Predicción de calidad en líneas de alfalfa tolerantes a sequía basado en tecnología NIRS
Loading...
Date
2026
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad de Concepción
Abstract
La alfalfa (Medicago sativa) es un cultivo forrajero estratégico para sistemas ganaderos de secano mediterráneo, siendo clave la selección de poblaciones con elevada calidad nutricional y adaptación a la sequía para la sostenibilidad productiva. En este contexto, la espectroscopía en el infrarrojo cercano (NIRS) surge como una herramienta eficiente para la determinación de calidad de forraje. Los objetivos de este trabajo fueron i) predecir calidad nutricional (proteína cruda (PC), fibra detergente neutra (FDN) y fibra detergente ácida (FDA)) en alfalfa mediante el ajuste de modelos predictivos basados en espectroscopía NIR; ii) estimar la calidad de forraje de 250 progenies de medios hermanos de alfalfa desarrolladas por INIA, y iii) seleccionar las progenies con mayor calidad de forraje en ambientes mediterráneos de secano. Se desarrollaron modelos NIRS a partir de 125 muestras de forraje de alfalfa mediante regresión PLS, los cuales presentaron elevada capacidad predictiva. Estos modelos permitieron estimar PC, FDN y FDA en 1.000 muestras de forraje de alfalfa provenientes de la Estación Experimental Cauquenes del INIA. Los resultados demuestran la eficacia de NIRS como herramienta robusta y precisa para la determinación de calidad de forraje en alfalfa, contribuyendo una base técnica sólida para programas de mejoramiento genético de alfalfa.
Alfalfa (Medicago sativa) is a key forage crop for Mediterranean rainfed livestock systems. The selection of populations with high nutritional quality and drought tolerance is essential for ensuring the long-term productivity of these systems. In this context, near-infrared spectroscopy (NIRS) emerges as an efficient tool for determining forage quality. This study aimed to i) predict the quality of alfalfa forage in terms of crude protein (CP), acid detergent fiber (ADF) and neutral detergent fiber (NDF) by adjusting predictive models based on NIRS; ii) estimate the forage quality of 250 alfalfa half-sib progenies developed by INIA, and iii) select the progenies with the highest forage quality in Mediterranean rainfed environments. NIRS models were developed using PLS regression on 125 alfalfa forage samples, which showed high predictive power. These models enabled the prediction of CP, NDF, and ADF in 1,000 alfalfa forage samples from INIA's Cauquenes Experimental Station. The results demonstrate the effectiveness of NIRS as a robust and accurate tool for determining forage quality in alfalfa and provide a solid technical basis for alfalfa breeding programs.
Alfalfa (Medicago sativa) is a key forage crop for Mediterranean rainfed livestock systems. The selection of populations with high nutritional quality and drought tolerance is essential for ensuring the long-term productivity of these systems. In this context, near-infrared spectroscopy (NIRS) emerges as an efficient tool for determining forage quality. This study aimed to i) predict the quality of alfalfa forage in terms of crude protein (CP), acid detergent fiber (ADF) and neutral detergent fiber (NDF) by adjusting predictive models based on NIRS; ii) estimate the forage quality of 250 alfalfa half-sib progenies developed by INIA, and iii) select the progenies with the highest forage quality in Mediterranean rainfed environments. NIRS models were developed using PLS regression on 125 alfalfa forage samples, which showed high predictive power. These models enabled the prediction of CP, NDF, and ADF in 1,000 alfalfa forage samples from INIA's Cauquenes Experimental Station. The results demonstrate the effectiveness of NIRS as a robust and accurate tool for determining forage quality in alfalfa and provide a solid technical basis for alfalfa breeding programs.
Description
Tesis presentada para optar al título de Ingeniero Agrónomo
Keywords
Forraje Análisis, Alfalfa Chile, Genética vegetal