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James H Cock

Biographic Data

ID5428200
NAMEJames H Cock
GIVEN NAMESJames H
FAMILY NAMECock
SIGNATURECOCK J H
AFFILIATIONSInternational Center for Tropical Agriculture
ORCID0000-0001-9857-3568
VERIFIEDYes
TOTAL WORKS3
TOTAL CITATIONS5
WORKS AS AUTHOR3
WORKS AS EDITOR0
FIRST PUBLICATION YEAR1977
LATEST PUBLICATION YEAR2023
H-INDEX1
  • Operations research and machine learning to manage risk and optimize production practices in agriculture

    Open Access•James H Cock, Daniel Jimenez et al.•ARTICLE•Current Opinion in Environmental…•2023•References: 10

    The potential for operations research (OR) with farmer-supplied data coupled with machine learning (ML) to improve crop management is explored through a series of case studies from developing countries. The information provided by the farmers ranged from solely yield to a description of the management of the crop and some details of the growth environment. The climate or weather conditions of the georeferenced farms were estimated from publicly a…

  • Benefits to cassava consumers and producers

    Open Access•James H Cock, Judith Lynam et al.•ARTICLE•Food Policy•1990

  • Centres of origin of crops and their productivity

    Open Access•Peter R Jennings, James H Cock•ARTICLE•Economic Botany•1977•Cited by: 5•References: 1

  • Centres of origin of crops and their productivity

    Open Access•Peter R Jennings, James H Cock•ARTICLE•Economic Botany•1977•Cited by: 5•References: 1

  • Centres of origin of crops and their productivity

    Open Access•Peter R Jennings, James H Cock•ARTICLE•Economic Botany•1977•Cited by: 5•References: 1

  • Benefits to cassava consumers and producers

    Open Access•James H Cock, Judith Lynam et al.•ARTICLE•Food Policy•1990

  • Operations research and machine learning to manage risk and optimize production practices in agriculture

    Open Access•James H Cock, Daniel Jimenez et al.•ARTICLE•Current Opinion in Environmental…•2023•References: 10

    The potential for operations research (OR) with farmer-supplied data coupled with machine learning (ML) to improve crop management is explored through a series of case studies from developing countries. The information provided by the farmers ranged from solely yield to a description of the management of the crop and some details of the growth environment. The climate or weather conditions of the georeferenced farms were estimated from publicly a…

Economics (3 works) · Agricultural economics (2 works) · Biology (2 works) · Business (2 works) · Geography (2 works) · Agricultural and Food Production Studies (1 work) · Agricultural engineering (1 work) · Agricultural risk and resilience (1 work) · Agricultural science (1 work) · Agriculture (1 work)

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