Stochastic simulation of maize productivity: spatial and temporal uncertainty

Detalhes bibliográficos
Autor(a) principal: GRIFO, ARL
Data de Publicação: 2015
Outros Autores: MARQUES DA SILVA, JR
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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spelling 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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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repository.mail.fl_str_mv
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