How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies.

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration.

Global evaluation of inhibitor impacts on ammonia and nitrous oxide emissions from agricultural soils: A meta-analysis.

Inhibitors are widely considered an efficient tool for reducing nitrogen (N) loss and improving N use efficiency, but their effectiveness is highly variable across agroecosystems. In this study, we synthesized 182 studies (222 sites) worldwide to evaluate the impacts of inhibitors (urease inhibitors [UI], nitrification inhibitors [NI] and combined inhibitors) on crop yields and gaseous N loss (ammonia [NH ] and nitrous oxide [N O] emissions) and explored their responses to different management and environmental factors including inhibitor application timing, fertilization regime, cropping system, water management, soil properties and climatic conditions using subgroup meta-analysis, meta-regression and multivariate analyses. The UI were most effective in enhancing crop yields (by 5%) and reducing NH volatilization (by 51%), whereas NI were most effective at reducing N O emissions (by 49%).

Exploring management strategies to improve yield and mitigate nitrate leaching in a typical radish field in northern China.

It is currently uncertain whether process-based models are capable of assessing crop yield and nitrogen (N) losses while helping to investigate best management practices from vegetable cropping systems. The objectives of this study were to (1) calibrate and evaluate the Denitrification-Decomposition (DNDC) model in simulating crop growth and nitrate leaching in a typical field radish system; (2) optimize management practices to improve radish yield and mitigate nitrate leaching under 20-year climate variability. A five-season in-situ field experiment of spring and autumn radish in northern China was established in the autumn of 2017 and measurements of radish yield, N uptake, soil temperature, soil moisture, drainage, and nitrate leaching were obtained under different N usage.

Regional climate influences manure temperature and methane emissions - A pan-Canadian modelling assessment.

This study explores the variation of liquid manure temperature (T) and CH emissions associated with contrasting regional climates, inter-annual weather variation, and manure storage emptying. As a case-study, six regions across Canada were used, spanning 11°32' latitude and 58°30' longitude. Annual average air temperatures ranged from 3.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking.

Modifying fertilizer rate and application method reduces environmental nitrogen losses and increases corn yield in Ontario.

Nitrogen (N) use in corn production is an important driver of nitrous oxide (NO) emissions and 4R (Right source, Right rate, Right time and Right place) fertilizer practices have been proposed to mitigate emissions. However, combined 4R practices have not been assessed for their potential to reduce NO emissions at the provincial-scale while also considering trade-offs with other N losses such as leaching or ammonia (NH) volatilization. The objectives of this study were to develop, validate, and apply a Denitrification-Decomposition model framework at 270 distinct soil-climate regions in Ontario to simulate corn yield and NO emissions across eleven fertilizer management scenarios during 1986-2015.

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N O emissions.

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N O) emissions for wheat, maize, rice and temperate grasslands.

Scenario analysis of fertilizer management practices for NO mitigation from corn systems in Canada.

Effective management of nitrogen (N) fertilizer application by farmers provides great potential for reducing emissions of the potent greenhouse gas nitrous oxide (NO). However, such potential is rarely achieved because our understanding of what practices (or combination of practices) lead to NO reductions without compromising crop yields remains far from complete. Using scenario analysis with the process-based model DNDC, this study explored the effects of nine fertilizer practices on NO emissions and crop yields from two corn production systems in Canada.

Impact of management strategies on the global warming potential at the cropping system level.

Estimating the greenhouse gas (GHG) emissions from agricultural systems is important in order to assess the impact of agriculture on climate change. In this study experimental data supplemented with results from a biophysical model (DNDC) were combined with life cycle assessment (LCA) to investigate the impact of management strategies on global warming potential of long-term cropping systems at two locations (Breton and Ellerslie) in Alberta, Canada. The aim was to estimate the difference in global warming potential (GWP) of cropping systems due to N fertilizer reduction and residue removal.

A Greenhouse Gas and Soil Carbon Model for Estimating the Carbon Footprint of Livestock Production in Canada.

To assess tradeoffs between environmental sustainability and changes in food production on agricultural land in Canada the Unified Livestock Industry and Crop Emissions Estimation System (ULICEES) was developed. It incorporates four livestock specific GHG assessments in a single model. To demonstrate the application of ULICEES, 10% of beef cattle protein production was assumed to be displaced with an equivalent amount of pork protein.