Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-26T21:43:58.009Z Has data issue: false hasContentIssue false
  • English
  • Français

Surveys of weed management on flooded rice yields in southern Brazil

Published online by Cambridge University Press:  27 December 2021

Anelise L. Silva Open the ORCID record for Anelise L. Silva [Opens in a new window] ,
Nereu A. Streck ,
Alencar J. Zanon Open the ORCID record for Alencar J. Zanon [Opens in a new window] ,
Giovana G. Ribas Open the ORCID record for Giovana G. Ribas [Opens in a new window] ,
Bruno L. Fruet  and
André R. Ulguim Open the ORCID record for André R. Ulguim [Opens in a new window]
Anelise L. Silva
Affiliation:
Graduate Student, Agronomy Graduate Program, Federal University of Santa Maria (UFSM), Santa Maria, Brazil
Nereu A. Streck
Affiliation:
Associate Professor, Federal University of Santa Maria (UFSM), Santa Maria, Brazil
Alencar J. Zanon
Affiliation:
Professor, Federal University of Santa Maria (UFSM), Santa Maria, Brazil
Giovana G. Ribas
Affiliation:
Graduate Student, Agronomy Graduate Program, Federal University of Santa Maria (UFSM), Santa Maria, Brazil
Bruno L. Fruet
Affiliation:
Undergraduate Student, Federal University of Santa Maria (UFSM), Santa Maria, Brazil
André R. Ulguim*
Affiliation:
Professor, Federal University of Santa Maria (UFSM), Santa Maria, Brazil
*
Author for correspondence: André R. Ulguim, Federal University of Santa Maria (UFSM), 1000 Roraima Avenue, 97105-900, Santa Maria, Brazil. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

One of the main limiting factors for high yields of flooded rice (Oryza sativa L.) is the presence of weeds, especially herbicide-resistant weeds. The aim of this study was to evaluate the association of weed management practices adopted by flooded rice farmers in the state of Rio Grande do Sul (RS), Brazil, with grain yield. For this purpose, 324 interview surveys were administered to farmers who supplied information about the history of weed management and yields. The answers to the survey indicated that weedy rice (Oryza sativa L.) and Echinochloa spp. were the most important weeds that occurred in flooded rice areas in RS. Advanced growth stage of weeds and inadequate environmental conditions such as air temperature and relative humidity were listed as the main reasons for low weed control efficacy. Farmers achieved greater rice yields when they adopted rice–soybean [Glycine max (L.) Merr.] (9,140 kg ha−1 average yield) and herbicide site of action rotations (8,801 kg ha−1 average yield) along with tank mixes (8,580 kg ha−1 average yield) as specific management practices for resistant weed control. The use of glyphosate with residual herbicides in a tank mix in the rice spiking stage is the main factor related to greater yields. The postemergence applications and their relationship to delaying of flooding in rice is a factor that reduces rice yield when no spiking glyphosate application was made. Identification of the most important weeds in terms of occurrence and knowledge of the main agronomic practices adopted by farmers are essential so that recommendations for integrated management practices can be adopted in an increasingly accurate and sustainable manner in flooded rice areas in southern Brazil.

Keywords

Farmersintegrated weed managementOryza sativa L.resistanceyield gap
Type
Research Article
Information
Weed Science , Volume 70 , Issue 2 , March 2022 , pp. 249 - 258
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Weed Science Society of America

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Associate Editor: Bhagirath Chauhan, The University of Queensland

References

Andres, A, Concenço, G, Melo, PTBS, Schmidt, M, Resende, RG (2007) Detection of Echinochloa sp. resistance to quinclorac in rice fields in Southern Brazil. Planta Daninha 25:221–226. PortugueseCrossRefGoogle Scholar
Avila, LA, Marchesan, E, Camargo, ER, Merotto, A Jr, Ulguim, AR, Noldin, JA, Andres, A, Mariot, CHP, Agostinetto, D, Dornelles, SHB, Markus, C (2021a) Eighteen years of Clearfield™ rice in Brazil: what have we learned? Weed Sci 69:585–597 CrossRefGoogle Scholar
Avila, LA, Noldin, JA, Mariot, CH, Massoni, PFS, Fipke, MV, Gehrke, VR, Merotto, A Jr, Tomita, FM, Matos, AB, Facioni, G, Vieira, EB, Rosa, ES, Santis, RP, Camargo, ER, Theisen, G, Roma-Burgos, N (2021b) Status of weedy rice (Oryza spp.) infestation and management practices in southern Brazil. Weed Sci 69:536–546 CrossRefGoogle Scholar
Beckie, HJ, Hall, LM (2014) Genetically-modified herbicide-resistant (GMHR) crops a two-edged sword? An Americas perspective on development and effect on weed management. Crop Prot 66:40–45 CrossRefGoogle Scholar
Breiman, L, Friedman, JH, Olshen, RA, Stone, CJ, eds (1984) Classification and Regression Trees. 1st ed. Boca Raton, FL: Chapman & Hall/CRC. 68 p Google Scholar
Burgos, NR, Norsworthy, JK, Scott, RC, Smith, KL (2008) Red rice (Oryza sativa) status after 5 years of imidazolinone-resistant rice technology in Arkansas. Weed Technol 22:200–208 CrossRefGoogle Scholar
[CONAB] Companhia Nacional de Abastecimento (2020) Série histórica das safras. https://www.conab.gov.br/info-agro/safras/serie-historica-das-safras?limitstart=0 Accessed: July 25, 2020Google Scholar
Counce, PA, Keisling, TC, Mitchell, AJ (2000) A uniform, objective, and adaptive system for expressing rice development. Crop Sci 40:436–443 CrossRefGoogle Scholar
Di Rienzo, JA, Casanoves, F, Balzarini, MG, Gonzalez, L, Tablada, M, Robledo, CW (2018) InfoStat versión 2018. Córdoba, Argentina: InfoStat Goup, FCA, Universidad Nacional de Córdoba. https://www.infostat.com.ar/index.php?mod=page&id=46&lang=en. Accessed: September 21, 2019Google Scholar
Eberhardt, DS, Oliveira Neto, AM, Noldin, JA, Vanti, RM (2016) Barnyardgrass with multiple resistance to synthetic auxin, ALS and ACCase inhibitors. Planta Daninha 34:823–832 CrossRefGoogle Scholar
Edwards, CB, Jordan, DL, Owen, MD, Dixon, PM, Young, BG, Wilson, RG, Weller, SC, Shaw, DR (2014) Benchmark study on glyphosate-resistant crop systems in the United States. Economics of herbicide resistance management practices in a 5 year field-scale study. Pest Manag Sci 70:1924–1929 CrossRefGoogle Scholar
[FAO] Food and Agriculture Organization of the United Nations (2020) Countries by Commodity. http://www.fao.org/faostat/es/#rankings/countries_by_commodity. Accessed: April 20, 2020Google Scholar
Fruet, B de L, Merotto, A, Ulguim, A da R (2020) Survey of rice weed management and public and private consultant characteristics in Southern Brazil. Weed Technol 34:351–356 CrossRefGoogle Scholar
Gazziero, DLP (2015) Mixture of pesticides in tank, in Brazilian farms. Planta Daninha 33:83–92. PortugueseCrossRefGoogle Scholar
Heap, I (2021) The International Herbicide-Resistant Weed Database. http://www.weedscience.org. Accessed: March 10, 2021Google Scholar
Hothorn, T, Hornik, K, A van de Wiel, M, Zeileis, A (2006) A Lego system for conditional inference. J Am Sta Assoc 60:257–263 Google Scholar
[IRGA] Instituto Rio Grandense do Arroz (2020) Safras. https://irga.rs.gov.br/safras. Accessed: July 26, 2020. PortugueseGoogle Scholar
Kalsing, A, dos Reis Goulart, ICG, Mariot, CHP, Menezes, VG, de Oliveira Matzenbacher, F, Merotto, A (2019) Spatial and temporal evolution of imidazolinone-resistant red rice in “Clearfield” rice cultivations. Pesqui Agropecu Bras 54:e00215 CrossRefGoogle Scholar
Kalsing, A, Tronquini, SM, Mariot, CHP, Rubin, R da S, Bundt, ADC, Fadin, DA, Marques, LH (2017) Susceptibility of Echinochloa populations to cyhalofop-butyl in Southern region of Brazil and impact of the weed phenology on its efficacy of control. Cienc Rural 47:e20160839 CrossRefGoogle Scholar
Kuhn, M (2008) Building predictive models in R using the caret package. J Stat Sofw 28:1–26 Google Scholar
Marchesan, E, Massoni, PFS, Villa, SCC, Grohs, M, Avila, LA, Sartori, GMS, Bruck, RF (2011) Productivity, injury and control of red rice in succession of irrigated rice cultivation in System Clearfield®. Cienc Rural 41:17–24. PortugueseCrossRefGoogle Scholar
Matzenbacher, FO, Bortoly, ED, Kalsing, A, Merotto, A (2015a) Distribution and analysis of the mechanisms of resistance of barnyardgrass (Echinochloa crus-galli) to imidazolinone and quinclorac herbicides. J Agric Sci 153:1044–1058 CrossRefGoogle Scholar
Matzenbacher, FO, Kalsing, A, Dalazen, G, Markus, C, Merotto, A Jr (2015b) Antagonism is the predominant effect of herbicide mixtures used for imidazolinone-resistant barnyardgrass (Echinochloa crus-galli) control. Planta Daninha 33:587–597 CrossRefGoogle Scholar
Menezes, VG, Mariot, CHP, Kalsing, A, Freitas, TFS, Grohs, DS, Matzenbacher, F de O (2013) Association of glyphosate and imidazolinones on red rice control in Clearfield® rice. Cienc Rural 43:2154–2159 CrossRefGoogle Scholar
Menezes, VG, Mariot, CHP, Kalsing, A, Goulart, ICGR (2009) Red rice (Oryza sativa) resistant to the herbicides imidazolinones. Planta Daninha 27:1047–1052. PortugueseCrossRefGoogle Scholar
Merotto, A Jr, Goulart, ICGR, Nunes, AL, Kalsing, A, Markus, C, Menezes, VG Wander, AE (2016) Evolutionary and social consequences of introgression of nontransgenic herbicide resistance from rice to weedy rice in Brazil. Evol Appl 9:837–846 CrossRefGoogle ScholarPubMed
Norsworthy, JK, Burgos, NR, Scott, RC, Smith, KL (2007) Consultant perspectives on weed management needs in Arkansas rice. Weed Technol 21:832–839 CrossRefGoogle Scholar
Norsworthy, JK, Ward, SM, Shaw, DR, Llewellyn, RS, Nichols, RL, Webster, TM, Bradley, KW, Frisvolt, G, Powles, SB, Burgos, NR, Witt, WW, Barrett, M (2012) Reducing the risks of herbicide resistance: best management practices and recommendations. Weed Sci 60:31–62 CrossRefGoogle Scholar
Riar, DS, Norsworthy, JK, Steckel, LE, Stephenson, DO, Eubank, TW, Bond, J, Scott, RC (2013a) Adoption of best management practices for herbicide-resistant weeds in Midsouthern United States cotton, rice, and soybean. Weed Technol 27:788–797 CrossRefGoogle Scholar
Riar, DS, Norsworthy, JK, Steckel, LE, Stephenson, DO, Eubank, TW Scott, RC (2013b) Assessment of weed management practices and problem weeds in the Midsouth United States—soybean: a consultant’s perspective. Weed Technol 27:612–622 CrossRefGoogle Scholar
Ribas, GG, Streck, NA, Duarte, AJ Jr, Ribeiro, BSMR, Pilecco, IB, Rossato, IG, Richter, GL, Bexaira, KP, Pereira, VF, Zanon, AJ (2020) An update of new flood-irrigated rice cultivars in the SimulArroz model. Pesqui Agropecu Bras 55:e00865 CrossRefGoogle Scholar
Roso, AC, Merotto, A Jr, Delatorre, CA, Menezes, VG (2010) Regional scale distribution of imidazolinone herbicide-resistant alleles in red rice (Oryza sativa L.) determined through SNP markers. Field Crop Res 119:175–182 CrossRefGoogle Scholar
Soares, MBB, Bianco, S, Finoto, EL, Bolonhezi, D, Albuquerque, JAA, Silva, AA (2016) Weed community in a raw sugarcane renovation area submitted to different soil managements. Planta Daninha 34:91–98 CrossRefGoogle Scholar
Sudianto, E, Beng-kah, S, Ting-Xiang, N, Saldain, NE, Scott, RC, Burgos, NR (2013) Clearfield® rice: its development, success, and key challenges on a global perspective. Crop Prot 49:40–51 CrossRefGoogle Scholar
Therneau, T M, Atkinson, EJ (1997) An Introduction to Recursive Partitioning Using the RPART Routine. Section of Biostatistics, Mayo Clinic, Technical Report 61. Rochester, MN: Mayo Foundation. 60 pGoogle Scholar
Ulguim, AR, Fruet, BL, Merotto, A Jr, Silva, AL (2021) Status of weed control in imidazolinone-herbicide resistant rice in Rio Grande do Sul. Adv Weed Sci 39:e237355 CrossRefGoogle Scholar
Ulguim, AR, Silva, BM, Agostinetto, D, Avila Neto, RC, Zandoná, RR (2019) Resistance mapping of the genus Cyperus in Rio Grande do Sul and selection pressure analysis. Planta Daninha 37:e019186679 CrossRefGoogle Scholar
Van Nguyen, N, Ferrero, A (2006) Meeting the challenges of global rice production. Paddy Water Environ 4:1–19 CrossRefGoogle Scholar
Vargas, L, Nohatto, MA, Agostinetto, D, Bianchi, MA, Gonçalves, EM, Toledo, RE (2011) Response of Euphorbia heterophylla biotypes to glyphosate rates. Planta Daninha 29:1121–1128. PortugueseCrossRefGoogle Scholar
Varanasi, A, Prasad, PVV, Jugulam, M (2016) Impact of climate change factors on weeds and herbicide efficacy. Adv Agron 135:107–146 CrossRefGoogle Scholar