Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.

QTL analysis of ergot resistance in sorghum

Sorghum ergot, caused predominantly by Claviceps africana Frederickson, Mantle, de Milliano, is a significant threat to the sorghum industry worldwide. The objectives of this study were firstly, to identify molecular markers linked to ergot resistance and to two pollen traits, pollen quantity (PQ) and pollen viability (PV), and secondly, to assess the relationship between the two pollen traits and ergot resistance in sorghum. A genetic linkage map of sorghum RIL population R931945-2-2 x IS 8525 (resistance source) was constructed using 303 markers including 36 SSR, 117 AFLP™, 148 DArT™ and two morphological trait loci. Composite interval mapping identified nine, five, and four QTL linked to molecular markers for percentage ergot infection (PCERGOT), PQ and PV, respectively, at a LOD >2.0. Co-location/linkage of QTL were identified on four chromosomes while other QTL for the three traits mapped independently, indicating that both pollen and non pollen-based mechanisms of ergot resistance were operating in this sorghum population. Of the nine QTL identified for PCERGOT, five were identified using the overall data set while four were specific to the group data sets defined by temperature and humidity. QTL identified on SBI-02 and SBI-06 were further validated in additional populations. This is the first report of QTL associated with ergot resistance in sorghum. The markers reported herein could be used for marker-assisted selection for this important disease of sorghum.

A comparison of plotless density estimators using Monte Carlo simulation on totally enumerated field data sets

Background: Plotless density estimators are those that are based on distance measures rather than counts per unit area (quadrats or plots) to estimate the density of some usually stationary event, e.g. burrow openings, damage to plant stems, etc. These estimators typically use distance measures between events and from random points to events to derive an estimate of density. The error and bias of these estimators for the various spatial patterns found in nature have been examined using simulated populations only. In this study we investigated eight plotless density estimators to determine which were robust across a wide range of data sets from fully mapped field sites. They covered a wide range of situations including animal damage to rice and corn, nest locations, active rodent burrows and distribution of plants. Monte Carlo simulations were applied to sample the data sets, and in all cases the error of the estimate (measured as relative root mean square error) was reduced with increasing sample size. The method of calculation and ease of use in the field were also used to judge the usefulness of the estimator. Estimators were evaluated in their original published forms, although the variable area transect (VAT) and ordered distance methods have been the subjects of optimization studies. Results: An estimator that was a compound of three basic distance estimators was found to be robust across all spatial patterns for sample sizes of 25 or greater. The same field methodology can be used either with the basic distance formula or the formula used with the Kendall-Moran estimator in which case a reduction in error may be gained for sample sizes less than 25, however, there is no improvement for larger sample sizes. The variable area transect (VAT) method performed moderately well, is easy to use in the field, and its calculations easy to undertake. Conclusion: Plotless density estimators can provide an estimate of density in situations where it would not be practical to layout a plot or quadrat and can in many cases reduce the workload in the field.

Multivariate analysis of fresh-cut carambola slices stored under different temperatures.

Quality of fresh-cut carambola (Averrhoa carambola L) is related to many chemical and biochemical variables especially those involved with softening and browning, both influenced by storage temperature. To study these effects, a multivariate analysis was used to evaluate slices packaged in vacuum-sealed polyolefin bags, and stored at 2.5 degrees C, 5 degrees C and 10 degrees C, for up to 16 d. The quality of slices at each temperature was correlated with the duration of storage, O(2) and CO(2) concentration in the package, physical chemical constituents, and activity of enzymes involved in softening (PG) and browning (PPO) metabolism. Three quality groups were identified by hierarchical cluster analysis, and the classification of the components within each of these groups was obtained from a principal component analysis (PCA). The characterization of samples by PCA clearly distinguished acceptable and non-acceptable slices. According to PCA, acceptable slices presented higher ascorbic acid content, greater hue angles ((o)h) and final lightness (L-5) in the first principal component (PC1). On the other hand, non-acceptable slices presented higher total pectin content. PPO activity in the PC1. Non-acceptable slices also presented higher soluble pectin content, increased pectin solubilisation and higher CO(2) concentration in the second principal component (PC2) whereas acceptable slices showed lower total sugar content. The hierarchical cluster and PCA analyses were useful for discriminating the quality of slices stored at different temperatures.