K. W. Finlay

The adaptation of barley varieties was studied by the use of grain yields of a randomly chosen group of 277 varieties from a world collection, grown in replicated trials for several seasons at three sites in South Australia. For each variety a linear regression of yield on the mean yield of all varieties for each site and season was computed to measure variety adaptation. In these calculations the basic yields were measured on a logarithmic scale, as it was found that a high degree of linearity was thereby induced. The mean yield of all varieties for each site and season provided a quantitative grading of the environments; and from the analysis described, varieties specifically adapted to good or poor seasons and those showing general adaptability may be identified. The study of the adaptation of the whole population of varieties was facilitated by the use of a two-dimensional plot (scatter diagram), with mean yield and regression coefficient as coordinates for each variety. Though wide variation was evident in both mean yield and sensitivity to environment as characterized by the regression coefficient, the variation in sensitivity was proportionately less among varieties with higher mean yield, and the varieties with highest mean yield exhibited, within very narrow limits (regression coefficients close to 0.8), a similar degree of adaptation to all environments over the wide range, especially of seasonal conditions, typical of the South Australian cereal belt. Varieties from particular geographic regions of the world showed a similarity in type of adaptation, which provides a useful basis for plant introduction. Phenotypic stability and physiological and morphological characteristics of groups of varieties with specific or general adaptability are discussed in relation to plant introduction and breeding.

weakiv calculates weak-instrument-robust tests of the coefficients on the endogenous regressors in instrumental variables (IV) estimation of models with any number of endogenous regressors. weakiv supports estimation of linear IV models by ivregress, ivreg2 and ivreg2h, panel data linear IV estimation (fixed effects and first differences) by xtivreg and xtivreg2, dynamic panel data estimation by xtabond2, and estimation of probit and tobit IV models by ivprobit and ivtobit. In an exactly-identified model with one instrument, weakiv reports the Anderson-Rubin (AR) test statistic. When the IV model is overidentified, weakiv also conducts the conditional likelihood ratio (CLR) test, the Lagrange multiplier K test, the J overidentification test, and a combination of the K and overidentification tests (the K-J test). Minimum Distance/Wald and LM versions of the tests are both supported. weakiv also provides graphing facilities for visual examination and presentation of rejection probabilities and confidence sets based on these tests. For individual endogenous regressors, weakiv will report confidence intervals and graph rejection probabilities that are robust to weak identification. For 2 endogenous regressors, weakiv constructs confidence sets and reports these using 2-D contour plots and 3-D surface plots. For linear IV and panel data model models, weakiv supports all the variance-covariance estimators supported by ivregress, ivreg2 and xtabond2 (robust, cluster-robust, HAC, 2-way clustering, Kiefer and Driscoll-Kraay SEs, etc.). weakiv builds on and extends the command rivtest by Finlay and Magnusson (Stata Journal 9(3), 2009). weakiv requires Stata 11 or above. Users with Stata 10 are recommended to install weakiv10, an older version of weakiv suitable for Stata 10 without many of the extensions available in weakiv.

A method of calculating a single numerical value as an index of germination behaviour is presented, based on germination under standard conditions after 24, 48 and 72 hr. This criterion is employed to obtain an estimate of the range of genetic and environmental variation in 800 barley varieties. A more detailed study has involved ten varieties chosen to represent the full range of available genetic variability. Further experiments on “time to germinate” and “percentage increase in weight” of individual seeds obtained as samples from a plot, a plant, and a single ear of the variety Research, demonstrate the effect of the environment on a uniform genetic background.

Ten barley varieties were grouped according to the rate and evenness of germination or “germination behaviour” of their seed. Varieties R1 and S2 constituted the rapid; M3, M4, M5, M6, M6, M7 and M8 the medium; and S9and S10 the slow groups. A diallele crossing test was performed, in which the ten varieties were crossed with each other to produce all possible F1 combinations. The general combining ability and specific combining ability analyses were carried out for each variety. The results are of significance in relation to the breeding of improved malting barley varieties.