Yield potential probability maps using the Rasch model
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/5048 |
Resumo: | Yield monitors commonly show that there are very large yield differences within a field which often differ from year to year. Because our ability to estimate reductions in growth and to quantify yield losses resulting from complex interactions and multiple stresses is limited, it does not appear feasible to analyse yield variability using a point to point strategy. For a farmer it is important to select parcels of land, or parts of a parcel, with a high yield probability. To analyse the high yield probability zones the Rasch model is used considering a multi-temporal yield data set. The Rasch measure for multi-temporal yield data makes it possible to place on a continuum axis the yield samples considered in terms of annual yield and vice versa. Using the Rasch measurement one can produce yield potential probabilistic maps taking into account each sample coordinate. From a quantitative point of view it is possible to find yield samples that do not support the model, or which do not reach the expected levels. Positive and negative mismatches can be analysed individually or according to a particular year yield. Thus, the Rasch model makes it possible to systematise the data, making it an effective tool for making appropriate decisions regarding areas with higher yield performance and greater stability over time. Also, it makes it possible to compare the yields of different samples and provide appropriate measures to correct, differentially, samples that obtained different inadequate levels. |
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Yield potential probability maps using the Rasch modelmaize yieldprobability mapsYield monitors commonly show that there are very large yield differences within a field which often differ from year to year. Because our ability to estimate reductions in growth and to quantify yield losses resulting from complex interactions and multiple stresses is limited, it does not appear feasible to analyse yield variability using a point to point strategy. For a farmer it is important to select parcels of land, or parts of a parcel, with a high yield probability. To analyse the high yield probability zones the Rasch model is used considering a multi-temporal yield data set. The Rasch measure for multi-temporal yield data makes it possible to place on a continuum axis the yield samples considered in terms of annual yield and vice versa. Using the Rasch measurement one can produce yield potential probabilistic maps taking into account each sample coordinate. From a quantitative point of view it is possible to find yield samples that do not support the model, or which do not reach the expected levels. Positive and negative mismatches can be analysed individually or according to a particular year yield. Thus, the Rasch model makes it possible to systematise the data, making it an effective tool for making appropriate decisions regarding areas with higher yield performance and greater stability over time. Also, it makes it possible to compare the yields of different samples and provide appropriate measures to correct, differentially, samples that obtained different inadequate levels.ELSEVIER2012-03-02T12:50:45Z2012-03-022012-02-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/5048http://hdl.handle.net/10174/5048porMarques da Silva, José Rafael; J. Rebollo, Francisco; Sousa, Adélia; Mesquita, Paulo (2012) Yield potential probability maps using the Rasch model. Biosystems Engeneering, 111(4), 369-380.111Biosystems EngineeringICAAMjmsilva@uevora.ptfrebollo@unex.esasousa@uevora.ptpaulomesquita00@gmail.com580Marques da Silva, José RafaelJ. Rebollo, FranciscoSousa, AdéliaMesquita, Pauloinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T18:43:23Zoai:dspace.uevora.pt:10174/5048Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:00:06.447459Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Yield potential probability maps using the Rasch model |
title |
Yield potential probability maps using the Rasch model |
spellingShingle |
Yield potential probability maps using the Rasch model Marques da Silva, José Rafael maize yield probability maps |
title_short |
Yield potential probability maps using the Rasch model |
title_full |
Yield potential probability maps using the Rasch model |
title_fullStr |
Yield potential probability maps using the Rasch model |
title_full_unstemmed |
Yield potential probability maps using the Rasch model |
title_sort |
Yield potential probability maps using the Rasch model |
author |
Marques da Silva, José Rafael |
author_facet |
Marques da Silva, José Rafael J. Rebollo, Francisco Sousa, Adélia Mesquita, Paulo |
author_role |
author |
author2 |
J. Rebollo, Francisco Sousa, Adélia Mesquita, Paulo |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Marques da Silva, José Rafael J. Rebollo, Francisco Sousa, Adélia Mesquita, Paulo |
dc.subject.por.fl_str_mv |
maize yield probability maps |
topic |
maize yield probability maps |
description |
Yield monitors commonly show that there are very large yield differences within a field which often differ from year to year. Because our ability to estimate reductions in growth and to quantify yield losses resulting from complex interactions and multiple stresses is limited, it does not appear feasible to analyse yield variability using a point to point strategy. For a farmer it is important to select parcels of land, or parts of a parcel, with a high yield probability. To analyse the high yield probability zones the Rasch model is used considering a multi-temporal yield data set. The Rasch measure for multi-temporal yield data makes it possible to place on a continuum axis the yield samples considered in terms of annual yield and vice versa. Using the Rasch measurement one can produce yield potential probabilistic maps taking into account each sample coordinate. From a quantitative point of view it is possible to find yield samples that do not support the model, or which do not reach the expected levels. Positive and negative mismatches can be analysed individually or according to a particular year yield. Thus, the Rasch model makes it possible to systematise the data, making it an effective tool for making appropriate decisions regarding areas with higher yield performance and greater stability over time. Also, it makes it possible to compare the yields of different samples and provide appropriate measures to correct, differentially, samples that obtained different inadequate levels. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-03-02T12:50:45Z 2012-03-02 2012-02-06T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/5048 http://hdl.handle.net/10174/5048 |
url |
http://hdl.handle.net/10174/5048 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
Marques da Silva, José Rafael; J. Rebollo, Francisco; Sousa, Adélia; Mesquita, Paulo (2012) Yield potential probability maps using the Rasch model. Biosystems Engeneering, 111(4), 369-380. 111 Biosystems Engineering ICAAM jmsilva@uevora.pt frebollo@unex.es asousa@uevora.pt paulomesquita00@gmail.com 580 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
ELSEVIER |
publisher.none.fl_str_mv |
ELSEVIER |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136483797368832 |