Large commercial sugarcane operations face complex replant planning decisions. The replant operation is costly and limited resources must be employed where they are likely to produce the largest yield improvement. These decisions are complicated further by the need to evaluate the benefit across multiple cutting seasons. Typically, replant field selection is based on historical performance, and poorest yielding fields on poorer soils tend to be prioritised for replant. However, this approach might not maximise estate-wide productivity. The decision making process needs to consider: which fields should be selected for replant in which season; what varieties should they be replanted back to; and what long-term harvest sequence should be followed to minimise harvest age effects; to maximise sucrose production in current and future seasons. A replant planning decision support framework was developed in CanePro, a commercially available Agricultural Management System, to assist with this complex task. Field selection was made by benchmarking actual field cane yields against potential yields discounted for soil type and ratoon using a soil type/ratoon matrix. Climatic potential yields were estimated using a simplified version of the CANEGRO crop simulation model. Each field's ideal replant ratoon age was estimated by maximising the total (across all fields) of the average (across all ratoons) expected yield of each field. Fields were assigned a replant date based on their ratio of current to ideal replant ratoon age within the estate's replant capacity constraints. Replant variety selection was made by optimising overall sucrose performance in a season using variety-specific sucrose curves. The harvest sequence was adapted to maximise overall sucrose production. To evaluate the methodology, four seasons of historical field data were obtained from a commercial operation in Swaziland. Actual estate practice was compared with the replant recommendations made using the CanePro framework. The relative performance of the scenarios was evaluated by comparing the overall sucrose yield simulated for each scenario. A 0.6% improvement was attributed to the CanePro field selection algorithm and a further 0.6% to the harvest sequencing algorithm.
