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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.

Analytical economic study of rice supply response in Egypt

Rehab A. H. Awad, Dina F. M. Enany, Amr S. S. El Sayed, Taher Kadah

Article ID: 2845
Vol 5, Issue 4, 2024

DOI: https://doi.org/10.54517/ama2845
VIEWS - 1717 (Abstract)

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Abstract

Recent studies have identified several challenges associated with rice production, which hinder the achievement of self-sufficiency in rice. As a crop that heavily relies on irrigation water, rice production has implications for the nation’s water security. Nevertheless, there is still hope for reducing the future food gap by implementing agricultural policies that encourage farmers to increase production. Therefore, it has become essential to understand the responsiveness of rice cultivation area supply to changes, as this is crucial for making decisions regarding agricultural policies related to production and farm prices during the period from 2010 to 2023. The aim is to estimate farmers’ responsiveness, and the time required to achieve this response, as well as the supply elasticity. To achieve the research objective, both descriptive and quantitative statistical analysis were used, applying the Stepwise method in its logarithmic form to estimate the Supply Response. The elasticity of supply response for the rice cultivation area was estimated to be approximately 1.86% in the short term and 2.09% in the long term. It was also found that the response time of the cultivated rice area supply in Egypt to changes is low, estimated at around 1.12. This may be due to the inability to expand the cultivated area of this crop in the following year, as rice cultivation requires large amounts of water, posing a challenge given the scarcity of water resources. Additionally, farmers aim to maximize profits from the crops they cultivate.

Keywords

supply response; R. solo model; correlation matrix; stepwise regression statistical flexibility

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Copyright (c) 2024 Rehab A. H. Awad, Dina F. M. Enany, Amr S. S. El Sayed, Taher Kadah

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Zhejiang University, China

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