The monitoring program and the use of reduced risk pesticides to control western flower thrips worked very effectively in the IPM greenhouses. This was a critical component of the entire program, because thrips are considered the key pest of roses. Significantly fewer western flower thrips were caught in the IPM houses than in the conventional houses across all nurseries. The largest differences in thrips levels between the two treatments occurred during the summer months, when western flower thrips pressure is generally highest . There were also greater fluctuations in the overall densities of western flower thrips in the conventional houses, as well as more variation between individual conventional houses during the time of peak thrips pressure, compared to the IPM greenhouses. We attribute both of these observations to the regular removal of open flowers in the lower canopy that occurred under IPM but not in the conventional houses. Powdery mildew. Our attempt to use the grape mildew model without modification to predict powdery mildew infection in greenhouse-grown roses was not satisfactory. The GMM is based on a sustained temperature threshold of 70°F to 85°F, which is a little higher than optimum for my celial growth of the rose mildew pathogen . For this reason, we attempted to improve the performance of the model by running it with a temperature range of either 65°F to 85°F or 65°F to 80°F. Generally, we found the model to be of limited value in Southern California; it showed a high level of disease risk most times of the year, and disease was a chronic problem. There was no clear start to a mildew season, and there was little success in identifying environmental changes associated with changes in disease pressure. On the other hand, Central California greenhouses did appear to have a seasonal component to disease,25 litre plant pot with powdery mildew on greenhouse roses starting in early spring and tapering off by early fall.
However, even under these conditions, the model was not successful in identifying triggering events. For example, there was a poor relationship between the powdery mildew index in a greenhouse near Monterey when the model was run with a temperature range of 65°F to 85°F . This relationship was improved somewhat by running the model for the same data using a temperature range of 65°F to 80°F . However, there were many times in the spring and early summer when the PMI indicated high disease risk but no disease was evident on the crop . We have no explanation for these persistent failures. Perhaps there was no inoculum in the greenhouse; perhaps we were not fully aware of all fungicide treatments; or perhaps greenhouse humidity is interacting in a way that confounds the model. Clearly a model that could predict the most opportune times for applying fungicide treatments to control powdery mildew on roses would be beneficial. We were encouraged by the fact that the model never indicated low risk when there was in fact significant disease , and that we sometimes saw a rise in mildew incidence after a rise in the index with an appropriate latent period lag . However, our research showed that the UC Davis powdery mildew risk assessment model for grapevines is not easily adapted to the challenge of powdery mildew on greenhouse roses. Additional research is needed to develop a more suitable modeling platform before it will be possible to effectively advise growers regarding risk periods. Secondary pests. Effective IPM implementation was hindered at two sites by the citrus mealybug . This pest is generally not a problem for rose growers until IPM is implemented, when the cessation of broad-spectrum pesticide applications can allow this pest to develop. It is generally a problem only at sites where roses are or were grown adjacent to other flower crops such as Stephanotis, an important citrus mealybug host plant. Unfortunately, natural enemies of the citrus mealybug are not regularly available at the commercial level, and the most effective mealybug pesticides are detrimental to spider mite predators.
We are working with the natural enemy suppliers to try to change this situation, and we continue to evaluate reduced-risk pesticides for efficacy against the citrus mealybug.Overall, we believe that the rose IPM program was successful. For example, most of the growers participating in the study wanted to abandon their conventional treatments in favor of using a biological control, predatory mites, to control two spotted spider mites; we allowed them to do so after we felt that enough data had been collected for a good comparison of the IPM and conventional treatments. Future work should concentrate on reducing the sampling effort while still collecting sufficient information to support good pest management decisions. In addition, more work is needed on refining the predictive powdery mildew model as well as on developing effective IPM techniques for secondary pests. This program represents the first and largest effort to demonstrate and implement an IPM strategy on floriculture crops in the United States. Drawing on the partnerships that are central to the Pest Management Alliance concept, we have shown that high-quality roses can be produced with substantially fewer pesticides and with the incorporation of biological control into mainstream floriculture. Effective partnering with the biological control industry has also been a hallmark of this program. This has led to the widespread use of predatory mites in commercial rose production in California, representing the largest use of biological control by the floriculture industry in the United States.Plants have pre-formed and inducible structural and biochemical mechanisms to prevent or arrest pathogen ingress and colonization. These defenses include barriers such as papillae and ligno-suberized layers to fortify cell walls, and low-molecular weight inhibitory chemicals . Plants undergo transcriptional changes upon perception of microbe associated molecular patterns or effectors to induce local and systemic resistance. The oomycete MAMPs, arachidonic acid and eicosapentaenoic acid , are potent elicitors of defense. These eicosapolyenoic acids were first identified as active components in Phytophthora infestans spore and mycelial extracts capable of eliciting a hypersensitive-like response, phytoalexin accumulation, lignin deposition, and protection against subsequent infection in potato tuber discs .
Further work demonstrated root treatment with AA protects tomato and pepper seedlings from root and crown rot caused by Phytophthora capsici, with associated lignification at sites of attempted infection . AA has been shown to induce resistance, elicit production of reactive oxygen species, and trigger programmed cell death in members of the Solanaceae and other families . Phaeophyta and Rhodophyta members contain numerous bioactive chemicals that can elicit defense responses in plants . The brown alga, Ascophyllum nodosum, is a rich source of polyunsaturated fatty acids, including AA and EPA, which comprise nearly 25% of its total fatty acid composition . A. nodosum and oomycetes belong to the major eukaryotic lineage, the Stramenopila, and share other biochemical features . Commercial extracts of A. nodosum,30 litre plant pots bulk used in organic and conventional agriculture as plant bio-stimulants, may also help plants cope with biotic and abiotic stresses. A proprietary A. nodosum extract, Acadian , has been shown to provide protection against bacterial and fungal pathogens . Studies in A. thaliana showed ANE induced systemic resistance to Pseudomonas syringae pv. tomato and Sclerotinia sclerotiorum . Investigation into ANE-induced resistance in A. thaliana and tomato suggest the role of ROS production, jasmonic acid signaling, and upregulation of defense-related genes and metabolites . As a predominant polyunsaturated fatty acid in ANE, AA may contribute to ANE’s biological activity. In a parallel study we demonstrated AA’s ability to systemically induce resistance and ANE’s capacity to locally and systemically induce resistance in tomato to different pathogens . Further, we showed that AA and ANE altered the phytohormone profile of tomato by modulating the accumulation of defense-related phytohormones . Through RNA sequencing, this same study revealed a striking level of transcriptional overlap in the gene expression profiles of AA- or ANEroot-treated tomato across tested time points . Gene ontology functional analysis of transcriptomic data revealed AA and ANE enriched similar categories of genes with nearly perfect overlap also observed in categories of under-represented genes. Both AA and ANE treatment protected seedings from challenge with pathogens with different parasitic strategies while eliciting expression of genes involved in immunity and secondary metabolism. The shared induced resistance phenotype and extensive transcriptional overlap of AA and ANE treatments suggested similar metabolic changes may be occurring in treated plants. In the current study, untargeted metabolomic analyses were conducted to assess global effects of root treatment with AA and the AA-containing complex extract, ANE, on the metabolome of tomato plants.Previous transcriptomic work revealed a high level of congruency in differentially expressed genes in AA- and ANE-treated tomato seedlings compared to H2O-treated controls . To further this line of investigation, the metabolomic profiles of AA- and ANEroot-treated plants were compared to H2O after 24-, 48-, 72-, and 96-hours exposure to their respective treatments. Locally-treated roots and distal leaves were harvested, flash frozen, extracted for metabolites, and subsequently analyzed via LC-MS, which with underivatized samples primarily captures the nonvolatile metabolome . Partial least squares score plots of tomato root tissue revealed distinct clustering by treatment irrespective of time point .
No overlap was observed in the 95% confidence ellipses for any treatment group. Likewise, heat map visualization of the log10 signal of metabolites showed clear clustering of metabolomic profiles by treatment . Features displayed in the heat map were filtered from the total dataset with a p-value < 10−6 and absolute fold change > 5 in roots. Less defined clustering was observed in PLS score plots of distal leaf tissue across sampled time points . Ellipses representing the 95% confidence interval for both H2O and AA treatments both partially overlap with the ANE treatment group. Similarly, a heat map depicting metabolite log10 signal showed more diffuse clustering by treatment . Visualized metabolites from leaves displayed in the heat map used a p-value < 0.001 and an absolute fold-change > 2. These findings are reflective of distal tissue not directly treated with either elicitor. Changes in the distal leaves were not as robust as in the directly treated roots, likely due to diminution of systemic signals that effect metabolic changes throughout the plant. An assessment of the total annotated features across the metabolomic analysis revealed shared and unique annotated features between roots and leaves . Roots and leaves share 44 features with leaves displaying the largest number of unique metabolic features . There were 330 unique identified features in leaves compared to 223 features unique to root tissue . A comparison of differential metabolic features for AA- and ANE-treated plants compared to the H2O control revealed robust overlap for both roots and leaves . AA- and ANE-treated roots shared 68 differential metabolic features, with AA and ANE treatments possessing 37 and 29 differential features unique to each, respectively . Less overlap was observed in leaves with 39 shared differential metabolites, with AA- and ANE-root treated plants displaying 34 and 19 uniquely differential metabolites, respectively . Chemical enrichment analyses were conducted to identify classes of metabolites whose accumulation was locally or systemically altered in AA- and ANE-root-treated tomato seedlings. Enrichment analyses of metabolites whose mean signal was significantly changed in AA- or ANE-treated plants compared to H2O identified numerous affected chemical classes . These changes were most robust in directly treated roots compared to distal leaves. Treatment of tomato seedlings with AA showed strong modulation of metabolomic features classified as triter penoids and linoleic acid and derivatives in roots. AA-treated roots also showed increases in hydroxycinnamic acids and derivatives and fatty acyl glycosides of mono- and disaccharides. ANE-treated roots showed modulation in the accumulation of triterpenoids, steroidal glycosides, and hydroxycinnamic acids and derivatives. Similar to AA-treated plants, the roots of plants treated with ANE also showed increases in metabolites classified as fatty acyl glycosides of mono- and di-saccharides. Although less striking than the chemical enrichment analysis of roots, leaf tissue of root treated plants did reveal an altered metabolome . These changes in metabolite accumulation occurred most prominently at 96 hours, the latest tested time point. Increases in sesquiter penoids and steroidal saponins were seen in leaves of AA-treated plants at 96 hours.