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Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively.

As a consequence of COVID-19, millions of households have suffered mobility restrictions and changes in their lifestyle over several months. The aim of this study is to evaluate the effects of COVID-19 home confinement on the food habits, lifestyle and emotional balance of the Spanish population. This cross-sectional study used data collected via an anonymous online questionnaire during the month before lockdown finished in Spain, with a total of 675 participants. 38.8% of the respondents experienced weight gain while 31.1% lost weight during confinement. The increase in body weight was positively correlated with age (Rs = 0.14, p < 0.05) and BMI (Rs = 0.20, p < 0.05). We also identified that 39.7% reported poorer quality sleep, positively correlated with BMI (Rs = −0.18, p < 0.05) and with age (Rs = −0.21, p < 0.05). 44.7% of the participants had not performed physical exercise during confinement with differences by sex (p < 0.05), by age (p < 0.05), by BMI (p < 0.05) and by sleep quality (p < 0.05). According to an emotional-eater questionnaire, 21.8% and 11% were classified as an emotional eater or a very emotional eater, respectively. We emphasize the importance of adopting a healthy lifestyle, as the COVID-19 pandemic is ongoing.

The ideal magnetocaloric material would lay at the borderline of a first-order and a second-order phase transition. Hence, it is crucial to unambiguously determine the order of phase transitions for both applied magnetocaloric research as well as the characterization of other phase change materials. Although Ehrenfest provided a conceptually simple definition of the order of a phase transition, the known techniques for its determination based on magnetic measurements either provide erroneous results for specific cases or require extensive data analysis that depends on subjective appreciations of qualitative features of the data. Here we report a quantitative fingerprint of first-order thermomagnetic phase transitions: the exponent n from field dependence of magnetic entropy change presents a maximum of n  > 2 only for first-order thermomagnetic phase transitions. This model-independent parameter allows evaluating the order of phase transition without any subjective interpretations, as we show for different types of materials and for the Bean–Rodbell model. Magnetocaloric materials often perform best when their magnetic transitions are at the boundary between first- and second-order behavior. Here the authors propose a simple criterion to determine the order of a transition, which may accelerate future magnetocaloric material searches.

The acquisition, processing, and interpretation of thermal images from unmanned aerial vehicles (UAVs) is becoming a useful source of information for agronomic applications because of the higher temporal and spatial resolution of these products compared with those obtained from satellites. However, due to the low load capacity of the UAV they need to mount light, uncooled thermal cameras, where the microbolometer is not stabilized to a constant temperature. This makes the camera precision low for many applications. Additionally, the low contrast of the thermal images makes the photogrammetry process inaccurate, which result in large errors in the generation of orthoimages. In this research, we propose the use of new calibration algorithms, based on neural networks, which consider the sensor temperature and the digital response of the microbolometer as input data. In addition, we evaluate the use of the Wallis filter for improving the quality of the photogrammetry process using structure from motion software. With the proposed calibration algorithm, the measurement accuracy increased from 3.55 °C with the original camera configuration to 1.37 °C. The implementation of the Wallis filter increases the number of tie-point from 58,000 to 110,000 and decreases the total positing error from 7.1 m to 1.3 m.

The world’s population continues to grow at a high rate, such that today’s population is twice that of 1960, and is projected to increase further to 9 billion by 2050. This situation has brought about a situation in which the percentage of the global energy used in cities is increasing considerably. Biomass is a resource that is present in a variety of different materials: wood, sawdust, straw, seed waste, manure, paper waste, household waste, wastewater, etc. Biomass resources have traditionally been used, and their use is becoming increasingly important due to their economic potential, as there are significant annual volumes of agricultural production, whose by-products can be used as a source of energy and are even being promoted as so-called energy crops, specifically for this purpose. The main objective of this work was to analyze the state of research and trends in biomass for renewable energy from 1978 to 2018 to help the research community understand the current situation and future trends, as well as the situation of countries in the international context, all of which provides basic information to facilitate decision-making by those responsible for scientific policy. The main countries that are investigating the subject of biomass as a renewable energy, as measured by scientific production, are the United States, followed by China, India, Germany and Italy. The most productive institutions in this field are the Chinese Academy of Sciences, followed by the National Renewable Energy Laboratory, Danmarks Tekniske Universitet and the Ministry of Education in China. This study also identifies communities based on the keywords of the publications obtained from a bibliographic search. Six communities or clusters were found. The two most important are focused on obtaining liquid fuels from biomass. Finally, based on the collaboration between countries and biomass research, eight clusters were observed. All this is centered on three countries belonging to different clusters: USA, India and the UK.

Biomass monitoring is one of the main pillars of precision farm management as it involves deeper knowledge about pest and weed status, soil quality, water stress, and yield prediction, among others. This research focuses on estimating crop biomass from high-resolution red, green, blue imaging obtained with an unmanned aerial vehicle. Onion, as one of the most cultivated vegetables, was studied for two seasons under non-controlled conditions in two commercial plots. Green canopy cover, crop height, and canopy volume (Vcanopy) were the predictor variables extracted from the geomatic products. Strong relationships were found between Vcanopy and dry leaf biomass and dry bulb biomass. Adjusted coefficient of determination (\\[{\\text{R}}_{\\text{adj}}^2\\]) values were 0.76 and 0.95, respectively. Nevertheless, crop management practices and leaf depletion at vegetative stages significantly affect the accuracy of the canopy model. These results suggested that obtaining biomass using aerial images are a good alternative to other sensors and platforms as they have high spatial and temporal resolution to perform high-quality biomass monitoring.

An sgRNA-scoring algorithm (CRISPRscan) based on molecular features that enhance activity allows users to predict the most efficient sgRNA for in vivo targets. CRISPR-Cas9 technology provides a powerful system for genome engineering. However, variable activity across different single guide RNAs (sgRNAs) remains a significant limitation. We analyzed the molecular features that influence sgRNA stability, activity and loading into Cas9 in vivo . We observed that guanine enrichment and adenine depletion increased sgRNA stability and activity, whereas differential sgRNA loading, nucleosome positioning and Cas9 off-target binding were not major determinants. We also identified sgRNAs truncated by one or two nucleotides and containing 5′ mismatches as efficient alternatives to canonical sgRNAs. On the basis of these results, we created a predictive sgRNA-scoring algorithm, CRISPRscan, that effectively captures the sequence features affecting the activity of CRISPR-Cas9 in vivo . Finally, we show that targeting Cas9 to the germ line using a Cas9-nanos 3′ UTR led to the generation of maternal-zygotic mutants, as well as increased viability and decreased somatic mutations. These results identify determinants that influence Cas9 activity and provide a framework for the design of highly efficient sgRNAs for genome targeting in vivo.

With the increasing competitiveness in the vine market, coupled with the increasing need for sustainable use of resources, strategies for improving farm management are essential. One such effective strategy is the implementation of precision agriculture techniques. Using photogrammetric techniques, the digitalization of farms based on images acquired from unmanned aerial vehicles (UAVs) provides information that can assist in the improvement of farm management and decision-making processes. The objective of the present work is to quantify the impact of the pest Jacobiasca lybica on vineyards and to develop representative cartography of the severity of the infestation. To accomplish this work, computational vision algorithms based on an ANN (artificial neural network) combined with geometric techniques were applied to geomatic products using consumer-grade cameras in the visible spectra. The results showed that the combination of geometric and computational vision techniques with geomatic products generated from conventional RGB (red, green, blue) images improved image segmentation of the affected vegetation, healthy vegetation and ground. Thus, the proposed methodology using low-cost cameras is a more cost-effective application of UAVs compared with multispectral cameras. Moreover, the proposed method increases the accuracy of determining the impact of pests by eliminating the soil effects.

Montoro-García et al. [...]

The peanut (Arachys hypogaea) is a plant of the Fabaceae family (legumes), as are chickpeas, lentils, beans, and peas. It is originally from South America and is used mainly for culinary purposes, in confectionery products, or as a nut as well as for the production of biscuits, breads, sweets, cereals, and salads. Also, due to its high percentage of fat, peanuts are used for industrialized products such as oils, flours, inks, creams, lipsticks, etc. According to the Food and Agriculture Organization (FAO) statistical yearbook in 2016, the production of peanuts was 43,982,066 t, produced in 27,660,802 hectares. Peanuts are grown mainly in Asia, with a global production rate of 65.3%, followed by Africa with 26.2%, the Americas with 8.4%, and Oceania with 0.1%. The peanut industry is one of the main generators of agroindustrial waste (shells). This residual biomass (25–30% of the total weight) has a high energy content that is worth exploring. The main objectives of this study are, firstly, to evaluate the energy parameters of peanut shells as a possible solid biofuel applied as an energy source in residential and industrial heating installations. Secondly, different models are analysed to estimate the higher heating value (HHV) for biomass proposed by different scientists and to determine which most accurately fits the determination of this value for peanut shells. Thirdly, we evaluate the reduction in global CO2 emissions that would result from the use of peanut shells as biofuel. The obtained HHV of peanut shells (18.547 MJ/kg) is higher than other biomass sources evaluated, such as olive stones (17.884 MJ/kg) or almond shells (18.200 MJ/kg), and similar to other sources of biomass used at present for home and industrial heating applications. Different prediction models of the HHV value proposed by scientists for different types of biomass have been analysed and the one that best fits the calculation for the peanut shell has been determined. The CO2 reduction that would result from the use of peanut shells as an energy source has been evaluated in all production countries, obtaining values above 0.5 ‰ of their total emissions.