Introduction
Rice is one of the most commonly grown agricultural commodities in the world (Childs Reference Childs2022) and contributes significantly to sources of human energy across the globe (Kondhia et al. Reference Kondhia, Tabien, Ibrahim, Kondhia, Tabien and Ibrahim2015). Global rice production is estimated to reach 467.2 million metric tons for the 2022 to 2023 year (Childs and LeBeau Reference Childs and LeBeau2022). In the United States, long-grain indica rice accounts for almost 75% of rice production, and japonica medium-grain and short-grain rice production make up the remainder (Childs Reference Childs2022). All U.S. rice is grown under irrigated conditions, which may vary by geographic distribution. The majority of California’s rice production, which accounts for 200,000 ha, is grown in a continuously flooded cropping system, where rice is pregerminated and seeded by airplane onto fields with a 10- to 15-cm standing flood (Ceseski and Al-Khatib Reference Ceseski and Al-Khatib2021; Espino et al. Reference Espino, Greer, Al-Khatib, Godfrey, Eckert, Fischer and Lawler2019).
Continuous flooding to suppress grass, sedge, and broadleaf weeds in rice fields is a method of weed control that has been extremely effective (Hill et al. Reference Hill, Smith and Bayer1994). However, decades of using continuous flooding, in addition to a lack of robust crop rotation in rice production areas, have selected weed species that exhibit ecological requirements and growing patterns that are similar to rice and can compete with rice resources (Hill et al. Reference Hill, Smith and Bayer1994). These flooded conditions favor weedy grasses that are well adapted to flood, which include watergrass species (Echinochloa spp.), bearded sprangletop [Leptochloa fusca (L.) Kunth ssp. fascicularis (Lam.) N. Snow], and weedy rice (Oryza sativa f. spontanea Rosh.) (Brim-DeForest et al. Reference Brim-DeForest, Al-Khatib and Fischer2017; Ceseski et al. Reference Ceseski, Godar and Al-Khatib2022).
Crop yields and harvest quality face the highest biological constraints because of weed infestations, and farmer inputs to weed management are expected to increase as herbicide resistance spreads worldwide (Brim-DeForest et al. Reference Brim-DeForest, Al-Khatib and Fischer2017). Certain weeds and weed groups cause more yield loss than others, even at lower infestation densities (Smith 1988). In rice systems, grasses are considered the most difficult weeds to control owing to the narrow selectivity between the crop and the grass weeds (Carey et al. Reference Carey, Hoagland and Talbert1995). Rice yield losses can amount to 79% after season-long interference from barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.] and have been recorded as high as 59% due to season-long competition with late watergrass [Echinochloa phyllopogon (Stapf). Koso-Pol.] (Gibson et al. Reference Gibson, Hill, Foin, Caton and Fischer2001; Smith 1968). Weedy rice is an increasingly problematic weed in rice-growing regions around the world, causing yield loss and contamination due to the critical weedy traits of seed shattering and seed dormancy (de Leon et al. Reference de Leon, Karn, Al-Khatib, Espino, Blank, Andaya, Andaya and Brim-Deforest2019), which build up a large soil seed reservoir for future years (Ziska et al. Reference Ziska, Gealy, Burgos, Caicedo, Gressel, Lawton-Rauh, Avila, Theisen, Norsworthy, Ferrero, Vidotto, Johnson, Ferreira, Marchesan, Menezes, Cohn, Linscombe, Carmona, Tang and Merotto2015). The weedy rice infestation threshold is 1 to 3 plants m−2, with higher ratios causing significant yield loss; weedy rice densities of 30 to 40 plants m−2 can reduce rice yields by 60% to 90%, depending on the height of the cultivar (Smith 1988; Ziska et al. Reference Ziska, Gealy, Burgos, Caicedo, Gressel, Lawton-Rauh, Avila, Theisen, Norsworthy, Ferrero, Vidotto, Johnson, Ferreira, Marchesan, Menezes, Cohn, Linscombe, Carmona, Tang and Merotto2015). In California, six biotypes of weedy rice have thus far been identified (de Leon et al. Reference de Leon, Karn, Al-Khatib, Espino, Blank, Andaya, Andaya and Brim-Deforest2019). Infestations of weedy rice cause harvest quality problems, increased production costs, and reduced yield, so an effective method of control is needed (de Leon et al. Reference de Leon, Karn, Al-Khatib, Espino, Blank, Andaya, Andaya and Brim-Deforest2019).
There are few options for weed control in California rice production. Although crop rotation would allow for alternative herbicides that may be able to manage resistant weeds, it is not a commonly used tactic in many rice production regions owing in part to the heavy clay or hardpan soils that typify many rice fields and result in low water drainage (Hill et al. Reference Hill, Williams, Mutters and Greer2006). Weed removal on field levees and ditches and the California statewide mandate on using certified clean seed assist in integrated weed management practices, but most rice growers rely solely on in-season herbicide applications and deepwater flooding for weed management (California Crop Improvement Association 2019; Hill et al. Reference Hill, Williams, Mutters and Greer2006).
Flooded rice agroecosystems are common worldwide in most rice production areas; however, certain regions have restrictions on the available herbicides to control weeds. For example, in California, largely owing to ecotoxicity concerns and strict regulatory structures (Ceseski and Al-Khatib Reference Ceseski and Al-Khatib2021; Hill et al. Reference Hill, Smith and Bayer1994), only 13 registered active ingredients across nine modes of action (MOAs) are available for use in flooded rice fields, which creates few opportunities for herbicide rotation to inhibit herbicide resistance development (Espino et al. Reference Espino, Greer, Al-Khatib, Godfrey, Eckert, Fischer and Lawler2019). By contrast, 60 active ingredients are registered for use in corn in the U.S. Midwest (Gerber Reference Gerber2021). Current herbicides in use in California rice systems include acetolactate synthase (ALS) inhibitors, protoporphyrinogen oxidase inhibitors, acetyl CoA carboxylase (ACCase) inhibitors, tubulin inhibitors, photosystem II (PSII) inhibitors, very-long-chain fatty-acid (VLCFA) inhibitors, auxin-mimics, 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors, and 1-deoxy-D-xylulose 5-phosphate inhibitors (Espino et al. Reference Espino, Greer, Al-Khatib, Godfrey, Eckert, Fischer and Lawler2019). Rice herbicides require proper selection in combination and sequence to provide adequate weed control. Early-season grass control applications commonly consist of field rates of carotenoid biosynthesis, HPPD, ALS, or VLCFA inhibitors (Brim-DeForest Reference Brim-DeForest2021). Late-season cleanup applications often use PSII, ALS, or ACCase inhibitors to control later-emerging grasses (Brim-DeForest Reference Brim-DeForest2021).
The continuous use of herbicides with similar MOAs has contributed to herbicide resistance evolution in several weeds found in rice systems. California arrowhead (Sagittaria montevidensis Cham. & Schltdl.) and smallflower umbrellasedge (Cyperus difformis L.) were the first confirmed cases of rice weeds with resistance to bensulfuron-methyl, an ALS inhibitor, in 1993 (Busi et al. Reference Busi, Vidotto, Fischer, Osuna, de Prado and Ferrero2006). Since then, eight other rice weed species have been identified, some with resistance to more than one MOA (Becerra-Alvarez and Al-Khatib Reference Becerra-Alvarez and Al-Khatib2022). The rise in herbicide resistance has increased the cost and difficulty of weed management, necessitating demand for novel herbicide development to postpone resistance expansion and assist in managing current herbicide-resistant weed biotypes (Qu et al. Reference Qu, He, Yang, Lin, Yang, Wu, Li and Yang2021).
Metribuzin is a selective and systemic herbicide that controls many broadleaf and some grass weeds (Armendáriz et al. Reference Armendáriz, de la Torre, Fernández and González2014). Metribuzin is a PSII inhibitor that belongs to the triazinone family and functions by binding to the QB binding site on the D1 protein of the PSII complex in the chloroplast thylakoid membranes. Once the chemical binds to the site, electron transport from QA to QB is blocked, and CO2 fixation and adenosine triphosphate and nicotinamide adenine dinucleotide phosphate diaphorase production are stopped, halting necessary resources for plant growth (Lambreva et al. Reference Lambreva, Russo, Polticelli, Scognamiglio, Antonacci, Zobnina, Campi and Rea2014). Foliar-applied metribuzin is absorbed into the plant at moderate rates with apoplastic translocation.
To date, metribuzin is labeled for use in alfalfa (Medicago sativa L.), asparagus (Asparagus L.), cereals, field corn (Zea mays L.), garbanzo bean (Cicer arietinum L.), lentil (Lens culinaris Medik.), peas, potatoes, sainfoin (Onobrychis viciifolia Scop.), soybean, sugarcane (Saccharum officinarum L.), and tomato (Solanum lycopersicum L.). Metribuzin has been successfully used to control broadleaf and grass weeds in wheat (Javaid et al. Reference Javaid, Mahmood, Bhatti, Waheed, Attia, Aziz, Nadeem, Khan, Al-Doss, Fiaz and Wang2022) and barley (Hordeum vulgare L.) (Volova et al. Reference Volova, Shumilova, Zhila, Sukovatyi, Shishatskaya and Thomas2020). There is no label for metribuzin for use in rice in California. Although information regarding the effect of metribuzin application rates and timing on weed control in rice is scant, recent studies from Mississippi have indicated that metribuzin applied post rice emergence at 42 g ai ha−1 caused 36% injury by 28 d after treatment (DAT) (Lawrence et al. Reference Lawrence, Bond, Golden, Allen, Reynolds and Bararpour2021). The same study found no correlation between rice injury from metribuzin and yield reduction, dry weight reduction, maturity delays, or seed germination (Lawrence et al. Reference Lawrence, Bond, Golden, Allen, Reynolds and Bararpour2021). Mahajan and Chauhan (Reference Mahajan and Chauhan2022) evaluated metribuzin at rates 72 and 144 g ai ha−1 and were able to reduce jungle rice [Echinochloa colona (L.) Link] biomass by 70% and 100%, respectively, compared to the untreated control.
Crop tolerance to herbicides may result from the ability of a crop to metabolize the chemical (Wright et al. Reference Wright, Norsworthy, Roberts, Scott, Hardke and Gbur2021). Selectivity differences among genotypes depends on accumulation of a critical amount of the active ingredient at the target site of action and a sufficient differential in chemical uptake, in-plant movement, and arrival of the chemical at the correct location in the active form (Cole Reference Cole1994). Although several factors may be involved in selectivity, the most imperative function is that of tolerant plants metabolizing and detoxifying herbicides rapidly and susceptible plants having reduced or no ability to do so (Cole Reference Cole1994).
Differential tolerance responses of soybean, pea, and wheat genotypes to foliar-applied metribuzin have been noted (Al-Khatib et al. Reference Al-Khatib, Libbey and Boydston1997; Barrentine et al. Reference Barrentine, Edwards and Hartwig1976; Hardcastle Reference Hardcastle1974; Javaid et al. Reference Javaid, Mahmood, Bhatti, Waheed, Attia, Aziz, Nadeem, Khan, Al-Doss, Fiaz and Wang2022). In rice, cultivar-specific responses to herbicide treatments have been previously identified after parent material was mutagenized using ethyl methanesulfonate (Shoba et al. Reference Shoba, Raveendran, Manonmani, Utharasu, Dhivyapriya, Subhasini, Ramchandar, Valarmathi, Grover, Krishnan, Singh, Jayaswal, Kale, Ramkumar, Mithra, Mohapatra, Singh, Singh, Sarla, Sheshshayee, Kar, Robin and Sharma2017). Herbicide-resistant rice lines, such as Clearfield® (BASF, Research Triangle Park, NC, USA) or FullPage® (ADAMA, Raleigh, NC, USA) and Provisia® (BASF) or Max-Ace® (ADAMA) rice, were developed using this genetic material, which conferred resistance to imidazolinones and quizalofop, respectively. Differing levels of sensitivity to triclopyr (Pantone and Baker Reference Pantone and Baker1992) and florpyrauxifen-benzyl (Wright et al. Reference Wright, Norsworthy, Roberts, Scott, Hardke and Gbur2021), synthetic auxin herbicides, have also been observed in various rice genotypes. The inherent genetic variability in rice genotypes may provide a resource for crop improvement through breeding (Okoshi et al. Reference Okoshi, Nishikawa, Akagi and Fujimura2018).
There is a need for additional and alternative herbicide programs to complement sustainable chemical weed control in rice systems. Investigation of differential responses to a chemical can reveal susceptible and tolerant crop genotypes that may prove useful in breeding programs. With limited knowledge of the response of rice genotypes to metribuzin, the objectives of this research were to evaluate the response of various rice genotypes to post rice emergence–applied metribuzin and to determine if early-season injury symptoms from foliar metribuzin application are correlated with reduced shoot biomass.
Materials and Methods
Growing Conditions
Experiments were conducted during 2021 to 2022 in greenhouses at the Rice Experiment Station (RES) in Biggs, CA (39.45°N, 121.72°W). Plastic perforated flats measuring 28 × 54 × 6 cm were prefilled with a Esquon-Neerdobe (fine, smectitic, thermic Xeric Epiaquerts and Duraquerts) silty clay with a pH of 5.11 and 2.6% organic matter that was sieved through a 2-cm mesh. One hundred forty-two rice genotypes sourced from the RES representing long-grain, medium-grain, and short-grain rice were selected, and 15 seeds of each genotype were sown in rows in the flats, with eight rice genotypes per flat and each row serving as a single experimental unit (Figure 1). Flats were placed in large basins filled with 5 cm of standing water for irrigation. Plants were grown in greenhouse conditions with average day/night temperatures of 32/18 C and 16-h photoperiod with supplemental light intensity of 250 mmol m2 s−1 photosynthetic photon flux density.
Figure 1. Rice line and grain type for 142 genotypes used in the greenhouse study to evaluate the differential rice response to postemergence foliar-applied metribuzin. Plant material was sourced from the Rice Experiment Station, Biggs, CA.
Metribuzin Treatments
Rice seedlings at the 3- to 4-leaf stage were treated with 0, 22, 44, 88, 176, and 352 g ai ha−1 metribuzin (Glory® 4L, ADAMA). Rates were 0X, 1/8X, 1/4X, 1/2X, 1X, and 2X the label rate for use in peas (Anonymous 2014). Treatments were applied with a research track bench sprayer (DeVries Manufacturing, Hollandale, MN, USA) equipped with a flat-fan TP8001EVS TeeJet® nozzle (TeeJet® Technologies, Wheaton, IL, USA) and calibrated to deliver 187 L ha−1 at 180 kPa. Control plants were treated with water. Each flat was sprayed at a height of 45 cm above plant canopy.
Data Collection
Visible rice injury was rated at 7, 14, 21, and 28 DAT. Visible injury ratings were based on a scale where 0% was no injury and 100% was plant death. At 28 DAT, rice height was recorded by measuring the plant from top leaf to soil, and plant biomass was harvested by removing all aboveground tissue. Hand-harvested samples were dried at 65 C for 10 d and weighed. Biomass and height data were reported as percent biomass and height reduction and were calculated as
$${\rm{\% \;reduction}} = \left[ {{{{\rm{UTC}} - B} \over {{\rm{UTC}}}}} \right] \times 100$$
where UTC is the mean biomass (g) or height (cm) of the untreated control for each respective rice cultivar and B is the biomass (g) or height (cm) of the experimental unit of interest (Ortmeier-Clarke et al. Reference Ortmeier-Clarke, Oliveira, Arneson, Conley and Werle2022).
Statistical Analysis
The experiment was a randomized complete-block design with a split-plot arrangement of treatments in which each treatment was replicated three times and the experiment was conducted twice; the experiment was planned so as to avoid climatic interference in the greenhouse during June to August 2021 (Abit et al. Reference Abit, Al-Khatib, Regehr, Tuinstra, Claassen, Geier, Stahlman, Gordon and Currie2009). The main plots were the rice genotypes, and the subplots were the herbicide rates. The experimental unit of interest was the row of plants representing each rice line in each flat. Secondary analysis based on averaged values from the rice genotypes combined within their respective grain types was also performed. Data from the two experimental runs and the three replications were combined, as the experimental runs and replications were considered random effects. The data were fitted to the four-parameter logistic model
$$y = a + {{\left( {a - c} \right)} \over {\left[ {1 + {{\left( {x/{x_0}} \right)}^b}} \right]}}$$
where a is the lower limit representing plant survival at increasingly large herbicide rates, c is the upper limit representing plant survival at low herbicide rates close to untreated controls, x 0 is the rate giving 50% plant response, and b is the slope around x 0. Metribuzin application rates that caused 50% visible injury (ID50), biomass reduction (GR50), and height reduction (HR50) were estimated for each rice line and each grain type using the ed function in the drc package in R (Ritz et al. Reference Ritz, Baty, Streibig and Gerhard2015) to create nonlinear regression models (R Development Core Team 2022). ID50, GR50, and HR50 values were analyzed using analysis of variance, and means were separated using Tukey–Kramer’s honestly significant difference at a 95% significance level. Correlation coefficient analysis on phytotoxicity versus height reduction and biomass reduction was estimated using JMP® Pro16 (SAS Institute, Cary, NC, USA).
Results and Discussion
There was no interaction across experimental runs for rice injury, height reduction, and biomass reduction, so the data were averaged over two experimental runs. Foliar application of metribuzin injured all rice genotypes at all rates. Metribuzin injury symptoms were characterized by stunting and leaf chlorosis originating at leaf margins, followed by necrosis. Estimations of injury were similar to the symptoms observed from other PSII-inhibiting herbicides (Smith 1965). As the study progressed, the damage symptoms became more apparent; symptoms on treated plants became more severe at 14 DAT than at 7 DAT (data not shown). Crop damage peaked at 21 DAT, with treated plants that remained alive at 21 DAT showing some recovery from injury by producing new, normal growth by 28 DAT. Crop phytotoxicity from metribuzin at the 352 g ha−1 use rate was more pronounced than it was at the use rate of 176 g ha−1 at all rating dates.
Phytotoxicity
There was no significant difference among rice genotypes in metribuzin injury response at any rate tested because there was significant variability among the phytotoxicity responses of the rice genotypes to the rates of metribuzin (Supplementary Table S1). Across all 142 rice genotypes tested, crop injury at 21 DAT ranged from 30% to 88% at the use rate of 176 g ha−1 and from 53% to 100% at the 352 g ha−1 use rate (data not shown). The average metribuzin application rate required to cause ID50 across all rice accessions was 163 g ha−1 metribuzin (P < 0.0001).
Differing grain type (long, short, and medium) was represented among the 142 rice genotypes tested. There were differences between crop injury response to metribuzin and the grain type of the rice genotypes (Figure 2). The average ID50 value for the short-grain rice genotypes was 136 g ha−1, which was significantly lower than the average ID50 for either long-grain or medium-grain rice genotypes, which were 172 g ha−1 and 182 g ha−1, respectively (P = 0.009) (Table 1). These results indicate that short-grain genotypes are more susceptible to phytotoxicity injury from foliar-applied metribuzin than are long-grain or medium-grain rice genotypes. Differences in grain type response to metribuzin may result from inherent differences in genetic background among the different grain types. Maeda et al. (Reference Maeda, Murata, Sakuma, Takei, Yamazaki, Karim, Kawata, Hirose, Kawagishi-Kobayashi, Taniguchi, Suzuki, Sekino, Ohshima, Kato, Yoshida and Tozawa2019) found a rice gene, HIS1, that was found to confer resistance to benzobicyclon and other β-triketone herbicides through chemical metabolism and detoxification; susceptible rice genotypes carried a defunct allele from a long-grain indica rice line that disabled functionality of the gene. The difference in grain types resulted in a genetic difference that altered the metabolic conversion of the toxic chemical and resulted in tolerant and susceptible rice genotypes (Lv et al. Reference Lv, Zhang, Yuan, Huang, Peng, Peng, Li, Tang, Liu, Zhou, Wang, Pan, Shao, Mao, Xin, Zhu, Zhao and Bai2021).
Figure 2. Rice phytotoxicity, height reduction, and biomass reduction as a result of increasing rates of metribuzin on long-grain, medium-grain, and short-grain rice, shown as ID50 (A), HR50 (B), and GR50 (C). The data are averaged from two experimental runs with three replicates. Curves represent four-parameter logistic regression. Equation is Y = a + (a − c)/[1 + (x/x 0) b ], where a and d are the maximum and minimum estimated values, respectively; b is the relative slope of regression about x 0; and x 0 is the rate giving 50% plant response.
Table 1. Average metribuzin application rate required to cause 50% visible injury, height reduction, and biomass reduction in the three rice grain types studied. a, b, c
a Plants were treated at the 3- to 4-leaf stage. Visible injury information was recorded at 21 d after treatment (DAT), and dry weights and heights were collected at 28 DAT. Standard errors are in parentheses.
b Abbreviations: GR50, rate required to cause 50% biomass reduction; HR50, rate required to cause 50% height reduction; ID50, rate required to cause 50% visible injury; LG, long grain, 59 genotypes; MG, medium grain, 52 genotypes; SG, short grain, 31 genotypes.
c Means accompanied by the same letter do not significantly differ with Tukey’s honestly significant difference at α = 0.05.
Height Reduction
Correlation coefficient analysis showed that rice phytotoxicity is highly correlated with rice cultivar height response (r = 0.727, P < 0.0001) (data not shown). There was no difference among any individual rice cultivar height response and rate of applied metribuzin, except at the 88 g ha−1 rate (P = 0.0407). When all the rice genotypes were tested, long-grain cultivar ‘RES14’ displayed an average 36% height increase at 88 g ha−1 metribuzin, contrary to expected results (data not shown). This height increase was different from short-grain rice line ‘RES223’ and long-grain rice line ‘Calmati-202,’ which displayed the highest amount of height reduction, 45% and 33%, respectively, at the 88 g ha−1 metribuzin application rate (data not shown). The average metribuzin application rate required to cause HR50 across all rice accessions was 187 g ha−1 (P < 0.0001) (Table 1).
The average height HR50 results for the rice genotypes showed no differences among the grain types evaluated (P = 0.002) (Table 1). Long-grain, medium-grain, and short-grain rice all required 173 to 198 g ha−1 for a 50% height reduction response (Table 1). These results indicate that metribuzin has an equivalent effect on height reduction across all grain types. The symptoms displayed after treatment with metribuzin correlate with Abou-Khater et al. (Reference Abou-Khater, Maalouf, Patil, Balech, Nacouzi, Rubiales and Kumar2021) and Bhoite et al. (Reference Bhoite, Si, Liu, Xu, Siddique and Yan2019), who noted similar symptoms in fava bean (Vicia faba L.) and wheat, respectively.
There were differences among the rice grain types and height reduction responses as a result of differing application rates of metribuzin. At 88, 176, and 352 g ha−1 metribuzin, all three grain types had significantly different types of height reduction responses (P < 0.0001) (Table 2). Long-grain and medium-grain rice accessions exhibited 33% and 14% less height reduction, respectively, than did short-grain accessions at 352 g ha−1 metribuzin. Short-grain rice genotypes consistently displayed the greatest crop height reduction in response to increasing rates of metribuzin, ranging from 17% to 87% height reduction at 88, 176, and 352 g ha−1 metribuzin.
Table 2. Average rice height and biomass reduction for each grain type at 28 d after foliar-applied metribuzin at varying rates. a, b, c
a Positive numbers indicate percent reduction as compared to the nontreated control plants, and negative numbers indicate percent increase as compared to the nontreated control plants. Standard errors are in parentheses.
b Abbreviations: LG, long grain, 59 genotypes; MG, medium grain, 52 genotypes; SG, short grain, 31 genotypes.
c Means accompanied by the same letter do not significantly differ with Tukey’s honestly significant difference at α = 0.05.
Biomass Reduction
Correlation coefficient analysis showed that rice phytotoxicity is moderately correlated with rice cultivar biomass response (r = 0.657, P < 0.0001) (data not shown). Reduction in plant biomass was observed for all genotypes at metribuzin application rates 176 and 352 g ha−1 (data not shown). The average metribuzin application rate required to cause GR50 across all rice accessions was 118 g ha−1 (P < 0.0001) (Table 1).
At 176 g ha−1 metribuzin, the biomass of short-grain genotype ‘RES223’ was significantly reduced, by 88% of the untreated control. Researchers in Australia found that rates of metribuzin as low as 36 g ha−1 were required to reduce the negative effect on rice biomass (Mahajan and Chauhan Reference Mahajan and Chauhan2022), so the results of the present study concur with this conclusion. Long-grain genotypes ‘RES8’, ‘CL271’, and ‘RES19’ produced the least biomass reduction, at 10%, 8%, and 4% of the untreated control, respectively. ‘RES8’ and ‘RES19’ are rice genotypes that were developed specifically for California water-seeded rice production. At 352 g ha−1 metribuzin, six rice genotypes responded with biomass reductions ranging from 90% to 94%: ‘RES223’, ‘RES213’, ‘RES226’, ‘RES216’, ‘RES230’, and ‘RES212’, all of which are short grain. At the 352 g ha−1 rate, seven genotypes had biomass reductions that were less than those previously mentioned; long-grain genotypes ‘L-205’, ‘RES36’, ‘L-201’, ‘RES35’, ‘Rex’, and ‘Della-2’ and medium-grain genotype ‘CL271’ had biomass reductions ranging from 6% to 20%. Of the long-grain rice genotypes that had fewer biomass reductions, four were developed for California rice conditions: ‘L-205’, ‘RES36’, ‘L-201’, and ‘RES35’. ‘CL271’ is a medium-grain Clearfield® genotype that was developed to harbor resistance to imidazolinone herbicides through a forcibly mutated gene that prevents inhibition of the acetolactate synthase enzyme. Yean et al. (Reference Yean, Dilipkumar, Rahman and Song2021) suggests that although imidazolinone resistance is conferred primarily through target site resistance, non–target site resistance can be an alternate mechanism that leads to herbicide resistance, which may contribute to ‘CL271’ rice having biomass reductions that were less than other assessed medium-grain rice genotypes. Abou-Khater et al. (Reference Abou-Khater, Maalouf, Patil, Balech, Nacouzi, Rubiales and Kumar2021) screened accessions of fava beans for innate tolerance to both metribuzin and imazethapyr and found three accessions that showed low visual damage and no reduction in yield after treatment with metribuzin and imazethapyr.
Biomass GR50 values varied among the grain types of rice genotypes evaluated in this study (Figure 2). The average GR50 for the medium-grain rice genotypes was 94 g ha−1, which was significantly lower than the GR50 for long-grain rice genotypes, which averaged 114 g ha−1 (P < 0.0001) (Table 1). These results would indicate that medium-grain genotypes are more susceptible to rice biomass reduction at a lower rate of foliar-applied metribuzin than long-grain rice genotypes. These findings are similar to results from research that showed differential responses of annual ryegrass genotypes to foliar-applied metribuzin and atrazine (Ma et al. Reference Ma, Lu, Han, Yu and Powles2020). The differential response of annual ryegrass genotypes to metribuzin may have been due to differences in metabolism of the foliar-absorbed herbicide. Annual ryegrass genotypes that were more tolerant to foliar-applied metribuzin metabolized metribuzin twice as quickly as the more sensitive genotypes (Ma et al. Reference Ma, Lu, Han, Yu and Powles2020).
There were differences between the rice grain types and the rate of biomass reduction as a result of differing rates of metribuzin. At 88, 176, and 352 g ha−1 metribuzin, all three grain types produced different biomass responses (P < 0.0001) (Table 2). Short-grain rice genotypes continually exhibited higher biomass reduction in response to increasing rates of metribuzin as compared to the other two grain types. Biomass reduction values for the short-grain genotypes ranged from 73% to 87% reduction at 88, 176, and 352 g ha−1 metribuzin.
At all tested rates, short-grain rice genotypes were more susceptible to metribuzin than long-grain or medium-grain rice genotypes. In general, short-grain rice genotypes had greater height reduction and produced less biomass than long-grain or medium-grain rice genotypes with the same rates of foliar-applied metribuzin. Crop injury from metribuzin was moderately correlated with biomass reductions and highly correlated with plant height reductions. However, further research is required to verify the extent of crop injury and resiliency from foliar-applied metribuzin under field conditions. The results of this research show that the level of metribuzin tolerance in rice is not adequate for commercial use; however, further research is needed to develop higher levels of tolerance by mutagenized rice cultivars.
Practical Implications
Weed infestations drive the greatest biological yield losses and quality reductions in rice production. However, owing to high costs of development and registration, few additional herbicides are currently available for rice growers, particularly herbicides that target grass weeds. The problem necessitates introducing novel active ingredients into existing weed control programs to allow for herbicide MOA rotation and reducing selection for herbicide resistance in weed populations. The work done in this research determines the effects of metribuzin applied at varying rates on several different genotypes of rice, indicating that innate metribuzin resistance in rice is not sufficient without substantial modification. If further research is conducted regarding metribuzin tolerance in California rice genotypes, focus should be placed on inducing herbicide resistance through mutagenesis in the long-grain or medium-grain lines. This work quantifies the correlation between metribuzin-induced leaf injury and biomass and height reductions and establishes a dose–response curve for rice injury from metribuzin.
Supplementary material
To view supplementary material for this article, please visit https://doi.org/10.1017/wet.2023.76
Acknowledgments
The authors acknowledge the California Rice Research Board for partial funding of this project and the Rice Experiment Station in Biggs, CA, for sourcing rice genotypes and providing greenhouse space. Also acknowledged are several past and present lab members, technicians, and student assistants who assisted with the labor and maintenance of this project. The authors declare no conflicts of interest.
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