Most recent issue published online in the International Journal of Sustainable Agricultural Management and Informatics.
International Journal of Sustainable Agricultural Management and Informatics
http://www.inderscience.com/browse/index.php?journalID=433&year=2024&vol=10&issue=1
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International Journal of Sustainable Agricultural Management and Informatics
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© 2024 Inderscience Publishers Ltd
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International Journal of Sustainable Agricultural Management and Informatics
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http://www.inderscience.com/browse/index.php?journalID=433&year=2024&vol=10&issue=1
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A standardised method for estimating environmental and agronomic covariates to discriminate the explanatory variables effects on bioindicators: a case study on soil fauna
http://www.inderscience.com/link.php?id=135401
A method was developed for the standardisation and analysis of environmental and agronomic covariates to discriminate the effects of specific explanatory variables on a given bioindicator. To test it, the effects of plant protection products (PPP) was assessed on soil fauna sampled in organic and conventional hazelnut orchards. More than 100 standardised covariates were numerically reduced, by Principal coordinates analysis (PCoA), to two derived covariates. Then, redundancy analysis (RDA) was applied using, as explanatory variables, two indexes referred to PPP input and the derived covariates. The results showed a marked differentiation of the soil fauna communities between the two groups of sampled sites and their clear response to the use of PPP. The procedure proved to be effective in reducing the 'background noise' determined by a great number of covariates. This method can be successfully applied in monitoring activities concerning the effects on biodiversity of several initiatives aimed at reducing PPP use.
A standardised method for estimating environmental and agronomic covariates to discriminate the explanatory variables effects on bioindicators: a case study on soil fauna
Stefano Macchio; Monica Vercelli; Michela Gori; Luisa Nazzini; Valter Bellucci; Pietro Massimiliano Bianco; Carlo Jacomini; Enrico Rivella; Susanna D'Antoni
International Journal of Sustainable Agricultural Management and Informatics, Vol. 10, No. 1 (2024) pp. 1 - 26
A method was developed for the standardisation and analysis of environmental and agronomic covariates to discriminate the effects of specific explanatory variables on a given bioindicator. To test it, the effects of plant protection products (PPP) was assessed on soil fauna sampled in organic and conventional hazelnut orchards. More than 100 standardised covariates were numerically reduced, by Principal coordinates analysis (PCoA), to two derived covariates. Then, redundancy analysis (RDA) was applied using, as explanatory variables, two indexes referred to PPP input and the derived covariates. The results showed a marked differentiation of the soil fauna communities between the two groups of sampled sites and their clear response to the use of PPP. The procedure proved to be effective in reducing the 'background noise' determined by a great number of covariates. This method can be successfully applied in monitoring activities concerning the effects on biodiversity of several initiatives aimed at reducing PPP use.]]>
10.1504/IJSAMI.2024.135401
International Journal of Sustainable Agricultural Management and Informatics, Vol. 10, No. 1 (2024) pp. 1 - 26
Stefano Macchio
Monica Vercelli
Michela Gori
Luisa Nazzini
Valter Bellucci
Pietro Massimiliano Bianco
Carlo Jacomini
Enrico Rivella
Susanna D'Antoni
Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy ' Strada Comunale Val Pattonera 184, 10133, Turin, Italy ' Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy ' Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy ' Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy ' Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy ' Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy ' Regional Agency for Environmental Protection of Piedmont (APRA Piemonte), Via Pio VII 9, 10135 Turin, Italy ' Italian Institute for Environmental Protection and Research (ISPRA), Vitaliano Brancati 48, 00144 Rome, Italy
plant protection products impact
agricultural management
covariates
bioindicators
organic farming
principal coordinates analysis
PCoA
canonical correspondence analysis
CCA
soil fauna
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The moderating role of digitalisation on smart-green production willingness in agriculture
http://www.inderscience.com/link.php?id=135402
In the era of the modern 4.0 economic revolution, most countries worldwide are well aware of the need to develop smart agriculture because the negative impacts of climate change are becoming increasingly evident on a large scale. Agricultural development is becoming a trend on a global scale at a rapid rate. Therefore, this study investigates the role of digitalisation in increasing smart-green production willingness in the agriculture sector from the farmers' perspective through the interaction with deforestation, mechanical power, organic fertiliser, and renewable organic resource. This study conducted multiple analyses to test these proposed relationships. The results found that the interactions between digitalisation with mechanical power, deforestation, and renewable organic resource enhance the smart-green production willingness. This study also contributes several implications to literature and practices based on these findings.
The moderating role of digitalisation on smart-green production willingness in agriculture
Nguyen Thi Hanh; Nguyen Thi Khanh Chi; Le Nhat Hoang; Nguyen Phuong Chi; Nguyen Thi To Uyen; Ngo Thi Nhu
International Journal of Sustainable Agricultural Management and Informatics, Vol. 10, No. 1 (2024) pp. 27 - 47
In the era of the modern 4.0 economic revolution, most countries worldwide are well aware of the need to develop smart agriculture because the negative impacts of climate change are becoming increasingly evident on a large scale. Agricultural development is becoming a trend on a global scale at a rapid rate. Therefore, this study investigates the role of digitalisation in increasing smart-green production willingness in the agriculture sector from the farmers' perspective through the interaction with deforestation, mechanical power, organic fertiliser, and renewable organic resource. This study conducted multiple analyses to test these proposed relationships. The results found that the interactions between digitalisation with mechanical power, deforestation, and renewable organic resource enhance the smart-green production willingness. This study also contributes several implications to literature and practices based on these findings.]]>
10.1504/IJSAMI.2024.135402
International Journal of Sustainable Agricultural Management and Informatics, Vol. 10, No. 1 (2024) pp. 27 - 47
Nguyen Thi Hanh
Nguyen Thi Khanh Chi
Le Nhat Hoang
Nguyen Phuong Chi
Nguyen Thi To Uyen
Ngo Thi Nhu
Foreign Trade University, 91 Chua Lang, Dong Da, Ha Noi, Vietnam ' Foreign Trade University, 91 Chua Lang, Dong Da, Ha Noi, Vietnam ' Foreign Trade University, 91 Chua Lang, Dong Da, Ha Noi, Vietnam ' Foreign Trade University, 91 Chua Lang, Dong Da, Ha Noi, Vietnam ' Foreign Trade University, 91 Chua Lang, Dong Da, Ha Noi, Vietnam ' Foreign Trade University, 91 Chua Lang, Dong Da, Ha Noi, Vietnam
digitalisation
deforestation
environment protection
smart-green agriculture
carbon emission
green production
innovation
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Copyright © 2023 Inderscience Enterprises Ltd.
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Impact of environmental cost on the production cost of crops: farmers' perspective
http://www.inderscience.com/link.php?id=135411
The aim of this study was to determine the impact of environmental factors on the production cost of paddy, corn, and potato crops in Bangladesh based on the farmers' perspective. A total of 210 cultivators of the three crops were surveyed through face-to-face interviews using an unstructured questionnaire. Environmental costs were represented by air, water, deforestation, and sound pollution costs. Multiple regression models were used to analyse the impact of environmental costs on the production cost. The results showed that air and water pollution costs have a statistically significant positive impact on the production cost of all three crops. On the other hand, sound pollution cost and deforestation cost had no significant impact on the production costs of all three crops, except for the case of deforestation cost on corn cultivation. The findings of this study can contribute to efforts to promote sustainable agriculture practices by considering intrinsic production costs that include environmental costs.
Impact of environmental cost on the production cost of crops: farmers' perspective
Rony Kumar Datta; Md. Main Uddin Ahammed; A.H.M. Ziaul Haq; Md. Shamim Hossain
International Journal of Sustainable Agricultural Management and Informatics, Vol. 10, No. 1 (2024) pp. 48 - 73
The aim of this study was to determine the impact of environmental factors on the production cost of paddy, corn, and potato crops in Bangladesh based on the farmers' perspective. A total of 210 cultivators of the three crops were surveyed through face-to-face interviews using an unstructured questionnaire. Environmental costs were represented by air, water, deforestation, and sound pollution costs. Multiple regression models were used to analyse the impact of environmental costs on the production cost. The results showed that air and water pollution costs have a statistically significant positive impact on the production cost of all three crops. On the other hand, sound pollution cost and deforestation cost had no significant impact on the production costs of all three crops, except for the case of deforestation cost on corn cultivation. The findings of this study can contribute to efforts to promote sustainable agriculture practices by considering intrinsic production costs that include environmental costs.]]>
10.1504/IJSAMI.2024.135411
International Journal of Sustainable Agricultural Management and Informatics, Vol. 10, No. 1 (2024) pp. 48 - 73
Rony Kumar Datta
Md. Main Uddin Ahammed
A.H.M. Ziaul Haq
Md. Shamim Hossain
Department of Finance and Banking, Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur, Bangladesh; Institute of Bangladesh Studies (IBS), University of Rajshahi, Bangladesh ' Department of Finance and Banking, Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur, Bangladesh ' Department of Finance, University of Rajshahi, Rajshahi, Bangladesh ' Department of Marketing, Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur, Bangladesh
environmental cost
production cost
pollution costs
agricultural crops
sustainable agriculture
2023-12-08T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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Development of a novel digestive cookies' recipe valorising rice by-products serving circular bioeconomy
http://www.inderscience.com/link.php?id=135400
Rice production generates several by-product streams with limited valorisation in human consumption. Among them, rice bran is the most promising to serve circular bioeconomy. The current study presents the development of a novel digestive cookies (DC) recipe fortified with rice bran (RB), rice bran oil (RBO) and emmer flour. Fifteen formulations of DC were prepared and four of them were analysed for their antioxidant capacity and nutraceutical status. Results indicated that RB and RBO incorporation elevated total phenolics, ABTS and total flavonoids. Moreover, it decreased saturated and increased polyunsaturated, monosaturated fatty acids and dietary fibres, changes in energy profile. Furthermore, sensory evaluation showed a slight increased preference of the DC concerning aroma, texture, and crunchiness attributes concerns. Thus, a novel recipe of heathy functional RBDC can be produced. However, rice industry should consider investing in the production of stabilised RB following the regulations for a product designated for human consumption.
Development of a novel digestive cookies' recipe valorising rice by-products serving circular bioeconomy
Ageliki Mygdalia; Sopio Ghoghoberidze; Themistoklis Sfetsas; Georgia Dimitropoulou; Sophia Zioupou; Tasos Mitsopoulos; Michalis Ioannou; Paschalis Lithoxopoulos; Dimitris Katsantonis
International Journal of Sustainable Agricultural Management and Informatics, Vol. 10, No. 1 (2024) pp. 74 - 91
Rice production generates several by-product streams with limited valorisation in human consumption. Among them, rice bran is the most promising to serve circular bioeconomy. The current study presents the development of a novel digestive cookies (DC) recipe fortified with rice bran (RB), rice bran oil (RBO) and emmer flour. Fifteen formulations of DC were prepared and four of them were analysed for their antioxidant capacity and nutraceutical status. Results indicated that RB and RBO incorporation elevated total phenolics, ABTS and total flavonoids. Moreover, it decreased saturated and increased polyunsaturated, monosaturated fatty acids and dietary fibres, changes in energy profile. Furthermore, sensory evaluation showed a slight increased preference of the DC concerning aroma, texture, and crunchiness attributes concerns. Thus, a novel recipe of heathy functional RBDC can be produced. However, rice industry should consider investing in the production of stabilised RB following the regulations for a product designated for human consumption.]]>
10.1504/IJSAMI.2024.135400
International Journal of Sustainable Agricultural Management and Informatics, Vol. 10, No. 1 (2024) pp. 74 - 91
Ageliki Mygdalia
Sopio Ghoghoberidze
Themistoklis Sfetsas
Georgia Dimitropoulou
Sophia Zioupou
Tasos Mitsopoulos
Michalis Ioannou
Paschalis Lithoxopoulos
Dimitris Katsantonis
Hellenic Agricultural Organization †'DEMETER', Institute of Plant Breeding and Genetic Resources, Elliniks Georgikis Sholis Av., Thermi-Thessaloniki, 57001, Greece ' Hellenic Agricultural Organization †'DEMETER', Institute of Plant Breeding and Genetic Resources, Elliniks Georgikis Sholis Av., Thermi-Thessaloniki, 57001, Greece ' Q-Lab, K. Karamanli 122, Delta, 57008, Greece ' Q-Lab, K. Karamanli 122, Delta, 57008, Greece ' ERGOPLANNING, Karatasou 7, 54626, Thessaloniki, Greece ' ERGOPLANNING, Karatasou 7, 54626, Thessaloniki, Greece ' Ioannou Confectiοnery Factory, Sindos Industrial Area, Î7-Block 18-Building 24, 57400, Thessaloniki, Greece ' Ioannou Confectiοnery Factory, Sindos Industrial Area, Î7-Block 18-Building 24, 57400, Thessaloniki, Greece ' Hellenic Agricultural Organization †'DEMETER', Institute of Plant Breeding and Genetic Resources, Elliniks Georgikis Sholis Av., Thermi-Thessaloniki, 57001, Greece
by-products
rice
bran
oil
emmer
phenolics
flavonoids
ABTS
sensory panel
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Automatic detection and diagnosis of cocoa diseases using mobile tech and deep learning
http://www.inderscience.com/link.php?id=135403
Cocoa is a cash crop that contributes about 3% to the gross domestic product (GDP) of Ghana's economy and makes up about 20% of total export receipts according to the Ghana statistical service. However, revenue has been hampered recently by the outbreak of cocoa diseases such as Swollen shoot and black pod thereby causing up to 11% loss of the crop. There is, therefore, a need for urgent intervention by all stakeholders within the cocoa production sector. In this research, we aim to employ mobile technology and machine learning (ML) techniques to enhance the early detection and diagnosis of the two major diseases that affect cocoa production namely - swollen shoot and black pod. Specifically, a distributed mobile application is developed that enables farmers to take a picture or video of the cocoa and the app will analyse and automatically detect the specific disease. The app consequently suggests the best treatment to undertake using an inbuilt-information guide. The automatic detection and diagnosis of diseases are based on deep convolutional neural networks (CNN) for image analysis, classification, and detection. The research analysed 2,828 cocoa images spread across three class labels. We built and trained four CNN models, namely CentreNet ResNet50 V2, EfficientDet D0, SSD MobileNet V2, and SSD ResNet50 V1 FPN. We found the best generalised and fastest model to be the SSD MobileNet V2 with a detection confidence score of approximately 88.0%.
Automatic detection and diagnosis of cocoa diseases using mobile tech and deep learning
Richard K. Lomotey; Sandra Kumi; Rita Orji; Ralph Deters
International Journal of Sustainable Agricultural Management and Informatics, Vol. 10, No. 1 (2024) pp. 92 - 119
Cocoa is a cash crop that contributes about 3% to the gross domestic product (GDP) of Ghana's economy and makes up about 20% of total export receipts according to the Ghana statistical service. However, revenue has been hampered recently by the outbreak of cocoa diseases such as Swollen shoot and black pod thereby causing up to 11% loss of the crop. There is, therefore, a need for urgent intervention by all stakeholders within the cocoa production sector. In this research, we aim to employ mobile technology and machine learning (ML) techniques to enhance the early detection and diagnosis of the two major diseases that affect cocoa production namely - swollen shoot and black pod. Specifically, a distributed mobile application is developed that enables farmers to take a picture or video of the cocoa and the app will analyse and automatically detect the specific disease. The app consequently suggests the best treatment to undertake using an inbuilt-information guide. The automatic detection and diagnosis of diseases are based on deep convolutional neural networks (CNN) for image analysis, classification, and detection. The research analysed 2,828 cocoa images spread across three class labels. We built and trained four CNN models, namely CentreNet ResNet50 V2, EfficientDet D0, SSD MobileNet V2, and SSD ResNet50 V1 FPN. We found the best generalised and fastest model to be the SSD MobileNet V2 with a detection confidence score of approximately 88.0%.]]>
10.1504/IJSAMI.2024.135403
International Journal of Sustainable Agricultural Management and Informatics, Vol. 10, No. 1 (2024) pp. 92 - 119
Richard K. Lomotey
Sandra Kumi
Rita Orji
Ralph Deters
The Pennsylvania State University †Beaver, Information Sciences and Technology, Monaca, PA, USA ' Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada ' Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada ' Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5C9, Canada
agriculture
mobile
deep learning
cocoa production
machine learning
ML
convolutional neural networks
CNN
classification
detection
2023-12-08T23:20:50-05:00
Copyright © 2023 Inderscience Enterprises Ltd.
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119
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