Abstract
The objective of this study was to evaluate the accuracy of the yield predictive models of Italian ryegrass (IRG, Lolium multiflorum Lam.) and forage rye (FR, Secale cereale L.) reported in previous studies through K-fold cross-validation method. In previous studies, statistical models were constructed for dry matter yield prediction of IRG and FR using general linear model based on climatic data by locations in the Republic of Korea. The yield predictive model for IRG cultivated in the southern region of the Korean Peninsula and Jeju Island were DMY = 78.178AGD–254.622MTJ + 64.156SGD–76.954PAT150 + 4.711SAP + 1028.295 + Location and DMY =–8.044AAT + 18.640SDS–7.542SAT + 9.610SAP + 17282.191, respectively. The yield predictive model for FR was as follows: DMY = 20.999AGD + 163.705LTJ + 113.716SGD + 64.379PAT100–4964.728 + Location. However, accuracy evaluation was not performed in the previous research. In this study, the reported models and the data set used for model construction were investigated. Subsequently, K-fold cross-validation was performed to assess the accuracy of the models. The results showed that the yield predictive models fit to the data sets well, while the accuracy of these models was in the common level since the data sources might keep major variances in cultivars, climatic conditions, and cultivated locations. Therefore, models with better fitness and accuracy might be constructed based on a data set with smaller variance. Hence, the standardization of the crop cultivation experiments is very necessary to decrease the variance in the historical data used for future crop yield modeling.
Similar content being viewed by others
References
Akhtar MK, Corzo GA, Van Andel SJ, Jonoski A. 2009. River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges River basin. Hydrol. Earth Syst. Sci. 13: 1607–1618
Behdani MA, Al-Ahmadi MJ, Fallahi HR. 2016. Biomass partitioning during the life cycle of saffron (Crocus sativus L.) using regression models. J. Crop Sci. Biotech. 1: 71–76
Chatterjee S, Bandopadhyay S. 2012. Reliability estimation using a genetic algorithm-based artificial neural network: An application to a load-haul-dump machine. Expert Syst. Appl. 39: 10943–10951
Excel. 2010. Microsoft Excel 2010. Microsoft Corp., Redmond, WA, USA
Feng HH. 2010. Studies on dynamic prediction of rice yield in county based on crop model and GIS. Master Thesis. Anhui Agricultural University, Hefei, China
Hawkins DM, Basak SC, Mills D. 2003. Assessing model fit by cross-validation. J. Chem. Inf. Comput. Sci. 43: 579–586
Kim NY, Chae HS, Woo JH, Cho IC, Cho SR, Cho WM, Park YS, Ko MS, Park NG. 2014. Changes of Feed Value and Productivity According to Supplemental Seeding Rates for Italian Ryegrass (Lolium multiflorum L.) in Jeju. Ann. Anim. Resour. Sci. 25, 23–28
Kim J, Sang W, Shin P, Cho H, Seo M, Yoo B, Kim KS. 2015. Evaluation of regional climate scenario data for impact assessment of climate change on rice productivity in Korea. J. Crop Sci. Biotech. 18: 257–264
Kim KS, Yoo B. 2015. Comparison of regional climate scenario data by a spatial resolution for the impact assessment of the uncertainty associated with meteorological inputs data on crop yield simulations in Korea. J. Crop Sci. Biotech. 18: 249–255
Ko J, Ahuja LR. 2013. Global warming likely reduces crop yield and water availability of the dryland cropping systems in the US Central Great Plains. J. Crop Sci. Biotech. 16: 233–242
Ko J, Kim HY, Jeong S, An JB, Choi G, Kang S, Tenhunen J. 2014. Potential impacts on climate change on paddy rice yield in mountainous highland terrains. J. Crop Sci. Biotech. 17: 117–126
Kozak A, Kozak R. 2003. Does cross validation provide additional information in the evaluation of regression models? Can. J. Forest. Res. 33: 976–987
Kuhn M, Johnson K. 2013. Applied Predictive Modeling. Springer, New York, pp 69–77
Lobell DB, Cassman KG, Field CB. 2009. Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34: 179
Maxwell SE. 2000. Sample size and multiple regression analysis. Psychol. Methods 5: 434–458
Osten DW. 1988. Selection of optimal regression models via cross–validation. J. Chemom. 2: 39–48
Peng JL, Kim MJ, Kim BW, Sung KI. 2016a. A yield estimation model of forage rye based on climate data by locations in South Korea using general linear model. J. Kor. Grassl. Forage. Sci. 36: 205–214
Peng JL, Kim MJ, Kim BW, Sung KI. 2016b. Models for estimating yield of Italian ryegrass in south areas of Korean Peninsula and Jeju Island. J. Kor. Grassl. Forage. Sci. 36: 223–236
Peng JL, Kim MJ, Kim YJ, Jo MH, Kim BW, Sung KI, Lv SJ. 2017. Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea. Grassl. Sci. 63: 184–195
Picard RR, Cook RD. 1984. Cross-validation of regression models. J. Am. Stat. Assoc. 79: 575–583
Refaeilzadeh P, Tang L, Liu H. 2009. Cross-validation. In: L Liu, MT Özsu, eds, Encyclopedia of Database Systems, Springer, New York, pp 532–538
Rinaldi M, Losavio N, Flagella Z. 2003. Evaluation and application of the OILCROP-SUN model for sunflower in southern Italy. Agricult. Sys. 78: 17–30
Salvacion AR, Martin AA. 2016. Climate change impact on corn suitability in Isabela Province, Philippines. J. Crop Sci. Biotech. 19: 223–229
Schaffer C. 1993. Selecting a classification method by crossvalidation. Mach. Learn. 13: 135–143
Seo S. 2016. Forage production, utilization, and animal husbandry in Korea. In Proceedings of the 6th Korea-China-Japan grassland conference, Jeju, pp 5–15
Shao J. 1993. Linear model selection by cross–validation. J. Am. Stat. Assoc. 88: 486–494
Shokri S, Marvast MA, Sadeghi MT, Narasimhan S. 2016. Combination of data rectification techniques and soft sensor model for robust prediction of sulfur content in HDS process. J. Taiwan Inst. Chem. Eng. 58: 117–126
StataCorp. 2011. Stata Statistical Software: Release 12. College Station, TX: StataCorp LP
Uno Y, Prasher SO, Patel RM, Strachan IB, Pattey E, Karimi Y. 2005. Development of field-scale soil organic matter content estimation models in Eastern Canada using airborne hyperspectral imagery. Can. Biosyst. Eng. 47: 1–14
Zhang Y, Yang Y. 2015. Cross-validation for selecting a model selection procedure. J. Econometrics 187: 95–112
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Peng, JL., Kim, MJ., Jo, MH. et al. Accuracy evaluation of the crop-weather yield predictive models of Italian ryegrass and forage rye using cross-validation. J. Crop Sci. Biotechnol. 20, 327–334 (2017). https://doi.org/10.1007/s12892-017-0090-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12892-017-0090-0