Ecogeographical determinants of the ecological niche of the common milkweed (Asclepias syriaca) on the basis of indices of remote sensing of land images

Kunah, O. М.; Папка, О. С.

doi:10.15421/011609

Cited by 12 publications

(9 citation statements)

References 19 publications

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“…High frequency yield components may have the character of noise and may have environmental origins as a consequence of such phenomena as the impact of diseases and pests, or the impact of weather anomalies. Changes in the abundance of pests depends on the characteris-tics of landscape diversity (Zhukov et al, 2015;Kunah, & Papka, 2016a). Analysis of the yield of grain and leguminous crops in Poltava region showed that the trend of the temporal dynamics is associated with the effect of systematic factors of an agroeconomic and agrotechnological nature .…”

Section: Discussionmentioning

confidence: 99%

Agroecological and agroeconomic aspects of the grain and grain legumes (pulses) yield dynamic within the Dnipropetrovsk region (period 1966–2016)

et al. 2018

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This paper reveals the spatial and temporal patterns of grain and leguminous crops yield dynamics in Dnipropetrovsk region and evaluates the role of agro-environmental and agro-economic factors in their formation. Crop data were obtained from the State Statistics Service of Ukraine. The data of the grain and grain legumes (pulses) yield during 1966–2016 on average per year in the administrative districts of Dnipropetrovsk region was analysed. The obtained data indicate that average yields of cereals and leguminous crops within Dnipropetrovsk region varies from 24.3 to 33.4 CWT/ha. The smallest interannual variability in yield is typical for Vasylkivskyi district (CV = 9.9%), and the largest is typical for Yurivskyi district (CV = 27.7%). As a result of the principal component analysis of the cereals and leguminous crops yields variability, three principal components were extracted which together explain 81.2% of the overall yield variability. Principal component 1 explains 69.4% of the total variability. It indicates the total synchronous yields variation within the area investigated as all examined variables have high loading values on principal component 1. The administrative districts that form a belt located in the direction from the north east to the south west of the region have the most coordinated variance, which is reflected by principal component 1. Principal component 2 explains 6.8% of the yield variability. This principal component is sensitive to opposite yield dynamics of central and south-western districts on the one hand and the eastern and northern districts – on the other. Principal component 3 explains 4.9% of the yield variability. This principal component reveals the opposite dynamics of productivity of the central districts on the one hand and the northern and south-eastern districts on the other. The cluster analysis of administrative districts was conducted based on the dynamics of the yield of grain and leguminous as a result of which four clusters were identified. The clusters are geographically defined administrative districts, together forming spatially connected areas. The similar temporal yield dynamics of grain and leguminous crops as a result of interaction between endogenous and exogenous ecological factors is the main principle for revealling such ecologically homogeneous territories. Spatial distribution of principal components indicates a continual pattern, but their overlapping allows one to extract spatially discrete units, which we identified as agroecological zones. Each zone is characterized by a certain character and dynamics of production capacity and has an invariant pattern of response to varying climatic, environmental, and agroeconomic factors.

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Section: Discussionmentioning

confidence: 99%

Agroecological and agroeconomic aspects of the grain and grain legumes (pulses) yield dynamic within the Dnipropetrovsk region (period 1966–2016)

et al. 2018

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“…Two groups of data were considered as ecogeographic predictors. The first group includes the digital elevation model (DEM) and its derivatives 5 . The second group includes a set of vegetation indices derived from the Landsat 8 satellite image 6 .…”

Section: Methodsmentioning

confidence: 99%

Reflection of the ecological niche of mute swan Cygnus Olor (Gmelin, 1803) in geographical and ecological space

Kunakh¹,

Zhukov²,

Holoborodko³

2021

Achievements of Ukraine and the Eu in Ecology, Biology, Chemistry, Geography and Agricultural Sciences

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INTRODUCTIONGeese live in all landscape-geographical zones. The biology of most species is associated with fresh, saline, and brackish water bodies. In nature, a rather limited number of habitats meet the nutritional requirements of gooseflies, primarily monotypic thickets of monocotyledons (in which meristem does not suffer from pinching), which are in the initial stages of growth. In winter, cereal meadows slightly south of the snow line are ideal for this purpose. In more southerly areas they grow quickly and coarsen, while to the north they are under snow or don't grow. Alternatively, succulent underwater parts of semi-aquatic grasses, reeds, sedges in shallow wetlands south of the freezing zone of water bodies. The results obtained earlier indicate that mute swan occupies only a part of the ecological space represented by the study area 1 .Relief is a set of different in its morphology, genesis and age forms and elements of the earth's surface, and is a reflection of their spatial relationships. Relief analysis is a means of cognition of the landscape based on the Digital Elevation Model. The spatial distribution of topographic attributes can be used to indirectly measure the spatial variability of hydrological, geomorphological and biological processes 2 . Landforms, or units of landforms, are important landform parameters,

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“…Концепція топографічного індексу вологості (Topographic wetness index -TWI) уперше була запропонована К. Бівеном та Н. Кіркбі (Beven & Kirkby, 1979). Топографічний індекс вологості обчислюється за формулою TWI = ln(a/tanβ), де a -дренажна площа (площа водозбору, розрахована на одиницю довжини замикаючого контуру); β -крутість схилу (Moore, Gessler, et al, 1993;Kunah & Papka, 2016a;Zhukov & Andryushchenko, 2017).…”

Section: матеріал і методи дослідженьunclassified

The role of geomorphological predictors for modeling spatial variation of environmental regimes measured using phytoindication

Жуков¹,

Жуков²,

Потапенко³

2019

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The paper established link between phytoindication estimated environmental regimes and geomorphological predictors and built environmental spatial patterns of variation modes within the Dnipropetrovsk region. 175 geobotanical descriptions were conducted in the 2016-2017 yy. within the Dnepropetrovsk region (Ukraine). Phytoindication assessment of the environmental regimes was made on the basis of the geobotanical description: soil water regime, variability of damping, soil aeration, soil acidity, total salt regime, carbonate content in soil, nitrogen content in soil, radiation balance, aridity or humidity, continentality, the rigours of the winter, light regime. The phytoindication assessments of the environmental regimes are characterized by correlation of geomorphological properties. Regression models can explain 10-31% variability of phytoindication environmental assessments modes. Most appeared to be dependent geomorphological regime nitrogen nutrition and moisture, and the least-the mode of variability of moisture and ombroclimat. Edaphic regimes for environmental information and most valuable predictor is the height of the relief and direct insolation (four statistically significant regression coefficient). For most modes of climate information and valuable factor in erosion, direct insolation and height of the channel network (two statistically significant regression coefficient). Entropy of the relief diversity is statistically significant predictor for salt regime, carbonate content and termoclimat. Digital elevation models and derivative information layers of spatial data (topographic wetness index, topographic position index, mass balance index, erosion factors, geomorphological assessment of direct and diffuse insolation, vertical distance to channel network, vector ruggedness measure, Shannon diversity of terrain) is a valuable information covariates (predictors) of the environmental regimes that obtained using the synphytoindication method. Procedure of the spatial extrapolation of the phytoindication assessments at regional level can be performed based on regression models by the method of support vectors. This approach is flexible and takes into account the specific environmental interactions in the system topography and vegetation and environmental regimes.

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Ecogeographical determinants of the ecological niche of the common milkweed (Asclepias syriaca) on the basis of indices of remote sensing of land images

Cited by 12 publications

References 19 publications

Agroecological and agroeconomic aspects of the grain and grain legumes (pulses) yield dynamic within the Dnipropetrovsk region (period 1966–2016)

Agroecological and agroeconomic aspects of the grain and grain legumes (pulses) yield dynamic within the Dnipropetrovsk region (period 1966–2016)

Reflection of the ecological niche of mute swan Cygnus Olor (Gmelin, 1803) in geographical and ecological space

The role of geomorphological predictors for modeling spatial variation of environmental regimes measured using phytoindication

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