Preprints
https://doi.org/10.5194/essd-2023-346
https://doi.org/10.5194/essd-2023-346
12 Oct 2023
 | 12 Oct 2023
Status: a revised version of this preprint is currently under review for the journal ESSD.

GGCP10: A Global Gridded Crop Production Dataset at 10km Resolution from 2010 to 2020

Xingli Qin, Bingfang Wu, Hongwei Zeng, Miao Zhang, and Fuyou Tian

Abstract. Spatial-temporal distribution information on global crop production is of is crucial for studying global food security and promoting sustainable agricultural development. However, the presently available datasets related to this subject are characterized by coarse resolution and discontinuous time spans. To tackle these problems, we have integrated multiple data sources, including statistical data, gridded production data, agroclimatic indicator data, agronomic indicator data, global land surface satellite products and ground data, to develop a data-driven crop production spatial allocation model, and generated the first global temporally continuous 10 km resolution gridded production dataset of four major crops (maize, wheat, rice and soybean) from 2010 to 2020 (Global gridded crop production dataset at 10 km, GGCP10). A set of data-driven models were trained based on agro-ecological zones to achieve accurate predictions of crop production for different agricultural regions. The performance of the models is demonstrated by the cross-validation results. The accuracy and reliability of GGCP10 have been evaluated from various perspectives using gridded, survey and statistical data. GGCP10 can reveal the spatial-temporal distribution patterns of global crop production and contribute to the understanding of the mechanisms driving changes in crop production. GGCP10 provides crucial data support for research on global food security and sustainable agricultural development. The GGCP10 dataset is available on Harvard Dataverse: https://doi.org/10.7910/DVN/G1HBNK (Qin et.al., 2023).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Xingli Qin, Bingfang Wu, Hongwei Zeng, Miao Zhang, and Fuyou Tian

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-346', Anonymous Referee #1, 07 Nov 2023
    • CC5: 'Reply on RC1', Qinghan Dong, 19 Feb 2024
    • AC1: 'Reply on RC1', Xingli Qin, 19 Mar 2024
  • CC1: 'Comment on essd-2023-346', Vishal Mishra, 30 Jan 2024
    • CC4: 'Reply on CC1', Qinghan Dong, 19 Feb 2024
    • AC4: 'Reply on CC1', Xingli Qin, 19 Mar 2024
  • CC2: 'Comment on essd-2023-346', Asfaw Kebede Kassa, 07 Feb 2024
    • AC5: 'Reply on CC2', Xingli Qin, 19 Mar 2024
  • CC3: 'Comment on essd-2023-346', Qinghan Dong, 19 Feb 2024
    • AC3: 'Reply on CC3', Xingli Qin, 19 Mar 2024
  • RC2: 'Comment on essd-2023-346', Anonymous Referee #2, 20 Feb 2024
    • AC2: 'Reply on RC2', Xingli Qin, 19 Mar 2024
Xingli Qin, Bingfang Wu, Hongwei Zeng, Miao Zhang, and Fuyou Tian

Data sets

GGCP10: A Global Gridded Crop Production Dataset at 10km Resolution from 2010 to 2020 Xingli Qin, Bingfang Wu, Hongwei Zeng, Miao Zhang, and Fuyou Tian https://doi.org/10.7910/DVN/G1HBNK

Xingli Qin, Bingfang Wu, Hongwei Zeng, Miao Zhang, and Fuyou Tian

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Short summary
We developed the first time-series gridded dataset of maize, wheat, rice and soybean production from 2010–2020. It offers detailed spatiotemporal information of crop production, allowing for advanced monitoring of agricultural productivity and for research into the factors that drive production trends across regions and over time. This dataset can help assess food security, evaluate climate impacts, and inform policies for more sustainable, resilient crop production worldwide.
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