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Germination of seeds and growth of seedling respond to seed priming as priming can guard the damage of salinity stress. A study conducted in the net house of the Department of Agronomy, BAU, Mymensingh during the period from November 2012 to April 2013 investigated the ameliorative effect of seed priming on seed germination and seedling attributes of maize under various salinity stress conditions. The experiment consisted of five seed priming and four salinity levels (NaCl) and laid out in a Completely Randomized Design (CRD) with three replications. Seed germination and seedling attributes of maize varied due to salinity stress and priming. The highest seed germination (95.7%) was recorded when no stress was imposed under hydropriming (48 hours). The germination of seeds performed even well (92.3%) when treated with hydropriming (48 hours) and NaCl@0.25 dS•m −1 which was followed by hydropriming (24 hours) under no salinity imposed and NaCl@0.25 dS•m −1 salinity. Hydropriming for 48 hours without salt stress performed the best on number of leaves seedling −1 (8), shoot length (28.2 cm), root length (14.5 cm), fresh weight (100.8 g) and dry weight of seedling (50.3 g). The germination and seedling growth parameters were reduced with the increase in salinity levels irrespective of priming while all seed priming treatments showed ameliorative effects. However, reduction in seed germination and seedling attributes were minimal with hydropriming for 48 hours. The results revealed that priming of maize seeds could be used for amelioration of salinity stress and hydropriming for 48 hours appeared as the best seed priming treatment.

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Purpose of the research is to investigate service recovery strategies in case of small restaurant brands, customer perception regarding these strategies and their expectations intensely. As the study is qualitative and exploratory in nature, data were collected via in-depth interviews from duo perspective; i.e. service providers’ and customers’ perspective. The results showed that even though the managements are very cautious as regards the service recovery strategies, and customers’ complaints, but sometimes even common service recovery actions fall below the customers’ anticipations. On the other hand, restaurant those are somehow innovative with their recovery strategies; i.e. designed with a clear understanding of customers’ needs, involving customers with the recovery strategies, empowering the service staffs, emphasizing on continuous improvement and customer engagement are getting more responses in case of customer satisfaction, loyalty; and finally, ensures firms sustainability in competitive market as well.

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One of the primary challenges of our time is to feed a growing and more demanding world population with degraded soil environments under more variable and extreme climate conditions. Conservation tillage (CS) and deep tillage (DT) have received strong international support to help address these challenges but are less used in major global food production in China. Hence, we conducted a large-scale literature search of English and Chinese publications to synthesize the current scientific evidence to evaluate the effects of CS and DT on soil protection and yield maintenance in the Northeast China Plain, which has the most fertile black soil (Mollisols) and is the main agricultural production area of China. As a result, we found that CS had higher soil bulk density, strong soil penetration resistance, greater water contents, and lower soil temperature, and was well-suited for dry and wind erosion-sensitive regions i.e., the southwest areas of the Northeast. Conversely, DT had better performance in the middle belt of the Northeast China Plain, which contained a lower soil temperature and humid areas. Finally, we created an original dataset from papers [dataset 1, including soil physio-chemical parameters, such as soil water, bulk density, organic carbon, sand, silt, clay, pH, total and available nitrogen (N), phosphorus (P), and potassium (K), etc., on crop biomass and yield], by collecting data directly from publications, and two predicted datasets (dataset 2 and dataset 3) of crop yield changes by developing random forest models based on our data.