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

Zuliang Zhao, Liu Zhe, Xiaodong Zhang, Xuli Zan, Xiaochuang Yao, Sijia Wang, Sijing Ye, Shaoming Li and Dehai Zhu
Additional contact information
Zuliang Zhao: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Liu Zhe: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Xiaodong Zhang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Xuli Zan: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Xiaochuang Yao: Satellite Data Technology Division Institute of Remote Sensing and Digital Earth, CAS, Beijing 100093, China
Sijia Wang: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Sijing Ye: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
Shaoming Li: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Dehai Zhu: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Sustainability, 2018, vol. 10, issue 5, 1-13

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.

Keywords: spatial clustering; spatial stratified sampling; Multi-environment; maize (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2071-1050/10/5/1424/pdf (application/pdf)
https://www.mdpi.com/2071-1050/10/5/1424/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:10:y:2018:i:5:p:1424-:d:144540

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().