Changing rice cropping patterns and their impact on food security in southern China

Jin, Tao; Zhong, Taiyang

doi:10.1007/s12571-022-01254-3

Cited by 16 publications

(5 citation statements)

References 31 publications

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“…The four main crops planted are rice, wheat, maize, and vegetables. These crops play an important role in national agricultural production [49][50][51]. Thus, the areas of paddy fields and irrigated dry land are further subdivided into these four main crop areas, and the woodland, grassland, and buffer zones of water bodies with similar characteristics in the regional CLUDs are reconstructed.…”

Section: Methods 41 Lusf Classificationmentioning

confidence: 99%

A New Land Use Dataset Fusion Algorithm for the Runoff Simulation Accuracy Improvement: A Case Study of the Yangtze River Basin, China

Zhang,

Sang,

Liu

et al. 2024

Sustainability

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Monitoring and understanding the development of agricultural management requires fine information on multiple agricultural land use classes. According to the main data bulletin of China’s third national land survey released in 2021, the proportion of arable land in China accounts for 16% of the total land area. After excluding areas that are not suitable for development (slope greater than 25 degrees), the remaining area represents arable land affected by human activities, constituting 96.69% of the total arable land area in the country. Artificially irrigated areas are products of the intertwined interactions between the natural environment and human society. However, the original remote sensing image (China’s land use/cover datasets, CLUDs), which is designed to depict the land use and cover patterns in mainland China, categorizes arable land into two main types: paddy fields and dryland. It lacks a subdivision of artificially irrigated areas. We also found a data discrepancy of more than 10% between the statistical data of rice and the paddy field data in CLUDs. To refine land use data, and then improve the simulation accuracy of the hydrological model, this article proposes a dual-source datasets fusion algorithm, learning based on big data, namely the LUCC statistical data fusion (LUSF) algorithm for integrating the remote sensing-based cropland area dataset and statistics dataset. The runoff simulation results show that, in the Yangtze River Basin, using LUSF datasets, the mean absolute percentage error value of monthly simulated runoff decreased by 0.74%, and the root mean square error value decreased by 0.22 million m3. At the basin scale, the absolute error of the simulated runoff is reduced by an average of 433 million m3 per year, and 36 million m3 every month. The LUSF datasets corrected the canopy interception coefficient effectively; the runoff simulation error was reduced by 2.96 billion m3/a. In the past 40 years, the runoff variation in Dongting Lake and Hanjiang River has been most strongly impacted by changes in the underlying surface. These results reveal that the new data fusion method has some significant improvement over the original method, applicable to the runoff simulation disturbed by strong human activities.

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Section: Methods 41 Lusf Classificationmentioning

confidence: 99%

A New Land Use Dataset Fusion Algorithm for the Runoff Simulation Accuracy Improvement: A Case Study of the Yangtze River Basin, China

Zhang,

Sang,

Liu

et al. 2024

Sustainability

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“…For example, the increase in farmers' incomes actively encourages the scientific management of cropland and enhances intensive farming and crop production [64]. As the profitability of staple crops declined from 2004 onwards (NSBC, 2005), crops with higher production and higher MCI (e.g., vegetables) replaced rice crops [65,66], which resulted in a change of cropping structure [67]. However, this is not the case in all regions.…”

Section: Impact Of Agricultural and Land Management Practicesmentioning

confidence: 99%

An In-Depth Assessment of the Drivers Changing China’s Crop Production Using an LMDI Decomposition Approach

Long

Wellens

et al. 2022

Remote Sensing

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Over the last decades, growing crop production across China has had far-reaching consequences for both the environment and human welfare. One of the emerging questions is “how to meet the growing food demand in China?” In essence, the consensus is that the best way forward would be to increase crop yield rather than further extend the current cropland area. However, assessing progress in crop production is challenging as it is driven by multiple factors. To date, there are no studies to determine how multiple factors affect the crop production increase, considering both intensive farming (using yield and multiple cropping index) and large-scale farming (using mean parcel size and number of parcels). Using the Logarithmic-Mean-Divisia-Index (LMDI) decomposition method combined with statistical data and land cover data (GlobeLand30), we assess the contribution of intensive farming and large-scale farming changes to crop production dynamics at the national and county scale. Despite a negative contribution from MPS (mean parcel size, ), national crop production increased due to positive contributions from yield (), MCI (multiple cropping index, ), as well as NP (number of parcels, ). This allowed China to meet the growing national crop demand. We further find that large differences across regions persist over time. For most counties, the increase in crop production is a consequence of improved yields. However, in the North China Plain, NP is another important factor leading to crop production improvement. On the other hand, regions witnessing a decrease in crop production (e.g., the southeast coastal area of China) were characterized by a remarkable decrease in yield and MCI. Our detailed analyses of crop production provide accurate estimates and therefore can guide policymakers in addressing food security issues. Specifically, besides stabilizing yield and maintaining the total NP, it would be advantageous for crop production to increase the mean parcel size and MCI through land consolidation and financial assistance for land transfer and advanced agricultural infrastructure.

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“…This requires effective policies that contribute to food security. The agricultural challenges for China include maintaining farmers' incomes, achieving sustainable agricultural development and ensuring national food security (Huang & Yang, 2017;Jin & Zhong, 2022). With only 6% of fresh water and 7% of arable land in the world, China has to feed nearly 20% of the world's population (Wong & Chan, 2016).…”

Section: Introductionmentioning

confidence: 99%

The impact of Chinese rice support policies on rice acreages

Jin,

Gardebroek,

Heerink

2024

Food Sec.

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Declining arable land and yield stagnation pose serious challenges to food security in China. Since 2004, the Chinese government has introduced rice support policies to stimulate rice production. A bundle of incentives, including a minimum procurement price, were introduced. Whether they were effective? Rice acreages have increased since the start of this policy, which could also result from rising rice price levels both nationally and globally. Using a natural experiment created by the minimum procurement price policy being introduced in a selected set of Chinese regions, we use a dynamic fixed effects model to perform a difference-in-differences analysis on the effectiveness of these rice support policies. We find that indica rice acreages do respond to changes in the rice prices, and, controlling for rice prices, that China’s rice support policies were effective in increasing rice acreages of both early and late indica after 2004. The paper concludes with policy implications.

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Changing rice cropping patterns and their impact on food security in southern China

Cited by 16 publications

References 31 publications

A New Land Use Dataset Fusion Algorithm for the Runoff Simulation Accuracy Improvement: A Case Study of the Yangtze River Basin, China

A New Land Use Dataset Fusion Algorithm for the Runoff Simulation Accuracy Improvement: A Case Study of the Yangtze River Basin, China

An In-Depth Assessment of the Drivers Changing China’s Crop Production Using an LMDI Decomposition Approach

The impact of Chinese rice support policies on rice acreages

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