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The effects of projected changes in climate and atmospheric CO2 concentration on productivity and nitrogen (N) leaching of characteristic arable and pig farming rotations in Denmark were investigated with the FASSET simulation model. The LARS weather generator was used to provide climatic data for the baseline period (1961–90) and in combination with two regional circulation models (RCM) to generate climatic data under the Intergovernmental Panel on Climate Change (IPCC) A1B emission scenario for four different 20-year time slices (denoted by midpoints 2020, 2040, 2060 and 2080) for two locations in Denmark, differing in soil and climate, and representative of the selected production systems. The CO2 effects were modelled using projected CO2 concentrations for the A1B emission scenario. Crop rotations were irrigated (sandy soil) and unirrigated (sandy loam soil), and all included systems with and without catch crops, with field operation dates adapted to baseline and future climate change. Model projections showed an increase in the productivity and N leaching in the future that would be dependent on crop rotation and crop management, highlighting the importance of considering the whole rotation rather than single crops for impact assessments. Potato and sugar beet in arable farming and grain maize in pig farming contributed most to the productivity increase in the future scenarios. The highest productivity was obtained in the arable system on the sandy loam soil, with an increase of 20% on average in 2080 with respect to the baseline. Irrigation and fertilization rates would need to be increased in the future to achieve optimum yields. Growing catch crops reduces N leaching, but current catch crop management might not be sufficient to control the potential increase of leaching and more efficient strategies are required in the future. The uncertainty of climate change scenarios was assessed by using two different climate projections for predicting crop productivity and N leaching in Danish crop rotations, and this showed the consistency of the projected trends when used with the same crop model.

The emissions of nitrous oxide (N2O) and leaching of nitrate (NO3) from agricultural cropping systems have considerable negative impacts on climate and the environment. Although these environmental burdens are less per unit area in organic than in non-organic production on average, they are roughly similar per unit of product. If organic farming is to maintain its goal of being environmentally friendly, these loadings must be addressed. We discuss the impact of possible drivers of N2O emissions and NO3 leaching within organic arable farming practice under European climatic conditions, and potential strategies to reduce these. Organic arable crop rotations are generally diverse with the frequent use of legumes, intercropping and organic fertilisers. The soil organic matter content and the share of active organic matter, soil structure, microbial and faunal activity are higher in such diverse rotations, and the yields are lower, than in non-organic arable cropping systems based on less diverse systems and inorganic fertilisers. Soil mineral nitrogen (SMN), N2O emissions andNO3 leaching are low under growing crops, but there is the potential for SMN accumulation and losses after crop termination, harvest or senescence. The risk of high N2O fluxes increases when large amounts of herbage or organic fertilisers with readily available nitrogen (N) and degradable carbon are incorporated into the soil or left on the surface. Freezing/thawing, drying/rewetting, compacted and/or wet soil and mechanical mixing of crop residues into the soil further enhance the risk of high N2O fluxes. N derived from soil organic matter (background emissions) does, however, seem to be the most important driver for N2O emission from organic arable crop rotations, and the correlation between yearly total N-input and N2O emissions is weak. Incorporation of N-rich plant residues or mechanical weeding followed by bare fallow conditions increases the risk of NO3 leaching. In contrast, strategic use of deep-rooted crops with long growing seasons or effective cover crops in the rotation reducesNO3 leaching risk. Enhanced recycling of herbage from green manures, crop residues and cover crops through biogas or composting may increase N efficiency and reduce N2O emissions and NO3 leaching. Mixtures of legumes (e.g. clover or vetch) and non-legumes (e.g. grasses orBrassica species) are as efficient cover crops for reducing NO3 leaching as monocultures of non-legume species. Continued regular use of cover crops has the potential to reduce NO3 leaching and enhance soil organic matter but may enhance N2O emissions. There is a need to optimise the use of crops and cover crops to enhance the synchrony of mineralisation with crop N uptake to enhance crop productivity, and this will concurrently reduce the long-term risks of NO3 leaching andN2O emissions.

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.

Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). 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.

Silage corn is a fodder with high starch content, organic matter and digestible fibre, and low nitrogen. From an environmental perspective, its use helps to reduce the volume of manure and N (Salcedo, 2011), emissions of NH +Subscript 3 -Subscript (Merino +Italic et al. -Italic , 2008), N 2 O (Arriaga , 2010), enteric CH 4 (Vellinga and Hovingy, 2010), carbon footprint (Nguyen , 2013) and increases nitrogen recovered in milk with respect to intake (Arriaga , 2009; Dewhurst, 2013). The aim of this study was to estimate whether the partial substitution of pasture (surface) and grass (feed) by forages with known production and nutritive potential (maize or maize and Lolium multiflorum ) and higher input needs (seeds, fertilizers, etc.) can contribute to reducing emissions of CO -eq/kg of milk, using the simulation model DairyCant 1.0.

Crop models are essential tools for assessing the threat of climate change to local and global food production(1). Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature(2). 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 degrees C of further temperature increase and become more variable over space and time.

Much of the uncertainty in crop and grassland model predictions of how arable and grassland systems respond to changes in management and environmental drivers can be attributed to differences in the structure of these models. This has created an urgent need for international benchmarking of models, in which uncertainties are estimated by running several models that simulate the same physical and management conditions to generate expanded envelopes of uncertainty in model predictions. This study presents some preliminary results on the uncertainty of outputs from 12 grassland models, whereas exploring differences in model response when increasing data resources are used for model calibration.

Linear variable differential transformer (LVDT) sensors were used to detect continuous diameter growth responses of Pinus pinea (stone pine) seedlings inoculated with the ectomycorrhizal fungus Rhizopogon roseolus. Colonised and non-colonised seedlings provided with sensors were submitted to different water regimes in two consecutive experiments established in a controlled-temperature greenhouse module (cycle 1), and in an adjacent module without temperature control (cycle 2). Under regular irrigation, colonised seedlings showed significantly higher growth than non-colonised seedlings. Water-stressed seedlings showed no benefit from inoculation in terms of growth. Also, seedlings with a high colonisation level recovered more slowly from water stress than control seedlings. A significant positive relationship between maximum daily shrinkage (amplitude of the daily stem contraction) and global radiation was observed only in the first water-stress period in cycle 1 and in regularly irrigated seedlings in both cycles. However, no differential responses due to inoculation were observed. The mycorrhizal colonisation of the seedlings at the end of the experiment was related with the initial colonisation level. Mycorrhizal colonisation by R. roseolus in old roots was maintained at significantly higher levels in seedlings which had an initial colonisation level >50% than in seedlings with <50% initial colonisation. Also, more newly formed roots became colonised in seedlings which had an initial colonisation level >50% than in seedlings with an initial colonisation <50%, which had almost no new root colonisation. From the results obtained, it can be concluded that LVDT sensors can be used to detect a differential response of plants according to water supply, mycorrhizal status and, in some cases, to their colonisation level. The results are discussed in relation to the predictive possibilities of the method for the selection of efficient mycorrhizal fungi for the promotion of plant growth.

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.

Many simulation studies have been carried out to predict the effect of climate change on crop yield. Typically, in such study, one or several crop models are used to simulate series of crop yield values for different climate scenarios corresponding to different hypotheses of temperature, CO2 concentration, and rainfall changes. These studies usually generate large datasets including thousands of simulated yield data. The structure of these datasets is complex because they include series of yield values obtained with different mechanistic crop models for different climate scenarios defined from several climatic variables (temperature, CO2 etc.). Statistical methods can play a big part for analyzing large simulated crop yield datasets, especially when yields are simulated using an ensemble of crop models. A formal statistical analysis is then needed in order to estimate the effects of different climatic variables on yield, and to describe the variability of these effects across crop models. Statistical methods are also useful to develop meta-models i.e., statistical models summarizing complex mechanistic models. The objective of this paper is to present a random-coefficient statistical model (mixed-effects model) for analyzing large simulated crop yield datasets produced by the international project AgMip for several major crops. The proposed statistical model shows several interesting features; i) it can be used to estimate the effects of several climate variables on yield using crop model simulations, ii) it quantities the variability of the estimated climate change effects across crop models, ii) it quantifies the between-year yield variability, iv) it can be used as a meta-model in order to estimate effects of new climate change scenarios without running again the mechanistic crop models. The statistical model is first presented in details, and its value is then illustrated in a case study where the effects of climate change scenarios on different crops are compared. See more from this Division: Special Sessions See more from this Session: Symposium--Perspectives on Climate Effects on Agriculture: The International Efforts of AgMIP