Impact Factors of Crop Protectants and Inoculants

Methods 5.0

Impact factors for crop protectants like pesticides require a more complex implementation. The FP v4.2 allowed users to indicate the number of applied products per season for each category (two herbicide products, four insecticide products, etc.), rather than asking users for the cumulative quantity of active ingredients (e.g., 1.3 lb of herbicide active ingredients in a growing season). The FP v4.2 had an application rate assumption for each product category. For example, if a grower indicated that four insecticide products were applied during the season, and the rate assumption for insecticides was 0.05 lb / acre per application per crop interval, then the total quantity of active ingredients applied would be \(4 * 0.05 = 0.2\ lb/ac\) of insecticides for a given crop interval.

With FP v5, we continue asking users for the number of pesticide products applied. We updated the energy use and GHG emission impact factors and the pesticide rate assumptions.

It is important to note that the energy use and GHG emission impact factors for pesticides have historically relied on data from Audsley et al. (2009), which in turn uses even older information from Green (1987). To our knowledge, there has been no publicly available literature to better understand the life cycle of modern pesticides to account for their manufacturing impact with more confidence.

Details: Crop Protectants

The environmental impact of pesticide production, including herbicides, fungicides, insecticides, growth regulators, and seed treatments, is assessed based on life cycle inventory data from Audsley et al. (2009) and Green (1987), which remain among the most widely used and comprehensive sources available for estimating pesticide emissions (LEIF 2025). However, given advancements in manufacturing processes, formulation efficiencies, and regulatory changes, some of the pesticides included in these references are no longer widely used. To ensure the relevance of the emission factors applied in this study, we cross-reference current pesticide usage data from publicly available agricultural statistics databases (USDA NASS 2024). The estimation of cradle-to-processing-gate emissions for pesticides considers multiple energy inputs and material flows. For each pesticide, the total amount of inherent energy retained within the chemical structures of key input materials, such as naphtha, natural gas, and coke is considered (Audsley et al. 2009; Green 1987). This inherent energy (mmbtu/kg active ingredient) is then multiplied by GREET 2023 cradle-to-gate emission factors (e.g. g CO₂ per mmBTU of LHV throughput) (Wang et al. 2023) to estimate the emissions associated with the production of raw chemical precursors.

Beyond inherent energy content, process energy emissions account for the direct energy use required to manufacture pesticides throughout the life cycle, including various stages of chemical synthesis, formulation, and packaging. Emissions from upstream process energy use are calculated by first dividing the cumulative energy demand per kg of active ingredient (Audsley et al. 2009) by the CED per unit of fuel throughput (Liu et al. 2023; LEIF 2025; Wang et al. 2023) resulting in total energy throughput/kg of active ingredient, and then multiplying this by the cradle-to-gate emissions per unit of energy throughput for processing (Wang et al. 2023; Liu et al. 2023). Combustion emissions from process energy use are estimated by multiplying the estimated energy throughput per kg of active ingredient by the EPA combustion-based factors, converted to per unit of LHV. Steam related emissions are estimated assuming an average 75% boiler efficiency for steam generation from natural gas.

For the formulation and packaging stage, we rely on energy use estimates for herbicides, fungicides, and insecticides (Audsley et al. 2009; Barber 2004). However, since these estimates also include distribution energy, we adjust these values by applying factors from Pimentel (2019), which distinguishes the energy contributions from formulation, packaging, and distribution. To prevent double counting of transportation emissions, only formulation and packaging energy use is considered, and it is assumed that electricity is the primary energy source for these activities. The final emissions per kilogram of active ingredient are determined by dividing total emissions by the percentage of active ingredient per kilogram of product (USEPA 2024; European Chemicals Agency 2024).

Due to limited data availability on fumigants used in U.S. agriculture, we use dichloropropene as a proxy for other fumigants such as metam sodium, chloropicrin, and metam potassium. Dichloropropene fumigant emissions are based on the material and energy input inventories provided by LCA databases (LEIF 2025; National Renewable Energy Laboratory 2012), and the GREET FD-CIC derived cradle-to-gate emission factors from input materials (Liu et al. 2023). Similarly, growth regulator emissions, such as those associated with ethephon (ethylene dichloride) are based on the material and energy input inventories specified in LCA databases (Wang et al. 2023; National Renewable Energy Laboratory 2012), with emissions from inputs based on GREET FD-CIC (Liu et al. 2023).

Details: Inoculants

Inoculants are biological soil amendments containing beneficial microorganisms that enhance nutrient availability and uptake by plants. While they are most commonly associated with enhancing nitrogen fixation in legume crops, such as B. Japonicum for soybeans, inoculants are also widely used in non-legume crops. Despite their growing importance, life cycle assessment data on inoculant production remains limited, making it challenging to develop comparable emission factors.

To estimate the cradle-to-gate emissions and CED of inoculants, we rely on the most comprehensive peer-reviewed studies available, which currently provide impact assessments for specific strains. For example, Mendoza Beltran et al. (2021) present LCA results of B. japonicum, while Kløverpris et al. (2020) provide data on P. bilaiae, a fungal inoculant used to increase phosphorus availability in cereals, oilseeds, and forage crops. These studies highlight significant variability in inoculant production impacts, with GHG emissions ranging from less than 1 kg CO₂e/kg to as high as 69 kg CO₂e/kg, and CED values spanning from 11 MJ/kg to over 600 MJ/kg (Mendoza Beltran et al. 2021; Kløverpris et al. 2020). The wide range suggests that production processes, microbial strains, energy carriers, and industrial fermentation techniques significantly influence the environmental footprint of inoculants. Given the diversity of inoculant types and their growing role in sustainable crop production, further research is needed to refine emission factors for different formulations.

The Fieldprint Platform v5 uses the factors from Mendoza Beltran et al. (2021).

Energy use factors

The energy use impact factors are described below.

• System Boundary: The impact factors in this section are attributed to the Upstream boundary.
• Source Category: It classifies the energy use to indicate it is associated with the production of crop protectants (e.g. pesticides, etc).
• Source Detail: A given pesticide option.
• MJ: Impact factor of megajoules per unit.
• Unit: Expected unit to use the MJ impact factor.

Table 1: Source Category: Energy use associated with production of pesticides (including inoculants).

System Boundary	Source Detail	MJ	Unit
Upstream	Fumigants	61.83	kg product
Upstream	Fungicides	344.74	kg active ingredient
Upstream	Growth Regulators	420.70	kg active ingredient
Upstream	Herbicides	431.68	kg active ingredient
Upstream	Herbicides (sulfuric acid)	2.79	kg active ingredient
Upstream	Inoculant	11.43	kg product
Upstream	Insecticides	405.78	kg active ingredient
Upstream	Seed Treatment	435.30	kg active ingredient

GHG emission factors

The GHG emission impact factors are described below.

• System Boundary: The impact factors in this section are attributed to the Upstream boundary.
• Source Category: It classifies the GHG emissions to indicate they are associated with the production of crop protectants (e.g. pesticides, etc)
• Source Detail: A given pesticide option.
• CO2_fossil: Impact factor for fossil CO₂ in kg of gas per unit.
• CH4_fossil: Impact factor of fossil CH₄ in kg of gas per unit.
• N2O: Impact factor for N₂O in kg of gas per unit.
• Unit: Expected unit to use the GHG emissions impact factor.

Table 2: Source Category: GHG emissions associated with production of pesticides (including inoculants).

System Boundary	Source Detail	CO2_fossil	CH4_fossil	N2O	Unit
Upstream	Fumigants	1.14	0.0107579	0.0003267	kg active ingredient
Upstream	Fungicides	14.99	0.0293242	0.0002793	kg active ingredient
Upstream	Growth Regulators	62.71	0.0604218	0.0041783	kg active ingredient
Upstream	Herbicides	19.57	0.0373498	0.0003573	kg active ingredient
Upstream	Herbicides (sulfuric acid)	0.03	0.0000514	0.0000005	kg active ingredient
Upstream	Inoculant	0.35	0.0000000	0.0000000	kg product
Upstream	Insecticides	18.23	0.0348022	0.0003351	kg active ingredient
Upstream	Seed Treatment	22.27	0.0222940	0.0021360	kg active ingredient

Application rate assumptions

We used USDA NASS survey data and the scientific literature to develop conservative estimates for pesticide rates per application per year. For some crops and pesticide categories, global assumptions were used, particularly when a crop had no history of receiving a given pesticide for field applications.

Here are some clarifications:

• These pesticides are for field applications and not for post-harvest processing or storage.
• Inoculants are only applicable to legume crops¹.
• Herbicides (sulfuric acid) are only applicable to potatoes.

1. This could change if users request that inoculants should be available to all crops.

Table 3: Assumed application rates of crop protectants, by crop. Units: active ingredient kg/ha.

Crop	Fumigants	Fungicides	Growth Regulators	Herbicides	Insecticides	Seed Treatment	Herbicides (sulfuric acid)
Alfalfa	32.48	0.10	0.00	0.43	0.05	0.05	0
Barley	32.48	0.09	0.26	0.17	0.06	0.05	0
Chickpeas (garbanzos)	32.48	0.10	0.00	1.10	0.04	0.05	0
Corn (grain)	32.48	0.08	0.00	0.33	0.06	0.05	0
Corn (silage)	32.48	0.08	0.00	0.33	0.06	0.05	0
Cotton	32.48	0.14	0.38	0.58	0.09	0.05	0
Dry Beans	32.48	0.10	0.00	1.00	0.04	0.05	0
Dry Peas	32.48	0.10	0.00	1.10	0.04	0.05	0
Fava Beans	32.48	0.10	0.00	1.00	0.04	0.05	0
Lentils	32.48	0.10	0.00	1.00	0.04	0.05	0
Lupin	32.48	0.10	0.00	1.00	0.04	0.05	0
Peanuts	32.79	0.19	0.07	0.35	0.23	0.05	0
Potatoes	180.48	0.19	2.25	0.54	0.08	0.05	296
Rice	32.48	0.16	0.07	0.41	0.11	0.05	0
Sorghum	32.48	0.08	0.07	0.86	0.35	0.05	0
Soybeans	32.48	0.10	0.00	0.43	0.05	0.05	0
Sugar beets	108.53	0.30	0.07	0.06	1.37	0.05	0
Wheat (durum)	32.48	0.10	0.11	0.10	0.03	0.05	0
Wheat (spring)	32.48	0.10	0.11	0.10	0.03	0.05	0
Wheat (winter)	32.48	0.10	0.11	0.10	0.03	0.05	0

← Return to list of impact factor categories

References

Audsley, Eric, K. F. Stacey, David J. Parsons, and Adrian G. Williams. 2009. “Estimation of the Greenhouse Gas Emissions from Agricultural Pesticide Manufacture and Use.” https://dspace.lib.cranfield.ac.uk/handle/1826/3913.

Barber, Andrew. 2004. “Seven Case Study Farms: Total Energy & Carbon Indicators for New Zealand Arable & Outdoor Vegetable Production.” AgriLINK New Zealand Ltd 288.

European Chemicals Agency. 2024. “Search for Chemicals.” https://echa.europa.eu/information-on-chemicals.

Green, MR. 1987. “Energy in Pesticide Manufacture, Distribution and Use.” Energy in Plant Nutrition and Pest Control.

Kløverpris, Jesper Hedal, Claus Nordstrøm Scheel, Jannick Schmidt, Brian Grant, Ward Smith, and Murray J Bentham. 2020. “Assessing Life Cycle Impacts from Changes in Agricultural Practices of Crop Production: Methodological Description and Case Study of Microbial Phosphate Inoculant.” The International Journal of Life Cycle Assessment 25: 19912007.

LEIF. 2025. “Impact Factors.” https://www.leifllc.com/.

Liu, Xinyu, Hao Cai, Hoyoung Kwon, and Michael Wang. 2023. “Feedstock Carbon Intensity Calculator (FD-CIC): Users’ Manual and Technical Documentation.”

Mendoza Beltran, Angelica, Claus Nordstrøm Scheel, Nuala Fitton, Jannick Schmidt, and Jesper Hedal Kløverpris. 2021. “Assessing Life Cycle Environmental Impacts of Inoculating Soybeans in Argentina with Bradyrhizobium Japonicum.” The International Journal of Life Cycle Assessment 26: 15701585.

National Renewable Energy Laboratory. 2012. “U.s. Life Cycle Inventory Database.” https://www.nrel.gov/analysis/lci.

Pimentel, David. 2019. Handbook of Energy Utilization in Agriculture. CRC press.

USDA NASS. 2024. “Surveys: Agricultural Chemical Use Program.”

USEPA. 2024. “Pesticide Product and Label System Database.” https://ordspub.epa.gov/ords/pesticides/f?p=PPLS:1.

Wang, Michael, Amgad Elgowainy, Uisung Lee, Kwang Hoon Baek, Sweta Balchandani, Pahola Thathiana Benavides, Andrew Burnham, et al. 2023. “Summary of Expansions and Updates in r&d GREET 2023.” Argonne National Laboratory (ANL), Argonne, IL (United States).