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Response of soil compaction to the seasonal freezing-thawing process andthe key controlling factors

Xianghao Wang, Chaozi Wang⁎, Xingwang Wang, Zailin Huo⁎

Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, PR China

A R T I C L E I N F O

Keywords:Freezing-thawingSoil compactionAutumn irrigationPlanting structureIrrigation regimeSpatial heterogeneity

A B S T R A C T

The seasonal freezing-thawing process can alleviate saline farmland soil compaction, and improve soil aerationand soil water retention. However, the response of soil compaction to the seasonal freezing-thawing process andthe key factors affecting soil compaction are still unclear. Therefore, in this study, we conducted a regionalmonitoring of soil compaction and other soil physical and chemical properties before and after the 2016–2017seasonal freezing-thawing period in Yongji Irrigation Area, Hetao Irrigation District, North China. We used acombination of genetic algorithm-projection pursuit evaluation, grey relational analysis and stepwise regressionto analyze the measured soil physical and chemical properties and their variations during the freezing-thawingperiod. Using this method combination, we obtained one projection value to represent the whole-profile soilcompaction, and found the most important influencing soil physical and chemical properties—soil water con-tent, groundwater level and total inorganic nitrogen. Ultimately, we developed a regression model to predict soilcompaction given the values of the predictive variables (soil water content, groundwater level and total in-organic nitrogen) at the same depth and time. The importance of autumn irrigation and planting structure, aswell as the spatial heterogeneity of field management regime on the relationship between freezing-thawingprocesses and soil compaction were highlighted. Our findings provide solid foundations for regional fieldmanagement policies, especially irrigation and drainage policies. Future studies are recommended to focus onthe effect of different irrigation regimes, different drainage regimes and different nitrogen fertilizer applicationregimes on the relationship between the seasonal freezing-thawing processes and saline farmland soil com-paction.

1. Introduction

The freezing-thawing process can alter soil structure by destroyingsoil aggregates through the phase change of water induced shrink-swellof soil pore spaces (Henry, 2007; Li and Fan, 2014; Unger, 1991). Thevariation of soil structure changes soil physical properties such as soilcompaction, bulk density, saturated hydraulic conductivity (Asareet al., 1999) and infiltration rate (Fouli et al., 2013). And, generally,reduced soil compaction benefits crop yields (Gameda et al., 1987).Therefore, understanding the impact of the seasonal freezing-thawingprocess on soil compaction is essential to soil hydrologic processesquantification, water resources management and food production.

Numerous soil compaction studies have concentrated on the effectsof agricultural traffic (Håkansson and Reeder, 1994; Sivarajan et al.,2018), grazing processes (Mapfumo et al., 1999; Steffens et al., 2008),tillage practices (Bogunovic et al., 2018; Lal, 1976) and crop rotation

regimes (Alakukku, 1998; Sojka and Arnold, 1980). Only a few studieshave evaluated the soil compaction alleviations resulted from freezingand thawing processes (Jabro et al., 2014; Sivarajan et al., 2018). Of thea few existing studies, nearly all of them were at the soil profile scale,and hardly any were at the regional scale. Therefore, a study on theeffect of freezing-thawing process on soil compaction at the regionalscale was in need.

Seasonal frozen soils mainly distribute in areas with latitudes higherthan 24°. In China, more than half of the national land is covered byseasonal frozen soil (Xu et al., 2010b), mostly in northwest China. Inmany arid irrigation areas in northwest China, e.g., Hetao IrrigationDistrict, there is usually an autumn irrigation in the whole region beforesoil freezing. As a traditional irrigation regime in arid saline farmlandafter autumn harvest (Liang et al., 2015; Shang et al., 1997), autumnirrigation aims to leach salt in root zone to deeper soil and keep suffi-cient soil water content for spring crop sowing (Feng et al., 2005). It has

https://doi.org/10.1016/j.catena.2019.104247Received 10 May 2019; Received in revised form 21 August 2019; Accepted 1 September 2019

⁎ Corresponding authors at: Center for Agricultural Water Research China, China Agricultural University, No.17 Tsinghua East Road, Haidian, Beijing, 100083, PRChina.

E-mail addresses: chaoziwang@cau.edu.cn (C. Wang), huozl@cau.edu.cn (Z. Huo).

Catena 184 (2020) 104247

Available online 09 September 20190341-8162/ © 2019 Elsevier B.V. All rights reserved.

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been found that autumn irrigation can increase soil porosity and alle-viate soil compaction in seasonal frozen area (Chen et al., 2010). Thefreezing and thawing after autumn irrigation can reduce soil bulkdensity of the plow pan and improve soil aeration and permeability,which facilitates water flow and solute transport in the soil (Hua andWang, 1993; Yao and Chen, 1986).

Generally, the effect of freezing and thawing process on soil struc-ture depends on land surface temperature, soil water content, soilsalinity, and groundwater level (Miao et al., 2017; Wu et al., 2015; Yiet al., 2014). Thus, by controlling the latter three factors, autumn ir-rigation influences the effect. However, the mechanism of soil structurevariation in the soil profile driven by the freezing and thawing processis still unclear, especially when the soil salinity and texture varies alongthe vertical soil profile. Therefore, soil compaction and related soilphysical and chemical properties at 77 sampling points were observedbefore and after the seasonal freezing-thawing process in Hetao Irri-gation District, a typical arid irrigation district with shallow ground-water. The objectives of this study are (1) to obtain the response of soilcompaction to the seasonal freezing-thawing process at regional scale,and (2) to find the key soil physical and chemical properties affectingsoil compaction and build a predictive model.

2. Materials and methods

2.1. Study area

Located in Hetao Irrigation District, Yongji Irrigation Area (YJIA)has a total irrigation area of 1.36×105 ha (HIDAB, 2018) (Fig. 1). Ithas an arid continental monsoon climate, with an average annual pre-cipitation of 130mm and an average annual evaporation of 2200mm(HIDAB, 2018). The annual average irrigation is 814.9million m3,

which is diverted from the Yellow River (HIDAB, 2018). The autumnirrigation quota is about 1500m3/ha (i.e., 150mm) (Fan and Zhang,2000). The main crops are maize, sunflower and wheat.

Due to the flood irrigation year after year and the desperate drai-nage systems, the groundwater level is very shallow, i.e., about 2mdeep from the land surface (Qu et al., 2003). Even those fields that arenot irrigated, the lateral water flow from adjacent fields and upwardwater movement during the freezing-thawing period make the 0–45 cmdeep soil almost saturated in early spring (Wang et al., 2019; Zenget al., 2018). Therefore, the secondary salinization is very serious,which threatens the agricultural sustainability (Chang et al., 2014). As aresult, the farmland soil there is categorized as the irrigation-silted soil(Gong et al., 2005), of which 75% is mild to moderate saline soil andthe other 25% is severe saline soil (Li et al., 2013).

In this area, the duration of the seasonal freezing-thawing period isabout 180 days (from about November 20th until about May 10th in thenext year) and the maximum frost depth can reach 1.3 m (Li et al.,2012). For the monitored period from November 2016 to April 2017,the maximum frost depth reached 1.1m. And the maximum andminimum air temperature was 27.6 and 21.2 °C, respectively (CMDC,2017).

2.2. Regional monitoring

There are 33 groundwater observation wells in the YJIA (blue dotsin Fig. 1). To detect the soil compaction variation during the enhancedfreezing-thawing process by autumn irrigation, the regional monitoringwas conducted twice: once (October 4th–6th 2016) before autumn ir-rigation and once (April 15th–17th 2017) before spring irrigation. Foreach regional monitoring, one maize field and one sunflower field wereselected near each observation well as sampling sites (N=64; for 2 of

Fig. 1. Locations of YJIA and groundwater monitoring wells. (For interpretation of the references to color in this figure, the reader is referred to the web version ofthis article.)

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the 33 wells, only one planting structure was available nearby). At themaize site and the sunflower site near well #143 at Fenzidi Experi-mental Station (about 20 ha) (Fig. 1), multiple sampling points(N=16) were selected. The multiple sampling points at the two siteswere selected to test if the soil condition at one site can be representedby one sampling point. At the other sampling sites, only one samplingpoint was selected.

At each sampling point, groundwater level, soil samples at differentdepths, undisturbed soil samples for in situ gravimetric water content(GWC), in situ soil compaction, and geographic coordinates were col-lected. The groundwater level was measured manually from the nearbygroundwater observation well. Soil samples at 20, 50, 80 and 100 cmdepths were collected using a one-meter hand-held soil sampler. Thesoil samples at 20 and 50 cm were divided into subsamples and thenmeasured for five physical and chemical properties—nitrate‑nitrogen(NO3

−-N), ammonium‑nitrogen (NH4+-N), soil salinity content (SSC),

pH and soil texture. The soil samples at 80 and 100 cm were onlymeasured for SSC, pH and soil texture. The GWC at each depth wasmeasured by oven-drying (105 °C) the undisturbed soil samples. Thesoil compaction (within a distance of 30 cm from the soil samplingpoint) was measured using a soil compaction meter (Field Scout SC900Soil Compaction Meter, Spectrum Technologies, Plainfield, IL, UnitedState of America) in 2.5 cm increments from soil surface to 45 cm depthwith three repetitions. The geographic coordinates of each samplingpoint were recorded by the GPS terminal device (Garmin eTrex 301,Garmin Corporation, Taiwan, China) during the October 2016 regionalmonitoring, to make sure that the soil samples, and soil compactionmeasurements were collected at the same points at the next time.

The five physical and chemical properties of soil samples—NO3−-N,

NH4+-N, SSC, pH and soil texture—were measured in laboratory. The

soil samples for nitrogen measurement had been kept frozen untilmeasured, to avoid transformation between different nitrogen species(i.e., NO3

−, NH4+, N2). Then the NO3

−-N and NH4+-N of the frozen

samples were measured by a continuous flow analyzer (Seal AutoAnalyzer 3, SEAL Analytical GmbH, Germany). Soil electrical con-ductivity (EC1:5, mS/cm) was obtained by measuring 1:5 soil to waterextract with an electrical conductivity meter (FE38-Meter. Mettler-Toledo Instruments (Shanghai) Co., Ltd., China). Then the measuredEC1:5 values were converted to total soil salinity content (SSC, %) by alocalized standard curve: SSC=0.3419EC1:5-0.021 (R2=0.9958;n=118). The localized standard curve was calibrated by ShahaoquExperimental Station of the Hetao Irrigation District AdministrationBureau. Soil pH was measured with a pH meter (FE28-Meter, Mettler-Toledo Instruments (Shanghai) Co., Ltd., China). Soil particle size dis-tribution was measured by a laser particle size analyzer (Mastersizer2000, Malvern Instruments Ltd. United Kingdom). And soil texture wascategorized according to the soil texture triangle defined by USDA(Fig. 2). Most of the soils distributed in this area were silt loam (Fig. 2).

2.3. Data analysis

2.3.1. Genetic algorithm-projection pursuit evaluationThe projection pursuit evaluation (PPE) was used to obtain one

variable to represent the soil compaction at 19 depths for each samplingpoint (please see Friedman and Tukey, 1974 and Posse, 1995 for de-tails). Briefly, the projection pursuit evaluation (1) find the optimalprojection direction, (2) project the original dataset to this direction,and (3) obtain one vector representing the original dataset (Fu andZhao, 2006). Specifically, after normalizing the dataset, a projectionindex function was constructed. The projection index function was theproduct of projection vector and the normalized dataset. The projectionvector was optimized by the genetic algorithm (GA, please see Fu et al.(2003) for details). Finally, one value was obtained for each soil profile,which can be used to evaluate the measured soil compaction of the0–45 cm profile. Please see Supplementary Materials S1 for detailedsteps of the genetic algorithm-projection pursuit evaluation.

2.3.2. Grey relational analysisThe basic idea of grey relational analysis is to decide whether the

relationship between two data sequences is close, according to the si-milarity of the curve geometry of the two sequences (please see Liu andForrest, 2010 for details). We used the grey relational analysis to ana-lyze the correlation between soil compaction and other physical andchemical properties, and to obtain the rank of the soil physical andchemical properties by their grey correlation degrees with soil com-paction. Please see Supplementary Materials S2 for basic steps of GreyRelational Analysis.

2.3.3. Stepwise regression analysisStepwise regression is a typical multi-variable linear regression to

select the best-fitted combination of independent variables by a for-ward-adding and backward deleting procedure (please see Draper andSmith, 2014 for details). We used the stepwise regression to select thedriving factors of soil compaction among the five soil physical andchemical properties.

3. Results and discussion

3.1. Variation of soil compaction

There were 42 and 35 sampling points located at sunflower andmaize fields, respectively. The mean (center lines) and standard error(shaded bands) of the soil compaction of the 77 soil profiles are shownin Fig. 3 (please see the full soil compaction profiles in SupplementaryMaterials Figs. S1 and S2). Under both planting structures, (1) the soilcompaction at each depth was significantly reduced after the freezing-thawing period (Supplementary Materials Table S1, ANOVA (maize)and ANOVA (sunflowers) p < 0.05 in the whole 0–45 cm range); (2) itgenerally increased from the soil surface to 45 cm depth (Fig. 3, all ofthe 4 lines); and (3) the soil compaction profiles in the range of 0–10 cmbefore the freezing-thawing and the soil compaction profiles in thewhole 0–45 cm after the freezing-thawing were very similar (Supple-mentary Materials Table S1, ANOVA_2016 in the range of 0–10 cm andANOVA_2017 in the whole 0–45 cm range, p > 0.05).

In contrast, the soil compaction before freezing-thawing of sun-flower fields sharply increased from soil surface until 10 cm depth(Fig. 3, red line); whereas, that of maize fields only sharply increasedfrom soil surface until 5 cm depth (Fig. 3, yellow line). This main dif-ference led to the larger soil compaction of sunflower than maize beforethe freezing-thawing at 10–45 cm (Supplementary Materials Table S1,ANOVA (2016) p < 0.05 in the range of 10–45 cm). The results in-dicated that the soil compaction diverged during crop growing period;whereas, it converged during the freezing-thawing period. The influ-encing factors, e.g., autumn irrigation, other soil properties, plantingstructure, will be discussed in the following sections.

3.2. The influence of autumn irrigation

The two essential mechanisms of soil loosen caused by freezing-thawing can be summarized as: (1) the soil frost heaving caused by thevolume expansion of pore water in the freezing period, and (2) thelegacy of increased pore volume after the drainage of pore water duringthe thawing period. And autumn irrigation is proposed to enhance thesoil loosen effect during both freezing and thawing periods.

There are two types of soil frost heaving: in situ frost heaving andsegregation frost heaving (Xu et al., 2010b). For the in situ frostheaving, the pore water freezes in situ; whereas, for the segregation frostheaving, the soil pore water moves to another location and freezes. Forboth types, the soil pore volume at the freezing location increases by9% due to the pore water phase transition. In our case, the soil watercontent in the vadose zone was largely increased by the autumn irri-gation, and the in situ soil frost heaving was enhanced accordingly.Moreover, after the autumn irrigation, the groundwater level was

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sharply increased, which enhanced the water movement in both thesaturated and unsaturated zones during the freezing period. The en-hanced water movement promotes the frozen front movement and thegrowth of ice lens (Konrad and Morgenstern, 1980; Konrad andMorgenstern, 1981; Miller, 1972), i.e. promoting the development ofsegregation frost heaving (Krumbach and White, 1964).

During the thawing process in spring, the ice in the soil poresconverted to liquid water, which drained to deeper soil. Without thesupport by solid ice, the soil pores collapsed, and the soil settled. Thefreezing expansion effect was stronger than the thawing settlement.Therefore, the overall effect of the freezing-thawing process was de-creased soil compaction. As a consequence, the soil bulk density de-creased, the porosity increased, and the saturated hydraulic

conductivity increased after soil freezing-thawing, which was consistentwith previous studies (Deng et al., 1998; Krumbach and White, 1964).Autumn irrigation not only largely promoted freezing expansion, butalso increased the soil water content in the whole soil profile. There-fore, at the beginning of the thawing period, the deeper soil was stillfrozen, and the liquid water in top soil was hard to drain. Thus, all thefields were almost saturated in the top soil layer at the beginning ofthawing.

3.3. Soil compaction evaluation

After 5 iterations of the genetic algorithm, the objective functionconverged, and the optimal projection index value was 7.32×108. Thecorresponding optimal projection direction is a*= (0.07232409,0.140979644, 0.156633722, 0.18150171, 0.300247822, 0.282654642,0.240244078, 0.259809404, 0.241075117, 0.257403317,0.201150508, 0.211687267, 0.220102026, 0.198792434,0.266865672, 0.20255705, 0.183340274, 0.298129293,0.299674844), which can be used to obtain the projection value of each0–45 cm soil profile before (Fig. 4, red bars) and after (Fig. 4, blue bars)the freezing and thawing process. It can be seen that for each samplingpoint, either at a maize field (Fig. 4a) or at a sunflower field (Fig. 4b),the soil compaction projection value decreased after the freezing-thawing process (each red bar is higher than the corresponding bluebar); however, the amplitude of decrease differed from point to point.

The mean of soil compaction projection values before and afterfreezing-thawing under different planting structures (Fig. 5) showedthat (1) the soil compaction was significantly reduced after the freezing-thawing period (Fig. 5 “All” red and blue bar, ANOVA,p=6.21E−28≪ 0.05); (2) the soil compaction before the freezing-thawing of sunflower fields was significantly larger than that of maizefields (Fig. 5 “Sunflower fields” red bar and “Maize fields” red bar,ANOVA, p=7.72E−4≪ 0.05); and (3) the soil compaction after thefreezing-thawing of both sunflower fields and maize fields were nearlythe same (Fig. 5 “Sunflower fields” blue bar and “Maize fields” blue bar,ANOVA, p=0.86≫ 0.05).

To our knowledge, this was the first time that the measured soilcompaction profile was projected onto a one-dimensional space by theGA-PPE model. The obtained soil compaction projection values retainedthe original multi-dimensional data characteristics to the optimum

Fig. 2. The soil texture of the 20 cm depth soil samples shown on the soil texture triangle defined by USDA.

Fig. 3. The mean (center lines) and standard error (shaded bands) of soilcompaction before (2016 Sunflower Field and 2016 Maize Field) and after(2017 Sunflower Field and 2017 Maize Field) the freezing-thawing periodunder different planting structures (sunflower and maize). (For interpretationof the references to color in this figure, the reader is referred to the web versionof this article.)

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extent, and provided an effective approach to evaluate the soil com-paction of the whole plow layer. The soil compaction projection valueswere further used to find controlling factors of soil compaction and itsvariation caused by freezing-thawing.

3.4. Correlation between soil compaction and other soil properties

The variation of SSC, soil total inorganic nitrogen (TIN, the sum ofNO3

−-N and NH4+-N), soil pH, GWC, and groundwater level (GWL)

during the freezing-thawing period were the potential factors influen-cing the variation of soil compaction during this period. Thus, the dif-ference of soil compaction projection values before (2016) and after(2017) the freezing-thawing period (SCPVD) was used as the referencesequence. And the differences of the five potential influencing soil

property factors before (2016) and after (2017) the freezing-thawingperiod (SSCD, TIND, GWCD and GWLD) were used as the comparisonsequences. Note that one soil compaction projection value was obtainedby the GA-PPE to represent the whole 0–45 cm profile; whereas, the fivepotential factors were measured at 20, 50, 80 and 100 cm depths. Thus,the average of the values at 20 and 50 cm was used, and then the dif-ference before and after the freezing-thawing period was obtained. Thevariation curves of the reference sequence and comparison sequencesare shown in Fig. 6.

The grey correlation degree between the variation of soil compac-tion and the variation of influencing soil properties (Table 1) showedthat the influence of GWC was the most significant, followed by GWL,TIN, SSC and pH. The results were quite consistent under differentplanting structures, and were also consistent with previous studies(Busscher, 1990; Ayers and Perumpral, 1982; Hernanz et al., 2000;Upadhyaya et al., 1982) suggesting that GWC was the dominant factor.Hetao Irrigation District is an arid area with shallow groundwater (Xuet al., 2010a) where GWL controls the GWC variation. Thus, the sig-nificant influence of GWL was understandable. SSC was expected to beof importance, as SSC could influence the structure and hydraulicconductivity of soil (Sameni, 1989). However, it was surprising that theinfluence of TIN was more significant than SSC. One explanation couldbe that the TIN reflected the activities of soil microorganisms whichcoincidently had a variation pattern similar to that of soil compaction.

3.5. Predictive model

The grey relational analysis determined the significance degrees ofthe influencing factors on soil compaction, but a quantitative re-lationship between them could be obtained from the stepwise regres-sion analysis. Using this method, soil compaction (SC) was estimated byconsidering the influencings of GWC, GWL, TIN, SSC and pH under thesame conditions (such as soil depth and date). Here we used the vari-able values at 20 cm (both before and after freezing-thawing) to showthe estimation results.

The stepwise regression analysis successively selected GWC, GWLand TIN, and then terminated. The final regression model based on theraw input data was:

Fig. 4. Comparison of soil compaction projection values before (red bars) andafter (blue bars) the freezing and thawing period at each sampling point (a) atmaize fields and (b) at sunflower fields. (For interpretation of the references tocolor in this figure legend, the reader is referred to the web version of thisarticle.)

Fig. 5. The mean of soil compaction projection values before (red bars) andafter (blue bars) the freezing-thawing period under different planting struc-tures. Error bars indicate the standard errors. (For interpretation of the refer-ences to color in this figure legend, the reader is referred to the web version ofthis article.)

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= − × + × + ×SC 2503.206 6942.903 GWC 162.611 GWL 0.871 TIN

The adjusted R2 was 0.466 and the p value obtained by F test was1.8E−17 (≪0.001), indicating that the regression was very significant.Since the variables in the model had different units, the magnitude ofthe regression coefficients could not indicate the extent to which the

independent variables influence the dependent variable. Therefore, allthe variables were standardized (STD, centered by means and scaled bystandard deviation) and the stepwise regression was rerun to obtain thestandard regression coefficients:

= − × + × + ×

STD_SC0.534 STD_GWC 0.216 STD_GWL 0.159 STD_TIN

The absolute value of each standard regression coefficient re-presents the magnitude of influence of the corresponding factor on soilcompaction; while, the positive or negative sign of each standard re-gression coefficient indicates the variation of the variable increases ordecreases soil compaction. The obtained models could be used to pre-dict soil compaction at 20 cm depth either before or after the freezing-thawing period with GWC, GWL and TIN measurements at the same

Fig. 6. The reference and comparison sequences under different planting structures: (a) at maize fields and (b) at sunflower fields.

Table 1Grey correlation degrees between the variation of soil compaction and thevariations of influencing soil properties.

GWC GWL TIN SSC pH

All sites 0.9915 0.9841 0.9829 0.9773 0.7827Maize field 0.9169 0.8741 0.8668 0.8099 0.7186Sunflower field 0.9762 0.9540 0.9511 0.9420 0.7581

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depth. Similar models could be obtained using the stepwise regressionanalysis to predict soil compaction under other conditions.

3.6. The influence of planting structure

Based on the data of soil compaction and soil water content at20 cm, we found that there was a significant difference (ANOVA,p=4.30E−4 < 0.01) in soil compaction between sunflower fields(2762.9 ± 191.7) and maize fields (1853.6 ± 146.2) before soilfreezing, but the soil water content under different planting structureswas similar (0.155 ± 0.007 for sunflower fields and 0.155 ± 0.009for maize fields, ANOVA, p=0.806 > 0.05). This seems to be con-tradictory to previous studies (Busscher, 1990; Ayers and Perumpral,1982; Hernanz et al., 2000; Upadhyaya et al., 1982) and our own re-sults that soil moisture is the most critical factor affecting soil com-paction. However, they were not contradictory, because the irrigationregimes during growth period were different under different plantingstructures. Therefore the wetting-drying processes were different,which was crucial to soil compaction (Abou Najm et al., 2010; Parkeret al., 1982). Under different planting structures, the root distribution(Elkins, 1985; Materechera et al., 1992; Singh and Sainju, 1998) andthe cementation of soil by root exudates (Shi et al., 2016) were alsodifferent, which could result in different soil compactions. The exactmechanism of how the planting structure influences soil compactionneeds more studies in the future.

After thawing, both the soil compaction (939.9 ± 109.2 for sun-flower fields and 944.1 ± 104.1 for maize fields, ANOVA,p=0.978 > 0.05) and soil water content (0.223 ± 0.007 for sun-flower fields and 0.225 ± 0.008 for maize fields, ANOVA,p=0.880 > 0.05) at 20 cm depth under different planting structureswere similar. The main reason was that during the autumn irrigation,the lateral flow of shallow groundwater and the followed capillary risemade the soil water content in the whole soil profiles in this regionalmost the same, regardless of planting structure. Then, during thefreezing-thawing period, similar freezing expansion and thawing set-tlement processes occurred in the whole soil profiles under differentplanting structures, resulted in the similar soil compaction and soilwater content after this process.

3.7. Implications of spatial variation in soil compaction for regionallandscape scale predictions

ANOVA was conducted to assess whether the soil compaction ofsamples collected at Fenzidi Experimental Station and from the regionalarea were similar or not. The results of maize fields obtained from the

two areas were significantly different before freezing-thawing (com-paring the red bars in Fig. 7a, p=0.009 < 0.05), but were relativelycloser after freezing-thawing (comparing the blue bars in Fig. 7a,p=0.925 > 0.05). Whereas, the results of sunflower fields and allfields from the two areas were similar before freezing-thawing (com-paring the red bars in Fig. 7b, p=0.749 > 0.05, and the red bars inFig. 7c, p=0.127 > 0.05), but significantly different after freezing-thawing (comparing the blue bars in Fig. 7b, p=0.001 < 0.05, andthe blue bars in Fig. 7c, p=1.06E−6 < 0.05).

The above results elucidate that it was hard to estimate the soilcompaction in the whole YJIA region by sampling at FenzidiExperimental Station, not to mention by measuring a single soil profile.This could attribute to the various field managements in the YJIA.Because the fields were owned by so many different farmers, and eachof them had his own field management regimes, for example, thesowing and harvesting dates, irrigation schedule and allocation of ir-rigation water at each time. Also, the soil textures were not the sameover the whole region (Fig. 2).

This study emphasized the importance of regional sampling to un-derstand the real-world relationship between freezing-thawing pro-cesses and soil compaction variation, and the influence of other soilphysical and chemical properties. Therefore, for landscape scale soilcompaction predictions, one way is to obtain the spatial distributions ofGWC, GWL and TIN under the interested condition (depth and date),and use the developed stepwise regression model to acquire the spatialdistribution of soil compaction under the same condition. Another wayis to first obtain the spatial distributions of irrigation and drainagenetworks, soil textures, and planting structures (including related til-lage practices). Then, superimpose the irrigation scheme and climatedata, and develop a distributed model to simulate the temporal andspatial distribution of GWC, GWL and, finally, soil compaction. BecauseGWC and GWL are the internal drivers of soil compaction, while TINmight just be a highly correlated factor.

4. Conclusions

In this study, we conducted a novel regional experiment on soilcompaction and related physical and chemical properties before andafter freezing-thawing in Yongji Irrigation Area. We creatively used acombination of genetic algorithm-projection pursuit evaluation, greyrelational analysis and stepwise regression. With the method combi-nation, we first obtained one projection value to represent the wholesoil profile compaction. Then, we found the most important influencingsoil physical and chemical properties—soil water content, groundwaterlevel and total inorganic nitrogen. At last, we obtained a regression

Fig. 7. Comparing the 16 sampling points at FenzidiExperimental Station and the 64 sampling pointsoutside the Fenzidi Experimental Station (RegionalPoints). The sampling points from (a) maize fields,(b) sunflower fields and (c) all fields were compared,respectively. The heights of the bars indicate themean values, and the error bars donate the standarderrors. The red bars and blue bars represent thesamples collected before and after the freezing-thawing period, respectively. (For interpretation ofthe references to color in this figure legend, thereader is referred to the web version of this article.)

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model to predict soil compaction given the values of the predictivevariables (soil water content, groundwater level and total inorganicnitrogen) under the same condition (depth and date).

The importance of autumn irrigation and planting structure, as wellas the spatial heterogeneity of field management regime were empha-sized on the relationship between freezing-thawing processes and soilcompaction. These results provide solid foundations for regional fieldmanagement, especially irrigation and drainage policies. Future studiesare recommended to focus on the effects of changed managementmeasures on the relationship between freezing-thawing processes andsoil compaction variation, such as different tillage, irrigation, drainageand nitrogen fertilizer application regimes.

Acknowledgement

This work was supported by National Key Research andDevelopment Program of China [grant number: 2017YFC0403301]; theNational Natural Science Foundation of China [grant numbers:51679236, 51790535, 51639009]. We are especially grateful to Ms.Qiangli Wei, Ms. Jiali Du, Ms. Hang Chen and Mr. Shuai Wang for theirhard work on the regional monitoring and laboratory tests. The con-tributions of the editor and anonymous reviewers whose comments andsuggestions significantly improved this article are also appreciated.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.catena.2019.104247.

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