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Gasque, M.; Martí, P.; Granero, B.; González Altozano, P. (2016). Effects of long-term summer deficit irrigation on 'Navelina' citrus trees. Agricultural Water Management. 169:140-147. doi:10.1016Management. 169:140-147. doi:10. /j.agwat.2016 The effects of long-term summer deficit irrigation (RDI) strategies on 'Navelina' 17 orange trees (Citrus sinensis L. Osbeck) were assessed in a drip-irrigated commercial orchard 18 located in Senyera (Valencia, Spain). Three irrigation treatments were applied during five 19 consecutive years (2007)(2008)(2009)(2010)(2011): a control treatment, without restriction, and two RDI 20 treatments, in which the water reduction was applied during the summer (initial fruit 21 enlargement phase). During the first three seasons, the trees under the control treatment 22 received 110% of the theoretically required irrigation dose (ID), and the RDI treatments 23 received 40% and 60% of the full ID during the deficit period. During the last two years of 24 the study, the control treatment was irrigated at 100% of the ID and the amount of water 25

ElsevierMartí Pérez, PC.; Gasque Albalate, M.; González Altozano, P. (2013). An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data. Computers and Electronics in Agriculture. 91:75-86. doi:10.1016Agriculture. 91:75-86. doi:10. /j.compag.2012.001. Dear Author, Please check your proof carefully and mark all corrections at the appropriate place in the proof (e.g., by using on-screen annotation in the PDF file) or compile them in a separate list. Note: if you opt to annotate the file with software other than Adobe Reader then please also highlight the appropriate place in the PDF file. To ensure fast publication of your paper please return your corrections within 48 hours.For correction or revision of any artwork, please consult http://www.elsevier.com/artworkinstructions.Any queries or remarks that have arisen during the processing of your manuscript are listed below and highlighted by flags in the proof. Click on the 'Q' link to go to the location in the proof. Location in articleQuery / Remark: click on the Q link to go Please insert your reply or correction at the corresponding line in the proof Q1Please confirm that given names and surnames have been identified correctly. Q2Kindly check whether the identification of corresponding author and the e-mail address are okay as typeset, and correct if necessary.Thank you for your assistance.Please check this box if you have no corrections to make to the PDF file Highlights " First step approach to estimate stem water potential from soil moisture and standard meteorological variables using ANNs. " Two principal components are enough to describe systematic variability of data. " Optimum input combination: temperature, relative humidity, solar radiation and soil moisture at 50 cm. " Artificial neural networks present higher accuracy than corresponding multi-linear regression models. COMPAG 2809No. of Pages 1, Model 5G , 1998;Hanson et al., 2000;Goldhamer and Fereres, 2001; 54 Dane and Topp, 2002;Intrigliolo and Castel, 2004;Jones, 2004). 55 In most of these, soil water status is considered a key factor for 56 planning irrigation doses (Campbell and Campbell, 1982).

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