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The recent surge in the plant-based protein market has resulted in high demands for soybean genotypes with improved grain yield, seed protein and oil content, and essential amino acids (EAAs). Given the quantitative nature of these traits, complex interactions among seed components, as well as between seed components and environmental factors and management practices, add complexity to the development of desired genotypes. In this study, the across-environment seed protein stability of 449 genetically diverse plant introductions was assessed, revealing that genotypes may display varying sensitivities to such environmental stimuli. The EAAs valine, phenylalanine, and threonine showed the highest variable importance toward the variation in stability, while both seed protein and oil contents were among the explanatory variables with the lowest importance. In addition, 56 single nucleotide polymorphism (SNP) markers were significantly associated with various seed components. Despite the strong phenotypic Pearson's correlation observed among most seed components, many independent genomic regions associated with one or few seed components were identified. These findings provide insights for improving the seed concentration of specific EAAs and reducing the negative correlation between seed protein and oil contents.

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KEY MESSAGE: Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with high-dimensional data such as climate and soil information is becoming a common practice in breeding programs. This study explored new ways to combine non-genetic information in genomic prediction models using machine learning. Using the multi-environment trial data from the Genomes To Fields initiative, different models to predict maize grain yield were adjusted using various inputs: genetic, environmental, or a combination of both, either in an additive (genetic-and-environmental; G+E) or a multiplicative (genotype-by-environment interaction; GEI) manner. When including environmental data, the mean prediction accuracy of machine learning genomic prediction models increased up to 7% over the well-established Factor Analytic Multiplicative Mixed Model among the three cross-validation scenarios evaluated. Moreover, using the G+E model was more advantageous than the GEI model given the superior, or at least comparable, prediction accuracy, the lower usage of computational memory and time, and the flexibility of accounting for interactions by construction. Our results illustrate the flexibility provided by the ML framework, particularly with feature engineering. We show that the feature engineering stage offers a viable option for envirotyping and generates valuable information for machine learning-based genomic prediction models. Furthermore, we verified that the genotype-by-environment interactions may be considered using tree-based approaches without explicitly including interactions in the model. These findings support the growing interest in merging high-dimensional genotypic and environmental data into predictive modeling.

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The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments.

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Soybean [Glycine max (L.) Merr.] is a globally important crop due to its valuable seed composition, versatile feed, food, and industrial end-uses, and consistent genetic gain. Successful genetic gain in soybean has led to widespread adaptation and increased value for producers, processors, and consumers. Specific focus on the nutritional quality of soybean seed composition for food and feed has further elucidated genetic knowledge and bolstered breeding progress. Seed components are historical and current targets for soybean breeders seeking to improve nutritional quality of soybean. This article reviews genetic and genomic foundations for improvement of nutritionally important traits, such as protein and amino acids, oil and fatty acids, carbohydrates, and specific food-grade considerations; discusses the application of advanced breeding technology such as CRISPR/Cas9 in creating seed composition variations; and provides future directions and breeding recommendations regarding soybean seed composition traits.

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The adoption of dicamba-tolerant (DT) soybean in the United States resulted in extensive off-target dicamba damage to non-DT vegetation across soybean-producing states. Although soybeans are highly sensitive to dicamba, the intensity of observed symptoms and yield losses are affected by the genetic background of genotypes. Thus, the objective of this study was to detect novel marker-trait associations and expand on previously identified genomic regions related to soybean response to off-target dicamba. A total of 551 non-DT advanced breeding lines derived from 232 unique bi-parental populations were phenotyped for off-target dicamba across nine environments for three years. Breeding lines were genotyped using the Illumina Infinium BARCSoySNP6K BeadChip. Filtered SNPs were included as predictors in Random Forest (RF) and Support Vector Machine (SVM) models in a forward stepwise selection loop to identify the combination of SNPs yielding the highest classification accuracy. Both RF and SVM models yielded high classification accuracies (0.76 and 0.79, respectively) with minor extreme misclassifications (observed tolerant predicted as susceptible, and vice-versa). Eight genomic regions associated with off-target dicamba tolerance were identified on chromosomes 6 [Linkage Group (LG) C2], 8 (LG A2), 9 (LG K), 10 (LG O), and 19 (LG L). Although the genetic architecture of tolerance is complex, high classification accuracies were obtained when including the major effect SNP identified on chromosome 6 as the sole predictor. In addition, candidate genes with annotated functions associated with phases II (conjugation of hydroxylated herbicides to endogenous sugar molecules) and III (transportation of herbicide conjugates into the vacuole) of herbicide detoxification in plants were co-localized with significant markers within each genomic region. Genomic prediction models, as reported in this study, can greatly facilitate the identification of genotypes with superior tolerance to off-target dicamba.

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Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crops, such as soybean and cotton, which has resulted in severe off-target dicamba exposure and substantial yield losses to non-tolerant crops. There is a strong demand for non-genetically engineered DT soybeans through conventional breeding selection. Public breeding programs have identified genetic resources that confer greater tolerance to off-target dicamba damage in soybeans. Efficient and high throughput phenotyping tools can facilitate the collection of a large number of accurate crop traits to improve the breeding efficiency. This study aimed to evaluate unmanned aerial vehicle (UAV) imagery and deep-learning-based data analytic methods to quantify off-target dicamba damage in genetically diverse soybean genotypes. In this research, a total of 463 soybean genotypes were planted in five different fields (different soil types) with prolonged exposure to off-target dicamba in 2020 and 2021. Crop damage due to off-target dicamba was assessed by breeders using a 1-5 scale with a 0.5 increment, which was further classified into three classes, i.e., susceptible (≥3.5), moderate (2.0 to 3.0), and tolerant (≤1.5). A UAV platform equipped with a red-green-blue (RGB) camera was used to collect images on the same days. Collected images were stitched to generate orthomosaic images for each field, and soybean plots were manually segmented from the orthomosaic images. Deep learning models, including dense convolutional neural network-121 (DenseNet121), residual neural network-50 (ResNet50), visual geometry group-16 (VGG16), and Depthwise Separable Convolutions (Xception), were developed to quantify crop damage levels. Results show that the DenseNet121 had the best performance in classifying damage with an accuracy of 82%. The 95% binomial proportion confidence interval showed a range of accuracy from 79% to 84% (p-value ≤ 0.01). In addition, no extreme misclassifications (i.e., misclassification between tolerant and susceptible soybeans) were observed. The results are promising since soybean breeding programs typically aim to identify those genotypes with 'extreme' phenotypes (e.g., the top 10% of highly tolerant genotypes). This study demonstrates that UAV imagery and deep learning have great potential to high-throughput quantify soybean damage due to off-target dicamba and improve the efficiency of crop breeding programs in selecting soybean genotypes with desired traits.

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The widespread adoption of genetically modified (GM) dicamba-tolerant (DT) soybean was followed by numerous reports of off-target dicamba damage and yield losses across most soybean-producing states. In this study, a subset of the USDA Soybean Germplasm Collection consisting of 382 genetically diverse soybean accessions originating from 15 countries was used to identify genomic regions associated with soybean response to off-target dicamba exposure. Accessions were genotyped with the SoySNP50K BeadChip and visually screened for damage in environments with prolonged exposure to off-target dicamba. Two models were implemented to detect significant marker-trait associations: the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) and a model that allows the inclusion of population structure in interaction with the environment (G×E) to account for variable patterns of genotype responses in different environments. Most accessions (84%) showed a moderate response, either moderately tolerant or moderately susceptible, with approximately 8% showing tolerance and susceptibility. No differences in off-target dicamba damage were observed across maturity groups and centers of origin. Both models identified significant associations in regions of chromosomes 10 and 19. The BLINK model identified additional significant marker-trait associations on chromosomes 11, 14, and 18, while the G×E model identified another significant marker-trait association on chromosome 15. The significant SNPs identified by both models are located within candidate genes possessing annotated functions involving different phases of herbicide detoxification in plants. These results entertain the possibility of developing non-GM soybean cultivars with improved tolerance to off-target dicamba exposure and potentially other synthetic auxin herbicides. Identification of genetic sources of tolerance and genomic regions conferring higher tolerance to off-target dicamba may sustain and improve the production of other non-DT herbicide soybean production systems, including the growing niche markets of organic and conventional soybean.

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The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture information and its interaction with molecular markers via covariance structures for enhancing predictive ability across breeding scenarios. A total of 797 soybean lines derived from 367 unique bi-parental populations were genotyped using the Illumina BARCSoySNP6K and tested for yield during 5 years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including the GBLUP model (M1), the reaction norm model (M2) including the interaction between molecular markers and the environment (G×E), an extended version of M2 that also includes soil type (S), and the interaction between soil type and molecular markers (G×S) (M3), and a parsimonious version of M3 which discards the G×E term (M4). Four cross-validation scenarios simulating progeny testing and line selection of tested-untested genotypes (TG, UG) in observed-unobserved environments [OE, UE] were implemented (CV2 [TG, OE], CV1 [UG, OE], CV0 [TG, UE], and CV00 [UG, UE]). Across environments, the addition of G×S interaction in M3 decreased the amount of variability captured by the environment (-30.4%) and residual (-39.2%) terms as compared to M1. Within environments, the G×S term in M3 reduced the variability captured by the residual term by 60 and 30% when compared to M1 and M2, respectively. M3 outperformed all the other models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). In addition to the Pearson correlation, other measures were considered to assess predictive ability and these showed that the addition of soil texture seems to structure/dissect the environmental term revealing its components that could enhance or hinder the predictability of a model, especially in the most complex prediction scenario (CV00). Hence, the availability of soil texture information before the growing season could be used to optimize the efficiency of a breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.

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KEY MESSAGE: This review provides a comprehensive atlas of QTLs, genes, and alleles conferring resistance to 28 important diseases in all major soybean production regions in the world. Breeding disease-resistant soybean [Glycine max (L.) Merr.] varieties is a common goal for soybean breeding programs to ensure the sustainability and growth of soybean production worldwide. However, due to global climate change, soybean breeders are facing strong challenges to defeat diseases. Marker-assisted selection and genomic selection have been demonstrated to be successful methods in quickly integrating vertical resistance or horizontal resistance into improved soybean varieties, where vertical resistance refers to R genes and major effect QTLs, and horizontal resistance is a combination of major and minor effect genes or QTLs. This review summarized more than 800 resistant loci/alleles and their tightly linked markers for 28 soybean diseases worldwide, caused by nematodes, oomycetes, fungi, bacteria, and viruses. The major breakthroughs in the discovery of disease resistance gene atlas of soybean were also emphasized which include: (1) identification and characterization of vertical resistance genes reside rhg1 and Rhg4 for soybean cyst nematode, and exploration of the underlying regulation mechanisms through copy number variation and (2) map-based cloning and characterization of Rps11 conferring resistance to 80% isolates of Phytophthora sojae across the USA. In this review, we also highlight the validated QTLs in overlapping genomic regions from at least two studies and applied a consistent naming nomenclature for these QTLs. Our review provides a comprehensive summary of important resistant genes/QTLs and can be used as a toolbox for soybean improvement. Finally, the summarized genetic knowledge sheds light on future directions of accelerated soybean breeding and translational genomics studies.

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This review summarizes the history and current state of the known genetic basis for soybean resistance to Soybean mosaic virus (SMV), and examines how the integration of molecular markers has been utilized in breeding for crop improvement. SVM causes yield loss and seed quality reduction in soybean based on the SMV strain and the host genotype. Understanding the molecular underpinnings of SMV-soybean interactions and the genes conferring resistance to SMV has been a focus of intense research interest for decades. Soybean reactions are classified into three main responses: resistant, necrotic, or susceptible. Significant progress has been achieved that has greatly increased the understanding of soybean germplasm diversity, differential reactions to SMV strains, genotype-strain interactions, genes/alleles conferring specific reactions, and interactions among resistance genes and alleles. Many studies that aimed to uncover the physical position of resistance genes have been published in recent decades, collectively proposing different candidate genes. The studies on SMV resistance loci revealed that the resistance genes are mainly distributed on three chromosomes. Resistance has been pyramided in various combinations for durable resistance to SMV strains. The causative genes are still elusive despite early successes in identifying resistance alleles in soybean; however, a gene at the Rsv4 locus has been well validated.

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Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.

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We investigated the effects of different performance goals (best time vs. beat the opponent) on pacing behaviour during a 10-km cycling race and explored the influence of different performance level of opponents on ratings of perceived exertion (RPE), affective feelings and self-efficacy. Thirteen cyclists performed two time-trials (TT) and two races against a faster (FAST +6%) or a slower (SLOW -3%) virtual opponent. Power output (PO), RPE, affective feelings and self-efficacy were recorded at each kilometre point. Race average and race phases [starting (P1 = first kilometre); first half (P2 = 2nd-5th kilometre); second half (P3 = 6th-9th kilometre) and final sprint (FS = last kilometre)] were analysed. There was no difference in performance, assessed by race time between conditions (p = .84). PO during TT was lower in P3 compared to FS (p = .03; ES 0.6; 90%CI 0.4-0.7). In SLOW and FAST, PO was higher in P1 compared to other phases (p < .05). PO in FS was higher in TT compared to FAST (p = .01; ES -0.97; 90%IC -1.4 to -0.5). RPE increased and affective feelings decreased during all conditions. Self-efficacy was stable through TT and SLOW, but decreased during FAST with higher values in P1 compared to P2 (p = .01; ES -1.1; 90%IC -1.6 to -0.6), P3 (p < .001; ES -2.2; 90%IC -2.8 to -1.6) and FS (p < .001; ES -2.6; 90%IC -3.3 to -1.8). Pacing behaviour, specifically starting and final sprint, was affected by virtual opponents independent of performance level, demonstrating the importance of goal orientation.HighlightsAdjustments in exercise intensity result from a complex decision-making process involving physiological, psychological, environmental and tactical information.Goal pursuit is an important determinant of pacing behaviour since athletes must balance their efforts with expectations of success.A competitive environment may be included to motivate participants to maintain their effort and at the same time to improve their self-confidence.The presence of a final sprint seems to be related to the goal orientation and perceived outcomes of success or failure.

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The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.

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The aim of this study was to examine whether (1) severe changes in salinity produced increased stress, and (2) vitamin C supplementation might reduce the observed damage in Nile tilapia. The parameters measured included condition factor, survival rate, and gene expression of catalase (CAT), heat shock protein 70 (HSP70), glutathione reductase (GSR), glutathione synthase (GSS), and glutathione peroxidase (GPx). The investigation was conducted with 160 Nile tilapia divided into four treatment groups: freshwater; 7 or 21 parts per thousand (‰) salinity, all fed a basal diet; as well as a fourth treatment group consisting of fish kept at 21‰ salinity fed a diet supplemented with vitamin C (1500 mg/kg). For gene expression analysis, liver samples were collected after 24 h or after 14 d. After 24 h, fish raised in 21‰ salinity and fed with the diet supplemented with vitamin C showed similar GPx expression as the control freshwater group. GSS expression in 21‰ salinity was similar to fish exposed to 7‰ salinity. Nile tilapia exposed to 21‰ salinity without vitamin C supplementation exhibited the highest HSP70 gene expression levels after 24 h. After 14-dtreatment, the lowest survival rate was observed in the 21‰ salinity group. After 14 d, the highest expression of GPx and GSR levels was detected in fish in the 21‰ salinity group that received vitamin C. Data indicate that vitamin C supplementation enhanced the expression of genes related to antioxidant capacity in Nile tilapia exposed to higher salinity, thereby increasing protection against the oxidative effects induced by high water salinity..

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O objetivo desse estudo foi caracterizar e comparar o somatotipo, a composição corporal e o perfil antropométrico de árbitros de futebol do Brasil atuantes em duas regiões distintas: Nordeste e Sul. A amostra desta pesquisa foi constituída por 18 árbitros pertencentes a Federação Norte-Rio-Grandense de Futebol (FNF, região Nordeste), com idade média de 31,9 ± 5,1 anos, peso de 75,5 ± 8,8 kg e estatura de 1,77 ± 0,06 m e 17 árbitros pertencentes a Federação Paranaense de Futebol (FPF, região Sul), com idade média de 34,2 ± 5,8 anos, peso 75,7 ± 6,7 kg e estatura de 1,76 ± 0,06 m. Foram mensuradas as variáveis antropométricas: massa corporal e estatura, a espessura de sete dobras cutâneas, quatro diâmetros ósseos e oito perímetros. Os dados estão descritos em média, desvio-padrão (DP) e frequência relativa (%). Para comparação dos grupos utilizou-se o teste t de Student para amostras independentes e o teste qui-quadrado. De acordo com os resultados do estudo, é possível concluir que há diferença entre o perfil corporal de árbitros de futebol das regiões Nordeste e Sul do Brasil, evidenciado, essencialmente, pela porcentagem de gordura corporal (14,5 ± 2,1% FNF e 18,2 ± 3,1% FPF, p<0,01) e somatotipo (3,3-5,2-2,1 e 4,6-5,0-1,8 respectivamente), que apontaram que os árbitros paranaenses estão com excesso de gordura corporal.

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This study aimed to assess the external and internal loads of Brazilian soccer referees during official matches. A total of 11 field referees (aged 36.2 ± 7.5 years) were monitored during 35 matches. The external (distance covered, mean and maximal speed) and internal load parameters (session ratings of perceived exertion [RPE] training load [TL], Edwards' TL, and time spent in different heart rate [HR] zones) were assessed in 3-4 matches per referee. External load parameters were measured using a wrist Global Positioning System (GPS) receiver. No differences in distance covered (5.219 ± 205 vs. 5.230 ± 237 m) and maximal speed (19.3 ± 1.0 vs. 19.4 ± 1.4 km·h(-1)) were observed between the halves of the matches (p > 0.05). However, the mean speed was higher in the first half of the matches (6.6 ± 0.4 vs. 6.4 ± 0.3 km·h(-1)) (p < 0.05) than in the second half. The mean HR during the matches was ~89% of HRmax. In ~95% of the matches, the referees demonstrated a HR ≥ 80% of HRmax. Nonetheless, the time spent at 90-100% of HRmax was higher in the first half (59.9 vs. 52.3%) (p < 0.05). Significant correlations between session RPE TL and distance covered at 90-100% of HRmax (r = 0.62) and session RPE TL and maximal speed (r = 0.54) (p < 0.05) were noted. Furthermore, there was a positive correlation between session RPE TL and Edwards' TL (r = 0.61) (p < 0.05). Brazilian soccer referees demonstrated high external and internal load demands during official matches. The portable GPS/HR monitors and session RPE method can provide relevant information regarding the magnitude of the physiological strain during official matches. Key PointsHigh external and internal loads were imposed on Brazilian soccer referees during official matches.There was a high positive correlation between a subjective marker of internal load (session RPE) and parameters of external load (distance covered between 90-100% of HRmax and maximal speed).There was a high positive correlation between session RPE method and Edwards' method.Session RPE seems to be a reliable marker of internal load.The portable GPS/HR monitors and the session RPE method can provide relevant information regarding the magnitude of external and internal loads of soccer referees during official matches.

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Os objetivos do presente estudo foram: a) descrever a demanda física imposta aos árbitros de futebol brasileiros durante partidas oficiais e b) analisar se o nível de aptidão física interfere no desempenho da arbitragem. Os árbitros (n = 11) foram avaliados durante jogos oficiais (n = 21) do campeonato Potiguar 2009. A média de idade foi de 36,36 ± 6,34 anos. A distância percorrida, a velocidade (média e máxima) e a frequência cardíaca (média e máxima) foram registradas durante as partidas. A análise da arbitragem foi realizada por avaliador credenciado pela Federação Norte-Rio-Grandense de Futebol (FNF), seguindo os critérios estabelecidos pela Confederação Brasileira de Futebol (CBF). A distância percorrida, a velocidade e a frequência cardíaca foram, respectivamente, 10,50 ± 0,35 km, 6,43 ± 0,26 km/h (média), 19,84 ± 1,56 km/h (máxima), 162,77 ± 7,44 bpm (média) e 182,22 ± 7,72 bpm (máxima). Foi evidenciada correlação significativa entre o VO2máx e a distância percorrida no segundo tempo (r = 0,517) (p < 0,05). O VO2máx também apresentou correlação com a velocidade máxima de deslocamento (r = 0,506) (p < 0,05). Já o percentual de gordura apresentou correlação negativa com a velocidade máxima no segundo tempo (r = -0,471) (p < 0,05). Foi detectada correlação positiva entre o desempenho da arbitragem e o VO2máx (r = 0,530) (p < 0,05). Com relação ao percentual de gordura, o mesmo apresentou correlação negativa com o desempenho do árbitro (r = -0,496) (p < 0,05). Os resultados do presente estudo indicam que os árbitros de futebol são submetidos à alta sobrecarga física/fisiológica durante as partidas. Os resultados obtidos também sugerem que os parâmetros associados com a aptidão física (composição corporal e o VO2máx) podem interferir no desempenho da arbitragem.

The aims of the present study were to: a) report the physical demands of brazilian soccer referees during official matches and, b) assess if the level of fitness interferes in the referees' performance. The referees were examined during official games (n = 21) of the 2009 Rio Grande do Norte Soccer Federation Championship. The referees (n = 11) mean age was 36.3 ± 6.3 years. Match analysis parameters (distance covered, speed and heart rate) were assessed during official matches. The referee performance evaluation was conducted by an official member of Rio Grande do Norte Soccer Federation. The average match distance covered was 10.50 ± 0.35 km. The average speed and maximum speed were 6.43 ± 0.26 km/h and 19.84 ± 1.56 km/h, respectively. Heart rate analysis revealed the intermittent nature of the referees' activities. The HRav and HRmax were 162.77 ± 7.44 bpm and 182.22 ± 7.72 bpm, respectively. There was a positive correlation (r = 0.517; p < 0.05) between VO2max and distance covered on the second half. VO2max was also correlated with the maximum speed (r = 0.506; p < 0.05). The fat mass was negatively correlated with maximum speed on the second half (r = -0.471; p < 0.05). Regarding referee performance, it was observed a positive correlation between VO2max and referee evaluation score (r = 0.530; p < 0.05). On the other hand, fat mass was negatively correlated to referee evaluation score (r = -0.496; p < 0.05). The results present herein suggest that soccer referees were submitted to a high level of physical demands during the match. The results also indicate that soccer referees' fitness level can interfere in their performance.

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