Stochastic simulation of maize productivity: spatial and temporal uncertainty
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/17355 https://doi.org/10.1007/s11119-015-9401-1 |
Resumo: | There is emerging interest in evaluating the uncertainty of agricultural production to support the production process and for guidance in decision making. The main objective of this work was to estimate the spatial and temporal maize yield uncertainty using stochastic simulation techniques to reduce the economic risk considering the producer risk profile and the international prices of maize and inputs. The results showed that (i) the class yield percentage variation in yield stochastic simulation depends on the sampling density; (ii) higher sampling densities promote an overestimation of low and high yield values compared to those of real yield data; (iii) reducing sampling density promotes the low and high values of overestimation reduction while increasing the central classes values compared to those of real yield data; (iv) the ideal point density for yield stochastic simulation is approximately 65 points/ha; (v) in Mediterranean environments, more than 3–4 years’ worth of real yield data considered as a whole do not seem to improve the parcel level of confidence when cropping irrigated maize; and (vi) the number of equiprobable surfaces that were generated by sequential Gaussian simulation helped to calculate the yield class uncertainty and permitted the study of class yield probabilities for a particular position of the parcel and, therefore, to manage the yield risk and support future decisions. The approach that is presented in this paper may increase prior knowledge of agricultural parcel behavior in the absence of multi-year data, thereby increasing the possibility of reducing economic risks. |
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Stochastic simulation of maize productivity: spatial and temporal uncertaintyMaizeYield spatial and temporal uncertaintyRisk managementStochastic simulationThere is emerging interest in evaluating the uncertainty of agricultural production to support the production process and for guidance in decision making. The main objective of this work was to estimate the spatial and temporal maize yield uncertainty using stochastic simulation techniques to reduce the economic risk considering the producer risk profile and the international prices of maize and inputs. The results showed that (i) the class yield percentage variation in yield stochastic simulation depends on the sampling density; (ii) higher sampling densities promote an overestimation of low and high yield values compared to those of real yield data; (iii) reducing sampling density promotes the low and high values of overestimation reduction while increasing the central classes values compared to those of real yield data; (iv) the ideal point density for yield stochastic simulation is approximately 65 points/ha; (v) in Mediterranean environments, more than 3–4 years’ worth of real yield data considered as a whole do not seem to improve the parcel level of confidence when cropping irrigated maize; and (vi) the number of equiprobable surfaces that were generated by sequential Gaussian simulation helped to calculate the yield class uncertainty and permitted the study of class yield probabilities for a particular position of the parcel and, therefore, to manage the yield risk and support future decisions. The approach that is presented in this paper may increase prior knowledge of agricultural parcel behavior in the absence of multi-year data, thereby increasing the possibility of reducing economic risks.SPRINGER2016-02-16T11:40:43Z2016-02-162015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/17355http://hdl.handle.net/10174/17355https://doi.org/10.1007/s11119-015-9401-1eng• GRIFO, A. R. L.; MARQUES DA SILVA, JOSÉ R. (2015). Stochastic simulation of maize productivity: spatial and temporal uncertainty. Precision Agriculture Journal, (16) 668–689ERUndjmsilva@uevora.pt580GRIFO, ARLMARQUES DA SILVA, JRinfo: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:RCAAP2024-01-03T19:04:43Zoai:dspace.uevora.pt:10174/17355Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:09:30.547718Repositório 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 |
Stochastic simulation of maize productivity: spatial and temporal uncertainty |
title |
Stochastic simulation of maize productivity: spatial and temporal uncertainty |
spellingShingle |
Stochastic simulation of maize productivity: spatial and temporal uncertainty GRIFO, ARL Maize Yield spatial and temporal uncertainty Risk management Stochastic simulation |
title_short |
Stochastic simulation of maize productivity: spatial and temporal uncertainty |
title_full |
Stochastic simulation of maize productivity: spatial and temporal uncertainty |
title_fullStr |
Stochastic simulation of maize productivity: spatial and temporal uncertainty |
title_full_unstemmed |
Stochastic simulation of maize productivity: spatial and temporal uncertainty |
title_sort |
Stochastic simulation of maize productivity: spatial and temporal uncertainty |
author |
GRIFO, ARL |
author_facet |
GRIFO, ARL MARQUES DA SILVA, JR |
author_role |
author |
author2 |
MARQUES DA SILVA, JR |
author2_role |
author |
dc.contributor.author.fl_str_mv |
GRIFO, ARL MARQUES DA SILVA, JR |
dc.subject.por.fl_str_mv |
Maize Yield spatial and temporal uncertainty Risk management Stochastic simulation |
topic |
Maize Yield spatial and temporal uncertainty Risk management Stochastic simulation |
description |
There is emerging interest in evaluating the uncertainty of agricultural production to support the production process and for guidance in decision making. The main objective of this work was to estimate the spatial and temporal maize yield uncertainty using stochastic simulation techniques to reduce the economic risk considering the producer risk profile and the international prices of maize and inputs. The results showed that (i) the class yield percentage variation in yield stochastic simulation depends on the sampling density; (ii) higher sampling densities promote an overestimation of low and high yield values compared to those of real yield data; (iii) reducing sampling density promotes the low and high values of overestimation reduction while increasing the central classes values compared to those of real yield data; (iv) the ideal point density for yield stochastic simulation is approximately 65 points/ha; (v) in Mediterranean environments, more than 3–4 years’ worth of real yield data considered as a whole do not seem to improve the parcel level of confidence when cropping irrigated maize; and (vi) the number of equiprobable surfaces that were generated by sequential Gaussian simulation helped to calculate the yield class uncertainty and permitted the study of class yield probabilities for a particular position of the parcel and, therefore, to manage the yield risk and support future decisions. The approach that is presented in this paper may increase prior knowledge of agricultural parcel behavior in the absence of multi-year data, thereby increasing the possibility of reducing economic risks. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-01-01T00:00:00Z 2016-02-16T11:40:43Z 2016-02-16 |
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/10174/17355 http://hdl.handle.net/10174/17355 https://doi.org/10.1007/s11119-015-9401-1 |
url |
http://hdl.handle.net/10174/17355 https://doi.org/10.1007/s11119-015-9401-1 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
• GRIFO, A. R. L.; MARQUES DA SILVA, JOSÉ R. (2015). Stochastic simulation of maize productivity: spatial and temporal uncertainty. Precision Agriculture Journal, (16) 668–689 ERU nd jmsilva@uevora.pt 580 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
SPRINGER |
publisher.none.fl_str_mv |
SPRINGER |
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) |
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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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1799136577267433472 |