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COMPARISON OF DIFFERENT IRRIGATION SCHEDULING STRATEGIES ON MAIZE

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Author(s): MORETHSON RESENDE | ANTÔNIO CARLOS OLIVEIRA

Journal: Revista Brasileira de Milho e Sorgo
ISSN 1676-689X

Volume: 4;
Issue: 2;
Start page: 205;
Date: 2005;
Original page

Keywords: irrigation scheduling | neural network | evapotranspiration | maize crop

ABSTRACT
Although it lacks precision, one of the most simple strategies for irrigation scheduling is the reference evapotranspiration average(ETo), estimated from historical climate data and through the soil water budget method. This permits a prediction of time and amount of water to be applied during the crop cycle. In cooperation with UFMG a work was carried out to increase the precision of this method by using Neural Artificial Network (NAN) to adjust and predict daily ETo from a historical climate data base. An experiment was conducted with two planting dates, (01/27/2003 and 09/02/2003) in order to test the precision of this method in relation to the daily estimated ETo (standard), the average ETo, the predicted ETo by using NAN without adjustment and ETo estimated by Evaporation of a Class A pan (ECA), using soil water balance in all ETo alternatives. The daily estimated ETo (standard), ajusted and predicted daily ETo and ETo estimated by ECA, brought the highest productivity. The irrigation scheduling using average ETo and one that used predicted ETo by using NAN without adjustment caused reduction in the productivity when compared with the standard treatment.
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