Temporal Variation Analysis of Rice Yield in the Jiangsu Province, China: Application of Decision Support System for Agrotechnology Transfer Model
PDF

Keywords

GIS
DSSAT
Rice yield
CERES-Rice model
Meteorological elements
Simulation and verification

How to Cite

1.
Yuqi P, Penghui J, Manchun L, Dengshuai C. Temporal Variation Analysis of Rice Yield in the Jiangsu Province, China: Application of Decision Support System for Agrotechnology Transfer Model. Glob. J. Agric. Innov. Res. Dev [Internet]. 2022 Sep. 2 [cited 2023 Feb. 3];9:81-99. Available from: https://avantipublishers.com/index.php/gjaird/article/view/1073

Abstract

The accuracy of grain yield estimation is critical for national food security. Because of the comprehensive influence of spatial differentiation conditions, such as temperature, precipitation, soil, rice variety, and irrigation, yield estimation requires integrated modeling that is based on dynamic conditions. These dynamic conditions include geographical background, biological factors, and human impact. Most existing studies focus on the observation and analysis of external factors; only a few reports on yield simulations are coupled with nature, management, and crop growth mechanism. Our study incorporates the crop growth mechanism of rice, along with data of rice varieties, soil, meteorology, and field management, to determine the rice yield in Jiangsu province, China. In addition, we have used a decision support system for the agrotechnology transfer model, along with Coupled Model Intercomparison Project data and geographic information system technology. Our results showed that: (1) A calibrated variety genetic coefficient could simulate rice biomass value (flowering stage, maturity stage, and yield) reasonably. The values of NRMSE (Normalized Root Mean Square Error) between the simulated and measured values after parameter calibration are all less than 10%, the values of d(index of agreement) are all close to 1, the simulated value of yield is in good agreement with the measured value. (2) A linear correlation between the meteorological elements and yield was observed. The linear correlation had regional differences. Notably, an increase in precipitation was conducive to the increase in yield. Except at the Huaiyin site, the other sites showed that the temperature rise could potentially lead to reduced production. We found that an increase in solar radiation was unfavorable to the production of rice in the northern and western sites in the Jiangsu province, whereas it was conducive in the southern and eastern sites. (3) Our study predicted the rice yield from typical sites in the Jiangsu province from 2019 to 2060 in the wake of climate change while excluding the extreme effects of diseases, pests, typhoons, and floods. The order of average yield per unit area is as follows: Xinghua site (8212.76 kg/ha) > Huaiyin site (7912.70 kg/ha) > Gaoyou site (7440.98 kg/ha) > Gaochun site (7512.29 kg/ha) > Ganyu site (7460.88 kg/ha) > Yixing site (7167.00 kg/ha). Notably, the average yields from the Xinghua and Huaiyin sites were higher than that from the Jiangsu province (7617.77 kg/ha). The fluctuation of the yield per unit area at each site was generally consistent with the fluctuation in the overall yield, showing a downward trend and tends to be stable. The dispersion of yield per unit area indicates that Gaochun had the most stable yield per unit area followed by Xinghua, Ganyu, Yixing, Huaiyin, and Gaoyou. The yield per unit area of the Huaiyin and Gaoyou sites was unstable and portrayed the biggest fluctuations. Future studies need to focus on how to deal with spatial variation and carry out adaptive verification to make the simulation results applicable to more dimensions.

https://doi.org/10.15377/2409-9813.2022.09.7
PDF

References

Chen S. Study on Integration of Remote Sensing Information and Crop Model based on Ensemnle Kalman Filter_A Case Study of Maize Yield Estimation in Northeast China[D]. Nanjing University of information Science & Technology, 2012. (in Chinese)

Cao J. Research on Spatio-terporal Coupling Relationship between Grain Production Capacity and Quality of Cultivated Land[D]. Central China Normal University, 2013. (in Chinese)

Cheng Z, Meng J. Research advances and perspectives on crop yield estimation models[J]. Chinese Journal of Eco-Agriculture,2015,23(04): 402-415. (in Chinese)

Yang X. Research on evaluation of Chinese food security based on the perspective of sustainable development[D]. Jilin University,2010. (in Chinese)

Zhao C. Advances of Research and Application in Remote Sensing for Agriculture[J]. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(12): 277-293. (in Chinese)

Wang F, Wang F, Hu J, Xie L, Xie J. Estimating and Mapping Rice Yield Using UAV-Hyperspectral Imager based Relative Spectral Variates[J]. Remote Sensing Technology and Application, 2020, 35(02): 458-468. (in Chinese)

Quarmby NA, Milnes M, Hindle TL, et al. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction[J]. International Journal of Remote Sensing, 1993, 14(2). https://doi.org/10.1080/01431169308904332

Wang C, Lin W. Winter wheat yield estimation based on MODIS EVI[J]. Transactions of the CASE, 2005, (10): 90-94. (in Chinese)

Cheng l. Irrigation management decision and response study to climate changes for winter wheat based on DSSAT in Henan province[D]. Nanjing University of Information Science & Technology, 2008. (in Chinese)

Jiang Z. Study on remote sensing data assimilation technology for regional winter wheat yield estimation[D]. Chinese academy of agricultural sciences,2012. (in Chinese)

Drury CF, Hoogenboom G. Optimizing Parameters of CSM-CERES-Maize Model to Improve Simulation Performance of Maize Growth and Nitrogen Uptake in Northeast China[J]. Journal of Integrative Agriculture, 2012, 11(11): 1898-1913. https://doi.org/10.1016/S2095-3119(12)60196-8

Jiang Z, Chen Z, Ren J, Zhou Q. Estimation of crop yield using CERES-Wheat model based on particle filter data assimilation method[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(14): 138-146. (in Chinese)

Liu Z, Yang X, Wang J, Lu S, Li K, Xun X, et al. Adaptability of APSIM Maize Model in Northeast China[J]. Acta Agronomica Sinica, 2012, 38(04): 740-746. (in Chinese) https://doi.org/10.3724/SP.J.1006.2012.00740

Wang W, Feng H. The progress and problems in the development of foreign crop models[J]. Water Saving Irrigation, 2012, (08): 63-68+73.

Zhang S, Zhang J, Li J, Cheng Y, Li G. Calibration and validation of WOFOST in main Maize-Producing regions in Henan[J]. Journal of Henan Agricultural Sciences, 2014, 43(08): 152-156. (in Chinese)

Zhang T, Fu C, Li J, Gu W, Xu W, Lu Y, et al. The Adaptability Test Analysis of AquaCrop and WOFOST Model Based on the Cold Spring Wheat[J]. Crops, 2013, (03): 121-126. (in Chinese)

Castrignanò A, Katerji N, Karam F, et al. A modified version of CERES-Maize model for predicting crop response to salinity stress[J]. Ecological Modelling, 1998, 111(2). https://doi.org/10.1016/S0304-3800(98)00084-2

Dettori M, Cesaraccio C, Motroni A, et al. Using CERES-Wheat to simulate durum wheat production and phenology in Southern Sardinia, Italy [J]. Field Crops Research, 2010, 120(1). https://doi.org/10.1016/j.fcr.2010.09.008

Quiring SM, Legates DR. Application of CERES-Maize for within-season prediction of rainfed corn yields in Delaware, USA[J]. Agricultural and Forest Meteorology, 2008, 148(6). https://doi.org/10.1016/j.agrformet.2008.01.009

Timsina J, Humphreys E. Performance of CERES-Rice and CERES-Wheat models in rice-wheat systems: A review[J]. Agricultural Systems, 2005, 90(1). https://doi.org/10.1016/j.agsy.2005.11.007

Bhatia VS, Piara S, Wani SP, et al. Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model[J]. Agricultural and Forest Meteorology, 2008, 148(8). https://doi.org/10.1016/j.agrformet.2008.03.004

Cabrera VE, Jagtap SS, Hildebrand PE. Strategies to limit (minimize) nitrogen leaching on dairy farms driven by seasonal climate forecasts[J]. Agriculture, Ecosystems and Environment, 2007, 122(4). https://doi.org/10.1016/j.agee.2007.03.005

Eitzinger J, Štastná M, Žalud Z, et al. A simulation study of the effect of soil water balance and water stress on winter wheat production under different climate change scenarios[J]. Agricultural Water Management, 2003, 61(3). https://doi.org/10.1016/S0378-3774(03)00024-6

Garcia AGY, Guerra LC, Hoogenboom G. Water use and water use efficiency of sweet corn under different weather conditions and soil moisture regimes[J]. Agricultural Water Management, 2009, 96(10). https://doi.org/10.1016/j.agwat.2009.04.022

Jones JW, Hoogenboom G, Porter CH, et al. The DSSAT cropping system model[J]. European Journal of Agronomy, 2003, 18(3). https://doi.org/10.1016/S1161-0301(02)00107-7

Heinemann AB, Hoogenboom G, Faria RTD. Determination of spatial water requirements at county and regional levels using crop models and GIS[J]. Agricultural Water Management, 2002, 52(3). https://doi.org/10.1016/S0378-3774(01)00137-8

O'neal MR, Frankenberger JR, Ess DR. Use of CERES-Maize to study effect of spatial precipitation variability on yield [J]. Agricultural Systems, 2002, 73(2). https://doi.org/10.1016/S0308-521X(01)00095-6

Xu K, Yang H, Zhang H, Gong J, Shen X, Tao X, et al. Latitudinal Difference of Rice Varieties Productivity in the Lower Yangtze and Huai Valleys and Its Rational Utilization[J]. Acta Agronomica Sinica, 2014, 40(05): 871-890. (in Chinese) https://doi.org/10.3724/SP.J.1006.2014.00871

Du J. Study on the modeling effect of conservation tillage on soil water and crop productivity in arid region[D]. Chinese Academy of Agricultural Sciences, 2008. (in Chinese)

Li J, Shao M, Fan T, Wang L. Databases creation of crop growth model DSSAT3 on the loess plateau region of China[J]. Agricultural Research in the Arid Areas, 2001, (01): 120-126. (in Chinese)

Zhou S, Zhu H. Economic Analysis of Climate Change Impact on the Rice Yield in Southern China and Its Adaptive Strategy[J]. China Population,Resources and Environment, 2010, 20(10): 152-157. (in Chinese)

Jin-Xia W, Ji-Kun H, Ting-Ting Y. Impacts of Climate Change on Water and Agricultural Production in Ten Large River Basins in China[J], 2013, 12(07): 1267-1278. https://doi.org/10.1016/S2095-3119(13)60421-9

Tian Y, Zhang J, He K, Feng J. Analysis on Farmers' agricultural low-carbon production behavior and its influencing factors -- Taking the application of chemical fertilizer and pesticide as an example[J]. China Rural Survey, 2015, (04): 61-70. (in Chinese)

Xu X, Sun M, Fang Y, He X, Xue F, Fu W, et al. Impact of Climatic Change on Rice Production and Response Strategies in Anhui Province[J]. Journal of Agro-Environment Science, 2011, 30(09): 1755-1763. (in Chinese)

Cui D. The scenario analysis of possible effect of warming climate on rice growing period[J]. Journal of Applied Meteorological Science, 1995, (03): 361-365. (in Chinese)

Shen C. Meteorological effects on rice yields in Jiangsu Province[J]. Acta Ecologica Sinica, 2015, 35(12): 4155-4167. (in Chinese) https://doi.org/10.5846/stxb201309212315

Wu C, Cui K. Progress on effect of high temperature on rice yield formation[J]. Journal of Agricultural Science and Technology, 2014, 16(03): 103-111. (in Chinese)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2022 Pan Yuqi, Jiang Penghui, Li Manchun, Chen Dengshuai