The Application of GGE Biplot Analysis for Evaluating Test Locations and Mega-Environment Investigation of Cotton Regional Trials

In the process to the marketing of cultivars, identification of superior test locations within multi-environment variety trial schemes is of critical relevance. It is relevant to breeding organizations as well as to governmental organizations in charge of cultivar registration. Where competition among breeding companies exists, effective and fair multi-environment variety trials are of utmost importance to motivate investment in breeding. The objective of this study was to use genotype main effect plus genotype by environment interaction（GGE） biplot analysis to evaluate test locations in terms of discrimination ability, representativeness and desirability, and to investigate the presence of multiple mega-environments in cotton production in the Yangtze River Valley（YaRV）, China. Four traits（cotton lint yield, fiber length, lint breaking tenacity, micronaire） and two composite selection indices were considered. It was found that the assumption of a single mega-environment in the YaRV for cotton production does not hold. The YaRV consists of three cotton mega-environments： a main one represented by 11 locations and two minor ones represented by two test locations each. This demands that the strategy of cotton variety registration or recommendation must be adjusted. GGE biplot analysis has also led to the identification of test location superior for cotton variety evaluation. Although test location desirable for selecting different traits varied greatly, Jinzhou, Hubei Province, China, was found to be desirable for selecting for all traits considered while Jianyang, Sichuan Province, China, was found to be desirable for none.

GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes

The experiments were conducted for three consecutive years across 14 locations using 9 non-waxy proso millet genotypes and 16 locations using 7 waxy proso millet genotypes in China. The objectives of this study were to analyze yield stability and adaptability of proso millets and to evaluate the discrimination and representativeness of locations by analysis of vari- ance （ANOVA） and genotype and genotype by environment interaction （GGE） biplot methods. Grain yields of proso millet genotypes were significantly influenced by environment （E）, genotype （G） and their interaction （GxE） （P〈0.1%）. GxE inter- action effect was six times higher than G effect in non-waxy group and seven times in waxy group. N04-339 in non-waxy and Neimi 6 （NM6） in waxy showed higher grain yields and stability compared with other genotypes. Also, Neimi 9 （NM9, a non-waxy cultivar） and 90322-2-33 （a waxy cultivar） showed higher adaptability in 7 and in 11 locations, respectively. For non-waxy, Dalat, Inner Mongolia （E2） and Wuzhai, Shanxi （E5） were the best sites among all the locations for maximizing the variance among candidate cultivars, and Yanchi, Ningxia （El0） had the best representativeness. Wuzhai, Shanxi （e9） and Yanchi, Ningxia （e14） were the best representative locations, and Baicheng, Jilin （e2） was better discriminating location than others for waxy genotypes. Based on our results, El0 and e14 have enhanced efficiency and accuracy for non-waxy genotypes and waxy genotypes selection, respectively in national regional test of proso millet varieties.

Effects of Paddy Field Rotation on Diseases,Pests and Weeds of Rice

[ Objective] The paper was to explore ecological effect of paddy field rotation on reducing disease, pests and weeds of rice. [ Method] With long-term continuous cropping rice as control, according to field location experiments for consecutive 14 years, the effects of paddy field rotation on diseases, pests and weeds of rice were studied in details. [ Result] Paddy field rotation in some extent could reduce diseases, pests and weeds of rice. The diseased plant rate and disease in- dex of rice sheath blight in early rice under paddy field rotation treatment were averagely 10% and 0.4% lower than those in continuous cropping treatment, respec- tively. The diseased plant rate and disease index of rice sheath blight in late rice were 17.7% and 13.3% lower than those in treatments with continuous cropping mode, respectively. The diseased plant rate and disease index of rice false smut in both early rice and late rice under rotation treatments were lower than those in treatments with continuous cropping mode. White leaf rate caused by rice leaf roller and dry heart rate caused by striped rice borer under rotation treatments were significantly lower than those under continuous cropping treatment. The growth status of weeds under rotation treatments was weaker than that under continuous cropping treatment. [ Conclusion] The paper has important significance on alleviating diseases, pests and weeds of rice and improvement of ecological environment of farmland, and provides theoretical basis for sustainable development of agriculture.

Projection-based High-dimensional Sign Test

This article is concerned with the high-dimensional location testing problem.For highdimensional settings,traditional multivariate-sign-based tests perform poorly or become infeasible since their Type I error rates are far away from nominal levels.Several modifications have been proposed to address this challenging issue and shown to perform well.However,most of modified sign-based tests abandon all the correlation information,and this results in power loss in certain cases.We propose a projection weighted sign test to utilize the correlation information.Under mild conditions,we derive the optimal direction and weights with which the proposed projection test possesses asymptotically and locally best power under alternatives.Benefiting from using the sample-splitting idea for estimating the optimal direction,the proposed test is able to retain type-I error rates pretty well with asymptotic distributions,while it can be also highly competitive in terms of robustness.Its advantage relative to existing methods is demonstrated in numerical simulations and a real data example.