Do Gridded Weather Datasets Provide High-Quality Data for Agroclimatic Research in Citrus Production in Brazil?

Abstract

Agrometeorological models are great tools for predicting yields and improving decision-making. High-quality climatic data are essential for using these models. However, most developing countries have low-quality data with low frequency and spatial coverage. In this case, two main options are available: gathering more data in situ, which is expensive, or using gridded data, obtained from several sources. The main objective here was to evaluate the quality of two gridded climatic databases for filling gaps of real weather stations in the context of developing agrometeorological models. Therefore, a comparative analysis of gridded database and INMET data (precipitation and air temperature) was conducted using an agrometeorological model for sweet orange yield estimation. Both gridded databases had high determination and concordance coefficients for maximum and minimum temperatures. However, higher errors and lower confidence coefficients were observed for precipitation data due to their high dispersion. BR-DWGD indicated more accurate results and correlations in all scenarios evaluated in relation to NasaPower, pointing out that BR-DWGD may be better at filling gaps and providing inputs to simulate attainable yield in the Brazilian citrus belt. Nevertheless, due to the BR-DWGD database’s geographical and temporal limitations, NasaPower is still an alternative in some cases. Additionally, when using NasaPower, it is recommended to use a measured precipitation source to improve prediction quality.

A Stochastic Bayesian Artificial Intelligence Framework to Assess Climatological Water Balance under Missing Variables for Evapotranspiration Estimates

“…Under a Bayesian approach (Bayesian regression model), we assume the same regression model from Equation (8), while considering Gaussian errors, with the regression parameters β i representing random quantities with specified probability density functions-also denoted as prior distributions-with hyperparameters φ i . These hyperparameters may be univariate or multivariate, whose values are usually known, as represented in Equation (9). If the hyperparameters are unknown, which comes from a hierarchical Bayesian approach, each hyperparameter also requires-over itself-a prior distribution.…”

“…Brazil is also a key contributor to global food security, ranking in 2021 as the world's largest producer of coffee beans, oranges, and soybeans; the second-largest in cattle meat; and the third in maize and chicken meat, among several other agricultural and livestock products [8]. The intricate interactions between soil, plants, and atmospheric systems play a pivotal role in food production [9]. Therefore, techniques that aid in producing nutritious food based on limited components of soil and water resources are paramount due to climate change and the scarcity of water resources [10].…”

The Relationship between Land Surface Temperature and Air Temperature in the Douro Demarcated Region, Portugal

“…Access to detailed temperature information is not always easy, however. Many studies looking at temperature in agricultural regions use gridded observational datasets [4][5][6][7][8][9]. Datasets like these are built with spatial and temporal interpolation methods using gaugebased data acquired at weather stations.…”