Connection between normalized difference vegetation index and yield in maizeó T., Nagy Z., Zsubori Z.T., Szőke C., Berzy T., Pintér J., Marton C.L. (2016): Connection between normalized difference vegetation index and yield in maize  . Plant Soil Environ., 62: 293-298.
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The preliminary estimation of expected yields and the accuracy of this evaluation provide information for decisions related to the harvest. The quantification of predictions makes it possible to estimate the accuracy of the prognosis. The yields that can be expected at the end of the vegetation season depend on the intensity of the photosynthetic activity. Numerous devices are now available to measure the quantity of photosynthetically active pigments in leaves, including the instrument GreenSeekerTM used in the present experiments, which records the value of normalized difference vegetation index. The present work attempted to answer the question of whether the yield could be predicted by means of multiple measurements during the vegetation period. Other questions raised were which phenophase was the most suitable for predicting yield, how values recorded at different times correlated with the yield, whether the strength of this correlation increased or decreased as harvest approached, and whether yield could be estimated at flowering, or in even earlier phenophases.  

Aparicio Nieves, Villegas Dolors, Casadesus Jaume, Araus Jose´ Luis, Royo Conxita (2000): Spectral Vegetation Indices as Nondestructive Tools for Determining Durum Wheat Yield. Agronomy Journal, 92, 83-
Bänziger M., Araus J. (2007): Recent advances in breeding maize for drought and salinity stress tolerance. In: Jenks M.A., Hasegawa P.M., Jain S.M. (eds): Advances in Molecular Breeding Toward Drought and Salt Tolerant Crops. Houten, Springer, 587–601.
Govaerts B., Verhulst N. (2010): The Normalized Difference Vegetation Index (NDVI) Greenseeker (TM) Handheld Sensor: Toward the Integrated Evaluation of Crop Management. Part A – Concepts and Case Studies. Mexico, CIMMYT, 1–16.
Parry Martin A. J., Hawkesford Malcolm J. (2010): Food security: increasing yield and improving resource use efficiency. Proceedings of the Nutrition Society, 69, 592-600
Raun William R., Solie John B., Johnson Gordon V., Stone Marvin L., Lukina Erna V., Thomason Wade E., Schepers James S. (2001): In-Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance. Agronomy Journal, 93, 131-
Raun William R., Solie John B., Johnson Gordon V., Stone Marvin L., Mullen Robert W., Freeman Kyle W., Thomason Wade E., Lukina Erna V. (2002): Improving Nitrogen Use Efficiency in Cereal Grain Production with Optical Sensing and Variable Rate Application. Agronomy Journal, 94, 815-
Rouse J.W., Haas R.H., Schell J.A., Deering D.W. (1974): Monitoring vegetation systems in the Great Plains with ERTS. In: Proceedings Third ERTS-1 Symposium, NASA Goddard, NASA SP-35, 309–317.
Sawasawa H.L.A. (2003): Crop Yield Estimation: Integrating RS, GIS and Management Factors. [MSc thesis] Enschede, International Institute for Geo-Information Science and Earth Observation Enschede, 43–44.
Shanahan J.F., Schepers J.S., Francis D.D., Varvel G.E., Wilhelm W., Tringe J.M., Schlemmer M.R., Major D.J. (2001): Use of remote-sensing imagery to estimate corn grain yield. Agronomy and Horticulture – Faculty Publications, Paper 9.
Teal R. K., Tubana B., Girma K., Freeman K. W., Arnall D. B., Walsh O., Raun W. R. (2006): In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index. Agronomy Journal, 98, 1488-
Tucker Compton J. (1979): Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127-150
Zhang M., Hendley P., Drost D., O’Neill M., Ustin S. (1999): Corn and soybean yield indicators using remotely sensed vegetation index. In: Robert P.C., Rust R.H., Larson W.E., Robert P.C., Rust R.H., Larson W.E. (eds.): Precision Agriculture. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 1475–1481.
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