Inicio  /  Agriculture  /  Vol: 12 Par: 9 (2022)  /  Artículo
ARTÍCULO
TITULO

Evaluation of Models for Utilization in Genomic Prediction of Agronomic Traits in the Louisiana Sugarcane Breeding Program

Subhrajit Satpathy    
Dipendra Shahi    
Brayden Blanchard    
Michael Pontif    
Kenneth Gravois    
Collins Kimbeng    
Anna Hale    
James Todd    
Atmakuri Rao and Niranjan Baisakh    

Resumen

Sugarcane (Saccharum spp.) is an important perennial grass crop for both sugar and biofuel industries. The Louisiana sugarcane breeding program is focused on improving sugar yield by incrementally increasing genetic gain. With the advancement in genotyping and (highthroughput) phenotyping techniques, genomic selection is a promising marker-assisted breeding tool. In this study, we assessed ridge regression best linear unbiased prediction (rrBLUP) and various Bayesian models to evaluate genomic prediction accuracy using a 10-fold cross validation on 95 commercial and elite parental clones from the Louisiana sugarcane breeding program. Datasets (individual and pooled in various combinations) were constructed based on soil type (light?Commerce silty loam, heavy?Sharkey clay) and crop (plant cane, ratoon). A total of 3906 SNPs were used to predict the genomic estimated breeding values (GEBVs) of the clones for sucrose content and cane and sugar yield. Prediction accuracy was estimated by both Spearman?s rank correlation and Pearson?s correlation between phenotypic breeding values and GEBVs. All traits showed significant variation with moderate (42% for sucrose content) to high (85% for cane and sugar yield) heritability. Prediction accuracy based on rank correlation was high (0.47?0.80 for sucrose content; 0.61?0.69 for cane yield, and 0.56?0.72 for sugar yield) in all cross-effect prediction models where soil and crop types were considered as fixed effects. In general, Bayesian models demonstrated a higher correlation than rrBLUP. The Pearson?s correlation without soil and crop type as fixed effects was lower with no clear pattern among the models. The results demonstrate the potential implementation of genomic prediction in the Louisiana sugarcane variety development program.

 Artículos similares

       
 
Jerry Gao, Charanjit Kaur Bambrah, Nidhi Parihar, Sharvaree Kshirsagar, Sruthi Mallarapu, Hailong Yu, Jane Wu and Yunyun Yang    
With the development of artificial intelligence, the intelligence of agriculture has become a trend. Intelligent monitoring of agricultural activities is an important part of it. However, due to difficulties in achieving a balance between quality and cos... ver más
Revista: Agriculture

 
Bao She, Jiating Hu, Linsheng Huang, Mengqi Zhu and Qishuo Yin    
To grasp the spatial distribution of soybean planting areas in time is the prerequisite for the work of growth monitoring, crop damage assessment and yield estimation. The research on remote sensing identification of soybean conducted in China mainly foc... ver más
Revista: Agriculture

 
Samuele Bumbaca and Enrico Borgogno-Mondino    
This work was aimed at developing a prototype system based on multispectral digital photogrammetry to support tests required by international regulations for new Plant Protection Products (PPPs). In particular, the goal was to provide a system addressing... ver más
Revista: Agronomy

 
Dilip Kumar Roy, Mohamed Anower Hossain, Mohamed Panjarul Haque, Abed Alataway, Ahmed Z. Dewidar and Mohamed A. Mattar    
This study addresses the crucial role of temperature forecasting, particularly in agricultural contexts, where daily maximum (???????? T m a x ) and minimum (???????? T m i n ) temperatures significantly impact crop growth and irrigation planning. While ... ver más
Revista: Agriculture

 
Yixin Huang, Rishi Srivastava, Chloe Ngo, Jerry Gao, Jane Wu and Sen Chiao    
Food shortage issues affect more and more of the population globally as a consequence of the climate crisis, wars, and the COVID-19 pandemic. Increasing crop output has become one of the urgent priorities for many countries. To raise the productivity of ... ver más
Revista: Agriculture