Quality: Dbt Fertilizer App High

By [Your Name/Company]

Modern agriculture runs on data. But in most farms, the path from a soil sample to a variable-rate fertilizer prescription is still littered with Excel spreadsheets, siloed databases, and gut feelings. dbt fertilizer app high quality

Enter dbt (Data Build Tool) .

In this post, we’ll walk through how we built a high-quality, production-grade fertilizer recommendation engine using dbt. We’ll cover the modeling logic, quality tests, and deployment patterns that turned raw agronomic data into actionable insights. By [Your Name/Company] Modern agriculture runs on data

Our raw data lands in Snowflake (or BigQuery/Redshift) from three sources: Our raw data lands in Snowflake (or BigQuery/Redshift)

All source tables are staged in stg_* models with light cleaning—renaming columns, casting types, filtering deleted records.

A high-quality dbt implementation within this context follows the standard ELT (Extract, Load, Transform) architecture.

with src as (
  select
    id as raw_id,
    vendor_sku,
    product_name,
    nutrient_type,
    nutrient_amount,
    nutrient_unit,
    application_rate,
    application_unit
  from  source('fertilizer', 'vendor_products') 
)
select
  raw_id,
  vendor_sku,
  lower(trim(product_name)) as product_name,
  nutrient_type,
  nutrient_amount::numeric as nutrient_amount,
   macros.convert_to_kg(nutrient_amount, nutrient_unit)  as nutrient_kg,
   macros.convert_to_kg_per_hectare(application_rate, application_unit)  as application_kg_per_ha
from src