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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.

Root exudates from weedy ryegrass hybrid type and selected crop plants affect soil microbial communities in two soil types of the Western Cape, South Africa

Michael Ignatius Ferreira, Anélia Marais, Alfred Botha, Carl Frederick Reinhardt, Marieta van der Rijst

Article ID: 2378
Vol 4, Issue 2, 2023

DOI: https://doi.org/10.54517/ama.v4i2.2378
VIEWS - 5010 (Abstract)

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Abstract

All growing plant roots have the ability to produce root exudates to which soil microbes are attracted. The objective of this study was to utilise the Biolog EcoPlate™ system to indicate the impact of soil type on soil microbial communities in the rhizosphere following treatment with pot leachates that contain various plant root exudates. A greenhouse experiment was conducted in 2021 and repeated in 2022 from May until July (southern hemisphere) in this winter rainfall area with Mediterranean climatic conditions. This ensured that natural daylight hours in the greenhouse coincided with those experienced in the field by winter-growing crops, from seeding until maturity (May to October). Pot leachate that contained various plant root exudates from six donor plant species (wheat, barley, two lupine cultivars, ryegrass pasture type, and weedy ryegrass hybrid type) was utilised as treatment for respective recipient pots, in which wheat (Triticum aestivum v. SST 027) was grown as a test plant. Recipient plants were grown in two sets of pots, each with two different soil types. Soil samples from recipient pots were used to inoculate the Biolog EcoPlate™ system, and the carbon utilisation patterns obtained in this process were compared to the soil microbial populations present in the soil samples collected prior to treatments. Pot leachate treatment effects on the two soil types differed. Similarly, the treatments had differential effects on the measured soil microbial populations of the recipient wheat plants. Results indicate that the pattern of substrate utilisation by the Biolog EcoPlate™ methodology indicates changes in the number of colony forming units in the soil. In this regard, it was clear that ryegrass pasture variety and weedy ryegrass hybrid type caused similar effects on the soil bacteria communities in the rhizosphere. It is concluded that the primary impact of soil type is distinct microbial communities as an important factor regulating plant and plant-microbe synergy. Secondly, due to the strong selective forces root exudates have on the soil microbiome, conspicuous microbial communities in the rhizosphere of each plant species will continue to develop over time.

Keywords

Biolog EcoPlate™ system; microbiome; pot leachate; rhizosphere; soil microbes

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Copyright (c) 2023 Michael Ignatius Ferreira, Anélia Marais, Alfred Botha, Carl Frederick Reinhardt, Marieta van der Rijst

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Editor-in-Chief

Prof. Zhengjun Qiu

Zhejiang University, China

Honorary Editor-in-Chief

Cheng Sun

Academician of World Academy of Productivity Science; Executive Chairman, World Confederation of Productivity Science China Chapter, China

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