Multilocation Yield Trials and Yield Stability Evaluation by GGE Biplot Analysis of Promising Large-Seeded Peanut Lines.

The demand by industries for large-seeded peanuts is increasing in Thailand and Southeast Asia. New large-seeded peanut lines were recently developed in Thailand to respond to the demand. In this study, a multilocation yield trial was performed to identify the best genotype(s) in Thailand's central region and investigate the genotype-environment interaction (GEI) on peanut production. Twelve promising large-seeded peanut lines and two check varieties (KU50 and KK6) were planted at 12 different planting locations during the dry and rainy seasons of 2018 and the dry season of 2019. This study found significant yield potential variability in the promising lines of peanuts evaluated at different planting locations. A combined analysis of variance presented that the environment and genotypes had a considerable impact (p < 0.001) on the pod and seed yield. The GEI showed a high impact (p < 0.01) on pod yield and an effect (p < 0.05) on seed yield. The environment presented the most significant influence on pod and seed yield variations, followed by genetics and GEI. The total variation in seed yield was 64.22%, composed of PC1 and PC2 values at 45.71% and 18.51%, respectively. The GGE biplot analysis of the yield potentials at each location indicated that KUP12BS029-1-1-3 was the ideal genotype, with a high yield potential and most stability at multilocations, followed by KUP12BS030-3-4-1 and KUP12BS030-1-4-3. These promising lines will be released as new peanut varieties in central Thailand and are recommended as parental lines in breeding programs for large-seeded and yield potential in Thailand and Southeast Asia.

Cassava root crown phenotyping using three-dimension (3D) multi-view stereo reconstruction.

Phenotypic analysis of cassava root crowns (CRCs) so far has been limited to visual inspection and very few measurements due to its laborious process in the field. Here, we developed a platform for acquiring 3D CRC models using close-range photogrammetry for phenotypic analysis. The state of the art is a low cost and easy to set up 3D acquisition requiring only a background sheet, a reference object and a camera, compatible with field experiments in remote areas. We tested different software with CRC samples, and Agisoft and Blender were the most suitable software for generating high-quality 3D models and data analysis, respectively. We optimized the workflow by testing different numbers of images for 3D reconstruction and found that a minimum of 25 images per CRC can provide high quality 3D models. Up to ten traits, including 3D crown volumes, 3D crown surface, root density, surface-to-volume ratio, root numbers, root angle, crown diameter, cylinder soil volume, CRC compactness and root length can be extracted providing novel parameters for studying cassava storage roots. We applied this platform to partial-inbred cassava populations and demonstrated that our platform provides reliable 3D CRC modelling for phenotypic analysis, analysis of genetic variances and supporting breeding selection.

Correlation Between Relative Gene Expression Patterns of Two Flowering locus T (MeFT1 and MeFT2) in Cassava Leaf and Flowering Traits Under Different Flowering Induction Conditions.

<b>Background and Objective:</b> <i>Flowering locus T</i> (<i>FT</i>) genes are involved in the flower induction mechanism in plants as florigen signals. The objective of this study was to study the relationship between the expression of <i>Flowering locus T</i> genes (<i>MeFTs</i>) in cassava and flowering traits under the different flowering induction conditions. <b>Materials and Methods:</b> The experimental design for flowering induction was RCBD for 4 replications. There were 5 treatment factors which were control, red light set from 5 pm to 7 am, 0.5 mM 6-benzyladenine (BA) with 2 mM silver thio-sulfate (STS), paclobutrazol for 6 g/plant and potassium chlorate (KCIO₃) for 250 g/plant. The number of plants with flower bunches and the average number of bunches per plant in two cassava varieties were collected each month from 5-9 months after planting (MAP). The leaf samples were collected from HB80 and R9 varieties at 5-7 MAP for RNA extraction to study <i>MeFT1</i> and <i>MeFT2</i> expression. <b>Results:</b> The results show that <i>MeFT1</i> expression level positively correlated with flowering traits in the same month, while <i>MeFT2</i> expression level positively correlated with flowering traits in the following months. <b>Conclusion:</b> Therefore, expression of <i>MeFT2</i> can be used for the prediction of cassava flowering in the following month which will assist the breeder for the crossing management.

Genome-wide association mapping and genomic prediction of yield-related traits and starch pasting properties in cassava.

KEY MESSAGE: GWAS identified eight yield-related, peak starch type of waxy and wild-type starch and 21 starch pasting property-related traits (QTLs). Prediction ability of eight GS models resulted in low to high predictability, depending on trait, heritability, and genetic architecture. Cassava is both a food and an industrial crop in Africa, South America, and Asia, but knowledge of the genes that control yield and starch pasting properties remains limited. We carried out a genome-wide association study to clarify the molecular mechanisms underlying these traits and to explore marker-based breeding approaches. We estimated the predictive ability of genomic selection (GS) using parametric, semi-parametric, and nonparametric GS models with a panel of 276 cassava genotypes from Thai Tapioca Development Institute, International Center for Tropical Agriculture, International Institute of Tropical Agriculture, and other breeding programs. The cassava panel was genotyped via genotyping-by-sequencing, and 89,934 single-nucleotide polymorphism (SNP) markers were identified. A total of 31 SNPs associated with yield, starch type, and starch properties traits were detected by the fixed and random model circulating probability unification (FarmCPU), Bayesian-information and linkage-disequilibrium iteratively nested keyway and compressed mixed linear model, respectively. GS models were developed, and forward predictabilities using all the prediction methods resulted in values of - 0.001-0.71 for the four yield-related traits and 0.33-0.82 for the seven starch pasting property traits. This study provides additional insight into the genetic architecture of these important traits for the development of markers that could be used in cassava breeding programs.

Cassava breeding and agronomy in Asia: 50 years of history and future directions.

In Asia, cassava (Manihot esculenta) is cultivated by more than 8 million farmers, driving the rural economy of many countries. The International Center for Tropical Agriculture (CIAT), in partnership with national agricultural research institutes (NARIs), instigated breeding and agronomic research in Asia, 1983. The breeding program has successfully released high-yielding cultivars resulting in an average yield increase from 13.0 t ha-1 in 1996 to 21.3 t ha-1 in 2016, with significant economic benefits. Following the success in increasing yields, cassava breeding has turned its focus to higher-value traits, such as waxy cassava, to reach new market niches. More recently, building resistance to invasive pests and diseases has become a top priority due to the emergent threat of cassava mosaic disease (CMD). The agronomic research involves driving profitability with advanced technologies focusing on better agronomic management practices thereby maintaining sustainable production systems. Remote sensing technologies are being tested for trait discovery and large-scale field evaluation of cassava. In summary, cassava breeding in Asia is driven by a combination of food and market demand with technological innovations to increase the productivity. Further, exploration in the potential of data-driven agriculture is needed to empower researchers and producers for sustainable advancement.