Effects of carbohydrate source and polyethylene glycol on maturation and germination of somatic embryos in walnut (<Emphasis Type="Italic">Juglans regia</Emphasis> L.)

Low efficiency of somatic embryo maturation, germination, and conversion to plantlets is a major problem in many species including J. regia L. Germination efficiency of somatic embryos is very low in walnut. In this study, effects of two carbohydrate sources, sucrose and maltose (each at 3% and 6%), and two kinds of PEG (4000 and 6000) (each at four levels of 1.5%, 3%, 5% and 7.5%) on maturation and germination of walnut embryos were tested. The number of somatic embryos increased conspicuously on medium containing PEG. Furthermore, higher levels of PEG-4000 (7.5%) could remarkably enhance the morphogenesis of somatic embryos and the number of embryos produced. PEG-4000 stimulated somatic embryo maturation of walnut. This stimulatory effect was dependent on the carbohydrate source used. Sucrose in combination with PEG-4000 produced 50% of cotyledonary and normal somatic embryos. Different concentrations of PEG were effective on the number of embryos with a shoot meristem. PEG-4000 7.5% and sucrose 3.0% produced the highest rate (50.0%) of normal shooting embryos. However, PEG (4000, 6000) and maltose caused an unfavorable effect and increased the frequency of abnormal shaped somatic embryos.     

Coping with imbalanced data problem in digital mapping of soil classes

An unsolved problem in the digital mapping of categorical soil variables and soil types is the imbalanced number of observations, which leads to reduced accuracy and the loss of the minority class (the class with a significantly lower number of observations compared to other classes) in the final map. So far, synthetic over- and under-sampling techniques have been explored in soil science; however, more efficient approaches that do not have the drawbacks of these techniques and guarantee retention of the minority classes in the produced map are essentially required. Such approaches suggested in the present study for digital mapping of soil classes include machine learning models of ensemble gradient boosting, cost-sensitive learning and one-class classification (OCC) of the minority class combined with multi-class classification. In this regard, extreme gradient boosting (XGB) as an ensemble gradient learner, a cost-sensitive decision tree (CSDT) within the C5.0 algorithm, and a one-class support vector machine combined with multi-class classification (OCCM) were investigated to map eight soil great groups with a naturally imbalanced frequency of observations in northwest Iran. A total of 453 profile data points were used for mapping the soil great groups of the study area. A data split was done manually for each class separately, which resulted in an overall 70% of the data for calibration and 30% for validation. The bootstrapping approach of calibration (with 10 runs) was performed to produce multiple maps for each model. The 10 bootstraps were evaluated against the hold-out validation dataset. The average values of accuracy measures, including Kappa (K), overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), were explored. In addition, the results of this study were compared with a previous study in the same area, in which resampling techniques were used to deal with imbalanced data for digital soil class mapping. The findings show that all three suggested methods can deal well with the imbalanced classification problem, with OCCM showing the highest K (= 0.76) and OA (= 82) in the validation stage. Also, this model can guarantee the retention of the minority classes in the final map. Comparing the present approaches with the previous study approach demonstrates that the three newly suggested methods can remarkably increase both overall and individual class accuracy for mapping.     

Predicting ESP and SAR by artificial neural network and regression models using soil pH and EC data (Miankangi Region,Sistan and Baluchestan Province,Iran)

Monitoring exchangeable sodium percentage (ESP) and sodium adsorption ratio (SAR) variability in soils is both time-consuming and expensive. However, in order to estimate the amounts of amendments and land management, it is essential to know ESP and SAR variations and values in sodic or saline and sodic soils. Thus, presenting a method which uses easily obtained indices to estimate ESP and SAR indirectly is more optimal and economical. Input data of the current research were 189 soil samples collected based on a regular networking approach from Miankangi region, Sistan plain, Iran. Then, their physicochemical properties were measured. Results showed that SAR = 3.8 × ln(EC) + 22.83 × ln(pH) – 44.37, (R² = 0.63), and ESP = 3.98×ln(EC) + 36.88(pH) – 56.98 (R² = 0.78) are the best regression models for estimating SAR and ESP, respectively. Moreover, multilayer perceptron (MLP), which explains 95–97% of parameters of soil sodicity using EC and pH as inputs, was the best neural network model. Therefore, MLP could be applied for ESP and SAR evaluation with high accuracy in the Miankangi region.     

Chemometrics Approaches to Monitoring of Biogenic Amines Changes in Three Fish Species

The biogenic amine contents in fillets of three freshwater fish of kutum (Rutilus frisii), common carp (Cyprinus carpio), and Caspian salmon (Salmon caspius) were monitored during storage at 4°C for 20 days. The amounts of biogenic amines were determined at regular intervals using high performance liquid chromatography after derivatization with benzoyl chloride. Significant differences in the levels of selected biogenic amines among the fish samples were found (p < 0.05) during storage times. While the amount of putrescine, cadaverine, histamine, and tyramine in the fish fillets increased during the storage time, the changes in spermine and spermidine contents of the fish species were minor and both showed low and inverse correlations with storage time. For each of the three fish species, the sensory acceptability limit was found to be 4–8 days. The quality and biogenic amine indices of fish samples showed high correlation with storage time (r > 0.9). The principal component analysis of the results indicated that the formation of biogenic amines displays a certain behavioral pattern in all fish samples.     

Effects of Some Organic Nitrogen Sources and Antibiotics on Callus Growth of Indica Rice Cultivars

A factorial experiment based on a completely randomized design with three replicates was conducted to evaluate effects of sixteen different mixtures of organic nitrogen sources and antibiotics which were supplemented to an Murashige and Skoog (MS) medium containing 4 mg L^?1 2,4-Dichlorophenoxyacetic acid (2,4-D), 0.4 mg L^?1 benzyl-aminopurine (BAP), 30 g L^?1 sucrose, 8 g L^?1 Agar-agar on fresh weight and dry weight (assessed after three days of drying at 70°C) of calli and in vitro callogenesis from scutellum of four indigenous Iranian Indica rice (Oryza sativa L.) cultivars, including ‘Hashemi’, ‘Gerdeh’, ‘Hasani’, and ‘Gharib’. It was found that proline at 2.8 g L^?1 is the most effective source of organic nitrogen in enhancing growth, whereas asparagine at 0.75 g L^?1 inhibited the calli growth. Hygromycin at 50 mg L^?1, with slight differences between the cultivars, could effectively stunt the growth of untransformed calli. Mixture of cefotaxime (250 mg L^?1) and vancomycin (250 mg L^?1) did not have any significant effects on calli growth, although this treatment was slightly phytotoxic.     

EFFECTS OF PHYTOHORMONES ON PROLINE CONTENT AND ANTIOXIDANT ENZYMES OF VARIOUS WHEAT CULTIVARS UNDER SALINITY STRESS

Salinity stress is one of the important agricultural problems in the world. A factorial experiment based on completely randomized design with four replications was conducted to evaluate the effects of phytohormones (gibberellic acid and abscisic acid) on the activity of antioxidant enzymes (peroxidase, superoxide dismutase and catalase), rubisco activity and content, and proline in three wheat cultivars (Gascogen, Zagros, and Kuhdasht) under control and salinity stress (3.5 and 7 dS m^?1). The results showed that salinity stress (3.5 and 7 dS m^?1) decreased the activity of catalase, rubisco, carboxylase, but increased peroxidase, superoxide dismutase activity and proline content. Gibberellic acid caused 58.03% increased in rubisco carboxylase activity in Zagros at 7 dS m^?1 in comparison with abscisic acid under salinity stress compared with the control plants in Kuhdasht. Activity of superoxide dismutase in Kuhdasht cultivar at 7 dS m^?1 salinity level showed 76.43% increased in Gascogen under salinity stress compared with the control plants with gibberellic acid application. The highest proline content as an osmolyte was found in Zagros at 7 dS m^?1 salinity level with abscisic acid (194 μmol g^?1 DM) application. Peroxidase activity increased 83.31% and catalase activity decreased 61.27% compared with the control plants in Zagros. Gibberellic acid application significantly prevented reduction in rubisco content under salinity stress. In conclusion, increased in peroxidase and superoxide dismutase activity and proline content decreased the adverse effects of salinity stress on studied cultivars. Also, the foliage spray of gibberellic acid enhanced and improved the growth condition. In this experiment, Zagros cultivar showed more tolerance to salinity stress than the other two cultivars.     

Integration of ANP and Fuzzy set techniques for land suitability assessment based on remote sensing and GIS for irrigated maize cultivation

Land suitability assessment can inform decisions on land uses suitable for maximizing crop yield while making best use, but not impairing the ability of natural resources such as soil to support growth. We assessed the suitability of maize to be produce in 12,000 ha land of Dasht-e-Moghan region of Ardabil province, northwest of Iran. Suitability criteria included soil depth, gypsum (%), CaCO₃ (%), pH, electrical conductivity (EC), exchangeable sodium percentage (ESP), slope (%) and climate data. We modified and developed a novel set of techniques to assess suitability: fuzzy set theory, analytic network process (ANP), remote sensing and GIS. A map of suitability was compared a map created using a traditional suitability technique, the square root method. The coefficient of determination between the land suitability index and observed maize yield for square root and ANP-fuzzy methods was 0.747 and 0.919, respectively. Owing to greater flexibility to represent different data sources and derive weightings for meaningful land suitability classes, the ANP-fuzzy method was a superior method to represent land suitability classes than the square root method.