Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-20T06:14:41.027Z Has data issue: false hasContentIssue false
  • English
  • Français

Characterizing spatial variability in soil water content for precision irrigation management

Published online by Cambridge University Press:  01 June 2017

A. de Lara ,
R. Khosla  and
L. Longchamps
A. de Lara
Affiliation:
Dep. of Soil and Crop Sciences, Colorado State Univ., Fort Collins, CO 80523-1170
R. Khosla*
Affiliation:
Dep. of Soil and Crop Sciences, Colorado State Univ., Fort Collins, CO 80523-1170
L. Longchamps
Affiliation:
Dep. of Soil and Crop Sciences, Colorado State Univ., Fort Collins, CO 80523-1170
*
†E-mail: RKhosla@Colostate.Edu
Get access

Abstract

One among many challenges in implementing precision irrigation is the reliable characterization of the soil water content (SWC) across spatially variable fields. For this purpose, commercial retailers are employing apparent soil electrical conductivity (ECa) to create irrigation prescription maps. The accuracy of this method at the field scale has received little attention from the scientific community. Hence, the objective of this study was to characterize spatial distribution of soil water content at the field scale for the purpose of precision irrigation management. Results showed mean SWC to be different across ECa derived management zones, indicating that soil ECa was able to characterize mean differences in SWC across management zones.

Keywords

soil water contentapparent soil electrical conductivityirrigationmanagement zones
Type
Soil Sensing and Variability
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 418 - 422
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barton, K 2016. MuMIn: Multi-Model Inference.Google Scholar
Bates, D, Maechler, M, Bolker, B and Walker, S 2015. Fitting Linear Mixed-Effects Models using “lme4”. Journal of Statistical Software 67, 1–48.CrossRefGoogle Scholar
Bhatti, A, Mulla, D and Frazier, B 1991. Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and thematic mapper images. Remote Sensing of Environment 37, 181–191.CrossRefGoogle Scholar
Burnham, KP and Anderson, DR 2003. Model selection and multimodel inference: a practical information-theoretic approach. Springer Science & Business Media, NY, USA.Google Scholar
Corwin, D and Lesch, S 2003. Application of soil electrical conductivity to precision agriculture. Agronomy Journal 95, 455–471.Google Scholar
de Lara, A, Khosla, R and Longchamps, L 2016. Soil water content and high-resolution imagery: maize yield. Unpublished manuscript, Department of Soil and Crop Sciences, Colorado State University, Fort Collins, USA.Google Scholar
Fleming, KL, Westfall, DG, Wiens, DW, Rothe, LE, Cipra, JE and Heermann, DF 1999. Evaluating farmer developed management zone maps for precision farming. Precision Agriculture 1, 335–343.Google Scholar
Fridgen, JJ, Kitchen, NR, Sudduth, KA, Drummond, ST, Wiebold, WJ and Fraisse, CW 2004. Management zone analyst (MZA). Agronomy Journal 96, 100–108.Google Scholar
Groeteke, J, Dotterer, L and Shanahan, J 2014. Variable Rate Irrigation. Crop Insights 24.Google Scholar
Hedley, C, Yule, I, Tuohy, M and Vogeler, I 2009. Key performance indicators for simulated variable-rate irrigation of variable soils in humid regions. Transactions of the ASABE 52, 1575–1584.CrossRefGoogle Scholar
Hedley, CB and Yule, IJ 2009. Soil water status mapping and two variable-rate irrigation scenarios. Precision Agriculture 10, 342–355.CrossRefGoogle Scholar
Hezarjaribi, A and Sourell, H 2007. Feasibility study of monitoring the total available water content using non-invasive electromagnetic induction-based and electrode-based soil electrical conductivity measurements. Irrigation and Drainage 56, 53–65.CrossRefGoogle Scholar
Khosla, R, Fleming, K, Delgado, J, Shaver, T and Westfall, D 2002. Use of site-specific management zones to improve nitrogen management for precision agriculture. Journal of Soil and Water Conservation 57, 513–518.Google Scholar
LaRue, JL 2011. Variable Rate Irrigation 2010 Field Results. 2011 Louisville, Kentucky, August 7–10, 2011. American Society of Agricultural and Biological Engineers.Google Scholar
Longchamps, L, Khosla, R, Reich, R and Gui, D 2015. Spatial and Temporal Variability of Soil Water Content in Leveled Fields. Soil Science Society of America Journal 79, 1446–1454.CrossRefGoogle Scholar
Martin, D, Smith, T, Kranz, W, Irmak, S, van Donk, S and Shanahan, J 2014. Soil Water Management. Crop Insights 24.Google Scholar
Mehrjardi, RT, Mahmoodi, S, Taze, M and Sahebjalal, E 2008. Accuracy assessment of soil salinity map in Yazd-Ardakan Plain, Central Iran, based on Landsat ETM+ imagery. Am.-Eurasian Journal Agricultural and Environmental Science 3, 708–712.Google Scholar
Paine, JG, Goldsmith, RS and Scanlon, BR 1998. Electrical conductivity and gamma-ray response to clay, water, and chloride content in fissured sediments, Trans-Pecos Texas. Environmental & Engineering Geoscience 4, 225–239.CrossRefGoogle Scholar
Peters, RT, Desta, K and Nelson, L 2013. Practical use of soil moisture sensors and their data for irrigation scheduling Washington State University Extension, USA.Google Scholar
Russell, VL 2016. Least-Squares Means: The (R) Package (lsmeans). Journal of Statistical Software 69, 1–33.Google Scholar
Schmitz, M and Sourell, H 2000. Variability in soil moisture measurements. Irrigation Science 19, 147–151.CrossRefGoogle Scholar
Sheets, KR and Hendrickx, JM 1995. Noninvasive soil water content measurement using electromagnetic induction. Water Resources Research 31, 2401–2409.CrossRefGoogle Scholar
Waskom, RM, Bauder, T, Davis, J and Cardon, G 2003. Diagnosing saline and sodic soil problems Colorado State University Cooperative Extension, USA.Google Scholar
Yong-Ling, W, Peng, G and Zhi-Liang, Z 2010. A spectral index for estimating soil salinity in the Yellow River Delta Region of China using EO-1 Hyperion data. Pedosphere 20, 378–388.Google Scholar