Why a data-driven approach to your crops can revolutionize your grow operation

Data-Driven Agriculture vs. Traditional Agriculture Techniques

We are in the era of data analysis. Everywhere you go, you’re surrounded by data collection and analytics. Oftentimes, this concept is met with some resistance by those with the mindset of “why fix something that isn’t broken,” but when it comes to your agricultural grow-operations, data analytics doesn’t necessarily fix the system, it simply improves upon it.

Traditionally, cannabis has been bred through phenotype hunting. In other words, growing out your plants, identifying traits (phenotypes), and then selecting plants to cross with others that have desirable traits. This crossing of plants or crossbreeding is a method breeders use when attempting to introduce new traits into a different plant.

So, how can data provide better methods than a technique that has been used for over 300 years? It doesn’t! It improves and expedites the process. Data-driven plant breeding techniques give growers access to reduced cycle times, improved selection accuracy, and can accelerate genetic gain. By collecting data, analyzing the genetics of a plant, and finding genetic markers at its seedling stage, we are able to determine — very early in the plant's life cycle — its potential at maturity. This reduces not only the time, but the space and resources required to grow selected plants to maturity.

How is Data Used to Identify Desired Plant Genetics?

There are numerous methods in which data is utilized to create more genetically desirable plants for growers. For example, by performing an in-depth statistical analysis of a plant’s genotype (e.g., DNA , SNPs and markers), its phenotype (e.g., yield rate, height, resistance to disease, etc.), and chemotype (e.g., cannabinoid composition and terpenes), advanced plant breeding techniques utilize differences and similarities between plant genomes. Additionally, this approach can unveil if genetic markers are linked with desired traits and accelerate selection for crossbreeding. Historically, this type of optimization has taken decades in other crops such as corn, soy, and canola.

Another example of data-driven plant breeding is marker-assisted selection (MAS). This method involves rapid selection of important traits in cannabis and hemp genetics leading to better varieties more quickly. MAS also enables breeders to work with more traits simultaneously in each breeding cycle.

How can a Data-Driven Approach to Farming Specifically Improve my Grow Operation?

Aside from stable genetics, there are several reasons why you should consider data analytics the secret weapon to revolutionizing your grow operation.

Cost Reduction - Breeding for agronomic traits such as better yield, drought tolerance, etc., can reduce input costs. A reduction of your costs translates to a higher margin and ultimately, a more competitive position in the marketplace.
Improved Energy Efficiency - Seeds that are adapted for outdoor or greenhouse (mixed light) production systems, like the autoflower varieties offered by Phylos, can reduce electricity requirements by up to 40 times when compared to indoor grows, which are notoriously expensive to run and maintain.
Improved Yield - With lowered costs, a higher yield increases your ROI and can also serve as a differentiator in an already competitive market.
Improved Space and Labor Efficiency - Uniform, consistent varieties can increase the efficiency of your production space because they exhibit predictable traits, such as height and width, branching structure, and density requirements. With autoflower or short-season varieties, you also can take advantage of staggered harvests giving you a consistent stream of revenue throughout the year.
Reduced Time to Market - Wouldn’t it be nice to be able to know exactly when your crop would reach maturity based on a schedule instead of day length? Genetics like Phylos’s autoflower AutoCBD and F1 hybrids give farmers this exact ability. The predictability of autoflower genetics allows growers to schedule multiple harvests in a season. Since light deprivation is not required with autoflower genetics, growers can also use a combination of outdoor and greenhouse production systems.
Improved Consistency - Consistency of plant growth for the grower can mean easier harvests, reliable maturation times, and reduced risk of crop loss. In terms of final product, consistency means that your end-users are receiving the same product composition each time and can rely on your product for a consistent experience.
Improved Germination Rates - Consistently high germ rates result in you spending less time and money dealing with replanting seed or wasted space.

Overall, growers and breeders using data-driven techniques in their operations can improve the ability to grow more plants at a more efficient and consistent rate. In short, data allows you to better understand your operation’s strengths and weaknesses.

Isn’t Big Data a Threat to Small Growers?

Not at all! Although Phylos’s plant genetics are developed in consideration for large-scale commercial growers, we want all growers to achieve the best results possible in terms of production, harvest, and ROI. Availability of data ultimately means access to more information and tools that can give all plant breeders a sundry of information around genetics and their influence on traits. This allows everyone to develop new varieties much more quickly than traditional, phenotype hunting techniques. Data-driven plant breeding is the future.

Ready to take the next step in using data to improve your grow operation? We recommend checking out our piece on questions to ask before planting and of course, Phylos is always available to provide additional support.

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