Antifungal activity of methylxanthines against grapevine trunk diseases

Methylxanthines, found in the seeds, leaves, and fruits of some plants, are receiving increasing attention as promising treatments for wood-degrading fungi. The aim of the study presented herein was to explore the potential applications of caffeine, four caffeine derivatives (viz. 8-bromo-caffeine, 8-iodo-caffeine, 8-(4-fluorophenoxy)-caffeine, and 8-(2, 3, 5, 6-tetrafluoroalcoxy)-caffeine), and theophylline as antifungals for Botryosphaeriaceae species associated with grapevine trunk diseases (GTDs). In vitro susceptibility tests were conducted to assess the antimycotic activity of the aforementioned compounds and their conjugated complexes with chitosan oligomers (COS). Caffeine, Br-caffeine, and I-caffeine exhibited higher efficacies than imidazole, the chosen antifungal control. Moreover, a strong synergistic behavior between COS and the methylxanthine derivatives was observed. The COS–I-caffeine complex showed the best overall performance against the phytopathogenic fungi with EC90 values of 471, 640, and 935 µg mL-1 for D. seriata, D. viticola, and N. parvum, respectively. In a second step, combinations of the new treatments with imidazole were also explored, resulting in further activity enhancement and EC90 values of 425, 271, and 509 mL-1 against D. seriata, D. viticola, and N. parvum, respectively, for the COS–I-caffeine-imidazole ternary compound. Given the high in vitro efficacy of these formulations for the control of GTDs, they may deserve further investigation with in vivo and field bioassays as an alternative to conventional fungicides. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

One step forward: contrasting the effects of Toe clipping and PIT tagging on frog survival and recapture probability

Amphibians have been declining worldwide and the comprehension of the threats that they face could be improved by using mark-recapture models to estimate vital rates of natural populations. Recently, the consequences of marking amphibians have been under discussion and the effects of toe clipping on survival are debatable, although it is still the most common technique for individually identifying amphibians. The passive integrated transponder (PIT tag) is an alternative technique, but comparisons among marking techniques in free-ranging populations are still lacking. We compared these two marking techniques using mark-recapture models to estimate apparent survival and recapture probability of a neotropical population of the blacksmith tree frog, Hypsiboas faber. We tested the effects of marking technique and number of toe pads removed while controlling for sex. Survival was similar among groups, although slightly decreased from individuals with one toe pad removed, to individuals with two and three toe pads removed, and finally to PIT-tagged individuals. No sex differences were detected. Recapture probability slightly increased with the number of toe pads removed and was the lowest for PIT-tagged individuals. Sex was an important predictor for recapture probability, with males being nearly five times more likely to be recaptured. Potential negative effects of both techniques may include reduced locomotion and high stress levels. We recommend the use of covariates in models to better understand the effects of marking techniques on frogs. Accounting for the effect of the technique on the results should be considered, because most techniques may reduce survival. Based on our results, but also on logistical and cost issues associated with PIT tagging, we suggest the use of toe clipping with anurans like the blacksmith tree frog4814801490CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP140684/2009-3; 309229/2009-0229611-02008/54472-2FADA-UNIFESP; INCTTOX; UNICAM

Life cycle analysis of macauba palm cultivation: A promising crop for biofuel production

The 450 Scenario, which limits the increase in global average temperature to 2¿°C, makes it necessary to take steps towards a low-carbon economy. Since the energy sector is a major contribution to anthropogenic greenhouse gas (GHG) emissions, the production of biofuels can play a key role in strategies aimed at climate change mitigation. In this regard, the oil derived from macauba palm (Acrocomia aculeata), mainly constituted of saturated organic chains, has been claimed to hold promise for the production of liquid fuels. The high potential yield, diversity of co-products and various positive features of this emerging energy crop make it an interesting option both from a social and an environmental point of view. Nonetheless, a full environmental evaluation is still missing. In the study presented herein, the impacts produced in its plantation, cultivation and harvesting phases and the associated cumulative energy demand have been determined using a life cycle analysis methodology, in addition to shedding some light on its GHG intensity relative to the other energy crops it can displace. Excluding land use changes and biogenic CO2 fixed by the crop, it was concluded that to produce one ton of macauba fruit in Brazil, the system would absorb 1810.21¿MJ, with GHG emissions of 158.69¿kg CO2eq in the 20-year timeframe, and of 140.04¿kg CO2eq in the 100-year timeframe (comparable to those of African oil palm). Damage to human health, ecosystem quality, and resources would add up to 16¿Pt·t-1 according to Eco-indicator 99 methodology. In order to account for the uncertainty derived from improvement and domestication programs, which should affect current production levels, a sensitivity analysis for different productivities was performed. In all analyses, fertilization was found to be responsible for ca. 90% of the impacts, and hence special attention should be paid to the development of alternative fertilizer management schemes

Bioenergy on islands: An environmental comparison of continental palm oil vs. local waste cooking oil for electricity generation

Producción CientíficaEnergy security on islands is a challenging issue due to their isolation from energy markets and fossil fuel dependence. In addition, islands’ average energy intensity has increased in recent years due to economic development. This research explores the environmental performance of two alternative non-variable bioelectricity feedstocks to increase energy resilience on islands. The study was developed for the Galápagos islands to address the environmental impacts from the direct use of waste cooking oil (WCO) and refined palm oil (RPO) to produce 1 MWh using the life cycle assessment methodological framework. A combination of primary and secondary data sources was used. The results show better performance for the electricity derived from WCO in all the impact categories considered when compared to RP

Prediction of Horizontal Daily Global Solar irradiation using artificial neural networks (ANNs) in the Castile and Leon Region, Spain

The next day's global horizontal solar irradiation is predicted using artificial neural networks (ANNs) for its application in agricultural science and technology. The time series of eight−years data is measured in an agrometeorological station, which belongs to the SIAR irrigation system (Agroclimatic Information System for Irrigation, in Spanish), located in Mansilla Mayor (León, Castile and León region, Spain). The zone has a Csb climate classification (i.e., Mediterranean Warm Summer Climate), according to Koppen−Geiger. The data for the years (2004−2010) are used for ANNs training and the 2011 as the validation year. ANN models were designed and evaluated with different numbers of inputs and neurons in the hidden layer. A neuron was used in the output layer, for all models, where the simulation of global solar irradiation for the next day on the horizontal surface results. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2 ·d), with two inputs [H(t), Kt(t)] and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2 ·d), with two inputs [H(t), J(t)] and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variable

Prediction of horizontal daily global solar irradiation using artificial neural networks (ANNs) in the Castile and León Region, Spain

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables

Predicción de la irradiación solar global diaria horizontal mediante redes neuronales artificiales en la región de Castilla y León, España

Resumen. Este artículo, se centra en la predicción de la irradiación solar global diaria horizontal, por ser el caso más interesante en la meteorología agrícola, por ejemplo, en las previsiones de necesidades de riego, utilizando la técnica de las redes neuronales artificiales (RNAs) de la inteligencia computacional, a partir de variables accesibles en las estaciones agrometeorológicas. El lugar donde fueron medidos los datos, utilizados para entrenar las RNAs, caracterizan donde se pueden volver a utilizar este tipo de modelos, en este estudio fueron las estaciones meteorológicas de la red SIAR en Castilla y León, en concreto la situada en Mansilla Mayor (León), durante los años 2004-2010. Los modelos RNAs se construyeron en la entrada con los datos medidos de irradiación solar global diaria de uno, dos y tres días anteriores, añadiendo el día del año J(t)=1..365, para predecir su valor el día siguiente. Los resultados obtenidos, validados durante el año 2011 completo RMSE=3,8012 MJ/(m2d), concluyen que las RNAs estudiadas mejoran los métodos clásicos comparados: 1) año típico CENSOLAR RMSE=5,1829 MJ/(m2d), 2) media móvil ponderada con la autocorrelación parcial de 11 días de retardo RMSE=3,9810 MJ/(m2d), 3) regresión lineal sobre el valor del día anterior RMSE=4,2434 MJ/(m2d), 4) año típico Fourier utilizado el 1er armónico RMSE=4,2747 MJ/(m2d), y 5) las matrices de transición de Markov para 33 estados posibles RMSE=4,3653 MJ/(m2d). Durante los días de cambio brusco en el nivel de irradiación solar, se observan los mayores errores de predicción. Se plantea utilizar en la entrada otras variables para mejorar la eficacia del modelo RNA. Una de las variables probadas fue el índice de claridad diario Kt=H/H0, resultando una mejora RMSE=3,7703 MJ/(m2d).Palabras clave: insolación, evapotranspiración, agrometeorología, inteligencia computacional