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Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making.
Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop-and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.
Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making.
Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop-and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.
Malnutrition, poor health, hunger, and even starvation are still the world's greatest challenges. Malnutrition is defined as deficiency of nutrition due to not ingesting the proper amounts of nutrients by simply not eating enough food and/or by consuming nutrient-poor food in respect to the daily nutritional requirements. Moreover, malnutrition and disease are closely associated and incidences of such diet-related diseases increase particularly in low- and middle-income states. While foods of animal origin are often unaffordable to low-income families, various neglected crops can offer an alternative source of micronutrients, vitamins, as well as health-promoting secondary plant metabolites. Therefore, agricultural and horticultural research should develop strategies not only to produce more food, but also to improve access to more nutritious food. In this context, one promising approach is to promote biodiversity in the dietary pattern of low-income people by getting access to nutritional as well as affordable food and providing recommendations for food selection and preparation. Worldwide, a multitude of various plant species are assigned to be consumed as grains, vegetables, and fruits, but only a limited number of these species are used as commercial cash crops. Consequently, numerous neglected and underutilized species offer the potential to diversify not only the human diet, but also increase food production levels, and, thus, enable more sustainable and resilient agro- and horti-food systems. To exploit the potential of neglected plant (NP) species, coordinated approaches on the local, regional, and international level have to be integrated that consequently demand the involvement of numerous multi-stakeholders. Thus, the objective of the present review is to evaluate whether NP species are important as “Future Food” for improving the nutritional status of humans as well as increasing resilience of agro- and horti-food systems.
The heme-undecapeptide microperoxidase-11 (MP-11) was immobilized on mesoporous antimony-doped tin oxide (ATO) thin-film electrodes modified with the positively charged binding promotor polydiallyldimethylammonium chloride. Surface concentrations of MP-11 of 1.5 nmol cm(-2) were sufficiently high to enable spectroelectrochemical analyses. UV/Vis spectroscopy and resonance Raman spectroscopy revealed that immobilized MP-11 adopts a six-coordinated low-spin conformation, as in solution in the presence of a polycation. Cathodic reduction of hydrogen peroxide at potentials close to +500mV versus Ag/AgCl indicates that the reaction proceeds via a Compound I-type like intermediate, analogous to natural peroxidases, and confirms mesoporous ATO as a suitable host material for adsorbing the heme-peptide in its native state. A hydrogen peroxide sensor is proposed by using the bioelectrocatalytic properties of the MP-11-modified ATO.
The abandonment of military areas leads to succession processes affecting valuable open-land habitats and is considered to be a major threat for European butterflies. We assessed the ability of hyper spectral remote sensing data to spatially predict the occurrence of one of the most endangered butterfly species (Hipparchia statilinus) in Brandenburg (Germany) on the basis of habitat characteristics at a former military training area. Presence-absence data were sampled on a total area of 36 km(2), and N = 65 adult individuals of Hipparchia statilinus could be detected. The floristic composition within the study area was modeled in a three-dimensional ordination space. Occurrence probabilities for the target species were predicted as niches between ordinated floristic gradients by using Regression Kriging of Indicators. Habitat variance could be explained by up to 81 % with spectral variables at a spatial resolution of 2 x 2 m by transferring PLSR models to imagery. Ordinated ecological niche of Hipparchia statilinus was tested against environmental predictor variables. N = 6 variables could be detected to be significantly correlated with habitat preferences of Hipparchia statilinus. They show that Hipparchia statilinus can serve as a valuable indicator for the evaluation of the conservation status of Natura 2000 habitat type 2330 (inland dunes with open Corynephorus and Agrostis grasslands) protected by the Habitat Directive (Council Directive 92/43/EEC). The authors of this approach, conducted in August 2013 at Doberitzer Heide Germany, aim to increase the value of remote sensing as an important tool for questions of biodiversity research and conservation.
The thiophene-modified iron porphyrin FeT3ThP and the respective iron Hangman porphyrin FeH3ThP, incorporating a carboxylic acid hanging group in the second coordination sphere of the iron center, were electropolymerized on glassy carbon electrodes using 3,4-ethylenedioxythiophene (EDOT) as co-monomer. Scanning electron microscopy images and Resonance Raman spectra demonstrated incorporation of the porphyrin monomers into a fibrous polymer network. Porphyrin/polyEDOT films catalyzed the reduction of molecular oxygen in a four-electron reaction to water with onset potentials as high as +0.14V vs. Ag/AgCl in an aqueous solution of pH7. Further, FeT3ThP/polyEDOT films showed electrocatalytic activity towards reduction of hydrogen peroxide at highly positive potentials, which was significantly enhanced by introduction of the carboxylic acid hanging group in FeH3ThP. The second coordination sphere residue promotes formation of a highly oxidizing reaction intermediate, presumably via advantageous proton supply, as observed for peroxidases and catalases making FeH3ThP/polyEDOT films efficient mimics of heme enzymes.
The present study reports a facile approach for sulfite biosensing, based on enhanced direct electron transfer of a human sulfite oxidase (hSO) immobilized on a gold nanoparticles modified electrode. The spherical core shell AuNPs were prepared via a new method by reduction of HAuCl4 with branched poly(ethyleneimine) in an ionic liquids resulting particles with a diameter less than 10 nm. These nanoparticles were covalently attached to a mercaptoundecanoic acid modified Au-electrode where then hSO was adsorbed and an enhanced interfacial electron transfer and electrocatalysis was achieved. UV/Vis and resonance Raman spectroscopy, in combination with direct protein voltammetry, are employed for the characterization of the system and reveal no perturbation of the structural integrity of the redox protein. The proposed biosensor exhibited a quick steady-state current response, within 2 s, a linear detection range between 0.5 and 5.4 mu M with a high sensitivity (1.85 nA mu M-1). The investigated system provides remarkable advantages in the possibility to work at low applied potential and at very high ionic strength. Therefore these properties could make the proposed system useful in the development of bioelectronic devices and its application in real samples.