|
Records |
Links ![sorted by URL field, descending order (down)](img/sort_desc.gif) |
|
Author |
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)
|
|
Title |
Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops |
Type |
Journal Article |
|
Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
|
|
Volume |
80 |
Issue |
|
Pages |
100-112 |
|
|
Keywords |
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 |
|
|
Abstract |
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. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
English |
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1364-8152 |
ISBN |
|
Medium |
Article |
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
CropM, ft_macsur |
Approved |
no |
|
|
Call Number |
MA @ admin @ |
Serial |
4724 |
|
Permanent link to this record |
|
|
|
|
Author |
Webber, H.; Ewert, F.; Kimball, B.A.; Siebert, S.; White, J.W.; Wall, G.W.; Ottman, M.J.; Trawally, D.N.A.; Gaiser, T. |
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Simulating canopy temperature for modelling heat stress in cereals |
Type |
Journal Article |
|
Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
|
|
Volume |
77 |
Issue |
|
Pages |
143-155 |
|
|
Keywords |
canopy temperature; heat stress; cereals; crop models; profile relationships; crop production; climate-change; spring wheat; field plots; growth; maize; water; yields; variability |
|
|
Abstract |
Crop models must be improved to account for the effects of heat stress events on crop yields. To date, most approaches in crop models use air temperature to define heat stress intensity as the cumulative sum of thermal times (TT) above a high temperature threshold during a sensitive period for yield formation. However, observational evidence indicates that crop canopy temperature better explains yield reductions associated with high temperature events than air temperature does. This study presents a canopy level energy balance using Monin ObukhovSimilarity Theory (MOST) with simplifications about the canopy resistance that render it suitable for application in crop models and other models of the plant environment. The model is evaluated for a uniform irrigated wheat canopy in Arizona and rainfed maize in Burkina Faso. No single variable regression relationships for key explanatory variables were found that were consistent across sowing dates to explain the deviation of canopy temperature from air temperature. Finally, thermal times determined with simulated canopy temperatures were able to reproduce thermal times calculated with observed canopy temperature, whereas those determined with air temperatures were not. (C) 2015 Elsevier Ltd. All rights reserved. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
English |
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1364-8152 |
ISBN |
|
Medium |
Article |
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
CropM, ft_macsur |
Approved |
no |
|
|
Call Number |
MA @ admin @ |
Serial |
4730 |
|
Permanent link to this record |
|
|
|
|
Author |
de Wit, A.; Boogaard, H.; van Diepen, K.; van Kraalingen, D.; Rötter, R.; Supit, I.; Wolf, J.; van Ittersum, M. |
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
WOFOST developer’s response to article by Stella et al., Environmental Modelling & Software 59 (2014): 44–58 |
Type |
Journal Article |
|
Year |
2015 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
|
|
Volume |
73 |
Issue |
|
Pages |
57-59 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
English |
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1364-8152 |
ISBN |
|
Medium |
Letter |
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
CropM, ftnotmacsur |
Approved |
no |
|
|
Call Number |
MA @ admin @ |
Serial |
4699 |
|
Permanent link to this record |
|
|
|
|
Author |
Kersebaum, K.C.; Boote, K.J.; Jorgenson, J.S.; Nendel, C.; Bindi, M.; Frühauf, C.; Gaiser, T.; Hoogenboom, G.; Kollas, C.; Olesen, J.E.; Rötter, R.P.; Ruget, F.; Thorburn, P.J.; Trnka, M.; Wegehenkel, M. |
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Analysis and classification of data sets for calibration and validation of agro-ecosystem models |
Type |
Journal Article |
|
Year |
2015 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
|
|
Volume |
72 |
Issue |
|
Pages |
402-417 |
|
|
Keywords |
field experiments; data quality; crop modelling; data requirement; minimum data; software; different climatic zones; soil-moisture sensors; spatial variability; nitrogen dynamics; crop models; systems simulation; wheat yields; elevated co2; growth; field |
|
|
Abstract |
Experimental field data are used at different levels of complexity to calibrate, validate and improve agroecosystem models to enhance their reliability for regional impact assessment. A methodological framework and software are presented to evaluate and classify data sets into four classes regarding their suitability for different modelling purposes. Weighting of inputs and variables for testing was set from the aspect of crop modelling. The software allows users to adjust weights according to their specific requirements. Background information is given for the variables with respect to their relevance for modelling and possible uncertainties. Examples are given for data sets of the different classes. The framework helps to assemble high quality data bases, to select data from data bases according to modellers requirements and gives guidelines to experimentalists for experimental design and decide on the most effective measurements to improve the usefulness of their data for modelling, statistical analysis and data assimilation. (C) 2015 Elsevier Ltd. All rights reserved. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
English |
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1364-8152 |
ISBN |
|
Medium |
Article |
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
CropM, ft_macsur |
Approved |
no |
|
|
Call Number |
MA @ admin @ |
Serial |
4563 |
|
Permanent link to this record |
|
|
|
|
Author |
Ewert, F.; Rötter, R.P.; Bindi, M.; Webber, H.; Trnka, M.; Kersebaum, K.C.; Olesen, J.E.; van Ittersum, M.K.; Janssen, S.; Rivington, M.; Semenov, M.A.; Wallach, D.; Porter, J.R.; Stewart, D.; Verhagen, J.; Gaiser, T.; Palosuo, T.; Tao, F.; Nendel, C.; Roggero, P.P.; Bartošová, L.; Asseng, S. |
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Crop modelling for integrated assessment of risk to food production from climate change |
Type |
Journal Article |
|
Year |
2015 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
|
|
Volume |
72 |
Issue |
|
Pages |
287-303 |
|
|
Keywords |
uncertainty; scaling; integrated assessment; risk assessment; adaptation; crop models; agricultural land-use; change adaptation strategies; farming systems simulation; agri-environmental systems; enrichment face experiment; high-temperature stress; change impacts; nitrogen dynamics; atmospheric co2; spring wheat |
|
|
Abstract |
The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
English |
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1364-8152 |
ISBN |
|
Medium |
Article |
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
CropM, ft_macsur |
Approved |
no |
|
|
Call Number |
MA @ admin @ |
Serial |
4521 |
|
Permanent link to this record |