scholarly journals Remote sensing and spatial statistical techniques for modellingOmmatissus lybicus(Hemiptera: Tropiduchidae) habitat and population densities

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3752 ◽  
Author(s):  
Khalifa M. Al-Kindi ◽  
Paul Kwan ◽  
Nigel R. Andrew ◽  
Mitchell Welch
Keyword(s):  
Remote Sensing ◽  
Management Strategies ◽  
Agricultural Practices ◽  
Analytical Techniques ◽  
Regional Level ◽  
Statistical Techniques ◽  
Palm Tree ◽  
Population Densities ◽  
Future Studies ◽  
Monitoring And Surveillance

In order to understand the distribution and prevalence ofOmmatissus lybicus(Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density ofO. lybicusin response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density ofO. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like theO. lybicuswith climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.

scholarly journals Remote sensing and geographical information analytical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities

2017 ◽  
Author(s):  
Khalifa M Al-Kindi ◽  
Paul Kwan ◽  
Nigel Andrew ◽  
Mitchell Welch
Keyword(s):  
Remote Sensing ◽  
Geographical Information Systems ◽  
Agricultural Practices ◽  
Analytical Techniques ◽  
Regional Level ◽  
Geographical Information ◽  
Palm Tree ◽  
Population Densities ◽  
Future Studies ◽  
Monitoring And Surveillance

In order to understand the distribution and prevalence of Ommatissus lybicus (Homoptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and highly sophisticated information on the environmental, climatic, and agricultural practices are essential. The analytical techniques available in modern spatial analysis packages, such as Remote Sensing and Geographical Information Systems, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental and human factors. The main objective of this paper is to review remote sensing and geographical information analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus in Oman. An exhaustive search of related literature revealed that there are few studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental and human practice related variables in the Middle East. Our review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance sites that are necessary in designing both local and regional level integrated pest management (IPM) policing of palm tree and other affected cultivated crops.

scholarly journals Remote sensing and geographical information analytical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities

2017 ◽  
Author(s):  
Khalifa M Al-Kindi ◽  
Paul Kwan ◽  
Nigel Andrew ◽  
Mitchell Welch
Keyword(s):  
Remote Sensing ◽  
Geographical Information Systems ◽  
Agricultural Practices ◽  
Analytical Techniques ◽  
Regional Level ◽  
Geographical Information ◽  
Palm Tree ◽  
Population Densities ◽  
Future Studies ◽  
Monitoring And Surveillance

In order to understand the distribution and prevalence of Ommatissus lybicus (Homoptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and highly sophisticated information on the environmental, climatic, and agricultural practices are essential. The analytical techniques available in modern spatial analysis packages, such as Remote Sensing and Geographical Information Systems, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental and human factors. The main objective of this paper is to review remote sensing and geographical information analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus in Oman. An exhaustive search of related literature revealed that there are few studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental and human practice related variables in the Middle East. Our review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance sites that are necessary in designing both local and regional level integrated pest management (IPM) policing of palm tree and other affected cultivated crops.

scholarly journals Application of Nematode Community Analyses-Based Models towards Identifying Sustainable Soil Health Management Outcomes: A Review of the Concepts

Soil Systems ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 32
Author(s):  
Haddish Melakeberhan ◽  
Gregory Bonito ◽  
Alexandra N. Kravchenko
Keyword(s):  
Nutrient Cycling ◽  
Health Management ◽  
Management Strategies ◽  
Soil Health ◽  
Agricultural Practices ◽  
Nematode Community ◽  
Parasitic Nematodes ◽  
Worst Case ◽  
Pests And Diseases ◽  
Water Holding

Soil health connotes the balance of biological, physicochemical, nutritional, structural, and water-holding components necessary to sustain plant productivity. Despite a substantial knowledge base, achieving sustainable soil health remains a goal because it is difficult to simultaneously: (i) improve soil structure, physicochemistry, water-holding capacity, and nutrient cycling; (ii) suppress pests and diseases while increasing beneficial organisms; and (iii) improve biological functioning leading to improved biomass/crop yield. The objectives of this review are (a) to identify agricultural practices (APs) driving soil health degradations and barriers to developing sustainable soil health, and (b) to describe how the nematode community analyses-based soil food web (SFW) and fertilizer use efficiency (FUE) data visualization models can be used towards developing sustainable soil health. The SFW model considers changes in beneficial nematode population dynamics relative to food and reproduction (enrichment index, EI; y-axis) and resistance to disturbance (structure index, SI; x-axis) in order to identify best-to-worst case scenarios for nutrient cycling and agroecosystem suitability of AP-driven outcomes. The FUE model visualizes associations between beneficial and plant-parasitic nematodes (x-axis) and ecosystem services (e.g., yield or nutrients, y-axis). The x-y relationship identifies best-to-worst case scenarios of the outcomes for sustainability. Both models can serve as platforms towards developing integrated and sustainable soil health management strategies on a location-specific or a one-size-fits-all basis. Future improvements for increased implementation of these models are discussed.

scholarly journals Analysis of Oil Palm Tree Recognition using Drone-Based Remote Sensing Images

2020 ◽  
Vol 596 ◽  
pp. 012070
Author(s):  
S A Hashim ◽  
S Daliman ◽  
I N Md Rodi ◽  
N Abd Aziz ◽  
N A Amaludin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Oil Palm ◽  
Palm Tree ◽  
Remote Sensing Images

scholarly journals Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1426
Author(s):  
Ahmed S. Abuzaid ◽  
Mohamed A. E. AbdelRahman ◽  
Mohamed E. Fadl ◽  
Antonio Scopa
Keyword(s):  
Remote Sensing ◽  
Soil Quality ◽  
Land Degradation ◽  
Quality Index ◽  
Management Strategies ◽  
Remote Sensing Data ◽  
Western Desert ◽  
Soil Quality Index ◽  
Climate Conditions ◽  
Sensing Data

Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for usingremote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security.

Sign in / Sign up

Export Citation Format

Share Document