Grain Maize Emergy Accounting and Sensitivity to Accounting Boundaries in Five Counties of Fars Province, Iran
Description
This dataset contains grain maize production inventories, unit emergy values (UEVs), reconstructed emergy accounts and accounting-boundary analyses for five counties in Fars Province, Iran: Marvdasht, Fasa, Darab, Arsanjan and Kazerun. Each county is represented by one supplied aggregate numerical profile; the dataset does not contain farm-level replicates. The Excel workbook comprises 12 worksheets covering documentation, county inventories, variable definitions and units, coefficients and conversion assumptions, input-level emergy calculations, sustainability indicators, mutually exclusive input shares, deterministic accounting cases, analytical sensitivity, source-formula verification, unresolved author queries and a cell-level transcription of the original workbook. The inventories include solar radiation, wind and rainfall energy; soil mineral, organic matter and nitrogen losses; river-water and groundwater energy; labour; seed expenditure; machinery; fertilisers; manure; agrochemicals; electricity; fuel; grain yield; and reference by-product biomass. Quantities retain their original per-hectare units. Soil-loss variables additionally carry an annual time basis. Grain energy is calculated using the source calorific assumption. Reported indicators include specific emergy (sej/g), transformity (sej/J), renewable emergy percentage, emergy yield and investment ratios, and unpartitioned and modified environmental loading and sustainability indices. Nine deterministic accounting cases examine renewable-pathway aggregation, soil valuation, labour and seed boundaries, and river-water classification. Exact local elasticities describe indicator sensitivity under fixed grain yield and accounting classifications. All 362 original formula cells were independently reproduced. Source values were retained without imputation or silent correction. Live spreadsheet calculations are explicitly distinguished from archived analytical outputs. The authors describe researcher-collected and official secondary information within the wider project. However, the observation period, sampling design, variable-specific provenance, some UEV attributions and potential overlap among soil valuations require confirmation. These limitations are documented within the workbook. The results represent conditional accounting comparisons and do not establish statistically significant county differences, management-treatment effects or empirical uncertainty intervals. The dataset supports transparent inspection, computational reproduction and methodological comparison of emergy accounting choices in agricultural systems.
Files
Steps to reproduce
Open Fars_Grain_Maize_Mendeley_Dataset.xlsx in spreadsheet software supporting standard Excel formulas. Read the Readme and Dictionary worksheets for variable definitions, units, accounting conventions and limitations. Retain an unchanged copy of the downloaded workbook. Inspect Raw_data, which contains one numerical profile for each of the five counties. Preserve the distinction between recorded quantities and calculated grain energy. The observation year is unconfirmed; blank year cells must not be interpreted as zero. Review Coefficients for the UEVs, conversion factors, renewable fractions and original source attributions. Derived water coefficients equal the parent volumetric coefficients divided by 4.94 × 10⁶ J/m³. Electricity uses 3.6 × 10⁶ J/kWh, fuel 3.86 × 10⁷ J/L, SOM loss 22,604.4 J/g, and grain energy 15,900 J/g. These assumptions reproduce the supplied accounting; their applicability remains subject to the documented qualifications. Recalculate Emergy. Each potential input contribution equals its quantity multiplied by the explicit conversion factor and applied UEV. Under the reference convention, local renewable emergy R is MAX(solar, wind, rain), not their sum. Local nonrenewable emergy N includes the three soil-loss valuations and both water categories. Purchased emergy F is partitioned into renewable FR and nonrenewable FN using the source fractions. Recalculate Indicators using: U = R + N + F; specific emergy = U/grain mass; transformity = U/grain energy; renewable percentage = 100(R + FR)/U; EYR = U/F; EIR = F/(R + N); ELR = (N + F)/R; modified ELR = (N + FN)/(R + FR); ESI = EYR/ELR; modified ESI = EYR/modified ELR. Confirm that the eight mutually exclusive categories in Input_shares sum to 100% for each county. Inspect Accounting_cases for the nine archived reference and boundary comparisons. To reproduce an alternative case, apply its stated inclusion, aggregation or classification rule to the same input contributions, reconstruct R, N, F, FR and FN, and recalculate all indicators. These cases are accounting diagnostics, not field-management treatments. Reproduce Sensitivity at fixed grain yield and active MAX branch. For counted contribution e, renewable fraction β and purchased-input flag p, the specific-emergy elasticity is e/U. The modified-ESI elasticity is e/U + βe/(R + FR) − pe/F − (1 − β)e/(N + FN). Inactive solar and rain pathways have zero local derivatives in the reference profiles. Use Source_formula_audit and Source_cells to trace the original calculations. Accounting_cases, Sensitivity and the source audit are archived outputs and do not update automatically when inputs change. Regenerate affected analyses before releasing a revised version. Consult Author_review before empirical interpretation or reuse.
Institutions
- Zabol UniversitySistan and Baluchestan, Zabol