Southeastern Brazil inland tropicalization: Köppen system applied for detecting climate change throughout 100 years of meteorological observed data.

Many regions around the world are facing climate changes, with substantial increase in air temperature over the past decades, which is mainly related to continental and global warming forced by the higher greenhouse gas (GHG) emissions. The objectives of this study were to use the Köppen climate classification to detect local climate change based on a historical series of 100 years and to assess if such change is related to those that are occurring in other spatial scales as a likely consequence of increasing GHG. This paper brings a content full of innovative results.

Assessing the performance of two gridded weather data for sugarcane crop simulations with a process-based model in Center-South Brazil.

High-quality measured weather data (MWD) are essential for long-term and in-season crop model applications. When MWD is not available, one alternative for crop simulations is to employ gridded weather data (GWD), which needs to be evaluated a priori. Therefore, this study aimed to evaluate the impact of weather data from two GWD sources (NASA and XAVIER), in the way that they are available for end users, on simulating sugarcane crop performance within the APSIM-Sugar model at traditional sites where sugarcane is grown in Center-South Brazil, compared to simulations with MWD.

Sugarcane yield gap analysis in Brazil - A multi-model approach for determining magnitudes and causes.

Brazil is the largest sugarcane producer in the world playing a pivotal role on global ethanol production. The sugarcane yield levels across the producing regions of the country vary substantially, resulting in yield gaps of different magnitudes, which represent a huge opportunity for increasing sugarcane and ethanol production. According to that, the present study aimed to investigate the sugarcane yield gap in Brazil, their magnitude and causes (water deficit or crop management), considering a multi-model approach.