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)

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

Multimodel Ensembles of Wheat Growth: More Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models

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

Uncertainty of wheat water use: Simulated patterns and sensitivity to temperature and CO₂

Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand