International Journal of Sustainable Agricultural Management and Informatics (17 papers in press)
Remote Sensing in Water Balance Modelling for Evapotranspiration at a Rural Watershed in Central Greece
by Nicolas Dalezios, Nicholas Dercas, Anna Blanta, Ioannis Faraslis
Abstract: There is an increasing trend in water balance modelling for remote sensing tools, which provide distributed information of watershed features. The papers objective consists of exploring the remote sensing potential for evapotranspiration estimation in water balance modelling of a small rural watershed with limited data, namely Portaikos rural mountainous watershed of 132 km2 in Thessaly, Greece. Three water balance models with small number of parameters are used, namely Abulohoms, GR2M and Xiong-Guo model, respectively. Two approaches are considered. At first, monthly potential evapotranspiration derived from conventional meteorological data is used as input to the models. Second, air temperature is derived from land surface temperature (LST) of NOAA/AVHRR satellite images for monthly evapotranspiration estimation. Time series of the above parameters for 13 years (1981-1993) are used. The remote sensing potential for evapotranspiration estimation is assessed by comparing the observed and simulated monthly runoff at the output of Portaikos watershed. Four error statistics are used for validation. The results are considered quite satisfactory for the four error statistics. The GR2M shows relatively better performance than the other models for all the error statistics, as well as for the satellite-based approach, which shows slightly better performance than the conventional approach.
Keywords: Remote sensing; water balance models; rural watershed; evapotranspiration; satellites; water resources management.
Residents concerns and attitudes towards policies and strategies of solid waste management facilities
by Stilianos Tampakis, Veronica Andrea, Paraskevi Karanikola, Despina Sidi
Abstract: The current study examines the concerns and attitudes of the residents in the Municipality of Lagadas, Greece and in particular the gateway community of Mavrorachi Sanitary Landfill (MSL) and it offers a general understanding of public perceptions regarding sanitary landfills and waste management issues from the residents point of view. The results of the present study indicate that there is a low acceptance for the MSL and also NIMB phenomena are spotted, while there is a positive view concerning financial issues and employment in a local level. The case study can be used as a valid tool in the planning, design and management of waste management facilities and especially for sanitary landfills, in areas all around the world facing similar challenges such as lack of space and NIMB phenomena.
Keywords: sanitary landfill; acceptability of a solid waste management facility; illegal dumpsites; quality of life; strategies and policies; means of information; NIMB.
A Hybrid Framework for detection of diseases in Apple and Tomato crops with Deep Feed Forward Neural Network
by Praneetha Rajupalepu, Vektatramaphanikumar Sistla, Venkata Krishna Kishore Kolli
Abstract: The traditional farming methods with low productivity and crop damage due to diseases have resulted in low economic growth of the farmer. To overcome this problem, an expert system capable of monitoring the crop growth and early detection of the diseases is highly essential for the farmer to take preventive steps. In this work, image processing and soft computing techniques were used to detect the diseases well in advance. First, a hybrid image enhancement technique was developed by fusion of threshold-based and principle component analysis colour enhancement techniques. Then the enhanced images were divided into three individual color channels (Red, Green, Blue) and then 27 statistical features comprising of texture, color and energy were computed from the individual channels. The same statistical features of the first-level wavelet decomposition were then appended to those 81 features. Finally, the features were classified by the deep feed forward neural network to identify the disease type. Performance evaluation of the method was validated on tomato and apple datasets. The proposed method outperformed the existing methods by yielding 94.06% true positive rate on tomato dataset and achieved 96.78% true positive rate on apple dataset.
Keywords: Precision Agriculture; Principle Component Analysis; Statistical features; Deep feed forward neural network; Wavelets; etc.
Factors impacting farm management decision making software adoption
by Kenneth David Strang, Sarah Bitrus, Rao Vajjhala
Abstract: In this study, we use an unconventional socio-cultural ideology to examine if Western African farmers think agricultural information systems (AISs) improve economic production, at the individual level of analysis. Food production is a necessity for our survival but it has been negatively impacted by unstable financial markets, climate change, political upheavals and health pandemics, especially in Western African-based developing countries. We developed a four factor model based on the information systems expectancy confirmation theory which could determine why Nigerian farmers adopt agricultural information systems. We used a survey to collect data from
farmers, we validated the questions using a pilot study, and we developed a structural equation model to quantitatively explain why farmers make the decision to adopt or discontinue the use of AIS electronic software. We found that farmers satisfaction was positively influenced by high confirmation experience with AIS. To a lesser extent we found that farmers satisfaction was positively impacted by AIS perceived usefulness (PU). Interestingly, we found no evidence that any factor was related to farmers behavioural intent for continued use of AIS. The results raise controversial issues concerning AIS effectiveness and government technology funding in Western African countries.
Keywords: agricultural information system; AIS; farm management; crop planning; decision making software; adoption; perceived use; continuance intention; satisfaction; confirmation experience; expectancy confirmation theory; Western Africa; Nigeria.
Selection of the most suitable tree species in urban areas based on their capability of capturing heavy metals. A Forest Policy approach.
by Stefanos Tsiaras, Theano Samara
Abstract: The aim of this paper to select the most suitable tree species in urban areas, taking into consideration their capability of capturing heavy metals. The selection is based on a broadly accepted tool for Forest Policy, Multiple Criteria Decision Analysis and more specifically, the PROMETHEE method. Five of the most common tree species for urban green in Greece (Cupressus arizonica, Albizia julibrissin, Celtis australis, Platanus orientals and Ligustrum japonicum) were examined, and the criteria were the captivity of seven heavy metals: manganese (Mn), zinc (Zn), copper (Cu), lead (Pd), cadmium (Cd), chromium and nickel (Ni). All heavy metals were considered of equal importance and therefore, the weights of the criteria were equal. According to the PROMETHEE ranking the best choice among the alternatives is the Arizona cypress; this is a reasonable outcome, as the cypress is an evergreen species and it absorbs heavy metals during the whole year, in contradiction with the deciduous tree species. Broadleaves species with compound leaves, such as Albizia julibrissin could also capture significant amount of heavy metals especially the manganese. The selection of these species would have important benefits for the citizens of Thessaloniki, a city characterized by its densification and lack of urban green. Forest Policy can play an important role in planning the green spaces in cities in an attempt to increase urban green, a vital decision with many benefits for the citizens.
Keywords: Air pollution; urban green; Multiple Criteria Decision Analysis; PROMETHEE; Forest Policy.
Soil Moisture Sensors for Sustainable Irrigation: Comparison and Calibration
by Vaibhav Bhatnagar, Ramesh C. Poonia, Jagdish Prasad
Abstract: Soil moisture is a vital component for plant growth. There are many electro-chemical low-cost sensors that sense soil moisture with immediate effect rather than other methods such as Gravimetric Method and others that are expensive as well as consume lot of time. Some of these electro-chemical sensors work on resistivity whereas some sensors work on capacitance. This paper compares the efficiency of resistivity based sensors with capacitance based soil moisture sensors so that farmers and experts can use the better one for designing IOT based irrigation monitoring and controller system. This comparison is performed with twenty one samples having different levels of soil water content. The Coefficient of Variation is calculated to compare the efficiency of both the sensors. After the comparison, calibration has also been performed that depicts the soil moisture content to LCD Display and alert the farmer by a Buzzer.
Keywords: IOT; Soil Moisture Sensor; Arduino; Calibration.
Satellite methodologies for rationalizing crop water requirements in vulnerable agroecosystems
by Nicolas Dalezios, Anna Blanta, Athanasios Loukas, Marios Spiliotopoulos, Ioannis Faraslis, Nicholas Dercas
Abstract: Vulnerability in agriculture is influenced, among others, by extended periods of water shortage in regions exposed to droughts. In order to assess irrigation water requirements, remote sensing techniques are integrated for the estimation and monitoring of cotton crop evapotranspiration ETc. Cotton fields in a small agricultural sub-catchment in Thessaly, central Greece, are used as an experimental site. Daily meteorological data and weekly field data are recorded throughout seven (2004-2010) growing seasons for the computation of reference evapotranspiration ETo, crop coefficient Kc and cotton crop ETc based on conventional data, which are compared during the corresponding period with the satellite-based method (Landsat TM) for the estimation of cotton crop coefficient Kc and cotton crop ETc, which also delineates its spatiotemporal variability. The methodology is applied for monitoring Kc and ETc during growing season in the selected sub-catchment. Several error statistics are used showing very good agreement with ground-truth observations.
Keywords: Crop evapotranspiration; remote sensing; crop water needs; vulnerable agriculture.
Special Issue on: AI2SD'2018 Advanced Intelligent Systems for Sustainable Development Applied to Agriculture and Environment
Investigating the impact of drying parameters on the dandelion root using full factorial design of experiments
by Haytem MOUSSAOUI, Hamza Lamsyehe, Ali Idlimam, Abdelkader Lamharrar, Mounir Kouhila
Abstract: Solar drying is regarded as the most convenient technique of conservation in the preservation field. This paper aims at studying the impact of drying parameters on the dandelion root as well as analyzing the interactions between the factors based on factorial experiments, statistical calculations, and ANOVA analysis. The drying of the dandelion plant process was carried out in an indirect solar dryer with a separate solar collector and a drying unit. The outcome is a model that helps to analyze the impact of drying parameters on the response, which is the time of drying. Determining the factors that affect the response is a prerequisite step so that we can analyze their influence on the drying time and on the quality of the sample.
Keywords: Solar energy; Dandelion root; drying parameters; semi-empirical models; ANOVA; full factorial design.
Predictable consequences of climate change for varieties of strawberry plants grown in Morocco
by Mohammed Ezziyyani, Ahlem Hamdache, Mostafa Ezziyyani, Loubna Cherrat
Abstract: The cultivation of strawberry in Morocco has developed remark-ably during the last 20 years. During the 2016-2017 crop year, this crop covers 3.050 hectares of land, including 180.378.742 straw-berry plants imported from various varieties: Sabrina, San Andreas, Fortuna, Festival, Camarosa, Splendor and others. The period from 1990 to 2010, the dominant varieties that were grown are Chandler, OsoGrande and especially Camarosa and this thanks to its very high productivity, profitability, precocity, quality and adaptation to agrocli-matic conditions of the perimeter of Luokkos. Moreover, from 2010, the Californian variety Camarosa (and others) experienced a dramatic decline. Farmers had lost patience because of low yields (very low productivity <500g / plant) and doubts were starting about the choice of the variety. This upset the choice of the distribution of varieties of strawberry plants imported in 2017. Today, many varieties are disap-pearing Moroccan producers, the choice being dictated by the produc-tion objectives.
Keywords: Climate change; Strawberry; Interannual variability; production; adaptation; Sabrina; San Andreas; Fortuna; Festival; Camarosa; Splendor; Loukkous.
Type 2 fuzzy TOPSIS for agriculture MCDM problems
by Mohamed EL ALAOUI, Khalid El Yassini, Hussain Ben-azza
Abstract: Multi Criteria Decision Making methods achieve an unprecedented success in various domains. However, they are not infallible. TOPSIS, one of the most used, does not check the decision makers assessments consistency nor their level of confidence. To take account of the preeminent uncertainty, we propose a TOPSIS based method with interval type 2 fuzzy numbers. An improved algorithm is presented to take into account both coherence and confidence level. An illustrative example treating an agriculture supply chain demonstrates a better sorting capacity of the proposed methodology. In addition, the suggested aims preventing rank reversal.
Keywords: TOPSIS; coherence; reliability; interval type 2 fuzzy numbers; MADM; sustainable agriculture.
Special Issue on: ICDSST & PROMETHEE DAYS 2018 Sustainable Approaches with MCDA Methods in Agri-Environment Policies
Mapping Soil Erosion Potential Zones with a Geo-Spatial Application of Multi Criteria Evaluation Technique Model in Highlands of Ethiopia
by Afera Halefom, Asirat Teshome, Ermias Sisay, Biruk Shewayirga, Mihret Dananto
Abstract: Soil erosion is considered one of the major problems affecting soil quality and water resources. Erosion and soil degradation increase significantly due to irregular and undulating topography, which is a serious problem of loss of fertile soils each year. Based on GIS with the integration of the MCE analysis, an attempt was made to combine a set of factors such as Slope, Topographic Wetness Index, Aspect, Stream Power Index, Elevation and Curvature to have a fruitful decision to fulfill to the stated objective in Megech Watershed. Out of the total watershed area (525.594 km2): 0.004 km2 (0.001%), 34.210 km2 (6.509%), 353.195 km2 (67.205%), 138.007 km2 (26.260%) and 0.132 km2 (0.025%) areas are very high, High, Medium, Low and Very low prone to soil erosion respectively. Therefore, for minimizing soil erosion problems in Megech watershed, biological and physical measurements of soil and water conservation techniques should be conducted.
Keywords: Erosion; Megech; MCE; GIS; Raster calculator; Susceptibility mapping.
Decision Making under the scope of Forest Policy: Sustainable Agroforestry Systems in Less Favoured Areas
by Stefanos Tsiaras, Jason Papathanasiou
Abstract: The aim of the paper is to apply Multiple Criteria Decision Analysis in order to propose suitable agroforestry systems as a sustainable choice for the forestation of abandoned agricultural land in a mountainous, less favoured area. The promotion of regional development through the increase of wooded land is a strategic target of Forest Policy in the European Union. MCDA was used for the selection of the most suitable agroforestry system taking into consideration all three pillars of sustainability: environment, society and economy. Fourteen agroforestry systems well adapted in the study area were analyzed with the PROMETHEE method under six criteria: cost, expected revenue, working hours, waiting period for the first yield, water demands and fertilizers. According to the results, the most suitable agroforestry system for the study area is the combination of pomegranate with medick. The findings could be useful in Forest Policy planning, contributing to the forestation of less favoured areas in Greece.
Keywords: Multiple Criteria Decision Analysis; Policy Making; Regional Development; Agroforestry; sustainability; PROMETHEE.
Towards a computer-based Decision Support System for aquaculture stakeholders in Greece in the context of climate change
by Orestis Stavrakidis-Zachou, Nikos Papandroulakis, Astrid Sturm, Panagiotis Anastasiadis, Frank Wätzold, Konstadia Lika
Abstract: Climate change constitutes an increasing concern for aquaculture. The uncertainty surrounding the future implications of this phenomenon coupled with the financial importance of the sector necessitate the development of appropriate frameworks and tools that can support management decisions and ensure the sustainability of future aquaculture production. Facilitated by emerging information technologies, Decision Support Systems (DSS) are becoming increasingly popular in sectors of primary production and deal with various aspects of decision-making from the operational to the strategic level. In this paper we present ongoing work, in the frame of the EU-ClimeFish project, towards the development of a computer-based DSS that simulates and visualizes the impacts of different climate change scenarios on Greek aquaculture, including economic impacts. The description contains details on the structure, constituent models, and current status of implementation of the DSS. The applicability of the generated tool in decision-making is discussed and planning for further development is outlined.
Keywords: Climate change; aquaculture; ClimeFish; DSS; European sea bass; DEB model; economic model;.
Building theory of agri-food supply chain resilience using Total Interpretive Structural Modelling and MICMAC analysis
by Guoqing Zhao, Shaofeng Liu, Carmen Lopez, Haiyan Lu, Sebastian Elgueta
Abstract: Agri-food supply chains are inherent with some unique characteristics and that can be easily disrupted compared with other supply chains. Therefore, supply chain resilience factors are relevant and can be taken into consideration. In this paper an attempt has been made to build a theoretical framework of resilience factors in agri-food supply chains with the help of Total Interpretive Structural Modelling and Matrix of Cross Impact Multiplications Applied to Classification analysis. The results of the total interpretive structural modelling demonstrate that leadership plays a vital role in enhancing the resilience of the agri-food supply chain. Furthermore, the matrix of cross impact multiplications applied to classification analysis results indicate that leadership and working team stability along with strong driving power should be given critical focus by agri-food supply chain managers to facilitate the improvement of agri-food supply chain resilience. This paper contributes to the extant theory building in the field of agri-food supply chain resilience, to fill the gap that a few researches have been conducted on agri-food supply chain resilience theory building.
Keywords: Agri-food supply chain resilience; Total Interpretive Structural Modelling; MICMAC analysis.
Identifying knowledge brokers, artefacts and channels for waste reduction in agri-food supply chains
by Huilan Chen, Shaofeng Liu, Guoqing Zhao, Festus Oderanti, Cécile Guyon, Biljana Mileva Boshkoska
Abstract: Knowledge mobilization has been proven crucial to increasing organizations efficiency, improving profitability and achieving competitive advantage. The paper aims to explore an approach to integrating knowledge mobilization within agri-food supply chains to enhance collaboration of all value chain actors and achieving a holistic reduction of waste. The research focus will be on the identification of knowledge brokers, artefacts and channels in order to facilitate knowledge mobilization crossing boundaries to reduce agri-food wastes. Cauliflower from Brittany, Frances largest cauliflower production and export region, provides the data underlying the following analysis. Research methods includes semi-structured interview and documentation for data collection and thematic analysis for data analysis. This study has great potential in helping make the right supply chain decisions for eliminating lean wastes in agri-food industry.
Keywords: Knowledge Mobilization; Knowledge Brokers; Artefacts; Knowledge Channels; Muda (waste) Reduction; Agri-food Supply Chains.
An open source approach for building web apps to support decision making with exploratory techniques. Case study: The Multiple Correspondence Analysis on real data from agritourism
by Stratos Moschidis, Athanasios Thanopoulos, Maro Vlachopoulou
Abstract: The purpose of this paper is to present a technical path in order to formulate the creation of small customized web applications that handle big data. Such web tools could be easily integrated in knowledge based or data driven decision support systems and provide added value. The paper studies the various ways in which relevant application can be developed and the findings resulted in a specific technical proposal. This is a combination of techniques and technologies that can be pursued to achieve more efficient and costless data analysis. To this direction, a novel demo web app was developed with open source code in which a user can login, upload data and analyze them in real time with the multivariate exploratory method of multiple correspondence analysis (MCA). The application was created under R programming language and underlines the use of Shiny library. The paper contributes to the scientific facilitation of researchers and professionals with a minimum technical background in order to perform online complex data analytics tasks. Eventually, this architecture enables extensibility and customization that suggests how decision support modules should be delivered in intelligent data systems.
Keywords: Decision support systems;multicriteria analysis;multiple correspondence analysis; R programming;exploratory statistics;big data;open source software.
Ranking the EU countries according to the Environmental Performance Index using PROMETHEE
by PANAGIOTA DIGKOLOU, Jason Papathanasiou
Abstract: The purpose of this work is to study how the PROMETHEE method interacts with the data of the Environmental Performance Index (EPI). For this purpose, we use as a basis two indicators of the EPI; the Environmental Health and the Ecosystem Vitality. These indicators include criteria covering a wide range of important issues, in an effort to cover the majority of the factors that form the perception of quality of life. Essentially, the EPI is a method that studies numerically the environmental performance of a country, and it consists of 24 performance indicators, with two main dimensions; the Environmental Health and the Ecosystem Vitality. The countries that are studied are those of the European Union. In this paper, we use the data from 2006 to 2018, and we form 6 annual scenarios, as Yale and Columbia Universities publish the EPI every two years. The selected data are processed with the well-known multi-criteria analysis method PROMETHEE. Composing the PROMETHEE ranking, we have the chance to observe the different interactions of data with the PROMETHEE method, and study the flows, as defined in PROMETHEE, of the EU countries in recent years on environmental issues, observing which countries are improving and which countries need to make additional efforts.
Keywords: multi-criteria analysis; PROMETHEE; Environmental Performance Index; EU countries.