Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-15T05:22:09.176Z Has data issue: false hasContentIssue false
  • English
  • Français

Utilization of Chlorophyll Fluorescence Imaging Technology to Detect Plant Injury by Herbicides in Sugar Beet and Soybean

Published online by Cambridge University Press:  20 June 2017

Jonas F. Weber ,
Christoph Kunz ,
Gerassimos G. Peteinatos ,
Hans-Joachim Santel  and
Roland Gerhards
Jonas F. Weber*
Affiliation:
Graduate Research Assistants, Department of Weed Science, University of Hohenheim, Stuttgart, Germany
Christoph Kunz
Affiliation:
Graduate Research Assistants, Department of Weed Science, University of Hohenheim, Stuttgart, Germany
Gerassimos G. Peteinatos
Affiliation:
Graduate Research Assistants, Department of Weed Science, University of Hohenheim, Stuttgart, Germany
Hans-Joachim Santel
Affiliation:
Weed Scientist and Professor, Department of Weed Science, University of Hohenheim, Stuttgart, Germany
Roland Gerhards
Affiliation:
Weed Scientist and Professor, Department of Weed Science, University of Hohenheim, Stuttgart, Germany
*
*Corresponding author’s E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Sensor technologies are expedient tools for precision agriculture, aiming for yield protection while reducing operating costs. A portable sensor based on chlorophyll fluorescence imaging was used in greenhouse experiments to investigate the response of sugar beet and soybean cultivars to the application of herbicides. The sensor measured the maximum quantum efficacy yield in photosystem II (PS-II) (Fv/Fm). In sugar beet, the average Fv/Fm of 9 different cultivars 1 d after treatment of desmedipham plus phenmedipham plus ethofumesate plus lenacil was reduced by 56% compared to the nontreated control. In soybean, the application of metribuzin plus clomazone reduced Fv/Fm by 35% 9 d after application in 7 different cultivars. Sugar beets recovered within few days from herbicide stress while maximum quantum efficacy yield in PS-II of soybean cultivars was reduced up to 28 d. At the end of the experiment, approximately 30 d after treatment, biomass was reduced up to 77% in sugar beet and 92% in soybean. Chlorophyll fluorescence imaging is a useful diagnostic tool to quantify phytotoxicity of herbicides on crop cultivars directly after herbicide application, but does not correlate with biomass reduction.

Keywords

Desmedipham;ethofumesate;flufenacet;lenacil;metamitron;metribuzin;phenmedipham;soybean, Glycine max (L.) Merr.;sugar beet, Beta vulgaris (L.) ssp. vulgaris.Chlorophyll fluorescencecrop injuryFv/Fmimaging sensorPS-IIstress detection.
Type
Weed Management-Major Crops
Information
Weed Technology , Volume 31 , Issue 4 , August 2017 , pp. 523 - 535
Copyright
© Weed Science Society of America, 2017 

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 for this paper: Ian Burke, Washington State University.

References

Literature Cited

Abbaspoor, M, Streibig, JC (2007) Monitoring the efficacy and metabolism of phenylcarbamates in sugar beet and black nightshade by chlorophyll fluorescence parameters. Pest Manag Sci 63:576–585 CrossRefGoogle ScholarPubMed
Andújar, D, Ribeiro, A, Carmona, R, Fernández-Quintanilla, C, Dorado, J (2010) An assessment of the accuracy and consistency of human perception of weed cover. Weed Res 50:638–647 Google Scholar
Arndt, F, Kötter, C (1968) Zur Selektivität von Phenmedipham als Nachauflaufherbizid in Beta-Rüben [Selectivity of phenmedipham as a post-emergence herbicide in sugar beet]. Weed Res 8:259–271 Google Scholar
Baker, NR (2008) Chlorophyll fluorescence: a probe of photosynthesis in vivo . Annu Rev Plant Biol 59:89–113 Google Scholar
Barbagallo, RP, Oxborough, K, Pallett, KE, Baker, NR (2003) Rapid, noninvasive screening for perturbations of metabolism and plant growth using chlorophyll fluorescence imaging. Plant Physiol 132:485–493 Google Scholar
Barrentine, WL, Hartwig, EE, Edwards, CJ (1982) Tolerance of three soybean (Glycine max) cultivars to metribuzin. Weed Sci 30:344–348 Google Scholar
Belfry, KD, Soltani, N, Brown, LR, Sikkema, PH (2015) Tolerance of identity preserved soybean cultivars to preemergence herbicides. Can J Plant Sci 95:719–726 CrossRefGoogle Scholar
Burke, JJ, Franks, CD, Burow, G, Xin, Z (2010) Selection system for the stay-green drought tolerance trait in sorghum germplasm. Agron J 102:1118–1122 Google Scholar
Carmer, SG, Nyquist, WE, Walker, WM (1989) Least significant differences for combined analyses of experiments with two-or three-factor treatment designs. Agron J 81:665–672 Google Scholar
Dayan, FE, Zaccaro, MLM (2012) Chlorophyll fluorescence as a marker for herbicide mechanisms of action. Pestic Biochem Physiol 102:189–197 CrossRefGoogle Scholar
Donald, WW (1998) Estimated soybean (Glycine max) yield loss from herbicide damage using ground cover or rated stunting. Weed Sci 46:454–458 Google Scholar
[EPPO] European and Mediterranean Plant Protection Organization. (2014) PP 1/135 (4) Phytotoxicity assessment. European and Mediterranean Plant Protection Organization Bulletin 44:265–273 Google Scholar
Fahlgren, N, Gehan, MA, Baxter, I (2015) Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 24:93–99 CrossRefGoogle ScholarPubMed
Fiorani, F, Schurr, U (2013) Future scenarios for plant phenotyping. Annu Rev Plant Biol 64:267–291 Google Scholar
Gehring, K, Festner, T, Gerhards, R, Hüsgen, K, Thyssen, S (2014) Chemical weed control in soybean (Glycine max, L.). Pages 701–708 in 26th German Conference on Weed Biology and Weed Control. Braunschweig, Germany: Julius Kühn Institut, Bundesforschungsinstitut für KulturpflanzenGoogle Scholar
Kaiser, YI, Menegat, A, Gerhards, R (2013) Chlorophyll fluorescence imaging: a new method for rapid detection of herbicide resistance in Alopecurus myosuroides . Weed Res 53:399–406 Google Scholar
Maxwell, K, Johnson, GN (2000) Chlorophyll fluorescence - a practical guide. J Exp Bot 51:659–668 Google Scholar
Moseley, C, Hatzios, KK, Hagood, ES (1993) Uptake, translocation, and metabolism of chlorimuron in soybean (Glycine max) and morningglory (Ipomoea spp.). Weed Technol 7:343–348 Google Scholar
Osborne, BT, Shaw, DR, Ratliff, RL (1995) Soybean (Glycine max) cultivar tolerance to SAN 582H and metolachlor as influenced by soil moisture. Weed Sci 43:288–292 CrossRefGoogle Scholar
Poston, DH, Nandula, VK, Koger, CH, Griffin, RM (2008) Preemergence herbicides effect on growth and yield of early-planted Mississippi soybean. Crop Manag. doi: 10.1094/CM-2008-0218-02-RS Google Scholar
R Development Core Team (2014) R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. 409 pGoogle Scholar
Roeb, J, Peteinatos, GG, Gerhards, R (2015) Using sensors to assess herbicide stress in sugar beets. Pages 563–570 in Stafford JV, ed. Precision Agriculture´15. Netherlands: Wageningen Academic Publishers Google Scholar
Salzman, FP, Renner, KA (1992) Response of soybean to combinations of clomazone, metribuzin, linuron, alachlor, and atrazine. Weed Technol 6:922–929 CrossRefGoogle Scholar
Smith, AE, Wilkinson, RE (1974) Differential absorption, translocation and metabolism of metribuzin [4-amino-6-tert-butyl-3-(metbylthio)-as-triazine-5(4H)one] by soybean cultivars. Physiol Plant 32:253–257 Google Scholar
Smith, GA, Schweizer, EE (1983) Cultivar X herbicide interaction in sugar beet. Crop Sci 23:325–328 CrossRefGoogle Scholar
Starke, RJ, Renner, KA (1996) Velvetleaf (Abutilon theophrasti) and sugar beet (Beta vulgaris) response to triflusulfuron and desmedipham plus phenmedipham. Weed Technol 10:121–126 Google Scholar
Thenkabail, PS, Lyon, JG, Huete, A, eds (2011) Hyperspectral Remote Sensing of Vegetation. Boca Raton, FL: CRC Press. 641 pGoogle Scholar
Vasel, EH, Ladewig, E, Märländer, B (2012) Weed composition and herbicide use strategies in sugar beet cultivation in Germany. J für Kult 64:112–125 Google Scholar
Voss, M, Renger, G, Kötter, C, Gräber, P (1984) Fluorometric detection of photosystem II herbicide penetration and detoxification in whole leaves. Weed Sci 32:675–680 Google Scholar
Wilson, RG (1999) Response of nine sugar beet (Beta vulgaris) cultivars to postemergence herbicide application. Weed Technol 13:25–29 Google Scholar
Wilson, RG, Yonts, CD, Smith, JA (2002) Influence of glyphosate and glufosinate on weed control and sugar beet (Beta vulgaris) yield in herbicide-tolerant sugar beet. Weed Technol 16:66–73 CrossRefGoogle Scholar