Soybean yield variability per plant in subtropical climate: sample size definition and prediction models for precision statistics

[dataset] FAO, 2018. FAOSTAT: Agricultural production. FAO. http://faostat.fao.org/ (acessed 10 January 2021). Alvez-Silva, 2018, How many leaves are enough? The influence of sample size on estimates of plant developmental instability and leaf asymmetry, Ecol. Indic., 89, 912, 10.1016/j.ecolind.2017.12.060 Anderson, 2017, Sample-size planning for more accurate statistical power: a method adjusting sample effect sizes for publication bias and uncertainty, Psychol. Sci., 28, 1547, 10.1177/0956797617723724 Brazilian Institute of Geography and Statistics, 2018. Agricultural production. http://www.sidra.ibge.gov.br/bda/pesquisas/pam/default.asp?o=18&i=P (Acessed 10 January 2021). Balbinot Junior, 2018, Phenotypic plasticity in a soybean cultivar with indeterminate growth type, Pesq. agrop. Bras., 53, 1038, 10.1590/s0100-204x2018000900007 Butturi-Gomes, 2014, Computer intensive methods for controlling bias in a generalized species diversity index, Ecol. Indic., 37, 90, 10.1016/j.ecolind.2013.10.004 Cargnelutti Filho, 2009, Measures of experimental precision in common bean and soybean genotype trials, Pesq. agropec. Bras., 44, 1225, 10.1590/S0100-204X2009001000003 Cargnelutti Filho, 2018, Number of replicates and experimental precision statistics in corn, Pesq. agropec. Bras., 53, 1213, 10.1590/s0100-204x2018001100003 Cargnelutti Filho, 2018, Number of leaves for modelling the leaf area of velvet bean according to leaf dimensions, Rev. Cienc. Agrovet., 17, 571, 10.5965/223811711732018571 Cargnelutti Filho, 2013, Sample size for estimation of the plastochron in pigeonpea, Eur. J. Agron., 48, 12, 10.1016/j.eja.2013.02.003 Confalonieri, 2009, Analysis of sample size for variables related to plant, soil, and soil microbial respiration in a paddy rice field, Field Crops Res., 113, 125, 10.1016/j.fcr.2009.04.014 CQFS - Comissão de Química e Fertilidade do Solo, 2016. Manual of fertilization and liming for the States of Rio Grande do Sul and Santa Catarina, eleventh ed. Sociedade Brasileira de Ciência do Solo, Porto Alegre. Cruz, C.D., Carneiro, P.C.S., Regazzi, A.J., 2012. Biometric models applied to genetical improvement, fourth ed. UFV, Viçosa. Döring, 2015, Taylor’s power law and the stability of crop yields, Field Crops Res, 183, 294, 10.1016/j.fcr.2015.08.005 Döring, 2018, Detecting global trends of cereal yield stability by adjusting the coefficient of variation, Eur. J. Agron., 99, 30, 10.1016/j.eja.2018.06.007 Duarte, 2018, Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches, Ecol. Model., 374, 51, 10.1016/j.ecolmodel.2018.02.007 Efron, 1979, Bootstrap methods: another look at the jackknife, Ann. Stat., 7, 1, 10.1214/aos/1176344552 Efron, 1986, Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy, Stat. Sci., 1, 54 Fehr, 1971, Stage of development descriptions for soybeans, Glycine max (L.) Merrill, 11, 929 Fisher, 1926, The arrangement of field experiments, J. Minist. Agric., 33, 503 Fisher, 1930, The arrangement field experiments and the statistical reduction of the results, Imp. Bur. Soil Sci. Tech. Commun., 10, 0 Gordón-Mendoza, 2015, Statistical selection for estimating the accuracy in experimental corn trials, Agron. Mesoam., 26, 55, 10.15517/am.v26i1.16920 Goulart, 2020, Cropping poorly-drained lowland soils: Alternatives to rice monoculture, their challenges and management strategies, Agry. Syst., 177 IRGA Instituto Rio Grandense do Arroz, 2020. Crop. https://irga.rs.gov.br/safras-2. (Acessed 10 January 2021). Khosravi, 2020, Application of bootstrap re-sampling method in statistical measurement of sustainability, Socio-Econ. Plan. Sci., 75 Lorentz, 2012, Proposal method for plot size estimation in crops, Rev. Ceres, 59, 772, 10.1590/S0034-737X2012000600006 Lúcio, 1997, Experimental precision parameters for main annual crops of Rio Grande do Sul state, Cienc. Rural, 27, 530, 10.1590/S0103-84781997000300029 Lúcio, 1999, Quality control of cultivar competition experiments through the analysis of the statistics employed, Pesq. Agrop. Gaúcha, 5, 99 Marchant, 2019, Establishing the precision and robustness of farmers’ crop experiments, Field Crops Res, 230, 31, 10.1016/j.fcr.2018.10.006 Masino, 2018, Spatial and temporal plant-to-plant variability effects on soybean yield, Eur. J. Agron., 98, 14, 10.1016/j.eja.2018.02.006 Maxwell, 2008, Sample size planning for statistical power and accuracy in parameter estimation, Annu. Rev. Psychol., 59, 537, 10.1146/annurev.psych.59.103006.093735 Mentges, 2016, Capacity and intensity soil aeration properties affected by granulometry, moisture, and structure in no-tillage soils, Geoderma, 263, 47, 10.1016/j.geoderma.2015.08.042 Moinester, 2014, Sample size estimation for correlations with pre-specified confidence interval, Quant. Meth. Psych., 10, 124, 10.20982/tqmp.10.2.p0124 Olivoto, 2018, Confidence interval width for Pearson’s correlation coefficient: a gaussian-independent estimator based on sample size and strength of association, Agron. J., 110, 1, 10.2134/agronj2017.09.0566 Passos, 2011, Yield per plant and other characteristics of soybean plants treated with kinetin and potassium nitrate, Ciênc. Agrotec., 35, 965, 10.1590/S1413-70542011000500014 Pimentel-Gomes, F., 1990. Experimental statistics course, thirteenth ed. Nobel, Piracicaba. Pimentel-Gomes, F., 1991. The variation index, an advantageous substitute for the variation coefficient, first ed. IPEF, Piracicaba. R Development Core Team, 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. Reichert, 2016, Conceptual framework for capacity and intensity physical soil properties affected by short and long-term (14 years) continuous no-tillage and controlled traffic, Soil Tillage Res, 158, 123, 10.1016/j.still.2015.11.010 Reichert, 2009, Reference bulk density and critical degree-of-compactness for no-till crop production in subtropical highly weathered soils, Soil Tillage Res, 102, 242, 10.1016/j.still.2008.07.002 Resende, 2007, Precision and quality control in variety trials, Pesq. Agropec Trop., 37, 182 Ribas, 2021, Assessing yield and economic impact of introducing soybean to the lowland rice system in southern Brazil, Agry. Syst., 188 Salvadori, J.R., Bacaltchuk, B., Deuner, C.C., Lamas Junior, G., Rizzardi, M.A., Langaro, N.C., Escosteguy, P.V., Boller, W., 2016. Technical indications for the soybean culture in Rio Grande do Sul and Santa Catarina, harvests of 2016/2017 and 2017/2018, first ed. UPF, Passo Fundo. Santos, H.G. dos, Jacomine, P.K.T., Anjos, L.H.C. dos, Oliveira, V.Á. de, Lumbreras, J.F., Coelho, M.R., Almeida, J.A. de, Araújo Filho, J.C. de, Oliveira, J.B. de, Cunha, T.J.F., 2018. Brasilian Soil Classification System. fifth ed. EMBRAPA, Brasília. Sartori, 2016, Growth and development of soybean roots according to planting management systems and irrigation in lowland areas, Cienc., Rural, 46, 1572, 10.1590/0103-8478cr20151579 Schumacher, 1939, A new growth curve and its application to timber yield studies, J., 37, 819 Siegel, 2016 Silva, 2017, Determination of maximum curvature point with the R package soilphysics, Int. J. Curr. Res., 9, 45241 Souza, 2021, Soybean grain yield in highland and lowland cultivation systems: A genotype by environment interaction approach, Annals of Applied Biology, 179, 302, 10.1111/aab.12709 Ståhle, 1989, Analysis of variance (ANOVA), Chemom. Intell. Lab Syst., 6, 259, 10.1016/0169-7439(89)80095-4 Storck, 2011, Partial collection of data on potato yield for experimental planning, Field Crops Res., 121, 286, 10.1016/j.fcr.2010.12.018 Storck, L., Garcia, D.C., Lopes, S.J., Estefanel, V., 2016. Plant Experimentation, third ed. UFSM, Santa Maria. Toebe, 2014, Sample size for estimating mean and coefficient of variation in maize, Pesq. agropec. Bras., 49, 860, 10.1590/S0100-204X2014001100005 Toebe, 2015, Sample dimensioning for estimating coefficients of correlation in maize hybrids, harvests and precision levels, Bragantia, 74, 16, 10.1590/1678-4499.0324 Toebe, 2018, Sample size for estimating mean and coefficient of variation in species of crotalarias, Acad. Bras. Cienc., 90, 1705, 10.1590/0001-3765201820170813 Wrege, M.S., Steinmetz, S., Reisser Júnior, C., Almeida, I.R. de, 2012. Climatic Atlas of the South Region of Brazil: States of Paraná, Santa Catarina and Rio Grande do Sul, second ed. EMBRAPA, Brasília. Zanon, 2016, Climate and management factors influence soybean yield potential in a subtropical environment, Agron. J., 108, 1447, 10.2134/agronj2015.0535 Zanon, 2015, Development of soybean cultivars as a function of maturation group and growth type in high lands and in lowlands, Bragantia, 74, 400, 10.1590/1678-4499.0043