1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Phytopathology
  4. Volume 61, 2023
  5. Article

Annual Review of Phytopathology

Volume 61, 2023

Integrated Nematode Management in a World in Transition: Constraints, Policy, Processes, and Technologies for the Future

Abstract

Plant-parasitic nematodes are one of the most insidious pests limiting agricultural production, parasitizing mostly belowground and occasionally aboveground plant parts. They are an important and underestimated component of the estimated 30% yield loss inflicted on crops globally by biotic constraints. Nematode damage is intensified by interactions with biotic and abiotic factors constraints: soilborne pathogens, soil fertility degradation, reduced soil biodiversity, climate variability, and policies influencing the development of improved management options. This review focuses on the following topics: () biotic and abiotic constraints, () modification of production systems, () agricultural policies, () the microbiome, () genetic solutions, and () remote sensing. Improving integrated nematode management (INM) across all scales of agricultural production and along the Global North–Global South divide, where inequalities influence access to technology, is discussed. The importance of the integration of technological development in INM is critical to improving food security and human well-being in the future.

Keyword(s): genetics, genome editing, integrated nematode management, microbiome, policy, processes, remote sensing, root health
Loading

Article metrics loading...

/content/journals/10.1146/annurev-phyto-021622-113058
2023-09-05
2024-04-27
Loading full text...

Full text loading...

/deliver/fulltext/phyto/61/1/annurev-phyto-021622-113058.html?itemId=/content/journals/10.1146/annurev-phyto-021622-113058&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Aiyer H, Fofana B, Fraser T, Caldwell C, McKenzie-Gopsill A et al. 2022. Choice of cover crop influences soil fungal and bacterial communities in Prince Edward Island, Canada. Can. J. Microbiol. 68:465–82
     [Google Scholar]
  2. 2.
    Arantes BHT, Moraes VH, Geraldine AM, Alves TM, Albert AM et al. 2021. Spectral detection of nematodes in soybean at flowering growth stage using unmanned aerial vehicles. Ciênc. Rural 51: https://doi.org/10.1590/0103-8478cr20200283
    [Google Scholar]
  3. 3.
    Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA et al. 2018. Structure and function of the global topsoil microbiome. Nature 560:233–37
     [Google Scholar]
  4. 4.
    Barker KR, Pederson GA, Windham GL, eds. 1998. Plant and Nematode Interactions Madison, WI: Am. Soc. Agron.
  5. 5.
    Bathiany S, Dakos V, Scheffer M, Lenton TM. 2018. Climate models predict increasing temperature variability in poor countries. Sci. Adv. 4:eaar5809
     [Google Scholar]
  6. 6.
    Bay G, Lee C, Chen C, Mahal NK, Castellano MJ et al. 2021. Agricultural management affects the active rhizosphere bacterial community composition and nitrification. mSystems 6:5e0065121
     [Google Scholar]
  7. 7.
    Been TH, Schomaker CH, Molendijk LPG. 2005. NemaDecide: a decision support system for the management of potato cyst nematodes. Potato in Progress: Science Meets Practice AJ Haverkort, PD Struik 143–55. Wageningen, Neth.: Wageningen Acad. Pub.
     [Google Scholar]
  8. 8.
    Behmann J, Mahlein AK, Rumpf T, Römer C, Plümer L. 2015. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precis. Agric. 16:3239–60
     [Google Scholar]
  9. 9.
    Blaxter M. 2011. Nematodes: the worm and its relatives. PLOS Biol. 9:4e1001050
     [Google Scholar]
  10. 10.
    Bock CH, Barbedo JGA, Del Ponte EM et al. 2020. From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. Phytopathol. Res. 2:9
     [Google Scholar]
  11. 11.
    Brugger A, Behmann J, Paulus S, Luigs HG, Kuska MT et al. 2019. Extending hyperspectral imaging for plant phenotyping to the UV-range. Remote Sens. 11:121401
     [Google Scholar]
  12. 12.
    Bressan M, Roncato MA, Bellvert F, Comte G, Haichar FEZ et al. 2009. Exogenous glucosinolate produced by Arabidopsis thaliana has an impact on microbes in the rhizosphere and plant roots. ISME J. 3:1243–57
     [Google Scholar]
  13. 13.
    Butler DM, Kokalis-Burelle N, Albano JP, McCollum TG, Muramoto J et al. 2014. Anaerobic soil disinfestation (ASD) combined with soil solarization as a methyl bromide alternative: vegetable crop performance and soil nutrient dynamics. . Plant Soil 378:365–81
     [Google Scholar]
  14. 14.
    Chen Z, Chen S, Dickson D, eds. 2004. Nematology: Advances and Perspectives, Vol. 2. Nematode Morphology, Physiology, and Ecology Wallingford, UK: CABI
     [Google Scholar]
  15. 15.
    Cortada L. 2022. Technologies for INM in smallholder farming systems: no one-size-fits-all. See Ref. 98 457–62
  16. 16.
    Cotton TEA, Pétriacq P, Cameron DD, Meselmani MA, Schwarzenbacher R et al. 2019. Metabolic regulation of the maize rhizobiome by benzoxazinoids. ISME J. 13:1647–58
     [Google Scholar]
  17. 17.
    Coyne DL, Cortada L, Dalzell JJ, Claudius-Cole AO, Haukeland S et al. 2018. Plant-parasitic nematodes and food security in sub-Saharan Africa. Annu. Rev. Phytopathol. 56:381–403
     [Google Scholar]
  18. 18.
    Cuscó A, Catozzi C, Viñes J, Sanchez A, Francino O. 2019. Microbiota profiling with long amplicons using nanopore sequencing: full-length 16S rRNA gene and the 16S-ITS-23S of the rrn operon. F1000Research 7:1755
     [Google Scholar]
  19. 19.
    Daub M. 2022. The beet cyst nematode (Heterodera schachtii, Schmidt): an ancient threat to sugar beet crops in Central Europe has become an invisible actor. See Ref. 98 394–400
  20. 20.
    Davies K, Spiegel Y. 2011. Root patho-systems nematology and biological control. Biological Control of Plant-Parasitic Nematodes K Davies, NY Spiegel 291–303. New York: Springer
     [Google Scholar]
  21. 21.
    de Guzman C. 2022. The crisis in Sri Lanka rekindles debate over organic farming. Time July 13. https://time.com/6196570/sri-lanka-crisis-organic-farming/
    [Google Scholar]
  22. 22.
    de Vrieze J. 2015. The littlest farmhands. Science 349:680–83
     [Google Scholar]
  23. 23.
    Desaeger J, Rao MR. 2001. The potential of mixed cover crops of Sesbania, Tephrosia and Crotalaria for minimizing nematode problems on subsequent crops. Field Crops Res. 70:111–25
     [Google Scholar]
  24. 24.
    Desaeger J, Sikora RA, Molendijk LPG. 2022. Outlook: a vision of the future of integrated nematode management. See Ref. 98 475–83
  25. 25.
    Desaeger J, Wram C, Zasada I. 2020. New reduced-risk agricultural nematicides: rationale and review. J. Nematol. 52:e2020–91
     [Google Scholar]
  26. 26.
    Doshi RA, King RL, Lawrence GW. 2010. Classification of Rotylenchulus reniformis numbers in cotton using remotely sensed hyperspectral data on self-organizing maps. J. Nematol. 42:3179–93
     [Google Scholar]
  27. 27.
    Downs SW Jr. 1974. Remote sensing in agriculture Rep. NASA-TM-X-64803 NASA Washington, DC:
  28. 28.
    EPA 2012. Fluopyram Technical. EPA Regist. No. 264-1077. https://www3.epa.gov/pesticides/chem_search/ppls/000264-01077-20120202.pdf
  29. 29.
    EPA 2014. FLUENSULFONE. EPA Regist. No. 66222-243. https://www3.epa.gov/pesticides/chem_search/ppls/066222-00243-20140911.pdf
  30. 30.
    Eur. Union 2022. A European green deal: striving to be the first climate-neutral continent. European Commission https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal_en
  31. 31.
    Eur. Union 2022. EU Commission Implementing Regulation (EU) 2022/1192 of 11 July 2022 establishing measures to eradicate and prevent the spread of Globodera pallida (Stone) Behrens and Globodera rostochiensis (Wollenweber) Behrens. Off. J. Eur. Union 65:2–26
     [Google Scholar]
  32. 32.
    Evans K, Barker AD. 2004. Economies in nematode management from precision agriculture—limitations and possibilities. Proceedings of the Fourth International Congress of Nematology R Cook, D Hunt 23–32. Leiden, Neth.: Brill
     [Google Scholar]
  33. 33.
    Evans K, Trudgill DL, Webster JM, eds. 1993. Plant Parasitic Nematodes in Temperate Agriculture Wallingford UK: CABI
  34. 34.
    Gair R, Mathias PL, Harvey PN. 1969. Studies of cereal nematode populations and cereal yields under continuous or intensive culture. Ann. Appl. Biol. 63:503–12
     [Google Scholar]
  35. 35.
    Gausman HW, Heald CM Jr., Escobar DE. 1975. Effect of Rotylenchulus reniformis on reflectance of cotton plant leaves. . J. Nematol. 7:4368–74
     [Google Scholar]
  36. 36.
    Gkarmiri K, Mahmood S, Ekblad A, Alström S, Högberg N, Finlay R. 2017. Identifying the active microbiome associated with roots and rhizosphere soil of oilseed rape. Appl. Environ. Microbiol. 83:22e01938–17
     [Google Scholar]
  37. 37.
    Grieve BD, Duckett T, Collison M, Boyd L, West J et al. 2019. The challenges posed by global broad acre crops in delivering smart agri-robotic solutions: a fundamental rethink is required. Glob. Food Secur. 23:116–24
     [Google Scholar]
  38. 38.
    Harkes P, Suleiman AKA, van den Elsen SJJ, de Haan JJ, Holterman M et al. 2019. Conventional and organic soil management as divergent drivers of resident and active fractions of major soil food web constituents. Sci. Rep. 9:13521
     [Google Scholar]
  39. 39.
    Harkes P, van Steenbrugge JJM, van den Elsen SJJ, Suleiman AKA, de Haan JJ et al. 2020. Shifts in the active rhizobiome paralleling low Meloidogyne chitwoodi densities in fields under prolonged organic soil management. Front. Plant Sci. 10:1697
     [Google Scholar]
  40. 40.
    Heald CM, Thames WH, Wiegand CL. 1972. Detection of Rotylenchulus reniformis infestations by aerial infrared photography. J. Nematol. 4:4298–300
     [Google Scholar]
  41. 41.
    Helder J, Heurer H. 2022. Let's be inclusive: the time of looking at individual plant parasitic nematodes is over, and new technologies allow for it. See Ref. 98 403–7
  42. 42.
    Hillnhütter C, Mahlein A-K, Sikora RA, Oerke E-C. 2011. Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields. Field Crops Res. 122:170–77
     [Google Scholar]
  43. 43.
    Jung J, Maeda M, Chang A, Bhandari M, Ashapure A, Landivar-Bowles J. 2021. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Curr. Opin. Environ. Biotechnol. 70:15–22
     [Google Scholar]
  44. 44.
    Karasov TL, Chae E, Herman JJ, Bergelson J 2017. Mechanisms to mitigate the trade-off between growth and defense. Plant Cell 29:666–80
     [Google Scholar]
  45. 45.
    Kearn J, Ludlow E, Dillon J, O'Connor V, Holden-Dye L 2014. Fluensulfone is a nematicide with a mode of action distinct from anticholinesterases and macrocyclic lactones. Pestic. Biochem. Physiol. 109:44–57
     [Google Scholar]
  46. 46.
    Kerdraon L, Laval V, Suffert F. 2019. Microbiomes and pathogen survival in crop residues, an ecotone between plant and soil. Phytobiomes J. 3:246–55
     [Google Scholar]
  47. 47.
    Kerry BR. 1982. The decline of Heterodera avenae populations. EPPO Bull. 12:491–96
     [Google Scholar]
  48. 48.
    Kimenju JW, Wendot PK, Thuo AK. 2022. Cumulative damage impact of plant-parasitic nematodes in smallholder maize cropping systems in East Africa. See Ref. 98 48–54
  49. 49.
    Kuska MT, Daub M, Mahlein AK. 2022. Emerging technologies for integrated nematode management: remote sensing or proximal sensing as a potential tool to detect and identify nematode infestation. See Ref. 98 414–20
  50. 50.
    Kuska MT, Heim RHJ, Geedicke I, Gold KM, Brugger A, Paulus S. 2022. Digital plant pathology: a foundation and guide to modern agriculture. J. Plant Dis. Prot. 129:3457–68
     [Google Scholar]
  51. 51.
    Lahm GP, Desaeger J, Smith BK, Pahutskia TF, Rivera MA et al. 2017. The discovery of fluazaindolizine: a new product for the control of plant parasitic nematodes. Bioorg. Med. Chem. Lett. 27:1572–75
     [Google Scholar]
  52. 52.
    Lawrence KS. 2022. Reniform nematodes (Rotylenchulus reniformis) and its interactions with cotton (Gossypium hirsutum). See Ref. 98 94–99
  53. 53.
    Leahy J, Mendelsohn M, Kough J, Jones R, Berckes N 2014. Biopesticide oversight and registration at the U.S. environmental protection agency. Biopesticides: State of the Art and Future Opportunities JN Seiber, J Coats, SO Duke, AD Gross 3–18. Washington, DC: Am. Chem. Soc.
     [Google Scholar]
  54. 54.
    Lennon JT, Jones SE. 2011. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microb. 9:119–30
     [Google Scholar]
  55. 55.
    Li J, Zou C, Xu J, Ji X, Niu X et al. 2015. Molecular mechanisms of nematode-nematophagous microbe interactions: basis for biological control of plant-parasitic nematodes. Annu. Rev. Phytopathol. 53:67–95
     [Google Scholar]
  56. 56.
    Lilley CJ, Wang D, Atkinson HJ, Urwin PE. 2011. Effective delivery of a nematode-repellent peptide using a root-cap-specific promoter. Plant Biotechnol. J. 9:2151–61
     [Google Scholar]
  57. 57.
    Liu X, Hannula SE, Li X, Hundscheid MPJ, klein Gunnewiek PJA et al. 2021. Decomposing cover crops modify root-associated microbiome composition and disease tolerance of cash crop seedlings. Soil Biol. Biochem. 160:108343
     [Google Scholar]
  58. 58.
    Lobell DB, Burke M, Tebaldi C, Mastrandrea M, Falcon W, Naylor R. 2008. Prioritizing climate change adaptation needs for food security in 2030. Science 319:607–10
     [Google Scholar]
  59. 59.
    Maes WH, Steppe K. 2019. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci. 24:2152–64
     [Google Scholar]
  60. 60.
    Mahlein AK. 2016. Plant disease detection by imaging sensors: parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100:2241–51
     [Google Scholar]
  61. 61.
    Mahlein AK, Kuska MT, Behmann J, Polder G, Walter A. 2018. Hyperspectral sensors and imaging technologies in phytopathology: state of the art. Annu. Rev. Phytopathol. 56:535–58
     [Google Scholar]
  62. 62.
    Mahlein AK, Kuska MT, Thomas S, Wahabzada M, Behmann J et al. 2019. Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!. Curr. Opin. Plant Biol. 50:156–62
     [Google Scholar]
  63. 63.
    Micallef SA, Channer S, Shiaris MP, Colón-Carmona A. 2009. Plant age and genotype impact the progression of bacterial community succession in the Arabidopsis rhizosphere. Plant Signal. Behav. 4:777–80
     [Google Scholar]
  64. 64.
    Molendijk PG, Sikora RS. 2022. Decision support systems in integrated nematode management: the need for a holistic approach. See Ref. 98 428–38
  65. 65.
    Moore GK. 1979. What is a picture worth? A history of remote sensing. Hydrol. Sci. B 24:477–85
     [Google Scholar]
  66. 66.
    Mueller J. 2022. Hoplolaimus columbus: a prime candidate for site-specific management in cotton and soybean production. See Ref. 98 80–86
  67. 67.
    Mueller JD, Khalilian A, Monfort WS, Davis RF, Kirkpatrick TL et al. 2010. Site-specific detection and management of nematodes. Precision Crop Protection: The Challenge and Use of Heterogeneity ed. E-C Oerke, R Gerhards, G Menz, RA Sikora 385–402. New York: Springer
     [Google Scholar]
  68. 68.
    Nerva L, Sandrini M, Moffa L, Velasco R, Balestrini R, Chitarra W. 2022. Breeding toward improved ecological plant-microbiome interactions. Trends Plant Sci. 27:1134–43
     [Google Scholar]
  69. 69.
    Nevins CJ, Nakatsu C, Armstrong S. 2018. Characterization of microbial community response to cover crop residue decomposition. Soil Biol. Biochem. 127:39–49
     [Google Scholar]
  70. 70.
    Nutter JFW, Tylka GL, Guan J, Moreira AJD, Marett CC et al. 2002. Use of remote sensing to detect soybean cyst nematode-induced plant stress. J. Nematol. 34:3222
     [Google Scholar]
  71. 71.
    Oerke EC. 2020. Remote sensing of diseases. Annu. Rev. Phytopathol. 58:225–52
     [Google Scholar]
  72. 72.
    Ofek M, Voronov-Goldman M, Hadar Y, Minz D. 2014. Host signature effect on plant root-associated microbiomes revealed through analyses of resident versus active communities. Environ. Microbiol. 16:2157–67
     [Google Scholar]
  73. 73.
    Oka Y, Saroya Y. 2019. Effect of fluensulfone and fluopyram on the mobility and infection of second-stage juveniles of Meloidogyne incognita and M. javanica. Pest Manag. Sci. 75:2095–106
     [Google Scholar]
  74. 74.
    Omidi R, Pourreza A, Moghimi A, Zuniga-Ramirez G, Jafarbiglu H et al. 2022. A semi-supervised approach to cluster symptomatic and asymptomatic leaves in root lesion nematode infected walnut trees. Comput. Electron. Agric. 194:106761
     [Google Scholar]
  75. 75.
    Ortiz BV, Sullivan D, Perry C, Vellidis G, Seymour L, Rucker K. 2007. Delineation of management zones for site specific management of parasitic nematodes using geostatistical analysis of measured field characteristics Paper presented at the Sixth European Conference of Precision Agriculture (6ECPA) Skiathos, Greece:
  76. 76.
    Padgham J. 2009. Agricultural Development Under a Changing Climate: Opportunities and Challenges for Adaptation Washington, DC: World Bank
     [Google Scholar]
  77. 77.
    Paulus S. 2019. Measuring crops in 3D: using geometry for plant phenotyping. Plant Methods 15:1103
     [Google Scholar]
  78. 78.
    Paulus S, Mahlein AK. 2020. Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale. GigaScience 9:8giaa090
     [Google Scholar]
  79. 79.
    Pépin N, Hebert FO, Joly DL. 2021. Genome-wide characterization of the MLO gene family in Cannabis sativa reveals two genes as strong candidates for powdery mildew susceptibility. Front. Plant Sci. 12:729261
     [Google Scholar]
  80. 80.
    Perry C, Vellidis G, Sulivan D, Rucker K, Kemerait RC. 2006. Predicting nematode hotspots using soil EC data. Paper presented at the 2006 Beltwide Cotton Conferences San Antonio, TX: Jan. 3–6. https://www.cotton.org/beltwide/proceedings/2005-2022/data/conferences/2006/pdfs/502-511.pdf
    [Google Scholar]
  81. 81.
    Plumblee MT, Mueller J. 2022. Implementing precision agriculture concepts and technologies into crops production and site-specific management of nematodes. See Ref. 98 421–27
  82. 82.
    Qin J, Wang B, Wu Y, Lu Q, Zhu H. 2021. Identifying pine wood nematode disease using UAV images and deep learning algorithms. Remote Sens. 13:2162
     [Google Scholar]
  83. 83.
    Radakovic ZS, Anjam MS, Escobar E, Chopra D, Cabrera J et al. 2018. Arabidopsis HIPP27 is a host susceptibility gene for the beet cyst nematode Heterodera schachtii. Mol. Plant Pathol. 19:81917–28
     [Google Scholar]
  84. 84.
    Rhoades HL. 1983. Effect of cover crops and fallowing on populations of Belonolaimus longicaudatus and Meloidogyne incognita and subsequent crop yields. Nematropica 13:9–16
     [Google Scholar]
  85. 85.
    Roesch LFW, Fulthorpe RR, Riva A, Casella G, Hadwin AKM et al. 2007. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 1:283–90
     [Google Scholar]
  86. 86.
    Ruwona J, Scherm H. 2021. Sensing and imaging of plant disease through the lens of science mapping. Trop. Plant Pathol. 47:74–84
     [Google Scholar]
  87. 87.
    Sacristán S, Goss EM, Eves-van den Akker S. 2021. How do pathogens evolve novel virulence activities?. Mol. Plant-Microbe Interact. 34:6576–86
     [Google Scholar]
  88. 88.
    Savary S, Willocquet L, Pethybride SJ, Esker P, McRoberts N, Nelson A. 2019. The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol. 3:430–43
     [Google Scholar]
  89. 89.
    Scheffers BR, De Meester L, Bridge TCL, Hoffmann AA, Pandolfi JM et al. 2016. The broad foot-print of climate change from genes to biomes to people. Science 354:aaf7671
     [Google Scholar]
  90. 90.
    Schleker ASS, Rist M, Matera C et al. 2022. Mode of action of fluopyram in plant-parasitic nematodes. Sci. Rep. 12:11954
     [Google Scholar]
  91. 91.
    Siddique S, Coomer A, Baum T, Williamson VM. 2022. Recognition and response in plant-nematode interactions. Annu. Rev. Phytopathol. 60:143–62
     [Google Scholar]
  92. 92.
    Siddique S, Eves-van den Akker S. 2022. Nematode management through genome editing. See Ref. 98 408–13
  93. 93.
    Siddique S, Radakovic ZS, Hiltl C, Pellegrin C, Baum TJ et al. 2021. The genome and lifestage-specific transcriptomes of a plant-parasitic nematode and its host reveal susceptibility genes involved in trans-kingdom synthesis of vitamin B5. bioRxiv 462558. https://doi.org/10.1101/2021.10.01.462558
  94. 94.
    Sikora RA. 1992. Management of the antagonistic potential in agricultural ecosystems for the control of plant parasitic nematodes. Annu. Rev. Phytopathol. 30:245–70
     [Google Scholar]
  95. 95.
    Sikora RA, Berg J, Oerke EC 2020. The big giveaway. Transforming Agriculture in Southern Africa: Constraints, Technologies, Policies and Processes RA Sikora, ER Terry, LG Vlek, J Chitja 45–55. London: Routledge
     [Google Scholar]
  96. 96.
    Sikora RA, Coyne D, Hallmann J, Timper P, eds. 2018. Plant-Parasitic Nematodes in Subtropical and Tropical Agriculture Wallingford, UK: CABI. , 3rd ed..
  97. 97.
    Sikora RA, Coyne D, Hallmann J, Timper P 2018. Reflections and challenges: nematology in subtropical and tropical agriculture. Plant-Parasitic Nematodes in Subtropical and Tropical Agriculture RA Sikora, D Coyne, J Hallmann, P Timper 1–9. Wallingford, UK: CABI. , 3rd ed..
     [Google Scholar]
  98. 98.
    Sikora RA, Desaeger J, Molendijk LPG, eds. 2022. Integrated Nematode Management: State-of-the-Art and Visions for the Future Wallingford, UK: CABI
     [Google Scholar]
  99. 99.
    Sikora RA, Molendijk LPG, Desaeger J. 2022. Integrated nematode management and crop health: future challenges and opportunities. See Ref. 98 3–10
  100. 100.
    Sikora RA, Padgham J, Desaeger J. 2022. The unpredictability of adapting integrated nematode management to climate variability. See Ref. 98 463–72
  101. 101.
    Sikora RA, Roberts P 2018. Management practices: an overview of integrated nematode management technologies. Plant Parasitic Nematodes in Subtropical and Tropical Agriculture RA Sikora, D Coyne, J Hallmann, P Timper 795–838. Wallingford, UK: CABI
     [Google Scholar]
  102. 102.
    Sikora RA, Terry ER, Vlek LG, Chitja J, eds. 2020. Transforming Agriculture in Southern Africa: Constraints, Technologies, Policies and Processes London: Routledge
     [Google Scholar]
  103. 103.
    Smith RF, Reynolds HT. 1966. FAO symposium on integrated pest control. Proc. FAO Symp. Int. Pest Control 1:11–17
     [Google Scholar]
  104. 104.
    Sparrow R, Howard M. 2021. Robots in agriculture: prospects, impacts, ethics, and policy. Precis. Agric. 22:3818–33
     [Google Scholar]
  105. 105.
    Stirling GR. 2011. Biological control of plant-parasitic nematodes: an ecological perspective, a review of progress and opportunities for further research. Biological Control of Plant-Parasitic Nematodes K Davies, Y Spiegel 1–38. London: Springer
     [Google Scholar]
  106. 106.
    Stringlis IA, Yu K, Feussner K, de Jonge R, Van Bentum S et al. 2018. MYB72-dependent coumarin exudation shapes root microbiome assembly to promote plant health. PNAS 115:E5213–22
     [Google Scholar]
  107. 107.
    Sun G, Lu H, Zhao Y, Zhou J, Jackson R et al. 2022. AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice. New Phytol. 236:41584–604
     [Google Scholar]
  108. 108.
    Sun Z, Wang Y, Pan L, Xie Y, Zhang B et al. 2022. Pine wilt disease detection in high-resolution UAV images using object-oriented classification. J. For. Res. 33:41377–89
     [Google Scholar]
  109. 109.
    Topalovic O, Hussain M, Heuer H. 2020. Plants and associated soil microbiota cooperatively suppress plant-parasitic nematodes. Front. Microbiol. 11:313
     [Google Scholar]
  110. 110.
    Van Ghelder C, Esmenjaud D, Callot C, Dubois E, Mazier M, Duval H. 2018. Ma orthologous genes in Prunus spp. shed light on a noteworthy NBS-LRR cluster conferring differential resistance to root-knot nematodes. Front. Plant Sci. 9:1269
     [Google Scholar]
  111. 111.
    Watson JR. 1922. Bunch velvet beans to control root-knot. Univ. Fla. Agric. Exp. Stn. Bull. 163:54–59
     [Google Scholar]
  112. 112.
    Williamson VM, Gleason CA. 2003. Plant-nematode interactions. Curr. Opin. Plant Biol. 6:4327–33
     [Google Scholar]
  113. 113.
    Wisniewski N. 2022. Trends in new molecule development: how crop protection companies are delivering innovative solutions. Agribusiness Global August 5. https://www.agribusinessglobal.com/agrochemicals/trends-in-new-molecule-development-how-crop-protection
    [Google Scholar]
  114. 114.
    Zadoks JC, van den Bosch F. 1994. On the spread of plant disease: a theory on foci. Annu. Rev. Phytopathol. 32:1503–21
     [Google Scholar]
  115. 115.
    Zhalnina K, Louie KB, Hao Z, Mansoori N, Da Rocha UN et al. 2018. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3:470–80
     [Google Scholar]
  116. 116.
    Zhu Y, Sun G, Ding G, Zhou J, Wen M et al. 2021. Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat. Plant Physiol. 187:2716–38
     [Google Scholar]
/content/journals/10.1146/annurev-phyto-021622-113058
Loading
Integrated Nematode Management in a World in Transition: Constraints, Policy, Processes, and Technologies for the Future
Annual Review of Phytopathology 61, 209 (2023); https://doi.org/10.1146/annurev-phyto-021622-113058
/content/journals/10.1146/annurev-phyto-021622-113058
/content/journals/10.1146/annurev-phyto-021622-113058
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error