عنوان مقاله [English]
Prediction of weed emergence time is one of the valuable decision-making tools in integrated weed management, which can be used to optimize weed control programs. Among human agricultural practices, tillage is one of the main drivers of weed communities. The emergence pattern has sensitive receptors that may change with a manipulation such as tillage or any other management activity. Johnson grass (Sorghum halepense (Pers.) L.) from the Poaceae family is a C4 perennial weed and one of the most invasive weeds in the world. This weed has spread in one third of the world's regions, which has led to a major reduction of biological and agricultural diversity in Asia, Africa, America and Europe. It ranks sixth in the list of 30 worst weeds in the world in 53 countries and has become endemic in millions of hectares of the world. Cutworms have been reported in crops including wheat (Triticum aestivum L.), soybean (Glycine max L.), corn (Zea mays L.), cotton (Gossypium hirsutum L.), vegetables and fruit trees.
Materials and Methods
In order to predict the time of emergence of seedlings from seeds and rhizomes, a field experiment was conducted during the spring and summer of 2022 in the research farm located in the Agricultural Campus of Tehran University located in Karaj. The experiment was conducted as a randomized complete block with four replications. The investigated treatments were four tillage dates including 15 May, 30 May, 15 June and 30 June. 20 plots with dimensions of 2 ×1 m2 were prepared in a part of the research farm which has a high level of Johnson grass contamination based on previous monitoring. Tillage is done up to a depth of 20 cm in the plots. In each plot, three quadrates with dimensions of 0.5 × 0.5 m2 were installed, and seed seedlings and rhizomes were counted at weekly intervals until the end of the season. Four models (Logistic, Sigmoidal, Gumpertz and Weibull) were used to investigate the emergence pattern of seedlings obtained from seeds and rhizomes. Analysis of variance was done with the help of SAS 9.1 software. Sigma plot software was used to brush the models.
Results and Discussion
The minimum square standard error model with an explanation coefficient of 0.85 and also the root mean square error of 5.35 has been able to predict the cumulative emergence of the seedlings of Johnson grass seeds. These values were 0.94 and 3.8 respectively in predicting the cumulative emergence of seedlings from the rhizome, which indicates the higher accuracy of the model in predicting the emergence from the rhizome. The results showed that, in general, the used models had close values of the corrected coefficient of explanation and root mean square error, so these indicators were not able to distinguish the more accurate model. But in general, since models with a lower Akaike index are more accurate anyway, the logistic model was chosen as the best model. The effect of plowing was significant on the germination pattern of seed and rhizomes. May plowing dates were associated with a high population of seed and rhizome seedlings, but in the same proportion in the population. Seed seedlings emergent in a smaller number and during a shorter period of the season, while rhizome shoots are observed in a longer period and with a much higher number in the field. In general, the emergence of seed seedlings started earlier, slightly before rhizome seedlings. With the gradual increase in temperature from May 15 to June 30, the response of rhizome seedlings to temperature has been variable among the studied squares. A similar trend regarding the increase of germination variance can be seen in the case of seeds, with the difference that the variance was much higher in the case of seed seedlings. This indicates that the seeds are more sensitive to the increase in temperature and their variable response to environmental changes.
The plowing dates during June were associated with a decrease in the population of seed seedlings and an increase in rhizome seedlings of the Johnson grass population. Considering that the rhizome population in the farm is high at all times, it is suggested to write and implement control programs based on the rhizome population. This point is more important in late tillage and late planting dates.