Crop-Yield Distributions Revisited
This article revisits the issue of crop-yield distributions using improved model specifications, estimation, and testing procedures that address the concerns raised in recent literature, which could have invalidated previous findings of yield nonnormality. It concludes that some aggregate and farm-l...
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Veröffentlicht in: | American journal of agricultural economics 2003-02, Vol.85 (1), p.108-120 |
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container_title | American journal of agricultural economics |
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creator | Ramirez, Octavio A. Misra, Sukant Field, James |
description | This article revisits the issue of crop-yield distributions using improved model specifications, estimation, and testing procedures that address the concerns raised in recent literature, which could have invalidated previous findings of yield nonnormality. It concludes that some aggregate and farm-level yield distributions are nonnormal, kurtotic, and right or left skewed, depending on the circumstances. The advantages of utilizing nonnormal versus normal probability distribution function models, and the consequences of incorrectly assuming crop-yield normality are explored. |
doi_str_mv | 10.1111/1467-8276.00106 |
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It concludes that some aggregate and farm-level yield distributions are nonnormal, kurtotic, and right or left skewed, depending on the circumstances. The advantages of utilizing nonnormal versus normal probability distribution function models, and the consequences of incorrectly assuming crop-yield normality are explored.</description><identifier>ISSN: 0002-9092</identifier><identifier>EISSN: 1467-8276</identifier><identifier>DOI: 10.1111/1467-8276.00106</identifier><identifier>CODEN: AJAEBA</identifier><language>eng</language><publisher>Malden: Oxford University Press</publisher><subject>Agricultural economics ; Agricultural production ; Agriculture ; Arid zones ; Corn Belt ; Cotton ; Crop insurance ; Crop yield ; crop-yields ; Crops ; Distribution ; Dryland farming ; Economics ; Expected utility ; Mathematical methods ; Modeling ; nonnormality ; Parametric models ; Probability ; probability distributions ; Q120 ; Random variables ; Simulations ; Skewed distribution ; Skewness ; Soybeans ; Statistical significance ; Texas ; Texas cotton ; U.S.A</subject><ispartof>American journal of agricultural economics, 2003-02, Vol.85 (1), p.108-120</ispartof><rights>Copyright 2003 American Agricultural Economics Association</rights><rights>Copyright 2003 American Agricultural Economics Association 2003</rights><rights>2003 Agricultural and Applied Economics Association</rights><rights>Copyright Blackwell Publishers Inc. Feb 2003</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5496-c46b5bff5cd574eb47546735161448b7ecd77e8f2363750cd200837fa5c15d423</citedby><cites>FETCH-LOGICAL-c5496-c46b5bff5cd574eb47546735161448b7ecd77e8f2363750cd200837fa5c15d423</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/1244930$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/1244930$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,1411,27901,27902,45550,45551,57992,58225</link.rule.ids></links><search><creatorcontrib>Ramirez, Octavio A.</creatorcontrib><creatorcontrib>Misra, Sukant</creatorcontrib><creatorcontrib>Field, James</creatorcontrib><title>Crop-Yield Distributions Revisited</title><title>American journal of agricultural economics</title><addtitle>American Journal of Agricultural Economics</addtitle><addtitle>American Journal of Agricultural Economics</addtitle><description>This article revisits the issue of crop-yield distributions using improved model specifications, estimation, and testing procedures that address the concerns raised in recent literature, which could have invalidated previous findings of yield nonnormality. It concludes that some aggregate and farm-level yield distributions are nonnormal, kurtotic, and right or left skewed, depending on the circumstances. The advantages of utilizing nonnormal versus normal probability distribution function models, and the consequences of incorrectly assuming crop-yield normality are explored.</description><subject>Agricultural economics</subject><subject>Agricultural production</subject><subject>Agriculture</subject><subject>Arid zones</subject><subject>Corn Belt</subject><subject>Cotton</subject><subject>Crop insurance</subject><subject>Crop yield</subject><subject>crop-yields</subject><subject>Crops</subject><subject>Distribution</subject><subject>Dryland farming</subject><subject>Economics</subject><subject>Expected utility</subject><subject>Mathematical methods</subject><subject>Modeling</subject><subject>nonnormality</subject><subject>Parametric models</subject><subject>Probability</subject><subject>probability distributions</subject><subject>Q120</subject><subject>Random variables</subject><subject>Simulations</subject><subject>Skewed distribution</subject><subject>Skewness</subject><subject>Soybeans</subject><subject>Statistical significance</subject><subject>Texas</subject><subject>Texas cotton</subject><subject>U.S.A</subject><issn>0002-9092</issn><issn>1467-8276</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><recordid>eNqFkc1LwzAYxoMoOKdnLx7GDh6EbPls2osw5twcguAHqJfQpilkdmtNWnX_vanViSKYS8j7_p7nfXkCwCFGA-zPELNAwJCIYIAQRsEW6Gwq26CDECIwQhHZBXvOLfwT4SjsgP7YFiV8MDpPe2fGVdYkdWWKletd6xfjTKXTfbCTxbnTB593F9ydT27HM3h5Nb0Yjy6h4iwKoGJBwpMs4yrlgumECe7nU44DzFiYCK1SIXSYERpQwZFKCUIhFVnMFeYpI7QLjlvf0hbPtXaVXBqndJ7HK13UTtKQkQgH3IP9X-CiqO3K7yYJpVhQJkIPDVtI2cI5qzNZWrOM7VpiJJvAZBOPbOKRH4F5xWmreDW5Xv-Hy9F8NGlqTenL4KQ1KOryTzn8Me2ohReuKuw3ThiLKPJt2Lb9n-i3TTu2T9KHKric3T_KaEbRHE2JvKHvUSKQHQ</recordid><startdate>200302</startdate><enddate>200302</enddate><creator>Ramirez, Octavio A.</creator><creator>Misra, Sukant</creator><creator>Field, James</creator><general>Oxford University Press</general><general>Blackwell Publishing</general><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>8BJ</scope><scope>C1K</scope><scope>FQK</scope><scope>JBE</scope><scope>SOI</scope></search><sort><creationdate>200302</creationdate><title>Crop-Yield Distributions Revisited</title><author>Ramirez, Octavio A. ; Misra, Sukant ; Field, James</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5496-c46b5bff5cd574eb47546735161448b7ecd77e8f2363750cd200837fa5c15d423</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Agricultural economics</topic><topic>Agricultural production</topic><topic>Agriculture</topic><topic>Arid zones</topic><topic>Corn Belt</topic><topic>Cotton</topic><topic>Crop insurance</topic><topic>Crop yield</topic><topic>crop-yields</topic><topic>Crops</topic><topic>Distribution</topic><topic>Dryland farming</topic><topic>Economics</topic><topic>Expected utility</topic><topic>Mathematical methods</topic><topic>Modeling</topic><topic>nonnormality</topic><topic>Parametric models</topic><topic>Probability</topic><topic>probability distributions</topic><topic>Q120</topic><topic>Random variables</topic><topic>Simulations</topic><topic>Skewed distribution</topic><topic>Skewness</topic><topic>Soybeans</topic><topic>Statistical significance</topic><topic>Texas</topic><topic>Texas cotton</topic><topic>U.S.A</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ramirez, Octavio A.</creatorcontrib><creatorcontrib>Misra, Sukant</creatorcontrib><creatorcontrib>Field, James</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>Environment Abstracts</collection><jtitle>American journal of agricultural economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ramirez, Octavio A.</au><au>Misra, Sukant</au><au>Field, James</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Crop-Yield Distributions Revisited</atitle><jtitle>American journal of agricultural economics</jtitle><stitle>American Journal of Agricultural Economics</stitle><addtitle>American Journal of Agricultural Economics</addtitle><date>2003-02</date><risdate>2003</risdate><volume>85</volume><issue>1</issue><spage>108</spage><epage>120</epage><pages>108-120</pages><issn>0002-9092</issn><eissn>1467-8276</eissn><coden>AJAEBA</coden><abstract>This article revisits the issue of crop-yield distributions using improved model specifications, estimation, and testing procedures that address the concerns raised in recent literature, which could have invalidated previous findings of yield nonnormality. It concludes that some aggregate and farm-level yield distributions are nonnormal, kurtotic, and right or left skewed, depending on the circumstances. The advantages of utilizing nonnormal versus normal probability distribution function models, and the consequences of incorrectly assuming crop-yield normality are explored.</abstract><cop>Malden</cop><pub>Oxford University Press</pub><doi>10.1111/1467-8276.00106</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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source | Jstor Complete Legacy; Wiley Online Library Journals Frontfile Complete; Business Source Complete |
subjects | Agricultural economics Agricultural production Agriculture Arid zones Corn Belt Cotton Crop insurance Crop yield crop-yields Crops Distribution Dryland farming Economics Expected utility Mathematical methods Modeling nonnormality Parametric models Probability probability distributions Q120 Random variables Simulations Skewed distribution Skewness Soybeans Statistical significance Texas Texas cotton U.S.A |
title | Crop-Yield Distributions Revisited |
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