Statistical decision economics, cost of experimentation modeling, variance reduction ROI, data collection cost-benefit, sampling efficiency analysis, and research investment optimization.
Live commodity yields and prices. Auto-populates price and yield fields in trial economics calculators (SF16-E.003, SF16-E.006).
Source: USDA NASS QuickStats API
ERS cost-of-production benchmarks. Auto-populates cost fields in SF16-E.001 and SF16-E.003.
USDA NIFA standard indirect rates, typical per-sample costs, and field trial cost benchmarks for grant budget development. Auto-populates SF16-E.001 cost fields.
Source: USDA NIFA Grants
Model SF16-E.001
Calculates total research project cost using USDA NIFA budget structure: sample collection, laboratory analysis, data management, and indirect (F&A) costs. Outputs per-sample cost, per-treatment cost, and total project budget. Essential for USDA NIFA competitive grant budget justifications and ARS research project proposals.
Model SF16-E.002
Analyzes the economic cost of achieving different power levels. Compares cost of under-powered studies (Type II error losses) against the cost of additional replication. Identifies the economically optimal sample size that balances statistical rigor with budget constraints using USDA ARS 80% power standard.
Model SF16-E.003
Full economic analysis of a randomized complete block design (RCBD) field trial. Calculates total trial cost, minimum detectable difference (MDD), economic value of the MDD at market prices, and trial sensitivity rating. Standard USDA ARS field trial economic framework (Steel & Torrie 1980).
Model SF16-E.004
Quantifies the economic return from a calibrated regression model. Calculates annual value of RMSE reduction, break-even accuracy threshold, payback period, and 5-year ROI. Relevant for yield forecasting, input response modeling, and price prediction systems used in USDA ARS and land-grant university research programs.
Model SF16-E.005
Compares CRD, RCBD, Latin Square, and split-plot designs on cost, power, and minimum detectable difference. Calculates cost per unit of statistical power for each design and identifies the most cost-efficient option within budget. Implements USDA ARS Biometrics Unit design efficiency standards.
Model SF16-E.006
Calculates return on investment from a completed statistical research study via extension adoption impact. Uses logistic diffusion adoption curve, yield improvement valuation across farm network, multi-year cumulative impact, and USDA NIFA benefit-cost ratio framework (OMB Circular A-4). For USDA ARS research program justification and NIFA impact reporting.
SF16-E provides 6 research economics models covering the full lifecycle of statistical research in agriculture: grant budget development (SF16-E.001), power-budget trade-off optimization (SF16-E.002), ANOVA field trial economics with MDD valuation (SF16-E.003), regression model predictive value and ROI (SF16-E.004), experimental design cost-efficiency comparison (SF16-E.005), and multi-year publication impact and benefit-cost analysis (SF16-E.006). All models apply USDA NIFA grant budget standards, USDA ERS cost-of-production benchmarks, USDA ARS Biometrics Unit design guidelines, and OMB Circular A-4 benefit-cost framework. Companion to SF16 Statistical Analysis Science Calculators.