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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.

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.

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.

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.