DOI: 10.6084/m9.figshare.27273177.v1
title: Climate anomalies due to Cerrado native vegetation loss

description: Description of the data and file structureThe Cerrado, occupying over 2 million square kilometers, is often referred to as the world's most biodiverse savanna. It plays a vital role in Brazil's agricultural economy, furnishing substantial portions of the nation's grain and beef production. However, this biome is heavily impacted by human activities, notably deforestation driven by the expansion of agricultural lands.Within this context, the dataset comprises detailed datasets that capture climate anomalies affecting the Cerrado:<b>Combined Max Temperature Anomalies</b> (First and Second Crop Seasons):These datasets measure deviations in maximum temperatures which can influence crop development, yield, and stress responses. Warmer temperatures during critical growth phases can lead to increased evapotranspiration rates and alter metabolic activities in crops, potentially curtailing productivity.<b>Combined Onset Anomalies Data:</b>Captures shifts in the timing of the agricultural rainy season. The onset of rains is crucial for planning planting dates and ensuring that crops have sufficient water at key growth stages. Delays or advances in the rainy season can significantly impact crop performance and resource planning.<b>Combined Rainfall Anomalies</b> (First and Second Crop Seasons):These datasets assess variations in rainfall volumes, crucial for understanding water availability. The Cerrado's agricultural success heavily relies on consistent rainfall, and any anomalies can profoundly affect soil moisture levels, irrigation needs, and crop health.Files and variables<b>File: combined_max_temperature_anomalies_first_crop_data.csv</b><b>Description:</b>\<br>This file contains data on anomalies in maximum temperature during the first crop season in the Brazilian Cerrado. The anomalies represent deviations in maximum temperature relative to historical averages, capturing the influence of climate variability and deforestation across different locations and years.<b>Variables:</b><b>x:</b> Longitude coordinate of the pixel, representing the east-west geographic position.<b>y:</b> Latitude coordinate of the pixel, representing the north-south geographic position.<b>1999-2019:</b> Each year's column provides the anomaly in maximum temperature for that specific year. Positive values signify higher-than-average temperatures, while negative values indicate cooler-than-average conditions during the first crop season.<b>File: combined_max_temperature_anomalies_second_crop_data.csv</b><b>Description:</b>\<br>This dataset captures anomalies in maximum temperature during the second crop season in the Cerrado. The anomalies reflect changes from historical temperature norms, highlighting variations potentially linked to deforestation and climate dynamics.<b>Variables:</b><b>x:</b> Longitude coordinate of the pixel.<b>y:</b> Latitude coordinate of the pixel.<b>1999-2019:</b> Annual maximum temperature anomalies for the second crop season, where positive values indicate warmer-than-average conditions, and negative values indicate cooler-than-average conditions.<b>File: combined_rainfall_anomalies_first_crop_data.csv</b><b>Description:</b>\<br>This file provides data on rainfall anomalies during the first crop season, essential for understanding water availability and seasonal climate impacts on agriculture in the Cerrado.<b>Variables:</b><b>x:</b> Longitude coordinate of the pixel.<b>y:</b> Latitude coordinate of the pixel.<b>1999-2019:</b> Each year's column details the rainfall anomaly, highlighting deviations from expected rainfall patterns, which can affect crop water requirements and agricultural outcomes.<b>File: combined_onset_anomalies_data.csv</b><b>Description:</b>\<br>This dataset includes anomalies related to the onset of the agricultural rainy season across the Cerrado. The data reveal variations in the expected timing of the rainy season's start, crucial for agricultural planning and resource management.<b>Variables:</b><b>x:</b> Longitude coordinate of the pixel.<b>y:</b> Latitude coordinate of the pixel.<b>1999-2019:</b> Annual onset anomalies, with positive values indicating a delayed start and negative values indicating an earlier onset of the rainy season.<b>File: combined_rainfall_anomalies_second_crop_data.csv</b><b>Description:</b><br>This file contains rainfall anomalies during the second crop season, providing insights into water availability changes and their potential impacts on seasonal agricultural activities.<b>Variables:</b><b>x:</b> Longitude coordinate of the pixel.<b>y:</b> Latitude coordinate of the pixel.<b>1999-2019:</b> Yearly columns represent deviations in rainfall volume from the norm for the second crop season, affecting irrigation needs and crop productivity.<b>Code/software</b>To analyze the CSV files in your dataset, you can use various software options, such as R and Microsoft Excel. R, a versatile open-source software, is particularly powerful for data manipulation, analysis, and visualization, with recommended packages like readr for reading CSVs, dplyr for data manipulation, and ggplot2 for visualization, ensuring a comprehensive analytical approach. Alternatively, Microsoft Excel, widely available as part of the Microsoft Office suite, offers a more intuitive interface for quick data inspection and basic visualizations. Excel allows you to easily open CSV files for straightforward analysis and visualization tasks, making it an ideal choice for users preferring a more user-friendly, spreadsheet-oriented approach.<br><b>Methodology</b>We utilized daily rainfall volume estimates (R<sub>i,j,t</sub> in mm.day<sup>-1</sup>) and daily maximum temperatures (T<sub>maxi,j,t</sub> in °C), from the Brazilian Daily Weather Gridded Database (BR-DWGD) at an original resolution of ≈1×1 km, which has a high correlation (r² ≈ 0.8-0.9) with in-situ data. The BR-DWGD data were aggregated into time-series maps at 28×28 km grid-cell resolution. To identify the deforestation signal on the onset of the agricultural rainy season (O<sup>′</sup><sub>i,j,t</sub>), rainfall volume (R<sup>′</sup><sub>i,j,t</sub>) and maximum temperature (T<sub>max</sub><sup>′</sup><sub>i,j,t</sub>) in each crop season, we had to remove the influence of geographic location, elevation and interannual variability (the latter due to large-scale climate mechanisms). We adopted a four-step process based on [20] (Fig. S5) to determine anomalies and to assess our detrending method's effectiveness. Our methodology includes the following steps: (1) using machine learning algorithms to model climate variability across the Brazilian Cerrado, (2) evaluating model accuracy, (3) applying a detrending procedure to remove trends related to other factors than deforestation, and (4) validating the detrending process to ensure its reliability.