Spatio-Temporal Changes in Wheat Crop Cultivation in India

Krishnan, P.; Aggarwal, Pramod K.; Mridha, Nilimesh; Bajpai, Vaishangi

doi:10.5194/isprs-archives-xlii-3-w6-385-2019

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…Mapping and spatiotemporal analysis are the most widely used spatiotemporal visualization methods. They are widely used in the fields of ecology (Krishnan, Aggarwal, Mridha, & Bajpai, 2019), environmental health (Fan, Zhao, & Yang, 2020) and emergency management (Kontokosta & Malik, 2018). In the urban management domain, distribution patterns and hotspots are both common applications in criminal patterns (Ajayakumar & Shook, 2020), public facilities (Shi, Yang, & Shen, 2020) and rainfall monitoring (Jing, Yu, Dai, Wei, & Du, 2016).…”

Section: Related Workmentioning

confidence: 99%

“…This model can help develop preventive measures to reduce such traffic accidents. Krishnan et al (2019) used the space‐time cube method to analyze the overall change trend in wheat crop cultivation data over time and used a spatiotemporal statistical model to evaluate the relationship. In the context of tourism management, Jing et al (2020) used the spatiotemporal cube model to develop a spatiotemporal dynamic model of Beijing inbound tourists under fine‐grained conditions with Flickr, thus providing a scientific basis for relevant departments to formulate inbound tourism policies.…”

Section: Related Workmentioning

confidence: 99%

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Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Jing

Zhu

et al. 2021

Transactions in GIS

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City service demand fluctuates across space and time. Although various data, such as 311 hotline data and social media data, have been used to explore the spatiotemporal patterns of city services, data uncertainty and the uneven distribution of service demand are overlooked to some extent and thus could result in bias. To overcome these shortcomings, top‐down collected city service data that fully cover urban areas are used as an emerging data source in this article. A visual analytical approach that employs a three‐dimensional model based on a space‐time cube combined with the Mann–Kendall algorithm is developed and applied in Xicheng District, Beijing, China. The results show that in comparison to other methods, the emerging data and visualization methods have more power to explain city services in terms of overall trends and micro‐scale details. For instance, city service cases demonstrate a significant downward trend. Meanwhile, the distribution of hotspots/coldspots is found to be related to the built environment and population density. For example, high‐incidence cases are located in some communities that are the key governance areas, indicating a demand to increase the staffing of grid administrators. The findings of this work can potentially benefit other cities in China and worldwide.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Jing

Zhu

et al. 2021

Transactions in GIS

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“…The use of the STC has proven to be a highly beneficial tool in agriculture. Krishnan et al [19] explained the use of the space-time cube for the quick and efficient analysis of wheat cultivation data in India, facilitating the identification of spatiotemporal patterns in agricultural production. This was particularly useful in understanding changes in wheat production over time and across different regions of the country, which, in turn, could contribute to enhancing agricultural management strategies and increasing wheat production in the future.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Winter Wheat (Triticum aestivum) Growth Pattern Using High Spatial Resolution Images: A Case Study at Lakehead University Agriculture Research Station, Thunder Bay, Canada

Fuentes,

Heenkenda,

Sahota

et al. 2024

Crops

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Remote sensing technology currently facilitates the monitoring of crop development, enabling detailed analysis and monitoring throughout the crop’s growing stages. This research analyzed the winter wheat growth dynamics of experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada using high spatial and temporal resolution remote sensing images. The spectral signatures for five growing stages were prepared. NIR reflectance increased during the growing stages and decreased at the senescence, indicating healthy vegetation. The space–time cube provided valuable insight into how canopy height changed over time. The effect of nitrogen treatments on wheat did not directly influence the plant count (spring/autumn), and height and volume at maturity. However, the green and dry weights were different at several treatments. Winter wheat yield was predicted using the XGBoost algorithm, and moderate results were obtained. The study explored different techniques for analyzing winter wheat growth dynamics and identified their usefulness in smart agriculture.

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Relationship between fungal bioaerosols and biotic stress on crops: a case study on wheat rust fungi

Varghese,

Krishnamoorthy,

Patel

et al. 2024

J Plant Dis Prot

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This study proposes the relationship between fungal bioaerosols and biotic stress on crops using a case study on wheat leaf rust reported from northern India. We sampled and quanti ed the size-resolved fungal bioaerosols using the Next-Generation Sequencing technique from a wheat crop eld during winter. Puccinia recondita, the fungal pathogen that causes wheat leaf rust, was identi ed during the study period. The pathogen is known for its frequent and widespread occurrence of new variants that causes disease-resistant crop varieties susceptible to infections. This reveals the need for frequent and systematic monitoring to prevent rust infection. In the current study, the size-resolved fungal bioaerosol characterisation was linked to the dispersal properties of the fungal propagules, and using a theoretical dispersion model, originating source and the areas of high risk for wheat leaf rust infection were identi ed. Our ndings may serve as a vital reference for crop pathologists, agro technologists, environmentalists, and policymakers to expand the investigation on the biotic stress caused by the invasion of fungal bioaerosols on various crops and to implement preventive measures to ensure global food security.

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Spatio-Temporal Changes in Wheat Crop Cultivation in India

Cited by 5 publications

References 35 publications

Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Analyzing Winter Wheat (Triticum aestivum) Growth Pattern Using High Spatial Resolution Images: A Case Study at Lakehead University Agriculture Research Station, Thunder Bay, Canada

Relationship between fungal bioaerosols and biotic stress on crops: a case study on wheat rust fungi

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