Records |
Author |
Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
Title |
Yield variability linked to climate uncertainty and nitrogen fertilisation |
Type |
Book Chapter |
Year |
2013 |
Publication |
|
Abbreviated Journal |
|
Volume |
|
Issue |
|
Pages |
427-434 |
Keywords |
CropM |
Abstract |
|
Address |
|
Corporate Author |
|
Thesis |
|
Publisher |
Springer |
Place of Publication |
|
Editor |
Stafford, J.V. |
Language |
|
Summary Language |
|
Original Title |
|
Series Editor |
|
Series Title |
Precision Agriculture ‘13. 9th ECPA – European Conference on Precision Agriculture, 7-11 June 2013, Lleida, Spain |
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
|
ISBN |
|
Medium |
|
Area |
|
Expedition |
|
Conference |
|
Notes |
|
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
2410 |
Permanent link to this record |
|
|
|
Author |
Dumont, B.; Basso, B.; Destain, J.-P.; Bodson, B.; Destain, M.-F. |
Title |
A Comparison of Optimal Nitrogen Fertilisation Strategies Using Current and Future Stochastically Generated Climatic Conditions |
Type |
Conference Article |
Year |
2014 |
Publication |
|
Abbreviated Journal |
|
Volume |
|
Issue |
|
Pages |
|
Keywords |
|
Abstract |
|
Address |
|
Corporate Author |
|
Thesis |
|
Publisher |
|
Place of Publication |
|
Editor |
|
Language |
|
Summary Language |
|
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
|
ISBN |
|
Medium |
|
Area |
|
Expedition |
|
Conference |
MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
Notes |
|
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
2412 |
Permanent link to this record |
|
|
|
Author |
Dumont, B.; Leemans, V.; Ferrandis Vallterra, S.; Vancutsem, F.; Seutin, B.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
Title |
A first step towards a real-time predictive yield support system |
Type |
Conference Article |
Year |
2012 |
Publication |
|
Abbreviated Journal |
|
Volume |
|
Issue |
|
Pages |
|
Keywords |
CropM |
Abstract |
|
Address |
|
Corporate Author |
|
Thesis |
|
Publisher |
|
Place of Publication |
|
Editor |
|
Language |
|
Summary Language |
|
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
|
ISBN |
|
Medium |
|
Area |
|
Expedition |
|
Conference |
CIGR-AgEng 2012, International Conference on Agricultural Engineering, Valencia (Spain)., 2012-07-07 to 2012-07-12 |
Notes |
|
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
2403 |
Permanent link to this record |
|
|
|
Author |
Dumont, B.; Leemans, V.; Ferrandis, S.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
Title |
Assessing the potential of an algorithm based on mean climatic data to predict wheat yield |
Type |
Journal Article |
Year |
2014 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
Volume |
15 |
Issue |
3 |
Pages |
255-272 |
Keywords |
stics model; yield prediction; real-time; proxy-sensing; stochastic weather generator; crop yield; mediterranean environment; simulation-model; variability; nitrogen; ensembles; forecasts; demeter; europe |
Abstract |
The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days. |
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 |
1385-2256 1573-1618 |
ISBN |
|
Medium |
Article |
Area |
|
Expedition |
|
Conference |
|
Notes |
CropM |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4621 |
Permanent link to this record |
|
|
|
Author |
Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
Title |
Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions |
Type |
Journal Article |
Year |
2015 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
Volume |
16 |
Issue |
4 |
Pages |
361-384 |
Keywords |
nitrogen management; climatic variability; lars-wg weather generator; stics soil-crop model; pearson system; probability risk assessment; crop model stics; fertilizer nitrogen; generic model; wheat yield; maize; simulation; skewness; field; agriculture; scenarios |
Abstract |
At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha(-1)), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of -1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha(-1)) of the distribution. |
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 |
1385-2256 |
ISBN |
|
Medium |
Article |
Area |
|
Expedition |
|
Conference |
|
Notes |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4519 |
Permanent link to this record |