International Journal of Sustainable Agricultural Management and Informatics (20 papers in press)

Regular Issues

• Applying deep neural networks to forecast the inside temperature of a semi-arid greenhouse  
  by Ahmed Badji, Abdelouahab Benseddik, Hocine Bensaha, Akila Boukhelifa, Salah Bouhoun, Badreddine Bezza, Djemoui Lalmi 
  Abstract: In this study, the air temperature inside a semi-arid greenhouse was investigated. The model database was built using greenhouse climatic data from a prototype greenhouse in Algeria’s Ghardaia region. Over January month, external and internal climatic data were collected in order to develop and validate models for simulating environmental conditions inside the greenhouse, such as relative humidity (RHext), total radiation (GH), air pressure (P), and external temperature (Text). The main objective of this study is to compare three deep neural network models feed forward networks (FFN), Nonlinear auto-regressive network with exogenous inputs (NARX), and recurrent neural networks (RNN-LSTM) to see which one predicted temperature changes in the environment the best. The results showed that the NARX-predicted results agreed closely with the measurements; additionally, RNN provided satisfactory results, while FFN was the weakest of the three models.
  Keywords: greenhouse; internal temperature; prediction; semi-arid climate; Ghardaia region.
  DOI: 10.1504/IJSAMI.2023.10059788
   
  
• Agricultural and business digitalisation degree in achieving sustainable development goals  
  by László Várallyai, Szilvia Botos, Levente Péter Bálint, Tamás Kovács, Róbert Szilágyi 
  Abstract: The agriculture 4.0 trend is moving manufacturing processes away from human resources and towards the world of machines. Smart technologies have become standard in production tools. Cloud services have allowed the processing of large amounts of data. The next step will be Agriculture 5.0, where robotics and artificial intelligence will be integrated. Today, data-driven business decisions have become a prerequisite for competitiveness. New technology can extract data, visualise relationships and use AI-based algorithms. That is why it is essential for education, mainly in agriculture. Agri-digitalisation could represent the most significant opportunity for the agricultural sector in the next decade, increasing the efficiency, profitability and competitiveness of production, and is expected to reduce environmental pressures, thus contributing to achieving the sustainable development goals of the European Union. This paper presents the role of digitalisation in agriculture and business and introduces the Agricultural and Business Digitisation BSc COURSE, combining professional knowledge.
  Keywords: collaborative systems; business decisions; digital transition; agri-digitalisation; sustainable development goals; SDGs.
  DOI: 10.1504/IJSAMI.2023.10060658
   
  
• A bibliometric analysis of drones-mediated precision farming  
  by Ajay Chandel, Rajesh Verma, Kiran Sood, Simon Grima 
  Abstract: The global agricultural drone market is expected to grow at a CAGR of 35.9% to $5.7 billion by 2025. Along with other orchestrated efforts, farming practices like drone-mediated farming can significantly impact agricultural output, contributing toward achieving the zero hunger sustainable development goal. They improve farmers productivity, save time and resources, and offer precision agriculture through IoT, AI, sensors, surveillance, spraying capabilities, lower prices, and government support. This research aims to provide a comprehensive and retrospective approach to understanding the vast body of knowledge on drone-mediated agriculture. A bibliometric investigation using VOSviewer and Biblioshiny was conducted to analyse domain development, hot topics, and research trends and to identify future research agendas through thematic mapping and content analysis.
  Keywords: drones; unmanned aerial vehicle; agriculture; precision agriculture; SDG goals.
  
  
• Application of data driven models in estimating daily reference evapotranspiration in a coastal region  
  by Mohammad Taghi Sattari, Halit Apaydin 
  Abstract: An accurate calculation of the amount of water requirements for plants can create a more effective irrigation program. In this study, the daily reference evapotranspiration (ETo) was calculated by FAO-Penman-Monteith method and also estimated by three data-driven based models; M5Rule, support vector regression, K-nearest neighbours and a long-short term memory (LSTM) model based on deep learning. Eight meteorological variables (maximum and minimum daily temperature, maximum and minimum relative humidity, wind speed, sunshine duration, dew point temperature and monthly time index) and 15 different input scenarios were considered for modelling in a coastal agricultural land, namely, Tekirdag, Turkey. The results showed that all the models used presented highly accurate estimations. However, the deep learning based LSTM model yielded the most accurate result with 0.99 as the correlation coefficient and 0.25 as the RMSE. The results concluded that, by using only the maximum temperature or minimum temperature, the amount of ETo can be estimated with a high degree of accuracy without the need for other meteorological variables and physically based equations.
  Keywords: deep learning; long-short term memory; LSTM; M5Rule; support vector regression; SVR; crop water requirement; irrigation; Tekirdag.
  
  
• Acceptance of artifical intelligence systems in agriculture: the role of performance expectancy and government supports  
  by Nguyen Thi Kim Ngan, Tu Thuy Anh, Bui Dang Thanh, Nguyen Thi Tuyet Nhung, Pham Thi My Dung, Nguyen Dieu Ninh 
  Abstract: In the era of digitalisation, most countries worldwide are well aware of the need to develop smart-green agriculture because the negative impacts of climate change are becoming increasingly apparent on a large-scale. The application of AI in agriculture supports automation and optimisation of production processes, helping farmers increase productivity and reduce production costs. AI also allows farmers to predict and avoid climate and disease risks. Therefore, this study investigates the role of perceived usefulness on farmers’ acceptance of AI systems through the interaction with personal attitude, personal innovativeness, green and lean practices, government support, and performance expectancy. This study conducted multiple analyses to test these proposed relationships. The results found that the interactions between perceived usefulness with personal attitude, personal innovativeness, government support, and performance expectancy enhance the farmers’ acceptance of AI systems. This study also contributes several implications to literature and practices based on these findings.
  Keywords: digitalisation; smart-green agriculture; AI acceptance; innovation; agricultural production; performance expectancy.
  DOI: 10.1504/IJSAMI.2023.10060780
   
  
• Evaluation and enhancement of accessibility of forest areas through the road network for conducting firefighting operations via GIS and fire susceptibility analysis  
  by Georgios Kolkos, Anastasia Stergiadou, Apostolos Kantartzis, Stergios Tampekis, Garyfallos Arabatzis 
  Abstract: Managing wildfires relies on comprehensive prevention studies and decision-making plans, with the forest road network serving as the primary means for ground firefighting forces. This research establishes a multi-criteria assessment and improvement system for forest areas accessibility, enhancing firefighting operations. Criteria like hiking time, distance from the road network, and terrain topography determine accessibility. Using the analytical hierarchy process (AHP) and spatial analysis considering slope, aspect, fuel type, and distances from human infrastructure, high fire risk areas are identified. This insight led to designing new roads in critical zones to enhance firefighting effectiveness. Re-evaluating accessibility post-road design demonstrates the percentage improvement achieved. Applied in mountainous, mid-altitude, and suburban Mediterranean forest ecosystems, this methodology offers guidelines for real-world forest management, enhancing the sustainability and resilience of forest ecosystems.
  Keywords: forest firefighting; wildfire prevention; forest roads planning; decision support system; analytical hierarchy process; AHP; multicriteria analysis.
  DOI: 10.1504/IJSAMI.2024.10062068
   
  
• A multilayer network methodological framework for transportation and development of mountainous areas  
  by Dimitrios Tsiotas, Apostolos Kantartzis, Georgios Kolkos, Panagiotis Lemonakis, Garyfallos Arabatzis 
  Abstract: This paper builds on the network paradigm to develop a novel multilayer network model for transportation planning and development of mountainous areas. Each layer of the proposed model encloses information on an environmental aspect, where a network analysis can be applied to provide insights into the network topology and functionality. The analysis of the aggregate graph model reveals the contribution of each layer to the total networks structure and functionality, maps the spatial distribution of each layer to the network layout, and highlights the realism of the proposed model compared to the observed forest complex. Overall, this paper develops a quantitative methodological framework for incorporating environmental information in a single model and provides insights into the study of the Koup Forest Complex road network, which is a remarkable case in terms of vegetation, land use, and forest diversity.
  Keywords: forest management; forest planning; forest policy; sustainability; multilayer graphs; Greece.
  
  
• Quest for less! Living with minimalism for building a better sustainable world - a qualitative study exploring millennials perspective  
  by Varghese Joy, Vijay Kumar Jain 
  Abstract: Today, consumption contributes significantly to environmental deterioration and accounts for up to 60% of greenhouse gas emissions. The current consumption model is not sustainable and jeopardise the needs of future generations and harm the environment. In order to afford current consumption practices, a new consumption habit is required since overconsumption has put sustainability at stake. The current study is motivated to address this problem and aims to identify the enablers of minimalism. A total of 16 enablers were chosen after literature review and experts consultation. Interpretive structural modelling (ISM) has been used to identify minimalist enabler prioritisation and interactions. The study found that attitude, cautious shopping, self-sufficiency, and clutter elimination, are most important enablers of minimalism. The findings of the study will help policymakers to develop new consumption practices which encourage people to adopt minimalism as a new lifestyle. By promoting minimalism habits and addressing negative externalities, the findings will contribute towards improved planetary well-being and environmental improvement.
  Keywords: biospheric values; circular economy; carbon footprint; unfettered consumption; modern aesthetics; planetary well-being; interpretive structural modelling; ISM.
  
  
• The effect of financial index analysis on productivity: the case of suckler cow farms in Greece  
  by Maria Tsiouni, Dimitris Gourdouvelis, Alexandra Pavloudi, Krystallia Tzotzou 
  Abstract: Suckler cow farming is an important sector in Greece, providing a significant contribution to the country’s economy and rural development. This sector plays a crucial role in preserving the country’s biodiversity and maintaining rural landscapes. Financial index analysis is an important tool that can be used to assess the performance of suckler cow farms and improve their financial viability and productivity. By analysing key financial indicators such as profitability, liquidity, solvency, and efficiency, farmers and policymakers can identify areas that require improvement and implement strategies to increase profitability and sustainability. The findings suggest that there are trade-offs between the different financial ratios and the production of suckler cow farms. Larger farms may benefit from economies of scale and greater efficiency, but they may also require higher levels of debt financing. Smaller farms may have higher financial efficiency but limited credit access.
  Keywords: financial index analysis; productivity; suckler cow farms; Central Macedonian region; Greece.
  DOI: 10.1504/IJSAMI.2024.10061356
   
  
• Effects of ecological farms on neighbouring farmers: the example of Lisinia nature ecological farm  
  by Hacer Celik Ates 
  Abstract: Lisinia Ecological Farm is one of the farms within the scope of the project TaTuTa (agricultural tourism in ecological farms and voluntary knowledge, experience exchange). One aim of the project is to support rural development and ecological farming. In this research, it is aimed to reveal the relations of the ecological farm with the farmers around it and the level of the farmers being affected by this farm. A survey was conducted with 128 farmers (in the villages bordering Lisinia Ecological Farm in Burdur province). A small number of younger farmers were positively influenced by the ecological farm. These farmers are in close communication with the farm and were most influenced by the farm’s natural farming practices. After Lisinia was established, these farmers adopted the ecological farming model and turned to activities such as dry farming, medicinal-aromatic plant farming and goat breeding.
  Keywords: ecologic farms; farmers; sustainability; environment; Lisinia.
  
  
• Enhancing crop yield prediction through machine learning regression analysis  
  by Seema Sharma, Anupriya Jain, Sachin Sharma, Pawan Whig 
  Abstract: The economic prosperity of any nation hinges significantly on its agricultural output, a cornerstone of sustained growth. The integration of advanced technology plays a pivotal role in enhancing agricultural productivity. Farmers today are leveraging breakthroughs in data mining, the internet of things (IoT), artificial intelligence (AI), and machine learning to optimise their practices. This paper is dedicated to the exploration of this transformative field, offering insights into its multifaceted applications. It delves into the assessment of diverse parameters to facilitate the cultivation of specific crops in a given region. Moreover, it collaborates closely with farmers, tailoring these parameters to maximise crop yields and diversify agricultural produce. The study also incorporates the deployment of AI algorithms, such as logistic regression and multiple regressions, to bolster decision-making processes in agriculture.
  Keywords: agriculture; artificial intelligence; logistic regression; machine learning; data mining.
  
  
• Water quality index prediction using artificial neural network: a case study of Selangor River, Malaysia  
  by Jia Jun Tan, Senthil Kumar Arumugasamy, Fang Yenn Teo 
  Abstract: Rapid urban development often leads to deterioration of river water quality, and water quality index (WQI) is a number that represents the water quality of a water body. According to Department of Environment, Malaysia parameters used to calculate WQI are dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), potential of hydrogen (pH), suspended solids (SS) and ammoniacal nitrogen (AN). Data was collected from the ASEAN Working Group on Water Resources Management website in Sungai-Selangor section. Two ANN models were developed, a prediction model to predict the current WQI, and a forecasting model to predict the future WQI. The prediction model gave good results with very low overall root mean squared error (1.15), an excellent overall regression value (0.97874), and a high correlation with the actual WQI (99.94%). The forecasting model did not provide good result with the overall RMSE of 4.80 and overall regression value of 0.752.
  Keywords: artificial neural networks; ANNs; water quality index; WQI; dissolved oxygen; DO; biochemical oxygen demand; BOD; chemical oxygen demand; COD; potential of hydrogen; pH; suspended solids; SS; ammoniacal nitrogen; AN; Malaysia.
  
  
• Integrating sustainable coastal tourism and aquaculture principles into higher education curricula: a comprehensive literature review  
  by Sofia K. Gkarane, Dimitra B. Manou 
  Abstract: Education can play a key role in achieving Goal 14 of the sustainable development goals (SDGs) by raising awareness of the importance of sustainable aquaculture and coastal tourism practices. Despite the global recognition of the importance of the SDGs and their significance in promoting the sustainable development of oceans, seas, and marine resources as well as the role of education in achieving these goals, only a few studies have focused on aquaculture and coastal tourism in higher education. The aim of this paper is to provide a comprehensive literature review that examines the current status of education in higher education institutions (HEIs) with regard to aquaculture and coastal tourism. Through an analysis of papers published between 2000 and 2023, this study offers a starting point for more discussion and also offers a conceptual framework, thus providing a basis for further research on the integration of sustainable coastal tourism and aquaculture principles into higher education curricula. Opportunities, implications and future research directions to enhance the role of HEIs in fostering a more sustainable coastal tourism and aquaculture sector are also discussed.
  Keywords: higher education; coastal tourism; aquaculture; sustainable development goals; SDGs; oceans; higher education institutions; HEIs.
  DOI: 10.1504/IJSAMI.2024.10062873
   
  
• Circular economy! Sustainable growth and achieving zero net emissions a way ahead for building sustainable tomorrow by optimising consumption  
  by ShivamKrishn Agrawal, Vijay Kumar Jain, Hemraj Verma 
  Abstract: The concept of a circular economy has received an increasing amount of focus in recent years due to its potential as a means to achieve long-term economic sustainability. The current study applies the interpretive structural modelling (ISM) methodology to investigate the complex relationships between a total of sixteen key variables from the literature that affect circular economy efforts. The findings imply that initiatives to advance technological know-how, awareness, resource sharing, recycling and reuse, and reverse logistics may have a substantial influence on the circular economy. As organisations seek to enhance their sustainability efforts, these insights can guide decision-makers in formulating effective strategies that harness technological advancements, optimise logistics operations, and raise awareness to foster a circular economy that benefits both the environment and society. This research based on ISM theory adds clarity on what helps spread the concept of a circular economy. It offers significant fresh insights for marketers and policy-makers.
  Keywords: circular economy; circular infrastructure; zero carbon emission; product utility; resources optimisation; frugal innovation.
  DOI: 10.1504/IJSAMI.2024.10062874
   
  
• Seeding sustainable practices: a value-attitude-behaviour analysis of farmers green innovation adoption  
  by Geetha Krishnan, Naga Venkata Raghuram Jeedigunta 
  Abstract: This study intends to investigate the impact of egoistic and altruistic values, farmers’ attitudes, and green innovation adoption behaviour using the value-attitude-behaviour framework. Altruistic value was represented by environmental concern, while egoistic value was represented by health concern. The researchers utilised purposive sampling to collect data from 211 farmers through a self-administered questionnaire survey. Structural equation modelling (SEM) was utilised to test the conceptual model. The results show that altruistic and egoistic values influence farmers’ green innovation adoption behaviour. Further research revealed that the association between both types of values and farmers’ green innovation adoption behaviour was mediated by attitude. These research findings have important implications for promoting sustainable agricultural practices and contribute to advancing environmental psychology and sustainable development theories. This study is one of the early attempts made in India to comprehend the significance of egoistic and altruistic values in relation to farmers’ green innovation adoption behaviour.
  Keywords: green innovation; altruistic value; egoistic value; health concern; environmental concern.
  DOI: 10.1504/IJSAMI.2024.10063014
   
  
• Promoting the concept of viability in agri-food supply chains  
  by Maria Kontopanou, Giannis T. Tsoulfas 
  Abstract: The rapid adaptation, and the reconfiguration of processes and structures, is necessary in response to unpredictable disruptions of supply chains. Sustainability and resilience are seen as the most promising approaches for dealing with the occurring disruptions. Both concepts focus on the ways in which supply chains should modify their functions to sufficiently respond to external or internal disturbances. Supply chains also need to reconfigure their long-term strategies to support their place in the market and preserve competitive advantage. The recently proposed concept of viability comes to reset the goals of the supply chain systems, aiming to ensure the long-term effectiveness of the measures taken while dealing with a crisis. This paper aims to define the concept of viability in the context of agri-food supply chains and propose interventions and ways of adoption for the existing supply chain systems.
  Keywords: viability; agri-food; supply chain; sustainability; resilience.
  DOI: 10.1504/IJSAMI.2024.10063191
   
  
• Blockchain-based application for grain production and trade: a case study of Huzhou storehouse tender mode, China  
  by Feiyu Hu, Taige Qin 
  Abstract: Grain security is vital for achieving Sustainable Development Goals and is a top national priority in China. To address natural disasters and emergencies, China has maintained a grain reserve system for decades. However, the current system faces issues in reservation efficiency and grain quality, risking waste in the grain industry. In order to solve these problems, in 2018, Huzhou City initiated an experiment named storehouse tender mode (STM) to improve the grain reserve system in production and trade. This paper provides an overview of STM, explores efficiency and security issues within the grain reserve system, and introduces a blockchain-based prototype system designed to bolster STM implementation. The system simulation highlights blockchain’s potential to establish a transparent, traceable, and trustworthy trade environment for STM. Findings confirm the feasibility and optimisation capacity of the proposed solution, signalling a transformative potential for the traditional grain reserve system.
  Keywords: blockchain; smart contract; storehouse tender mode; STM; information system; China.
  DOI: 10.1504/IJSAMI.2024.10063471
   
  
• Deep learning and machine learning approaches for data-driven risk management and decision support in precision agriculture  
  by Mounia Mikram, Chouaib Moujahdi, Maryem Rhanoui 
  Abstract: Modern agriculture grapples with challenges such as unpredictable weather, biosecurity threats, market volatility, evolving regulations, and farmer health concerns. Effectively addressing these issues while maintaining sustainability demands informed decision-making. Data-driven technologies, especially deep learning (DL), emerge as crucial solutions. This study introduces a sustainable multivariate risk management system for precision agriculture, encompassing plant disease detection, weed detection, fire and smoke detection, and crop recommendation modules. Empowering farmers with tools to navigate risks and enhance operational efficiency, the system leverages DL techniques to uncover correlations among diverse risk factors. Enabling well-informed decisions on risk mitigation, this innovative system has the potential to revolutionise precision agriculture, fostering sustainability and profitability. Insights from the study set a benchmark for adopting data-driven, sustainable practices in smart agriculture. Farmers can utilise the system to conduct informed assessments, proactively mitigate crop damage, and redefine their approach to modern agriculture, ensuring improved yields and enhanced monitoring.
  Keywords: deep learning; precision agriculture; risk management; farming; risk mitigation strategies; smart agriculture.
  DOI: 10.1504/IJSAMI.2024.10063472
   
  
• Farmers profile and their entrepreneurial orientation in Greece  
  by Athanasios Falaras, Odysseas Moschidis, Katerina Gotzamani, Demetres Subeniotis 
  Abstract: The research aim was to create the human geographic map of farmers in Greece. So a survey was conducted, where 735 answers were collected. The study employed MCA to extract critical data insights, AHC to form clusters of farmers, and tests to identify statistically significant categories within each cluster. Three clusters emerged: The innovative group of farmers is consisted of farmers in Epirus, Western Greece and Crete and they are young, well-educated and wealthy farmers featuring strong entrepreneurial orientation characteristics. The inefficient cluster consists of farmers in Central and Western Macedonia, earning 20.001-30.000 per year, who demonstrate weak entrepreneurial orientation characteristics. The old guard cluster consists of farmers in Eastern Macedonia and Thrace, Thessaly and Central Greece, who possess low educational level, income and arable land size. The innovative cluster along with the suitable soil and climate of Greece propose a hopeful future for agriculture in Greece.
  Keywords: entrepreneurship; innovation; agriculture; Greece.
  DOI: 10.1504/IJSAMI.2024.10063574
   
  
• A review on the relation between European protected areas, farming systems and labelled agri-food products with a protected area logo  
  by Athina Koutsouki, Stefanos Nastis, Dimitrios Zikos 
  Abstract: Agricultural practices exert significant influence on the conservation and sustainable management of European protected areas (PAs). Traditional low-intensity farming systems have become unprofitable leading to either abandonment or intensification of farming practices. These changes have contributed to the environmental degradation of biodiversity-rich agricultural landscapes and the loss of valuable cultural knowledge. The development of certification and labelling schemes of high quality agri-food products with a PA logo could generate environmental improvements and positive socio-economic changes. This paper review presents the findings of existing studies focusing on the relationship between the European PAs, farming systems and certification/labelling schemes of agri-food products with a PA logo. Academic research on the subject is limited but provides valuable insights. The findings can serve as a starting point for discussions and reveal opportunities for further research to better understand the interrelations and additional effects emerging from the labelling/certification of high quality agri-food products within PAs.
  Keywords: European protected areas; farming systems; low-intensity farming; agri-food products; labelling schemes; sustainable rural development.