Visual Estimation of Botanical Composition in Simple Legume‐Grass Mixtures<sup>1</sup>

Marten, G. C.

doi:10.2134/agronj1964.00021962005600060008x

Cited by 6 publications

(5 citation statements)

References 3 publications

(8 reference statements)

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“…Plant populations and percent vegetative cover of seeded natives and volunteer weeds were determined during late July in a 1.0 by 0.5 m quadrants placed in a representative area of each plot, while a 30-cm border from the edge was maintained. Due to the height variation of the plant material and the fact that taller plants covered a larger portion of the plot with shorter species intermixed, it was challenging to accurately assign vegetative cover values to individual forb and legume species (Kennedy and Addison, 1987;Marten, 1964). Th erefore, populations of legumes and forbs were determined by counting the number of seedlings per 0.5 m 2 .…”

Section: Methodsmentioning

confidence: 99%

Native Perennial Grassland Species for Bioenergy: Establishment and Biomass Productivity

et al. 2011

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Proposed perennial bioenergy cropping systems include both native grass monocultures and polycultures of grasses and forbs. We determined the eff ect of species richness and composition on establishment and initial biomass production of native plant polycultures. Twelve treatments with varying levels of species richness (1-24 species) were established. Establishment success and yield varied over eight locations. Th e number of species established in polyculture increased linearly as the number of species seeded increased. Average biomass yield ranged from 1.2 to 6.0 Mg ha −1 with the highest yielding treatments being grass monocultures or an eight species grass-legume mixture. An increase in species richness from one to eight species increased yield an average of 28%, but increasing species richness from 8 to 12 or 24 species had no yield advantage at most locations. Early successional species, Canada milkvetch (Astragalus canadensis L.) and Maximilian sunfl ower (Helianthus maximilian Schrad.), were dominant in mixtures and contributed a majority of the biomass to the yield. Even in high diversity plots, biomass was from only a few plant species with a single species dominating the mixture. Our results suggest that selected low diversity mixtures (one to fi ve species) likely off er the best combination of species establishment and high yield during stand establishment. However, we expect that early successional species that were dominant during the establishment phase of our experiment will contribute less biomass as stands mature and later successional species will become dominant and provide greater biomass.

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Section: Methodsmentioning

confidence: 99%

Native Perennial Grassland Species for Bioenergy: Establishment and Biomass Productivity

et al. 2011

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“…However, determining the legume content in a grass–legume pasture directly is difficult due to the heterogeneity of the distribution. A number of indirect methods for estimating legume content of grass–legume mixtures have been developed, such as visual estimation (Marten 1964), point quadrat method (Vankeuren and Ahlgren 1957), and the dry‐weight rank method (Mannetje and Haydock 1963). However, accuracy of visual estimation depends on the experience of the observer, and hand‐separation of samples to estimate the legume content is time‐consuming (Shibayama 2003).…”

Section: Introductionmentioning

confidence: 99%

Waveband selection using a phased regression with a bootstrap procedure for estimating legume content in a mixed sown pasture

et al. 2011

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Legume content in grass–legume mixtures is a key parameter for deciding the forage quality and the amount of fertilizer application to the pasture due to nitrogen (N) fixation. To estimate legume content in a grass‐white clover (WC) mixed pasture in Hokkaido, we searched for robust hyperspectral wavebands from in situ canopy reflectance spectra over the 400–2350 nm range comparing a phased regression with a bootstrap procedure (PHR‐BS) (Ferwerda et al. 2006) and forward stepwise multiple linear regression (FS‐MLR). Canopy reflectance data and plant samples were obtained from 50 selected sites during two seasons (n = 100); spring (May) and summer (July) 2007. Although selected wavebands were similar in the PHR‐BS and FS‐MLR, PHR‐BS gave a higher predictive accuracy (44–74%) than FS‐MLR (35–73%). Selected wavebands in the final models were blue (400–456 nm) and red bands (659–670 nm) in visible wavelength, red‐edge region (704–724 nm), near infrared regions (813, 937, and 1121 nm), and shortwave infrared regions (2303–2344 nm) that are mainly linked to known biochemical components such as chlorophyll, N, lignin and cellulose. These results suggest that legume content in grass–legume mixtures can be predicted by in situ canopy reflectance, and that the predictive ability of the model can be improved by wavelength selection using the PHR‐BS method.

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“…A number of indirect methods for estimating botanical composition of mixtures of grasses and legumes have been developed. Some common methods are visual estimation (Marten, 1964;Tanner et al, 1966;Tiwari et al, 1963), point quadrat methods (Leasure, 1949;VanKeuren and Ahlgren, 1957), and the dry-weight rank method (Mannetje and Haydock, 1963;Walker, 1970). Each of these methods has advantages and disadvantages.…”

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Indirect Estimation of Botanical Composition of Alfalfa‐Smooth Bromegrass Mixtures

1990

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Botanical composition of grass‐legume mixtures greatly influences the productivity and quality of the sward and is therefore an important variable in many agronomic studies. Four indirect methods of estimating the botanical composition of mixed swards of alfalfa (Medicago sativa L.) and smooth bromegrass (Bromus inermis Leyss.) were evaluated to determine their relative efficacy. The methods were the constituent differential method using either neutral detergent fiber (NDF) concentration or crude protein (CP) concentration as variables, a modified constituent differential method where NDF and CP concentrations were used simultaneously as variables, and near infrared reflectance spectroscopy (NIRS). Mean deviations of predicted alfalfa percentages from known values were lowest for NIRS at all stages of maturity and averaged 1.5 percentage units. Of the constituent differential procedures, NDF was the most reliable variable for predicting alfalfa percentage over all maturities with deviations averaging 2.9 percentage units. Based upon the results of this study, NIRS would be the preferred method for estimating botanical composition of grass‐legume mixtures; however, in cases where NIRS is unavailable or inappropriate, the constituent differential method using NDF as a single variable would be an acceptable alternative.

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Visual Estimation of Botanical Composition in Simple Legume‐Grass Mixtures¹

Cited by 6 publications

References 3 publications

Native Perennial Grassland Species for Bioenergy: Establishment and Biomass Productivity

Native Perennial Grassland Species for Bioenergy: Establishment and Biomass Productivity

Waveband selection using a phased regression with a bootstrap procedure for estimating legume content in a mixed sown pasture

Indirect Estimation of Botanical Composition of Alfalfa‐Smooth Bromegrass Mixtures

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Visual Estimation of Botanical Composition in Simple Legume‐Grass Mixtures1

Cited by 6 publications

References 3 publications

Native Perennial Grassland Species for Bioenergy: Establishment and Biomass Productivity

Native Perennial Grassland Species for Bioenergy: Establishment and Biomass Productivity

Waveband selection using a phased regression with a bootstrap procedure for estimating legume content in a mixed sown pasture

Indirect Estimation of Botanical Composition of Alfalfa‐Smooth Bromegrass Mixtures

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Visual Estimation of Botanical Composition in Simple Legume‐Grass Mixtures¹