Performance of improved guayule lines in Australia

Guayule (Parthenium argentatum Gray) is a potential source of commercial natural rubber. Its commercialisation depends mainly on economical plant production. The objective of this study was to evaluate the performance of improved lines in Australia. Seeds from five improved lines (AZ-1, AZ-2, AZ-3, AZ-5 and AZ-6) and two previously developed guayule lines (N 565 and 11591) were obtained from the Agricultural Research Service (ARS) of the United States Department of Agriculture (USDA). Seedlings from these lines were grown in a glasshouse for 3 months and later transplanted in a field experiment in early September 2001. Plant height and width were monitored from transplanting to 62 weeks at regular intervals. After 62 weeks, plant dry matter production, rubber and resin content, and yields were analysed. Plant height and width of the improved lines were higher than N 565 and 11591. Plant dry matter, rubber and resin yields were significantly different among lines. Of the five lines, AZ-1 and AZ-2 produced rubber yields of 620 and 550 kg/ha, respectively and these yields were significantly greater than for N 565 (371 kg/ha) and 11591 (391 kg/ha). AZ-1 and AZ-2 also produced significantly higher resin yields, 727 and 668 kg/ha, respectively, than those for N 565 (436 kg/ha) and 11591 (325 kg/ha). Rubber and resin yield increase of lines, AZ-1 and AZ-2, were in the range of 41-68% and 53-123%, respectively over N 565 and 11591. AZ-1 tended to produce higher rubber and resin yields than AZ-2 but exhibited highly variable plant height (CV = 25%) and width (CV = 41%) indicating potential for further genetic improvement. AZ-2 offers the best combination of desirable characters including early vigour, uniformity and comparatively higher rubber and resin yields. (C) 2003 Published by Elsevier B.V.

Rotary atomizer drop size distribution database

Wind tunnel measurements of drop Size distributions from Micronair A U4000 and A U5000 rotary atomizers were collected to develop a database for model use. The measurements varied tank mix, flow rate, air speed, and blade angle conditions, which were correlated by multiple regressions (average R-2 = 0.995 for A U4000 and 0.988 for AU5000). This database replaces an outdated set of rotary atomizer data measured in the 1980s by the USDA Forest Service and fills in a gap in data measured in the 1990s by the Spray Drift Task Force. Since current USDA Forest Service spray projects rely on rotary atomizers, the creation of the database (and its multiple regression interpolation) satisfies a need seen for ten years.

Estimating variable returns to scale production frontiers with alternative stochastic assumptions

Two stochastic production frontier models are formulated within the generalized production function framework popularized by Zellner and Revankar (Rev. Econ. Stud. 36 (1969) 241) and Zellner and Ryu (J. Appl. Econometrics 13 (1998) 101). This framework is convenient for parsimonious modeling of a production function with returns to scale specified as a function of output. Two alternatives for introducing the stochastic inefficiency term and the stochastic error are considered. In the first the errors are added to an equation of the form h(log y, theta) = log f (x, beta) where y denotes output, x is a vector of inputs and (theta, beta) are parameters. In the second the equation h(log y,theta) = log f(x, beta) is solved for log y to yield a solution of the form log y = g[theta, log f(x, beta)] and the errors are added to this equation. The latter alternative is novel, but it is needed to preserve the usual definition of firm efficiency. The two alternative stochastic assumptions are considered in conjunction with two returns to scale functions, making a total of four models that are considered. A Bayesian framework for estimating all four models is described. The techniques are applied to USDA state-level data on agricultural output and four inputs. Posterior distributions for all parameters, for firm efficiencies and for the efficiency rankings of firms are obtained. The sensitivity of the results to the returns to scale specification and to the stochastic specification is examined. (c) 2004 Elsevier B.V. All rights reserved.