Agriculture
Seyed Masoud Ziaei; hasan feizi; abbas khashei; hossein sahabi
Abstract
Saffron is a valuable plant that generally faces water stress in its life cycle. Therefore, in order to investigate the effect of corm priming of saffron on physiological and corm characteristics of this product under drought stress conditions, a split plot experiment was carried out in a randomized ...
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Saffron is a valuable plant that generally faces water stress in its life cycle. Therefore, in order to investigate the effect of corm priming of saffron on physiological and corm characteristics of this product under drought stress conditions, a split plot experiment was carried out in a randomized complete block design with three replications. The experimental treatments included two levels of irrigation based on 70 and 50% of field capacity as a main plot and six corm priming treatments including no priming (control), potassium nitrate, auxin, gibberellin, silicon dioxide nanoparticles, and hydro-priming as sub plot. The results showed that the increase of intensity in drought stress from 70 to 50% of field capacity caused an increase of 38.5%, 59.1% and 57.3% in the amount of chlorophyll a, chlorophyll b and carotenoids, respectively, and a decrease of 32.6% and 20% to the ratio of chlorophyll a/b and the amount of protein respectively. Priming of mother corn with two hormones of auxin and gibberellin, significantly increased the amount of protein and the highest weight of daughter corm was observed at gibberellin hormone treatment at the rate of 3.72 grams per plant. The two treatment levels of gibberellin and auxin hormones, under conditions of medium drought stress, significantly showed the highest number of daughter corms and gibberellin hormone, under medium drought stress conditions, significantly increased the diameter of daughter corm at the rate of 28 mm. In generally, corm priming of saffron with two hormones of gibberellin and auxin, is recommended to improve the physiological traits and tuber characteristics, especially in the conditions of drought stress.
Hossien Riahi Modavar; Abbas Khashei-Siuki; Akram Seifi
Abstract
Because of saffron yield sensitivity and the effects of climate on its performance, and also due to the nonlinear nature of crop yield functions, the Artificial Neural Network (ANN) model is employed in this study for prediction and uncertainty analysis of saffron yield in the South Khorasan province ...
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Because of saffron yield sensitivity and the effects of climate on its performance, and also due to the nonlinear nature of crop yield functions, the Artificial Neural Network (ANN) model is employed in this study for prediction and uncertainty analysis of saffron yield in the South Khorasan province based on 20 years of data. The input vector of the ANN model was optimized from 37 parameters through correlation and variance inflation. The optimum architecture of the model was derived as 1-2-4-11 with a sigmoidal activation function based on the results at three stages of training, testing and verification. The root mean square error (RMSE) and mean absolute error (MAE) were equal to 0.3 and 0.5 in the training step and 0.7 and 1 in the test step, respectively. These results indicate that the ANN is a suitable model for predicting saffron yield. Uncertainty analysis based on R2, d-factor and 95%PPU showed that despite use of inadequate data, model prediction showed acceptable prediction bounds and predicted a satisfactorily saffron yield trend. The R2 values were equal to 0.92 and 0.58 in the training and test steps, respectively, which are statistically significant at the P
