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Abstract Spatially detailed information on agricultural nitrogen (N) budgets is relevant to identify regions where there is a need for a reduction in inputs in view of various forms of N pollution. However, at the scale of the European Union, there is a lack of consistent, reliable, high spatial resolution data necessary for the calculation of regional N losses. To gain insight in the reduction in uncertainty achieved by using higher spatial resolution input data. This was done by comparing spatially disaggregated agricultural N budgets for Denmark for the period 2000–2010, generated by two versions of the European scale model Integrator, a version using high spatial resolution national data for Denmark (Integrator-DK) and a version using available data at the EU scale (Integrator-EU). Results showed that the national N fluxes in the N budgets calculated by the two versions of the model were within 1–5% for N inputs by fertilizer and manure excretion, but inputs by N fixation and N mineralisation differed by 50–100% and N uptake also differed by ca 25%, causing a difference in N leaching and runoff of nearly 50%. Comparison with an independently derived Danish national budget appeared generally to be better with Integrator-EU results in 2000 but with Integrator-DK results in 2010. However, the spatial distribution of manure distribution and N losses from Integrator-DK were closer to observed distributions than those from Integrator-EU. We conclude that close attention to local agronomic practices is needed when using a leaching fraction approach and that for effective support of environmental policymaking, Member States need to collect or submit high spatial resolution agricultural data to Eurostat. Graphical abstract Unlabelled Image Highlights • Spatially disaggregated agronomic nitrogen (N) budgets for Denmark are compared. • Effects of using national rather than EU input data are analysed. • Detail of data causes large differences in N excretion and N losses to air and water. • Results obtained with detailed Danish input data were closer to observed distributions. • Good policy support requires high spatial resolution input data. [ABSTRACT FROM AUTHOR]

Phosphorus is an essential element to enhance the needed increase in crop production in the forthcoming century. On the other hand environmental losses of phosphorus cause eutrophication of surface waters. Both problems call for reliable models to predict the behaviour of phosphorus in agricultural soils. In this study the performances of five different sorption approaches were evaluated. The ultimate aim was to identify the most suitable concept for large scale predictions of P dynamics in soils, in terms of a high comparability between observations and predictions with a minimum amount of input data. The model results were compared with unique data from long term (10–15 years) experimental field studies of grassland including situations with P mining, equilibrium P fertilization and P surpluses and a pot experiment with P mining. The model performance was evaluated while using site specific constants and generic constants for adsorption and desorption. Three rate limited models (DPPS, INITIATOR and ANIMO) showed good performance when site specific constants were used but the performance of the equilibrium model (NEWS-Dynamic) was reasonably comparable. Model performance was better for experiments with a P surplus than for P mining and was also better for sandy soils as compared to clay and peat soils. However, long term desorption rates had to be calibrated for each application rate. The performance of all models declined when generic data were used. We conclude that none of the included models properly describe what happens when the soil changes its P status, considering that parameterization needs to be treatment-specific to obtain reliable predictions. Considering this flaw, models of intermediate complexity, including both equilibrium and rate limited sorption, and a limited data demand, like DPPS and INITIATOR, seem most suited for regional model application. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N2O) direct soil emissions from agriculture are often estimated using the default IPCC emission factor (EF) of 1%. However, a large variation in EFs exists due to differences in environment, crops and management. We developed an approach to determine N2O EFs that depend on N-input sources and environmental factors. The starting point of the method was a monitoring study in which an EF of 1% was found. The conditions of this experiment were set as the reference from which the effects of 16 sources of N input, three soil types, two land-use types and annual precipitation on the N2O EF were estimated. The derived EF inference scheme performed on average better than the default IPCC EF. The use of differentiated EFs, including different regional conditions, allows accounting for the effects of more mitigation measures and offers European countries a possibility to use a Tier 2 approach. [Copyright &y& Elsevier]

A European Union (EU-27) wide N2O emission estimate for the agricultural sector is presented, using the INTEGRATOR model, combined with detailed GIS-based environmental data and farming data. INTEGRATOR links various modules, calculating NH3, NOx, N2O, CO2, and CH4 emissions from housing and manure storage systems, agricultural and non-agricultural soils and surface waters, while accounting for the interaction between agricultural and non-agricultural systems through an emission-deposition model for NH3 and NOx. This study is limited to N2O emissions from housing and manure storage systems and agricultural soils. All emissions are based on N2O emission factors which depend on environment (weather and soil conditions), land use (grassland, arable land, crop residues), and management (type of manure and fertilizer, etc). We assessed the plausibility by a comparison of calculated present nitrous oxide emissions at country level with those derived by more simple emission factor approaches and detailed ecosystem models. The model was used to assess the impact of various farming practice packages to mitigate N2O emissions. For the agricultural sector of the EU-27, a total N2O emission of 347 kton N2O-N is calculated for the year 2000, which is comparable to other estimates. The overall achievable reduction in N2O emissions with the evaluated mitigation measures is about 25%. For the EU-27, the most effective measures are balanced fertilization and maximum manure application, but this is highly variable per country. [ABSTRACT FROM AUTHOR]

Various model approaches have been developed for assessing emissions of different forms of reactive nitrogen in various parts of Europe at various geographic resolutions and for various time periods. The modeling approaches include emission factor approaches, empirical models, simple process-based models, and detailed ecosystem models. In this study, we compared three relatively simple process-based models, developed for the national scale (Integrated NITrogen Impact AssessmenT model On a Regional Scale (INITIATOR2)), European scale (MITERRA) and global scale (integrated model to assess the global environment (IMAGE)), with respect to their response to structural and technological changes in the agricultural systems based on the IPCC B2 baseline scenario for the period 2000-2030. Changes are predicted by the IMAGE model and relate to crop yield, crop area, animal numbers, and N fertilizer inputs. The predicted relative changes by IMAGE are used in INITIATOR2 and MITERRA while relating the animal categories and crop categories in IMAGE to those in the latter models. A comparison was made of NH3, N2O and NOx emissions and N leaching to ground water. We compared predictions for the years 2000 and 2030 for: (i) the Netherlands between INITIATOR2 and MITERRA and (ii) Europe (EU-27 countries) between MITERRA and IMAGE. The results of the comparison are presented and evaluated in view of differences in model structure and the effect of aggregating input data at larger spatial scales. [ABSTRACT FROM AUTHOR]

• Harvesting forest residues may reduce soil fertility due to increased nutrient removal. • Harvest removal is compared with deposition and weathering inputs minus leaching. • Forest harvesting guidelines are developed based on balances of major nutrients. • These guidelines refer to major tree species, soil types and regions in the Netherlands. • Whole tree harvesting enhances the risk of phosphorus and potassium depletion in sandy soils. The substitution of biomass for fossil fuels in energy consumption is a measure to decrease the emissions of greenhouse gases and thereby mitigate global warming. During recent years, this has led to an increasing interest to use tree harvest residues as feedstock for bioenergy. An important concern related to the removal of harvesting residues is, however, the potential adverse effects on soil fertility caused by increased nutrient removal, relative to conventional stem-only harvesting. In the Netherlands this is a major concern, since most forests are located on poor sandy soils. To develop forest harvesting guidelines, we applied a mass balance approach comparing nutrient inputs by deposition and weathering with nutrient outputs by harvesting and leaching for various timber harvesting scenarios, including both stem-only harvesting and additional removal of tree tops and branches. A distinction was made in seven major tree species, six soil types (three sandy soils, loam, clay and peat soils) and nine regions, with clear variations in atmospheric deposition of phosphorus (P), calcium (Ca), magnesium (Mg) and potassium (K). For each region-tree-soil combination we calculated the maximum amounts that can be harvested such that the output of the nutrients Ca, Mg, K and P is balanced with the inputs. Results showed that at current harvesting rates, a negative balance of Ca, Mg, K or P is hardly calculated for the richer loamy to clayey soil types, while depletion can occur for the poorer sandy soils, particularly of P and K. Results are used to derive forest biomass harvesting guidelines, taking the uncertainties in the mass balance approach into account. The role of mitigating management approaches is also discussed. [ABSTRACT FROM AUTHOR]

In this study, we compare the relative importance of climate change to technological, management, price and policy changes on European arable farming systems. This required linking four models: the SIMPLACE crop growth modelling framework to calculate future yields under climate change for arable crops; the CAPRI model to estimate impacts on global agricultural markets, specifically product prices; the bio-economic farm model FSSIM to calculate the future changes in cropping patterns and farm net income at the farm and regional level; and the environmental model INTEGRATOR to calculate nitrogen (N) uptake and losses to air and water. First, the four linked models were applied to analyse the effect of climate change only or a most likely baseline (i.e. B1) scenario for 2050 as well as for two alternative scenarios with, respectively, strong (i.e. A1-b1) and weak economic growth (B2) for five regions/countries across Europe (i.e. Denmark, Flevoland, Midi Pyrenées, Zachodniopomorski and Andalucia). These analyses were repeated but assuming in addition to climate change impacts, also the effects of changes in technology and management on crop yields, the effects of changes in prices and policies in 2050, and the effects of all factors together. The outcomes show that the effects of climate change to 2050 result in higher farm net incomes in the Northern and Northern–Central EU regions, in practically unchanged farm net incomes in the Central and Central–Southern EU regions, and in much lower farm net incomes in Southern EU regions compared to those in the base year. Climate change in combination with improved technology and farm management and/or with price changes towards 2050 results in a higher to much higher farm net incomes. Increases in farm net income for the B1 and A1-b1 scenarios are moderately stronger than those for the B2 scenario, due to the smaller increases in product prices and/or yields for the B2 scenario. Farm labour demand slightly to moderately increases towards 2050 as related to changes in cropping patterns. Changes in N 2 O emissions and N leaching compared to the base year are mainly caused by changes in total N inputs from the applied fertilizers and animal manure, which in turn are influenced by changes in crop yields and cropping patterns, whereas NH 3 emissions are mainly determined by assumed improvements in manure application techniques. N emissions and N leaching strongly increase in Denmark and Zachodniopomorski, slightly decrease to moderately increase in Flevoland and Midi-Pyrenées, and strongly decrease in Andalucia, except for NH 3 emissions which zero to moderately decrease in Flevoland and Denmark. [ABSTRACT FROM AUTHOR]

Impacts of climate change on European agricultural production, land use and the environment depend on its impact on crop yields. However, many impact studies assume that crop management remains unchanged in future scenarios, while farmers may adapt their sowing dates and cultivar thermal time requirements to minimize yield losses or realize yield gains. The main objective of this study was to investigate the sensitivity of climate change impacts on European crop yields, land use, production and environmental variables to adaptations in crops sowing dates and varieties' thermal time requirements. A crop, economic and environmental model were coupled in an integrated assessment modelling approach for six important crops, for 27 countries of the European Union (EU27) to assess results of three SRES climate change scenarios to 2050. Crop yields under climate change were simulated considering three different management cases; (i) no change in crop management from baseline conditions (NoAd), (ii) adaptation of sowing date and thermal time requirements to give highest yields to 2050 (Opt) and (iii) a more conservative adaptation of sowing date and thermal time requirements (Act). Averaged across EU27, relative changes in water-limited crop yields due to climate change and increased CO 2 varied between − 6 and + 21% considering NoAd management, whereas impacts with Opt management varied between + 12 and + 53%, and those under Act management between − 2 and + 27%. However, relative yield increases under climate change increased to + 17 and + 51% when technology progress was also considered. Importantly, the sensitivity to crop management assumptions of land use, production and environmental impacts were less pronounced than for crop yields due to the influence of corresponding market, farm resource and land allocation adjustments along the model chain acting via economic optimization of yields. We conclude that assumptions about crop sowing dates and thermal time requirements affect impact variables but to a different extent and generally decreasing for variables affected by economic drivers. [ABSTRACT FROM AUTHOR]

Changes in climate, technology, policy and prices affect agricultural and rural development. To evaluate whether this development is sustainable, impacts of these multiple drivers need to be assessed for multiple indicators. In a case study area in the Netherlands, a bio-economic farm model, an agent-based land-use change model, and a regional emission model have been used to simulate rural development under two plausible global change scenarios at both farm and landscape level. Results show that in this area, climate change will have mainly negative economic impacts (dairy gross margin, arable gross margin, economic efficiency, milk production) in the warmer and drier W + scenario, while impacts are slightly positive in the G scenario with moderate climate change. Dairy farmers are worse off than arable farmers in both scenarios. Conversely, when the W + scenario is embedded in the socio-economic Global Economy (GE) scenario, changes in technology, prices, and policy are projected to have a positive economic impact, more than offsetting the negative climate impacts. Important is, however, that environmental impacts (global warming, terrestrial and aquatic eutrophication) are largely negative and social impacts (farm size, number of farms, nature area, odour) are mixed. In the G scenario combined with the socio-economic Regional Communities (RC) scenario the average dairy gross margin in particular is negatively affected. Social impacts are similarly mixed as in the GE scenario, while environmental impacts are less severe. Our results suggest that integrated assessments at farm and landscape level can be used to guide decision-makers in spatial planning policies and climate change adaptation. As there will always be trade-offs between economic, social, and environmental impacts stakeholders need to interact and decide upon most important directions for policies. This implies a choice between production and income on the one hand and social and environmental services on the other hand. [ABSTRACT FROM AUTHOR]