| Records |
| Author |
Dono, G.; Cortignani, R.; Doro, L.; Giraldo, L.; Ledda, L.; Pasqui, M.; Roggero, P.P. |
| Title |
Adapting to uncertainty associated with short-term climate variability changes in irrigated Mediterranean farming systems |
Type |
Journal Article |
| Year |
2013 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
| Volume |
117 |
Issue |
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Pages |
1-12 |
| Keywords |
changed climate variability; dsp; epic; adaptation; water management; irrigation; simulating impacts; co2 concentration; crop production; productivity; maize; yield; growth; model; photosynthesis; agriculture |
| Abstract |
Short-term perspectives appear to be relevant in formulating adaptation measures to changed climate variability (CCV) as a part of the European Rural Development Policy (RDP). Indeed, short-run CCV is the variation that farmers would perceive to such an extent that a political demand would be generated for adapting support measures. This study evaluates some relevant agronomic and economic impacts of CCV as modelled in a near future time period at the catchment scale in a rural district in Sardinia (Italy). The effects of CCV are assessed in relation to the availability of irrigation water and the irrigation needs of maize. The Environmental Policy Integrated Climate (EPIC) model was used to simulate the impact of key climatic variables on the irrigation water requirements and yields of maize. A three-stage discrete stochastic programming model was then applied to simulate management and economic responses to those changes. The overall economic impact of a simulated CCV was found to be primarily caused by reduced stability in the future supply of irrigation water. Adaptations to this instability will most likely lead to a higher level of groundwater extraction and a reduction in the demand for labour. Changed climate variability will most likely reduce the income potential of small-scale farming. The most CCV-vulnerable farm typologies were identified, and the implications were discussed in relation to the development of adaptation measures within the context of the Common Agricultural Policy of European Union. (C) 2013 Elsevier Ltd. All rights reserved. |
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English |
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| ISSN |
0308521x |
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Article |
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| Notes |
CropM, ftnotmacsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4489 |
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| Author |
Hidy, D.; Barcza, Z.; Haszpra, L.; Churkina, G.; Pintér, K.; Nagy, Z. |
| Title |
Development of the Biome-BGC model for simulation of managed herbaceous ecosystems |
Type |
Journal Article |
| Year |
2012 |
Publication |
Ecological Modelling |
Abbreviated Journal |
Ecol. Model. |
| Volume |
226 |
Issue |
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Pages |
99-119 |
| Keywords |
biogeochemical model; biome-bgc; grassland; management; soil moisture; bayesian calibration; carbon flux model; regional applications; bayesian calibration; use efficiency; general-model; exchange; balance; climate; grassland; variability |
| Abstract |
Apart from measurements, numerical models are the most convenient instruments to analyze the carbon and water balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based Biome-BGC model is widely used to simulate the storage and flux of water, carbon, and nitrogen within the vegetation, litter, and soil of unmanaged terrestrial ecosystems. Considering herbaceous vegetation related simulations with Biome-BGC, soil moisture and growing season control on ecosystem functioning is inaccurate due to the simple soil hydrology and plant phenology representation within the model. Consequently, Biome-BGC has limited applicability in herbaceous ecosystems because (1) they are usually managed; (2) they are sensitive to soil processes, most of all hydrology; and (3) their carbon balance is closely connected with the growing season length. Our aim was to improve the applicability of Biome-BGC for managed herbaceous ecosystems by implementing several new modules, including management. A new index (heatsum growing season index) was defined to accurately estimate the first and the final days of the growing season. Instead of a simple bucket soil sub-model, a multilayer soil sub-model was implemented, which can handle the processes of runoff, diffusion and percolation. A new module was implemented to simulate the ecophysiological effect of drought stress on plant mortality. Mowing and grazing modules were integrated in order to quantify the functioning of managed ecosystems. After modifications, the Biome-BGC model was calibrated and validated using eddy covariance-based measurement data collected in Hungarian managed grassland ecosystems. Model calibration was performed based on the Bayes theorem. As a result of these developments and calibration, the performance of the model was substantially improved. Comparison with measurement-based estimate showed that the start and the end of the growing season are now predicted with an average accuracy of 5 and 4 days instead of 46 and 85 days as in the original model. Regarding the different sites and modeled fluxes (gross primary production, total ecosystem respiration, evapotranspiration), relative errors were between 18-60% using the original model and 10-18% using the developed model; squares of the correlation coefficients were between 0.02-0.49 using the original model and 0.50-0.81 using the developed model. (c) 2011 Elsevier B.V. All rights reserved. |
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English |
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0304-3800 |
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Article |
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| Notes |
LiveM |
Approved |
no |
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MA @ admin @ |
Serial |
4472 |
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| Author |
Cortignani, R.; Dono, G. |
| Title |
Agricultural policy and climate change: An integrated assessment of the impacts on an agricultural area of Southern Italy |
Type |
Journal Article |
| Year |
2018 |
Publication |
Environmental Science and Policy |
Abbreviated Journal |
Environ. Sci. Pol. |
| Volume |
81 |
Issue |
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Pages |
26-35 |
| Keywords |
Agricultural policy; Climate change; Bio-economic model; Integrated Assessment; Temperature-Humidity Index; Adaptation Pathways; Maximum-Entropy; Model; Cap; Uncertainty; Irrigation; Management; Scenarios; Systems |
| Abstract |
The European Union (EU) has recently reformed its Common Agricultural Policy (CAP) and, in parallel, has completely abolished the production quotas for milk. These changes will have important consequences for the use of land, of inputs (i.e., water and chemicals) and on the economic performance of rural areas. It is of interest to evaluate the integrated impact of these modifications and of climate change (CC), since the latter could neutralize or reverse some desired effects of the former. For this purpose, this paper evaluates the potential impact of the abolition of milk quotas, as well as of the reform of the first pillar of CAP in two different climate scenarios (present and near future). A bio-economic model simulates the possible adaptation of various farm types in an agricultural area of Southern Italy to these changes, given the available technological options and current market conditions. The main results show that the considered policy changes have small positive impacts on economic and environmental factors of the study area. However, some farm types are more affected. CC can effectively attenuate or reverse several of those effects, especially in some farm types. These results can inform the planning of future changes to the CAP, which will have to act in the context of deeper climate alteration. |
| Address |
2018-03-02 |
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English |
Summary Language |
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Series Issue |
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Edition |
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| ISSN |
1462-9011 |
ISBN |
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Article |
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| Notes |
TradeM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5193 |
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| Author |
Holman, I.P.; Brown, C.; Carter, T.R.; Harrison, P.A.; Rounsevell, M. |
| Title |
Improving the representation of adaptation in climate change impact models |
Type |
Journal Article |
| Year |
2019 |
Publication |
Regional Environmental Change |
Abbreviated Journal |
Reg. Environ. Change |
| Volume |
19 |
Issue |
3 |
Pages |
711-721 |
| Keywords |
Adaptive capacity; Limits; Water; Land; Decision making; Integrated assessment; Land-Cover Change; Global Change; River-Basin; Integrated Assessment; Adaptive Capacity; Vulnerability; Variability; Precautionary; Agriculture; Management |
| Abstract |
Climate change adaptation is a complex human process, framed by uncertainties and constraints, which is difficult to capture in existing assessment models. Attempts to improve model representations are hampered by a shortage of systematic descriptions of adaptation processes and their relevance to models. This paper reviews the scientific literature to investigate conceptualisations and models of climate change adaptation, and the ways in which representation of adaptation in models can be improved. The review shows that real-world adaptive responses can be differentiated along a number of dimensions including intent or purpose, timescale, spatial scale, beneficiaries and providers, type of action, and sector. However, models of climate change consequences for land use and water management currently provide poor coverage of these dimensions, instead modelling adaptation in an artificial and subjective manner. While different modelling approaches do capture distinct aspects of the adaptive process, they have done so in relative isolation, without producing improved unified representations. Furthermore, adaptation is often assumed to be objective, effective and consistent through time, with only a minority of models taking account of the human decisions underpinning the choice of adaptation measures (14%), the triggers that motivate actions (38%) or the time-lags and constraints that may limit their uptake and effectiveness (14%). No models included adaptation to take advantage of beneficial opportunities of climate change. Based on these insights, transferable recommendations are made on directions for future model development that may enhance realism within models, while also advancing our understanding of the processes and effectiveness of adaptation to a changing climate. |
| Address |
2019-04-27 |
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English |
Summary Language |
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Edition |
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1436-3798 |
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Article |
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| Notes |
TradeM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5220 |
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| Author |
Sanz-Cobena, A.; Misselbrook, T.H.; Hernaiz, P.; Vallejo, A. |
| Title |
Impact of rainfall to the effectiveness of pig slurry shallow injection method for NH3 mitigation in a Mediterranean soil |
Type |
Journal Article |
| Year |
2019 |
Publication |
Atmospheric Environment |
Abbreviated Journal |
Atm. Environ. |
| Volume |
216 |
Issue |
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Pages |
116913 |
| Keywords |
ammonia; micrometeorological method; slurry incorporation; trade-offs; nitrous oxide; mediterranean agroecosystems; nitrous-oxide emissions; field-applied manure; organic fertilizers; ammonia emissions; methane emissions; N2O emissions; animal manures; management; losses; grassland |
| Abstract |
Ammonia emission from fertilized cropping systems is an important concern for stakeholders, particularly in regions with high livestock densities producing large amounts of manure. Application of pig slurries can result in very large losses of N through NH3 volatilization, thus decreasing the N use efficiency (NUE) of the applied manure. Shallow incorporation has been shown to significantly abate these losses. In this field study, we assessed the impact of contrasting weather conditions on the effectiveness of shallow injection to abate NH3 emissions from pig slurry application to a Mediterranean soil. As potential trade-offs of NH3 abatement, greenhouse gas emissions were also measured under conditions of high soil moisture. Compared with surface application of slurry, shallow injection effectively and significantly decreased NH3 losses independently of weather conditions, but reductions of NH3 emission were greater after heavy rainfall. In contrast, under these conditions, shallow injection triggered higher emissions of N2O and CH4. Our findings reinforce the idea that any single-pollutant abatement strategy needs to be designed and assessed in a regional context and considering potential trade-offs in the form of other pollutants. |
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2020-06-08 |
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article |
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| Notes |
LiveM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5234 |
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