Regenerative agriculture: Merging farming and natural resource conservation profitably

Abstract

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 regenerative 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.

Full text

Submitted 12 December 2017
Accepted 9 February 2018
Published 26 February 2018
Corresponding author
Jonathan G. Lundgren,
jgl.entomology@gmail.com
Academic editor
Sheila Colla
Additional Information and
Declarations can be found on
page 8
DOI 10.7717/peerj.4428
Copyright
2018 LaCanne and Lundgren
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Regenerative agriculture: merging
farming and natural resource conservation
profitably
Claire E. LaCanne1and Jonathan G. Lundgren2
1Natural Resource Management Department, South Dakota State University, Brookings, SD, USA
2Ecdysis Foundation, Estelline, SD, USA
ABSTRACT
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
INTRODUCTION
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
(latitude, longitude)
Cover crop
(yes: 1; no: 0)
Insecticide
(no: 1; yes: 0)
Other
pesticides
(no: 1; yes: 0)
Tillage
(yes: 1; no: 0)
Grazed
corn field
(yes: 1; no: 0)
Composite
rank score
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
livestock).
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
(latitude, longitude)
SOM
(%)
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.
CONCLUSIONS
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

ACKNOWLEDGEMENTS
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
Funding
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.
Grant Disclosures
The following grant information was disclosed by the authors:
USDA PMAP Award: #2013-34381-21245.
NC-SARE: GNC16-227.
Ecdysis Foundation.
Competing Interests
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).
Author Contributions
•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.
Data Availability
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
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.4428#supplemental-information.
REFERENCES
Barbosa P, Hines J, Kaplan I, Martinson H, Szczepaniec A, Szendrei Z. 2009. Associa-
tional resistance and susceptibility: having right or wrong neighbors. Annual Review
of Ecology, Evolution & Systematics 40:1–20
DOI 10.1146/annurev.ecolsys.110308.120242.
Beard JD, Umbach DM, Hoppin JA, Richards M, Alavanja MCR, Blair A, Sandler DP,
Kamel F. 2014. Pesticide exposure and depression among male private pesticide
applicators in the agricultural health study. Environmental Health Perspectives
122:984–991 DOI 10.1289/ehp.1307450.
Beman JM, Chow C-E, King AL, Feng Y, Fuhrman JA, Andersson A, Bates NR, Popp
BN, Hutchings DA. 2011. Global declines in oceanic nitrification rates as a conse-
quence of ocean acidification. Proceedings of the National Academy of Sciences of the
United States of America 108:208–213 DOI 10.1073/pnas.1011053108.
Bullock DG. 1992. Crop rotation. Critical Reviews in Plant Sciences 11:309–326
DOI 10.1080/07352689209382349.
Butler SJ, Vickery JA, Norris K. 2007. Farmland biodiversity and the footprint of
agriculture. Science 315:381–384 DOI 10.1126/science.1136607.
Cambardella CA, Elliott ET. 1992. Particulate soil organic-matter changes across a
grassland cultivation sequence. Soil Science Society of America Journal 56:777–783
DOI 10.2136/sssaj1992.03615995005600030017x.
Carlsson-Kanyama A, Gonzalez AD. 2009. Potential contributions of food con-
sumption patterns to climate change. The American Journal of Clinical Nutrition
89:1704S–1709S DOI 10.3945/ajcn.2009.26736AA.
Davies BE. 1974. Loss-on-ignition as an estimate of soil organic matter. Soil Science Soci-
ety of America Journal 38:150–151 DOI 10.2136/sssaj1974.03615995003800010046x.
De Ponti T, Rijk B, Van Ittersum MK. 2012. The crop yield gap between organic and
conventional agriculture. Agricultural Systems 108:1–9
DOI 10.1016/j.agsy.2011.12.004.
Ding G, Liu X, Herbert S, Novak J, Amarasiriwardena D, Xing B. 2006. Effect
of cover crop management on soil organic matter. Geoderma 130:229–239
DOI 10.1016/j.geoderma.2005.01.019.
Ebelhar SA, Frye WW, Blevins RL. 1984. Nitrogen from legume cover crops for no-
tillage corn. Agronomy Journal 76:51–55
DOI 10.2134/agronj1984.00021962007600010014x.
Fausti SW, Lundgren JG. 2015. The causes and unintended consequences of a paradigm
shift in corn production practices. Environmental Science & Policy 52:41–50
DOI 10.1016/j.envsci.2015.04.017.
LaCanne and Lundgren (2018), PeerJ, DOI 10.7717/peerj.4428 9/12

Haaland C, Naisbit RE, Bersier L-F. 2011. Sown wildflower strips for insect conserva-
tion: a review. Insect Conservation and Diversity 4:60–80
DOI 10.1111/j.1752-4598.2010.00098.x.
Hutchison WD, Burkness EC, Mitchell PD, Moon RD, Leslie TW, Fleischer SJ,
Abrahamson M, Hamilton KL, Steffey KL, Gray ME, Hellmich RL, Kaster LV,
Hunt TE, Wright RJ, Pecinovsky K, Rabaey TL, Flood BR, Raun ES. 2010. Areawide
suppression of European corn borer with Bt maize reaps savings to non-Bt maize
grower. Science 330:222–225 DOI 10.1126/science.1190242.
Hutchison WD, Burkness E, Moon R, Leslie T, Fleischer S, Abrahamson M, Hamilton
K, Steffey K, Gray M. 2007. Evidence for regional suppression of European corn
borer populations in Bt maize in the midwestern US: analysis of long-term time
series’ from three states. In: XVI international plant protection congress. Glasgow,
Scotland, 512–513.
Johnston CA. 2014. Agricultural expansion: land use shell game in the US Northern
Plains. Landscape Ecology 29:81–95 DOI 10.1007/s10980-013-9947-0.
Karlen DL, Mausbach MJ, Doran JW, Cline RG, Harris RF, Schuman GE. 1997. Soil
quality: a concept, definition, and framework for evaluation. Soil Science Society of
America Journal 61:4–10 DOI 10.2136/sssaj1997.03615995006100010001x.
Khan ZR, Pickett JA, Wadhams LJ, Hassanali A, Midega CAO. 2006. Combined control
of Striga hermonthica and stemborers by maize-Desmodium spp. intercrops. Crop
Protection 25:989–995 DOI 10.1016/j.cropro.2006.01.008.
Krupke CH, Holland JD, Long EY, Eitzer BD. 2017. Planting of neonicotinoid-treated
maize poses risks for honey bees and other non-target organisms over a wide area
without consistent crop yield benefit. Journal of Applied Ecology Epub ahead of print
May 22 2017.
Kuo S, Sainju UM, Jellum EJ. 1997. Winter cover crop effects on soil organic carbon
and carbohydrate in soil. Soil Science Society of America Journal 61:145–152
DOI 10.2136/sssaj1997.03615995006100010022x.
Lal R. 2004. Soil carbon sequestration impacts on global climate change and food
security. Science 304:1624–1627 DOI 10.1126/science.1097396.
Lal R, Reicosky DC, Hanson JD. 2007. Evolution of the plow over 10,000 years and the
rationale for no-till farming. Soil & Tillage Research 93:1–12
DOI 10.1016/j.still.2006.11.004.
Landis DA, Gardiner MM, Van der Werf W, Swinton SM. 2008. Increasing corn for
biofuel production reduces biocontrol services in agricultural landscapes. Proceedings
of the National Academy of Sciences of the United States of America 105:20552–20557
DOI 10.1073/pnas.0804951106.
Lehman RM, Cambardella CA, Stott DE, Acosta-Martinez V, Manter DK, Buyer
JS, Maul JE, Smith JL, Collins HP, Halvorson JJ, Kremer RJ, Lundgren JG,
Ducey TF, Jin VL, Karlen DL. 2015. Understanding and enhancing soil biological
health: the solution for reversing soil degradation. Sustainability 7:988–1027
DOI 10.3390/su7010988.
LaCanne and Lundgren (2018), PeerJ, DOI 10.7717/peerj.4428 10/12

Letourneau DK, Jedlicka JA, Bothwell SG, Moreno CR. 2009. Effects of natural
enemy biodiversity on the suppression of arthropod herbivores in terrestrial
ecosystems. Annual Review of Ecology, Evolution, and Systematics 40:573–592
DOI 10.1146/annurev.ecolsys.110308.120320.
Lundgren JG, Fausti SW. 2015. Trading biodiversity for pest problems. Science Advances
1:e1500558 DOI 10.1126/sciadv.1500558.
Lundgren JG, Fergen JK. 2011. Enhancing predation of a subterranean insect pest: a
conservation benefit of winter vegetation in agroecosystems. Applied Soil Ecology
51:9–16 DOI 10.1016/j.apsoil.2011.08.005.
Lundgren JG, McDonald TM, Rand TA, Fausti SW. 2015. Spatial and numerical
relationships of arthropod communities associated with key pests of maize. Journal
of Applied Entomology 136:446–456 DOI 10.1111/jen.12215.
McIntosh WLW, Spies E, Stone DM, Lokey CN, Trudeau A-R, Bartholow B. 2016.
Suicide rates by occupational group—17 states, 2012. MMWR Morbity and Mortality
Weekly Report 2016 65:641–645.
Morrissey CA, Mineau P, Devries JH, Sanchez-Bayo F, Liess M, Cavallaro MC, Liber
K. 2015. Neonicotinoid contamination of global surface waters and associated
risk to aquatic invertebrates: a review. Environment International 74:291–303
DOI 10.1016/j.envint.2014.10.024.
National Agriculture Statistics Service (NASS). 2017. National Agriculture Statistics
Service. Washington, D.C., USDA. Available at http:// www.nass.usda.gov .
Perkins JH. 1982. Insects, experts, and the insecticide crisis. New York: Plenum Press.
Pikul Jr JL, Osborne SE, Ellsbury MM, Riedell WE. 2007. Particulate organic matter and
water-stable aggregation of soil under contrasting management. Soil Science Society of
America Journal 71:766–776 DOI 10.2136/sssaj2005.0334.
Pretty JN. 1995. Regenerating agriculture: policies and practice for sustainability and self-
reliance. Washington, D.C.: Joseph Henry Press.
Rodale R. 1983. Breaking new ground: the search for a sustainable agriculture. The
Futurist 17:15–20.
Russelle MP, Entz MH, Franzluebbers AJ. 2010. Reconsidering integrated crop-livestock
systems in North America. Agronomy Journal 99:325–334.
Schipanski ME, MAcDonald GK, Rosenzweig S, Chappell MJ, Bennett EM, Kerr
RB, Blesh J, Crews TE, Drinkwater LE, Lundgren JG, Schnarr C. 2016. Realizing
resilient food systems. Bioscience 66:600–610 DOI 10.1093/biosci/biw052.
Schmid RB, Lehman RM, Brözel VS, Lundgren JG. 2015. Gut bacterial symbiont
diversity within beneficial insects linked to reductions in local biodiversity. Annals
of the Entomological Society of America 108:993–999 DOI 10.1093/aesa/sav081.
Six J, Elliott ET, Paustian K. 1999. Aggregate and soil organic matter dynamics un-
der conventional and no-tillage systems. Soil Science Society of America Journal
63:1350–1358 DOI 10.2136/sssaj1999.6351350x.
Stern VM, Smith RF, Van den Bosch R, Hagen KS. 1959. The integrated control concept.
Hilgardia 29:81–101 DOI 10.3733/hilg.v29n02p081.
LaCanne and Lundgren (2018), PeerJ, DOI 10.7717/peerj.4428 11/12

Teague WR, Apfelbaum S, Lal R, Kreuter UP, Rowntree J, Davies CA, Wang F. 2016.
The role of ruminants in reducing agriculture’s carbon footprint in North America.
Journal of Soil and Water Conservation 71:156–164 DOI 10.2489/jswc.71.2.156.
Tiessen H, Cuevas E, Chacon P. 1994. The role of soil organic matter in sustaining soil
fertility. Nature 371:783–785 DOI 10.1038/371783a0.
Tracy BF, Zhang Y. 2008. Soil compaction, corn yield response, and soil nutrient
pool dynamics within an integrated crop-livestock system in Illinois. Crop Science
48:1211–1218 DOI 10.2135/cropsci2007.07.0390.
Tscharntke T, Clough Y, Wanger TC, Jackson L, Motzke I, Perfecto I, Vander-
meer J, Whitbread A. 2012. Global food security, biodiversity conservation and
the future of agricultural intensification. Biological Conservation 151:53–59
DOI 10.1016/j.biocon.2012.01.068.
Welch KD, Lundgren JG. 2016. An exposure-based, ecology-driven framework for
selection of indicator species for insecticide risk assessment. Food Webs 9:46–54
DOI 10.1016/j.fooweb.2016.02.004.
Wright CK, Wimberly MC. 2013. Recent land use change in the Western Corn Belt
threatens grasslands and wetlands. Proceedings of the National Academy of Sciences
of the United States of America 110:4134–4319 DOI 10.1073/pnas.1215404110.
LaCanne and Lundgren (2018), PeerJ, DOI 10.7717/peerj.4428 12/12