# Spatial Variability and Lateral Location of Soil Moisture Monitoring Points on Cotton Mulched Drip Irrigation Field

Xiaoyun Lei, Fangsong Li, Shijun Zhou, Yan Li, Dachun Chen, Huanxian Liu, Yu Pan, Xiangmin Shen

► To cite this version:

Xiaoyun Lei, Fangsong Li, Shijun Zhou, Yan Li, Dachun Chen, et al. Spatial Variability and Lateral Location of Soil Moisture Monitoring Points on Cotton Mulched Drip Irrigation Field. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. pp.247-257, 10.1007/978-3-642-27281-3_31. hal-01351817

HAL Id: hal-01351817

https://inria.hal.science/hal-01351817

Submitted on 4 Aug 2016

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Spatial variability and lateral location of soil moisture monitoring

points on cotton mulched drip irrigation field

Xiaoyun LEI¹, Fangsong LI², Shijun ZHOU³, YAN LI¹, Dachun CHEN¹, Huanxian LIU⁴, YU PAN², Xiangmin SHEN²

1. College of Civil and Hydraulic Engineering, Xinjiang Agriculture University, Urumqi, 830052;

2. Xinjiang Institute of water Resources and Hydropower Research, Urumqi, 830049; 3. Suining

Water Service Bureau, Sichuan Province, 629000; 4. College of Grass and Environmental Science, Xinjiang Agriculture University, Urumqi, 830052}

**Abstract:** This experiment was conducted from April to September in 2009 in Baotou-lake Farm of Korla city, Xinjiang region, China. Two experiment schemes were designed for analyzing the spatial variability and lateral orientation of monitoring points for soil moisture. In scheme 1, 44 monitoring points were positioned with sampling depth of 0~60cm. In scheme 2, 20 monitoring points were placed with sampling depth of 0~80cm. Samples were taken in the whole cotton growing period before and after irrigation in both schemes. Statistical analyses including Q-Q test, descriptive statistics, t-test and geo-statistical analysis were performed to the soil moisture data. The results indicate that the increase of soil moisture can enhance their spatial variability. In drip-irrigated cotton cultivation, spatial variability of soil moisture resulted from the combination of several random variables and the structural factors such as climate, topography, soil form. Semi-variogram models belong to spherical model and the range of spherical model for soil moisture is about 9.40m~35.35m. The accuracy of the fitted model decreases as the soil moisture content increases. The monitoring points should be placed at the range of 0~0.475m from the drip tape and it is not suitable to place them at a farther place. It can be concluded that it is best to position the monitoring points at outward cotton row in consideration of management and soil

**This Research is sponsored by following projects:** the National Natural Fund Project on the Spatial Variability Mechanism of Soil Moisture and the Sensors' Layout Plan on Cotton Mulched Drip Irrigation (51060002); High Technology Research and Development Program in Xinjiang Autonomous Region (200712111); the National Technology Support Program for the 11th five-year Plan (2007BAD38B05); the Key Discipline Construction Fund of Hydrology and Water Resources in Xinjiang.

**Author:** Xiaoyun LEI, 1961, female, Master's degree, professor, doctoral supervisor, currently at College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China. E-mail address: lxyyx88888@sohu.com

**Corresponding author:** Fangsong LI, 1982, male, Master's degree, engineer, Xinjiang Institute of Water Resources and Hydropower Research, Urumqi, 830049, China. Email address: lfsws132@sina.com.
moisture monitoring. The outcome of this study can be used for the proper design and placement of
monitoring points for soil moisture.

**Key words:** Cotton, Mulched drip irrigation, Soil moisture, Spatial variability, Monitoring point, Geo-statistics

# 1 Introduction

Xinjiang is not only one of the important cotton production bases but also it has the largest acreage of drip irrigated cotton area in China. In the last decades, Xinjiang has created a miracle of micro-irrigation development in the world. Until the year of 2009, drip irrigated cotton cultivation acreage in Xinjiang has reached about $9 \times 10^5 \text{hm}^2$ and the region has become the main area for research & development and application of drip irrigation advanced technology in China. With the promotion of drip irrigation, the technology development, automation and information management have become increasingly important. However, the implementation of irrigation decision-making on cotton still depends on the farmers' experience at present and it's still difficult to achieve accurate irrigation by the real-time monitoring information of soil moisture in field. In recent years, research of accurate drip irrigation on cotton has carried out in Xinjiang region to solve the key scientific problems such as the drip irrigation design theory and the methods for soil moisture monitoring system. However, these issues are extremely difficult to solve due to the unknown spatial variability of soil moisture. For the time being, drip irrigation system designers reduce the layout density of sensor randomly in order to reduce input costs. As a result, these practices affect the reliability of soil moisture information collection and accuracy. Thus the collection system of soil moisture has not yet really worked in decision-making on cotton drip irrigation.

Under drip irrigation, only soils near the root zone is moisturized, which is different to traditional surface irrigation. In addition to the influence of soil factor itself, there are many other factors that have significant impacts on the spatial variability of soil moisture, such as emitter flow, emitter spacing, drip tape uniformity and layout in field. After three years of experiment research, a database management system was established including soil moisture of cotton field, crop growth, irrigation water and social economy. The data measured by soil moisture sensors were transferred to Central Control Computer via the Remote Telemetry Control Unit (RTU) and the automatic evaluation of gathered data was processed
through Central Control Computer by experts' system on cotton production and management. Practically,
the solenoid valves will be opened or closed through RTU based on the orders issued by central control
computer system. For details, see Fig.1.

There are many articles on the researches of spatial variability of soil moisture using statistical
methods in literatures [2-10]. However, fewer scholars have done systematic study about method of setting
soil moisture sensor in automatic monitoring system for growing cotton. In this study, we focused on this
issue. The research results of this study could provide theoretical basis for the scientific design of soil
moisture monitoring system and its high-level application in field to conduct study on the characteristics
of spatial variability of soil moisture and reasonable layout method of soil moisture sensor. The outcomes
from this study could also offer technical support for soil moisture automatic acquisition and processing
information, accurate forecasting of soil moisture content, soil moisture intelligent diagnosis and
real-time irrigation decision-making. It has important practical significance to realize precise irrigation
and efficient use of water in agriculture in Xinjiang.

## 2 Materials and methods

### 2.1 Experimental site

The study site is located in Baotou-lake Farm in China, East longitude 86°08′~86°26′, north latitude
41°45′~41°56′, which is some 30km away from Korla city. The irrigation condition is very good with
Yongfeng Channel on the east and Kongque River on the west. The climate is dry and warm, the annual
average temperature is 10.7℃, the annual average sunshine hours is 2886.8h, the annual average
accumulated temperature is 4192.1℃, the average annual frost-free period is 132~181 days, the
temperature difference between day and night is 12℃~17℃, the annual rainfall is about 103mm. It is
abundant in sunshine with a long growing season, less rainfall in summer and less snow in winter.

The study was conducted on a 68-ha cotton field (Automatic Irrigation Demonstration Area on Cotton
Mulched Drip Irrigation and the details of the control system are shown in Fig.1). The sources of
irrigation network are well. Experimental site selected is at a spacing of 45m×54m and average drip tape
space is 1.25m and emitter space is 0.30 m. Soil texture is sandy loam and soil bulk density is 1.45g/cm³.
The cultivation mode of cotton is four lines under a film with one drip tape placed in the center. The
cotton growing direction is from east to west and cultivation mode is: 25cm+45cm+ 25cm+40cm. The
details are shown in Fig. 2.

### 2.2 Experimental schemes
**Scheme 1: Analysis of spatial variability of soil moisture**

As indicated in Fig. 3, the grid method was adopted in the layout of sampling points. Five rows of
sampling points were designed along drip tape direction and row interval was 10 meters, space between
consecutive points was 5 meters along submain direction. The experiment is divided into two stages. The
first stage was from April 22nd to June 13th without any precipitation (winter irrigation was occurred at
December 15th, 2008) and belonged to water shortage period; detailed experiment times are shown in
Table 1、Table 2 and Table 3. The soil moisture was measured by drying and weighing method in three
layers (0~20cm, 20~40cm and 40~60cm)

Scheme 2: Analysis of lateral orientation of soil moisture monitoring points

In 2009, a control area in one typical drip tape was selected. Four rows of sampling points were placed along vertical direction to drip tape. Row interval was 2 meters, each row has five sampling points and their locations were 0m,0.225m, 0.350m, 0.475m and 0.675m from drip tape respectively (namely located in drip tape, inward cotton row, in the center of two cotton rows, outward cotton row and at ditch between two neighboring membranes). The details are shown in Fig.3. Detailed sampling times are shown in Table 6. Soil moisture was measured by drying and weighing method in four layers (0~20cm, 20~40cm, 40~60cm and 60~80cm).

**3 Methodology**

The traditional and geo-statistical methods were utilized to analyze data in this study. Both methods have been used extensively in the literatures[1-10], readers are referred to above literatures for detailed description of the methods. In this study, a normal Q-Q test was performed and descriptive statistics were computed to the soil moisture data in scheme 1 using SPSS17.0 (Table 1, Table 2 and Table 3). Take the data of Jun 4, Aug 5, and Aug 10 for example, normal Q-Q tests were shown in Fig.4, Fig.5 and Fig 6; T test was performed to the soil moisture data in scheme 2 and the results are shown in Table 5. Geo-statistics analysis was performed to the soil moisture data in scheme 1 by GS+ software and results are shown in Table 4. The isogram of soil moisture for each depth was drawn by Kriging interpolation through Surfer 8.0 software. The details are shown in Fig.7.

**4 Results and discussion**

**4.1 Traditional statistical analysis of spatial variability for soil moisture on**
**cotton mulched drip irrigation**

Traditional statistical analysis[11-12] to the sample data of soil moisture in field were analyzed by SPSS
17.0 (only discuss it in two-dimensional plane), and the results are shown in Table 1, Table 2 and Table 3.
As indicated from the Tables, in stage one, mean value, minimum value and maximum value of soil
moisture in the whole experimental site decrease as time goes except some unusual value (the maximum
of June 11th) which maybe attributed to the measurement error. However, the tendency of coefficient of
variation Cv is opposite. Coefficient of variation Cv can express the dispersion degree of random
variable. Generally speaking, when Cv≤0.1, it indicates weak variation; when 0.1< Cv <1.0, it indicates
moderate variation; when Cv≥1.0, it indicates strong variation [13-14]. In stage one, Cv value vary in the
range of 0.097~0.107, an approximate value is 0.1, thus it should belongs to weak variation; in stage
two (including after irrigation and before irrigation), Cv value vary in the range of 0.104~0.173 and
should belongs to moderate variation. This showed that the increase of soil moisture can enhance the
spatial variability, namely, the spatial variability vary directly with soil moisture.

## 4.2 Geo-statistical analysis of spatial variability of soil moisture on cotton mulched drip irrigation

Wang et al. (2001) [15] suggested that the ratio of nugget variance to sill variance expressed as a percentage is an indication of the spatial dependence of the variable concerned. Cline et al. (1989) [16] and Cambardella et al.(1994) [17] stated that ratios between 25% and 75% represent moderate spatial dependence, those below 25% strong spatial dependence, and all others weak dependence (Qi Feng et al,2004) [18] when it was not suitable to do spatial forecast by interpolation.

According to Table 4, both in stage one and stage two, Nugget/ Sill variance of soil moisture in all three depths are between 25% and 75%,which represents moderate spatial dependence or autocorrelation.
Thus this case of soil moisture spatial variability is resulted from the combination of several random variables and structural factors including climate, topography, and soil form and so on. From the coefficient of determination in Table 4, we can obtain that the best theory model is spherical model [18] for each stage. However, with the increment of soil moisture content, the accuracy of regression model has a tendency of decreasing. From Table 4, we can easily found that the range of spherical model is about 9.40m~35.35m, about the same value for placing monitoring points for soil moisture. However, fractal dimension did not show significant changing rule.

From the results of Geo-statistical analysis above, we obtained that it was suitable to do spatial interpolation in consideration of the moderate spatial dependence of soil moisture. So, the isogram [19-22] of soil moisture for each depth was obtained by Surfer 8.0, the details are shown in Fig.7. As indicated in
Fig.7, lines of the isogram are not too dense in all the three depth layers which indicate that the spatial correlation of soil moisture is strong while its variability is weak.

## 4.3 Lateral location research on monitoring points of soil moisture

The purpose of performing lateral location research of monitoring points on soil moisture is to obtain the best location of monitoring points by seeking the best vertical distance from monitoring points to the drip tape. This can improve the layout efficiency of monitoring points on soil moisture and their monitoring accuracy. The vertical profile of soils was divided into four layers, 0~20cm、20~40cm、40~60cm and 60~80cm. In order to perform t-test, the soil moisture data was divided into two Groups. Group 1 is composed from data of No.2~No.5 monitoring points observed from Jun 11, Jun 29, Aug 5 and Sep 3, and Group 2 is including the data of No.1 observed from Jun 11, Jun 29, Aug 5 and Sep 3 (monitoring points of No.1 are used to locate points in drip tape). A t-test to the data between Group 1 and Group 2 was performed to estimate its significance of soil moisture difference in vertical profile distribution and to determine the relative position between monitoring points and drip tape. The results are shown in Tab.5.

When $|t| \ge t_{\alpha/2}(n-1)$, the difference from the two data observed is significant; when $|t| < t_{\alpha/2}(n-1)$, there is no significant difference. As indicated in Tab.5, compared to No.1 point, there are significant differences in vertical profile 0~20cm soil moisture from No.2 point but 20~80cm; At 0~60cm vertical profile, there is no significant differences between No.3 and No.4 points. Compared to No.1 point, there is significant difference in No.5 point at 0~80cm. This research showed that: At top 0~60cm layer, there are no significant differences about soil moisture distribution among the points which are at 0.225m, 0.350m and 0.475m from the drip tape, respectively. However, there are significant differences between drip tape and at points 0.675m away from it. So, it is better to place the monitoring points at the range of 0~0.475m from drip tape while farther points from the drip tape is not suitable for placing monitoring points.

## 5 Conclusions

Through the analysis of spatial variability of soil moisture and lateral orientation of soil moisture monitoring points, following conclusions can be made: (1) The increase of soil moisture can enhance their spatial variability. (2) In the drip irrigation system and cotton cultivation mode, spatial variability of soil moisture is resulted from the combination of several random variables and the structural factors
including climate, topography, and soil form and so on. (3) Semi-variogram models belong to spherical model and the range of spherical model for soil moisture is about 9.40m~35.35m (4) With the increment of soil moisture content, the accuracy of regression models have a tendency of decreasing. (5) The monitoring points should be placed at the range of 0~0.475m from drip tape and it is not suitable to place them at a farther place. It is best to place them at outward cotton row in consideration of management and monitoring of soil moisture.

In this research, the range of spatial variability, field size and monitoring accuracy of soil moisture are considered in analysis of monitoring point lateral layout for soil moisture. The results from this research are helpful to guide the monitoring points' layout of soil moisture. As for how to place vertical direction monitoring points, it still needs a further study.

## References

[1] Hillel D. Fundaments of Soil Physics[M]. New York: Academic Press, 1980.

[2] Burgess T M, Webster R. Optimal interpolation and isarithmic and isarithmic mapping of soli properties[J]. Soil Sci, 1980, 31: 315~341.

[3] Nielsen D R, Biggar J W. Monitoring soil water properties utilizing geostatistical techniques [J]. ISRSS China, 1985, 21: 211~223.

[4] A. Bárdossy, W. Lehmann. Spatial distribution of soil moisture in a small catchment. Part 1: geostatistical analysis[J]. Hydrology, 1998, 206: 1~15.

[5] F. Anctil, Renaud Mathieu, Leon-Etienne Parent, Alain A. Viau, Mathali Sbih, Masoud Hessami. Geostatistics of near-surface moisture in bare cultivated organic soils [J]. Hydrology 2002 (260)30-37.

[6] Wilson D J, Western A W, Rodger B Grayson. Spatial distribution of soil moisture over 6 and 30 cm depth, Mahurangi river catchment, New Zealand[J]. Hydrology, 2003, 276: 254~274.

[7] A.W. Western. Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes[J]. Hydrology. 2004, 286: 113~13.

[8] VERONESE JUNIOR V., CARVALHO M.P., DAFONTE J., FREDDI O.S., VIDAL VAZQUEZ E., INGARAMO O.E.. Spatial variability of soil water content and mechanical resistance of BraZilian ferralsol[J]. Soil & Tillage Research, 2006, 85: 166~177.

[9] Huang Jing, Du Zhen-dong, Zhu Xin-guo, Liu Li, Wu Wen-hong, Liu Bing. Spatial Variability Research of Soil Moisture[J]. Water Saving Irrigation, 2010(10):20-21,25.

[10] BI Huaxing, LI Xiaoyin, LIU Xin, GUO Mengxia, and LI Jun. A case study of spatial heterogeneity of soil moisture in the Loess Plateau, western China: A geostatistical approach. International Journal of Sediment Research, 2009, 24(1): 63-73

[11] L.Brocca, R.Morbidelli, F.Melone, T.Moramarco. Soil moisture spatial variability in experimental areas of central Italy.[J]. Hydrology, 2007, 333, 356-373

[12] J. S. Famiglietti, J. A. Devereaux, C. A. Laymon, T. Tsegaye, P. R. Houser, T. J. Jackson, S. T. Graham, M. Rodell and P. J. van Oevelen. Ground-based investigation of soil moisture variability within remote sensing footprints during the Southern Great Plains 1997 (SGP97) Hydrology Experiment.

[13] Fengmei Yao, Yinglong Xu, Erda Lin, Masayuki Yokozawa, Jiahua Zhang. Assessing the impacts of climate change on rice yields in the main rice areas of China. Climatic Change 2007, 80:395-409.DOI
[14] Hangsheng Lin, Dan Wheeler, Jay Bell, Larry Wilding. Assessment of soil spatial variability at multiple scales. Ecological Modelling, 2005, 182: 271-290.

[15] Wang, J., Fu, B.J., Qiu, Y., Chen, L.D., Wang, Z.. Geostatistical analysis of soil moisture variability on Da Nangou catchments of the loess plateau, China. Environmental Geology, 2001, 41 (1): 113-116.

[16] Cline, T.J., Molinas, A., Julien, P.Y. An auto-CAD-based watershed information system for the Hydrologic Model HEC-1. Water Resources Bulletin, 1989, 25 (3): 641-652.

[17] Cambardella, C.A., T.B. Moorman, T.B. Parkin, D.L. Karlen, R.F.Turco, and A.E. Konopka. Field scale variability of soil properties in Central Iowa soils. Soil Sci. Soc. Am. J. 1994, 58:1501-1511.

[18] Qi Feng, Yansui Liu, Masao, Mikami. Geostatistical analysis of soil moisture variability in grassland. Journal of Arid Environments, 2004, 58: 357-372.

[19] Andrea S. Taschetto and Matthew H. England. An analysis of late twentieth century trends in Australian rainfall. International Journal of Climatology, 2008, Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/joc.1736.

[20] C.S. De Silva, E.K. Weatherhead, J.W. Knox, J.A. Rodriguez-Diaz. Predicting the impacts of climate change—A case study of paddy irrigation water requirements in Sri Lanka. Agricultural water management, 2007, 93:19-29.

[21] H. LIN, X.ZHOU. Evidence of subsurface preferential flow using soil hydrologic monitoring in the Shale Hills catchment. European Journal of Soil Science, February 2008, 59, 34-49.

[22] Richard M. Petrone, J. S. Price, S. K. Carey and J. M. Waddington. Statistical characterization of the spatial variability of soil moisture in a cutover peatland. Hydrol. Process, 2004, 18, 41-52.
<table><caption>Tab.1 Descriptive statistics of soil moisture content in Stage One</caption><thead><tr><th>Sampling Time</th><th>Sample Number</th><th>Minimum</th><th>Maximum</th><th>Mean</th><th>Standard deviation</th><th>Variation coefficient</th><th>Skewness</th><th>Kurtosis</th></tr></thead><tbody><tr><td>Apr 22</td><td>44</td><td>0.184</td><td>0.258</td><td>0.217</td><td>0.021</td><td>0.097</td><td>0.311</td><td>-0.961</td></tr><tr><td>May 15</td><td>44</td><td>0.174</td><td>0.256</td><td>0.209</td><td>0.021</td><td>0.100</td><td>0.583</td><td>-0.620</td></tr><tr><td>May 24</td><td>44</td><td>0.166</td><td>0.238</td><td>0.198</td><td>0.021</td><td>0.106</td><td>0.405</td><td>-0.915</td></tr><tr><td>Jun 4</td><td>44</td><td>0.156</td><td>0.233</td><td>0.198</td><td>0.021</td><td>0.106</td><td>-0.005</td><td>-0.990</td></tr><tr><td>Jun 11</td><td>44</td><td>0.141</td><td>0.235</td><td>0.187</td><td>0.020</td><td>0.107</td><td>-0.003</td><td>-0.029</td></tr></tbody></table>

<table><caption>Tab.2 Descriptive statistics of soil moisture before irrigation in Stage Two</caption><thead><tr><th>Sampling Time</th><th>Sample Number</th><th>Minimum</th><th>Maximum</th><th>Mean</th><th>Standard deviation</th><th>Variation coefficient</th><th>Skewness</th><th>Kurtosis</th></tr></thead><tbody><tr><td>Jun 29</td><td>44</td><td>0.101</td><td>0.225</td><td>0.168</td><td>0.029</td><td>0.173</td><td>0.017</td><td>-0.692</td></tr><tr><td>Jul 13</td><td>44</td><td>0.169</td><td>0.242</td><td>0.197</td><td>0.022</td><td>0.112</td><td>0.625</td><td>-0.835</td></tr><tr><td>Jul 24</td><td>44</td><td>0.140</td><td>0.256</td><td>0.183</td><td>0.025</td><td>0.137</td><td>1.039</td><td>0.923</td></tr><tr><td>Aug 5</td><td>44</td><td>0.138</td><td>0.257</td><td>0.189</td><td>0.026</td><td>0.138</td><td>0.518</td><td>-0.059</td></tr><tr><td>Aug 17</td><td>44</td><td>0.124</td><td>0.242</td><td>0.183</td><td>0.026</td><td>0.142</td><td>0.360</td><td>-0.128</td></tr><tr><td>Aug 27</td><td>44</td><td>0.185</td><td>0.291</td><td>0.222</td><td>0.023</td><td>0.104</td><td>0.626</td><td>0.216</td></tr></tbody></table>

<table><caption>Tab.3 Descriptive statistics of soil moisture after irrigation in Stage Two</caption><thead><tr><th>Sampling Time</th><th>Sample Number</th><th>Minimum</th><th>Maximum</th><th>Mean</th><th>Standard deviation</th><th>Variation coefficient</th><th>Skewness</th><th>Kurtosis</th></tr></thead><tbody><tr><td>Jun 17</td><td>44</td><td>0.185</td><td>0.287</td><td>0.237</td><td>0.025</td><td>0.105</td><td>0.434</td><td>-0.270</td></tr><tr><td>Jul 4</td><td>44</td><td>0.181</td><td>0.287</td><td>0.232</td><td>0.027</td><td>0.116</td><td>0.328</td><td>-0.514</td></tr><tr><td>Jul 15</td><td>44</td><td>0.190</td><td>0.319</td><td>0.258</td><td>0.029</td><td>0.112</td><td>0.300</td><td>-0.316</td></tr><tr><td>Jul 31</td><td>44</td><td>0.185</td><td>0.288</td><td>0.234</td><td>0.028</td><td>0.120</td><td>0.236</td><td>-0.919</td></tr><tr><td>Aug 10</td><td>44</td><td>0.158</td><td>0.289</td><td>0.223</td><td>0.031</td><td>0.139</td><td>-0.097</td><td>-0.535</td></tr><tr><td>Aug 20</td><td>44</td><td>0.177</td><td>0.287</td><td>0.229</td><td>0.027</td><td>0.118</td><td>-0.047</td><td>-0.397</td></tr><tr><td>Sep 3</td><td>44</td><td>0.197</td><td>0.299</td><td>0.236</td><td>0.026</td><td>0.110</td><td>0.826</td><td>-0.022</td></tr></tbody></table>

<table><caption>Tab. 4 Corresponding parameters of Semivariogram models and test parameters of regression models for soil moisture</caption><thead><tr><th></th><th>Depth<br>cm</th><th>Theory<br>Model</th><th>Nugget<br>10<sup>4</sup></th><th>Sill<br>10<sup>4</sup></th><th>Range<br>A<sub>0</sub></th><th>Nugget/ Sill<br>C<sub>0</sub>/(C<sub>0</sub>+C)</th><th>Decision<br>R<sup>2</sup></th><th>Fractal Dimension<br>D<sub>0</sub></th></tr></thead><tbody><tr><td rowspan="3">Stage<br>one</td><td>0~20</td><td>spherical</td><td>1.75</td><td>3.14</td><td>16.45</td><td>0.56</td><td>0.80</td><td>1.91</td></tr><tr><td>20~40</td><td>spherical</td><td>1.80</td><td>3.14</td><td>19.27</td><td>0.57</td><td>0.49</td><td>1.95</td></tr><tr><td>40~60</td><td>spherical</td><td>2.77</td><td>4.88</td><td>15.04</td><td>0.57</td><td>0.82</td><td>1.83</td></tr><tr><td rowspan="3">Before<br>Irrigation<br>in Stage<br>two</td><td>0~20</td><td>spherical</td><td>1.05</td><td>3.59</td><td>9.40</td><td>0.29</td><td>0.73</td><td>1.91</td></tr><tr><td>20~40</td><td>spherical</td><td>6.00</td><td>6.00</td><td>18.80</td><td>1.00</td><td>0.64</td><td>1.90</td></tr><tr><td>40~60</td><td>spherical</td><td>5.40</td><td>11.59</td><td>11.75</td><td>0.47</td><td>0.38</td><td>1.92</td></tr><tr><td rowspan="3">After<br>Irrigation<br>in Stage<br>two</td><td>0~20</td><td>spherical</td><td>5.50</td><td>9.33</td><td>21.92</td><td>0.59</td><td>0.70</td><td>1.82</td></tr><tr><td>20~40</td><td>spherical</td><td>6.60</td><td>11.59</td><td>19.27</td><td>0.57</td><td>0.59</td><td>1.84</td></tr><tr><td>40~60</td><td>spherical</td><td>4.00</td><td>12.99</td><td>35.35</td><td>0.31</td><td>0.30</td><td>1.98</td></tr></tbody></table>
<table><caption>Tab.5 Significance test for soil moisture between soil monitoring points before irrigation</caption><thead><tr><th rowspan="2">Scheme</th><th colspan="2">0-20cm</th><th colspan="2">20-40cm</th><th colspan="2">40-60cm</th><th colspan="2">60-80cm</th></tr><tr><th>t</th><th>t<sub>2</sub><sup>a/(n-1)</sup></th><th>t</th><th>t<sub>2</sub><sup>a/(n-1)</sup></th><th>t</th><th>t<sub>2</sub><sup>a/(n-1)</sup></th><th>t</th><th>t<sub>2</sub><sup>a/(n-1)</sup></th></tr></thead><tbody><tr><td>No.1~No.2</td><td>0.625</td><td>0.576</td><td>-0.340</td><td>0.756</td><td>-0.132</td><td>0.903</td><td>-0.307</td><td>0.779</td></tr><tr><td>No.1~No.3</td><td>-0.131</td><td>0.904</td><td>0.121</td><td>0.911</td><td>0.395</td><td>0.719</td><td>-0.692</td><td>0.539</td></tr><tr><td>No.1~No.4</td><td>0.264</td><td>0.809</td><td>-0.333</td><td>0.761</td><td>-0.441</td><td>0.689</td><td>2.810</td><td>0.067</td></tr><tr><td>No.1~No.5</td><td>0.918</td><td>0.426</td><td>-0.612</td><td>0.584</td><td>-1.614</td><td>0.205</td><td>0.609</td><td>0.586</td></tr></tbody></table>

<table><caption>Tab.6 Irrigation time and Irrigation requirement at different periods</caption><thead><tr><th rowspan="2">Period of growth</th><th rowspan="2">Irrigation requirement<br>(m³/hm²)</th><th colspan="2">Time</th></tr><tr><th>Before irrigation</th><th>After irrigation</th></tr></thead><tbody><tr><td>Seedling stage</td><td>300</td><td>Jun 11</td><td>Jun 13</td></tr><tr><td>Budding period</td><td>300</td><td>Jun 29</td><td>Jul 1</td></tr><tr><td>Flowering stage</td><td>300</td><td>Aug 5</td><td>Aug 7</td></tr><tr><td>Boll opening stage</td><td>300</td><td>Sep 3</td><td>Sep 4</td></tr></tbody></table>

<figure><img src="image_2.png" /><figcaption>Fig. 1 Automatic control system in drip irrigation demonstration</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Fig. 2 Cotton cultivated pattern and layout of sampling points</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig.3 Distribution of sampling points for soil moisture</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig.4 Normal Q-Q plot of June 4th</figcaption></figure>

<caption>Fig.5 Normal Q-Q plot of August 5th</caption>

<caption>Fig.6. Normal Q-Q plot of August 8th</caption>
<figure><img src="image_6.png" /><figcaption>Fig.7 Isogram of soil moisture at three depths in June 4th、August 5th and July 31st</figcaption></figure>