New Software Models Increase Efficiency of Olive Oil Production

Summary

A soft­ware mod­el­ing pro­gram devel­oped in Chile helps olive grow­ers make plant­ing and har­vest­ing deci­sions, result­ing in a 4% increase in olive oil pro­duc­tion. The pro­gram con­sid­ers fac­tors like bud­get, cli­mate risks, and syn­chro­niza­tion with refiner­ies, pro­vid­ing advice on when to har­vest and how to man­age resources effi­ciently.

Scientists in Chile have devel­oped a soft­ware mod­el­ing pro­gram to assist olive grow­ers in mak­ing key deci­sions about when to plant and har­vest their crops.

This pro­gram takes fac­tors such as the har­vester’s bud­get, poten­tial cli­mate risks and the level of syn­chro­niza­tion between plan­ta­tion and refiner­ies into account before pro­vid­ing advice to olive grow­ers.

“In olive oil pro­duc­tion two main agents are involved, the deci­sion-maker in the field and the deci­sion-maker in the mill,” said Eduardo Alvarez-Miranda, a researcher on the project. ​“The pro­posed model allows both deci­sions to be uni­fied by deter­min­ing the best flow of fruit from the field to the mill.”

The pro­gram was tested on an olive plan­ta­tion in the Region del Maule in cen­tral Chile and led to an increase in oil pro­duc­tion.

“With the same input of resources, they had a higher out­put of olive oil,” said Alvarez-Miranda. ​“The plan gen­er­ated by the model extracted four per­cent more olive oil than the plan given by the tra­di­tional man­ual pro­ce­dure.”

The way in which the mod­el­ing improved pro­duc­tiv­ity the most was by pro­vid­ing data about when to har­vest the olives and how many peo­ple and har­vest­ing machines were needed to do so.

“For the plan­ning stage it was espe­cially impor­tant,” said Alvarez-Miranda. ​“The mod­els told them how many peo­ple they needed to hire at cer­tain times dur­ing the har­vest sea­son as well as when to rent machines. It turned out they needed fewer har­vest­ing machines than they thought.”

The soft­ware pro­gram also pro­vided advice to the plan­ta­tion own­ers about man­ag­ing their resources more effi­ciently. The com­pany had been rent­ing the refin­ery to other olive pro­duc­ers in order to earn some extra income.

“We man­aged to incor­po­rate this into the model,” said Alvarez-Miranda. ​“We found that they were, in fact, los­ing money by let­ting their olives sit while they extracted oil from other pro­duc­ers’ olives.”

Olives must be processed soon after har­vest­ing in order to main­tain oil con­tent and avoid oxi­da­tion.

Alvarez-Miranda believes there is huge poten­tial in Chile for this type of sta­tis­ti­cal mod­el­ing.

A sim­i­lar model has already been used in Chile for har­vest­ing grapes. Scientists from Pontificia Universidad Catolica de Chile were able to decrease the oper­a­tional costs of one grape har­vester by 27 per­cent and labor costs by 16 per­cent.

However, there are a cou­ple of issues pre­vent­ing the pro­gram from being widely adopted among olive grow­ers: spe­cific com­puter soft­ware is required and not many olive pro­duc­ers are will­ing to pur­chase it. Then the data must be input into the soft­ware and the pro­gram must be run.

“Only one per­son knows how to use the tool at that plan­ta­tion,” said Alvarez-Miranda. ​“Even so, the plan­ta­tions own­ers paid for it and are likely to keep using it as long as that per­son stays on.”

• Eduardo Alvarez-Miranda


• International Journal of Production Economics