The occurrence of extreme climate events (ECEs) in the Amazon basin (AMZ) and northeast Brazil (NEB), such as torrential rains and severe droughts, varies in both spatial and temporal scales. Spatial analysis of trends allows observing changes in behaviour and determining in which regions a particular variable has been experiencing changes over time. Thus, the objective of this study is to analyse trends of 21 climate extremes indices, relative to maximum and minimum precipitation and temperature, as defined by the World Meteorological Organization, for the AMZ and NEB. The 21 indices were selected according to meteorological and climate characteristics of the regions. Through annual analysis it was possible to observe an increase in most of the climate extremes indices for air temperature in all AMZ and NEB subregions. As for extreme precipitation, only a few of the selected indices presented significant increase and/or decrease in their values. Overall, the eastern Amazon subregion in the AMZ presented the highest significant indices for temperature and precipitation. In NEB, both Northern Coast and Southern Coast subregions presented substantial increase or decrease in precipitation and temperature indices.

The objective of this study was to analyze the influence of large-scale atmospheric–oceanic mechanisms (El Niño–Southern Oscillation—ENSO and the inter-hemispheric thermal gradient of the Tropical Atlantic) on the spatial–temporal variability of soy yield in MATOPIBA. The following, available in the literature, were used: (i) daily meteorological data from 1980 to 2013 (Xavier et al., 2016); (ii) (chemical, physical, and hydric) properties of the predominant soil class in the area of interest, available at the World Inventory of Soil Emission Potentials platform; (iii) genetic coefficients of soybean cultivar with Relative Maturity Group adapted to the conditions of the region. The simulations were performed using the CROPGRO-Soybean culture model of the Decision Support System for Agrotechnology Transfer (DSSAT) system, considering sowing dates between the months of October and December of 33 agricultural years, as well as for three meteorological scenarios (climatology, favorable-wet, and unfavorable-dry). Results showed that the different climate scenarios can alter the spatial patterns of agricultural risk. In the favorable-wet scenario, there was a greater probability of an increase in yield and a greater favorable window for sowing soybean, while in the unfavorable-dry scenario these values were lower. However, considering the unfavorable-dry scenario, in some areas the reduction in yield losses will depend on the chosen planting date.

The occurrence of rainfall extreme events leads to several environmental, social, cultural, and economic consequences, heavily impacting agriculture. The analysis of climate extreme indices at the municipal level is of the uttermost importance to the overall study of climate variability and regional food security. Corn, bean, and cassava are among the most cultivated temporary subsistence crops. Thus, the objective of this study was to analyze the relationship between subsistence agriculture productivity and the behavior of rainfall extreme indices in the Rio Grande do Norte state in the period from 1980 to 2013. We used the dataset provided by Xavier (2016) and the climate extreme indices obtained through the Expert Team on Climate Change Detection and Indices. Crop productivity data were retrieved from the Municipal Agriculture Survey from the Brazilian Institute of Geography and Statistics system. The methodology evaluated the behavior and the relationship between agricultural productivity time series and extreme precipitation indicators. We applied the following statistical techniques: descriptive analysis, time series trend analysis by the Mann-Kendall test, cluster analysis, and analysis of variance to check for equal means between identified groups. Cluster analysis was considered an adequate tool for the comprehension of data spatial distribution, allowing the identification of five homogenous subregions with different precipitation patterns. Rainfall extreme indices allowed the analysis of regional conditions regarding consecutive dry days, annual precipitation in wet days, and heavy rainfall. Trends were identified in these indices and they were significantly correlated with dryland crops productivity, indicating a direct relationship between water availability and regional agroclimatic stress.