Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach

Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.</p

Differences between the real and the desired worlds in the results of clinical trials

OBJECTIVE:We refer to the effectiveness (known as pragmatic or real world) and efficacy (known as explanatory or desired or ideal world) of interventions. However, these terms seem to be randomly chosen by investigators who design clinical trials and do not always reflect the true purpose of the study. A pragmatic-explanatory continuum indicator summary tool was thus developed with the aim of identifying the characteristics of clinical trials that distinguish between effectiveness and efficacy issues. We verified whether clinical trials used the criteria proposed by the indicator summary tool, and we categorized these clinical trials according to a new classification.METHOD:A systematic survey of randomized clinical trials was performed. We added a score ranging from 0 (more efficacious) to 10 (more effective) to each domain of the indicator summary tool and proposed the following classifications: high efficacy

Necrotrophism Is a Quorum-Sensing-Regulated Lifestyle in Bacillus thuringiensis

How pathogenic bacteria infect and kill their host is currently widely investigated. In comparison, the fate of pathogens after the death of their host receives less attention. We studied Bacillus thuringiensis (Bt) infection of an insect host, and show that NprR, a quorum sensor, is active after death of the insect and allows Bt to survive in the cadavers as vegetative cells. Transcriptomic analysis revealed that NprR regulates at least 41 genes, including many encoding degradative enzymes or proteins involved in the synthesis of a nonribosomal peptide named kurstakin. These degradative enzymes are essential in vitro to degrade several substrates and are specifically expressed after host death suggesting that Bt has an active necrotrophic lifestyle in the cadaver. We show that kurstakin is essential for Bt survival during necrotrophic development. It is required for swarming mobility and biofilm formation, presumably through a pore forming activity. A nprR deficient mutant does not develop necrotrophically and does not sporulate efficiently in the cadaver. We report that necrotrophism is a highly regulated mechanism essential for the Bt infectious cycle, contributing to spore spreading

IlsA, A Unique Surface Protein of Bacillus cereus Required for Iron Acquisition from Heme, Hemoglobin and Ferritin

The human opportunistic pathogen Bacillus cereus belongs to the B. cereus group that includes bacteria with a broad host spectrum. The ability of these bacteria to colonize diverse hosts is reliant on the presence of adaptation factors. Previously, an IVET strategy led to the identification of a novel B. cereus protein (IlsA, Iron-regulated leucine rich surface protein), which is specifically expressed in the insect host or under iron restrictive conditions in vitro. Here, we show that IlsA is localized on the surface of B. cereus and hence has the potential to interact with host proteins. We report that B. cereus uses hemoglobin, heme and ferritin, but not transferrin and lactoferrin. In addition, affinity tests revealed that IlsA interacts with both hemoglobin and ferritin. Furthermore, IlsA directly binds heme probably through the NEAT domain. Inactivation of ilsA drastically decreases the ability of B. cereus to grow in the presence of hemoglobin, heme and ferritin, indicating that IlsA is essential for iron acquisition from these iron sources. In addition, the ilsA mutant displays a reduction in growth and virulence in an insect model. Hence, our results indicate that IlsA is a key factor within a new iron acquisition system, playing an important role in the general virulence strategy adapted by B. cereus to colonize susceptible hosts

Using SEAS5 Seasonal Weather Forecasts for Regional Crop Yield Prediction in a Land Surface Modelling Approach

Seasonal weather forecasts can provide important information for water resources and agricultural planning. However, their coarse spatial and temporal resolution limit the usage for modelling applications such as crop and land surface models and have hindered their widespread use in such models. In this study, we applied sub-seasonal and seasonal weather forecasts from the latest ECMWF SEAS5 forecasting system in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for multiple years forced with sub-seasonal and seasonal weather forecasts over two different domains, one over the German state of North Rhine-Westphalia characterized by heterogeneous land cover and diverse agricultural land use, the other over the Australian state of Victoria that is dominated by large agricultural fields of mostly rainfed winter grain crops. Our results show that the simulations forced with seasonal and sub-seasonal forecasts were able to reproduce recorded inter-annual trends of crop yield, but the inter-annual variability of crop yields was significantly lower compared to the records. The forecast-forced simulations were able to reproduce the generally higher inter-annual variability in crop yield throughout the Australian domain (approx. 50 % inter-annual variability in recorded yields and 20 % in simulated yields) compared to the German domain (approx. 15 % inter-annual variability in recorded yields and 5 % in simulated yields). Also, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 throughout the German domain, thus capturing one of the main contributing factors to the low annual crop yield. While general soil moisture trends, such as the European drought in 2018, were reproduced in the results from the sub-seasonal and seasonal experiments, we found systematic biases compared to satellite products that could also be observed in the reference simulations forced with reanalysis weather data. The observed biases in the representation of soil moisture, as well as the relatively low inter-annual variability of simulated crop yield, indicate that the representation of these variables in CLM5 still needs to be improved to increase the model sensitivity to drought stress and other crop stressors (e.g., pests, hail, wind)

Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0

The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy, and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site-specific field data focusing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields, as well as water, energy, and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines following Lu et al. (2017) in CLM5; (2) implementing plant-specific parameters for sugar beet, potatoes, and winter wheat, thereby adding the two crop functional types (CFTs) for sugar beet and potatoes to the list of actively managed crops in CLM5; and (3) introducing a cover-cropping subroutine that allows multiple crop types on the same column within 1 year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity as it reduces erosion and improves soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop-specific parameterization and the winter wheat subroutines led to a significant simulation improvement in terms of energy fluxes (root-mean-square error, RMSE, reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange, and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover-cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes, seasonal cycle of LAI, and latent heat flux (reduction of wintertime RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes

Guia prático de medicina baseada em evidências

Neste livro são expostos, para uso de profissionais da área, os princípios básicos e conceitos fundamentais, além dos procedimentos necessários para a prática da Medicina Baseada em Evidências (MBE). A MBE é uma prática relativamente nova, criada em 1992, pelo cientista epidemiologista Gordon Guyatt, na canadense McMaster University. Trata-se da utilização e do desenvolvimento de métodos rigorosos que respondam a questões clinicas sobre efetividade, eficiência e segurança de determinado tratamento e prevenção, bem como sobre a sensibilidade e especificidade de testes diagnósticos de certa doença na área de saúde. A MBE propõe realizar pesquisas de boa qualidade metodológica e livres de vieses e conflitos de interesse, de forma que as respostas sejam adequadas para auxiliar na tomada de decisão clínica. Segundo a MBE, a melhor forma de saber se uma medicação específica é eficaz no tratamento de determinada doença é a revisão sistemática de ensaios clínicos randomizados, que mapeiam os estudos publicados e não publicados realizados mundialmente sobre o assunto. Tal revisão deve ainda contar com metodologia rigorosa que busque, por exemplo, explicar resultados contraditórios sobre a mesma questão e comparar estudos com diferentes amostras para detectar possível diferença estatística. De forma didática e detalhada, mas mantendo todo o rigor científico, esta obra tem caráter de manual. Ao longo dos capítulos, seus autores explicam conceitos e apresentam o passo a passo da utilização da MBE para os profissionais de saúde