Agricultural residues are efficient abrasive tools for weed control

  • Manuel Perez-Ruiz
  • Rocío Brenes
  • Jose M. Urbano
  • David C. Slaughter
  • Frank Forcella
  • Antonio Rodríguez-Lizana
Research Article


Non-chemical control of weeds is essential for organic farming and is a potential solution to address herbicide-resistant weeds, but too few non-chemical control methods exist. Consumers, farmers, and regulators want organic produce, new tools, and fewer xenobiotics. New weed management strategies focused on the integration of different tools, and strategies are needed to minimize dependence on broad-spectrum herbicides. Accordingly, we assessed abrasive grits from eight agricultural sources (almond shell, grape seed, maize cob, olive seed, poultry manure, sand, soybean meal, and walnut shell) as weed-abrading materials when delivered at high air pressures. Grit efficacies were determined in laboratory trials on weeds common to tomato, sugar beet, and olive: Amaranthus retroflexus L., Chenopodium murale L., and Centaurea cyanus L., respectively. Additionally, application rates and costs of residues were estimated. Control of two- to three-leaf stage weed seedlings ranged from 30 to 100%. In 88% of the trials, weed control exceeded 80%. Except for sand, the effectiveness of the grits was not species dependent. Significant differences in the mass flow of grits suggested that effective doses may vary up to 100% among grit materials. The residue yield ratio (percent control per gram of grit) varied among residues, ranging from 2.8 to 7.1% g−1. We demonstrate that the best combination of weed control, grit dose, and residue yield ratio was provided by maize cob and olive seed, with control rates of 93 and 90%, respectively. This pioneering study simultaneously assessed residues from both herbaceous and woody crops as well as animal wastes and indicated that a more efficient and effective use of these resources for weed control is feasible.


Abrasion Alternative weed control Non-chemical application Organic farming Precision farming 



The research was supported in part by the Spanish Ministry of Economic and Competence (Project: AGL2013-46343-R) and the Regional Government of Andalucía (Project: P12-AGR-1227).


  1. Carbonell-Bojollo RM, Repullo-Ruibérriz de Torres MA, Rodríguez-Lizana A, Ordóñez-Fernández R (2012) Influence of soil and climate conditions on CO2 emissions from agricultural soils. Water Air Soil Pollut 223:3425–3435. CrossRefGoogle Scholar
  2. Clarke JH, Wynn SC, Twining SE (2011) Impact of changing pesticide availability. Aspect Appl Biol 10:263–267Google Scholar
  3. Curran WE (2016) Persistence of herbicides in soil. Crops and Soils Magazine. American Society of Agronomy, Madison, pp 16–21. Google Scholar
  4. Erazo-Barradas M, Forcella F, Humburg D, Clay SA (2017) Propelled abrasive grit for weed control in organic silage corn. Renewable Agric Food Syst 1–8.
  5. Fontanelli M, Raffaelli M, Martelloni L, Frasconi C, Peruzzi A (2013) The influence of non-living mulch, mechanical and thermal treatments on weed population and yield of rainfed fresh-market tomato (Solanum lycopersicum L.) Span J Agric Res 11:593–602. CrossRefGoogle Scholar
  6. Forcella F (2009) Potential use of abrasive air-propelled agricultural residues for weed control. Weed Res 49:341–345. CrossRefGoogle Scholar
  7. Forcella F (2012) Air-propelled abrasive grit for postemergence in-row weed control in field corn. Weed Technol 26:161–164. CrossRefGoogle Scholar
  8. Forcella F (2013) Soybean seedlings tolerate abrasion from air-propelled grit. Weed Technol 27:631–635. CrossRefGoogle Scholar
  9. Forcella F, James T, Rahman A (2011) Post-emergence weed control through abrasion with an approved organic fertilizer. Renewable Agric Food Syst 26(1):31–37. CrossRefGoogle Scholar
  10. Gill HK, Garg H (2014) Pesticide: environmental impacts and management strategies. In: Solenski S, Larramenday ML (eds) Pesticides- toxic effects. Intech. Rijeka, Croatia, pp 187–230. Google Scholar
  11. González R (2006) Métodos para el control de malas hierbas. (1) culturales. Instituto de Ciencias Agrarias (ICA). Centro de Ciencias Medioambientales (CCMA) Consejo Superior de Investigaciones Científicas (CSICI). Hoja divulgativa Núm. 2119Google Scholar
  12. Gruber S, Claupein W (2009) Effect of tillage intensity on weed infestation in organic farming. Soil Tillage Res 105:104–111. CrossRefGoogle Scholar
  13. Gutjahr C, Gerhards R (2010) Decision rules for site-specific weed management. In E.-C. Oerke et al. (ed) Precision Crop Protection – the Challenge and the Use of Heterogeneity. Springer Science. pp 223–239.
  14. Hull R, Tatnell LV, Cook SK, Beffa R, Moss SR (2014) Current status of herbicide-resistant weeds in the UK. Asp Appl Biol 127:261–272Google Scholar
  15. Levene H (1960) Robust tests for equality of variances. In: Ingram Olkin; Harold Hotelling; et al. contributions to probability and statistics: essays in honor of Harold Hotelling. Stanford University Press, pp 278–292Google Scholar
  16. Ministerio de Agricultura, Alimentación y Medio Ambiente (2015) Encuesta sobre superficies y rendimientos de cultivos. Resultados nacionales y autonómicos. Accessed 05 Dec 2016
  17. Pastor M (2005) Mantenimiento del suelo en olivar de regadío: manejo del suelo y los herbicidas. In: Pastor M (ed) Cultivo del olivo con riego localizado. Junta de Andalucía & Mundi-Prensa, Madrid, pp 589–624Google Scholar
  18. Perez-Ruiz M, Slaughter DC, Gliever C, Upadyaya S (2012) Tractor-based real-time kinematic-global positioning system (RTK-GPS) guidance system for geospatial mapping of row crop transplant. Biosyst Eng 111:64–71. CrossRefGoogle Scholar
  19. Pérez-Ruiz M, Slaughter DC, Fathallah FA, Gliever CJ, Miller BJ (2014) Co-robotic intra-row weed control system. Biosyst Eng 126:45–55. CrossRefGoogle Scholar
  20. Pérez-Ruiz M, Gonzalez-de-Santos P, Ribeiro A, Fernandez-Quintanilla C, Peruzzi A, Vieri M, Tomic S, Agüera J (2015) Highlights and preliminary results for autonomous crop protection. Comput Electron Agric 110:150–161. CrossRefGoogle Scholar
  21. Podolsky K, Blackshaw RE, Entz MH (2016) A comparison of reduced tillage implements for organic wheat production in western Canada. Agron J 108:2003–2014. CrossRefGoogle Scholar
  22. Qin J, Hu F, Li D, Li H, Lu J, Yu R (2010) The effect of mulching, tillage and rotation on yield in non-flooded compared with flooded Rice production. J Agron Crop Sci 196:397–406. CrossRefGoogle Scholar
  23. R Core Team (2015) R: a language and environment for statistical comput- ing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  24. Reicosky DC, Forcella F (1998) Cover crop and soil quality interactions in agroecosystems. J Soil Water Conserv 53:224–229Google Scholar
  25. Reisch L, Eberle U, Lorek S (2013) Sustainable food consumption: an overview of contemporary issues and policies. Sustain: Sci Pract Policy 92:7–25Google Scholar
  26. Repullo-Ruibérriz de Torres MA, Carbonell-Bojollo R, Alcántara-Braña C, Rodríguez-Lizana A, Ordóñez-Fernández R (2012) Carbon sequestration potential of residues of different types of cover crops in olive groves under mediterranean climate. Spanish J Agric Res 10:649–661. CrossRefGoogle Scholar
  27. Rodríguez-Lizana A, Carbonell R, González P, Ordóñez R (2010) N, P and K released by the field decomposition of residues of a pea-wheat-sunflower rotation. Nutr Cycl Agroecosyst 87:199–208. CrossRefGoogle Scholar
  28. Rodríguez-Lizana A, Pereira MJ, Ribeiro MC, Soares A, Márquez-García F, Ramos A, Gil-Ribes JA (2017) Assessing soil protection uncertainty through stochastic simulations. Land Degrad Dev 28:2086–2097. CrossRefGoogle Scholar
  29. Sivesind EC, Leblanc ML, Cloutier DC, Seguin P, Stewart KA (2009) Weed response to flame weeding at different developmental stages. Weed Technol 23:438–443. CrossRefGoogle Scholar
  30. Slaughter DC, Giles DK, Downey D (2008) Autonomous robotic weed control systems: a review. Comput Electron Agric 61:63–78. CrossRefGoogle Scholar
  31. Ulloa SM, Datta A, Knezevic SZ (2010) Tolerance of selected weed species to broadcast flaming at different growth stages. Crop Prot 29:1381–1388. CrossRefGoogle Scholar
  32. Van Evert FK, Samson J, Polder G, Vijn M, Van Dooren H, Lamaker A, Der Heijden GWAM V, Van der Zalm T, Lotz LA (2011) A robot to detect and control broad-leaved dock (Rumex obtusifolius L.) in grassland. J Field Robot 28:264–277. CrossRefGoogle Scholar
  33. Walz E (1999) Third biennial national organic farmer’s survey. Organic Farming Research Foundation, Santa CruzGoogle Scholar
  34. Wooldridge JM (2013) Introductory econometrics. A modern approach, 5th edn. South Western Cengage Learning, United StatesGoogle Scholar
  35. Wortman SE (2014) Integrating weed and vegetable crop management with multifunctional air-propelled abrasive grits. Weed Technol 28:243–252. CrossRefGoogle Scholar
  36. Wortman SE (2015) Air-propelled abrasive grits reduce weed abundance and increase yields in organic vegetable production. Crop Prot 77:157–162. CrossRefGoogle Scholar
  37. Yuen KK (1974) The two-sample trimmed t for unequal population variances. Biometrika 61:165–170. CrossRefGoogle Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  • Manuel Perez-Ruiz
    • 1
  • Rocío Brenes
    • 1
  • Jose M. Urbano
    • 2
  • David C. Slaughter
    • 3
  • Frank Forcella
    • 4
  • Antonio Rodríguez-Lizana
    • 1
  1. 1.Área de Ingeniería Agroforestal. Dpto. de Ingeniería Aeroespacial y Mecánica de FluidosUniversidad de SevillaSevilleSpain
  2. 2.Dpto. de Ciencias AgroforestalesUniversidad de SevillaSevilleSpain
  3. 3.Department of Biological and Agricultural EngineeringUniversity of CaliforniaDavisUSA
  4. 4.North Central Soil Conservation Research LaboratoryUSDA-ARSMorrisUSA

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