A protected cultivation trial database has two distinct failure modes: losing the genetic identity of an accession, and losing the treatment history that explains why one plant performed differently than its siblings in the same row. The Matityaho Nethouse template prevents both.
The Problem with Nethouse Trial Documentation
Inside a nethouse or screenhouse, the protected environment creates conditions that differ fundamentally from the open field — modified light spectrum, altered pest pressure, different thermal regime. A phenological observation recorded in this environment isn't directly comparable to the same cultivar in open field conditions without knowing the trial context. The template's implicit structure — linking Date planted to the full phenological sequence ending at Ripening date — creates a complete developmental timeline that a nethouse trial researcher can use for comparative analysis without having to reconstruct the context from scattered field notes.
The Row and Number in row spatial coordinates are even more important in a nethouse than in an open field because nethouse layouts are designed, not organic. Row position determines microclimate exposure: proximity to the netting walls affects temperature fluctuation, wind exposure, and humidity gradients. If a specific row shows systematically earlier Full bloom date than the rest of the trial, the spatial data is what lets you determine whether you're seeing a genetic signal or a microclimate artifact.
Position also determines irrigation zone exposure, proximity to entry points where aphid or thrips pressure enters, and distance from any light-diffusing overhead structures. Without the spatial record, you're analyzing performance data from what looks like a homogeneous population but isn't.
The Treatment and Pest Record as a Research Variable
The Treatmens and Pests/diseases fields are the additions that distinguish this template from a pure phenology database. In a breeding trial, treatments are not just operational farm management — they are experimental variables. If one portion of the trial receives a preventive fungicide application and another section doesn't, and the performance data later shows differential fruit set, the treatment record is what allows you to distinguish genotype effect from treatment effect.
Pests/diseases creates the incidence log that transforms anecdotal observation into a patterned dataset. If a specific rootstock combination consistently shows elevated susceptibility to crown rot under the nethouse irrigation regime, that pattern only becomes visible when each plant's disease observation is linked to its Rootstock field and Date planted. The per-plant record is the unit of analysis.
Condition as a freetext field captures current plant status in a way that the structured fields can't — a plant with split trunk damage from a mechanical contact isn't fitting cleanly into a disease or pest category, but its condition materially affects whether its phenological data should be included in the season's analysis.
The Genetics Layer in a Protected Trial
Parent 1, Parent 2, Or parent 2, and Alleles transform a cultivation record into a pedigree node. In a nethouse trial evaluating F1 crosses or backcross populations, each plant's phenological data has meaning in proportion to how confidently its genetic identity is known. The Self compatible field is particularly consequential here: self-incompatibility in a population cross affects fruit set rates in ways that can be misread as vigor differences if the genetic status of each plant isn't tracked.
Latin name handles the taxonomic precision that cultivar names alone can't provide. Named commercial cultivars in fruit tree programs sometimes cross species boundaries through introgression breeding. Knowing the Latin designation of each entry is what allows the research record to remain accurate when common names prove inconsistent across sources.
Branching 3.25 and Branching 1.26 — the two date-anchored branching counts — capture the vegetative architecture at defined points in the season. In rootstock evaluation programs, branching habit is a direct indicator of scion-rootstock compatibility and future canopy management requirements. The specific dates embedded in the field names mean these observations are always comparable across years without relying on the researcher remembering what "early season" meant.