Controle de qualidade de análises de solos da rede Rolas - RS/SC e procedimentos estatísticos alternativos - Griebeler, Gustavo (2012)
Soil chemical analysis must be accurate to avoid errors in the recommendations of lime and fertilizers. The quality control program of the ROLAS-RS/SC network evaluates the analysis accuracy by the distance of a result, through standard deviation, from the median of four soil samples analyzed monthly during a year. This way of judgment requires that data sets must have normal distribution to insure that the median can be considered an estimation of the true value. Outliers should be eliminated because they change the standard deviation spreads and consequently, the accuracy. The mathematical procedure of the accuracy calculation may also allow that asterisks attributed to accurate data might be poised by accurate ones. In addition, the cancellation criteria of the asterisks may be at odds with the uncertainty associated with analytical methods. Therefore, the objective of this work was to check the normal distribution of data sets, identify outliers, evaluate procedures of accuracy calculation and quantify analytically the uncertainty associated to the extractions and determination methods of P and K in order to verify how these aspects may affect laboratories accuracy. The Lilliefors test was run to check the normality and outliers were identified through the quartile test. Procedures to evaluate the accuracy by normality adjustment through outliers elimination of data sets were tested. The substitution of the median by the average as criteria of central reference and calculation of accuracy for each attribute analyzed, instead of annual average accuracy was also tested. Repetitions of the analysis of P and K were carried out to determine the intrinsic variability of the methods. Only 59% of data followed normal distribution, indicating that 41% of the attributes analyzed were considered in disaccordance with statical assumptions. When outliers were eliminated of the data sets, analyzes with normal distribution increased up to 75%, which decreased the number of laboratories that had the minimum accuracy required by the laboratories network. When data sets have normal distribution, the use of the average instead of median showed to be better for the estimation of the true value. Data sets out of the analytical expected range should be eliminated, while those framed within it, if amplitude is less than 1,5 interquartile distances, should not be excluded for the calculation of the accuracy. The procedure to calculate annual average accuracy hide attributes less accurate then the minimal the required. The intrinsic variability associated with the methods of analysis indicates that the criteria for the cancellation of asterisks from P should be reassessed, but the criteria for K seem to be appropriate. Studies on the variability of each analytical method are needed.