Navegar

Colección

Datos Estadísticas

Artículo

Estamos trabajando para incorporar este artículo al repositorio
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

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.

Registro:

Documento: Artículo
Título:Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites
Autor:d'Orgeval, T.; Boulanger, J.-P.; Capalbo, M.J.; Guevara, E.; Penalba, O.; Meira, S.
Filiación:Centro de Investigacion del Mar y la Atmosfera, Buenos Aires, Argentina
Laboratoire de Météorologie Dynamique, Paris, France
Laboratoire d'Océanographie et du Climat, Expérimentation et Analyse Numérique, Paris, France
Instituto Nacional de Tecnología Agropecuaria, Pergamino, Argentina
Palabras clave: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
Año:2010
Volumen:98
Número:3
Página de inicio:565
Página de fin:580
DOI: http://dx.doi.org/10.1007/s10584-009-9746-4
Título revista:Climatic Change
Título revista abreviado:Clim. Change
ISSN:01650009
CODEN:CLCHD
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650009_v98_n3_p565_dOrgeval

Referencias:

  • Barnston, A.G., Mason, S., Goddard, L., Dewitt, D., Zebiak, S., Multimodel ensembling in seasonal climate forecasting at IRI (2003) Bull Am Meteorol Soc, 84, pp. 1783-1796
  • Bert, F., Satorre, E., Toranzo, F., Podestá, G., Climatic information and decision-making in maize crop production systems of the Argentinean pampas (2006) Agric Sys, 88, pp. 180-204
  • Boulanger, J., Leloup, J., Penalba, O., Rusticucci, M., Lafon, F., Vargas, W., Observed precipitation in the Paraná-Plata hydrological basin: Long-term trends, extreme conditions and ENSO teleconnections (2005) Clim Dyn, 24, pp. 393-413
  • Breiman, L., Friedman, J., Olshen, R., Stone, C., (1984) Classification and Regression Trees, p. 384. , Boca Raton: Chapman & Hall/CRC
  • Friedman, J.H., Multivariate adaptive regression splines (with discussion) (1991) Ann Stat, 19, p. 1
  • Grimm, A., Barros, V., Doyle, M., Climate variability in Southern South America associated with el Niño and la Niña events (2000) J Climate, 13, pp. 53-58
  • Guevara, E., Meira, S., Using CERES-maize in Argentina (1995) 2nd International Symposium on Systems Approaches for Agricultural Development, IRRI (International Rice Research Institute), , Los Baños, Filipinas, 6-8 December
  • Guevara, E., Meira, S., Calibration and evaluation of CERES-maize model for subtropical environments (1999) The third international symposium on systems approaches for agricultural development (SAAD III), , Lima, Peru, 8-10 November
  • Guevara, E., Meira, S., Peper, A., Hernandorena, C., Yield prediction of five maize hybrids using CERES-maize model in the corn belt of Argentina (1998) 28th Annual Crop Simulation Workshop, , Beltsville, Maryland, USA, 5-8 April
  • Guevara, E., Meira, S., Maturano, M., Coca, M., Maize simulation for different environments of Argentina (1999) International Symposium Modelling Cropping Systems, , Leida, Espaa, 21-23 June
  • Hartmann, H., Pagano, T., Sorooshian, S., Bales, R., Confidence builders. Evaluating seasonal climate forecasts from user perspectives (2002) Bull Am Meteorol Soc, 83, pp. 683-698
  • Jones, J., Hoogenboom, G., Porter, C., Boote, K., Batchelor, W., Hunt, L., Wilkens, P., Ritchie, J., DSSAT cropping system model (2003) EurJ Agron, 18, pp. 235-265
  • Lemos, M., Finan, T., Fox, F., Nelson, D., Tucker, J., The use of seasonal climate forecasting in policy-making: Lessons from Northeast Brazil (2002) Clim Change, 55, pp. 479-507
  • Letson, D., Llovet, I., Podestá, G., Royce, F., Brescia, V., Lema, D., Parallada, G., User's perspectives of climate forecasts: Crop producers in Pergamino, Argentina (2001) Clim Res, 19, pp. 57-67
  • Meira, S., Baigorri, H., Guevara, E., Maturano, M., Calibration of soybean cultivars for the SOYGRO model in two environments of Argentina (1999) IV World Soybean Research Conference, , 4-7 August, Chicago, Illinois, USA
  • Messina, C., Hansen, J., Hall, A., Land allocation conditioned on El Niño Southern Oscillation phases in the pampas of Argentina (1999) Agric Sys, 60, pp. 197-212
  • Penalba, O., Vargas, W., Interdecadal and interannual variations of annual and extreme precipitation over central-northeastern Argentina (2004) Int J Climatol, 24, pp. 1565-1580
  • Podestá, G., Messina, C.D., Grondona, M., Magrin, G., Associations between grain crop yields in central-eastern Argentina and El Niño-Southern Oscillation (1999) J Appl Meteorol, 38, pp. 1488-1498
  • Roncoli, C., Ethnographic and participatory approaches to research on farmers' responses to climate predictions (2006) Clim Res, 33, pp. 81-99
  • Ropelewski, C.F., Halpert, M.S., Quantifying southern oscillation-precipitation relationships (1996) J Climate, 9, pp. 1043-1059
  • Trenberth, K.E., The definition of El Niño (1997) Bull Am Meteorol Soc, 78, pp. 2771-2777
  • Vargas, W., Penalba, O., Minetti, J., Las precipitaciones mensuales and zonas de la Argentina y el ENSO. un enfoque hacia problemas de decisión (1999) Meteorológica, 24, pp. 2-22
  • Vogel, C., O'Brien, K., Who can eat information? Examining the effectiveness of seasonal climate forecasts and regional climate-risk management strategies (2006) Clim Res, 33, pp. 111-122

Citas:

---------- APA ----------
d'Orgeval, T., Boulanger, J.-P., Capalbo, M.J., Guevara, E., Penalba, O. & Meira, S. (2010) . Yield estimation and sowing date optimization based on seasonal climate information in the three CLARIS sites. Climatic Change, 98(3), 565-580.
http://dx.doi.org/10.1007/s10584-009-9746-4
---------- CHICAGO ----------
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" . Climatic Change 98, no. 3 (2010) : 565-580.
http://dx.doi.org/10.1007/s10584-009-9746-4
---------- MLA ----------
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" . Climatic Change, vol. 98, no. 3, 2010, pp. 565-580.
http://dx.doi.org/10.1007/s10584-009-9746-4
---------- VANCOUVER ----------
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. Clim. Change. 2010;98(3):565-580.
http://dx.doi.org/10.1007/s10584-009-9746-4