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Author |
De Swaef, T.; Bellocchi, G.; Aper, J.; Lootens, P.; Roldan-Ruiz, I. |
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Title |
Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality |
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Journal Article |
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Year |
2019 |
Publication |
Journal of Experimental Botany |
Abbreviated Journal |
J. Experim. Bot. |
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Volume |
70 |
Issue |
9 |
Pages |
2587-2604 |
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Keywords |
Breeding; grassland modelling; identifiability analysis; perennial; ryegrass; phenotyping; sensitivity analysis; pasture simulation-model; practical identifiability; crop; water; parameters; systems; carbon; uncertainty; sensitivity; emissions |
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Abstract  |
Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phe-notyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized. |
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2020-02-14 |
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English |
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0022-0957 |
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LiveM, ft_macsur |
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MA @ admin @ |
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5231 |
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Author |
Sanz-Cobena, A.; Misselbrook, T.H.; Hernaiz, P.; Vallejo, A. |
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Title |
Impact of rainfall to the effectiveness of pig slurry shallow injection method for NH3 mitigation in a Mediterranean soil |
Type |
Journal Article |
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Year |
2019 |
Publication |
Atmospheric Environment |
Abbreviated Journal |
Atm. Environ. |
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Volume |
216 |
Issue |
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Pages |
116913 |
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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 |
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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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LiveM, ft_macsur |
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MA @ admin @ |
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5234 |
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Holman, I.P.; Brown, C.; Carter, T.R.; Harrison, P.A.; Rounsevell, M. |
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Title |
Improving the representation of adaptation in climate change impact models |
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Journal Article |
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Year |
2019 |
Publication |
Regional Environmental Change |
Abbreviated Journal |
Reg. Environ. Change |
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Volume |
19 |
Issue |
3 |
Pages |
711-721 |
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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 |
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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. |
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2019-04-27 |
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English |
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ISSN |
1436-3798 |
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TradeM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
5220 |
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Author |
Nguyen, T.P.L.; Seddaiu, G.; Roggero, P.P. |
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Title |
Declarative or procedural knowledge? Knowledge for enhancing farmers’ mitigation and adaptation behaviour to climate change |
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Journal Article |
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Year |
2019 |
Publication |
Journal of Rural Studies |
Abbreviated Journal |
Journal of Rural Studies |
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Volume |
67 |
Issue |
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Pages |
46-56 |
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Keywords |
Farming systems; Knowledge; Attitude; Practice; Social construction |
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Abstract |
Climate change poses a major challenge for farmers, but agricultural sustainability, mitigation, and adaptation can effectively decrease climate impacts on agricultural systems. Changes in farming practices are necessary to reduce emissions and to adapt to climate change. However, such modifications to common practices depend, to a large extent, on farmers’ knowledge and attitudes towards climate risks. An empirical study of farmers’ attitudes and knowledge of climate change mitigation and adaptation practices is useful to understand how farmers’ knowledge influences their attitudes and practices towards climate change mitigation and adaptation. Based on a case study characterised by four agricultural farming systems (extensive dairy sheep, intensive dairy cattle, horticultural farming, and rice farming) in the Province of Oristano in Italy, this study contains an investigation of (i) farmers’ knowledge of climate change causes and effects, how they construct such knowledge, and how they adapt to the phenomenon; (ii) what and how are farmers’ attitudes towards climate change causes are shaped under their contextual social interests and values; and (iii) if their practices in responding to climate variability are influenced by their constructed knowledge. The research results showed that farmers’ declarative knowledge of climate change did not affect their adaptation practices but directed farmers’ attitudes towards climate change causes. The findings also underscore the necessity of facilitating social learning spaces for enhancing virtuous behaviours towards climate change mitigation and the sharing and co-production of procedural knowledge for developing shared sustainable climate adaptation practices at the farm level. |
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2019-02-19 |
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0743-0167 |
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CropM, TradeM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
5217 |
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Author |
Gomara, I.; Bellocchi, G.; Martin, R.; Rodriguez-Fonseca, B.; Ruiz-Ramos, M. |
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Title |
Influence of climate variability on the potential forage production of a mown permanent grassland in the French Massif Central |
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Journal Article |
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Year |
2020 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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Volume |
280 |
Issue |
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Pages |
107768 |
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Keywords |
climate variability; grasslands; potential yield; climate services; forage production forecasts; french massif central; pasture simulation-model; dry-matter production; atmospheric; circulation; crop yield; SST anomalies; maize yield; managed grasslands; storm track; ENSO; impacts |
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Abstract |
Climate Services (CS) provide support to decision makers across socio-economic sectors. In the agricultural sector, one of the most important CS applications is to provide timely and accurate yield forecasts based on climate prediction. In this study, the Pasture Simulation model (PaSim) was used to simulate, for the period 1959–2015, the forage production of a mown grassland system (Laqueuille, Massif Central of France) under different management conditions, with meteorological inputs extracted from the SAFRAN atmospheric database. The aim was to generate purely climate-dependent timeseries of optimal forage production, a variable that was maximized by brighter and warmer weather conditions at the grassland. A long-term increase was observed in simulated forage yield, with the 1995–2015 average being 29% higher than the 1959–1979 average. Such increase seems consistent with observed rising trends in temperature and CO2, and multi-decadal changes in incident solar radiation. At interannual timescales, sea surface temperature anomalies of the Mediterranean (MED), Tropical North Atlantic (TNA), equatorial Pacific (El Niño Southern Oscillation) and the North Atlantic Oscillation (NAO) index were found robustly correlated with annual forage yield values. Relying only on climatic predictors, we developed a stepwise statistical multi-regression model with leave-one-out cross-validation. Under specific management conditions (e.g., three annual cuts) and from one to five months in advance, the generated model successfully provided a p-value<0.01 in correlation (t-test), a root mean square error percentage (%RMSE) of 14.6% and a 71.43% hit rate predicting above/below average years in terms of forage yield collection. This is the first modeling study on the possible role of large-scale oceanic–atmospheric teleconnections in driving forage production in Europe. As such, it provides a useful springboard to implement a grassland seasonal forecasting system in this continent. |
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2020-06-08 |
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LiveM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
5233 |
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