Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites
The present article is a contribution to the CLARIS WorkPackage "Climate and Agriculture", and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites (Pergamino, Marcos Juarez and Anguil) which are representative...
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todo:paper_01650009_v98_n3_p565_dOrgeval2023-10-03T15:02:29Z Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites d'Orgeval, T. Boulanger, J.-P. Capalbo, M.J. Guevara, E. Penalba, O. Meira, S. Artificial intelligence Classification (of information) Climate models Crops Decision support systems Forecasting Grain (agricultural product) Information use Soil testing Uncertainty analysis Agrotechnology transfer Classification trees Climate information Multivariate adaptive regression splines Regional climate changes Seasonal forecasts Technology change Temperature forecasts Climate change climate change crop yield data set optimization precipitation (climatology) regional climate sowing soybean weather forecasting Argentina Glycine max Zea mays The present article is a contribution to the CLARIS WorkPackage "Climate and Agriculture", and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites (Pergamino, Marcos Juarez and Anguil) which are representative of different climate and soil conditions in Argentina. Considering that we focus on the use of climate information only, and that official long time yield series are not always reliable and often influenced by both climate and technology changes, we decided to build a dataset with yields simulated by the DSSAT (Decision Support System for Agrotechnology Transfer) crop model, already calibrated in the selected three sites and for the two crops of interest (maize and soybean). We simulated yields for three different sowing dates for each crop in each of the three sites. Also considering that seasonal forecasts have a higher skill when using the 3-month average precipitation and temperature forecasts, and that regional climate change scenarios present less uncertainty at similar temporal scales, we decided to focus our analysis on the use of quarterly precipitation and temperature averages, measured at the three sites during the crop cycle. This type of information is used as input (predictand) for non-linear statistical methods (Multivariate Adaptive Regression Splines, MARS; and classification trees) in order to predict yields and their dependency to the chosen sowing date. MARS models show that the most valuable information to predict yield amplitude is the 3-month average precipitation around flowering. Classification trees are used to estimate whether climate information can be used to infer an optimal sowing date in order to optimize yields. In order to simplify the problem, we set a default sowing date (the most representative for the crop and the site) and compare the yield amplitudes between such a default date and possible alternative dates sometimes used by farmers. Above normal average temperatures at the beginning and the end of the crop cycle lead to respectively later and earlier optimal sowing. Using this classification, yields can be potentially improved by changing sowing date for maize but it is more limited for soybean. More generally, the sites and crops which have more variable yields are also the ones for which the proposed methodology is the most efficient. However, a full evaluation of the accuracy of seasonal forecasts should be the next step before confirming the reliability of this methodology under real conditions. © Springer Science + Business Media B.V. 2009. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_01650009_v98_n3_p565_dOrgeval |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Artificial intelligence Classification (of information) Climate models Crops Decision support systems Forecasting Grain (agricultural product) Information use Soil testing Uncertainty analysis Agrotechnology transfer Classification trees Climate information Multivariate adaptive regression splines Regional climate changes Seasonal forecasts Technology change Temperature forecasts Climate change climate change crop yield data set optimization precipitation (climatology) regional climate sowing soybean weather forecasting Argentina Glycine max Zea mays |
| spellingShingle |
Artificial intelligence Classification (of information) Climate models Crops Decision support systems Forecasting Grain (agricultural product) Information use Soil testing Uncertainty analysis Agrotechnology transfer Classification trees Climate information Multivariate adaptive regression splines Regional climate changes Seasonal forecasts Technology change Temperature forecasts Climate change climate change crop yield data set optimization precipitation (climatology) regional climate sowing soybean weather forecasting Argentina Glycine max Zea mays d'Orgeval, T. Boulanger, J.-P. Capalbo, M.J. Guevara, E. Penalba, O. Meira, S. Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites |
| topic_facet |
Artificial intelligence Classification (of information) Climate models Crops Decision support systems Forecasting Grain (agricultural product) Information use Soil testing Uncertainty analysis Agrotechnology transfer Classification trees Climate information Multivariate adaptive regression splines Regional climate changes Seasonal forecasts Technology change Temperature forecasts Climate change climate change crop yield data set optimization precipitation (climatology) regional climate sowing soybean weather forecasting Argentina Glycine max Zea mays |
| description |
The present article is a contribution to the CLARIS WorkPackage "Climate and Agriculture", and aims at testing whether it is possible to predict yields and optimal sowing dates using seasonal climate information at three sites (Pergamino, Marcos Juarez and Anguil) which are representative of different climate and soil conditions in Argentina. Considering that we focus on the use of climate information only, and that official long time yield series are not always reliable and often influenced by both climate and technology changes, we decided to build a dataset with yields simulated by the DSSAT (Decision Support System for Agrotechnology Transfer) crop model, already calibrated in the selected three sites and for the two crops of interest (maize and soybean). We simulated yields for three different sowing dates for each crop in each of the three sites. Also considering that seasonal forecasts have a higher skill when using the 3-month average precipitation and temperature forecasts, and that regional climate change scenarios present less uncertainty at similar temporal scales, we decided to focus our analysis on the use of quarterly precipitation and temperature averages, measured at the three sites during the crop cycle. This type of information is used as input (predictand) for non-linear statistical methods (Multivariate Adaptive Regression Splines, MARS; and classification trees) in order to predict yields and their dependency to the chosen sowing date. MARS models show that the most valuable information to predict yield amplitude is the 3-month average precipitation around flowering. Classification trees are used to estimate whether climate information can be used to infer an optimal sowing date in order to optimize yields. In order to simplify the problem, we set a default sowing date (the most representative for the crop and the site) and compare the yield amplitudes between such a default date and possible alternative dates sometimes used by farmers. Above normal average temperatures at the beginning and the end of the crop cycle lead to respectively later and earlier optimal sowing. Using this classification, yields can be potentially improved by changing sowing date for maize but it is more limited for soybean. More generally, the sites and crops which have more variable yields are also the ones for which the proposed methodology is the most efficient. However, a full evaluation of the accuracy of seasonal forecasts should be the next step before confirming the reliability of this methodology under real conditions. © Springer Science + Business Media B.V. 2009. |
| format |
JOUR |
| author |
d'Orgeval, T. Boulanger, J.-P. Capalbo, M.J. Guevara, E. Penalba, O. Meira, S. |
| author_facet |
d'Orgeval, T. Boulanger, J.-P. Capalbo, M.J. Guevara, E. Penalba, O. Meira, S. |
| author_sort |
d'Orgeval, T. |
| title |
Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites |
| title_short |
Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites |
| title_full |
Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites |
| title_fullStr |
Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites |
| title_full_unstemmed |
Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites |
| title_sort |
yield estimation and sowing date optimization based on seasonal climate information in the three claris sites |
| url |
http://hdl.handle.net/20.500.12110/paper_01650009_v98_n3_p565_dOrgeval |
| work_keys_str_mv |
AT dorgevalt yieldestimationandsowingdateoptimizationbasedonseasonalclimateinformationinthethreeclarissites AT boulangerjp yieldestimationandsowingdateoptimizationbasedonseasonalclimateinformationinthethreeclarissites AT capalbomj yieldestimationandsowingdateoptimizationbasedonseasonalclimateinformationinthethreeclarissites AT guevarae yieldestimationandsowingdateoptimizationbasedonseasonalclimateinformationinthethreeclarissites AT penalbao yieldestimationandsowingdateoptimizationbasedonseasonalclimateinformationinthethreeclarissites AT meiras yieldestimationandsowingdateoptimizationbasedonseasonalclimateinformationinthethreeclarissites |
| _version_ |
1807317129227141120 |