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Mueller, L.; Schindler, U.; Shepherd, T.G.; Ball, B.C.; Smolentseva, E.; Hu, C.; Hennings, V.; Schad, P.; Rogasik, J.; Zeitz, J.; Schlindwein, S.L.; Behrendt, A.; Helming, K.; Eulenstein, F. |
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Title |
A framework for assessing agricultural soil quality on a global scale |
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Journal Article |
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Year |
2012 |
Publication |
Archives of Agronomy and Soil Science |
Abbreviated Journal |
Archives of Agronomy and Soil Science |
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58 |
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sup1 |
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S76-S82 |
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Keywords |
soil quality; indicators; muencheberg soil quality rating |
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Abstract |
This paper provides information about a novel approach of rating agricultural soil quality (SQ) and crop yield potentials consistently over a range of spatial scales. The Muencheberg Soil Quality Rating is an indicator-based straightforward overall assessment method of agricultural SQ. It is a framework covering aspects of soil texture, structure, topography and climate which is based on 8 basic indicators and more than 12 hazard indicators. Ratings are performed by visual methods of soil evaluation. A field manual is then used to provide ratings from tables based on indicator thresholds. Finally, overall rating scores are given, ranging from 0 (worst) to 100 (best) to characterise crop yield potentials. The current approach is valid for grassland and cropland. Field tests in several countries confirmed the practicability and reliability of the method. At field scale, soil structure is a crucial, management induced criterion of agricultural SQ. At the global scale, climate controlled hazard indicators of drought risk and soil thermal regime are crucial for SQ and crop yield potentials. Final rating scores are well correlated with crop yields. We conclude that this system could be evolved for ranking and controlling agricultural SQ on a global scale. |
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0365-0340 |
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CropM, TradeM, ftnotmacsur |
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MA @ admin @ |
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4589 |
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Rusu, T.; Moraru, P.; Coste, C.; Cacovean, H.; Chetan, F.; Chetan, C. |
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Title |
Impact of climate change on climatic indicators in Transylvanian Plain, Romania |
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Journal Article |
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Year |
2014 |
Publication |
Journal of Food, Agriculture and Environment |
Abbreviated Journal |
Journal of Food, Agriculture and Environment |
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12 |
Issue |
1 |
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469-473 |
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Keywords |
Climate change; climatic indicators; Transylvanian plain |
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The condition of land degradation in Transylvanian Plain and its effects, being the result of local extreme physical-geographical conditions, is susceptible to degradation (evidenced by the erodibility index), which overlaps the extreme climatic conditions. Thermal and hydric regime monitoring is necessary in order to identify and implement measures of adaptation to the impacts of climate change. Soil moisture and temperature regimes were evaluated using a set of 20 data logging stations positioned throughout the plain. Each station stores electronic data of ground temperature at 3 depths (10, 30, 50 cm), the humidity at the depth of 10 cm, the air temperature (at 1 m) and precipitations. Climate change in the past few years has significantly altered the climatic indicators of the Transylvanian Plain. Precipitations, although deficient in terms of annual amounts, through their regime, have a negative influence on the plant carpet. Pluvial aggressiveness index reveals, for the research period, a first peak of pluvial aggressiveness during the months of February-April, then in July and in autumn, the months of October-November. This requires special measures for soil conservation, both in autumn and early spring, soil tillage measures being recommended, which ensure the presence of plant debris and vegetation in early spring but especially in summer and autumn. Climatic indicators determined for the period 2008 – 2012 point out, in Transylvanian Plain, a semi-arid Mediterranean climate through the rain factor Lang, respectively semi-arid (in the South) – semi-wet (in the North) according to the De Martonne index. This climatic characterization requires special technological measures for soil conservation. |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4638 |
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Eza, U.; Shtiliyanova, A.; Borras, D.; Bellocchi, G.; Carrère, P.; Martin, R. |
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Title |
An open platform to assess vulnerabilities to climate change: An application to agricultural systems |
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Journal Article |
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2015 |
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Ecological Informatics |
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Ecological Informatics |
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30 |
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389-396 |
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climate change; grasslands; modeling platform; vulnerability assessment; pasture simulation-model; software component; solar-radiation; crop production; change impacts; adaptation; indicator; makers |
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Numerous climate futures are now available from global climate models. Translation of climate data such as precipitation and temperatures into ecologically meaningful outputs for managers and planners is the next frontier. We describe a model-based open platform to assess vulnerabilities of agricultural systems to climate change on pixel-wise data. The platform includes a simulation modeling engine and is suited to work with NetCDF format of input and output files. In a case study covering a region (Auvergne) in the Massif Central of France, the platform is configured to characterize climate (occurrence of arid conditions in historical and projected climate records), soils and human management, and is then used to assess the vulnerability to climate change of grassland productivity (downscaled to a fine scale). We demonstrate how using climate time series, and process-based simulations vulnerabilities can be defined at fine spatial scales relevant to farmers and land managers, and can be incorporated into management frameworks. (C) 2015 Elsevier B.V. All rights reserved. |
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1574-9541 |
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CropM LiveM, ft_macsur |
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MA @ admin @ |
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4708 |
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Gutzler, C.; Helming, K.; Balla, D.; Dannowski, R.; Deumlich, D.; Glemnitz, M.; Knierim, A.; Mirschel, W.; Nendel, C.; Paul, C.; Sieber, S.; Stachow, U.; Starick, A.; Wieland, R.; Wurbs, A.; Zander, P. |
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Agricultural land use changes – a scenario-based sustainability impact assessment for Brandenburg, Germany |
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2015 |
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Ecological Indicators |
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Ecological Indicators |
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48 |
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505-517 |
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scenarios; impact assessment; agricultural intensification; land use change; irrigation; bioenergy; social and environmental indicators; climate-change; landscape; model |
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Decisions for agricultural management are taken at farm scale. However, such decisions may well impact upon regional sustainability. Two of the likely agricultural management responses to future challenges are extended use of irrigation and increased production of energy crops. The drivers for these are high commodity prices and subsidy policies for renewable energy. However, the impacts of these responses upon regional sustainability are unknown. Thus, we conducted integrated impact assessments for agricultural intensification scenarios in the federal state of Brandenburg, Germany, for 2025. One Irrigation scenario and one Energy scenario were contrasted with the Business As Usual (BAU) scenario. We applied nine indicators to analyze the economic, social and environmental effects at the regional, in this case district scale, which is the smallest administrative unit in Brandenburg. Assessment results were discussed in a stakeholder workshop involving 16 experts from the state government. The simulated area shares of silage maize for fodder and energy were 29%, 37% and 49% for the BAU, Irrigation, and Energy scenarios, respectively. The Energy scenario increased bio-electricity production to 41% of the demand of Brandenburg, and it resulted in CO2 savings of up to 3.5 million tons. However, it resulted in loss of biodiversity, loss of landscape scenery, increased soil erosion risk, and increased area demand for water protection requirements. The Irrigation scenario led to yield increases of 7% (rapeseed), 18% (wheat, sugar beet), and 40% (maize) compared to the BAU scenario. It also reduced the year-to-year yield variability. Water demand for irrigation was found to be in conflict with other water uses for two of the 14 districts. Spatial differentiation of scenario impacts showed that districts with medium to low yield potentials were more affected by negative impacts than districts with high yield potentials. In this first comprehensive sustainability impact assessment of agricultural intensification scenarios at regional level, we showed that a considerable potential for agricultural intensification exists. The intensification is accompanied by adverse environmental and socio-economic impacts. The novelty lies in the multiscale integration of comprehensive, agricultural management simulations with regional level impact assessment, which was achieved with the adequate use of indicators. It provided relevant evidence for policy decision making. Stakeholders appreciated the integrative approach of the assessment, which substantiated ongoing discussions among the government bodies. The assessment approach and the Brandenburg case study may stay exemplary for other regions in the world where similar economic and policy driving forces are likely to lead to agricultural intensification. (C) 2014 The Authors. Published by Elsevier Ltd. |
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1470-160x |
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CropM |
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MA @ admin @ |
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4561 |
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Author |
Sanna, M.; Bellocchi, G.; Fumagalli, M.; Acutis, M. |
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A new method for analysing the interrelationship between performance indicators with an application to agrometeorological models |
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Journal Article |
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2015 |
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Environmental Modelling & Software |
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Env. Model. Softw. |
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73 |
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286-304 |
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model evaluation; performance indicators; stable correlation; solar-radiation; simulation-model; environmental-models; statistical-methods; crop nitrogen; validation; rice; uncertainty; calibration; software |
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The use of a variety of metrics is advocated to assess model performance but correlated metrics may convey the same information, thus leading to redundancy. Starting from this assumption, a method was developed for selecting, from among a collection of performance indicators, one or more subsets providing the same information as the entire set. The method, based on the definition of “stable correlation”, was applied to 23 performance indicators of agrometeorological models, calculated on large sets of simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation. Two subsets were determined: {Squared Bias, Root Mean Squared Relative Error, Coefficient of Determination, Pattern Index, Modified Modelling Efficiency}, {Persistence Model Efficiency, Root Mean Squared Relative Error, Coefficient of Determination, Pattern Index}. The method needs corroboration but is statistically founded and can support the implementation of standardized evaluation tools. (C) 2015 Elsevier Ltd. All rights reserved. |
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1364-8152 |
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CropM LiveM, ftnotmacsur |
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MA @ admin @ |
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4503 |
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