CP 13-4100' (Reg. no. CV-202, PI 698546) sugarcane cultivar (a complex hybrid of Saccharum spp.) was developed through cooperative research conducted by the USDA-ARS, the University of Florida, and the Florida Sugar Cane League, Inc. The Florida Sugarcane Variety Committee (FSVC) released CP 13-4100 in June 2020 to growers for commercial cultivation on mineral (sand) soils. It originated from a polycross made at Canal Point (CP) on 3 Dec. 2008 where CP 96-1252 was the female parent and the male parent was unknown and could be any one of genotypes used in the polycross. CP 13-4100 was released by FSVC for its high cane and sucrose yields, acceptable commercial recoverable sucrose, and resistances to orange rust, leaf scald, Sugarcane mosaic virus strain E (mosaic), and smut, and acceptable levels of susceptibility to brown rust. CP 13-4100 and commercial reference checks (i.e., CL 88-4730, CP 96-1252, CPCL 97-2730) yield data were collected from 12 harvests (i.e. three crop cycles-plant cane, first, and second ratoon-at four sand-soil locations) in final-stage replicated yield trials. Compared with CL 88-4730 and CPCL 97-2730, CP 13-4100 had significantly (P ≤ .0001) higher cane yield (tonnes of cane per hectare: 18.7 and 29.5%), sucrose yield (tonnes of sugar per hectare: 26.3 and 30.7%), and economic index (EI; 10.9-16.9%) and no significant differences from CP 96-1252 in these traits. CP 13-4100 tested negative for the Bru1 marker, which is linked to a quantitative major locus of brown rust resistance. CP 13-4100 could be considered moderately tolerant to freezing among 23 tested genotypes.
Sugarcane (Saccharum spp. interspecific hybrids), a high biomass perennial crop, in which manual data collection for early yield prediction, through its growth cycle (∼12 mo long), is labor intensive and time consuming. Alternately, aerial imagery can be explored to predict yield-related components and high-throughput phenotyping for genetic selection. In this study, aerial imagery and ground data were collected in Stage IV (final stage of genotype selection) of the Florida sugarcane cultivar development program to evaluate the use of unmanned aerial vehicles in yield prediction (tons of cane per hectare [TCH], sucrose concentration, and tons of sugar per hectare [TSH]) in multiple new genotypes (13 in plant cane crop, nine in first ratoon crop). Aerial imagery data were collected using hyperspectral sensor, and yield data were collected through manual sampling of sugarcane stalks at harvest. The gradient-boosting regression tree model was selected based on low mean absolute percentage error on multiple dates (April, July, and September) to determine the best timing of yield predictions. Results showed that yield was predicted with greater accuracy in July in plant crop and April in the first ratoon crop. Also, sucrose percentage was predicted with greater accuracy (94% in plant crop and 93% in first ratoon crop) than TCH and TSH. Although only two out of the top five genotypes were common in both selection methods (measured vs. predicted yields) in Stage IV, high accuracy in TCH and sucrose percentage shows that aerial imagery may be useful in making genotype selection in early stages when actual yield estimation is not feasible.Abbreviation: GBRT, gradient-boosting regression tree; LRT, likelihood ratio test; MAPE, mean absolute percentage error; ML, machine learning; TCH, tons of cane per hectare; TSH, tons of sugar per hectare; UAV, unmanned aerial vehicle.
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