The Digital Agricultural Revolution - Группа авторов

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for estimating stem water potential in grapevines: Comparison between natural reference and baseline approaches, in: VIII International Symposium on Irrigation of Horticultural Crops, vol. 1150, pp. 189–194, 2015.

      69. Gowda, P.H., Jose, L., Paul, D.C., Steve, R.C., Terry, A.E., Judy, H., Tolk, A., ET mapping for agricultural water management: present status and challenges. Irrig. Sci., 26, 23–237, 2008.

      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.

      73. Tabari, H., Marofi, S., Sabziparvar, A.A., Estimation of daily pan evaporation using artificial neural network and multivariate nonlinear regression. Irrig. Sci., 28, 5, 399–406, 2010.

      74. Kumar, M., Raghuwanshi, N.S., Singh, R., Wallender, W.W., Pruitt, W.O., Estimating evapotranspiration using artificial neural network. J. Irrig. Drain. Eng., 128, 4, 224–233, 2002.

      75. Uno, Y., Prasher, S.O., Lacroix, R., Goel, P.K., Karimi, Y., Viau, A., Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data. Comput. Electron. Agric., 47, 2, 149–161, 2005.

      76. Prasad, A.K., Chai, L., Ramesh, P.S., Kafatos, M., Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Obs. Geoinf., 8, 26–33, 2006.

      77. Simoes, M.D., Rocha, S.J.V., Lamparelli, R.A.C., Spectral variables, growth analysis and yield of sugarcane. Sci. Agric., 62, 3, 199–207, 2005.

      1 * Corresponding author: [email protected]

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