Modelling Germination Pattern of Two Pigweed Ecotypes in Response to Temperature

Document Type : Research Article

Authors

• reza deihimfard ¹
• shahram nazari ²
• mohammad ali aboutalbian ²

¹ Assistant Professor Department of Agroecoloy

² University of Bu-Ali Sina

10.22067/jpp.v30i2.46562

Abstract

Introduction: Seed germination is an important stage in the life history of plant affecting seedling development, survival, and population dynamics. Germination begins with seed, water uptake and terminates with the elongation of the embryonic axis from the seed coat. Germination and seedling emergence are the most important phonological development stages in pigweed and have a vital role in its establishment. Accordingly, predicting the phenological stages would be resulted in improvement of crop management as the number and time of pigweed emergence could be quantified. Sigmoidal curves, also known as growth models, have wide application in agricultural research and can be evaluated by means of nonlinear models, which operates through data modelling by a nonlinear combination of parameters depending on one or more independent variables. This study was conducted to evaluate various regression models to describe the response of germination rate to temperature range in two pigweed ecotypes (Alborz and Fars).
Materials and Methods: A glasshouse experiment was carried out as a completely randomized design with four replicates. The seeds were sterilized by soaking in a 3% solution of hypochlorite sodium for 30 seconds. After the treatment, the seeds were washed several times with distilled water. 25 seeds were put in each Petri dish (with 9 cm diameter). The petri dish is monitored on a daily basis and afterwards germinated seeds (according to exit of radicles to the size of 2 mm) were measured and recorded daily in each Petri dish. Six regression models were applied to quantify the germination patterns of two pigweed ecotypes (Alborz and Fars) under a range of temperature between 5 to 35 °C. For both regions, during spring and summer, the range of temperatures was selected in order to simulate the temperature changes. The models were included Weibull, Lognormal, Logistic, Gompertz, Sigmoidal and Chapman. Some criteria were used to describe the goodness of fits of the models, including coefficient of determination (R2), root mean square of error (RMSE) and Akaike index (AIC). Moreover, a simple program called Germin was used to calculate D10, D50 and D90 (the time interval to maximum 10, 50 and 90% of germination, respectively).
Results and Discussion: Results showed that Weibull four-parameter and logistic models were the best for describing the germination rate in Alborz and Fars ecotypes, respectively compared to the other models. The difference between ecotypes could be attributed to their base temperatures and thermal time requirements at each developmental stage. Therefore, it can be concluded weed germination during different seasons is not a random phenomenon. However, the germination and emergence of a clear pattern over time follows the pattern of different environmental conditions is subject to change. Results also indicated that the time to D90 was only 4 and 5 days in Alborz and Fars ecotypes, respectively meaning that with increasing temperature during early spring, this weed would germinate much sooner than spring crops and consequently resulted in crop damage. The results showed that the in Alborz population, with increases temperature from 10 to 30 °C, germination percentage linearly increased and with increasing temperature to the desired temperature, it decreased. However, germination in Fars ecotype showed that, in temperatures 10, 15 and 20 °C respectively, the germination was 36, 56 and 84 percent, while, with an increase in temperature from 25 to 35 °C, this component was always a constant process. Germination rate increased with increasing temperature from 10 to 35 °C which was higher in Alborz ecotype compared to Fars. At lower temperatures, the main reason for less germination rate could be decrement of water imbibition and enzyme activity in biochemical processes of germination.
Conclusion: Increasing public awareness and concern about the impacts of herbicides on the environment, development of herbicide-resistant weeds, and the high economic cost of herbicides have increased the need to reduce the amount of herbicides used in agriculture. Prediction of weed emergence timing would help reduce herbicide use through the optimization of the timing of weed control. It was concluded that Weibull four-parameter and logistic models could be used as decision making tools in Alborz and Fars, respectively, to predict seed emergence of pigweed which in turn resulted in efficient management as well as reduction of herbicides usage. Future research should be addressed to determine a wider validation of the models, which could be valuable tools for farmers and practitioners for adequate timing of control in pigweed weed.

Keywords

• Germination
• Regression Models
• Weeds

20.1001.1.20084749.1395.30.2.16.8

---

References

1- Asgarpour R., Mijani S., and Ghorbabi R. 2014. Effect of temperature on germination rate of Russian thistle (Salsola kali L.) based on regression models. Journal of Plant Protection, 27(4): 476-483. (in Persian with English abstract).

2- Azimi R., Khaje Hosseini M., and Falahpor F. 2014. Evaluation of seed germination features of Bromus kopetdaghensis Drobov under different temperature. Journal of Range and Watershed Management. 67(2): 253-261. (in Persian with English abstract).

3- Bavec F., and Mlakar S.G. 2002. Effects of soil and climatic conditions on emergence of grain amaranths. European Journal of Agronomy, 17: 93-103.

4- Beheshtian M. M. 2011. Modeling seeding emergence patterns of wild barley (Hordeum spontaneum) and canary grass (Phalaris minor) weeds. Ph.D. thesis.University of Tehran. (in Persian).

5- Blackshaw R.E. 1991. Soil temperature and moisture effects on downy brome Vs. winter canola, wheat, and rye emergence. Crop Science, 31: 1034-1040.

6- Bosh Z., Yousefi A.R., Tavakoli A., and Nikbakht J. 2013. Emergence prediction of Amaranthus retroflexus L. under different irrigation systems in sunflower (Helianthus annuus L.) production. Iranian Journal of Field Crop Science, 44(1): 119-127. (in Persian with English abstract).

7- Burnham K.P., and Anderson D.R. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer Verlag, New York.

8- Chantre G.R., Blanco A.M., Lodovichi M.V., Bandoni A.J., Sabbatini M.R., Lopez R.L., Viga M.R., and Gigon R. 2013. Modeling Avena fatua seedling emergence dynamics: An artificial neural network approach. Computers and Electronics in Agriculture, 88: 95-102.

9- Dehghan A., Bannayan Awal M., Khajeh Hossaini M., Izadi E and Mijani S. 2013. Simulation of emergence pattern of weeds species in corn (Zea mays L.) field based on sigmoidal models. Journal of Plant Protection, 26(4): 457-466. (in Persian with English abstract).

10- Derakhshan A., Gherekhloo J., and Paravar E. 2013. Estimation of cardinal temperatures and thermal time requirement for Cyperus difformis seed germination. Iranian Journal of Weed Science, 9(1): 27-38. (in Persian with English abstract).

11- Donald W.W. 2000. A degree-day model of Cirsium arvense shoot emergence from adventitious root buds in spring. Weed Science, 48: 333-341.

12- Dorado J., Sousa E., Calha I.M., Gonzalez-Andujar J.L., and Frenandez-Quintalilla C. 2009. Predicting weed emergence in maize crops under two contrasting climatic conditions. Weed Research, 59: 1-9.

13- Ebrahimi E., Eslami S.V., Jami Al-Ahmadi M., and Mahmodi S. 2011. Studying the effect of different environmental factors on germination of Ceratocarpus arenarius L. Bluk seed. Iranian Journal of Weed Science, 7(1): 45-87. (in Persian with English abstract).

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

15- Ghaderi Far F., Alimagham S.M., Rezaei Moghadam H., and Hagheghi M. 2013. Influence of environmental factors on seed germination and seedling emergence of rye (Secale cereale L.) as a volunteer plant in wheat fields. Electronic Journal of Crop Production, 5(4): 121-133. (in Persian with English abstract).

16- Hasagawa P.M., Bressan R.A., Zhu J.K., and Bohner H.J. 2000. Plant cellular and molecular responses to high salinity. Annual Review of Plant Physiology and Plant Molecular Biology, 51: 463-499.

17- Jalilian A., Mazaheri D., Rahimian H., Tavakkol Afshari R., Abdolahian M., and Gohari J. 2004. Estimation of base temperature and the investigation of germination and field emergence trend of monogerm sugar beet under various temperatures. Journal of Sugar Beet, 40(2): 97-112. (in Persian with English abstract).

18- Izquierdo J., Bastida F., Lezaun J.M., Sanchez M.J., and Gonzalez-Andujar J.L. 2013. Development and evaluation of a model for predicting Lolium rigidum emergence in winter cereal crops in the Mediterranean area. Weed Research, 53(4): 269-278.

19- Maiti R., and Wesche-Ebeling P. 2001. Advance in Chickpea Science. Science Publishers, Inc. 410 pp.

20- Rafael A.M., Randall S.C., Michael J.H., and John B.J. 2001. Interference of palmer amaranth in corn. Weed Science, 49: 202-208.

21- Rashed Mohassel M.H., Rastgoo M., Mousavi S.K., Valiollahpour R.H., and Haghighi A.A. 2006. An introduction to weeds science. Trans. Ferdowsi University of Mashad Press, 536 pp. (in Persian).

22- Sarabi V., Nassiri Mahallati M., Nezami A., and Rashed Mohassel M.H. 2010. The effect of relative tmergence time and density of Common lambsquarters (Chenopodium album L.) on corn (Zea mays L.) grain and biological yield. Iranian Journal of Field Crops Research, 8(5): 862-870. (in Persian with English abstract).

23- Shaban M. 2013. Effect of water and temperature on seed germination and emergence as a seed hydrothermal time model. International Journal of Advanced Biological and Biomedical Research, 1(12): 1686-1691.

24- Soltani E., Akram Ghaderi F., and Memar H. 2008. The effect of priming on germination components and seedling growth of cotton seeds under drought. Journal of Agricultural Sciences and Natural Resources, 14(5): 9-16. (in Persian with English abstract).

25- Soltani, A. and Madah, V. 2010. Simple applied programs for education and research in agronomy. Iranian Society of Ecological Agriculture. Tehran. Iran. 80p. (in Persian with English abstract).

26- Steckel L.E., Christy L.S., Edward W.S., and Wax L.M. 2004. Temperature effects on germination of nine Amaranthus species. Weed Science, 52(2): 217-221.

27- Vleeshouwers L.M. 1997. Modelling the effect of temperature, soil penetration resistance, burial depth and seed weight on preemergence growth of weeds. Annals of Botany, 79: 553-563.

28- Yousefi A.R., Rastgoo M., Ghanbari Motlagh M., and Ebrahimi M. 2013. Predicting seedling emergence of Flixweed (Descurainia sophia (L.) Webb.) and Hoary cress (Cardaria draba (L.) Desv.) in rapeseed (Brassica napus) field in Zanjan conditions. Journal of Plant Protection, 27(1): 48-54.(in Persian with English abstract).