English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

This paper reports results of regression analyses of the use of a capacitance meter to estimate herbage weight. Estimation of dry weights was found to be as accurate as estimation of green weights. Analysis of covariance for three factors, site, season of year, and year of data collection, showed only season significantly (PC.01) affected the relationship between herbage weight and meter reading. Simple linear regressions were compared to natural logarithmic regressions. Logarithmic regressions were found to be better predictors of herbage weight as determined by Furnival’s Index. Winter and spring proved to be the best time to use the capacitance meter, probably due to decreased effect of moisture fluctuations on the meter’s performance. Neal and Neal (1973) reported electronic capacitance meters are accurate for estimating weights of standing vegetation under a wide variety of conditions. Neal and Neal (1965) and Back et al. (1969) concluded that a double sampling procedure (consisting of clipping and weighing a subsample of the plots where meter readings are recorded) can produce rapid, accurate estimates of biomass. Estimation is improved if standing biomass within a three dimensional plot (Currie et al. (1973)) that just encompasses the sensors of the meter is clipped-as opposed to the more conventional clipping of material rooted within a two dimensional plot delineated on the ground. Carpenter et al. (1973) showed that eliminating woody stems from weight measurements also improves herbage estimation. This is in agreement with data of Morris et al. (1976), whose study indicated that the capacitance meter mainly senses plant parts with the highest moisture content. The capacitance of plant moisture is several orders of magnitude higher than that of free water, due principally to the electrolyte content of the former. Consequently, green succulent plants produce considerably higher meter readings than do standing dead material. They both can contribute significantly to variation of regression estimates (Currie et al. (1973)). Herbage capacitance meters have been used in Florida since 1975 as a nondestructive means of measuring forage production. However, simple linear regressions of dry live weight on meter readings yielded poor predictors, even when regressions were run on a day-to-day basis. Analysis reported here suggests the superiority of separate logarithmic regression models for each of the four seasons of the year. Study Area and Methods Herbage capacitance meters were used in three separate studies at two locations within the state, with both sites having the same basic vegetation type. Two of the studies were conducted in south central Florida flatwoods vegetation type; the third took place in the north Florida flatwoods vegetation type at the University of Florida’s Beef Research Unit. South Florida flatwoods vegetation consists principally of small shrubs, forbs, grasses, and grasslikes dominated by Serenoa repens (saw palmetto), Elephantopus tomentosus (elephant’s foot), Aristida stricta (pineland threeawn), Authors are research biologist, Range Ecosystem Management, School of Forest Resources and Conservation, University of Florida, Gainesville 3261 I; and currently senior Environmental Engineer, Utah International Incorporated, San Francisco, CA 94104 and formerly assistant professor Range Ecosystem Management, School of Forest Resources and Conservation University of Florida, Gainesville; and research forester. U.S. Dee. Agr. Forest Service, Southeastern Forest Experiment Station, Gainesville 32611: This paper is Journal Series No. 1938, Institute of Food and Agricultural Sciences, University of Florida, Gainesville. Guidance in data collection by Dr. Larry White is gratefully acknowledged. Manuscript received October 11, 1979. 240 Schizachyrium stolonifer (creeping bluestem), and Rhynchospora spp. (beakrush), respectively. The north Florida flatwoods site contained similar understory vegetation with a greater abundance of trees, mainly Pinus palustris (longleaf pine), and various Quercus spp. (oaks). Study sites were sampled using the Model 18-2000 herbage capacitance meter from Neal Electronics, Burbank, California. At each plot several meter readings were taken until they stabilized, and then were recorded. The meter was removed, herbage standing within the space previously occupied by the meter’s sensors (3 1 cm X 61 cm X 51 cm) was clipped, and all standing dead material was removed. The live material was oven-dried at 65” C for 48 hours and then weighed. In south Florida 210 such plots were measured in the spring of 1975, and 290 in the fall. Green weights also were recorded on 210 plots to allow comparison of the predictability of green versus dry weights. The Beef Research Unit site was sampled in 1976 and 1977 where 142plots were measured in winter (December-February), 233 in spring (March-May), 456 in summer (June-August), and 571 in fall (September-November). Analysis of covariance, with weight as the response variable and meter reading as the covariable, was used to test for site, season, and year effects and interactions. Logarithmic regressions and linear regressions were compared using Furnival’s Index (Furnival I96 I). Data were analyzed using the Statistical Analysis System on an Amdahl470 V/6-1 1 with OS/MV Release 3.8 and JES2/NJE Release 3. Computing was done using the facilities of the Northeast Regional Data Center of the State University System of Florida located on the campus of the University of Florida in Gainesville. Results and Discussion A question addressed early was whether dry weights could be predicted as well as green weights based on meter readings. Dry weights had simple correlations with meter readings comparable to those of green weights. Neal et al. (1976) also found that the capacitance meter could accurately predict dry weights of herbage biomass. With this assurance only dry weights were used in all remaining analyses. An analysis of covariance was conducted to determine whether any of the factors (site, season, or year of data collection), or their interactions, affect dry weights adjusted for meter readings. The results indicated that the only significant (K.01) factor was season of the year. Simple linear regressions of dry weights on meter readings were plotted for each of the four seasons. The resulting graphs suggested that a logarithmic regression model resulted in a better fit than a linear model. The linear regression model fitted was w= a, + a1 w +E, (1) where W= dry weight, w = meter reading, e is random error, and a, and al are regression coefficients estimated from the data. The logarithmic regression model was P nW=b+bl en w +a, (2) where b W= natural log of dry weight & w = natural log of meter reading, u is a random error, and bo and bl are regression coefficients from the transformed data. JOURNAL OF RANGE MANAGEMENT 34(3), May 1981 LINEAR (r*: .493) -LOGARITHMIC (r*: .664) where 11 = index for equation (l), and S1 = standard error of regression for equation (I). The Index for the logarithmic regression of equation (2) is:

Herbage Capacitance Meter: An Evaluation of Its Accuracy in Florida Rangelands