Skip to main content
×
×
Home

Maize yield forecasting by linear regression and artificial neural networks in Jilin, China

  • K. MATSUMURA (a1) (a2), C. F. GAITAN (a2), K. SUGIMOTO (a3), A. J. CANNON (a4) and W. W. HSIEH (a2)...
Summary

Forecasting the maize yield of China's Jilin province from 1962 to 2004, with climate conditions and fertilizer as predictors, was investigated using multiple linear regression (MLR) and non-linear artificial neural network (ANN) models. Yield was set to be a function of precipitation from July to August, precipitation in September and the amount of fertilizer used. Fertilizer emerged as the dominant predictor and was non-linearly related to yield in the ANN model. Given the difficulty of acquiring fertilizer data for maize, the current study was also tested using the previous year's yield in the place of fertilizer data. Forecast skill scores computed under both cross-validation and retroactive validation showed ANN models to significantly outperform MLR and persistence (i.e. forecast yield is identical to last year's observed yield). As the data were non-stationary, cross-validation was found to be less reliable than retroactive validation in assessing the forecast skill.

Copyright
Corresponding author
* To whom all correspondence should be addressed. Email: kanichi1@mbox.kyoto-inet.or.jp
References
Hide All
Alvarez, R. (2009). Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach. European Journal of Agronomy 30, 7077.
Campbell, C. A., Zentner, R. P. & Johnson, P. J. (1988). Effect of crop rotation and fertilization on the quantitative relationship between spring wheat yield and moisture use in southwestern Saskatchewan. Canadian Journal of Soil Science 68, 116.
Campbell, C. A., Selles, F., Zentner, R. P., McConkey, B. G., Brandt, S. A. & McKenzie, R. C. (1997 a). Regression model for predicting yield of hard red spring wheat grown on stubble in the semiarid prairie. Canadian Journal of Plant Science 77, 4352.
Campbell, C. A., Selles, F., Zenter, R. P., McConkey, B. G., McKenzie, R. C. & Brandt, S. A. (1997 b). Factors influencing grain N concentration of hard red spring wheat in the semiarid prairie. Canadian Journal of Plant Science 77, 5362.
Chantre, G. R., Blanco, A. M., Forcella, F., Van Acker, R. C., Sabbatini, M. R. & Gonzalez-Andular, J. L. (2014). A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence. Journal of Agricultural Science, Cambridge 152, 254262.
Chen, C. Q., Lei, C. X., Deng, A. X., Qian, C. R., Hoogmoed, W. & Zhang, W. J. (2011). Will higher minimum temperatures increase corn production in Northeast China? An analysis of historical data over 1965–2008. Agricultural and Forest Meteorology 151, 15801588.
China Maize (2012). Corn Acreage and Yield in Chinese Provinces over Years. Beijing: Institute of Crop Science, Chinese Academy of Agricultural Sciences (ICS-CAAS) (in Chinese). Available from: www.chinamaize.com.cn/scgk/ (accessed November 2011).
CRU (Climatic Research Unit) (2010). CRU TS3.10: Climatic Research Unit (CRU) Time-Series (TS) Version 3.10 of High Resolution Gridded Data of Month-by-Month Variation in Climate (January 1901–December 2009). Norwich: Climatic Research Unit, University of East Anglia. Available from: http://badc.nerc.ac.uk/view/badc.nerc.ac.uk__ATOM__ACTIVITY_fe67d66a-5b02-11e0-88c9-00e081470265/ (accessed 20 February 2014).
Dai, X., Huo, Z. & Wang, H. (2011). Simulation for response of crop yield to soil moisture and salinity with artificial neural network. Field Crops Research 121, 441449.
ESRI (2008). ESRI Data and Maps: Country-Based Boundary Datasets, CD/DVD set. Kranzberg: Esri Deutschland GmbH.
FAOSTAT (2012). FAOSTAT Database. Rome: FAO. Available from: http://faostat.fao.org/site/567/default.aspx#ancor (accessed February 2014).
Foresee, F. D. & Hagan, M. T. (1997). Gauss–Newton approximation to Bayesian regularization. In Proceedings of the 1997 International Joint Conference on Neural Networks, Vol. 3, pp. 19301935. Piscataway, NJ: IEEE.
Fortin, J. G., Anctil, F., Parent, L. E. & Bolinder, M. A. (2011). Site-specific early season potato yield forecast by neural network in Eastern Canada. Precision Agriculture 12, 905923.
Gaitan, C. F., Hsieh, W. W., Cannon, A. J. & Gachon, P. (2013). Evaluation of linear and non-linear downscaling methods in terms of daily variability and climate indices: surface temperature in southern Ontario and Quebec, Canada. Atmosphere-Ocean, doi: 10.1080/07055900.2013.857639.
Ghodsi, R., Yani, R. M., Jalali, R. & Ruzbahman, M. (2012). Predicting wheat production in Iran using an artificial neural networks approach. International Journal of Academic Research in Business and Social Sciences 2, 3447.
Gilleland, E. (2010). Confidence Intervals for Forecast Verification, NCAR Technical Note NCAR/TN-479+STR. Boulder, CO: National Center for Atmospheric Research. Available from: http://nldr.library.ucar.edu/repository/assets/technotes/TECH-NOTE-000-000-000-846.pdf (accessed 20 February 2014).
Guo, W. W. & Xue, H. R. (2012). An incorporative statistic and neural approach for crop yield modelling and forecasting. Neural Computing and Applications 21, 109117.
Hill, B. D., McGinn, S. M., Korchinski, A. & Burnett, B. (2002). Neural network models to predict the maturity of spring wheat in western Canada. Canadian Journal of Plant Science 82, 713.
Hsieh, W. W. (2004). Nonlinear multivariate and time series analysis by neural network methods. Reviews of Geophysics 42, RG1003, doi: 10.1029/2002RG000112.
Hsieh, W. W. (2009). Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels. Cambridge: Cambridge University Press.
Hsieh, W. W., Tang, B. & Garnett, E. R. (1999). Teleconnections between Pacific sea surface temperatures and Canadian prairie wheat yield. Agricultural and Forest Meteorology 96, 209217.
IFA (International Fertilizer Industry Association) (2012). IFADATA Statistical Databases. Available from: www.fertilizer.org//En/Statistics/IFADATA.aspx (accessed September 2012).
Ji, B., Sun, Y., Yang, S. & Wan, J. (2007). Artificial neural networks for rice yield prediction in mountainous regions. Journal of Agricultural Science, Cambridge 145, 249261.
Jiang, D., Yang, X., Clinton, N. & Wang, N. (2004). An artificial neural network model for estimating crop yields using remotely sensed information. International Journal of Remote Sensing 25, 17231732.
Kaul, M., Hill, R. L. & Walthall, C. (2005). Artificial neural networks for corn and soybean yield prediction. Agricultural Systems 85, 118.
Liu, H. L., Yang, J. Y., Drury, C. F., Reynolds, W. D., Tan, C. S., Bai, Y. L., He, P., Jin, J. & Hoogenboom, G. (2011). Using the DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuous maize production. Nutrient Cycling in Agroecosystems 89, 313328.
Lobell, D. B. (2007). Changes in diurnal temperature range and national cereal yields. Agricultural and Forest Meteorology 145, 229238.
Lobell, D. B. & Field, C. B. (2007). Global scale climate crop yield relationships and the impacts of recent warming. Environmental Research Letters 2, 014002, doi: 10.1088/1748-9326/2/1/014002
Lobell, D. B. & Burke, M. B. (2010). On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology 150, 14431452.
Luo, Q. Y., Wen, L., McGregor, J. L. & Timbal, B. (2013). A comparison of downscaling techniques in the projection of local climate change and wheat yields. Climatic Change 120, 249261.
Ma, L., Trout, T. J., Ahuja, L. R., Bausch, W. C., Saseendran, S. A., Malone, R. W. & Nielsen, D. C. (2012). Calibrating RZWQM2 model for maize responses to deficit irrigation. Agricultural Water Management 103, 140149.
MacKay, D. J. C. (1992). A practical Bayesian framework for backpropagation networks. Neural Computation 4, 448472.
O'Neal, M. R., Engel, B. A., Ess, D. R. & Frankenberger, J. R. (2002). Neural network prediction of maize yield using alternative data coding algorithms. Biosystems Engineering 83, 3145.
Panda, S. S., Ames, D. P. & Panigrahi, S. (2010). Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sensing 2, 673696.
Qian, B., Jong, R. D., Warren, R., Chipanshi, A. & Hill, H. (2009). Statistical spring wheat yield forecasting for the Canadian prairie provinces. Agricultural and Forest Meteorology 149, 10221031.
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations (Eds Rumelhart, D. E. & McClelland, J. L.), pp. 318362. Cambridge, MA: MIT Press.
Shabbar, A. & Kharin, V. (2007). An assessment of cross validation for estimating skill of empirical seasonal forecasts using a global coupled model simulation. CLIVAR Exchanges 12, 1012.
Sun, B. J. & Van Kooten, G. C. (2013). Weather effects on maize yields in northern China. Journal of Agricultural Science, Cambridge 152, 523533.
Yang, C., Peterson, C. L., Shropshire, G. J. & Otawa, T. (1998). Spatial variability of field topography and wheat yield in the Palouse region of the Pacific Northwest. Transactions of the ASAE 41, 1727.
Yang, H. S., Dobermann, A., Lindquist, J. L., Walters, D. T., Arkebauer, T. J. & Cassman, K. G. (2004). Hybrid-maize – a maize simulation model that combines two crop modeling approaches. Field Crops Research 87, 131154.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

The Journal of Agricultural Science
  • ISSN: 0021-8596
  • EISSN: 1469-5146
  • URL: /core/journals/journal-of-agricultural-science
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed