Bactrocera zonata (Saunders) is one of the most harmful species of Tephritidae. It causes extensive damage in Asia and threatens many countries located along or near the Mediterranean Sea. The climate mapping program, CLIMEX 3.0, and the GIS software, ArcGIS 9.3, were used to model the current and future potential geographical distribution of B. zonata. The model predicts that, under current climatic conditions, B. zonata will be able to establish itself throughout much of the tropics and subtropics, including some parts of the USA, southern China, southeastern Australia and northern New Zealand. Climate change scenarios for the 2070s indicate that the potential distribution of B. zonata will expand poleward into areas which are currently too cold. The main factors limiting the pest's range expansion are cold, hot and dry stress. The model's predictions of the numbers of generations produced annually by B. zonata were consistent with values previously recorded for the pest's occurrence in Egypt. The ROC curve and the AUC (an AUC of 0.912) were obtained to evaluate the performance of the CLIMEX model in this study. The analysis of this information indicated a high degree of accuracy for the CLIMEX model. The significant increases in the potential distribution of B. zonata projected under the climate change scenarios considered in this study suggest that biosecurity authorities should consider the effects of climate change when undertaking pest risk assessments. To prevent the introduction and spread of B. zonata, enhanced quarantine and monitoring measures should be implemented in areas that are projected to be suitable for the establishment of the pest under current and future climatic conditions.

Our previous study suggested that green light promotes broiler growth during the early stage [posthatching day (P) 0 to P26], and blue light enhances growth during the later stage (P27 to P49). The purpose of this study was to improve broiler growth and productive performance by using a combination of monochromatic lights at critical points between the early and later stages of growth. A total of 512 male Arbor Acres broilers on P0 were reared under white light (W), red light (R), green light (G), and blue light (B) by using light-emitting diode lamps at 15 ± 0.2 lx from P0 to P26 (16 replicate pens/group, 8 birds/pen), and then switching to another color of light until P49 (4 replicate pens/group, 8 birds/pen). As compared with single monochromatic lights, broilers reared in environments under combinations of monochromatic lights, W→G, R→B, G→B, and B→G, attained heavier BW than those reared in environments under W→W (3.18 to 12.00%), R→R (1.96 to 18.14%), G→G (0.85 to 5.08%), and B→B (0.39 to 4.70%), respectively. In addition, feed conversion ratios in the W→B, R→B, and G→B combinations were lower than feed conversion ratios for W→W (15.86%, P < 0.05), R→R (18.41%, P < 0.05), and G→G (3.37%), respectively. Moreover, the eviscerated carcass weight and breast, thigh, and crus muscle weights under G→B were greater by 0.40 to 56.23% than were those for the other light groups except W→B (eviscerated carcass) and B→G (breast muscle). The results suggest that the application of the G→B and B→G exchanges can be used successfully to improve growth and productive performance in broilers.

Genomic selection has been widely implemented in national and international genetic evaluation in the dairy cattle industry, because of its potential advantages over traditional selection methods and the availability of commercial high-density (HD) single nucleotide polymorphism (SNP) panels. However, this method may not be cost-effective for cow selection and for other livestock species, because the cost of HD SNP panels is still relatively high. One possible solution that can enable other species to benefit from this promising method is genomic selection with low-density (LD) SNP panels. In this simulation study, LD SNP panels designed with different strategies and different SNP densities were compared. The effects of number of quantitative trait loci, heritability, and effective population size were evaluated in the framework of genomic selection with LD SNP panels. Methodologies of Bayesian variable selection; BLUP with a trait-specific, marker-derived relationship matrix; and BLUP with a realized relationship matrix were employed to predict genomic estimated breeding values with both HD and LD SNP panels. Up to 95% of accuracy obtained by using an HD panel can be obtained by using only a small proportion of markers. The LD panel with markers selected on the basis of their effects always performs better than the LD panel with evenly spaced markers. Both the genetic architecture of the trait and the effective population size have a significant effect on the performance of the LD panels. We concluded that, to implement genomic selection with LD panels, a training population of sufficient size and genotyped with an HD panel is necessary. The trade-off between the LD panels with evenly spaced markers and selected markers must be considered, which depends on the number of target traits in a breeding program and the genetic architecture of these traits. Genomic selection with LD panels could be feasible and cost-effective, though before implementation, a further detailed genetic and economic analysis is recommended.

Genomic selection using dense markers covering the whole genome is a tool for the genetic improvement of livestock and is revolutionizing the breeding system in dairy cattle. Progeny-tested bulls have been used to form reference populations in almost all countries where genomic selection has been implemented. In this study, the accuracy of genomic prediction when cows are used to form the reference population was investigated. The reference population consisted of 3,087 cows. All individuals were genotyped with Illumina BovineSNP50. After genotype imputation and editing, 48,676 single nucleotide polymorphisms were available for analysis. Two methods, genomic BLUP (GBLUP) and BayesB, were used to render genomic estimated breeding values (GEBV) for 5 milk production traits. Accuracies of GEBV were assessed in 3 ways: r(GEBV,EBV) (the correlation between GEBV and conventional EBV) in 67 progeny-tested bulls, rGEBV,EBV from a 5-fold cross validation in the 3,087 cow reference population, and the theoretical accuracy (for GBLUP) calculated in the same way as for conventional BLUP. The results showed that using GBLUP, the r(GEBV,EBV) and theoretical accuracy of genomic prediction in Chinese Holstein ranged from 0.59 to 0.76 and 0.70 to 0.80, respectively, which was 0.13 to 0.30 and 0.23 to 0.33 higher than the accuracies of conventional pedigree index, respectively. The results indicate that, as an alternative, genomic selection using cows in the reference population is feasible.