Machine learning approach for climate change impact assessment in agricultural production

Singh, Swati; Babu, K. V. Suresh; Singh, Shivani

doi:10.1016/b978-0-323-99714-0.00012-1

Cited by 7 publications

(2 citation statements)

References 90 publications

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“…The potential for the application of machine learning techniques to the analysis of complex ecological data is enormous [69]. By integrating environmental factors such as temperature, precipitation, and soil properties, machine learning models can more accurately predict the effects of climate change on species cultivation [70]. Studies have shown that using models such as random forests and support vector machines can forecast crop yields under climate change [71], crop type mapping [72], and optimize farming and management practices [73].…”

Section: Future Research Perspectivesmentioning

confidence: 99%

Ecological Adaptation and Sustainable Cultivation of Citrus reticulata by Applying Mixed Design Principles under Changing Climate in China

Yang,

Wang,

et al. 2024

Remote Sensing

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Climate change is heavily altering plant distributions, posing significant challenges to conventional agricultural practices and ecological balance. Meanwhile, mixed species planting emerges as a potent strategy to enhance agricultural resilience, counteract climate change, preserve ecological balance, and provide a solution to economic instability. The MaxEnt model was used to predict the suitable area of Citrus reticulata under five climate scenarios and to explore affecting environmental factors. Litchi chinensis, Punica granatum, and Lycium chinense were selected as mixed species to analyze the spatial distribution and centroid migration trend of potentially suitable areas. The research results show the following: (1) The primary environmental factors impacting C. reticulata distribution are annual precipitation (1000–4000 mm), precipitation of driest quarter over 100 mm, and mean temperature of coldest quarter (12–28 °C). Crucially, the mixed species exhibited similar environmental sensitivities, indicating mutual mixing suitability. (2) Currently, the C. reticulata suitable area is of 240.21 × 104 km2, primarily in South, East, Central, and Southwest China, with potential for expansion to 265.41 × 104 km2 under the 2090s SSP1-2.6 scenario. (3) The geometric center of the moderately-to-highly suitable areas for C. reticulata is located in Hunan Province. Future scenarios show the C. reticulata’s centroid migrating northwest, with distances of less than 110 km. Mixed planting trends toward higher latitudes, fluctuating from 6 km to 210 km. (4) Mixed planting area planning: C. reticulata and L. chinensis are suitable for mixed planting in South China. C. reticulata and P. granatum, C. reticulata and L. chinense are suitable for mixed planting in most areas of Central, East, Southwest, and South China. This research presents a new perspective on using mixed design principles for ecological adaptation and the sustainable mixed planting of C. reticulata, in response to China’s changing climate. This approach is expected to help the economic fruit tree industry enhance ecological resilience and economic stability in the face of future climate change challenges.

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Section: Future Research Perspectivesmentioning

confidence: 99%

Ecological Adaptation and Sustainable Cultivation of Citrus reticulata by Applying Mixed Design Principles under Changing Climate in China

Yang,

Wang,

et al. 2024

Remote Sensing

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“…Also, weather conditions are the key factor in agricultural production. As climate change is the primary factor, both components are internally tied to one another in many ways [138,139]. These data are critical for agricultural preparation, handling water resources, as well as disaster preparation.…”

Section: Climate Sciencementioning

confidence: 99%

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Chaudhry,

Shafi,

Mahnoor

et al. 2023

Symmetry

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Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.

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Use of Tissue Culture Methods to Improve Stress Tolerance in Plants

Mazri,

Koufan,

Rham

et al. 2023

Environmental Science and Engineering

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Machine learning approach for climate change impact assessment in agricultural production

Cited by 7 publications

References 90 publications

Ecological Adaptation and Sustainable Cultivation of Citrus reticulata by Applying Mixed Design Principles under Changing Climate in China

Ecological Adaptation and Sustainable Cultivation of Citrus reticulata by Applying Mixed Design Principles under Changing Climate in China

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Use of Tissue Culture Methods to Improve Stress Tolerance in Plants

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