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. The study area presented an average annual air temperature increase by 0.9 °C between 1917 and 2016, rising from 21.4 °C for the first climatological normal (1917-1946) to 22.3 °C for the last one (1987-2016). Furthermore, in the summer months, the temperature rose from 24.5 to 25.3 °C, and in the winter months, such increase was from 17.1 (1917-1946) to 18.3 °C (1987-2016). Our findings showed the subtropical conditions (Cfa in Köppen's classification) in the study area persisted from the beginning of the analysis (1917-1946) until the climatological normal of 1979-2008, with a clear tendency of tropicalization after that with a change in the climate type of Piracicaba from subtropical to tropical, which can now be classified as tropical with dry winter (Aw climate type). The local average air temperature showed concordances with the long-term air temperature anomalies from regional, continental, and global scales, indicating that all of them may be linked with increasing GHG emissions, since well-defined long-term linear relationships (r 2 = 0.99) were observed between continental and global average air temperature anomalies and atmospheric CO2 concentration observed at the NOAA Lab in Mauna Loa in the last 59 years. While the local and regional forcing effects remain to be fully unraveled, our study provided a valid and strong scientific sound evidence that climate change occurred in Piracicaba, southeastern Brazil, in the last 100 years.

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. Besides, this study also evaluated the impact of replacing GWD rainfall by the site-specific measured data on such simulations. A common sugarcane cropping system was repeatedly simulated between 1997 and 2015 for different combinations of climate input. Both NASA and XAVIER appear to be interesting for applications that only require temperature and solar radiation for predictions, such as crop phenology and potential yield. Nonetheless, GWD should be used with caution for crop model applications that rely on accurate estimation of crop water balance, canopy development, and biomass accumulation, at least with crop models that run at a daily time-step. The replacement of gridded rainfall with measured rainfall was pivotal for improving sugarcane simulations, as observed for cane yield, by increasing both agreement (NASA d index from 0.67 to 0.90; XAVIER d from 0.73 to 0.93) and R2 (NASA from 0.35 to 0.76; XAVIER from 0.43 to 0.79) and reducing root mean square errors (RMSE) from 32.8 to 16.3 t/ha when simulated with other variables of NASA data and from 27.9 to 12.7 t/ha when having XAVIER data as input. Therefore, while using both GWD sets without any correction, it is recommended to replace gridded rainfall by measured values, whenever possible, to improve sugarcane simulations in Center-South Brazil.

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. Three different sugarcane simulation models, FAO-Agroecological Zone, DSSAT/CANEGRO and APSIM-Sugarcane, properly calibrated and validated for sugarcane in Brazil, were used to estimate potential and water-limited yields and yield gaps for 30 locations across the country. The average of total yield gap for the 30 locations was 124 t ha-1. The main proportion of the total yield gap was caused by water deficit (about 89 t ha-1, representing 73% of the total), followed by sub-optimal crop management (about 35 t ha-1, representing 27% of the total). The highest yield gap by water deficit was found in the Northeastern region, whereas São Paulo State showed the lowest yield gap by crop management. The main causes of yield gap by crop management are possibly related to the production system based on long-term monoculture and soil compaction due to intense crop mechanization in recent years. Reducing sugarcane yield gap caused by crop management by 20 to 100% would allow to diminish the cultivated area with this crop, respectively, from 9 to 32%. Possible solutions to mitigate the yield gaps, such as use of irrigation, adoption of drought tolerant cultivars, better traffic control, periodical crop rotation, among others, were also discussed.