Iranian Journal of Seed Research

Extended abstract
Introduction: Germination speed is one of the most important germination indices, used in most studies to compare the effects of different treatments on seed germination. Researchers use the reverse time up to 50% maximum germination (1/D50) to calculate the germination rate. One of the methods used for calculating the D50 is the utilization of nonlinear regression models such as Logestic, Gompertz, Richard, Weibull and Hill. In addition, for the purpose of calculating this parameter, simple empirical models such as the model presented by Farooq et al. and Ellis and Roberts are used. The question which arises is which of these methods has more precision predicting D50. The purpose of this study was to calculate D50, using different methods in seed germination of cotton.
Material and Methods: In this experiment, cottonseeds were placed at three temperatures of 15, 25 and 40°C with three replications, and germinated seeds were counted daily several times. To calculate D50, several nonlinear regression models including Gompertze, Logestic, Hill (the four-parameter), Richard and Weibull models were used. Moreover, for the purpose of calculating D50, the models presented by Farooq et al. and Ellis and Roberts were used.
Results: The results showed that all nonlinear regression models exhibited suitable fit to germination data. However, logestic, Hill and Weibull showed better predictability of D50, compared with other models. Besides, D50 calculated by the Farooq model was similar to that estimated by nonlinear regression models, whereas D50 estimated by the Ellis and Roberts model was higher than that estimated by other models.
Conclusions: The results of this study showed that both non-linear regression models and the model developed by Farooq could be used to calculate D50 of cottonseed. In general, the results of this study showed that nonlinear regression models could be used to calculate D50. In this research, Logestic, Hill, and Weibull showed good fit for cumulative seed germination data of cotton seeds versus time at different temperatures. These models have coefficients that have a biological concept that includes maximum germination percentage, time to 50% maximum germination and time to start germination. Moreover, when researchers only seek to measure D50 and are not familiar with the statistical software, they can use the empirical formula presented in this research.
 
Highlights:

1. Calculating D50 in cottonseeds, using different methods.
2. Using nonlinear regression models to calculate D50 in cottonseeds.
3. Developing a proper method which is more accurate, and better lends itself to calculating D50 of cottonseeds.

Extended Abstract
Introduction: Seed germination and seedling emergence depend on the genetics of plant species and are also influenced by environmental factors. Genetics and nutritional status of the maternal plant, maturity stage at a time of harvest, and environmental factors such as temperature, salinity, drought, and soil fertility influence seed germination. Seed vigor as the main parameter of seed quality decreases due to accelerated aging and storage. The objective of this study was to evaluate the response of accelerated aged Chia seed to different levels of salinity stress.
Material and Methods: Two-way factorial experiment with experimental factors, including five levels of seed accelerated aging durations (0, 24, 48, 72, 96 h) and six levels of salinity stress (0, 50, 100, 150, 200, and 250 mM) was arranged based on a complete randomized block design with three replications. The experiment was conducted at seed technology laboratory Khuzestan Agricultural Sciences and Natural Resources, University of Khuzestan, in 2019.
Results: Results of analysis of variance revealed that the effect of seed accelerating aging, salinity stress, and interaction effects of both factors on all measured germination traits were significant (p<0.01). The best pattern of seed germination was evaluated using three-parameter sigmoid models (logistic, Gompertz, and sigmoidal) and two polynomial models (quadratic and cubic), then the performance of all models was compared using (R²_adj), root square of the mean (RMSE) and corrected Akaike index (AICc). Results showed that at accelerated aging duration, models' performance to describe Chia seed germination response varied at different levels of salinity stress. At no aging and 72h of accelerated aging treatments, the sigmoidal model exhibited the best fit on final seed germination, whereas for the other levels of accelerated aging, Gompertz exhibited the best fit. Based on the output of the sigmoidal model, for no aging and 72 hours of accelerated aging, 50% of seed germination was declined at 171.7 and 76.9 mM, respectively, and based on the results of the Gompertz model, after 24 and 48 h of accelerated aging, seed germination declined to 50% at 163.8 and 129.6 mM. Results obtained from fitting polynomial models on seed germination showed that the cubic model provides reasonable descriptions for studied traits such as seed vigor.
Conclusion: Chia seed germination was sensitive to salinity and accelerated aging treatments. At no aging condition, Chia seeds tolerate salinity stress up to 200 mM and were able to germinate. By increasing aging durations, seed germination declined dramatically at all salinity levels and after 96 hours of aging, there was no seed germination at 150 mM.

 
Highlights:
1- The best nonlinear model to study accelerated Chia seed response to salinity stress was selected using the model selection criterion.
2- Chia seed germination threshold to salinity stress was determined for not- aged and aged seeds.

Extended abstract
Introduction: The germination process is one of the most critical stages of a plant's growth and determines the success of the emergence of a weed in an agroecosystem because it is the first stage in which the weed competes for a niche. Various environmental factors, including temperature and moisture, affect the germination of weed seeds. Modeling techniques are capable of predicting germination, seedling emergence, and establishment of weed species. The ability to predict weed germination in response to environmental conditions is very effective for the development of control programs. The experiment was conducted to determine the cardinal temperature and evaluate the best model for quantifying the response of the germination rate of Western ragweed weed seeds under different water stress conditions.
Materials and Methods: A factorial experiment was conducted in the form of a completely randomized design in three replications. The investigated factors include temperature with eight levels (5, 10, 15, 20, 25, 30, 35, and 40 C˚) and water potential with six levels (0, -0.3, -0.6, -0.9, -1.2, and -1.5 MPa) on the germination of Western ragweed. In order to quantify the response of Western ragweed germination rate to temperature, three non-linear Dent-like, Beta, and Segmented regression models were used.
Results: The results showed that the effect of temperature, water potential, and their interactions on maximum germination, germination rate, and time required to reach 10, 50, and 90 percent germination were significant. Also, the results showed that by increasing the temperature from 10 to 25 C˚, the percentage and rate of germination increased whereas by increasing water potential, the percentage and rate of germination decreased. In comparing the models, based on RMSE, R², CV, and coefficients a and b parameters, the Beta model was the most suitable for estimating the temperatures of cardinal Western ragweed. The base, optimum, and ceiling temperatures using the Beta model were 3.88, 25, and 40 C˚, respectively.
Conclusions: The use of the Beta model to quantify the germination response of Western ragweed seeds to different levels of water potential at different temperatures had acceptable results. Therefore, by using the output of these models at different temperatures, it is possible to predict the germination rate at different potentials.

Highlights:
1- Germination cardinal temperatures and the effect of water potential on western ragweed weed were investigated.
2- Estimation of different models to quantify the response of germination rate to temperature and different water potentials.