Integration of animal husbandry and nature conservation on grassland farms

A farming model for peat grassland has been designed as an instrument in physical planning, integrating equally nature conservation and animal husbandry by combining the conditions for existence of both. It includes a subdivision of the farm in production grassland and nature grassland, such as marsh marigold Caltha palustris hayfield and blue grassland. Each type of grassland ha a suitable combination of groundwater level, P- and N-controlled animal and plant production, and a regime of mowing and grazing depending on its function for flora, (avi)fauna and animal husbandry

Grazing animal husbandry based on sustainable nutrient management

Sustainable husbandry systems for grazing animals (cattle and sheep) can be achieved by sustainable nutrient management (SNM). This implies the tuning of inputs to outputs of nutrients, to achieve and maintain optimum ranges of agronomically wanted and ecologically acceptable reserves of single nutrients in the soil. P is presented as the ‘boss cow of the nutrient herd’ and its optimum range of available reserves, in the Netherlands expressed as P-AL count, is quantified as a P-AL count of 30–55. SNM is elaborated into two scenarios. In both, output of milk and meat is compensated for by a P-equivalent input from concentrates. However, in the scenario ‘off-take manure’, on soils with a P-AL count greater than 55, all manure produced indoors is to be removed from the farm until the P-AL count is 55 or less. If large-scale manure processing is not a realistic option, the scenario ‘own concentrates’ can be followed. In this case, on soils with a P-AL count of 55–100, output of milk and meat can no longer be compensated for by a P-equivalent input from concentrates, so concentrates are to be produced on the farm. Furthermore, soils with a P-AL count greater than 100 need maximum sanitation by off-take of all plant produce, so grazing and manure application are no longer allowed. At the farm level, SNM is elaborated into a quota system for stocking rate (livestock units ha-1) and milk production (kg milk 4% fat ha-1). If applied on a national level in the Netherlands, SNM will extensify grazing animal husbandry through a reduction in stocking rate by 27–41% and in milk production by 16–34% in the scenarios ‘off-take manure’ and ‘own concentrates’, respectively. At the same time, livestock and milk quotas will be redistributed across regions and farms. Consequently, current surpluses on the annual P balance-sheet of the national grazing animal husbandry will turn into ‘shortages’, implying a gradual decline in excessive soil P reserves. In the scenario ‘off-take manure’, this is achieved by a more than 50% reduction in the import of concentrates and by the export of stable manure. In the scenario ‘own concentrates’, it is achieved by a complete replacement of import of concentrates by concentrates produced on-farm. In a similar way, SNM reduces the surpluses on the N balance-sheet of the national grazing animal husbandry

Multifunctionele landbouw; ruimtelijke verkenning van de landelijke behoefte op gemeenteniveau

In deze studie staan twee beleidsvragen centraal. De eerste is in welke gebieden in Nederland er behoefte is aan een grotere bijdrage van de landbouw aan een aantrekkelijke leefomgeving of beheer van strategische voorraden. De tweede is waar grote behoeften aan multifunctionele landbouw van de functies aantrekkelijke leefomgeving of beheer van strategische voorraden samenvallen met de behoefte om de economische productiefunctie versterken. De verkenning is uitgevoerd op gemeenteniveau. Per functie isgezocht naar een indicator die de behoefte aan multifunctionele landbouw weergeeft. Per indicator zijn de gemeenten ingedeeld in drie behoefteklassen en het resultaat is ruimtelijk weergegeven op monofunctionele kaarten. Tenslotte is per gemeente de behoefte aan multifunctionele landbouw vanuit de functies tezamen vastgesteld en weergegeven op multifunctionele kaarten

Intercomparison of NO2, O4, O3 and HCHO slant column measurements by MAX-DOAS and zenith-sky UV¿visible spectrometers during CINDI-2

40 pags., 22 figs., 13 tabs.In September 2016, 36 spectrometers from 24 institutes measured a number of key atmospheric pollutants for a period of 17¿d during the Second Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) that took place at Cabauw, the Netherlands (51.97¿¿N, 4.93¿¿E). We report on the outcome of the formal semi-blind intercomparison exercise, which was held under the umbrella of the Network for the Detection of Atmospheric Composition Change (NDACC) and the European Space Agency (ESA). The three major goals of CINDI-2 were (1) to characterise and better understand the differences between a large number of multi-axis differential optical absorption spectroscopy (MAX-DOAS) and zenith-sky DOAS instruments and analysis methods, (2) to define a robust methodology for performance assessment of all participating instruments, and (3) to contribute to a harmonisation of the measurement settings and retrieval methods. This, in turn, creates the capability to produce consistent high-quality ground-based data sets, which are an essential requirement to generate reliable long-term measurement time series suitable for trend analysis and satellite data validation. The data products investigated during the semi-blind intercomparison are slant columns of nitrogen dioxide (NO2), the oxygen collision complex (O4) and ozone (O3) measured in the UV and visible wavelength region, formaldehyde (HCHO) in the UV spectral region, and NO2 in an additional (smaller) wavelength range in the visible region. The campaign design and implementation processes are discussed in detail including the measurement protocol, calibration procedures and slant column retrieval settings. Strong emphasis was put on the careful alignment and synchronisation of the measurement systems, resulting in a unique set of measurements made under highly comparable air mass conditions. The CINDI-2 data sets were investigated using a regression analysis of the slant columns measured by each instrument and for each of the target data products. The slope and intercept of the regression analysis respectively quantify the mean systematic bias and offset of the individual data sets against the selected reference (which is obtained from the median of either all data sets or a subset), and the rms error provides an estimate of the measurement noise or dispersion. These three criteria are examined and for each of the parameters and each of the data products, performance thresholds are set and applied to all the measurements. The approach presented here has been developed based on heritage from previous intercomparison exercises. It introduces a quantitative assessment of the consistency between all the participating instruments for the MAX-DOAS and zenith-sky DOAS techniques.CINDI-2 received funding from the Netherlands Space Office (NSO). Funding for this study was provided by ESA through the CINDI-2 (ESA contract no. 4000118533/16/ISbo) and FRM4DOAS (ESA contract no. 4000118181/16/I-EF) projects and partly within the EU 7th Framework Programme QA4ECV project (grant agreement no. 607405). The BOKU MAX-DOAS instrument was funded and the participation of Stefan F. Schreier was supported by the Austrian Science Fund (FWF): I 2296-N29. The participation of the University of Toronto team was supported by the Canadian Space Agency (through the AVATARS project) and the Natural Sciences and Engineering Research Council (through the PAHA project). The instrument was primarily funded by the Canada Foundation for Innovation and is usually operated at the Polar Environment Atmospheric Research Laboratory (PEARL) by the Canadian Network for the Detection of Atmospheric Change (CANDAC). Funding for CISC was provided by the UVAS (“Ultraviolet and Visible Atmospheric Sounder”) projects SEOSAT/INGENIO, ESP2015-71299- R, MINECO-FEDER and UE. The activities of the IUP-Heidelberg were supported by the DFG project RAPSODI (grant no. PL 193/17-1). SAOZ and Mini-SAOZ instruments are supported by the Centre National de la Recherche Scientifique (CNRS) and the Centre National d’Etudes Spatiales (CNES). INTA recognises support from the National funding projects HELADO (CTM2013-41311-P) and AVATAR (CGL2014-55230-R). AMOIAP recognises support from the Russian Science Foundation (grant no. 16-17-10275) and the Russian Foundation for Basic Research (grant nos. 16-05- 01062 and 18-35-00682). Ka L. Chan received transnational access funding from ACTRIS-2 (H2020 grant agreement no. 654109). Rainer Volkamer recognises funding from NASA’s Atmospheric Composition Program (NASA-16-NUP2016-0001) and the US National Science Foundation (award AGS-1620530). Henning Finkenzeller is the recipient of a NASA graduate fellowship. Mihalis Vrekoussis recognises support from the University of Bremen and the DFG Research Center/Cluster of Excellence “The Ocean in the Earth System-MARUM”. Financial support through the University of Bremen Institutional Strategy in the framework of the DFG Excellence Initiative is gratefully appreciated for Anja Schönhardt. Pandora instrument deployment was supported by Luftblick through the ESA Pandonia Project and NASA Pandora Project at the Goddard Space Flight Center under NASA Headquarters’ Tropospheric Composition Program. The article processing charges for this open-access publication were covered by BK Scientific