Estimating Plant - Available Water Capacity for Claypan Landscapes Using Apparent Electrical Conductivity

Soil Sci. Soc. Am. J. 71:1902-1908 doi:10.2136/sssaj2007.0011 Received 6 Jan. 2007. *Corresponding author ([email protected]). © Soil Science Society of America 677 S. Segoe Rd. Madison WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. The ability of soil to store and supply water to plants is one of its fundamental properties related to crop production. Knowledge about plant-available water (PAW) capacity (PAWc) is useful for many soil management practices as well as for crop yield modeling applications. Quantitative determination of PAWc, however, is not an easy task. Determination of PAW involves determining the two limits (i.e., fi eld capacity and permanent wilting point), which can be either monitored from fi eld measurements (Ritchie, 1981) or approximated under laboratory conditions (Jamison and Kroth, 1958). The former requires permanent installation of soil moisture devices and repeated monitoring, while the latter involves destructive sampling and water extraction. Either way, the time-consuming nature prohibits extensive assessment of the spatial variability of this soil property for a given fi eld or watershed. Further diffi culties include the limited value of soil survey information (e.g., texture and bulk density) for estimating PAW due to potentially large errors and bias in the estimation (Fortin and Moon, 1999). A key component of site-specifi c management is quantifi cation of the spatial variability of soil properties that affect crop yields (Atherton et al., 1999). A map of PAWc would help advance management decisions such as adjusting fertilizer input and optimizing water management options. This information could also be incorporated in management zone delineation or in crop models. To meet this need, alternative approaches have been proposed (Timlin et al., 2001b; Morgan et al., 2003). Timlin et al. (2001b) used a simple water budget model to simulate yield, and then applied a procedure to match the simulation to observed yield. During the matching procedure, the amount of PAW was varied until the closest match between predicted and observed yield was found. Then available water was estimated at the closest match. By similar principles, Morgan et al. (2003) devised an inverse yield model to create a “look-up” table where corn yields were simulated at a range of PAW levels. Using this correspondence, a map of PAW could be inversely generated based on yield maps. These approaches take advantage of readily available yield data made possible through yield-mapping technologies and assume that Pingping Jiang* Dep. of Environmental Sciences 2323 Geology Bldg. Univ. of California Riverside, CA 92521