Agricultural Waste Feasibility Analysis
The Business Challenge
A tribal organization was considering a waste collection and fertilizer production business but had no data to assess feasibility. The business model required collecting agricultural waste from commercial producers and processing it into fertilizer products. They needed to answer two critical questions before committing resources: Was there enough waste being generated to create a viable business? Which producers generated sufficient volumes to justify collection costs?
The client's initial request was general - they wanted to know if the business concept was viable but had no baseline information about waste availability, generation patterns, or potential customer base. They were unfamiliar with what data sources existed or what insights could be extracted.
Analysis Strategy
I recommended using Washington State's public agricultural traceability data as the foundation for feasibility assessment. This dataset represented the most comprehensive source of waste generation information available - providing actual production data from hundreds of commercial operations rather than theoretical estimates or small-sample surveys.
I structured the analysis around three business-critical questions: What total waste volumes were being generated across the state? Which individual producers generated sufficient quantities to justify targeted collection efforts? How predictable was waste generation timing for operational planning?
The December 2020 dataset contained approximately 37 million batch-level production records tracking waste measurements, producer identifiers, plant growth stages, harvest dates, and timestamps. Analysis focused on harvested-stage waste as this represented material suitable for collection and fertilizer processing.
Data Preparation
The dataset required correction of several systematic data quality issues: negative waste values (likely sign errors during data entry), missing plant stage classifications for records with recorded waste, and unit inconsistencies. Cleaned data yielded approximately 210,000 waste generation events at the harvested stage - the subset relevant to the business model.
Initial validation confirmed that waste was concentrated in post-harvest processing. Other production stages (propagation, packaging, transfer) showed negligible waste amounts, validating the focus on harvested material for collection operations.
Key Findings
Total Volume Assessment
Analysis revealed waste generation volumes substantially exceeded the client's initial expectations. Aggregating waste measurements across all producers and dates demonstrated that sufficient material existed to support commercial-scale fertilizer production operations.
Producer Targeting
Grouping waste by producer ID and calculating mean and median volumes identified which operations generated consistent, high-volume waste streams versus those with occasional large batches. This producer-level analysis enabled prioritization of collection relationships - distinguishing producers worth targeting for regular service contracts from those generating insufficient volumes to justify collection costs.
Timing Predictability
Temporal analysis of waste generation patterns revealed predictable, episodic production cycles rather than random or continuous generation. Waste occurred on specific harvest dates with consistent gaps between events. This predictability was critical for operational planning - it meant collection routes and processing schedules could be planned in advance rather than requiring on-demand response capabilities.
Business Impact
The analysis answered both critical feasibility questions. First, total waste volumes were sufficient to support a commercial operation - substantially more material was available than the client had anticipated. Second, the producer-level analysis identified specific high-volume operations that could anchor the business with reliable waste streams.
The timing predictability finding addressed an operational concern the client hadn't initially articulated but proved essential to their decision. Knowing that waste generation followed predictable harvest cycles meant they could plan collection logistics and processing capacity around scheduled events rather than maintaining expensive on-demand infrastructure.
The combination of higher-than-expected volumes and predictable generation patterns convinced the client to move forward with the business. The analysis provided the data foundation they lacked - transforming a vague feasibility question into concrete evidence that sufficient waste existed with characteristics suitable for a viable collection and processing operation.
Methodology
Analysis used pandas for data manipulation and aggregation. The dataset size (37 million records) required memory optimization through explicit dtype specification and selective column loading. Multi-level groupby operations calculated waste totals by producer and date, with statistical aggregations identifying high-volume producers and temporal patterns.
Development Environment
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn