Abstract:
The focus of this study is to gain a better understanding of the hazards affecting the transportation of avocados from farm to packhouse by developing an effective risk assessment tool farmers can use. The transport related factors considered in this study encompass all hazards which may affect the avocado, from the point the fruit is picked to the point the avocado is packed at the packhouse.
The study has been undertaken in five stages, namely:
A literature study split into four main stages, including an investigation into avocado specific hazards, transportation related hazards, market influencers and investigating analysis tools.
Data collection (including environmental indicators, accelerations and GPS measurements) stemming from field tests conducted with a smart avocado device (smAvo);
Data analysis of the smAvos, including assessing the kinetic energy the avocado experiences;
Risk analysis and Bayesian Network Development including those hazards identified in the literature study as well as from the smAvo, and
Bayesian Network analysis, using Delphi Fuzzy methodology and smAvo data to determine the influence of the combination of risk factors identified.
The risk assessment tool was developed through the use of Bayesian Networks. This tool eliminates the guesswork of what causes the largest reduction in shelf life/waste and therefore profit. The Network considers the joint probability of these hazards, and posterior probabilities of any subset of variables when evidence is introduced.
The Bayesian Network is analysed and optimised by means of finding factors that will cause the greatest improvement of shelf life and decreased damage. A converse analysis is done by determining the effect of, for example poor road conditions or truck type. The result of this analysis provides the farmer with a decision-making tool which will optimise processes, increase profits (by reducing waste) and eliminate any guesswork. The Network can be used by the farmer and updated as new evidence is discovered.
The analysis concludes with the most damaging areas within the network is at harvest, followed by truck transportation effects, packhouse conditions and lastly farm transportation effects. In order to optimise the network, emphasis is put on the plant condition, followed by any delay in transportation and the picking technique used during harvest. A “what-if” analysis was done which concluded poor road conditions can increase overall damage by 0.44 per cent, whereas poor harvest conditions can increase this to 12.57 per cent.
