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

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  • Zuliang Zhao

    (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
    Current address: Department of Computer Science, The City College of New York, New York, NY, USA.)

  • Liu Zhe

    (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)

  • Xiaodong Zhang

    (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)

  • Xuli Zan

    (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)

  • 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
    Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, 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
    Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, China)

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.

Suggested Citation

  • Zuliang Zhao & Liu Zhe & Xiaodong Zhang & Xuli Zan & Xiaochuang Yao & Sijia Wang & Sijing Ye & Shaoming Li & Dehai Zhu, 2018. "Spatial Layout of Multi-Environment Test Sites: A Case Study of Maize in Jilin Province," Sustainability, MDPI, vol. 10(5), pages 1-13, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1424-:d:144540
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    References listed on IDEAS

    as
    1. Dan Wang & Guangyong Li & Yan Mo & Mingkun Cai & Xinyang Bian, 2017. "Effect of Planting Date on Accumulated Temperature and Maize Growth under Mulched Drip Irrigation in a Middle-Latitude Area with Frequent Chilling Injury," Sustainability, MDPI, vol. 9(9), pages 1-16, August.
    2. Anton Grafström & Niklas L. P. Lundström & Lina Schelin, 2012. "Spatially Balanced Sampling through the Pivotal Method," Biometrics, The International Biometric Society, vol. 68(2), pages 514-520, June.
    3. Manfred M. Fischer & Jinfeng Wang, 2011. "Spatial Data Analysis," SpringerBriefs in Regional Science, Springer, number 978-3-642-21720-3.
    4. Qingfeng Meng & Peng Hou & David Lobell & Hongfei Wang & Zhenling Cui & Fusuo Zhang & Xinping Chen, 2014. "The benefits of recent warming for maize production in high latitude China," Climatic Change, Springer, vol. 122(1), pages 341-349, January.
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    Cited by:

    1. Ebelechukwu Maduekwe & Walter Timo de Vries, 2019. "Random Spatial and Systematic Random Sampling Approach to Development Survey Data: Evidence from Field Application in Malawi," Sustainability, MDPI, vol. 11(24), pages 1-27, December.
    2. Xuli Zan & Zuliang Zhao & Wei Liu & Xiaodong Zhang & Zhe Liu & Shaoming Li & Dehai Zhu, 2019. "The Layout of Maize Variety Test Sites Based on the Spatiotemporal Classification of the Planting Environment," Sustainability, MDPI, vol. 11(13), pages 1-15, July.
    3. Ye, Sijing & Song, Changqing & Shen, Shi & Gao, Peichao & Cheng, Changxiu & Cheng, Feng & Wan, Changjun & Zhu, Dehai, 2020. "Spatial pattern of arable land-use intensity in China," Land Use Policy, Elsevier, vol. 99(C).

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