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

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 (R 2 = 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.

Climatic and synoptic characterization of heat waves in Brazil

“…Several studies applied to Brazil established a relationship between the occurrence of periods marked by high temperatures (often accompanied by high air pollution levels) and an increase in deaths related to cardiovascular/cerebrovascular accidents and respiratory diseases (Gouveia et al, ; Hajat et al, ; Bell et al, ). From an economic point of view, some studies show that the occurrence of exceptionally high temperatures in Brazil can be related to shortages in agriculture and animal production (Gusso et al, ; McKechnie and Wolf, ; Olesen et al, ) and increases in energy consumption due to air conditioning demand. All these socio‐economic impacts are particularly relevant in vast countries like Brazil that present a wide range of ecoregions, a large and diverse population and with several economic sectors heavily weather dependent (e.g.…”

Spatial Multivariate Cluster Analysis for Defining Target Population of Environments in West Africa for Yam Breeding

“…However, the importance of spectral vegetation indices such as NDVI and EVI, as indicator of crop performance is well documented in literature. For instance, Gusso et al, (2014) found good correlation between EVI and crop yield in Brazil while Kogan et al, (2012) concluded that NDVI is a cumulative indicator of crop growth in Kansas, USA. These observations from other researchers confirm the relative importance of satellite derived vegetation related products in delineating yam growing environment observed in this study.…”

Canopy temperatures distribution over soybean crop fields using satellite data in the Amazon biome frontier

“…Optimal canopy-LST values between 298 K (25°C) and 309 K (35°C), with values nearby 303 K (30°C) are most frequently considered the ideal conditions for soybean development (Schlenker & Roberts, 2009). Through a different ecosystem than MT, an important contribution regarding this research is given by Gusso et al (2014) when studying the southern region of Brazil. Those studies observed that slightly warmer than the optimal conditions for growth can potentially increase drought effects (if any) and yield loss, by overheating the vegetation canopy and intensifying plant damage.…”

“…(4) By combining the two LST composites of 16 days (DOY 001-017) as a function of the maximum EVI, we obtain a 32-day composite of canopy-LST when crop canopy vegetation is its maximum. This is an important step because the high incidence of cloud cover exerts a confusing factor during key periods of the vegetation profile (DOY 353 to 033) in MT (Gusso et al, 2014(Gusso et al, , 2017. Additionally, in order to overcome the cloud cover and cloud shadows challenge, the 8-day product (MYD11A2) was combined into a maximum LST of 16 days.…”