Caio Simplicio Arantes

Sugarcane ( Saccharum officinarum L.) is one of the largest crops in Brazil, and its productivity varies according to the environment and management practices adopted. In this study, tons of sugar per hectare (TSH) are estimated using a heteroscedastic gamma (GA) regression model, which considers several explanatory variables, one of which is the normalized difference green vegetation index (GNDVI), obtained from multispectral images in two locations over two consecutive growing seasons. The modeling considers regression structures in the parameters representing the mean and coefficient of variation, respectively. The results show that there is an influence of location, cultivar, cycle, accumulated precipitation, and GNDVI. To verify if the model is well-fitted to the data, the analysis of quantile residuals shows that the model is adequate. Therefore, the results indicate that heteroscedastic GA regression is an alternative model for predicting TSH and can assist in decision-making in sugarcane cultivation.

Climate data is essential for agriculture, supporting resilience, decision-making and food security under both current conditions and climate change scenarios. It is also essential for research, education, and applied sciences. However, its use is often limited by sparse monitoring networks, time series that are not continuous, platforms that handle immense volumes of data, and technical barriers that require programming and statistical expertise. These challenges are particularly pronounced in developing countries. To address these issues, we have developed ClimaPlots: an open-source QGIS plugin that simplifies the access to, and analysis of, temperature, precipitation, solar radiation, and relative humidity data from NASA’s POWER database. The tool enables users to extract data from any location, generate interactive visualisations, analyse trends and calculate climate extremes. While not a complete solution to all limitations in accessing data, ClimaPlots reduces technical and economic barriers, thus improving the usability of global climate data for agriculture, research, education, and planning in regions where data is scarce. ClimaPlots is freely available in the QGIS repository, promoting open and reproducible science by transforming large datasets into actionable knowledge.

This research explores the estimation of sugarcane (Saccharum officinarum L.) productivity at harvest by leveraging average satellite image time series collected during the growth phase. The study aims to evaluate the effectiveness of diverse modeling approaches, including a Heteroskedastic Gamma Regression model, Random Forest, and Neural Networks, in predicting sugarcane yield using satellite-derived vegetation indices and environmental variables. Key covariates such as sugarcane varieties, production cycle, accumulated precipitation during the growth phase, and the mean GNDVI vegetation index during the growth phase were analyzed in two different locations for two consecutive growth seasons. The research highlights the integration of satellite data with advanced statistical and machine learning techniques to enhance productivity forecasting in agricultural systems, with a focus on sugarcane cultivation. Additionally, the Heteroskedastic Gamma Regression model demonstrated superior performance in explaining productivity variability, particularly in commercial sugarcane fields, achieving an R2 of 0.89. The findings underscore the potential of these models to support informed decision-making and optimize agricultural practices, offering valuable insights for precision farming.

O Brasil é o maior produtor mundial de cana-de-açúcar e o monitoramento das safras agrícolas por satélites é de importância estratégica para o setor canavieiro. Nesse contexto, este trabalho descreve o desenvolvimento e a aplicação de um novo complemento no Sistema de Informação Geográfica QGIS, denominado “ComparaSafras, que possibilita gerar perfis temporais a partir do consumo dos dados dos índices de vegetação do sensor MODIS, por meio de requisições de consulta a API SATVeg, possibilitando a comparação entre sucessivas safras de cana-de-açúcar.