TY - JOUR
T1 - Synergistic use of optical and biophysical traits to assess Bromus inermis pasture performance and quality under different management strategies in Eastern Nebraska, U.S.
AU - Mazis, A.
AU - Awada, T.
AU - Erickson, G. E.
AU - Wardlow, B.
AU - Wienhold, B. J.
AU - Jin, V.
AU - Schmer, M.
AU - Suyker, A.
AU - Zhou, Y.
AU - Hiller, J.
N1 - Funding Information:
Funding: This work was supported by the Long-Term Agroecosystem Research (LTAR) - Department of Agriculture (ARS), United States Department of Agriculture ( USDA ) [Award number: 58-3042-9-014 ]. A. Mazis acknowledges the Arthur W. Sampson Fellowship (University of Nebraska-Lincoln) which provided partial support for this research.
Publisher Copyright:
© 2023 The Authors
PY - 2023/6/1
Y1 - 2023/6/1
N2 - We investigated the synergic use of optical and biophysical traits to characterize Bromus inermis (smooth bromegrass) pasture lands and assess the combined effects of long-term (15-years) rotational grazing and management strategies of (i) no fertilization (C), (ii) mineral nitrogen (N) fertilization (HF), and (iii) supplemented fertilization through dry distiller grains plus soluble (DDGS; SF)- on forage growth, performance, and quality. We found that fertilization improved pasture's biomass, specific leaf area, leaf area index (LAI), as well as forage quality. The use of N fertilization did not offer an advantage (e.g., forage quality, yield) over DDGS under both grazed and ungrazed conditions. Optical, proximal sensing techniques allowed the characterization of pasture lands in a non-invasive and time-efficient manner. We tested established vegetation indices (VIs) for their accuracy in identifying and quantifying important physiological and morphological traits. Results showed that the Normalized Difference Vegetation Index (NDVI) and Vogelmann (VOG) were among the best performing indices. Results contribute to our understanding of the impact of long-term fertilization management on Bromus inermis pastures and validate the use of proximal sensing methods. Proximal sensing methods provide direct, non-invasive, and time efficient tool for assessment of the performance and health of vegetation, keys to successful integrative management strategies.
AB - We investigated the synergic use of optical and biophysical traits to characterize Bromus inermis (smooth bromegrass) pasture lands and assess the combined effects of long-term (15-years) rotational grazing and management strategies of (i) no fertilization (C), (ii) mineral nitrogen (N) fertilization (HF), and (iii) supplemented fertilization through dry distiller grains plus soluble (DDGS; SF)- on forage growth, performance, and quality. We found that fertilization improved pasture's biomass, specific leaf area, leaf area index (LAI), as well as forage quality. The use of N fertilization did not offer an advantage (e.g., forage quality, yield) over DDGS under both grazed and ungrazed conditions. Optical, proximal sensing techniques allowed the characterization of pasture lands in a non-invasive and time-efficient manner. We tested established vegetation indices (VIs) for their accuracy in identifying and quantifying important physiological and morphological traits. Results showed that the Normalized Difference Vegetation Index (NDVI) and Vogelmann (VOG) were among the best performing indices. Results contribute to our understanding of the impact of long-term fertilization management on Bromus inermis pastures and validate the use of proximal sensing methods. Proximal sensing methods provide direct, non-invasive, and time efficient tool for assessment of the performance and health of vegetation, keys to successful integrative management strategies.
KW - Biomass
KW - Bromegrass
KW - Dry distiller grains plus soluble
KW - Fertilization
KW - Morphology
KW - Pasture
KW - Proximal sensing
KW - Vegetation indices
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U2 - 10.1016/j.agee.2023.108400
DO - 10.1016/j.agee.2023.108400
M3 - Article
AN - SCOPUS:85148677474
SN - 0167-8809
VL - 348
JO - Agriculture, Ecosystems and Environment
JF - Agriculture, Ecosystems and Environment
M1 - 108400
ER -