What are you seeing?Here you choose a ready-made story to learn how Yolia supports a plant or batch over time.
Why it mattersEach of the five featured cases teaches a different type of tracking, so the one you choose changes what you will learn.
How to think about itRead each case's problem and pick the one closest to what you want to understand. If your question is about weather changes, look for the case that mentions heat or humidity.
Very simple exampleIf you want to see how Yolia reacts to a heat wave, choose the case whose summary describes exactly that problem.
What are you seeing?The five featured cases show tracking, use of control plants, sector-based management, recovery after stress, and the limits of the system.
Why it mattersTogether they teach a central idea: a decision can change when the available evidence changes during the cycle.
How to think about itCompare goal, risk, number of plants, and type of tracking before choosing a case — don't just go by the title.
Very simple exampleA lot divided into sectors should not be corrected as if all plants were the same; The case of sectors shows you exactly why.
What are you seeing?Yolia 40 represents a compressed longitudinal series: states, events, observations, comparators and recalibrations chained in time.
Why it mattersIt allows teaching real temporal causality and prevents confusing a one-off correlation with a consistent, sustained response.
How to think about itIn each case, look at what longitudinal evidence it provides and what control mechanism it introduces (for example, a control plant) before accepting the conclusion it shows.
Very simple exampleThe control plant works as the contextual reference for THAT case, not as universal proof of causality applicable to any other crop.