Food Security in China: A Brief View of Rice Production in Recent 20 Years

Tang, Ling; Risalat, Hamdulla; Cao, Rong; Hu, Qinan; Pan, Xiaoya; Hu, Yaxin; Zhang, Guoyou

doi:10.3390/foods11213324

Cited by 20 publications

(5 citation statements)

References 43 publications

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“…Ketika hasil panen melimpah, maka suatu daerah akan mampu memenuhi kebutuhan pangan penduduknya, yaitu kebutuhan beras (Kartiasih & Setiawan, 2019). Sesuai dengan Tang et al (2022) bahwa produksi beras berdampak pada ketahanan pangan, ketersediaan produksi padi dalam jumlah yang cukup dapat mengurangi risiko kelaparan dan kekurangan pangan.…”

Section: Analisis Inferensiaunclassified

Mampukah Sulawesi Selatan Mendukung Ketahanan Pangan di Kawasan Timur Indonesia?

Alfi,

Budiasih,

Rohmah

et al. 2023

semnasoffstat

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Pangan merupakan salah satu faktor yang menjadi kebutuhan pokok bagi kehidupan manusia. Secara umum, Kawasan Timur Indonesia (KTI) merupakan wilayah yang rentan rawan pangan. Meskipun demikian, Provinsi Sulawesi Selatan memiliki produksi padi tertinggi di Kawasan Timur Indonesia (KTI) pada tahun 2019. Hal ini berimplikasi pada ketetapan pemerintah menjadikan Sulawesi Selatan sebagai sentra ketahanan pangan KTI. Oleh karena itu, penelitian ini menyajikan bukti kuat yang mendukung Sulawesi Selatan sebagai sentra ketahanan pangan di Kawasan Timur Indonesia. Metode yang digunakan adalah regresi linier berganda untuk membuktikan pengaruh produksi padi dan share pengeluaran per kapita untuk makanan terhadap tingkat ketahanan pangan. Hasil penelitian ini mendukung keputusan pemerintah yang menetapkan Provinsi Sulawesi Selatan sebagai sentra ketahanan pangan di Kawasan Timur Indonesia (KTI).

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Section: Analisis Inferensiaunclassified

Mampukah Sulawesi Selatan Mendukung Ketahanan Pangan di Kawasan Timur Indonesia?

Alfi,

Budiasih,

Rohmah

et al. 2023

semnasoffstat

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“…In China, rice accounts for almost 30% of global production and 28% of global consumption [ 22 ]. The study found that more than 50% of the global population relies on rice [ 23 ] to meet their dietary needs, and its production plays an important role in shaping the environmental profile of agricultural systems [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Chinese organic rice transition spatial econometrics empirical analysis

Luo,

Huang

2024

PLoS ONE

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Based on the integrated model of Super-SBM model, spatial Durbin model (SDM) and Grey neural network model, this paper analyzes the panel data of various provinces in China from multiple angles and dimensions. It was found that there were significant differences in eco-efficiency between organic rice production and conventional rice production. The response of organic rice to climate change, the spatial distribution of ecological and economic benefits and the impact on carbon emission were analyzed. The results showed that organic rice planting not only had higher economic benefits, but also showed a rising trend of ecological benefits and a positive feedback effect. This finding highlights the importance of organic rice farming in reducing carbon emissions. Organic rice farming effectively reduces greenhouse gas emissions, especially carbon dioxide and methane, by improving soil management and reducing the use of fertilizers and pesticides. This has important implications for mitigating climate change and promoting soil health and biodiversity. With the acceleration of urbanization, the increase of organic rice planting area shows the trend of organic rice gradually replacing traditional rice cultivation, further highlighting the potential of organic agriculture in emission reduction, environmental protection and sustainable agricultural production. To this end, it is recommended that the Government implement a diversified support strategy to encourage technological innovation, provide guidance and training, and raise public awareness and demand for organic products. At the same time, private sector participation is stimulated to support the development of organic rice cultivation through a public-private partnership model. Through these measures, further promote organic rice cultivation, achieve the dual goals of economic benefits and environmental benefits, and effectively promote the realization of double carbon emission reduction targets.

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“…The examination extensively examines China as a prominent case study. By examining the spatial factors affecting rice output, we not only fulfil the critical requirement for well-informed decision-making in agriculture but also provide valuable contributions to sustainable food production and global food security [7].…”

Section: Introductionmentioning

confidence: 99%

Identifying Spatial Determinants of Rice Yields in Main Producing Areas of China Using Geospatial Machine Learning

Wang,

Sun,

Yang

2024

IJGI

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Rice yield is essential to global food security under increasingly frequent and severe climate change events. Spatial analysis of rice yields becomes more critical for regional action to ensure yields and reduce climate impacts. However, the understanding of the spatially varied geographical, climate, soil, and environmental factors of rice yields needs to be improved, leading to potentially biased local rice yield prediction and responses to climate change. This study develops a spatial machine learning-based approach that integrates machine learning and spatial stratified heterogeneity models to identify the determinants and spatial interactions of rice yields in the main rice-producing areas of China, the world’s largest rice-producing nation. A series of satellite remote sensing-derived variables are collected to characterize varied geographical, climate, soil, and environmental conditions and explain the spatial disparities of rice yields. The first step is to explore the spatial clustering patterns of the rice yield distributions using spatially global and local autocorrelation models. Next, a Geographically Optimal Zones-based Heterogeneity (GOZH) model, which integrates spatial stratified heterogeneity models and machine learning, is employed to explore the power of determinants (PD) of individual spatial variables in influencing the spatial disparities of rice yields. Third, geographically optimal zones are identified with the machine learning-derived optimal spatial overlay of multiple geographical variables. Finally, the overall PD of various variables affecting rice yield distributions is calculated using the multiple variables-determined geographically optimal zones and the GOZH model. The comparison between the developed spatial machine learning-based approach and previous related models demonstrates that the GOZH model is an effective and robust approach for identifying the spatial determinants and their spatial interactions with rice yields. The identified spatial determinants and their interactions are essential for enhancing regional agricultural management practices and optimizing resource allocation within diverse main rice-producing regions. The comprehensive understanding of the spatial determinants and heterogeneity of rice yields of this study has a broad impact on agricultural strategies and food security.

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Food Security in China: A Brief View of Rice Production in Recent 20 Years

Cited by 20 publications

References 43 publications

Mampukah Sulawesi Selatan Mendukung Ketahanan Pangan di Kawasan Timur Indonesia?

Mampukah Sulawesi Selatan Mendukung Ketahanan Pangan di Kawasan Timur Indonesia?

Chinese organic rice transition spatial econometrics empirical analysis

Identifying Spatial Determinants of Rice Yields in Main Producing Areas of China Using Geospatial Machine Learning

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