Investigating the Spatial Distribution and Influencing Factors of Non-Grain Production of Farmland in South China Based on MaxEnt Modeling and Multisource Earth Observation Data DOI Creative Commons
Juntao Chen,

Zhuochun Lin,

Jinyao Lin

и другие.

Foods, Год журнала: 2024, Номер 13(21), С. 3385 - 3385

Опубликована: Окт. 24, 2024

Excessive non-grain production of farmland (NGPF) seriously affects food security and hinders progress toward Sustainable Development Goal 2 (Zero Hunger). Understanding the spatial distribution influencing factors NGPF is essential for agricultural management. However, previous studies on identification have mainly relied high-cost methods (e.g., visual interpretation). Furthermore, common machine learning techniques difficulty in accurately identifying based solely spectral information, as not merely a natural phenomenon. Accurately at grid scale elucidating its emerged critical scientific challenges current literature. Therefore, aims this study are to develop grid-scale method that integrates multisource remote sensing data enhance precision provide more comprehensive understanding factors. To overcome these challenges, we combined images, natural/anthropogenic factors, maximum entropy model reveal scale. This combination can detailed information quantify integrated influences multiple from microscale perspective. In case Foshan, China, area under receiver operating characteristic curve 0.786, with results differing by only 1.74% statistical yearbook results, demonstrating reliability method. Additionally, total error our result lower than using information. Our enhances resolution effectively detects small fragmented farmlands. We identified elevation, farming radius, population density dominant affecting NGPF. These offer targeted strategies mitigate excessive The advantage lies independence negative samples. feature applicability other cases, particularly regions lacking high-resolution grain crop-related data.

Язык: Английский

Short-term evaluation of soil physical, chemical and biochemical properties in an abandoned cropland treated with different soil organic amendments under semiarid conditions DOI
Ana B. Villafuerte, Rocío Soria, Natalia Rodríguez-Berbel

и другие.

Journal of Environmental Management, Год журнала: 2023, Номер 349, С. 119372 - 119372

Опубликована: Окт. 27, 2023

Язык: Английский

Процитировано

11
Understanding Climate Change and Air Quality Over the Last Decade: Evidence From News and Weather Data Processing DOI Creative Commons
Alin-Gabriel Văduva, Mihai Munteanu, Simona‐Vasilica Oprea

и другие.

IEEE Access, Год журнала: 2023, Номер 11, С. 144631 - 144648

Опубликована: Янв. 1, 2023

Climate change is a phenomenon that sometimes denied or trivialized. However, in recent years, we have faced extreme phenomena such as fires, floods, excessive temperatures, etc. which affect our physical and mental condition the environment, often leading to significant material damage. To understand these problems highlight meteorological phenomenological changes encountered last decade, time series were web-scraped analyzed from several open data sources: weather news broadcast Romania, air quality, temperature, The extraction organization of recorded between 2009 2023 are formulated framework can be reproduced replicated continue monitoring. exploratory analysis categorical numerical highlights intricate patterns correlations within conditions across regions seasons. From temperature trends quality fluctuations, study underscores dynamic interplay phenomena, paving way for informed forecasting deeper climate research. At same time, processing includes Latent Dirichlet Allocation, K-prototype clustering analysis, addition K-means with dimensional reduction techniques, all employed further reveal higher occurrence. Therefore, this paper, propose multiple datasets analytics, extracting valuable information on identifying exposed phenomena.

Язык: Английский

Процитировано

10
Assessing and mapping cropland abandonment risk in China DOI
Jie Zeng,

Ting Luo,

Wanxu Chen

и другие.

Land Degradation and Development, Год журнала: 2024, Номер 35(8), С. 2738 - 2753

Опубликована: Апрель 3, 2024

Abstract Cropland abandonment (CA) in China worsens the human‐land conflict and endangers national food sustainability. Scientifically assessing cropland risk (CAR) can provide valuable information for early warning prevention of CA. Despite extensive literature on identification, determinants, consequences CA, research CAR still needs to be improved, especially a grid scale. Therefore, this study constructed an evaluation indicators system regarding farming conditions, socio‐economic, patch characteristics used optimal parametric geographical detector structural equation modeling assess from 2010 2020. The results show China's decreased west east. Very high areas were plateaus mountains western China. Medium mainly central southeastern low Sichuan Basin eastern plains. In 2010, medium CARs accounted larger share area, 24.814% 24.759%, respectively. area very low, was 19.294%, 19.501%, 11.633%, By 2020, both increased, while opposite true other grades CAR. increased most evidently Loess Plateau. Although decreased, 43,327 km 2 converted CAR's centers gravity located at junction Plateau Huang‐Huai‐Hai Plain have shifted northwest by 5445.34 m. findings will assist stakeholders developing targeted protection strategies prevent CA efficiently allocate resources agricultural production.

Язык: Английский

Процитировано

4
Uphill cropland and stability assessment of gained cropland in China over the preceding 30 years DOI
Tingting He, Jianhua Li, Maoxin Zhang

и другие.

Journal of Geographical Sciences, Год журнала: 2024, Номер 34(4), С. 699 - 721

Опубликована: Апрель 1, 2024

Язык: Английский

Процитировано

4
Investigating the Spatial Distribution and Influencing Factors of Non-Grain Production of Farmland in South China Based on MaxEnt Modeling and Multisource Earth Observation Data DOI Creative Commons
Juntao Chen,

Zhuochun Lin,

Jinyao Lin

и другие.

Foods, Год журнала: 2024, Номер 13(21), С. 3385 - 3385

Опубликована: Окт. 24, 2024

Excessive non-grain production of farmland (NGPF) seriously affects food security and hinders progress toward Sustainable Development Goal 2 (Zero Hunger). Understanding the spatial distribution influencing factors NGPF is essential for agricultural management. However, previous studies on identification have mainly relied high-cost methods (e.g., visual interpretation). Furthermore, common machine learning techniques difficulty in accurately identifying based solely spectral information, as not merely a natural phenomenon. Accurately at grid scale elucidating its emerged critical scientific challenges current literature. Therefore, aims this study are to develop grid-scale method that integrates multisource remote sensing data enhance precision provide more comprehensive understanding factors. To overcome these challenges, we combined images, natural/anthropogenic factors, maximum entropy model reveal scale. This combination can detailed information quantify integrated influences multiple from microscale perspective. In case Foshan, China, area under receiver operating characteristic curve 0.786, with results differing by only 1.74% statistical yearbook results, demonstrating reliability method. Additionally, total error our result lower than using information. Our enhances resolution effectively detects small fragmented farmlands. We identified elevation, farming radius, population density dominant affecting NGPF. These offer targeted strategies mitigate excessive The advantage lies independence negative samples. feature applicability other cases, particularly regions lacking high-resolution grain crop-related data.

Язык: Английский

Процитировано

4