Mathematical Modeling in Agriculture E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
The main goal of the book is to explore the idea behind data modeling in smart agriculture using information and communication technologies and tools to make agricultural practices more functional, fruitful and profitable.
The research in the book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers’ choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models were utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies.
Farm management information systems (FMIS) have constantly advanced in complexity as they have incorporated new technology, the most recent of which is the internet. However, few FMIS have fully tapped into the internet’s possibilities, and the newly developing idea of precision agriculture receives little or no support in the FMIS that are now being sold. FMIS for precision agriculture must meet a few more criteria beyond those of regular FMIS, which increases the technological complexity of these systems’ deployment in a number of ways. In order to construct an FMIS that meet these extra needs, the authors here evaluated various cutting-edge web-based methods. The goal was to determine the requirements that precision agriculture placed on FMIS.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Analyzing the Impact of Food Safety Regulations on Agricultural Supply Chains: A Mathematical Modeling Perspective
1.1 Introduction
1.2 Resources and Techniques
1.3 Results and Analysis
1.4 Conclusion
References
2 Modeling the Effects of Land Degradation on Agricultural Productivity: Implications for Legal and Policy Interventions
2.1 Introduction
2.2 Materials and Procedures
2.3 Results and Analysis
2.4 Conclusion
References
3 Mathematical Modeling of Carbon Sequestration in Agricultural Soils: Implications for Climate Change Mitigation Policies
3.1 Introduction
3.2 Resources and Techniques
3.3 Results
3.4 Discussion
3.5 Conclusions
References
4 Optimizing Livestock Feed Formulation for Sustainable Agriculture: A Mathematical Modeling Approach
4.1 Introduction
4.2 Managing Swine Herds Using Modeling
4.3 Models of a Sow Herd
4.4 Discussion
4.5 Conclusions
References
5 Modeling the Economic Impact of Agricultural Regulations: A Case Study on Environmental Compliance Costs
5.1 Introduction
5.2 Mechanisms Study Time and Location
5.3 Sampling
5.4 Analysis, Both Physical and Chemical
5.5 Module for Water Quality
5.6 Particulate Phosphorus and Suspended Solids
5.7 Calculation of PP
5.8 Model Caliphy
5.9 Scientifications Described by the Model
5.10 Simulation of Sediment Trap
5.11 Pumping Profile Modifications Simulation
5.12 Conclusion
References
6 Quantifying the Economic Benefits of Precision Agriculture Technologies: A Mathematical Modeling Study
6.1 Introduction
6.2 Method and Materials
6.3 Conclusion and Results
6.4 Conclusions
References
7 Optimizing Resource Allocation in Agribusinesses: A Mathematical Modeling Approach Considering Legal Factors
Introduction
Methods
A Framework for the Transmission and Command of Brucellosis: A Case Study Overview
Brucellosis Nominal Transmission Modeling
Modeling Disease Costs and Control Capabilities
Creating a Cost Model and Confronting the Challenge of Control Design
Analysis, Design, and Parameterization Techniques
Overview of the Control and Surveillance Design
Network Model Identification and Validation for Zoonoses
Results
Indicative Model
Control Strategy Modeling
Optimized Approaches
Parameterization
Discussion
Wide-Ranging Perspectives on High-Performance Control
Talking About Parameterzing Models
Conclusion
References
8 Modeling the Dynamics of Agricultural Cooperatives and Legal Implications for Farmer Organizations
8.1 Introduction
8.2 Resources and Techniques
8.3 Conclusion
References
9 Optimizing Agroforestry Systems for Sustainable Agriculture: A Mathematical Modeling Approach
9.1 Introduction
9.2 Relationships Between Structure and Activity (SAR) and the Level of Toxicological Involvement
9.3 Threshold Approaches
9.4 Reciprocal Analysis
9.5 Chemical-Specific Adjustments
Conclusion
References
10 Simulating the Effects of Climate-Smart Agriculture Practices on Farm Resilience: A Mathematical Modeling Approach
10.1 Introduction
10.2 Definitions, Concepts, and Methods for the Analytical Framework
10.3 Results
10.4 Consequences for Political Implementations
10.5 Advanced Research
10.6 Conclusions
References
11 Modeling the Dynamics of Agrochemical Regulations and Impacts on Agricultural Productivity
11.1 Introduction
11.2 Resources and Techniques
11.3 Results
11.4 Discussion
11.5 Conclusion
References
12 Optimizing Energy Consumption in Greenhouse Production: A Mathematical Modeling Approach
12.1 Introduction
12.2 Literature Review
12.3 The Creation of Mathematical Models a Range of Models
12.4 Formulation of a Model
12.5 Modeling of Groundwater Quality
12.6 Conclusion
References
13 Analyzing the Economic and Legal Impacts of Intellectual Property Rights on Plant Breeding Innovations: A Mathematical Modeling Study
13.1 Introduction
13.2 Competition Postulates
13.3 Transparent Competition
13.4 Concurrence Inter-Specific
13.5 Dynamic Plant Growth and Competition Models
13.6 Aspects Impacting the Result of Competitiveness
13.7 Crop-Weed Competition Models Applied in Practical Situations
13.8 Conclusion
References
14 Simulating the Effects of Land Use Regulations on Agricultural Land Values: A Mathematical Modeling Study
14.1 Introduction
14.2 Models of Component Agricultural Systems
14.3 Present-Day Farming System Frameworks in Relation to Certain Application Situations
14.4 Discussion
References
15 Simulating the Effects of Agricultural Land Fragmentation on Farm Efficiency: A Mathematical Modeling Analysis
15.1 Introduction
15.2 Conceptual Foundation
15.3 Resources and Techniques Household Polls
15.4 Results
15.5 Discussion
15.6 Conclusions
References
16 Simulating the Effects of Land Use Policies on Agricultural Productivity: A Mathematical Modeling Perspective
16.1 Introduction
16.2 Upcoming Applications of NextGen Farming Frameworks
16.3 Envisioned Consumers of the Application Chain Beneficiaries
16.4 Conclusion and Research Plan
References
17 Quantifying the Economic Benefits of Agricultural Extension Services: A Mathematical Modeling Analysis
17.1 Introduction
17.2 Creating New Models for the Future: A Demand-Driven, Prospective Strategy
17.3 Potential Improvements to Model Elements
17.4 Conclusions
References
18 Modeling the Impact of Agricultural Investment Incentives on Rural Development: Legal and Economic Perspectives
18.1 Introduction
18.2 Approach
18.3 Conversation
18.4 Conclusion
References
19 Optimizing Harvest Scheduling in Agriculture: A Mathematical Modeling Approach Considering Legal Restrictions
19.1 Initialization
19.2 Structure of the System
19.3 Irrigation Community Event
19.4 Assessment and Authentication
19.5 Conclusions
References
20 Quantifying the Economic Benefits of Agricultural Data Sharing: A Mathematical Modeling Perspective
20.1 Introduction
20.2 Model for Data Mining Process
20.3 Techniques for Machine Learning
20.4 Website Tools
20.5 Case Study: Grading of Mushrooms
20.6 Conclusion
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 2
Table 2.1 The experimental soil’s physical characteristics and chemical analys...
Table 2.2 The commercial brown sea algae extract’s chemical make-up.
Chapter 3
Table 3.1 Places in Manchester, UK, with their average annual temperature and ...
Chapter 6
Table 6.1 Determinants of nutrient movement in the soil.
List of Illustrations
Chapter 1
Figure 1.1 The 16 parameters that affect the requirement for modeling in diffe...
Figure 1.2 By nation type, company size, and their involvement in the food ind...
Chapter 4
Figure 4.1 The main occurrences and cyclical pattern in sow reproduction.
Chapter 5
Figure 5.1 The Everglades Location illustrates the EAA in relation to the diff...
Figure 5.2 The overall flow paths, BMP structures, and the hydraulic system co...
Figure 5.3 Comparison of the overall phosphorus constituent to the cumulative ...
Figure 5.4 CEM(0) correlation as an equation for exponential growth.
Figure 5.5 Event 237 flow and cumulative dissolved solids materials.
Figure 5.6 Implemented and noticed cumulative dissolved solids materials for E...
Figure 5.7 Summation of PP load for Event 237.
Figure 5.8 Implemented and noticed cumulative dissolved solids materials for E...
Figure 5.9 Sum of PP load for Event 355.
Figure 5.10 Dissolved solids implementation at particular positions for Event ...
Figure 5.11 Erodible mass implementation at chosen positions for Event 237.
Figure 5.12 Implementation of Events 220 and 258.
Chapter 6
Figure 6.1 Actual system.
Chapter 7
Figure 7.1 Spread graph example. Brucellosis spread chart.
Figure 7.2 Nominal spread model simulation based on an initial infection in a ...
Figure 7.3 Modeling the notional spread of an initial illness in a sedentary h...
Figure 7.4 Comparing immunization programs quantitatively. A vaccination progr...
Figure 7.5 Examination of a policy for monitoring and control. Enhancing bruce...
Figure 7.6 Creating the best possible immunization program. The top figure dis...
Figure 7.7 The spread framework determines the rate of human disease. In an ep...
Figure 7.8 Fundamental reproduction ratio for ideal layout. For the 20-herd ex...
Figure 7.9 Animal-level and animal-to-human controls are compared. Using the t...
Figure 7.10 Model recognition using data from snapshots. A nonlinear SIR frame...
Figure 7.11 Identification of the framework using time-course data. After a va...
Figure 7.12 Hardness. The resilience of the model to parameter modifications i...
Chapter 8
Figure 8.1 The calculation plan for the solid-state drainage pipeline having a...
Figure 8.2 A simulation of the impact of a transverse fracture in a drainage d...
Figure 8.3 The transverse crack’s critical stresses as it crosses the drainage...
Figure 8.4 A simulation of the impact of a fault in the structure of the demol...
Figure 8.5 How to simulate the impact of a flaw that results in the destructio...
Figure 8.6 How to simulate the impact of a fault that results in the destructi...
Chapter 9
Figure 9.1 Decision-making framework utilized by the JECFA to assess flavoring...
Figure 9.2 Data on the oral toxicity of methoxychlor for humans and animals we...
Figure 9.3 The 101-fold uncertainty feature divided into toxicokinetics and to...
Figure 9.4 Chemical-specific CSAF creation. The CSAF is calculated by dividing...
Chapter 12
Figure 12.1 Flowchart of the Model Creation method.
Figure 12.2 A contrast of the hydraulic heads computed and measured.
Figure 12.3 Remaining at calibration targets from a comparison of measured and...
Figure 12.4 A simulated change in hydraulic head due to a parameter value chan...
Figure 12.5 The simulated uncertainty in the calibration objectives for hydrau...
Figure 12.6 Predicted range in hydraulic heads.
Figure 12.7 Simulated wellhead protection zones utilizing a variety of hydraul...
Figure 12.8 Simulated pollutant concentrations.
Chapter 14
Figure 14.1 A diagram of the production scenario is used to identify the aspec...
Figure 14.2 The DSSAT Cropping System Model includes elements for land, soil, ...
Figure 14.3 A theoretical framework for simulating “agricultural systems”.
Figure 14.4 Diagram of a farming system that highlights the relationships and ...
Figure 14.5 The AgMIP Regional Integrated Assessment framework, which places a...
Chapter 15
Figure 15.1 An integrated evaluation of the management techniques and rice pro...
Figure 15.2 A map of the research area that shows where the houses were scruti...
Figure 15.3 Temporal alterations in rice-dependent agricultural frameworks in ...
Figure 15.4 The variation in farm level indicators for several farmer goals (h...
Figure 15.5 The correlation between rice productions and labor yield, cumulati...
Figure 15.6 The differences in farm size, hired labor, and non-rice revenue ac...
Chapter 16
Figure 16.1 Knowledge pyramid which connects data to information to knowledge ...
Figure 16.2 Application chains are a way of describing how data and informatio...
Figure 16.3 The parts of the infrastructure for data, modeling, and dispatch b...
Figure 16.4 A schematic diagram illustrating the interconnections between farm...
Chapter 17
Figure 17.1 Links between the competitive space of knowledge product growth an...
Figure 17.2 Connections between data and decision-making resources at the fiel...
Figure 17.3 Components of the value chain for the agriculture industry.
Figure 17.4 Lateral linkages between different scales and environmental elemen...
Chapter 18
Figure 18.1 The research articles pertaining to “big data in agriculture” are ...
Chapter 19
Figure 19.1 A specific data model for managing agricultural plots.
Figure 19.2 The planned design in broad strokes.
Figure 19.3 The data framework for applications at the IoT level.
Figure 19.4 The case study’s layout.
Figure 19.5 Analysis of module scalability. CPU utilization (a) RAM usage (b).
Figure 19.6 Reaction time for activities using various techniques of operation...
Figure 19.7 Reaction time in accordance with the platform’s tiers at the level...
Figure 19.8 Growth of apricot crop humidity depending on predetermined water l...
Figure 19.9 The development of effective irrigation while contrasting soil moi...
Chapter 20
Figure 20.1 Data flow diagram for the process framework for an ML implementati...
Figure 20.2 Applet for grading mushrooms.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Mathematical Modeling in Agriculture
Edited by
Sabyasachi Pramanik
Niranjanamurthy M.
Ankur Gupta
and
Ahmed J. Obaid
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-23369-4
Front cover images supplied by Pixabay.comCover design by Russell Richardson
Preface
The research in the book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers’ choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models were utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies.
Farm management information systems (FMIS) have constantly advanced in complexity as they have incorporated new technology, the most recent of which is Internet connection. However, few FMIS have fully tapped into the Internet’s possibilities, and the newly developing idea of precision agriculture receives little or no support in the FMIS that are now being sold. FMIS for precision agriculture must meet a few more criteria beyond those of regular FMIS, which increases the technological complexity of these systems’ deployment in a number of ways. In order to construct an FMIS that meet these extra needs, the authors here evaluated various cutting-edge web-based methods. The goal was to determine the requirements that precision agriculture placed on FMIS.
1Analyzing the Impact of Food Safety Regulations on Agricultural Supply Chains: A Mathematical Modeling Perspective
Nimit Kumar1, Shwetha M.S.2, Govind Shay Sharma3, Nitin Ubale4, Nuzhat Fatima Rizvi5 and Dharmesh Dhabliya6*
1College of Agriculture Sciences, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
2Department of Food Technology, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Karnataka, India
3Department of Mathematics, Vivekananda Global University, Jaipur, Rajasthan, India
4Department of Horticulture, College of Agriculture, Parul University, Limda, Dist-Vadodara, Gujarat, India
5Symbiosis Law School, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
6Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
Abstract
With several mathematical models developed for diverse food subjects, mathematical modeling plays a significant role in the field of food engineering. There is, however, little data available on the utilization of statistical models in the foodstuff industry. This essay intends to analyze the scope and circumstances of mathematical model use in connection to the North American foodstuff and beverage sector. It examines the understanding, characteristics, and present utilization of modeling methodologies in connection to the key features of the industry. This study covered 203 food firms overall from 12 different North American nations. The size of the firm and the nation in which it operates are shown to be more relevant determinants of the utilization of statistical models than the kind of food industry. The most advanced nations are located with a better degree of knowledge and model use. Similar patterns were seen at the micro level, demonstrating that smaller and mid-category businesses had limited resources, expertise, and model adoption.
Keywords: Food industry mathematical models, modeling expertise using models
1.1 Introduction
A valuable technique for determining the influence of various system and process features on the output of a process is mathematical modeling. Modeling diverse food items and/or processes is difficult, mostly because the phenomena are not well understood, modeling experiments are complex, and there are unknowns about trustworthy data and food attributes.
While statistical models on foodstuff nature through the foodstuff chain facilitate knowledge based on food features and various events which take place, every activity, grade and foodstuff safety properties is a huge worry of both clients and the foodstuff industry.
The study of competing options is improved by foodstuff process modeling and/or multiscale simulations from foodstuff components up to the complete foodstuff supply chain (SC). It is important to remember that foodstuff tissues are multiscale aggregation having unique properties at every spatial scale, necessitating the use of multiscale modeling. Because of this, the primary goals in technological food procedures are to comprehend a specific phenomena using current theoretical knowledge and accessible data, create methods, and regulate those processes. According to [1], there are two main purposes for using foodstuff technique modeling: (i) to well realize a method, and (ii) to test potential “what-if” situations. Additionally, modeling of food products and processes might be done using sophisticated model-based methodologies. These methods could involve model-based production control or mathematically based product/process optimization. Locating “good” data points or feature representations is the goal of knowledge transfer techniques in order to improve the target model’s predictability and believability.
The use of models in the foodstuff sector often depends on static, simplistic models which don’t provide a meaningful assessment of observable methods, conditions for quality or safety, or environmental effect. Additionally, these models streamline the explanations of the food system’s mechanics and rate equations for change. Models may be divided into three categories, analytical models, numerical models, and observational models. Models may also be divided into three types based on the point of view: product, process, and product-process interactions. A newer model called multiscale [2] modeling has emerged to address the difficulty of modeling at several geographical scales. According to [3] the complexity of modeling depends on the fact that a variety of skills are required, including knowledge in food technology, applied math and statistics, technology, information technology, etc. It’s crucial to remember that every modeling technique has its drawbacks. Even though many models have been developed, [4] claims that there is very little evidence of their use in actual situations. The authors of [5] provide some scientific guidelines in modeling information extraction and formalization for addressing the function of foodstuff operators in little businesses.
Therefore, the goal of the current study was to assess the utilization of statistical models in the foodstuff sector related to the understanding of statistical approaches, the extent to which these tools are used by businesses, and obstacles to adopting mathematical modeling. Environmental aims and indicators as well as modeling of environmental consequences were also studied. This study determined the need for food modeling across a range of application sectors. According to the nation in which the firms operate, the functions of the organizations in the food chain, and the dimension of the organizations, the results were distributed.
Review of the literatureBy examining published work utilizing the academic databases Web of Science, Scopus, a critical literature review was carried out. These databases located the best scientific publications about the quality and safety of food items as well as environmental models for food processes and products. No topographical limitations were used, and the hunt was only allowed to turn up research that had been published during the previous ten years. Modeling foodstuff products and dangers from a foodstuff safety belief was the main emphasis of the bulk of papers linked to modeling in the food industry.
The degree of understanding of a certain phenomenon is correlated with how difficult it is to analyze this issue. For instance, during the processing of food, a variety of heat transfer activities, like chilling, disinfecting, chilling, making, roasting, etc., take place in numerous unit operations. Mass transfer is a typical subject discussed in many publications in addition to heat transfer. Analysis of foodstuff processing processes such as baking, freezing, hydrating, filtering, dissipation, draining, osmosis, membrane separation, remoistening, aggregating, removal and repository requires modeling of mass movement. In food processing, simultaneous heat and mass transmission may be seen in roasting processes, baking processes, and food drying models. As the developing automations have distinct forms of action relying on the origin of energy transfer, modeling assumptions might vary depending on whether a food process occurs using traditional or non-thermal technologies. To ensure food safety while maintaining food quality, non-thermal processing has been developed. [6] gave one of the most recent updates on modeling heat transport in traditional and cutting-edge technology.
[7] developed quality modeling of many foodstuff grade characteristics, like taste, complexion, presence, and nutritive value. Some of the most recent efforts to model quality index were addressed by [8]. Foodstuff safety models range from risk assessment to food security to modeling in order to improve transportation and shelf life. Modeling of ecological effects and environmental indicators in the foodstuff industry is becoming more important in light of the significance of the Sustainable Development Goals established by the UN (UNESCO, 2022). Food firms, food processes, and food products are the three views that make up the scale of environmental models in the food chain.
The authors of this work recognized this as a research need since they found that examination of the deployment of statistical models [9] in foodstuff firms was not the center of the research. This study’s working premise was that food corporations don’t often employ mathematical models.
1.2 Resources and Techniques
Specifications of the surveyThe investigation took place in the first half of 2018. An English-language questionnaire was created and then translated into the native tongues of the participating nations. There were 203 food firms overall, from 12 different European nations, and they were split into two groups: Asian Nations (AN) and Other North American (ONA) countries. The System Partnership Accord between the American Organization in Technology defines ITC as less research-intensive nations. Companies were picked from every region of the tested nations. The authors acknowledge that this approach only provides a “convenience sample” of food firms rather than a genuinely random sample of them. The specimen is compared to numerous produced surveys on the execution of specific tools in various countries having fewer than 80 food companies in a nation like quality management, hygiene practices, pest control, or food fraud. This is true despite the sample’s small number of companies per country. Under certain conditions of care, the broader European food industry might benefit from our findings.
The authors wanted to disseminate a questionnaire on the use of statistical models in the foodstuff business, so they contacted firms in advance and indicated that the survey is anonymous.
QuestionnaireTo assess the food industry’s use of modeling and optimization techniques from computer science and mathematics, a questionnaire has been created. The collection of responses offered the chance to examine how tools are now used for a variety of purposes, including the creation of products and processes, process control, foodstuff safety, decision assistance, and ecological effects.
The firms’ basic characteristics (nation of origin, size, industry of operation, and adopted enterprise systems) were covered in the first part. The understanding of different mathematical procedures, the extent of tool usage in businesses, and challenges associated with applying mathematical modeling were all examined in the second portion. A 5-point Likert scale [10] having the choice of one for “strongly disagree,” two for “disagree,” three for “no opinion,” four for “agree,” and five for “strongly agree” allowed the respondents to express their level of agreement. The analysis of modeling requirements in different application domains made up the third segment. The respondents were given the chance to rank their needs on a scale of 0 (“not applicable in our company”), 1 (“there is no need for mathematical models in my company”), 2 (“we would like to use some mathematical models”), 3 (“there is some (limited) use, we would like to expand our knowledge in this area”), and 4 (“we have an extensive use of models in this area”). The fourth section of the survey asked respondents to rate their awareness of environmental impacts on a scale of 0 (no analysis of this impact is done), 1 (organization analyses fundamental ecological data), 2 (organization computes particular ecological measures for this influence), 3 (organization changes basic data to compute ecological affect per technique/functional unit), and 4 (organization computes ecological footprints connected to this thrust). Using the similar 5-point Likert scale from 1 (firmly disagree) to 5 (strongly agree), organizations were asked to indicate if they had recorded environmental aims or indicators in the last section.
Statistical analysisSince the data were not normally distributed, Likert scale results were treated as ordinal estimates and non-parametric statistical tests were utilized. We classified the noticed remarks using a cluster analysis. The food industry, firm size, and nation type were used as categorical variables in a two-step cluster analysis. For finding statistically major differences between the clusters, the Mann-Whitney U test was performed.
In order to better understand the general relationships in the two data sets, a PCA [11] was performed on 10 statements assessing knowledge of environmental implications and 16 statements indicating desired requirements for modeling in diverse application areas. Prior to analysis, the appropriateness of PCA was determined. With specific KMO measures all more than 0.8 and ratings ranging from “meritorious” to “marvelous,” the entire Kaiser-Meyer-Olkin (KMO) [12] estimate relating to the demands for modeling was 0.925. Data were probably factorable since Bartlett’s test [13] of sphericity was statistically significant (p .0005). The total KMO score [14] for awareness of environmental effects was 0.747, and all every KMO scores were over 0.75, earning them the designation “meritorious.” Statistics were significant for Bartlett’s test of sphericity (p .0004). The PCA retrieved two elements explaining 73.7% of the cumulative variance for the analysis of modeling requirements and two components showing 81.4% of the summed variance for recognition of ecology effects based on the criterion of eigen values [15] above one. MS Excel and SPSS [16] were used to process the statistical data. A 0.05 threshold for statistical significance was established.
1.3 Results and Analysis
1.3.1 Knowledge, Application, and Obstacles to Food Modeling
The food industry, firm size, and nation type were all used as categorical variables in a two-cluster analysis. Entire findings indicate that the greatest degree of agreement across businesses was connected to their understanding of transportation phenomena and mechanics, production planning, real-time process control [17], and their regular usage of production planning models. They don’t agree that response surface modeling should be used often, and they don’t think their product is too basic to benefit from any modeling. Food modeling calls a solid knowledge of applied mathematics, computer science, and food technology, stressing the requirement.
1.3.2 Obstacles to Our Company’s Use of Mathematical Modeling
It is important to note that they did not express a position on environmental issues such as the need to save energy and water and to prevent ecosystem and greenhouse gas degradation. Because most models are not “user-friendly,” this occurs as a consequence. In the past, models have been created for scientific objectives, and then customized to fit the demands of the user. As a result, many users still find it challenging to acquire model results or to otherwise utilize models. Two modeling clusters, referred to as “developing” and “developed,” were identified via cluster analysis [18]. Cluster 1 (100 firms - “developing”) is made up of ITC nations, small and midsized businesses, and businesses that primarily work in the food producing industry. Respondents acknowledged their lack of understanding (scores ranging from 1.8 to 3.4) and use of models (scores ranging from 1.8 to 3.1) for the majority of the models in this cluster. Insufficient resources (knowledge and infrastructure) were also verified by them.
When it comes to business success, knowledge transmission is crucial, and both scientists and industry professionals are always looking for the best ways to innovation, a result of quality and environmental management. According to a recent research on environmental models in the food chain, it is important to establish simpler models for evaluating environmental performance so that they may be widely and easily used in the food industry. To meet different corporate profiles, a broad variety of models created at the research level need better knowledge transfer.
Cluster 2 (125 respondents; “developed”) included OEC nations, large corporations, and businesses engaged in the foodstuff service industry. In this cluster, respondents agreed that they knew about modeling (scores ranging from 3.4 to 4.1) and used these models (scores ranging from 2.9 to 4.2), but they disagreed that there weren’t enough resources for modeling. When it comes to environmental concerns, the responses that were most often given in this cluster revealed that businesses seek to enhance ecological performance and prevent pollution (rating ranging from 3.5 to 4.2). It demonstrates that environmental factors have a favorable impact on company performance as a whole. Pollution [19] prevention was identified as the primary impetus for developing any environmental management system in a study on the impact of certified enterprises’ environmental management. The fact that many of the businesses in this cluster have an accredited environmental management system is noteworthy. Outcomes from the cluster were better than those from Cluster 1, having 26 out of 28 assertions exhibiting statistically significant differences across clusters (p .05). The first set of findings demonstrates that larger organizations are, predictably, best equipped, having sophisticated modeling abilities and with knowledge of environmental impact concerns.
Level of modeling requirementsIn order to assess the scale’s dependability, the reliability of 18 elements was assessed by computing the Cronbach’s alpha coefficient. Our scale has a Cronbach’s alpha [20] of 0.941 that is a good degree of internal consistency. Our findings indicated that there is little modeling [21] of foodstuff quality and safety, having modeling of microbial development. The most typical response to these claims was that businesses employ models extensively. This is understandable as product quality control is a specific industrial application where the approach of knowledge transmission is used. The usage of quality tools in foodstuff industries has been examined in previous research, ranging from simple tools to more complicated tools that need specialized training. According to several rules on food safety, food businesses must use mathematical modeling that predicts the development or survival of the microorganisms that are of concern. Lowest results, however, indicate a lack of any environmental mathematical modeling. The most common response was that businesses would want to utilize certain mathematical models [22]. The usage of ecological models in the foodstuff business depends on many factors, including the model’s applicability, user-friendliness, cost (if applicable), and need for specialized environmental knowledge.
Figure 1.1 displays the PCA result for the data matrix. Dimension reduction by PCA divided the observed factors into two specific directions which were accepted as two dimensions: a “product-based dimension” (PC1) that was driven toward modeling numerous product-dependent models and a “risk-based dimension” (PC2) that was a dimension focused on environmental or foodstuff safety risks. Regulators and policymakers continually propose standards in response to hazards with the aim of detecting and reducing risks. Food safety management standards (BRC, 2018; IFS, 2014; ISO, 2018) and environmental management standards (ISO, 2015b) are examples of risk-based standards. In our research, 41.4% of the companies examined had an environmental system in place, while 76.8% had a food safety system (Table 1). Confirmation of the two aspects, the product-dependent and the risk-dependent, can enhance the study of foodstuff modeling by expanding on the body of research already supporting modeling in the food sector.
Figure 1.1 The 16 parameters that affect the requirement for modeling in different application areas are shown by PCA loadings (a) and scores (b) plots according to the type of nation, the size of the enterprises, and their activity in the food industry. 67.7% of the total variation is explained by the two retrieved components. Factors include: product development (DE), process development (PR), real-time process control (RT), food storage optimization and control (ST), food quality control (QC), microbial growth modeling (MB), food safety (FS), characterizing food quality (QU), value chain management (VC), decision control (DC), productivity analysis (PD), life cycle assessment (LC), carbon footprint (CF), water footprint (WF), energy footprint (EF), and waste management (WM). Country type: OEC - Other European countries (Denmark, France, Germany, Greece, United Kingdom); Company size - small, medium, and large.
A loading plot (Figure 1.1a) shows the outcomes in detail. In Figure 1.1a, it is clear that every outcome has positive loadings, indicating that it has a significant positive impact on the “product-based” components. All statements loaded the “product-based dimension” (PC1) strongly (> 0.65). In terms of the “risk-based” dimension (PC2), environmental modeling (carbon footprint, water footprint, and energy footprint) has the largest positive loading, whereas modeling of food safety, microbiological growth, and quality control has the highest negative loading.
Nine of the statements (item and process development, real-time process control, decision support, food storage, value-chain, productivity, life-cycle evaluation, and waste management) have loadings that may be changed. Productivity and value chain are examples of quality-oriented approaches where risks are mostly related to (not) meeting customer expectations.
Scoring guidelines: “0” indicates that it does not apply to our business; “1” indicates that we do not need to use mathematical models; “2” indicates that we would like to utilize a few statistical models; “3” indicates that we have few models (limited utilization) and would like to explore our expertise in the domain; and “4” indicates that we utilize models extensively in this business. In the first extracted components item loadings the raw data was used to get the Mean values [23], Standard deviations [24], and modes [25].
Customer satisfaction is achieved when a product satisfies customer wants and expectations (ISO). In order to identify significant sources, pollutants, receptors, and exposure routes across a product’s life cycle, life cycle assessment from a risk viewpoint may be helpful. In addition, waste management puts human health and groundwater in danger.
The scores plot (Figure 1.1b) summarizes the connections between the nations and the businesses. Large and small businesses, each representing a company with a different modeling strategy, were in opposition to one another. Companies were clustered towards the center according to their activity and nation type, suggesting that they had comparable average modeling practice ratings. The management of safety-related hazards is a particularly sensitive area of the food sector in accordance to customer expectations, as seen by the second family of findings.
Our scale has a good degree of internal uniformity, as shown by the Cronbach’s alpha value of 0.934 for the dependability of 10 items. A summary of the findings (Table 4) revealed that enterprises only had a rudimentary understanding of environmental information about trash, water use, and electric energy use. This is because rising energy and water costs are being attributed to economic and legal problems. Thus, in order to comply with regulatory obligations, businesses are primarily focused on reducing costs as well as tracking waste production. [26] also support the economic component of environmental performance.
Factors, Items, and ResultsAccording to the polluter-pays concept defined in EU law, corporations are obligated to have some information about the trash they produce (EC, 2008). Fig. displays the PCA result for the data matrix. The detected components were reduced in size by using PCA to split them into two independent dimensions: a “level of awareness dimension” (PC1) and a “type of impact dimension” (PC2). Figure 1.2a’s loading plot displays the outcomes in detail. All of the findings had positive loadings, indicating that they have a significant positive impact on the component measuring “level of awareness.” Outcomes that provide related information are classified as below.
With all assertions, the “level of awareness dimension” (PC1) had a high load (> 0.69). According to their degree of environmental consciousness, [27] distinguished between two categories of businesses: those that develop the necessary competencies to just comply with environmental regulations and those that consider environmental performance while making business decisions. In relation to the kind of negative loading is representing resource depletion consequences like energy and water consumption pacts (atmospheric pollution, water pollution, soil contamination, climate alteration and waste disposal). Environmental effects of the food chain affect how much natural resources (mostly water and energy) are used and how the environment is purged.
Figure 1.2 By nation type, company size, and their involvement in the food industry, PCA loadings (a) and scores (b) plots for the 10 variables impacting knowledge of environmental implications are shown. 76.8% of the total variation is explained by the two retrieved components. Factors include the following: EE stands for electrical energy, TE for thermal energy, ES for energy sources, WC for water use, AP for air pollution, WP for water pollution, SC for soil contamination, EC for ecosystem, CC for climate change, and WA for waste produced. Country type: OEC - Other European countries.
The scores plot (Figure 1.1b) summarizes the connections between the nations and the businesses. Big firms were opposed by small businesses and those in the food service industry, while those in the plant origin industry were in opposition to the former.
Scoring guidelines: “0” means that the environmental impact has not been analyzed; “1” means that the company has done so; “2” means that the company has calculated particular environmental measures for this consequence; “3” means that the organization has converted the basic data to compute environmental consequences per process or functional unit; and “4” means that the organization has calculated environmental footprints based on this impact.
The first extracted component’s item loadings. The raw data was used to calculate the Mean values and Standard deviations.
There is a lot of untapped potential for using data and models in different “knowledge products” to improve their efficiency. This third family of data therefore seems to suggest that, in addition to the company’s size (as was previously noted above), the company’s sector, animal, or plant influences its environmental consciousness.
Implications for food chain stakeholders on a practical levelThis method of examining the on-site implementation of mathematical models adds value in terms of the study of existing methods in the food chain and the degree of comprehension of food models in the foodstuff industry. These results encourage everyone involved in the food chain, particularly foodstuff businesses and academics, to step up knowledge transfer initiatives. Results show variations when company size, primary business, and nation of origin are taken into consideration. Managers may find opportunities to enhance their goods and procedures by taking into account the advantages of modeling in the food business. These findings could be useful for food consultants looking to increase the range of services they provide to the food industry. Last but not least, our results may be used as a guide to create a variety of user-friendly approaches designed for certain foodstuff industries and for smaller and midsized businesses to boost their competitive edge.
1.4 Conclusion
At the international level, nations use models in many ways. The more advanced nations utilize the available modeling and are positioned at a better degree of understanding. Similar patterns may be seen at the micro level, where smaller and midsized businesses show a restricted usage of models as a result of their shortage of resources and understanding. Most of the models reviewed here did not show significant differences across nations, and the usage of statistical models in the foodstuff business may be regarded as lower to medium. It is crucial because huge corporations may have operations in many nations, while small and medium-sized businesses function on a national basis. In light of the complexity of food matrices, the results show low to medium levels of knowledge in relation to the numerous models used by the food industry, with greater levels of knowledge being connected with certain food processes (and related foodstuff safety/environmental issues) than food items. Having restricted utilization of environmental models in foodstuff production, the most often employed models relate to comprehending and enhancing different elements of food safety and food quality. To achieve a greater level of proficiency and understanding of its commercial potential, the researchers feel that education attempts regarding modeling and simulation tools must be strengthened in both the industry (particularly in SMEs) and academics. The study’s limitations include that no on-site evaluations were carried out to determine if the findings reported by the firms were accurate since the study was focused on the attitudes and beliefs of companies on the usage of mathematics. Additional restrictions are connected to the survey’s sample size, corporate profiles, and the number of participating European nations. With a certain amount of care, the findings of this poll may be extrapolated to the whole European food industry.
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Note
*
Corresponding author
:
Nimit Kumar: Orcid:
https://orcid.org/0009-0005-4341-0565
Shwetha M.S.: Orcid:
https://orcid.org/0000-0002-9651-8095
Govind Shay Sharma: Orcid:
https://orcid.org/0000-0002-5884-4674
Nitin Ubale: Orcid:
https://orcid.org/0000-0002-1270-481x
2Modeling the Effects of Land Degradation on Agricultural Productivity: Implications for Legal and Policy Interventions
Amit Verma1, Istita Auddy2, Murli Manohar Gour3, Dhwani Bartwal4, Sukhvinder Singh Dari5 and Ankur Gupta6*
1College of Law, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
2Department of Food Technology, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Karnataka, India
3Department of Mathematics, Vivekananda Global University, Jaipur, Rajasthan, India
4Department of Agronomy, College of Agriculture, Parul University, Limda, Dist-Vadodara, Gujarat, India
5Symbiosis Law School, Nagpur Campus, Symbiosis International (Deemed University), Pune, India
6Department of Computer Science and Engineering, Vaish College of Engineering, Rohtak, Haryana, India
Abstract
Using seaweed extracts as a natural plant growing energizer might be a beneficial farming strategy in the development of sustainable and organic crops. Thus, during the winter growth seasons of 2020 and 2021, the present research was conducted in a private farm in Manchester, UK. The impact of BSE on the vegetable growth, production, and some nutritive uses of the leaves and roots of Celesta F1 hybrid RR plants was evaluated. BSE was utilized as a pre-sowing seed soaking for 10 hours at a rate of 5 ml/l, as well as foliar spraying once at rates of 2, 3 or 5 ml/l after 30 days from the sowing date. Three duplicates of the implementation were used in a split plot design. The findings clearly showed that seaweed-immersed seeds greatly outperformed water-immersed seeds in terms of all examined features. It was quite clear that, when compared to the other treatments, the foliage spraying therapy with the greatest concentration of SE (5 ml/l) produced considerably the finest results for all evaluated features. With the exception of leaves and roots dry weight, leaves Fe and roots Fe and Zn concentrations in all seasons, chlorophyll an in the first season and chlorophyll b and carotenoids in the second season, the interaction significantly affected all measured parameters. Seaweed-soaked seeds that were then sown and foliar spraying SE at a rate of 5 ml/l once 15 days after sprinkling were determined to be the most successful treatments.
Keywords: Mineral contents, phenols, flavonoids, phytochemicals
2.1 Introduction
The annual cool-season root vegetable crop is known as the RR. RR roots, leaves, and sprouting seeds may all be eaten raw or in salads right away. The radish root’s skin may be white, red, pink, purple, or yellow, but the root’s meat is a crisp white hue having a strong taste. The pigment anthocyanin is what gives root skin its red color. Vitamin C, glucosinolates, polyphenolic chemicals, S, Ca, K, and P are all present in sufficient amounts in root. According to [1], the Brassicaceae family of vegetables includes potent anti-oxidants that are physiologically active and have been linked to positive health impacts. According to [2], these compounds may help reduce or prevent the risk of a number of illnesses, including cardiovascular, some malignancies, hypertension, stroke and various chronic disorders.
SE is regarded as a natural and organic plant growth energizer according to [3] definition of a plant growth stimulant from 2015. Depending on their colour, marine macroalgae may be divided into three groups: green algae, brown algae, and red algae.
Ulva species (Chlorophyta); red algae, Lithophyllum spp.; brown algae, Sargassum spp., and Ascophyllum spp. and Cladophora spp. and Asparagopsis varieties. The extracts of brown algae, a kind of sea algae, have gained significant attention and are often employed in contemporary, sustainable, and farming as bio-fertilizers or plant growing stimulants. This may be explained by the fact that these algae have larger concentrations of natural phytohormones, micronutrients, and macronutrients than other varieties of algae.
According to several studies, SE contains a variety of functional organic active materials, including polyphenols, alginates, pigments, free amino acids, vitamins, micro and macronutrients, and natural phytohormones. According to [4], the stimulant impact of SE can be connected to every compound existing which may alter the cellular metabolic techniques of treated vegetations. This might explain all familiar positive benefits of SE. Many researchers have shown that SE stimulates seed germination, early seedling development and vigor and enhancing seedling foundation in a range of crops.
Additionally, the utilization of SEs expanded the amount of cumulative chlorophyll in the leaves that had an effect on the extent and proficiency of the photosynthetic technique and the amount of organic C in the soil, nutrient availability, and nutritional absorption. Using SEs improved plant tolerance to various environmental stress conditions; it also made plants more resistant to diseases and nematodes; and it increased plant resilience to nematodes. Overall, SE application shows growth-energizing activation of the plant growing, production, and condition in various farming crops.
SE may be administered using one approach, such as seed priming before sowing, root dipping for seedlings before transplantation, root or soil soaking, foliar sprinkling, or a combination of two or additional ways. According to the plant type and development phases, the suggested application technique, timing, and rate varied substantially. To address this issue, [5] showed that applying SE early in a plant’s development cycle resulted in more prominent stimulatory effects.
