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Covering almost a quarter of Brazil, the Cerrado is the world's most biologically rich tropical savanna. Fire is an integral part of the Cerrado but current land use and agricultural practices have been changing fire regimes, with undesirable consequences for the preservation of biodiversity. In this study, fire frequency and fire return intervals were modelled over a 12-year time series (1997-2008) for the Jalapão State Park, a protected area in the north of the Cerrado, based on burned area maps derived from Landsat imagery. Burned areas were classified using object based image analysis. Fire data were modelled with the discrete lognormal model and the estimated parameters were used to calculate fire interval, fire survival and hazard of burning distributions, for seven major land cover types. Over the study period, an area equivalent to four times the size of Jalapão State Park burned and the mean annual area burned was 34%. Median fire intervals were generally short, ranging from three to six years. Shrub savannas had the shortest fire intervals, and dense woodlands the longest. Because fires in the Cerrado are strongly responsive to fuel age in the first three to four years following a fire, early dry season patch mosaic burning may be used to reduce the extent of area burned and the severity of fire effects.

Insects are the most diverse group of animals on the planet, comprising over 90% of all metazoan life forms, and have adapted to a wide diversity of ecosystems in nearly all environments. They have evolved highly sensitive chemical senses that are central to their interaction with their environment and to communication between individuals. Understanding the molecular bases of insect olfaction is therefore of great importance from both a basic and applied perspective. Odorant binding proteins (OBPs) are some of most abundant proteins found in insect olfactory organs, where they are the first component of the olfactory transduction cascade, carrying odorant molecules to the olfactory receptors. We carried out a search for OBPs in the genome of the parasitoid wasp Nasonia vitripennis and identified 90 sequences encoding putative OBPs. This is the largest OBP family so far reported in insects. We report unique features of the N. vitripennis OBPs, including the presence and evolutionary origin of a new subfamily of double-domain OBPs (consisting of two concatenated OBP domains), the loss of conserved cysteine residues and the expression of pseudogenes. This study also demonstrates the extremely dynamic evolution of the insect OBP family: (i) the number of different OBPs can vary greatly between species; (ii) the sequences are highly diverse, sometimes as a result of positive selection pressure with even the canonical cysteines being lost; (iii) new lineage specific domain arrangements can arise, such as the double domain OBP subfamily of wasps and mosquitoes.

Exploration of an unknown environment is a fundamental concern in mobile robotics. This paper presents an approach for cooperative multi-robot exploration, fire searching and mapping in an unknown environment. The proposed approach aims to minimize the overall exploration time, making it possible to localize fire sources in an efficient way. In order to achieve this goal, the robots should cooperate in an effective way, so they can individually and simultaneously explore different areas of the environment while they identify fire sources. The proposed approach employs a decentralized frontier based exploration method which evaluates the cost-gain ratio to navigate to target way-points. The target way-points are obtained by an A* search variant algorithm. The potential field method is used to control the robots motion while avoiding obstacles. When a robot detects a fire, it estimates the flame's position by triangulation. The communication between the robots is done in a decentralized control way where they share the necessary data to generate the map of the environment and to perform cooperative actions in a behavioral decision making way. This paper presents simulation and experimental results of the proposed exploration and fire search method and concludes with a discussion of the obtained results and future improvements. 1

Geostatistical tools were used to estimate spatial relations between wheat yield and soil parameters under organic farming field conditions. Thematic maps of each factor were created as raster images in R software using kriging. The Geographic Resources Analysis Support System (GRASS) calculated the principal component analysis raster images for soil parameters and yield. The correlation between the raster arising from the PC1 of soil and yield parameters showed high linear correlation (r = 0.75) and explained 48.50% of the data variance. The data show that durum wheat yield is strongly affected by soil parameter variability, and thus, the average production can be substantially lower than its potential. Soil water content was the limiting factor to grain yield and not nitrate as in other similar studies. The use of precision agriculture tools helped reduce the level of complexity between the measured parameters by the grouping of several parameters and demonstrating that precision agriculture tools can be applied in small organic fields, reducing costs and increasing wheat yield. Consequently, site-specific applications could be expected to improve the yield without increasing excessively the cost for farmers and enhance environmental and economic benefits. Foundation for Science and Technology (Fundacao para a Ciencia e a Tecnologia), Portugal [SFRH/BD/8303/2002]; Research Center of Spatial and Organizational Dynamics (CIEO); Ministery of Science, Culture and Sport, Israel; Bundesmenisterium fuer Bildung and Forschung (BMBF)

Acacia spp. are widespread all over the Portuguese territory, representing a threat to local biodiversity and to the productivity of the forest sector. The measures adopted in some countries for their eradication or to control their propagation are expensive, have been considered unfeasible from practical and economical perspectives, and have generated large amounts of residue that must be valorized in a sustainable way. This review brings together information on the valorization of bark, wood, leaves, flowers, pods, seeds, roots, and exudates from Acacia spp., through the production of high-value bioactive extracts (e.g., antioxidant, antimicrobial, anti-inflammatory, antidiabetic, antiviral, anthelmintic, or pesticidal agents, suitable to be explored by pharmaceutical, nutraceutical, cosmetics, and food and feed industries), its incorporation in innovative materials (e.g., polymers and composites, nanomaterials, low-cost adsorbents), as well as through the application of advanced thermochemical processes (e.g., flash pyrolysis) and pre-treatments to decompose biomass in its structural components, regarding the production of biofuels along with valuable chemicals derived from cellulose, hemicellulose, and lignin. The knowledge of this research is important to encourage an efficient and sustainable valorization of Acacia spp. within a biorefinery concept, which can bring a significant economic return from the valorization of these residues, simultaneously contributing to forest cleaning and management, to reduce the risk of fires, and to improve the social-economic development of rural areas.

In recent years, deep learning algorithms have been successfully applied in the development of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases, weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as lack of natural resources and climate change, an efficient decision support system for irrigation is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved the values of mean square error values within the range of 0.014 to 0.056 and R2 ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error as loss function performed better than the model with other loss functions. Project Centro-01-0145-FEDER000017-EMaDeS-Energy, Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020), within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia (FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST).

Producción Científica Pines are major components of native forests and plantations in Europe, where they have both economic significance and an important ecological role. Diseases of pines are mainly caused by fungal and oomycete pathogens, and can significantly reduce the survival, vigor, and yield of both individual trees and entire stands or plantations. Pine pitch canker (PPC), caused by Fusarium circinatum (Nirenberg and O’Donnell), is among the most devastating pine diseases in the world, and is an example of an emergent invasive disease in Europe. The effects of microbial interactions on plant health, as well as the possible roles plant microbiomes may have in disease expression, have been the focus of several recent studies. Here, we describe the possible effects of co-infection with pathogenic fungi and oomycetes with F. circinatum on the health of pine seedlings and mature plants, in an attempt to expand our understanding of the role that biotic interactions may play in the future of PPC disease in European nurseries and forests. The available information on pine pathogens that are able to co-occur with F. circinatum in Europe is here reviewed and interpreted to theoretically predict the effects of such co-occurrences on pine survival, growth, and yield. Beside the awareness that F. circinatum may co-occurr on pines with other pathogens, an additional outcome from this review is an updating of the literature, including the so-called grey literature, to document the geographical distribution of the relevant pathogens and to facilitate differential diagnoses, particularly in nurseries, where some of them may cause symptoms similar to those induced by F. circinatum. An early and accurate diagnosis of F. circinatum, a pathogen that has been recently introduced and that is currently regulated in Europe, is essential to prevent its introduction and spread in plantings and forests. Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (Proyect AGL2015-69370-R) Russian Foundation for Basic Research (Grant No. 17-04-01486) Portuguese Foundation for Science and Technology (PTDC/AGR-FOR/2768/2014) Ministry of Education, Youth and Sports of the Czech Republic and the European Regional Development Fund, Grant No. CZ.02.1.01/0.0/0.0/15_003/0000453. Romanian National projects PN18040105/2018 and PN19070206/2019

project number POCI-01-0145-FEDER-007688. Electronic skin (e-skin) is pursued as a key component in robotics and prosthesis to confer them sensing properties that mimic human skin. For pressure monitoring, a great emphasis on piezoresistive sensors was registered due to the simplicity of sensor design and readout mechanism. For higher sensitivity, films composing these sensors may be micro-structured, usually by expensive photolithography techniques or low-cost and low-customizable molds. Sensors commonly present different sensitivities in different pressure ranges, which should be avoided in robotics and prosthesis applications. The combination of pressure sensing and temperature is also relevant for the field and has room for improvement. This work proposes an alternative approach for film micro-structuration based on the production of highly customizable and low-cost molds through laser engraving. These bimodal e-skin piezoresistive and temperature sensors could achieve a stable sensitivity of -6.4 × 10-3 kPa-1 from 1.6 kPa to 100 kPa, with a very robust and reproducible performance over 27,500 cycles of objects grasping and releasing and an exceptionally high temperature coefficient of resistance (TCR) of 8.3%/°C. These results point toward the versatility and high benefit/cost ratio of the laser engraving technique to produce sensors with a suitable performance for robotics and functional prosthesis. publishersversion published