural Engineers, 25:1485–1488.

7- Buhler D.D., Hartzler R.G., Forcella F., and Gunsolus J.L. 1997. Sustainable agriculture: Relative emergence sequence for weeds of corn and soybeans. Iowa State University Extension Bulletin, SA-11, 4 p.

8- Burnside O.C., Wilson R.G., Weisberg S., and Hubbard K.G. 1996. Seed longevity of 41 weed species buried 17 years in eastern and western Nebraska. Weed Science, 44:74–86.

9- Conn J.S., Beattie K.L., and Blanchard A. 2006. Seed viability and dormancy of 17 weed species after 19.7 years of burial in Alaska. Weed Science, 54:464–470.

10- Davis A.S., Schutte B.J., Iannuzzi J., and Renner K.A. 2008. Chemical and physical defense of weed seeds in relaion to soil seedbank persistence. Weed Science, 56:676–684.

11- Deihimfard R., Nazari Sh., and Aboutalbian M.A. 2016. Modelling Germination Pattern of Two Pigweed Ecotypes in Response to Temperature. Journal of Plant Protection (Agricultural Science and Technology), 30(2):328-336. (in Persian with English abstract).

12- Derksen D.A., Lafond G.P., Thomas A.G., Loeppky H.A., and Swanton C.J. 1993. Impact of agronomic practices on weed communities: tillage systems. Weed Science, 41:409–417.

13- Doll H. 1997. The ability of barley to compete with weeds. Biological Agriculture and Horticulture, 14:43–51.

14- Dorado J., Sousa E., Calha I.M., Gonzalez-Avdujar J.L., Fernandez L.A., and couintanilla, I.L. 2009. Predicting weed emergence in maize crops under two contrasting climatic conditions. Weed Research, 49:251–260.

15- Ervio L.R. 1972. Growth of weeds in cereal population. Journal of the Science of Food and Agriculture, 44:19–27.

16- Forcella F., Wilson R.G., Dekker J., Kremer R., Cardina J., Anderson R.L., Alm D., Renner K.A., Harvey R.G., Clay S., and Buhler D.D. 1997. Weed seed bank emergence across the Corn Belt. Weed Science, 67:123–129.

17- Forcella F., Benech-Arnold R.L., Sanchez R.E., and Ghersa C.M. 2000. Modeling seedling emergence. Field Crops Research, 67:123–139.

18- Gomez-Campo C. 1999. Biology of Brassica Coenospecies. Elsevier Science, p. 40.

19- Grundy A.C. 2003. Predicting weed emergence: a review of approaches and future challenges. Weed Research, 43:1–11.

20- Gummerson R.J. 1986. The effect of constant temperatures and osmotic potential on the germination of sugar beet. Journal of Experimental Botany, 41:1431–1439.

21- Gunsolus J.L. 1990. Mechanical and cultural weed control in cornand soybeans. Am. Journal Alternate Agriculture, 5:114-119.

22- Hakansson S. 1997. Competitive effects and competitiveness in annual plant stands, 2: measurements of plant growth as influenced by density and relative time of emergence. Swedish Journal of Agricultural Research, 27:75–94.

23- Halloway K.l., Kookana R.S., Noy D.M., Smith J.G., and Wilhelm N. 2006. Crop damage caused by residual Acetolactate synthase herbicides in the soils of south-eastern Australia. Australian Journal of Experimental Agriculture, 46:1323-1331.

24- Hartzler R.G., Buhler D.D., and Stoltenberg D.E. 1999. Emergence characteristics of four annual weed species. Weed Science, 47:578–584.

25- Holt J.S., and Orcutt D.R. 1996. Temperature thresholds for bud sprouting in perennial weeds and seed germination in cotton. Weed Science, 44:523–533.

26- Klingman T.E., King C.A., and Oliver L.R. 1992. Effect of application rate, weed species, and weed stage of growth on imazethapyr activity. Weed Science, 40:227-232.

27- Leguizamon E.S., Fernandez-Quintanilla C., Barroso J., and Gonzalez-Andujar J.L. 2005. Using thermal and hydrothermal time to model seedling emergence of Avena sterilis ssp. ludoviciana in Spain. Weed Research, 45:149–156.

28- Lemerle D., Gill C.E., Murphy S.R., Walker R.D., Cousens S., Mokhtari S.J., Peltzer R., Coleman C., and Luckett D.J. 2001. Genetic improvement and agronomy for enhanced wheat competitiveness with weeds. Australian Journal of Agricultural Research, 52:527–548.

29- Lindquist J.L., Mortensen D.A., Clay S.A., Schmenk R., Kells, J.J., Howatt K., and Westra P. 1996. Stability of corn (Zea mays)-velvetleaf (Abutilon theophrasti) interference relationships. Weed Science, 44:309–313.

30- Lindquist J.L., Mortensen D.A., and Johnson B.E. 1998. Mechanism of corn tolerance and velvetleaf suppressive ability. Agronomy Journal, 90:787–792.

31- Maddonni G.A., Otegui M.E., and Cirilo A.G. 2001. Plant population density, row spacing and hybrid effects on maize canopy architecture and light attenuation. Field Crops Research, 71:183-193.

32- McCloskey M., Firbank G., Watkinson A.R., and Webb D.J. 1996. The dynamics of experimental arable weed communities under different management practices. Journal of Vegetation Science, 7:799–808.

33- Medd R.W., Auld B.A., Kemp D.R., and Musison R.D. 1985. The influence of wheat density and spatial arrangement on annual ryegrass, Lolium rigidum, competition. Australian Journal of Agricultural Research, 36:361–371.

34- Mohler C.L. 1996. Ecological bases for the cultural control of annual weeds. Journal of Production Agriculture, 9:468–474.

35- Mohler C. 2001. Ecological Management of Agricultural Weeds. Cambridge: Cambridge University Press, 532 pp.

36- Moyer J.R., and Hamman W.M. 2001. Factors affecting the toxicity of MON 37500 residues to following crops. Weed Technology, 15:42-47.

37- Murphy S.D., Yakubu Y., Weise S.F., and Swanton C.J. 1996. Effect of planting patterns and inter row cultivation on competition between corn (Zea mays) and late-emerging weeds. Weed Science, 44:856–870.

38- Myers M.M., Curran W.S., Vangessel M.J., Calvin D.D., Mortensen D.A., Majek B.A., Karsten H.D., and Roth G.W. 2004. Predicting weed emergence for eight annual species in the northeastern United States. Weed Science, 52:913–919.

39- Nelson K.A., and Renner K.A. 2002. Yellow nutsedge control and tuber production with glyphosate and ALS-inhibiting herbicides. Weed Technology, 16:512-519.

40- Ogg A.G.Jr., and Dawson J.H. 1984. Time of emergence of eight weed species. Weed Science, 32:327–335.

41- Radosevish S., Holt J., and Ghersa C. 1997. Weed Ecology. 2nd edn. New York, 589 p.

42- Schwinning S., and Weiner J. 1998. Mechanisms determining the degree of sizeasymmetry in competition among plants. Oecologia, 113:447–455.

43- Steinmaus S.J., Prather T.S., and Holt J.S. 2000. Estimation of base temperatures for nine weed species. Journal of Experimental Botany, 51:275–286.

44- Stoller E.W., and Wax L.M. 1973. Periodicity of germination and emergence of some annual weeds. Weed Science, 21:74–580.

45- Teasdale, J.R., Beste C.E., and Potts W.E. 1991. Response of weeds to tillage and cover crop residue. Weed Science, 39:195–199.

46- Vangessel M.J., and Renner K.A. 1990. Effect of soil type, hilling time, and weed interference on potato (Solanum tuberosum) development and yield. Weed Technology, 4: 299–305.

47- Weiner J. 1990. Asymmetric competition in plant populations. Trends in Ecology and Evolution, 5:360–364.

48- Weiner J., Griepentrog H.W., and Kristensen L. 2001. Suppression of weeds by spring wheat (Triticum aestivum) increases with crop density and spatial uniformity. Journal of Applied Ecology, 38:784–790.

49- Weiner J., Andersen S.B., Wille W.K.M., Griepentrog H.W., and Olsen J.M. 2010. Evolutionary Agroecology: the potential for cooperative, high density, weed-suppressing cereals. Evolutionary Applications, 3:473–479.

50- Werle R., Sandell L.D., Buhler D.D., Hartzler R.G., and Lindquist J.L. 2014. Predicting Emergence of 23 Summer Annual Weed Species. Weed Science, 62:267–279.

51- Zhang H., Tian Y., and Zhou D. 2015. A Modified Thermal Time Model Quantifying Germination Response to Temperature for C3 and C4 Species in Temperate Grassland. Agriculture, 5:412-426.