Introduction: Water and nitrogen deficits are the most important limiting factors for plant growth and crop production in the world. Drought stress would be amplified by the global warming. Moreover, nitrogen scarcity is occurred in most arid and semi-arid areas. Cumin (Cuminum cyminum L.) is an important plant due to export benefits and low water demand. This study was aimed to evaluate nitrogen fertilizer effect on yield and some physiological characteristics of cumin under different irrigation regimens. Methods: The experiment was performed based on a split plot as randomized complete block design. Experiment treatments were irrigation regimens (field capacity, irrigation by draining 40% of soil water as middle stress, and irrigation by draining 80% of soil water as severe stress) and nitrogen fertilizers (60 kg ha-1 urea, 30 kg ha-1 urea, Nitroxin, and Nitroxin + 30 kg ha-1 urea). Results: Drought stress reduced cumin dry weight, seed yield, and chlorophyll content. In contrary, proline content, malondialdehyde (MDA) rate, phenol content, anthocyanin amount, and activity of catalase (CAT) and peroxidase (POX) increased by water stress. Increment urea use resulted in amending cumin growth and seed yield in the field capacity. Also, nitrogen use and raising its rate under the middle water stress caused to improve cumin drought tolerance. However, under the severe water stress, nitrogen application had not a significant impress on drought acclimation and seed yield. Conclusion: Nitroxin inoculation with use of 30 kg ha-1 urea was the most effective treatment to ameliorate seed yield and drought tolerance.

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Poor germination capacity of stevia is a major problem in its cultivation. Moreover, drought stress is one of the most major environmental constraints, which influences seed germination and early seedling growth of many crops. The aim of this research was to evaluate the impact of seed priming with salicylic acid (SA), zinc (Zn) and iron (Fe) on some germination parameters and physiological attributes of stevia seedlings under drought stress induced by polyethylene glycol (PEG) 6000 (0, -3, -6 and -9 bars). The results revealed that germination traits (germination percentage, germination rate, mean germination time, germination value, seedling length, and seedling vigor index) and chlorophyll (Chl) content were negatively affected by drought stress. However, the reduction of germination parameters in seedlings exposed to drought stress in most cases was moderated by seed priming, which also increased the Chl content at all levels of drought stress as compared with the control. Drought stress also increased the proline accumulation and the enzymatic activity of catalase (CAT) and peroxidase (POD) in all priming treatments, but these enhancements were significantly higher in primed seedlings than those in unprimed ones. Among all priming treatments, priming with SA + Fe + Zn was found to be more effective than other treatments to improve growth and physiological characteristics under normal and drought stress conditions. Thus, we suggest that seed priming with SA, Fe, Zn and particularly the integrated application of these three agents at a suitable concentration can promote the poor germination performance of stevia and improve the seedling growth by increasing the antioxidant capacity under drought conditions.

Storage of potatoes is one of the most important concerns in maintaining freshness and nutritional quality in the storage process. To achieve this, an experiment was carried out with five different storage conditions at various temperatures using fresh rosemary leaves and branches with three replicates. The results revealed that storage of potatoes at 25°C with rosemary leaves and branches resulted in the lowest sprout development and weight loss after 10 weeks. This was significantly different from either 4°C or 30°C. The findings indicated the potential of rosemary fresh leaves and branches to improve potato storage life considering its simplicity and efficacy in decreasing the storage cost, the weight loss and sprouting without causing any environmental toxicity. In addition, the potential of rosemary could be used to other fruits and vegetables to investigate further the possible role of rosemary application to prevent fungal rot.

Geostatistical interpolation is widely used to map spatial variability in physical and chemical properties of soil, such as organic matter content, particle density; and pH. Geostatistical interpolation is a branch of applied science which predicts spatial concentrations at unknown locations at a study area by incorporating limited measured data, which is a major advantage over classical statistics. Although many studies applied geostatistical interpolation in agricultural land, there are still gaps in knowledge in selecting suitable models to map soil properties on a large geographical location. The objectives of this paper were to examine and to map the spatial distribution of the soil physico-chemical properties, including electric conductivity (EC), pH, sodium absorption ratio (SAR), organic matter (OM), percentage of sand, silt and clay, bulk density (ρb), saturate percentage (SP), and mean weight diameter (MWD), at 800 hectares of agro-industrial land at Sharifabad, Qazvin, Iran. The soil samples were collected in total 275 points in a regular grid (100 × 100m) over the study area. The exploratory statistical analysis was applied on the collected data for understanding the distribution of the dataset. The silt content, clay content and OM data showed normal frequency distribution, and the pH data show near to normal frequency distribution. The remaining soil properties data, including SAR, EC, SP, MWD, sand content and bulk density showed log-normal distribution, which was identified by the normality test of Kolmogorov-Smirnov with an error probability of 1%. The spatial characteristics of the dataset were assessed by semivariogram models in GS + and GIS 10.3 software. Among the four different semivariogram models, namely linear, exponential, Gaussian and spherical, the best performing model was chosen following the highest R 2 and lowest error values. The predictive geostatistical interpolation maps for each variable were drawn using ordinary kriging model.