Reducing cane harvest losses is an important part of farm management. Harvest waste consists of two parts: the collected waste and non-collectable waste. These cane losses are mainly caused by the operation of harvesting machines and the performance of their operators. In Australia, the Norris Crop Energy Technology Company designed software to manage sugarcane harvesting system (SCHLOT) and we found that it is a valuable model for Iranian sugarcane harvesters that can optimise Austoft 7000 harvester machines. Factors including linear ground speed of the harvester, the speed of fan rotation, conditions of field harvest variety, field density and yield, the type of harvest (green and burned), the condition of cane lodging in field, and the age of the farm can affect harvesting losses. This experiment has been carried out on 3 000 hectares of Salman Farsi agro-industry farms. This design consists of two treatments, a control treatment and a harvesting optimisation model (application of linear speed of harvester and fan rotation according to the factors involved). The tested surface was 1 500 ha per treatment. Regarding the field conditions such as height, number of stalks per hectare, age of field and field moisture the farms were divided into four age groups, and the linear and optimal fan speeds of harvesters were regulated. The selected 1 500 ha fields were harvested based on harvest optimisation treatment and another 1 500 ha were selected as control and harvested based on conventional method. The results showed that the harvest losses were significantly reduced by using the harvest optimisation counting model. In harvesting optimisation model, average harvest losses were 2.1 t/ha and in the previous and usual method it was 4.4 t/ha. Harvest losses with the optimised harvesting method were 2.4 t/ha lower. Harvesting sugarcane fields with the optimisation method will increase the farmer's or company's income from each hectare of harvested cane. Reducing cane harvest losses is an important part of farm management. Harvest waste consists of two parts: the collected waste and non-collectable waste. These cane losses are mainly caused by the operation of harvesting machines and the performance of their operators. In Australia, the Norris Crop Energy Technology Company designed software to manage sugarcane harvesting system (SCHLOT) and we found that it is a valuable model for Iranian sugarcane harvesters that can optimise Austoft 7000 harvester machines. Factors including linear ground speed of the harvester, the speed of fan rotation, conditions of field harvest variety, field density and yield, the type of harvest (green and burned), the condition of cane lodging in field, and the age of the farm can affect harvesting losses. This experiment has been carried out on 3 000 hectares of Salman Farsi agro-industry farms. This design consists of two treatments, a control treatment and a harvesting optimisation model (application of linear speed of harvester and fan rotation according to the factors involved). The tested surface was 1 500 ha per treatment. Regarding the field conditions such as height, number of stalks per hectare, age of field and field moisture the farms were divided into four age groups, and the linear and optimal fan speeds of harvesters were regulated. The selected 1 500 ha fields were harvested based on harvest optimisation treatment and another 1 500 ha were selected as control and harvested based on conventional method. The results showed that the harvest losses were significantly reduced by using the harvest optimisation counting model. In harvesting optimisation model, average harvest losses were 2.1 t/ha and in the previous and usual method it was 4.4 t/ha. Harvest losses with the optimised harvesting method were 2.4 t/ha lower. Harvesting sugarcane fields with the optimisation method will increase the farmer's or company's income from each hectare of harvested cane.
