1 Purpose

This section includes additional details for various topics, methods, and reference data. This document contains technical information. The language used here will, at times, be repetitive to reduce ambiguity.

2 Describing the concept of crop intervals

A crop interval is defined as the period between two dates in which on-farm and field activities are conducted to produce a cash crop. Defining a crop interval is critical to creating a well-defined and continuous sequence of crop management activities for a given field.

A crop interval starts after the last day of harvest of the previous cash crop and ends with the harvest of the next cash crop. Crop drying is included in the crop interval regardless of when the drying operation is conducted. A crop interval might start on the same day of harvest when there is a field operation on that day (stalk shredding, tillage, etc.); otherwise, the crop interval starts on the day after harvest. The energy use and GHG emissions associated with on-farm and field activities that occur during the crop interval are attributed to the cash crop being produced. Crop intervals typically straddle calendar years; however, there are some exceptions, such as short-season double-crops. Crop intervals are named using the crop name and harvest year or month-year of harvest. For example, a crop interval for cotton harvested in 2023 would be named 2023 cotton or 2023-10 cotton to specify that is was harvested in October of 2023. The month-harvest designation will only be used when there are two crop harvests of the same crop in one calendar year.

To close a crop interval, the crop must result in economic gain. Economic gain is defined as crop harvest, either with machinery (grain, forage removal) or consumed by livestock on-site. Fallow years, crop failures, and crop abandonment require specific treatment; these scenarios are discussed in the sections below.

Growing a cover crop for soil cover, green manure, or other soil health benefits does not count as economic gain to define a crop interval. The energy use and GHG emissions associated with cover cropping activities are attributed to the following cash crop.

Below, we include eight scenarios to illustrate the concept of crop intervals. To be concise, we show a simplified list of field activities, omitting crop transportation, drying, and irrigation events, along with other operations.

2.1 Case 1: A grower produces one cash crop per season

In the simplest case, a crop interval starts after the harvest of the previous cash crop and ends with the harvest of the following cash crop.

The following figure demonstrates the crop intervals for a corn-soybean rotation.

Figure 1: Case 1, crop rotation with one cash crop per season

In Case 1, the energy use and GHG emissions associated with the field activities after the harvest of the 2022 corn grain are attributed to the 2023 soybean crop, the energy use and GHG emissions associated with the activities after harvest of soybeans are attributed to the 2024 corn crop, and so on. For the two crops with complete information shown above, the crop intervals would be delineated as follows:

2023 soybeans, 10-31-2022 to 10-10-2023
2024 corn grain, 10-11-2023 to 10-20-2024

2.2 Case 2: A grower has a rotation with double-cropping

In many regions of the US, growers can add a second, short-season crop to their rotations. A common double-crop rotation is producing soybeans after a winter wheat harvest.

The following figure demonstrates the crop intervals for a rotation with double-cropping.

Figure 2: Case 2, crop rotation with double-cropping

In this rotation, the 2023 soybeans have a short crop interval that lasts around four months without straddling a calendar year. For the crops with complete information shown above, the crop intervals would be delineated as follows:

2022 corn grain: 10/31/2021 to 9/20/2022
2023 winter wheat: 9/21/2022 to 6/15/2023
2023 soybeans: 6/16/2023 to 10/15/2023

The energy use and GHG emissions associated with the on-farm and field activities within the dates of the crop interval are attributed to the cash crop named in the crop interval.

2.3 Case 3: A grower produces a cash crop per year and adds cover cropping

In this case, a grower introduces cover crops to the rotation while harvesting one cash crop per year. The cover cropping activities (planting, seed inputs, chemical or mechanical termination, etc.) are included in the crop interval for the cash crop.

The following figure demonstrates the crop intervals for a rotation with cash crops and cover crops.

Figure 3: Case 3, crop rotation with one cash crop per year with cover cropping

As before, the energy use and GHG emissions associated with the on-farm and field activities are attributed to the crop named in the crop interval. If the cover crop were to be harvested for forage or grain, or if it was consumed by livestock, that would make it a cash crop, and the crop interval would be named and delineated accordingly.

For the crops with complete information shown above, the crop intervals would be delineated as follows:

2022 corn grain: 10/17/2021 to 9/20/2022
2023 cotton: 9/21/2022 to 10/25/2023

2.4 Case 4: A grower has fallow years in the rotation

Fallow cropping is common in dryland systems in many regions of the US, including the Great Plains, the Pacific Northwest, and the Rocky Mountain region. Fallow cropping refers to leaving a field unplanted during one or more growing seasons. This is typically done to build up soil water reserves for the following cash crop, among other reasons. Fallow years might have light tillage operations or applications of herbicides to control weeds. The energy use and GHG emissions of those activities, plus any estimated soil N₂O emissions or other sources of emissions, are attributed to the following cash crop.

The following figure demonstrates the crop intervals for a rotation with fallow cropping.

Figure 4: Case 4, crop rotation with fallow cropping

In the example above, 2021 is a fallow year with multiple light tillage passes to control weeds. The 2022 spring wheat crop interval spans two years. For the crops with complete information shown above, the crop intervals would be delineated as follows:

2019 corn grain: 10/16/2018 to 10/15/2019
2020 corn grain: 10/16/2019 to 10/15/2020
2022 spring wheat: 10/16/2020 to 8/5/2022

2.5 Case 5: A grower experiences crop failure or crop abandonment

In the unfortunate case in which a crop is killed by weather events, such as hail, flood, or drought, the on-farm and field activities for the failed or abandoned crop are assigned to the following cash crop. This is similar to how fallow cropping is treated.

The following figure demonstrates the crop intervals for a rotation with a failed crop.

Figure 5: Case 5, a cash crop fails or has to be abandoned

In the example above, the 2020 corn crop suffered a hail event that killed the growing crop (the event is shown in bold font). The grower shredded and incorporated the corn biomass into the soil and returned in the 2021 season to grow spring wheat. The energy use and GHG emissions associated with the failed corn crop are attributed to the 2021 spring wheat.

For the crops with complete information shown above, the crop intervals would be delineated as follows:

2021 spring wheat: 10/16/2019 to 8/5/2021

2.6 Case 6: A grower produces a rice crop with ratooning

Rice producers, mostly in Texas and Louisiana, can benefit from harvesting two rice crops from the same planted seeds. This presents a unique situation for which there is little guidance for GHG emission accounting methodologies, and it blurs the lines of crop interval delineation. We are faced with three complications:

The method for CH₄ emissions from flooded rice cultivation, as published by Ogle et al. (2024), does not contain guidance about how to account for ratoon emissions.
Rice ratooning cannot be classified strictly as a double crop because it is harvested from the regrowth of the first rice crop planted at the beginning of the season. It is also not a given that both crops will be managed with the same level of inputs, such as fertilizers.
The guidance published in IPCC (2019) indicates that the area harvested for the main crop and the ratoon crop should be summed together, which leads to having to sum together the crop production outputs from the two harvests (primary rice crop + ratoon crop).

To solve these challenges, Field to Market proposes using the following approach until better guidance emerges:

If a field produces annual rice with no ratooning, the method CH₄ emissions from flooded rice cultivation is run, and the accounting of energy use and GHG emissions is similar to an annual cash crop described in Section 2.1.
If a field produces rice with a ratoon crop, the method CH₄ emissions from flooded rice cultivation is run aggregating inputs and outputs for both rice crops, and extending the season length from the planting of the first rice crop to the harvest of the ratoon crop (approximately 133 days + 60 days). The estimated energy use and GHG emissions are representative of both crops. This will require filling in some assumptions. Taking this approach will typically result in higher emissions per area (e.g., kg CO₂e / ha) and lower emissions per crop production unit (e.g., kg CO₂e / kg rice crop) compared to producing a single rice crop per year.

The following figure demonstrates the crop intervals for a rotation with rice ratooning. To be concise, many field activities were omitted from the timeline of operations.

Figure 6: Case 6, a rice crop with ratooning

For the crops with complete information shown above, the crop intervals would be delineated as follows:

2020 rice with ratooning: 10/16/2019 to 10/15/2020

2.7 Case 7: A grower has a rotation with multi-year alfalfa and annual crops

Alfalfa is currently the only perennial crop in Field to Market’s programs. In addition, alfalfa can be harvested multiple times per growing season. The Fieldprint Platform needs the following rules to account for crop intervals for alfalfa:

The first crop interval for alfalfa will start after the harvest of the last cash crop, and end after the last harvest of the first year with biomass removal. The first crop interval will typically span two years, since the first establishment year is unlikely to include any biomass harvesting.
For the rest of the alfalfa stand, crop intervals will start after the last harvest in the previous calendar year, and end with the last harvest in the following year. After the first establishment crop interval, all other intervals will accumulate the multiple harvests conducted in a given calendar year.

The following figure demonstrates the crop intervals for a rotation with alfalfa. To be concise, many field activities were omitted from the timeline of operations. Alfalfa was planted in August 2015, and the first harvest occurred in the spring of 2016.

Figure 7: Case 7, a rotation with multi-year alfalfa

To illustrate the first two crop intervals, the delineation would be as follows:

2026 alfalfa: 10/16/2014 to 9/15/2016. This includes the establishment year (2015) and five biomass harvests in 2016.
2017 alfalfa: 9/16/2016 to 9/15/2017. This includes five biomass harvests.

Alfalfa crop intervals will continue in the same manner until the crop is terminated, either mechanically with heavy plowing or a mix of tillage and chemical applications. In the example above, the alfalfa stand produced 25 biomass harvests in the span of six years.

2.8 Case 8: Fields with rotations that include crops not part of Field to Market’s program

For FP v5, the system will fill the crop rotation sequence for a given field from 2008 to the latest available year using the Cropland Data Layer (Boryan et al. 2011). This will bring crops that are not yet part of Field to Market’s programs. We will use the crop rotation to model soil carbon stock changes and soil erosion; however, the FP v5 cannot produce output for energy use and GHG emissions for those crop intervals.

The following figure demonstrates a crop interval with a non-FTM crop.

Figure 8: Case 8, a rotation with a non-FTM crop

3 Area planted or harvested for Field to Market crops

In 2024, USDA NASS surveys reported the following area planted (area harvested for alfalfa) for the crops in Field to Market’s program. The Fieldprint Platform can be used to quantify the impact of approximately 267 million acres (108 million hectares) of United States cropland.

Table 1: Crops and national planted area that can be assessed with the Fieldprint Platform

Geographic Level	Crop	Data Category	Acres (2024)	Hectares (2024)
US Total	Barley	Barley - Acres Planted	2,373,000	960,340
US Total	Beans	Beans, Dry Edible, (Excl Chickpeas) - Acres Planted	1,533,000	620,397
US Total	Chickpeas	Chickpeas - Acres Planted	502,000	203,157
US Total	Corn	Corn - Acres Planted	90,594,000	36,662,890
US Total	Cotton	Cotton - Acres Planted	11,174,000	4,522,056
US Total	Peanuts	Peanuts - Acres Planted	1,801,000	728,855
US Total	Peas	Peas, Dry Edible - Acres Planted	976,000	394,982
US Total	Potatoes	Potatoes - Acres Planted	930,000	376,366
US Total	Rice	Rice - Acres Planted	2,910,000	1,177,661
US Total	Sorghum	Sorghum - Acres Planted	6,300,000	2,549,575
US Total	Soybeans	Soybeans - Acres Planted	87,050,000	35,228,652
US Total	Sugarbeets	Sugarbeets - Acres Planted	1,104,300	446,904
US Total	Wheat	Wheat - Acres Planted	46,079,000	18,647,916
US Total	Hay	Hay, Alfalfa - Acres Harvested	14,612,000	5,913,395
US Total	All Crops Above	All Above	267,938,300	108,433,144

4 Global Warming Potential factors

In FP v5, individual growers and project administrators aggregating grower data will be able to select the Global Warming Potential (GWP) factor that meets their needs.

Biogenic CO₂, excluding emissions from direct land use change and soil carbon stock changes, can be manually removed by users, since those emissions are part of the natural carbon cycle.

The default GWP will be AR6 with 100-yr horizon:

Table 2: Default Global Warming Potential Factors

Assessment Report (AR)	Time Horizon	Gas	Global Warming Potential
AR6	100-yr	CO2_fossil	1.0
AR6	100-yr	CO2_biogenic	1.0
AR6	100-yr	CH4_biogenic	27.0
AR6	100-yr	CH4_fossil	29.8
AR6	100-yr	N2O	273.0
AR6	100-yr	NF3	17400.0
AR6	100-yr	SF6	25200.0

Including the default factors shown above, the following options will be available for 100- and 20-yr horizons:

Table 3: IPCC Assessment Reports Available in FP v5

Assessment Report (AR)
AR6
AR5 (with climate-carbon feedback)
AR5 (without climate-carbon feedback)
AR4

The complete list of GWP factors is shown in the table below.

Table 4: Global Warming Potential Factors Available for Selection in FP v5

Assessment Report (AR)	Time Horizon	Gas	Global Warming Potential
AR6	100-yr	CO2_fossil	1.0
AR6	100-yr	CO2_biogenic	1.0
AR6	100-yr	CH4_biogenic	27.0
AR6	100-yr	CH4_fossil	29.8
AR6	100-yr	N2O	273.0
AR6	100-yr	NF3	17400.0
AR6	100-yr	SF6	25200.0
AR5 (with climate-carbon feedback)	100-yr	CO2_fossil	1.0
AR5 (with climate-carbon feedback)	100-yr	CO2_biogenic	1.0
AR5 (with climate-carbon feedback)	100-yr	CH4_biogenic	34.0
AR5 (with climate-carbon feedback)	100-yr	CH4_fossil	36.0
AR5 (with climate-carbon feedback)	100-yr	N2O	298.0
AR5 (with climate-carbon feedback)	100-yr	NF3	17885.0
AR5 (with climate-carbon feedback)	100-yr	SF6	26087.0
AR5 (without climate-carbon feedback)	100-yr	CO2_fossil	1.0
AR5 (without climate-carbon feedback)	100-yr	CO2_biogenic	1.0
AR5 (without climate-carbon feedback)	100-yr	CH4_biogenic	28.0
AR5 (without climate-carbon feedback)	100-yr	CH4_fossil	30.0
AR5 (without climate-carbon feedback)	100-yr	N2O	265.0
AR5 (without climate-carbon feedback)	100-yr	NF3	16100.0
AR5 (without climate-carbon feedback)	100-yr	SF6	23500.0
AR4	100-yr	CO2_fossil	1.0
AR4	100-yr	CO2_biogenic	1.0
AR4	100-yr	CH4_biogenic	25.0
AR4	100-yr	CH4_fossil	25.0
AR4	100-yr	N2O	298.0
AR4	100-yr	NF3	17200.0
AR4	100-yr	SF6	22800.0
AR6	20-yr	CO2_fossil	1.0
AR6	20-yr	CO2_biogenic	1.0
AR6	20-yr	CH4_biogenic	79.7
AR6	20-yr	CH4_fossil	82.5
AR6	20-yr	N2O	273.0
AR6	20-yr	NF3	13400.0
AR6	20-yr	SF6	18300.0
AR5 (with climate-carbon feedback)	20-yr	CO2_fossil	1.0
AR5 (with climate-carbon feedback)	20-yr	CO2_biogenic	1.0
AR5 (with climate-carbon feedback)	20-yr	CH4_biogenic	86.0
AR5 (with climate-carbon feedback)	20-yr	CH4_fossil	87.0
AR5 (with climate-carbon feedback)	20-yr	N2O	268.0
AR5 (with climate-carbon feedback)	20-yr	NF3	13008.0
AR5 (with climate-carbon feedback)	20-yr	SF6	17783.0
AR5 (without climate-carbon feedback)	20-yr	CO2_fossil	1.0
AR5 (without climate-carbon feedback)	20-yr	CO2_biogenic	1.0
AR5 (without climate-carbon feedback)	20-yr	CH4_biogenic	84.0
AR5 (without climate-carbon feedback)	20-yr	CH4_fossil	85.0
AR5 (without climate-carbon feedback)	20-yr	N2O	264.0
AR5 (without climate-carbon feedback)	20-yr	NF3	12800.0
AR5 (without climate-carbon feedback)	20-yr	SF6	17500.0
AR4	20-yr	CO2_fossil	1.0
AR4	20-yr	CO2_biogenic	1.0
AR4	20-yr	CH4_biogenic	72.0
AR4	20-yr	CH4_fossil	72.0
AR4	20-yr	N2O	289.0
AR4	20-yr	NF3	12300.0
AR4	20-yr	SF6	16300.0

5 Impact factors

Impact factors represent the cumulative energy demand (CED) and associated GHG emissions with manufacturing a unit of output for a given category, such as a kg of fertilizer, a MWh of electricity, a gallon of fuel, etc.

The impact factors are as follows.

5.1 Electricity

Due to a data restriction, Field to Market is unable to post the electricity impact factors here; however, we are able to present the data to interested reviewers.

In broad terms, electricity factors represent 27 energy grids in the United States, and include the cumulative energy demand and associated GHG emissions for electricity generation, transmission, and distribution. The electric grid selected is tied to the field location entered by a user.

In the FP v5, electricity impact factors are used by two farming operations:

Irrigation operations
Crop drying

5.2 Fuels

For FP v5, the fuel options have been reduced and tied to specific operations. We have also incorporated more reliable data and separated the impact factors by on-farm combustion and upstream manufacturing impacts for the procured fuel.

The FP v5 won’t include biogenic CO₂ from biofuels in the final estimate for the GHG Emissions metric, as permitted by the Greenhouse Gas Reporting Program (GHGRP).

The fuels are linked to operations as follows:

Irrigation pumping
- Diesel (ag equipment)
- Gasoline
- LPG
- Natural gas
Crop drying
- Diesel (ag equipment)
- Gasoline
- LPG
- Natural gas
Crop transportation
- Biodiesel (on-road heavy-duty truck). This is based on B100.
- Diesel (on-road medium-heavy duty truck)
Manure transportation
- Diesel (on-road medium-heavy duty truck)
Field operations
- Diesel (ag equipment)
Agricultural input transportation
- Diesel (on-road medium-heavy duty truck)

5.2.1 Energy use

The energy use impact factors are explained below.

System Boundary: This enables the disaggregation of upstream and combustion factors. Combustion factors are attributed to On-Farm Mechanical or Post-harvest boundaries. The combustion factors for On-Farm Mechanical or Post-harvest are the same.
Source Category: It classifies the energy use to indicate whether it is associated with production of fuels (upstream), mobile machinery (field equipment, crop transportation, manure transportation), and stationary machinery (irrigation pumps and crop dryers).
Source: A given fuel.
MJ: Impact factor of megajoules per unit.
Unit (LHV): Expected unit to use the MJ impact factor.

The energy use impact factors are given below.

Table 5: Energy use impact factors for fuels

System Boundary	Source Category	Source	MJ	Unit (LHV)
Upstream	Energy use associated with production of fuels	Biodiesel (on-road heavy-duty truck)	74.34	gallon
Upstream	Energy use associated with production of fuels	Diesel (ag equipment)	15.94	gallon
Upstream	Energy use associated with production of fuels	Diesel (on-road medium-heavy duty truck)	15.94	gallon
Upstream	Energy use associated with production of fuels	Gasoline	26.93	gallon
Upstream	Energy use associated with production of fuels	LPG	12.70	gallon
Upstream	Energy use associated with production of fuels	Natural gas	0.11	SCF
Upstream	Energy use associated with transportation of agricultural inputs	Diesel (on-road medium-heavy duty truck)	160.88	gallon
Post-Harvest	Energy use associated with mobile machinery	Diesel (on-road medium-heavy duty truck)	144.94	gallon
Post-Harvest	Energy use associated with mobile machinery	Biodiesel (on-road heavy-duty truck)	126.13	gallon
Post-Harvest	Energy use associated with stationary machinery	Diesel (ag equipment)	144.94	gallon
Post-Harvest	Energy use associated with stationary machinery	Gasoline	118.29	gallon
Post-Harvest	Energy use associated with stationary machinery	LPG	88.89	gallon
Post-Harvest	Energy use associated with stationary machinery	Natural gas	1.08	SCF
On-Farm Mechanical	Energy use associated with mobile machinery	Diesel (ag equipment)	144.94	gallon
On-Farm Mechanical	Energy use associated with mobile machinery	Diesel (on-road medium-heavy duty truck)	144.94	gallon
On-Farm Mechanical	Energy use associated with mobile machinery	Biodiesel (on-road heavy-duty truck)	126.13	gallon
On-Farm Mechanical	Energy use associated with stationary machinery	Diesel (ag equipment)	144.94	gallon
On-Farm Mechanical	Energy use associated with stationary machinery	Gasoline	118.29	gallon
On-Farm Mechanical	Energy use associated with stationary machinery	LPG	88.89	gallon
On-Farm Mechanical	Energy use associated with stationary machinery	Natural gas	1.08	SCF

5.2.2 GHG emissions

The GHG emissions impact factors are explained below.

System Boundary: This enables the disaggregation of upstream and combustion factors. Combustion factors are attributed to On-Farm Mechanical or Post-harvest boundaries. The combustion factors for On-Farm Mechanical or Post-harvest are the same.
Source Category: It classifies the GHG emissions to indicate whether it is associated with production of fuels (upstream), mobile machinery (field equipment, crop transportation, manure transportation), and stationary machinery (irrigation pumps and crop dryers).
Source: A given fuel.
CO2_fossil: Impact factor for fossil CO₂ in kg of gas per unit.
CO2_biogenic: Impact factor for biogenic CO₂ in kg of gas per unit. It is important to note that the Greenhouse Gas Reporting Program (GHGRP) allows for the non-inclusion of biogenic CO₂ from inventories, except for those CO₂ emissions from direct land use change and soil carbon stock changes.
CH4_fossil: Impact factor of fossil CH₄ in kg of gas per unit.
CH4_biogenic: Impact factor of biogenic CH₄ in kg of gas per unit.
N2O: Impact factor for N₂O in kg of gas per unit.
Unit (LHV): Expected unit to use the impact factors for each GHG gas.

The GHG emission impact factors are given below.

Table 6: GHG emissions impact factors for fuels

System Boundary	Source Category	Source	CO2_fossil	CO2_biogenic	CH4_fossil	CH4_biogenic	N2O	Unit (LHV)
Upstream	GHG emissions associated with production of fuels	Biodiesel (on-road heavy-duty truck)	2.24	0.00	0.0037037	0.00e+00	0.0010976	gallon
Upstream	GHG emissions associated with production of fuels	Diesel (ag equipment)	0.97	0.00	0.0023290	0.00e+00	0.0000195	gallon
Upstream	GHG emissions associated with production of fuels	Diesel (on-road medium-heavy duty truck)	0.97	0.00	0.0023290	0.00e+00	0.0000195	gallon
Upstream	GHG emissions associated with production of fuels	Gasoline	1.68	0.00	0.0046768	0.00e+00	0.0003232	gallon
Upstream	GHG emissions associated with production of fuels	LPG	0.91	0.00	0.0025506	0.00e+00	0.0000152	gallon
Upstream	GHG emissions associated with production of fuels	Natural gas	0.01	0.00	0.0001952	0.00e+00	0.0000013	SCF
Upstream	GHG emissions associated with transportation of agricultural inputs	Diesel (on-road medium-heavy duty truck)	11.18	0.00	0.0032384	0.00e+00	0.0003245	gallon
Post-Harvest	GHG emissions associated with transportation of crop production	Biodiesel (on-road heavy-duty truck)	0.00	9.48	0.0000000	9.95e-05	0.0000142	gallon
Post-Harvest	GHG emissions associated with transportation of crop production	Diesel (on-road medium-heavy duty truck)	10.20	0.00	0.0009094	0.00e+00	0.0003050	gallon
Post-Harvest	GHG emissions associated with stationary machinery	Diesel (ag equipment)	10.20	0.00	0.0012692	0.00e+00	0.0010693	gallon
Post-Harvest	GHG emissions associated with stationary machinery	Gasoline	8.72	0.00	0.0003727	0.00e+00	0.0000745	gallon
Post-Harvest	GHG emissions associated with stationary machinery	LPG	5.64	0.00	0.0002744	0.00e+00	0.0000549	gallon
Post-Harvest	GHG emissions associated with stationary machinery	Natural gas	0.05	0.00	0.0000010	0.00e+00	0.0000001	SCF
On-Farm Mechanical	GHG emissions associated with mobile machinery	Diesel (ag equipment)	10.20	0.00	0.0012692	0.00e+00	0.0010693	gallon
On-Farm Mechanical	GHG emissions associated with transportation of crop production	Biodiesel (on-road heavy-duty truck)	0.00	9.48	0.0000000	9.95e-05	0.0000142	gallon
On-Farm Mechanical	GHG emissions associated with transportation of crop production	Diesel (on-road medium-heavy duty truck)	10.20	0.00	0.0009094	0.00e+00	0.0003050	gallon
On-Farm Mechanical	GHG emissions associated with stationary machinery	Diesel (ag equipment)	10.20	0.00	0.0012692	0.00e+00	0.0010693	gallon
On-Farm Mechanical	GHG emissions associated with stationary machinery	Gasoline	8.72	0.00	0.0003727	0.00e+00	0.0000745	gallon
On-Farm Mechanical	GHG emissions associated with stationary machinery	LPG	5.64	0.00	0.0002744	0.00e+00	0.0000549	gallon
On-Farm Mechanical	GHG emissions associated with stationary machinery	Natural gas	0.05	0.00	0.0000010	0.00e+00	0.0000001	SCF
On-Farm Mechanical	GHG emissions associated with mobile machinery	Diesel (on-road medium-heavy duty truck)	10.20	0.00	0.0009094	0.00e+00	0.0003050	gallon

5.3 Fertilizers

For FP v5, fertilizers have been enhanced with more reliable data and better options compared to FP v4.2. The impact factors for fertilizers in this section are associated with the energy use and GHG emissions from the manufacturing process, and these impacts are attributed to the Upstream boundary. The soil GHG emissions related to the use of nitrogen fertilizers, lime, and urea are accounted by methods from Ogle et al. (2024).

5.3.1 Energy use

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 fertilizers.
Source Detail: A given fertilizer option.
MJ: Impact factor of megajoules per unit.
Unit: Expected unit to use the MJ impact factor.

The energy use impact factors are given below.

Table 7: Energy use impact factors for fertilizers

System Boundary	Source Category	Source Detail	MJ	Unit
Upstream	Energy use associated with production of fertilizers	Ammonia (aqueous)	0.01	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Ammonia (aqueous) (green ammonia)	0.01	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Ammonia (conventional)	37.75	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Ammonia (green)	43.27	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Ammonium nitrate	14.70	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Ammonium nitrate (green ammonia)	15.87	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Ammonium sulfate	18.04	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Ammonium sulfate (green ammonia)	19.47	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Calcium ammonium nitrate	13.66	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Calcium ammonium nitrate (green ammonia)	14.48	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Diammonium phosphate	23.91	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Diammonium phosphate (green ammonia)	25.14	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Gypsum	0.00	kg fertilizer
Upstream	Energy use associated with production of fertilizers	K2O	8.73	kg K2O
Upstream	Energy use associated with production of fertilizers	Lime (calcitic)	0.03	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Lime (dolomitic)	0.03	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Micronutrient (boron)	12.15	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Micronutrient (manganese)	28.84	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Micronutrient (zinc)	32.32	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Monoammonium phosphate	22.41	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Monoammonium phosphate (green ammonia)	23.16	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Potash (MOP)	5.24	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Potassium nitrate	14.94	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Sulfur	6.64	kg fertilizer
Upstream	Energy use associated with production of fertilizers	US average nitrogen fertilizer	55.42	kg N
Upstream	Energy use associated with production of fertilizers	US average phosphate fertilizer	48.18	kg P2O5
Upstream	Energy use associated with production of fertilizers	Urea	28.22	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Urea (green ammonia)	39.74	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Urea ammonium nitrate	53.58	kg fertilizer
Upstream	Energy use associated with production of fertilizers	Urea ammonium nitrate (green ammonia)	67.82	kg fertilizer

5.3.2 GHG emissions

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 fertilizers.
Source Detail: A given fertilizer 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.

The GHG emission impact factors are given below.

Table 8: GHG emissions impact factors for fertilizers

System Boundary	Source Category	Source Detail	CO2_fossil	CH4_fossil	N2O	Unit
Upstream	GHG emissions associated with production of fertilizers	Ammonia (aqueous)	0.54	0.0015586	0.0000095	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Ammonia (aqueous) (green ammonia)	0.03	0.0000610	0.0000006	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Ammonia (conventional)	2.16	0.0062343	0.0000381	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Ammonia (green)	0.05	0.0001044	0.0000010	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Ammonium nitrate	0.24	0.0061190	0.0037640	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Ammonium nitrate (green ammonia)	0.20	0.0048329	0.0037565	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Ammonium sulfate	0.14	0.0016980	0.0000109	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Ammonium sulfate (green ammonia)	0.10	0.0001405	0.0000019	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Calcium ammonium nitrate	0.45	0.0022787	0.0000544	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Calcium ammonium nitrate (green ammonia)	0.14	0.0013677	0.0000489	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Diammonium phosphate	0.84	0.0029129	0.0000262	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Diammonium phosphate (green ammonia)	0.80	0.0015764	0.0000185	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Gypsum	0.03	0.0000462	0.0000002	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	K2O	0.45	0.0009157	0.0000068	kg K2O
Upstream	GHG emissions associated with production of fertilizers	Lime (calcitic)	0.01	0.0000114	0.0000000	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Lime (dolomitic)	0.01	0.0000114	0.0000000	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Micronutrient (boron)	0.47	0.0009837	0.0001148	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Micronutrient (manganese)	1.78	0.0029312	0.0002378	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Micronutrient (zinc)	1.59	0.0041184	0.0002050	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Monoammonium phosphate	0.89	0.0026207	0.0000257	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Monoammonium phosphate (green ammonia)	0.87	0.0018060	0.0000211	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Potash (MOP)	0.27	0.0005494	0.0000041	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Potassium nitrate	0.42	0.0014692	0.0106350	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Sulfur	0.33	0.0010044	0.0000071	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	US average nitrogen fertilizer	0.76	0.0111846	0.0026369	kg N
Upstream	GHG emissions associated with production of fertilizers	US average phosphate fertilizer	1.80	0.0057498	0.0000540	kg P2O5
Upstream	GHG emissions associated with production of fertilizers	Urea	-0.21	0.0046282	0.0000293	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Urea (green ammonia)	-0.30	0.0012093	0.0000087	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Urea ammonium nitrate	0.68	0.0138985	0.0054250	kg fertilizer
Upstream	GHG emissions associated with production of fertilizers	Urea ammonium nitrate (green ammonia)	0.53	0.0083196	0.0053933	kg fertilizer

5.4 Pesticides

Impact factors for pesticides require a more complex implementation. The FP v4.2 allowed users to indicate the number of applied pesticide 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 has a pesticide rate assumption for each product category. For example, if a grower indicates that four insecticide products were applied during the season, and the rate assumption for insecticides is 0.05 lb / acre per application per crop interval, then the total quantity of pesticide active ingredients applied would be 4 X 0.05 = 0.2 lb of insecticides for a given crop interval.

For FP v5, we will continue with the approach of asking users for the number of pesticide products applied. We provide an update for 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.

We include inoculants here to avoid creating a new data storage category.

5.4.1 Energy use

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 pesticides.
Source Detail: A given pesticide option.
MJ: Impact factor of megajoules per unit.
Unit: Expected unit to use the MJ impact factor.

The energy use impact factors are given below.

Table 9: Energy use impact factors for pesticides

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

5.4.2 GHG emissions

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 pesticides.
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.

The GHG emission impact factors are given below.

Table 10: GHG emissions impact factors for pesticides

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

5.4.3 Pesticide 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 crops. This could change if users request that inoculants should be available to all crops.
Herbicides (sulfuric acid) are only applicable to potatoes.

The rates for each crop are as follows, in units of kg active ingredient / ha:

Table 11: Assumptions for pesticide rates by crop

Crop	Fumigants	Fungicides	Growth Regulators	Herbicides	Inoculant	Insecticides	Seed Treatment	Herbicides (sulfuric acid)
Alfalfa	32.48	0.10	0.00	0.43	7.3	0.05	0.05	0
Barley	32.48	0.09	0.26	0.17	0.0	0.06	0.05	0
Chickpeas (garbanzos)	32.48	0.10	0.00	1.10	7.3	0.04	0.05	0
Corn (grain)	32.48	0.08	0.00	0.33	0.0	0.06	0.05	0
Corn (silage)	32.48	0.08	0.00	0.33	0.0	0.06	0.05	0
Cotton	32.48	0.14	0.38	0.58	0.0	0.09	0.05	0
Dry Beans	32.48	0.10	0.00	1.00	7.3	0.04	0.05	0
Dry Peas	32.48	0.10	0.00	1.10	7.3	0.04	0.05	0
Fava Beans	32.48	0.10	0.00	1.00	7.3	0.04	0.05	0
Lentils	32.48	0.10	0.00	1.00	7.3	0.04	0.05	0
Lupin	32.48	0.10	0.00	1.00	7.3	0.04	0.05	0
Peanuts	32.79	0.19	0.07	0.35	7.3	0.23	0.05	0
Potatoes	180.48	0.19	2.25	0.54	0.0	0.08	0.05	296
Rice	32.48	0.16	0.07	0.41	0.0	0.11	0.05	0
Sorghum	32.48	0.08	0.07	0.86	0.0	0.35	0.05	0
Soybeans	32.48	0.10	0.00	0.43	7.3	0.05	0.05	0
Sugar beets	108.53	0.30	0.07	0.06	0.0	1.37	0.05	0
Wheat (durum)	32.48	0.10	0.11	0.10	0.0	0.03	0.05	0
Wheat (spring)	32.48	0.10	0.11	0.10	0.0	0.03	0.05	0
Wheat (winter)	32.48	0.10	0.11	0.10	0.0	0.03	0.05	0

5.5 Seed production

Seeds are another agricultural input for which we need to estimate the associated energy use and GHG emissions for their manufacturing. Most seed production is conducted by the private industry, and, to our knowledge, no recent source of seed production data by crop exists.

To update the impact factors for seed production, we conducted an analysis with national level assumptions for the production of a crop, which required filling in the inputs listed in Foreground Activity Data. The Commodity Flow Survey from BLS (2017) provided an estimate of transportation miles, while USDA NASS surveys and the census of agriculture provided some of the crop production information. Other inputs were filled using data published in journal articles and enterprise crop budgets created by Universities.

5.5.1 Energy use

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 seeds.
Source Detail: A given crop seed option.
MJ: Impact factor of megajoules per unit.
Unit: Expected unit to use the MJ impact factor.

The energy use impact factors are given below.

Table 12: Energy use impact factors for seed production

System Boundary	Source Category	Source Detail	MJ	Unit
Upstream	Energy use associated with production of seed	Seed \| Alfalfa	49.9	kg seed
Upstream	Energy use associated with production of seed	Seed \| Barley	13.4	kg seed
Upstream	Energy use associated with production of seed	Seed \| Chickpeas (garbanzos)	9.0	kg seed
Upstream	Energy use associated with production of seed	Seed \| Corn (grain)	5.2	kg seed
Upstream	Energy use associated with production of seed	Seed \| Corn (silage)	5.2	kg seed
Upstream	Energy use associated with production of seed	Seed \| Cotton	37.6	kg seed
Upstream	Energy use associated with production of seed	Seed \| Dry Beans	22.6	kg seed
Upstream	Energy use associated with production of seed	Seed \| Dry Peas	14.6	kg seed
Upstream	Energy use associated with production of seed	Seed \| Fava Beans	4.6	kg seed
Upstream	Energy use associated with production of seed	Seed \| Lentils	8.3	kg seed
Upstream	Energy use associated with production of seed	Seed \| Lupin	33.3	kg seed
Upstream	Energy use associated with production of seed	Seed \| Peanuts	8.4	kg seed
Upstream	Energy use associated with production of seed	Seed \| Potatoes	1.6	kg seed
Upstream	Energy use associated with production of seed	Seed \| Rice	6.4	kg seed
Upstream	Energy use associated with production of seed	Seed \| Sorghum	25.6	kg seed
Upstream	Energy use associated with production of seed	Seed \| Soybeans	9.0	kg seed
Upstream	Energy use associated with production of seed	Seed \| Sugar beets	32.6	kg seed
Upstream	Energy use associated with production of seed	Seed \| Wheat (durum)	20.2	kg seed
Upstream	Energy use associated with production of seed	Seed \| Wheat (spring)	20.2	kg seed
Upstream	Energy use associated with production of seed	Seed \| Wheat (winter)	20.2	kg seed

5.5.2 GHG emissions

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 seeds.
Source Detail: A given crop seed 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.

The GHG emission impact factors are given below.

Table 13: GHG emissions impact factors for seed production

System Boundary	Source Category	Source Detail	CO2_fossil	CH4_fossil	CH4_biogenic	N2O	Unit
Upstream	GHG emissions associated with production of seed	Seed \| Alfalfa	1.12	0.0009169	0.0000000	0.0005078	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Barley	0.30	0.0002104	0.0000000	0.0007847	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Chickpeas (garbanzos)	0.27	0.0001453	0.0000000	0.0004184	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Corn (grain)	0.16	0.0000870	0.0000000	0.0006974	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Corn (silage)	0.16	0.0000870	0.0000000	0.0006974	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Cotton	0.78	0.0008796	0.0000000	0.0001907	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Dry Beans	0.56	0.0003781	0.0000000	0.0003397	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Dry Peas	0.35	0.0002500	0.0000000	0.0001253	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Fava Beans	0.12	0.0000794	0.0000000	0.0001048	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Lentils	0.28	0.0001261	0.0000000	0.0001416	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Lupin	0.83	0.0005924	0.0000000	0.0001643	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Peanuts	0.22	0.0001892	0.0000000	0.0002691	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Potatoes	0.03	0.0000195	0.0000000	0.0000083	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Rice	0.15	0.0001194	0.0593208	0.0003496	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Sorghum	0.59	0.0004656	0.0000000	0.0002721	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Soybeans	0.29	0.0001805	0.0000000	0.0001454	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Sugar beets	0.87	0.0005382	0.0000000	0.0000376	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Wheat (durum)	0.49	0.0003382	0.0000000	0.0004094	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Wheat (spring)	0.49	0.0003382	0.0000000	0.0004094	kg seed
Upstream	GHG emissions associated with production of seed	Seed \| Wheat (winter)	0.49	0.0003382	0.0000000	0.0004094	kg seed

6 Explaining disaggregation of energy use and GHG emission sources

One of the significant limitations of FP v4.2 is that the sources of energy use and GHG emission are too broad and do not provide enough information to separate upstream sources from on-farm sources. The lack of detail also makes it hard to compare results from the FP v4.2 with other sustainability platforms.

In addition, many Field to Market members have requested alignment with standard-setter organizations. There were three major components to consider for alignment:

Greenhouse gas separation: The FP v4.2 aggregates all emissions into CO₂e starting from the reference data, which prevents the separation into the main gases of interest (CO₂, CH₄, N₂O). This was a key component of why this major revision was needed.
Addition of a method to estimate land use change emissions: We are confident the method we are proposing is a transparent and conservative choice to start estimating land use change in the FP. The method will estimate conversions from deforestation and grasslands to croplands.
Addition of a method to estimate soil carbon stock changes: With the implementation of SWAT+ (Soil and Water Assessment Tool Plus), it is now possible to estimate annual soil carbon stock changes with a process-based (Tier 3) model.

Figure 2 shows the major disaggregated components within the FP v5 system boundary. In this section, we show the complete disaggregation for the Energy Use and GHG Emissions metrics. Of course, the metrics will have final, aggregated scores, which are shown in Table 2.

6.1 Energy Use metric

The Energy Use metric would be disaggregated as follows.

Three system boundaries:

Upstream
On-Farm Mechanical
Post-Harvest

Eight source categories:

Energy use associated with electricity generation and distribution
Energy use associated with production of fuels
Energy use associated with transportation of agricultural inputs
Energy use associated with mobile machinery
Energy use associated with stationary machinery
Energy use associated with production of fertilizers
Energy use associated with production of pesticides
Energy use associated with production of seed

The complete set of sources for Energy Use is shown below. For an analysis, the itemization would be reduced, since a Fieldprint Analysis would include one subregion for the energy grid, two or three fuels associated with stationary and mobile machinery, two or three sources of fertilizers, one source of crop seed, and so on.

Table 14: Sources of Energy Use in FP v5

System Boundary	Source Category	Source Detail
Upstream	Energy use associated with electricity generation and distribution	Crop Drying \| Electricity (grid)
Upstream	Energy use associated with electricity generation and distribution	Irrigation Operations \| Electricity (grid)
Upstream	Energy use associated with production of fertilizers	Ammonia (aqueous)
Upstream	Energy use associated with production of fertilizers	Ammonia (aqueous) (green ammonia)
Upstream	Energy use associated with production of fertilizers	Ammonia (conventional)
Upstream	Energy use associated with production of fertilizers	Ammonia (green)
Upstream	Energy use associated with production of fertilizers	Ammonium nitrate
Upstream	Energy use associated with production of fertilizers	Ammonium nitrate (green ammonia)
Upstream	Energy use associated with production of fertilizers	Ammonium sulfate
Upstream	Energy use associated with production of fertilizers	Ammonium sulfate (green ammonia)
Upstream	Energy use associated with production of fertilizers	Calcium ammonium nitrate
Upstream	Energy use associated with production of fertilizers	Calcium ammonium nitrate (green ammonia)
Upstream	Energy use associated with production of fertilizers	Diammonium phosphate
Upstream	Energy use associated with production of fertilizers	Diammonium phosphate (green ammonia)
Upstream	Energy use associated with production of fertilizers	Gypsum
Upstream	Energy use associated with production of fertilizers	K2O
Upstream	Energy use associated with production of fertilizers	Lime (calcitic)
Upstream	Energy use associated with production of fertilizers	Lime (dolomitic)
Upstream	Energy use associated with production of fertilizers	Micronutrient (boron)
Upstream	Energy use associated with production of fertilizers	Micronutrient (manganese)
Upstream	Energy use associated with production of fertilizers	Micronutrient (zinc)
Upstream	Energy use associated with production of fertilizers	Monoammonium phosphate
Upstream	Energy use associated with production of fertilizers	Monoammonium phosphate (green ammonia)
Upstream	Energy use associated with production of fertilizers	Potash (MOP)
Upstream	Energy use associated with production of fertilizers	Potassium nitrate
Upstream	Energy use associated with production of fertilizers	Sulfur
Upstream	Energy use associated with production of fertilizers	US average nitrogen fertilizer
Upstream	Energy use associated with production of fertilizers	US average phosphate fertilizer
Upstream	Energy use associated with production of fertilizers	Urea
Upstream	Energy use associated with production of fertilizers	Urea (green ammonia)
Upstream	Energy use associated with production of fertilizers	Urea ammonium nitrate
Upstream	Energy use associated with production of fertilizers	Urea ammonium nitrate (green ammonia)
Upstream	Energy use associated with production of fuels	Crop Drying \| Diesel (ag equipment)
Upstream	Energy use associated with production of fuels	Crop Drying \| Gasoline
Upstream	Energy use associated with production of fuels	Crop Drying \| LPG
Upstream	Energy use associated with production of fuels	Crop Drying \| Natural gas
Upstream	Energy use associated with production of fuels	Crop Transportation \| Biodiesel (on-road heavy-duty truck)
Upstream	Energy use associated with production of fuels	Crop Transportation \| Diesel (on-road medium-heavy duty truck)
Upstream	Energy use associated with production of fuels	Field Operations \| Diesel (ag equipment)
Upstream	Energy use associated with production of fuels	Irrigation Operations \| Diesel (ag equipment)
Upstream	Energy use associated with production of fuels	Irrigation Operations \| Gasoline
Upstream	Energy use associated with production of fuels	Irrigation Operations \| LPG
Upstream	Energy use associated with production of fuels	Irrigation Operations \| Natural gas
Upstream	Energy use associated with production of fuels	Manure Transportation \| Diesel (on-road medium-heavy duty truck)
Upstream	Energy use associated with production of pesticides	Fumigants
Upstream	Energy use associated with production of pesticides	Fungicides
Upstream	Energy use associated with production of pesticides	Growth Regulators
Upstream	Energy use associated with production of pesticides	Herbicides
Upstream	Energy use associated with production of pesticides	Herbicides (sulfuric acid)
Upstream	Energy use associated with production of pesticides	Inoculant
Upstream	Energy use associated with production of pesticides	Insecticides
Upstream	Energy use associated with production of pesticides	Seed Treatment
Upstream	Energy use associated with production of seed	Seed \| Alfalfa
Upstream	Energy use associated with production of seed	Seed \| Barley
Upstream	Energy use associated with production of seed	Seed \| Chickpeas (garbanzos)
Upstream	Energy use associated with production of seed	Seed \| Corn (grain)
Upstream	Energy use associated with production of seed	Seed \| Corn (silage)
Upstream	Energy use associated with production of seed	Seed \| Cotton
Upstream	Energy use associated with production of seed	Seed \| Dry Beans
Upstream	Energy use associated with production of seed	Seed \| Dry Peas
Upstream	Energy use associated with production of seed	Seed \| Fava Beans
Upstream	Energy use associated with production of seed	Seed \| Lentils
Upstream	Energy use associated with production of seed	Seed \| Lupin
Upstream	Energy use associated with production of seed	Seed \| Peanuts
Upstream	Energy use associated with production of seed	Seed \| Potatoes
Upstream	Energy use associated with production of seed	Seed \| Rice
Upstream	Energy use associated with transportation of agricultural inputs	Agricultural Input Transportation \| Diesel (on-road medium-heavy duty truck)
Post-Harvest	Energy use associated with mobile machinery	Crop Transportation \| Biodiesel (on-road heavy-duty truck)
Post-Harvest	Energy use associated with mobile machinery	Crop Transportation \| Diesel (on-road medium-heavy duty truck)
Post-Harvest	Energy use associated with stationary machinery	Crop Drying \| Diesel (ag equipment)
Post-Harvest	Energy use associated with stationary machinery	Crop Drying \| Gasoline
Post-Harvest	Energy use associated with stationary machinery	Crop Drying \| LPG
Post-Harvest	Energy use associated with stationary machinery	Crop Drying \| Natural gas
On-Farm Mechanical	Energy use associated with mobile machinery	Crop Transportation \| Biodiesel (on-road heavy-duty truck)
On-Farm Mechanical	Energy use associated with mobile machinery	Crop Transportation \| Diesel (on-road medium-heavy duty truck)
On-Farm Mechanical	Energy use associated with mobile machinery	Field Operations \| Diesel (ag equipment)
On-Farm Mechanical	Energy use associated with mobile machinery	Manure Transportation \| Diesel (on-road medium-heavy duty truck)
On-Farm Mechanical	Energy use associated with stationary machinery	Crop Drying \| Diesel (ag equipment)
On-Farm Mechanical	Energy use associated with stationary machinery	Crop Drying \| Gasoline
On-Farm Mechanical	Energy use associated with stationary machinery	Crop Drying \| LPG
On-Farm Mechanical	Energy use associated with stationary machinery	Crop Drying \| Natural gas
On-Farm Mechanical	Energy use associated with stationary machinery	Irrigation Operations \| Diesel (ag equipment)
On-Farm Mechanical	Energy use associated with stationary machinery	Irrigation Operations \| Gasoline
On-Farm Mechanical	Energy use associated with stationary machinery	Irrigation Operations \| LPG
On-Farm Mechanical	Energy use associated with stationary machinery	Irrigation Operations \| Natural gas

6.2 GHG Emissions metric

In a similar fashion, the GHG Emissions metric would be disaggregated as follows.

Four system boundaries:

Upstream
On-Farm Mechanical
Post-Harvest
On-Farm Non-Mechanical Sources and Sinks

Seventeen source categories:

GHG emissions associated with electricity generation and distribution
GHG emissions associated with production of fuels
GHG emissions associated with transportation of agricultural inputs
GHG emissions associated with mobile machinery
GHG emissions associated with transportation of crop production
GHG emissions associated with stationary machinery
GHG emissions associated with production of fertilizers
GHG emissions associated with production of pesticides
GHG emissions associated with production of seed
CH₄ flux from non-flooded soils
CO₂ from carbonate lime applications to soils
CO₂ from urea fertilizer applications
Non-CO₂ emissions from biomass burning
Direct land use change emissions
Soil N₂O
CH₄ emissions from flooded rice cultivation
Soil carbon stock changes

The complete set of sources for GHG Emissions is shown below. For an analysis, the itemization would be reduced, since a Fieldprint Analysis would include one subregion for the energy grid, two or three fuels associated with stationary and mobile machinery, two or three sources of fertilizers, one source of crop seed, and so on.

Table 15: Sources of GHG emissions in FP v5

System Boundary	Source Category	Source Detail
Upstream	GHG emissions associated with electricity generation and distribution	Crop Drying \| Electricity (grid)
Upstream	GHG emissions associated with electricity generation and distribution	Irrigation Operations \| Electricity (grid)
Upstream	GHG emissions associated with production of fertilizers	Ammonia (aqueous)
Upstream	GHG emissions associated with production of fertilizers	Ammonia (aqueous) (green ammonia)
Upstream	GHG emissions associated with production of fertilizers	Ammonia (conventional)
Upstream	GHG emissions associated with production of fertilizers	Ammonia (green)
Upstream	GHG emissions associated with production of fertilizers	Ammonium nitrate
Upstream	GHG emissions associated with production of fertilizers	Ammonium nitrate (green ammonia)
Upstream	GHG emissions associated with production of fertilizers	Ammonium sulfate
Upstream	GHG emissions associated with production of fertilizers	Ammonium sulfate (green ammonia)
Upstream	GHG emissions associated with production of fertilizers	Calcium ammonium nitrate
Upstream	GHG emissions associated with production of fertilizers	Calcium ammonium nitrate (green ammonia)
Upstream	GHG emissions associated with production of fertilizers	Diammonium phosphate
Upstream	GHG emissions associated with production of fertilizers	Diammonium phosphate (green ammonia)
Upstream	GHG emissions associated with production of fertilizers	Gypsum
Upstream	GHG emissions associated with production of fertilizers	K2O
Upstream	GHG emissions associated with production of fertilizers	Lime (calcitic)
Upstream	GHG emissions associated with production of fertilizers	Lime (dolomitic)
Upstream	GHG emissions associated with production of fertilizers	Micronutrient (boron)
Upstream	GHG emissions associated with production of fertilizers	Micronutrient (manganese)
Upstream	GHG emissions associated with production of fertilizers	Micronutrient (zinc)
Upstream	GHG emissions associated with production of fertilizers	Monoammonium phosphate
Upstream	GHG emissions associated with production of fertilizers	Monoammonium phosphate (green ammonia)
Upstream	GHG emissions associated with production of fertilizers	Potash (MOP)
Upstream	GHG emissions associated with production of fertilizers	Potassium nitrate
Upstream	GHG emissions associated with production of fertilizers	Sulfur
Upstream	GHG emissions associated with production of fertilizers	US average nitrogen fertilizer
Upstream	GHG emissions associated with production of fertilizers	US average phosphate fertilizer
Upstream	GHG emissions associated with production of fertilizers	Urea
Upstream	GHG emissions associated with production of fertilizers	Urea (green ammonia)
Upstream	GHG emissions associated with production of fertilizers	Urea ammonium nitrate
Upstream	GHG emissions associated with production of fertilizers	Urea ammonium nitrate (green ammonia)
Upstream	GHG emissions associated with production of fuels	Crop Drying \| Diesel (ag equipment)
Upstream	GHG emissions associated with production of fuels	Crop Drying \| Gasoline
Upstream	GHG emissions associated with production of fuels	Crop Drying \| LPG
Upstream	GHG emissions associated with production of fuels	Crop Drying \| Natural gas
Upstream	GHG emissions associated with production of fuels	Crop Transportation \| Biodiesel (on-road heavy-duty truck)
Upstream	GHG emissions associated with production of fuels	Crop Transportation \| Diesel (on-road medium-heavy duty truck)
Upstream	GHG emissions associated with production of fuels	Field Operations \| Diesel (ag equipment)
Upstream	GHG emissions associated with production of fuels	Irrigation Operations \| Diesel (ag equipment)
Upstream	GHG emissions associated with production of fuels	Irrigation Operations \| Gasoline
Upstream	GHG emissions associated with production of fuels	Irrigation Operations \| LPG
Upstream	GHG emissions associated with production of fuels	Irrigation Operations \| Natural gas
Upstream	GHG emissions associated with production of fuels	Manure Transportation \| Diesel (on-road medium-heavy duty truck)
Upstream	GHG emissions associated with production of pesticides	Fumigants
Upstream	GHG emissions associated with production of pesticides	Fungicides
Upstream	GHG emissions associated with production of pesticides	Growth Regulators
Upstream	GHG emissions associated with production of pesticides	Herbicides
Upstream	GHG emissions associated with production of pesticides	Herbicides (sulfuric acid)
Upstream	GHG emissions associated with production of pesticides	Inoculant
Upstream	GHG emissions associated with production of pesticides	Insecticides
Upstream	GHG emissions associated with production of pesticides	Seed Treatment
Upstream	GHG emissions associated with production of seed	Seed \| Alfalfa
Upstream	GHG emissions associated with production of seed	Seed \| Barley
Upstream	GHG emissions associated with production of seed	Seed \| Chickpeas (garbanzos)
Upstream	GHG emissions associated with production of seed	Seed \| Corn (grain)
Upstream	GHG emissions associated with production of seed	Seed \| Corn (silage)
Upstream	GHG emissions associated with production of seed	Seed \| Cotton
Upstream	GHG emissions associated with production of seed	Seed \| Dry Beans
Upstream	GHG emissions associated with production of seed	Seed \| Dry Peas
Upstream	GHG emissions associated with production of seed	Seed \| Fava Beans
Upstream	GHG emissions associated with production of seed	Seed \| Lentils
Upstream	GHG emissions associated with production of seed	Seed \| Lupin
Upstream	GHG emissions associated with production of seed	Seed \| Peanuts
Upstream	GHG emissions associated with production of seed	Seed \| Potatoes
Upstream	GHG emissions associated with production of seed	Seed \| Rice
Upstream	GHG emissions associated with transportation of agricultural inputs	Agricultural Input Transportation \| Diesel (on-road medium-heavy duty truck)
Post-Harvest	GHG emissions associated with stationary machinery	Crop Drying \| Diesel (ag equipment)
Post-Harvest	GHG emissions associated with stationary machinery	Crop Drying \| Gasoline
Post-Harvest	GHG emissions associated with stationary machinery	Crop Drying \| LPG
Post-Harvest	GHG emissions associated with stationary machinery	Crop Drying \| Natural gas
Post-Harvest	GHG emissions associated with transportation of crop production	Crop Transportation \| Biodiesel (on-road heavy-duty truck)
Post-Harvest	GHG emissions associated with transportation of crop production	Crop Transportation \| Diesel (on-road medium-heavy duty truck)
On-Farm Mechanical	GHG emissions associated with mobile machinery	Field Operations \| Diesel (ag equipment)
On-Farm Mechanical	GHG emissions associated with mobile machinery	Manure Transportation \| Diesel (on-road medium-heavy duty truck)
On-Farm Mechanical	GHG emissions associated with stationary machinery	Crop Drying \| Diesel (ag equipment)
On-Farm Mechanical	GHG emissions associated with stationary machinery	Crop Drying \| Gasoline
On-Farm Mechanical	GHG emissions associated with stationary machinery	Crop Drying \| LPG
On-Farm Mechanical	GHG emissions associated with stationary machinery	Crop Drying \| Natural gas
On-Farm Mechanical	GHG emissions associated with stationary machinery	Irrigation Operations \| Diesel (ag equipment)
On-Farm Mechanical	GHG emissions associated with stationary machinery	Irrigation Operations \| Gasoline
On-Farm Mechanical	GHG emissions associated with stationary machinery	Irrigation Operations \| LPG
On-Farm Mechanical	GHG emissions associated with stationary machinery	Irrigation Operations \| Natural gas
On-Farm Mechanical	GHG emissions associated with transportation of crop production	Crop Transportation \| Biodiesel (on-road heavy-duty truck)
On-Farm Mechanical	GHG emissions associated with transportation of crop production	Crop Transportation \| Diesel (on-road medium-heavy duty truck)

Mobile machinery refers to:

Field operations
Manure transportation
Crop transportation

Stationary machinery refers to:

Irrigation operations
Crop dryers

7 Revised methods

The following methods represent significant sources of energy use and associated GHG emissions. For this revision, we updated the methods to reflect the latest science, simplify the algorithms when possible, and clarify the documentation.

7.1 Irrigation operations

Energy is required to lift and pressurize water for irrigation (Eisenhauer et al. 2021). Pumping surface and ground waters to irrigate crops can represent a significant source of energy use and associated GHG emissions. The direct energy use of the pump will be calculated in FP v5 as such:

\[ PE = \frac{E_{ideal}}{e_i} = \frac{(P + L) \times D_g \times A \times CF_{pump} \times CF_{mj}}{e_p \times e_o \times e_q} \]

where $P$ is pressure, $L$ is lift (including elevation changes), $D_g$ is gross depth (volume) of water pumped per area, $A$ is the total field area, $CF$ refers to conversion factors, and $e_i$ is the overall irrigation system efficiency comprised of efficiency values for the pump mechanism ( $e_p$ ), drive/power transfer ( $e_o$ ), and thermal losses ( $e_q$ ).

In FP v4.2, the direct energy use (and therefore GHG emissions) was underestimated due to an omission of thermal energy losses that affect the power unit efficiency (Hoffman, Howell, and Solomon 1990). As an example, consider a case in which diesel fuel was used as the pump’s energy source. Let’s assume the energy needed to pump water was 1 unit of energy. If 1 unit of diesel fuel contains 1 unit of energy, we might assume 1 unit of diesel was needed and used. However, this assumption ignores thermal losses. When that unit of diesel was burned, only ~30% of the energy was converted by the power unit into work; the rest was lost as heat. Therefore, the pump actually needs ~3.3 units of diesel to provide 1 unit of energy.

The improved method for FP v5 gives overall system efficiency values comparable to those reported in literature (Martin et al. 2011; Arkansas 2024; Harrison 2012). As such, users can expect energy use and GHG emissions from irrigation operations to double or triple compared to estimates from FP v4.2, depending on the pump’s energy source.

7.1.1 Irrigation Fuel Consumption Example

Let’s take for example the pumping system described by Fipps (1995):

Pumping lift: 250 ft lift + 37 ft elevation change (287 ft)
Pressure: 45 psi
Gross water pumped: 276 ac-ft (3312 ac-in for whole field)

Friction losses related to pipe diameters and materials and fittings were included in the Fipps example, but let’s assume friction losses to be negligible to align with the USDA-NRCS Irrigation Energy Estimator tool. The NRCS tool uses national averages and indicates the potential for variability (USDA-NRCS, n.d.). Such variability is seen in Fipps (1995) with Case 1 based on a 6-in diameter pipe, and Case 2 on an 8-in pipe.

Based on the system parameters above, the results would be as follows:

Model	Pump Diesel Consumption (gal)	System Boundary	BTU/ac	lb CO2e/ac
FP v4.2	3,844	On-farm	2,869,000	485
FP v5	12,686	On-farm	9,470,000	1,600
FP v5		On-farm + upstream	10,511,000	1,760
NRCS (no upgrades)	13,934	Not specified	10,400,000 - 11,545,000	1,760 - 1,930
NRCS (w/ upgrades)	12,862	Not specified	9,600,000 - 10,656,000	1,620 - 1,790
Fipps (Case 2)	12,960	Not specified	9,673,000 - 10,738,000	1,640 - 1,800
Fipps (Case 1)	16,160	Not specified	12,062,000 - 13,389,000	2,040 - 2,240

The fuel consumption estimates from FP v5 are reasonably within 1.3-8.9% of NRCS and 2.1% of Case 2.

7.2 Manure transportation

Manure loading and transportation to the field that will receive the manure application could be a low to moderate source of energy use and GHG emissions, depending on the manure type and rate. The FP v4.2 relied on expert opinion for the impact factor related to manure loading and transportation. For FP v5, the impact factor has been updated to match the factor reported by Liu et al. (2023).

The energy use and GHG emissions associated with the field application of manure are accounted for in the Field Operations source.

Energy use and GHG emission estimates in FP v5 for manure transportation will be similar to those from FP v4.2.

7.3 Field operations

For FP v5, the energy use and GHG emissions from field operations will be accounted for under the Field Operations source. In FP v4.2, the impact of field operations was accounted for under Applications and Management, mixing upstream and on-farm sources.

Each field operation has an estimate of fuel use. The source of data is the Conservation Resources Land Management Operations Database (CRLMOD) (Kucera and Coreil 2023), which can be accessed via this link (file named Operations spreadsheet.xlsx).

Energy use and GHG emission estimates in FP v5 for field operations will be similar to those from FP v4.2, with the difference of being aggregated into a single category rather than separated into Applications and Management.

7.4 Crop transportation

Crop transportation refers to taking the harvested crop from the field of origin to the next step of the supply chain (crop dryer, grain elevator, buying point, ginning facility, etc.). Crop transportation impact factors were revised and improved with better documentation, along with incorporating the harvest moisture into the algorithm.

Energy use and GHG emission estimates in FP v5 for crop transportation will be slightly higher than those from FP v4.2. This will be due to crop transportation being estimated for the crop at standard moisture in FP v4.2. Crop transportation in FP v5 will be estimated based on the weight of the crop at harvest moisture.

7.5 Crop drying

Crop drying typically represents a low to medium source of energy use and GHG emissions. Crop drying impact factors, drying options, and algorithms were reviewed and updated for most crops.

Most dryers have moving parts (stirrers, fans, augers) powered by electricity and a heating element powered by a fuel such as natural gas or propane. For FP v5, the energy use and GHG emissions from drying operations will account separately for the impact of electricity and the impact of the fuel powering the heating element, which will improve the accuracy of the estimates.

7.6 CH₄ emissions from flooded rice cultivation

For FP v5, we revised the FP v4.2 method with the one from Ogle et al. (2024). The proposed solution to account for rice ratooning is described in Section 2.6. The revised method removes the impact of rice cultivars and adds the options of pre-season water management and length of the growing season.

In rice production, the annual methane (CH₄) emission (kg CH₄) from soils results from the balance of two processes: methanogenesis, which occurs under anaerobic conditions, and methanotrophy, which is the dominant process under aerobic conditions. The magnitude of both processes depends on the length of flooding periods during cropping season and other management practices.

The method for estimating CH₄ emissions from rice fields is formulated on a baseline emission factor $EF_i$, at which CH₄ is produced daily by unit of land for rice production with continuously flooded conditions and no organic amendments. The baseline emission factor is scaled according to the practices and conditions for the land parcel, including water management, organic amendments, use of sulfur products, residue amount, and seeding practices.

The method described below is based on IPCC’s Tier 2 method with specific factors for the two major rice production regions in the U.S.: the Mid-South (Arkansas, Louisiana, Mississippi, and some counties in Missouri and Texas) and California.

In case of ratooning, CH₄ flux is computed as if the two rice crops (main crop and ratooning crop) were one crop by aggregating the management variables applied to each crop (fertilizers, etc.). In the case of fertilizers, since both crops share the same area, the rates would be expressed as the simple average among the individual rates. The same approach would be used for yield. In contrast, the length of the cultivation period would be the sum of each cultivation period.

Although not ideal, this approach would enable the accounting for whole field emissions of both crops within the same scenario. The following equations are used to calculate the CO₂e emissions for both crops (main and ratooning crop if present) combined. At the end, if ratooning is present, per area and per crop production unit estimates are adjusted by the corresponding area and production levels.

7.6.1 Annual total (whole field) CH₄ flux

\[ [CH_4]^{total} = EF \times t \times A \]

$[CH_4]^{total}$ = the annual total CH₄ flux (kg CH₄)
$EF$ = integrated daily emission factor based on management for the growing season (kg CH₄ ha^-1 day^-1)
$t$ = cultivation period for the entire growing season (days). If ratooning crop is present, it would be the days between the planting date of the main crop and the harvest of the ratooning crop.
$A$ = the area of the land parcel (ha)

The emission factor for each growing season ($EF_i$) is estimated using the following equation:

\[ EF_i = EF_{base} \times SF_w \times SF_p \times SF_o \times SF_s \times SF_r \times SF_e \]

where:

$EF_{base}$ = baseline emission factor for continuously flooded fields (kg CH₄ ha^-1 day^-1)
$SF_w$ = scaling factor for water regime during the cultivation period (dimensionless)
$SF_p$ = scaling factor to account for the differences in water regime in the preseason before the cultivation period (dimensionless)
$SF_o$ = scaling factor for both type and amount of organic amendment applied (dimensionless)
$SF_s$ = scaling factor for sulfur amendments to soils (dimensionless)
$SF_r$ = scaling factor for residue litter amount (dimensionless)
$SF_e$ = scaling factor for seeding method in California (dimensionless)

The $EF_{base}$ for continuously flooded fields can be estimated using region-specific conditions using the following equation:

\[ EF_{base} = \{ EF_{sa} - [ (Clay-BPC ) \times C_f ] \} \times CP^{-1} \]

where:

$EF_{base}$ = baseline emission factor for continuously flooded fields (kg CH₄ ha^-1 day^-1)
$EF_{sa}$ = average seasonal CH₄ emissions (kg CH₄ ha^-1 season^-1)
$Clay$ = percent clay associated with the soil texture (percentage); percent clay values that are greater than 54% are assigned a value of 54%
$BPC$ = base percent clay (percentage)
$C_f$ = clay factor (kg CH₄ ha^-1 season^-1)
$CP$ = average cultivation period for the seasonal CH₄ emissions (days)

The following equation estimates the scaling factor for sulfur amendments $SF_s$ as a function of the sulfur amendment rate ($SR$):

\[ SF_s = \begin{cases} 1 & \text{for $SR = 0$ kg S ha}^{-1}\\ 1 - (SR \times 0.00133) & \text{for 0 $\lt$ $SR$ $\le$ 338 kg S ha}^{-1}\\ 0.55 & \text{for $SR > 338$ kg S ha}^{-1} \end{cases} \]

where:

$SR$ = sulfur application rate (kg S ha^-1)

Sulfur application rate is computed by adding up the sulfur inputs entered by a Fieldprint Platform user.

\[ SR = [\sum_i^n (FR_i \times S^{prop}_i) ] \times 10^3 \]

$FR_i$ = the annual applied rate of fertilizer i^th (kg ha^-1)
$S^{prop}$ = the proportion of sulfur in fertilizer i^th [kg sulfur (kg fertilizer^-1)]

The following equation estimates the scaling factor for organic amendments $SF_o$ as a function of the type and rate of amendments ($ROA$):

\[ SF_o = [ 1 + \sum_i^n (ROA_i \times 10^{-3} \times CFOA_i) ] ^ {0.59} \]

$ROA_i$ = rate of application of the i^th organic amendment type (kg ha^-1)
$CFOA_i$ = conversion factor for the i^th organic amendment type (dimensionless)

7.6.2 Annual total CH₄ flux per area and per crop production unit

Methane emissions per area and crop production unit are estimated by the following equations:

\[ [CH_4]^{area} = [CH_4]^{total} \times (A \times i)^{-1} [CH_4]^{prod} = [CH_4]^{total} \times (Y \times A \times i)^{-1} \]

where:

$[CH_4]^{total}$ = the annual total CH₄ emissions (kg CH₄)
$[CH_4]^{area}$ = the annual total CH₄ emissions per area (kg CH₄ ha^-1)
$[CH_4]^{prod}$ = the annual total CH₄ emissions per crop production unit (kg CH₄ [crop prod unit]^-1)
$A$ = the land parcel area (ha)
$Y$ = the crop yield (crop production units ha^-1)
$i$ = the number of rice crops during the season, 1 for a single crop and 2 for a main crop followed by ratooning (dimensionless)

7.6.3 Conversion of CH₄ to CO₂e

Finally, methane flux can be expressed in units of CO₂e as follows:

\[ \begin{align} [CO_2\text{e}]^{total} &= [CH_4]^{total} \times [CH_4]^{gwp} \\ [CO_2\text{e}]^{area} &= [CH_4]^{area} \times [CH_4]^{gwp} \\ [CO_2\text{e}]^{prod} &= [CH_4]^{prod} \times [CH_4]^{gwp} \end{align} \]

where:

$[CO_2\text{e}]^{total}$ = the annual total CO₂e flux (kg CO₂e)
$[CO_2\text{e}]^{area}$ = the annual total CO₂e flux per area (kg CO₂e ha ^-1)
$[CO_2\text{e}]^{prod}$ = the annual total CO₂e flux per crop production unit (kg CO₂e [crop production units]^-1)
$[CH_4]^{gwp}$ = the global warming potential factor for CH₄ (kg CO₂e/kg CH₄)

7.6.4 Example

The following is a sample of the results produced by the method. For illustration purposes, the scenarios considered have the following properties:

Rice field from East Carroll (LA) under conventional tillage
Ratoon crop present
Field area: 40.5 ha
Average rates of NPK fertilizers
Water management: flooded preseason (> 30 days) and continuously flooded during season.

Table 16 summarizes the differences between the two scenarios.

Table 16: Scenarios details

Scenario	Ratooning	Planting date	Harvest date	Cultivation period	Main crop yield	Ratooning crop yield	Overall yield
1	Yes	2023-05-01	2023-12-20	233	7663	3832	5747.618
2	No	2023-05-01	2023-09-15	137	7663	NA	7663.491

Table 17 summarizes the results for each scenario:

Table 17: Output from the method for CH4 from rice cultivation

Scenario	Ratooning	Metric	System Boundary	Source Category	kg CO2e / whole field	kg CO2e / ha	kg CO2e / kg rice
1	Yes	GHG Emissions	On-Farm Non-Mechanical Sources and Sinks	CH4 emissions from flooded rice cultivation	1933121	23884.2	4.155
2	No	GHG Emissions	On-Farm Non-Mechanical Sources and Sinks	CH4 emissions from flooded rice cultivation	1136642	28087.0	3.665

For these 40-ha rice fields, the whole field emissions were about 1,933 and 1,136 tonnes of CO₂e with and without ratooning crop, respectively. The emissions from the scenario with ratooning were about 1.7 times the base scenario without ratooning due to the longer growing season.

When converted to emissions by area, the scenario with ratooning crop has lower emissions because the whole-field emissions are divided by twice the area, following the guidance from IPCC (2019). Emissions by crop production unit are higher for the scenario with ratooning because the sum of the two harvests (main crop + ratooning) are not enough to offset the higher amount of emissions when producing a ratoon crop compared to a single rice crop per season.

7.7 CH₄ flux from non-flooded soils

This is an addition for FP v5, using the method from Ogle et al. (2024). This represents a very minor source of GHG emissions or sequestration.

Annual methane (CH₄) net uptake or emission (tonne CH₄) from soils results from the balance of two processes: methanogenesis, which occurs under anaerobic conditions, and methanotrophy, which is the dominant process under aerobic conditions.

In non-flooded mineral soils (NFMS), aerobic conditions are predominant and net uptake or negative fluxes of CH₄ are expected. The rate of uptake will depend on the land use. For these situations, the annual CH₄ flux is determined by the average CH₄ uptake in soils with natural vegetation ($[CH_4]^{base}$) and a multiplying factor related to the current land use ($MF$) as follows:

\[ [CH_4]^{total} = [CH_4]^{base} \times MF \times A \times 10^3 \]

$[CH_4]^{total}$ = the annual total CH₄ flux (kg CH₄)
$A$ = the area of the land parcel (ha)
$[CH_4]^{base}$ = the base annual CH₄ flux per area for mineral soils with natural vegetation (tonne CH₄ ha^-1)
$MF$ = the management factor for cropland and grazing land NFMS (dimensionless)

The methane flux per area and per crop production unit can be estimated with the following equations:

7.7.1 Emissions per area and per crop production unit

Provided the area and crop yield, the annual total CH₄ emissions can be computed per area and per crop production unit as follows:

\[ \begin{align} [CH_4]^{area} &= [CH_4]^{total} \times A^{-1} \\ [CH_4]^{prod} &= [CH_4]^{total} \times (A \times Y)^{-1} \end{align} \]

where:

$[CH_4]^{area}$ = the annual total CH₄ flux per area (kg CH₄ ha^-1)
$[CH_4]^{prod}$ = the annual total CH₄ flux per crop production unit (kg CH₄ [crop prod unit]^-1)
$A$ = the area of the land parcel (ha)
$Y$ = the crop yield (crop production units ha^-1)

7.7.2 Conversion CH₄ to CO₂e

Finally, methane flux can be expressed as CO₂e as follows:

where:

$[CO_2\text{e}]^{total}$ = the annual total CO₂e flux (kg CO₂e)
$[CO_2\text{e}]^{area}$ = the annual total CO₂e flux per area (kg CO₂e ha ^-1)
$[CO_2\text{e}]^{prod}$ = the annual total CO₂e flux per crop production unit (kg CO₂e [crop production units]^-1)
$[CH_4]^{gwp}$ = the global warming potential factor for CH₄ (kg CO₂e / kg CH₄)

7.7.3 Example

The following is a sample of the results produced by the method. For illustration purposes, the scenario considered has the following characteristics:

Corn field from Champaign (IL) under reduced tillage
Field area: 40.5 ha
Yield: 10607 kg corn / ha
Average rates of NPK fertilizers
$[CH_4]^{base}$ and $MF$ for grassland natural vegetation and annual cropland.

Table 18 summarizes the output from the method.

Table 18: Output from the method for CH4 from non-flooded soils

Metric	System Boundary	Source Category	kg CO2e / whole field	kg CO2e / ha	kg CO2e / kg corn
GHG Emissions	On-Farm Non-Mechanical Sources and Sinks	CH4 flux from non-flooded soils	-892	-22	-0.002

As expected, a negative flux is produced due to the predominant aerobic conditions. About 892 kg CO₂e are captured (negative emissions) for the whole field, which represents -22 kg CO₂e per hectare and -0.002 kg CO₂e per kg of corn.

7.8 CO₂ from carbonate lime applications to soils

This is an addition for FP v5, using the method from Ogle et al. (2024). The carbon dioxide (CO₂) emissions from carbonate lime applications are directly proportional to the amount of lime applied ($M$) and are modulated by the emission factor ($EF$) based on the lime type. This method requires data on the amount and type of lime applied to soils.

The following equation is used to estimate the total CO₂ emissions: \[ [CO_2\text{e}]^{total} = M \times EF \times [CO_2\text{e}]^{mw} \]

$[CO_2\text{e}]^{total}$ = the annual total CO₂ emissions (kg CO₂e)
$M$ = the annual lime application (kg crushed limestone or dolomite)
$EF$ = the emission factor, based on the lime type (kg CO₂-C [kg lime]^-1)
$[CO_2\text{e}]^{mw}$ = ratio of molecular weight of CO₂ to carbon (kg CO₂-C [kg C]^-1)

7.8.1 Emissions per area and per crop unit

Provided the area and crop yield, the annual total CO₂ emissions from lime per area and per crop production unit can be estimated by the following equation:

\[ \begin{align} [CO_2\text{e}]^{area} &= [CO_2\text{e}]^{total} \times A^{-1} \\ [CO_2\text{e}]^{prod} &= [CO_2\text{e}]^{total} \times(A\times Y)^{-1} \end{align} \]

where:

$[CO_2\text{e}]^{area}$ = the annual total CO₂ emissions per area (kg CO₂e ha^-1)
$[CO_2\text{e}]^{prod}$ = the annual total CO₂ emissions per crop production unit (kg CO₂e [crop prod unit]^-1)
$Y$ = the crop yield (crop production units ha^-1)
$A$ = the area of the land parcel (ha)

7.8.2 Example

The following is a sample of the outputs produced by the method. For illustration purposes, the scenario considered has the following characteristics:

Corn field from Champaign (IL) under reduced tillage
Field area: 40.5 ha
Yield: 10607 kg corn / ha
Application of 1120 kg/ha (~ 0.5 ton/ac) of Lime (calcitic).

Table 19 summarizes the outputs from the method.

Table 19: Output from the method for CO2 from lime application

Metric	System Boundary	Source Category	kg CO2e / whole field	kg CO2e / ha	kg CO2e / kg corn
GHG Emissions	On-Farm Non-Mechanical Sources and Sinks	CO2 from carbonate lime applications to soils	4906	121.2	0.011

According to this result, an application of ~ 1120 kg/ha of Lime (calcitic) to a ~ 40 ha field will produce an emission of 4906 kg CO₂e for the whole field, which represents 121.2 kg CO₂e/ha and 0.011 kg CO₂e / kg of corn.

7.9 CO₂ from urea fertilizer applications

This is an addition for FP v5, using the method from Ogle et al. (2024). It has has been adopted from the methodology developed by IPCC and uses the IPCC default emission factor.

The CO₂ emissions from urea fertilizers are directly proportional to the amount of urea applied ($M$) and are modulated by the emission factor ($EF$) based on the proportion of carbon in urea.

The following equation is used to estimate the total CO₂ emissions from urea based fertilizers.

\[ \begin{align} [CO_2\text{e}]^{total} &= M \times EF \times [CO_2\text{e}]^{mw} \\ M &= \sum_i^n (FR_i \times A \times U^{prop}_i) \end{align} \]

$[CO_2\text{e}]^{total}$ = the annual total CO₂ emissions (kg CO₂e)
$M$ = the annual Urea application (kg urea)
$FR$ = the annual applied rate of fertilizer i^th (kg ha^-1)
$A$ = the area of the field (ha)
$U^{prop}$ = the proportion of Urea in fertilizer i^th [kg urea (kg fertilizer^-1)]
$EF$ = the emission factor, based on the proportion of carbon in urea (kg tons CO₂-C [kg tons urea]^-1)
$[CO_2\text{e}]^{mw}$ = ratio of molecular weight of CO₂ to carbon (kg CO₂-C [kg C]^-1)

7.9.1 Emissions per area and per crop unit

Carbon dioxide total emissions from urea per area and per crop units can be estimated by the following equations:

\[ \begin{align} [CO_2\text{e}]^{area} &= [CO_2\text{e}]^{total} \times A^{-1}\\ [CO_2\text{e}]^{prod} &= [CO_2\text{e}]^{total} \times(A\times Y)^{-1} \end{align} \]

where:

$[CO_2\text{e}]^{area}$ = the annual total CO₂ emissions per area (kg CO₂e ha^-1)
$[CO_2\text{e}]^{prod}$ = the annual total CO₂ emissions per crop production unit (kg CO₂e [crop prod unit]^-1)
$A$ = the area of the land parcel (ha)
$Y$ = the crop yield (crop production units ha^-1)

7.9.2 Example

The following is a sample of the outputs produced by the method. For illustration purposes, the scenario considered has the following characteristics:

Corn field from Story (IA) under conventional tillage
Field area: 40.5 ha
Yield: 11298 kg corn / ha
Application of 150 kg/ha of Urea and 50 kg/ha of DAP

Table 20 summarizes the outputs from the method.

Table 20: Output from the method for CO2 from urea fertilizer application

Metric	System Boundary	Source Category	kg CO2e / whole field	kg CO2e / ha	kg CO2e / kg corn
GHG Emissions	On-Farm Non-Mechanical Sources and Sinks	CO2 from urea fertilizer applications	4452	110	0.01

For this scenario, the application of 150 kg/ha of urea resulted in 4452 kg CO₂e / ha for the whole field, representing 110 kg CO₂e / ha and 0.01 kg CO₂e / kg corn.

7.10 Direct land use change

This is an addition to FP v5, using a method based on IPCC (2019) and EC–European Commission (2010). This method is a tailored implementation to represent Field to Market crops and feedback from stakeholders.

Land use change will be estimated when the Cropland Data Layer (Boryan et al. 2011) detects one of the following categories for a given field boundary, starting in the year 2008:

Forest
Deciduous Forest
Evergreen Forest
Mixed Forest
Shrubland
Grassland

Land use change emissions are the emissions associated with the change from one of the IPCC land cover categories shown above to annual or perennial (alfalfa) cropland for a given field boundary. The emissions account for the carbon lost from the biomass and the soil.

To develop this methodology, we used the IPCC Generic Methodologies Applicable to Multiple Land Use Categories (IPCC 2019) and the Guidelines for the Calculation of Land Carbon Stocks for the Purpose of Annex V to Directive 2009/28/EC (EC–European Commission 2010).

To calculate the emissions, we establish a reference land use value and the current land use value. The reference land use must have happened in the past 20 years. To calculate the amount of C stored, we use standard values that can be found in the guidelines mentioned above. A limitation is that the Cropland Data Layer can detect land use for the contiguous United States starting in 2008, representing a coverage of 16 years. At the US national level, approximately 40% of cropland is rented or leased (Bigelow 2014). It is unlikely that a grower farming a rented or leased field in 2024 would know the history of the same field starting in 2004. As a consequence, until 2028, the Cropland Data Layer will have a 1-4-year gap to meet the 20-year look-back period requirement.

Users of the FP sometimes draw field boundaries by hand rather than importing a shapefile field boundary from a farm management system. A boundary might erroneously intersect areas outside the cropland. For this reason, we propose implementing a threshold of detecting at least 10% of the area within the field boundary to be one of the categories above (forest, shrubland, etc.) before informing the user that the land use change method will be part of the field’s footprint and to review the field boundary to fix any errors. There will be a period of iteration and adjustments in the FP v5 to reach a robust methodology to implement this method.

The following equations is used to estimate the soil C stock $CS^{soil}$ for the i^th situation, i.e. reference and current land use:

\[ CS^{soil}_i = \sum_{c,s} (SOC^{ref}_{c,s} \times F^{LU}_{c,s} \times F^{MG}_{c,s} \times F^{I}_{c,s}) \]

$CS^{soil}_i$ = total mineral soil organic C stock at a defined time for the i^th situation (tonne C ha^-1)
$SOC_{ref}$ = the soil organic C stock for mineral soils in the reference condition (tonne C ha^-1)
$F^{LU}$ = stock change factor for mineral soil organic C land-use systems or sub-systems for a particular land-use (dimensionless)
$F^{MG}$ = stock change factor for mineral soil organic C for management regime (dimensionless)
$F^{I}$ = stock change factor for mineral soil organic C for the input of organic amendments (dimensionless)
$c,s$ denotes climate regions and soil types

The biomass C stock $CS^{veg}_i$ (tonne C ha^-1) for the i^th situation (reference and current land use) is taken from standard values which consider all the necessary C pools, including above and below-ground biomass, as well as living and dead organic matter. These values are selected using the information from: type of vegetation, ecological or Climate zone, and the species or the age of the plants.

Factors for both soil and biomass C stock can be found in the Guidelines for the calculation of land carbon stocks for the purpose of Annex V to Directive 2009/28/EC (EC–European Commission 2010).

Then, the C stock $CS$ for the i^th situation (reference and current state) is the sum of both above mentioned stock:

\[ CS_i = (CS^{soil}_i + CS^{veg}_i) \times A \times 10^{3} \]

$CS_i$ = C stock for the i^th situation (reference and reference state) (kg C)
$A$ = land area of the stratum being estimated (ha)

Once soil and vegetation carbon stocks are estimated, the total emissions are computed as follows:

\[ [CO_2]^{total} = (CS_R - CS_A) \times [CO_2]^{mw} \times AEF(t) \times A \]

$[CO_2]^{total}$ = the emissions from land use change (kg CO₂ ha^-1)
$CS_R$ and $CS_C$ = carbon stock at reference and current situation (kg C ha^-1)
$AEF(t)$ = allocated emission fraction for year $t$ (dimensionless)
$[CO_2]^{mw}$ = ratio of molecular weight of CO₂ to carbon, 44/12 (kg CO₂ [kg C]^-1)
A = area of the land parcel (ha)

We propose that the calculated emissions from LUC are allocated differently across the 20 years, with the impacts decreasing gradually across the period described by the following equation:

\[ AEF(t) = 0.1025 - (0.005 *t) \]

Thus, the first year after the LUC accounts for 9.75% of the total emissions, while the 20th year accounts for 0.25%. Each year represents 0.5% less emissions than the previous year. This allocation method attributes more importance to years closer to the LUC event, which follows what really happens in the natural systems.

7.10.1 Emissions per area and per crop production unit

Provided the area and crop yield, the annual total CO₂ emissions can be computed per area and per crop production unit as follows:

\[ \begin{align} [CO_2]^{area} &= [CO_2]^{total} \times A^{-1} \\ [CO_2]^{prod} &= [CO_2]^{total} \times (Y \times A)^{-1} \end{align} \]

$[CO_2]^{area}$ = the emissions from land use change per area (kg CO₂ ha^-1)
$[CO_2]^{prod}$ = the annual total CO₂ emissions per crop production unit (kg CO₂ [kg crop production]^-1)
$A$ = the area of the land parcel (ha)
$Y$ = crop yield (kg ha^-1)

7.10.2 Constants and factors required for calculation

Symbol	Name	Value	Units
CO2_MW	ratio of molecular weight of CO₂ to carbon	44/12	kg CO₂ [kg C]^-1

Vegetation carbon stock reference values (Download). First 10 values showed here:

Type of vegetation	Ecological/Climate Zone	Species or Age	CS_veg
Cropland	Not applicable	Not applicable	0.0
Cropland (sugar cane)	Subtropical Steppe	Not applicable	4.8
Cropland (sugar cane)	Subtropical Humid Forest	Not applicable	4.8
Perennial crop	Temperate (all moisture regimes)	Not applicable	43.2
Perennial crop	Tropical (dry)	Not applicable	6.2
Perennial crop	Tropical (moist)	Not applicable	14.4
Perennial crop	Tropical (wet)	Not applicable	34.3
Perennial (coconuts)	Not applicable	Not applicable	75.0
Perennial (Jatropha)	Not applicable	Not applicable	17.5
Perennial (Jojoba)	Not applicable	Not applicable	2.4

Soil organic carbon reference values by soil type and climate regions (Download). First 10 values showed here:

Climate region	Soil type	SOC
Boreal, moist	High activity clay soils	68
Cool temperate, dry	High activity clay soils	50
Cool temperate, moist	High activity clay soils	95
Warm temperate, dry	High activity clay soils	38
Warm temperate, moist	High activity clay soils	88
Tropical, dry	High activity clay soils	38
Tropical, moist	High activity clay soils	65
Cool temperate, dry	Low activity clay soils	33
Cool temperate, moist	Low activity clay soils	85
Warm temperate, dry	Low activity clay soils	24

Soil classes and soil types look up table (Download). First 10 values showed here:

Soil_subunit	Soil_unit	Soil type
Af	Acrisol	Low activity clay soils
Ag	Acrisol	Low activity clay soils
Ah	Acrisol	Low activity clay soils
Ao	Acrisol	Low activity clay soils
Ap	Acrisol	Low activity clay soils
NA	Albeluvisol	High activity clay soils
NA	Alisol	High activity clay soils
Th	Andosol	Volcanic soils
Tm	Andosol	Volcanic soils
To	Andosol	Volcanic soils

US soil map (Download) extracted from the Word Soil Map by FAO.

Koppen-Geiger climate classification by county (Download). Sample of 10 values showed here:

State	County	Code
Alabama	Jackson	Cfa
Arizona	Greenlee	Csb
Arkansas	Washington	Cfa
California	Mono	Csb
Colorado	Las Animas	BSk
Connecticut	Middlesex	Dfa
Delaware	Sussex	Cfa
Florida	Union	Cfa
Georgia	Schley	Cfa
Idaho	Bear Lake	Dfb

A dictionary for matching Koppen-Geiger classification to climate regions is provided here (Download)

Code	Climate Description	Climate region
Af	Tropical Rainforest	Tropical, moist
Am	Tropical Monsoon	Tropical, moist
Aw	Tropical Savanna (Wet and Dry Climate)	Tropical, dry
BWk	Cold Desert Climate	Cool temperate, dry
BWh	Hot Desert Climate	Tropical, dry
BSk	Cold Semi-Arid Climate	Cool temperate, dry
BSh	Hot Semi-Arid Climate	Tropical, dry
Csa	Hot-Summer Mediterranean Climate	Warm temperate, dry
Csb	Warm-Summer Mediterranean Climate	Warm temperate, dry
Csc	Temperate, Dry Summer, Cold Summer	Warm temperate, dry

Factors for computing soil carbon stock (Download).

Climate region	Land use	Management	Input	FLU	FMG	FI
Boreal, moist	Cultivated	Full-tillage	Low	0.69	1.00	0.92
Boreal, moist	Cultivated	Full-tillage	Medium	0.69	1.00	1.00
Boreal, moist	Cultivated	Full-tillage	High with manure	0.69	1.00	1.44
Boreal, moist	Cultivated	Full-tillage	High without manure	0.69	1.00	1.11
Boreal, moist	Cultivated	Reduced tillage	Low	0.69	1.08	0.92
Boreal, moist	Cultivated	Reduced tillage	Medium	0.69	1.08	1.00
Boreal, moist	Cultivated	Reduced tillage	High with manure	0.69	1.08	1.44
Boreal, moist	Cultivated	Reduced tillage	High without manure	0.69	1.08	1.11
Boreal, moist	Cultivated	No till	Low	0.69	1.15	0.92
Boreal, moist	Cultivated	No till	Medium	0.69	1.15	1.00

7.10.3 Example

The following is a sample of the outputs produced by the method. For illustration purposes, the land use change scenario is as follows:

Table 21: Scenario for direct land use change emissions

Attribute	Previous state	Current state
Inputs	Not applicable	High without manure
Landuse	Native forest (non-degraded)	Cultivated
Vegetation	Forest land (excluding forest plantations) - more than 30 % canopy cover	Cropland
Management	Not applicable	Full-tillage
Ecozone	Temperate Continental Forest	Not applicable
Species	> 20 y	Not applicable
Time Since Change	NA	5 yr
Area (Ha)	40.5	40.5
Yield (Kg/Ha)	NA	11298

As it can be seen from table Table 21, this field has switched from a deciduous forest to cropland (currently corn) five years ago. Due to the decrease of soil and vegetation C stocks, CO₂ emissions proportional to the time of the land use change is expected. The following table shows the final output of this method expressed as whole field emissions, emissions per area, and emissions per crop production unit.

Table 22: Output from the method for direct land use change emissions

Metric	System Boundary	Source Category	kg CO2e / whole field	kg CO2e / ha	kg CO2e / kg corn
GHG Emissions	On-Farm Non-Mechanical Sources and Sinks	Direct land use change emissions	1325235	32747	2.898

As expected, due to the land use change occurred five years ago, the current year is attributed approximately 1300 tonnes of CO₂e for the entire field, which represents 32.7 tonnes CO₂e/ha and 2.89 kg CO₂e / kg of corn.

7.11 Non-CO₂ emissions from biomass burning

For FP v5, we revised the FP v4.2 method with the one from Ogle et al. (2024). The method works in a similar manner, with the enhancement of greenhouse gas separation (CH₄ and N₂O) and allowing users to indicate the size of the area of the field that was set on fire (e.g., 50% of the field rather than the entire field).

The emissions from biomass burning area proportional to the mass of fuel available for combustion in the land parcel. The method to estimate the mass of fuel $M$ varies depending on the land use (cropland, perennial crop) and requires input data. CO₂ emissions from crop biomass burning are considered ephemeral (CO₂ is sequestered and released within < 1 year) and are not considered by this method.

The mass available for combustion is estimated by the following equations.

Crop residue biomass from croplands:

\[ M = [(Y_b \times HI^{-1}) - Y] \times DM \]

$Y$ = crop harvest or forage yield of the crop burned (kg ha^-1)
$HI$ = harvest index ratio of yield to aboveground biomass (kg yield [kg biomass]^-1)
$DM$ = dry matter content of harvested crop biomass or forage (kg dry matter [kg biomass]^-1)

\[ [GHG]^{burning}_{total} = AB \times M \times C_e \times EF \]

$[GHG]^{burning}_{total}$ = the annual total GHG emissions (kg GHG)
$AB$ = area burned of the land parcel (ha)
$EF$ = the emission factor for each GHG based on land use category (g GHG kg^-1)
$C_e$ = combustion efficiency for each land use category (dimensionless)

7.11.1 Emissions per area and per crop production unit

\[ \begin{align} [GHG]^{burning}_{area} &= [GHG]^{burning}_{total} \times A^{-1} \\ [GHG]^{burning}_{prod} &= [GHG]^{burning}_{total} \times (Y \times A)^{-1} \end{align} \]

$[GHG]^{burning}_{area}$ = the annual total GHG emissions per area (kg GHG ha^-1)
$[GHG]^{burning}_{prod}$ = the annual total GHG emissions per crop production unit (kg GHG [kg crop production]^-1)
$A$ = the area of the current cash crop (ha)
$Y$ = current cash crop yield (kg ha^-1)

7.11.2 Conversion of GHG emissions to CO₂e

Total, per area and per crop production units of GHG emissions can be converted to CO₂e by applying the corresponding global warming potential factor of each GHG.

\[ \begin{align} [CO_2\text{e}]^{burning}_{total} &= [GHG]^{burning}_{total} \times [GHG]^{gwp} \\ [CO_2\text{e}]^{burning}_{area} &= [GHG]^{area}_{total} \times [GHG]^{gwp} \\ [CO_2\text{e}]^{burning}_{prod} &= [GHG]^{prod}_{total} \times [GHG]^{gwp} \end{align} \]

where:

$[CO_2\text{e}]^{burning}_\cdot$ = total, per area and per crop production unit CO₂e emissions (kg CO₂e)
$[GHG]^{burning}_\cdot$ = total, per area and per crop production unit GHG emissions (kg CO₂e)
$[GHG]^{gwp}$ = global warming potential factor for each GHG

7.11.3 Constants and factors required for calculation

Symbol	Name	Value	Units
F_c	carbon fraction of aboveground biomass	0.45	kg C/kg dry matter

Harvest indices and dry matter contents for crops (Download):

crop	DM	HI
Alfalfa	0.880	0.95
Barley	0.855	0.46
Chickpeas (garbanzos)	0.840	0.46
Corn (grain)	0.845	0.53
Corn (silage)	0.350	0.95
Cotton	0.920	0.40
Dry Beans	0.840	0.46
Dry Peas	0.840	0.46
Fava Beans	0.840	0.46
Lentils	0.840	0.46
Lupin	0.840	0.46
Peanuts	0.910	0.40
Potatoes	0.200	0.50
Rice	0.860	0.42
Rye	0.860	0.50
Sorghum	0.860	0.44
Soybeans	0.870	0.42
Sugar beets	0.150	0.40
Wheat (durum)	0.865	0.39
Wheat (spring)	0.865	0.39
Wheat (winter)	0.865	0.39
All other crops	0.860	0.39

Combustion efficiency factors for crops (Download):

Crop	Stage_of_burning	C_e
Alfalfa	Early season burn	0.74
Alfalfa	Mid-late season burn	0.77
Barley	NA	0.90
Chickpeas (garbanzos)	NA	0.80
Corn (grain)	NA	0.80
Corn (silage)	NA	0.80
Cotton	NA	0.80
Dry Beans	NA	0.80
Dry Peas	NA	0.80
Fava Beans	NA	0.80
Lentils	NA	0.80
Lupin	NA	0.80
Peanuts	NA	0.80
Potatoes	NA	0.80
Rice	NA	0.90
Rye	NA	0.90
Sorghum	NA	0.80
Soybeans	NA	0.80
Sugar beets	NA	0.80
Wheat (durum)	NA	0.90
Wheat (spring)	NA	0.90
Wheat (winter)	NA	0.90
All other crops	NA	0.90

The followings are the values for $[N_2O]^{gwp}$ and $[CH_4]^{gwp}$ available in this reference table (Download).

Assessment Report (AR)	Time Horizon	Gas	Global Warming Potential
AR6	100-yr	CH4_biogenic	27
AR6	100-yr	N2O	273

7.11.4 Example

The following is a sample of the results produced by the method. For illustration purposes, the scenario considered has the following characteristics:

Corn field from Ripley (KS) under reduced tillage
Field area: 40.5 ha
Yield: 10607 kg corn / ha
40% of the area with previous crop (wheat) residue was burned

Table 23 summarizes the output from the method.

Table 23: Output from the method for Non-CO2 emissions from biomass burning

Metric	System Boundary	Source Category	kg CO2e / whole field	kg CO2e / ha	kg CO2e / kg corn
GHG Emissions	On-Farm Non-Mechanical Sources and Sinks	Non-CO2 emissions from biomass burning	9107	225	0.021

According to this result, burning 40% of the area of wheat residue results in whole field emissions of 9107 kg CO₂e of non-CO₂ GHG. In terms of emission per area and per crop production unit, the emissions are 225 kg CO₂e of non-CO₂ GHG per hectare and 0.021 per kg of corn respectively.

7.12 Soil carbon stock changes

This is described in the Soil Carbon metric section.

Here we include a reduced example demonstrating the output from SWAT+ and how it will be used by FP v5.

SWAT+ produces estimates of soil carbon stock changes on a calendar-year basis. Table 24 is an example of the annual outputs from SWAT+ and the conversions to use the model simulations for a given field.

The explanation of Table 24 is as follows:

Year: sequence of years of the simulation, from 2008 to the most recent year.
Annual SOC Stock (kg C / ha): Annual output from SWAT+, in units of kg of carbon.
Annual SOC Stock (kg CO₂ / ha): The output from SWAT+ is multiplied by 44/12 (ratio of molecular weights, CO₂ to carbon) to obtain units of kg CO₂ / ha.
Annual Change (kg CO₂ / ha): Difference in SOC stocks in consecutive calendar years.
Annual Emissions (kg CO₂ / ha): The variable Annual Change (kg CO2 / ha) is multiplied to reverse its sign to represent GHG emissions.

Table 24: Demonstration of SWAT+ output for a given field

Year	Annual SOC Stock (kg C / ha)	Annual SOC Stock (kg CO2 / ha)	Annual Change (kg CO2 / ha)	Annual Emissions (kg CO2 / ha)
2008	7580	27793.3	NA	NA
2009	7520	27573.3	-220.0	220.0
2010	7486	27448.7	-124.7	124.7
2011	7431	27247.0	-201.7	201.7
2012	7376	27045.3	-201.7	201.7
2013	7345	26931.7	-113.7	113.7
2014	7294	26744.7	-187.0	187.0
2015	7268	26649.3	-95.3	95.3
2016	7208	26429.3	-220.0	220.0
2017	7165	26271.7	-157.7	157.7
2018	7108	26062.7	-209.0	209.0
2019	7076	25945.3	-117.3	117.3
2020	7022	25747.3	-198.0	198.0
2021	6991	25633.7	-113.7	113.7
2022	6947	25472.3	-161.3	161.3
2023	6905	25318.3	-154.0	154.0
2024	6860	25153.3	-165.0	165.0

In the case above, the field is slowly depleting its SOC stock every year. For the FP v5, GHG emissions will be represented with positive numbers (e.g., 259 kg CO₂ / ha / year) so they add to the total, and GHG emissions sequestrations will be represented with negative numbers (e.g., -450 kg CO₂ / ha / year) so they subtract from the total.

7.12.1 Attribution of CO₂ emissions or sequestration by crop interval

The Fieldprint Platform will assign CO₂ sequestration or emissions to a crop interval according to the number of days the crop interval covers within a calendar year. The following scenario illustrates the approach:

Calendar year: 2023
Soil carbon stock change estimate: 425 kg CO₂ / ha / year or 1.16 kg CO₂ / ha / day.
Two crop intervals in 2023:
- 2023 soybeans (2022-10-31 to 2023-10-10), covering 283 days in 2023. This crop interval gets assigned 1.16 kg CO₂ / ha / day * 283 days = 329.5 kg CO₂ / ha.
- 2024 corn grain, (2023-10-11 to 2024-10-20), covering 82 days in 2023. This crop interval gets assigned 1.16 kg CO₂ / ha / day * 82 days = 95.5 kg CO₂ / ha.
The soybean crop interval would also include emissions from 2022, and the corn crop interval would include emissions for 2024, using the same approach.

7.13 Soil N₂O

For FP v5, we revised the FP v4.2 method with the method from Ogle et al. (2024). The method outlined here is based on an adaptation of the IPCC Tier 1 method with emission and scaling factors to address management factors, input type, and climate. The method includes direct and indirect N₂O emissions.

7.13.1 Direct emissions

According to Ogle et al. (2024), nitrous oxide (N₂O) is directly emitted from soils where there are nitrogen additions such as mineral or organic fertilization, or management practices that influence nitrogen mineralization from soil organic matter.

\[ [N_2O]^{direct} = [N_2O]^{input} \times [N_2O]^{mw} \]

where:

$[N_2O]^{input}$ = annual soil N₂O emissions from nitrogen inputs to the land parcel (kg N₂O-N)
$[N_2O]^{mw}$ = ratio of molecular weights of N₂O to N₂O-N = 44/28

The $[N_2O]^{input}$ is calculated from the fertilizer application data and factors using this equation.

\[ \begin{align} [N_2O]^{synthetic} &= [ F_{sn} \times EF_{sn} \times (1 + S_{sr}) \times (1 + S_{inh}) ] \\ [N_2O]^{organic} &= [(F_{on} + F_{cr}) \times EF_{on} ] \\ [N_2O]^{animal} &= (F_{prp} \times EF_{prp}) \\ [N_2O]^{input} &= ([N_2O]^{synthetic} + [N_2O]^{organic} + [N_2O]^{animal}) \times (1 + S_{till}) \times (1 + S_{bc}) \end{align} \]

where:

$F_{sn}$ = synthetic fertilizer nitrogen inputs to the land parcel (kg N)
$EF_{sn}$ = emission factor for synthetic nitrogen input to soils (kg N₂O-N /kg N)
$S_{sr}$ = scaling factor for slow-release fertilizers, 0 where no effect (dimensionless)
$S_{inh}$ = scaling factor for nitrification inhibitors, 0 where no effect (dimensionless)
$F_{on}$ = organic fertilizer/manure nitrogen inputs to the land parcel (kg N)
$F_{cr}$ = crop residue and forage renewal nitrogen inputs to the land parcel (kg N)
$EF_{on}$ = emission factor for other nitrogen inputs, i.e., organic fertilizer/manure and crop/forage residue nitrogen input to soils (kg N₂O-N [kg N]^-1)
$F_{prp}$ = manure nitrogen deposited directly onto the land parcel by livestock (kg N)
$EF_{prp}$ = emission factor for manure deposited directly onto the land parcel by the livestock (kg N₂O-N [kg N]^-1)
$S_{till}$ = scaling factor for no-tillage, 0 except for no-till (dimensionless)
$S_{bc}$ = scaling factor for biochar addition—mineral soils only, 0 with no addition or organic soils (dimensionless)

The total amount of N from synthetic fertilizer $F_{sn}$ is calculated by:

\[ F_{sn} = A \times \sum_i^n ( R^{sn}_i \times N^{prop}_i) \]

where:

$R^{sf}_i$ = is the rate of synthetic i^th fertilizer (kg fertilizer [ha^-1])
$N^{prop}_i$ = is the proportion of N in the fertilizer i^th (kg N [kg fertilizer]^-1)
$A$ = is the area of the field (ha)

The total amount of N from organic fertilizer $F_{on}$ is calculated by:

\[ F_{on} = A \times \sum_i^n ( R^{of}_i \times N^{prop}_i) \]

where:

$R^{of}_i$ = is the rate of organic fertilizer i^th (kg fertilizer [ha^-1])
$N^{prop}_i$ = is the proportion of N in the fertilizer i^th (kg N [kg fertilizer]^-1)
$A$ = is the area of the field (ha)

The total amount of N from crop residue $F_{cr}$ calculated by:

\[ \begin{align} F_{cr} &= CRN_a + CRN_b \\ CRN_b &= CBa \times (1 + R) \times N_b \\ CRN_a &= (CB_a - Y \times A \times DM \times N_a) \times (1-R_m) \\ CB_a &= (Y \times HI^{-1}) \times A \times DM \end{align} \]

where:

$CRN_a$ = aboveground crop and forage renewal residue inputs to the land parcel (kg N)
$CRN_b$ = belowground crop and forage renewal residue inputs to the land parcel (kg N)
$CB_a$ = aboveground crop and forage biomass in dry matter units (kg of dry matter)
$R$ = aboveground biomass to belowground biomass (root-to-shoot) ratio (kg belowground dry matter [kg aboveground dry matter]^-1)
$N_b$ = N content in the belowground residue (kg N [kg dry matter]^-1)
$Y$ = fresh weight of crop harvest yield or peak grazing land forage amount (kg ha^-1)
$A$ = area of a parcel of land (ha)
$N_a$ = N content in the aboveground residue (kg N [kg dry matter]^-1)
$R_m$ = proportion of crop or forage residue removed by burning, grazing, or harvesting residues (kg dry matter removed [kg dry matter produced]^-1)
$HI$ = harvest index: ratio of crop yield or forage removal to total aboveground biomass (kg biomass [kg yield]^-1)
$DM$ = dry matter content of harvested crop biomass or forage (kg dry matter [kg biomass]^-1)

7.13.2 Indirect emissions

Indirect emissions occur when reactive nitrogen is volatilized as NH₃ or NO_x or transported via surface runoff or leaching in soluble forms from cropland or grazing lands where nitrogen additions are occurring, or management practices are influencing nitrogen mineralization from organic matter.

\[ [N_2O]^{indirect} = ([N_2O]^{vol} + [N_2O]^{leach} ) \times [N_2O]^{mw} \]

$[N_2O]^{vol}$ = N₂O emitted by the ecosystem receiving volatilized nitrogen (kg N₂O-N)
$[N_2O]^{leach}$ = N₂O emitted by ecosystem receiving leached and runoff nitrogen (kg N₂O-N)
$[N_2O]^{mw}$ = ratio of molecular weights of N₂O to N₂O-N = 44/28 (kg N₂O [kg N₂O-N]^-1)

The $[N_2O]^{vol}$ is calculated from the fertilizer application data and factors using this equation:

\[ [N_2O]^{vol} = \{ (F_{sn} \times FR_{sn}) + [ ( F_{on} + F_{prp}) \times FR_{on} ] \} \times EF_{vol} \]

where:

$F_{sn}$ = synthetic nitrogen fertilizer applied (kg N)
$FR_{sn}$ = fraction of synthetic nitrogen (NSN) that volatilizes as NH₃ and NO_x (kg N [kg N in synthetic fertilizer]^-1)
$F_{on}$ = nitrogen fertilizer applied of organic origin, including manure, sewage sludge, compost, and other organic amendments (kg N)
$F_{prp}$ = manure nitrogen deposited directly onto the land parcel by livestock (kg N)
$FR_{on}$ = fraction or proportion of $F_{on}$ that volatilizes as NH₃ and NO_x (kg N [kg N in organic fertilizer]^-1)
$EF_{vol}$ = emission factor for volatilized nitrogen or proportion of nitrogen volatilized as NH₃ and NO_x that is transformed to N₂O in receiving ecosystem (kg N₂O-N [kg N]^-1)

And the $[N_2O]^{leach}$ is calculated from the fertilizer application data and factors using this equation:

\[ [N_2O]^{leach} = ([N_2O]^{input} \times FR_{leach}) \times EF_{leach} \]

where:

$[N_2O]^{input}$ = nitrogen inputs, including mineral fertilizer, organic amendments, manure nitrogen deposited by livestock, and residues (kg N)
$FR_{leach}$ = fraction of nitrogen inputs ($[N_2O]^{input}$) that is leached or runs off the land parcel (kg N [kg N in nitrogen inputs]^-1)
$EF_{leach}$ = proportion of leached and runoff nitrogen that is transformed to N₂O in the receiving ecosystem (kg N₂O-N [kg N]^-1)

7.13.3 Total emissions

Finally, the total N₂O emissions are the sum of direct and indirect emissions.

\[ [N_2O]^{total} = [N_2O]^{direct} + [N_2O]^{indirect} \]

$[N_2O]^{total}$ = the annual total $[N_2O]$ emissions (kg N₂O)
$[N_2O]^{direct}$ = the annual total direct $[N_2O]$ emissions (kg N₂O)
$[N_2O]^{indirect}$ = the annual total indirect $[N_2O]$ emissions (kg N₂O)

7.13.4 Emissions per area and per crop production unit

To get emissions per area:

\[ \begin{align} [N_2O]^{area} &= [N_2O]^{total} \times A^{-1} \\ [N_2O]^{area}_{direct} &= [N_2O]^{direct} \times A^{-1} \\ [N_2O]^{area}_{indirect} &= [N_2O]^{indirect} \times A^{-1} \end{align} \]

To get emissions per crop production unit:

\[ \begin{align} [N_2O]^{prod} &= [N_2O]^{total} \times (Y \times A)^{-1} \\ [N_2O]^{prod}_{direct} &= [N_2O]^{direct} \times (Y \times A)^{-1} \\ [N_2O]^{prod}_{indirect} &= [N_2O]^{indirect} \times (Y \times A)^{-1} \end{align} \]

7.13.5 Conversion N₂O to CO₂e

Finally, all N₂O emissions can be converted to CO₂e by applying the Global Warming Potential factor for N₂O.

\[ \begin{align} [CO_2\text{e}]^{total} &= [N_2O]^{total} \times [N_2O]^{gwp} \\ [CO_2\text{e}]^{direct} &= [N_2O]^{direct} \times [N_2O]^{gwp} \\ [CO_2\text{e}]^{indirect} &= [N_2O]^{indirect} \times [N_2O]^{gwp} \\ [CO_2\text{e}]^{total}_{area} &= [N_2O]^{total}_{area} \times [N_2O]^{gwp} \\ [CO_2\text{e}]^{direct}_{area} &= [N_2O]^{direct}_{area} \times [N_2O]^{gwp} \\ [CO_2\text{e}]^{indirect}_{area} &= [N_2O]^{indirect}_{area} \times [N_2O]^{gwp} \\ [CO_2\text{e}]^{total}_{prod} &= [N_2O]^{total}_{prod} \times [N_2O]^{gwp} \\ [CO_2\text{e}]^{total}_{prod} &= [N_2O]^{direct}_{prod} \times [N_2O]^{gwp} \\ [CO_2\text{e}]^{total}_{prod} &= [N_2O]^{indirect}_{prod} \times [N_2O]^{gwp} \end{align} \]

where:

$[CO_2\text{e}]$ = annual $[N_2O]$ emissions expressed in (kg CO₂e)
$[N_2O]^{direct}$ = the annual total direct $[N_2O]$ emissions (tonnes CO₂e)
$[N_2O]^{indirect}$ = the annual total indirect $[N_2O]$ emissions (tonnes CO₂e)

7.13.6 Constants and factors required for this method

The following values are considered constants for the N₂O method.

symbol	name	value	Units
N2O_MW	ratio of molecular weight of N₂O to N₂O-N	44/28	kg N₂O [kg N₂O-N]^-1
FR_on	Fraction of nitrogen in organic amendments (excluding crop residues) and PRP nitrogen (FON,PRP) that volatilizes as NH₃ and NO_x	0.21	dimentionless
EF_leach	Proportion of leached and runoff nitrogen that is transformed to N2O in the receiving ecosystem (metric tons N2O-N/metric tons N)	0.011	dimentionless
S_bc	biochar scaling factor	-0.23	dimentionless

Emission and scaling factors related to climate conditions (Download):

symbol	name	climate	value
EF_sn	Emission factor for synthetic nitrogen input	Arid/Semi-arid	0.005
EF_sn	Emission factor for synthetic nitrogen input	Wet/Mesic	0.016
S_sr	Slow-release fertilizer use scaling factor	Arid/Semi-arid	-0.380
S_sr	Slow-release fertilizer use scaling factor	Wet/Mesic	-0.200
S_inh	Nitrification inhibitor use factor	Arid/Semi-arid	-0.460
S_inh	Nitrification inhibitor use factor	Wet/Mesic	-0.330
EF_on	Emission factor for other nitrogen inputs (organic fertilizer, manure and crop residue)	Arid/Semi-arid	0.006
EF_on	Emission factor for other nitrogen inputs (organic fertilizer, manure and crop residue)	Wet/Mesic	0.005
EF_vol	Indirect soil N2O emission factor for volatilized nitrogen losses	Arid/Semi-arid	0.005
EF_vol	Indirect soil N2O emission factor for volatilized nitrogen losses	Wet/Mesic	0.014

Emission and scaling factors related to climate conditions and tillage practice (Download):

tillage	climate	S_till
Conventional or reduced till	Arid/Semi-arid	0.000
Conventional or reduced till	Wet/Mesic	0.000
< 10 years following no-till adoption	Arid/Semi-arid	0.380
< 10 years following no-till adoption	Wet/Mesic	-0.015
>= 10 years following no-till adoption	Arid/Semi-arid	-0.330
>= 10 years following no-till adoption	Wet/Mesic	-0.090

Emission and scaling factors related to climate conditions and livestock (Download):

climate	livestock	EF_prp
Arid/Semi-arid	Dairy and beef cattle	0.002
Wet/Mesic	Dairy and beef cattle	0.006
Arid/Semi-arid	Sheep	0.003
Wet/Mesic	Sheep	0.003

Climates are divided into the following categories:

class	region	condition
Wet/Mesic	Temperate/Boreal	ratio of mean annual precipitation to potential evapotranspiration is greater than 0.8
Wet/Mesic	Tropical/Subtropical	mean annual precipitation grather than 1000 mm
Arid/Semi-arid	Temperate/Boreal	ratio of mean annual precipitation to potential evapotranspiration is lower or equal than 0.8
Arid/Semi-arid	Tropical/Subtropical	mean annual precipitation lower or equal than 1000 mm

The U.S. counties from the 48 contiguous states were grouped into these two broad classes using the Koppen-Geiger climate classification map at the county level provided by Kottek et al. (2006).

The table with climate classification by county can be downloaded here. The following is a sample of ten rows:

state	county	cls	climate
Nevada	Nye	BSk	Arid/Semi-arid
Texas	Castro	BSk	Arid/Semi-arid
Colorado	Yuma	BSk	Arid/Semi-arid
Texas	Glasscock	BSk	Arid/Semi-arid
Montana	Liberty	BSk	Arid/Semi-arid
Georgia	Cook	Cfa	Wet/Mesic
Texas	Leon	Cfa	Wet/Mesic
Ohio	Wyandot	Dfa	Wet/Mesic
New Hampshire	Belknap	Dfb	Wet/Mesic
Michigan	Eaton	Dfb	Wet/Mesic

N content and volatilization fraction from fertilizers (Download):

Source Detail	Nprop	FR_sn
Ammonia (aqueous)	0.200	0.08
Ammonia (aqueous) (green ammonia)	0.200	0.08
Ammonia (conventional)	0.820	0.08
Ammonia (green)	0.820	0.08
Ammonium nitrate	0.350	0.05
Ammonium nitrate (green ammonia)	0.350	0.05
Ammonium sulfate	0.210	0.08
Ammonium sulfate (green ammonia)	0.210	0.08
Calcium ammonium nitrate	0.270	0.05
Calcium ammonium nitrate (green ammonia)	0.270	0.05
Diammonium phosphate	0.180	0.08
Diammonium phosphate (green ammonia)	0.180	0.08
Monoammonium phosphate	0.120	0.08
Monoammonium phosphate (green ammonia)	0.120	0.08
Potassium nitrate	0.138	0.01
Urea	0.460	0.15
Urea (green ammonia)	0.460	0.15
Urea ammonium nitrate	0.320	0.10
Urea ammonium nitrate (green ammonia)	0.320	0.10
US average nitrogen fertilizer	1.000	0.10
Ammonia (aqueous)	0.200	0.08
Ammonia (aqueous) (green ammonia)	0.200	0.08
Ammonia (conventional)	0.820	0.08
Ammonia (green)	0.820	0.08
Ammonium nitrate	0.350	0.05
Ammonium nitrate (green ammonia)	0.350	0.05
Ammonium sulfate	0.210	0.08
Ammonium sulfate (green ammonia)	0.210	0.08
Calcium ammonium nitrate	0.270	0.05
Calcium ammonium nitrate (green ammonia)	0.270	0.05
Diammonium phosphate	0.180	0.08
Diammonium phosphate (green ammonia)	0.180	0.08
Monoammonium phosphate	0.120	0.08
Monoammonium phosphate (green ammonia)	0.120	0.08
Potassium nitrate	0.138	0.01
Urea	0.460	0.15
Urea (green ammonia)	0.460	0.15
Urea ammonium nitrate	0.320	0.10
Urea ammonium nitrate (green ammonia)	0.320	0.10
US average nitrogen fertilizer	1.000	0.10

N content from manure (Download) for different regions, manure sources and types:

region	animal_category	moisture_designation	pct_as_applied
Midwest	Beef	Solid	0.780
Northeast	Dairy	Liquid	0.130
Northern Plains	Poultry	Semi-solid	0.688
Pacific Northwest	Beef	Semi-solid	0.650
Southeast	Poultry	Solid	2.680
Southern Plains	Dairy	Solid	0.950
Southwest	Swine	Semi-solid	0.870

States by region (Download)

N content from other org fertilizers (Download) for different regions, manure sources and types:

fert_type	Nprop
Green manure	0.0325
Compost	0.0125
Sewage sludge/Biosolids	0.0300

N content from crop residues (Download)

crop	N_a	N_b
Alfalfa	0.027	0.019
Barley	0.007	0.014
Chickpeas (garbanzos)	0.008	0.008
Corn (grain)	0.006	0.007
Corn (silage)	0.006	0.007
Cotton	0.012	0.007
Dry Beans	0.008	0.008
Dry Peas	0.008	0.008
Fava Beans	0.008	0.008
Lentils	0.008	0.008
Lupin	0.008	0.008
Other crops	0.006	0.009
Other grain crops	0.006	0.009
Peanuts	0.016	0.014
Potatoes	0.019	0.014
Rice	0.007	0.009
Sorghum	0.007	0.006
Soybeans	0.008	0.008
Sugar beets	0.019	0.014
Wheat (durum)	0.006	0.009
Wheat (spring)	0.006	0.009
Wheat (winter)	0.006	0.009

Harvest indices and dry matter contents for crops (Download):

crop	DM	HI	R
Alfalfa	0.880	0.95	0.87
Barley	0.855	0.46	0.11
Chickpeas (garbanzos)	0.840	0.46	0.08
Corn (grain)	0.845	0.53	0.18
Corn (silage)	0.350	0.95	0.18
Cotton	0.920	0.40	0.17
Dry Beans	0.840	0.46	0.08
Dry Peas	0.840	0.46	0.08
Fava Beans	0.840	0.46	0.08
Lentils	0.840	0.46	0.08
Lupin	0.840	0.46	0.08
Peanuts	0.910	0.40	0.07
Potatoes	0.200	0.50	0.07
Rice	0.860	0.42	0.22
Rye	0.860	0.50	0.20
Sorghum	0.860	0.44	0.18
Soybeans	0.870	0.42	0.19
Sugar beets	0.150	0.40	0.43
Wheat (durum)	0.865	0.39	0.20
Wheat (spring)	0.865	0.39	0.20
Wheat (winter)	0.865	0.39	0.20
All other crops	0.860	0.39	0.20

Leaching fraction factors (Download):

cover_crop	FR_leach
Without cover crops	0.24
With leguminous cover crops	0.18
With non-leguminous cover crops	0.09

The followings are the values for $[N_2O]^{gwp}$ available in this reference table (Download).

symbol	name	Source	Assessment Report (AR)	Time Horizon	value	units
N2O_GWP	Global Warming Potential	ref table	AR6	100-yr	273	kg CO₂-eq/kg N₂O
N2O_GWP	Global Warming Potential	ref table	AR5 (with climate-carbon feedback)	100-yr	298	kg CO₂-eq/kg N₂O
N2O_GWP	Global Warming Potential	ref table	AR5 (without climate-carbon feedback)	100-yr	265	kg CO₂-eq/kg N₂O
N2O_GWP	Global Warming Potential	ref table	AR4	100-yr	298	kg CO₂-eq/kg N₂O
N2O_GWP	Global Warming Potential	ref table	AR6	20-yr	273	kg CO₂-eq/kg N₂O
N2O_GWP	Global Warming Potential	ref table	AR5 (with climate-carbon feedback)	20-yr	268	kg CO₂-eq/kg N₂O
N2O_GWP	Global Warming Potential	ref table	AR5 (without climate-carbon feedback)	20-yr	264	kg CO₂-eq/kg N₂O
N2O_GWP	Global Warming Potential	ref table	AR4	20-yr	289	kg CO₂-eq/kg N₂O

7.13.7 Example

The following is a sample of the results produced by the method. For illustration purposes, Table 25 presents the scenarios considered:

Table 25: Scenarios used for demonstration of the N2O emissions method

Scenario	Area (ha)	Yield (kg/ha)	Fertilizer source	Rate (kg/ha)	Slow release	N Inhibitor
1	40.5	10607.7	US average nitrogen fertilizer	151.3	no	no
2	40.5	10607.7	US average nitrogen fertilizer	151.3	no	yes
3	40.5	10607.7	US average nitrogen fertilizer	151.3	yes	no

Base scenario: a corn field from Champaign, IL, under reduced till, regular management practices, and average N fertilizer rate
N inhibitor: base scenario with the use of an N inhibitor
Slow release: base scenario with the use of a slow-release fertilizer

The following table summarizes the results from the N₂O method.

Table 26: Output from the method for Soil N2O emissions

Scenario	Metric	System Boundary	Source Category	kg CO2e / whole field	kg CO2e / ha	kg CO2e / kg corn
1	GHG Emissions	On-Farm Non-Mechanical Sources and Sinks	Soil N2O	77500	1915.1	0.181
2	GHG Emissions	On-Farm Non-Mechanical Sources and Sinks	Soil N2O	60127	1485.8	0.140
3	GHG Emissions	On-Farm Non-Mechanical Sources and Sinks	Soil N2O	66971	1654.9	0.156

The explanation of Table 26 is as follows:

Emissions are expressed as kg CO₂e:
The base scenario, with conventional N fertilizers and no N inhibitors produced approx. 77500 kg CO₂e.
The use of N inhibitors and slow release fertilizers reduced the direct emissions. For instance, the whole-field emissions are 60127 and 66971 kg CO₂e for the scenarios with N inhibitors and slow release, respectively.
In addition, the use of N inhibitors and slow release fertilizers reduced the indirect emissions by decreasing the amount of N at risk of being potentially leached or volatilized.
In these scenarios, the addition of N inhibitors resulted in the largest reduction of N₂O emissions.

8 Allocation

8.1 Cotton

The cotton industry requested an 83% allocation of burden to the lint, based on the economic value of cotton lint and seed.

9 Life cycle analysis consultant

Field to Market contracted with Rylie Pelton, Ph.D., to assist with the Fieldprint Platform revision. Dr. Pelton is the founder and CEO of LEIF, and a Research Scientist of Industrial Ecology at the University of Minnesota Institute on the Environment, specializing in methods to assess and improve the impacts of complex supply chains. Dr. Pelton holds a Ph.D. in Industrial Ecology, a Ph.D. minor in Public Health, and a M.S. and B.S. in Corporate Environmental Management from the University of Minnesota.

10 Scenarios

Here we include scenarios demonstrating results from the revised methods and impact factors. Crop production inputs were obtained from the literature, University crop enterprise budgets, USDA NASS averages, and Ogle et al. (2024). These scenarios are meant to represent realistic conditions; however, they should not be considered a comprehensive assessment of the GHG Emission metric estimate for a given region or crop.

Below are notes that apply to all scenarios:

Scenarios do not include an estimate for soil carbon stock changes.
Scenarios do not include an estimate for direct land use change.
We applied global warming potential factors from AR6 with 100-yr time horizon.
GHG emissions from NF₃ and SF₆ associated with electricity generation and distribution are excluded from the graphs in this section; the amounts are not visible.
Unless noted for a given scenario, the transportation fuel is diesel.

10.1 Corn for grain

10.1.1 Production inputs

Location: Champaign County, Illinois.
Yield: 225 bushel/acre.
Diesel used for field operations: 8 gallon/acre.
N-P₂O₅-K₂O rates: 175-125-75; no use of slow-release N fertilizers or N inhibitors.
Lime rate: 500 lb/acre.
Rainfed.
Transportation distance from the field to the farm grain drying system: 15 miles.
Amount of moisture removed through drying: 3%.
Seeding rate of 30 lb/acre or approximately 40,000 seeds/acre.
Pesticides: 3 herbicide products, 1 insecticide product, 1 fungicide product, treated seed.

10.1.2 Results

The aggregated estimate is 0.26 kg CO₂e/kg corn grain.
Disaggregated estimates are shown in Figure 9.
67% of GHG emissions are coming from soil N₂O.
Area-based emissions are 3,706 kg CO₂e/hectare. Considering the small amount of estimated biogenic CH₄ being sequestered, net emissions are 3,684 kg CO₂e/hectare.

Figure 9: Disaggregated estimates for the GHG Emissions metric for corn (grain)

10.2 Soybeans

Location: Des Moines County, Iowa.
Yield: 65 bushel/acre.
Diesel used for field operations: 7 gallon/acre.
N-P₂O₅-K₂O rates: 0-42-80.
Lime rate: 450 lb/acre.
Rainfed.
Transportation distance from the field to the farm grain drying system: 35 miles.
Transportation fuel: biodiesel.
Amount of moisture removed through drying: 2%.
Seeding rate of 167 lb/acre or approximately 250,000 seeds/acre.
Pesticides: 3 herbicide products, 2 insecticide products, 2 fungicide products, inoculant, treated seed.

10.2.1 Results

The aggregated estimate is 0.22 kg CO₂e/kg soybeans.
Disaggregated estimates are shown in Figure 10.
43% of GHG emissions are coming from soil N₂O.
Area-based emissions are 973 kg CO₂e/hectare. Considering the small amount of estimated biogenic CH₄ being sequestered, net emissions are 951 kg CO₂e/hectare.
The emissions associated with biogenic CO₂ from biodiesel are estimated at 17 kg CO₂e/hectare, which are shown in Figure 10 but are not included in the estimates per hectare and per kg of soybean as allowed by the GHG Reporting Program. In this scenario, biodiesel was used to power the trucks transporting the soybean crop output to the grain dryer for a distance of 35 miles.

Figure 10: Disaggregated estimates for the GHG Emissions metric for soybeans

10.3 Winter wheat

Location: Hodgeman County, Kansas.
Yield: 62 bushel/acre.
Diesel used for field operations: 7 gallon/acre.
N-P₂O₅-K₂O rates: 101-30-25.
Lime rate: 500 lb/acre.
Rainfed.
Transportation distance from the field to the farm grain drying system: 25 miles.
Amount of moisture removed through drying: 3%.
Seeding rate of 157 lb/acre or approximately 1,500,000 seeds/acre.
Approximately half the field was set on fire before planting to remove corn residue from the previous year.
Pesticides: 3 herbicide products, 2 insecticide products, 2 fungicide products, 1 growth regulator, treated seed.

10.3.1 Results

The aggregated estimate is 0.59 kg CO₂e/kg wheat.
Disaggregated estimates are shown in Figure 11.
57% of GHG emissions are coming from soil N₂O.
Area-based emissions are 2,461 kg CO₂e/hectare. Considering the small amount of estimated biogenic CH₄ being sequestered, net emissions are 2,439 kg CO₂e/hectare.
Setting crop residue on fire for half the field contributed 230 kg CO₂e/hectare.

Figure 11: Disaggregated estimates for the GHG Emissions metric for winter wheat

10.4 Cotton

10.4.1 Production inputs

Location: Tift County, Georgia.
Yield: 1,200 lb/acre of lint.
Diesel used for field operations: 9 gallon/acre.
N-P₂O₅-K₂O rates: 90-70-70; no use of slow-release N fertilizers or N inhibitors.
Lime rate: 660 lb/acre.
Irrigation rate of 13 acre-inches/acre; well depth of 250 ft; pump pressure of 50 psi; pump powered by electricity.
Transportation distance from the field to the ginning facility: 25 miles.
Cotton lint wetter than normal.
Seeding rate of 19 lb/acre or approximately 85,000 seeds/acre.
Pesticides: 3 herbicide products, 2 insecticide products, 2 fungicide products, 2 growth regulators, treated seed.

10.4.2 Results

The aggregated estimate is 2.21 kg CO₂e/kg cotton output (lint and seed). Considering the 83% allocation to lint, the final estimate is 1.8 kg CO₂e/kg cotton lint.
Disaggregated estimates are shown in Figure 12.
36% of GHG emissions are coming from soil N₂O.
Area-based emissions are 2,804 kg CO₂e/hectare for production of lint and seed. Considering the small amount of estimated biogenic CH₄ being sequestered, net emissions are 2,780 kg CO₂e/hectare, or 2,307 kg CO₂e/hectare allocated to the lint.

Figure 12: Disaggregated estimates for the GHG Emissions metric for cotton (lint and seed)

10.5 Peanuts

10.5.1 Production inputs

Location: Tift County, Georgia.
Yield: 4,700 lb/acre.
Diesel used for field operations: 12 gallon/acre.
No N-P₂O₅-K₂O needed; 1 application of micronutrients (boron).
Gypsum rate: 1,000 lb/acre.
Irrigation rate of 8 acre-inches/acre; well depth of 250 ft; pump pressure of 50 psi; pump powered by diesel.
Transportation distance from the field to the peanut buying point: 15 miles.
Amount of moisture removed through drying: 7%.
Seeding rate of 130 lb/acre or approximately 59,800 seeds/acre.
Pesticides: 3 herbicide products, 2 insecticide products, 6 fungicide products, inoculant, treated seed.

10.5.2 Results

The aggregated estimate is 0.47 kg CO₂e/kg peanuts.
Disaggregated estimates are shown in Figure 13.
39% of GHG emissions are coming from soil N₂O. Although there were no applications of N fertilizer, the soil N₂O model considers crop residue as another input that can result in a release of N₂O emissions.
Area-based emissions are 2,492 kg CO₂e/hectare. Considering the small amount of estimated biogenic CH₄ being sequestered, net emissions are 2,469 kg CO₂e/hectare.

Figure 13: Disaggregated estimates for the GHG Emissions metric for peanuts

10.6 Chickpeas (garbanzos)

10.6.1 Production inputs

Location: Fallon County, Montana.
Yield: 1,574 lb/acre.
Diesel used for field operations: 6 gallon/acre.
N-P₂O₅-K₂O rates: 10-25-10; no use of slow-release N fertilizers or N inhibitors.
Rainfed.
Transportation distance from the field to the processing facility: 45 miles.
Crop does not need to be dried.
Seeding rate of 159 lb/acre.
Pesticides: 2 herbicide products, 2 insecticide product, 1 fungicide product, inoculant, treated seed.

10.6.2 Results

The aggregated estimate is 0.3 kg CO₂e/kg chickpeas.
Disaggregated estimates are shown in Figure 14.
37% of GHG emissions are coming from soil N₂O.
Area-based emissions are 527 kg CO₂e/hectare. Considering the small amount of estimated biogenic CH₄ being sequestered, net emissions are 505 kg CO₂e/hectare.

Figure 14: Disaggregated estimates for the GHG Emissions metric for chickpeas (garbanzos)

10.7 Potatoes

10.7.1 Production inputs

Location: Bingham County, Idaho.
Yield: 420 cwt/acre.
Diesel used for field operations: 24 gallon/acre.
N-P₂O₅-K₂O-S rates: 260-235-215-85; no use of slow-release N fertilizers or N inhibitors.
Irrigation rate of 21 acre-inches/acre; well depth of 500 ft; pump pressure of 250 psi; pump powered by electricity.
Transportation distance from the field to the farm storage system: 5 miles.
Seeding rate of 6,965 lb/acre or approximately 34,800 seeds/acre.
Pesticides: 4 herbicide products, 4 insecticide products, 3 fungicide products, 1 growth regulator, 1 fumigant, 1 application of sulfuric acid, treated seed.

10.7.2 Results

The aggregated estimate is 0.15 kg CO₂e/kg potatoes.
Disaggregated estimates are shown in Figure 15.
38% of GHG emissions are coming from soil N₂O.
Area-based emissions are 7,176 kg CO₂e/hectare. Considering the small amount of estimated biogenic CH₄ being sequestered, net emissions are 7,152 kg CO₂e/hectare.

Figure 15: Disaggregated estimates for the GHG Emissions metric for potatoes

10.8 Sorghum

10.8.1 Production inputs

Location: Hodgeman County, Kansas.
Yield: 66 bushel/acre.
Diesel used for field operations: 6 gallon/acre.
N-P₂O₅-K₂O rates: 140-60-15; no use of slow-release N fertilizers or N inhibitors.
Irrigation rate of 15 acre-inches/acre; well depth of 150 ft; pump pressure of 75 psi; pump powered by electricity.
Transportation distance from the field to the storage facility: 15 miles.
Crop does not need to be dried.
Seeding rate of 12 lb/acre or approximately 100,000 seeds/acre.
Pesticides: 2 herbicide products, 1 insecticide product, 1 fungicide product, treated seed.

10.8.2 Results

The aggregated estimate is 0.73 kg CO₂e/kg sorghum.
Disaggregated estimates are shown in Figure 16.
55% of GHG emissions are coming from soil N₂O.
Area-based emissions are 3,009 kg CO₂e/hectare. Considering the small amount of estimated biogenic CH₄ being sequestered, net emissions are 2,987 kg CO₂e/hectare.

Figure 16: Disaggregated estimates for the GHG Emissions metric for sorghum)

10.9 Sugar beets

10.9.1 Production inputs

Location: Crook County, Oregon.
Yield: 22 ton/acre with 18% sugar content (2,000 lb/ton).
Diesel used for field operations: 14 gallon/acre.
N-P₂O₅-K₂O rates: 106-148-46; no use of slow-release N fertilizers or N inhibitors.
Lime rate of 500 lb/acre.
Irrigation rate of 25 acre-inches/acre; well depth of 250 ft; pump pressure of 50 psi; pump powered by electricity.
Transportation distance from the field to the farm storage system: 25 miles.
Seeding rate of 20 lb/acre.
Pesticides: 2 herbicide products, 1 insecticide product, 4 fungicide products, 1 growth regulator, 1 fumigant, treated seed.

10.9.2 Results

The aggregated estimate is 0.57 kg CO₂e/kg sugar.
Disaggregated estimates are shown in Figure 17.
57% of GHG emissions are coming from soil N₂O.
Area-based emissions are 5,093 kg CO₂e/hectare. Considering the small amount of estimated biogenic CH₄ being sequestered, net emissions are 5,068 kg CO₂e/hectare.

Figure 17: Disaggregated estimates for the GHG Emissions metric for sugar beets

10.10 Rice (Mid-South)

10.10.1 Production inputs

Location: Monroe County, Arkansas.
Yield: 74 cwt/acre.
Diesel used for field operations: 8 gallon/acre.
N-P₂O₅-K₂O rates: 170-93-46; no use of slow-release N fertilizers or N inhibitors.
Lime rate: 500 lb/acre.
Irrigation rate of 30 acre-inches/acre; well depth of 50 ft; pump pressure of 50 psi; pump powered by electricity.
Transportation distance from the field to the storage facility: 25 miles.
Amount of moisture removed: 3%.
Water management: continuously flooded.
Soil clay content: 23%.
Non-flooded pre-season.
Planting into low residue; drill seeded.
Seeding rate of 156 lb/acre.
Pesticides: 2 herbicide products, 2 insecticide products, 1 fungicide product, treated seed.

10.10.2 Results

The aggregated estimate is 1.18 kg CO₂e/kg rice.
Disaggregated estimates are shown in Figure 18.
65% of GHG emissions are coming from CH₄.
Area-based emissions are 9,262 kg CO₂e/hectare.

Figure 18: Disaggregated estimates for the GHG Emissions metric for rice in Arkansas)

10.11 Rice (California)

10.11.1 Production inputs

Location: Colusa County, California.
Yield: 85 cwt/acre.
Diesel used for field operations: 8 gallon/acre.
N-P₂O₅-K₂O rates: 170-93-46; no use of slow-release N fertilizers or N inhibitors.
Lime rate: 500 lb/acre.
Irrigation rate of 30 acre-inches/acre; well depth of 50 ft; pump pressure of 50 psi; pump powered by electricity.
Transportation distance from the field to the storage facility: 25 miles.
Amount of moisture removed: 3%.
Water management: continuously flooded.
Soil clay content: 46%.
Flooded pre-season > 30 days.
Planting into high residue; water seeded.
Seeding rate of 156 lb/acre.
Pesticides: 2 herbicide products, 2 insecticide products, 1 fungicide product, treated seed.

10.11.2 Results

The aggregated estimate is 1.06 kg CO₂e/kg rice.
Disaggregated estimates are shown in Figure 19.
66% of GHG emissions are coming from CH₄.
Area-based emissions are 9,713 kg CO₂e/hectare.

Figure 19: Disaggregated estimates for the GHG Emissions metric for rice in California)

References

Arkansas. 2024. “Pumping Plant Efficiency.” https://www.uaex.uada.edu/environment-nature/water/agriculture-irrigation/pumping-plant-efficiency.aspx.

Audsley, Eric, K Stacey, DJ Parsons, and AG Williams. 2009. “Estimation of the Greenhouse Gas Emissions from Agricultural Pesticide Manufacture and Use.” https://doi.org/10.13140/RG.2.1.5095.3122.

Bigelow, Daniel. 2014. “USDA Economic Research Service-Farmland Ownership and Tenure.” https://www.ers.usda.gov/topics/farm-economy/land-use-land-value-tenure/farmland-ownership-and-tenure.

BLS. 2017. “Commodity Flow Survey (CFS).” Bureau of Transportation Statistics. https://www.bts.gov/cfs.

Boryan, Claire, Zhengwei Yang, Rick Mueller, and Mike Craig. 2011. “Monitoring US Agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program.” Geocarto International 26 (5): 341–58.

EC–European Commission. 2010. “2010/335/: Commission Decision of 10 June 2010 on Guidelines for the Calculation of Land Carbon Stocks for the Purpose of Annex v to Directive 2009/28/EC (Notified Under Document c(2010) 3751).” Official Journal of the European Union. https://eur-lex.europa.eu/eli/dec/2010/335/oj.

Eisenhauer, Dean E, Derrel L Martin, Derek M Heeren, and Glenn J Hoffman. 2021. Irrigation Systems Management. American Society of Agricultural; Biological Engineers (ASABE).

Fipps, Guy. 1995. “Calculating Horsepower Requirements and Sizing Irrigation Supply Pipelines.”

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

Harrison, Kerry. 2012. “Irrigation Pumping Plants and Energy Use.” https://extension.uga.edu/publications/detail.html?number=B837&title=irrigation-pumping-plants-and-energy-use.

Hoffman, Glenn J, TA Howell, and Kenneth H Solomon. 1990. Management of Farm Irrigation Systems. Joseph, MI, USA: American Society of Agricultural Engineers.

IPCC. 2019. “2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.” Agriculture, Forestry and Other Land Use. IPCC Geneva, Switzerland:

Kottek, Markus, Jürgen Grieser, Christoph Beck, Bruno Rudolf, and Franz Rubel. 2006. “World Map of the Köppen-Geiger Climate Classification Updated.” Meteorologische Zeitschrift 15 (3): 259–63. https://doi.org/10.1127/0941-2948/2006/0130.

Kucera, Michael J., and Christopher B. Coreil. 2023. “Overview of USDA-NRCS Erosion Prediction Technology and Conservation Resources - Land Management Operations Database and Use.” Soil Erosion Research Under a Changing Climate, January 8-13, 2023, Aguadilla, Puerto Rico, USA. https://doi.org/10.13031/soil.23007.

Liu, Xinyu, Hao Cai, Hoyoung Kwon, and Michael Wang. 2023. “Feedstock Carbon Intensity Calculator (Fd-Cic): Users’ Manual and Technical Documentation.” Argonne National Laboratory (ANL), Argonne, IL (United States).

Martin, Derrel L, Tom W Dorn, Steve R Melvin, Alan J Corr, and William Kranz. 2011. “Evaluating Energy Use for Pumping Irrigation Water.” In.

Ogle, Stephen M, Paul R Adler, Gary Bentrup, Justin Derner, Grant Domke, Stephen Del Grosso, Johannes Lehmann, Michele Reba, and Dominic Woolf. 2024. “Chapter 3: Quantifying Greenhouse Gas Sources and Sinks in Cropland and Grazing Land Systems.” In: Hanson, Wes L.; Itle, Cortney; Edquist, Kara, Eds. Quantifying Greenhouse Gas Fluxes in Agriculture and Forestry: Methods for Entity-Scale Inventory. Technical Bulletin Number 1939, 2nd Edition. Washington, DC: US Department of Agriculture, Office of the Chief Economist. 6-1-6-23. Chapter 3. 1939: 3–1.

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1 Purpose

2 Describing the concept of crop intervals

2.1 Case 1: A grower produces one cash crop per season

2.2 Case 2: A grower has a rotation with double-cropping

2.3 Case 3: A grower produces a cash crop per year and adds cover cropping

2.4 Case 4: A grower has fallow years in the rotation

2.5 Case 5: A grower experiences crop failure or crop abandonment

2.6 Case 6: A grower produces a rice crop with ratooning

2.7 Case 7: A grower has a rotation with multi-year alfalfa and annual crops

2.8 Case 8: Fields with rotations that include crops not part of Field to Market’s program

3 Area planted or harvested for Field to Market crops

4 Global Warming Potential factors

5 Impact factors

5.1 Electricity

5.2 Fuels

5.2.1 Energy use

5.2.2 GHG emissions

5.3 Fertilizers

5.3.1 Energy use

5.3.2 GHG emissions

5.4 Pesticides

5.4.1 Energy use

5.4.2 GHG emissions

5.4.3 Pesticide rate assumptions

5.5 Seed production

5.5.1 Energy use

5.5.2 GHG emissions

6 Explaining disaggregation of energy use and GHG emission sources

6.1 Energy Use metric

6.2 GHG Emissions metric

7 Revised methods

7.1 Irrigation operations

7.1.1 Irrigation Fuel Consumption Example

7.2 Manure transportation

7.3 Field operations

7.4 Crop transportation

7.5 Crop drying

7.6 CH4 emissions from flooded rice cultivation

7.6.1 Annual total (whole field) CH4 flux

7.6.2 Annual total CH4 flux per area and per crop production unit

7.6.3 Conversion of CH4 to CO2e

7.6.4 Example

7.7 CH4 flux from non-flooded soils

7.7.1 Emissions per area and per crop production unit

7.7.2 Conversion CH4 to CO2e

7.7.3 Example

7.8 CO2 from carbonate lime applications to soils

7.8.1 Emissions per area and per crop unit

7.8.2 Example

7.9 CO2 from urea fertilizer applications

7.9.1 Emissions per area and per crop unit

7.9.2 Example

7.10 Direct land use change

7.10.1 Emissions per area and per crop production unit

7.10.2 Constants and factors required for calculation

7.10.3 Example

7.11 Non-CO2 emissions from biomass burning

7.11.1 Emissions per area and per crop production unit

7.11.2 Conversion of GHG emissions to CO2e

7.11.3 Constants and factors required for calculation

7.11.4 Example

7.12 Soil carbon stock changes

7.12.1 Attribution of CO2 emissions or sequestration by crop interval

7.13 Soil N2O

7.13.1 Direct emissions

7.13.2 Indirect emissions

7.13.3 Total emissions

7.13.4 Emissions per area and per crop production unit

7.13.5 Conversion N2O to CO2e

7.13.6 Constants and factors required for this method

7.13.7 Example

8 Allocation

8.1 Cotton

9 Life cycle analysis consultant

10 Scenarios

10.1 Corn for grain

10.1.1 Production inputs

10.1.2 Results

10.2 Soybeans

10.2.1 Results

7.6 CH₄ emissions from flooded rice cultivation

7.6.1 Annual total (whole field) CH₄ flux

7.6.2 Annual total CH₄ flux per area and per crop production unit

7.6.3 Conversion of CH₄ to CO₂e

7.7 CH₄ flux from non-flooded soils

7.7.2 Conversion CH₄ to CO₂e

7.8 CO₂ from carbonate lime applications to soils

7.9 CO₂ from urea fertilizer applications

7.11 Non-CO₂ emissions from biomass burning

7.11.2 Conversion of GHG emissions to CO₂e

7.12.1 Attribution of CO₂ emissions or sequestration by crop interval

7.13 Soil N₂O

7.13.5 Conversion N₂O to CO₂e