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FORAGE & GRAZINGLANDS

Determining the Contribution of Legumes in Legume–Grass Mixtures Using Digital Image Analysis

Dep. of Grassland Science and Renewable Plant Resources, Univ. of Kassel, Steinstr. 19, 37213 Witzenhausen, Germany

^* Corresponding author (mhimstedt{at}uni-kassel.de).

Digital image analysis could be a rapid and precise technique for estimating legume proportions in grass swards. In 2004, we conducted a pot study to evaluate a digital image analysis (DIA) system for estimation of legume dry matter (DM) contribution in legume–grass mixtures. Examination of pure swards and binary legume–grass mixtures of red clover (Trifolium pratense L.), white clover (T. repens L.), alfalfa (Medicago sativa L.), and perennial ryegrass (Lolium perenne L.) took place after 35, 49, and 63 d of growth. To estimate the cover percentage of legumes in the swards, a total of 64 digital pictures were taken. The DM contribution of legumes (% of total biomass) showed a significant relationship with the proportion of image area covered by legumes (% of total area), which was classified visually. A DIA system for grayscale images was developed with the software Optimas. We found that DIA could be used to accurately predict legume contribution in mature swards. Legume contribution, as estimated by DIA, was significantly correlated with DM contribution of red clover (R² = 0.87), white clover (R² = 0.85), and alfalfa (R² = 0.79). Bare ground reduced the predictive ability of DIA in young or open swards. Use of DIA may be limited until we refine the method to deal with bare ground and different leaf shapes associated with various weed species.

Abbreviations: AG, alfalfa–ryegrass • DIA, digital image analysis • DM, dry matter • G, perennial ryegrass • ANIRS, near infrared spectroscopy • R, red clover • R2G, red clover–ryegrass, sown at sown at 2 and 20 kg ha^–1 • R8G, red clover–ryegrass, sown at sown at 8 and 20 kg ha^–1 • W, white clover • WG, white clover–ryegrass