ANNUAL AND SEASONAL PATTERNS OF SOIL PROFILE TEMPERATURE FOR 2003 IN BROOKINGS, SOUTH DAKOTA

Transcription

1 Proceedings of the South Dakota Academy of Science, Vol. 88 (2009) 81 ANNUAL AND SEASONAL PATTERNS OF SOIL PROFILE TEMPERATURE FOR 2003 IN BROOKINGS, SOUTH DAKOTA I. E. Vahyala 1, B. Shmagin 2 and T. E. Schumacher 1 1 Department of Plant Science 2 Department of Agriculture and Biosystems Engineering South Dakota State University Brookings, SD ABSTRACT Hourly soil temperature data at 0.05, 0.1, 0.2, 0.5 and 1.0 m soil depths for Brookings, South Dakota, in 2003 were obtained from AG DATA of South Dakota Climate and Weather for analysis of unique trends and variations in time and season. A graphical approach based on temperature at 0.05 m depth was used to select four periods of distinct patterns (positive/negative /flat sloping trends). A 120 x 365 matrix was created from the data (120 = 24 hrs a day multiplied by 5 soil depths; 365 days in a year). Basic statistical analysis was carried out using STATISTICA (Statsoft Version 6). Summer (21.6 ± 2.2 C) and winter (-2.5 ± 2.4 C) had the least variations in temperature at 0.05 m depth, while greatest variations were observed in spring (9.3 ± 6.4 C); similar trends were observed at the other depths. A lag of about 40 days was observed between 0.05 m & 1.0 m depth for the rising trend in soil temperature during spring season. Keywords Soil temperature patterns, Brookings, South Dakota INTRODUCTION Soil temperature is an important physical property which influences seed germination, soil respiration rates (plant roots and soil organisms), and the overall energy budget of the crop environment. The biological process for nutrient transformations, availability, and uptake are influenced by soil temperature. Soils in the Brookings, SD, area are classified as frigid (fine-silty, mixed, superactive, frigid Aquic Hapludolls) with warmer temperatures in summer when compared to winter, a mean annual temperature < 8 C, and the difference between mean winter and mean summer temperature more than 5 C at 0.5 m soil depth (South Dakota: Soil Classification Key. 2003). Two major crops grown in Brookings are corn (Zea mays) and soybean (Glycine max). Wheat crop may replace corn in the rotation due to market opportunities, grazing livestock and soil health conditions. The minimum threshold temperature for corn germination and emergence is about 50 F (10 C) at which germination is slow (longer

2 82 Proceedings of the South Dakota Academy of Science, Vol. 88 (2009) than 7 days and uneven), however, uniform emergence will occur in less than 7 days at about 12.8 C. Soybean can germinate easily at 10 C (could take up to 3 weeks), but the ideal temperature for soybean germination is 25 C (Nielson 2009). This study presents observed distinct patterns in soil daily and seasonal temperature for 2003 in Brookings to increase our understanding of unexpected incidences in soil temperature regimes. METHODS Hourly soil temperature data at 0.05, 0.1, 0.2, 0.5 and 1.0 m soil depths for Brookings, South Dakota, in 2003 were obtained from automatic weather data network (AWDN), AG DATA of South Dakota Climate and Weather (AG- DATA 2009) for analysis of unique trends and variations in time and season. Additional data for 2002 (October-December) were also obtained to facilitate the study of the winter season based on a hydrologic year calendar (In this study, our hydrologic year was 11/21/02 to 11/20/03). A graphical approach, based on temperature at 0.05 m depth was used to select four periods of distinct patterns (positive/ negative /flat sloping trends in the hydrologic year period): November 21, 2002 to March 10, 2003 (winter); March 11 to June 16 (spring, 2003); June 17 to August 28 (summer, 2003); August 29 to November 20 (fall, 2003). A 120 x 365 matrix was created from the data (120 = 24 hrs a day multiplied by 5 soil depths; 365 days in a year). Basic statistical analysis was carried out using STATISTICA (Statsoft Version 6). RESULTS Basic statistics show that soil temperature at a specified depth and time (hour of day) was most consistent in the summer season and most variable in spring (Table 1). The great variations in spring were probably due to erratic events of rainfall, snow and snowmelts. On the other hand, the lower variations observed during summer may have been due to a more uniform profile of moisture content over the period (AGDATA 2009), and grass cover which tends to act as a buffer against rapid fluctuations in temperature. The spring season had a mean and standard deviation of 9.3 ± 6.4 C and a very large range of 29.8 C (Table 1), while these values were 21.6 ± 2.2 C and range 12.4 C at 0.05 m depth in the summer.

3 Proceedings of the South Dakota Academy of Science, Vol. 88 (2009) 83 Table 1. Soil temperature ( C) statistics for each season and the hydrologic year. Valid *N is total number of hours. Source: AWDN AGDATA Brookings, SD, station. Soil is Vienna Brookings complex (Web Soil Survey 2009). Depth(m) Valid *N Mean Std. Dev. Abs. Dev. Maximum Minimum Range Winter (11/21/02-3/10/03) Spring (3/11/03-6/16/03) Summer (6/17/03-8/28/03) Fall (8/29/03-11/20/03) Hydrologic year (11/21/02 11/ 20/03) The period described as summer in this study started June 17, which was consistent with late planting dates assuming adequate moisture. The pattern ob-

4 84 Proceedings of the South Dakota Academy of Science, Vol. 88 (2009) served at 0.05 m depth was consistent to about 0.5 m depth. We also observed a wave-like pattern (Figures 1-4) exhibited in diurnal soil temperature as described by Van Wijk equations (Poulovassilis et al. 1998, Holmes et al. 2008) over a 24 hr period for 0.05, 0.1, 0.2, and 0.5 m depths with some damping at 0.5 m. The 1.0 m depth did not exhibit any wave-like pattern. In Figures 1a-4a, the daily peak/maximum temperature is presented for each season as captured using hourly observations over a 24 hr period. Graphs presented in Figures 1b-4b show daily minimums. Maximum daily temperature was attained at 1700 hrs (5:00 pm) in spring and summer seasons, while in fall and winter, it was at 1600 hrs (4:00 pm) and 1500 hrs (3:00 pm), respectively. Minimum temperatures were at 0800 hrs in summer and spring and 0900 hrs in fall and winter. Figures 5 and 6 present the temperature pattern of all seasons on the same graph for the 0.05 m and 1.0 m soil depths, respectively. There was a generally rising trend in mean daily temperature during the spring season except in early spring, when the 1.0 m depth did not show any apparent change in mean daily temperature, leading to a lag of 40 days behind the rising trend at 0.05 m depth (see circle on Figure 6). DISCUSSION Simple statistical analysis of seasonal soil temperature pattern can be helpful in understanding variability and unique trends in soil temperature. It took about 9 hrs for the soil to attain maximum levels from the minimum and about hrs to drop back to minimum position. This shows lack of symmetry in diurnal soil temperature distribution about its origin with time, commonly reported in most soil physics books (e.g., Scott 2000). The mean temperatures from the second to third week of June are well above the critical value of 10 C needed for good germination of corn/soybean and variations from the mean over the period were low (standard deviation < 2). It should be realized that the 40 day lag in rise of mean daily temperature at 1.0 m depth may be responsible for rapid loss of heat gained at or nearer the soil surface, which would otherwise favor warmer temperatures for an early planting. In Brookings, we suggest using a single depth observations model to predict temperatures at soil depths (Holmes et al. 2008) restricted to 0.5 m or less. Starting from the second half of July, soil temperatures create favorable conditions for germination of corn and soybean. Long term data are required for improved soil temperature simulation model to be used for assessing performance of cultivated crops in Brookings for a sustainable cropping system. We hope to carry out further studies using at least 10 years data and a modeling approach in addition to this statistical approach.

5 Proceedings of the South Dakota Academy of Science, Vol. 88 (2009) 85 Figure 1a. Maximum hourly temperature in winter. Figure 1b. Minimum hourly temperature in winter.

6 86 Proceedings of the South Dakota Academy of Science, Vol. 88 (2009) Figure 2a. Maximum hourly temperature in spring. Figure 2b. Minimum hourly temperature in spring.

7 Proceedings of the South Dakota Academy of Science, Vol. 88 (2009) 87 Figure 3a. Maximum hourly temperature in summer. Figure 3b. Minimum hourly temperature in summer.

8 88 Proceedings of the South Dakota Academy of Science, Vol. 88 (2009) Figure 4a. Maximum hourly temperature in fall. Figure 4b. Minimum hourly temperature in fall.

9 Proceedings of the South Dakota Academy of Science, Vol. 88 (2009) 89 Figure 5. Mean daily temperature at 0.05m depth for all seasons. Figure 5. Mean daily temperature at 1m depth for all seasons.

10 90 Proceedings of the South Dakota Academy of Science, Vol. 88 (2009) ACKNOWLEDGMENTS Special thanks to Douglas Malo and Zuhair Massri for taking their time to review this work. The research was completed during spring 2009 semester as part of a matrix statistics class Data analysis of climate and water resources of SD taught by Boris Shmagin. LITERATURE CITED AG DATA South Dakota Climate and Weather. South Dakota State University. (URL accessed 2009 March 28). Holmes, T.R., M. Owe, R. A. M. De Jeu and H. Kooi Estimating the soil temperature profile from a single depth observation: a simple empirical heatflow solution. Water Resources Research Vol. 44: Nielson, R.L Requirements for Uniform Germination and Emergence of Corn. Corny News Network, Purdue University. (Revised 2000 May; URL accessed 2009 March 28). Poulovassilis, A., P. Kerkides, S. Alexandris, and S. A Rizos Contribution to the study of the water and energy balances of an irrigated soil profile: Heat flux estimates. Soil and Tillage Research 45: Scott, D. H Soil Physics, Agricultural and Environmental Applications. Iowa State University Press. Ames, IA. 421pp. StatSoft, Inc. (2004). STATISTICA (data analysis software system), version 6.0 (Revised 2004; URL accessed 2009 March 30) South Dakota: Soil Classification Key USDA, NRCS. (Revised 2003 September; URL accessed 2009 July 16). articles/tb96.pdf. Web Soil Survey. USDA, NRCS (Revised 2009 July 14; URL accessed 2009 July 16).