Drying and related post-harvest processing

Methods 5.0

Energy and emissions related to post-harvest processing activities like drying and ginning.

Introduction

Post-harvest processes like crop drying may contribute significantly to the total GHGs in agriculture sector (Panigrahi et al. 2023). Drying is the most common form of post-harvest energy use and emissions in the Fieldprint Platform. Drying energy is based on the amount of water removed from a crop and the efficiency of the selected system to remove that water. There are many conditions that affect drying, and values can range from 1200-3200 BTU/lb of water removed. Many options are provided in the methods.

The silos of Mt. Aire Farms in Trappe, Md., rise above agricultural fields on July 2, 2010. (Photo by Matt Rath/Chesapeake Bay Program)

Methods

Logic for System Boundaries

Whether the transportation energy use and associated GHG emissions are assigned to “On-Farm Mechanical” or “Post-Harvest” (i.e. “Off-Farm”) depends on the crop and the location of post-harvest processing.

If the user indicates that an on-farm facility was used for drying/storage, the Calculator will assign drying energy and emissions to “On-Farm Mechanical”.

If the user indicates that an off-farm facility was used for drying/storage, the Calculator will assign transportation energy and emissions to “Post-harvest”.

Most grain crops + pulses

This first section is applicable to corn, pulse crops, sorghum, soybean, barley, rice, and wheat.

To calculate drying energy, we must first estimate the mass of water removed. The difference between the initial moisture content and the final moisture content after drying is expressed as percentage points. The percentage points of water removed is correlated to mass of water removed per unit of crop, using the following regression Equation 1 below. It was determined to use one regression equation for most of the listed crops, except wheat.

\[ m_{water\ per\ unit} = 0.0113483 \times \Delta_{M} + 0.0001711 \times {\Delta_{M}}^2 \tag{1}\]

One formula now serves as a reasonable drying function for most crops.

For example, if you dry a corn grain crop from 20% moisture to 16% moisture (4% points), we estimate 0.0841 kg of water were removed per kg of corn dried. Multiplying this by the total production of the field (keep same units) gives the total amount of water removed through drying, for that field.

The user indicates what drying system was used based on the options in Table 6. Each option has an associated energy footprint for gas and electricity meaning that, in addition to the total drying energy for the field, the GHG emissions associated with the gas and electric can be calculated later on. Of course, any of the these results can be divided by crop production or field area to calculate efficiency metrics.

One source states that

the Propane Education and Research Council (PERC) estimates that about 80 percent of grain dryers in the U.S. use propane.

Wheat

From Ford (2024)

In-bin wheat drying processes can utilize either natural air (unheated) or low temperature air (slightly heated usually less than 10 °F) to dry grain in bins (see figure 1). The air is forced up through the grain with fans until the grain moisture content is sufficiently reduced.

High temperature batch or continuous flow dryers are usually used to dry large capacities of wheat. These units typically have very high airflow rates, and they do not require supplemental heat for daytime drying when harvesting wheat at 18-20% moisture range.

In previous metric versions, wheat had slightly lower values for water removed. Keeping with this and in agreement with values in Ford (2024), wheat will have its own drying formula and generic drying option in the Table 6.

\[ m_{water\ per\ unit} = 0.0106173 \times \Delta_{M} + 0.0001592 \times {\Delta_{M}}^2 \tag{2}\]

Inputs

Input	Value	Units	Symbol
Crop yield (standardized)	User entry	kg/ac	\(Y_{s}\)
Field area	User defined boundary	acre	\(A\)
Points of moisture removed	Difference between initial moisture content and final moisture after drying	percentage (0 - 100%)	\(\Delta_{M}\)
Drying system options and energy use	See Table 6	MJ kg-1 water removed	\(e_{gas}\) \(e_{electric}\)
Energy and emission factors	Table 7

Steps

The following should be in harmony with the logic described in the Crop Transportation page.

1. Was the crop was dried using energy?

   • If not, as in the case of natural air drying, energy and emissions are zero.

2. Where was the crop dried?

   • If the crop was dried using an on-farm system, ask What type of drying system was used?

   • If the crop was dried off-farm, the Calculator automatically selects a Commercial Drying system

     • The energy factors for this default are a reasonable “average” across drying systems, including the alfalfa options.

   • Where the crop was dried and stored affects the system boundary (see logic)

3. How much moisture was removed by drying?

4. Using the points of moisture removed¹, calculate the amount of water removed per kg of crop using either Equation 1 or Equation 2.

5. Calculate the total amount of water removed (kg) by multiplying the Step 2 result by total crop transported (kg).

6. Using the thermal efficiency values from Table 6, multiply the amount of water removed by the MJ values for the gas and electric components.

7. Convert MJ of gas to quantity units, and convert electricity from MJ to MWh.

8. Multiply the quantities of gas and electricity by their respective emission factors.

¹ There should be a warning if the entered value for the total moisture removed is greater than 15% points. This warning will not stop the calculation unless total moisture exceeds 30%.

Formulas

\[ m_{water} = m_{water\ per\ unit} \times Y_s \ A \]

\[ E_{gas} = m_{water} \times e_{gas} \]

\[ E_{electric} = m_{water} \times e_{electric} \]

\[ E_{drying} = E_{gas} + E_{electric} \]

Example for most grain crops

A 100-acre field produced 65 bu/ac of soybean which was dried on-farm by 2 moisture points. Their farm is in the Midwest, in the SRMW eGrid subregion.

NoteTotal emissions for both electric and gas components of drying soybeans

1,227 kg CO₂e

Table 1: Emissions associated with soy drying example.

system_boundary	source_category	drying_system	CO2_fossil	CO2_biogenic	CH4_fossil	CH4_biogenic	N2O	NF3	SF6	units
Upstream	production of fuels	Combination High/Low Temp Bin	91.1	0	7.58	0	0.415	NA	NA	kg_CO2e
On-Farm Mechanical	stationary machinery	Combination High/Low Temp Bin	563	0	0.815	0	1.49	NA	NA	kg_CO2e
Upstream	electricity generation and distribution	Combination High/Low Temp Bin	544	NA	15.5	0.0213	2.45	0.0000136	0.00532	kg_CO2e

Alfalfa

The drying energy for alfalfa is calculated for each cutting. The sum of the cuttings represents the total drying energy. The harvest moisture value entered by the user in the Calculator represents the percent moisture content after baling, as the bales will be transported and possibly loaded into a forced-air drying system.

Air flow system with propane gas burner for drying hay. Photo from Savoie and Joannis (2006).

As discussed in the first section above on grain crops, the following regression also describes drying for alfalfa.

Inputs

Input	Value	Units	Symbol
Crop yield	User entry	ton	\(Y\)
Field area	User defined boundary	acre	\(A\)
Harvest moisture (after baling)	User entry	Percentage (0 - 100%)	\(M\)
Final moisture	12 (standard moisture in the Platform)	Percentage (0 - 100%)	\(M_s\)
Points of moisture removed	Difference between harvest moisture content and final moisture after drying; default = 18%	percentage (0 - 100%)	\(\Delta_{M}\)
Hay drying system	Two hay dryer options in

Table

Parker et al. (1992) reported 142 kg water removed per tonne of alfalfa to remove 11.9% moisture. Using our regression, we would expect about 151 kg water removed per tonne to remove 11.9% moisture, meaning our formula is reasonably within 10% of a real data point. Parker et al. (1992) also gave numbers for energy use in forced drying hay (standardized to 18% moisture content):

• Fan: 100-150 kWh tonne^-1; 1.05 kWh/kg water removed (3.78 MJ kg^-1)

• Fan + LP Gas: 300-600 kWh tonne^-1; ~1.5 kWh/kg water removed (5.4 MJ kg^-1)

Arinze et al. (1996) reported a specific energy consumption around 2060 BTU/lb-water removed, which is in agreement with our table of drying systems and their associated energy.

Two options for alfalfa have been added to Table 6.

drying_system	gas_mj_per_kg_water	electric_mj_per_kg_water
Hay Dryer Without Gas Heating	0	3.72
Hay Dryer With Gas Heating	3.84	1.28

Formulas

\[ m_{water} = m_{water\ per\ unit} \times Y_s \ A \]

\[ E_{gas} = m_{water} \times e_{gas} \]

\[ E_{electric} = m_{water} \times e_{electric} \]

\[ E_{drying} = E_{gas} + E_{electric} \]

where \(m_{water}\) is the mass of water removed. This is multiplied by the energy per unit of water removed

\[ E_{drying} = m_{water} \times E_{system} \times Y_s \ A \]

Steps

1. Was the crop was dried using energy?
   • If not, as in the case of natural air drying, energy and emissions are zero.
2. Where was the crop dried?

   • If the crop was dried using an on-farm system, ask What type of drying system was used?

   • If the crop was dried off-farm, the Calculator automatically selects a Hay Dryer With Gas Heating system

   • Where the crop was dried and stored affects the system boundary (see logic)

3. How much moisture was removed by drying?
4. Using the points of moisture removed², calculate the amount of water removed per kg of crop using Equation 1.
5. Calculate the total amount of water removed (kg) by multiplying the Step 2 result by total crop transported (kg).
6. Using the thermal efficiency values from Table 6, multiply the amount of water removed by the values for the gas and electric components.
7. Convert MJ of gas to quantity units, and convert electricity from MJ to MWh.
8. Multiply the quantities of gas and electricity by their respective emission factors.

² There should be a warning if the entered value for the total moisture removed is greater than 15% points.

Example

Let’s say 7 ton/ac of alfalfa was harvested³ from an 100 acre field located in the SRMW grid region. The initial moisture was 22%. The crop was baled and dried off-farm in a gas-heated, forced-air system. How much energy was used during this post-harvest process?

³ The drying energy are calculated separately for each cutting, but for simplicity in this example, cuttings are combined.

NoteTotal energy used for both electric and gas components of drying alfalfa

909,444 MJ

Table 2: Energy use associated with alfalfa drying example.

system_boundary	source_category	drying_system	MJ	units
Upstream	production of fuels	Hay Dryer With Gas Heating	35400	MJ
Post-Harvest	stationary machinery	Hay Dryer With Gas Heating	248000	MJ
Upstream	electricity generation and distribution	Hay Dryer With Gas Heating	626000	MJ

Cotton

In the case of cotton, where lint drying occurs at the gin and is not in direct control of the grower, the user is asked to qualitatively assess the moisture content of their cotton crop upon delivery to the gin. Based on this qualitative grouping, the energy used for drying and ginning are found in a lookup table developed by Dr. Ed Barnes, Senior Director Agricultural & Environmental Research, Cotton Incorporated.

In the case of cotton, the energy use and GHG emissions associated with post-harvest processing like ginning and drying are assigned to the Upstream and On-Farm Mechanical system boundaries (as with cotton crop transportation).

Inputs

Input	Value	Units	Symbol
Crop yield (standardized)	User entry	lbs lint	\(Y_{s}\)
Field area	User defined boundary	acre	\(A\)
Moisture content	User selection from 4 choices in Table 3	qualitative

Table 3: Source: Ed Barnes, Cotton Incorporated

cotton_region	cotton_moisture_level	cotton_gas_source	gas_mj_per_kg_lint	electric_mj_per_kg_lint
SE	Very Dry	LPG	0.209	0.705
SE	Normal	LPG	0.628	0.751
SE	Wetter than Normal	LPG	1.05	0.798
SE	Very Wet	LPG	1.47	0.844
SW	Very Dry	Natural gas	0.209	0.705
SW	Normal	Natural gas	0.628	0.751
SW	Wetter than Normal	Natural gas	1.05	0.798
SW	Very Wet	Natural gas	1.47	0.844

Formula

\[ E_{postharvest} = Y_s\ A\ (E_{drying} + E_{ginning}) \]

Example

A cotton grower in Georgia (eGrid subregion SRSO) harvested 1200 lb-lint per acre on a 100 acre field. She estimated the moisture content was wetter than normal. What emissions are associated with drying and ginning her cotton crop?

NoteTotal emissions for ginning and drying cotton

9,755 kg CO₂e

Table 4: Emissions associated with cotton drying example.

system_boundary	source_category	drying_system	CO2_fossil	CO2_biogenic	CH4_fossil	CH4_biogenic	N2O	NF3	SF6	units
Upstream	production of fuels	Commercially Dried	586	0	48.7	0	2.67	NA	NA	kg_CO2e
On-Farm Mechanical	stationary machinery	Commercially Dried	3620	0	5.24	0	9.6	NA	NA	kg_CO2e
Upstream	electricity generation and distribution	Commercially Dried	5270	NA	196	1.56	17.9	0.000321	0.000401	kg_CO2e

Peanuts

For peanuts, drying energy is calculated using a set of equations developed by staff at USDA ARS in Georgia, which are based on empirical data and previous research (Blankenship and Chew 1979). The peanut drying energy considers energy for electric fans (kWh ton^-1) blowing air past a gas burner (BTU ton^-1).

Inputs

Input	Value	Units	Symbol
Crop yield (standardized)	User entry	ton	\(Y_{s}\)
Field area	User defined boundary	acre	\(A\)
Initial moisture content	User entry	percentage (0 - 100%)	\(M\)

Formula

The original equations were given in units of BTU ton^-1 and kWh ton^-1.

\[ E_{gas} = 62618\ M - 578344 \]

\[ E_{electric} = 2.991\ M - 27.7 \]

\[ E_{postharvest} = Y_s\ A\ (E_{gas} + E_{electric}) \tag{3}\]

Important

The results for gas and electric energy are each converted into BTU lb^-1 before proceeding to Equation 3.

Example

The grower harvested 4700 lbs/ac of peanuts from an 100 acre field in southern Georgia (eGrid subregion SRSO). The peanuts were delivered to the curing facility with an initial moisture content upon arrival of 16%. Provide an energy and emissions table.

Table 5: Emissions associated with peanut drying example.

scn_id	state	crop	metric	system_boundary	source_category	source_detail	CO2_fossil	CH4_fossil	CH4_biogenic	N2O	NF3	SF6	MJ	units
44	Georgia	Peanuts	Energy Use	Upstream	production of fuels	Crop Drying | LPG	0	0	0	0	NA	NA	16300	MJ
44	Georgia	Peanuts	Energy Use	Post-Harvest	stationary machinery	Crop Drying | LPG	0	0	0	0	NA	NA	114000	MJ
44	Georgia	Peanuts	Energy Use	Upstream	electricity generation and distribution	Crop Drying | Electricity (grid)	0	0	0	0	0	0	116000	MJ
44	Georgia	Peanuts	GHG Emissions	Upstream	production of fuels	Crop Drying | LPG	1170	97.3	0	5.33	NA	NA	0	kg_CO2e
44	Georgia	Peanuts	GHG Emissions	Post-Harvest	stationary machinery	Crop Drying | LPG	7220	10.5	0	19.2	NA	NA	0	kg_CO2e
44	Georgia	Peanuts	GHG Emissions	Upstream	electricity generation and distribution	Crop Drying | Electricity (grid)	2240	83.4	0.665	7.62	0.000137	0.000171	0	kg_CO2e

Corn Silage, Potatoes, and Sugar Beets

Corn silage, potatoes, and sugar beets do not yet have energy associated with post-harvest processing activities like storage and drying. While corn silage may be wrapped and/or stored, and potatoes have energy associated with storage and refrigeration, the associated energy is not fully accounted for currently in version 5.0 of the Fieldprint Calculator. Only the energy to transport the crop from the field to the storage is accounted.

Field to Market would welcome collaborations to include these components in a future release of the Platform.

Tables

Drying System Options

Table 6: MJ of energy associated with each kg of water removed

drying_system	gas_mj_per_kg_water	electric_mj_per_kg_water
Natural Air Only	0	0
No Heat Bin	0	3.49
Low Temp Bin	0	3.84
Combination High/Low Temp Bin	2.09	0.698
Continuous/Mixed Flow In Bin	4.56	0.0931
High Temp Batch Dryer	5.47	0.112
PTO-driven Batch Dryer	7.3	0.149
Continuous Cross Flow Dryer	7.3	0.149
Hay Dryer Without Gas Heating	0	3.72
Hay Dryer With Gas Heating	3.84	1.28
Commercially Dried	4.56	0.0931

Energy and Emission Factors

Table 7: Sample of data in full table. Units for GHG gases are kg per unit of energy source. Units for energy (mj) are MJ per unit of energy source.

metric	system_boundary	source_category	source_detail	subregion	CO2_fossil	CO2_biogenic	CH4_fossil	CH4_biogenic	N2O	NF3	SF6	MJ
GHG Emissions	Upstream	GHG emissions associated with production of fuels	Crop Drying | Diesel (ag equipment)	NA	0.975	0	0.00233	0	0.0000195	NA	NA	0
GHG Emissions	Upstream	GHG emissions associated with production of fuels	Crop Drying | Gasoline	NA	1.68	0	0.00468	0	0.000323	NA	NA	0
GHG Emissions	Upstream	GHG emissions associated with production of fuels	Crop Drying | LPG	NA	0.914	0	0.00255	0	0.0000152	NA	NA	0
GHG Emissions	Upstream	GHG emissions associated with production of fuels	Crop Drying | Natural gas	NA	0.00614	0	0.000195	0	0.00000131	NA	NA	0

References

Arinze, E. A., S. Sokhansanj, G. J. Schoenau, and F. G. Trauttmansdorff. 1996. “Experimental Evaluation, Simulation and Optimization of a Commercial Heated-Air Batch Hay Drier: Part 1, Drier Functional Performance, Product Quality, and Economic Analysis of Drying.” Journal of Agricultural Engineering Research 63 (4): 301–14. https://doi.org/10.1006/jaer.1996.0033.

Blankenship, Paul D., and Victor Chew. 1979. “Peanut Drying Energy Consumption.” Peanut Science 6 (1): 10–13. https://doi.org/10.3146/i0095-3679-6-1-3.

Ford, Vic. 2024. Wheat Drying and Storage. https://www.uaex.uada.edu/farm-ranch/crops-commercial-horticulture/Grain_drying_and_storage/wheat_drying_and_storage.aspx.

Panigrahi, Shubham Subrot, Kaushik Luthra, Chandra B. Singh, Griffith Atungulu, and Kenny Corscadden. 2023. “On-Farm Grain Drying System Sustainability: Current Energy and Carbon Footprint Assessment with Potential Reform Measures.” Sustainable Energy Technologies and Assessments 60 (December): 103430. https://doi.org/10.1016/j.seta.2023.103430.

Parker, BF, GM White, MR Lindley, et al. 1992. “Forced-Air Drying of Baled Alfalfa Hay.” Transactions of the ASAE 35 (2): 607–15.

Savoie, P, and H Joannis. 2006. “Bidirectional Drying of Baled Hay with Air Recirculation and Cooling.” Canadian Biosystems Engineering 48: 3. https://library.csbe-scgab.ca/docs/journal/48/c0614.pdf.