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Abstract According to McKinsey & Company, about a third of food produced is lost or wasted every year, amounting to a $940 billion economic hit. Inefficiencies in planting, harvesting, water use, reduced animal contributions, as well as uncertainty about weather, pests, consumer demand and other intangibles contribute to the loss. Precision Agriculture (PA) and Precision Livestock Farming (PLF) come to assist in optimizing agricultural and livestock production and minimizing the wastes and costs aforementioned. PA is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. PLF is also a technology-enabled, data-driven approach to livestock production management, which exploits technology to quantitatively measure the behavior, health and performance of animals. Big data delivered by a plethora of data sources related to these domains, has a multitude of payoffs including precision monitoring of fertilizer and fungicide levels to optimize crop yields, risk mitigation that results from monitoring when temperature and humidity levels reach dangerous levels for crops, increasing livestock production while minimizing the environmental footprint of livestock farming, ensuring high levels of welfare and health for animals, and more. By adding analytics to these sensor and image data, opportunities also exist to further optimize PA and PLF by having continuous data on how a field or the livestock is responding to a protocol. For these domains, two main challenges exist: 1) to exploit this multitude of data facilitating dedicated improvements in performance, and 2) to make available advanced infrastructure so as to harness the power of this information in order to benefit from the new insights, practices and products, efficiently time-wise, lowering responsiveness down to seconds so as to cater for time-critical decisions. The current paper aims to introduce CYBELE, a platform aspiring to safeguard that the stakeholders involved in the agri-food value chain (research community, SMEs, entrepreneurs, etc.) have integrated, unmediated access to a vast amount of very large scale datasets of diverse types and coming from a variety of sources, and that they are capable of actually generating value and extracting insights out of these data, by providing secure and unmediated access to large-scale High Performance Computing (HPC) infrastructures supporting advanced data discovery, processing, combination and visualization services, solving computationally-intensive challenges modelled as mathematical algorithms requiring very high computing power and capability.

AbstractWhile several recent studies have focused on global insect population trends, all are limited in either space or taxonomic scope. As global monitoring programs for insects are currently not implemented, inherent biases exist within most data. Expert opinion, which is often widely available, proves to be a valuable tool where hard data are limited. Our aim is to use global expert opinion to provide insights on the root causes of potential insect declines worldwide, as well as on effective conservation strategies that could mitigate insect biodiversity loss. We obtained 753 responses from 413 respondents with a wide variety of spatial and taxonomic expertise. The most relevant threats identified through the survey were agriculture and climate change, followed by pollution, while land management and land protection were recognized as the most significant conservation measures. Nevertheless, there were differences across regions and insect groups, reflecting the variability within the most diverse class of eukaryotic organisms on our planet. Lack of answers for certain biogeographic regions or taxa also reflects the need for research in less investigated settings. Our results provide a novel step toward understanding global threats and conservation measures for insects.

The use of weather satellite recordings has been growing rapidly over the last three decades. Determining the patterns between meteorological and topographical features is an important scientific job. Cloud cover analysis and properties can be of the utmost significance for potential cloud seeding. Here, the analysis of the cloud properties was conducted by means of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite recordings. The resolution of used data was 1 km2 within the period of 30 years (1989–2019). This research showed moderate changing of cloudiness in the territory of Serbia with a high cloudiness in February, followed by cloudiness in January and November. For the past three decades, May has been the month with the highest cloudiness. The regions in the east and south-west, and particularly in the west, have a high absolute cloudiness, which is connected with the high elevation of the country. By means of long term monitoring, the whole territory of Serbia was analyzed for the first time, in terms of cloudiness. Apart from the statistical and numerical results obtained, this research showed a connection between relief and clouds, especially in the winter season. Linear regression MK (Mann-Kendall test) has proven this theory right, connecting high elevation sides with high absolute cloudiness through the year. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Symbiotic di-nitrogen fixation of grain legumes has a substantial impact on crop performance, harvest product quality, and nitrogen (N) balance of crop rotations, particularly under organic management regimes. In soybean breeding, selection for increased nitrogen fixation is desirable for improving seed protein content and N balance of cropping systems. However, the lack of high-throughput screening methods for direct measurement of N2 fixation rates prohibits practical breeding efforts. Therefore, hyperspectral canopy reflectance measurement as a field-based phenotyping method was evaluated in three environments for indirect estimation of N fixation and uptake of soil nitrogen in a set of early maturity soybean genotypes exhibiting a wide range in seed protein content. Reflectance spectra were collected in repeated measurements during flowering and early seed filling stages. Subsequently, various spectral reflectance indices (SRIs) were calculated for characterizing nitrogen accumulation of individual genotypes. Moreover, prediction models for seed protein content as an end-of-season target trait were developed utilizing full spectral information in partial-least-square regression (PLSR) models. A number of N-related SRIs calculated from spectral reflectance data recorded at the beginning of the seed filling stage were significantly correlated to seed protein content. The best prediction of seed protein content, however, was achieved in PLSR models (validation R2 = 0.805 across all three environments). Environments lower in initial soil mineral N content appeared as more favorable selection sites in terms of prediction accuracy, because N fixation is not masked by soil N uptake in such environments. Hyperspectral reflectance data proved to be a valuable method for determining genetic variation in crop N accumulation, which might be implemented in high-throughput screening protocols for N fixation in plant breeding programs.

Green Public Procurement (GPP) became an efficient instrument to achieve the objectives of environmental policy expressed by the European Commission in its Communications. At the same time, it must be addressed by the public authorities as a complex process, in which all purchased goods and services must integrate perfectly into an entire puzzle-like system of legislation, the construction field, innovation, healthcare, food, and education. Scientific references published in the Web of Science (WoS) mainly between 2017 and 2020 were investigated, and they analyze the implications of green public procurement in various fields, as presented by scientific communities. This article brings as a novelty in this context the identification of some barriers in the adoption of these processes, so that they can be overcome. Based on good practices and international standards and trends, the article shows how aspects related to the implementation of green procurement in society can be taken into account. In the second stage, we added a case study on Romanian green agriculture and discussions regarding inter-correlation between different fields and GPP. info:eu-repo/semantics/publishedVersion

Natural beauties of Eastern Serbia combined with the culture, traditions, food specialties and music, can become a recognizable tourism brand, which could improve the image of this part of Serbia, and that is the reason why Eastern Serbia has been recently discovering its potential for tourism development. The aim of this paper is to analyze the current and potential elements for tourism development of Eastern Serbia’s products. Based on the relevant literature and factual situation on the feld, the goal is to rank strategies for tourism development on the territory of Eastern Serbia in order to enable better positioning of tourism in Eastern Serbia on the Serbian tourist map. In order to rank tourism development strategies we have used neutrosophic sets. The justification and usability of the proposed approach for the selection of tourism development strategy is demonstrated in the implemented numerical example.

A stand-alone lidar-based method for detecting airborne hazards for aviation in near real time (NRT) is presented. A polarization lidar allows for the identification of irregular-shaped particles such as volcanic dust and desert dust. The Single Calculus Chain (SCC) of the European Aerosol Research Lidar Network (EARLINET) delivers high-resolution preprocessed data: the calibrated total attenuated backscatter and the calibrated volume linear depolarization ratio time series. From these calibrated lidar signals, the particle backscatter coefficient and the particle depolarization ratio can be derived in temporally high resolution and thus provide the basis of the NRT early warning system (EWS). In particular, an iterative method for the retrieval of the particle backscatter is implemented. This improved capability was designed as a pilot that will produce alerts for imminent threats for aviation. The method is applied to data during two diverse aerosol scenarios: first, a record breaking desert dust intrusion in March 2018 over Finokalia, Greece, and, second, an intrusion of volcanic particles originating from Mount Etna, Italy, in June 2019 over Antikythera, Greece. Additionally, a devoted observational period including several EARLINET lidar systems demonstrates the network’s preparedness to offer insight into natural hazards that affect the aviation sector. European Commission European Commission Joint Research Centre 725698 E-shape (EuroGEOSS Showcases: Applications Powered by Europe) 820852 Ministry of Research and Innovation, Ontario 19PFE/17.10.2018 Romanian National Core Program 18N/2019 ACTRIS preparatory phase 739530 EUNADICS-AV 723986 ACTRIS-2 654109