The value of seasonal climate information for agricultural decision-making in the three CLARIS sites

D ORGEVAL · JEAN-PHILIPPE BOULANGER · M. J. CAPALBO · E. GUEVARA · O. PENALBA · S. MEIRA

CLIMATIC CHANGE

SPRINGER

Año: 2010 vol. 98 p. 565 - 580

0165-0009

The present article is a contribution to the CLARIS WorkPackage?Climate and Agriculture?, and aims at testing whether it is possible to predictyields and optimal sowing dates using seasonal climate information at three sites(Pergamino, Marcos Juarez andAnguil) which are representative of different climateand soil conditions in Argentina. Considering that we focus on the use of climateinformation only, and that official long time yield series are not always reliable andoften influenced by both climate and technology changes, we decided to build adataset with yields simulated by theDSSAT (Decision Support System for AgrotechnologyTransfer) crop model, already calibrated in the selected three sites andfor the two crops of interest (maize and soybean). We simulated yields for threedifferent sowing dates for each crop in each of the three sites. Also considering thatseasonal forecasts have a higher skill when using the 3-month average precipitationand temperature forecasts, and that regional climate change scenarios present lessuncertainty at similar temporal scales, we decided to focus our analysis on the useof quarterly precipitation and temperature averages, measured at the three sitesduring the crop cycle. This type of information is used as input (predictand) for nonlinearstatistical methods (Multivariate Adaptive Regression Splines, MARS; and classification trees) in order to predict yields and their dependency to the chosensowing date.MARS models show that the most valuable information to predict yieldamplitude is the 3-month average precipitation around flowering. Classification treesare used to estimate whether climate information can be used to infer an optimalsowing date in order to optimize yields. In order to simplify the problem, we set adefault sowing date (the most representative for the crop and the site) and comparethe yield amplitudes between such a default date and possible alternative datessometimes used by farmers. Above normal average temperatures at the beginningand 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 dateformaize but it is more limited for soybean.More generally, the sites and crops whichhave more variable yields are also the ones for which the proposed methodology isthe most efficient. However, a full evaluation of the accuracy of seasonal forecastsshould be the next step before confirming the reliability of this methodology underreal conditions.