UAV multisensor data and GAMLSS improve forage biomass estimation in Cerrado integrated crop–livestock pastures

Abstract

Introduction Accurate estimation of aboveground biomass (AGB) is essential for monitoring pasture productivity and supporting sustainable management of integrated crop–livestock (ICL) systems. We hypothesized that integrating multispectral, thermal, and canopy-structural information derived from unmanned aerial vehicles (UAVs) would improve AGB prediction relative to spectral information alone, and that Generalized Additive Models for Location, Scale and Shape (GAMLSS) would accommodate seasonal heteroscedasticity while maintaining predictive performance comparable to Random Forest (RF) and Support Vector Machine (SVM) models. Methods We collected 280 destructive biomass samples from two ICL paddocks and one continuously grazed pasture in the Brazilian Cerrado between 2022 and 2024. Twenty-four UAV-derived predictors, including spectral bands, vegetation indices, canopy surface temperature, and canopy height, were evaluated using repeated five-fold cross-validation. Model transferability was assessed by withholding one management paddock at a time. Results and discussion Under repeated five-fold cross-validation, GAMLSS achieved the lowest prediction error (R² = 0.69 ± 0.01; RMSE = 2.15 ± 0.04 Mg ha⁻¹), followed closely by SVM (R² = 0.68 ± 0.01; RMSE = 2.19 ± 0.03 Mg ha -1 ); RF showed lower accuracy (R 2 = 0.53 ± 0.01; RMSE = 2.63 ± 0.02 Mg ha -1 ). In the paddock-transferability assessment, GAMLSS also showed the lowest error (R 2 = 0.63 ± 0.04; RMSE = 2.34 ± 0.26 Mg ha -1 ). For GAMLSS, the complete multisensor configuration reduced RMSE by 6.2% compared with the spectral-only configuration. The selected model was used to generate spatially explicit maps of AGB and standing aboveground biomass carbon, estimated from the mean measured carbon concentration of forage biomass. Integrating multispectral, thermal, and structural UAV data with distributional regression improves AGB estimation and enables spatial monitoring of tropical pastures under contrasting management conditions.

Plant phenotyping relevance

UAVのマルチセンサーデータと統計モデルを用いて牧草の地上部バイオマスを推定し、交差検証と圃場間移 transferability 評価を行っており、植物形質の取得・推定手法が研究の中心である。

Code and data availability

The supplied blocks describe UAV multispectral/thermal/structural data collection, 280 destructive biomass samples, and R-based modeling (GAMLSS, RF, SVM), but contain no public deposit, repository, or authors' URL for the phenotype dataset, UAV imagery, analysis code, or trained models. The only URLs present (redeilpf

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