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

Basics of Sustainable Diets and Tools for Assessing Dietary Sustainability: A Primer for Researchers and Policy Actors

Climate change can have economic consequences, affecting the nutritional intake of populations and increasing food insecurity, as it negatively affects diet quality parameters. One way to mitigate these consequences is to change the way we produce and consume our food. A healthy and sustainable diet aims to promote and achieve the physical, mental, and social well-being of the populations at all life stages, while protecting and safeguarding the resources of the planet and preserving biodiversity. Over the past few years, several indexes have been developed to evaluate dietary sustainability, most of them based on the EAT-Lancet reference diet. The present review explains the problems that arise in human nutrition as a result of climate change and presents currently available diet sustainability indexes and their applications and limitations, in an effort to aid researchers and policy actors in identifying aspects that need improvement in the development of relevant indexes. Overall, great heterogeneity exists among the indicators included in the available indexes and their methodology. Furthermore, many indexes do not adequately account for the diets’ environmental impact, whereas others fall short in the economic impact domain, or the ethical aspects of sustainability. The present review reveals that the design of one environmentally friendly diet that is appropriate for all cultures, populations, patients, and geographic locations is a difficult task. For this, the development of sustainable and healthy diet recommendations that are region-specific and culturally specific, and simultaneously encompass all aspects of sustainability, is required

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