Skip to main content

Land cover and climate changes drive regionally heterogeneous increases in US insecticide use

Abstract

Context

Global environmental change is expected to dramatically affect agricultural crop production through a myriad of pathways. One important and thus far poorly understood impact is the effect of land cover and climate change on agricultural insect pests and insecticides.

Objectives

Here we address the following three questions: (1) how do landscape complexity and weather influence present-day insecticide use, (2) how will changing landscape characteristics and changing climate influence future insecticide use, and how do these effects manifest for different climate and land cover projections? and (3) what are the most important drivers of changing insecticide use?

Methods

We use panel models applied to county-level agriculture, land cover, and weather data in the US to understand how landscape composition and configuration, weather, and farm characteristics impact present-day insecticide use. We then leverage forecasted changes in land cover and climate under different future scenarios to predict insecticide use in 2050.

Results

We find different future scenarios—through modifications in both landscape and climate conditions—increase the amount of area treated by ~ 4–20% relative to 2017, with regionally heterogeneous impacts. Of note, we report large farms are more influential than large crop patches and increased winter minimum temperature is more influential than increased summer maximum temperature. However, our results suggest the most important determinants of future insecticide use are crop composition and farm size, variables for which future forecasts are sparse.

Conclusions

Both landscape and climate change are expected to increase future insecticide use. Yet, crop composition and farm size are highly influential, data-poor variables. Better understanding of future crop composition and farm economics is necessary to effectively predict and mitigate increases in pesticide use.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  • Barreca A, Deschênes O, Guldi M (2018) Maybe next month? Temperature shocks and dynamic adjustments in birth rates. Demography 55:1269–1293

    PubMed  PubMed Central  Google Scholar 

  • Baston D (2020) exactextractr: fast extraction from Raster Datasets using Polygons. R package version 0.1.2. https://CRAN.R-project.org/package=exactextractr

  • Berrisford P, Dee DP, Fielding M, Fuentes M, Kållberg PW, Kobayashi S, Uppala S (2009) The ERA-interim archive. ERA Rep Ser 1:1–16

    Google Scholar 

  • Berrisford P, Kållb, P, Kobayashi S, Dee D, Uppala S, Simmons AJ, Poli P, Sato H (2011) Atmospheric conservation properties in ERA-Interim. Q J Royal Meteorol Soc 137:1381–1399

  • Chaplin-Kramer R, Kremen C (2012) Pest control experiments show benefits of complexity at landscape and local scales. Ecol Appl 22:1936–1948

    PubMed  Google Scholar 

  • Chaplin-Kramer R, O’Rourke ME, Blitzer EJ, Kremen C (2011) A meta-analysis of crop pest and natural enemy response to landscape complexity. Ecol Lett 14:922–932

    PubMed  Google Scholar 

  • Chen CC, McCarl BA (2001) An investigation of the relationship between pesticide usage and climate change. Clim Change 50:475–487

    Google Scholar 

  • Conley TG (1999) GMM estimation with cross sectional dependence. J Econom 92:1–45

    Google Scholar 

  • Conley TG (2008) Spatial econometrics. In: Durlauf SN, Blume LE (eds) The new Palgrave dictionary of economics. Palgrave Macmillan, London, pp 741–747. https://doi.org/10.1057/9780230226203.1582

    Chapter  Google Scholar 

  • Costello C, Quérou N, Tomini A (2017) Private eradication of mobile public bads. Eur Econ Rev 94:23–44

    Google Scholar 

  • Damien M, Tougeron K (2019) Prey-predator phenological mismatch under climate change. Curr Opin Insect Sci. https://doi.org/10.1016/j.cois.2019.07.002

    Article  PubMed  Google Scholar 

  • Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597

    Google Scholar 

  • Deschênes O, Greenstone M (2007) The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather. Am Econ Rev 97:354–385

    Google Scholar 

  • Deutsch CA, Tewksbury JJ, Tigchelaar M, Battisti DS, Merrill SC, Huey RB, Naylor RL (2018) Increase in crop losses to insect pests in a warming climate. Science 361:916–919

    CAS  PubMed  Google Scholar 

  • Emerson SS, Spieker AJ, Williamson BD, Hee Wai TY, Lim S (2018) uwIntroStats: descriptive statistics, inference, regression, and plotting in an introductory statistics course. R package version 0.0.7. https://CRAN.R-project.org/package=uwIntroStats

  • Fahrig L, Girard J, Duro D, Pasher J, Smith A, Javorek S, King D, Lindsay KF, Mitchell S, Tischendorf L (2015) Farmlands with smaller crop fields have higher within-field biodiversity. Agric Ecosyst Environ 200:219–234

    Google Scholar 

  • Foley JA, Defries R, Asner GP, Barford C, Bonan G, Carpenter SR, Chapin FS, Coe MT, Daily GC, Gibbs HK, Helkowski JH, Holloway T, Howard EA, Kucharik CJ, Monfreda C, Patz JA, Prentice IC, Ramankutty N, Snyder PK (2005) Global consequences of land use. Science 309:570–574

    CAS  PubMed  Google Scholar 

  • García CB, García J, López Martín MM, Salmerón R (2014) Collinearity: revisiting the variance inflation factor in ridge regression. J Appl Stat 42:648–661

    Google Scholar 

  • Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasch PJ, Vertenstein M, Worley PH, Yang Z-L, Zhang M (2011) The Community Climate System Model Version 4. J Clim 24:4973–4991

    Google Scholar 

  • Gerard PJ, Barringer JRF, Charles JG, Fowler SV, Kean JM, Phillips CB, Tait AB, Walker GP (2012) Potential effects of climate change on biological control systems: case studies from New Zealand. BioControl 58:149–162

    Google Scholar 

  • Gross K, Rosenheim JA (2011) Quantifying secondary pest outbreaks in cotton and their monetary cost with causal-inference statistics. Ecol Appl 21:2770–2780

    PubMed  Google Scholar 

  • Hesselbarth MHK, Sciaini M, With KA, Wiegand K, Nowosad J (2019) landscapemetrics: an open-source Rtool to calculate landscape metrics. Ecography 42:1648–1657

    Google Scholar 

  • IPCC (2000) In: Nakićenović N, Swart R (eds) Special report on emissions scenarios: a special report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, pp 1–608

  • IPCC (2014) In: Pachauri RK, Meyer LA (eds) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland, pp 1–151

  • Jin S, Homer C, Yang L, Danielson P, Dewitz J, Li C, Zhu Z, Xian G, Howard D (2019) Overall methodology design for the United States National Land Cover Database 2016 products. Remote Sens 11:2971–3032

    Article  Google Scholar 

  • Karger DN, Conrad O, Böhner J, Kawohl T, Kreft H, Soria-Auza RW, Zimmermann NE, Linder HP, Kessler M (2017) Climatologies at high resolution for the earth’s land surface areas. Sci Data 4:170122–170220

    PubMed  PubMed Central  Google Scholar 

  • Karger DN, Conrad O, Böhner J, Kawohl T, Kreft H, Soria-Auza RW, Zimmermann NE, Linder HP, Kessler M (2018) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digit Repos. https://doi.org/10.5061/dryad.kd1d4

    Article  Google Scholar 

  • Karp DS, Chaplin-Kramer R, Meehan TD, Martin EA, DeClerck F, Grab H, Gratton C, Hunt L, Larsen AE, Martínez-Salinas A, O’Rourke ME, Rusch A, Poveda K, Jonsson M, Rosenheim JA, Schellhorn NA, Tscharntke T, Wratten SD, Zhang W, Iverson AL, Adler LS, Albrecht M, Alignier A, Angelella GM, Anjum MZ, Avelino J, Batáry P, Baveco JM, Bianchi FJJA, Birkhofer K, Bohnenblust EW, Bommarco R, Brewer MJ, Caballero-López B, Carrière Y, Carvalheiro LG, Cayuela L, Centrella M, Ćetković A, Henri DC, Chabert A, Costamagna AC, De la Mora A, de Kraker J, Desneux N, Diehl E, Diekötter T, Dormann CF, Eckberg JO, Entling MH, Fiedler D, Franck P, van Veen FJF, Frank T, Gagic V, Garratt MPD, Getachew A, Gonthier DJ, Goodell PB, Graziosi I, Groves RL, Gurr GM, Hajian-Forooshani Z, Heimpel GE, Herrmann JD, Huseth AS, Inclán DJ, Ingrao AJ, Iv P, Jacot K, Johnson GA, Jones L, Kaiser M, Kaser JM, Keasar T, Kim TN, Kishinevsky M, Landis DA, Lavandero B, Lavigne C, Le Ralec A, Lemessa D, Letourneau DK, Liere H, Lu Y, Lubin Y, Luttermoser T, Maas B, Mace K, Madeira F, Mader V, Cortesero AM, Marini L, Martinez E, Martinson HM, Menozzi P, Mitchell MGE, Miyashita T, Molina GAR, Molina-Montenegro MA, O’Neal ME, Opatovsky I, Ortiz-Martinez S, Nash M, Östman Ö, Ouin A, Pak D, Paredes D, Parsa S, Parry H, Perez-Alvarez R, Perović DJ, Peterson JA, Petit S, Philpott SM, Plantegenest M, Plećaš M, Pluess T, Pons X, Potts SG, Pywell RF, Ragsdale DW, Rand TA, Raymond L, Ricci B, Sargent C, Sarthou J-P, Saulais J, Schäckermann J, Schmidt NP, Schneider G, Schüepp C, Sivakoff FS, Smith HG, Whitney KS, Stutz S, Szendrei Z, Takada MB, Taki H, Tamburini G, Thomson LJ, Tricault Y, Tsafack N, Tschumi M, Valantin-Morison M, Van Trinh M, van der Werf W, Vierling KT, Werling Ben P, Wickens JB, Wickens VJ, Woodcock Ben A, Wyckhuys K, Xiao H, Yasuda M, Yoshioka A, Zou Y (2018) Crop pests and predators exhibit inconsistent responses to surrounding landscape composition. Proc Natl Acad Sci USA 111:E7863–E7870

    Google Scholar 

  • Krauss J, Gallenberger I, Steffan-Dewenter I (2011) Decreased functional diversity and biological pest control in conventional compared to organic crop fields. PLoS One 6:e19502–e19509

    CAS  PubMed  PubMed Central  Google Scholar 

  • Landis DA (2017) Designing agricultural landscapes for biodiversity-based ecosystem services. Basic Appl Ecol 18:1–12

    Google Scholar 

  • Landis DA, Wratten SD, Gurr GM (2000) Habitat management to conserve natural enemies of arthropod pests in agriculture. Annu Rev Entomol 45:175–201

    CAS  PubMed  Google Scholar 

  • Larsen AE (2013) Agricultural landscape simplification does not consistently drive insecticide use. Proc Natl Acad Sci 110:15330–15335

    CAS  PubMed  Google Scholar 

  • Larsen AE, Gaines SD, Deschênes O (2015) Spatiotemporal variation in the relationship between landscape simplification and insecticide use. Ecol Appl 25:1976–1983

    PubMed  Google Scholar 

  • Larsen AE, Noack F (2017) Identifying the landscape drivers of agricultural insecticide use leveraging evidence from 100,000 fields. Proc Natl Acad Sci 114:5473–5478

    CAS  PubMed  Google Scholar 

  • Larsen AE, Noack F. Provisionally accepted. Impact of local and landscape complexity on the stability of field-level pest control. Nat Sustain

  • Larsen AE, Patton M, Martin EA (2019) High highs and low lows: elucidating striking seasonal variability in pesticide use and its environmental implications. Sci Total Environ 651:828–837

    CAS  PubMed  Google Scholar 

  • Lehmann P, Ammunét T, Barton M, Battisti A, Eigenbrode SD, Jepsen JU, Kalinkat G, Neuvonen S, Niemelä P, Terblanche JS, Økland B, Björkman C (2020) Complex responses of global insect pests to climate warming. Front Ecol Environ. https://doi.org/10.1002/fee.2160

    Article  Google Scholar 

  • Lesk C, Coffel E, D’Amato AW, Dodds K, Horton R (2017) Threats to North American forests from southern pine beetle with warming winters. Nat Clim Change 7:713–717

    Google Scholar 

  • Macfadyen S, Muller W (2013) Edges in agricultural landscapes: species interactions and movement of natural enemies. PLoS One 8:e59659

    CAS  PubMed  PubMed Central  Google Scholar 

  • Mall D, Larsen A, Martin EA (2018) Investigating the (mis)match between natural pest control knowledge and the intensity of pesticide use. Insects 9:2–13

    PubMed Central  Google Scholar 

  • Marino P, Landis D (1996) Effect of landscape structure on parasitoid diversity and parasitism in agroecosystems. Ecol Appl 6:276–284

    Google Scholar 

  • Martin EA, Dainese M, Clough Y, Báldi A, Bommarco R, Gagic V, Garratt MPD, Holzschuh A, Kleijn D, Hostyánszki AK, Marini L, Potts SG, Smith HG, Al Hassan D, Albrecht M, Andersson GKS, Asís JD, Aviron S, Balzan MV, Picón LB, Bartomeus I, Batáry P, Burel F, López BC, Concepción ED, Coudrain V, Dänhardt J, Diaz M, Diekötter T, Dormann CF, Duflot R, Entling MH, Farwig N, Fischer C, Frank T, Garibaldi LA, Hermann J, Herzog F, Inclán D, Jacot K, Jauker F, Jeanneret P, Kaiser M, Krauss J, Le Féon V, Marshall J, Moonen AC, Moreno G, Riedinger V, Rundlöf M, Rusch A, Scheper J, Schneider G, Schüepp C, Stutz S, Sutter L, Tamburini G, Thies C, Tormos J, Tscharntke T, Tschumi M, Uzman D, Wagner C, Anjum MZ, Dewenter IS (2019) The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe. Ecol Lett 22:1083–1094

    PubMed  Google Scholar 

  • Martin EA, Seo B, Park C-R, Reineking B, Steffan-Dewenter I (2016) Scale-dependent effects of landscape composition and configuration on natural enemy diversity, crop herbivory, and yields. Ecol Appl 26:448–462

    PubMed  Google Scholar 

  • McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. University of Massachusetts, Amherst

    Google Scholar 

  • McNaughton S (1977) Diversity and stability of ecological communities: a comment on the role of empiricism in ecology. Am Nat 111:515–525

    Google Scholar 

  • Meehan TD, Werling BP, Landis DA, Gratton C (2011) Agricultural landscape simplification and insecticide use in the Midwestern United States. Proc Natl Acad Sci 108:11500–11505

    CAS  PubMed  Google Scholar 

  • Meisner MH, Zaviezo T, Rosenheim JA (2016) Landscape crop composition effects on cotton yield, Lygus hesperus densities and pesticide use. Pest Manag Sci 73:232–239

    PubMed  Google Scholar 

  • Menne MJ, Durre I, Korzeniewski B, McNeal S, Thomas K, Yin X, Anthony S, Ray R, Vose RS, Gleason BE, Houston TG (2012) Global Historical Climatology Network—Daily (GHCN-Daily). Dataset Version 3.12. National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center. https://doi.org/10.7289/V5D21VHZ

  • Morandin LA, Kremen C (2013) Hedgerow restoration promotes pollinator populations and exports native bees to adjacent fields. Ecol Appl 23:829–839

    PubMed  Google Scholar 

  • Multi-Resolution Land Characteristics Consortium (MRLC) (2019) NLCD Land Cover (CONUS) All Years. U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. Data available at https://www.mrlc.gov/data

  • Oerke EC (2006) Crop losses to pests. J Agric Sci 144:31–43

    Google Scholar 

  • O’Rourke ME, Jones LE (2011) Analysis of landscape-scale insect pest dynamics and pesticide use: an empirical and modeling study. Ecol Appl 21:3199–3210

    Google Scholar 

  • R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

  • Rhodes LA, McCarl BA (2020) An analysis of climate impacts on herbicide, insecticide, and fungicide expenditures. Agronomy 10:745

    Google Scholar 

  • Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Boote KJ, Folberth C, Glotter M, Khabarov N, Neumann K, Piontek F, Pugh TAM, Schmid E, Stehfest E, Yang H, Jones JW (2014) Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci USA 111:3268–3273

    CAS  PubMed  Google Scholar 

  • Rusch A, Bommarco R, Jonsson M, Smith HG, Ekbom B (2013) Flow and stability of natural pest control services depend on complexity and crop rotation at the landscape scale. J Appl Ecol 50:345–354

    Google Scholar 

  • Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc Natl Acad Sci 106:15594–15598

    CAS  PubMed  Google Scholar 

  • Segre H, Carmel Y, Segoli M, Tchetchik A, Renan I, Perevolotsky A, Rotem D, Shwartz A (2019) Cost-effectiveness of uncultivated field-margins and semi-natural patches in Mediterranean areas: a multi-taxa, landscape scale approach. Biol Conserv 240:108262

    Google Scholar 

  • Sexton SE, Lei Z, Zilberman D (2007) The economics of pesticides and pest control. Int Rev Environ Resour Econ 1:271–326

    Google Scholar 

  • Sirami C, Gross N, Baillod AB, Bertrand C, Carrié R, Hass A, Henckel L, Miguet P, Vuillot C, Alignier A, Girard J, Batáry P, Clough Y, Violle C, Giralt D, Bota G, Badenhausser I, Lefebvre G, Gauffre B, Vialatte A, Calatayud F, Gil-Tena A, Tischendorf L, Mitchell S, Lindsay K, Georges R, Hilaire S, Recasens J, Solé-Senan XO, Robleño I, Bosch J, Barrientos JA, Ricarte A, Marcos-Garcia MÁ, Miñano J, Mathevet R, Gibon A, Baudry J, Balent G, Poulin B, Burel F, Tscharntke T, Bretagnolle V, Siriwardena G, Ouin A, Brotons L, Martin J-L, Fahrig L (2019) Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc Natl Acad Sci USA 116:16442–16447

    CAS  PubMed  Google Scholar 

  • Sohl TL, Sayler KL, Bouchard MA, Reker RR, Friesz AM, Bennett SL, Sleeter BM, Sleeter RR, Wilson T, Soulard C, Knuppe M, Van Hofwegen T (2014) Spatially explicit modeling of 1992–2100 land cover and forest stand age for the conterminous United States. Ecol Appl 24:1015–1036

    PubMed  Google Scholar 

  • Sohl TL, Sayler KL, Bouchard MA, Reker RR, Freisz AM, Bennett SL, Sleeter BM, Sleeter RR, Wilson T, Soulard C, Knuppe M, Van Hofwegen T (2018) Conterminous United States Land Cover Projections—1992 to 2100: U.S. Geological Survey data release. https://doi.org/10.5066/P95AK9HP

  • Thomson LJ, Macfadyen S, Hoffmann AA (2010) Predicting the effects of climate change on natural enemies of agricultural pests. Biol Control 52:296–306

    Google Scholar 

  • TIGER (Topologically Integrated Geographic Encoding and Referencing)/Line Shapefiles (2018) Machine readable data files, prepared by the U.S. Census Bureau, 2018. Data available at: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2018.html

  • Tscharntke T, Clough Y, Wanger TC, Jackson L, Motzke I, Perfecto I, Vandermeer J, Whitbread A (2012) Global food security, biodiversity conservation and the future of agricultural intensification. Biol Conserv 151:53–59

    Google Scholar 

  • Tscharntke T, Klein AM, Kruess A, Steffan-Dewenter I, Thies C (2005) Landscape perspectives on agricultural intensification and biodiversity-ecosystem service management. Ecol Lett 8:857–874

    Google Scholar 

  • Tscharntke T, Steffan-Dewenter I, Kruess A, Thies C (2002) Characteristics of insect populations on habitat fragments: a mini review. Ecol Res 17:229–239

    Google Scholar 

  • U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) (2007) Agricultural Statistics Districts. Data available as County and District Codes at: https://www.nass.usda.gov/Data_and_Statistics

  • U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) (2017a) Census of Agriculture. Data available at: www.nass.usda.gov/AgCensus

  • U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) (2017b) Cropland Data Layer. Data available at: https://nassgeodata.gmu.edu/CropScape/. https://doi.org/10.1080/10106049.2011.562309

  • van Vuuren DP, Carter TR (2014) Climate and socio-economic scenarios for climate change research and assessment: reconciling the new with the old. Clim Change 122:415–429

    Google Scholar 

  • Waterfield G, Zilberman D (2012) Pest management in food systems: an economic perspective. Annu Rev Environ Resour 37:223–245

    Google Scholar 

  • Wooldridge JM (2002) Econometric analysis of cross section and panel data. MIT Press, Cambridge

    Google Scholar 

  • Yang L, Jin S, Danielson P, Homer C, Gass L, Bender SM, Case A, Costello C, Dewitz J, Fry J, Funk M, Granneman B, Liknes GC, Rigge M, Xian G (2018) A new generation of the United States National Land Cover Database requirements, research priorities, design, and implementation strategies. ISPRS J Photogramm Remote Sens 146:108–123

    Google Scholar 

Download references

Acknowledgements

We thank O. Deschenes for sharing data and for methodological insights, and A. MacDonald for insightful comments on an earlier draft.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ashley E. Larsen.

Ethics declarations

Conflict of interest

We declare no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 1025 kb)

Supplementary file2 (XLSX 6549 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Larsen, A.E., McComb, S. Land cover and climate changes drive regionally heterogeneous increases in US insecticide use. Landscape Ecol 36, 159–177 (2021). https://doi.org/10.1007/s10980-020-01130-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10980-020-01130-5

Keywords

  • Climate change
  • Agricultural intensification
  • Landscape simplification
  • Landscape complexity
  • Pest management
  • Pesticides