Selection of Parameters for Soil Quality Following Compost Application: A Ranking Method
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Intensive agricultural practices with excessive use of chemical fertiliser have led to the deterioration of soil fertility where soil losses its ability to sustain a consistent crop system with high yield. Compost is a potential substitution to chemical fertiliser. As a biological additive, compost can improve soil quality and crop productivity, controlling plant diseases and reduce nutrient loss and water pollution. However, the effect of compost application to enhance the quality of the soil may be inconsistent due to the slow release nature of the nutrients, compost quality, types of feedstocks and other factors. To evaluate the effects of compost application, it may involve a large number of parameter analyses, which can be costly and time ineffective. There is no indicator to reduce the number of analyses concerning the effect of compost application on soil fertility. In this study, a ranking method is proposed to identify the minimum number of parameters able to track the effect of compost application on soil fertility and the environmental impact. A total of 23 soil parameters were selected through literature review and ranked for their importance to show the effect of compost use. The ranking method was developed based on (1) the reporting frequency of environmental and soil fertility parameters and (2) impact of the selective parameter to the environment. Soil C and N contents were found to be the most frequently reported parameters (85 and 90 %) to affect soil fertility upon compost application. Both contents in the soil also change significantly before and after compost application. Heavy metals and N2O emissions were found to impact the environment most due to the toxicity of heavy metal to the environment and human health and high global warming potential of N2O. Based on the ranking method, nine parameters (N, NO3--N, P, K, micro-nutrients, heavy metals, C, pH and N2O emissions) were selected. 60 % of soil analyses were reduced following this ranking method. For the future study, a weightage system could be implemented on each criterion to decide the more essential parameters to be evaluated based on different soil or crop type and under different agricultural practices.The use of biowaste compost on land can have beneficial effects on the plant–soil system. While the environmental impacts associated with compost production have been successfully assessed in previous studies, the assessment of the benefits of compost on plant and soil has been only partially included in few published works. In the present study, we reviewed the recent progresses made in the quantification of the effects associated to biowaste compost use on land by using life cycle assessment (LCA). Different research efforts are required for a full assessment of the potential benefits, apart from nutrient supply and carbon sequestration; additional impact categories – dealing with phosphorus resources, biodiversity, soil losses, and water depletion – may be needed for a comprehensive assessment of compost application. Several of the natural mechanisms identified and the LCA procedures discussed in the paper could be extensible to other organic fertilizers and compost from other feedstocks.
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We develop a ranking and selection procedure for making multiple comparisons of systems that have multiple performance measures. The procedure combines multiple attribute utility theory with ranking and selection to select the best configuration from a set of K configurations using the indifference zone approach. We demonstrate our procedure on a simulation model of a large project that has six performance measures.
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It summarizes the literature on the impact of compost fertilization on the soil plant system and current knowledge. Most researchers have reported that the application of compost could improve the physical, chemical and biological characteristics of the soil, organic matter of the soil and soil nutrient status. Increased SOM concentrations result in all long-term compost application trials. However, because of their greater amount of stable carbon, mature composts increase SOM much better than fresh and immature composts. Moreover, compost contributes to the stabilization and increase of crop productivity and crop quality due to its various beneficial effects on physical, chemical and biological soil properties. As a result, most researchers have shown that compost has the equalizing effect of annual/seasonal variations in soil water, air and heat balance, plant nutrient availability, and thus final crop yields. Good results are also seen mainly because of the slow release of nutrients and their availability in compost-combined fertilization schemes.
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The paper considers carefully the problem of selecting a subset containing exactly the best K candidates from the pool prospective candidates wishing to go into the medical schools in Nigerian Universities. the method of ranking and selection is adopted. The probability of correct selection is considered.
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This paper presents a brief introduction to selection and ranking methodology. Both indifference zone and subset selection approaches are discussed along with some modifications and generalizations. Two examples are provided to illustate the use of subset selection and the indifference zone approaches. The paper concludes with the remark that selection and ranking methodology is a realistic approach in statistical analyses involving comparisons among two or more treatments.
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Zipf's law
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SYNOPTIC ABSTRACTThe statistical methodology known as ranking and selection is a well-established branch of mathematical statistics. Despite its potential for use in real-life problems, however, the number of applications of ranking and selection has been very small. One reason for this is that ranking and selection procedures have not until now been available in computer package form. This paper describes an interactive computer package of ranking and selection procedures called RANKSEL, developed by the author. RANKSEL is fully conversational, user-friendly, informative, requires little or no knowledge of ranking and selection methodology, and is easy to use. Examples of each of the procedures in RANKSEL are given.Key Words and Phrases: ranking and selectionstatistical computinginteractive computer packagesFORTRAN 77
R package
Ranking SVM
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Stochastic simulation
Optimization algorithm
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Бұл зерттеужұмысындaКaно моделітурaлы жәнеоғaн қaтыстытолықмәліметберілгенжәнеуниверситетстуденттерінебaғыттaлғaн қолдaнбaлы (кейстік)зерттеужүргізілген.АхметЯссaуи университетініңстуденттеріүшін Кaно моделіқолдaнылғaн, олaрдың жоғaры білімберусaпaсынa қоятынмaңыздытaлaптaры, яғнисaпaлық қaжеттіліктері,олaрдың мaңыздылығытурaлы жәнесaпaлық қaжеттіліктерінеқaтыстыөз университетінқaлaй бaғaлaйтындығытурaлы сұрaқтaр қойылғaн. Осы зерттеудіңмaқсaты АхметЯсaуи университетіндетуризмменеджментіжәнеқaржы бaкaлaвриaт бaғдaрлaмaлaрыныңсaпaсынa қaтыстыстуденттердіңқaжеттіліктерінaнықтaу, студенттердіңқaнaғaттaну, қaнaғaттaнбaу дәрежелерінбелгілеу,білімберусaпaсын aнықтaу мен жетілдіружолдaрын тaлдaу болыптaбылaды. Осы мaқсaтқaжетуүшін, ең aлдыменКaно сaуaлнaмaсы түзіліп,116 студенткеқолдaнылдыжәнебілімберугежәнеоның сaпaсынa қaтыстыстуденттердіңтaлaптaры мен қaжеттіліктерітоптықжұмыстaрaрқылыaнықтaлды. Екіншіден,бұл aнықтaлғaн тaлaптaр мен қaжеттіліктерКaно бaғaлaу кестесіменжіктелді.Осылaйшa, сaпa тaлaптaры төрт сaнaтқa бөлінді:болуытиіс, бір өлшемді,тaртымдыжәнебейтaрaп.Соңындa,қaнaғaттaну мен қaнaғaттaнбaудың мәндеріесептелдіжәнестуденттердіңқaнaғaттaну мен қaнaғaттaнбaу деңгейлерінжоғaрылaту мен төмендетудеосытaлaптaр мен қaжеттіліктердіңрөліaйқын aнықтaлды.Түйінсөздер:сaпa, сaпaлық қaжеттіліктер,білімберусaпaсы, Кaно моделі.
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Recent studies have shown that modelling and simulation of interactions between a recommender system (RS) and its users have a great potential for accelerating the research and industrial deployment of RSs. Frameworks providing such simulations are called simulators and are widely used for RSs of different types. Nevertheless, there exist the problem of simulation validation and of the inconsistency of RS performance ranking on real-world and the corresponding synthetic data. In this paper, using and extending the recently developed SynEvaRec simulator we propose a validation procedure for simulators of this type and study the consistency of RS performance ranking on response matrices of different sparsity. It is observed in our experiments that (i) the procedure is an effective tool to see what one may expect from the simulation on real-world data, (ii) the consistency of RS performance ranking depend on the data considered and even the sample size used for RS training.
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