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The use of SST and SOI anomalies as indicators of crop yield variability
Author(s) -
Travasso Maria I.,
Magrin Graciela O.,
Grondona Martin O.,
Rodriguez Gabriel R.
Publication year - 2009
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.1701
Subject(s) - sunflower , yield (engineering) , crop , environmental science , sea surface temperature , climatology , crop yield , agronomy , el niño southern oscillation , biology , geology , materials science , metallurgy
Interannual climate variability accounts for most of the observed crop yield fluctuations in the main agricultural region of Argentina. Moreover, in this region climatic variations are related to sea surface temperatures (SST) and the Southern Oscillation Index (SOI). In the present study, we aimed to obtain indicators of crop yield variability based on these drivers. For this purpose, monthly anomalies corresponding to SSTs from the Equatorial Pacific (SSTN3) and South Atlantic (SSTSA) Oceans and the SOI were related to maize, sunflower and soybean grain yield anomalies. Historical data (1923–2000 for maize, 1934–2000 for sunflower and 1969–2000 for soybean) were used to obtain grain yield anomalies at the county level after removing technology trends by smoothing techniques. By means of correlation analysis, we obtained the counties presenting significant association ( p < 0.05) between monthly SST/SOI anomalies and yield anomalies, for the period 1950–1997. Those indicators showing spatial consistency were classified in percentiles, and the values corresponding to the upper and lower terciles showed to be useful to discriminate between positive and negative yield anomalies (high and low yields). In general, SOI for maize and SSTSA for soybean and sunflower were the best indicators of crop yield variability. SOI corresponding to September and May were useful in counties contributing to 71% of maize production. SSTa_SA (June) was the best for soybean in the main producing region, which includes 72% of the total production. SSTa_SA (March) could be useful for sunflower in the northern part of the region, which accounts for 27% of the regional production. Copyright © 2008 Royal Meteorological Society