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Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures., This study was a part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 and was supported by the French National Research Institute for Agriculture, Food (INRAE) and the International Maize and Wheat Improvement Center (CIMMYT) through the International Wheat Yield Partnership (IWYP, grant IWYP115). SD and PM acknowledge support from the metaprogram Agriculture and forestry in the face of climate change: adaptation and mitigation (CLIMAE) of INRAE. YC and FT acknowledge support from the National Natural Science Foundation of China (No. 31761143006). RPR and GBM acknowledge financial support from BARISTA project (031B0811A) through ERA-NET SusCrop under EU-FACCE JPI. KCK was funded by the German Federal Ministry of Education and Research (BMBF) through the BonaRes project ’’I4S’’ (031B0513I). AS and TG acknowledge funding by the German Federal Ministry of Education and Research (BMBF) through the BonaRes Project “Soil3” (FKZ 031B0026A). KCK and JEO were supported by the Ministry of Education, Youth and Sports of Czech Republic through SustES—Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/000797). FE acknowledges support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2070 – 390732324”. TKDW was funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017). MAS and NS at Rothamsted Research received grant-aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) through Designing Future Wheat [BB/P016855/1] and Achieving Sustainable Agricultural Systems [NE/N018125/1] jointly funded with NERC. TP and FT are supported by the DivCSA project funded by the Academy of Finland (decision no. 316215).

This repositoty contains crop model campaign and output files from the gridded version of the Decision Support System for Agrotechnology Transfer (DSSAT) crop model to support the following publication: Partridge, T.F., Winter, Kendall, A.D. Pei, L., Basso, B., Hyndman, D.W., Projecting the value of current and expanded irrigation from groundwater as a climate adaptation strategy. Model Campaign Files: Historic and future campaign files for simulating maize and soybean growth across cultivated areas in the United States. Maize campaign files contain state level oberseved planting dates aquired from the USDA NASS quickstats repository andannual approximations of planted cultivars based on plant progress data and growing degree days. A calibrated soil producitivty coefficient (pDSSAT_calibrated_slpf_maize.nc)is included to adjust simulated maize yields to reported county level yield values through minimzing30-year RMSE values. Future planting dates and cultivar parameters are scaled by changes in growing season meterological metrics for each time period and downscaled global climate model included in this analysis.Please see the corresponding paper for a full description of phenology and yield approximation and estimation of future adaptations. Soybeancampaignfiles contain state level observed planting dates from the USDA NASS quickstats repository and annual estimates of soybean cultivar parameters. Future soybean planting dates are approximated based on changes in growing season conditions for each time period and global climate model inlcuded in this analysis, but cultivar parameters are held constant during future periods.Please see the corresponding paper for a full description of phenology and yield approximation and estimation of future planting dates. Model Output: Gridded crop model simulation outputfor both rainfed and fully irrigated maize and soybean across all cultivated areas in the Conterminous United States from the parallelizedDecision Support System for Agrotechnology Transfer model (pDSSAT). Model runs include three time periods:historical (1981-2010), mid-century (2036-2065), and end-of-century (2071-2100) conditions under moderate and high emissions trajectories(SSP245 and SSP585) from five statistically downscaled CMIP6 global climate models. Future crop simulations approximate changes in planting date andcultivar selection (maize only) and historically planted cultivars. Model output variables include annual values of crop yield (HWAM), total biomass (CWAM), applied Irrigation (IRCM), planting date (PDAT), crop anthesis date (ADAT), crop maturity date (MDAT), seasonal ET (ETCM), growing season ET (ETCP), seasonal drainage from the bottom of the model (DRCM), seasonal precipitation (PRCM), growing season precipitation (PRCP), seasonal runoff (ROCM), growing season average maximum temperature (TMAXA), growing season average minimum temperature (TMINA). water stress during the growing season (WSPDGS), water stress during flowering (WSPDGF), water stress during grain filling (WSPDGS, crop yield irrigation productivity (YPIM), and crop yield precipitation productivity (YPPM).&nbsp

Acute lung injury (ALI) is a disease of high prevalence and is characterized by the excessive production of inflammatory mediators in the lungs of people sick. Inflammation is the major characteristic of ALI and studies report that inhibition of inflammatory cytokines could be an alternative treatment. Statins such as Simvastatin (SV) are known to their use for cholesterol reduction but also for inflammatory and immunoregulatory processes. In this study, we evaluated the effects of SV on LPS-induced alveolar macrophages and in ALI mice model. Our study has demonstrated the protective effects of SV on LPS-activated alveolar macrophages RAW 264.7 and LPS-induced ALI in mice. SV treatment significantly inhibited the alveolar macrophages activation by decreasing the iNOS, IL-1β, and IL-6 gene expression in vitro and in vivo. The treatment also decreased the inflammatory cells migration and the cytokines gene expression. Our findings suggest that SV can act as an anti-inflammatory agent for acute lung injury.

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 +/- 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.

A fibrose hep?tica ? uma doen?a que tem o seu desenvolvimento ligado ao dano cr?nico no f?gado, sendo muitas vezes consequ?ncia de infec??es virais, abuso alco?lico, toxinas, ou ac?mulo de metais. O dano cr?nico no f?gado leva a um processo inflamat?rio que pode resultar na perda do equilibro da s?ntese e degrada??o de elementos de matriz extracelular, sendo a ativa??o das c?lulas hep?ticas estreladas um dos principais fatores respons?veis por iniciar o processo fibr?tico. Geralmente, a progress?o da fibrose hep?tica, ? detectada somente em est?gios avan?ados, podendo resultar no ?ltimo est?gio de doen?as cr?nicas hep?ticas, conhecido como cirrose, com isso levando ao risco de insufici?ncia hep?tica. Atualmente, n?o existe um tratamento bem estabelecido, por?m, se busca primeiramente identificar e remover o agente causador do dano cr?nico e utilizar juntamente terapias capazes de reduzir a ativa??o, prolifera??o ou at? mesmo induzir apoptose em c?lulas estreladas hep?ticas. No entanto, mol?culas com efeito antioxidante t?m mostrado efeitos significativos na diminui??o do dano por estresse oxidativo. Sendo assim, a busca por novos candidatos ao tratamento da fibrose hep?tica ? necess?ria, e para isso, a investiga??o de mol?culas de fontes naturais e renov?veis parece ser promissora, pois sabe-se que os metab?litos secund?rios de muitas esp?cies de plantas possuem potencial para o desenvolvimento de novos f?rmacos. Dessa forma, o presente estudo investigou os efeitos de dois tratamentos que possuem em comum, sua fonte de origem natural e sua composi??o fitoqu?mica, composta principalmente por compostos fen?licos. No primeiro estudo in vitro e in vivo, a mol?cula do metoxieugenol, 5 demonstrou efeito modulador em c?lulas hep?ticas estreladas ativadas, reduzindo seus marcadores de ativa??o (Col-1, ?-SMA e TGF-? e) e revertendo ao seu fen?tipo quiescente. Da mesma forma, o tratamento foi capaz de ativar o fator de transcri??o PPAR-?, e quando na presen?a do seu inibidor, GW9662, o efeito do metoxieugenol foi suprimido, mostrando a intera??o da ativa??o de PPAR-? nos efeitos observados. O efeito hepatoprotetor in vivo em camundongos foi observado, atenuando o desenvolvimento da fibrose e diminuindo a ativa??o de vias inflamat?rias mediadas por NF-kB. No segundo estudo in vivo, o tratamento com o extrato de Baccharis anomala DC. diminui o dano por estresse oxidativo (TBARS e DCFH-DA), reduziu a ativa??o de genes pro inflamat?rios (TNF-?, NF-kB, IL-6 e iNOS) e protegeu o f?gado dos animais do desenvolvimento da fibrose. Os principais componentes do extrato identificados por HPLC foram os ?cidos fen?licos caf?ico, g?lico e cum?rico. Liver fibrosis is a disease that has its development linked to chronic liver damage, often due to viral infections, alcohol and drugs abuse toxins or metal accumulation. Chronic liver damage leads to a continuous inflammatory process that can result in activation of hepatic stellate cells. The activated phenotype of hepatic stellate cells disrupts the balance between synthesis and degradation of extracellular matrix elements, and this process is responsible for initiating the fibrotic process. The progression of liver fibrosis is often detected in late stages of disease and may result in the last stage of chronic liver disease, known as cirrhosis, leading to the risk of liver failure. Currently, liver fibrosis has no well-established treatment, however, the first goal for therapy is to identify and remove the cause of chronic damage and to use therapies capable of reducing activation, proliferation or even inducing apoptosis in hepatic stellate cells. In addition, molecules with an antioxidant effect have shown good effects in decreasing oxidative stress damage. Thus, the search for new candidates for the treatment of liver fibrosis is necessary, and for this, the investigation of molecules from natural and renewable sources seems to be promising, since it is currently known that the secondary metabolites of many plant species have high applicability for the development of new drugs. Thus, the present study investigated the effects of two treatments that have in common, their natural source and their phytochemical composition, mainly composed of phenolic compounds. In the first in vitro and in vivo study, the methoxyeugenol molecule demonstrated a modulatory effect on activated hepatic stellate cells, reducing its activation markers (Col-1, ?-SMA and 7 TGF-? e) and inducing to its quiescent phenotype. Also, the treatment was able to activate the PPAR-? transcription factor, and when in the presence of its inhibitor, GW9662, the effect of methoxyeugenol was suppressed, showing the interaction of PPAR-? activation in the observed effects. Furthermore, it showed hepatoprotective effect in vivo in mice, attenuating the development of fibrosis and decreasing the activation of NF-kB-mediated inflammatory pathways. In the second in vivo study, treatment with B.anomala extract decreases oxidative stress damage (TBARS and DCFH-DA), reduced activation of pro-inflammatory genes (TNF-?, NF-kB, IL-6 and iNOS ) and protected the animals' livers from the development of fibrosis. The main components of the extract identified by HPLC were caffeic, gallic and coumaric phenolic acids. Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES

Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.

International audience; Crop yield can be affected by crop water use (evapotranspiration, ET) and vice versa, so when trying to simulate one or the other, it can be important to simulate both well. Method: To determine how well 29 maize growth models can simulate ET, an inter-comparison study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project) using eddy cova-riance data from Ames, IA as the standard for comparison (Kimball et al., 2019).

Octyl gallate (OG) is an antioxidant commonly used in food, although there is no definition of its acceptable daily intake. There are reports in vitro and in vivo showing that food additives and drugs can alter lipid metabolism. Lipid droplet accumulation in hepatic cells is one of the main findings in the unregulated lipid metabolism and is strongly related to the development of nonalcoholic fatty liver disease (NAFLD). In this study, we investigated the effects of OG on lipid metabolism in the hepatocellular carcinoma cell line (HepG2). The results have shown, for the first time, that treatment with OG increased the overall amount of lipids, the triglyceride concentration, the lipid droplet area, and SREBP-1c and PPAR-γ gene expression. Taken together, the findings indicate that OG induces lipid droplet accumulation in HepG2 cells through the regulation of SREBP-1c and PPAR-γ gene expression without involving mTOR/S6K1 and may contribute to NAFLD when used as a food additive., EXCLI Journal;Vol. 19 2020

Plant phenology, which describes the timing of plant development, is a major aspect of plant response to environment and for crops, a major determinant of yield. Many studies have focused on comparing model equations for describing how phenology responds to climate but the effect of crop model calibration, also important for determining model performance, has received much less attention. The objectives here were to obtain a rigorous evaluation of prediction capability of wheat phenology models, to analyze the role of calibration and to document the various calibration approaches. The 27 participants in this multi-model study were provided experimental data for calibration and asked to submit predictions for sites and years not represented in those data. Participants were instructed to use and document their “usual” calibration approach. Overall, the models provided quite good predictions of phenology (median of mean absolute error of 6.1 days) and did much better than simply using the average of observed values as predictor. The results suggest that calibration can compensate to some extent for different model formulations, specifically for differences in simulated time to emergence and differences in the choice of input variables. Conversely, different calibration approaches were associated with major differences in prediction error between the same models used by different groups. Given the large diversity of calibration approaches and the importance of calibration, there is a clear need for guidelines and tools to aid with calibration. Arguably the most important and difficult choice for calibration is the choice of parameters to estimate. Several recommendations for calibration practices are proposed. Model applications, including model studies of climate change impact, should focus more on the data used for calibration and on the calibration methods employed.

The therapeutic potential of Baccharis anomala DC. extracts was evaluated through its cytotoxic and antiproliferative effect and their phenotypic reversion property in activated hepatic stellate cells (HSCs). Baccharis anomala is distributed in Brazil (southeastern and south regions) and used for diuretic effect in folk medicine. Four fractions were obtained from the fractionation of the methanolic extract. Fractions III and IV decreased cell proliferation without increasing cell necrosis markers levels and induced cell cycle arrest in G1 phase. Fraction III induced phenotypic reversion through PPAR-γ activation pathway, while fraction IV did not alter PPAR-α/γ expression levels, suggesting that there is an independent PPAR-α/γ pathway involved. Hydroxybenzoic, chlorogenic and coumaric acids were identified. Fractions III and IV showed antiproliferative effect and ability to induce reversion of activated phenotype of HSCs., EXCLI Journal;Vol. 18 2019

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The therapeutic potential of Baccharis anomala DC. extracts was evaluated through its cytotoxic and antiproliferative effect and their phenotypic reversion property in activated hepatic stellate cells (HSCs). Baccharis anomala is distributed in Brazil (southeastern and south regions) and used for diuretic effect in folk medicine. Four fractions were obtained from the fractionation of the methanolic extract. Fractions III and IV decreased cell proliferation without increasing cell necrosis markers levels and induced cell cycle arrest in G1 phase. Fraction III induced phenotypic reversion through PPAR-γ activation pathway, while fraction IV did not alter PPAR-α/γ expression levels, suggesting that there is an independent PPAR-α/γ pathway involved. Hydroxybenzoic, chlorogenic and coumaric acids were identified. Fractions III and IV showed antiproliferative effect and ability to induce reversion of activated phenotype of HSCs., EXCLI Journal; 18:Doc91; ISSN 1611-2156

Esta pesquisa teve como objetivo analisar as contribui??es do OA ?Batalha do Milh?o? para o rendimento dos alunos em matem?tica, bem como analisar a percep??o dos estudantes do 6? ano do Ensino Fundamental sobre o uso do OA em sala de aula, identificar altera??es de resultados em avalia??es com a aplica??o do OA, e avaliar a pertin?ncia da utiliza??o do OA no ensino de matem?tica, na vis?o dos estudantes e pela an?lise dos resultados obtidos. Foi feita uma an?lise do referencial te?rico sobre objetos de aprendizagem e as caracter?sticas necess?rias para ser considerado como tal. Ao se confirmar que a ferramenta utilizada se tratava de um OA, fez-se primeiramente uma avalia??o em formato de prova para avaliar os conhecimentos dos alunos a partir das aulas tradicionais. Em seguida foi feita a aplica??o do OA com os estudantes, e depois uma nova avalia??o para avaliar os conhecimentos. A partir de um question?rio com perguntas abertas e fechadas e de uma an?lise estat?stica do rendimento dos alunos nas duas avalia??es, p?de-se verificar que houve um melhor aproveitamento dos conte?dos e uma melhora nas notas. Al?m disso, observou-se tamb?m uma satisfa??o por parte dos estudantes, que consideraram ter melhorado os seus conhecimentos e aprendizado com um objeto que lhes proporcionou divers?o e entretenimento. Destacou-se a intera??o durante as perguntas e a an?lise de erros, passando o professor a ser um mediador e n?o somente um transmissor de conhecimentos, e o aluno um protagonista de sua aprendizagem. This research has aimed to analyze the contributions of LO (Learning Objects) ?Batalha do Milh?o? upon the Mathematics students? output, as well as analyze 6th grade students? perceptions about the LO application in the classroom, identify changes in results in evaluations based on the use of LO and, evaluate the importance of using this to teach Mathematics under the student?s opinion after their results. An analysis of the LO theory and its characteristics was made to check which ones could be classified as LO. After confirming that the tool used fit the LO characteristics, a test based on traditional classes was applied to the students. Afterwards LO based activities were applied to the students who had another test to check their knowledge. From a questionnaire made of open and closed questions, some statistic data from both tests was rated showing the result was better score and understanding of the contents in the second term. Besides, the students showed themselves more satisfied, perceiving more learning and fun through the LO activities. Interactivity during the activities and analysis of their mistakes was noticed, turning the teacher more into a mediator of the tasks, rather than the author of any teaching, and yet, the students the protagonists of their own learning process. Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES

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The response of wheat crops to elevated CO 2 (eCO 2) was measured and modelled with the Australian Grains Free‐Air CO 2 Enrichment experiment, located at Horsham, Australia. Treatments included CO 2 by water, N and temperature. The location represents a semi‐arid environment with a seasonal VPD of around 0.5 kPa. Over 3 years, the observed mean biomass at anthesis and grain yield ranged from 4200 to 10 200 kg ha−1 and 1600 to 3900 kg ha−1, respectively, over various sowing times and irrigation regimes. The mean observed response to daytime eCO 2 (from 365 to 550 μmol mol−1 CO 2) was relatively consistent for biomass at stem elongation and at anthesis and LAI at anthesis and grain yield with 21%, 23%, 21% and 26%, respectively. Seasonal water use was decreased from 320 to 301 mm (P = 0.10) by eCO 2, increasing water use efficiency for biomass and yield, 36% and 31%, respectively. The performance of six models (APSIM‐Wheat, APSIM‐Nwheat, CAT‐Wheat, CROPSYST, OLEARY‐CONNOR and SALUS) in simulating crop responses to eCO 2 was similar and within or close to the experimental error for accumulated biomass, yield and water use response, despite some variations in early growth and LAI. The primary mechanism of biomass accumulation via radiation use efficiency (RUE) or transpiration efficiency (TE) was not critical to define the overall response to eCO 2. However, under irrigation, the effect of late sowing on response to eCO 2 to biomass accumulation at DC65 was substantial in the observed data (~40%), but the simulated response was smaller, ranging from 17% to 28%. Simulated response from all six models under no water or nitrogen stress showed similar response to eCO 2 under irrigation, but the differences compared to the dryland treatment were small. Further experimental work on the interactive effects of eCO 2, water and temperature is required to resolve these model discrepancies.

O g?nero Baccharis pertence ? fam?lia Asteraceae e tem v?rias esp?cies amplamente utilizadas na medicina popular, possuindo muitos compostos de interesse farmac?utico. O objetivo deste trabalho foi avaliar o efeito citot?xico, antiproliferativo e a revers?o fenot?pica do extrato de Baccharis anomala em c?lulas estreladas hep?ticas ativadas (HSCs) e identificar os compostos presentes no extrato. Para avaliar o efeito citot?xico e antiproliferativo do extrato fracionado, foram utilizadas a libera??o de lactato desidrogenase (LDH) e o m?todo de exclus?o de azul de Tripan. A revers?o do fen?tipo foi avaliada por colora??o com Oil Red e pela express?o de PPAR-? por RT-PCR. As fra??es obtidas a partir do extrato metan?lico foram caracterizadas pela sua composi??o fen?lica por HPLC. Os resultados mostraram que duas fra??es do extrato metan?lico diminu?ram a prolifera??o celular sem aumentar os n?veis de libera??o de LDH. A parada do ciclo celular foi avaliada usando 7-AAD e colora??o por DAPI foi utilizada para avaliar a apoptose celular. As fra??es de B.anomala induziram a parada do ciclo celular em fase G1, a colora??o por DAPI n?o revelou morte celular por apoptose. Colora??o por Oil-RedO (ORO) mostrou a capacidade de indu??o da revers?o fenot?pica, a avalia??o da express?o mRNA de PPAR-y por RT-PCR n?o foi alterada, sugerindo que exista uma via independente de PPAR-y envolvida. Os principais componentes identificados nas fra??es que apresentaram atividade biol?gica foram os ?cidos hidroxibenz?ico, clorog?nico e cum?rico. Nosso estudo foi capaz de mostrar o efeito antiproliferativo das fra??es de extrato metan?lico de B.anomala, seu potencial para reverter o fen?tipo ativado das c?lulas estreladas hep?ticas e mostrou seu potencial para o tratamento da fibrose hep?tica. The genus Baccharis belongs to the family Asteraceae and has several species widely used in folk medicine and own many compounds of pharmaceutical interest. The aim of this work was to evaluate the cytotoxic, antiproliferative effect and phenotypic reversion of Baccharis anomala extract on activated hepatic stellate cells (HSCs) and identify the compounds present in the extract. In order to evaluate the cytotoxic and antiproliferative effect of the fractionated extract, lactate dehydrogenase (LDH) release and the trypan blue exclusion method were used. The phenotype reversion was evaluated by staining with Oil Red and PPAR-? expression by RT-PCR. The fractions obtained from the methanolic extract were characterized for their phenolic composition by HPLC. The results showed that two fractions of methanolic extract decreased cell proliferation without increasing LDH release levels. Cell cycle arrest was evaluated using 7-AAD and DAPI staining was used to evaluate indications of apoptosis. Fractions of B.anomala induced cell cycle arrest in G1 phase, while DAPI staining did not reveal cell death by apoptosis. Oil-Red (ORO) staining showed the ability of fractions to induce phenotypic reversion and the evaluation of PPAR-? mRNA expression was not altered, suggesting that there is an independent PPAR-? pathway involved. The main components identified in the fractions that presented biological activity were hydroxybenzoic, chlorogenic and coumaric acid. Our study was able to show the antiproliferative effect of methanolic extract fractions of B.anomala, its potential to reverse the activated phenotype of hepatic stellate cells and showed its potential for the treatment of liver fibrosis. Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data., Highlights • Data limitations exist for all components of agricultural systems. • Model limitations also exist, more severely in constrained, complex systems. • Knowledge products for informing decisions and policy remain very limited. • Use cases provide an important context for model evaluation and improvement. • More emphasis is needed on models, systems analyses of agricultural systems.

Conservation tillage (i.e., reduced- RT and no till - NT) is frequently proposed as mitigation practices as it can contribute to increase soil organic carbon (SOC) compared to conventional mouldboard ploughing (CT). In this study, we assessed the long-term effects of different tillage management practices on crop yield and SOC stock dynamics in Mediterranean rainfed cereal cropping systems at current and future climate scenarios. We relied on data obtained from long term experiments (LTEs) coming from ICFAR network coupled with four simulation models (APSIM, DSSAT, EPIC, SALUS). Two LTEs dataset were used: AN (Ancona, Marche, 1994-2015) characterized by a two-year durum wheat-maize rotation (NT vs CT: 40 cm deep mouldboard ploughing) and PI2 (Pisa, Toscana) based on a maize continuous crop from 1994 to 1998 followed by a durum wheat-maize rotation (RT: 15 cm disc tillage; vs CT: 30 cm deep ploughing). Climate scenarios were generated by setting up a statistical model using predictors from ERA40 reanalysis and seasonal indices of temperature and precipitation from E-OBS gridded data for the period 1958-2010. The statistical downscaling model was applied to CMCC-CM predictors to obtain climate scenarios at local scale over the period 1971-2000 and 2021-2050 (RCP45 and RCP85 emission scenarios). The multi-model mean was able to better reproduce and with less uncertainty SOC dynamics than a single model, hence better SOC predictions are also expected to occur in the future assessment. Overall, our study showed a decrease of SOC stocks in both sites and tillage systems in future scenarios. However, even if conservation tillage was more affected by climate change losing more SOC than CT, these systems were still able to stock more soil organic carbon also under future scenarios.

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

Submitted by Lâmoni null (lamoni@stj.jus.br) on 2015-12-16T14:21:33Z No. of bitstreams: 1 juizados_especiais_fazenda_mello.pdf: 615988 bytes, checksum: 30b6c7792f5f125054a42a2cb285b7be (MD5) Approved for entry into archive by rmarins@stj.jus.br (rmarins@stj.jus.br) on 2015-12-16T17:59:59Z (GMT) No. of bitstreams: 1 juizados_especiais_fazenda_mello.pdf: 615988 bytes, checksum: 30b6c7792f5f125054a42a2cb285b7be (MD5) Made available in DSpace on 2015-12-16T17:59:59Z (GMT). No. of bitstreams: 1 juizados_especiais_fazenda_mello.pdf: 615988 bytes, checksum: 30b6c7792f5f125054a42a2cb285b7be (MD5) Previous issue date: 2015

Impact response surfaces (IRSs) were constructed to depict the sensitivity of modelled spring and winter wheat yields to systematic changes in baseline temperature (between -2°C and +9°C) and precipitation (-50 to +50%) as simulated by a 26-member ensemble of process-based crop simulation models. The study was conducted across a latitudinal transect for sites in Finland, Germany and Spain. In spite of large differences in simulated yield responses to both baseline and changed climate between models, sites, crops and years, several common messages emerged. Ensemble average yields decline with warming (3-7% per 1°C) and decreased precipitation (3-9% per 10% decrease), but benefit from increased precipitation (0-8% per 10% increase). Yields are more sensitive to temperature than precipitation changes at the Finnish site while sensitivities are mixed at the other sites. Inter-model variability is highest for baseline climate at the Spanish site but is affected little by changed climate. Model responses diverge most under warming at the Finnish and German sites for winter wheat. The IRS pattern of yield reliability tracks average yield levels. Optimal temperatures for present-day cultivars are below the baseline at the German and Spanish sites suggesting that adoption of cultivars with higher temperature requirements might already be advantageous, and increasingly so at all sites under future warming. The study was conducted in the CropM component of the FACCE-JPI/MACSUR project.

Projections of climate change impacts on crop performances are inherently uncertain. However, multimodel uncertainty analysis of crop responses is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we report on the Agricultural Model Intercomparison and Improvement Project ensemble of 30 wheat models tested using both crop and climate observed data in diverse environments, including infra-red heating field experiments, for their accuracy in simulating multiple crop growth, N economy and yield variables. The relative error averaged over models in reproducing observations was 24-38% for the different end-of-season variables. Clusters of wheat models organized by their correlations with temperature, precipitation, and solar radiation revealed common characteristics of climatic responses; however, models are rarely in the same cluster when comparing across sites. We also found that the amount of information used for calibration has only a minor effect on model ensemble climatic responses, but can be large for any single model. When simulating impacts assuming a mid-century A2 emissions scenario for climate projections from 16 downscaled general circulation models and 26 wheat models, a greater proportion of the uncertainty in climate change impact projections was due to variations among wheat models rather than to variations among climate models. Uncertainties in simulated impacts increased with atmospheric [CO2] and associated warming. Extrapolating the model ensemble temperature response (at current atmospheric [CO2]) indicated that warming is already reducing yields at a majority of wheat-growing locations. Finally, only a very weak relationship was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs. In conclusion, uncertainties in prediction of climate change impacts on crop performance can be reduced by improving temperature and CO2 relationships in models and are better quantified through use of impact ensembles.

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The sensitivity of a 26 member ensemble of process based wheat simulation models to perturbations in baseline temperature and precipitation was examined to construct impact response surfaces (IRS) of simulated yields.

AgMIP is an international program bringing together research projects on climate, crop modelling and regional agriculture adaptation to climate change. One objective is to better assess the projections of global food availability depending on different staple crops (wheat, rice and maize), taking into account the projections of climate change for the end of century and the uncertainty attached to them. The need for robust estimates, i.e. good crop models for yields and use of natural resources is a prerequisite to benchmark the various cropping systems and local solutions that will ultimately be explored in order to cope with climate change, without bringing about any negative side effects on the environment. Modelers hence work together internationally in order to compare and improve process-based crop simulation models. Maize is a strategic crop, exhibiting high potential radiation and water use efficiencies and is cultivated worldwide. In a first phase, the impacts of CO2 and temperature on the maize yields and water use were studied using 23 crop models on 4 sites with contrasted cool or hot climate conditions, under no water limitation (Lusignan in France, Ames in the United States, Morogoro in Tanzania and Rio Verde in Brasil). Models were run using local soil conditions and climate variables for 30 years (1980-2010) after adjusting the cultivar parameters to the ones used in one experiment in each site. At the four sites studied, the average values across models of simulated yields were closer to the observed local experimental results than the simulation of any individual model. This indicated that ensemble modelling could be a relevant way to approach the impact of climate change on maize yields. There was also a broad agreement between models to simulate a reduction in maize yield in response to temperature, roughly - 0.5 Mg ha-1 per °C increase, with no significant impact on water use, although the latter variable was estimated with a large variability between models. Plant phenology was the mostly altered process with increasing temperature. Shortening of the duration from flowering to maturity in particular reduced the gain in grain weight during that phase. This suggests that genetics could hence play a key role in adapting maize production to climate change, at least under high water availability. Doubling [CO2] from 360 to 720 μmole mole-1 increased grain yield by 7.5% on average across models and sites, with a slight decrease of water use, bringing about an increase in water use efficiency. However, the variability of the response to [CO2] was very high, bringing about the need to better simulate the role of CO2, especially on plant transpiration. In a second phase, models are therefore now being tested against Free Air CO2 Enrichment experimental data, so that variability can be reduced and the actual impact of global change on water use can be assessed with a relevant precision to adaptating agricultural practices. (Texte intégral)

This study was conducted as part of the impact uncertainty research of the crop modelling (CropM) component of MASUR, a project funded by national funding agencies and the EC.

(+)-Catechin is a type of catechin present in large amounts in açaí fruits and cocoa seeds. Besides its antioxidant and anti-inflammatory activities, little is known about its effects in the liver, especially during hepatic fibrosis. We report here the effects of (+)-catechin on hepatic stellate cells. (+)-Catechin induced quiescent phenotype in GRX cells, along with an increase in lipid droplets. Proliferator-activated receptor γ mRNA expression was upregulated, whereas type I collagen mRNA expression was downregulated. Pro-inflammatory cytokines were not influenced by (+)-catechin, whereas the levels of interleukin 10 were significantly increased. The data provide evidence that (+)-catechin can reduce hepatic stellate cell activation.

Impact response surfaces (IRSs) depict the response of an impact variable to changes in two explanatory variables as a plotted surface. Here, IRSs of spring and winter wheat yields were constructed from a 25-member ensemble of process-based crop simulation models. Twenty-one models were calibrated by different groups using a common set of calibration data, with calibrations applied independently to the same models in three cases. The sensitivity of modelled yield to changes in temperature and precipitation was tested by systematically modifying values of 1981-2010 baseline weather data to span the range of 19 changes projected for the late 21st century at three locations in Europe.

The IC-FAR project (2013-2016), funded by the Italian ministry of University, Research and Education, aims to use datasets from 16 Italian long term agronomic experiments (LTEs) to assess the reliability of different cropping system models over a range of Mediterranean environments and cropping systems. The selected models will be used for scenario and uncertainty analyses vs near-future climate change. The LTEs are located in seven sites: Turin, Padua, Bologna, Ancona, Pisa, Perugia, Foggia. The project’s is linked to international projects such as MACSUR, AgMIP, ANAEE, ESFRI and GRA, and has model developer teams as associate partners. IC-FAR is structured in five WPs. WP1 is focused on building a common dataset and sampling protocols. The field data will be implemented in the WP2 to calibrate, validate and assess the performances of different models across Italian environments. An uncertainty analysis will be performed in relation to the model types, cropping system typologies and climate scenarios (WP3). WP4 and WP5 are focused on capacity building on modeling and on dissemination, including networking with other European LTE platforms (WP4), and to the project coordination (WP5).The next step of IC-FAR will be the design and realization of a special issue summarizing a selection of the most important results from the LTEs, that will be the starting point towards the full implementation of the data sharing policy of this project.

Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations of satellites – including the RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free and open data policies – can be used to create stacks of dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet to date, the contribution of SAR imagery to operational crop mapping is often limited to that of a gap-filler, compensating for optical data obscured by clouds. The Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-Comparison Experiment is a multi-year, multi-partner project focused on evaluating methods for SAR-based crop classification. Stacks of dense time-series SAR imagery, from RADARSAT-2 and Sentinel-1 satellites, were acquired for 10 sites located in six countries. Decision Tree (DT) and Random Forest (RF) classification methodologies were applied to these SAR data-stacks, as well as to data-stacks of optical only, and optimized SAR/optical data combinations. For the dense time-series SAR stacks, overall classification accuracies above 85% and 80% were obtained for 6 of 10 and 8 of 10 sites, respectively. For maize, the SAR-only data delivered user’s and producer’s accuracies greater than 90% for half the sites. For soya bean, accuracies greater than 80% were reported for 5 of 9 sites and classification accuracies were greater than 80% for wheat on half the sites. Classification results were influenced by the mix and number of agriculture classes present at each site, the available SAR imagery, as well as the training and validation data sets for individual crop types. These results have important operational implications for regions of the world dominated by cloudy conditions and the lack of adequate amounts of optical imagery to support satellite-based crop monitoring.

Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations of satellites – including the RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free and open data policies – can be used to create stacks of dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet to date, the contribution of SAR imagery to operational crop mapping is often limited to that of a gap-filler, compensating for optical data obscured by clouds. The Joint Experiment for Crop Assessment and Monitoring (JECAM) SAR Inter-Comparison Experiment is a multi-year, multi-partner project focused on evaluating methods for SAR-based crop classification. Stacks of dense time-series SAR imagery, from RADARSAT-2 and Sentinel-1 satellites, were acquired for 10 sites located in six countries. Decision Tree (DT) and Random Forest (RF) classification methodologies were applied to these SAR data-stacks, as well as to data-stacks of optical only, and optimized SAR/optical data combinations. For the dense time-series SAR stacks, overall classification accuracies above 85% and 80% were obtained for 6 of 10 and 8 of 10 sites, respectively. For maize, the SAR-only data delivered user’s and producer’s accuracies greater than 90% for half the sites. For soya bean, accuracies greater than 80% were reported for 5 of 9 sites and classification accuracies were greater than 80% for wheat on half the sites. Classification results were influenced by the mix and number of agriculture classes present at each site, the available SAR imagery, as well as the training and validation data sets for individual crop types. These results have important operational implications for regions of the world dominated by cloudy conditions and the lack of adequate amounts of optical imagery to support satellite-based crop monitoring.

The Experimental Zooprophylactic Institute of Southern Italy (Istitituto Zooprofilattico Sperimentale del Mezzogiorno, IZSM) is a public health institution operating within the Italian National Health Service. Over the past 5 years [IZSM] has promoted several research studies and interventions in an effort to tackle the ‘Land of Fires’ phenomenon, caused by the continued trafficking and uncontrolled incineration of waste that has affected some areas of Campania for decades. In this article, a mathematical model that generates a municipality index of environmental pressure is presented. The model was developed by a multidisciplinary team led by an environmental engineer and included researchers in the fields of veterinary and human medicine, biology and computer science. This model may serve as a geostratification tool useful for the design of human biomonitoring studies, although it may also be employed for strategic planning of remediation programs and public health interventions., Lay abstract The complex environmental scenario of the ‘Land of Fires’ phenomenon is caused by the continued trafficking and uncontrolled incineration of waste, and has affected some areas of Campania in Italy for decades. In this article a mathematical model that generates a municipality index of environmental pressure is presented. This index may be used for the design of biomonitoring studies as well as for planning of remediation interventions.

Simulation models, informed and validated with datasets from long term experiments (LTEs), are considered useful tools to explore the effects of different management strategies on soil organic carbon (SOC) dynamics and evaluate suitable mitigative options for climate change. But, while there are several studies which assessed a better prediction of crop yields using an ensemble of models, no studies are currently available on the evaluation of a model ensemble on SOC stocks. In this study we assessed the advantages of using an ensemble of crop models (APSIM-NWheat, DSSAT, EPIC, SALUS), calibrated and validated with datasets from LTEs, to estimate SOC dynamics. Then we used the mean of the model ensemble to assess the impacts of climate change on SOC stocks under conventional (CT) and conservation tillage practices (NT: No Till; RT: Reduced Tillage). The assessment was completed for two long-term experiment sites (Agugliano – AN and Pisa – PI2 sites) in Italy under rainfed conditions. A durum wheat (Triticum turgidum subsp. durum (Desf.) Husn.) – maize (Zea mays L.) rotation system was evaluated under two different climate scenarios over the periods 1971–2000 (CP: Present Climate) and 2021–2050 (CF: Future Climate), generated by setting up a statistical model based on canonical correlation analysis. Our study showed a decrease of SOC stocks in both sites and tillage systems over CF when compared with CP. At the AN site, CT lost −7.3% and NT −7.9% of SOC stock (0–40 cm) under CF. At the PI2 site, CT lost −4.4% and RT −5.3% of SOC stocks (0–40 cm). Even if conservation tillage systems were more impacted under future scenarios, they were still able to store more SOC than CT, so that these practices can be considered viable options to mitigate climate change. Furthermore, at the AN site, under CF, NT demonstrated an annual increase of 0.4%, the target value suggested by the 4 per thousand initiative launched at the 21st meeting of the Conference of the Parties in Paris. However, RT at the PI2 needs to be coupled with other management strategies, as the introduction of cover crops, to achieve such target.

Fabio Orecchini, Gino Bella,Salvatore Pietro Cicconardi