| Records |
| Author |
Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Ruane, A.C.; Boote, K.J.; Thorburn, P.; Antle, J.M.; Nelson, G.C.; Porter, C.; Janssen, S.; Asseng, S.; Basso, B.; Ewert, F.; Wallach, D.; Baigorria, G.; Winter, J.M. |
| Title |
The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies |
Type |
Journal Article |
| Year |
2013 |
Publication  |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
| Volume |
170 |
Issue |
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Pages |
166-182 |
| Keywords |
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| Abstract |
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| Corporate Author |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0168-1923 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
CropM, ftnotmacsur, IPCC-AR5 |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4927 |
| Permanent link to this record |
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| Author |
Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
| Title |
Estimating model prediction error: Should you treat predictions as fixed or random |
Type |
Journal Article |
| Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
| Volume |
84 |
Issue |
|
Pages |
529-539 |
| Keywords |
Crop model; Uncertainty; Prediction error; Parameter uncertainty; Input uncertainty; Model structure uncertainty |
| Abstract |
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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Thesis |
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Place of Publication |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1364-8152 |
ISBN |
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Medium |
Article |
| Area |
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Expedition |
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Conference |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4773 |
| Permanent link to this record |
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| Author |
Wallach, D.; Nissanka, S.P.; Karunaratne, A.S.; Weerakoon, W.M.W.; Thorburn, P.J.; Boote, K.J.; Jones, J.W. |
| Title |
Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: A case study on rice |
Type |
Journal Article |
| Year |
2016 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
| Volume |
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Issue |
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Pages |
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| Keywords |
Uncertainty; Phenology; Parameter uncertainty; Multi-model ensemble; Generalized least squares; Rice; Crop model; APSIM; DSSAT |
| Abstract |
We consider predictions of the impact of climate warming on rice development times in Sri Lanka. The major emphasis is on the uncertainty of the predictions, and in particular on the estimation of mean squared error of prediction. Three contributions to mean squared error are considered. The first is parameter uncertainty that results from model calibration. To take proper account of the complex data structure, generalized least squares is used to estimate the parameters and the variance-covariance matrix of the parameter estimators. The second contribution is model structure uncertainty, which we estimate using two different models. An ANOVA analysis is used to separate the contributions of parameter and model uncertainty to mean squared error. The third contribution is model error, which is estimated using hindcasts. Mean squared error of prediction of time from emergence to maturity, for baseline +2 °C, is estimated as 108 days2, with model error contributing 86 days2, followed by model structure uncertainty which contributes 15 days2 and parameter uncertainty which contributes 7 days2. We also show how prediction uncertainty is reduced if prediction concerns development time averaged over years, or the difference in development time between baseline and warmer temperatures. |
| Address |
2016-09-13 |
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Thesis |
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Place of Publication |
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Language |
Summary Language |
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Original Title |
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Series Title |
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Series Issue |
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Edition |
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| ISSN |
1161-0301 |
ISBN |
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Medium |
Article |
| Area |
CropM |
Expedition |
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Conference |
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| Notes |
CropM; wos; ftnotmacsur; wsnotyet; |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4777 |
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| Author |
De Sanctis, G.; Roggero, P.P.; Seddaiu, G.; Orsini, R.; Porter, C.H.; Jones, J.W. |
| Title |
Long-term no tillage increased soil organic carbon content of rain-fed cereal systems in a Mediterranean area |
Type |
Journal Article |
| Year |
2012 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
| Volume |
40 |
Issue |
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Pages |
18-27 |
| Keywords |
N fertilization; C dynamics; DSSAT; Wheat; Maize; Weed fallow; sandy loam soil; cropping systems; agricultural systems; climate-change; winter-wheat; sequestration; matter; model; fertilization; dynamics |
| Abstract |
The differential impact on soil organic carbon (SOC) of applying no tillage (NT) compared to conventional tillage (CT, i.e. mouldboard ploughing), along with three rates of nitrogen (N) fertilizer application (0,90 and 180 kg ha(-1) y(-1)), was studied under rain-fed Mediterranean conditions in a long-term experiment based on a durum wheat-maize rotation, in which crop residues were left on the soil (NT) or incorporated (CT). Observed SOC content following 8 and 12 years of continuous treatment application was significantly higher in the top 10 cm of the soil under NT than CT, but it was similar in the 10-40 cm layer. NT grain yields for both maize and durum wheat were below those attained under CT (on average 32% and 14% lower respectively) at a given rate of N fertilizer application. Soil, climate and crop data over 5 years were used to calibrate DSSAT model in order to simulate the impact of the different management practices over a 50-year period. Good agreement was obtained between observed and simulated values for crops grain yield, above-ground biomass and observed SOC values. Results from the simulations showed that under NT the weeds growing during the intercrop fallow period made a significant contribution to the observed SOC increase. When the contribution of the weed fallow was considered, NT significantly increased SOC in the top 40 cm of the soil at an average rate of 0.43, 0.31 and 0.03 t ha(-1) per year, respectively for 180,90 and 0 kg N ha(-1) year(-1), within the simulated 50 years. Under CT, a significant SOC increase was simulated under N180 and a significant decrease when no fertilizer was supplied. |
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Place of Publication |
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| Language |
English |
Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1161-0301 |
ISBN |
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Medium |
article |
| Area |
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Expedition |
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Conference |
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| Notes |
CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4469 |
| Permanent link to this record |
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| Author |
Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
| Title |
Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions |
Type |
Report |
| Year |
2016 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
8 |
Issue |
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Pages |
C4.1-D |
| Keywords |
MACSUR_ACK; CropM |
| Abstract |
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. Several ways of quantifying prediction uncertainty have been explored in the literature, but there have been no studies of how the different approaches are related to one another, and how they are related to some overall measure of prediction uncertainty. Here we show that all the different approaches can be related to two different viewpoints about the model; either the model is treated as a fixed predictor with some average error, or the model can be treated as a random variable with uncertainty in one or more of model structure, model inputs and model parameters. We discuss the differences, and show how mean squared error of prediction can be estimated in both cases. The results can be used to put uncertainty estimates into a more general framework and to relate different uncertainty estimates to one another and to overall prediction uncertainty. This should lead to a better understanding of crop model prediction uncertainty and the underlying causes of that uncertainty. This study was published as (Wallach et al. 2016) |
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Approved |
no |
| Call Number |
MA @ office @ |
Serial |
2954 |
| Permanent link to this record |
