Assessing Agricultural Risks of Climate Change in the 21st Century in a Global Gridded Crop Model Intercomparison

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies

Comparing correction methods of RCM outputs for improving crop impact projections in the Iberian Peninsula for 21st century

Assessment of climate change impacts on crops in regions of complex orography such as the Iberian Peninsula (IP) requires climate model output which is able to describe accurately the observed climate. The high resolution of output provided by Regional Climate Models (RCMs) is expected to be a suitable tool to describe regional and local climatic features, although their simulation results may still present biases. For these reasons, we compared several post-processing methods to correct or reduce the biases of RCM simulations from the ENSEMBLES project for the IP. The bias-corrected datasets were also evaluated in terms of their applicability and consequences in improving the results of a crop model to simulate maize growth and development at two IP locations, using this crop as a reference for summer cropping systems in the region. The use of bias-corrected climate runs improved crop phenology and yield simulation overall and reduced the inter-model variability and thus the uncertainty. The number of observational stations underlying each reference observational dataset used to correct the bias affected the correction performance. Although no single technique showed to be the best one, some methods proved to be more adequate for small initial biases, while others were useful when initial biases were so large as to prevent data application for impact studies. An initial evaluation of the climate data, the bias correction/reduction method and the consequences for impact assessment would be needed to design the most robust, reduced uncertainty ensemble for a specific combination of location, crop, and crop management

Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies

Cytotoxic Withanolide Constituents of Physalis longifolia

Fourteen new withanolides, 1–14, named withalongolides A–N, respectively, were isolated from the aerial parts of Physalis longifolia together with eight known compounds (15–22). The structures of compounds 1–14 were elucidated through spectroscopic techniques and chemical methods. In addition, the structures of withanolides 1, 2, 3, and 6 were confirmed by X-ray crystallographic analysis. Using a MTS viability assay, eight withanolides (1, 2, 3, 7, 8, 15, 16, and 19) and four acetylated derivatives (1a, 1b, 2a, and 2b) showed potent cytotoxicity against human head and neck squamous cell carcinoma (JMAR and MDA-1986), melanoma (B16F10 and SKMEL-28), and normal fetal fibroblast (MRC-5) cells with IC50 values in the range between 0.067 and 9.3 μM

Withanolides and related steroids

Since the isolation of the first withanolides in the mid-1960s, over 600 new members of this group of compounds have been described, with most from genera of the plant family Solanaceae. The basic structure of withaferin A, a C28 ergostane with a modified side chain forming a δ-lactone between carbons 22 and 26, was considered for many years the basic template for the withanolides. Nowadays, a considerable number of related structures are also considered part of the withanolide class; among them are those containing γ-lactones in the side chain that have come to be at least as common as the δ-lactones. The reduced versions (γ and δ-lactols) are also known. Further structural variations include modified skeletons (including C27 compounds), aromatic rings and additional rings, which may coexist in a single plant species. Seasonal and geographical variations have also been described in the concentration levels and types of withanolides that may occur, especially in the Jaborosa and Salpichroa genera, and biogenetic relationships among those withanolides may be inferred from the structural variations detected. Withania is the parent genus of the withanolides and a special section is devoted to the new structures isolated from species in this genus. Following this, all other new structures are grouped by structural types. Many withanolides have shown a variety of interesting biological activities ranging from antitumor, cytotoxic and potential cancer chemopreventive effects, to feeding deterrence for several insects as well as selective phytotoxicity towards monocotyledoneous and dicotyledoneous species. Trypanocidal, leishmanicidal, antibacterial, and antifungal activities have also been reported. A comprehensive description of the different activities and their significance has been included in this chapter. The final section is devoted to chemotaxonomic implications of withanolide distribution within the Solanaceae. Overall, this chapter covers the advances in the chemistry and biology of withanolides over the last 16 years.Fil: Misico, Rosana Isabel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Orgánica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos Aplicados a la Química Orgánica (i); ArgentinaFil: Nicotra, V.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Orgánica; ArgentinaFil: Oberti, Juan Carlos María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Orgánica; ArgentinaFil: Barboza, Gloria Estela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Farmacia; ArgentinaFil: Gil, Roberto Ricardo. University Of Carnegie Mellon; Estados UnidosFil: Burton, Gerardo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Orgánica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos Aplicados a la Química Orgánica (i); Argentin

Evaluating the utility of dynamical downscaling in agricultural impacts projections

Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling — nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output — to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO 2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios ( < 10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections.Michael Glotter, Joshua Elliott, David McInerney, Neil Best, Ian Foster, and Elisabeth J. Moye