جستجوی مقالات مرتبط با کلیدواژه « non-liner regression models » در نشریات گروه « زراعت »

One of the most important stages in the life cycle is germination, which is controlled by various environmental and genetic factors. Temperature and water potential are the most important factors in germination control. Different models for quantization of germination response to temperature and osmotic potential have been used. Quantification of germination response to osmotic potential and temperature is possible using non-liner regression models. Therefore, this research was carried out to determine the cardinal temperature of germination (base temperature, optimum and maximum germination) of safflower of Sofeh cultivar under different osmotic stress (drought stress) conditions and seed deterioration.

In this study germination response to water potential in different temperature were studied. Treatments included osmotic levels (0, -0.4 and -0.8 MPa), temperature (5, 10, 15, 20, 30, 35 and 40 °C) and seed deterioration (0 and 5 days). Ccumulative germination response of seeds to differential water potential and temperature were quantified using three-parameter sigmoidal model. For quantifying response of germination rate to temperature for different osmotic potential were used of 3 non-linear regression models (segmented, dent-like and beta). The root mean square of errors (RMSE), coefficient of determination (R2), CV and SE for relationship between the observed and the predicted germination percentage were used to select the superior model from among the employed methods. Germination percentage and time to 50% maximum seed germination of safflower were calculated for the different temperatures and osmotic potential by fitting 3-parameter sigmoidal functions to cumulative germination data.

Results indicated that temperature in addition to germination percentage also on germination rate was effective. Also results showed that germination percentage and germination rate increased with increasing temperature, while germination percentage and germination rate reduced as a result of water potential increment. Results indicated that under different osmotic potential as 0, -0.4 and -0.8 MPa, the segmented model estimated base temperature as 2.23, 3.67 and 4.33 °C, the dent model estimated base temperature as 3, 3.96 and 4.33 °C, the beta model estimated base temperature as -1.22, -1.28 and -2.28 °C, the segmented model estimated optimum temperature as 23.05, 25.44 and 24.19 °C, the optimum temperature using beta model as 28.89, 28.99 and 26.46 °C, the dent-like model estimated lower limit of optimum temperature and upper limit of optimum temperature as 21.12, 21.92 and 20.16 and 30.07, 25 and 23.27 °C, ceiling temperature using segmented model were 40, 40 and 35 °C, using dent-like model were 40, 39.83 and 35 °C, using beta model were 40, 35 and 34.82 °C, the segmented model estimated fo as 23.02, 69.51 and 84.17 h, the dent-like model estimated as 27, 75.99 and 83.87 h and using beta model were 26.09, 75.09 and 103.41 h, respectively. In compared 3 models according to the root mean square of errors (RMSE) of germination time, the coefficient of determination (R2), CV and SE the best model for determination of cardinal temperatures of seed control of safflower for 0 MPa was dent-like model and for -0.4 and -0.8 MPa was segmented model and for seed deterioration of safflower in all osmotic potential was segmented model. In general, results indicated that lower limit of optimum temperature and upper limit of optimum temperature and ceiling temperature reduced but fo increased as a result of water potential increment.

Germination of safflower response to different temperatures and osmotic potentials, led to acceptable results. Utilizing the output of non-liner models at different temperatures can be useful in prediction of germination rate in different water potential.

Temperature is one of the primary environmental regulators of seed germination. Seed priming technique has been known as a challenge to improving germination and seedling emergence under different environmental stresses. Quantification of germination response to temperature and priming is possible, using non-liner regression models. Therefore, the objective of this study was to evaluate the effect of temperature and priming on germination and determination of cardinal temperatures (base, optimum and maximum) of Brassica napus L.

Treatments included priming levels (non-priming, priming with water, gibberellin 50 and 100 mg/l) and temperature (5, 10, 15, 20, 30, 35 and 40 °C). Germination percentage and time to 50% maximum seed germination of Brassica napus L. were calculated for different temperatures and priming by fitting 3-parameter logistic functions to cumulative germination data. For the purpose of quantifying the response of germination rate to temperature, use was made of 3 nonlinear regression models (segmented, dent-like and beta). The root mean square of errors (RMSE), coefficient of determination (R2), CV and SE for the relationship between the observed and the predicted germination percentage were used to compare the models and select the superior model from among the methods employed.

The results indicated that temperature and priming were effective in both germination percentage and germination rate. In addition, the results showed that germination percentage and rate increase with increasing temperature to the optimum level and using priming. As for the comparison of the 3 models, according to the root mean square of errors (RMSE) of germination time, the coefficient of determination (R2), CV and SE, the best model for the determination of cardinal temperatures of Brassica napus L. for non-primed seeds was the segmented model. For hydro-priming and hormone-priming with 50 mg/l GA, the best models were segmented and dent-like models and for hormone-priming with 100 mg/l GA,  the dent-like model was the best. The results showed that for non-priming, hydropriming with water, gibberellin 50 and 100 mg/l treatments, the segmented model estimated base temperature as 3.54, 2.57, 2.34 and 2.34 °C and dent-model estimated base temperature as 3.34, 2.45, 2.21 and 2.83 °C, respectively. The segmented model estimated optimum temperature as 24.62, 23.23, 23.69 and 24.38 °C. The dent-model estimated lower limit of optimum temperature and upper limit of optimum temperature as 20.01, 19.62, 16.25, 19.87 and 28.81, 27.38, 29.58 and 27.31 °C.

Utilizing non-liner models (segmented, dent-like and beta) for quantification of germination of Brassica napus L. response to different temperatures and priming produced desirable results. Therefore, utilizing the output of these models at different temperatures can be useful in the prediction of germination rate in different treatments.

IntroductionSeed germination is a complex biological process that is influenced by various environmental and genetic factors. Temperature and water potential are two primary environmental regulators of seed germination. Quantification of germination response to osmotic potential and temperature is possible using non-liner regression models. Tall mallow (Malva sylvestris) is an important invasive weed in southwest Iran and also a medicinal plant. ). Tall mallow is native home in Western Europe, North Africa and Asia. This plant frequently found in cultivated fields, orchards, gardens, farmyards near manure piles, along roadsides, in towns, and in waste places and, can grow anywhere from 60 to 120 cm in length. Not published information exists concerning effect of osmotic potential on cardinal temperatures, Therefore, the objective of this research was to evaluate the effect of osmotic potential and different temperatures on germination and determination cardinal temperatures (base, optimum and maximum) of Malva sylvestris under osmotic stress.
Material and methodsIn this study germination response to water potential in different temperature were studied. Treatments included osmotic levels (0, -0.2, -0.4, -0.6 and -0.8 MPa) and temperature (5, 10, 15, 20, 30, 35 and 40 °C). Cumulative germination response of seeds to differential water potential and temperature were quantified using three-parameter sigmoidal model. For quantifying response of germination rate to temperature for different osmotic potential were used of 3 non-linear regression models (segmented, dent-like and beta). The root mean square of errors (RMSE), coefficient of determination (R2), CV and SE for relationship between the observed and the predicted germination percentage were used to select the superior model from among the employed methods. Germination percentage and time to 50% maximum seed germination of Malva sylvestris were calculated for the different temperatures and osmotic potential by fitting 3-parameter sigmoidal functions to cumulative germination data.
ResultsResults indicated that temperature in addition to germination percentage also on germination rate was effective. Also results showed that germination percentage and germination rate increased with increasing temperature, while germination percentage and germination rate reduced as a result of water potential increment. Results indicated that under different osmotic potential as 0, -0.2, -0.4, -0.6 and -0.8 MPa, the segmented model estimated base temperature as 1.46, 1.82, 1.29, 0.43 and 4.06 °C, the dent model estimated base temperature as 1.23, 1.82, 3.04, 2.63 and 4.07 °C, the beta model estimated base temperature as -4.32, 4.46, 1.86, 1.61 and 4.13 °C, the segmented model estimated optimum temperature as 28.29, 27.58, 22.24, 22.51 and19.69 °C, the optimum temperature using beta model as 27.89, 25.41, 23.18 and 21.05 °C, the dent-like model estimated lower limit of optimum temperature and upper limit of optimum temperature as 23.16 and 33.58, 16.86 and 30, 16.1 and 25, 15.81 and 25, 19.51 and 1987 °C, ceiling temperature using segmented model were 42.9, 40, 40, 40 and 34.96 °C, using dent-like model were 42, 40, 40, 40 and 34.96 °C, using beta model were 42.01, 40.02, 39.96, 39.98 and 34.83 °C, the segmented model estimated fo as 13.87, 18.45, 19.43, 25.24 and 36.13 h, the dent-like model estimated as 16.65, 23.28, 23.43, 30.48 and 36.56 h and using beta model were 16.06, 21.34, 22.21, 28.92 and 42.89 h, respectively. In compared 3 models according to the root mean square of errors (RMSE) of germination time, the coefficient of determination (R2), CV and SE the best model for determination of cardinal temperatures of Malva sylvestris for 0 to -0.6 MPa was dent-like model and for -0.8 MPa was segmented model. In general, results indicated that lower limit of optimum temperature and upper limit of optimum temperature and ceiling temperature reduced but fo increased as a result of water potential increment.
ConclusionGermination of Malva sylvestris response to different temperatures and osmotic potentials, led to acceptable results. Utilizing the output of non-liner models at different temperatures can be useful in prediction of germination rate in different water potential.