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Consolidation of agricultural land can contribute to agricultural sustainability in China

Nature Food volume 2, pages 1014–1022 (2021)Cite this article

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An Author Correction to this article was published on 31 January 2022

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Abstract

China’s agricultural sector is dominated by smallholder farms, which mostly exhibit relatively low nutrient use efficiency, low agricultural income and substantial non-point-source pollution. Here we assess the spatial feasibility and cost-effectiveness of agricultural land consolidation in China by integrating data from over 40,000 rural surveys, ecological modelling and geostatistical analysis. We found that 86% of Chinese croplands could be consolidated to establish a large-scale farming regime with an average field size greater than 16 ha. This would result in a 59% and 91% increase in knowledge exchange and machinery use, respectively, contributing to a 24% reduction in total nitrogen input, an 18% increase in nitrogen use efficiency and a 39% reduction in labour requirement, while doubling labour income. Despite requiring a one-time investment of approximate US$370 billion for land consolidation, total agricultural profits would double due to agricultural production costs being halved.

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Fig. 1: Field size distribution across China.
Fig. 2: Changes in nitrogen input, NUE and nitrogen surplus between current level and large-scale farming.
Fig. 3: Cost of cropland consolidation.
Fig. 4: Changes of agricultural labour, labour productivity and agricultural cost for large-scale farming.

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Data availability

Data supporting the findings of this study are available within the article and its supplementary information files, or are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

The spatial analysis is run in ArcGIS v.10.2 and the statistical analysis was completed in Stata v.12.0. All code is available upon request.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (41822701, 41773068, 42061124001 and 41721001).

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Author notes

  1. Joint first authors.

Authors and Affiliations

  1. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Jiakun Duan, Sitong Wang, Jianming Xu & Baojing Gu

  2. Policy Simulation Laboratory, Zhejiang University, Hangzhou, China

    Jiakun Duan, Chenchen Ren & Baojing Gu

  3. Department of Land Management, Zhejiang University, Hangzhou, China

    Chenchen Ren

  4. School of Agriculture and Food, The University of Melbourne, Melbourne, Victoria, Australia

    Xiuming Zhang

  5. UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, UK

    Stefan Reis

  6. University of Exeter Medical School, European Centre for Environment and Health, Knowledge Spa, Truro, UK

    Stefan Reis

  7. Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, Hangzhou, China

    Jianming Xu & Baojing Gu

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Contributions

B.G. designed the study. J.D. and C.R. conducted the research. B.G., J.D. and C.R. wrote the first draft of the paper, S.W., X.Z., S.R. and J.X. revised the paper. All authors contributed to the discussion and revision of the paper.

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Correspondence to Baojing Gu.

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The authors declare no competing interests.

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Nature Food thanks David Norse, Yuelai Lu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 China Land use (2017).

This map is derived from FROM-GLC 2017v1. It shows the land use of China in 2017. There are 10 types of land, namely cropland, forest, grassland, shrubland, wetland, water, tundra, impervious surface, bareland and snow/ice. We extract cropland from this map for our analysis. The geographic coordinates of maps can be found in Fig. 1a. The base map was applied without endorsement using data from the Database of Global Administrative Areas (GADM; https://gadm.org/).

Extended Data Fig. 2 Categories of regions.

We divide the country’s provinces into four categories according to terrain and local economic conditions. HP refers to high-income plain region. LP refers to low-income plain region. HM represents for high-income mountainous region. LM represents for low-income mountainous region. The geographic coordinates of maps can be found in Fig. 1a. The base map was applied without endorsement using data from the Database of Global Administrative Areas (GADM; https://gadm.org/).

Extended Data Fig. 3 Distribution of sample sites.

The sample sites for field size are from the table of dominant field size provided by Lesiv et al. There are 5421 sites, detailed data can be downloaded at http://pure.iiasa.ac.at/id/eprint/15526/. And the data was transferred to point shapefile by ArcGIS 10.2. Yellow area is cropland. The geographic coordinates of maps can be found in Fig. 1a. The base map was applied without endorsement using data from the Database of Global Administrative Areas (GADM; https://gadm.org/).

Source data

Extended Data Fig. 4 Field size share in different regions.

This figure shows the percentage of different field size in the four regions mentioned above. And SF refers to scale farming. The color is consistent with Fig. 1. The red color represents for field which is less than 0.6 hectare (ha), yellow for 0.6–2.6 ha, green for 2.6–16 ha, light blue for 16–100 ha and dark blue for field larger than 100 ha.

Extended Data Fig. 5 Land consolidation sites.

We collected land consolidation data from the website. It shows the distribution of land consolidation projects that almost cover all of China’s provinces. The geographic coordinates of maps can be found in Fig. 1a. The base map was applied without endorsement using data from the Database of Global Administrative Areas (GADM; https://gadm.org/).

Source data

Extended Data Fig. 6 Consolidation cost share.

The cropland share of every region is calculated from cropland map. We calculated the proportion change based on Extended Data Fig. 4 and the cropland area to get the consolidation area. Consolidation cost was calculated by cost per hectare and consolidation area. Here we only show the share of each region, details see Supplementary Table 3.

Extended Data Fig. 7 Slope of China.

The slope of China is range from 0 to 45 degrees. And we divided it into 6 levels, namely <2, 2–5, 5–8, 8-15, 15-25 and >25 degrees. It can be seen that most of the land is less than 8 degrees while great slopes located mainly in southwest region. The geographic coordinates of maps can be found in Fig. 1a. The base map was applied without endorsement using data from the Database of Global Administrative Areas (GADM; https://gadm.org/).

Extended Data Fig. 8 Slope share of different field size.

We choose slope to reflect the quality of land. And in this bar charts, we divided the arable land into 11 groups. The slope classification is according to ‘Regulation for gradation on agriculture land quality’ of China. It is divided into 6 levels, namely <2, 2–5, 5–8, 8-15, 15-25 and >25 degrees, respectively. Here we didn’t show the last class because it’s little. As the increase of field size, the share of first slope class is increasing, too. It shows the rise in the quality of arable land.

Source data

Extended Data Fig. 9 Recommended N input.

We use sowing area and recommended N fertilizer (Details see Supplementary Table 8) for crops (rice, wheat, corn, millet, sorghum, barley, beans, potato, peanut, rapeseeds, cotton, hemp, tobacco, sugar beet, sugar cane, vegetable, fruits) to calculate the recommended N input for each county. And we compared this value with N fertilizer input for large-scale farming. The green area which occupied 74% cropland in (b) is the area where N input reached the recommended value. The geographic coordinates of maps can be found in Fig. 1a. The base map was applied without endorsement using data from the Database of Global Administrative Areas (GADM; https://gadm.org/).

Source data

Extended Data Fig. 10 Changes of agricultural output and profit.

(a) Current agricultural output; (b) Current agricultural profit; (c) Predicted agricultural output of large-scale farming; (d) Predicted agricultural profit of large-scale farming; (e) Agricultural output decrease; (f) Agricultural profit increase. Agricultural output is total market value of all crop yields directly reported by farmers. It includes all grains and crash crops. Agricultural profit equals to the difference between total agricultural output and cost. Current data is from China Agricultural Yearbook 2017. The predicted calculation is based on current values and changes in the field size showed in Fig. 1d and according to relations between farm size and agricultural output and profit in China (See Table 1). The changes are the differences between predicted value and the current one. The geographic coordinates of maps can be found in Fig. 1a. The base map was applied without endorsement using data from the Database of Global Administrative Areas (GADM; https://gadm.org/).

Source data

Supplementary information

Supplementary Information

Supplementary methods, references and Tables 1–7.

Reporting Summary.

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Duan, J., Ren, C., Wang, S. et al. Consolidation of agricultural land can contribute to agricultural sustainability in China. Nat Food 2, 1014–1022 (2021). https://doi.org/10.1038/s43016-021-00415-5

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