The Digital Agricultural Revolution. Группа авторов

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a Southern California chaparral ecosystem. Remote Sens. Environ., 103, 3, 289–303, 2005.

      66. Peng, Z., Hu, M., Liu, Y., Application of RS and GIS Technique to Estimate Regional Water-saving Potentiality, 2007.

      67. Singh, R.K. and Prajneshu, Artificial Neural Network Methodology for Modelling and Forecasting Maize Crop Yield. Agric. Econ. Res. Rev., 21, 1, 152–156, 2008.

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      70. Wart, J.V., Kersebaum, K.C., Peng, S., Milner, M., Cassman., K.G., Estimating crop yield potential at regional to national scales. Field Crops Res., 143, 34–4, 2013.

      71. Sirisha, A., Raghuwanshi, N.S., Mishra, A., Tiwari, M.K., Evapotranspiration Modeling Using Second-Order Neural Networks. J. Hydrol. Eng., 19, 6, 1131–1140, 2014.

      72. Martí, P. and Gasque, M., Ancillary data supply strategies for improvement of temperature-based ETo ANN models. Agric. Water Manage., 97, 7, 939–955, 2010.

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      1 * Corresponding author: [email protected]

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