Submitted 12 December 2017
Accepted 9 February 2018
Published 26 February 2018
Jonathan G. Lundgren,
Additional Information and
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2018 LaCanne and Lundgren
Creative Commons CC-BY 4.0
Regenerative agriculture: merging
farming and natural resource conservation
Claire E. LaCanne1and Jonathan G. Lundgren2
1Natural Resource Management Department, South Dakota State University, Brookings, SD, USA
2Ecdysis Foundation, Estelline, SD, USA
Most cropland in the United States is characterized by large monocultures, whose
productivity is maintained through a strong reliance on costly tillage, external fertilizers,
and pesticides (Schipanski et al., 2016). Despite this, farmers have developed a regen-
erative model of farm production that promotes soil health and biodiversity, while
producing nutrient-dense farm products profitably. Little work has focused on the
relative costs and benefits of novel regenerative farming operations, which necessitates
studying in situ, farmer-defined best management practices. Here, we evaluate the
relative effects of regenerative and conventional corn production systems on pest
management services, soil conservation, and farmer profitability and productivity
throughout the Northern Plains of the United States. Regenerative farming systems
provided greater ecosystem services and profitability for farmers than an input-
intensive model of corn production. Pests were 10-fold more abundant in insecticide-
treated corn fields than on insecticide-free regenerative farms, indicating that farmers
who proactively design pest-resilient food systems outperform farmers that react to
pests chemically. Regenerative fields had 29% lower grain production but 78% higher
profits over traditional corn production systems. Profit was positively correlated with
the particulate organic matter of the soil, not yield. These results provide the basis for
dialogue on ecologically based farming systems that could be used to simultaneously
produce food while conserving our natural resource base: two factors that are pitted
against one another in simplified food production systems. To attain this requires a
systems-level shift on the farm; simply applying individual regenerative practices within
the current production model will not likely produce the documented results.
Subjects Agricultural Science, Biodiversity, Ecology, Entomology, Soil Science
Keywords Agroecology, Biodiversity, Conservation agriculture, Corn, Pest management, Yield,
Profit, Soil organic matter
Development of synthetic fertilizers, hybrid crops, genetically modified crops, and policies
that decouple farmer decisions from market demands all helped create a modern food
production system which reduces the diversity of foods that are produced (Fausti &
Lundgren, 2015;Pretty, 1995). This simplification of our food system contributes to
climate change (Carlsson-Kanyama & Gonzalez, 2009), rising pollution (Beman et al., 2011;
Morrissey et al., 2015), biodiversity loss (Butler, Vickery & Norris, 2007;Landis et al., 2008),
How to cite this article LaCanne and Lundgren (2018), Regenerative agriculture: merging farming and natural resource conservation
profitably. PeerJ 6:e4428; DOI 10.7717/peerj.4428
and damaging land use changes (Johnston, 2014;Wright & Wimberly, 2013) that affect
the sustainability, profitability and resilience of farms (Schipanski et al., 2016). Farmers
experience the highest suicide rate of any profession in the United States, a rate nearly
five-fold higher than the general public (McIntosh et al., 2016); the driving depression
rates are related to conventional production practices (Beard et al., 2014). The scale of our
food production system provides opportunities for solving some of these planetary scale
problems (Lal, 2004;Teague et al., 2016), but requires a systems-level shift in the values
and goals of our food production system that de-prioritizes solely generating high yields
toward one that produces higher quality food while conserving our natural resource base.
The goal of regenerative farming systems (Rodale, 1983) is to increase soil quality and
biodiversity in farmland while producing nourishing farm products profitably. Unifying
principles consistent across regenerative farming systems include (1) abandoning tillage
(or actively rebuilding soil communities following a tillage event), (2) eliminating spatio-
temporal events of bare soil, (3) fostering plant diversity on the farm, and (4) integrating
livestock and cropping operations on the land. Further characterization of a regenerative
system is problematic because of the myriad combinations of farming practices that
comprise a system targeting the regenerative goal. Other comparisons of conventional
agriculture with alternative agriculture schemes do not compare in situ best management
practices developed by farmers, and frequently ignore a key driver to decision making on
farming operations: the examined systems’ relative net profit to the farmer (De Ponti, Rijk
& Van Ittersum, 2012).
MATERIALS AND METHODS
Corn (Zea mays L.) was selected for our study due to its pre-eminence as a food crop in
North America and globally. Corn is planted on 39.9% of all crop acres (NASS, 2017), or
4.8% (37.1 million ha) of the terrestrial land surface of the contiguous 48 states. In 2012,
it generated 30.3% ($64,319 billion) of all gross crop value in the US (NASS, 2017). Nearly
100% of cornfields are treated annually with insecticides (NASS, 2017). We used a matrix
of specific production practices (Table 1) to define each farm into one of two systems
(regenerative or conventional). The most regenerative systems (n=40 fields on 10 farms)
used mixed multispecies cover crops (ranging from 2–40 plant species), were never-till,
used no insecticides, and grazed livestock on their cropland. The most conventional farms
practiced tillage at least annually (36 fields on eight farms), applied insecticides (as GM
insect-resistant varieties and neonicotinoid seed treatments), and left their soil bare aside
from the cash crop.
Soil organic matter, insect pest populations, and corn yield and profit were assessed
for each field. Soil cores (8.5 cm deep, 5 cm in diameter; 30 g of soil each; n=4 samples
per field that were made a composite sample; only one field was sampled per farm-
selected by the producer- and two farms were omitted due to adverse weather during the
sampling event) were collected at least 10 m from one another during anthesis. Samples
were cleaned of plant residue, ground, and dried to constant weight at 105 ◦C. Particulate
soil organic matter (POM) was determined by screening each sample (soaked in 5 g L−1
LaCanne and Lundgren (2018), PeerJ, DOI 10.7717/peerj.4428 2/12
Table 1 Trait matrix used to assign farms to regenerative or conventional corn production systems. The composite rank scores are based on
the number of regenerative practices used on a particular farm. Farms whose rank scores are in the top 50% of farms are considered regenerative
(shaded rows); those with rank scores in the lower half are conventional (white rows). To aid interpretation, additional traits of each system could
be included in enhanced trait matrices. Organic operations are indicated by an asterisk in the ‘‘Reference town’’ column.
Reference town Farm locations
(yes: 1; no: 0)
(no: 1; yes: 0)
(no: 1; yes: 0)
(yes: 1; no: 0)
(yes: 1; no: 0)
Bladen, NE 40.31971, −98.57358 yes no yes no no 3
Bladen, NE 40.33703, −98.56301 no yes yes yes no 0
York, NE 40.63054, −97.66534 yes no yes no no 3
York, NE 40.97390, −97.49031 no yes yes yes no 0
Bismarck, ND 46.85280, −100.60131 yes no no no yes 5
Bismarck, ND 46.85280, −100.35145 no yes yes no no 1
Bismarck, ND 46.81734, −100.51257 yes no yes no yes 4
Bismarck, ND 47.14250, −100.19720 no yes yes no no 1
White, SD* 44.42572, −96.58806 yes no no yes no 3
White, SD 44.41155, −96.60008 no yes yes yes no 0
Pipestone, MN* 44.11446, −96.32468 yes no no yes no 3
Pipestone, MN 44.12416, −96.36422 no yes yes yes no 0
Toronto, SD 44.59248, −96.57923 yes yes yes no no 3
Toronto, SD 44.57960, −96.58367 no yes yes yes no 0
Gary, SD* 44.80565, −96.34708 yes no no yes yes 4
Gary, SD 44.80689, −96.35465 no yes yes yes no 0
Arlington, SD 44.41566, −97.18795 yes no yes no yes 4
Arlington, SD 44.42644, −97.25077 no yes yes yes no 0
Lake Norden, SD 44.58976, −97.08649 yes yes yes no yes 3
Lake Norden, SD 44.55.6839, −97.243820 no yes yes yes no 0
aqueous hexametaphosphate) through 500 um (course POM) and 53 um (fine POM) sieves
and then applying the loss on ignition (LOI) technique (Davies, 1974). Insect pests were
enumerated through dissections of all aboveground plant tissues (25 plants per field). Major
pests of corn (rootworm adults, caterpillar pests, and aphids) are all present in cornfields
at this crop developmental stage (Lundgren et al., 2015), and this was substantiated in the
observations in this study as well. Yields were gathered from three randomly selected 3.5 m
sections of row from each field. Gross revenue for each field were considered as yield
and return on grain, and additional revenue streams (e.g., animal weight gain resulting
from grazing). Total direct costs for each field were calculated based on the costs of corn
seed, cover crop seed, drying/cleaning grain, crop insurance, tillage, planting, fertilizers,
pesticides, and irrigation.
RESULTS AND DISCUSSION
Insect pest populations were more than 10 fold higher on the insecticide-treated farms than
on the insecticide-free regenerative farms (ANOVA; F1,77 =13.52, P<0.001; Fig. 1). Pest
populations were numerically dominated by aphids, but each of the individual pest species
followed the same pattern of the aggregated data; none of these pests were at economically
LaCanne and Lundgren (2018), PeerJ, DOI 10.7717/peerj.4428 3/12
Figure 1 Insecticide-treated cornfields had higher pest abundance than untreated, regenerative corn-
fields. Values presented are mean ±SEM total pests (corn rootworm adults, European corn borers, West-
ern bean cutworm, other caterpillars, and aphids) per m2, and were assessed during corn anthesis. The sys-
tems were regarded as best-management practices for the sampled region by the farmers themselves. All
conventional farms planted neonicotinoid-treated, Bt corn seed to prophylactically reduce pests, and some
cornfields were also sprayed with insecticides. Regenerative farms included >3 of the following practices:
use of a multispecies cover crop, abandonment of insecticide, abandonment of tillage, and the cropland
was grazed, etc. Pest abundance was significantly different in the two systems (α=0.05; n=39 regenera-
tive cornfields and 40 conventional cornfields).
Full-size DOI: 10.7717/peerj.4428/fig-1
damaging levels, as observed in other work in the region (Hutchison et al., 2010;Lundgren
et al., 2015). Pest problems in agriculture are often the product of low biodiversity and
simple community structure on numerous spatial scales (Tscharntke et al., 2012). Hundreds
of invertebrate species have been inventoried from cornfields of the Northern Plains of
the US (Lundgren et al., 2015;Welch & Lundgren, 2016), but these communities represent
only 25% of the insect species that lived in ancestral habitats (e.g., prairie) that cornfields
replaced in this region (Schmid et al., 2015). Pest abundance is lower in cornfields that
have greater insect diversity, enhanced biological network strength and greater community
evenness (Lundgren & Fausti, 2015). Suggested mechanisms to explain how invertebrate
diversity and network interactions reduce pests include predation (Letourneau et al., 2009),
competition (Barbosa et al., 2009), and other processes that may not be easily predicted.
What practices foster diversity in agroecosystems? In our studies, farmers that replaced
insecticide use with agronomic forms of plant diversity invariably had fewer pest problems
than those with strict monocultures. Reducing insect diversity and relying solely on
insecticide use establishes a scenario whereby pests persist and resurge through adaptation,
as was observed by our forebears (Perkins, 1982;Stern et al., 1959). Applying winter cover
crops (Lundgren & Fergen, 2011), lengthening crop rotations (Bullock, 1992), diversifying
field margins using conservation mixes (Haaland, Naisbit & Bersier, 2011), and allowing or
promoting non-crop plants between crop rows (Khan et al., 2006) are other agronomically
LaCanne and Lundgren (2018), PeerJ, DOI 10.7717/peerj.4428 4/12
Figure 2 Regenerative corn fields generate nearly twice the profit of conventionally managed corn
fields. The heights of the bars represent average gross profits across all 40 fields (in each treatment). Profit
was calculated using direct costs and revenues for each field and excludes any overhead and indirect ex-
penses. Regenerative cornfields implemented three or more practices such as planting a multispecies cover
mix, eliminating pesticide use, abandoning tillage, and integrating livestock onto the crop ground. Con-
ventional cornfields used fewer than two of these practices. The regenerative systems had 70% higher
profit than conventional cornfields (α=0.05; n=36 fields in each system). Seed drying, corn planting,
and cover crop planting are present on the graphs, but were negligible costs.
Full-size DOI: 10.7717/peerj.4428/fig-2
sound practices that regenerative farmers successfully apply to improve the resilience of
their system to pest proliferation.
Despite having lower grain yields, the regenerative system was nearly twice as profitable
as the conventional corn farms (ANOVA; F1,70 =14.35, P< 0.001; Fig. 2). Regenerative
farms produced 29% less corn grain than conventional operations (8,481±684 kg/ha vs.
11,884 ±648 kg/ha; ANOVA; F1,70 =8.39, P=0.01). Yield reductions are commonly
reported in more ecologically based food production systems relative to conventional
systems (De Ponti, Rijk & Van Ittersum, 2012). However, only 4% of calories produced as
corn grain is eaten directly by humans, and almost none is consumed as grain. Thirty-six
percent of grain is fed to livestock (NASS, 2017), and corn-fed beef contains only 13% of the
total calories produced by corn grain. Two ways that regenerative systems could increase
the human food produced per ha in cornfields would be to increase the diversity of livestock
on the field, or increasing the duration of grazing current stock. The relative profitability
in the two systems was driven by the high seed and fertilizer costs that conventional
farms incurred (32% of the gross income went into these inputs on conventional fields,
versus only 12% in regenerative fields), and the higher revenue generated from grain and
other products produced (e.g., meat production) on the regenerative corn fields (Fig. 2).
The high seed costs on conventional farms are largely attributable to premiums paid by
farmers for prophylactic insecticide traits (no insecticide was applied as spray on these
fields), whose value is questionable due to pest resistance and persistent low abundance
for some targeted pests in the Northern Plains (Hutchison et al., 2007;Krupke et al., 2017).
Regenerative farmers reduced their fertilizer costs by including legume-based cover crops
LaCanne and Lundgren (2018), PeerJ, DOI 10.7717/peerj.4428 5/12
Figure 3 Corn fields with high particulate organic matter and low bulk density in the soil have greater
profits. Corn fields were managed under either conventional or regenerative systems, and profit was cal-
culated using direct costs and revenues for each field and excludes any overhead and indirect expenses.
(general linear regression model; F1,16 =7.84; P=0.01; r2=0.34; profit =29.68[POM]–66.94; bulk den-
sity; F1,19 =5.23; P=0.03; r2=0.24; profit = −975 [POM] +1,593).
Full-size DOI: 10.7717/peerj.4428/fig-3
on their fields during the fallow period (Ebelhar, Frye & Blevins, 1984), adopting no-till
practices (Lal, Reicosky & Hanson, 2007), and grazing the crop field with livestock (Russelle,
Entz & Franzluebbers, 2010). They also received higher value for their crop by receiving
an organic premium, by selling their grain directly to consumers as seed or feed, and by
extracting more than just corn revenue from their field (e.g., by grazing cover mixes with
The profitability of a corn field was not related to grain yields (F1,70 <0.001; P=0.98;
r2<0.01; profit = −0.0006[yield] +1,274), but was positively correlated with the level
of POM in the soil, and inversely related to the bulk density of the soil (Fig. 3; the SOM
quantities upon which %POM are presented here are reported in Table 2). Organic matter
is considered by some as the basis for productivity in the soil (Karlen et al., 1997;Tiessen,
Cuevas & Chacon, 1994), and soils with high SOM typically have lower bulk density. SOM
increases water infiltration rates, and supports greater microbial and animal abundance
and diversity (Lehman et al., 2015). The components of POM are the labile portion of this
SOM, and are frequently used to study the effects of management-based differences in SOM
(Cambardella & Elliott, 1992). The only way to generate SOM in situ in cropland is through
fostering biology, which inherently is driven by plant communities through sequestration of
CO2from the atmosphere. Eliminating tillage (Pikul Jr et al., 2007;Six, Elliott & Paustian,
LaCanne and Lundgren (2018), PeerJ, DOI 10.7717/peerj.4428 6/12
Table 2 Soil organic matter on regenerative and conventional corn farms. Shaded rows represent re-
generative corn farms.
Reference town Farm locations
Bladen, NE 40.31971, −98.57358 6.23
Bladen, NE 40.33703, −98.56301 4.52
York, NE 40.63054, −97.66534 6.21
York, NE 40.97390, −97.49031 5.55
Bismarck, ND 46.85280, −100.60131 4.19
Bismarck, ND 46.85280, −100.35145 N/A
Bismarck, ND 46.81734, −100.51257 5.82
Bismarck, ND 47.14250, −100.19720 3.85
White, SD 44.42572, −96.58806 N/A
White, SD 44.41155, −96.60008 5.52
Pipestone, MN 44.11446, −96.32468 N/A
Pipestone, MN 44.12416, −96.36422 4.75
Toronto, SD 44.59248, −96.57923 7.60
Toronto, SD 44.57960, −96.58367 6.38
Gary, SD 44.80565, −96.34708 7.53
Gary, SD 44.80689, −96.35465 7.36
Arlington, SD 44.41566, −97.18795 8.17
Arlington, SD 44.42644, −97.25077 8.18
Lake Norden, SD 44.58976, −97.08649 4.56
Lake Norden, SD 44.55.6839, −97.243820 6.26
1999), implementing cover crops (Ding et al., 2006;Kuo, Sainju & Jellum, 1997), and
cycling plant residue through livestock (Tracy & Zhang, 2008) all enhance this process, and
all are important practices used in regenerative food systems that raise POM in the soil.
The farmers themselves have devised an ecologically based production system comprised of
multiple practices that are woven into a profitable farm that promotes ecosystem services.
Regenerative farms fundamentally challenge the current food production paradigm that
maximizes gross profits at the expense of net gains for the farmer. Key elements of this
successful approach to farming include
1. By promoting soil biology and organic matter and biodiversity on their farms,
regenerative farmers required fewer costly inputs like insecticides and fertilizers,
and managed their pest populations more effectively.
2. Soil organic matter was a more important driver of proximate farm profitability than
yields were, in part because the regenerative farms marketed their products differently
or had a diversified income stream from a single field.
LaCanne and Lundgren (2018), PeerJ, DOI 10.7717/peerj.4428 7/12
We thank our 20 farmers throughout the Northern Plains for providing us with study sites
and management information. E Adee, M Bredeson, J Fergen, D Grosz, K Januschka, N
Koens, R LaCanne, M La Vallie, A Leiferman, J Lundgren, A Martens, C Mogren, K Nemec,
A Nikolas, J Pecenka, G Schen, C Snyder, & K Weathers assisted field work. R Conser,
M Entz, C Morrissey, & R Teague provided comments on earlier drafts. M Longfellow
and L Hesler identified invertebrates. Mention of trade names or commercial products in
this publication does not imply recommendation or endorsement by South Dakota State
University or Ecdysis Foundation.
ADDITIONAL INFORMATION AND DECLARATIONS
The project was supported by USDA PMAP Award # 2013-34381-21245, a NC-SARE
graduate student fellowship GNC16-227, and donations of farmers and beekeepers to
Ecdysis Foundation. The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
The following grant information was disclosed by the authors:
USDA PMAP Award: #2013-34381-21245.
Jonathan G. Lundgren is the CEO for Blue Dasher Farm and director of the Ecdysis
Foundation. Claire E. LaCanne is an employee of the University of Minnesota, and was a
graduate student for South Dakota State University during her thesis program (this work
is part of that thesis).
•Claire E. LaCanne conceived and designed the experiments, performed the experiments,
analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the
paper, approved the final draft.
•Jonathan G. Lundgren conceived and designed the experiments, analyzed the data,
contributed reagents/materials/analysis tools, prepared figures and/or tables, authored
or reviewed drafts of the paper, approved the final draft.
The following information was supplied regarding data availability:
The raw data is provided as a Supplemental File.
LaCanne and Lundgren (2018), PeerJ, DOI 10.7717/peerj.4428 8/12
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
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