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Coffee pest severity by agrometeorological models in subtropical climate

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Abstract

This study aimed to estimate the number of generations and cycle duration of the southern red mite, coffee berry borer, and coffee leaf miner using the thermal index to assist in controlling these main coffee pests in the state of Paraná, Brazil. The data of maximum and minimum air temperature (°C) and precipitation (mm) of all municipalities in the state from 1984 to 2018 were collected from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER). The reference evapotranspiration was estimated using the (Camargo Campinas IAC Boletim 116:9, 1971) method and the water balance was calculated using the method of ( Thornthwaite C, Mather J (1955) The water balance publications in climatology, 8 (1). DIT, Laboratory of climatology, Centerton, NJ, USA). The basal temperature of each pest minus the average temperature of the years was used to calculate the degrees-day, the duration of the pest cycle, and the number of generations per year. The influence of altitude on the development of coffee pests was measured using the Pearson correlation. The thermal index is able to estimate the damage caused by coffee pests in the state of Pará, Brazil. Coffee pests show greater severity in the north of Paraná, in the regions with the highest temperatures. It is the same region that concentrates most of the coffee production of the state. The results of the life cycle and number of generations were interpolated for the entire state using the kriging method. Coffee pests showed the highest severity in the north region of the state of Paraná, more specifically in the Northwest, North Central, and West Central mesoregions. These regions have concentrated most of the state’s coffee production. Mesoregions with the highest coffee production in the state showed higher susceptibility to coffee pests. Altitude showed a high correlation (r > 0.6) with the cycle variability and number of generations of coffee pests. The average cycles of the coffee berry borer, coffee leaf miner, and southern red mite are 24.13 (± 8.34), 45.64 (± 18.61), and 21.51 (± 3.51) days, respectively. The average annual generation was 16.67 (± 4.77), 9.02 (± 2.75), and 17.32 (± 2.63) generations, for the coffee berry borer, the coffee red mite, and the southern red mite, respectively.

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Fig. 1

Adapted from DaMatta et al. (2007)

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modified from Thornthwaite and Mather (1955). Legend: P is precipitation (mm), PET is evapotranspiration, AET is the real evapotranspiration (mm), STO is the soil water storage (mm), CAD is the soil water capacity (mm), NAC is the negative accumulated (mm), meaning the potential drying of the soil, ALT is the alteration of STO, SUR is the water surplus in the soil–plant-atmosphere system (mm), DEF is the water deficit of the soil–plant-atmosphere system (mm), and i a given period, i-1 previous period. Adapted by

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References

  • Agegnehu E, Dawa D (2015) Potential impact of climate change on dynamics of coffee berry borer (Hypothenemus hampi Ferrari) in Ethiopia. Open Access Libr J 2:1

    Google Scholar 

  • Alba-Alejandre I, Alba-Tercedor J, Vega FE (2018) Observing the devastating coffee berry borer (Hypothenemus hampei) inside the coffee berry using micro-computed tomography. Sci Rep 8:17033. https://doi.org/10.1038/s41598-018-35324-4

    Article  CAS  Google Scholar 

  • Alvares CA, Stape JL, Sentelhas PC, de MoraesGonçalves JL (2013) Modeling monthly mean air temperature for Brazil. Theor Appl Climatol 113:407–427. https://doi.org/10.1007/s00704-012-0796-6

    Article  Google Scholar 

  • Alvares CA, Stape JL, Sentelhas PC, et al (2013a) Köppen’s climate classification map for Brazil. https://www.ingentaconnect.com/content/schweiz/mz/2013a/00000022/00000006/art00008. Accessed 14 May 2020

  • Androcioli HG, Hoshino AT, Menezes Júnior A de O, et al (2018) Coffee leaf miner incidence and its predation by wasp in coffee intercropped with rubber trees

  • Aristizábal L, Bustillo A, Arthurs S (2016) Integrated pest management of coffee berry borer: strategies from Latin America that could be useful for coffee farmers in Hawaii. Insects 7:6. https://doi.org/10.3390/insects7010006

    Article  Google Scholar 

  • Atalla T, Gualdi S, Lanza A (2018) A global degree days database for energy-related applications. Energy 143:1048–1055. https://doi.org/10.1016/j.energy.2017.10.134

    Article  Google Scholar 

  • Augustinus B, Sun Y, Beuchat C, et al (2020) Predicting impact of a biocontrol agent: integrating distribution modeling with climate‐dependent vital rates. Ecol Appl 30: https://doi.org/10.1002/eap.2003

  • Azrag AGA, Murungi LK, Tonnang HEZ et al (2017) Temperature-dependent models of development and survival of an insect pest of African tropical highlands, the coffee antestia bug Antestiopsis thunbergii (Hemiptera: Pentatomidae). J Therm Biol 70:27–36. https://doi.org/10.1016/j.jtherbio.2017.10.009

    Article  Google Scholar 

  • Barreto C, Branfireun BA, McLaughlin JW, Lindo Z (2021) Responses of oribatid mites to warming in boreal peatlands depend on fen type. Pedobiologia 89:150772. https://doi.org/10.1016/j.pedobi.2021.150772

    Article  Google Scholar 

  • Batista LA, Guimarães RJ, Pereira FJ et al (2010) Anatomia foliar e potencial hídrico na tolerância de cultivares de café ao estresse hídrico. Rev Ciênc Agron 41:475–481. https://doi.org/10.1590/S1806-66902010000300022

    Article  Google Scholar 

  • Bohl MT, Gross C, Souza W (2019) The role of emerging economies in the global price formation process of commodities: evidence from Brazilian and US coffee markets. Int Rev Econ Financ 60:203–215

    Article  Google Scholar 

  • Brazil (1981) RADAMBRASIL Project

  • Camargo AP (1971) Water balance in the state of São Paulo. Campinas IAC Boletim 116:9

    Google Scholar 

  • Camargo ÂPD, Camargo MBPD (2001) Definição e esquematização das fases fenológicas do cafeeiro arábica nas condições tropicais do Brasil. Bragantia 60:65–68. https://doi.org/10.1590/S0006-87052001000100008

    Article  Google Scholar 

  • Campera M, Budiadi B, Adinda E et al (2021) Fostering a wildlife-friendly program for sustainable coffee farming: the case of small-holder farmers in Indonesia. Land 10:121

    Article  Google Scholar 

  • Caramori P, Caviglione J, Wrege M et al (2001) Climatic risk zoning for coffee (Coffea arabica L.) in Paraná state Brazil. Rev Bras De Agrometeorologia 9:486–494

    Google Scholar 

  • Carneiro ALC, Silva LDB, and Faulin MSAR (2021) Artificial intelligence for detection and quantification of rust and leaf miner in coffee crop. arXiv preprint arXiv:2103.11241.

  • Carvalho CF, Carvalho SM, Souza B (2019) Coffee. In: Souza B, Vázquez LL, Marucci RC (eds) Natural enemies of insect pests in neotropical agroecosystems. Springer International Publishing, Cham, pp 277–291

    Chapter  Google Scholar 

  • Castro-Moretti FR, Cocuron J-C, Vega FE, Alonso AP (2020) Differential metabolic responses caused by the most important insect pest of coffee worldwide, the coffee berry borer ( Hypothenemus hampei ). J Agric Food Chem 68:2597–2605. https://doi.org/10.1021/acs.jafc.9b07363

    Article  CAS  Google Scholar 

  • Chaloner TM, Gurr SJ, Bebber DP (2021) Plant pathogen infection risk tracks global crop yields under climate change. Nat Clim Chang 11:710–715. https://doi.org/10.1038/s41558-021-01104-8

    Article  Google Scholar 

  • CONAB CNDAC (2021) Acompanhamento da safra brasileira: café

  • Costa JF, da Silva TGF (2016) Prospecção do Nordeste brasileiro para a incidência da mosca-das-frutas em cenários de mudanças climáticas. Rev Brasileira De Geogr Física 9:2148–2163

    Google Scholar 

  • da Consolação RM, de Araújo GJ, Pallini A, Venzon M (2021) Cover crop intercropping increases biological control in coffee crops. Biol Control 160:104675

    Article  Google Scholar 

  • da Costa GV, Neves CSVJ, Telles TS (2020) Spatial dynamics of orange production in the state of Paraná. Brazil. Rev Bras Frutic 42:e-525. https://doi.org/10.1590/0100-29452020525

    Article  Google Scholar 

  • da Costa SL, José JV, Bender FD et al (2020) Climate change in the Paraná state, Brazil: responses to increasing atmospheric CO2 in reference evapotranspiration. Theor Appl Climatol 140:55–68. https://doi.org/10.1007/s00704-019-03057-7

    Article  Google Scholar 

  • DaMatta FM, Ronchi CP, Maestri M, Barros RS (2007) Ecophysiology of coffee growth and production. Braz J Plant Physiol 19:485–510

    Article  CAS  Google Scholar 

  • Dantas J, Motta IO, Vidal LA et al (2021) A comprehensive review of the coffee leaf miner leucoptera coffeella (Lepidoptera: Lyonetiidae)—a major pest for the coffee crop in Brazil and others neotropical countries. Insects 12:1130. https://doi.org/10.3390/insects12121130

    Article  Google Scholar 

  • de Camargo MBP (2010) The impact of climatic variability and climate change on arabic coffee crop in Brazil. Bragantia 69:239–247. https://doi.org/10.1590/S0006-87052010000100030

    Article  Google Scholar 

  • de VasconcellosViana R, Medeiros PMA Rodrigues (2017) A economia cafeeira no Brasil e a importância das inovações para essa cadeia. A Economia em Revista-AERE 25:1–12

    Google Scholar 

  • de Carvalho HP, de Melo B, Rabelo PG et al (2011) Bioclimatic indices for the coffee crop. Rev Bras de Engenharia Agríc e Ambient 15:601–606. https://doi.org/10.1590/S1415-43662011000600010

    Article  Google Scholar 

  • de OlivieraAparecido LE, de Souza Rolim G, Richetti J et al (2016) Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil. Ciência e Agrotecnologia 40:405–417. https://doi.org/10.1590/1413-70542016404003916

    Article  Google Scholar 

  • de Souza Tuelher E, de Oliveira E, Guedes R, Magalhaes L (2003) Occurrence of coffee leaf-miner (Leucoptera coffeella) influenced by season and altitude. Acta Scientiarum-Agronomy 25:119–124

    Google Scholar 

  • F de Macedo Soares Guimarães (1942) Divisão regional do Brasil. Serviço gráfico do instituto Brasileiro de geografia e estatística

  • de Souza Rolim G, de Oliveira Aparecido LE, de Souza PS, et al (2020) Climate and natural quality of Coffea arabica L. drink. Theoretical and Applied Climatology 1–12

  • Dhooria MS (2016) Fundamentals of applied acarology. Springer

    Book  Google Scholar 

  • dos Santos Renato N, Silva JBL, Sediyama GC, Pereira EG (2013) Influência dos métodos para cálculo de graus-dia em condições de aumento de temperatura para as culturas de milho e feijão. Revista Brasileira de Meteorologia 28:382–388

    Article  Google Scholar 

  • Duarte YCN, Sentelhas PC (2020) Intercomparison and performance of maize crop models and their ensemble for yield simulations in Brazil. Int J Plant Prod 14:127–139. https://doi.org/10.1007/s42106-019-00073-5

    Article  Google Scholar 

  • Escobar-Ramírez S, Grass I, Armbrecht I, Tscharntke T (2019) Biological control of the coffee berry borer: main natural enemies, control success, and landscape influence. Biol Control 136:103992. https://doi.org/10.1016/j.biocontrol.2019.05.011

    Article  Google Scholar 

  • Escola JPL, Guido RC, da Silva IN et al (2020) Automated acoustic detection of a cicadid pest in coffee plantations. Comput Electron Agric 169:105215. https://doi.org/10.1016/j.compag.2020.105215

    Article  Google Scholar 

  • Esgario JGM, de Castro PBC, Tassis LM, Krohling RA (2021) An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning. Inf Process Agric S2214317321000044. https://doi.org/10.1016/j.inpa.2021.01.004

  • Fand BB, Nagrare V, Bal S et al (2021) Degree day-based model predicts pink bollworm phenology across geographical locations of subtropics and semi-arid tropics of India. Sci Rep 11:1–18

    Article  Google Scholar 

  • Fragoso DB, Jusselino-Filho P, Guedes RNC, Proque R (2001) Seletividade de inseticidas a vespas predadoras de Leucoptera coffeella (Guér.-Mènev.) (Lepidoptera: Lyonetiidae). Neotrop Entomol 30:139–143. https://doi.org/10.1590/S1519-566X2001000100020

    Article  CAS  Google Scholar 

  • Gomes LC, Bianchi FJJA, Cardoso IM et al (2020) Agroforestry systems can mitigate the impacts of climate change on coffee production: a spatially explicit assessment in Brazil. Agr Ecosyst Environ 294:106858. https://doi.org/10.1016/j.agee.2020.106858

    Article  Google Scholar 

  • Gouveia NM (1984) Estudo da diferenciação e crescimento de gemas florais de Coffea arabica L. observações sobre antese e maturação dos frutos

  • Gusmão MR, Picanço M, Gonring AHR, Moura MF (2000) Seletividade fisiológica de inseticidas a Vespidae predadores do bicho-mineiro-do-cafeeiro. Pesq Agrop Brasileira 35:681–686

    Article  Google Scholar 

  • Hajjar R, Newton P, Adshead D et al (2019) Scaling up sustainability in commodity agriculture: transferability of governance mechanisms across the coffee and cattle sectors in Brazil. J Clean Prod 206:124–132. https://doi.org/10.1016/j.jclepro.2018.09.102

    Article  Google Scholar 

  • IBGE IBDGEE (2020) Sistema IBGE de Recuperação Automática - SIDRA: Produção Agrícola Municipal. In: Sistema IBGE de Recuperação Automática. https://sidra.ibge.gov.br/pesquisa/ppm/quadros/brasil/2020. Accessed 28 Jan 2021

  • ICO (2021) International Coffee Organization. Historical data on the global coffee trade. http://www.ico.org/new_historical.asp. Accessed 18 Aug 2021

  • Jeran N, Grdiša M, Varga F et al (2021) Pyrethrin from Dalmatian pyrethrum (Tanacetum cinerariifolium (Trevir.) Sch. Bip.): biosynthesis, biological activity, methods of extraction and determination. Phytochem Rev 20:875–905

    Article  CAS  Google Scholar 

  • Kath J, Byrareddy VM, Craparo A et al (2020) Not so robust: robusta coffee production is highly sensitive to temperature. Glob Change Biol 26:3677–3688. https://doi.org/10.1111/gcb.15097

    Article  Google Scholar 

  • Kogo BK, Kumar L, Koech R (2021) Climate change and variability in Kenya: a review of impacts on agriculture and food security. Environ Dev Sustain 23:23–43

    Article  Google Scholar 

  • Krige DG (1951) A statistical approach to some basic mine valuation problems on the Witwatersrand. J South Afr Inst Min Metall 52:119–139

    Google Scholar 

  • Leite SA, Santos MPD, Resende-Silva GA et al (2020) Area-wide survey of chlorantraniliprole resistance and control failure likelihood of the neotropical coffee leaf miner Leucoptera coffeella Lepidoptera: Lyonetiidae. J Econ Entomol toaa017. https://doi.org/10.1093/jee/toaa017

    Article  Google Scholar 

  • Mauri R, Coelho RD, Fraga Junior EF et al (2017) Water relations at the initial sugarcane growth phase under variable water deficit. Eng Agríc 37:268–276. https://doi.org/10.1590/1809-4430-eng.agric.v37n2p268-276/2017

    Article  Google Scholar 

  • Melo EF, Fernandes-Brum CN, Pereira FJ et al (2014) Anatomic and physiological modifications in seedlings of Coffea arabica cultivar Siriema under drought conditions. Ciênc Agrotec 38:25–33. https://doi.org/10.1590/S1413-70542014000100003

    Article  Google Scholar 

  • Mendes LO (1949) Determinação do potencial biótico da" broca do café": Hypothenemus Hampei (Ferr.)-E considerações sôbre o crescimento de sua população. II-A importância da diminuição do índice inicial de infestação no grau final de frutos de café atacados pela praga. Bragantia 9:203–214

    Article  Google Scholar 

  • Mendonça AP, Nonato JVA, Andrade VT et al (2016) Coffea arabica clones resistant to coffee leaf miner. Crop Breed Appl Biotechnol 16:42–47. https://doi.org/10.1590/1984-70332016v16n1a7

    Article  CAS  Google Scholar 

  • Merle I, Tixier P, de MeloVirginioFilho E et al (2020) Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica. Crop Prot 130:105046. https://doi.org/10.1016/j.cropro.2019.105046

    Article  CAS  Google Scholar 

  • Mesquita CM de, REZENDE J de, Carvalho J, et al (2016) Manual do café: distúrbios fisiológicos, pragas e doenças do cafeeiro (Coffea arabica L.). Belo Horizonte: EMATER-MG 22–42

  • Mitiku F, de Mey Y, Nyssen J, Maertens M (2017) Do private sustainability standards contribute to income growth and poverty alleviation? A comparison of different coffee certification schemes in Ethiopia. Sustain 9:246. https://doi.org/10.3390/su9020246

    Article  Google Scholar 

  • Moreto VB, Rolim GDS (2015) Agrometeorological models for groundnut crop yield forecasting in the Jaboticabal, São Paulo State region. Brazil Acta Sci Agron 37:403. https://doi.org/10.4025/actasciagron.v37i4.19766

    Article  Google Scholar 

  • Morris JR, Perfecto I (2016) Testing the potential for ant predation of immature coffee berry borer (Hypothenemus hampei) life stages. Agric, Ecosystems Environ 233:224–228. https://doi.org/10.1016/j.agee.2016.09.018

    Article  Google Scholar 

  • Morris JR, Vandermeer J, Perfecto I (2015) A keystone ant species provides robust biological control of the coffee berry borer under varying pest densities. PLoS ONE 10:e0142850. https://doi.org/10.1371/journal.pone.0142850

    Article  CAS  Google Scholar 

  • Motta IO, Dantas J, Vidal L et al (2021) The coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae): identification of the larval instars and description of male and female genitalia. Rev Bras Entomol 65:e20200122. https://doi.org/10.1590/1806-9665-rbent-2020-0122

    Article  Google Scholar 

  • Neves AD, Oliveira RF, Parra JR (2006) A new concept for insect damage evaluation based on plant physiological variables. An Acad Bras Ciênc 78:821–835

    Article  Google Scholar 

  • Oliveira CM, Auad AM, Mendes SM, Frizzas MR (2014) Crop losses and the economic impact of insect pests on Brazilian agriculture. Crop Prot 56:50–54. https://doi.org/10.1016/j.cropro.2013.10.022

    Article  Google Scholar 

  • Pantoja-Gomez LM, Corrêa AS, de Oliveira LO, Guedes RNC (2019) Common origin of Brazilian and Colombian populations of the neotropical coffee leaf miner, Leucoptera coffeella (Lepidoptera: Lyonetiidae). J Econ Entomol 112:924–931. https://doi.org/10.1093/jee/toy416

    Article  Google Scholar 

  • PARANÁ G do E (2006) Instituto Paranaense de Desenvolvimento Econômico e Social–IPARDES. Os Vários Paranás: linhas de ação para as dimensões econômica, social e institucional: subsídios à política de desenvolvimento regional Curitiba: IPARDES

  • Parra J (1985) Biologia comparada de Perileucoptera coffeella (Guérin-Mèneville, 1842)(Lepidoptera, Lyonetiidae) visando ao seu zoneamento ecológico no Estado de São Paulo. Rev Brasileira De Entomologia 29:45–76

    Google Scholar 

  • Pathak TB, Maskey ML, Rijal JP (2021) Impact of climate change on navel orangeworm, a major pest of tree nuts in California. Sci Total Environ 755:142657. https://doi.org/10.1016/j.scitotenv.2020.142657

    Article  CAS  Google Scholar 

  • Pezzopane JRM, da Silveira Castro F, Pezzopane JEM et al (2010) Zoneamento de risco climático para a cultura do café Conilon no Estado do Espírito Santo. Revista Ciência Agronômica 41:341–348

    Article  Google Scholar 

  • Plata-Rueda A, Martínez LC, Costa NCR et al (2019) Chlorantraniliprole–mediated effects on survival, walking abilities, and respiration in the coffee berry borer, Hypothenemus hampei. Ecotoxicol Environ Saf 172:53–58. https://doi.org/10.1016/j.ecoenv.2019.01.063

    Article  CAS  Google Scholar 

  • Polanczyk RA, Celestino FN, Ferreira LS et al (2011) Desenvolvimento de Oligonychus ilicis em Coffea canephora sob diferentes temperaturas. Bragantia 70:370–374. https://doi.org/10.1590/S0006-87052011000200017

    Article  Google Scholar 

  • Reis PR, Chiavegato LG, Moraes GJ et al (1998) Seletividade de agroquímicos ao ácaro predador Iphiseiodes zuluagai Denmark & Muma (Acari: Phytoseiidae). An Soc Entomol Bras 27:265–274. https://doi.org/10.1590/S0301-80591998000200013

    Article  CAS  Google Scholar 

  • Reis P, de Souza J, Venzon M (2002) Manejo ecológico das principais pragas do cafeeiro. Informe Agropecuário 23:83–99

    Google Scholar 

  • Rena AB, Maestri M (1986) Fisiologia do cafeeiro. Cultura do cafeeiro: fatores que afetam a produtividade 1–87

  • Resende NC, Miranda JH, Cooke R et al (2019) Impacts of regional climate change on the runoff and root water uptake in corn crops in Parana, Brazil. Agric Water Manag 221:556–565. https://doi.org/10.1016/j.agwat.2019.05.018

    Article  Google Scholar 

  • Righi CA, Campoe OC, Bernardes MS et al (2013) Influence of rubber trees on leaf-miner damage to coffee plants in an agroforestry system. Agroforest Syst 87:1351–1362. https://doi.org/10.1007/s10457-013-9642-9

    Article  Google Scholar 

  • Sarmiento-Soler A, Vaast P, Hoffmann MP et al (2020) Effect of cropping system, shade cover and altitudinal gradient on coffee yield components at Mt. Elgon, Uganda. Agric Ecosystems Environ 295:106887. https://doi.org/10.1016/j.agee.2020.106887

    Article  Google Scholar 

  • Schneider L, Comte V, Rebetez M (2021) Increasingly favourable winter temperature conditions for major crop and forest insect pest species in Switzerland. Agric for Meteorol 298–299:108315. https://doi.org/10.1016/j.agrformet.2020.108315

    Article  Google Scholar 

  • Silva EA, Reis PR, Zacarias MS, Marafeli PP (2010) Fitoseídeos (Acari: Phytoseiidae) associados a cafezais e fragmentos florestais vizinhos. Ciênc Agrotec 34:1146–1153. https://doi.org/10.1590/S1413-70542010000500010

    Article  Google Scholar 

  • Sparovek G, De Jong Van Lier Q, Dourado Neto D (2007) Computer assisted Koeppen climate classification: a case study for Brazil. International Journal of Climatology, 27, 257-266. https://doi.org/10.1002/joc.1384

  • Stackhouse PW, Westberg D, Hoell JM, et al (2015) Prediction of Worldwide Energy Resource (POWER)-agroclimatology methodology-(1.0 latitude by 1.0 longitude spatial resolution). Prediction of Worldwide Energy Resource (POWER)-Agroclimatology methodology-(1.0 latitude by 1.0 longitude spatial resolution).

  • Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38:55–94

    Article  Google Scholar 

  • Thornthwaite C, Mather J (1955) The water balance publications in climatology, 8 (1). DIT, Laboratory of climatology, Centerton, NJ, USA

  • Toledo MA, Reis PR, Liska GR, Cirillo MÂ (2018) Biological control of southern red mite, Oligonychus ilicis (Acari: Tetranychidae), in coffee plants. Adv Entomol 6:74

    Article  Google Scholar 

  • Vega VJ, Mariño YA, Deynes D et al (2020) A beetle in a haystack: are there alternate hosts of the coffee berry borer (Hypothenemus hampei) in Puerto Rico? Agron 10:228. https://doi.org/10.3390/agronomy10020228

    Article  CAS  Google Scholar 

  • Wojda I (2017) Temperature stress and insect immunity. J Therm Biol 68:96–103. https://doi.org/10.1016/j.jtherbio.2016.12.002

    Article  CAS  Google Scholar 

  • WTO WTO (2020) Statistics on merchandise trade. In: World trade organization. timeseries.wto.org/. Accessed 28 May 2020

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This study was funded by IFMS Campus of Naviraí and IFSULDEMINAS Campus Muzambinho.

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de Oliveira Aparecido, L.E., Lorençone, P.A., Lorençone, J.A. et al. Coffee pest severity by agrometeorological models in subtropical climate. Int J Biometeorol 66, 957–969 (2022). https://doi.org/10.1007/s00484-022-02252-y

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