Radiation Use Efficiency of Forage Resources: A Meta‐Analysis. (1st July 2019)

• Record Type: Journal Article
• Authors: Druille, Magdalena; Oyarzabal, Mariano; Oesterheld, Martín
• Is Part Of: Agronomy Journal. Volume 111:Number 4(2019)
• Page Start: 1770
• Page End: 1778
• Publication Date: 2019-07-01
• Subjects: Agronomy -- Periodicals; 630
• DOI: 10.2134/agronj2018.10.0645
• Languages: English
• ISSNs: 0002-1962

Abstract

Abstract : Core Ideas: This is the first meta‐analysis of radiation use efficiency (RUE) of forage resources. RUE was most responsive to genotype, followed by N, water and light availability. Mean intraspecific differences were as large as interspecific ones. Most responses of RUE to the environment depended on the level of other factors. These results will enhance the accuracy of radiation‐based plant growth models. Forage production is increasingly monitored through models based on remote sensing. Radiation use efficiency (RUE) is a key input to these models. However, no study has synthesized the published values of RUE of forage resources. Our objective was to quantitatively synthesize through a meta‐analysis the variation of RUE of forage resources and its main controls. We gathered 496 RUE values and assessed their variation according to genotype, resource availability and phenological stage. Mean RUE was 1.93 ± 1.2 g of dry matter (DM) per megajoule of intercepted photosynthetically active radiation (IPAR) (±SD). The RUE was most responsive to genotype and to direct and indirect manipulations of resource availability. Within genotype treatments, mean effect size of C4 species identity, cultivar identity and C3 species identity was 61, 58, and 42%, respectively. Within resource availability treatments, mean effect size was 43% for N fertilization, 40% for shading and inter‐annual environmental variation and 23% for irrigation and defoliation frequency. For the phenology treatment, mean effect size of reproductive vs. vegetative stage did not differ from zero. This large variability implies a challenge to select RUE values as input to estimate productivity through plant‐growth models, such as those based on remote sensing, but also highlight the margin for increasing RUE through breeding and management practices.