Preprints
https://doi.org/10.5194/essd-2022-273
https://doi.org/10.5194/essd-2022-273
 
29 Aug 2022
29 Aug 2022
Status: this preprint is currently under review for the journal ESSD.

AsiaRiceYield4km: Seasonal Rice Yield in Asia from 1995 to 2015

Huaqing Wu1,2,, Jing Zhang1,2,, Zhao Zhang1,2, Jichong Han1,2, Juan Cao1,2, Liangliang Zhang1,2, Yuchuan Luo1,2, Qinghang Mei1,2, Jialu Xu2, and Fulu Tao3,4 Huaqing Wu et al.
  • 1Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education Beijing Normal University, Beijing 100875, People’s Republic of China
  • 2School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875 / Zhuhai 519087, People’s Republic of China
  • 3Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, People’s Republic of China
  • 4College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
  • These authors contributed to the work equally and should be regarded as co-first authors.

Abstract. Rice is the most important staple food in Asia. However, high-spatiotemporal-resolution rice yield datasets are very limited over a large region. The lack of such products hugely hinders the studies on accurately assessing the impacts of climate change and simulating agricultural production. Based on dynamic rice maps in Asia, we incorporated four predictor categories into three machine learning (ML) models to generate a high-spatial-resolution (4 km) rice yield dataset (AsiaRiceYield4km) for main rice seasons from 1995 to 2015. Four predictor categories considered the most comprehensive rice growing conditions and the optimal ML model was determined for each rice season based on an inverse proportional weight method. The results showed that AsiaRiceYield4km has a good accuracy for seasonal rice yield prediction (single rice: R2 = 0.88, RMSE = 920 kg/ha, double rice: R2 = 0.91, RMSE = 554 kg/ha, and triple rice: R2 = 0.93, RMSE = 588 kg/ha). Compared with Spatial Production Allocation Model (SPAM), R2 of grided rice yields was improved by 0.20 and RMSE was reduced by 618 kg/ha on average for single rice. Particularly, constant environmental conditions including longitude, latitude, elevation, and soil properties contributed the most (~45 %) to rice yield prediction. As for different growing periods of rice, we found that the predictors in reproductive period had more impacts on rice yield prediction than those of the vegetative period and the whole growing period. AsiaRiceYield4km is a novel high-spatial-resolution gridded rice yield dataset that can fill the unavailability of seasonal yield products across major rice production areas and promote more relevant studies on agricultural sustainability in the world. AsiaRiceYield4km can be downloaded from an open-data repository (DOI: https://doi.org/10.5281/zenodo.6901968; Wu et al., 2022).

Huaqing Wu et al.

Status: open (until 24 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-273', Anonymous Referee #1, 13 Sep 2022 reply

Huaqing Wu et al.

Data sets

AsiaRiceYield4km: Seasonal Rice Yield in Asia from 1995 to 2015 Huaqing Wu, Jing Zhang, Zhao Zhang, Jichong Han, Juan Cao, Liangliang Zhang, Yuchuan Luo, Qinghang Mei, Jialu Xu, Fulu Tao https://doi.org/10.5281/zenodo.6901968

Huaqing Wu et al.

Viewed

Total article views: 279 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
209 63 7 279 23 2 1
  • HTML: 209
  • PDF: 63
  • XML: 7
  • Total: 279
  • Supplement: 23
  • BibTeX: 2
  • EndNote: 1
Views and downloads (calculated since 29 Aug 2022)
Cumulative views and downloads (calculated since 29 Aug 2022)

Viewed (geographical distribution)

Total article views: 264 (including HTML, PDF, and XML) Thereof 264 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Sep 2022
Download
Short summary
High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed an explicit method to predict rice yield based on machine learning methods and generated a seasonal 4 km resolution rice yield dataset across Asia (AsiaRiceYield4km) from 1995 to 2015. The seasonal rice yield accuracy of AsiaRiceYield4km is high and has a big improvement compared with previous dataset. AsiaRiceYield4km will fill the dataset blank and better support agricultural monitoring systems.