EU-Rotate_N – a decision support system – to predict environmental and economic consequences of the management of nitrogen fertiliser in crop rotations

A model has been developed which assesses the economic and environmental performance of crop rotations, in both conventional and organic cropping, for over 70 arable and horticultural crops, and a wide range of growing conditions in Europe. The model, though originally based on the N_ABLE model, has been completely rewritten and contains new routines to simulate root development, the mineralisation and release of nitrogen (N) from soil organic matter and crop residues, and water dynamics in soil. New routines have been added to estimate the effects of sub-optimal rates of N and spacing on the marketable outputs and gross margins. The model provides a mechanism for generating scenarios to represent a range of differing crop and fertiliser management strategies which can be used to evaluate their effects on yield, gross margin and losses of nitrogen through leaching. Such testing has revealed that nitrogen management can be improved and that there is potential to increase gross margins whilst reducing nitrogen losses

Modeling GHG emissions, N and C dynamics in Spanish agricultural soils.

To date, only few initiatives have been carried out in Spain in order to use mathematical models (e.g. DNDC, DayCent, FASSET y SIMSNIC) to estimate nitrogen (N) and carbon (C) dynamics as well as greenhouse gases (GHG) in Spanish agrosystems. Modeling at this level may allow to gain insight on both the complex relationships between biological and physicochemical processes, controlling the processes leading to GHG production and consumption in soils (e.g. nitrification, denitrification, decomposing, etc.), and the interactions between C and N cycles within the different components of the continuum plant-soil-environment. Additionally, these models can simulate the processes behind production, consumition and transport of GHG (e.g. nitrous oxide, N2O, and carbon dioxide, CO2) in the short and medium term and at different scales. Other sources of potential pollution from soils can be identified and quantified using these process-based models (e.g. NO3 y NH3)

Aortic root remodelling in competitive athletes.

BACKGROUND: Controversy remains about the cut-off limits for detecting aortic dilatation in athletes, particularly in large-sized individuals. The allometric scaling model has been used to obtain size-independent measurements in cardiovascular structures in the general population. AIM: The purpose of this study was to validate the use of allometric scaling in the measurement of the aortic root for competitive athletes and to offer reference values. METHODS: This was a cross-sectional study that analyses the dimensions of aortic root found in the echocardiogram performed as part of pre-participation sports screening in competitive athletes between 2012-2015. Beta exponents were calculated for height and body surface area in the whole cohort. In order to establish whether a common exponent could be used in both genders the following model was assessed y = axb*exp(c*sex). If a common exponent could not be applied then sex-specific beta exponents were calculated. RESULTS: Two thousand and eighty-three athletes (64% men) were included, from a broad spectrum of 44 different sports disciplines, including basketball, volleyball and handball. The mean age was 18.2 ± 5.1 years (range 12-35 years) and all athletes were Caucasian, with a training load of 12.5 ± 5.4 h per week. Indexed aortic root dimension showed a correlation with ratiometric scaling by body surface area (r: -0.419) and generated size independence values with a very light correlation with height (r: -0.084); and with the allometric scaling by body surface area (r: -0.063) and height (r: -0.070). The absolute value of aortic root was higher in men than in women (p < 0.001). These differences were maintained with allometric scaling. CONCLUSION: Size-independent aortic root dimension values are provided using allometric scaling by body surface area and height in a large cohort of competitive athletes. Aortic root values were larger in men than in women, both in absolute values and after allometric scaling. The use of these indexed aortic reference ranges can be useful for the early detection of aortic pathologies

Uncertainty in Simulating Wheat Yields Under Climate Change

Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1,3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking

Strategies for greenhouse gas emissions mitigation in Mediterranean agriculture: A review

[EN] An integrated assessment of the potential of different management practices for mitigating specific components of the total GHG budget (N2O and CH4 emissions and C sequestration) of Mediterranean agrosystems was performed in this study. Their suitability regarding both yield and environmental (e.g. nitrate leaching and ammonia volatilization) sustainability, and regional barriers and opportunities for their implementation were also considered. Based on its results best strategies to abate GHG emissions in Mediterranean agro-systems were proposed. Adjusting N fertilization to crop needs in both irrigated and rain-fed systems could reduce N2O emissions up to 50% compared with a non-adjusted practice. Substitution of N synthetic fertilizers by solid manure can be also implemented in those systems, and may abate N2O emissions by about 20% under Mediterranean conditions, with additional indirect benefits associated to energy savings and positive effects in crop yields. The use of urease and nitrification inhibitors enhances N use efficiency of the cropping systems and may mitigate N2O emissions up to 80% and 50%, respectively. The type of irrigation may also have a great mitigation potential in the Mediterranean region. Drip-irrigated systems have on average 80% lower N2O emissions than sprinkler systems and drip-irrigation combined with optimized fertilization showed a reduction in direct N2O emissions up to 50%. Methane fluxes have a relatively small contribution to the total GHG budget of Mediterranean crops, which can mostly be controlled by careful management of the water table and organic inputs in paddies. Reduced soil tillage, improved management of crop residues and agro-industry by-products, and cover cropping in orchards, are the most suitable interventions to enhance organic C stocks in Mediterranean agricultural soils. The adoption of the proposed agricultural practices will require farmers training. The global analysis of life cycle emissions associated to irrigation type (drip, sprinkle and furrow) and N fertilization rate (100 and 300 kg N ha(-1) yr(-1)) revealed that these factors may outweigh the reduction in GHG emissions beyond the plot scale. The analysis of the impact of some structural changes on top-down mitigation of GHG emissions revealed that 3-15% of N2O emissions could be suppressed by avoiding food waste at the end-consumer level. A 40% reduction in meat and dairy consumption could reduce GHG emissions by 20-30%. Reintroducing the Mediterranean diet (i.e. similar to 35% intake of animal protein) would therefore result in a significant decrease of GHG emissions from agricultural production systems under Mediterranean conditions. (C) 2016 Elsevier B.V. All rights reserved.The authors would like to thank the Spanish National R+D+i Plan (AGL2012-37815-C05-01, AGL2012-37815-C05-04) and very specifically the workshop held in December 2016 in Butron (Bizkaia) to synthesize the most promising measures to reduce N2O emissions from Spanish agricultural soils. BC3 is sponsored by the Basque Government. M. L. Cayuela thanks Fundacion Seneca for financing the project 19281/PI/14.Sanz-Cobeña, A.; Lassaletta, L.; Aguilera, E.; Del Prado, A.; Garnier, J.; Billen, G.; Iglesias, A.... (2017). Strategies for greenhouse gas emissions mitigation in Mediterranean agriculture: A review. Agriculture Ecosystems & Environment. 238:5-24. https://doi.org/10.1016/j.agee.2016.09.038S52423

Strategies for GHG mitigation in Mediterranean cropping systems. A review

In this review we aimed to synthetize and analyze the most promising GHGs mitigation strategies for Mediterranean cropping systems. A description of most relevant measures, based on the best crop choice and management by farmers (i.e., agronomical practices), was firstly carried out. Many of these measures can be also efficient in other climatic regions, but here we provide particular results and discussion of their efficiencies for Mediterranean cropping systems. An integrated assessment of management practices on mitigating each component of the global warming potential (N2O and CH4 emissions and C sequestration) of production systems considering potential side-effects of their implementation allowed us to propose the best strategies to abate GHG emissions, while sustaining crop yields and mitigating other sources of environmental pollution (e.g. nitrate leaching and ammonia volatilization)

О перспективе извлечения йода из продукта утилизации окислителя ракетного топлива

Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32 degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degree C of further temperature increase and become more variable over space and time

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects

The International Heat Stress Genotype Experiment for modeling wheat response to heat: field experiments and AgMIP-Wheat multi-model simulations

The data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models