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Open AccessArticle

Spatial Layout of Multi-Environment Test Sites: A Case Study of Maize in Jilin Province

College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, China
Satellite Data Technology Division Institute of Remote Sensing and Digital Earth, CAS, Beijing 100093, China
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
Author to whom correspondence should be addressed.
Current address: Department of Computer Science, The City College of New York, New York, NY, USA.
Sustainability 2018, 10(5), 1424;
Received: 9 April 2018 / Revised: 23 April 2018 / Accepted: 26 April 2018 / Published: 4 May 2018
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. View Full-Text
Keywords: spatial clustering; spatial stratified sampling; Multi-environment; maize spatial clustering; spatial stratified sampling; Multi-environment; maize
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Zhao, Z.; Zhe, L.; Zhang, X.; Zan, X.; Yao, X.; Wang, S.; Ye, S.; Li, S.; Zhu, D. Spatial Layout of Multi-Environment Test Sites: A Case Study of Maize in Jilin Province. Sustainability 2018, 10, 1424.

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