Amounts of the insecticide thiamethoxam required for 50% mortality of western corn rootworm larvae, Diabrotica virgifera virgifera LeConte, were reduced 100-fold when extracts of germinating corn, Zea mays L., were used to entice neonate larvae to feed on it. In behavioral bioassays, neonate rootworm larvae fed vigorously on filter paper disks treated with liquid pressed from corn roots. Moreover, disks treated with an acetone extract of corn (dried and rewetted with water) also elicited strong feeding from larvae. Larvae wandered away from filter paper disks treated with distilled water without feeding. Dilutions of thiamethoxam were tested in the bioassay alone or with corn extract and the efficacy of this insecticide was improved by the addition of the corn extract. For solutions containing 10 ppm thiamethoxam, 95% larval mortality occurred after 30 min of exposure when corn extract was present, but only 38% mortality occurred when the same concentration of insecticide alone (no feeding stimulants) was tested. Larval mortality after 24 h was significantly higher for corn extract-treated disks with 0.01, 0.1, 1, or 10 ppm insecticide than for the same concentrations without corn extract. Thiamethoxam did not deter larval feeding on corn extract, even at the highest concentration of thiamethoxam tested.
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http://dx.doi.org/10.1603/0022-0493-98.4.1150 | DOI Listing |
Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields.
View Article and Find Full Text PDFAnal Chim Acta
February 2025
Food Laboratory of Zhongyuan, Luohe, 462000, Henan Province, PR China.
Background: Edible oils are susceptible to contamination with polycyclic aromatic hydrocarbons (PAHs) throughout production, storage, and transportation processes due to their lipophilic nature. The necessity of quantifying PAHs present in complex oil matrices at trace levels, which bind strongly to impurities in oil matrices, poses a major challenge to the accurate quantification of these contaminants. Therefore, the development of straightforward and effective methods for the separation and enrichment of PAHs in oil samples prior to instrumental analysis is paramount to guaranteeing food safety.
View Article and Find Full Text PDFPolymers (Basel)
December 2024
College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.
Corn stalk fibers extracted from cattle manure (CSFCM) represent a unique class of natural fibers that undergo biological pre-treatment during ruminant digestion. This study systematically investigates the optimization of CSFCM-reinforced friction materials through controlled silane treatment (2-10 wt.%).
View Article and Find Full Text PDFPlants (Basel)
December 2024
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Accurate crop density estimation is critical for effective agricultural resource management, yet existing methods face challenges due to data acquisition difficulties and low model usability caused by inconsistencies between optical and radar imagery. This study presents a novel approach to maize density estimation by integrating optical and radar data, addressing these challenges with a unique mapping strategy. The strategy combines available data selection, key feature extraction, and optimization to improve accuracy across diverse growth stages.
View Article and Find Full Text PDFPlants (Basel)
December 2024
School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun 130052, China.
The precise identification of maize kernel varieties is essential for germplasm resource management, genetic diversity conservation, and the optimization of agricultural production. To address the need for rapid and non-destructive variety identification, this study developed a novel interpretable machine learning approach that integrates low-field nuclear magnetic resonance (LF-NMR) with morphological image features through an optimized support vector machine (SVM) framework. First, LF-NMR signals were obtained from eleven maize kernel varieties, and ten key features were extracted from the transverse relaxation decay curves.
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