Result 1 - 10 of total 204
FACCE MACSUR Reports, 8:SP8-6
2016
| Language | English |
|---|---|
| Document type | Article |
| DOI | 10.4126/FRL01-006413166 |
| Database | Repository for Life Sciences |
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.
FACCE MACSUR Reports, 8:SP8-7
2016
| Language | English |
|---|---|
| Document type | Article |
| DOI | 10.4126/FRL01-006413168 |
| Database | Repository for Life Sciences |
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.
FACCE MACSUR Reports, 8:SP8-15
2016
| Language | English |
|---|---|
| Document type | Article |
| DOI | 10.4126/FRL01-006413192 |
| Database | Repository for Life Sciences |
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.
FACCE MACSUR Reports, 5:SP5-26
2015
Abstract: Farm-scale models quantify the cycling of nitrogen (N) and carbon (C) so are powerful tools for assessing the impact of management-related decisions on greenhouse gas (GHG) emissions, especially on dairy cattle farms, where the internal cycling is ... ...
| Abstract | Farm-scale models quantify the cycling of nitrogen (N) and carbon (C) so are powerful tools for assessing the impact of management-related decisions on greenhouse gas (GHG) emissions, especially on dairy cattle farms, where the internal cycling is particularly important. Farm models range in focus (economic, environmental) and the detail with which they represent C and N cycling. We compared four models from this range in terms of on-farm production and emissions of GHGs, using standardized scenarios. The models compared were SFarMod, DairyWise, FarmAC and HolosNor. The scenarios compared were based on two soil types (sandy clay versus heavy clay), two roughage systems (grass only versus grass and maize), and two climate types (Eindhoven versus Santander). Standard farm characteristics were; area (50 ha), milk yield (7000 kg/head/year), fertiliser (275 kg N and 150 kg N/ha/year for grass and maize, respectively). Potential yields for grass 10t dry matter (DM)/ha/year in both areas, maize 14 t DM/ha/ year in Eindhoven and 18t DM/ha/ year in Santander. The import of animal feed and the export/import manure and forages was minimized. Similar total farm direct GHG emissions for all models disguised a variation between models in the contribution of the different on-farm sources. There were large differences between models in the predictions of indirect GHG emission from nitrate leaching. Results could be explained by differences between models in the assumptions made and detail with which underlying processes were represented. We conclude that the choice of an appropriate farm model is highly dependent upon the role it should play and the context within which it will operate, so the current diversity of farm models will continue into the future. |
|---|---|
| Language | English |
| Document type | Article |
| DOI | 10.4126/FRL01-006413589 |
| Database | Repository for Life Sciences |
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.
FACCE MACSUR Reports, 3(Supplement):CP3-13
2014
Abstract: In the EU Joint-Programming-Initiative: Modelling European Agriculturewith Climate Change for Food Security (MACSUR, LiveM: http://www.macsur.eu/index.php/livestock-modelling) we develop a research frameworkfor the modelling and sustainability assessment ...
| Abstract | In the EU Joint-Programming-Initiative: Modelling European Agriculturewith Climate Change for Food Security (MACSUR, LiveM: http://www.macsur.eu/index.php/livestock-modelling) we develop a research frameworkfor the modelling and sustainability assessment of livestock and grasslandbased farming systems at farm and regional scales.Based on results from related research and model development in Denmark,methodologies used for regional scaling, the description of data requirementsand sources, and methods to predict the effect and effectiveness of climate-and environment related policy measures are developed. In this study we present results from farm modelling in a study areaaround Viborg, Western Denmark using the http://www.Farm-N.dk/ model (Env.Pol. 159 3183-3192), including thedistribution of N-surpluses into different types of losses, and a comparisonwith empirical studies of farm nitrogen balances in the Danish study and fiveadditional European landscapes (Biogeosciences 9, 5303–5321). Based on this,methods and development needs for the mapping and uncertainty assessment ofnutrient losses and greenhouse gas emissions are discussed, referring to the presentdevelopment of the Farm-AC model and ongoing scenario studies in e.g. the www.dNmark.org project. In these scenarios, regional-scale policy measures areimplemented via the responses of a range of stakeholders, such as farmers,public interest groups, regulators and politicians. When modelling the outcomeof the policy measures implementation, it is often assumed that stakeholdersrespond as economically rational entities. However, social and cultural factorsare also known to play a role and modelling methods that permit these factorsto be taken into account will also be discussed. |
|---|---|
| Language | English |
| Document type | Article |
| DOI | 10.4126/FRL01-006414104 |
| Database | Repository for Life Sciences |
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.
Environmental science & policy. 2020 Dec., v. 114
2020
Abstract: Requirements for substantial reductions in environmental pollution such as nitrogen (N) loading to aquatic environments, and in greenhouse gase (GHG) emissions impose challenges for agriculturally-intensive regions in Europe. Here we use afforestation to ...
| Abstract | Requirements for substantial reductions in environmental pollution such as nitrogen (N) loading to aquatic environments, and in greenhouse gase (GHG) emissions impose challenges for agriculturally-intensive regions in Europe. Here we use afforestation to illustrate how high-spatial resolution data can be used to improve the efficiency of implementation of an environmental measure. Since afforestation of agricultural land has the potential to reduce both aquatic N load and GHG emissions, targeting the reduction of one pollutant will also affect the non-targeted pollutant. We developed a method to use nationally-available, high-resolution data to minimise the agricultural area selected for potential afforestation, for a given reduction of N load or GHG emissions, and assess the co-reduction in the non-targeted pollutant. To illustrate the effect of imposing policy restrictions on the implementation of measures, two restrictions were investigated; limitations on the maximum proportion of each farm that could be afforested and threshold proportions of the farm area, above which the whole farm must be afforested.For N load, both the N leaching below the root zone and the efficiency of denitrification between the bottom of the root zone and the recipient aquatic ecosystem were significant factors determining the selection of fields for afforestation. Since N leaching was the only location-dependent GHG emission source and these were only a minor contributor to the total GHG emissions, the selection of land for afforestation to reduce GHG emissions depended more on farm-scale than field-scale characteristics. In a case study area, the availability of high-resolution data allowed the use of a targeted afforestation selection method that significantly reduced the land area required, relative to a non-targeted approach. With the targeted approach, reducing N load or GHG emissions by 25 % of the maximum potential reduction required the afforestation of 14 % and 18 %, respectively, of the case study area. This represented reductions in area of 42 % and 24 % compared to the untargeted approach. Measures targeting one pollutant also substantially reduced the non-targeted pollutant. We conclude that the implementation efficiency of some environmental policy interventions in agriculture depends on the availability of high-resolution agricultural and landscape data, and adequate methods to utilize these data. |
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| Keywords | afforestation ; aquatic ecosystems ; case studies ; denitrification ; farm area ; farms ; greenhouse gas emissions ; greenhouses ; landscapes ; nitrogen ; pollutants ; rhizosphere ; Europe |
| Language | English |
| Dates of publication | 2020-12 |
| Size | p. 14-21. |
| Publishing place | Elsevier Ltd |
| Document type | Article |
| Note | NAL-AP-2-clean |
| ZDB-ID | 1454687-5 |
| ISSN | 1462-9011 |
| ISSN | 1462-9011 |
| DOI | 10.1016/j.envsci.2020.07.019 |
| Database | NAL-Catalogue (AGRICOLA) |
| Z 7104: Show issues |
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Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.
FACCE MACSUR Reports, 10:L1.1-D1
2017
Abstract: Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute ... ...
| Abstract | Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensi- fication of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a prior- ity. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are ap- plied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. De- veloping the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems re- lated to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks |
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| Keywords | Agriculture ; Climate Change ; Food security ; Joint Programming Initiative ; Livestock systems ; Modeling ; Pastoral systems ; Policy support ; Ruminants |
| Language | English |
| Document type | Article |
| Database | Repository for Life Sciences |
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.
Science of the total environment. 2021 June 10, v. 772
2021
Abstract: Reducing nitrate leaching may not result in a significant improvement of groundwater quality. The amount of nitrate reaching groundwater depends not only on the hazard related to agricultural activities but also on-site specific groundwater vulnerability. ...
| Abstract | Reducing nitrate leaching may not result in a significant improvement of groundwater quality. The amount of nitrate reaching groundwater depends not only on the hazard related to agricultural activities but also on-site specific groundwater vulnerability. Using national databases and other compiled datasets, the agricultural hazard was calculated as the ratio of (i) the nitrate leached estimated from the N surplus, and (ii) the water surplus, a proxy of the percolating water below the root zone. By combining the hazard with a multi-parameter groundwater vulnerability, a spatially explicit groundwater contamination risk, developed for mainland Portugal, was computed for 1999 and 2009. Results show an increase from 8,800 to 82,679 ha of the territory rated with a very high contamination risk. The priority areas were successfully screened by the Index, coinciding with the current Vulnerable Zones, although additional hotspots were detected in southern Portugal. Percolation, including both irrigation activity and precipitation, was found to be a key driver for the groundwater contamination risk due to its opposite effects in the hazard and in the vulnerability. Reducing nitrogen leaching may be insufficient to reduce the risk of nitrate contamination if there is a relatively larger reduction in precipitation. This index is particularly useful when applied to contrasting situations of vulnerability and hazard, which require distinct mitigation measures to mitigate groundwater contamination. |
|---|---|
| Keywords | aquifers ; data collection ; environment ; groundwater ; groundwater contamination ; irrigation ; nitrates ; nitrogen ; rhizosphere ; risk ; risk reduction ; water quality ; Portugal |
| Language | English |
| Dates of publication | 2021-0610 |
| Publishing place | Elsevier B.V. |
| Document type | Article |
| Note | NAL-AP-2-clean |
| ZDB-ID | 121506-1 |
| ISSN | 1879-1026 ; 0048-9697 |
| ISSN (online) | 1879-1026 |
| ISSN | 0048-9697 |
| DOI | 10.1016/j.scitotenv.2021.145032 |
| Database | NAL-Catalogue (AGRICOLA) |
| Z 74/341: Show issues |
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.
(DIAS report : Plant production ; 60)
2001
| Author's details | S. G. Sommer, N. J. Hutchings & O. T. Carton |
|---|---|
| Series title | DIAS report : Plant production ; 60 DJF rapport / Ministeriet for Fødevarer, Landbrug og Fiskeri, Danmarks JordbrugsForskning DIAS report ; Plant production |
| Collection | DJF rapport / Ministeriet for Fødevarer, Landbrug og Fiskeri, Danmarks JordbrugsForskning DIAS report ; Plant production |
| Language | Danish |
| Size | 112 S. : Ill., graph. Darst. |
| Publisher | DIAS |
| Publishing place | Tjele |
| Publishing country | Denmark |
| Document type | Book |
| HBZ-ID | HT014479227 |
| Database | Catalogue ZB MED Nutrition, Environment, Agriculture |
| Shelf mark | Loan status | Location |
|---|---|---|
| 038/1330 | available for loan (reading room) | Freihandmagazin (U1) |
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.
The Science of the total environment
2021 Volume 772, Page(s) 145032
Abstract: Reducing nitrate leaching may not result in a significant improvement of groundwater quality. The amount of nitrate reaching groundwater depends not only on the hazard related to agricultural activities but also on-site specific groundwater vulnerability. ...
| Abstract | Reducing nitrate leaching may not result in a significant improvement of groundwater quality. The amount of nitrate reaching groundwater depends not only on the hazard related to agricultural activities but also on-site specific groundwater vulnerability. Using national databases and other compiled datasets, the agricultural hazard was calculated as the ratio of (i) the nitrate leached estimated from the N surplus, and (ii) the water surplus, a proxy of the percolating water below the root zone. By combining the hazard with a multi-parameter groundwater vulnerability, a spatially explicit groundwater contamination risk, developed for mainland Portugal, was computed for 1999 and 2009. Results show an increase from 8,800 to 82,679 ha of the territory rated with a very high contamination risk. The priority areas were successfully screened by the Index, coinciding with the current Vulnerable Zones, although additional hotspots were detected in southern Portugal. Percolation, including both irrigation activity and precipitation, was found to be a key driver for the groundwater contamination risk due to its opposite effects in the hazard and in the vulnerability. Reducing nitrogen leaching may be insufficient to reduce the risk of nitrate contamination if there is a relatively larger reduction in precipitation. This index is particularly useful when applied to contrasting situations of vulnerability and hazard, which require distinct mitigation measures to mitigate groundwater contamination. |
|---|---|
| Language | English |
| Publishing date | 2021-02-02 |
| Publishing country | Netherlands |
| Document type | Journal Article |
| ZDB-ID | 121506-1 |
| ISSN | 1879-1026 ; 0048-9697 |
| ISSN (online) | 1879-1026 |
| ISSN | 0048-9697 |
| DOI | 10.1016/j.scitotenv.2021.145032 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
| Zs.A 949: Show issues | Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG) |
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