Exact neutrosophic analysis of missing value in augmented randomized complete block design

Abstract The augmented randomized complete block design (ARCBD) is widely used in plant breeding programs to screen numerous new treatments. error variance estimated based on the replicated control treatments run over a and test that are administrated each once extended units of blocks. However, one or more observations corresponding may be missed experiments, making difficulties, e.g., biased estimates. An approximate common approach deal with this problem imputation value which some uncertainties. Moreover, real-life there sources uncertainty cause conflict-indeterminate, vague, imprecise, erroneous data increases complexity analysis. In paper, an exact scheme utilized missing treatment ARCBD. To overcome indeterminacies data, novel neutrosophic analysis proposed. Specifically, ARCBD for uncertain environment resolved analytically by considering incomplete framework statistics so-called (NARCBD). approach, proposing model, estimations as well mathematical adjusted sums squares derived table provided. model applied genotype example safflower assessed simulation study. Furthermore, code R software was written analyze proposed fill calculation gap NARCBD value. light results observed, it can concluded method performs better than classic presence uncertainty.