Stachys pilifera Benth is an endemic species of Iran where it is found in the mountainous habitats of Zagros region. The plant is a rich source of secondary metabolites endowed with different medicinal properties. Here, for the first time, the essential oil variability among eleven wild populations of S. pilifera, was investigated. Results indicated that the variation of the chemical profile of essential oils was remarkable. The most abundant components were cis-chrysanthenyl acetate (19.1-48.2%), viridiflorol (1.4-19.1%), trans-caryophyllene (2.3-11.9%), caryophyllene oxide (1.9-11.0%), limonene (2.0- 5.9%) and spathulenol (0.0- 9.5). Based on the cluster analyses (CA), four main chemotypes were recognized: chemotype I (cis-chrysanthenyl acetate), chemotype II (cis-chrysanthenyl acetate/viridiflorol), chemotype III (cis-chrysanthenyl acetate/viridiflorol/spathulenol), chemotype IV (cis-chrysanthenyl acetate/trans-caryophyllene/α-pinene). The chemical variation detected can be useful to consider these populations for pharmaceutical industries and industrial applications as well as for domestication and conservation purposes. Graphical Abstract

The pollution of soils and groundwater by heavy metal pollutants is a widespread environmental problem, particularly in Iran. One cost-effective method to remediate the polluted soils is phytoremediation, in which different plants are used to extract the heavy metals from soil and accumulate them in their organs. In this study, the phytoremediation potential of native plants of Southwestern Iran in removing nickel and lead from polluted soil was assessed separately and in combination with organic amendments of compost and biochar.Hence, the pot experiments were conducted in the greenhouse using soil gathered from the Pazanan oil refineryin Gachsaran, located at Kohgiluyeh and Boyer-Ahmad Province. Then, the hierarchical cluster analysis was applied to classify the different treatments of amendments.The results indicated that the phytoremediation strength of three native species for Ni uptake was C. procera> St. Plumosa > M. sativa; and for Pb uptake was St. Plumosa > C. procera> M. sativa. Also, the utilisation of organic materials originated from urban waste caused to increase in the Ni and Pb accumulation in root and shoot of these species as well as the residual concentration of the metals in soil. Based on the cluster analysis, the treatment of biochar 2% showed the most effect on the residual concentration of heavy metals in soil (1128.00 mgkg⁻¹);the treatment of biochar 1% represented the highest accumulation of metals at the root (158.82 mgkg⁻¹), while all treatments of different amounts of amendments have the same effect on the accumulation of heavy metals in the shoot (151.38 mgkg⁻¹). In general, cluster analysis showed that the organic amendments, especially biochar even at low amounts can improve the phytoremediation capability of the studied native species in removing Ni and Pb from polluted soil.

Festuca ovinaL. (sheep fescue), a perennial grass plant found in mountainous regions, is important from both an ecological and economic viewpoint. However, the variability of biological yield of sheep fescue due to its reliance on different characteristics makes it difficult to accurately prediction using classic modeling techniques. In this study, machine learning methods and multiple regression models (linear and non-linear) are used to investigate the interdependence of various morphological and physiological characteristics on accurate prediction of the biological yield (BY) of sheep fescue. Principal components analysis and stepwise regression were used to select six agronomic parameters i.e. thousand seed weight (TSW), relative water content (RWC), canopy cover (CC), leaf area index, number of florescence, and viability (VA), while the output variable was BY. To optimized the artificial neural network (ANN) structure, different transfer functions and training algorithms, different number of neurons in each layer, different number of hidden layers and training iteration were tested. The accuracy of the models and algorithms is analyzed by root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R²). According to the findings, ANN models were more accurate than regression models. The ANN model with two hidden layers (i.e. structure of 6–4–8–1) which had RMSE, MAE and R²scores of 0.087, 0.065 and 0.96, respectively, was discovered as the best model for predicting the BY. In addition, result of the sensitivity analysis showed TSW, RWC and CC, in that order, were the variables most important for high-quality BY estimation in both models regardless of input combination. Finally, the paper concludes that early flowering sheep fescue genotypes with long maturation and great TSW must be regarded as the most suitable model for increasing BY in breeding projects.