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Author |
Dumont, B.; Basso, B.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Assessing and modeling economic and environmental impact of wheat nitrogen management in Belgium |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
79 |
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184-196 |
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Tactical nitrogen management; Climatic variability; Probability risk; assessment; LARS-WG; Crop model; STICS; stics crop model; generic model; simulation; yield; water; soil; fertilizer; behavior; climate; maize |
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Future progress in wheat yield will rely on identifying genotypes & management practices better adapted to the fluctuating environment Nitrogen (N) fertilization is probably the most important practice impacting crop growth. However, the adverse environmental impacts of inappropriate N management (e.g., lixiviation) must be considered in the decision-making process. A formal decisional algorithm was developed to tactically optimize the economic & environmental N fertilization in wheat. Climatic uncertainty analysis was performed using stochastic weather time-series (LARS-WG). Crop growth was simulated using STICS model. Experiments were conducted to support the algorithm recommendations: winter wheat was sown between 2008 & 2014 in a classic loamy soil of the Hesbaye Region, Belgium (temperate climate). Results indicated that, most of the time, the third N fertilization applied at flag-leaf stage by farmers could be reduced. Environmental decision criterion is most of the time the limiting factor in comparison to the revenues expected by farmers. (C) 2016 Elsevier Ltd. All rights reserved. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4749 |
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Zhao, G.; Hoffmann, H.; Yeluripati, J.; Xenia, S.; Nendel, C.; Coucheney, E.; Kuhnert, M.; Tao, F.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Kiese, R.; Eckersten, H.; Haas, E.; Cammarano, D.; Kassie, B.; Moriondo, M.; Trombi, G.; Bindi, M.; Biernath, C.; Heinlein, F.; Klein, C.; Priesack, E.; Lewan, E.; Kersebaum, K.-C.; Rötter, R.; Roggero, P.P.; Wallach, D.; Asseng, S.; Siebert, S.; Gaiser, T.; Ewert, F. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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80 |
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Pages |
100-112 |
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Crop model; Stratified random sampling; Simple random sampling; Clustering; Up-scaling; Model comparison; Precision gain; species distribution models; systems simulation; weather data; large-scale; design; soil; optimization; growth; apsim; autocorrelation |
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We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4724 |
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Ruane, A.C.; Hudson, N.I.; Asseng, S.; Camarrano, D.; Ewert, F.; Martre, P.; Boote, K.J.; Thorburn, P.J.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, &rew J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.F.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.C.; Kumar, S.N.; Müller, C.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Rötter, R.P.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J.W.; Wolf, J. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Multi-wheat-model ensemble responses to interannual climate variability |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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81 |
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86-101 |
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Crop modeling; Uncertainty; Multi-model ensemble; Wheat; AgMIP; Climate; impacts; Temperature; Precipitation; lnterannual variability; simulation-model; crop model; nitrogen dynamics; winter-wheat; large-area; systems simulation; farming systems; yield response; growth; water |
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We compare 27 wheat models’ yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models’ climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R-2 <= 0.24) was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Published by Elsevier Ltd. |
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CropM, ft_macsur |
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MA @ admin @ |
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4769 |
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Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Estimating model prediction error: Should you treat predictions as fixed or random |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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84 |
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529-539 |
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Keywords |
Crop model; Uncertainty; Prediction error; Parameter uncertainty; Input uncertainty; Model structure uncertainty |
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Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEPfixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEPuncertain(X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEPuncertain(X) can be estimated using a random effects ANOVA. It is argued that MSEPuncertain(X) is the more informative uncertainty criterion, because it is specific to each prediction situation. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4773 |
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Author |
Ghaley, B.B.; Vesterdal, L.; Porter, J.R. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Quantification and valuation of ecosystem services in diverse production systems for informed decision-making |
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Journal Article |
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Year |
2014 |
Publication |
Environmental Science & Policy |
Abbreviated Journal |
Environmental Science & Policy |
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39 |
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139-149 |
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bio-physical quantification; combined food and energy system; economic valuation field measurements; land management; marketable and non-marketable ecosystem services; land-use change; carbon; farm; efficiency; crops; china; model; scale; field |
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The empirical evidence of decline in ecosystem services (ES) over the last century has reinforced the call for ES quantification, monitoring and valuation. Usually, only provisioning ES are marketable and accounted for, whereas regulating, supporting and cultural ES are typically non-marketable and overlooked in connection with land-use or management decisions. The objective of this study was to quantify and value total ES (marketable and non-marketable) of diverse production systems and management intensities in Denmark to provide a basis for decisions based on economic values. The production systems were conventional wheat (Cwheat), a combined food and energy (CFE) production system and beech forest. Marketable (provisioning ES) and non-marketable ES (supporting, regulating and cultural) ES were quantified by dedicated on-site field measurements supplemented by literature data. The value of total ES was highest in CFE (US$ 3142 ha(-1) yr(-1)) followed by Cwheat (US$ 2767 ha (1) yr(-1)) and beech forest (US$ 2328 ha(-1) yr(-1)). As the production system shifted from Cwheat – CFE-beech, the marketable ES share decreased from 88% to 75% in CFE and 55% in beech whereas the non-marketable ES share increased to 12%, 25% and 45% of total ES in Cwheat, CFE and beech respectively, demonstrating production system and management effects on ES values. Total ES valuation, disintegrated into marketable and non-marketable share is a potential way forward to value ES and `tune’ our production systems for enhanced ES provision. Such monetary valuation can be used by policy makers and land managers as a tool to assess ES value and monitor the sustained flow of ES. The application of ES-based valuation for land management can enhance ES provision for maintaining the productive capacity of the land without depending on the external fossil-based fertilizer and chemical input. (C) 2013 Elsevier Ltd. All rights reserved. |
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1462-9011 |
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MA @ admin @ |
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4623 |
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