A Field-Scale Decision Support System for Assessment and Management of Soil Functions

peer-reviewedAgricultural decision support systems (DSSs) are mostly focused on increasing the supply of individual soil functions such as, e.g., primary productivity or nutrient cycling, while neglecting other important soil functions, such as, e.g., water purification and regulation, climate regulation and carbon sequestration, soil biodiversity, and habitat provision. Making right management decisions for long-term sustainability is therefore challenging, and farmers and farm advisors would greatly benefit from an evidence-based DSS targeted for assessing and improving the supply of several soil functions simultaneously. To address this need, we designed the Soil Navigator DSS by applying a qualitative approach to multi-criteria decision modeling using Decision Expert (DEX) integrative methodology. Multi-criteria decision models for the five main soil functions were developed, calibrated, and validated using knowledge of involved domain experts and knowledge extracted from existing datasets by data mining. Subsequently, the five DEX models were integrated into a DSS to assess the soil functions simultaneously and to provide management advices for improving the performance of prioritized soil functions. To enable communication between the users and the DSS, we developed a user-friendly computer-based graphical user interface, which enables users to provide the required data regarding their field to the DSS and to get textual and graphical results about the performance of each of the five soil functions in a qualitative way. The final output from the DSS is a list of soil mitigation measures that the end-users could easily apply in the field in order to achieve the desired soil function performance. The Soil Navigator DSS has a great potential to complement the Farm Sustainability Tools for Nutrients included in the Common Agricultural Policy 2021–2027 proposal adopted by the European Commission. The Soil Navigator has also a potential to be spatially upgraded to assist decisions on which soil functions to prioritize in a specific region or member state. Furthermore, the Soil Navigator DSS could be used as an educational tool for farmers, farm advisors, and students, and its potential should be further exploited for the benefit of farmers and the society as a whole

Development of an Agricultural Primary Productivity Decision Support Model: A Case Study in France

Agricultural soils provide society with several functions, one of which is primary productivity. This function is defined as the capacity of a soil to supply nutrients and water and to produce plant biomass for human use, providing food, feed, fiber, and fuel. For farmers, the productivity function delivers an economic basis and is a prerequisite for agricultural sustainability. Our study was designed to develop an agricultural primary productivity decision support model. To obtain a highly accurate decision support model that helps farmers and advisors to assess and manage the provision of the primary productivity soil function on their agricultural fields, we addressed the following specific objectives: (i) to construct a qualitative decision support model to assess the primary productivity soil function at the agricultural field level; (ii) to carry out verification, calibration, and sensitivity analysis of this model; and (iii) to validate the model based on empirical data. The result is a hierarchical qualitative model consisting of 25 input attributes describing soil properties, environmental conditions, cropping specifications, and management practices on each respective field. An extensive dataset from France containing data from 399 sites was used to calibrate and validate the model. The large amount of data enabled data mining to support model calibration. The accuracy of the decision support model prior to calibration supported by data mining was ~40%. The data mining approach improved the accuracy to 77%. The proposed methodology of combining decision modeling and data mining proved to be an important step forward. This iterative approach yielded an accurate, reliable, and useful decision support model for the assessment of the primary productivity soil function at the field level. This can assist farmers and advisors in selecting the most appropriate crop management practices. Embedding this decision support model in a set of complementary models for four adjacent soil functions, as endeavored in the H2020 LANDMARK project, will help take the integrated sustainability of arable cropping systems to a new level

Composite Surrogate for Likelihood-Free Bayesian Optimisation in High-Dimensional Settings of Activity-Based Transportation Models

Activity-based transportation models simulate demand and supply as a complex system and therefore large set of parameters need to be adjusted. One such model is Preday activity-based model that requires adjusting a large set of parameters for its calibration on new urban environments. Hence, the calibration process is time demanding, and due to costly simulations, various optimisation methods with dimensionality reduction and stochastic approximation are adopted. This study adopts Bayesian Optimisation for Likelihood-free Inference (BOLFI) method for calibrating the Preday activity-based model to a new urban area. Unlike the traditional variant of the method that uses Gaussian Process as a surrogate model for approximating the likelihood function through modelling discrepancy, we apply a composite surrogate model that encompasses Random Forest surrogate model for modelling the discrepancy and Gaussian Mixture Model for estimating the its density. The results show that the proposed method benefits the extension and improves the general applicability to high-dimensional settings without losing the efficiency of the Bayesian Optimisation in sampling new samples towards the global optima.Peer reviewe

Influence of olive oil on the fatty acids composition of coarse chopped boiled sausages

The paper examines the influence of ordinary and cold pressed olive oil on the fatty acid composition of coarse chopped boiled sausages. For this purpose, cold pressed olive oil was added in the production of the Folk sausages and in the production of Kranj sausages was added ordinary olive oil. In both production batches, olive oil is added in the amount of 3, 4 and 5 g/kg. In the examined production batches of Folk sausage, the content of palmitic and stearic fatty acids (C16: 0 and C18: 0) is within the limits of other meat products. A smaller percentage representation is observed in the content of C16: 0, and greater in the content of (C18: 0) in the production batches of Kranj sausages. The ratio of PUFA / SFA in bought production batches of sausages is up to 0.4%, which means that the sausages full field the quality requirements of the product according to the lipid content that means that the addition of olive oil in this type of batches is appropriate. Key words: Folk sausage, Kranj sausage, monounsaturated fatty acids, polyunsaturated fatty acids, saturated fatty acids, qualit

BAG-DSM: A Method for Generating Alternatives for Hierarchical Multi-Attribute Decision Models Using Bayesian Optimization

Funding Information: Funding: This work was partially funded by the Slovenian Research Agency (ARRS) under research core funding Knowledge Technologies No. P2-0103 (B), and by the Slovenian Ministry of Education, Science and Sport (funding agreement No. C3330-17-529020). Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Multi-attribute decision analysis is an approach to decision support in which decision alternatives are evaluated by multi-criteria models. An advanced feature of decision support models is the possibility to search for new alternatives that satisfy certain conditions. This task is important for practical decision support; however, the related work on generating alternatives for qualitative multi-attribute decision models is quite scarce. In this paper, we introduce Bayesian Alternative Generator for Decision Support Models (BAG-DSM), a method to address the problem of generating alternatives. More specifically, given a multi-attribute hierarchical model and an alternative representing the initial state, the goal is to generate alternatives that demand the least change in the provided alternative to obtain a desirable outcome. The brute force approach has exponential time complexity and has prohibitively long execution times, even for moderately sized models. BAGDSM avoids these problems by using a Bayesian optimization approach adapted to qualitative DEX models. BAG-DSM was extensively evaluated and compared to a baseline method on 43 different DEX decision models with varying complexity, e.g., different depth and attribute importance. The comparison was performed with respect to: the time to obtain the first appropriate alternative, the number of generated alternatives, and the number of attribute changes required to reach the generated alternatives. BAG-DSM outperforms the baseline in all of the experiments by a large margin. Additionally, the evaluation confirms BAG-DSM’s suitability for the task, i.e., on average, it generates at least one appropriate alternative within two seconds. The relation between the depth of the multi-attribute hierarchical models—a parameter that increases the search space exponentially— and the time to obtaining the first appropriate alternative was linear and not exponential, by which BAG-DSM’s scalability is empirically confirmed.Peer reviewe

Use of cold-pressed olive oil for improvement the quality of Kranj sausages Vladimir Kuzmanovski*, Aco Kuzelov1, Elena Joshevska2

Abstract This paper presents the results from examination on impact of cold-pressed olive oil on the chemical composition and oxidative changes (degree of acidity and peroxide value) of Kranj sausage. For this purpose, four groups of national sausage have been produced. The first group was produced without addition of olive oil (control group), the second one with addition of 3g/kg, and the third one with addition of 4g/kg and the fourth group with addition of 5g/kg olive oil. After production, the groups of sausages were vacuumed and stored in refrigerator at temperature from 0 to +4°С. The chemical composition of the groups of sausages was examined on the first and on the fortieth day of production, and the degree of acidity and peroxide value were examined on 1st,10th,20th,30th and 40th day of production. The degree of acidity of the control group of sausages ranges from 3.13 to 5.21, while the degree of acidity of the other groups ranges from 2.03 to 3.84. The peroxide value of the control groups of sausages ranges from 0.39 to 1.31, and the acidity of other groups of sausages ranges from 0.38 to 1.33. The obtained low values of degree of acidity and peroxide value most likely result from small concentrations of coldpressed olive oil and vacuuming of the sausages. Used concentrations of olive oil in the groups of sausages do not have statistically significant impact on the chemical composition of the sausages

INFLUENCE OF OLIVE OIL ON THE FATTY ACIDS COMPOSITION OF COARSE CHOPPED BOILED SAUSAGES

The paper examines the influence of ordinary and cold pressed olive oil on the fatty acid composition of coarse chopped boiled sausages. For this porpoise, cold pressed olive oil was added in the production of the Folk sausages and in the production of Kranj sausages was added ordinary olive oil. In both production batches, olive oil is added in the amount of 3, 4 and 5 g/kg. In the examined production batches of Folk sausage, the content of palmitic and stearic fatty acids (C16: 0 and C18: 0) is within the limits of other meat products. A smaller percentage representation is observed in the content of C16: 0, and greater in the content of (C18: 0) in the production batches of Kranj sausages. The ratio of PUFA / SFA in bought production batches of sausages is up to 0.4%, which means that the sausages full field the quality requirements of the product according to the lipid content that means that the addition of olive oil in this type of batches is appropriate. Key words: Folk sausage, Kranj sausage, monounsaturated fatty acids, polyunsaturated fatty acids, saturated fatty acids, qualit

Semi-Supervised Multi-View Learning for Gene Network Reconstruction

The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently observed, however, that no single inference method performs optimally across all datasets. It has also been shown that the integration of predictions from multiple inference methods is more robust and shows high performance across diverse datasets. Inspired by this research, in this paper, we propose a machine learning solution which learns to combine predictions from multiple inference methods. While this approach adds additional complexity to the inference process, we expect it would also carry substantial benefits. These would come from the automatic adaptation to patterns on the outputs of individual inference methods, so that it is possible to identify regulatory interactions more reliably when these patterns occur. This article demonstrates the benefits (in terms of accuracy of the reconstructed networks) of the proposed method, which exploits an iterative, semi-supervised ensemble-based algorithm. The algorithm learns to combine the interactions predicted by many different inference methods in the multi-view learning setting. The empirical evaluation of the proposed algorithm on a prokaryotic model organism (E. coli) and on a eukaryotic model organism (S. cerevisiae) clearly shows improved performance over the state of the art methods. The results indicate that gene regulatory network reconstruction for the real datasets is more difficult for S. cerevisiae than for E. coli. The software, all the datasets used in the experiments and all the results are available for download at the following link: http://figshare.com/articles/Semi_supervised_Multi_View_Learning_for_Gene_Network_Reconstruction/1604827

Boletín de Segovia: Número 43 - 1876 abril 11

Copia digital. Madrid : Ministerio de Cultura. Subdirección General de Coordinación Bibliotecaria, 200