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Maize fumonisin (FB) contamination is strongly driven by the weather conditions and crop resistance. Logistic regression techniques were used to quantify the effect of weather-based variables on total FB content in kernel samples coming from many locations of the Argentinean Pampas region (over three growing seasons), and collected immediately after arriving at export terminals. The samples were analyzed by the HPLC method and grouped according to their proximity to the available weather stations (n = 52). The highest correlations between binary and ordinal FB levels and weather variables were found in an early critical period (17 December to 15 January) where maize silking phase (Si) frequently occurs and in a late period (15 February to 2 April) around physiological maturity (PM). The best-fitted models included variables calculated around Si that would meet the requirements of infection of F. verticillioides (precipitationinduced wetness events, high humidity and warm temperatures). Around PM, the effect of the number of days with precipitation combined with lower temperatures (13.3°to 25°C) that would slow the kernel drying process was included, increasing the FB accumulation. An integrated system for FB management in the maize value chain should use validated weather-based models as tools for estimating seasonal kernel FB contamination levels in the Pampas region, being able to improve kernel sampling efficiency at export terminals and mills.

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L. Abbott, S. Filippini, H. Delfino and S. Pistorale. 2012. Stability analysis of forage production in Bromus catharticus (prairie grass) using three methodologies. Cien. Inv. Agr. 39(2): 331-338. Thirteen genotypes of Bromus catharticus (prairie grass) were evaluated for forage production over three years using completely randomized trials with six replicates. The genotype x environment interaction was statistically significant and indicates that the behavior of genotypes differs over time. Once this interaction was detected, we used three methodologies to assess the stability of genotypes: Wricke's ecovalence, the Lin and Binns index, and the Eberhart and Russell model. The methods of Lin and Binns and Eberhart and Russell indicate that genotypes 11, 9, 3 and 4 are stable. They also rule out possible selection of genotypes 2, 10, 1 and 12 for lack of stability or poor adaptation. The correlation among these indices was statistically significant (r=0.61). When using Wricke's ecovalence, there is agreement among the indices for the selection of genotypes 9 and 4, which show good stability. There is no agreement with the other two methods for ruling out unstable genotypes. Considering the three methodologies used, the Lin and Binns index is easiest to apply and interpret because higher productivity always correlates with greater stability, and there are no restrictions on the use of regression. However, it is necessary to accumulate more data prior to the widespread use of these methods.