Several spatial methods exist for the adjustment of local trend in one dimension. The aim of this study was to evaluate and compare the precision of different spatial methods. For this purpose, 293 sugar beet (Beta vulgaris) and 64 multienvironment barley (Hordeum vulgare) trials of two German plant breeding companies were analyzed using a baseline model, which comprised a block and replicate effect, and different one‐dimensional spatial models augmenting the baseline model. Model fit was assessed using the Akaike Information Criterion (AIC), the phenotypic correlation of the adjusted genotype means between two environments, and the relative efficiency. For the sugar beet and barley trials the baseline model outperformed the spatial models in the majority of cases, while in some cases the addition of a spatial component proved beneficial. Based on these results we propose a conservative approach to spatial modeling that starts with a baseline model and then checks whether adding a spatial component improves the fit. Among the alternative models studied, the linear variance and the first‐order autoregressive models were the most promising candidates.
The maize (Zea mays L.) growing area in India is divided into five zones for cultivar testing. During triannual testing of genotypes in official trials within the All‐India Coordinated Maize Improvement Program (AICMIP), a large number of entries is rejected each year. Therefore, only a low number of entries is carried forward to the advanced stage of testing. The subdivision of the breeding sites into zones results in limited data per zone. Hence, the question arises how to select the best genotypes per zone and how information can be borrowed across zones to improve the accuracy of selection within zones. We compared the performance of best linear unbiased prediction (BLUP) using the correlation of genetic effects between zones with best linear unbiased estimation (BLUE) based on data per zone. In both cases, data were analyzed using a mixed model. We used simulations to calculate correlations between the true simulated values and the predicted genotype values obtained by BLUE and BLUP using the same models. The data structure and the variance components used in simulations were based on the analysis of 40 triannual series of four different maize maturity groups. Best linear unbiased prediction outperformed BLUE in 38 out of 40 series and on average across all series. An advantage of BLUP was observed for varying genetic correlations between zones. We conclude that the use of BLUP enhanced the estimation accuracy in zoned AICMIP maize testing trials and can be recommended for future use in these trials.
Plant breeding and official variety testing involve the challenge to design multienvironmental trials in several years and locations. Several variables influence the performance and, therefore, the possible selection gain of such trials. We provide a simulation‐based approach using SAS to vary these variables and to allow a comparison of different scenarios for the design of series of trials regarding selection gain. Our approach builds on the FORTRAN software tool SELSYS. Three examples of its application are given.
Japanese bindweed was found to be one of the most abundant and most difficult-to-control weed species during a 2-yr weed survey in more than 100 winter wheat fields in the North China Plain region. Multivariate data analysis showed that Japanese bindweed is most abundant at sites with comparative low nitrogen (N) fertilization intensities and low crop densities. To gain deeper insights into the biology of Japanese bindweed under various N fertilization intensities, winter wheat seeding rates, herbicide treatments, and their interactions, a 2-yr field experiment was performed. In nonfertilized plots, a herbicide efficacy (based on density reduction) of 22% for 2,4-D, and of 25% for tribenuron-methyl was found. The maximum herbicide efficacy in Nmin-fertilized plots (target N value based on expected crop yield minus soil mineral nitrogen content,) was 32% for 2,4-D and 34% for tribenuron-methyl. In plots fertilized according to the farmer's practices, a maximum herbicide efficacy of 72% for 2,4-D and of 64% for tribenuron-methyl could be observed. Furthermore, medium and high seeding rates improved the herbicide efficacy by at least 39% for tribenuron-methyl and 44% for 2,4-D compared to the low seeding rate. Winter wheat yield was not significantly affected by seeding rate itself, whereas at low and medium seeding rates, Nminfertilization was decreasing winter wheat yield significantly compared to the farmer's usual fertilization practice. At the highest seeding rate, Nminfertilization resulted in equal yields compared to the farmer's practices of fertilization.
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