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The state of Rio Grande do Sul (RS) is the third largest sunflower producer in Brazil. This study verified the existence of linear relationships between grain yield, oil content, and oil yield in sunflower (Helianthus annuus L.) genotypes evaluated in RS. The data on grain yield (GY), oil content (OC), and oil yield (OY) were obtained from 73 sunflower cultivar trials in 21 municipalities in RS from 1999 to 2018. Scatter diagrams were createdto investigate the relationships between the variables GY, OC, and OY. Then, Pearson's linear correlation coefficient (r) was calculated between the variables, and the Student's t-test was performed at a 5% significance level. There is a positive and significant linear relationship between PG and OY. However, no linear relationship was found between grain yield and oil content in sunflower genotypes. Additionally, a positive linear association of high magnitude (r = 0.9672) was observed between grain yield and oil yield, while a low magnitude (r = 0.2245) was found between oil content and oil yield. [ABSTRACT FROM AUTHOR]

The objectives of this work were to verify the adjustment of time series of monthly minimum (Tmín) and monthly maximum (Tmáx) air temperatures, in the State of Rio Grande do Sul, to the normal, log-normal, gamma and weibull probability distributions, to determine probabilities of occurrence and prepare maps with the interpolation of isolines. The Kolmogorov-Smirnov test was applied to 216 time series of Tmín and 216 time series of Tmáx (18 cities × 12 months). Based on the normal distribution, values of Tmín were determined, whose probability of occurrence of Tmín less than or equal to this value is 90%, and values of Tmáx, whose probability of occurrence of Tmáx greater or equal to this value is 90%. From these values, maps were prepared with interpolation of isolines with a difference of 0.5°C. The Tmín and Tmáx data adjust the normal, log-normal, gamma and weibull distributions, showing better adherence to the normal distribution. The maps with the isolines are useful to verify the probability of occurrence of harmful or favorable Tmín and Tmáx for agricultural crops in the State of Rio Grande do Sul, during the months of the year. [ABSTRACT FROM AUTHOR]

Genotype x environment interaction (GEI) causes constant interference in soybean [Glycine max (L.) Merr.] grain yield. This complexity tends to increase when comparing highland and lowland cultivation systems, and there has been little referenced work for these in the situation of subtropical conditions. Hence, the aims of this study were to verify the effects of genotypes, environments and GEI for soybean grain yield in highland and lowland areas with subtropical climate and to compare the adaptability and stability methodologies. The trials were carried out in two locations of the state of Rio Grande do Sul, Brazil, with 20 soybean cultivars on three sowing dates for each location. The experiments were conducted in a randomised block design with three replications. Given the observed magnitude of the GEI, its simple and complex parts were quantified for further analysis of adaptability and stability, using the Additive Main Effect and Multiplicative Interaction (AMMI), Genotype plus Genotype–Environment interaction (GGE) and Best Linear Unbiased Prediction (BLUP) modelling approaches, followed by the verification of similarity between the methodologies via Spearman's correlation coefficient. The complex part of the GEI represented 82.11% of the total variation associated with the GEI, whereas the simple part accounted for 17.89% of that variation. The second sowing date, in both locations, was the one that presented the best rankings according to the models used, and also, cases of specific genotype adaptability were identified in each environment. The highest yield averages were obtained in the highlands. The GGE and BLUP techniques presented genotypic ranking concordance. [ABSTRACT FROM AUTHOR]

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Biometric techniques, including the principal component analysis, are commonly applied in soybean genetic divergence studies. However, the sample size used in such studies is often determined empirically, neglecting its potential impact on inference interpretation. Therefore, this study aimed (i) to analyze the response of principal components to the number of plants sampled per experimental unit (plot); (ii) to determine the multivariate representative sample size, and (iii) to construct a methodology to predict sample size for principal components as a function of the precision level defined a priori. Six experiments were performed in two locations in Rio Grande do Sul, Brazil, with three experiments being carried out in each location. All experiments were conducted using a complete randomized block design with three repetitions, and 20 soybean genotypes were utilized, resulting in 360 plots (60 plots per experiment). From each plot, twenty plants were sampled, totaling 7200 plants. A resampling bootstrap procedure was applied to the principal component technique for 10 biometric traits. Posteriorly, the sample size was defined based on predefined precision levels, and power logistic models were parametrized to predict the sample size per experimental unit. The precision of the eigenvalues obtained from the principal component analysis gradually improves with larger sample sizes. Eigenvalues that capture a higher variance tend to require smaller sample sizes for accurate estimation. The fitted models demonstrated satisfactory predictive ability in determining the optimal number of plants per experimental unit, serving as a complementary tool for defining sample size at the desired precision level. Eighteen plants per experimental unit are enough to estimate the eigenvalues of the first two soybean principal components reliably. The developed predictive methodology and sample dimensioning per experimental unit can support future research aimed at identifying divergent genotypes, making them useful tools for soybean plant breeding programs. [Display omitted] • Principal component estimates are improved as sample sizes increases. • Eighteen plants per experimental unit were enough to analyze genetic divergence. • An accurate methodology to predict sample size per experimental unit was developed. [ABSTRACT FROM AUTHOR]

Planning agricultural activities requires that climatic risks in each location and time of year are estimated with maximum precision. In this study data from 15 locations of Rio Grande do Sul, Brazil, were used with the objective of studing spatial and temporal variability of the precision estimates average of monthly rainfall, sunshine, solar radiation global and temperatures minimum, mean and maximum. It was estimated the estimation error (half-amplitude of the confidence interval) (EE) in percentage of estimate average monthly rainfall, sunshine, solar radiation global and temperatures minimum, mean and maximum for each month and location, which was used as a measure of precision. From the EE, for each meteorological elements, months and locations were clustered by hierarchical method of Ward. The Pearson's linear correlation coefficient between the arrays of the standardized average euclidian distance and graphics was calculated in order to check the clusters consistency. It was concluded that the precision estimates, average of months and locations, between the meteorological elements, increases in the following order: rainfall (EE=21.66%), sunshine (EE=6.74%), global solar radiation (EE=5.52%) and temperatures minimum (EE=4.40%), mean (EE=2.63%) and maximum (EE=2.27%). Results showed that there is variability in precision estimate of monthly average rainfall, sunshine, global solar radiation and minimum, mean and maximum temperatures in Rio Grande do Sul, both in temporal and spatial scale. Estimates based on 30 years data are less precision in April, May, June, July, August and September in relation to the others. [ABSTRACT FROM AUTHOR]

The objective of this research was to determine the number of environments (experiments) that were necessary to identify the diversity in common bean cultivars (Phaseolus vulgaris L.), by cluster methods based on productive, phenologic and morphologic traits. Thirteen common bean cultivars were evaluated in nine experiments conducted in Santa Maria, in Rio Grande do Sul State, Brazil (latitude 29°42'S, longitude 53°49'W, altitude 95m) in the agricultural years of 2000/2001 and 2004/2005. Randomized blocks design with three replications was installed to evaluate the following characters: grain yield, number of pods per plant, number of seeds per pod, weight of 100 grains, final population of plants, number of days of the emergency to flowering, number of days of the emergency to harvest, height of first pod insertion, height of the final pod insertion and degree of the down. In the central depression region of Rio Grande do Sul State, Brazil, seven and six experiments are sufficient to identify the diversity in common bean cultivars, by hierarchical methods of the Ward and optimizing Tocher, respectively, for productive, phenologic and morphologic traits. [ABSTRACT FROM AUTHOR]

Grain yield data were used from 65 maize cultivar trials carried out in the State of the Rio Grande do Sul, Brazil, in the years of 2002/2003, 2003/2004 and 2004/2005, with the objective of comparing the following methods of adaptability and stability analysis: Yates and Cochran, Plaisted and Peterson, Wricke, Annicchiarico, Finlay and Wilkinson, Eberhart and Russell, Tai, Toler, Silva and Barreto, Cruz et al., Huehn, Lin and Binns modified by Carneiro and analysis AMMI. To verify the degree of association between the estimates of adaptability and stability parameters, obtained by the different methods, the Spearman correlation coefficient was used. There is an agreement in the indication of maize cultivars by the methods of Plaisted and Peterson and Wricke, of Annicchiarico and Lin and Binns modified by Carneiro, of Eberhart and Russell and Tai, and of Toler and Silva and Barreto, which counter-indicates their concomitant use. To a lesser degree of association the AMMI analysis agrees with the estimates of the stability parameters, Plaisted and Peterson, Wricke, Eberhart and Russell, Tai, Silva and Barreto and Cruz et al. [ABSTRACT FROM AUTHOR]

Empirical decision-making on the number of plants to be sampled per experimental unit when assessing soybean grain yield is a commonly observed situation. The cause for this is unknowing how that decision affects experimental precision statistics, which has not yet been reported. Therefore, the aims of this study were: to analyze the response of experimental precision statistics as a function of the variations in the number of plants collected per experimental unit in highland and lowland systems; to define the optimal sample size per experimental unit for each precision statistic; and, to propose predictive models for estimating the precision of experiments with soybean. Field experiments were carried out in two locations of Rio Grande do Sul, Brazil, using 30 cultivars in the highland trials and 20 cultivars in the lowland trials. A validation trial was also performed using 8 cultivars. A randomized block design with three repetitions was used for all trials. The grain yield of 20 plants per experimental unit was assessed, thus totaling 9000 plants. In the validation experiment, grain yield was measured in 100 plants per experimental unit, totaling 2400 plants more. From these experiments, trials with sample scenarios of 1, 2, ..., 1000 plants per experimental unit were simulated with replacement. The confidence interval width of the precision statistics reduced as the number of plants measured per experimental unit increased, allowing a higher precision of its estimates. In general, up from values of ≥ 70%, precision gain, in the estimates of experimental precision statistic determined in soybean highland and lowland systems, tends to stabilize. The optimal sample number per experimental unit varied between systems and experimental precision statistics. The sampling of 18 plants per experimental unit is sufficient to estimate most precision indicators reliably. Interestingly, the mechanistic response of the confidence interval width is easily modeled and can be used for predicting experimental precision in highland and lowland cropping systems. From this analysis, a methodology for predicting experimental precision was outlined based on sample size per experimental unit, thus bringing new insight into future experimental planning for soybean experiments in highland and lowland systems in subtropical climate. [Display omitted] • The accuracy of the precision statistics was improved in greater sampling scenarios. • Eighteen plants per experimental unit were sufficient in highlands and lowlands. • The proposed methodology is adequate for subtropical climate highlands and lowlands. • Precision predictability is improved in samplings ≥ 18 plants per experimental unit. [ABSTRACT FROM AUTHOR]