Journal of Agricultural Science; Vol. 10, No. 9; 2018 ISSN 1916-9752 E-ISSN 1916-9760 Published by Canadian Center of Science and Education 209 Sample Sufficiency for Mean Estimation of Productive Traits of Sunn Hemp Denison Esequiel Schabarum 1 , Alberto Cargnelutti Filho 2 , Cláudia Marques de Bem 1 , Giovani Facco 1 , Jéssica Andiara Kleinpaul 3 & Cleiton Antonio Wartha 3 1 Postgraduate Program in Agronomy, Federal University of Santa Maria, Santa Maria, Brazil 2 Department of Crop Science, Federal University of Santa Maria, Santa Maria, Brazil 3 Student of Agronomy, Federal University of Santa Maria, Santa Maria, Brazil Correspondence: Alberto Cargnelutti Filho, Department of Crop Science, Federal University of Santa Maria, Avenida Roraima, nº 1000, Bairro Camobi, CEP 97105-900, Santa Maria, RS, Brazil. Tel: 55-55-3220-8899. E-mail: alberto.cargnelutti.filho@gmail.com Received: May 11, 2018 Accepted: June 18, 2018 Online Published: August 15, 2018 doi:10.5539/jas.v10n9p209 URL: https://doi.org/10.5539/jas.v10n9p209 Abstract Sunn hemp (Crotalaria juncea L.) is an annual cycle legume with high potential for biological nitrogen fixation, being widely used in crop rotation for biomass formation and control of nematodes. The objectives of this study were to determine the sample size for the mean estimation of productive traits of sunn hemp and verify the sample size variability between traits and sowing dates. Two uniformity trials were performed in the agricultural year 2014/2015, with sowing in October (trial 1) and December (trial 2). At the crop flowering stage, 300 plants of each trial were harvested and fresh and dry matter of leaves, stem, root, aerial part, and total weight were evaluated. The normality and randomness tests were performed for each trait and the sample size was calculated for the semi-amplitudes of the confidence interval (estimation errors) of 2, 4, 6, 8, 10, 12, 14, 16, 18 and 20% of the mean estimate. There is sample size variability between productive traits and between sowing dates. The assessment of at least 101 plants is required for mean estimation of productive traits with maximum estimation error of 20% of the mean and 95% confidence level. Keywords: Crotalaria juncea L., sample planning, sample size, soil cover crops, sowing dates 1. Introduction Sunn hemp (Crotalaria juncea L.) is a native species from India, with wide adaptation to tropical regions. This legume is a shrub, standing upright 2 to 3 meters high and with deep root system, which assists in soil decompression and nutrient recycling. This species is used in crop rotation and stands out among the soil cover crops due to the P and Mg accumulation, high biomass production, reaching up to 16.5 t ha -1 of dry matter and nitrogen fixation up to 298 kg ha -1 of N (Mangaravite, Passos, Andrade, Burak, & Mendonça, 2014; Silva et al., 2014; Xavier, Oliveira, & Silva, 2017). Sunn hemp is a viable option for crop rotation in areas infested with root-knot nematodes (Meloidogyne enterolobii) (Rosa, Westerich, & Wilcken, 2015). Due to its importance, carrying out further research is essential to assure security in the use of new technologies related to the crop by technicians and growers. In agricultural experiments, factors such as availability of time, labor, financial and human resources generally limit the evaluation of all plants of the entire experimental unit (plot). Thus, commonly only part of the plot is evaluated, i.e., only a few plants (sample) in order to minimize the limiting factors. Therefore, the sample should be representative of the plants of the experimental unit (Storck, Garcia, Lopes, & Estefanel, 2016). The results obtained from the samplings are subject to a certain degree of uncertainty because the data measured in samples present random variation corresponding to the evaluation method and the experimental material, besides only considering part of the population. Meanwhile, these errors can be reduced by employing more accurate measuring instruments and sample sized for the desired precision. Data heterogeneity and the desired confidence level in the mean estimation of one trait are factors that directly influence the sample size. The sample size can be calculated by setting the desired precision degree. Lower values of admitted estimation error (greater precision) increase the number of observations to be evaluated (Bussab & Morettin, 2017).