Using biochar for environmental recovery and boosting the yield of valuable non-food crops: The case of hemp in a soil contaminated by potentially toxic elements (PTEs)

Hemp (Cannabis sativa L.) is known to tolerate high concentrations of soil contaminants which however can limit its biomass yield. On the other hand, organic-based amendments such as biochar can immobilize soil contaminants and assist hemp growth in soils contaminated by potentially toxic elements (PTEs), allowing for environmental recovery and income generation, e.g. due to green energy production from plant biomass. The aim of this study was therefore to evaluate the suitability of a softwood-derived biochar to enhance hemp growth and promote the assisted phytoremediation of a PTE-contaminated soil (i.e., Sb 2175 mg kg-1; Zn 3149 mg kg-1; Pb 403 mg kg-1; and Cd 12 mg kg-1). Adding 3% (w/w) biochar to soil favoured the reduction of soluble and exchangeable PTEs, decreased soil dehydrogenase activity (by ∼2.08-fold), and increased alkaline phosphomonoesterase and urease activities, basal respiration and soil microbial carbon (by ∼1.18-, 1.22-, 1.22-, and 1.66-fold, respectively). Biochar increased the abundance of selected soil culturable microorganisms, while amplicon sequencing analysis showed a positive biochar impact on α-diversity and the induction of structural changes on soil bacterial community structure. Biochar did not affect root growth of hemp but significantly increased its aboveground biomass by ∼1.67-fold for shoots, and by ∼2-fold for both seed number and weight. Biochar increased the PTEs phytostabilisation potential of hemp with respect to Cd, Pb and Zn, and also stimulated hemp phytoextracting capacity with respect to Sb. Overall, the results showed that biochar can boost hemp yield and its phytoremediation effectiveness in soils contaminated by PTEs providing valuable biomass that can generate profit in economic, environmental and sustainability terms.

Estimating annual GHG and particulate matter emissions from rural and forest fires based on an integrated modelling approach

Rural and forest fires represent one of the most significant sources of emissions in the atmosphere of trace gases and aerosol particles, which significantly impact carbon budget, air quality, and human health. This paper aims to illustrate an integrated modelling approach combining spatial and non-spatial inputs to provide and enhance the estimation of GHG and particulate matter emissions from surface fires using Italy as a case study over the period 2007-2017. Three main improvements characterize the approach proposed in this work: (i) the collection and development of comprehensive and accurate data inputs related to burned area; (ii) the use of the most recent data on fuel type and load; and (iii) the modelling application to estimate fuel moisture, burning efficiency, and fuel consumption considering meteorological factors and combustion phases. On average, Italy's GHG and particulate matter emissions were 2621 Gg yr-1, ranging from a minimum of 772 Gg yr-1 in 2013 to a maximum of 7020 Gg yr-1 in 2007. Emissions from fire disturbances in broadleaf forests, shrublands, and agricultural fuel types account for about 76 % of the total. Results were compared with global and national inventories and showed good agreement, especially considering CO2 and particulate matter. The approach of this study added confidence in emission estimates, and the results can be utilized in decision support systems to address air quality management and fire impact mitigation policies.

Modeling Phenological Phases across Olive Cultivars in the Mediterranean

Modeling phenological phases in a Mediterranean environment often implies tangible challenges to reconstructing regional trends over heterogenous areas using limited and scattered observations. The present investigation aimed to project phenological phases (i.e., sprouting, blooming, and pit hardening) for early and mid-late olive cultivars in the Mediterranean, comparing two phenological modeling approaches. Phenoflex is a rather integrated but data-demanding model, while a combined model of chill and anti-chill days and growing degree days (CAC_GDD) offers a more parsimonious and general approach in terms of data requirements for parameterization. We gathered phenological observations from nine experimental sites in Italy and temperature timeseries from the European Centre for Medium-Range Weather Forecasts, Reanalysis v5. The best performances of the CAC_GDD (RMSE: 4 days) and PhenoFlex models (RMSE: 5-9.5 days) were identified for the blooming and sprouting phases of mid-late cultivars, respectively. The CAC_GDD model was better suited to our experimental conditions for projecting pit hardening and blooming dates (correlation: 0.80 and 0.70, normalized RMSE: 0.6 and 0.8, normalized standard deviation: 0.9 and 1.0). The optimization of the principal parameters confirmed that the mid-late cultivars were more adaptable to thermal variability. The spatial distribution illustrated the near synchrony of blooming dates between the early and mid-late cultivars compared to other phases.

A Systematic Review on the Impacts of Climate Change on Coffee Agrosystems

Coffee production is fragile, and the Intergovernmental Panel on Climate Change (IPCC) reports indicate that climate change (CC) will reduce worldwide yields on average and decrease coffee-suitable land by 2050. This article adopted the systematic review approach to provide an update of the literature available on the impacts of climate change on coffee production and other ecosystem services following the framework proposed by the Millenium Ecosystem Assessment. The review identified 148 records from literature considering the effects of climate change and climate variability on coffee production, covering countries mostly from three continents (America, Africa, and Asia). The current literature evaluates and analyses various climate change impacts on single services using qualitative and quantitative methodologies. Impacts have been classified and described according to different impact groups. However, available research products lacked important analytical functions on the precise relationships between the potential risks of CC on coffee farming systems and associated ecosystem services. Consequently, the manuscript recommends further work on ecosystem services and their interrelation to assess the impacts of climate change on coffee following the ecosystem services framework.

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO₂, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Measurement(s)	net ecosystem exchange • carbon dioxide • water • energy
Technology Type(s)	eddy covariance • measurement device
Sample Characteristic - Environment	terrestrial biome • atmosphere
Sample Characteristic - Location	Earth (planet)

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12295910

Modeling the effects of different fuel treatment mosaics on wildfire spread and behavior in a Mediterranean agro-pastoral area

Wildfire spread and behavior can be limited by fuel treatments, even if their effects can vary according to a number of factors including type, intensity, extension, and spatial arrangement. In this work, we simulated the response of key wildfire exposure metrics to variations in the percentage of treated area, treatment unit size, and spatial arrangement of fuel treatments under different wind intensities. The study was carried out in a fire-prone 625 km² agro-pastoral area mostly covered by herbaceous fuels, and located in Northern Sardinia, Italy. We constrained the selection of fuel treatment units to areas covered by specific herbaceous land use classes and low terrain slope (<10%). We treated 2%, 5% and 8% of the landscape area, and identified priority sites to locate the fuel treatment units for all treatment alternatives. The fuel treatment alternatives were designed create diverse mosaics of disconnected treatment units with different sizes (0.5-10 ha, LOW strategy; 10-25 ha, MED strategy; 25-50 ha, LAR strategy); in addition, treatment units in a 100-m buffer around the road network (ROAD strategy) were tested. We assessed pre- and post-treatment wildfire behavior by the Minimum Travel Time (MTT) fire spread algorithm. The simulations replicated a set of southwestern wind speed scenarios (16, 24 and 32 km h^-1) and the driest fuel moisture conditions observed in the study area. Our results showed that fuel treatments implemented near the existing road network were significantly more efficient than the other alternatives, and this difference was amplified at the highest wind speed. Moreover, the largest treatment unit sizes were the most effective in containing wildfire growth. As expected, increasing the percentage of the landscape treated and reducing wind speed lowered fire exposure profiles for all fuel treatment alternatives, and this was observed at both the landscape scale and for highly valued resources. The methodology presented in this study can support the design and optimization of fuel management programs and policies in agro-pastoral areas of the Mediterranean Basin and herbaceous type landscapes elsewhere, where recurrent grassland fires pose a threat to rural communities, farms and infrastructures.

Contrasting effects of nitrogen addition on soil respiration in two Mediterranean ecosystems

Increased atmospheric nitrogen (N) deposition is known to alter ecosystem carbon source-sink dynamics through changes in soil CO₂ fluxes. However, a limited number of experiments have been conducted to assess the effects of realistic N deposition in the Mediterranean Basin, and none of them have explored the effects of N addition on soil respiration (R _s ). To fill this gap, we assessed the effects of N supply on R _s dynamics in the following two Mediterranean sites: Capo Caccia (Italy), where 30 kg ha^-1 year^-1 was supplied for 3 years, and El Regajal (Spain), where plots were treated with 10, 20, or 50 kg N ha^-1 year^-1 for 8 years. Results show a complex, non-linear response of soil respiration (R _s ) to N additions with R _s overall increasing at Capo Caccia and decreasing at El Regajal. This suggests that the response of R _s to N addition depends on dose and duration of N supply, and the existence of a threshold above which the N introduced in the ecosystem can affect the ecosystem's functioning. Soil cover and seasonality of precipitations also play a key role in determining the effects of N on R _s as shown by the different responses observed across seasons and in bare soil vs. the soil under canopy of the dominant species. These results show how increasing rates of N addition may influence soil C dynamics in semiarid ecosystems in the Mediterranean Basin and represent a valuable contribution for the understanding and the protection of Mediterranean ecosystems.

Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas

We used simulation modeling to assess potential climate change impacts on wildfire exposure in Italy and Corsica (France). Weather data were obtained from a regional climate model for the period 1981-2070 using the IPCC A1B emissions scenario. Wildfire simulations were performed with the minimum travel time fire spread algorithm using predicted fuel moisture, wind speed, and wind direction to simulate expected changes in weather for three climatic periods (1981-2010, 2011-2040, and 2041-2070). Overall, the wildfire simulations showed very slight changes in flame length, while other outputs such as burn probability and fire size increased significantly in the second future period (2041-2070), especially in the southern portion of the study area. The projected changes fuel moisture could result in a lengthening of the fire season for the entire study area. This work represents the first application in Europe of a methodology based on high resolution (250 m) landscape wildfire modeling to assess potential impacts of climate changes on wildfire exposure at a national scale. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.

Assessing landscape scale wildfire exposure for highly valued resources in a Mediterranean area

We used a fire simulation modeling approach to assess landscape scale wildfire exposure for highly valued resources and assets (HVR) on a fire-prone area of 680 km(2) located in central Sardinia, Italy. The study area was affected by several wildfires in the last half century: some large and intense fire events threatened wildland urban interfaces as well as other socioeconomic and cultural values. Historical wildfire and weather data were used to inform wildfire simulations, which were based on the minimum travel time algorithm as implemented in FlamMap. We simulated 90,000 fires that replicated recent large fire events in the area spreading under severe weather conditions to generate detailed maps of wildfire likelihood and intensity. Then, we linked fire modeling outputs to a geospatial risk assessment framework focusing on buffer areas around HVR. The results highlighted a large variation in burn probability and fire intensity in the vicinity of HVRs, and allowed us to identify the areas most exposed to wildfires and thus to a higher potential damage. Fire intensity in the HVR buffers was mainly related to fuel types, while wind direction, topographic features, and historically based ignition pattern were the key factors affecting fire likelihood. The methodology presented in this work can have numerous applications, in the study area and elsewhere, particularly to address and inform fire risk management, landscape planning and people safety on the vicinity of HVRs.

Analyzing seasonal patterns of wildfire exposure factors in Sardinia, Italy

In this paper, we applied landscape scale wildfire simulation modeling to explore the spatiotemporal patterns of wildfire likelihood and intensity in the island of Sardinia (Italy). We also performed wildfire exposure analysis for selected highly valued resources on the island to identify areas characterized by high risk. We observed substantial variation in burn probability, fire size, and flame length among time periods within the fire season, which starts in early June and ends in late September. Peak burn probability and flame length were observed in late July. We found that patterns of wildfire likelihood and intensity were mainly related to spatiotemporal variation in ignition locations, fuel moisture, and wind vectors. Our modeling approach allowed consideration of historical patterns of winds, ignition locations, and live and dead fuel moisture on fire exposure factors. The methodology proposed can be useful for analyzing potential wildfire risk and effects at landscape scale, evaluating historical changes and future trends in wildfire exposure, as well as for addressing and informing fuel management and risk mitigation issues.

Effects of the Ras homolog Rhes on Akt/protein kinase B and glycogen synthase kinase 3 phosphorylation in striatum

G protein-coupled receptors (GPCR) signal not only through heterotrimeric G proteins, but also through alternate pathways. Thus, dopamine D2 receptors in the striatum signal through Gαi/o and also by promoting formation of a multi-protein complex containing β-arrestin2, protein phosphatase 2A (PP2A), and Akt in order to dephosphorylate Akt. Lithium, on the other hand, disrupts this complex to increase Akt phosphorylation. Rhes is a striatally enriched GTP-binding protein that has been shown to inhibit dopamine receptor-mediated behavior and signaling through heterotrimeric G proteins. Therefore, our objective was to test whether Rhes similarly affects signaling through the Akt/GSK3 pathway in the striatum. Rhes(-/-) mice showed basally increased Akt and GSK3β phosphorylation relative to rhes(+/+) mice that was not further enhanced by lithium treatment. Furthermore, they responded to the D1/D2 agonist apomorphine with increased Akt and GSK3 phosphorylation. Co-immunoprecipitation experiments revealed that apomorphine treatment recruits PP 2A-C to Akt in both rhes(+/+) and rhes(-/-) mice. Lithium did not disrupt their interaction in rhes(-/-) mice as there was little basal interaction. Rhes co-immunoprecipitated with β-arrestins, suggesting that it is integral to the multi-protein complex. Thus, Rhes is necessary for Akt dephosphorylation by the striatal multi-protein complex, and in its absence, a lithium-treated phenotype results.

Thermal optimality of net ecosystem exchange of carbon dioxide and underlying mechanisms

• It is well established that individual organisms can acclimate and adapt to temperature to optimize their functioning. However, thermal optimization of ecosystems, as an assemblage of organisms, has not been examined at broad spatial and temporal scales. • Here, we compiled data from 169 globally distributed sites of eddy covariance and quantified the temperature response functions of net ecosystem exchange (NEE), an ecosystem-level property, to determine whether NEE shows thermal optimality and to explore the underlying mechanisms. • We found that the temperature response of NEE followed a peak curve, with the optimum temperature (corresponding to the maximum magnitude of NEE) being positively correlated with annual mean temperature over years and across sites. Shifts of the optimum temperature of NEE were mostly a result of temperature acclimation of gross primary productivity (upward shift of optimum temperature) rather than changes in the temperature sensitivity of ecosystem respiration. • Ecosystem-level thermal optimality is a newly revealed ecosystem property, presumably reflecting associated evolutionary adaptation of organisms within ecosystems, and has the potential to significantly regulate ecosystem-climate change feedbacks. The thermal optimality of NEE has implications for understanding fundamental properties of ecosystems in changing environments and benchmarking global models.

Purification, primary structure, and properties of Euphorbia characias latex purple acid phosphatase

A purple acid phosphatase was purified to homogeneity from Euphorbia characias latex. The native protein has a molecular mass of 130 ± 10 kDa and is formed by two apparently identical subunits, each containing one Fe(III) and one Zn(II) ion. The two subunits are connected by a disulfide bridge. The enzyme has an absorbance maximum at 540 nm, conferring a characteristic purple color due to a charge-transfer transition caused by a tyrosine residue (Tyr172) coordinated to the ferric ion. The cDNA nucleotide sequence contains an open reading frame of 1392 bp, and the deduced sequence of 463 amino acids shares a very high degree of identity (92-99%) to other purple acid phosphatases isolated from several higher plants. The enzyme hydrolyzes well p-nitrophenyl phosphate, a typical artificial substrate, and a broad range of natural phosphorylated substrates, such as ATP, ADP, glucose-6-phosphate, and phosphoenolpyruvate. The enzyme displays a pH optimum of 5.75 and is inhibited by molybdate, vanadate, and Zn2+, which are typical acid phosphatase inhibitors.

Estrogen-induced proliferation in cultured hepatocytes involves cyclin D1, p21(Cip1) and p27(Kip1)

The purpose of this study was to establish if estrogen-induced hepatocyte proliferation in vitro involves the cell cycle regulators cyclin D1, p21(Cip1), and p27(Kip1). Male rat hepatocytes were cultured in presence of 17-beta-estradiol (E2) +/- ICI-182780, a pure estrogen antagonist, and [3H]-thymidine, as required. DNA synthesis as well as p21(Cip1), p27(Kip1), and cyclin D1mRNA and protein levels were evaluated at different times (12, 24, 36, and 48 hours) of incubation. E2-increased DNA synthesis was correlated with cyclin D1 and p21(Cip1) (mRNA and protein) variations that were reversed by the addition of ICI-182780. p27(Kip1) protein levels progressively increased regardless of the presence of E2 or ICI-182780. Our data confirm that estrogens' stimulatory effect is related to their ability to increase cyclin D1 levels. The increase of p21(Cip1) is probably related to the reentry of hepatocytes in the quiescent state. p27(Kip1) protein is not able to arrest hepatocyte proliferation.

An improved model for determining degree-day values from daily temperature data

Although using hourly weather data offers the greatest accuracy for estimating growing degree-day values, daily maximum and minimum temperature data are often used to estimate these values by approximating the diurnal temperature trends. This paper presents a new empirical model for estimating the hourly mean temperature. The model describes the diurnal variation using a sine function from the minimum temperature at sunrise until the maximum temperature is reached, another sine function from the maximum temperature until sunset, and a square-root function from then until sunrise the next morning. The model was developed and calibrated using several years of hourly data obtained from five automated weather stations located in California and representing a wide range of climate conditions. The model was tested against an additional data-set at each location. The temperature model gave good results, the rootmean-square error being less than 2.0 degrees C for most years and locations. The comparison with published models from the literature showed that the model was superior to the other methods. Hourly temperatures from the model were used to calculate degree-day values. A comparison between degree-day estimates determined from the model and those obtained other selected methods is presented. The results showed that the model had the best accuracy in general regardless of the season.

An improved model for determining degree-day values from daily temperature data

Although using hourly weather data offers the greatest accuracy for estimating growing degree-day values, daily maximum and minimum temperature data are often used to estimate these values by approximating the diurnal temperature trends. This paper presents a new empirical model for estimating the hourly mean temperature. The model describes the diurnal variation using a sine function from the minimum temperature at sunrise until the maximum temperature is reached, another sine function from the maximum temperature until sunset, and a square-root function from then until sunrise the next morning. The model was developed and calibrated using several years of hourly data obtained from five automated weather stations located in California and representing a wide range of climate conditions. The model was tested against an additional data-set at each location. The temperature model gave good results, the rootmean-square error being less than 2.0 degrees C for most years and locations. The comparison with published models from the literature showed that the model was superior to the other methods. Hourly temperatures from the model were used to calculate degree-day values. A comparison between degree-day estimates determined from the model and those obtained other selected methods is presented. The results showed that the model had the best accuracy in general regardless of the season.

Temperature data for phenological models

In an arid environment, the effect of evaporation on energy balance can affect air temperature recordings and greatly impact on degree-day calculations. This is an important consideration when choosing a site or climate data for phenological models. To our knowledge, there is no literature showing the effect of the underlying surface and its fetch around a weather station on degree-day accumulations. In this paper, we present data to show that this is a serious consideration, and it can lead to dubious models. Microscale measurements of temperature and energy balance are presented to explain why the differences occur. For example, the effect of fetch of irrigated grass and wetting of bare soil around a weather station on diurnal temperature are reported. A 43-day experiment showed that temperature measured on the upwind edge of an irrigated grass area averaged 4% higher than temperatures recorded 200 m inside the grass field. When the single-triangle method was used with a 10 degrees C threshold and starting on May 19, the station on the upwind edge recorded 900 degree-days on June 28, whereas the interior station recorded 900 degree-days on July 1. Clearly, a difference in fetch can lead to big errors for large degree-day accumulations. Immediately after wetting, the temperature over a wet soil surface was similar to that measured over grass. However, the temperature over the soil increased more than that over the grass as the soil surface dried. Therefore, the observed difference between temperatures measured over bare soil and those over grass increases with longer periods between wettings. In most arid locations, measuring temperature over irrigated grass gives a lower mean annual temperature, resulting in lower annual cumulative degree-day values. This was verified by comparing measurements over grass with those over bare soil at several weather stations in a range of climates. To eliminate the effect of rainfall frequency, using temperature data collected only over irrigated grass, is recommended for long-term assessment of climate change effects on degree-day accumulation. In high evaporative conditions, a fetch of at least 100 m of grass is recommended. Our results clearly indicate that weather stations sited over bare soil have consistently higher degree-day accumulations. Therefore, especially in arid environments, phenology models based on temperature collected over bare soil are not transferable to those based on temperature recorded over irrigated grass. At a minimum, all degree-day-based phenology models reported in the literature should clearly describe the weather station site.