Resolvable row-column designs are widely used in field trials to control variation and improve the precision of treatment comparisons. Further gains can often be made by using a spatial model or a combination of spatial and incomplete blocking components. Martin, Eccleston, and Gleeson presented some general principles for the construction of robust spatial block designs which were addressed by spatial designs based on the linear variance (LV) model. In this article we define the two-dimensional form of the LV model and investigate extensions of the Martin et al. principles for the construction of resolvable spatial row-column designs. The computer construction of efficient spatial designs is discussed and some comparisons made with designs constructed assuming an autoregressive variance structure.

In the UK, there have been reports of significant reductions in paediatric emergency attendances and visits to the general practitioners due to COVID-19. A national survey undertaken by the UK Association of Children’s Diabetes Clinicians found that the proportion of new-onset type 1 diabetes (T1D) presenting with diabetes ketoacidosis (DKA) during this COVID-19 pandemic was higher than previously reported, and there has been an increase in presentation of severe DKA at diagnosis in children and young people under the age of 18 years. Delayed presentations of T1D have been documented in up 20% of units with reasons for delayed presentation ranging from fear of contracting COVID-19 to an inability to contact or access a medical provider for timely evaluation. Public health awareness and diabetes education should be disseminated to healthcare providers on the timeliness of referrals of children with T1D.

A method is given for constructing row and column designs for situations where replicates are contiguous. Designs of this type are needed in cotton variety trials. A table of generating arrays is given from which a series of resolvable designs can be constructed; these designs are called latinized a-designs. Some results from cotton variety trials are presented.

Summary Generalized lattice designs are defined. They include as special cases the square and rectangular lattice designs, and the α‐designs defined by Patterson and Williams (1976). An iterative procedure is given for the combined estimation of variety effects in generalized lattice designs with optimal or near optimal efficiency factors. This procedure, together with an approximate variance matrix, enables the analysis of efficient generalized lattice designs to be carried out on mini computers.

A variety trial sometimes requires a resolvable block design in which the replicates are set out next to each other. The long blocks running through the replicates are then of interest. A t-latinized design is one in which groups of these t long blocks are binary. In this paper examples of such designs are given. It is shown that the algorithm described by John & Whitaker (1993) can be used to construct designs with high average efficiency factors. Upper bounds on these efficiency factors are also derived.