Forthcoming and Online First Articles

International Journal of Sustainable Agricultural Management and Informatics

International Journal of Sustainable Agricultural Management and Informatics (IJSAMI)

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International Journal of Sustainable Agricultural Management and Informatics (17 papers in press)

Regular Issues

  • Applying deep neural networks to forecast the inside temperature of a semi-arid greenhouse   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

  • Economic sustainability of organic farming: an empirical study on farmer's prospective   Order a copy of this article
    by Shubham Garg, Karam Pal Narwal, Sanjeev Kumar 
    Abstract: The shifting from conventional farming to organic farming creates a lot of hurdles and economic constraints for farmers. Therefore, the current study endeavours to examine the perspective and barriers perceived by the farmers in conversion to organic farming in Haryana by employing exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) for developing a comprehensive instrumental scale. The proposed instrument is validated with 45 experts selected using snowball random sampling. Finally, the study has collected a random sample of 276 farmers using stratified random sampling. The result of EFA affirms five major barrier factors perceived by farmers in conversion to organic farming explaining 65.166% of the total variance. Moreover, the results of CFA confirm the five factors and proposed instrument. This study will surely assist the government and policymakers in formulating policies on organic farming in making it more viable in India.
    Keywords: constraints; confirmatory factor analysis; CFA; economic viability; exploratory factor analysis; EFA; premium prices; prospective; sustainability.
    DOI: 10.1504/IJSAMI.2023.10057686
     
  • Farmers' training needs in bioeconomy: evidence from Greece   Order a copy of this article
    by Aikaterini Paltaki, Efstratios Loizou, Fotios Chatzitheodoridis, Maria Partalidou, Stefanos Nastis, Christina-Ioanna Papadopoulou, Anastasios Michailidis 
    Abstract: The rapid growth of the world's population leads to intensive global food production, which contributes to greenhouse gas emissions, loss of biodiversity, and utilises large amounts of natural resources. Bioeconomy is becoming one of the most critical topics in the 21st century as it is a way to secure a sustainable future. This paper explores the current situation of bioeconomy development in a typical Greek rural area located in the Region of Western Macedonia; Kozani. The main aim of this paper is to investigate farmers' attitudes and current knowledge in bioeconomy as well as their training needs. Summary statistics and multivariate analysis were performed for the data analysis. Some indicative survey results highlight farmers' low familiarity with bioeconomy and the importance of constant and relevant training. These outcomes would be useful for understanding the current development of bioeconomy in Greece and future research on this subject.
    Keywords: assessment; bioeconomy; multivariate statistics analysis; rural policy; training needs; Western Macedonia; Greece.
    DOI: 10.1504/IJSAMI.2023.10059912
     
  • Lightweight CNN and blockchain integrated secured model for crop disease information system   Order a copy of this article
    by Neenu Johnson, M.B. Santosh Kumar, T. Dhannia 
    Abstract: Crop diseases are a major threat to the farming community that reduce yield and affect the income of farmers. Higher profitability, attaining sustainability, minimising workload, and economic development of the nation are the main driving forces for the adoption of smart farming technologies. Deep learning has emerged as an accurate tool for prediction and decision-making in smart farming. The integration of blockchain with deep learning is efficient in developing a secured data sharing framework. A crop disease data management framework that leverages the benefits of deep learning, blockchain, and interplanetary file system is proposed to assist farmers in crop disease detection and secure sharing of crop disease data. In the framework, a lightweight convolutional neural network architecture-based banana crop disease detection module is included that achieves an accuracy of 99.91%. A system architecture based on blockchain for the crop disease communication module is included to ensure secured crop data sharing.
    Keywords: deep learning; blockchain; lightweight convolutional neural networks; lightweight-CNNs; agriculture; crop disease detection; hyperledger fabric; interplanetary file system; IPFS; smart contract; TensorFlow lite model; Raspberry Pi 4.
    DOI: 10.1504/IJSAMI.2024.10062431
     
  • Costs and benefits analysis of applying agro-climate information to agricultural cultivation for ethnic minority farmers in the mountainous communities of Vietnam   Order a copy of this article
    by Ngo Cong Chinh, Phan Dang Thang, Vu Minh Hai, Nguyen Thi Hong Mai, Le Thi Hong Phuong 
    Abstract: The agricultural sector in Vietnam has faced challenges in accessibility to agro-climate information. The question is how to provide evidence to convince farmers in applying agro-climate information to enhance livelihood and increase resilience to climate change. An analysis of cost-benefit analysis (CBA) when farmers use and apply the agro-climate information services in their agriculture production was conducted with 213 farmers in two mountainous provinces in the North of Vietnam. The findings show that production costs were reduced in rice, coffee, and tea cultivation households that applied agro-climate information. Reducing production costs leads to higher agricultural profits for households. The CBA results show that the intervention achieves high benefits of economic efficiency compared to investment costs when farmers applied agro-climate information. The research concludes that the meteo-hydrological station, department of agriculture and rural development, centre for agricultural services, provincial television and radio need to adopt and broadcast agro-climate information to farmers.
    Keywords: cost-benefit analysis; CBA; agro-climate information; farmers; ethnic minority; Vietnam.
    DOI: 10.1504/IJSAMI.2024.10062432
     
  • Stakeholders' opinions and perceptions on the cultivation of legumes for livestock in Greece   Order a copy of this article
    by Efstratios Michalis, Marisol Dar Ali, Athanasios Ragkos 
    Abstract: Grain legumes constitute an important alternative to soy meal in animal nutrition but also a crop sector that can be adopted by farmers in order to be included in rotations. This study presents orientations for strategic and policy design towards the inclusion of grain legumes in livestock production and their adoption by crop farmers in Greece, based on experts' opinions. To meet this objective, a structured questionnaire was addressed to two groups of stakeholders: 1) researchers and academics; 2) agronomists, advisors and representatives of cooperatives. By means of descriptive statistics and multivariate analysis methods, the study pinpointed technical, economic and policy lock-ins hindering the adoption of the sector across the country. This study constitutes one of the first research endeavours for the examination of this issue in Greece, suggesting integrated strategies which need to focus on the development of regional supply chains but also on efficient training and advisory support.
    Keywords: crop rotations; livestock production; animal nutrition; questionnaire survey; stakeholders; principal component analysis; PCA; adoption barriers; strategic and policy design.
    DOI: 10.1504/IJSAMI.2024.10062423