Location: Sugarcane Field StationTitle: Genotype-by-environment interaction analysis across three crop cycles in sugarcane
|DAVIDSON, WAYNE - Florida Sugarcane League|
|MCCORD, PER - Washington State University|
|SANDHU, HARDEV - University Of Florida|
|BALTAZAR, MIGUEL - Florida Sugarcane League|
|COTO ARBELO, ORLANDO - University Of Florida|
Submitted to: Journal of Crop Improvement
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/27/2020
Publication Date: 9/13/2020
Citation: Momotaz, A., Davidson, W.R., Zhao, D., Mccord, P.H., Sandhu, H.S., Baltazar, M., Islam, M.S., Coto Arbelo, O. 2020. Genotype-by-environment interaction analysis across three crop cycles in sugarcane. Journal of Crop Improvement. 35(2):276-290. https://doi.org/10.1080/15427528.2020.1817220.
Interpretive Summary: Sugarcane (Saccharum spp.) is an economically important crop in the tropical and subtropical areas. Sugarcane varieties are being developed through a collaborative sugarcane cultivar development program of the USDA–ARS, the University of Florida, and the Florida Sugar Cane League, Inc., based at Canal Point (CP), Florida. Cane tonnage yield is complex in nature which depends on a number of other agronomic traits i.e. stalk weight and stalk numbers that comprise biomass accumulation. These traits are highly influenced by genetic and environmental factors. Genotype by environment interaction reduces the performance of quantitative traits in sugarcane across the diverse environments. It is very important to study the effect of environment and their interaction with genotypes. Our objective was to finely tailor clone(s) to a particular environment by incorporating direct measurement of the environmental factors. Thirteen sugarcane clones were evaluated in five different environments with three year crop cycles (plant cane, first ratoon and second ratoon) in the Florida organic (muck) soils. The experiment was conducted in a randomized complete block design with six replications. The data were analyzed using additive main effects and multiplicative interaction (AMMI) and the genotype plus genotype-by-environment interaction (GGE) method. The AMMI analysis of variance for stalk weight and cane yield showed significant differences for genotype, environment and genotype-environment interaction for both traits. According to GGE biplots, clone CP12-2479 was for all trait values across all crop cycles followed by CP12-1417 over the checks. Both AMMI and GGE biplots are powerful tools for visual comparison of mitigating the confounding effects of GEI to identify superior clones in an easier and faster way.
Technical Abstract: Genotype-by-environment interaction (GEI) is encountered in multi-environment trials. In sugarcane (Saccharum spp.), GEI affects crop growth and cane yield and complicates the selection of superior genotypes. The objective of this study was to assess and analyze GEI for cane yield and stalk weight to identify high-yielding, stable genotypes. Thirteen Canal Point (CP) sugarcane clones and three check varieties were evaluated at five different locations (environments) across three crop cycles (plant cane, first ratoon and second ratoon) on the Florida organic (muck) soils. At each location, the experiment was conducted as a randomized complete block design with six replications. Mean stalk weight and cane yield data were collected and analyzed via the additive main effects and multiplicative interaction (AMMI) and genotype main effect (G) plus genotype × environment interaction (GEI), i.e., GGE biplot. The AMMI analysis for mean stalk weight and cane yield revealed that variation attributable to genotypes, environments and GEI for these traits was significant. The GGE biplot analysis indicated that the five locations formed two mega-environments, with different winning genotypes. Two locations, Okeelanta and Duda, were non-representative of the Florida muck-soils. Among all the genotypes, CP 12-1417 had the highest mean cane yield and had the most stable performance across crop cycles. We concluded that the application of GGE biplot in our final-stage sugarcane testing program could help us identify clones best adapted to specific locations and enhance selection efficiency by helping us identify locations that provide similar information.