The influence of slope exposure on the yield characteristics of winter wheat and spring barley in the Oka River basin, Russia

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.

The influence of slope exposure on the yield characteristics of winter wheat and spring barley in the Oka River basin, Russia

P. A. Shary, L. S. Sharaya, O. V. Rukhovich

Article ID: 2435
Vol 5, Issue 1, 2024

DOI: https://doi.org/10.54517/ama.v5i1.2435
VIEWS - 2631 (Abstract)

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Abstract

We studied the relationship of the yield of winter wheat and spring barley with slope exposure components in the west of the Oka River basin. The size of the study area was 250 km by 360 km. The yield characteristics included the maximal yield obtained when applying the optimal dose of fertilizers, the yield without applying fertilizers (control), and the maximal addition to yield, that is, their difference. The addition is shown to be most sensitive to climatic factors. For wheat, the addition increased on the warmer southwestern slopes, and for barley—on the wetter north-eastern slopes. The high sensitivity of the addition of barley to moisture is shown using its comparison with climatic water deficit. To compare slopes by the energy of incident solar radiation, we used the slope insolation in energy units. Although the difference in energy between the southwest and northeast slopes was only 2.2%, wheat addition on these slopes varied by more than a factor of two. The reasons for this are discussed. The results obtained show that when choosing locations for crop areas, it is advisable to take into account the exposure of the slopes.

Keywords

slope exposure; solar irradiation; winter wheat; spring barley; water deficit; incident solar energy

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Zhejiang University, China

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

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