DEXi-Dairy: an ex post multicriteria tool to assess the sustainability of dairy production systems in various European regions

Growing awareness of global challenges and increasing pressures on the farming sector, including the urgent requirement to rapidly cut greenhouse gases (GHG) emissions, emphasize the need for sustainable production, which is particularly relevant for dairy production systems. Comparing dairy production systems across the three sustainability dimensions is a considerable challenge, notably due to the heterogeneity of production conditions in Europe. To overcome this, we developed an ex post multicriteria assessment tool that adopts a holistic approach across the three sustainability dimensions. This tool is based on the DEXi framework, which associates a hierarchical decision model with an expert perspective and follows a tree shaped structure; thus, we called it the DEXi-Dairy tool. For each dimension of sustainability, qualitative attributes were defined and organized in themes, sub-themes, and indicators. Their choice was guided by three objectives: (i) better describe main challenges faced by European dairy production systems, (ii) point out synergies and trade-offs across sustainability dimensions, and (iii) contribute to the identification of GHG mitigation strategies at the farm level. Qualitative scales for each theme, sub-theme, and indicator were defined together with weighting factors used to aggregate each level of the tree. Based on selected indicators, a list of farm data requirements was developed to populate the sustainability tree. The model was then tested on seven case study farms distributed across Europe. DEXi-Dairy presents a qualitative method that allows for the comparison of different inputs and the evaluation of the three sustainability dimensions in an integrated manner. By assessing synergies and trade-offs across sustainability dimensions, DEXi-Dairy is able to reflect the heterogeneity of dairy production systems. Results indicate that, while trade-offs occasionally exist among respective selected sub-themes, certain farming systems tend to achieve a higher sustainability score than others and hence could serve as benchmarks for further analyses

Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data

[EN] The agricultural sector faces the challenge of ensuring food security without an excessive burden on the environment. Simulationmodels provide excellent instruments for researchers to gainmore insight into relevant processes and best agricultural practices and provide tools for planners for decision making support. The extent to which models are capable of reliable extrapolation and prediction is important for exploring new farming systems or assessing the impacts of future land and climate changes. A performance assessmentwas conducted by testing six detailed state-of-the-artmodels for simulation of nitrate leaching (ARMOSA, COUPMODEL, DAISY, EPIC, SIMWASER/STOTRASIM, SWAP/ANIMO) for lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to nitrate leaching. Three consecutive phases were distinguished to gain insight in the predictive power of themodels: 1) a blind test for 2005 2008 in which only soil hydraulic characteristics, meteorological data and information about the agricultural management were accessible; 2) a calibration for the same period in which essential information on field observations was additionally available to the modellers; and 3) a validation for 2009 2011 with the corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root mean squared error, index of agreement,model efficiency, root relative squared error, Pearson's linear correlation coefficient) was applied for testing the results and comparing the models. None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high-input farming systems had difficulties in accurately predicting leaching in low-input farming systems that are strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to identify years and crops with high- and low-leaching rates.This research was made possible by the GENESIS project of the EU 7th Framework Programme (Project No. 226536; FP7-ENV-2008-1). We are grateful for the experimental data provided by Joanneum Raum (Graz, Austria). The modelling team of Democritus University of Thrace would like to thank Per-Erik Jansson (Royal Institute of Technology, Stockholm, Sweden) for his valuable help during the application of Coup Model.Groenendijk, P.; Heinen, M.; Klammler, G.; Fank, J.; Kupfersberger, H.; Pisinaras, V.; Gemitzi, A.... (2014). Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data. Science of the Total Environment. 499:463-480. https://doi.org/10.1016/j.scitotenv.2014.07.002S46348049

Pilotage de la fertilisation azotée du blé d'hiver sur la base d'une évaluation précoce de la réflectance radiométrique ou du taux de couverture du sol, en vue d'une application à l'agriculture de précision

* INRA Unité de Bioclimatologie Grignon (FRA) Diffusion du document : INRA Unité de Bioclimatologie Grignon (FRA) Diplôme : Dr. Ing

Grasslands for soil organic matter storage in crop-livestock systems.

International audienc

Paramétrisation d'un modèle architectural de blé au cours du tallage. Estimation des paramètres de structure par photogrammétrie

National audienc

Caractérisation de l'état azoté d'un peuplement de blé par des variables physiologiques et radiométriques. Simulation de l'indice foliaire et de la réflectance par une modélisation de la structure tridimensionnelle du blé

National audienc

3D plant characterization and modelling aimed at the remote control of winter wheat growth

National audienc

Rice straw composting and its effect on soil properties

International audienceIn Egypt, recycling rice straw and organic wastes is of great concern as well as improvement of soil properties. Rice straw compost could improve both organic waste recycling and soil quality. The aim of this study was to evaluate the effect of the rice straw compost, with or without water treatment residuals (WTR), on soil chemical properties and dry weight of canola. The results showed that the addition of rice straw compost and WTR decreased soil salinity and increased Ca(+2), K(+) and organic matter. The addition of compost and WTR (2:1 wet weight ratio) at level of 10 g dry weight kg(-1) dry soil gave the best reduction in soil salinity compared to compost or WTR only or for level 10 g dry weight kg(-1) dry soil. The results also showed that the available P decreased with the application of WTR while it increased with the application of compost. The study demonstrated that the dry weight and relative increase (R.I %) of dry weight of canola plants increased with the application of WTR and compost to soil. Combinations of WTR and compost to soils had a greater effect on increasing yield and improved the efficiency of WTR on soil properties

Rice straw composting and its effect on soil properties

In Egypt, recycling rice straw and organic wastes is of great concern as well as improvement of soil properties. Rice straw compost could improve both organic waste recycling and soil quality. The aim of this study was to evaluate the effect of the rice straw compost, with or without water treatment residuals (WTR), on soil chemical properties and dry weight of canola. The results showed that the addition of rice straw compost and WTR decreased soil salinity and increased Ca(+2), K(+) and organic matter. The addition of compost and WTR (2:1 wet weight ratio) at level of 10 g dry weight kg(-1) dry soil gave the best reduction in soil salinity compared to compost or WTR only or for level 10 g dry weight kg(-1) dry soil. The results also showed that the available P decreased with the application of WTR while it increased with the application of compost. The study demonstrated that the dry weight and relative increase (R.I %) of dry weight of canola plants increased with the application of WTR and compost to soil. Combinations of WTR and compost to soils had a greater effect on increasing yield and improved the efficiency of WTR on soil properties