Visual selection of Urochloa ruziziensis genotypes for green biomass yield

Abstract

The breeding program of Urochloa ruziziensis evaluates many genotypes in initial phases. Evaluations through grades might make the selection less costly. The aim of this study was to verify the efficiency of visual selection for green biomass yield in relation to different selection strategies, such as mass selection by phenotypic mean, BLUP (Best Linear Unbiased Prediction) and at random. For this purpose, 2,309 regular genotypes were evaluated in an augmented block design in two cuts. The evaluators gave grades for plant vigor, and later, the plots were measured for green biomass yield. The coincidences of the selected genotypes were estimated by different selection strategies. Then, 254 clones of the genotypes selected in different strategies were evaluated in a clonal test in a triple lattice design in four cuts. The statistical analyses were performed in SAS using the Mixed procedure. The regular genotype level and clone-mean basis heritabilities were 31.16 and 62.91%, respectively, for green mass yield. The expected selection gains were 21.09% (visual), 25.43% (phenotypic mean), and 27.5% (BLUP). Moreover, the realized heritabilities for these strategies were 15.58, 11.87, and 15.86%, respectively, which might be associated with genotype by environment interaction. Therefore, the visual selection could be a useful strategy in initial phases of a U. ruziziensis breeding program because the efficiency was moderate to high in relation to phenotypic mean and BLUP.

Genetic improvement of Ilex paraguariensis in western Santa Catarina State: estimate of phenotypic and genetic parameters

“…The average estimate of commercial biomass productivity (mass of leaves and thin branches < 7 mm) based on visual estimation, was underestimated by approximately 25% in Guatambu and 5% in Chapecó compared to the biomass measured on a scale. Visual estimation is a low-cost alternative that requires less work to qualitatively define the production of individuals (Teixeira et al, 2020). However, its efficiency is strongly related to the traits of high heritability, as well as to the degree of training of the evaluator.…”

Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypes

“…Nevertheless, in both cases (selecting versus discarding), it is important to consider that perennial grass breeding programs have a peculiar regrowth evaluation (Berchembrock et al 2020), which limits the number of generations and selection cycles on a per year basis. Various forage breeding programs have used visual evaluation to assess traits at different stages of selection cycles for crops such as red clover (Riday 2009), U. ruziziensis (Souza Sobrinho et al 2010, Teixeira et al 2020, U. decumbens (Matias et al 2020), U. humidicola (Figueiredo et al 2019), and Urochloa spp. (Gouveia et al 2020).…”

“…(Gouveia et al 2020). Teixeira et al (2020) showed the effectiveness of visual selection for green biomass yield based on plant vigor in U. ruziziensis. Souza Sobrinho et al (2010) also highlighted the efficiency of visual evaluation in U. ruziziensis by indirectly selecting genotypes resistant to spittlebugs.…”

“…However, none of these studies explained why the respective numbers of graders were adopted. Similarly, in U. ruziziensis, some studies used visual evaluation to derive phenotypic measurements (Teixeira et al 2020), but the importance or effect of the number of graders on the results presented was not discussed.…”

Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing

“…Currently, phenotyping for forage yield traits is performed by low-cost inaccurate visual evaluations [7] or by high-cost low throughput measurements [8]. For the latter strategy, samples are air-forced dried in a drying chamber to calculate DMY.…”