Validmeth: um aplicativo shiny para automatizar o método de validação de testes de sementes

Abstract

Seed quality is the key factor for the agriculture success, contributing directly to increase productivity, sustainability, stress resistance, and efficient use of inputs. This outcome is made possible not only by advances in genetic improvement, biotechnology, and good agricultural practices, but also by the structured operation of the National Seed and Seedling System (SNSM). The SNSM is coordinated by the Ministry of Agriculture and Livestock (MAPA) and regulated by Law No. 10.711/2003 and Decree No. 10.586/2020, and it establishes rules for the production, certification, analysis, commercialization, and inspection of seeds and seedlings. Through the certification system, standards for germination, physical purity, and genetic identity are defined and monitored, ensuring that seeds offered to the market the minimum quality requirements needed for the safe and efficient development of Brazilian agriculture. Therefore, seed quality tests are frequently performed and they form the basis for technical, commercial, and regulatory decision-making. Failures in the quality of these tests may result in significant losses, such as financial damages to producers, restrictions on commercialization, phytosanitary risks, as well as legal fines and penalties. Thus, it is essential to ensure that the results of the applied analytical methods are accurate and capable of demonstrating their reliability within acceptable variation limits. This warranty is provided through a rigorous validation procedure, which certifies the precision, repeatability, and reproducibility of the method used. In this context, the application of statistical procedures by using an automated way has led to significant gains in efficiency, time savings, and reduction of risks associated with operational errors. In this study, the VALIDMeth web app was developed using the shiny package in R, aiming to automate the statistical analysis required in the validation of methods according to ISTA rules. The tool performs, in an integrated manner, outlier detection, ANOVA, Cochran’s test, repeatability and reproducibility estimates, and Mandel’s k and h tests. With a user-friendly interface, generation of interactive graphics, and reports, the app aims to enhance the efficiency, standardization, and reliability of laboratory testing in seeds, benefiting laboratories, research institutions, and regulatory bodies.