The arable farmer as the assessor of within-field soil variation

Feasible, fast and reliable methods of mapping within-field variation are required for precision agriculture. Within precision agriculture research much emphasis has been put on technology, whereas the knowledge that farmers have and ways to explore it have received little attention. This research characterizes and examines the spatial knowledge arable farmers have of their fields and explores whether it is a suitable starting point to map the within-field variation of soil properties. A case study was performed in the Hoeksche Waard, the Netherlands, at four arable farms. A combination of semi-structured interviews and fieldwork was used to map spatially explicit knowledge of within-field variation. At each farm, a field was divided into internally homogeneous units as directed by the farmer, the soil of the units was sampled and the data were analysed statistically. The results show that the farmers have considerable spatial knowledge of their fields. Furthermore, they apply this knowledge intuitively during various field management activities such as fertilizer application, soil tillage and herbicide application. The sample data on soil organic matter content, clay content and fertility show that in general the farmers’ knowledge formed a suitable starting point for mapping within-field variation in the soil. Therefore, it should also be considered as an important information source for highly automated precision agriculture systems

Motivational Factors in the Use of Videoconferences to Carry out Tutorials in Spanish Universities in the Post-Pandemic Period

Many of the tools used for virtual teaching during the pandemic had not been used previously, but they could continue to be used when traditional teaching returns. For this reason, this study focused on locating the key motivational factors for the possible continuation of the use of one of these tools, videoconferencing, to carry out tutorials in Spanish universities as a complement to face-to-face tutorials. For this, a literary review was conducted to obtain a list of motivational factors that may influence teachers to continuing using it, and a causal study was performed with university professors (through fuzzy cognitive maps) to identify the causal relationships among these factors and classify them by their relevance in making a decision. The most influential factors are intention, attitude and perceived compatibility with how tutorials are given, and the negative factors include quality management and trus

Using fuzzy cognitive maps for predicting river management responses: A case study of the Esla River basin, Spain

The planning and management of river ecosystems affects a variety of social groups (i.e., managers, stakeholders, professionals and users) who have different interests about water uses. To avoid conflicts and reach an environmentally sustainable management, various methods have been devised to enable the participation of these actors. Mathematical modelling of river systems is highly recommended to forecast, but we do not always have enough information to do it. In these cases, the soft and meta-models can be valid alternatives to simulate these complex systems. The Fuzzy Cognitive Maps (FCMs) are presented as a tool that facilitates the modelling of ecological systems, functions and services. FCM networking concepts are intertwined through causal relationships. The FCM concept spatial arrangement and the use of fuzzy logic facilitate the integration of different expert opinions. In our study, from a panel of seven experts from representatives of different social sectors, an aggregated FCM was obtained. The most central concept in the aggregated map was cross barriers, dams and weirs. Using our FCM expert model, we performed a number of simulations from different possible scenarios, such as the continuous degradation of natural conditions and the improvement of river natural conditions. A regular increment in the natural conditions generates a substantial enhance in variables as natural water flow and sediment transport. Conversely, the increment in human activities as agro-forestry production addresses to a deterioration of river banks among other variables. In the Esla River, the FCM indicators showed an ecosystem that was greatly influenced by human activity, especially by the presence of barriers, in which the economic variables presented high network influence even though their centrality indices were relatively low. Meanwhile, the essential elements for the proper functioning of this ecosystem, as a natural flow regime, showed very low values that were visibly affected by anthropogenic variables. FCM methodology enabled us not only to understand the perception of current fluvial ecosystems but also to generate plausible management scenarios based on expert knowledge in this field

Using mental-modelling to explore how irrigators in the Murray-Darling Basin make water-use decisions

Study region: Water stress and over-allocation are at the forefront of water management and policy challenges in Australia, especially in the Murray–Darling Basin (MDB). Because irrigated agriculture is a major social and economic component of the MDB, farmer decision-making plays a major role in water sustainability in the region. Study focus: This study used a fuzzy cognitive mapping methodology, ‘mental modeling’, to understand the perceived constraints of irrigator water-use decisions in the MDB, for two different types of irrigation: permanent and annual crops. The approach elicits and documents irrigator insights into the complex and networked nature of irrigation water use decisions in relation to farm-based dynamics. New hydrological insights for the region: Results suggest support for greater local and irrigator involvement in water management decisions. Many, if not most, of the irrigators understood the need for, or at least the inevitability of, governmental policies and regulations. However, a lack of accountability, predictability, and transparency has added to the uncertainty in farm-based water decision-making. Irrigators supported the concept of environmental sustainability, although they might not always agree with how the concept is implemented. The mental modelling approach facilitated knowledge sharing among stakeholders and can be used to identify common goals. Future research utilizing the mental modelling approach may encourage co-management and knowledge partnerships between irrigators, water managers and government officials.Ellen M. Douglas, Sarah Ann Wheeler, David J. Smith, Ian C. Overton, Steven A. Gray, Tanya M. Doody, Neville D. Crossma

An FCM-Based Dynamic Modelling of Integrated Project Delivery Implementation Challenges in Construction Projects

Question: What are the Integrated Project Delivery Implementation challenges in construction projects, their interrelationships and their effects on the project time, cost and quality?Purpose: The Purpose of this study is applying an efficient method to determine the most important challenges to IPD implementation in construction industry, and also to evaluate the interrelationships among these challenges and their effects on the project time, cost, and quality.Research Method: This study models available Integrated Project Delivery challengesusing a real case data, through applying Fuzzy Cognitive Mapping technique.Findings: Results show that contractual factors have the major influence compared with others. This shows the significance of paying attention to why project stakeholders must be integrated throughout the project life cycle since early contract documentation stage.Limitations/Implications: This study is limited to the caseselectedfrom Tehran of Iran.Value for authors: This study is significantdue to identifying, classifying and determining the intensity of effects of IPD implementation challenges on cost, time, and quality of construction projects. It results in planning, resolving the challenges, enhancing the quality of constructions and lastly saving the construction cost and time

Artificial intelligence and machine learning in environmental impact prediction for soil pollution management – case for EIA process

Scientific predictions are a key component of Environmental Impact Assessments (EIA), which can indicate the level of change within an environmental sphere (e.g., soil). As part of the EIA process, decision-making in mitigating complex environmental problems such as maintaining soil quality can be challenging, especially in data-sparse locations. Artificial Intelligence (AI) can ameliorate but the literature suggests that the deployment of Machine Learning (ML) techniques in soil research is concentrated mostly in developed countries. The potential of ML in managing soil pollution from complex mixture of heavy metals, petroleum hydrocarbons, and physicochemical factors is rarely explored. To address this research gap, we built robust models that increase the accuracy of impact prediction based on new experimental soil data from a data-sparse region of Africa (i.e., Nigeria). The algorithms applied are artificial neural networks (ANN), support vector regression (SVR), regression tree (RT), and random forest (RF). The study also implemented a multivariate linear regression (MLR) model as a baseline. Key findings include (a) the MLR model performed less than the machine learning models largely due to the nonlinearity of data; (b) Log-normalization helped to improve the predictive capability of all models as the effects of statistical variability were removed; (c) the RF model had the best performance in terms of correlation coefficient, mean absolute error, and root mean square error, and (d) the machine learning models showed improved performance with increased correlation and lower error between the actual and predicted soil electrical conductivity values. Our results imply that data sparsity may no longer be an excuse for the non-use of quantitative impact prediction in Environmental Impact Assessment (EIA) processes. This could change how EIAs are conducted and enhance sustainability in natural resource exploitation, globally. Future work will apply algorithms for automated feature selection to obtain optimal subset of soil quality measurements that will further improve the accuracy of the models

Fuzzy Cognitive Maps and their Application in Social Science Research: A Study of their Main Problems

La metodología de los Mapas Cognitivos Difusos (MCD) es una de las más relevantes en el estudio del conocimiento y, probablemente, una de las más utilizadas en los últimos tiempos por los inves-tigadores en sus estudios y proyectos. Este artículo analiza a través de una revisión bibliográfica por las principales bases de datos científicas, el uso de esta herramienta en la investigación cientí-fica y su aplicación en las ciencias sociales, además de aportar referencias históricas al respecto. Muestra las principales características de la metodología, sus posibilidades y limitaciones, sus dif-erencias con su antecesor (los Mapas Cognitivos), las fases operativas para su aplicación y las dif-erentes formas de captación de información. Además de demostrar la idoneidad de su aplicación en el ámbito de las ciencias sociales y estudiar sus mayores problemáticas, la identificación de los conceptos involucrados dentro del sistema a estudiar, la selección de expertos y los coeficientes o pesos de los conocimientos de cada uno de estos, ofreciendo sugerencias y aportaciones para una correcta identificación de los conceptos involucrados, una correcta selección de estos expertos y una correcta cuantificación de sus coeficientes de “conocimiento experto”.The methodology of Fuzzy Cognitive Maps (FCMs) is one of the most relevant in the study of knowledge and, probably, one of the most used in recent times by researchers in their studies and projects. This paper analyzes, through a bibliographic review by the main scientific databases, the use of this tool in scientific research and its application in the social sciences, as well as pro viding historical references in this regard. It shows the main characteristics of the methodology, its possibilities and limitations, its differences with its predecessor (Cognitive Maps), the opera tional phases for its application and the different ways of capturing information. In addition to demonstrating the suitability of its application in the field of social sciences and studying its major problems, the identification of the concepts involved within the system to be studied, the selection of experts and the coefficients or weights of the knowledge of each of these ones, offering sugges tions and contributions for a correct identification of the concepts involved, a correct selection of these experts and a correct quantification of their coefficients of "expert knowledge"