Water stress is the most important environmental factor that limits plant growth and yield. However, plant growth-promoting rhizobacteria (PGPR) can stimulate resistance of the plant host in unsuitable environmental conditions such as water stress. In order to evaluate whether PGPR improve morphological, physiological, and phytochemical traits of the savory plant Satureja hortensis L., the effects of two PGPR strains of Pseudomonas fluorescens Migula (PF-135 and PF-108) under two water conditions (well-watered and 50 % field capacity) were studied by performing a factorial experiment based on randomized complete block design with three replications under commercial greenhouse. The highest values of root and shoot dry matter, root length, plant height, leaf number, and branch number were observed in PF-135-inoculated plants under well-watered conditions, whereas the abovementioned parameters were found to be the lowest in noninoculated plants under water stress condition. Chlorophyll a, b, total chlorophyll, and carotenoid contents significantly changed under water stress conditions. The H 2 O 2 and MDA contents of root and shoot significantly decreased in plants inoculated with PF-135, whereas their contents increased in non-inoculated plants under water stress condition. The highest shoot oil yield was observed in plants inoculated with PF-135 under water stress condition, while the lowest shoot oil yield was observed in plants inoculated with PF-108 under well-watered condition. Twenty-eight components were found in the essential oils of S. hortensis. Carvacrol (56.81-78.15 %), c-terpinene (9.08-22.87 %), and p-cymene (5.78-14.28 %) were identified as the major components in all treatments. Plants under water stress conditions showed the highest yield of these components when inoculated with bacteria. Thus, we could suggest that the promising strains of P. fluorescens are able to minimize the deleterious effects of water stress on plant growth and improve the morphological and physiological traits of plants as well as increase the essential oil yield and quality.

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SUMMARYThe aim of this research was to determine the effective drought indices such as NDVI, VCI, EVI, TVX, VTCI, VHI, TCI and NVSWI based on some criteria. The satellite-based drought indices have different data from vegetation and temperature conditions. Linear and trend analysis showed that satellite-based drought indices based on vegetation condition were not more efficient indices such as NDVI and VCI. It can be said that the indices with combination of vegetation and temperature condition have more efficiency. A good agreement was observed among drought indices and precipitation in the arid and semi-arid climatic regions. Application of NDVI, VCI and EVI was limited for real time drought monitoring. Investigation the topographic effect on drought indices indicated that VTCI and NDVI had maximum impressed from height variations. The synthesized drought indices based on effective indices led to better performance. Drought indices investigation using different criteria indicated the high performance of NVSWI, TCI, VHI and TVX. The indices have high correlation with each other. Therefore, using one of them is suitable for drought monitoring.

<p>Investigations of the relation between crop yield and climate variables are crucial for agricultural studies and decision making related to crop monitoring. Multiple linear regression (MLR) and support vector regression (SVR) are used to identify and model the impact of climate variables on barley yield. The climate variables of 36 years (1982–2017) are gathered from three provinces of Iran with different climate: Yazd (arid), Zanjan (semi-arid), Gilan (very humid). Air temperature by high correlation coefficient with barley yield was introduced as the dominant climate variable. According to evaluation criteria, SVR provided accurate estimation of crop yield in comparison with MLR. The diversity of climate impressed the estimated yield in which UI, decreasing from Gilan to Yazd provinces, was 47.77%. Support vector machine (SVM) with capturing the nonlinearity of time series, could improve barley yield estimation, with the minimum UI for Yazd province. Also, the minimum correlation coefficient between the observed and simulated yield was found in Gilan province. Based on GMER calculations, SVM forecasts were underestimated in three provinces. All findings show that SVM is able to have high efficiency to model the climate effect on crop yield.</p>

Precipitation forecast, especially on monthly and annual scales, is a key for optimal water resources management and planning, especially in semiarid climates with scarce water. The traditional hybrid models, in which two statistical models are used to separate and simulate linear and nonlinear components of precipitation time series, are still unable to provide accurate precipitation forecasts. This research aims to improve hybrid forecast models by combining one linear model and three nonlinear models with two preprocessing configurations: 1) using residuals of a linear model, representing the nonlinear component with different time steps and 2) using original time series of observations with different time steps, linear model simulations and residuals. Gene Expression Programming (GEP), Support Vector Regression (SVR) and Group Method of Data Handling (GMDH) models were used individually as in the traditional hybrid models and combinedly as in the proposed hybrid models in this study. The performance of the hybrid models was improved by different methods such as inverse variance (Iv) as an error-based method, least square regression, genetic algorithm and SVR. Two weather stations of Tabriz (annual) and Rasht (monthly) in Iran were selected to test the developed models. The results showed that Theil’s coefficient, UII, decreased in configuration one for the Tabriz station by 9% and 15% for SVR and GMDH relative to GEP, suggesting that these two models performed better than GEP in the precipitation forecast. The error criteria used in developing the proposed hybrid models with all forecast combination methods better represent observations than the hybrid model. MSE decreased by 67% and Nash Sutcliffe increased by 5% in the Rasht station in configuration two when we combined the three models using GA to obtain the improved hybrid model relative to the hybrid model combined with SVR. Generally, the hybrid models when SVR, the error based methods and GA were incorporated showed better performance than traditional hybrid models. The developed models have implications for modeling highly nonlinear systems using full advantages of machine learning methods.