Near-Infrared Spectroscopy and Wet Chemistry Dataset for Forage Nutritional Quality Assessment in Urochloa humidicola .

Abstract

Assessing the nutritional quality traits of pastures is crucial for germplasm and breeding evaluations, enabling the selection of high-quality forages to enhance livestock productivity. However, traditional laboratory analytical methods are logistically demanding and costly, particularly in large-scale trials, underscoring the need for rapid, precise, and high-throughput evaluation methods. Near-Infrared Spectroscopy (NIRS) optimizes the estimation of forage nutritional quality parameters by developing chemometric models that predict these parameters with high accuracy and precision, based on the association between NIRS data and wet chemistry analyses. This dataset, collected over ten years by the Tropical Forages Program at the International Center for Tropical Agriculture (CIAT) in Colombia, comprises 1112 samples. It includes 995 measurements of Neutral Detergent Fiber (NDF), 996 of Acid Detergent Fiber (ADF), 995 of In Vitro Dry Matter (IVDMD), and 469 of Crude Protein (CP), all obtained through wet chemistry methodologies. Additionally, the 1112 samples contain absorbance data spanning 400 to 2498 nanometers (nm) in 2 nm intervals, generating 1050 spectral data points per sample. Finally, this dataset is a valuable resource for predicting forage nutritional quality beyond conventional parameters, incorporating plant reflectance attributes to enhance selection strategies for optimized forage selection.

Plant phenotyping relevance

牧草の栄養品質形質をNIRSスペクトルから推定するための大規模データセットであり、湿式化学値との対応付けとケモメトリックモデル構築が中心的な方法的貢献である。

Code and data availability

The paper is a data descriptor for a paper-specific public dataset: 1112 Urochloa humidicola samples with wet-chemistry traits (NDF, ADF, IVDMD, CP) and 1050-point NIR absorbance spectra (400–2498 nm), deposited in the Harvard Dataverse (DOI 10.7910/DVN/XPNIQY). The deposit is explicitly public and actionable; however,