This deliverable focuses on the collation, screening, and consolidation of data for selected grassland sites in Europe and peri-Mediterranean regions

Bellocchi, Gianni

Braunmiller, Katharina

Köchy, Martin

Ma, Shaoxiu

FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)

   Instrument: Joint Programming Initiative Topic: Agriculture, Food Security, and Climate Change Project: Modelling European Agriculture with Climate Change for Food Security (FACCE-MACSUR) Start date of project: 1 June 2012 Duration: 36 months Theme, Work Package:  LiveM 2 Deliverable reference num.: D-L2.1.2 Deliverable lead partner: INRA Due date of deliverable: month 18 Submission date: 2013-10-25 Confidential till: —   Revision Changes Date 1.0 First Release 2013-10-25  i FACCE-MACSUR Datasets classification and criteria for data requirements  Gianni Bellocchi1*, Shaoxiu Ma1, Martin Köchi2, Katharina Braunmiller2  1 French National Institute for Agricultural Research, 5 chemin de Beaulieu, 63039 Clermont-Ferrand, France 2 Thünen Institute of Market Analysis, Bundesallee 50, 38116 Braunschweig, Germany  *gianni.bellocchi@clermont.inra.fr          1 Abstract/Executive summary This deliverable focuses on the collation, screening, and consolidation of data for selected grassland sites in Europe and peri-Mediterranean regions.    Table of Contents Datasets classification and criteria for data requirements ........................................ i	  Abstract/Executive summary ........................................................................... 1	  Table of Contents ........................................................................................ 1	  Introduction ............................................................................................... 2	  Grassland datasets ....................................................................................... 2	  References ................................................................................................ 7	     2 Introduction Pasture growth simulation models are a major part of integrated agro-ecosystem models, which are applied to support decision making at different spatial and temporal scales. Applications range from field to global scales, targeting the evaluation of management and policy options. With increasing spatial extent of the area under investigation, input data for mechanistic grassland and livestock models are scarce and uncertain and data to test relevant state variables are insufficiently available. There is also an increasing demand by both model users and decision makers for analysis of the robustness of models and the uncertainties of model results. The bulk of pasture and livestock models faces with a range of continuously evolving environmental conditions (e.g. greenhouse gas emissions, extreme weather events), which can also be substantially different from those under which these models have been originally developed and evaluated. Currently there are climate data being generated for the 5th Report of the Intergovernmental Panel on Climate Change, in a large scale and well-coordinated effort in an attempt to describe and maybe to narrow uncertainties in future climate projections. However, such projections are often fed into impact models that in some cases have not been properly evaluated with experimental data concerning unprecedented climate and atmospheric CO2 conditions, have seldom been inter-compared and for which the uncertainties in estimates can thus be large. Efforts have been limited at global scale to evaluate grassland and livestock models across the same datasets with the purpose to identify uncertainties. Also there has been little focus on methodologies for applying these models across a range of spatial scales and for coupling them with socio-economical models. Supporting climate change impact studies include an up-to-date geographical coverage of climate, soil and vegetation data, and access to secondary information (e.g. soil information obtained via transfer functions from the primary data), as associated with changes in management options.  Grassland datasets  Nine datasets were collected on permanent grassland sites either from multi-year field experiments or observational studies (Table 1). They cover a broad gradient of geographic and climatic conditions in Europe and peri-Mediterranean regions as well as a variety of management practices.  Table 1. List of permanent grassland sites. Site Latitude Longitude Elevation (m a.s.l.) Notes Laqueuille, France 45°38’ N 02°44′ E 1040 Flux-tower grazed site, either extensively or intensively managed Oensingen, Switzerland 47°17′ N 07°44′ E 450 Flux-tower mowed site, established on a ley-arable rotation Monte Bondone, Italy 46°00′ N 11°02′ E 1500 Flux tower Alpine hay meadow with occasional grazing in late autumn Grillenburg, Germany 50°57° N 13°30’ E 380 Flux-tower mowed, extensively managed site Kempten, Germany 47°43° N 10°20’ E 730 Experimental sward with different levels of nitrogen and cutting management Lelystad, The Netherlands 52°30’ N 05°28’ E -4 Experimental sward with alternative nitrogen management options Matta, Israel 31°42’ N 35°03’ E 620 Dwarf shrubland in association with herbaceous annual species Rothamsted, United Kingdom 51°48° N 00°21’ E 128 Experimental sward with alternative nitrogen management options Sassari, Italy 40°39’ N 08°21’ E 68 Mixed Mediterranean grassland with   3 annual self-reseeding grasses   The grassland sites of Table 1 are described in LiveM deliverable L.2.1.1. Further details are supplied in Table 2 with respect of the usability of datasets (in terms of both inputs and outputs) for model-based analyses and testing. This is essential to permit development of a uniform protocol for handling model inter-comparison and evaluating skills of alternative models in simulating relevant processes in grassland-livestock production systems.  Input data: gap filling Gaps in weather and soil data collection were filled using a scheme of expert rules or transfer functions making use of one or more readily available data to estimate values of a missing variable (e.g. Acock and Pachepski, 2000; Pachewksi and Rawls, 2004; Donatelli et al., 2010). For small gaps in weather data series, estimations were made using data from before and after the day with no data (Kempten, Germany; Matta, Israel; Rothamsted, United Kingdom; Sassari, Italy). Relative humidity was estimated at Matta (Israel) via an empirical relationship with the available air temperature data, supplied by Linacre (1992) and based on a parameterization by Pérez et al. (1994). At Rothamsted (United Kingdom), relative humidity was derived by measured data of actual vapour pressure and temperature (Tetens, 1930). At Matta (Israel), measured data of maximum daily wind speed were converted into mean daily wind speed using a cyclical daily function based on daylight estimates (Porter et al., 2000). At this site, a model based on Bristow and Campbell (1994) was used to calculate daily solar radiation based on daily temperature range and a parameterization supplied with RadEst software (through http://www.sipeaa.it/tools/ RadEst/RadEst.htm) for a nearby site (Tel Hadya, Syria, 36º40’ N; 37 º 20’; 390 m a.s.l.). Daily mean values of relative humidity and temperature were available at Lelystad (The Netherlands), which were used to generate maximum and minimum daily temperatures (based on Tetens, 1930). Wind speed data were missing at Sassari and they were replaced with data taken at Alghero airport (40°38’ N; 8°18’ E; 23 m a.s.l.), located at about 10 km away from the experimental site (small gaps were filled in by interpolation). As textural components (sand, silt and clay) were available from soil surveys at Lelystad (The Netherlands), Matta (Israel), Rothamsted (United Kingdom) and Sassari (Italy), pedotransfer functions were used to add value to this basic information by translating it into estimates of other soil properties functions. According to Saxton et al. (1986), sand and clay fractions were used to determine soil hydraulic characteristics such as water content at field capacity, permanent wilting point and saturation, as well as hydraulic conductivity and bulk density. For Lelystad (The Netherlands), bulk density data were available and were used to estimate some missing components of soil texture. Both transfer functions and expert rules have been flagged in Table 2 to provide an indication of the quality and uncertainties associated with the input data used in the modelling exercise.  Output data Flux-tower sites are the most data-rich grasslands in Europe. Each of these sites is equipped with an eddy covariance system to determine the net ecosystem exchange (NEE) of CO2. They thus allow covering a variety of components of grassland ecosystem, including gross primary production (GPP), an estimate of the plant production of organic compounds from atmospheric CO2, and ecosystem respiration (RECO), the latter playing an important role to estimate global carbon balances of terrestrial ecosystems (NEE = GPP - RECO). At two sites, Oensingen (Switzerland) and Monte Bondone (Italy), data are also available of leaf area index (LAI). Calculated as one sided green leaf area per unit ground area, LAI is a measure of the plant light use capacity, which is basic to prediction of photosynthetic primary production, evapotranspiration and vegetation growth. The biophysical knowledge   4 of the grassland ecosystem is complemented at flux-tower sites with two soil variables, water content and temperature, and actual evapotranspiration, the latter being the major loss flux of water from the system (depending on both air and ground surface temperatures). Other sites essentially focus on grass yield, the amount of dry matter biomass that is removed from the field at each cutting event, that is, simultaneous removal of carbon and nitrogen from mown and mown/grazed grassland systems. Each of these sites strengthens the ability to model different grassland systems while expanding geographical coverage and the variety of management options tested.    5  Table 2. Database of permanent grassland sites with associated information. Coloured cells indicate that a complete series was constructed by interpolating across the observations (yellow), estimating missing variable values based on the available values of surrogate variables (red), replacing missing values with values from another site in the area (orange). Different approaches were applied in association if needed. Type of data Permanent grassland sites Flux-tower sites Other sites Laqueuille (France) Oensingen (Switzerland) Monte Bondone (Italy) Grillenburg (Germany) Kempten (Germany) Lelystad (The Netherlands) Matta (Israel) Rothamsted (United Kingdom) Sassari (Italy) Input data Site description X X X X X X X X X Years 2004-2010 2008-2008 2003-2010 2004-2008 2004-2009 1994-1998 2007-2011 1981-2011 1983-1988 Climate X X X X X X X X X precipitation hourly hourly hourly hourly daily daily daily daily daily relative humidity or vapour pressure hourly hourly hourly hourly daily daily daily daily daily temperature (max, min) hourly hourly hourly hourly daily daily daily daily daily solar radiation hourly hourly hourly hourly daily daily daily daily daily wind speed hourly hourly hourly hourly daily daily daily  daily daily  Soil X X X X X X X X X depth X X X X x x x x x layers X X X X x x x x x texture X X X X x x x x x hydrologic properties X X X X x x x x x Management X X X X X X X X X Cutting / grazing X X X X x X X X X Nitrogen fertilization X X X X x X X X X  Output data Carbon fluxes (daily) X X X X - - - - - Gross primary productivity X X X X Net ecosystem exchange X X X X Ecosystem respiration X X X X Energy fluxes (daily) X X X X - - - - - Actual evapotranspiration X X X X Soil temperature (top 10 cm) X X X X Soil moisture (top 10 cm) X X X X Plant growth (occasional) X X X X X X X X X Harvested aboveground biomass - - - - x x x x x Standing aboveground biomass X X X X - - - - - Leaf area index - X X - - - - - -     6 Acknowledgements This report is a contribution to the FACCE MACSUR knowledge hub. We thank all Partner Institutions for providing datasets. We also acknowledge technical support from the European Fluxes Database Cluster (http://www.europe-fluxdata.eu).   7 References Acock, M.C., Pachepski, Ya.A., 2000. Estimating missing weather data for agricultural simulations using group method of data handling. Journal of Applied Meteorology 39, 1176-1184. Bristow, K.L., Campbell, G.S., 1984. On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology 31, 159-166. Donatelli, M., Russell, G., Rizzoli, A.E., Acutis, M., Adam, M., Athanasiadis, I., Balderacchi, M., Bechini, L., Belhoucette, H., Bellocchi, G., Bergez, J.E., Botta, M., Braudeau, E., Bregaglio, S., Carlini, L., Casellas, E., Celette, F., Ceotto, E., Charron-Moirez, M.E., Confalonieri, R., Corbeels, M., Criscuolo, L., Cruz, P., Di Guardo, A., Ditto, D., Dupraz, C., Duru, M., Fiorani, D., Gentile, A., Ewert, F., Gary, C., Habyarimana, E., Jouany, C., Kansou, K., Knapen, M.J.R., Lanza Filippi, G., Leffelaar, P., Manici, L., Martin, P., Meuter, E.C., Mugueta, N., Mulia, R., Noordwijk, V.M., Oomen, R., Rosenmund, A., Rossi, V., Salinari, F., Serrano, A., Sorce, A., Vincent, G., Theau, J.P., Therond, O., Trevisan, M., Trevisiol, P., van Evert, F.K., Wallach, D., Wery, J., Zerourou, A., 2010. A component-based framework for simulating agricultural production and externalities, in: Brouwer, F., van Ittersum, M. (Eds.), Environmental and agricultural modelling: integrated approaches for policy impact assessment. Springer, Dordrecht, The Netherlands, pp. 63-108. Linacre, E., 1992. Climate data and resources: a reference and guide. Routledge, London, United Kingdom. Pachepsky, Y., Rawls, W., 2004. Development of pedotransfer functions in soil hydrology. Elsevier, Amsterdam, The Netherlands. Pérez, P.J., Castellví, F., Villar, J.M., Rosell, J.I., Ibañez, M., 1994. An empirical relationship between diurnal vapor pressure deficit and daily minimum and maximum temperatures in irrigated areas, in: Proceedings of the 3rd European Society for Agronomy Congress, September 18–22, Abano (PD), Italy, pp. 402-303. Porter, C.H., Pickering, N.B., Jones, J.W., Hoogenboom, G., 2000. Weather module in DSSAT v. 4.0 documentation and source code listing. Agricultural and Biological Engineering Department Research Report No. 2000-1203. University of Florida, Gainesville, FL, USA. Saxton, K.E., Rawls, W.J., Romberger, J.S., Papendick, R.I., 1986. Estimating generalized soil-water characteristics from texture. Soil Science Society of America Journal 50, 1031-1036. Tetens, O., 1930. Über einige meteorologische Begriffe. Zeitschrift für Geophysik 6, 297-309. (in German)  

Datasets classification and criteria for data requirements

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Datasets classification and criteria for data requirements

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