Bacillus pumilus and Stenotrophomonas maltophilia as two potentially causative agents involved in Persian oak decline in Zagros forests (Iran)

The oak decline is known as one of the most destructive complex diseases causing high economic losses around the world, especially in Iran. The main objective of the present study was to investigate the possible role of bacteria as causative agents of oak decline in the Zagros forests of Iran. To do this, stem, root and leaf samples were taken from symptomatic Persian oak trees (Quercus brantii) in different zones of Zagros forests (Ilam Province, Iran). From 150 bacterial isolates, 20 showed pathogenicity against Geranium seedlings. Among 20 hypersensitivity test positive strains, four strains showed pathogenicity against oak saplings. Based on morphological and 16S rRNA gene sequence analysis, three strains were identified as Bacillus pumilus and one strain as non‐sporulating Gram‐negative Stenotrophomonas maltophilia. Pathogenicity studies of different B. pumilus and S. maltophilia strains revealed that they have potential to cause the disease in oak saplings and symptoms of disorder in Persian oak trees. To our knowledge, there are no previous records of B. pumilus and S. maltophilia causing decline on Fagaceous trees like Q. brantii. More detailed field and molecular studies are required to confirm the absolute role of such bacteria in occurrence of oak decline in Zagros forests.     

The study of seed yield and seed yield components of lentil (Lens culinaris Medik) under normal and drought stress conditions

In this study yield and seed yield components of twenty lentil (Lens culinaris Medik) genotypes were compared in a split plot RCBD based design with 3 replications at the Zanjan University Research Farmland in 2004. The main plots were lentil planted under drought stress and non stress (irrigation) condition and subplots were twenty of genotype lentil. There were significant differences between traits in lentil genotypes. The seed yield per plant was sensitive to drought stress but 100 seed weight was more tolerance and stable trait in drought condition. As correlation analysis of traits in various stress condition, the harvest index, seed yield per plant, pods per plant and biological yield were correlated with grain yield. In addition harvest index, seed yield per plant, pods/plant and biological yield were the most important traits that have a relationship with grain yield.     

Prediction of soil saturated water content using evolutionary polynomial regression (EPR)

The measurement of saturated water content (SWC) is necessary in the estimation of soil water retention and unsaturated hydraulic conductivity curves. In several studies, pedotransfer functions (PTFs) were developed to predict SWC. Among them, evolutionary polynomial regression (EPR) is one that can operate on large quantities of data in order to capture nonlinear and complex interactions between the variables of the system. In this study, the evolutionary data-mining technique was used to derive new PTFs and different methods were evaluated, such as the soil porosity method, Rosetta method, and others, for the estimation of SWC. For this purpose, 270 soil samples (3:1 ratio for development and validation) from three data sets were used. Among 190 PTFs provided by EPR, one equation with the highest accuracy and the least number of inputs was selected. The EPR predictions were compared with the experimental results as well as the PTFs proposed in previous studies. Comparison of the statistical indicators showed that the ‘proposed PTF’ and ‘porosity method’ are the best and worst methods for the prediction of SWC, respectively. Also, good predictions were achieved from the proposed approaches by the groups of Scheinost, Vereecken, and Williams.     

Broiler litter and inorganic fertilizer effects on seed yield and productivity of buckwheat and fenugreek in row intercropping

We present data of a 2 years field experiment on influence of intercropping and N source on yield and yield components of fenugreek (Trigonella foenum-graecum)–buckwheat (Fagopyrum esculentum) intercrops. The experiment was conducted at the research farm, College of Agriculture, Shahrekord, Iran during 2014 and 2015. Treatments included sole cropping of fenugreek (F), sole cropping of buckwheat (B), and three intercropping ratios. Second factor was N fertilizer type: mineral chemical fertilizer (CF) or broiler litter (BL). Intercropping improved yield of fenugreek and buckwheat compared with sole cropping and BL was the more effective fertilizer especially in intercrops. Fenugreek and buckwheat in intercrops used available environmental resources for increasing pods plant^?1, seeds pod^?1, 1000-seed weight, and harvest index (HI) (for fenugreek) and clusters plant^?1 and 1000-seed weight (for buckwheat) compared to the respective sole crops in both years. The resulting land equivalent ratios (LERs) of intercrops varied from 0.99 to 1.72 and the highest LER was observed in F:B = 2:1 treated with BL. This study shows a potential yield benefit of fenugreek–buckwheat intercropping under semiarid growing conditions when planting fenugreek and buckwheat at a ratio of 2:1, respectively, and applying BL compared with sole cropping with inorganic fertilizer application.     

Improving dairy yield predictions through combined record classifiers and specialized artificial neural networks

A multi-network system, comprising a neural classifier and two specialized neural predictors, was developed for prediction of milk yield from monthly records of Holstein dairy cattle. The classifier categorized records based on high- (≥9000 kg) and low-yields (<9000 kg) after which each set of classified records was fed to a network specialized, or semi-specialized, in prediction of yields for that category. Two differently trained classifiers were developed and compared; one trained with records covering the entire range of milk production, and the other with yields bordering on the cut-off point value of 9000 kg. Training files for the specialized predictors included records from only one yield category while those of the semi-specialized networks contained some records from the other category as well. The classifier trained with border cut-off point yields, had a considerably higher classification rate than the other (99.7% versus 92.3%). In the case of the specialized networks, the addition of records from the opposite category to the training files slightly degraded predictions for the records in the category of their specialization, but considerably improved those of the misclassified records. When misclassifications were marginal, however, the specialized networks produced the highest overall performance. Compared to a network trained with mixed records the combined classifier-predictor system improved the results, although it required an increase in development time.