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Several studies show that the El Niño-Southern Oscillation (ENSO) is an important factor in determining interannual rainfall variability in South America. This signal is detected in the region including Northeastern Argentina, Uruguay and part of southern Brazil, in the form of excessive rains and big floods in the regional rivers. To check that this relationship was similar in the past the objectives of this paper were to construct a time series of large floods in the Paraná River region from documentary records, during the sixteenth to eighteenth centuries, and to evaluate the relationship between that historical record of extreme floods, the ENSO documentary evidence (Ortlieb, The Documented Historical Record of El Niño Events in Perú: An Update of the Quinn Record (Ortlieb, sixteenth through nineteenth centuries, Diaz and Markgraf, (eds.), El Niño and the southern oscillation. Multiscale variability and global and regional Impacts. Cambridge University Press, pp. 207-295, 2001; Quinn and Neal, The historical record of El Niño events, Bradley and Jones (eds.), Climate since a.d. 1500, Routledges, pp. 623-648, 1992) and the temperature index of the Pacific Ocean (Mann ME et al., Global temperature patterns in past centuries: an interactive presentation, IGBP pages/world data center for paleoclimatology data contribution series #2000-075. NOAA/NGDC Paleoclimatology Program, Boulder CO, USA, 2000). Considering that the period 1904-2000, where 11 out of 16 floods occurred during El Niño events, it can be concluded that the proportion of years with exceptional flow volume in the Paraná River in years with El Niño events in the seventeenth and eighteenth centuries was relatively lower than that of the twentieth century. The reason for this difference is discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The present investigation aims at identifying the needs of the Argentinean agro-business sector for climate research and information. Our approach is focused on any type of climate information from past observations to seasonal prediction and climate change projections. We tried, in particular, to evaluate the possible existence of specific research themes that would be of major interest for these agriculture end-users. We interviewed representatives of seventeen companies from three major agrobusiness sectors: (1) regional/national entities such as agricultural associations, cooperatives and commercial trade boards; (2) insurance companies in the Argentinean agro-insurance market; and (3) large national and international companies representing cereal producers, agro-chemistry and agro-seeds. While all the interviewees recognized the strong influence of climate on their activities, they all pointed out that, at the time of making decision, they considered the political and economic risks rather than the climate one. In many aspects, climate is often considered as a fatality against which it is difficult to be protected. An interesting result is the confidence of the private sectors in the climate information provided by public sources (INTA, SMN, Universities) although they contract private consultants for frequent reports and tendencies. Finally, we explain how such sectors could make a better use of climate information (seasonal prediction, climate change scenarios) that could be integrated in their business projections. In particular, the development of new financial contracts (climate derivatives) open in countries with a relatively poor insurance history new ways to protect the different agricultural sectors, from the producer to large companies. [ABSTRACT FROM AUTHOR]