Temporal variability of soil water storage evaluated for a coffee field
L. C. Timm A F , D. Dourado-Neto B , O. O. S. Bacchi C , W. Hu D , R. P. Bortolotto B , A. L. Silva E , I. P. Bruno B and K. Reichardt CA Department of Rural Engineering, FAEM, UFPel, Pelotas, RS C.P. 354-96001-970, Brazil.
B Crop Science Department, ESALQ, USP, Piracicaba, SP C.P. 9, 13418-900, Brazil.
C Soil Physics Laboratory, CENA, USP, Piracicaba, SP C.P. 96, 13416-903, Brazil.
D Key Laboratory of Water Cycle and Related Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
E CTC, Fazenda Santo Antônio, Piracicaba, SP 13400-970, Brazil.
F Corresponding author. Email: lctimm@ufpel.edu.br
Soil Research 49(1) 77-86 https://doi.org/10.1071/SR10023
Submitted: 19 January 2010 Accepted: 13 July 2010 Published: 4 February 2011
Abstract
Sampling field soils to estimate soil water content and soil water storage (S) is difficult due to the spatial variability of these variables, which demands a large number of sampling points. Also, the methodology employed in most cases is invasive and destructive, so that sampling in the same positions at different times is impossible. However, neutron moderation, time domain reflectrometry, and, more recently, frequency domain reflectrometry methodologies allow measurements at the same points over long time intervals. This study evaluates a set of neutron probe data, collected at 15 positions placed randomly along a coffee crop contour line, over 2 years at 14-day intervals. The temporal stability of S was again demonstrated, so that wetter or dryer locations remain so over time, and the definition of such positions in the field reduces the number of sampling points in future S evaluations under similar conditions. An analysis was made to determine the minimum number of sampling points to obtain the average S of the field within a chosen level of significance. Classical statistical analysis indicated that the 15 measurement positions could be reduced to four or even to one position to obtain a reliable field S average. State–time analysis showed S estimations depend more on previous measurements of rainfall P (52%) than on evapotranspiration ET (28%) and S (20%). The analysis also showed that ET was not realistically estimated from previous measurements of S; it was more dependent on previous measurements of ET (59%) than on P (30%) and S (9%). This statistical procedure showed great advantages over classical multiple regressions. Future studies of this type should be carried out at regularly spaced observation points in a grid, in order to allow a 2-D and 3-D state–space–time analysis.
Additional keywords: multiple regression, neutron probe, sampling number, state–space, state–time.
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