Proposed water valuation scheme for a more sustainable agricultural water productivity

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.

Proposed water valuation scheme for a more sustainable agricultural water productivity

Khashayar Peyghan, Omid Raja, Masoud Parsinejad, Fatemeh Karandish

Article ID: 3098
Vol 6, Issue 1, 2025

DOI: https://doi.org/10.54517/ama3098

Received: 26 November 2024; Accepted: 8 January 2025; Available online: 14 January 2025; Issue release: 31 March 2025

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Abstract

Growing depletion of groundwater resources is a global threat and intensified under improper water valuation systems. Here, we proposed an irrigation water valuation framework based on the opportunity cost concept (IWVF), to better differentiate the value of surface water, renewable, and nonrenewable groundwater. In this study, a 10-years dataset was used in Marvdasht-Kharameh irrigation networks (MKINs) in southern Iran, where groundwater depletion overshoots the sustainable level with an annual average rate of 1.42 m y⁻¹. Irrigation water use, net incomes and losses, and economic water productivities (EWP) were estimated under the common and newly developed valuation methods. The reflections of adopting IWVF on EWPs were assessed under current condition and the proposed WP improvement scenarios, including removing irrigation efficiency gaps, changing the cropping calendar, and application of different levels of deficit irrigation. Results showed that the value of irrigation water supply ($436.91 million) exceeds gross income through crop production ($139.01 million) under current condition, which results in a net loss of $297.9 million in the study area. Hence, economic WP loss will be −0.33 $ m⁻³, meaning that consuming a unit of blue water causes $0.33 income loss under current condition. Applying WP improvement scenarios can reduce value of irrigation water by 27%, and gross income by 5.3%. Accordingly, common WP can increase by 6.6% from 1.81 to 1.93 kg m⁻³. However, the negative sign of EWP loss under management scenarios (−0.22 $ m⁻³) indicates that crop production in the study area is not viable due to its considerable environmental damages. Hence, current irrigation system should be revisited when sustainable agriculture is considered. The proposed water valuation method can help decision makers to better assess the consequences of WP improvement strategies, if the true value of different water resources is ignored.

Keywords

nonrenewable groundwater resources; opportunity cost; irrigation water valuation; environmental deterioration; economic water productivity

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Zhejiang University, China

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