scholarly journals Geostatistical modeling of “lethal coconut palm crown atrophy”, a new disease in the State of Pará

2021 ◽  
Vol 42 (6supl2) ◽  
pp. 3603-3616
Author(s):  
Adriano da Silva Gama ◽  
◽  
Paulo Roberto Silva Farias ◽  
Keyword(s):  
Distribution Pattern ◽  
Temporal Distribution ◽  
Spherical Model ◽  
Coconut Palm ◽  
Coefficient Of Determination ◽  
Data Variability ◽  
Range Vector ◽  
Spatial Estimates ◽  
Spreading Disease ◽  
Kriging Maps

’Lethal Coconut Palm Crown Atrophy’ (LCCA) is a rapidly spreading disease in Brazil, capable of quickly killing coconut trees and threatening the commercial exploration of this plant. The objective of this work was to characterize the spatial and temporal distribution pattern of LCCA in green dwarf coconut commercial plantation areas, located the municipality of Santa Izabel, mesoregion of Northeastern Pará, Brazil. Surveys were carried out at monthly intervals between January 2014 and December 2018, checking for plants with LCCA-characteristic symptoms. Geostatistics was applied to perform spatial-temporal disease estimates based on semivariogram modeling and preparation of ordinary kriging maps. These spatial estimates are conducted through interpolations that characterize data variability in the area. The spherical model yielded the best fit to the spatial distribution of the disease, as it presented the best coefficient of determination (R²), with the range varying between 14m and 45m. The Spatial Dependence Index (SDI) was moderate in the evaluations carried out between 2014 and 2017 (in the 0.26-0.64 range), but not in 2018, when it was strong (0.23). The values of the clustering intensity of LCCA-symptomatic plants were estimated in non-sampled points. The spherical fit model of the data indicates an aggregated distribution pattern, shown by aggregation patches in the plantation, graded by values of dissemination intensity. The kriging maps allowed the observation that the disease expands between plants in the same line, suggesting the possibility of the presence of a short-range vector.

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

scholarly journals Temporal Distribution of Gastropods In Rocky Intertidal Area In North Manokwari, West Papua

2019 ◽  
pp. 1
Author(s):  
Dandi Saleky ◽  
Simon P.O Leatemia ◽  
Yuanike Yuanike ◽  
Irman Rumengan ◽  
I Nyoman Giri Putra
Keyword(s):  
Community Structure ◽  
Distribution Pattern ◽  
Similarity Index ◽  
Temporal Distribution ◽  
Population History ◽  
Rocky Intertidal ◽  
Systematic Sampling ◽  
Intertidal Area ◽  
Ecological Features ◽  
West Papua

Gastropods is an important organism that commonly found inhabiting the rocky intertidal area. Distribution pattern of this species is influenced by various factors such as population history, microhabitat, predation and a complex interactions between oceanographic dynamics and ecological features. This study aims to compare the temporal distribution pattern of gastropods at two different rocky intertidal area. This research was conducted at the rocky intertidal area of Amban and Nuni, North Manokwari District, West Papua. Data collection was performed during the daylight and night in April and June 2012 using systematic sampling method. The results showed that both physical and chemical factors are suite for supporting gastropods life. Furthermore, these factors seem to have an impact on gastropod zoning patterns observed in the study area. The similarity index values ​​indicate that the similarity of gastropod species between the two locations is low, which means that the species of gastropods found in each location is quite different. The community structure of gastropod at the study area is stable. In addition, we found that the gastropods diversity were higher during the night than the daylight because gastropods are classified as a nocturnal animals. Key Words: Gastropod; Rocky Intertidal; Community Structure; Nocturnal

scholarly journals FUZZY LOGIC IN THE SPATIAL AND TEMPORAL DISTRIBUTION IN THE QUALITY OF THE BEVERAGE IN CONILON COFFEE

2019 ◽  
Vol 14 (2) ◽  
pp. 163
Author(s):  
Abel Souza Da Fonseca ◽  
Julião Soares De Souza Lima ◽  
Samuel De Assis Silva ◽  
Maria Christina Junger Delôgo Dardengo ◽  
Alexandre Candido Xavier
Keyword(s):  
Spatial Dependence ◽  
Temporal Variability ◽  
Temporal Distribution ◽  
Spherical Model ◽  
Coffea Canephora ◽  
Descriptive Statistics ◽  
Fuzzy Classification ◽  
Spatial And Temporal Variability ◽  
Mouth Feel

<p>The objective in this study was to evaluate the spatial and temporal variability of the beverage quality by applying the fuzzy classification in the final global sensory analysis, for Coffea canephora Pierre ex A. Froehner, in two consecutive harvests. The studied variables were: fragrance (aroma), flavor, bitterness (sweetness), set, balance, cleaning, aftertaste, mouth feel, uniformity, salinity (acidity) and drink (global note). To the average overall scores of the drinks obtained on the cup-tasting at 80.0 points of a sampling, the mesh has applied the function of association of the fuzzy classification linear model to determine the degree of pertinence. The data were analyzed by the descriptive statistics and then by geostatistics to verify the existence and quantify the degree of spatial dependence of the variables. In the interval classified as “very good coffee” is found in the global average grade, in the two harvests. The methodology fuzzy applied in the global beverage note of the coffee conilon seminal made it possible to determine their spatial variability in the same distribution pattern in the two harvests, close ranges, and adjustments to the spherical model, which was confirmed by the spatial correlation of 61.6% among the fuzzy maps for the global score</p>

Spatial and Temporal Distribution Pattern of Camptothecin in Seeds and Fruits of Pyrenacantha volubilis Hook. (Icacinaceae) during Different Fruit Developmental Stages

2017 ◽  
Vol 112 (05) ◽  
pp. 1034 ◽  
Author(s):  
Hirenallur Kumarappa Suma ◽  
Vadlapudi Kumar ◽  
Patel Mohana Kumara ◽  
Amitava Srimany ◽  
Gudasalamani Ravikanth ◽  
...  
Keyword(s):  
Distribution Pattern ◽  
Developmental Stages ◽  
Temporal Distribution ◽  
Spatial And Temporal Distribution ◽  
Seeds And Fruits

scholarly journals Spatial and temporal distribution of bats (Chiroptera) in bright summer nights

2016 ◽  
Vol 66 (1) ◽  
pp. 65-80 ◽  
Author(s):  
Tore Christian Michaelsen
Keyword(s):  
Spatial Distribution ◽  
Distribution Pattern ◽  
Temporal Distribution ◽  
Spatial Distribution Pattern ◽  
Spatial And Temporal Distribution ◽  
Pronounced Increase ◽  
Low Salinity ◽  
Non Linear ◽  
Over Time

Most bat species show plasticity in their choice of habitat and landscape. This study focuses on the distribution and activity of bats along the hillsides and onto the shores of a low salinity marine Norwegian fiord at 62°N. Ultrasound was recorded using D500 detectors in June and July at 42 different sites from the shoreline and up the hillsides to around 200 m. Detectors were placed in well-preserved woodlands. OnlyPipistrellussp., northern batsEptesicus nilssoniiand bats of theMyotisgenus were common. There was a clear non-linear spatial distribution pattern along these slopes, with a pronounced increase in the number of recorded bats at short distances from the shore. On all six nights, the detector closest to the shore had the highest number of recorded bats. A pattern was also seen in bat distribution over time.Pipistrellussp., northern bats andMyotisspecies all had a peak near the shore during the darkest part of the night, which is around 01.35 h in mid-summer at this latitude. At greater distances,Pipistrellussp. and northern bats had a peak around 40 minutes to one hour before the darkest part of the night, respectively. Here,Myotisspp. peaked about an hour after 01.35.

scholarly journals Frequency of Arthropods Daily Visits on Centella asiatica L. and Synedrlla nodiflora (L) Gaertn in the Wonosari Singosari Tea Plantation Area in Malang Regency

2020 ◽  
Vol 5 (01) ◽  
pp. 11-21
Author(s):  
Dora Dayu Rahma Turista ◽  
Fatchur Rohman ◽  
Bagyo Yanuwiadi
Keyword(s):  
Distribution Pattern ◽  
Control Method ◽  
Temporal Distribution ◽  
Centella Asiatica ◽  
Visual Control ◽  
Wild Plants ◽  
Wild Plant ◽  
Tea Plantation ◽  
Duration Data ◽  
Synedrella Nodiflora

The purpose of this research is to find the temporal distribution pattern of the visiting of Arthropods on these wild plants. This descriptive and explorative research was carried out in the Wonosari tea plantation, Singosari, Malang in March 2011. The visual control method for these purposes was adopted from the modification of the visual control method developed by Frei and Manhart (1992). Observations were done with 10 repeats, consisting of 10 tempos with 15 minutes duration. Data relating to the temporal distribution of Arthropods on these wild plants were presented as diagrams and interpreted descriptively. The result of this research can be described as follows: There was a difference between the temporal distribution of Arthropod visiting the wild plants Centella asiatica L. and Synedrella nodiflora (L) Gaertn. For the Centella asiatica L the highest visitor number was at 10.00 am while for the wild plant Synedrella nodiflora (L) Gaertn it was at 11.20 am.

scholarly journals Applicability of CHIRPS-based satellite rainfall estimates for South Africa

2021 ◽  
Vol 63 (3) ◽  
pp. 1-12
Author(s):  
J A du Plessis ◽  
J K Kibii
Keyword(s):  
South Africa ◽  
Temporal Distribution ◽  
Pairwise Comparison ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Good Precision ◽  
Alternative Source

Long-term rainfall data with good spatial and temporal distribution is essential for all climate-related analyses. The availability of observed rainfall data has become increasingly problematic over the years due to a limited and deteriorating rainfall station network, occasioned by limited reporting and/or quality control of rainfall and, in some cases, closure of these stations. Remotely sensed satellite-based rainfall data sets offer an alternative source of information. In this study, daily and monthly rainfall data derived from Climate Hazards Group InfraRed Precipitation (CHIRPS) is compared with observed rainfall data from 46 stations evenly distributed across South Africa. Various metrics, based on a pairwise comparison between the observed and CHIRPS data, were applied to evaluate CHIRPS performance in the estimation of daily and monthly rainfall. The results show that CHIRPS data correlate well with observed monthly rainfall data for all stations used, having an average coefficient of determination of 0.6 and bias of 0.95. This study concludes that monthly CHIRPS data corresponds well, with good precision and relatively little bias when compared to observed monthly rainfall data, and can therefore be considered for use in conjunction with observed rainfall data where no or limited data is available in South Africa for hydrological analysis.

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