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Soybean is the main agricultural crop produced and exported by Brazil, and the whitefly, Bemisia tabaci (Gennadius, 1889) (Hemiptera: Aleyrodidae), is one of the main pests of this crop. Whitefly control is carried out mainly with chemical insecticides, which has led to an increase in number of applications and evolution towards resistant populations. There is a need to study alternatives for the control of this insect pest, and biological control using entomopathogenic fungi can be an effective tool, offering less risk to the environment. This study aimed to design and evaluate four management programs in two soybean cropping seasons, including two integrated management alternatives, a conventional chemical management alternative and a biological (fungal) control alternative. Two sequential sprays set 10 days apart were performed for each program, and the results of ANOVA and Scott-Knott test (P ≤ 0.05) revealed differences among treatments. The integrated management consisting of a sequential spray of chemical insecticide followed by a fungal one stood out as the best control option, presenting 60% of control efficacy for whitefly adults and 34% for whitefly nymphs in the first cropping season; 83% of control efficacy for whitefly adults and 53% for whitefly nymphs in the second cropping season. Therefore, integrating fungi-based products with chemical insecticides may provide an efficient alternative for controlling B. tabaci in soybean. • Integrating entomopathogenic fungi-based products with chemical insecticides enhances B. tabaci control in soybean. • Applying fungi-based products after two sprays of chemical insecticides provides 60–83% of control for whitefly adults. • This strategy is more effective for B. tabaci control than applying fungal products in the first spray or alone. [ABSTRACT FROM AUTHOR]

Abstract: The objectives of this study were to estimate the plastochron in pigeonpea (Cajanus cajan (L.) Millsp.) during the period between emergence and flowering using three methods of calculating the average daily air temperature and to determine the sample size (number of plants) needed to estimate the plastochron. A uniformity test (blank experiment) was conducted in an area of 1440m2 containing a pigeonpea crop. The area was divided into 360 plots of 2m×2m, and 1 plant per plot was marked at random. In each of these 360 plants, the number of nodes on the main stem was counted at 37, 43, 50, 57, 64, 71, 78, 85, 93, 99, 106, 114 and 120 days after emergence (DAE). The average daily air temperature (T average) was calculated using three methods: method 1: T average =(T minimum + T maximum)/2; method 2: T average =(T 0h + T 1h + T 2h +⋯+T23h)/24; and method 3: T average =(T minimum + T maximum + T 9h +2T 21h)/5. For the three methods, the daily and cumulative thermal times were calculated from the date of emergence to early flowering and fitted to a linear regression of the average number of nodes on the main stem as a function of the accumulated thermal time. The plastochron was then calculated under each method as the inverse of the slope of the linear regression, and the required sample size (number of plants) to estimate the plastochron was determined by resampling with replacement. Plastochron values determined from the average daily air temperature calculated based on the three methods are different, and the use of the arithmetic mean of the hourly temperatures (method 2) should be favoured. Under method 2, the plastochron for pigeonpea was determined to be 21.34°Cdaynode−1. To estimate the plastochron with 95% confidence interval amplitudes equal to 1, 2 and 3°Cdaynode−1, it was necessary to count the number of nodes in 194, 50 and 24 pigeonpea plants, respectively. [Copyright &y& Elsevier]

The whitefly Bemisia tabaci is the second most widespread and economically important arthropod pest in the world, with frequent population outbreaks occurring in Brazil and increasing economic damage on soybean crops. High oviposition rate, short life cycle and uneven distribution along the plants' canopy favour the dissemination of B. tabaci within soybean crops and compromise the efficacy of control measures. The objective of this work was to quantify soybean yield reduction caused by B. tabaci damage and propose an economic injury level (EIL) for this pest in soybean crops. Experimental design was randomized blocks with four replicates. Soybean grain yield and its components (number of pods, number of grains and 1000-grain weight) were quantified in relation to different levels of B. tabaci infestation in two cropping seasons, obtained through insecticide sprays carried out at different growth stages of the soybean plants. The resulting data were submitted to variance analysis, mean separation test and regression analysis. We found that a population density of one whitefly trifoliate−1 reduces from 0.88 to 1.79 grains plant−1, 0.16 ± 0.01 g plant−1 or 31.24 ± 4.21 kg ha−1of grain yield, and varies in response for pods plant−1 and 1000-grains weight. Based on this information, we suggest an economic injury level of 1.465 ± 0.195 B. tabaci trifoliate−1, considering a control cost of US$ 20.00 ha-1 and soybean crop value of US$ 400.00 tonne−1. • The number of B. tabaci soybean trifoliate−1 is decisive for the level of control. • The population peak was reached during soybean reproductive phases. • The economic injury level of B. tabaci in soybean is 1.465 ± 0.195 trifoliate−1. • One B. tabaci trifoliate−1 reduced 0.16 ± 0.01 g plant−1 or 31.24 ± 4.21 kg ha−1. • One B. tabaci trifoliate−1 reduced 0.88 to 1.79 grains plant−1. [ABSTRACT FROM AUTHOR]

Abstract Aquatic macrophytes are potentially useful for phytoremediation on flooded areas. A field study in Brazil was conducted to evaluate Eleocharis acutangula (E), Cyperus papyrus (C) and Typha domingensis (T) in monocropping and intercropping, aiming to phytoremediate barium-polluted flooded soils. The treatments were: monocroppings (E, C and T); double intercroppings (EC, ET and CT); and triple intercropping (ECT). The 180-d field trial was performed in a flooded area with high barium content, with a randomized complete block design and three replicates. Plant stand size, biomass yield, and Ba concentration aboveground/Ba concentration in roots (translocation factor – TF) as well as Ba mass aboveground/Ba mass in roots (mass translocation factor – mTF) were determined. Most of the treatments did not differ on dry biomass, except for EC, which showed the lowest yield. Consistently with its biology, E. acutangula in monocropping showed the largest plant stand. Otherwise, intercroppings with T. domingensis achieved the highest amounts of barium absorbed from the soil and transferred most of the barium content from belowground to aboveground (mTF > 1.0), especially ET, which showed the highest mTF among the intercroppings (2.03). Remarkably, TF values did not reflect such phytoextraction ability for CT and ECT. Thus, mTF was more appropriate than TF to assess phytoextraction capacity. Furthermore, it was demonstrated that intercropping can increase barium uptake from flooded soils. Particularly, the intercropping ET constituted the most cost-effective treatment, with the cyperaceous species providing high plant coverage while T. domingensis facilitated barium removal by translocating it to the aboveground biomass. Highlights • Three aquatic macrophytes were evaluated for Ba removal from a flooded soil. • A field trial compared the Ba removal efficiency of monocropping and intercropping. • The highest Ba phytoextraction levels were found in the intercropping treatments. • Typha domingensis played a pivotal role in phytoremediation outcomes. [ABSTRACT FROM AUTHOR]

The use of barite (BaSO4) in drilling fluids for oil and gas activities makes barium a potential contaminant in case of spills onto flooded soils, where low redox conditions may increase barium sulfate solubility. In order to select plants able to remove barium in such scenarios, the following species were evaluated on barium phytoextraction capacity: Brachiaria arrecta , Cyperus papyrus , Eleocharis acutangula , E. interstincta , Nephrolepsis cf. rivularis , Oryza sativa IRGA 424, O. sativa BRS Tropical, Paspalum conspersum , and Typha domingensis . Plants were grown in pots and exposed to six barium concentrations: 0, 2.5, 5.0, 10.0, 30.0, and 65.0 mg kg −1 . To simulate flooding conditions, each pot was kept with a thin water film over the soil surface (∼1.0 cm). Plants were evaluated for biomass yield and barium removal. The highest amount of barium was observed in T. domingensis biomass, followed by C. papyrus . However, the latter exported most of the barium to the aerial part of the plant, especially at higher BaCl 2 doses, while the former accumulated barium preferentially in the roots. Thus, barium removal with C. papyrus could be achieved by simply harvesting aerial biomass. The high amounts of barium in T. domingensis and C. papyrus resulted from the combination of high barium concentration in plant tissues with high biomass production. These results make T. domingensis and C. papyrus potential candidates for phytoremediation schemes to remove barium from flooded soils. [ABSTRACT FROM AUTHOR]

The aims of this study were to determine the adequate sample size of cauliflower seedlings per experimental unit to estimate experimental precision statistics, and to forecast experimental precision statistics. From a reference experiment in which 20 seedlings per experimental unit were sampled and the number of leaves, shoot length, root length, and total length were measured, a bootstrapping procedure was applied with replacement, recreating experiments with sample sizes ranging from 1 to 100 seedlings per experimental unit, obtaining 10,000 resamples for each size. For each resample, 13 precision statistics were estimated, for which the 95% confidence interval widths were calculated, fitting these values to the sampling scenarios through traditional power models. The sample size was then determined through the maximum curvature point through the perpendicular distances' method for each precision statistic and trait, for which a modified power model was used for forecasting. Also the contribution of the precision statistics to sample size definition was verified via principal component analysis. The reference experiment showed high experimental precision, which varied according to the trait and precision statistic considered. An exponential decrease in the confidence interval width was observed as the sample number was increased, and the optimal sample size varied slightly among statistics and traits, where the maximum value found was 20 seedlings per experimental unit, however, considering the statistics that contributed the most to the total variation, 16 seedlings per experimental unit represent the sufficient sample size for estimating experimental precision statistics in experiments with cauliflower seedlings. Also, the forecasting models showed excellent fitting quality and can be used and adapted by researchers performing experiments on seedling production of cauliflower or other horticultural crops. [Display omitted] [ABSTRACT FROM AUTHOR]

Counting traits are commonly measured in soybean research, however, the number of plants to be sampled per experimental unit needs to be standardized for estimating precision statistics from these traits, considering experimental designs; The current study evaluated the impact of plant variability for selected soybean agronomic traits; established the ideal sample size to estimate experimental precision statistics; and modeled the response of plant variability per experimental unit to forecast experimental precision statistics; Trials were conducted with 30 genotypes in highlands and 20 in lowland areas of Brazil. A complete randomized block design with three repetitions was used, measuring four counting traits in 20 plants per experimental unit, which represents a total of 9000 plants. From these experiments, trials with sampling scenarios from 1 to 100 plants per experimental unit were simulated; Results showed an exponential decreasing response of the 95% confidence intervals as a function of the increase in sample size per experimental unit for all precision statistics, stabilizing when ≥ 21 plants per experimental unit were sampled. Twenty-one plants per experimental unit were considered representative for the soybean traits here studied. The response of the 95% confidence interval width to sample size enabled the creation of good-quality forecasting formulas and equations to predict experimental precision in experiments with soybean that evaluate these traits; The sample size recommendations made in this work can be applied in similar experimentations. [Display omitted] • Twenty-one plants per experimental unit were enough for soybean counting traits. • Precision statistics were predicted from subtropical-climate soybean counting traits. • Forecasting precision improved after reaching 21 plants per experimental unit. [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]