Factors affecting the establishment and growth of annual legumes in semi-arid mediterranean grasslands

Abstract Legumes are an important component of mediterranean grasslands with a significant ecological and economic role. The aim of this study was to investigate the factors that affect their establishment and growth and how they survive in a highly variable and unpredictable environment. The research was carried out in a grassland characterised by a semi-arid mediterranean climate and located on a calcareous substrate at about 150 m a.s.l., in Macedonia, northern Greece. It was dominated by annual legumes such as Hippocrepis multisiliquosa, Medicago disciformis, Medicago minima, Onobrychis aequindentata, Trifolium angustifolium, Trifolium campestre and Trifolium scabrum. It was subjected to the following treatments for four consecutive years: prescribed burning, irrigation, digging, cutting, P fertilization and control. Total legume density was measured in late autumn and in the following spring each year, while total legume biomass was measured only in spring. Dominant legume species densities and biomasses were measured only in spring in the last 3 years. Also, monthly precipitation and air temperature were recorded in a nearby weather station. A great reduction of both legume density and biomass occurred at the third growing season due to adverse weather conditions. Among treatments, P fertilization affected the positively annual legume density and biomass. The other treatments such as burning, irrigation, digging and cutting influenced positively or negatively annual legume density and biomass depending on the climatic characteristics of the particular growing season involved. It is concluded that in semi-arid mediterranean grasslands with cold winters, weather conditions strongly interact with human disturbances in affecting establishment and growth of annual legumes

Digital Implementation of Various Locking Schemes of Ultrastable Photonics Systems

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Toward Big Data Manipulation for Grape Harvest Time Prediction by Intervals' Numbers Techniques

The automation of agricultural production calls for accurate prediction of the harvest time. Our interest in particular here is in grape harvest time. Nevertheless, the latter prediction is not trivial also due to the scale of data involved. We propose a novel neural network architecture that processes whole histograms induced from digital images. A histogram is represented by an Intervals' Number (IN); hence, all-order data statistics are represented. In conclusion, the proposed IN Neural Network, or INNN for short, emerges with the capacity of predicting an IN from past INs. We demonstrate a proof-of-concept, preliminary application on a time series of digital images of grapes taken during their growth to maturity. Compared to a conventional Back Propagation Neural Network (BPNN), the results by INNN are superior not only in terms of prediction accuracy but also because the BPNN predicts only first-order data statistics, whereas the INNN predicts all-order data statistics

Frequency and quantity of egg intake is not associated with dyslipidemia: The hellenic national nutrition and health survey (HNNHS)

Background: Gaps remain on the safety of egg intake on cardiovascular health, setting the study’s aim to investigate the association between quantity and frequency of egg consumption, with established dyslipidemia. Methods: Study participants (N = 3558, 40.3% males) included individuals from the Hellenic National and Nutrition Health Survey (HNNHS), of national representation. Quantity and frequency of egg consumption was determined. Minimally adjusted, multivariable logistic and linear analysis were used to assess egg consumption and dyslipidemia. Results: The more frequent egg consumption compared to no or rare egg consumption significantly decreased the odds of dyslipidemia in the minimally adjusted (Odds Ratio (OR) for frequency: 0.83; 95% Confidence Interval (CI): 0.752, 0.904; OR for quantified frequency: 0.87; 95% CI: 0.796, 0.963) and the fully adjusted models (OR for frequency: 0.80; 95% CI: 0.718, 0.887; OR for quantified frequency: 0.85; 95%CI: 0.759, 0.945). Level of serum cholesterol and LDL-c were significantly lower with higher frequency and quantified frequency of egg consumption in all models. Conclusion: Eggs do not increase the risk of dyslipidemia and can be consumed as part of a healthy diet that is high in fiber and low in saturated fat, without excessive energy intake, by all individuals. © 2019 by the authors. Licensee MDPI, Basel, Switzerland