Abstract
Variety regional tests based on multiple environments play a critical role in understanding the high yield and adaptability of new crop varieties. However, the current approach mainly depends on experience from breeding experts and is difficulty to promote because of inconsistency between testing and actual situation. We propose a spatial layout method based on the existing systematic regional test network. First, the method of spatial clustering was used to cluster the planting environment. Then, we used spatial stratified sampling to determine the minimum number of test sites in each type of environment. Finally, combined with the factors such as the convenience of transportation and the planting area, we used spatial balance sampling to generate the layout of multi-environment test sites. We present a case study for maize in Jilin Province and show the utility of the method with an accuracy of about 94.5%. The experimental results showed that 66.7% of sites are located in the same county and the unbalanced layout of original sites is improved. Furthermore, we conclude that the set of operational technical ideas for carrying out the layout of multi-environment test sites based on crop varieties in this paper can be applied to future research.
Highlights
Variety regional test, as a key to new crop variety performance and market prospects, has an irreplaceable role in breeding [1]
We propose a three-stage spatial layout method: (1) based on meteorological data, soil nutrient data, and topographical data during the phenophase period, we clustered the planting environment by spatial clustering method; (2) we used spatial stratified sampling to determine the minimum number of test sites in each type of environment; and (3) combined with factors such as the convenience of transportation and the planting area, the layout of multi-environment test sites was constructed according to spatially balanced sampling method
We proposed an integrated clustering algorithm for spatial attributes based on ISOData method [34,35], where the implementation is in three phases
Summary
As a key to new crop variety performance and market prospects, has an irreplaceable role in breeding [1]. Since 2000, United States constructed a regional test network based on hundreds of test sites to represent almost all types of planting environments [2]. China has built a systematic regional test network [3,4,5]. To accurately assess each variety within 2–3 years, every test site must be highly representative of planting environments, which cover several elements such as weather, soil, terrain, biological factors, etc., called multi-environments. Regional test results are still inconsistent with actual crop results. An important reason for this result is that neither the number nor the locations of test sites could adequately represent the multi-environments
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