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Digitized photographic images of turf plots composed of bermudagrass, buffalo grass, tall fescue, and zoysiagrass were taken at a height of about 150 cm with a 28-mm lens. Fast Fourier transforms of these images were performed, and a radial plot of the power spectrum was obtained from each image. Hurst plots (log frequency vs. log intensity) were used to subtract “background” from the power spectra, so peaks would be more evident. The peak of the power spectrum occurs at the average spacing between leaves (more precisely, between areas of the canopy that reflects a significant amount of light) and defines the characteristic dimension. Zoysiagrass had the lowest characteristic dimension, while tall fescue had the highest. The width of the power spectrum is indicative of the variability of the characteristic dimension within the canopy. The minimum characteristic dimension (occurring at the highest frequency) was less than 1.7 cm, whereas all the other species had about the same minimum characteristic dimension of ≈1.9 cm. The maximum characteristic dimension was greatest for fescue (6.9 cm), followed by buffalo grass (3.8 cm), bermudagrass (3.3 cm), and zoysiagrass (2.8 cm). These results indicate that the characteristic dimension can be a useful tool for discriminating between turfgrass species in digitized images.

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Seed vigor has a very subtle effect on the productivity of greenhouses producing vegetable transplants, celery, cauliflower, lettuce, etc. and on todays highly mechanized automatic or semi-automatic transplanting operations. As greenhouse production technology moves from traditional bare root to plug/tray growing systems and as automatic and semi-automatic transplanting operations increase in number, the impact of poor seed vigor is realized.

Measures to mitigate the impact of poor seed vigor in the nursery are: Seed density grading; increased growing cycle in the nursery, hand culling or replanting. Measures to mitigate the impact of poor seed vigor in automatic transplanting operations: increase the number of people following the planter to replace poor vigor plants; use hand fed transplanters.

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A system for the digital analysis of photographic prints of turfgrass plots is being developed. The 3-year-old turfgrass plots included Meyer zoysiagrass, Midlawn bermudagrass, Prairie buffalograss and Mustang tall fescue. The plots were photographed by a camera with a small dual bubble level on the camera back and a 28-mm-wide angle lens. Photographs were digitized with flatbed scanners. The images can then be analyzed in a variety of ways. For example, a series of photographs were taken from mid-Sept. through late Oct 1995 and spectral analysis of the resultant digital images were made. The initial RGB (red-greenblue) format of the images was converted to HSI (hue-saturation-intensity) for analysis. The results indicate, obviously, that hue changed from 104 (i.e., green) to 75.7 degrees (i.e., brownish) between the beginning and end of Oct. 1995. Similarly, intensity changed from ≈0.12 to ≈0.16 during the same time period, indicating that the images became darker over time. These phenomena were observed in all four species examined. However, the saturation value evoked a significant species * date interaction. The three warm-season species showed a decrease in saturation, while Mustang had no significant decrease during Oct. Spectral as well as textural analysis are likely the two most useful techniques in the digital analysis of turfgrass plots. Examples of both will be presented.

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Abstract

The process of ice formation in media at container capacity was followed by sectioning the frozen medium and determining solute content in each sample using sodium fluorescein as a tracer. H2O apparently moved from the bottom and central interior regions of the container medium to the sides of the container. The phase in which movement occurred is unknown. Fluorescein moved down from the top and central interior regions to the bottom and sides of the container. The final distribution of fluorescein should indicate the location of the majority of liquid H2O due to the exclusion of solutes by ice. In the partially frozen state the greatest amount of liquid water therefore occurs near the container sides – a region normally occupied by a large proportion of the root system.

Open Access

During 1995, 33 poinsettia cultivars were evaluated for Colorado greenhouse production conditions. Plants were supplied by the Paul Ecke Poinsettia Ranch, Fischer Geraniums USA, Oglevee, and Mikkelsens. At the end of the production period, Colorado greenhouse growers were invited to an open house and asked to judge the cultivars for plant, bract, and cyathia quality. As rated by the 24 growers, the best red cultivars in overall performance were `Freedom Red', `Nutcracker Red', `Cortez', and `Bonita', respectively. The best pink cultivars in overall performance were `Nutcracker Pink', `Maren', and `Flirt', respectively. The best white cultivars in overall performance were `Nutcracker White' and `V-17 Angelika White', respectively. The best novelty cultivars in overall performance were `Puebla' and `Monet', respectively.

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The prediction of which species will do well in various microclimates is of obvious interest to horticulturists as well as homeowners. To this end, the following 5 species of trees and shrubs where planted at 5 disparate sites across Kansas in spring 1985 and growth and environment measured for the 4 following years: Phellodendron amurense, Acer rubrum, Acer platanoides `Greenlace', Quercus acutissima, and Cercocarpus montanus. Preliminary analysis of trunk diameter growth vs. environment indicates few simple relationships and several rather complex relationships. Rather simplistic linear relationships (growth vs. a single environmental parameter) are largely meaningless, and often misleading. For instance, growth of Q. acutissima was negatively correlated with the highest maximum temperature prior to the growing season and positively correlated with the lowest minimum temperature prior to the growing season. More complex, and reasonable, relationships will be presented.

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This study develops a method of estimating wind machine effectiveness. The method captures the important variables affecting cost-effectiveness and can be applied at little cost. The present-value method outlined may be applied when evaluating frost protection for other crops and other risk-reducing inputs, such as irrigation equipment. Oranges in California are presented as a case study. The empirical results presented indicate that wind machines are generally not cost-effective for California orange producers. However, when the nonfinancial benefits of yield risk reduction are included, it is possible that wind machines are cost-effective for some growers.

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The following model simulates hourly temperature fluctuations at 6 Kansas stations:
\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \[T_{h}=\frac{(T_{x}-T_{n})}{2}\left[\mathrm{exp}\left(\frac{0.693h}{DL_{M}}\right)-1\right]+T_{n};{\ }0{\leq}h{\leq}DL_{M}\] \end{document}
\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \[T_{h}=\frac{(T_{x}-T_{n})}{2}\left[1+\mathrm{sin}\frac{{\pi}(h-DL_{M})}{2(23-DL_{M})}\right]+T_{n};{\ }DL_{M}{\leq}h{\leq}23\] \end{document}
where h = time (hours after sunrise), DLM = 20.6 - 0.6 * daylength (DL), Th = temperature at time h, and TX and Tn = maximum and minimum temperature, respectively. Required inputs are daily TX and Tn and site latitude (for the calculation of DL). Whereas other models have been derived by fitting equations to chronological temperatures, this model was derived by daily fitting of hourly temperatures sorted by amplitude. Errors from this model are generally lower, and less seasonally biased, than those from other models tested.
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The following model simulates hourly temperature fluctuations at 6 Kansas stations:
T h = ( T x T n ) 2 [ exp ( 0.693 h D L M ) 1 ] + T n ; 0 h D L M T h = ( T x T n ) 2 [ 1 + sin π ( h D L M ) 2 ( 23 D L M ) ] + T n ; D L M h 23
where h = time (hours after sunrise), DLM = 20.6 - 0.6 * daylength (DL), Th = temperature at time h, and TX and Tn = maximum and minimum temperature, respectively. Required inputs are daily TX and Tn and site latitude (for the calculation of DL). Whereas other models have been derived by fitting equations to chronological temperatures, this model was derived by daily fitting of hourly temperatures sorted by amplitude. Errors from this model are generally lower, and less seasonally biased, than those from other models tested.
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Scanning electron micrographs of grape berry surfaces, which resemble mountainscapes, contain a wealth of structural information. A typical statistical characterization of features such as root mean square peak-to-peak spacings, peak density, etc., is readily performed on these images. However, a much richer base of information is accessible by analyzing the images with fractal geometry. Fractal box dimension is a quantitative measure of surface roughness, and varies with the contour at which it is determined in both cultivars `Foch' and `Perlette', suggesting that the surfaces are multifractal structures. Fourier spectral analyses of the surfaces produce a similar conclusion. Thus, the unambiguous quantitative resolution of cultivars on the basis of their wax surface structure looks promising, but requires further work.

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