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Meerburg, B.G., Verhagen, A., Jongschaap, R.E.E., Franke, A.C., Schaap, B.F., Dueck, T.A. and van der Werf, A. (2009) Do Nonlinear Temperature Effects Indicate Severe Damages to US Crop Yields under Climate Change? Proceedings of the National Academy of Sciences, 106, 120. http://dx.doi.org/10.1073/pnas.0910618106

has been cited by the following article:

• TITLE: Monitoring Heat Waves and Their Impacts on Summer Crop Development in Southern Brazil

  AUTHORS: Anibal Gusso, Jorge Ricardo Ducati, Mauricio Roberto Veronez, Victor Sommer, Luiz Gonzaga da Silveira Junior

  KEYWORDS: Land Surface Temperature; Soybean; Remote Sensing; Canopy; Vegetation Index; MODIS Data

  JOURNAL NAME: Agricultural Sciences, Vol.5 No.4, March 21, 2014

  ABSTRACT: Periods in the soybean summer cycle that are sensitive to the occurrence of high temperatures were studied. An analysis was performed on the variability of soybean yields associated with crop canopy temperatures during key development periods. A land surface temperature (LST) data series from MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua satellite was processed between 2003 and 2012 that covered the entire state of Rio Grande do Sul, in Brazil. Enhanced vegetation index (EVI) data from MODIS on the Terra satellite were used to monitor the LST during different phenological stages. Spatially interpolated maps of soybean yield distributions were generated using data obtained from Instituto Brasileiro de Geografia e Estatística (IBGE) at state and municipality levels. The results indicate that canopy-LST occurrence in mid-February, during the grain filling, is most correlated to yield reduction (R2 = 0.82 and RMSD = 14.4%). At the state level, the average yield is 2003 kg·ha-1 with a standard deviation of 308 kg·ha-1. The overall average of the canopy-LST is 305.0 K (31.8°C) with a standard deviation of 1.9 K. The slope of the downward linear relationship between canopy-LST and yield was -28.7%. These results indicate that monitoring heat wave events can provide important information for characterising agriculture vulnerability.