Agricultural inputs on rice yield: A case study of domestic rice production in Preah Vihear province, Cambodia

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.

Agricultural inputs on rice yield: A case study of domestic rice production in Preah Vihear province, Cambodia

Siek Darith, Lim Kim Eav, Loeurng Chymann, Leng Channy, Din Chamroeun, Yun Chihouy, Siengly Hang

Article ID: 2966
Vol 6, Issue 1, 2025

DOI: https://doi.org/10.54517/ama2966

Received: 30 November 2024; Accepted: 2 January 2025; Available online: 23 January 2025; Issue release: 31 March 2025

VIEWS - 1135 (Abstract)

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Abstract

The key factors affecting domestic rice production in Preah Vihear Province, Cambodia, are investigated in this study. Cambodia’s economy depends largely on rice, which is both a main food and a major export. It helps Cambodia’s economy and food security. However, rice production is difficult due to lack of water, small farms, and geographical problems. Preah Vihear province is a key rice-producing region with hills and small farms. It helps increase income and sell rice to other countries with unique and famous rice seeds. To analyze the factors affecting domestic rice production in Preah Vihear, a statistical model known as ordinary least squares (OLS) will be used to examine the relationship between various input variables (costs) and rice yield. Data were gathered from a sample of 51 rice-farming households across five villages in Preah Vihear province. Additionally, both regression models indicated that herbicide costs were the most significant factor affecting rice yield, demonstrating high significance at the 5% level. This was closely followed by the price for renting rice harvesters and the total land area for rice production (ton). This study provides crucial information that can be used to develop targeted strategies to increase rice production and enhance the livelihoods of rural farmers. This will also empower stakeholders in the agricultural sector to propose campaigns that promote sustainable rice cultivation in local communities.

Keywords

agricultural inputs; agricultural economics; rice yield; domestic rice production; Preah Vihear; Cambodia

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Copyright (c) 2025 Siek Darith, Lim Kim Eav, Loeurng Chymann, Leng Channy, Din Chamroeun, Yun Chihouy, Siengly Hang

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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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