5,239 research outputs found
Use of consumer-grade cameras to assess wheat N status and grain yield
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 techniquesβA review
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
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
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
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 needsβ.
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 companyβs 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 UAVβs, 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 needsβ.
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 companyβs 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 UAVβs, 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
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
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
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
ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠ² Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ΅Π»ΡΡΠΊΠΎΡ ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΡΡ ΠΊΡΠ»ΡΡΡΡ
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.ΠΡΡΠΎΡΠΎΡΠΎΡΡΠ΅ΠΌΠΊΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π±Π΅ΡΠΏΠΈΠ»ΠΎΡΠ½ΡΡ
Π»Π΅ΡΠ°ΡΠ΅Π»ΡΠ½ΡΡ
Π°ΠΏΠΏΠ°ΡΠ°ΡΠΎΠ² ΠΈ ΠΌΡΠ»ΡΡΠΈΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠ°ΠΌΠ΅ΡΡ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡ Π΄Π»Ρ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΏΠΎΡΠ΅Π²ΠΎΠ² ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ΅Π»ΡΡΠΊΠΎΡ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΡΡ
ΠΊΡΠ»ΡΡΡΡ. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ ΡΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΡΠΌΠΈ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
Π΄Π»ΠΈΠ½ Π²ΠΎΠ»Π½ ΡΠΌΠΏΠΈΡΠΈΡΠ΅ΡΠΊΠΈ ΠΏΠΎΠ΄Π±ΠΈΡΠ°ΡΡ ΠΈ ΡΠ°ΡΡΡΠΈΡΡΠ²Π°ΡΡ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΈΠ½Π΄Π΅ΠΊΡΡ, ΡΠΎΡΡΠ°Π²Π»ΡΡ ΠΊΠ°ΡΡΡ. ΠΡΠΈ ΠΎΡΠ΅Π½ΠΊΠ΅ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΏΠΎΡΠ΅Π²ΠΎΠ² Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡ Π»ΠΈΠΌΠΈΡΠΈΡΡΡΡΠΈΠ΅ ΡΠ°ΠΊΡΠΎΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠ².(Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ) ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ, ΠΎΡΠ΅Π½ΠΈΡΡ ΠΈ Π²ΡΠ±ΡΠ°ΡΡ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΈΠ½Π΄Π΅ΠΊΡΡ Π΄Π»Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ, ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ΅Π»ΡΡΠΊΠΎΡ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΡΡ
ΠΊΡΠ»ΡΡΡΡ ΠΈ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ
ΡΠΏΡΠ°Π²Π»Π΅Π½ΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ.(ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ) ΠΠ·ΡΡΠΈΠ»ΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π½Π°ΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π·ΠΎΠ½Π΄ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π±Π΅ΡΠΏΠΈΠ»ΠΎΡΠ½ΡΡ
Π»Π΅ΡΠ°ΡΠ΅Π»ΡΠ½ΡΡ
Π°ΠΏΠΏΠ°ΡΠ°ΡΠΎΠ² ΠΈ ΠΌΡΠ»ΡΡΠΈΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠ°ΠΌΠ΅Ρ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΠΏΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠ² Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΡΠ΅Π»ΡΡΠΊΠΎΡ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΡΡ
ΠΊΡΠ»ΡΡΡΡ Π² ΡΠΈΡΡΠ΅ΠΌΠ΅ ΡΠΎΡΠ½ΠΎΠ³ΠΎ Π·Π΅ΠΌΠ»Π΅Π΄Π΅Π»ΠΈΡ. ΠΠΏΡΠ΅Π΄Π΅Π»ΠΈΠ»ΠΈ Π»ΠΈΠΌΠΈΡΠΈΡΡΡΡΠΈΠ΅ ΡΠ°ΠΊΡΠΎΡΡ Π΄Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠ²: ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½Π½ΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎΒ ΠΌΠΎΠ½ΠΎΡ
ΡΠΎΠΌΠ½ΡΡ
ΠΊΠ°ΠΌΠ΅Ρ Π² ΠΏΠΎΠΏΡΠ»ΡΡΠ½ΡΡ
ΠΌΡΠ»ΡΡΠΈΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠ°ΠΌΠ΅ΡΠ°Ρ
; ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ Π΄Π»Ρ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΠ΅Π»ΡΡΠΊΠΎΡ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΡΡ
ΠΊΡΠ»ΡΡΡΡ, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΠ΅ Π°Π³ΡΠΎΠ½ΠΎΠΌΠ°ΠΌ. ΠΠΎΡΠ»Π΅ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π°ΡΡΠΎΡΠΎΡΠΎΡΠ½ΠΈΠΌΠΊΠΎΠ² Ρ Π±Π΅ΡΠΏΠΈΠ»ΠΎΡΠ½ΠΎΠ³ΠΎ Π»Π΅ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ Π°ΠΏΠΏΠ°ΡΠ°ΡΠ° ΡΠΎΠ·Π΄Π°Π»ΠΈ Π²ΡΡΠΎΠΊΠΎΡΠΎΡΠ½ΡΠΉ ΠΎΡΡΠΎΡΠΎΡΠΎΠΏΠ»Π°Π½, ΡΠΈΡΡΠΎΠ²ΡΡ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΠΎΠ»Ρ ΠΈ ΠΊΠ°ΡΡΡ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠ².(Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅) ΠΠ±Π½Π°ΡΡΠΆΠΈΠ»ΠΈ Π±ΠΎΠ»Π΅Π΅ 150 Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠ². ΠΠ΅ Π²ΡΠ΅ ΠΈΡ
Π½ΠΈΡ
ΡΠΎΠ·Π΄Π°Π²Π°Π»ΠΈΡΡ ΠΏΡΡΠ΅ΠΌ Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠΉ ΠΈ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ². Π Π°ΡΡΠΌΠΎΡΡΠ΅Π»ΠΈ ΡΠΈΡΠΎΠΊΠΎΠΏΠΎΠ»ΠΎΡΠ½ΡΠ΅ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΈΠ½Π΄Π΅ΠΊΡΡ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΏΠΎΡΠ΅Π²ΠΎΠ² Π½Π° ΠΏΠΎΠ»ΡΡ
. ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π»ΠΈ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΈΠ½Π΄Π΅ΠΊΡΡ ΠΏΠΎΡΠ΅Π²ΠΎΠ² ΡΠΎΠΈ ΠΈ ΠΎΠ·ΠΈΠΌΠΎΠΉ ΠΏΡΠ΅Π½ΠΈΡΡ Π² ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΡΠ°Π·Π°Ρ
Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΈ.(ΠΡΠ²ΠΎΠ΄Ρ) ΠΡΡΠ²ΠΈΠ»ΠΈ, ΡΡΠΎ ΠΊΠ°ΠΆΠ΄ΡΠΉ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΠΉ ΠΈΠ½Π΄Π΅ΠΊΡ ΠΈΠΌΠ΅Π΅Ρ ΡΠ²ΠΎΡ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ΅ΡΠΊΡΡ ΡΡΠ΅ΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ, ΠΎΠ³ΡΠ°Π½ΠΈΡΠΈΠ²Π°ΡΡΠΈΠ΅ ΡΠ°ΠΊΡΠΎΡΡ ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΊΠ°ΠΊ ΠΎΡΠ΄Π΅Π»ΡΠ½ΠΎ, ΡΠ°ΠΊ ΠΈ Π² ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ΅ Ρ Π΄ΡΡΠ³ΠΈΠΌΠΈ ΠΈΠ½Π΄Π΅ΠΊΡΠ°ΠΌΠΈ. Π Π΅ΠΊΠΎΠΌΠ΅Π½Π΄ΠΎΠ²Π°Π»ΠΈ ΠΏΡΠΈ ΡΠ°ΡΡΠ΅ΡΠ΅ Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΈΠ½Π΄Π΅ΠΊΡΠΎΠ² Π΄Π»Ρ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²ΠΎΠ²Π°ΡΡΡΡ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌΠΈ ΠΌΡΠ»ΡΡΠΈΡΠΏΠ΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠ°ΠΌΠ΅Ρ ΠΈ ΡΡΠΈΡΡΠ²Π°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΈΠ½Π΄Π΅ΠΊΡΠ° Π½Π° ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΡΠ°Π΄ΠΈΡΡ
Π²Π΅Π³Π΅ΡΠ°ΡΠΈΠΈ
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