Abstract
An appropriate layout of crop multi-environment trial (MET) sites is imperative for evaluating new crop varieties’ performance in terms of agronomic traits and stress tolerance, and this information is used to determine the utilization value and suitable promotion region of new varieties. Actually, traditional maize test sites have been selected according to the experience of breeding experts, which leads to the strong subjective and unscientific conclusions regarding sites, as well as test results that are not representative of the target population of environments (TPE). Therefore, in this study, we proposed a new method for MET sites layout. Meteorological data, maize growth period data, and county-level maize planting area data were collected for the spatiotemporal classification of a given maize planting region to analyze change rules in the environmental category of each minimum research unit within the study period. If the occurrence frequency of its final attribution category reaches a certain threshold (50%), this minimum research unit is classified as a typical environment region; otherwise, it is classified as an atypical environment region. Then, the number of test sites in each environmental category is allocated by spatial stratified sampling. At last, we establish the optimal test sites layout and a reliability measurement (test adequacy) methods. The practicability of this method was proved by taking the Three Northeastern Provinces of China as the study area. The result shows that there should be 112 test sites in the study area, the distribution of the test sites is uniform, and the environmental representation is high. Test adequacy analysis of the test sites reveals that most of the environmental categories have a test adequacy that reaches 1 in each test period. The method proposed in this paper provides support for the scientific layout of crop varieties test sites and helps to improve the representative and reliability of variety test results while optimizing resources.
Highlights
Maize (Zea mays L) is one of the main food, feed, and industrial material crops produced all over the world, accounting for about 13% of the world’s arable land [1]
This study aims to solve problems such as poor distribution and representativeness of current multi-environment trial (MET) sites of maize varieties, the inability to adapt to the changes in planting environments, and the high risk associated with the promotion of a new maize variety
In this paper, using the Three Northeastern Provinces of China as the study area, we propose a maize variety test sites layout method that is based on the spatiotemporal classification of planting environment
Summary
Maize (Zea mays L) is one of the main food, feed, and industrial material crops produced all over the world, accounting for about 13% of the world’s arable land [1]. In order to adapt maize to different planting environments and meet the needs of food safety and economic development, breeders continuously select or improve new maize varieties. Before being planted on a large-scale in the target population of environments (TPE), new maize varieties need to be tested in multi-environment trial (MET) to estimate their productivity and adaptability, as well as determine the appropriate promotion region [5,6,7,8,9].determining how to set up or optimize the test sites of MET is key to breeding and promoting new maize varieties in different ecological environments. The first one relies on expert experience to select test sites, the second involves analyzing the size of test sites using classical sampling theory, and the third entails calculating the test size and sites layout based on the spatial clustering results of the crop planting environment
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