Genomic prediction is becoming a daily tool for plant breeders. It makes use of genotypic information to make predictions used for selection decisions. The accuracy of the predictions depends on the number of genotypes used in the calibration; hence, there is a need of combining data across years. A proper phenotypic analysis is a crucial prerequisite for accurate calibration of genomic prediction procedures. We compared stage-wise approaches to analyse a real dataset of a multi-environment trial (MET) in rye, which was connected between years only through one check, and used different spatial models to obtain better estimates, and thus, improved predictive abilities for genomic prediction. The aims of this study were to assess the advantage of using spatial models for the predictive abilities of genomic prediction, to identify suitable procedures to analyse a MET weakly connected across years using different stage-wise approaches, and to explore genomic prediction as a tool for selection of models for phenotypic data analysis. Using complex spatial models did not significantly improve the predictive ability of genomic prediction, but using row and column effects yielded the highest predictive abilities of all models. In the case of MET poorly connected between years, analysing each year separately and fitting year as a fixed effect in the genomic prediction stage yielded the most realistic predictive abilities. Predictive abilities can also be used to select models for phenotypic data analysis. The trend of the predictive abilities was not the same as the traditionally used Akaike information criterion, but favoured in the end the same models. Making predictions using weakly linked datasets is of utmost interest for plant breeders. We provide an example with suggestions on how to handle such cases. Rather than relying on checks we show how to use year means across all entries for integrating data across years. It is further shown that fitting of row and column effects captures most of the heterogeneity in the field trials analysed.

The medicinal use of cannabinoids renewed the interest in industrial hemp (Cannabis sativa L.). The aim of this study was to evaluate the impact of growth stage and biomass fractions of seven industrial hemp genotypes. The study focused on biomass yield, content of cannabidiolic acid/cannabidiol (CBDA/CBD), cannabigerolic acid/cannabigerol (CBGA/CBG), and tetrahydrocannabinolic acid (THCA). The experiment was conducted in 2017 and 2018. The biomass samples were taken at the vegetative (S1), bud (S2), full-flowering (S3) and seed maturity stage (S4). Plants were fractionated into inflorescence, upper and lower leaves. The average inflorescence dry yield of genotypes Futura75, Fédora17, Félina32 and Ferimon ranged between 257.28 g m⁻² to 442.00 g m⁻², resulting in a maximum yield of CBDA at S4, with 4568.26 mg m⁻², 6011.20 mg m⁻², 4975.60 mg m⁻² and 1929.60 mg m⁻², respectively. CBGA was exclusively found in genotype Santhica27, with a maximum CBGA yield of 5721.77 mg m⁻² in inflorescence at growth stage S4 and a dry weight yield of 408.99 g m⁻². Although these industrial hemp genotypes are mainly cultivated for fibre and seed production, however, cannabinoids offer an additional value. For an optimized harvest result, yield of extractable material and overall yield of cannabinoids must be considered.

The definition of optimum harvest and pruning interventions are important factors varying inflorescence yield and cannabinoid composition. This study investigated the impact of (i) harvest time (HT) and (ii) pruning techniques (PT) on plant biomass accumulation, CBD and CBDA-concentrations and total CBD yield of a chemotype III medical cannabis genotype under indoor cultivation. The experiment consisted of four HTs between 5 and 11 weeks of flowering and three PTs-apical cut (T); removal of side shoots (L) and control (C), not pruned plants. Results showed that inflorescence dry weight increased continuously, while the total CBD concentration did not differ significantly over time. For the studied genotype, optimum harvest time defined by highest total CBD yield was found at 9 weeks of flowering. Total CBD-concentration of inflorescences in different fractions of the plant’s height was significantly higher in the top (9.9%) in comparison with mid (8.2%) and low (7.7%) fractions. The T plants produced significantly higher dry weight of inflorescences and leaves than L and C. Total CBD yield of inflorescences for PTs were significantly different among pruned groups, but do not differ from the control group. However, a trend for higher yields was observed (T > C > L).

The impact of exogenously applied plant growth regulators (PGR), 1-naphthalenaecetic acid (NAA), 6-benzylaminopurine (BAP), and a mixture of both (NAA/BAP-mix), was investigated in regard to plant height, length of axillary branches, number of internodes, biomass yield and cannabinoid content of three different phytocannabinoid-rich (PCR) Cannabis genotypes. The results showed that total plant height was significantly reduced under the application of NAA (28%), BAP (18%), and NAA/BAP-mix treated plants (15%). Axillary branch length was also significantly reduced by 58% (NAA) and 30% (NAA/BAP-mix). BAP did not significantly reduce the length of axillary branches. The number of internodes was reduced by NAA (19%), BAP (10%), and the NAA/BAP-mix (14%) compared to the untreated control. NAA application influenced the plant architecture of the tested cv. KANADA beneficially, resulting in a more compact growth habitus, while inflorescence yield (23.51 g plant⁻¹) remained similar compared to the control (24.31 g plant⁻¹). Inflorescence yield of v. 0.2x and cv. FED was reduced due to PGR application while cannabinoid content remained stable. Overall, the application of PGR could be used on a genotype-specific level to beneficially influence plant architecture and optimize inflorescence yield per unit area and thus cannabinoid yield, especially in the presence of space limitations under indoor cultivation.