Spatial distribution of grain yield based on different sample scales and partitioning schemes and its error correction
Spatialization of grain yield can contribute to comprehensive analysis of grain yield with other natural and cultural data. Grain production has a close relationship with the distribution of farmland. Therefore, information on spatial distribution of farmland is an important parameter for spatializa...
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| Veröffentlicht in: | Nong ye gong cheng xue bao 2015-08, Vol.31 (15), p.272-278 |
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| container_title | Nong ye gong cheng xue bao |
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| creator | Ji, Guangxing Liao, Shunbao Yue, Yanlin Hou, Pengmin Yang, Xu |
| description | Spatialization of grain yield can contribute to comprehensive analysis of grain yield with other natural and cultural data. Grain production has a close relationship with the distribution of farmland. Therefore, information on spatial distribution of farmland is an important parameter for spatialization of grain yield, and the statistical analysis and modeling are the basic means to realize spatialization of grain yield. Spatialization of nationwide grain yield relates to sample scales and partitioning schemes. Different sample scales and partitioning schemes will inevitably lead to different errors of spatialization. In this paper, models considering farmland distribution and sample scales and partition schemes were proposed to estimate grain yield and its spatial distribution. The grain yield data were collected from 2005 Yellow Book of China. Data of paddy field, irrigated land, and dry land areas in each county or district were calculated. This research made up for the deficiency of spatial error analysis |
| doi_str_mv | 10.11975/j.issn.1002-6819.2015.15.037 |
| format | Article |
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Grain production has a close relationship with the distribution of farmland. Therefore, information on spatial distribution of farmland is an important parameter for spatialization of grain yield, and the statistical analysis and modeling are the basic means to realize spatialization of grain yield. Spatialization of nationwide grain yield relates to sample scales and partitioning schemes. Different sample scales and partitioning schemes will inevitably lead to different errors of spatialization. In this paper, models considering farmland distribution and sample scales and partition schemes were proposed to estimate grain yield and its spatial distribution. The grain yield data were collected from 2005 Yellow Book of China. Data of paddy field, irrigated land, and dry land areas in each county or district were calculated. This research made up for the deficiency of spatial error analysis</description><identifier>ISSN: 1002-6819</identifier><identifier>DOI: 10.11975/j.issn.1002-6819.2015.15.037</identifier><language>chi</language><subject>Error analysis ; Farmlands ; Grains ; Partitioning ; Samples ; Spatial distribution ; Statistical analysis ; Statistical methods</subject><ispartof>Nong ye gong cheng xue bao, 2015-08, Vol.31 (15), p.272-278</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,778,782,27911,27912</link.rule.ids></links><search><creatorcontrib>Ji, Guangxing</creatorcontrib><creatorcontrib>Liao, Shunbao</creatorcontrib><creatorcontrib>Yue, Yanlin</creatorcontrib><creatorcontrib>Hou, Pengmin</creatorcontrib><creatorcontrib>Yang, Xu</creatorcontrib><title>Spatial distribution of grain yield based on different sample scales and partitioning schemes and its error correction</title><title>Nong ye gong cheng xue bao</title><description>Spatialization of grain yield can contribute to comprehensive analysis of grain yield with other natural and cultural data. Grain production has a close relationship with the distribution of farmland. Therefore, information on spatial distribution of farmland is an important parameter for spatialization of grain yield, and the statistical analysis and modeling are the basic means to realize spatialization of grain yield. Spatialization of nationwide grain yield relates to sample scales and partitioning schemes. Different sample scales and partitioning schemes will inevitably lead to different errors of spatialization. In this paper, models considering farmland distribution and sample scales and partition schemes were proposed to estimate grain yield and its spatial distribution. The grain yield data were collected from 2005 Yellow Book of China. Data of paddy field, irrigated land, and dry land areas in each county or district were calculated. 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Grain production has a close relationship with the distribution of farmland. Therefore, information on spatial distribution of farmland is an important parameter for spatialization of grain yield, and the statistical analysis and modeling are the basic means to realize spatialization of grain yield. Spatialization of nationwide grain yield relates to sample scales and partitioning schemes. Different sample scales and partitioning schemes will inevitably lead to different errors of spatialization. In this paper, models considering farmland distribution and sample scales and partition schemes were proposed to estimate grain yield and its spatial distribution. The grain yield data were collected from 2005 Yellow Book of China. Data of paddy field, irrigated land, and dry land areas in each county or district were calculated. This research made up for the deficiency of spatial error analysis</abstract><doi>10.11975/j.issn.1002-6819.2015.15.037</doi><tpages>7</tpages></addata></record> |
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| language | chi |
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| source | IngentaConnect Free/Open Access Journals |
| subjects | Error analysis Farmlands Grains Partitioning Samples Spatial distribution Statistical analysis Statistical methods |
| title | Spatial distribution of grain yield based on different sample scales and partitioning schemes and its error correction |
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