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The present paper describes the quality-control component of an automatic procedure (APACH: A Procedure for Automated Quality Control and Homogenization of Weather Station Data) developed to control quality and homogenize the historical daily temperature and precipitation data from meteorological stations. The quality-control method is based on a set of decision-tree algorithms analyzing separately precipitation and minimum and maximum temperature. All our tests are non-parametric and therefore are potentially useful in regions or countries presenting different climates as those observed in Argentina. The method is applied to the 1959–2005 historical daily database of the Argentine National Weather Service. Our results are coherent with the history of the Weather Service and more specifically with the history of implementation of systematized quality control processes. In temperature, our method detects a larger number of suspect values before 1967 (when there was no quality control) and after 1997 (when only real-time quality control had been applied). In precipitation, the detection of error in extreme precipitations is complex, but our method clearly detected a strong decrease in the number of potential outliers after 1976 when the National Weather Service was militarized, and the network was strongly reduced, focusing more on airport weather stations. Also in precipitation, we analyze in detail the long dry sequences and are able to identify potential long erroneous sequences. This is important for the use of the data for hydrological or agricultural impact studies. Finally, all the data are flagged with codes representing the path followed by the record in our decision-tree algorithms. While each code is associated to one of the categories (“Useful”, “Need-Check”, “Doubtful” or “Suspect”), the final user is free to redefine such category-assignment. [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]