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Citation: Smith, M.M.; Bentrup, G.;
Kellerman, T.; MacFarland, K.;
Straight, R.; Ameyaw, L. Agroforestry
Extent in the United States: A Review
of National Datasets and Inventory
Efforts. Agriculture 2022,12, 726.
https://doi.org/10.3390/
agriculture12050726
Academic Editors: Dimitris Fotakis
and Thomas Papachristou
Received: 19 April 2022
Accepted: 19 May 2022
Published: 21 May 2022
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agriculture
Review
Agroforestry Extent in the United States: A Review of National
Datasets and Inventory Efforts
Matthew M. Smith 1, * , Gary Bentrup 1, Todd Kellerman 1, Katherine MacFarland 2, Richard Straight 1
and Lord Ameyaw 3
1USDA National Agroforestry Center, Lincoln, NE 68583, USA; gary.bentrup@usda.gov (G.B.);
todd.kellerman@usda.gov (T.K.); richard.straight@usda.gov (R.S.)
2USDA National Agroforestry Center, Burlington, VT 05405, USA; katherine.macfarland@usda.gov
3Nebraska Forest Service, University of Nebraska-Lincoln, Lincoln, NE 68503, USA; lameyaw2@unl.edu
*Correspondence: matthew.smith4@usda.gov
Abstract:
A comprehensive understanding of agroforestry adoption across a landscape is critical for
effective agroforestry planning. The objectives of this study are to identify the sources of agroforestry
data that can be used in the United States (U.S.) for national inventory purposes, discuss the possible
uses and nuances of the datasets, synthesize the data to create regional maps, and provide recom-
mendations for improving future agroforestry inventory efforts. To accomplish this, we queried
multiple government databases containing agroforestry inventory data and spoke with agency repre-
sentatives with in-depth knowledge of each dataset. Data from federal conservation programs were
found to be useful for assessing practice-level adoption through a conservation program but not for
general inventory use, since agroforestry systems can be established without federal assistance. For
inventory purposes, the 2017 U.S. Census of Agriculture was found to be the most comprehensive
dataset, with 30,853 farm operations reporting agroforestry use, representing 1.5% of all U.S. farms.
However, this value is likely an underestimate, due to respondent unfamiliarity with agroforestry
terminology. We propose several strategies to improve the accuracy of future agroforestry surveys,
since a greater understanding of agroforestry adoption will influence decisions related to agricultural
policies, technical assistance, and planning of these integrated systems.
Keywords:
windbreak; silvopasture; riparian forest buffer; alley cropping; forest farming;
silvopastoral;
intercropping; survey; census of agriculture; conservation programs
1. Introduction
Agroforestry is the intentional integration of trees and shrubs into crop and animal
farming systems. These systems encompass a suite of multifunctional land-use approaches
that can enhance agricultural production, while delivering ecosystem services [
1
–
6
]. Al-
though the benefits of agroforestry have a strong literature base from a biophysical perspec-
tive, advancement of agroforestry requires a comprehensive understanding of adoption
by producers. Having a greater understanding of how many producers use agroforestry,
along with associated demographic and biophysical variables is important for:
1.
Helping producers make more informed decisions about their operations when de-
signing and planning agroforestry systems.
2. Understanding how agroforestry implementation is changing over time.
3. Identifying trends and factors that may help increase successful adoption.
4. Identifying market opportunities for agroforestry-produced products and services.
5. Better matching of financial and technical support with producer demand.
6.
Informing decision-making related to farm policies, funding, programs, research, and
extension delivery.
7.
Better estimating the ecosystem goods and services provided by agroforestry practices.
Agriculture 2022,12, 726. https://doi.org/10.3390/agriculture12050726 https://www.mdpi.com/journal/agriculture
Agriculture 2022,12, 726 2 of 17
In the United States (U.S.), several recent reviews have been conducted on agroforestry
adoption by aggregating data from regional producer surveys [
7
,
8
]. These reviews identi-
fied a need for a thorough analysis of agroforestry implementation on the national scale,
since no such analysis has occurred in the U.S. Currently, the only national datasets on
agroforestry implementation in the U.S. reside in United States Department of Agriculture
(USDA) databases. One dataset comes from a single question in the 2017 Census of Agri-
culture (COA), which is administered by the USDA National Agricultural Statistics Service
(NASS). The Yes/No question asked, “At any time in 2017, did this operation practice alley
cropping, silvopasture, or forest farming, or have riparian forest buffers or windbreaks?” [
9
].
Because this question aggregated multiple agroforestry practices together, the data can-
not be refined further by specific agroforestry practices. Additionally, these data cannot
be compared with an earlier 2012 COA agroforestry question, which only asked about
silvopasture and alley cropping. Unfortunately, these nuances sometimes result in data
misinterpretation. For example, Bergmann et al. [
10
] described a SilvopastureRatio, which
used the 2012 COA agroforestry question description for silvopasture and alley cropping
and 2017 COA agroforestry data for all five practices to make inferences about silvopasture.
Because of the aggregate nature of these data, such practice-level interpretations cannot
be made.
National agroforestry data can also be found in other government databases focused
on federal conservation programs, such as those from the USDA Natural Resources Conser-
vation Service (NRCS) and the USDA Farm Service Agency (FSA). By design, these national
datasets are specific to agroforestry systems established through federal conservation pro-
grams. However, there have been instances where conservation practice implementation
data have been extrapolated to make inferences about the prevalence of specific agroforestry
practices in the U.S. For instance, Davis and Rausser [
11
] used conservation program data
to describe how silvopasture adoption is low in the U.S. because the number of acres
established through USDA conservation programs was low. Unfortunately, interpretations
like this cannot be made since agroforestry practices can be implemented without funding
or technical assistance from federal conservation programs.
While national-level datasets exist for agroforestry implementation in the U.S., the
data are in several different government databases and can be nuanced, especially when
trying to assess trends over time. This leads to misrepresentation and incorrect use of data
in both peer reviewed and popular literature. Additionally, while many of these data are
publicly available, the agriculture, forestry, and conservation communities may be unaware
of their existence or not know how to access the raw data. As such, the objectives of this
review are to:
1. Identify federal sources of agroforestry data for inventorying purposes in the U.S.
2. Discuss the possible uses and nuances of the various datasets.
3.
Synthesize the available agroforestry inventory data to create mapping products
showcasing agroforestry adoption by state and county.
4.
Provide strategies for improving future agroforestry survey and inventory efforts in
the U.S. and abroad.
By providing insight into the various national agroforestry datasets available from the
U.S. government, along with synthesizing information to create state-level maps, we hope
to provide a greater understanding of how many producers use agroforestry, where systems
exist across the country, and how these data can support a variety of policy, extension,
research, and planning needs.
2. Materials and Methods
National datasets for agroforestry adoption in the U.S. were identified through recently
published systematic reviews by the authors of this study [
7
,
8
]. These systematic reviews
identified government databases as the only source for national-level datasets related to
agroforestry adoption. As such, three reports describing agroforestry programs across
Agriculture 2022,12, 726 3 of 17
the U.S. government were used as a starting point to identify potential data sources. The
reports included:
1. Agroforestry Across USDA Agencies [12].
2. Guide to USDA Agroforestry Research Funding Opportunities [13].
3. USDA Agroforestry Strategic Framework: Fiscal Years 2019–2024 [14].
Additionally, representatives from the USDA Interagency Agroforestry Team (IAT)
were contacted to inquire about additional data sources. The IAT is comprised of eight
USDA agencies and is focused on identifying, assessing, and prioritizing agroforestry
science and technology needs and outcomes across the U.S. [
14
]. Through this approach,
data sources were identified for all eight IAT agencies.
Of the agroforestry datasets identified during the data collection phase, only the 2017
U.S. Census of Agriculture from NASS was utilized for creating regional maps, due to data
availability for every state and county within the U.S. More specifically, NASS’s Quick Stats
2.0 was used to query the 2017 COA database to develop a customized table of agroforestry
responses at the county level for all fifty states [
15
]. To determine the number of operations
that answered “Yes” to using agroforestry, the query string was
Census > Economics > Farm
and Land and Assets > Practices > Area > Data Item > Practices, Alley Cropping and
Silvopasture—Number of Operations > County > 2017. Although the Data Item menu
just shows Alley Cropping and Silvopasture, this selection includes all five practices if
the year 2017 is selected. For total number of farms per county, the query string was
Census > Economics > Farm&Land&Assets > Farm Operations > Operations > Farm
Operations—Number of Operations > Total > County > 2017. The agroforestry responses
for individual states by county can also be found in Table 43 of Chapter 2—Selected
Practices using the Census Data Query Tool. To map the data, the tables were copied into a
Microsoft Excel spreadsheet and were merged with county spatial data using ArcGIS Pro.
The U.S. County layer was downloaded as a shapefile from Esri [
16
]. Federal Information
Processing System (FIPS) county codes were added to the NASS data in Excel. FIPS codes
are a unique number identifier for each county. The Excel table and the spatial County
layer were joined using the Join Field tool in ArcGIS Pro (version 2.73, Esri) using the FIPS
codes. The resulting layer contained the NASS numbers according to county and were
checked for accuracy before being mapped.
Agroforestry data from other government databases were reviewed but not included
in mapping products due to the limited scope of the databases. However, these datasets
are discussed in depth from a qualitative perspective in the following sections.
3. Results and Discussion
3.1. USDA NASS Census of Agriculture
Based on the 2017 COA, 30,853 farm operations responded that they had at least one
agroforestry practice, representing 1.5% of all farm operations in the U.S. (Table 1and Figure 1).
The states with the highest number of farms with an agroforestry practice include
Texas, Pennsylvania, Missouri, Virginia, and Oregon. When viewed as a percentage of
farms with an agroforestry practice at the state level, the top five states are Vermont (7.2%),
Maine (4.8%), Hawaii (4.7%), Massachusetts (4.1%), and New Hampshire (4.1%), while the
lowest state percentages were found in Arizona (0.2%), Nevada (0.3%), Utah (0.3%), Texas
(0.5%), North Dakota (0.6%), and Wyoming (0.6%).
At the county level, the percentage of farms with agroforestry practices ranges from
0.0% to 25.0% (Figure 2). In general, counties with a higher percentage of farms with
agroforestry were found in the Pacific Northwest, Mid-Atlantic, and Northeast regions.
Agriculture 2022,12, 726 4 of 17
Table 1.
Total number of U.S. farms and farms with agroforestry based on the 2017 Census of
Agriculture [17].
State Number of Farms Number of Farms with at Least
One Agroforestry Practice
Percentage of Farms with at
Least One Agroforestry Practice
Alabama 40,592 635 1.6
Alaska 990 35 3.5
Arizona 19,086 42 0.2
Arkansas 42,625 585 1.4
California 70,521 1064 1.5
Colorado 38,893 361 0.9
Connecticut 5521 188 3.4
Delaware 2302 48 2.1
Florida 47,590 803 1.7
Georgia 42,439 969 2.3
Hawaii 7328 347 4.7
Idaho 24,996 317 1.3
Illinois 72,651 604 0.8
Indiana 56,649 594 1.0
Iowa 86,104 822 1.0
Kansas 58,569 438 0.7
Kentucky 75,966 1028 1.4
Louisiana 27,386 349 1.3
Maine 7600 362 4.8
Maryland 12,429 473 3.8
Massachusetts 7241 299 4.1
Michigan 47,641 957 2.0
Minnesota 68,822 1011 1.5
Mississippi 34,988 542 1.5
Missouri 95,320 1311 1.4
Montana 27,048 298 1.1
Nebraska 46,332 458 1.0
Nevada 3423 11 0.3
New Hampshire 4123 170 4.1
New Jersey 9883 263 2.7
New Mexico 25,044 201 0.8
New York 33,438 1187 3.5
North Carolina 46,418 1162 2.5
North Dakota 26,364 155 0.6
Ohio 77,805 1156 1.5
Oklahoma 78,531 514 0.7
Oregon 37,616 1467 3.9
Pennsylvania 53,157 1657 3.1
Rhode Island 1043 37 3.5
South Carolina 24,791 667 2.7
South Dakota 29,968 252 0.8
Tennessee 69,983 938 1.3
Texas 248,416 1347 0.5
Utah 18,409 61 0.3
Vermont 6808 492 7.2
Virginia 43,225 1526 3.5
Washington 35,793 1075 3.0
West Virginia 23,622 384 1.6
Wisconsin 64,793 1120 1.7
Wyoming 11,938 71 0.6
Total 2,042,220 30,853 1.5
Agriculture 2022,12, 726 5 of 17
Agriculture 2022, 12, x FOR PEER REVIEW 5 of 18
Wisconsin
64,793
1120
1.7
Wyoming
11,938
71
0.6
Total
2,042,220
30,853
1.5
The states with the highest number of farms with an agroforestry practice include
Texas, Pennsylvania, Missouri, Virginia, and Oregon. When viewed as a percentage of
farms with an agroforestry practice at the state level, the top five states are Vermont
(7.2%), Maine (4.8%), Hawaii (4.7%), Massachusetts (4.1%), and New Hampshire (4.1%),
while the lowest state percentages were found in Arizona (0.2%), Nevada (0.3%), Utah
(0.3%), Texas (0.5%), North Dakota (0.6%), and Wyoming (0.6%).
Figure 1. Number of U.S. farms by county reporting using at least one of the five common agrofor-
estry practices (windbreaks, riparian forest buffers, alley cropping, silvopasture and/or forest farm-
ing) [17].
At the county level, the percentage of farms with agroforestry practices ranges from
0.0% to 25.0% (Figure 2). In general, counties with a higher percentage of farms with ag-
roforestry were found in the Pacific Northwest, Mid-Atlantic, and Northeast regions.
Figure 1.
Number of U.S. farms by county reporting using at least one of the five common agroforestry
practices (windbreaks, riparian forest buffers, alley cropping, silvopasture and/or forest farming) [
17
].
Figure 3offers an example of the county-level data at an individual state scale. Sup-
plement File S1 provides individual county-level state maps and tables for all fifty states.
Some counties can have particularly high percentages even when their state percentage is
low. Factors that contribute to these differences may be due to local interest and expertise
on implementing agroforestry practices, familiarity with practice names when responding
to the COA question, or federal and state program availability. Future studies could expand
upon this dataset to better understand some of these nuances and whether trends exist.
These studies could also include detailed analysis of correlations between agroforestry
adopters and other demographic or land-use decisions, such as conservation practices or
organic management.
Agriculture 2022,12, 726 6 of 17
Agriculture 2022, 12, x FOR PEER REVIEW 6 of 18
Figure 2. Percent of U.S. farms by county reporting using at least one of the five common agrofor-
estry practices (windbreaks, riparian forest buffers, alley cropping, silvopasture, and/or forest farm-
ing) [17].
Figure 3 offers an example of the county-level data at an individual state scale. Sup-
plement 1 provides individual county-level state maps and tables for all fifty states. Some
counties can have particularly high percentages even when their state percentage is low.
Factors that contribute to these differences may be due to local interest and expertise on
implementing agroforestry practices, familiarity with practice names when responding to
the COA question, or federal and state program availability. Future studies could expand
upon this dataset to better understand some of these nuances and whether trends exist.
These studies could also include detailed analysis of correlations between agroforestry
adopters and other demographic or land-use decisions, such as conservation practices or
organic management.
Figure 2.
Percent of U.S. farms by county reporting using at least one of the five common agro-
forestry practices (windbreaks, riparian forest buffers, alley cropping, silvopasture, and/or forest
farming) [17].
Comparison between the 2017 and 2012 Census of Agriculture
One of the benefits of the COA is the ability to assess agricultural trends over time since
it occurs once every five years. In Table 43 of Chapter 2 from the 2017 COA, agroforestry
data are presented for both the 2017 and 2012 COA years. However, the 2012 data have
a footnote which says, “data for 2012 exclude operations that practiced forest farming or
had riparian forest buffers or windbreaks” [
17
]. This footnote is included because the 2012
COA agroforestry question only asked if producers had practiced alley cropping and/or
silvopasture. As such, the 2012 data cannot be compared with the 2017 agroforestry data.
While the footnote details this important distinction, there have been media articles that
have incorrectly suggested that the number of farm operations practicing agroforestry
increased 10-fold from 2012 to 2017.
Agriculture 2022,12, 726 7 of 17
Agriculture 2022, 12, x FOR PEER REVIEW 7 of 18
Figure 3. (a) Number of farms in California by county with at least one agroforestry practice based
on the 2017 Census of Agriculture. (b) Percentage of farms in California by county with at least one
agroforestry practice based on the 2017 Census of Agriculture [17].
Comparison between the 2017 and 2012 Census of Agriculture
One of the benefits of the COA is the ability to assess agricultural trends over time
since it occurs once every five years. In Table 43 of Chapter 2 from the 2017 COA, agro-
forestry data are presented for both the 2017 and 2012 COA years. However, the 2012 data
have a footnote which says, “data for 2012 exclude operations that practiced forest farm-
ing or had riparian forest buffers or windbreaks” [17]. This footnote is included because
the 2012 COA agroforestry question only asked if producers had practiced alley cropping
and/or silvopasture. As such, the 2012 data cannot be compared with the 2017 agroforestry
data. While the footnote details this important distinction, there have been media articles
that have incorrectly suggested that the number of farm operations practicing agrofor-
estry increased 10-fold from 2012 to 2017.
3.2. National Agroforestry Data from Other USDA Agencies
Few national data sources exist related to how many producers implement agrofor-
estry on their operation other than the COA. Some researchers and practitioners have
looked to data related to federal conservation program funding to understand implemen-
tation and retention of agroforestry and other conservation practices [18-20]. For example,
Basche et al. [19] used data from EQIP to explore the potential to generate environmental
health outcomes. This approach is effective at understanding conservation program adop-
tion and the ecosystem service implications of participation in conservation programs.
However, it has several limitations for understanding agroforestry adoption more broadly
because it excludes systems established without the use of federal conservation funding
or technical assistance. While incentives often increase practice adoption [21], studies have
found that some producers prefer not to work with or receive funding or technical assis-
tance from the federal government [22,23]. One reason is that the conservation focus of
some federal programs is not a good fit for the production goals of their operation’s
Figure 3.
(
a
) Number of farms in California by county with at least one agroforestry practice based
on the 2017 Census of Agriculture. (
b
) Percentage of farms in California by county with at least one
agroforestry practice based on the 2017 Census of Agriculture [17].
3.2. National Agroforestry Data from Other USDA Agencies
Few national data sources exist related to how many producers implement agro-
forestry on their operation other than the COA. Some researchers and practitioners have
looked to data related to federal conservation program funding to understand implementa-
tion and retention of agroforestry and other conservation practices [
18
–
20
]. For example,
Basche et al. [19]
used data from EQIP to explore the potential to generate environmen-
tal health outcomes. This approach is effective at understanding conservation program
adoption and the ecosystem service implications of participation in conservation programs.
However, it has several limitations for understanding agroforestry adoption more broadly
because it excludes systems established without the use of federal conservation funding
or technical assistance. While incentives often increase practice adoption [
21
], studies
have found that some producers prefer not to work with or receive funding or technical
assistance from the federal government [
22
,
23
]. One reason is that the conservation focus
of some federal programs is not a good fit for the production goals of their operation’s
agroforestry practices. Furthermore, some producers are farming on rented land, where
accessing federal programs requires approval by their landlords and may be logistically
challenging [
24
]. Finally, funding for federal conservation programs is finite and controlled
by the Farm Bill and annual appropriations. The number of contracts for agroforestry
practices does not necessarily reflect demand for that funding, but instead may reflect
funding availability. Conservation data can also be nuanced since some states do not offer
the same federal programs for all the agroforestry practices.
3.2.1. USDA Natural Resources Conservation Service
NRCS has several conservation programs that include agroforestry. These data can be
found on public-facing websites, such as the NRCS Soil and Water Resources Conservation
Act (RCA) Data Viewer and NRCS Environmental Quality Incentives Program (EQIP)
Data Dashboard [
25
]. In relation to EQIP, the program offers financial assistance through
contracts to implement conservation practice standards that address specific resource
Agriculture 2022,12, 726 8 of 17
concerns. These practice standards each have a unique code number. National Conservation
Practice Standards for the five most common agroforestry practices were developed in the
1990s and early 2000s, and include Alley Cropping (311), Forest Farming (379), Riparian
Forest Buffers (391), Silvopasture (381), and Windbreak/Shelterbelt Establishment and
Renovation (380) [
26
]. Each NRCS state office decides whether it is appropriate to offer
a conservation practice in their state based on priority resource concerns, farmer interest,
and other factors. Many NRCS state offices do not offer the complete suite of agroforestry-
related conservation practices. Each NRCS state office maintains a list of Conservation
Practice Standards that are currently offered in the state through their electronic Field Office
Technical Guide [27].
Data available for NRCS EQIP include contract information for windbreaks, silvopas-
ture, forest farming, alley cropping, and riparian forest buffers. These data may infer
some helpful biophysical, agronomic, or economic information. For example, these data
indicate that there are few EQIP contracts for forest farming in Great Plains states because
this practice does not often address the natural resource concerns in that region where
forest resources are relatively limited. The data may also reflect programmatic approaches
unique to each state. For example, state NRCS offices may use various EQIP Conservation
Practice Standards to support Silvopasture (381), such as Tree/Shrub Establishment (612)
and Prescribed Grazing (528). This support would not be reflected in the EQIP data for
silvopasture, even though the support may have resulted in silvopasture implementation.
EQIP may also support agroforestry practice establishment through supporting practices
such as Access Control (472), Brush Management (314), Fence (382), Forest Stand Improve-
ment (666), Prescribed Burning (338), Tree/Shrub Establishment (612), Tree/Shrub Site
Preparation (490), and many others.
In addition, NRCS state offices may offer different practices over time as priorities,
capacity, demand, and other factors change. The ranking process for program applications
reflects priority resource concerns, Farm Bill requirements, funding pools, and other factors.
Depending on available funds and whether agroforestry practices match these criteria,
agroforestry-related applications may not rank high enough to be funded. In addition,
these data represent contracts with a federal agency for specific agroforestry practices
and do not provide information on whether these practices are maintained by the farmer
after the practice lifespan is over. These contracts also do not provide information on
whether other agroforestry practices exist on these farms, even though an agroforestry
practice is generally integrated into a whole farm conservation and production system.
Prokopy et al. [28]
found that previous adoption of conservation practices is positively
related to adoption of other conservation practices. For these reasons, EQIP contract
enrollment data should only be used to make inferences related to agroforestry established
through federal conservation programs and not agroforestry establishment or inventory
in general.
In addition to EQIP, agroforestry data can be found for other NRCS programs. One ex-
ample is the Conservation Stewardship Program (CSP), which provides funding to enhance
existing conservation practices, including some enhancements for agroforestry. In addition,
CSP offers enhancements that can be applied to any forestry-related practice, which could
be used for agroforestry. Another example is the Agricultural Management Assistance
(AMA) program, which is designed to help agricultural producers manage financial risk
through diversification, marketing, or natural resource conservation in 16 states where Fed-
eral Crop Insurance Program participation is historically low. These states offer support for
different conservation practices, with some states currently funding riparian forest buffers,
hedgerows, windbreak/shelterbelt establishment, and windbreak/shelterbelt renovation.
Financial and conservation practice implementation data about CSP and AMA are available
through the RCA Data Viewer [
25
]. As with EQIP, data from CSP and AMA provide valu-
able information related to agroforestry establishment via a specific federal conservation
program but are not meant to be used for general inventory purposes. Additionally, NRCS
conservation programs change over time. For example, in the 2014 Farm Bill, the Farm
Agriculture 2022,12, 726 9 of 17
and Ranch Lands Protection Program (FRPP), Grassland Reserve Program (GRP), and
Wetlands Reserve Program (WRP) were merged into the new Agricultural Conservation
Easement Program (ACEP). The Agricultural Water Enhancement Program (AWEP) and
Chesapeake Bay Watershed Initiative (CBWI) were merged into the new Regional Conser-
vation Partnership Program (RCPP). The Wildlife Habitat Incentive Program (WHIP) was
merged into the existing Environmental Quality Incentives Program (EQIP). These changes
make it challenging to track agroforestry adoption over time when using data from federal
conservation programs.
3.2.2. USDA Farm Service Agency
The USDA Farm Service Agency (FSA) provides many programs and services that are
available to farmers, ranchers, and forest owners, some of whom may practice agroforestry.
FSA’s most substantial program supporting establishment of agroforestry practices is the
Conservation Reserve Program (CRP), which began with the 1985 Farm Bill. While the
program is funded through FSA, the technical assistance for CRP is provided by NRCS.
Since the initial CRP began, several associated programs have been created, including the
Conservation Reserve Enhancement Program (CREP), Farmable Wetlands Program (FWP),
Transition Incentives Program (TIP), Grassland CRP, and several specific initiatives. For
purposes of this discussion, these programs will be combined and referred to as CRP.
Over the years, CRP has included over forty conservation practices, each with its own
code number. There are currently four CRP conservation practices related to agroforestry
: Field
Windbreak Establishment (CP-5A), Shelterbelt Establishment (CP-16A), Living Snow Fences
(CP-17A), and Riparian Buffers (CP-22). Other tree-related CRP conservation practices
could also be used to advance agroforestry and include Tree Planting (CP-3/3A), Bottom-
land Timber Establishment on Wetlands (CP-31), and Longleaf Pine Establishment (CP-36).
These tree practices are often lumped together for analysis of program delivery and impacts.
A complete list of CRP practices can be found on the FSA CRP Practice Library website [
29
].
CRP includes financial assistance for establishment of the conservation practices along
with a contract for a 10-to-15-year annual rental payment, depending on the practice and
the specific program. The annual rental rate is for each acre under contract and is primar-
ily based on foregone returns to crop production. After the contract period expires, the
landowner is not required to maintain the conservation practice. However, there are a vari-
ety of options to re-enroll land back into the program. Because the landowner can change
the land use after contract expiration, past CRP establishment data may not correlate to
the current land use. As such, these data should not be used for a general agroforestry
inventory. However, they can be used effectively to track trends in conservation practice es-
tablishment and retention, including agroforestry practices. For example, Bigelow et al. [
30
]
used CRP data to explore land-use patterns following the expiration of CRP contracts. This
national assessment found that CRP contracts in tree-cover practices, including those in
agroforestry, were more likely to be re-enrolled when compared to non-tree practices. From
an ecosystem services perspective, this has important implications, as 80% of the land
non-re-enrolled was converted to some type of crop production [
30
]. Researchers have also
used CRP data to quantify and value ecosystem service impacts of land enrolled in the
program [31].
3.2.3. Other USDA Agencies
Like data from USDA conservation programs, information about participants in
other USDA programs can provide insights into agroforestry examples, topics, interest,
and research but are not designed for general inventory purposes. For example, the
USDA National Institute of Food and Agriculture (NIFA) provides annual capacity grants
and competitive grants that support agroforestry research, extension, and education. In
particular, the Sustainable Agriculture Research and Education (SARE) program has a long
history of supporting agroforestry-related projects. Detailed information about agroforestry
projects can be gleaned from NIFA’s Data Gateway [
32
]. The USDA National Agroforestry
Agriculture 2022,12, 726 10 of 17
Center also created the SARE Agroforestry Grants Index [
33
], which is a searchable database
for SARE agroforestry projects. Similarly, the USDA Agricultural Marketing Service (AMS)
Specialty Crop Block Grant Program (SCBGP) has advanced agroforestry efforts through
providing information about key crops used in agroforestry systems. Project information
is available on the SCBGP grant website [
34
]. USDA Rural Development (RD) has also
advanced agroforestry through loans, grants, and loan guarantees, with project information
being available on their website. In relation to research, the USDA Forest Service and USDA
Agricultural Research Service (ARS) both have a long history of advancing agroforestry,
with detailed information available on their research project websites [
35
,
36
]. Additional
examples of how USDA programs have been used to support agroforestry and potential
sources for data can be found in Agroforestry Across USDA Agencies [
12
] and the Guide
to USDA Agroforestry Research Funding Opportunities [13].
3.3. Strategies to Enhance Future Estimates of Agroforestry Adoption
When looking at strategies to improve upon future estimates of agroforestry adoption,
the COA offers one of the best mechanisms, since it is designed to be the most uniform and
comprehensive source of agriculture data for the U.S. The strategies that can be used to
improve upon the national estimate fall into three broad categories, which include:
1. Ensuring that all applicable farm and ranch operations fill out the COA.
2.
Ensuring that producers who have an agroforestry practice answer “Yes” to the
agroforestry question in the COA and those who do not have an agroforestry system
answer “No.”
3.
Using other national and regional surveys and inventory methods to supplement the
national estimate.
While these strategies may seem obvious, there are several known issues and mis-
understandings related to agroforestry that can lead to misinformation being reported in
questionnaires and/or no information being reported at all. The following sections discuss
some of these issues and possible solutions, which may increase the accuracy of future
estimates of agroforestry use in the U.S. and abroad.
3.3.1. Ensuring Operations with Agroforestry Answer the Census of Agriculture
The COA is a count of all U.S. farms and ranches where $1000 or more of agricultural
products were produced and sold, or normally would have been sold during the census
year [
9
]. However, one challenge with inventorying agroforestry is that it spans both
agriculture and forestry. This could be problematic for some producers that practice forest
farming/multi-story cropping, which is the deliberate cultivation of crops under a canopy
of trees since the producer may not consider their land as a farm or ranch. They may
also be unsure of whether their products are considered as an agricultural crop or a forest
product. This uncertainty could result in an undercount of some operations who may not
appear to be a farm but are in fact producing crops, products, and resources in a forest
farming system.
While it is uncertain whether forest farming systems were under-counted in the most
recent COA, potential insight could be gleaned from the National Woodland Owner Survey
(NWOS), which is conducted by the USDA Forest Service, Forest Inventory and Analysis
program. The NWOS collects information from private forest owners related to attitudes,
behaviors, and other characteristics of forest ownership [
37
]. In recent versions of the
survey, there is a specific question related to whether the operation collected any nontimber
forest products (NTFPs), which can be grown in a forest farming system. NTFPs include
edible and culinary products, specialty wood products, floral and decorative products,
and medicinal and dietary supplements. In 2018, 1,296,000 family forest owners with
4 hectares or more of forest indicated that they have harvested NTFPs since owning
their forestland. Furthermore, 780,000 family forest owners indicated that NTFPs were
moderately important, important, or very important for why they owned forestland [
38
].
While not all family forest owners who collect NTFPs are practicing forest farming, the
Agriculture 2022,12, 726 11 of 17
prevalence of collecting NTFPs by private forestland owners warrants further investigation
as to what percent of these owners are practicing forest farming. This could be accomplished
through a national survey or a series of regional surveys targeting family forest owners that
asks: (1) whether these operations are wild harvesting NTFPs or practicing forest farming,
(2) what products are being harvested and/or sold, (3) what is the market value of those
products, and (4) whether the respondent filled out the 2017 COA. Through such a study,
inferences could be made as to whether forest farming was under-reported in the national
agroforestry estimate. This type of survey could also serve as a learning opportunity for
some producers on what is and is not forest farming and may also inform respondents of
whether they should register to receive a COA survey.
3.3.2. Ensuring Producers Answer the Agroforestry Census Question Correctly
Another factor that can influence the national agroforestry estimate is unfamiliarity
with the terminology and nomenclature used for the various agroforestry practices. While
windbreak and riparian forest buffers are more familiar, several studies have identified
unfamiliarity among producers with the agroforestry practices of silvopasture, forest
farming, and alley cropping [
7
,
39
]. While definitions for these practices are provided in
the COA Report Form Guide [
9
], it is uncertain how many producers referenced these
definitions prior to filling out the agroforestry question. In some cases, these uncertainties
may result in an underestimation of the true value of agroforestry use, while other times,
it may result in overestimation. In addition, respondents who use agroforestry practices
other than the five mentioned in the COA will not be counted. Producers may also say no
to using agroforestry because they use different terms for an agroforestry practice. Table 2
illustrates the five agroforestry practices identified in the 2017 COA, along with associated
practices and terms used in the U.S.
Table 2.
Agroforestry practices identified in the 2017 Census of Agriculture and other terms associated
with those practices in the U.S.
Terms Used in the 2017 Census of Agriculture Associated Terms
Windbreak Shelterbelt, timberbelt, hedgerow, living snow fence, vegetated
environmental buffer
Riparian forest buffer
Streamside forests, riparian management zone, streamside
management zone, vegetated buffer strips, woody riparian buffers,
conservation buffers, riparian forest corridor
Forest farming Multi-story cropping, food forest, forest garden, polyculture
Alley cropping
Intercropping, mixed cropping, polyculture, multifunctional woody
polyculture
Silvopasture Silvopastoral, woodland grazing, forest grazing
Confusion also exists because certain practices can be considered as agroforestry in
some instances, while not in others. The distinction is often based on management and
the interactions between the tree, crop, and/or livestock components. For example, living
snow fences and riparian forest buffers can be considered as agroforestry when deliberately
used in an agricultural setting, but not when used for non-agricultural purposes. Likewise,
tapping trees for maple syrup or other saps can be considered as a type of agroforestry
(forest farming) depending on the type of management used. This may have important im-
plications, as 9492 operations in the U.S. reported selling maple syrup in the 2017 COA [
17
].
While it is uncertain how many producers were tapping trees in an agroforestry system, the
prevalence warrants further investigation. This could be achieved by conducting a survey
of maple syrup producers that asks about management practices.
A second source of potential error related to agroforestry nomenclature is confusion
with other agricultural practices. For example, a recent study found that some producers
Agriculture 2022,12, 726 12 of 17
using silvopasture management are unfamiliar with the term and instead refer to their
system as woodland or forest grazing [
7
]. While woodland and forest grazing have some
similarities with silvopasture, they do not often involve rotational or management-intensive
grazing, which is a requirement of silvopasture management. When looking at the 2017
COA data, 326,279 farm operations reported having wooded pasture and 265,162 reported
practicing rotational or management-intensive grazing [
17
]. However, it is uncertain how
many producers were using both forms of management simultaneously, which may be
indicative of silvopasture. One strategy to better understand these relationships would be
to conduct a survey in collaboration with USDA NASS, specifically targeting producers
that reported both wooded pasture and use of rotational or management-intensive grazing
to assess whether they are using silvopasture. If these data were compared to their response
to the agroforestry COA question, it would provide insight into whether silvopasture may
have been under- or over-counted.
Confusion also exists with the term agroforestry itself. On the surface, one may
think that any system that combines agriculture and forestry would be considered as
agroforestry. Our team of agroforestry researchers and technology transfer specialists have
encountered producers and NRPs who identify tree farms, orchards, farm woodlots, or tree
plantations as agroforestry, since the trees in these systems can be grown in an agricultural
setting and/or are managed using agricultural practices. While some of these systems
do occasionally meet the intentional, intensive, integrational, and interactive criteria for
being agroforestry, more often they do not. In some cases, this confusion has extended
to government programs. In the late 1990s, the Minnesota Agro-Forestry Cooperative
provided low-interest loans to assist landowners to grow hybrid poplar (Populus) trees
for commercial harvest [
40
,
41
]. While agroforestry practices were included, other systems
(block, biomass, and bioenergy plantings) were also considered as an agroforestry practice
because the trees were grown as an agricultural crop. This confusion also extends to research
and academia. For example, Colletti et al. [
42
] reported a short-rotation woody crop system
as an agroforestry demonstration, when the planting was more focused on biomass-to-
energy. While this type of planting could qualify as an agroforestry system if used as a
windbreak or riparian buffer, as the authors suggested, the system described was not an
actual agroforestry demonstration. Similar issues were apparent in
Chapman et al. [43]
,
where a Christmas tree farm was described as agroforestry, Patch and Felker [
44
] for honey
mesquite (Prosopis glandulosa) plantations, and Matthews et al. [
45
] for farm woodlots.
These examples illustrate confusion of what qualifies as agroforestry, which could lead to
errors in agroforestry surveys.
While government programs and academic research have increased the consistency
in agroforestry nomenclature, many terms are still confusing and unfamiliar. Unfortu-
nately, this can result in missing or incorrect data being reported in agroforestry surveys.
One strategy
to reduce issues with nomenclature in surveys is to provide definitions and pic-
tures for each agroforestry practice. Include and exclude prompts for specific agroforestry
questions may also be warranted. While these additions take up space in a questionnaire,
the quality of the results will likely improve. A second strategy is to encourage use around
a consistent set of established agroforestry practice names and definitions and minimize the
use of new terms, which can complicate inventorying efforts. Improving the accuracy of
future agroforestry surveys will also require a more deliberate effort to increase awareness
about each agroforestry practice through training, education, and demonstration sites [8].
3.3.3. Utilizing Regional Agroforestry Surveys
Regional agroforestry surveys can play a vital role, since these instruments can be
tailored to the unique characteristics of the region and producer community. As described
earlier, regional surveys can be used to identify operations that may have an agroforestry
system but do not meet the definition of a farm as defined in the COA. This may be
true for some forest farming systems, home gardens, hobby farms, or those that produce
agricultural products for only personal consumption. Regional surveys are also more likely
Agriculture 2022,12, 726 13 of 17
to have the space to provide detailed descriptions and pictures for the various agroforestry
systems to help producers identify whether they are practicing agroforestry or not. These
surveys may also identify agroforestry systems that do not fit under the five primary
practices included in the COA, such as urban food forests, hedgerows, or indigenous
agroforestry systems.
Regional surveys can also be used to better understand the national agroforestry
estimate if structured with that in mind. For example, Stubblefield [
46
] surveyed producers
in 12 counties in the state of Missouri and found that 15.0% were practicing agroforestry.
This contrasts with 1.4% identified in the 2017 COA for the same counties. It should be
noted that the Missouri study was conducted four years after the COA and the sampling
frame included owners of agricultural land, which may have captured respondents not
meeting the stricter definition of an agricultural operation as defined in the COA. However,
the large discrepancy is important to consider. Is it due an increase in agroforestry over the
past four years, a slightly varied definition of a farm operation, and/or other variables? If
one of the objectives of future regional agroforestry surveys is to make comparisons to the
COA, a few targeted questions could be asked, such as (1) what agroforestry practice(s)
was the respondent using during the census year, (2) what was the market value of the
agricultural products sold during that year, and (3) whether they filled out a COA. Such
correlating questions, among others, would help identify possible reasons for any discrep-
ancies between regional and national estimates. It may also help inform some producers
on whether they should register to receive a COA survey.
3.3.4. Utilizing the Census of Agriculture for Follow-Up Surveys
One effective method to strengthen and build off the existing COA agroforestry data
is to conduct a follow up survey in collaboration with USDA NASS. NASS provides tech-
nical expertise and conducts surveys for other federal agencies, state governments, and
private organizations on a reimbursable basis. More specifically, through the reimbursable
program, NASS provides support and assistance with questionnaire and sample design,
data collection and editing, analysis of survey results, and training [
47
]. The USDA Na-
tional Agroforestry Center is currently using this option to conduct a national survey of
agroforestry adoption across the U.S., sampling operations that indicated they practiced
agroforestry in the 2017 COA. The National Agroforestry Survey is structured to collect
data for each agroforestry practice on number of acres, when and how the system was
established, primary benefits and challenges, products and resources and where they were
sold, maintenance and management requirements, and future projections on whether the
practice will be continued. This comprehensive dataset will provide foundational informa-
tion for developing national-scale value-added assessments. For instance, practice-level
data on extent, age, and management of systems from the National Agroforestry Survey
can support development of Tier 1 and 2 IPCC methods for soil organic and biomass
carbon storage in agroforestry systems [
48
]. These data may also facilitate ecosystem ser-
vice valuations including soil erosion protection, water quality, pollination, and biological
control [49,50].
3.3.5. Utilizing Remote Sensing to Augment National Estimates
With advances in spatial assessment technologies, remote sensing offers potential
opportunities to supplement survey methodologies in developing national estimates of
agroforestry use [
51
,
52
]. In the U.S., high-resolution aerial imagery and machine-learning
classification systems are being used to develop 1 m resolution maps of tree cover [
53
],
which can then be used to identify windbreaks and riparian forest buffers based on their
shape [
54
]. This approach can be used to estimate land area and locations for these types
of linear agroforestry practices and may be a way to cross check numbers derived from
survey methods. Challenges remain in accurately identifying forest farming, silvopasture,
and similar block-type agroforestry practices from other forest land covers and uses [
55
].
Remotely sensed data will need to be augmented with producer-based surveys that can
Agriculture 2022,12, 726 14 of 17
provide key information, including number of adopters and their demographics as well as
practice implementation and management factors.
4. Conclusions
Understanding how many producers use agroforestry across the landscape is impor-
tant, as it influences decisions related to agricultural policy. When sufficient producer
information is available, these datasets can also be used to understand how agroforestry
implementation is changing over time, identify trends related to adoption, and be used
for ecosystem services valuation, including climate change mitigation and adaptation.
Through this review of national agroforestry datasets in the U.S., we found the data to be
nuanced due to changing government programs, methods of reporting, and datasets being
distributed across several government databases. These datasets also contained varying
levels of producer information related to their agroforestry systems, resulting in different
opportunities and challenges for value-added analyses. More specifically, we found data
from the 2017 U.S. Census of Agriculture to be the most robust for assessing who is using
agroforestry across the U.S., as the raw county-level data allow for detailed agroforestry
maps and tables to be generated for all 50 states (Supplement File S1). However, these
data are not delineated by agroforestry practice, nor do they contain acreage data neces-
sary for conducting robust ecosystem service valuations. National datasets containing
practice-specific agroforestry information, along with data on acreage, were found in other
government databases, including the Natural Resources Conservation Service’s EQIP, CSP,
and AMA and the Farm Service Agency’s CRP. While these data can be used for ecosystem
valuation, the analysis is limited to systems established through conservation programs,
which may only represent a fraction of the agroforestry systems across the landscape.
Looking forward, we suggest that an effective mechanism to strengthen future es-
timates of agroforestry adoption across the U.S. is to focus on enhancing the COA agro-
forestry statistic and through regional agroforestry surveys. A key strategy to improve
upon these surveys will be through increased outreach and education to producers and
natural resource professionals on what is and is not agroforestry, since the nomenclature
used for several agroforestry practices is still unfamiliar to many. Unfortunately, this
unfamiliarity in terminology is likely causing misinformation to be reported in surveys,
causing an undercount of agroforestry systems across the U.S. The issue of agroforestry
terminology has also been reported in studies investigating agroforestry adoption in other
countries [45,56–58], suggesting this problem and potential solutions offered in this study
are applicable beyond the U.S. Such an undercount in agroforestry surveys could have
negative consequences, since funding, programs, research, and extension delivery are often
directed toward agricultural practices that are more prevalent. With improved understand-
ing of what agroforestry is and is not, the accuracy of future surveys will increase, along
with response rates from adopters using these integrated systems.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/agriculture12050726/s1, File S1: State and county maps of agro-
forestry use across the United States using 2017 Census of Agriculture data.
Author Contributions:
Conceptualization, M.M.S., G.B., T.K., K.M., R.S., and L.A.; methodology,
M.M.S., G.B., T.K., K.M., R.S., and L.A.; software, T.K., and G.B.; validation, M.M.S., G.B., T.K., K.M.,
R.S., and L.A.; formal analysis, M.M.S., G.B., T.K., K.M., R.S., and L.A.; investigation, M.M.S., G.B.,
T.K., K.M., R.S., and L.A.; resources, M.M.S., G.B., T.K., K.M., R.S., and L.A.; data curation, M.M.S.,
G.B., T.K., K.M., R.S., and L.A.; writing—original draft preparation, M.M.S., G.B., T.K., K.M., R.S., and
L.A.; writing—review and editing, M.M.S., G.B., T.K., K.M., R.S., and L.A.; visualization, T.K., and
G.B.; supervision, M.M.S.; project administration, M.M.S.; funding acquisition, M.M.S. All authors
have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Agriculture 2022,12, 726 15 of 17
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data that support the findings of this study are available within
the article and Supplementary Materials.
Acknowledgments:
This work was supported in part by the U.S. Department of Agriculture (USDA),
Forest Service. The findings and conclusions in this publication are those of the authors and should
not be construed to represent any official USDA or U.S. Government determination or policy.
Conflicts of Interest: The authors declare no conflict of interest.
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