Modelling uncertainty in field grown iceberg lettuce production for decision support

Harwood, Thomas D.; Said, Fahad A. Al; Pearson, Simon; Houghton, S. J.; Hadley, P.

doi:10.1016/j.compag.2009.12.003

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…However, few FMIS deal with uncertainty in farm management problems (Engel et al, 2003;Bange et al, 2004;Harwood et al, 2010), while most revert to solely deterministic aspects (Thomson and Willoughby, 2004;Sahu and Raheman, 2008;Papadopoulos et al, 2011). In this regard, uncertainty assessment is the least well understood and implemented capability of farm management and DSSs.…”

Section: Reportingmentioning

confidence: 94%

Farm management information systems: Current situation and future perspectives

Fountas

Carli

Sørensen

et al. 2015

Computers and Electronics in Agriculture

282

122

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a b s t r a c tFarm Management Information Systems (FMIS) in agriculture have evolved from simple farm recordkeeping into sophisticated and complex systems to support production management. The purpose of current FMIS is to meet the increased demands to reduce production costs, comply with agricultural standards, and maintain high product quality and safety. This paper presents current advancements in the functionality of academic and commercial FMIS. The study focuses on open-field crop production and centeres on farm managers as the primary users and decision makers. Core system architectures and application domains, adoption and profitability, and FMIS solutions for precision agriculture as the most information-intensive application area were analyzed. Our review of commercial solutions involved the analysis of 141 international software packages, categorized into 11 functions. Cluster analysis was used to group current commercial FMIS as well as examine possible avenues for further development. Academic FMIS involved more sophisticated systems covering compliance to standards applications, automated data capture as well as interoperability between different software packages. Conversely, commercial FMIS applications targeted everyday farm office tasks related to budgeting and finance, such as recordkeeping, machinery management, and documentation, with emerging trends showing new functions related to traceability, quality assurance and sales.

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Section: Reportingmentioning

confidence: 94%

Farm management information systems: Current situation and future perspectives

Fountas

Carli

Sørensen

et al. 2015

Computers and Electronics in Agriculture

282

122

View full text Add to dashboard Cite

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“…Decision support rules and models facilitate the use of a combination of techniques (including pesticides) to control pests and diseases [20]. Challenges relating to real-world use include accessing commercial data to validate and improve models [22] and little continued adoption due to the complexity of the models [23]. The limitations of these models include inaccuracy in capturing late-season trends at higher latitudes or inaccuracy due to slower growth rates [22].…”

Section: Lettuce Production and Decision Support Systemsmentioning

confidence: 99%

A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks

Barons,

Walsh,

Salakpi

et al. 2024

Agriculture

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The EU Green Deal requires the reduction in pesticides and fertilisers in food crop production, whilst the sustainable development goals require reductions in food loss and food waste. In a complex and interacting system like the food system, these goals are difficult to coordinate. Here, we show an approach using Bayesian network modelling for decision support. Bayesian networks are important tools for modelling complex systems which may develop emergent behaviour and for providing quantitative comparisons for different candidate policies, approaches or interventions under the Integrating Decision Support System paradigm. Using lettuce as an exemplar crop, we demonstrate that expected food loss changes under different agricultural input reduction and integrated pest management combinations can be quantified to aid decision making for growers.

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“…Uncertainties, such as mechanical failure and weather change [29,30] , would impact the implementation of a farming plan made at the beginning of the growing season, especially for machinery cooperatives that providing services to external customers [23] . How to handle uncertainties reveals the capacity of a machinery cooperatives to adjust according to changes and exploit opportunities from internal and external changes [13] .…”

mentioning

confidence: 99%

Classification and evaluation of uncertain influence factors for farm machinery service

Cai²,

Bingbing

et al. 2017

International Journal of Agricultural and Biological Engineering

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Uncertainty extremely interferes with the execution of farm machinery operation. Treating uncertainties is especially important for machinery cooperatives providing social service since they face more uncertain influence factors (UIFs) than family farms. Under social service circumstance, uncertainties may arise from participants and environments. Classification and evaluation of UIFs were studied in this research. According to the production system, 32 UIFs are defined and classified into six categories, which include supply, demand, interactivity, nature, society and others. Uncertainty composite index (UCI) is defined to evaluate the importance of UIFs, which is the square root of the product of occurrence frequency (OF) and impact degree (ID) calculated from the well-designed questionnaire responded by farm machinery operators. UCI is divided into five ranks based on normalization distribution test to illustrate the level of importance. Results from questionnaire showed that natural UIFs have an extreme impact on farm operation, UIFs of the demand and the supply have a serious influence on farm operation, UIFs of interactivity cannot be ignored, and social UIFs have a weak impact on farm operations. This study discovered the uncertainty problems under the specific circumstance of farm machinery service, which may provide a theoretical basis and potential methods for risk management of machinery cooperatives.

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Modelling uncertainty in field grown iceberg lettuce production for decision support

Cited by 8 publications

References 14 publications

Farm management information systems: Current situation and future perspectives

Farm management information systems: Current situation and future perspectives

A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks

Classification and evaluation of uncertain influence factors for farm machinery service

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