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Jeffery C. Kallestad, Theodore W. Sammis, John G. Mexal, and John White

Optimal pecan (Carya illinoiensis) production in the southwestern United States requires 1.9 to 2.5 m of irrigation per year depending on soil type. For many growers, scheduling flood irrigation is an inexact science. However, with more growers using computers in their businesses, and with soil moisture sensors and computerized data-collection devices becoming more inexpensive and accessible, there is potential to improve irrigation and water use efficiencies. In this project two low-cost soil monitoring instruments were introduced to a group of pecan producers. They were also given instruction on the use of Internet-based irrigation scheduling resources, and assistance in utilizing all of these tools to improve their irrigation scheduling and possibly yield. The objectives were to determine whether the technology would be adopted by the growers and to assess the performance of the sensors at the end of the season. Three out of the five growers in the project indicated they used either the granular matrix (GM) sensors or tensiometer to schedule irrigations, but compared to the climate-based irrigation scheduling model, all growers tended to irrigate later than the model's recommendation. Graphical analysis of time-series soil moisture content measured with the GM sensors showed a decrease in the rate of soil moisture extraction coincident with the model's recommended irrigation dates. These inflection points indicated the depletion of readily available soil moisture in the root zone. The findings support the accuracy of the climate-based model, and suggest that the model may be used to calibrate the sensors. Four of the five growers expressed interest in continued use of the tensiometer, but only one expressed a desire to use the GM sensor in the future. None of the participants expressed interest in using the climate-based irrigation scheduling model.

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Xu Zhang, Na Ta, Wei-ye Tian, Li-jun Gao, and Wei Jiao

University Harbin, China Minasny, B. McBratney, A. 2002 The neuro-m method for fitting neural network parametric pedotransfer functions Soil Sci. Soc. Amer. J. 66 2 352 361 Roxy, M.S. Sumithran, V.B. Renuka, G. 2014 Estimation of