Phase 2: Agricultural Life Cycle Inventory Dataset (Inputs, Outputs, and Data Sources)

Abstract

Life cycle inventory (LCI) data constitute Phase 2 of life cycle assessment (LCA) and serve as the basis for calculating environmental impacts. In agricultural LCA studies, LCI data are often reported using different scopes, stages, units, and calculation methods, making it difficult to reproduce results and compare studies. The objective of this study is to compile and standardize Phase 2 LCI data reported in agricultural LCA studies into a structured dataset. The dataset is based on 184 peer-reviewed agricultural LCA studies published between 1999 and 2025. Data were collected through a systematic review using Google Scholar, and studies were included if they applied LCA to crop production systems and reported inventory data such as inputs, outputs, emission factors, or calculation equations. Inventory data were manually extracted from each study, including inputs and outputs, emission factors, equations, and data sources. The dataset is provided as an Excel workbook containing linked sheets for study identifiers, inputs, outputs, emission factors, equations, and data sources. Rather than providing newly harmonized inventory values, the dataset organizes extracted and categorized information reported in the reviewed studies using standardized identifiers and categories. The dataset includes more than 2000 inputs, around 1000 outputs, about 200 emission factors, and over 600 data sources. It is intended for researchers, practitioners, and tool developers to support LCI development, cross-study comparison, and integration into databases, knowledge bases, and decision-support tools.

Dataset: https://doi.org/10.5281/zenodo.20030339.

Dataset License: Creative Commons Attribution 4.0 International (CC BY 4.0)

1. Summary

Life cycle assessment (LCA) is widely used to evaluate the environmental impacts of agricultural production systems. The second phase (Phase 2), known as life cycle inventory (LCI), includes the identification, quantification, and organization of all inputs, outputs, emission factors, and data sources used in the assessed system. These choices guide later impact assessment and interpretation steps and determine how material, energy, and emission flows are included, calculated, and compared. This dataset provides information on Phase 2 reported in 184 agricultural LCA studies published between 1999 and 2025.

In agricultural LCA studies, Phase 2 is reported in different ways depending on the assessment’s purpose, crop type, scope, included production stages, and data availability. However, this information is often reported inconsistently and with varying levels of detail, making it difficult to reproduce results and limiting the reliability and comparability of agricultural LCA studies. In many cases, studies do not clearly allocate inventory data to specific production stages, requiring a detailed reading of the full paper and expert judgment to identify and extract relevant information. This inconsistency is not only a reporting issue but also affects the reliability of LCA results. Differences in how inventory data are defined, grouped, and calculated lead to differences in later impact assessment results and conclusions. That creates a domino effect, where inconsistencies in inventory data are carried through emission calculations and then into impact assessment, leading to different results even for similar systems. As a result, it becomes difficult to compare studies or perform meta-analysis across the reviewed studies. To address this issue, this dataset compiles and standardizes Phase 2 information from the reviewed studies. Agricultural LCA applications represented in the reviewed literature cover a broad range of crop production systems, management practices, and technological innovations, ranging from conventional crop production to specialized systems such as vineyard frost-protection technologies [1]. This dataset includes more than 2000 inputs, around 1000 outputs, about 200 emission factors, and more than 600 data sources. The data show how agricultural LCA studies report their inventory data and how these values overlap across studies.

Each input, output, emission factor, and data source is assigned standardized identifiers to support traceability and reuse. Inventory data are linked to clearly defined life-cycle stages, with consistent stage definitions. All studies are assigned Stable_IDs to maintain traceability to the original publications across tables. The dataset organizes LCI inputs, outputs, emission factors, and references into standardized categories to reduce inconsistencies in terminology and reporting across agricultural LCA studies. Inputs include materials, fertilizers, pesticides, fuel, water, electricity, seeds, and machinery-related flows. Outputs include crop yield, co-products, emissions to air, water, and soil, and waste streams. Emission factors and equations reported in the reviewed studies are also included where available.

This dataset goes beyond compiling reported Phase 2 information by organizing inputs, outputs, emission factors, and data sources into a structured format with consistent identifiers and linked tables. Unlike conventional LCI databases, which often focus primarily on inventory flows, this dataset also documents the emission factors, equations, and data sources used to generate inventory results as reported in the reviewed studies. Unlike systematic reviews and agricultural LCA compilations, which typically summarize findings at the study level, this dataset extracts and organizes detailed Phase 2 inventory information at the record level and links related information across multiple sheets. It standardizes inventory elements and links inputs, outputs, emission factors, and equations across defined life cycle stages. The main contribution of the dataset is its exclusive focus on Phase 2 (Life Cycle Inventory) information and its use of Stable_ID identifiers to connect inputs, outputs, emission factors, equations, and data sources to their original studies. The use of standardized identifiers organizes the dataset in a clear and structured manner, allowing data from different sheets to be linked so users can connect inventory flows, production stages, and studies without confusion. That makes it easier to filter, search, and reuse the data across different applications. The dataset supports comparison across LCA studies and helps users select appropriate inventory data, emission factors, and calculation methods when designing new assessments. Because the data are structured with standardized identifiers, they can serve as a machine-readable knowledge base, with inventory data, emission factors, and equations clearly linked. The dataset, therefore, serves not only as a repository of reported inventory information but also as a structured knowledge base that preserves relationships among studies, inventory elements, and life cycle stages. That allows the dataset to be used directly in Excel-based knowledge systems for developing LCA frameworks, tools, and decision-support applications, and to support integration into rule-based and expert systems, where relationships between inputs, emissions, and outputs can be processed automatically.

The Excel sheets are ready for integration into databases, expert systems, and other applications that require the structured Phase 2 information provided in Supplementary File S1. In addition, the dataset is not limited to conventional agricultural systems and can be extended to emerging technologies that combine food and energy production. For example, recent studies on plant microbial fuel cells (P-MFCs) show that agricultural systems can simultaneously increase crop yields and generate renewable energy [2,3]. These types of systems introduce more complex inventory data, in which inputs, outputs, and emissions are linked to multiple types of outputs within the same system. The structure of this dataset supports such cases by organizing inputs, outputs, and emission factors using standardized identifiers and linking them to defined life cycle stages. That enables tracking multiple outputs, such as agricultural products and energy, within the same system while maintaining consistency in how inventory data are reported and used. That provides a clear structure for handling multifunctional agricultural systems in Phase 2 inventory development.

This dataset focuses only on Phase 2 (life cycle inventory) and does not include goal and scope definition, impact assessment, or interpretation results. Therefore, it should be used as a structured basis for inventory development rather than a complete LCA, and users must integrate it with other phases to perform a full assessment. Datasets for Phases 1, 3, and 4 have been developed or are being reported separately.

2. Data Description

The dataset organizes Phase 2 LCI inputs, outputs, emission equations, emission factors, and data sources from 184 studies. Due to the large volume and level of detail, all Phase 2 elements are provided in Supplementary File S1.

2.1. Inputs

The life cycle of agricultural processes involves several stages, each with specific key inputs. Common inputs include fertilizers, pesticides, seeds, diesel fuel, energy, machinery, packaging materials, and water [4,5]. Transportation relies on diesel fuel and vehicles [6], while field preparation and crop growth stages involve tractors, diesel, fertilizers, seeds, and irrigation [4,7]. Harvesting requires machinery and labor, and post-harvest processing demands energy and containers [8]. Packaging uses plastics, adhesives, and energy, while Final Product Transport again uses diesel-fueled vehicles [9]. Consumption and use demand energy and water; at the same time, End-of-Life Management relies on fuel and chemicals to recycle or dispose of [10].

Some studies, like [11], identified fuel and machinery as inputs for in-field operations but did not specify the exact stages. This study systematically allocated these inputs across relevant stages to avoid double-counting. Similarly, ref. [12] listed labor, machinery, chemicals, and electricity without stage details, distributed appropriately, but excluded from multiple counts. Ref. [7] reported diesel use for tractors (land preparation to harvesting) and pumps (groundwater extraction), while [13] placed nurseries under raw material production and field activities under preparation.

The 61 newly added studies expanded input data across systems such as hydroponics [14], organic farming [15], and region-specific practices in Asia and the Middle East [16]. They included irrigation powered by solar systems [17], new fertilizer types like biochar and micronutrients [18], and digital tools such as chlorophyll sensors and automation platforms [19].

These inputs were quantified using equations reported in the reviewed studies and were used to calculate material and energy flows during the inventory stage.

Table 1. Equation to calculate used substances.

Eq_ID	Equations	Related Impact	Source
EM_FERT_TOTAL	$$\begin{gathered} \mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{N}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{P}\mathrm{f}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \\ =\mathrm{C}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{y}\mathrm{a}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e}\mathrm{N}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{P}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e} \\ \times \mathrm{C}\mathrm{r}\mathrm{o}\mathrm{p}\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \end{gathered}$$	Abiotic depletion potential (Minerals, fossils, elements) Freshwater aquatic ecotoxicity potential (FAETP)	[20,21,22]
EN_FOSSIL	$\mathrm{E}\mathrm{N}\mathrm{F}=\sum {\mathrm{E}\mathrm{U}}_{\mathrm{d}}+\sum {\mathrm{E}\mathrm{U}}_{\mathrm{i}}$	Energy Footprint	[23]
EN_TOTAL	$\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{E}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}\mathrm{u}\mathrm{s}\mathrm{e}=$ $\begin{array}{cc}\hfill \mathrm{L}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}+& \mathrm{A}\mathrm{n}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{l}\hfill \\ & +\mathrm{M}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{y}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}\hfill \\ & +\mathrm{S}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}\hfill \\ & +\mathrm{E}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{p}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}\hfill \\ & +\mathrm{C}\mathrm{h}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}\hfill \end{array}$	Energy use Energy Footprint	[16,24,25,26]
EN_LABOR	Human labor energy (MJ/h): ${\mathrm{E}}_{\mathrm{h}\mathrm{u}\mathrm{m}\mathrm{a}\mathrm{n}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{o}\mathrm{r}}={\displaystyle \frac{{\mathrm{W}}_{\mathrm{h}}\times {\mathrm{H}}_{\mathrm{l}}\times \mathrm{E}\mathrm{n}.\mathrm{E}\mathrm{q}\mathrm{r}}{\mathrm{A}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a}}}$
EN_ANIMAL	Animal draft energy (MJ/h): ${\mathrm{E}}_{\mathrm{a}\mathrm{n}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{l}}={\displaystyle \frac{{\mathrm{W}}_{\mathrm{h}}\times {\mathrm{H}}_{\mathrm{l}}\times \mathrm{E}\mathrm{n}.\mathrm{E}\mathrm{q}\mathrm{r}}{\mathrm{A}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a}}}$
EN_MECH_FUEL	$\mathrm{M}\mathrm{e}\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{n}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}=$ ${\displaystyle \frac{\mathrm{F}\mathrm{e}\times \mathrm{N}\mathrm{o}\times \mathrm{W}\mathrm{h}\times \mathrm{d}\mathrm{a}\mathrm{y}\mathrm{s}\times \mathrm{E}\mathrm{n}.\mathrm{E}\mathrm{q}\mathrm{r}}{\mathrm{P}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a}}}$
EN_MECH_WEIGHT	${\mathrm{A}\mathrm{M}}_{\mathrm{E}}={\displaystyle \frac{\mathrm{W}}{\mathrm{L}\times \mathrm{A}}}\times {\mathrm{C}}_{\mathrm{F}}\times \mathrm{T}$
EN_SEED	${\mathrm{E}}_{\mathrm{s}\mathrm{e}\mathrm{e}\mathrm{d}}={\displaystyle \frac{\mathrm{S}\mathrm{e}\mathrm{e}\mathrm{d}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}(\mathrm{k}\mathrm{g}/\mathrm{h}\mathrm{a})\times \mathrm{E}\mathrm{n}.\mathrm{E}\mathrm{q}\mathrm{r}}{\mathrm{C}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a}}}$
EN_CHEM	$\mathrm{C}\mathrm{h}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{E}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}=$ ${\displaystyle \frac{\mathrm{A}\mathrm{m}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{o}\mathrm{f}\mathrm{c}\mathrm{h}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l}\times \mathrm{E}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{g}\mathrm{y}\mathrm{e}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{n}\mathrm{t}}{\mathrm{A}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a}}}$
WATER_WRPC	$\mathrm{W}\mathrm{R}\mathrm{P}\mathrm{C}={\displaystyle \frac{\mathrm{W}\mathrm{R}}{\mathrm{P}\mathrm{o}\mathrm{p}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}}$	Freshwater aquatic ecotoxicity potential (FAETP)	[27]
WATER_WTA	$\mathrm{W}\mathrm{T}\mathrm{A}={\displaystyle \frac{\sum \mathrm{W}\mathrm{U}}{\mathrm{W}\mathrm{A}}}$

Note: Detailed parameter definitions, variable descriptions, coefficients, conversion factors, and units associated with each equation are provided in the Equation_Parameters and Variable_Definitions sheets of Supplementary File S1.

summarizes the equations used to calculate resource inputs, their parameters, the related impacts reported in the source studies, and the literature sources. The listed impacts are included to preserve the context of the original studies and to show how the resulting inventory flows were subsequently used in environmental assessments. They do not represent impact calculations performed within this dataset. Detailed values and parameters are provided in Supplementary File S1.

2.2. Outputs

Outputs are generated at each stage, often linked to specific inputs. For instance, Raw Materials Extraction produces CO₂, methane, slag, dust, and wastewater [4,28]. Material processing adds NOx, SO₂, VOCs, and scrap waste [5,21]. Manufacturing contributes air emissions, chemical residues, and waste heat [12,29]. Transportation emits CO₂, particulates, noise, and odors [6,30].

Field preparation and crop growth release fertilizer and machinery-related emissions [4,12], while harvesting adds diesel emissions and crop yield outputs [4,7]. Post-harvest, storage, packaging, and final transport produce CO₂, wastewater, packaging remains, refrigeration emissions, spoiled goods, and leachate [4,5,8,31], as well as waste from plastics, cardboard, and metals [9,28].

Also, Final Product Transport contributes to emissions, noise, and additional packaging waste [6,30]. Consumption and use generate emissions, food waste, and packaging waste [8,32]. End-of-Life End-of-life stages create landfill waste, incineration emissions, recyclables, and leachate [10,21].

Recent studies recorded CH₄ from anaerobic digestion [33], leachate from hydroponic [14], phosphorus runoff [34], and nitrogen volatilization under different climates [35]. Dynamic models also track seasonal N₂O emissions from fertilizer use [36,37].

Table 2. Equation to calculate the mass of the emitted.

Eq_ID	Equations	Related Impact	Source
EM_FERT	$\begin{array}{cc}\hfill \mathrm{E}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}& = \mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{N}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{P}\mathrm{f}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\hfill \\ & \times \mathrm{E}\mathrm{F}\hfill \end{array}$	Abiotic depletion potential (Minerals, fossils, elements) Freshwater aquatic ecotoxicity potential (FAETP)	[20,21,22,26,38]
EM_FUEL	${\mathrm{E}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{a}\mathrm{i}\mathrm{r}\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t},\mathrm{f}\mathrm{u}\mathrm{e}\mathrm{l}}={\mathrm{C}}_{\mathrm{f}\mathrm{u}\mathrm{e}\mathrm{l}}\times {\mathrm{E}\mathrm{F}}_{\mathrm{a}\mathrm{i}\mathrm{r}\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{l}\mathrm{u}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{t},\mathrm{f}\mathrm{u}\mathrm{e}\mathrm{l}}$	GWP Marine aquatic ecotoxicity potential (MAETP) Non-renewable energy (minerals, fossil fuels) Land occupation Terrestrial acidification/nitrification Aquatic acidification Aquatic eutrophication Ozone layer depletion potential Aquatic ecotoxicity Terrestrial Ecotoxicity Carcinogens Non-carcinogens Respiratory inorganics Respiratory organics Ionizing radiation Human toxicity potential (HTP) Or Human toxicity impact Eutrophication potential (EP)	[38]
EM_ENERGY	${\mathrm{E}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{i}}={\mathrm{C}}_{\mathrm{D}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}\times {\mathrm{P}\mathrm{C}\mathrm{I}}_{\mathrm{D}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}\times {\mathrm{E}}_{\mathrm{D}\mathrm{i}\mathrm{e}\mathrm{s}\mathrm{e}\mathrm{l}}\times {\mathrm{F}\mathrm{E}}_{\mathrm{i}}$	Cumulative Energy Demand Human toxicity potential (HTP) Or Human toxicity impact Eutrophication potential (EP)	[39]
EM_WATER_N	${\displaystyle \frac{{NO}_3^{-}-Nleachedtowater}{TotalNapplied}}$	Freshwater aquatic ecotoxicity potential (FAETP) Eutrophication potential (EP)	[40]
EM_WATER_P	${\displaystyle \frac{\mathrm{P}\mathrm{h}\mathrm{o}\mathrm{s}\mathrm{p}\mathrm{h}\mathrm{o}\mathrm{r}\mathrm{u}\mathrm{s}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}}{\mathrm{P}\mathrm{h}\mathrm{o}\mathrm{s}\mathrm{p}\mathrm{h}\mathrm{o}\mathrm{r}\mathrm{u}\mathrm{s}\mathrm{a}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{d}}}$
EM_P_BALANCE	$0={\mathrm{P}}_{\mathrm{i}\mathrm{n}}-{\mathrm{P}}_{\mathrm{o}\mathrm{u}\mathrm{t}}-{\mathrm{P}}_{\mathrm{d}\mathrm{i}\mathrm{f}\mathrm{f}\mathrm{s}\mathrm{o}\mathrm{i}\mathrm{l}}$	Eutrophication potential (EP)	[34,40,41,42]
EM_P_SIMPLE	${\mathrm{P}\mathrm{h}\mathrm{o}\mathrm{s}\mathrm{p}\mathrm{h}\mathrm{o}\mathrm{r}\mathrm{u}\mathrm{s}}_{\mathrm{i}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t}}-{\mathrm{P}\mathrm{h}\mathrm{o}\mathrm{s}\mathrm{p}\mathrm{h}\mathrm{o}\mathrm{r}\mathrm{u}\mathrm{s}}_{\mathrm{o}\mathrm{u}\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{t}}$	Eutrophication potential (EP)
P_INPUT	${\mathrm{P}}_{\mathrm{i}\mathrm{n}}={\mathrm{P}}_{\mathrm{m}\mathrm{i}\mathrm{n}}+{\mathrm{P}}_{\mathrm{m}\mathrm{a}\mathrm{n}}+{\mathrm{P}}_{\mathrm{i}\mathrm{r}\mathrm{r}+\mathrm{d}\mathrm{e}\mathrm{p}}$	Phosphorus Depletion (PD) Eutrophication Potential (EP) Global Warming Potential (GWP)
P_OUTPUT	${\mathrm{P}}_{\mathrm{o}\mathrm{u}\mathrm{t}}={\mathrm{P}}_{\mathrm{u}\mathrm{p}\mathrm{t}\mathrm{a}\mathrm{k}\mathrm{e}}+{\mathrm{P}}_{\mathrm{l}\mathrm{o}\mathrm{s}\mathrm{t}}$
P_ACCUMULATION	${\mathrm{P}}_{\mathrm{a}\mathrm{c}\mathrm{c}\mathrm{u}\mathrm{m}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}={\mathrm{P}}_{\mathrm{i}\mathrm{n}}+{\mathrm{P}}_{\mathrm{o}\mathrm{u}\mathrm{t}}$
P_EMITTED_PO4_EQ	${\mathrm{E}}_{\mathrm{m}\mathrm{i}\mathrm{n}}^{\mathrm{P}}={\mathrm{P}}_{\mathrm{i}\mathrm{n}}\times {\mathrm{e}}^{\mathrm{x}}\times$ 3.06
EM_N2O_TOTAL	$\begin{array}{cc}\hfill {\mathrm{N}}_2{\mathrm{O}}_{\mathrm{f}\mathrm{e}\mathrm{r}\mathrm{t},\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}& = ({\mathrm{N}}_2{\mathrm{O}}_{\mathrm{D}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}}+{\mathrm{N}}_2{\mathrm{O}}_{\mathrm{A}\mathrm{T}\mathrm{D}}+{\mathrm{N}}_2{\mathrm{O}}_{\mathrm{l}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}}\hfill \\ & + {\mathrm{N}}_2{\mathrm{O}}_{\mathrm{O}\mathrm{S}}+{\mathrm{N}}_2{\mathrm{O}}_{\mathrm{P}\mathrm{R}\mathrm{P}})\hfill \end{array}$	Aquatic eutrophication Terrestrial Eutrophication Potentials Global Warming Potential (GWP)	[35,36,37,43,44]
EM_N2O_DIRECT	${\mathrm{N}}_2{\mathrm{O}}_{\mathrm{D}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}}=\left({\mathrm{D}}_{\mathrm{t}\mathrm{s}}\times {\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{N}}_{\mathrm{t}\mathrm{o}\mathrm{t}}\right)\times {\displaystyle \frac{44}{28}}\times {\mathrm{E}\mathrm{F}}_1$
EM_N2O_ATD	$\begin{array}{cc}\hfill {\mathrm{N}}_2{\mathrm{O}}_{\mathrm{A}\mathrm{T}\mathrm{D}}& = \left({\mathrm{D}}_{\mathrm{t}\mathrm{s}}\times {\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{N}}_{\mathrm{t}\mathrm{o}\mathrm{t}}\right)\times \mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{G}\mathrm{A}\mathrm{S}\mathrm{F}\times {\displaystyle \frac{44}{28}}\hfill \\ & \times {\mathrm{E}\mathrm{F}}_4\hfill \end{array}$
EM_N2O_LEACH	$\begin{array}{cc}\hfill {\mathrm{N}}_2{\mathrm{O}}_{\mathrm{l}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}}& = \left({\mathrm{D}}_{\mathrm{t}\mathrm{s}}\times {\mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{N}}_{\mathrm{t}\mathrm{o}\mathrm{t}}\right)\times \mathrm{F}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{L}\mathrm{E}\mathrm{A}\mathrm{C}\mathrm{H}\times {\displaystyle \frac{44}{28}}\\ & \times {\mathrm{E}\mathrm{F}}_5\hfill \end{array}$
EM_N2O_OS	${\mathrm{N}}_2{\mathrm{O}}_{\mathrm{O}\mathrm{S}}={\mathrm{F}\mathrm{O}\mathrm{S}}_{\mathrm{t}\mathrm{y}\mathrm{p}\mathrm{e}}\times {\mathrm{E}\mathrm{F}}_{2\mathrm{t}\mathrm{y}\mathrm{p}\mathrm{e}}$
EM_N2O_PRP	${\mathrm{N}}_2{\mathrm{O}}_{\mathrm{P}\mathrm{R}\mathrm{P}}={\mathrm{F}\mathrm{P}\mathrm{R}\mathrm{P}}_{\mathrm{a}\mathrm{n}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{l}}\times {\mathrm{E}\mathrm{F}}_{3\mathrm{a}\mathrm{n}\mathrm{i}\mathrm{m}\mathrm{a}\mathrm{l}}$
EM_CH4_PADDY	$\mathrm{D}\mathrm{C}\mathrm{E}=\mathrm{B}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}+(\mathrm{E}\mathrm{F}\times \mathrm{T}\mathrm{O}\mathrm{M})$
EM_CH4_CO2EQ	${\mathrm{C}\mathrm{E}}_{{\mathrm{C}\mathrm{H}}_4}=({\mathrm{F}}_{{\mathrm{C}\mathrm{H}}_4}\times 28)$

Note: Detailed parameter definitions, variable descriptions, coefficients, conversion factors, and units associated with each equation are provided in the Equation_Parameters and Variable_Definitions sheets of Supplementary File S1.

presents the equations used to calculate the mass of emitted substances. These equations translate inventory inputs, such as fertilizer application rates, fuel use, energy consumption, and nutrient balances, into quantified emission outputs. The related impacts shown in Table 2 were extracted from the reviewed studies that reported the corresponding equations. They are provided as contextual information to document how the resulting inventory flows were used in later impact assessment stages within the source studies. The dataset does not calculate characterization factors, impact scores, or results for normalization or weighting. The equations summarize the results of the literature review and serve as the basis for generating output flows prior to the impact assessment. All identified outputs were organized by scope stages, with detailed values and parameters documented in Supplementary File S1. Emission factors were calculated as $EF={\displaystyle \frac{TotalEmissions}{TotalActivity}}$, covering substances such as NH₃, NOx, CH₄, CO₂, and metals. All outputs and emission factors are consolidated in Supplementary File S1 to ensure consistency and transparency.

2.3. Data Sources

Supplementary File S1 documents the different types of data sources in the reviewed studies, including academic literature, databases, surveys, reports, experiments, government publications, and industry data. Each source provides information relevant to different LCA stages.

• Literature Reviews: Academic studies provide input–output data from field experiments, surveys, and simulations. Examples include fertilizers, machinery, emissions, and yields [4,7]. Recent reviews aggregated findings across cropping systems [45,46,47].
• Databases such as Ecoinvent, GaBi, and SimaPro provide standardized LCI data. Ecoinvent supports emissions and energy modeling [21,31]. Recent works applied Ecoinvent, SimaPro, and Agribalyse for region-specific systems [14,16,17,19,36,48].
• Field Surveys and Interviews: Field surveys, interviews, and farm documentation provide firsthand data capture of real-world practices [21,49]. New studies relied on structured interviews and on-site measures for organic, hydroponic, and regional systems [16,36,48,50].
• Professional and Technical Reports Industry reports cover case studies and pilot projects, focusing on energy use and emissions [28,31]. New reports expand coverage of anaerobic digestion and greenhouse systems [33,46].
• Experimental Data: Controlled field trials provided precise data on fertilizer, yields, and emissions [5,29]. New contributions include seasonal N volatilization [35], P runoff [34], regenerative systems [15], and detailed trial energy use [51].
• Government: Agencies such as USDA-NASS and IPCC provide standardized emission factors and guidelines [10,52]. Recent studies applied IPCC 2006/2019 guidelines and national coefficients [34,35,36,37].
• Industry Databases and Surveys: Surveys and databases provide operational values for equipment, energy use, and emissions [13]. Recent studies documented greenhouse systems, digital tools, and machinery [17,53,54].
• However, 54 studies did not specify their data sources, including works by [55,56,57]. This lack of data source transparency can affect reproducibility. By integrating these diverse sources into a structured Excel-based dataset, Supplementary File S1 standardizes inputs, outputs, equations, and references, enhances consistency across the Life Cycle Inventory phase, and supports future life cycle assessment applications.

3. Methods

This dataset was created by systematically reviewing published studies to collect LCI data for agricultural LCA. The review followed the PRISMA method to clearly document the selection and screening of studies (Figure 1). The literature search was carried out using Google Scholar. Google Scholar was selected because it provides broad coverage of peer-reviewed articles, reports, and other sources relevant to agricultural LCA. Google Scholar was used as the primary search database because it indexes a wide range of scholarly literature from multiple publishers, disciplines, and document types within a single platform. Databases such as Scopus, Web of Science, AGRICOLA, and ScienceDirect were not used in this data description paper because the objective was to identify a broad set of agricultural LCA studies using a consistent search strategy and screening process. To reduce the possibility of missing relevant studies, reference lists of selected studies and review papers were also examined, and an updated search was conducted in 2025.

Nevertheless, the use of a single database may have limited the retrieval of some studies indexed exclusively in other databases, thereby limiting the dataset. On 15 April 2022, the keywords “impact categories” and “life cycle assessment” returned about 38,400 results. To reduce this number, the term ecotoxicity was added as an example impact category, resulting in 13,600. Other keyword combinations returned too few studies to be useful. The search terms “crop production system,” “impact categories,” and “life cycle assessment” yielded 161 studies, which were selected for further screening. Several keyword combinations were tested during the search process. These search terms were selected because they returned a manageable number of studies while remaining directly relevant to the study objectives. The 161 records were screened using the eligibility criteria shown in Figure 1. Studies were included if they (1) applied the LCA methodology, (2) focused on crop production systems, and (3) assessed environmental impacts. A total of 39 studies were excluded because 31 were not accessible, 6 provided only an abstract, and 2 were not published in English. The remaining 122 studies were included in the original review, including eight review papers.

An updated search in May 2025 identified 66 additional studies, of which 62 were included. In total, 184 peer-reviewed studies, including 13 review papers, were used to build the dataset. Studies were included if they used LCA to study crop production and reported inventory data such as inputs, outputs, emission factors, or calculation equations. No limits were placed on crop type, region, or production system. The reference lists of selected studies and review papers were also examined to ensure that no relevant studies were missed. The studies included in the dataset represent a wide range of agricultural crop production systems and geographic regions. These include field crops, fruit crops, vegetable crops, greenhouse production systems, hydroponic systems, biomass and bioenergy crops, precision agriculture systems, and both conventional and organic farming systems. Multifunctional agricultural systems were also included when crop production was a primary component of the assessment. No restrictions were placed on crop type, production system, or geographic location. Studies focusing exclusively on livestock production, forestry systems, or non-agricultural products were excluded unless they were directly connected to crop production or integrated agricultural systems. Review papers were included because some reported inventory information, equations, emission factors, and data covering multiple crops and agricultural systems. They were also used to support the understanding of relationships among LCA phases. All sources, including review papers and original studies, were assigned unique Stable_ID values. Records were retained as reported in the original sources, even when similar information appeared in multiple studies, because the dataset’s objective was to document reported inventory information rather than to combine or reconcile values across studies. The use of Stable_ID allows users to trace each record back to its original source.

Inventory data were collected manually from each study using a structured extraction format developed for this review. The extracted information included scope definitions, production stages, inputs (such as fertilizers, energy, water, and machinery), outputs (such as emissions to air, water, and soil), emission factors, equations, and data sources. The same extraction structure was applied across all reviewed studies to maintain consistency in data collection and organization. When studies reported data without clearly assigning them to stages, the data were placed into the most appropriate scope stage based on the study description, using consistent rules to avoid counting the same input more than once. All collected data were organized using Microsoft Excel for Microsoft 365, Version 2607 (Microsoft Corporation, Redmond, WA, USA), with standardized units, variable names, and categories across studies, and each data entry linked to its original study using a unique study ID.

Clear classification rules were used to distinguish between inputs, outputs, emission factors, equations, and equation parameters. Parameter definitions, coefficients, and conversion factors associated with the equations were documented separately in the Equation_Parameters sheet. Inputs include material and energy flows entering the system; outputs include products, emissions, and waste streams; emission factors represent conversion relationships between activities and emissions; and equations describe how inventory values are calculated. The equations in the dataset were extracted from the reviewed studies and reformatted where necessary to ensure consistent notation and presentation. The underlying mathematical relationships and calculations reported in the original studies were not modified. To improve transparency and interpretation, parameter definitions, coefficients, conversion factors, default values, and units associated with the equations were documented in the Equation_Parameters sheet. At the same time, variable names and descriptions were provided in the Variable_Definitions sheet. For example, fertilizers, pesticides, fuel, electricity, water, and machinery use were classified as inputs.

In contrast, crop yield, co-products, emissions to air, water, and soil, and waste streams were classified as outputs. Emission factors were recorded when studies reported factors linking an activity or input to a quantified emission, such as emission factors for fertilizer application, fuel combustion, or electricity use. Equations were recorded when studies provided mathematical expressions used to calculate inventory flows, emissions, nutrient balances, or resource use. Data sources were recorded separately and included databases, published references, reports, and other secondary sources used to obtain inventory data or emission factors. When an item could be interpreted in more than one way, classification was based on its function within the inventory. For example, diesel fuel consumption was classified as an input, while the corresponding fuel-combustion coefficient used to estimate emissions was classified as an emission factor. These rules were applied consistently across all studies.

The reviewed studies used different functional units, system boundaries, terms, and reporting styles. To make the data easier to organize and compare, variable names, units, and categories were standardized while keeping the original values reported in each study. Unit standardization was limited to the consistent recording of units and naming conventions across the dataset. Reported values were not converted to a common unit, and the original units were retained as reported in the source studies to preserve traceability. Data from different studies were not combined into a single inventory and were not converted to a common functional unit. Instead, each record remained linked to its original study through the Stable_ID. When the same inventory element was reported using different terms, it was assigned to a common category. If a study did not clearly indicate the life cycle stage for a specific inventory item, the item was assigned to the most appropriate stage based on the study description and the stage definitions used in this dataset. The assignment was based on the activity associated with the inventory item. Inputs related to the production or acquisition of materials, such as seeds, fertilizers, and pesticides, were assigned to raw material production stages. Inputs related to field activities, such as land preparation, planting, fertilization, irrigation, and pest management, were assigned to field preparation or crop growth stages. Inputs related to harvesting operations were assigned to the harvesting stage. In contrast, activities such as drying, storage, milling, packaging, and transportation were assigned to their corresponding post-harvest, packaging, storage, or transport stages. When studies reported inventory items for a general operation without specifying the exact stage, the study description was used to determine the most appropriate stage. For example, ref. [4] reported fuel and machinery for in-field operations without identifying specific stages, and these inputs were assigned to field preparation, crop growth, and harvesting stages based on the reported activities. Similarly, ref. [12] reported labor, machinery, chemicals, and electricity without stage-specific information, and these inputs were assigned to the relevant stages based on their intended use. Ref. [7] reported diesel use for tractors and pumps, which were assigned according to their reported functions, while ref. [13] classified nursery activities under raw material production and infrastructure, machinery, fertilization, and pest management under field preparation. These assignment rules were consistently applied across the dataset, and each inventory item was recorded only once to avoid duplicate counting. When different studies reported different values for the same inventory element, all values were kept as separate records rather than being averaged or modified, allowing users to trace each record back to its original source. Standardization focused on harmonizing variable names, categories, identifiers, and data structure across studies. Original units reported by the source studies were retained to preserve traceability. Where different terms referred to the same inventory element, a standardized variable name was assigned. Differences in system boundaries, regional assumptions, and modeling approaches were retained as reported in the original studies and linked through study-specific identifiers. Because the reviewed studies used different functional units, system boundaries, geographical conditions, crop types, and methodological approaches, inventory values may not be directly comparable across studies. Functional unit and system boundary information were retained as reported in the original studies and are documented in the related Phase 1 dataset [58]. These records can be linked to the Phase 2 inventory data through the shared Stable_ID, allowing users to interpret inventory information within its original study context.

Because the dataset is large, all detailed Phase 2 inventory data are provided in Supplementary File S1. No formal quality assessment was applied during data collection. All studies meeting the inclusion criteria were retained, and the extracted information was linked to the original publications through Stable_ID values to preserve traceability. Data extraction and classification were performed manually, which may introduce some subjectivity despite the use of consistent extraction and classification rules. To reduce this risk, the same extraction structure, classification criteria, and stage-assignment rules were applied throughout the review process. All records remained linked to their original sources through Stable_ID values, allowing users to verify the extracted information against the original publications. Because the dataset compiles information from studies conducted in different years, regions, production systems, and methodological contexts, data reliability, completeness, temporal validity, geographical representativeness, and methodological consistency may vary among studies. In addition, 31 records identified during the screening process could not be accessed and were therefore excluded from the dataset. The exclusion of these studies may have reduced the representativeness of the dataset if they contained inventory data, crop systems, geographical regions, or methodological approaches that differed from those included in the final dataset. Users should consult the original studies when evaluating methodological quality or selecting data for specific applications. Using a single database is a limitation, as relevant studies indexed in databases such as Web of Science or Scopus may not have been captured. Excluding inaccessible studies may also have introduced selection bias. However, the large number of studies identified, the updated search conducted in 2025, and the inclusion of review papers helped improve the coverage and representativeness of the dataset. Furthermore, differences in functional units, system boundaries, inventory reporting practices, and methodological assumptions across the reviewed studies limit the degree of harmonization achievable within a single dataset.

4. User Notes

This dataset is provided as Supplementary File S1 (Phase 2) in Excel format. The workbook contains separate sheets for guidance, study IDs, life-cycle inventory inputs and outputs, emission equations, emission factors, equation parameters, and data sources. The first three sheets (ReadMe, User_Guide, and Variable_Definitions) help users understand the file structure, how to use the workbook, and the meaning of the variables used in the dataset.

• The ReadMe sheet gives a short overview of the dataset and workbook contents.
• The User_Guide sheet explains how to move between sheets and use the shared IDs.
• The Variable_Definitions sheet explains column names and gives examples.
• The Study_ID Logic sheet explains how study references were cleaned, matched, and linked across sheets.
• The Study_ID_Registry sheet includes Stable_ID, Short_Cite, Clean_ID, Clean_Normalized, Match_Key, and related fields. That is the main sheet for linking each reviewed study to a unique ID.
• The Study_ID_Lookup sheet can be used to search for study references or Stable_ID values.
• The Phase2_Inputs sheet includes reported inputs such as fertilizers, pesticides, fuel, water, electricity, machinery-related inputs, amounts, units, products, and life cycle stages.
• The Phase2_Outputs sheet includes reported outputs such as crop yield, co-products, emissions, wastes, amounts, units, categories, products, and stages.
• The Phase2_Emission_Equations sheet includes Eq_ID, Emission_Type, Description, Output_Name, Expression, Required_Inputs, Unit, and Notes for equations reported in the reviewed studies.
• The Equation_Parameters sheet includes parameter definitions, coefficients, conversion factors, default values, and units associated with the equations reported in the Phase2_Emission_Equations sheet.
• The Phase2_Emission_Factors sheet includes reported emission factor values, substances, emission compartments, and units.
• The Phase2_Data_Sources sheet includes secondary data sources, databases, and references used in the reviewed studies.

All sheets are linked using Stable_ID and supporting fields such as Match_Key and Clean_Normalized. The Study_ID_Registry sheet serves as the central linking table, while Stable_ID serves as the primary key that connects records across the inventory inputs, outputs, emission equations, emission factors, and data source sheets. Users can filter by product, stage, category, substance, or source, and then use the Stable_ID to trace related records across the Phase2_Inputs, Phase2_Outputs, Phase2_Emission_Equations, Phase2_Emission_Factors, and Phase2_Data_Sources sheets. For example, users can identify a study in the Study_ID_Registry sheet and use the same Stable_ID to locate its associated inventory flows, emission factors, equations, and data sources throughout the workbook.

If users add new studies, a new Stable_ID should be assigned in the Study_ID_Registry sheet, and the study reference should be added to the Study_ID_Registry sheet. Phase 2 data, including inputs, outputs, emission factors, equations, and data sources, should then be extracted and entered into the corresponding sheets using the same Stable_ID. All entries should follow the existing units, naming conventions, and categories used in the dataset. Any new variables or categories should be added using the same structure, and all data should be verified against the original source to maintain traceability and consistency. Example of dataset use. A user interested in Stable_ID = St_66,which corresponds to the dataset record “Balasuriya et al., 2022,” can use the Stable_ID to retrieve related records across the workbook. In the Phase2_Inputs sheet, the user can locate reported fertilizer inputs. In the Phase2_Outputs sheet, the user can retrieve the corresponding crop-related outputs. In the Phase2_Emission_Equations sheet, the same Stable_ID links to equations such as EM_FERT (emissions from fertilizer application) and EM_FUEL (emissions from fuel combustion). In the Phase2_Emission_Factors sheet, the user can retrieve the emission factors used in the study. That allows all inventory information associated with a specific study to be traced and connected through a single Stable_ID.

All data were taken from published studies, including peer-reviewed journal articles and selected scholarly sources. Users should check the original references for more details, assumptions, or units before reusing. The dataset is provided in Excel format and can be used with Microsoft Excel. The dataset includes studies published between 1999 and 2025. Because agricultural production systems, technologies, input management practices, and emission estimation methods have evolved over this period, inventory data reported in older studies may differ from those reported in more recent studies. They may not fully represent current agricultural practices. Users should therefore consider the publication period and original study context when selecting data for new assessments. This dataset focuses only on Phase 2 (Life Cycle Inventory). Goal and scope definition, impact assessment, and interpretation are reported separately in the related Phase 1, Phase 3, and Phase 4 datasets.