5,239 research outputs found

    Use of consumer-grade cameras to assess wheat N status and grain yield

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    Relationships between (a) fractional Intercepted PAR (fIPAR), and (b) aboveground biomass (Biomass) and (c) grain yield at harvest with the Normalized Difference Vegetation Index (NDVI) derived either from a spectroradiometer or a conventional camera at final grain filling (n = 12).Postprint (published version

    Leaf nitrogen determination using non-destructive techniques–A review

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    Β© 2017 Taylor & Francis Group, LLC. The optimisation of plant nitrogen-use-efficiency (NUE) has a direct impact on increasing crop production by optimising use of nitrogen fertiliser. Moreover, it protects environment from negative effects of nitrate leaching and nitrous oxide production. Accordingly, nitrogen (N) management in agriculture systems has been major focus of many researchers. Improvement of NUE can be achieved through several methods including more accurate measurement of foliar N contents of crops during different growth phases. There are two types of methods to diagnose foliar N status: destructive and non-destructive. Destructive methods are expensive and time-consuming, as they require tissue sampling and subsequent laboratory analysis. Thus, many farmers find destructive methods to be less attractive. Non-destructive methods are rapid and less expensive but are usually less accurate. Accordingly, improving the accuracy of non-destructive N estimations has become a common goal of many researchers, and various methods varying in complexity and optimality have been proposed for this purpose. This paper reviews various commonly used non-destructive methods for estimating foliar N status of plants

    High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

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    Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields

    Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables

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    The development of small unmanned aerial vehicles and advances in sensor technology have made consumer digital cameras suitable for the remote sensing of vegetation. In this context, monitoring the in-field variability of maize (Zea mays L.), characterized by high nitrogen fertilization rates, with a low-cost color-infrared airborne system could be the basis for a site-specific nitrogen (N) fertilization support system. An experimental field with different N treatments applied to silage maize was monitored during the years 2014 and 2015. Images of the field and reference destructive measurements of above ground biomass, its N concentration and N uptake were taken at V6 and V9 development stages. Classical normalized difference vegetation indices (NDVI) and the indices adjusted by crop ground cover were calculated and regressed against the measured variables. Finally, image colorgrams were used to explore the potential of band-related information in variable estimation. A colorgram is a linear signal that summarizes the color content of each digital image. It is composed of a sequence of the frequency distribution curves of the camera bands, of their related parameters and of results of the principal components analysis applied to each image. The best predictors were found to be the ground cover and the adjusted green-based NDVI: regression equation at V9 resulted in R2 of 0.7 and RRMSE < 25% in external validation. Colorgrams did not improve prediction performance due to the spectral limitations of the camera. Therefore, the feasibility of the method should be tested in future research. In spite of limitations of sensor setup, the modified camera was able to estimate maize biomass due to the very high spatial resolution. Since the above ground biomass is a robust proxy of N status, the modified camera could be a promising tool for a low-cost N fertilization support system

    Use of remote sensing techniques to analyse lodging level in cereal crops

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    openNowadays, there is high attention to sustainability in all areas of human activities. But what does sustainability mean? As the World Commission on Environment and Development says, sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. Definitively, it is possible to affirm that sustainability is based on three main pillars: the social one, the economic one and the environment. If all those pillars will be met, sustainability can be reached. What about agriculture? There are several definitions for Sustainable Agriculture, one says that Sustainable Agriculture is the efficient production of safe, high quality agricultural products, in a way that protects and improves the natural environment, the social and economic conditions of farmers, their employees and local communities, and safeguards the health and welfare of all farmed species (Sustainable Agriculture Initiative Platform). The aim of this dissertation is to illustrate how Precision Agriculture can help not only farmers, but also agriculture business operators to process the right decision in order to satisfy sustainable principles. New technologies are useful to manage resources employed in agricultural processes such as soil, water, fertilizers or pesticide, but also to reduce wastage maximizing yields and, consequently farm profit. In particular, the dissertation is going to illustrate how monitoring technologies implementation is useful to manage soil, crop and weather with proximal and remote sensing. Those collected data can be processed, corrected and interpreted by operators in order to generate Decision Support System which is useful to improve company’s decision-making capability. The study focuses on one of the main extensive crops, barley, in particular on its lodging. Different barley varieties were tested on 195 plots located in Idice, province of Bologna, North-East Italy to asses which one can better resist to lodge and to demonstrate how Unmanned Aerial Vehicle can be useful to monitor crop evolution. In fact, UAV was employed to collect data and, to validate them, crop smart scouting was necessary. After data collection and correction, a Digital Elevation Model has been created in order to evaluate three classes: laid crop, partial laid crop, no laid crop. The study evidences how remote sensing, in particular UAV’s, can help to process data otherwise hard to collect, giving useful information to farmers and business operators to make the right decision with high accuracy in short time.Nowadays, there is high attention to sustainability in all areas of human activities. But what does sustainability mean? As the World Commission on Environment and Development says, sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. Definitively, it is possible to affirm that sustainability is based on three main pillars: the social one, the economic one and the environment. If all those pillars will be met, sustainability can be reached. What about agriculture? There are several definitions for Sustainable Agriculture, one says that Sustainable Agriculture is the efficient production of safe, high quality agricultural products, in a way that protects and improves the natural environment, the social and economic conditions of farmers, their employees and local communities, and safeguards the health and welfare of all farmed species (Sustainable Agriculture Initiative Platform). The aim of this dissertation is to illustrate how Precision Agriculture can help not only farmers, but also agriculture business operators to process the right decision in order to satisfy sustainable principles. New technologies are useful to manage resources employed in agricultural processes such as soil, water, fertilizers or pesticide, but also to reduce wastage maximizing yields and, consequently farm profit. In particular, the dissertation is going to illustrate how monitoring technologies implementation is useful to manage soil, crop and weather with proximal and remote sensing. Those collected data can be processed, corrected and interpreted by operators in order to generate Decision Support System which is useful to improve company’s decision-making capability. The study focuses on one of the main extensive crops, barley, in particular on its lodging. Different barley varieties were tested on 195 plots located in Idice, province of Bologna, North-East Italy to asses which one can better resist to lodge and to demonstrate how Unmanned Aerial Vehicle can be useful to monitor crop evolution. In fact, UAV was employed to collect data and, to validate them, crop smart scouting was necessary. After data collection and correction, a Digital Elevation Model has been created in order to evaluate three classes: laid crop, partial laid crop, no laid crop. The study evidences how remote sensing, in particular UAV’s, can help to process data otherwise hard to collect, giving useful information to farmers and business operators to make the right decision with high accuracy in short time

    Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest

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    Β© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 7 (2017): 1267, doi:10.1038/s41598-017-01260-y.Changes in plant phenology affect the carbon flux of terrestrial forest ecosystems due to the link between the growing season length and vegetation productivity. Digital camera imagery, which can be acquired frequently, has been used to monitor seasonal and annual changes in forest canopy phenology and track critical phenological events. However, quantitative assessment of the structural and biochemical controls of the phenological patterns in camera images has rarely been done. In this study, we used an NDVI (Normalized Difference Vegetation Index) camera to monitor daily variations of vegetation reflectance at visible and near-infrared (NIR) bands with high spatial and temporal resolutions, and found that the infrared camera based NDVI (camera-NDVI) agreed well with the leaf expansion process that was measured by independent manual observations at Harvard Forest, Massachusetts, USA. We also measured the seasonality of canopy structural (leaf area index, LAI) and biochemical properties (leaf chlorophyll and nitrogen content). We found significant linear relationships between camera-NDVI and leaf chlorophyll concentration, and between camera-NDVI and leaf nitrogen content, though weaker relationships between camera-NDVI and LAI. Therefore, we recommend ground-based camera-NDVI as a powerful tool for long-term, near surface observations to monitor canopy development and to estimate leaf chlorophyll, nitrogen status, and LAI.This research was supported by US Department of Energy Office of Biological and Environmental Research Grant DE-SC0006951, National Science Foundation Grants DBI-959333 and AGS-1005663, and the University of Chicago and the MBL Lillie Research Innovation Award to J.T. and China Scholarship Council (CSC) to H.Y

    Unmanned aerial vehicle to estimate nitrogen status of turfgrasses

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    Spectral reflectance data originating from Unmanned Aerial Vehicle (UAV) imagery is a valuable tool to monitor plant nutrition, reduce nitrogen (N) application to real needs, thus producing both economic and environmental benefits. The objectives of the trial were i) to compare the spectral reflectance of 3 turfgrasses acquired via UAV and by a ground-based instrument; ii) to test the sensitivity of the 2 data acquisition sources in detecting induced variation in N levels. N application gradients from 0 to 250 kg ha-1 were created on 3 different turfgrass species: Cynodon dactylon x transvaalensis (Cdxt) Patriot, Zoysia matrella (Zm) Zeon and Paspalum vaginatum (Pv) Salam. Proximity and remote-sensed reflectance measurements were acquired using a GreenSeeker handheld crop sensor and a UAV with onboard a multispectral sensor, to determine Normalized Difference Vegetation Index (NDVI). Proximity-sensed NDVI is highly correlated with data acquired from UAV with r values ranging from 0.83 (Zm) to 0.97 (Cdxt). Relating NDVI-UAV with clippings N, the highest r is for Cdxt (0.95). The most reactive species to N fertilization is Cdxt with a clippings N% ranging from 1.2% to 4.1%. UAV imagery can adequately assess the N status of turfgrasses and its spatial variability within a species, so for large areas, such as golf courses, sod farms or race courses, UAV acquired data can optimize turf management. For relatively small green areas, a hand-held crop sensor can be a less expensive and more practical option

    Remote Sensing for Precision Nitrogen Management

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    This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

    ΠŸΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… индСксов для ΠΎΡ†Π΅Π½ΠΊΠΈ состояния ΡΠ΅Π»ΡŒΡΠΊΠΎΡ…ΠΎΠ·ΡΠΉΡΡ‚Π²Π΅Π½Π½Ρ‹Ρ… ΠΊΡƒΠ»ΡŒΡ‚ΡƒΡ€

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    Monitoring of the state of agricultural crops and forecasting the crops development begin with aerial photography using a unmanned aerial vehicles and a multispectral camera. Vegetation indexes are selected empirically and calculated as a result of operations with values of diff erent spectral wavelengths. When assessing the state of crops, especially in breeding, it is necessary to determine the limiting factors for the use of vegetation indexes.(Research purpose) To analyze, evaluate and select vegetation indexes for conducting operational, high-quality and comprehensive monitoring of the state of crops and the formation of optimal management decisions.(Materials and Methods) The authors studied the results of scientifi c research in the fi eld of remote sensing technology using unmanned aerial vehicles and multispectral cameras, as well as the experience of using vegetation indexes to assess the condition of crops in the precision farming system. The limiting factors for the vegetation indexes research were determined: a limited number of monochrome cameras in popular multispectral cameras; key indicators for monitoring crops required by agronomists. After processing aerial photographs from an unmanned aerial vehicle, a high-precision orthophotomap, a digital fi eld model, and maps of vegetation indexes were created.(Results and discussion) More than 150 vegetation indexes were found. Not all of them were created through observation and experimentation. The authors considered broadband vegetation indexes to assess the status of crops in the fi elds. They analyzed the vegetation indexes of soybean and winter wheat crops in the main phases of vegetation.(Conclusions) The authors found that each vegetative index had its own specifi c scope, limiting factors and was used both separately and in combination with other indexes. When calculating the vegetation indexes for practical use, it was recommended to be guided by the technical characteristics of multispectral cameras and took into account the index use eff ectiveness at various vegetation stages.ΠΡΡ€ΠΎΡ„ΠΎΡ‚ΠΎΡΡŠΠ΅ΠΌΠΊΡƒ с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ бСспилотных Π»Π΅Ρ‚Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… Π°ΠΏΠΏΠ°Ρ€Π°Ρ‚ΠΎΠ² ΠΈ ΠΌΡƒΠ»ΡŒΡ‚ΠΈΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½ΠΎΠΉ ΠΊΠ°ΠΌΠ΅Ρ€Ρ‹ ΠΏΡ€ΠΈΠΌΠ΅Π½ΡΡŽΡ‚ для ΠΌΠΎΠ½ΠΈΡ‚ΠΎΡ€ΠΈΠ½Π³Π° состояния посСвов ΠΈ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π° развития ΡΠ΅Π»ΡŒΡΠΊΠΎΡ…ΠΎΠ·ΡΠΉΡΡ‚Π²Π΅Π½Π½Ρ‹Ρ… ΠΊΡƒΠ»ΡŒΡ‚ΡƒΡ€. Π’ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π΅ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΉ со значСниями Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… Π΄Π»ΠΈΠ½ Π²ΠΎΠ»Π½ эмпиричСски ΠΏΠΎΠ΄Π±ΠΈΡ€Π°ΡŽΡ‚ ΠΈ Ρ€Π°ΡΡΡ‡ΠΈΡ‚Ρ‹Π²Π°ΡŽΡ‚ Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Π΅ индСксы, составляя ΠΊΠ°Ρ€Ρ‚Ρ‹. ΠŸΡ€ΠΈ ΠΎΡ†Π΅Π½ΠΊΠ΅ состояния посСвов Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎ ΠΎΠΏΡ€Π΅Π΄Π΅Π»ΡΡ‚ΡŒ Π»ΠΈΠΌΠΈΡ‚ΠΈΡ€ΡƒΡŽΡ‰ΠΈΠ΅ Ρ„Π°ΠΊΡ‚ΠΎΡ€Ρ‹ примСнСния Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… индСксов.(ЦСль исслСдования) ΠŸΡ€ΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ, ΠΎΡ†Π΅Π½ΠΈΡ‚ΡŒ ΠΈ Π²Ρ‹Π±Ρ€Π°Ρ‚ΡŒ Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Π΅ индСксы для провСдСния ΠΎΠΏΠ΅Ρ€Π°Ρ‚ΠΈΠ²Π½ΠΎΠ³ΠΎ, качСствСнного ΠΈ комплСксного ΠΌΠΎΠ½ΠΈΡ‚ΠΎΡ€ΠΈΠ½Π³Π° состояния ΡΠ΅Π»ΡŒΡΠΊΠΎΡ…ΠΎΠ·ΡΠΉΡΡ‚Π²Π΅Π½Π½Ρ‹Ρ… ΠΊΡƒΠ»ΡŒΡ‚ΡƒΡ€ ΠΈ формирования ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹Ρ… управлСнчСских Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ.(ΠœΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Ρ‹ ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹) Π˜Π·ΡƒΡ‡ΠΈΠ»ΠΈ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ Π½Π°ΡƒΡ‡Π½Ρ‹Ρ… исслСдований Π² области Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ дистанционного зондирования с использованиСм бСспилотных Π»Π΅Ρ‚Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… Π°ΠΏΠΏΠ°Ρ€Π°Ρ‚ΠΎΠ² ΠΈ ΠΌΡƒΠ»ΡŒΡ‚ΠΈΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΊΠ°ΠΌΠ΅Ρ€, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΎΠΏΡ‹Ρ‚ примСнСния Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… индСксов для ΠΎΡ†Π΅Π½ΠΊΠΈ состояния ΡΠ΅Π»ΡŒΡΠΊΠΎΡ…ΠΎΠ·ΡΠΉΡΡ‚Π²Π΅Π½Π½Ρ‹Ρ… ΠΊΡƒΠ»ΡŒΡ‚ΡƒΡ€ Π² систСмС Ρ‚ΠΎΡ‡Π½ΠΎΠ³ΠΎ зСмлСдСлия. ΠžΠΏΡ€Π΅Π΄Π΅Π»ΠΈΠ»ΠΈ Π»ΠΈΠΌΠΈΡ‚ΠΈΡ€ΡƒΡŽΡ‰ΠΈΠ΅ Ρ„Π°ΠΊΡ‚ΠΎΡ€Ρ‹ для исслСдования Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… индСксов: ΠΎΠ³Ρ€Π°Π½ΠΈΡ‡Π΅Π½Π½ΠΎΠ΅ количСство монохромных ΠΊΠ°ΠΌΠ΅Ρ€ Π² популярных ΠΌΡƒΠ»ΡŒΡ‚ΠΈΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΊΠ°ΠΌΠ΅Ρ€Π°Ρ…; основныС ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΠΈ для ΠΌΠΎΠ½ΠΈΡ‚ΠΎΡ€ΠΈΠ½Π³Π° ΡΠ΅Π»ΡŒΡΠΊΠΎΡ…ΠΎΠ·ΡΠΉΡΡ‚Π²Π΅Π½Π½Ρ‹Ρ… ΠΊΡƒΠ»ΡŒΡ‚ΡƒΡ€, Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΡ‹Π΅ Π°Π³Ρ€ΠΎΠ½ΠΎΠΌΠ°ΠΌ. ПослС ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ аэрофотоснимков с бСспилотного Π»Π΅Ρ‚Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠ³ΠΎ Π°ΠΏΠΏΠ°Ρ€Π°Ρ‚Π° создали высокоточный ΠΎΡ€Ρ‚ΠΎΡ„ΠΎΡ‚ΠΎΠΏΠ»Π°Π½, Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΡƒΡŽ модСль поля ΠΈ ΠΊΠ°Ρ€Ρ‚Ρ‹ Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… индСксов.(Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΠΈ обсуТдСниС) ΠžΠ±Π½Π°Ρ€ΡƒΠΆΠΈΠ»ΠΈ Π±ΠΎΠ»Π΅Π΅ 150 Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… индСксов. НС всС ΠΈΡ… Π½ΠΈΡ… создавались ΠΏΡƒΡ‚Π΅ΠΌ наблюдСний ΠΈ экспСримСнтов. РассмотрСли ΡˆΠΈΡ€ΠΎΠΊΠΎΠΏΠΎΠ»ΠΎΡΠ½Ρ‹Π΅ Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Π΅ индСксы для ΠΎΡ†Π΅Π½ΠΊΠΈ состояния посСвов Π½Π° полях. ΠŸΡ€ΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π»ΠΈ Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Π΅ индСксы посСвов сои ΠΈ ΠΎΠ·ΠΈΠΌΠΎΠΉ ΠΏΡˆΠ΅Π½ΠΈΡ†Ρ‹ Π² основных Ρ„Π°Π·Π°Ρ… Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΈ.(Π’Ρ‹Π²ΠΎΠ΄Ρ‹) Выявили, Ρ‡Ρ‚ΠΎ ΠΊΠ°ΠΆΠ΄Ρ‹ΠΉ Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹ΠΉ индСкс ΠΈΠΌΠ΅Π΅Ρ‚ свою ΡΠΏΠ΅Ρ†ΠΈΡ„ΠΈΡ‡Π΅ΡΠΊΡƒΡŽ сфСру примСнСния, ΠΎΠ³Ρ€Π°Π½ΠΈΡ‡ΠΈΠ²Π°ΡŽΡ‰ΠΈΠ΅ Ρ„Π°ΠΊΡ‚ΠΎΡ€Ρ‹ ΠΈ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅Ρ‚ΡΡ ΠΊΠ°ΠΊ ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½ΠΎ, Ρ‚Π°ΠΊ ΠΈ Π² комплСксС с Π΄Ρ€ΡƒΠ³ΠΈΠΌΠΈ индСксами. Π Π΅ΠΊΠΎΠΌΠ΅Π½Π΄ΠΎΠ²Π°Π»ΠΈ ΠΏΡ€ΠΈ расчСтС Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… индСксов для практичСского примСнСния Ρ€ΡƒΠΊΠΎΠ²ΠΎΠ΄ΡΡ‚Π²ΠΎΠ²Π°Ρ‚ΡŒΡΡ тСхничСскими характСристиками ΠΌΡƒΠ»ΡŒΡ‚ΠΈΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΊΠ°ΠΌΠ΅Ρ€ ΠΈ ΡƒΡ‡ΠΈΡ‚Ρ‹Π²Π°Ρ‚ΡŒ ΡΡ„Ρ„Π΅ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ примСнСния индСкса Π½Π° Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… стадиях Π²Π΅Π³Π΅Ρ‚Π°Ρ†ΠΈΠΈ
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