Imagine having the ability to successfully gauge the productivity of a cannabis plant without having to fully finish the cultivation cycle. This would provide a massive time saving in terms of phenohunting, screening and selection of yield-associated traits, and choosing plants for targeted breeding. Authors from a publication featured in BMC Plant Biology in June 2021 titled ‘The characterization of key physiological traits of medicinal cannabis (Cannabis sativa L.) as a tool for precision breeding’ (lead author Erez Naim-Feil) have claimed to have successfully devised a formula for the successful prediction of plant dry cannabis bud yield, based on growth rate parameters.
Providing evidence that floral cannabis bud yield is correlated to plant height and stem diameter gives growers an earlier than previously possible opportunity to know how a crop may finish, and also gives breeders a chance to race through generations, predicting yield without having to take every plant to full maturity – although it would still be recommended to take new genotypes to completion to compare the prediction to the actual yield.
To generate a study of this quality, the authors had to ensure some normalisation between genotypes, and within genotypes. Therefore, as well as using mother-plant stock to create clones for the trial, the authors took other measures to increase the validity of the comparison. This resulted in, for example, fixed optimised climatic conditions, a single set-length vegetative growth period, and pre-vegetative screening and selection based on plant height and vigour, so that all genotypes were within a fixed range. Controlling some of these variables in this way could result in limiting factors, such as some genotypes producing a dry bud yield below what is possible in longer vegetative periods, or the amount of time in vegetative growth could possibly result in other correlations not observed in this study. However, there are always ways in which research can be improved upon, and the authors themselves acknowledged this in the text, stating that much more follow-up work is needed to firm up this type of predictive study.
Using mother stocks for each of the 121 genotypes, 10 clonal cuttings within the length range of 10-11cm were taken. After 30 days, the 6 or 7 most consistent of the 10 were selected for further work. Determination of consistency was based on plant height and vigour. The final selection process for standardisation of growth was imposed by choosing the 4 or 5 most uniform plants to take into the 42-day vegetative growth stage. After this, the reproductive/flowering growth stage was initiated by reduction of day length from 18/6 to 12 hour light/dark cycles.
The authors measured many parameters for this study throughout the growing cycle. These measurements included: growth rate: plant height and stem diameter tracked over time to determine the rate of growth; this was done in both vegetative and flowering stages. The final stem diameter was also measured along with the days to maturity (note the authors determine maturity not as the time in reproductive/flowering growth until harvest, but rather the time taken for a plant to produce 3 floral buds with styles changing colour to brown). Harvest time was determined when 70% of the whole plant styles had changed colour to brown. Internode length and frequency was also recorded as well as vegetative dry weight, trimmed waste, dry bud weight – the main output metric – and harvest index.
Much of the study focused on finding the correct parameters which would provide an association to dry bud yield. A correlation is determined by scoring how one variable is linked to another in a straightforward linear fashion, usually resulting in both variables move together. However, when this link includes constants or expresses the relationship using an equation it is then called regression, which is a cause-and-effect relationship (what impact one variable has on the other when changed). Both correlation and regression use the variables to determine the direction (positive or negative) and the strength (R value) of an association. The associations can be represented by a line on an x and y scatter plot (figure 1).
How tightly two variables are associated is written as an r = N (N = a number from -1 to 1). When between -1 and 0, the association is negative (As Y increase, X decreases or the opposite (figure 1 D,E,F)). A positive relationship is shown when r is between 0 and 1 (Y increases therefore- X increases) (Figure 1 A,C)). The closer to 0 the less associated the relationship is, and the closer to 1 and -1 indicates the association between the 2 variables is extremely strong. In addition to this, the relationship is then tested using a probability score, which determines the probability that the observed association occurs by chance, which determines how statistically significant the association is.
Figure 1. Scatter plots showing associations expressed as r values. When the data is tight to the red line the association is strong and r values are close to the upper or lower limit i.e. -1 or 1. Weak associations show data which is more varied, as in examples C and F. And no correlation results in r = 0, shown in example B.
One of the conclusions from the study describes how growth parameters at certain points in the plant’s life can be used to predict dry bud yield. Plant height at week 2, growth rate in vegetative growth, and stem diameter in vegetative growth are the key measurements for predicting dry bud weight. As would be expected, different factors influence bud yield in greater and lesser ways. For example, the authors found that final plant height could be used as a reliable prediction of dry bud weight, with an r = 0.59, which is a fairly strong positive association, and days to maturity linked with an r value of -0.47, which is a fairly strong negative association. However, this is less useful as you need to ensure the plant has stopped growing in height – something the authors observed ceased by the 3rd week of flower initiation for most, but not all, genotypes – and that maturity has been reached. One conclusion made for these observations was that taller plants tend to mature earlier and are generally more productive.
Growth rate itself, measured by changes in plant height and stem diameter over time, decreased substantially when flowering was initiated, by 64% for plant height and 76% for stem diameter. Interestingly, days to maturity was separate from all other variables, and days to maturity was negatively associated with growth traits, especially plant height where the r value was around -0.47.
Harvest index, internode length and vegetative plant height negatively correlates with internode count – this is often a metric used in determining one variety over the other for registration (at least in the European Union).
The most impressive part of this study describes how tracking plant height growth rate, and stem diameter growth rate, can allow growers to use a fairly basic equation to predict the dry bud yield. The equation is as follows:
Dry bud weight in grams =
(-77.33 + 4.33(growth rate in veg (cm/week)) + 5.94(Stem diameter growth rate in veg (mm/week) + 119.88(Plant height in week 2(m))
Additionally, the authors scored the heritability of each trait, and showed that days to maturity, plant height and stem diameter are the most predictable through generations, whereas dry bud yield, trimmed waste and harvest index scored the lowest in broad-sense heritability (Naim-Feil et al 2021).
This study uses various growing metrics to gauge how productive cannabis plants can be. This involved creating set standards between plants within a genotype and across genotypes for fairer comparison. Using the described method limits the genotypes to photoperiod induced flowering cultivars and is not tested on autoflower/photoperiod independent flowering cultivars. Many associations are described, showing how each parameter is related to the yield of the plants. The study does an excellent job of providing insight into how to successfully predict the yield of a growing plant based on the rate of growth. The data provided can be extrapolated and built upon for more accurate, less standardised studies in the future. This is an example of how combining botany, statistics and plant science can really provide deeper insight to the link between genotype and phenotype, and physiology.
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