Pesticide residue in cucumber-exposed plants, and its associated effects on soil nematode population

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Pesticide residue in cucumber-exposed plants, and its associated effects on soil nematode population

Praise Ewomaoghene Imonikebe, Obemeata Emmanuel Oriakpono, Helen Imafidor, Aroloye Ofo Numbere

Article ID: 3196
Vol 6, Issue 2, 2025

DOI: https://doi.org/10.54517/ama3196

Received: 31 December 2024; Accepted: 7 April 2025; Available online: 15 April 2025; Issue release: 30 June 2025

VIEWS - 64 (Abstract)

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Abstract

This study investigates pesticide residues in cucumber plants and their impact on soil nematode populations while evaluating the effect of pesticides on cucumber growth and yield. Gas Chromatography Tandem Mass Spectrometry (GC-MS/MS) was used to quantify pesticide residues, comparing the results to the Maximum Residue Limits (MRLs) defined by the Codex Alimentarius. Significant differences in residue levels were found between various pesticides and application rates. Diazinon residues ranged from 0.86 to 2.28 mg/kg, exceeding the MRL of 0.1 mg/kg, indicating soil contamination. Endosulfan had the lowest residues, from 0.44 to 1.75 mg/kg, which were within acceptable limits. Conversely, Malathion and Methoxychlor residues notably surpassed their MRLs, raising potential safety concerns. Further analysis using a linear regression model revealed a negative correlation between pesticide application and soil nematode populations. There was a proportional decrease in nematode populations with increasing pesticide application rate, with Malathion having the most significant impact, followed by Endosulfan, Methoxychlor, and Diazinon. The impact of pesticide application on cucumber plant growth and yield was assessed using one-way ANOVA, which uncovered significant differences across treatment groups. While pesticides are effective for pest control, their application must be carefully managed to avoid phytotoxicity and ensure optimal plant and environmental health, thereby enhancing maximum productivity.

Keywords

pesticide; cucumber; Maximum Residue Limits (MRLs); nematode

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