Low Label Rates Research Articles

Toward Few-Label Vertical Federated Learning

Federated Learning (FL) provides a novel paradigm for privacy-preserving machine learning, enabling multiple clients to collaborate on model training without sharing private data. To handle multi-source heterogeneous data, Vertical Federated Learning (VFL) has been extensively investigated. However, in the context of VFL, the label information tends to be kept in one authoritative client and is very limited. This poses two challenges for model training in the VFL scenario. On the one hand, a small number of labels cannot guarantee to train a well VFL model with informative network parameters, resulting in unclear boundaries for classification decisions. On the other hand, the large amount of unlabeled data is dominant and should not be discounted, and it is worthwhile to focus on how to leverage them to improve representation modeling capabilities. To address the preceding two challenges, we first introduce supervised contrastive loss to enhance the intra-class aggregation and inter-class estrangement, which is to deeply explore label information and improve the effectiveness of downstream classification tasks. Then, for unlabeled data, we introduce a pseudo-label-guided consistency mechanism to induce the classification results coherent across clients, which allows the representations learned by local networks to absorb the knowledge from other clients, and alleviates the disagreement between different clients for classification tasks. We conduct sufficient experiments on four commonly used datasets, and the experimental results demonstrate that our method is superior to the state-of-the-art methods, especially in the low-label rate scenario, and the improvement becomes more significant.

Boosting efficient attention assisted cyclic adversarial auto-encoder for rotating component fault diagnosis under low label rates

In practical engineering scenarios with limited labeled samples, conventional semi-supervised diagnostic methods face challenges in achieving satisfactory identification outcomes. To address the aforementioned issues, this paper introduces an incremental semi-supervised learning (ISL) approach based on boosting efficient attention (BEA) assisted cyclic adversarial auto-encoder (CAAE), referred to as BEA-CAAE. The CAAE enhances unsupervised feature representation by simultaneously constraining the distribution of encoded features and aligning elements of the reconstructed samples through a cyclic encoding strategy. The BEA improves classical attention weights' activation strength to better capture vital information, thereby boosting the feature extraction capabilities of both CAAE and the classifier. The ISL employs a stepwise pseudo-label propagation strategy to incrementally filter high-confidence samples, enhancing sample and label utilization, and improving diagnostic accuracy under low label rate conditions. Experiments conducted on multiple test rigs with simple structures as well as large-scale rotating components test rigs that mimic real-world engineering conditions have demonstrated that the proposed method exhibits a significant advantage over existing semi-supervised fault diagnosis approaches in terms of fault diagnosis accuracy and generalization capability, especially under low label rate conditions.

Less Is More: Label Recommendation for Weakly Supervised Point Cloud Semantic Segmentation

Weak supervision has proven to be an effective strategy for reducing the burden of annotating semantic segmentation tasks in 3D space. However, unconstrained or heuristic weakly supervised annotation forms may lead to suboptimal label efficiency. To address this issue, we propose a novel label recommendation framework for weakly supervised point cloud semantic segmentation. Distinct from pre-training and active learning, the label recommendation framework consists of three stages: inductive bias learning, recommendations for points to be labeled, and point cloud semantic segmentation learning. In practice, we first introduce the point cloud upsampling task to induct inductive bias from structural information. During the recommendation stage, we present a cross-scene clustering strategy to generate centers of clustering as recommended points. Then we introduce a recommended point positions attention module LabelAttention to model the long-range dependency under sparse annotations. Additionally, we employ position encoding to enhance the spatial awareness of semantic features. Throughout the framework, the useful information obtained from inductive bias learning is propagated to subsequent semantic segmentation networks in the form of label positions. Experimental results demonstrate that our framework outperforms weakly supervised point cloud semantic segmentation methods and other methods for labeling efficiency on S3DIS and ScanNetV2, even at an extremely low label rate.

Semi-Supervised Fault Diagnosis Method Based on Dual-Stream Attention Cyclic Auto-Encoder for Rotating Components With Low Label Rate

Semi-Supervised Fault Diagnosis Method Based on Dual-Stream Attention Cyclic Auto-Encoder for Rotating Components With Low Label Rate

Label Efficient Regularization and Propagation for Graph Node Classification.

An enhanced label propagation (LP) method called GraphHop was proposed recently. It outperforms graph convolutional networks (GCNs) in the semi-supervised node classification task on various networks. Although the performance of GraphHop was explained intuitively with joint node attribute and label signal smoothening, its rigorous mathematical treatment is lacking. In this paper, we propose a label efficient regularization and propagation (LERP) framework for graph node classification, and present an alternate optimization procedure for its solution. Furthermore, we show that GraphHop only offers an approximate solution to this framework and has two drawbacks. First, it includes all nodes in the classifier training without taking the reliability of pseudo-labeled nodes into account in the label update step. Second, it provides a rough approximation to the optimum of a subproblem in the label aggregation step. Based on the LERP framework, we propose a new method, named the LERP method, to solve these two shortcomings. LERP determines reliable pseudo-labels adaptively during the alternate optimization and provides a better approximation to the optimum with computational efficiency. Theoretical convergence of LERP is guaranteed. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of LERP. That is, LERP outperforms all benchmarking methods, including GraphHop, consistently on five common test datasets, two large-scale networks, and an object recognition task at extremely low label rates (i.e., 1, 2, 4, 8, 16, and 20 labeled samples per class).

Graph based semi-supervised learning using spatial segregation theory

In this work, we address graph based semi-supervised learning using the theory of the spatial segregation of competitive systems. First, we define a discrete counterpart over connected graphs by using direct analogue of the corresponding competitive system. This model turns out does not have a unique solution as we expected. Nevertheless, we suggest gradient projected and regularization methods to reach some of the solutions. Then we focus on a slightly different model motivated from the recent numerical results on the spatial segregation of reaction–diffusion systems. In this case we show that the model has a unique solution and propose a novel classification algorithm based on it. Finally, we present numerical experiments showing the method is efficient and comparable to other semi-supervised learning algorithms at high and low label rates.

View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes.

Performing transductive learning on graphs with very few labeled data, that is, two or three samples for each category, is challenging due to the lack of supervision. In the existing work, self-supervised learning via a single view model is widely adopted to address the problem. However, recent observation shows multiview representations of an object share the same semantic information in high-level feature space. For each sample, we generate heterogeneous representations and use view-consistency loss to make their representations consistent with each other. Multiview representation also inspires to supervise the pseudolabels generation by the aid of mutual supervision between views. In this article, we thus propose a view-consistent heterogeneous network (VCHN) to learn better representations by aligning view-agnostic semantics. Specifically, VCHN is constructed by constraining the predictions between two views so that the view pairs can supervise each other. To make the best use of cross-view information, we further propose a novel training strategy to generate more reliable pseudolabels, which thus enhances predictions of the VCHN. Extensive experimental results on three benchmark datasets demonstrate that our method achieves superior performance over state-of-the-art methods under very low label rates.

Dual Contrastive Learning for Semi-Supervised Fault Diagnosis Under Extremely Low Label Rate

Dual Contrastive Learning for Semi-Supervised Fault Diagnosis Under Extremely Low Label Rate

Digital fingerprinting of microstructures

Finding efficient means of quantitatively describing material microstructure is a critical step towards harnessing data-centric machine learning approaches to understanding and predicting processing–microstructure–property relationships. Current quantitative descriptors of microstructure tend to consider only specific, narrow features such as grain size or phase fractions, but these metrics discard vast amounts of information. Since the gain in traction of machine learning and computer vision, more abstract methods for describing image data in a concise and quantitative manner have become available, but have yet to be fully exploited within materials science. The main aim of this paper is to investigate some of these methods as tools for constructing compressed numerical descriptions of microstructural image data, which are here referred to as “microstructural fingerprints”. A statistical framework is developed to combine some of these methods which includes some classical computer vision methods as special cases.The effectiveness of fingerprints, in this case, is assessed via a series of classification tasks, which take the fingerprints as input along with some label to describe the microstructure, and aim to predict the class label. The classification tasks are a simple way to assess the information content of differently constructed fingerprints, using readily available data. However, the potential applications for such fingerprints, in theory, extend far beyond this, provided a suitably labelled training dataset. For example, fingerprints constructed from image data labelled with some processing parameters or mechanical properties could be fed into regression-based tasks to predict properties from microstructure or predict the processing parameters required for a target microstructure. The ultimate purpose is to rapidly fingerprint sample images in the context of various high-throughput design/make/test scenarios. Such fingerprints would enable, for example, quantification of the disparity between microstructures for quality control, classifying microstructures, predicting materials properties from image data and identifying potential processing routes to engineer new materials with specific properties.Here, the approach is applied to two distinct datasets to illustrate various aspects of the fingerprints and some recommendations are made based on the findings. In particular, methods that leverage transfer learning with convolutional neural networks (CNNs), pretrained on the ImageNet dataset, are generally shown to outperform other methods. Additionally, dimensionality reduction of these CNN-based fingerprints is shown to have negligible impact on classification accuracy for the supervised learning approaches considered. In situations where there is a large dataset with only a handful of images labelled, graph-based label propagation to unlabelled data is shown to be favourable over discarding unlabelled data and performing supervised learning. In particular, label propagation by Poisson learning is shown to be highly effective at low label rates. The supplementary code is available on GitHub (White and Tarakanov, 2021).

Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution

This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel graph transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains.

EVALUATION OF PYRETHROID AND BOTANICAL BARRIER INSECTICIDES AGAINST AEDES ALBOPICTUS IN THE LABORATORY AND FIELD

Outdoor residual insecticide applications are useful for preventing or reducing mosquito populations at focal areas. Until recently, pyrethroids have been the only option for barrier sprays in mosquito control. In this study, three pyrethroid (Onslaught, Cyzmic CS, DeltaGard) and two botanical (Nature-Cide, Essentria IC3 ) outdoor residual insecticides were comparatively tested at low, mid, and high label rates against adult Aedes albopictus in both laboratory bioassays and field trials in St. Augustine, FL, from May-August 2017. Bioassays indicated NatureCide and Cyzmic CS were the most toxic across all three dilution ratios followed by DeltaGard, Onslaught, and Essentria IC3 , respectively. In field trials Nature-Cide and Onslaught were the only products that reduced mosquito abundance at the low rate. However, at the mid rate NatureCide and Onslaught caused ~90% percent reduction of adult female Ae. albopictus in the field, the highest of all tested products. The performance of DeltaGard (79% reduction in field counts), Essentria IC 3 (64%), and Cyzmic CS (36%) in the field were not similar to the laboratory results. The universally high performance of Nature-Cide indicates that mosquito control operations should expand consideration to botanical based insecticides for field operations.

Mutual teaching for graph convolutional networks

Graph convolutional networks generate reasonable predictions of unlabeled samples because of transductive label propagation. Samples can have different predicted confidences, and therefore, we consider high-confidence predictions as pseudo labels to select more samples for updating models. We propose a new training strategy called mutual teaching, wherein dual models are first trained and they then teach each other during each batch process. Each network feeds forward all samples, and the samples with high-confidence predictions are used to expand the label set; then, each model is updated by the selected samples of its peer network. We consider the high-confidence predictions as useful knowledge, and each network teaches its peer network using this knowledge. In the proposed strategy, the pseudo-label set of a network is derived from its peer network, and this strategy helps improve the performance significantly. Experiments are conducted on the three citation network datasets and experimental results demonstrate that our method achieves superior performance over state-of-the-art methods under the condition of very low label rates.

Laboratory evaluation of CX-10282 containing Beauveria bassiana (Hypocreales: Cordycipitaceae) strain GHA against adult Rhagoletis indifferens (Diptera: Tephritidae)

Western cherry fruit fly, Rhagoletis indifferens Curran (Diptera: Tephritidae), is a pest of cherries (Prunus spp.) in western North America that potentially could be managed using the fungus Beauveria bassiana (Balsamo-Crivelli) Vuillemin (Hypocreales: Cordycipitaceae). Here, the product CX-10282 containing 11.3% B. bassiana strain GHA was evaluated as a potential control agent for adult R. indifferens by determining whether it can cause 100% fly mortality in the laboratory. Low or high label rates of 5.4 × 107 or 1.7 × 108 conidia/ml in five different modes of application and application volumes were tested. When one 1-μl drop of CX-10282 containing 1.6 × 105 conidia was applied onto a single female fly, 100% mortality was reached by 4 or 7 d. When CX-10282 was sprayed into a container with flies to produce 2.2 × 105 to 6.9 × 105 conidia/cm2 and flies left in containers, 76.2–99.5% mortality was reached by 15–21 d. When sprayed flies were moved into clean containers, 74.8% mortality was reached by 25 d. When a 9-cm diam filter paper with 2.8 × 107 conidia/cm2 was exposed to flies inside a container, 100% mortality was reached by 9 d. When flies were exposed to CX-10282 mixed with or without sucrose on filter paper, mortality was 98.2 or 99.7% by 20 d. Dead treatment flies had mycoses rates of 68.8–93.0%. Findings suggest that field studies to evaluate CX-10282 as an organic control product for adult R. indifferens are worth pursuing.

Degradation and residual efficacy of beta-cyfluthrin as a surface treatment for control of Tribolium castaneum Herbst: Effects of temperature and environment

Concrete arenas were treated with the low and high label rates of beta-cyfluthrin Tempo SC Ultra® (10 and 20 mg Active Ingredient [AI]/m2, respectively) and stored during the summer and autumn in two separate years inside an empty grain bin, inside a rice mill, and inside a laboratory at an approximate ambient temperature of 27 °C. Bioassays were conducted by removing the arenas at 0 (1 day) to 10 weeks post-treatment from the three locations where they were stored, and exposing adults of Tribolium castaneum (Herbst), the red flour beetle, for 0.5–3 h on the arenas. Rapidity of knockdown was used to assess residual efficacy. Application rate was significant (P < 0.05) for rapidity of knockdown for the summer storage period but not for autumn (P ≥ 0.05). As the weeks progressed during summer, knockdown was progressively slower during the 0.5 to 3-h exposure period on arenas held inside the grain bin compared to the arenas inside the rice mill and the laboratory. During autumn the order was reversed for the grain bin and laboratory as the weeks progressed but at 10 weeks knockdown after 3 h was still much less on arenas held inside the bin and mill compared to the laboratory. Extreme high temperatures in the grain bin during the summer, and the fluctuating temperatures during autumn, may have promoted residual degradation on arenas held in the bin and mill compared to the constant temperatures in the laboratory. Results show that residual persistence of contact insecticides for control of stored product insects may be less on treated surfaces outside of a laboratory setting, and caution is warranted when predicting time periods for residual efficacy in sites that are outside of research laboratories with stable environmental conditions.

Efficacy of Entomopathogenic Fungal Products for Biological Control of Spotted Wing Drosophila (Diptera: Drosophilidae) under Laboratory Conditions

Spotted wing drosophila, Drosophila suzukii Matsumura (Diptera: Drosophilidae), is a major pest of bushberries, caneberries, and other thinskinned fruits around the world. Current control programs rely solely on the use of frequent insecticide applications. Entomopathogenic fungi have shown some efficacy for tephritid fruit flies and D. suzukii. In this study, formulated products BotaniGard ES (Beauveria bassiana), and PFR-97 20% WDG (Isaria fumosorosea) were evaluated for efficacy on adult D. suzukii in laboratory fruit-dip studies. Blueberry fruits were dipped in high and low label rates of each fungal suspension before exposure to D. suzukii. Mortality was assessed at 24, 48, 72, and 168 h (1 wk) after D. suzukii release. After 1 wk exposure, the remaining live flies, dead flies, and fruit were removed from containers. The live flies were discarded whereas dead flies were placed in separate containers and checked for mycosis after 1 wk. Fruits were placed in small solo cups with lids and checked after 2 wk for emergence of D. suzukii. The high rate of BotaniGard ES caused significant mortality after 24 h and both rates of BotaniGard ES reduced emergence from fruit compared with the control. BotaniGard ES may be useful in rotation with other effective compounds, but further research under field conditions is needed to confirm this finding.

Field efficacy of Bacillus thuringiensis galleriae strain SDS-502 for the management of alfalfa weevil and its impact on Bathyplectes spp. parasitization rate.

Alfalfa weevil, Hypera postica Gyllenhal, is an important pest in forage alfalfa worldwide, and especially so on the Northern Plains of North America. Neither the weevil-specific fungus, Erynia phytonomi, nor the weevil's parasitoids are able to routinely suppress outbreaks as they do in the eastern U.S. A new Bacillus thuringiensis var. galleriae, having a Cry8Da coleopteran-active toxin, has been recently commercialized. We examined the efficacy of this B. thuringiensis product against the H. postica in replicated field trials in north central Montana. Because it has been suggested that efficiency of the parasitoids, Bathyplectes curculionis and Oomyzus incertus, was inversely proportional to host numbers (i.e., parasitoid efficiency increased when host population is low), we also sought to determine if a partial reduction of larval H. postica populations with a B. thuringiensis would yield to greater parasitoid efficiency, manifested as higher percent parasitism among the surviving larvae. The B. thuringiensis gave 27-40% reduction in weevil numbers at the low label rate, 55-59% for the high label rate. Mean parasitism at the two research locations varied from 5-26% and 17-36% respectively, but application of the B. thuringiensis had no significant effect on parasitism levels, i.e. parasitism was not greater in treated than in carrier control plots.

First Report of Penicillium expansum Isolates With Low Levels of Resistance to Fludioxonil From Commercial Apple Packinghouses in Washington State

HomePlant DiseaseVol. 101, No. 5First Report of Penicillium expansum Isolates With Low Levels of Resistance to Fludioxonil From Commercial Apple Packinghouses in Washington State PreviousNext DISEASE NOTES OPENOpen Access licenseFirst Report of Penicillium expansum Isolates With Low Levels of Resistance to Fludioxonil From Commercial Apple Packinghouses in Washington StateA. Amiri, K. A. Mulvaney, and L. K. PanditA. AmiriSearch for more papers by this author, K. A. MulvaneySearch for more papers by this author, and L. K. PanditSearch for more papers by this authorAffiliationsAuthors and Affiliations A. Amiri K. A. Mulvaney L. K. Pandit , Washington State University, Tree Fruit Research and Extension Center, Wenatchee 98801. Published Online:1 Mar 2017https://doi.org/10.1094/PDIS-09-16-1353-PDNAboutSections ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmailWechat Fludioxonil has been used since 2004 in the state of Washington to control several postharvest pathogens of pome fruit, including Penicillium expansum, the causal agent of blue mold. In a recent survey conducted between February and May of 2016, decayed apples were collected from multiple packinghouses (n = 95) across Washington State. Blue-mold symptomatic fruit were cut in half with a sterile scalpel and a small plug was taken from the inside of the lesion and transferred to Petri plates containing potato dextrose agar (PDA) acidified to pH 3.5 using lactic acid. Plates were incubated for 5 days at 22°C and a mycelial plug was cut from the growing colony and transferred to PDA plates and incubated until sporulation was observed. Isolates were single-spored, stored in 30% glycerol at –80°C, and identified to species level as described by Pitt (2002). A total of 1,200 isolates were collected and tested for sensitivity to fludioxonil using a germ tube elongation inhibition assay on PDA amended with 0.5 and 1.0 µg/ml fludioxonil (Scholar SC, Syngenta Crop Protection, Inc.). The 0.5 µg/ml dose discriminated lab-mutants with low resistance to fludioxonil (Li and Xiao 2008) whereas the 1.0 µg/ml dose was used in this study to detect highly resistant isolates. Overall, germ tube elongation on 0.5 µg/ml-amended PDA was observed in 128 (10.6%) P. expansum isolates, whereas none germinated at 1.0 µg/ml. The ability of fludioxonil to control P. expansum isolates with different levels of sensitivity was evaluated on detached apples. Four sensitive isolates and six isolates with different germ tube length in vitro were tested. Organically grown Red Delicious apples were surface sterilized in 0.8% sodium hypochlorite for 2 min, rinsed twice with tap water, and allowed to dry. Fruit were wounded twice (4 mm wide × 3 mm deep) near the stem-end area and immersed for 30 s in a Scholar SC suspension at 0.78 and 1.25 ml/liter, which correspond to the low and high label rates, respectively. Nontreated fruit were immersed in water and served as controls. Fruit were inoculated 4 h post treatment with 25 µl of spore suspension at 105 spores/ml and incubated for 6 days at 20°C in sealed boxes containing 200 ml of sterile water. Eight fruit were inoculated for each isolate and the assay was repeated twice. All isolates developed blue mold on control fruit, whereas only six isolates characterized with low resistance in vitro developed blue mold lesions on fruit treated with Scholar at 0.78 ml/liter. Disease severity in low-resistant isolates ranged from 51.1 to 71.4% relative to nontreated control. All isolates were fully controlled on fruit treated with the higher labeled rate regardless of their in vitro sensitivity. This is the first report of low resistance to fludioxonil in P. expansum from commercial apple packinghouses in Washington State. The emergence of such a population is likely due to frequent fludioxonil usage in recent years because of the widespread resistance to thiabendazole and the emergence of resistance to pyrimethanil in Washington and Pennsylvania (Caiazzo et al. 2014; Yan et al. 2014), the two other postharvest fungicides registered on pome fruit. These findings warrant continuous resistance monitoring and the implementation of adequate management strategies to avoid selecting for highly resistant pathogen populations.

Limited impacts of truck-based ultra-low-volume applications of mosquito adulticides on mortality in honey bees (Apis mellifera).

Adulticides applied against mosquitoes can reduce vector populations during times of high arbovirus transmission. However, impacts of these insecticides on pollinators and other non-target organisms are of concern to mosquito control professionals, beekeepers and others. We evaluated mortality of Culex quinquefasciatus and Apis mellifera when caged insects were exposed to low and high label rates of four common adulticides (Aqua-Pursuit™ [permethrin], Duet® [prallethrin + sumithrin], Fyfanon® [malathion] and Scourge® [resmethrin]) at six distances up to 91.4 m from a truck-mounted ultra-low-volume sprayer. Honey bee mortality was both absolutely low (61 m had limited impacts on honey bee mortality while providing effective mosquito control.

Strategies to Provide Fertilizer for Both Production and Consumer Phases of Petunia

The objective of this study was to compare strategies using water-soluble fertilizers (WSF) and controlled-release fertilizers (CRF) to provide adequate nutrition during both production and consumer phases of petunia (Petunia ×hybrida). Strategies included a CRF with a second prill coating (DCT) that delayed initial nutrient release, compared with a conventional single-coated CRF (OSM) and WSF. Rooted cuttings of petunia were grown for 42 days in trade 1-gal (2.84-L) containers (the “production phase”) with WSF only, a low rate of combined WSF and substrate-incorporated OSM, or low and high label rates of WSF and top-dressed (TD) OSM (WSF + OSM TD), WSF and substrate-incorporated DCT (WSF + DCT), OSM, or a commercial blend of substrate-incorporated OSM and DCT (OSM + DCT). By the end of production phase after 42 days, all fertilizer strategies tested produced horticulturally acceptable plants in terms of chlorophyll index and number of flowers. In a subsequent “consumer phase,” plants were maintained in containers or were transplanted into a landscape and irrigated with clear water for 98 days. Plant performance [number of flowers, SPAD chlorophyll index, dry weight, and tissue nitrogen (N) level] was greater during the consumer phase in treatments with high rates of CRF compared with WSF only or lower rates of CRF. On the basis of nutrient release in a sand substrate without plants at 10, 21, or 32 °C, the DCT had delayed nutrient release compared with single-coated CRF. The release rates of all CRF products and the duration of the delay in release from DCT were temperature dependent. A partial budget found that the lowest cost treatment was WSF only at $0.02/container. Comparing at high application rates, using WSF + DCT ($0.085/container) was more expensive than incorporated OSM ($0.05/container) and had a similar cost to WSF + OSM TD ($0.084/container). The greatly improved consumer performance for plants with residual fertilizer compared with WSF provides an opportunity to add value and profitability if a slightly higher sales price could be obtained. Several fertilizer strategies are available depending on material and labor cost and availability and preferred crop management style.

Control of Green Peach Aphid on Green Cabbage, 2014: Table 1

The objective of this study was to evaluate the residual efficacy of three industry standard insecticides at low label rates against green peach aphid in cabbage tested under desert growing conditions. Cabbage “Gazzelle” was direct seeded into double row beds on 42-inch centers on 24 January 2014. Plots were two beds in width by 45 ft in length and bordered by two untreated beds. Stand establishment was achieved using overhead sprinkler irrigation and irrigated via furrow thereafter. Four replications of each treatment were arranged in an RCB design. Formulations …