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Efficacy of chemical, botanical, and microbial pesticides against mite and insect pests on zucchini


Eggs, nymphs, and adult silverleaf whitefly on zucchini.  Photo by Surendra Dara

A study was conducted in the summer of 2017 to evaluate the efficacy of various chemical, botanical, and microbial pesticides against arthropod pests on zucchini.  Zucchini plants initially had a high aphid infestation, but populations gradually declined due to natural control by lady beetle activity.  However, heavy silverleaf whitefly (Bemisia tabaci) infestations developed by the time the study was initiated.  Other pests that were present during the study period were aphids (possibly melon aphids), the western flower thrips (Frankliniella occidentalis), and the pacific spider mite (Tetranychus pacificus).

Pacific spider mite (egg, male, and females), western flower thrips larva, and unknown aphids on zucchini.  Photo by Surendra Dara

Methodology

Experiment was conducted using a randomized complete block design with 10 treatments.  Each treatment had two 38” wide and 300' long rows of zucchini replicated four times.  Treatments included i) untreated control, ii) Sivanto 200 SL (flupyradifurone) 14 fl oz/ac, iii) Sequoia (sulfoxaflor) 2.5 fl oz/ac, iv) Venerate XC (heat-killed bacterium, Burkholderia rinojensis strain A396) 4 qrt/ac, v) PFR-97 20% WDG (entomopathogenic fungus, Isaria fumosorosea Apopka strain 97) 2 lb/ac, vi) I1800AA (undisclosed botanical extract) 10.3 fl oz/ac, vii) I1800A 12.7 fl oz, viii) I1800A 17.1 fl oz, ix) I1800A 20.5 fl oz, and x) VST-00634LC (based on a peptide in spider venom) 25%.  A spray volume of 50 gpa for all treatments except for VST-00634LC, which had 25 gpa.  Treatments were applied on 28 August and 4 September, 2017 using a tractor-mounted sprayer with three Teeject 8003vs flat spry nozzles that covered the top and both sides of each bed.

Pest populations were counted before the first spray application and 4 days after each application.  On each sampling date, one mid-tier leaf was collected from each of the five randomly selected plants within each plot.  A 2-square inch disc was cut out from the middle of each leaf and the number of aphids, eggs and nymphs of silverleaf whitefly, larvae of western flower thrips, and eggs and mobile stages of pacific spider mite were counted under a dissecting microscope.  Data were analyzed using Statistix software and Tukey's HSD test was used to separate significant means.


Spraying, sampling, and counting

Results

Efficacy varied among different treatments and for different pests.

Aphid: There was a general decline in aphid populations during the study period and there was no difference (P > 0.05) among the treatments (Fig. 1). 


Fig. 1. Aphid numbers and percent change from pre-treatment counts

Western flower thrips: Nymphal numbers declined in most of the treatments during the observation period (Fig. 2).  However, significant differences (P = 0.0220) only after the second spray application where Sivanto treatment had significantly fewer thrips than Venerate treatment (Fig. 2).  There was a 92.5% decline by the end of the study, compared to the pre-treatment counts, from PFR-97 application, followed by 88.1% decline in Sivanto, 85.4% in VST-00634, and 82.9% in I1800AA at 10.3 fl oz.


Fig. 2. Western flower thrips larvae and percent change from pre-treatment counts

Pacific spider mite: There was an increase in mite eggs in all treatments after the first spray application followed by a decline after the second one without significant differences (P > 0.05) (Fig. 3)  Similar trend was also seen in mobile stages in some treatments.  Number of mobile stages was significantly different (P = 0.0025) only after the first spray where untreated control, PFR-97, Venerate, and I1800AA at 20.5 fl oz had the lowest.  When percent change in egg numbers from the pre-treatment counts, only I1800AA treatments reduced egg numbers after the second spray with a 33.8% decline at 10.3 fl oz rate, 35.7% at 20.5 fl oz, and 60% at 17.1 fl oz.  There was also a decline in the mobile stages after the second spray with 54.1% reduction in untreated control to 67.7% in PFR-97 treatment.


Fig. 3. Pacific spider mite egg and mobile stages and percent change from pre-treatment counts

Silverleaf whitefly:  There was a general increase in the egg and nymphal stages of whitefly during the study (Fig. 4).  Significant differences were seen pre-treatment counts of egg (P = 0.0330) and nymphal stages (P = 0.0011), and after the second spray in nymphal stages (P = 0.0220).  Compared to the untreated control, both Sivanto and Sequoia resulted in a significant reduction in egg numbers after the first spray, whereas Sequoia, Venerate, and I1800AA at 20.5 fl oz reduced nymphal stages after the second spray.  When the percent change from the pre-treatment counts was compared, only Sivanto and Sequoia reduced whitefly egg numbers after both sprays.  There was also a reduction in eggs after the first spray from I1800AA at 17.1 fl oz.  However, there was a reduction in nymphal stages after the first spray in Sivanto, I1800AA at 17.1 fl oz, and VST-00634, and after the second spray in Sivanto, Sequoia, Venerate, and I1800AA at 17.1 and 20.5 fl oz.


Fig. 4. Silverleaf whitefly egg and nymphal stages and percent change from pre-treatment counts

All arthropod pests: When all data were combined for different pests and their life stages, Sivato, Sequia, and PF-97 resulted in a significant (P = 0.0001) decline in pest numbers compared to untreated control after the first spray.  Only Sivanto and Sequoia caused such a reduction (P = 0.0048) after the second spray.


Fig. 5. All arthropod pest numbers and percent change from pre-treatment counts

In general, both the chemical pesticides (Sivanto and Sequoia) provided a very good pest control.  The efficacy of the botanical extract was moderate to good depending on the pest, life stage, or the application date.  Spider venom-based product also provided a good control while microbial products had a moderate impact.  Although chemical pesticides appeared to be very efficacious, non-chemical alternatives were also effective.  It is important to consider all these options to apply in combinations or rotations to obtain desired pest suppression without posing the risk of insecticide resistance.

Acknowledgements: Thanks for the financial support of Arysta LifeScience, CertisUSA, Dow AgroSciences, and Vestaron, and the technical assistance of Neal Hudson.


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Posted on Friday, December 22, 2017 at 12:52 PM
  • Author: Surendra K. Dara
  • Author: Sumanth S. R. Dara
  • Author: Suchitra S. Dara
  • Author: Ed Lewis

Biopesticides and integrated pest management

Biopesticide refers to a pesticide which originates from animals, microorganisms, or plants.  In addition to preventing yield losses through pest and disease control, biopesticides improve environmental and human health by contributing to the reduction of chemical pesticides as well as by improving the quality of produce (Popp et al., 2012).  Additionally, these products have the potential to improve harvest and shipping flexibility, assist with environmental stewardship, and assist growers to achieve sustainability goals. Biopesticides are also important tools in integrated pest management (IPM) programs and reducing the risk of resistance to chemical pesticides (Pretty and Bharucha, 2015), improving worker safety through short restricted entry intervals (Valland, n.d.), conserving natural enemies, and maintaining environmental health (EPA, 2017a).

Biopesticides are inherently less toxic than conventional pesticides. Most affect only the target pest and closely related organisms, in contrast to broad spectrum conventional pesticides that may affect nontarget organisms such as beneficial insects, birds, wildlife, aquatic animals, and mammals. The majority of biopesticides often rapidly decompose, resulting in decreased exposure as well as preventing many pollution problems commonly associated with conventional pesticides. Although relatively safer than chemical pesticides, users or applicators should follow safety guidelines and wear personal protective equipment according to the label directions (EPA, 2017a).  It is also important to follow guidelines for spray volume, application rates, droplet size, water pH, compatibility with tank-mix partners, time and frequency of application, and other details to ensure efficacy of the biopesticides (van Zyl et al., 2010; Wang & Liu, 2007; Whitford et al., 2009).

Biopesticides use has been increasing in the recent years.  They can be used as standalone treatments or combined or rotated with other pesticides in both organic and conventional production systems.  The fact that there are no residues is a huge benefit for exported commodities, as maximum residue limit issues continue to be a challenge in this arena (Berger, 2013).

In expanding upon the role of biopesticides in biocontrol, the topic of resistance management is a key consideration. Pest resistance to conventional chemical pesticides is a significant concern. Scientific research has repeatedly demonstrated that continuous use of the same class of pesticides, especially those reliant on a single mode of action, will result in the emergence of a pest population resistant to those products (Osteen et al., 2012). Populations of insect pests, plant pathogens, nematodes, and weeds all have the ability to develop resistance quickly, even to different types of functionally similar chemistries. This phenomenon is called cross-resistance and is caused by multi-chemistry detoxification mechanisms present in many pest populations (Horowitz and Ishaaya, 2009).

Because of the increasing number of novel, low-impact chemistries available, educators and growers have additional tools to manage resistance within IPM programs (EPA, 2017a). Biopesticides have long been used in combination with synthetic chemistries to provide the basis for excellent control programs that effectively manage resistance. Additionally, they typically have modes of action that are different from synthetic pesticides and do not rely on a single target site for efficacy. Properly used, these products have the potential to extend the effective field life of all products by curtailing the development of resistant pest populations (Horowitz and Ishaaya, 2009).

According to the United States Environmental Protection Agency (EPA), “IPM is an effective and environmentally sensitive approach to pest management that relies on a combination of common-sense practices” (2017b, p. 1). The University of California Statewide Integrated Pest Management Program (UCIPM) (2017) defines the IPM approach as combining prevention, cultural, physical, biological and chemical means to control pests, all the while minimizing economic, public health, beneficial as well as non-target organism, and environmental risks. Biopesticides are noted among the low-risk and most highly effective tools for achieving crop protection in IPM systems. The challenges of farming require that IPM systems actively integrate multiple management approaches to balance optima productivity with sustainability (BPIA, 2017).

Biopesticides should be considered as a component of a holistic total program and used at an appropriate time and pest density. Today, many forward looking IPM professionals are incorporating biopesticides into traditionally conventional pest management strategies (EPA, 2017b). However, education and training are needed to address biopesticide best use practices, the methods of integrating them into IPM programs; as well as instruction to promote an understanding of their unique modes of action (EPA, 2017b). Part of the educational process involves research through fair and realistic field trials that evaluate biopesticides both as standalone treatments as well as in combination and rotation with other options with an objective of improving IPM practices (Abler et al., 1992; Kumar and Singh, 2015).  All of these learning experiences are useful in demonstrating the science of biopesticide use and establishing best use practices.  A better understanding of biopesticide potential and the mode of action of different active ingredients, increased grant support to promote biopesticide research, and productive grower-industry-researcher collaborations to generate applied research data and design IPM strategies are necessary to make the best use of biopesticides and for environmental sustainability.

References

Abler, D.G., G.P. Rauniyar, and F.M. Goode. 1992. Field trials as an extension technique: The case of Swaziland. NJARE 21(1): 30-35.

Berger, L. 2013. MRL issues and international trade commodity perspectives, pp 3-48. In Proceedings: Idaho Pesticide MRL Workshop, 2 December 2013, Boise, ID. AgBusiness Resources, Visalia, CA.

(BPIA). Biological Products Industry Alliance. 2014. Biopesticides in a program with traditional chemicals offer growers sustainable solutions. http://www.bpia.org/wp-content/uploads/2014/01/grower-final.pdf

(BPIA). Biological Products Industry Alliance. 2017. Benefits of biological products. http://www.bpia.org/benefits-of-biological-products/

(EPA). U.S. Environmental Protection Agency. 2017a. Biopesticides. https://www.epa.gov/pesticides/biopesticides#what

(EPA). U.S. Environmental Protection Agency. 2017b. Integrated pest management principles. https://www.epa.gov/safepestcontrol/integrated-pest-management-ipm-principles

Horowitz, A. and I. Ishaaya. (2009). Biorational control of arthropod pests: Application and resistance management. Springer, New York, NY.

Kumar, S., and A. Singh. 2015. Biopesticides: Present status and the future prospects. J Fertil Pestic 6: e129. doi:10.4172/2471-2728.1000e129

Osteen, C., J. Gottlieb, and U. Vasavada (eds.). 2012. Agricultural Resources and Environmental Indicators. EIB-98, U.S. Department of Agriculture, Economic Research Service, August 2012.

Popp, J., K. Peto, and J. Nagy. 2012. Pesticide productivity and food security: A review. Agron Sustain Dev 33: 243–255. DOI 10.1007/s13593-012-0105-x.

Pretty, J. and Z.P. Bharucha. 2015. Integrated pest management for sustainable intensification of agriculture in Asia and Africa. Insects 6(1): 152–182.

(UCIPM). University of California Statewide Integrated Pest Management Program. 2017. What is integrated pest management (IPM)? http://www2.ipm.ucanr.edu/WhatIsIPM/

Vallad, G.E. n.d. Use of biopesticides for the management of vegetable diseases. University of Florida Gulf Coast Research and Extension Center. http://ipm.ifas.ufl.edu/pdfs/Bio-Pesticides_Slides_IPM_site.pdf

van Zyl, S.A., J. Brink, F.J. Calitz, S. Coertze, and P.J. Fourie. 2009. The use of adjuvants to improve spray deposition and Botrytis cinerea control on Chardonnay grapevine leaves. Crop Prot 29(1): 58-67. https://doi.org/10.1016/j.cropro.2009.08.012

Wang, C.J. and Z.Q. Liu. 2007. Foliar uptake of pesticides: Present status and future challenge. Pest Biochem Phys 87(1): 1-8. https://doi.org/10.1016/j.pestbp.2006.04.004

Whitford, F., D. Penner, B. Johnson, L. Bledsoe, N. Wagoner, et al. 2009. The impact of water quality on pesticide performance. Purdue Extension Publication PPP-86. https://www.extension.purdue.edu/extmedia/ppp/ppp-86.pdf

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Posted on Tuesday, December 19, 2017 at 11:48 AM

Insect resistance to biopesticides


Mechanisms of insecticide resistance in insects.

Use of biopesticides or non-chemical pesticides is encouraged as a part of integrated pest management (IPM) for environmental and human safety and to reduce the risk of insecticide resistance.  With the increase in biopesticide use in both organic and conventional cropping systems, it is a good time to review the potential of insect resistance to botanical and microbial pesticides. 

Insects and mites develop resistance to chemical pesticides through genetic, metabolic, or behavioral changes resulting in reduced penetration of toxin, increased sequestration or excretion, reduced binding to the target site, altered target site that prevents binding of the toxin, or reduced exposure to the toxin through modified behavior.  When the active ingredient is a toxic molecule and has the mode of action similar to that of a chemical compound, regardless of the plant or microbial origin, arthropods are more likely to develop resistance through one or more of the abovementioned mechanisms.  When the mode of action is infection by a microorganism, rather than a toxin, arthropods are less likely to develop resistance.  Under natural circumstances, plants, insects, natural enemies, and beneficial or harmful microbes continuously co-evolve and adapt to changing environment.  When there is a higher selection pressure, such as indiscriminate use of chemical pesticides, increased mutagenesis can lead to resistance issues.  A good understanding of insect resistance to biopesticides will help minimize potential risks and improve their efficient use in IPM.

Resistance to botanical pesticides

            Nicotine, an alkaloid from Nicotiana spp., is one of the earlier botanical pesticides known.  Although nicotine is not currently used as an insecticide, its synthetic alternatives – neonecotinoids – are commonly used against several pests.  Botanical insecticide pyrethrum, extracted from the flowers of Chrysanthemum cinerariaefolium, contains insecticidal pyrethrins (synthetic pyrethrins are referred to as pyrethroids).  Although insect resistance to pyrethrum or pyrethroid compounds has been known (Whitehead, 1959; Immaraju et al., 1992; Glenn et al., 1994), they have been effectively used against a number of pests through careful placement in IPM, organic, or conventional management strategies.  Additionally, pyrethrin products have been effectively used along with piperonyl butoxide, which acts as a synergist and resistance breaker (Gunning et al. 2015).

Another botanical insecticidal compound, azadirachtin, is a tetranortriterpenoid limonoid from neem (Azadirachta indica) seeds, which acts as an insecticide, antifeedant, repellent and insect growth regulator.  While neem oil, which has a lower concentration of azadirachtin, has been used in the United States as a fungicide, acaricide, and insecticide for a long time, several azadirachtin formulations in powder and liquid forms have become popular in recent years and were found effective in managing important pests (Dara 2015a and 2016).  Feng and Isman (1995) reported that the green peach aphid, Myzus persicae developed resistance to pure azadirachtin under artificially induced selection pressure after 40 generations, but did not develop resistance to a refined neem seed extract.  They suggested that natural blend of azadirachtin compounds in a biopesticide would not exert selection pressure that could lead to resistance.  Additionally, Mordue and Nisbet (2000) discussed that azadirachtin can play a role in insecticide resistance management because it reduces the detoxification enzyme production as a protein synthesis inhibitor.  Azadirachtin also improved the efficacy of other biopesticides in multiple studies (Trisyono and Whalon, 2000; Dara, 2013 and 2015b).

Insects feeding on plant allelochemicals can develop cross-resistance to insecticides (Després et al., 2007).  For example, overproduction of detoxification enzymes such as glutathione S-transferases and monooxygenases in the fall armyworm, Spodoptera frugiperda,when it fed on corn and cowpea, respectively, imparted cross-resistance to various chemical pesticides.  It is important to keep this in mind when botanical pesticides are used to detect potential resistance issues.

Resistance to bacterial biopesticides

            Bacillus thuringiensis (Bt)is a gram-positive soil bacterium, which contains crystalline toxic protein that is activated upon ingestion by an insect host, binds to the receptor sites in the midgut, and eventually causes insect death.  Since the mode of action involves a toxin rather than bacterial infection, several insects developed resistance to Bt pesticides or transgenic crops that contain Bt toxins (Tabashnik et al., 1990; McGaughey and Whalon, 1992; Tabashnik, 1994; Iqbal et al., 1996).  However, Bt pesticides are still very popular and used against a variety of lepidopteran (Bt subsp. aizawai and Bt subsp. kurstaki), dipteran (Bt subsp. israelensis and Bt subsp. sphaericus), and coleopteran (Bt subsp. tenebrionis) pests.

            Spinosad is a mixture of macrocyclic lactones, spinosyns A and spinosyns D, derived from Saccharopolyspora spinosa, an actinomycete gram-positive bacterium, and is used against dipteran, hymenopteran, lepidopteran, thysanopteran, and other pests. Spinosad products, while naturally derived are registered as chemical pesticides, not as biopesticides. Insect resistance to spinosad later led to the development of spinetoram, which is a mixture of chemically modified spinosyns J and L.  Both spinosad and spinetoram are contact and stomach poisons and act on insect nervous system by continuous activation of nicotinic acetylcholine receptors.  However, insect resistance to both spinosad (Sayyed et al., 2004; Bielza et al., 2007) and spinetoram (Ahmad and Gull, 2017) has been reported due to extensive use of these pesticides.  Cross-resistance between spinosad and some chemical insecticides has also occurred in some insects (Mota-Sanchez et al., 2006; Afzal and Shad, 2017).

Resistance to viral biopesticides

            Baculovirus infections in lepidoptera have been known for centuries, especially in silkworms.  Currently, there are several commercial formulations of nucleopolyhedroviruses (NPV) and granuloviruses (GV).  When virus particles are ingested by the insect host, usually lepidoptera, they invade the nucleii of midgut, fatbody, or other tissue cells and kill the host.  Baculoviruses are generally very specific to their host insect species and can be very effective in bringing down the pest populations.  However, variations in the susceptibility of certain insect populations and development of resistant to viruses has occurred in several host species (Siegwart et al., 2015).  Resistance to different isolates of Cydia pomonella granulovirus (CpGV-M, CpGV-S) in codling moth (Cydia pomonella) populations is well known in Germany and other parts of Europe (Sauer et al., 2017a & b).

Resistance to fungal biopesticides

            There are several fungi that infect insects and mites.  Fungal infection starts when fungal spores come in contact with an arthropod host.  First, they germinate and gain entry into the body by breaching through the cuticle.  Fungus later multiplies, invades the host tissues, kills the host, and emerges from the cadaver to produce more spores.  Entomophthoralean fungi such as Entomophthora spp., Pandora spp., and Neozygites spp. can be very effective in pest management through natural epizootics, but cannot be cultured in vitro for commercial scale production.  Hypocrealean fungi such as Beauveria bassiana, Isarea fumosorosea, Metarhizium brunneum, and Verticillium lecanii,on the other hand, can be mass-produced in vitro and are commercially available.  These fungi are comparable to broad-spectrum insecticides and are pathogenic to a variety of soil, foliar, and fruit pests of several major orders.  Since botanical, bacterial, and viral biopesticides have insecticidal metabolites, proteins, or viral particles that have specific target sites and mode of action, insects have a higher chance of developing resistance through one or more mechanisms.  Although fungi also have insecticidal proteins such as beauvericin in B. bassiana and I. fumosorosea and dextruxin in M. anisopliae and M. brunneum, their mode of action is more through fungal infection and multiplication and arthropods are less prone to developing resistance to entomopathogenic fungi.  However, insects can develop resistance to entomopathogenic fungi through increased melanism, phenoloxidase activity, protease inhibitor production, and antimicrobial and antifungal peptide production (Wilson et al., 2001; Zhao et al., 2012; Dubovskiy et al., 2013).  It appears that production of detoxification enzymes in insects against fungal infections can also impart resistance to chemical pesticides.  Infection of M. anisopliae in the larvae of greater wax moth, Galleria mellonella, increased dexotification enzyme activity and thus resistance to malathion (Serebrov et al., 2006).

            These examples show that insects can develop resistance to biopesticides in a manner somewhat similar to chemical pesticides, but due to the typically more complex and multiple modes of action, at a significantly lesser rate depending on the kind of botanical compound or microorganism involved.  Resistance to entomopathogenic fungi is less common than with other entomopathogens. Since biopesticide use is not as widespread as chemical pesticides, the risk of resistance development is less for the former.  However, excessive use of any single tool has the potential for resistance or other issues and IPM, which uses a variety of management options, is always a good strategy.

Acknowledgements: Thanks to Pam Marrone for reviewing the manuscript.

References

Afzal, M.B.S. and S. A. Shad.  2017.  Spinosad resistance in an invasive cotton mealybug, Phenacoccus solenopsis: cross-resistance, stability and relative fitness.  J. Asia-Pacific Entomol. 20: 457-462.

Ahmad M. and S. Gull.  2017.  Susceptibility of armyworm Spodoptera litura (Lepidoptera: Noctuidae) to novel insecticides in Pakistan.  The Can. Entomol. 149: 649-661.

Bielza, P., V. Quinto, E. Fernández, C. Grávalos, and J. Contreras.  2007.  Genetics of spinosad resistance in Frankliniella occidentalis (Thysanoptera: Thripidae).  J. Econ. Entomol. 100: 916-920.

Dara, S. K.  2013.  Strawberry IPM study 2012: managing insect pests with chemical, botanical, and microbial pesticides.  UCANR eJournal Strawberries and Vegetables March 13, 2013 (http://ucanr.edu/blogs/blogcore/postdetail.cfm?postnum=9595).

Dara, S. K.  2015a.  Root aphids and their management in organic celery.  CAPCA Adviser 18(5): 65-70.

Dara, S. K.  2015b. Strawberry IPM study 2015: managing insect pests with chemical, botanical, microbial, and mechanical control options.  UCANR eJournal Strawberries and Vegetables November 30, 2015 (http://ucanr.edu/blogs/blogcore/postdetail.cfm?postnum=19641).

Dara, S. K. 2016.  Managing strawberry pests with chemical pesticides and non-chemical alternatives.  Intl. J. Fruit Sci. https://doi.org/10.1080/15538362.2016.1195311

Després, L., J.-L. David, and C. Gallet.  2007.  The evolutionary ecology of insect resistance to plant chemicals.  Trends in Ecol. Evol. 22: 298-307.

Dubovskiy, I. M., M.M.A. Whitten, O. N. Yaroslavtseva, C. Greig, V. Y. Kryukov, E. V. Grizanova, K. Mukherjee, A. Vilcinskas, V. V. Glupov, and T. M. Butt.  2013.  Can insects develop resistance to insect pathogenic fungi? PloS one 8: e60248.

Feng, R. and M. B. Isman.  1995.  Selection for resistance to azadirachtin in the green peach aphid, Myzus persicae.  Experientia 51: 831-833.

Glenn, D. C., A. A. Hoffmann, and G. McDonald.  1994.  Resistance to pyrethroids in Helicoverpa armigera (Lepidoptera: Noctuidae) from corn: adult resistance, larval resistance, and fitness effects.  J. Econ. Entomol. 87: 1165-1171.

Gunning, R., G. Moores, and M. Balfe.  2015.  Novel use of pyrethrum to control resistant insect pests on cotton.  Acta Hort. 1073: 113-118. https://doi.org/10.17660/ActaHortic.2015.1073.16

Immaraju, J. A., T. D. Paine, J. A. Bethke, K. L. Robb, and J. P Newman.  1992.  Western flower thrips (Thysanoptera: Thripidae) resistance to insecticides in coastal California greenhouses.  J. Econ. Entomol. 85: 9-14.

Iqbal, K., R.H.J. Vekerk, M. J. Furlong, P. C. Ong, S. A. Rahman, and D. J. Wright.  1996.  Evidence for resistance to Bacillus thuringiensis (Bt) subsp. kurstaki HD-1, Bt subsp. aizawai and Abamectin in field populations of Plutella xylostella from Malaysia.  Pest Manag. Sci. 48: 89-97.

McGaughey, W. H. and M. E. Whalon.  1992.  Managing insect resistance to Bacillus thuringiensis toxins.  Science 258: 1451-1455.

Mordue, A. J. and A. J. Nisbet.  2000.  Azadirachtin from the neem tree Azadirachta indica: its action against insects.  An. Soc. Entomol. Bras. 29: 615-632.

Mota-Sanchez, D., R. M. Hollingworth, E. J. Grafius, and D. D. Moyer.  2006.  Resistance and cross-resistance to neonicotinoid insecticides and spinosad in the Colorado potato beetle, Leptinotarsa decemlineata (Say) (Coleoptera: Chrysomelidae).  Pest Manag. Sci. 62: 30-37.

Sauer, A. J., E. Fritsch, K. Undorf-Spahn, P. Nguyen, F. Marec, D. G. Heckel, and J. A. Jehle.  2017a.  Novel resistance to Cydia pomonella granulovirus (CpGV) in codling moth shows autosomal and dominant inheritance and confers cross-resistance to different CpGV genome groups.  PLoS ONE 12(6): e0179157.

Sauer, A. J., S. Schulze-Bopp, E. Fritsch, K. Undorf-Spahn, and J. A. Jehle.  2017b.  A third type of resistance of codling moth against Cydia pomonella granulovirus (CpGV) shows a mixture of a Z-linked and autosomal inheritance pattern.  Appl. Environ. Microbiol. AEM-01036.

Sayyed, A. H., D. Omar, and D. J. Wright.  2004.  Genetics of spinosad resistance in a multi-resistant field-selected population of Plutella xylostella.  Pest Manag. Sci. 60: 827-832.

Serebrov, V. V., O. N. Gerber, A. A. Malyarchuk, V. V. Martemyanov, A. A. Alekseev, and V. V. Glupov.  Effect of entomopathogenic fungi on detoxification enzyme activity in greater wax moth Galleria mellonella L. (Lepidoptera, Pyralidae) and role of detoxification enzymes in development of insect resistance to entomopathogenic fungi.  Anim. Human Physiol. 33: 581-586.

Siegwart, M., B. Graillot, C. B. Lopez, S. Besse, M. Bardin, P. C. Nicot, and M. Lopez-Ferber.  2015.  Resistance to bio-insecticides or how to enhance their sustainability: a review.  Front. Plant Sci. 6:381. https://doi.org/10.3389/fpls.2015.00381

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Trisyono, A. and M. E. Whalon.  2000.  Toxicity of neem applied alone and in combination with Bacillus thuringiensis to Colorado potato beetle (Coleoptera: Chrysomelidae).  J. Econ. Entomol. 92: 1281-1288.

Whitehead, G. B.  1959. Pyrethrum resistance conferred by resistance to DDT in the blue tick.  Nature 184: 378-379.

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Zhao, P., Z. Dong, J. Duan, G. Wang, L. Wang, Y. Li, Z. Xiang, and Q. Xia.  2012.  Genome-wide identification and immune response analysis of serine protease inhibitor genes in the silkworm, Bombyx mori.  PloS one 7: e31168.

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Posted on Wednesday, December 6, 2017 at 8:43 AM

Evaluating a solar-powered UV light trap as a pest management option in strawberry

The western tarnished plant bug or lygus bug (Lygus hesperus) continues to be a major pest of strawberry on the Central Coast.  Most growers typically rely on chemical or biological pesticides to manage pest.  Some growers also use tractor-mounted vacuums to remove the pest, but the western tarnished plant bug is a major concern as it causes significant losses to marketable yields by deforming developing berries.  Considering the status of the pest, having additional control options is critical both to reduce yield losses and also to strengthen the current integrated pest management (IPM) strategies.

A solar-powered UV light trap was reported to be a potential tool for controlling a variety of coleopteran, lepidoptera, hemipteran and other pests including the western tarnished plant bug.  To evaluate its role as a potential IPM tool for strawberry pests a study was in conducted in fall-planted organic and conventional strawberry fields in Santa Maria. 

Specifications of the trap: UV light trap known as Solar Powered Pest Control Machine (Model GFS-8) is manufactured by GreenFuture Equipment based in Sacramento, CA.  It has a 30W solar power panel and a 12V battery to power a dual color UV light bulb and a rotating grill/grid for two nights on one day of charging.  The grill surrounds the light produces 3600 volts of electricity with a surface area of 2.37 sqft and electrocutes insects as they are attracted to the light.  Each light trap is supposed to cover 3-4 acres of area.  A rubber flap brushes off insects into the container in the bottom of the trap as the grill rotates periodically.


Solar-powered UV light traps in conventional (left) and organic (middle) fields and insects collected in the container (right)

Experimental set up: One light trap was set up in a conventional strawberry field on West Main St (Manzanita Berry Farms) and another one in an organic field on Solomon Rd (Eraud Farms) in late March, 2017.  Contents of the container were collected each week in a bag, taken back to the laboratory, and pest and beneficial insects were categorized and enumerated.  Observations were made on 13 sampling dates between 2 May and 26 July, 2017.

Results: There were several groups of beneficial and pest insects were found attracted to the light trap.  However, the western tarnished plant bugs were not seen throughout the observation period although field scouting indicated their presence.  In general, the western tarnished plant bug infestations were lower this year and grower was able to manage pest populations by regular vacuuming.  Pesticides were not used to control this pest during this period.

Among the pest insects trapped, corn earworm (Helicoverpa zea) adults were the only ones known to be a pest of strawberry in California.  Insects that are generally recognized as pests included tiger moths, owlet moths, corn earworm adults, eucalyptus moths, sphinx moths, and mosquitoes while the beneficial insects included crane flies, lady beetles, parasitic wasps, neuropterons (such as lace wings), and soldier beetles.  Some crane flies are important in the ecosystem as a prey for some animals and birds or through the activity of the larvae on decaying organic matter in the soil.  However, their impact in strawberry is unknown.


Number of various pes (above) and benefifcial (below) insects collected on each sampling date

All pest and beneficial insects collected on different sampling dates (blue line: organic, orange line: conventional)

The number of both pest and beneficial insects was higher in the organic field than in the conventional field.  Seasonal average of all insects per sampling date was 177 for the organic field and 98 for the conventional field.  The proportion of the beneficial insects was about 27 in the organic field and 5 in the conventional field.


Seasonal average of various insects collected (Click on the image for a bigger version)

Although the role of UV light traps as a control option for the western tarnished plant bug could not be determined, it appeared to be a good tool for trapping corn earworm adults and other moths.  This light trap could probably be useful for managing lepidopteran pests in strawberry or other corps.

Acknowledgements: Thanks to Dave Peck for his collaboration, GreenFuture Equipment for donating the light traps, and Maria Murrietta, Tamas Zold, and Chris Martinez for their technical assistance.


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Posted on Friday, September 29, 2017 at 10:32 AM

Influence of weather on strawberry crop and development of a yield forecasting model

California offers ideal weather conditions for both nursery plant and strawberry fruit production. Variations in weather conditions in three strawberry production regions in California complement fruit production from each other and help avoid market glut. The warmer Oxnard area, the milder Santa Maria area, and the colder Watsonville area with minimal overlapping of their peak fruit production seasons allow yearlong strawberry production.

Weather influences on strawberry have been well documented in various strawberry producing regions across the world (Palencia et al., 2013; Li et al., 2010; Waister et al., 1972). Examples of key weather parameters correlated with strawberry yield include temperature, precipitation, solar radiation, relative humidity, and wind speed. Crop growth is weather dependent, thus, it is a common practice to estimate fruit yield based on weather variables. Since strawberry production spreads across 4–5 months, evaluating relationships between meteorological parameters and strawberry yield can provide valuable information and early indications of yield estimations that growers can utilize to their advantage. Objective of this research was to evaluate correlations of meteorological parameters on strawberry yield for Santa Maria region and to develop weather based statistical yield forecasting models for strawberries.

Strawberry yield data

Daily strawberry yield data for the Santa Maria region was obtained from published sources (California Strawberry Commission). This information is publically available and is originally compiled from the United States Department of Agriculture Market News/Fruits & Vegetables website. Daily strawberry yield data for the month of April through July were aggregated to weekly values. For this analysis we used weekly strawberry yield data for 2009 through 2015.

Weather data

Weather data were obtained from the California Irrigation Management Information System (http://www.cimis.water.ca.gov/), a network of over 145 automated weather stations in California. Specific meteorological parameters used in this study were net radiation, air temperature (minimum and maximum), relative humidity (minimum and maximum), dew point temperature, soil temperature (minimum and maximum), vapor pressure (minimum and maximum), reference evapotranspiration, and average wind speed.

Correlation analysis

Weekly values of meteorological parameters from October of the year prior to harvest to February of current year of strawberry harvest were correlated with weekly strawberry yield from April through July and tested for significance. Each meteorological variable was correlated with strawberry yields from April to July. This thorough correlation analysis was done in order to understand influence of meteorological parameters on strawberry yield on a more detailed basis.

Fall and winter weather conditions during the vegetative growth period of strawberry have a significant influence on the fruit yields in the following spring and summer for the Santa Maria region. Results show that net radiation, relative humidity, vapor pressure, wind speed, and temperature showed significant correlations with strawberry yields at various temporal scales. In general, it was evident that many meteorological parameters during the early stages of strawberry growth and development phase exhibit statistically significant correlation with strawberry yields during the peak fruit production period. This finding is consistent with the findings of Lobell et al. (2006) for strawberry and other crops in California.


Table 1. Correlation matrix of monthly meteorological parameters and strawberry yields that were statistically significant.

Statistical yield model

Weather parameters that showed significant correlations were used to develop strawberry yield forecasting model. Instead of using weather parameters as explanatory variables, they were transformed into principal components to develop yield–forecasting models.

Figure 1 shows observed versus predicted yields for April (top) and June (bottom)

It is important to note that there are limitations on how much variability in yield data that can be explained by meteorological parameters as many other factors such as management practices, pests, diseases, varieties, other stress factors can also influence yield variability. Additionally, historic strawberry yield data provided an average estimate for the region and might not represent accurate observations.

These results demonstrate the potential to predict strawberry yield using weather variables relevant to the Santa Maria strawberry growing region. In order to make these results usable for decision–making, it could be refined to be utilized at the field scale. Additionally, skills of these models can be further improved by combining weather parameters and relevant physiological parameters of strawberry at the field scale.

A full version of this article (Pathak et al., 2016) can be viewed at: https://www.hindawi.com/journals/amete/2016/9525204/

References

Palencia, P., F. Martínez, J.J. Medina, and J. López– Medina. 2013. Strawberry yield efficiency and its correlation with temperature and solar radiation. Hortic. Bras.31(1): 93–99

Li, H., T. Li, R.J. Gordon, S.K. Asiedu, and K. Hu. 2010. Strawberry plant fruiting efficiency and its correlation with solar irradiance, temperature and reflectance water index variation. Environ. Exp. Bot. 68, 165–174.

Waister, P. D. 1972. Wind as a limitation on the growth and yield of strawberries. hort. Sci. 47: 411 – 418.

Lobell, D.B., K. Cahill, and C. Field. 2006. Weather–based yield forecasts developed for 12 California crops. California Agriculture, 60(4): 211–215.

Pathak, T.B., Dara, S.K., Biscaro, A. 2016. Evaluating correlations and development of meteorology based yield forecasting model for strawberry. Advances in Meteorology, vol. 2016, article ID 9525204. doi:10.1155/2016/9525204

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Posted on Wednesday, September 27, 2017 at 9:14 AM
  • Author: Tapan Pathak, CE Specialist, UC Merced ( http://ucanr.edu/?facultyid=28062)
  • Author: Surendra K. Dara

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