References
- Asseng, S., F. Ewert, P. Martre, R. P. Rotter, D. B. Lobell, D. Cammarano, B. A. Kimball, M. J. Ottman, G. W. Wall, J. W. White, M. P. Reynolds, P. D. Alderman, P. V. V. Prasad, P. K. Aggarwal, J. Anothai, B. Basso, C. Biernath, A. J. Challinor, G. De Sanctis, J. Doltra, E. Fereres, M. Garcia- Vila, S. Gayler, G. Hoogenboom, L. A. Hunt, R. C. Izaurralde, M. Jabloun, C. D. Jones, K. C. Kersebaum, A. K. Koehler, C. Müller, S. Naresh Kumar, C. Nendel, G. O'Leary, J. E. Olesen, T. Palosuo, E. Priesack, E. Eyshi Rezaei, A. C. Ruane, M. A. Semenov, I. Shcherbak, C. Stockle, P. Stratonovitch, T. Streck, I. Supit, F. Tao, P. J. Thorburn, K. Waha, E. Wang, D. Wallach, J. Wolf, Z. Zhao, and Y. Zhu, 2015: Rising temperatures reduce global wheat production. Nature Climate Change 5(2), 143-147. https://doi.org/10.1038/nclimate2470
- Boote, K., C. Porter, J. Jones, P. Thorburn, K. Kersebaum, G. Hoogenboom, J. White, and J. Hatfield, 2016: Sentinel site data for model improvement-definition and characterization. Advances in Agricultural Systems Modeling 7, 125-158.
- Corbeels, M., D. Berre, L. Rusinamhodzi, and S. Lopez-Ridaura, 2018: Can we use crop modelling for identifying climate change adaptation options? Agricultural and Forest Meteorology 256, 46-52. https://doi.org/10.1016/j.agrformet.2018.02.026
- He, D., E. Wang, J. Wang, and M. J. Robertson, 2017: Data requirement for effective calibration of process-based crop models. Agricultural and Forest Meteorology 234, 136-148. https://doi.org/10.1016/j.agrformet.2016.12.015
- Hoogenboom, G., J. W. Jones, P. C. Traore, and K. J. Boote, 2012: Experiments and data for model evaluation and application. Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT), 9-18.
- Hyun, S., and K. S. Kim, 2019: Calibration of cultivar parameters for cv. Shindongjin for a rice growth model using the observation data in a low quality. Korean Journal of Agricultural and Forest Meteorology 21(1), 42-54. https://doi.org/10.5532/KJAFM.2019.21.1.42
- Kersebaum, K. C., K. J. Boote, J. Jorgenson, C. Nendel, M. Bindi, C. Frühauf, T. Gaiser, G. Hoogenboom, C. Kollas, and J. E. Olesen, 2015: Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Environmental Modelling & Software 72, 402-417. https://doi.org/10.1016/j.envsoft.2015.05.009
- Kim, J., C. K. Lee, H. Kim, B. W. Lee, and K. S. Kim, 2015: Requirement analysis of a system to predict crop yield under climate change. Korean Journal of Agricultural and Forest Meteorology 17(1), 1-14. https://doi.org/10.5532/KJAFM.2015.17.1.1
- Kim, J., W. Sang, P. Shin, J. Baek, C. Cho, and M. Seo, 2019: History and future direction for the development of rice growth models in Korea. Korean Journal of Agricultural and Forest Meteorology 21(3), 167-174. https://doi.org/10.5532/KJAFM.2019.21.3.167
- Kim, J., W. Sang, P. Shin, H. Cho, and M. Seo, 2018a: Calibration of crop growth model CERESMAIZE with yield trial data. Korean Journal of Agricultural and Forest Meteorology 20(4), 277-283. https://doi.org/10.5532/KJAFM.2018.20.4.277
- Kim, K. S., S.-O. Kim, J. H. Kim, K. H. Moon, J. H. Shin, and J. Cho, 2018b: Development and application of crop models in Korea. Korean Journal of Agricultural and Forest Meteorology 20(2), 145-148. https://doi.org/10.5532/KJAFM.2018.20.2.145
- Kim, Y., K.-M. Shim, M.-P. Jung, I.-T. Choi, and K.-K. Kang, 2016: Classification of agroclimatic zones considering the topography characteristics in South Korea. Journal of Climate Change Research 7(4), 507-512. https://doi.org/10.15531/ksccr.2016.7.4.507
- Lee, K.-D., C.-W. Park, K.-H. So, and S.-I. Na, 2017: Selection of optimal vegetation indices and regression model for estimation of rice growth using UAV aerial images. Korean Journal of Soil Science and Fertilizer 50(5), 409-421. https://doi.org/10.7745/KJSSF.2017.50.5.409
- Lee, S., and K. S. Kim, 2018: Estimation of fresh weight for chinese cabbage using the Kinect sensor. Korean Journal of Agricultural and Forest Meteorology 20(2), 205-213. https://doi.org/10.5532/KJAFM.2018.20.2.205
- Lobell, D. B., and S. Asseng, 2017: Comparing estimates of climate change impacts from processbased and statistical crop models. Environmental Research Letters 12(1), 015001. https://doi.org/10.1088/1748-9326/aa518a
- Marinello, F., A. Pezzuolo, D. Cillis, and L. Sartori, 2016: Kinect 3d reconstruction for quantification of grape bunches volume and mass. Engineering for Rural Development 15, 876-881.
- Moon, K. H., H. H. Seo, M. J. Shin, E. Y. Song, and S. Oh, 2019: Production of Farm-level Agroinformation for adaptation to climate change. Korean Journal of Agricultural and Forest Meteorology 21(3), 158-166. https://doi.org/10.5532/KJAFM.2019.21.3.158
- Rosenzweig, C., J. W. Jones, J. L. Hatfield, A. C. Ruane, K. J. Boote, P. Thorburn, J. M. Antle, G. C. Nelson, C. Porter, and S. Janssen, 2013: The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies. Agricultural and Forest Meteorology 170, 166-182. https://doi.org/10.1016/j.agrformet.2012.09.011
- Yoo, B. H., K. S. Kim, and H.-Y. Ban, 2018: Development of a gridded crop growth simulation system for the DSSAT model using script languages. Korean Journal of Agricultural and Forest Meteorology 20(3), 243-251. https://doi.org/10.5532/KJAFM.2018.20.3.243
- Zhao, C., B. Liu, L. Xiao, G. Hoogenboom, K. J. Boote, B. T. Kassie, W. Pavan, V. Shelia, K. S. Kim, and I. M. Hernandez-Ochoa, 2019: A SIMPLE crop model. European Journal of Agronomy 104, 97-106. https://doi.org/10.1016/j.eja.2019.01.009