Determinants and Methods of Quality Management in Agriculture and Food Processing

Topic Information

Dear Colleagues,

Both the COVID-19 pandemic and the recent war in Ukraine have had a significant impact on the markets of raw materials and food products. In 2020–2021, the pandemic was responsible for record increases in the prices of cereals and cereal products. In 2022, there is an increase in the prices of all raw materials, especially food and energy raw materials. On the other hand, the recent war in Ukraine has severely disrupted the supply of these raw materials and led to historically soaring prices in many commodity markets. In particular, the increase in energy prices over the year was the largest since 1973. Thus, the comprehensive effects of the COVID-19 pandemic and the war in Ukraine have had a profound impact on global commodity markets. The aim of this Topic is to provide a collection of high-quality research papers covering a wide range of topics with a fresh look at the quality of goods in an environment of economic and political uncertainty. We welcome original empirical and theoretical contributions to all aspects of quality determinants in commodity and food markets. We also welcome documents related to the situation and problems of agricultural exchanges. Quality plays a key role in commodity science and is the subject of a wide range of topics related to key areas of economic and social life. It is an interdisciplinary research area and is therefore the subject of research across many scientific disciplines. It is expressed in the way the organization is managed and is an important aspect that determines the success of the organization and at the same time constantly evolves. Currently, quality no longer relates to the extent to which a given product or service meets customer requirements, but also relates to management processes.

Quality management is the deliberate performance of the management function in terms of optimizing the use of resources and other factors, as well as process rationalization, and focuses on obtaining high-quality products and their continuous improvement. Therefore, in quality management, the goal should always be to improve the quality of products, aiming to satisfy the expectations and needs of the customer. Quality management is an approach that aims to improve the efficiency and flexibility of an organization in order to meet the requirements, needs and expectations of customers.

Currently, there is an increased interest in quality issues, which results from the development of the market and the desire to increase the competitiveness of enterprises. The continuous scientific and technical developments of the agricultural and processing industries and the need for the constant modification of products, with the aim to increase the efficiency of enterprises, means that more attention is paid to the quality of processes and final products. Striving to improve the quality of production, in turn, puts pressure on the introduction of innovative technologies. This results in an increase in labor productivity, the creation of a culture supporting innovation, modern products, a lower consumption of raw materials, increased pro-ecological effect, better use of the means of production, better satisfaction of the growing needs of customers and an increase in the pace of development of enterprises and the entire economy.

Prof. Dr. Barbara Sawicka
Dr. Barbara Krochmal-Marczak
Dr. Piotr Barbaś
Dr. Dominika Skiba
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.3	4.9	2011	19.2 Days	CHF 2600	Submit
Businesses businesses	-	-	2021	24.7 Days	CHF 1000	Submit
Processes processes	2.8	5.1	2013	14.9 Days	CHF 2400	Submit
Standards standards	-	-	2021	30.6 Days	CHF 1000	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit

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Published Papers (2 papers)

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13 pages, 3093 KiB  

Open AccessArticle

Quality of Surface and Groundwater in the Sierra de Amula Region, Jalisco, Mexico

by Oscar Raúl Mancilla-Villa, Fabiola Villafaña-Castillo, Álvaro Can-Chulim, Rubén Darío Guevara-Gutiérrez, José Luis Olguín-López, Elia Cruz-Crespo, Jonas Alan Luna-Fletes and Juan Uriel Avelar-Roblero

Agriculture 2025, 15(3), 278; https://doi.org/10.3390/agriculture15030278 - 28 Jan 2025

Viewed by 607

Abstract

Water is a valuable natural resource, indispensable in the productive, economic, and social development of human beings, agriculture, and domestic and industrial uses throughout the world. Two samplings were established to evaluate the quality of surface and underground water for agricultural irrigation in [...] Read more.

Water is a valuable natural resource, indispensable in the productive, economic, and social development of human beings, agriculture, and domestic and industrial uses throughout the world. Two samplings were established to evaluate the quality of surface and underground water for agricultural irrigation in the Sierra de Amula Region, Jalisco, Mexico. The first was performed during the dry season from November 2021 to April 2022, and the second was performed during the rainy season from July to September 2022 through completely random probabilistic sampling and a longitudinal descriptive study. In total, 25 surface water and 30 groundwater samples were taken. Each sample was evaluated for its pH, electrical conductivity, and ionic concentration (Ca²⁺, Mg²⁺, Na⁺, K⁺, CO₃²⁻, HCO₃⁻, CI⁻, SO₄²⁻). For data analysis, we determined the ionic concentrations and the salinity and sodicity indexes, including the electrical conductivity, pH, sodium adsorption ratio (SAR), and cationic ratio of soil structural stability (CROSS). The results indicate that the ionic concentration is mainly due to calcium bicarbonate, probably due to the geology of the region through water–rock interactions, and the pH is between 6.64 and 7.77; with respect to EC, most of the sampled sites are concentrated in medium-salinity waters of 250–750 µS cm⁻¹. The sodium adsorption ratio (SAR) showed that the waters have high ionic concentrations of calcium and magnesium and low sodium. The CROSS values were lower than the SAR values, showing that the concentration of potassium ions K⁺ is low in the evaluated waters. With respect to salinity and sodicity, the water quality of the sampled sites, both surface and groundwater, can be considered good for agricultural use. Given that it was sampled in two seasons, the concentration of ions varies in the rainy season, with the dragging of materials causing the ions to concentrate to a greater extent. This type of research benefits farmers in reducing production costs, having knowledge of water quality, and decision making. We recommend that the alkaline pH of the surface or groundwater be conditioned according to the requirements of the crop to be grown and the irrigation method to be used. Full article

(This article belongs to the Topic Determinants and Methods of Quality Management in Agriculture and Food Processing)

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Figure 1

Figure 1
<p>Study sites in the Sierra de Amula Region, Jalisco, Mexico.</p> Full article ">Figure 2
<p>Ionic concentration in surface and underground water during the dry season in the Sierra de Amula Region, Jalisco.</p> Full article ">Figure 3
<p>Ionic concentration in surface and underground water during the rainy season in the Sierra de Amula Region, Jalisco.</p> Full article ">Figure 4
<p>Electric conductivity and pH during the dry season.</p> Full article ">Figure 5
<p>Electric conductivity and pH during the rainy season.</p> Full article ">Figure 6
<p>(<b>a</b>,<b>b</b>). Classification diagram of sodium adsorption ratio and electric conductivity for surface and underground water samples obtained in the Sierra de Amula Region, Jalisco, Mexico.</p> Full article ">Figure 7
<p>Residual sodium carbonate during the dry and rainy seasons.</p> Full article ">Figure 8
<p>Classification of surface and underground water electrical conductivity and structural stability cationic ratio (CROSS) in the Sierra de Amula Region, Jalisco, during the (<b>a</b>) dry and (<b>b</b>) rainy seasons.</p> Full article ">

21 pages, 5901 KiB  

Open AccessArticle

A Rapid Identification Method for Cottonseed Varieties Based on Near-Infrared Spectral and Generative Adversarial Networks

by Qingxu Li, Hao Li, Renhao Liu, Xiaofeng Dong, Hongzhou Zhang and Wanhuai Zhou

Agriculture 2024, 14(12), 2177; https://doi.org/10.3390/agriculture14122177 - 29 Nov 2024

Viewed by 581

Abstract

China is a major cotton-growing country with numerous cotton varieties, each exhibiting significant differences in yield and fiber quality. However, the current management of cottonseed varieties is disorganized, resulting in severe homogenization and the presence of counterfeit and mislabeled varieties. The detection of [...] Read more.

China is a major cotton-growing country with numerous cotton varieties, each exhibiting significant differences in yield and fiber quality. However, the current management of cottonseed varieties is disorganized, resulting in severe homogenization and the presence of counterfeit and mislabeled varieties. The detection of cottonseed variety information has become a critical issue for the Chinese cotton industry. In this study, we collected near-infrared (NIR) spectral data from six cottonseed varieties and constructed a GAN for cottonseed NIR data (GAN-CNIRD) model to generate additional cottonseed NIR data. The Euclidean distance method was used to label the generated NIR data according to the characteristics of the true NIR data. We then applied Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Normalization algorithms to preprocess the combined dataset of generated and real cottonseed NIR data. Feature wavelengths were extracted using Bootstrap Soft Shrinkage (BOSS) and Competitive Adaptive Reweighted Sampling (CARS) algorithms. Subsequently, we developed Linear Discriminant Analysis (LDA), Random subspace method (RSM), and convolutional neural network (CNN) models to classify the cottonseed varieties. The results showed that for the LDA model, the use of feature wavelengths extracted after Normalization-BOSS processing achieved the best performance with an accuracy of 97.00%. For the RSM model, the use of feature wavelengths extracted after SNV-CARS processing achieved the best performance with an accuracy of 98.00%. For the CNN model, the use of feature wavelengths extracted after MSC-CARS processing achieved the best performance with an accuracy of 100.00%. Data augmentation using GAN-CNIRD-generated cottonseed data improved the accuracy of the three optimal models by 6%, 5%, and 6%, respectively. This study provides a crucial reference for the rapid detection of cottonseed variety information and has significant implications for the standardized management of cottonseed varieties. Full article

(This article belongs to the Topic Determinants and Methods of Quality Management in Agriculture and Food Processing)

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Figure 1

Figure 1
<p>Six different cottonseed varieties.</p> Full article ">Figure 2
<p>The NIR data acquisition system for cottonseeds.</p> Full article ">Figure 3
<p>WGAN-GP.</p> Full article ">Figure 4
<p>GAN-CNIRD.</p> Full article ">Figure 5
<p>CNN.</p> Full article ">Figure 6
<p>Loss of GAN-CNIRD.</p> Full article ">Figure 7
<p>Generated and real cottonseed data.</p> Full article ">Figure 8
<p>The preprocessed cottonseed data.</p> Full article ">Figure 9
<p>Selection of cottonseed features using the CARS.</p> Full article ">Figure 10
<p>Feature wavelengths selected by CARS.</p> Full article ">Figure 11
<p>The variation of RMSECV with the number of iterations.</p> Full article ">Figure 12
<p>The weight distribution at the 23rd iteration.</p> Full article ">Figure 13
<p>Feature wavelengths selected by BOSS.</p> Full article ">

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