Brazil is an agricultural country, with 190 Mha of pastures sustaining 209 million cattle. Fewer than 10% of the cattle are fattened in feedlots, whereas cattle reared on pastures have a competitive advantage for export, eliminating the risks presented by the mad cow disease (bovine spongiform encephalopathy) and considerations related to animal welfare. Brazil has been the world’s largest exporter of beef since 2004 and has the largest commercial herd in the world. In 2011, 16.5% of its production was exported, and the livestock sector contributed 30.4% of the gross national product from agribusiness and 6.73% of the total GNP. Many forage breeding programs, mainly at Embrapa, have contributed to the development of improved pastures, and cultivars of Brachiaria brizantha, B. decumbens, B. humidicola and Panicum maximum are the main pastures used in the country. All have apomictic reproduction, which means there are few cultivars occupying very large, continuous areas, thus suggesting a risk to the productive system. Such is the case of B. brizantha cv. Marandu, which occupies around 50 Mha. The Brazilian tropical forage seed industry is also important, and Brazil is the main seed exporter, supplying all Latin American countries. Due to pasture degradation, around 8 Mha is renovated or recovered each year. Forages are also used and planted each year in integrated crop–livestock and integrated crop–livestock–forest systems. Nowadays, these systems occupy 4 Mha. Improved pastures are thus a major asset in Brazil not only for the beef production chain but also for the dairy industry.

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Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species Panicum maximum Jacq. Experiments were conducted in the Brazilian Cerrado with 110 genotypes with three replications, totaling 330 plots. Two regression models based on Convolutional Neural Networks (CNNs) named AlexNet and ResNet18 were evaluated, and compared to VGGNet—adopted in previous work in the same thematic for other grass species. The predictions returned by the models reached a correlation of 0.88 and a mean absolute error of 12.98% using AlexNet considering pre-training and data augmentation. This proposal may contribute to forage biomass estimation in breeding populations and livestock areas, as well as to reduce the labor in the field.

A tropical forage breeding program contains several peculiarities, especially when it involves polyploid species and facultative apomixis. Urochloa spp. are excellent perennial forages, and the identification of superior genotypes depends on the selection of many characteristics under complex genetic control, with high cost and time‐consuming evaluation. Therefore, the use of tools such as multivariate analysis and diallel analyses could contribute to improving the efficiency of breeding programs. Thus, the objectives were to estimate (i) the contribution of additive and nonadditive effects on agronomical and nutritional traits in a population of interspecific hybrids of Urochloa spp., originated from a partial diallel between five apomictic and four sexual parents, and (ii) the accuracy of multivariate index selection efficiency. Genetic variability was detected between the parents, crosses, and hybrids for all the traits. There was no clear trend of the importance of the additive and nonadditive genetic effects on agronomical and nutritional traits. Furthermore, the predominant component of genetic variance changed depending on the characteristic. Moreover, there was no parent or cross that was outstanding for all traits simultaneously, showing the high variability generated from these crosses. The Mulamba and Mock index associated with principal components analysis allowed a more significant gain only for agronomic characteristics. However, the per se index, at the univariate level, promoted a more balanced response to selection for all traits.

Urochloa decumbens (Stapf) R. D. Webster is one of the most important African forage grasses in Brazilian beef production. Currently available genetic-genomic resources for this species are restricted mainly due to polyploidy and apomixis. Therefore, crucial genomic-molecular studies such as the construction of genetic maps and the mapping of quantitative trait loci (QTLs) are very challenging and consequently affect the advancement of molecular breeding. The objectives of this work were to (i) construct an integrated U. decumbens genetic map for a full-sibling progeny using GBS-based markers with allele dosage information, (ii) detect QTLs for spittlebug ( Notozulia entreriana ) resistance, and (iii) seek putative candidate genes involved in defense against biotic stresses. We used the Setaria viridis genome a reference to align GBS reads and selected 4,240 high-quality SNP markers with allele dosage information. Of these markers, 1,000 were distributed throughout nine homologous groups with a cumulative map length of 1,335.09 cM and an average marker density of 1.33 cM. We detected QTLs for resistance to spittlebug, an important pasture insect pest, that explained between 4.66 and 6.24% of the phenotypic variation. These QTLs are in regions containing putative candidate genes related to defense against biotic stresses. Because this is the first genetic map with SNP autotetraploid dosage data and QTL detection in U. decumbens , it will be useful for future evolutionary studies, genome assembly, and other QTL analyses in Urochloa spp. Moreover, the results might facilitate the isolation of spittlebug-related candidate genes and help clarify the mechanism of spittlebug resistance. These approaches will improve selection efficiency and accuracy in U. decumbens molecular breeding and shorten the breeding cycle.