Soil & Tillage Research 146 (2015) 174–183

Maize (Zea mays L.) yield response to nitrogen as influenced by spatio-temporal variations of soil–water-topography dynamics$

Qing Zhu a,b,*, John P. Schmidt c, Ray B. Bryant b

aKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73 East Beijing Road,Nanjing 210008, ChinabUSDA-ARS, Pasture Systems & Watershed Management Research Unit, Building 3702, Curtin Road, University Park, PA 16802, USAc Pioneer Hi-Bred International Inc., Champaign Research Center, 985 County Road 300 E, Ivesdale, IL 61851, USA

A R T I C L E I N F O

Article history:Received 22 August 2013Received in revised form 15 September 2014Accepted 14 October 2014

Keywords:HydropedologyNon-point contaminationPrecision agricultureSoil moisture

A B S T R A C T

Applying N fertilizer at rates that satisfy both economic and environmental objectives are critical forsustainable agriculture. The hypothesis of this study was that the spatial variability in maize (Zea mays L.)yield and its response to N rate were influenced by soil–water-topography dynamics. In 2008 and 2009, astudy was conducted along an agricultural hillslope in the Northern Appalachian Ridge and ValleyPhysiographic Province in the USA with Cambisols according to FAO soil classification. Minimum,maximum, and delta yields and optimum N rate at different slope positions were determined usingquadratic-plateau maize yield – N rate models. Results confirmed our hypothesis. The spatial variabilityof maize yield and its response to N rate was influenced by silt content, soil depth, profile curvature, slope,soil wetness and degree of soil water content temporal variation. In both dry year (2008) and wet year(2009), optimum N rates positively correlated (P < 0.05) with the temporal variation of soil water content,which is an indicator of subsurface flow paths. In 2008, maize yield was little varied along this hillslope(11.7–12.0 Mg ha�1), while greater yield response to N rate (represented as delta yield, 5.6 Mg ha�1) wasobserved in upper convex and steep slope areas with low minimum yield (6.1 Mg ha�1). However, in2009, greater maximum maize yield (13.5 Mg ha�1) and yield response to N rate (8.7 Mg ha�1) wereobserved in lower concave slope areas with deeper soil depth and thus greater water storage. Resultsfrom this study suggested that site-specific N applications could be improved by considering within fieldvariability of soil, topography and hydrology.

ã 2014 Elsevier B.V. All rights reserved.

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1. Introduction

Nitrogen (N) contamination in surface and groundwater is aregulatory and social issue threatening potable water supplies andendangering wildlife habitat (Carpenter et al.,1998). Nitrogen fromagriculture, specifically the widespread use of N fertilizers,application of livestock manures, legumes, and mineralization ofsoil N, has been identified as one of the largest nonpoint sources ofreactive N in the environment (e.g., Jemison and Fox, 1994;Carpenter et al., 1998; Schilling and Libra, 2000; Hatfield et al.,

$ Mention of trade names or commercial products in this publication is solely forthe purpose of providing specific information and does not imply recommendationor endorsement by the U.S. Department of Agriculture. USDA is an equalopportunity provider and employer.* Corresponding author at: Key Laboratory of Watershed Geographic Sciences,

Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, 73East Beijing Road, Nanjing 210008, China. Tel.: +86 15261454920; fax: +862557714759.

E-mail address: qzhu@niglas.ac.cn (Q. Zhu).

http://dx.doi.org/10.1016/j.still.2014.10.0060167-1987/ã 2014 Elsevier B.V. All rights reserved.

2009). To reduce N loss from agricultural lands, especially frommaize lands, applying N at rates that satisfy both economic andenvironmental objectives is critical for sustainable agriculture(Blackmer and White,1998; Scharf et al., 2005). Soil, hydrology andtopography and weather have been widely accepted as majorinfluencing factors of soil N loss and crop N utilization.

Recent research has illustrated that N requirements for maizewere spatially varied due to the spatial heterogeneity of soil(Blackmer and White, 1998; Scharf et al., 2005). Variability in soiltexture, drainage class and organic C influenced soil N availabilityand crop yield by affecting N leaching, mineralization anddenitrification (Qian and Schoenau, 1995; Sogbedji et al., 2001;Dharmakeerthi et al., 2006). Blackmore et al. (1999) reported that anet benefit may exist from variably applying N based on soil type,as opposed to a uniform N application. Sogbedji et al. (2000,2001),) found that poorly drained and fine-textured soils lost moreNO3 through denitrification, while better-drained and coarsertextured soils experience NO3 losses primarily through leaching.

Hydrology (e.g., evapotranspiration and soil water distribution)controls soil N availability and crop yield by influencing water

Q. Zhu et al. / Soil & Tillage Research 146 (2015) 174–183 175

availability and N leaching, denitrification and volatilization. In the“hole-in-the-pipe” conceptual model that analogs the nitrificationand denitrification as a fluid flowing through a leaky pipe, the sizesof holes through which the NO, N2O, N2, and NO3 leaks arecontrolled by soil water content (Davidson et al., 2000). In a studyby Pennock et al. (2001), the optimum N rate of canola (Brassicarapa var. Maverick) was significantly correlated to spring soil watercontent (R2 = 0.73). Zhu et al. (2009) also observed that thesubsurface lateral flow could reduce the soil N availability, and thusreduce crop yield.

Topography influences spatial variations of microclimate, soilproperties, runoff, evaporation and transpiration. These factors, inturn, affect soil N availability and crop yield (Raghubanshi, 1992;Goovaerts and Chiang, 1993; Brubaker et al., 1994). For example,Pennock (2005) pointed out that improvements in N use efficiencythrough site-specific management have great potential to reducenitrous oxide emissions from lower slope positions. The most andleast maize N uptake occurred in (a) backslope position, and (b)toe-slope and foot slope positions, respectively (Jowkin andSchoenau, 1998; Dharmakeerthi et al., 2006).

Effective ways to incorporate these soil, hydrology andtopography into site-specific N management have not been fullydeveloped. With the development of precision agriculture, within-field variation can be accounted into the optimized N application.However, the dominant management for maize production is toapply a single rate of N over whole fields or farms. This is probablydue to that the complex interaction among soil, topography, andhydrology that affect N availability has not been adequatelycharacterized (Loecke and Robertson, 2009; Zhu et al., 2009). Inaddition, how soil, topography and hydrology influence maizeyield and N availability in different years with distinct weathers(e.g., precipitation and temperature) have not been fully under-stood. Site-specific N recommendations depend more on anexplicit understanding of interacting causal factors rather thanthe general understanding required for mean N recommendations,which are usually developed from the mean response of many site-years of results (Schmidt et al., 2011).

Fig. 1. Treatments and plot layouts of the study hillslope. In 2008, experiment was cotopopositions 1, 2, 3, 4, and 5. In each topoposition, six TDR (time domain reflectometry) asix N treatments.

The hypothesis of this study was that the spatial variability inmaize yield and its response to N rate were influenced by soil–water-topography dynamics. Therefore, specific objective of thisstudy was to investigate how temporal and spatial variability ofsoil water content, spatial variations of pedological propertiesand terrain attributes impacted maize yield and N use efficiencyat different hillslope positions in comparatively wet and dryyears.

2. Materials and methods

2.1. Study site

An experiment was conducted in 2008–2009 at the Russell E.Larson Agronomy Research farm at Rock Springs in centralPennsylvania, along a 300-m hillslope with a westerly aspectand 10 m of vertical relief (Fig. 1). This farm is located in a typicalvalley in the Northern Appalachian Ridge and Valley PhysiographicProvince in the USA, and has been under no-till for more than10 years. Slopes ranged from 1.5 to 5.4% along the hillslope. Soilswere Hagerstown silt loams or silty clay loams (fine, mixed,semiactive, mesic Typic Hapludalfs) or Opequon silty clay loams(clayey, mixed, active, mesic Lithic Hapludalfs) according to USDASoil Taxonomy or Cambisols according to FAO soil classification(Schmidt et al., 2011). These well-drained soils formed in limestoneresiduum. The Hagerstown soils were mainly distributed at the toeslope positions with solum >1.0 m thick, while the Opequon weremainly distributed in the upper slope positions with solum <0.5 mthick. This field did not receive any manure application within thepast 20 years. The previous crop in each year was soybean[Glycine max (L.) Merr.]. The specific research area alternatedbetween two adjacent areas along the same hillslope (Fig.1). Maizewas no-till planted during the first 2 weeks of May with rowsparallel to the slope. Infiltration always exceeds precipitation onthis hillslope and visual signs of runoff (e.g., rills) were notobserved during this study. Typical practices were followed(i.e., no-till, herbicides and pesticides to control weeds and pests)

nducted in topopositions 1, 2.5, 3.5, and 5. In 2009, experiment was conducted inccessing tubes were installed in the second row to measure soil water content for all

176 Q. Zhu et al. / Soil & Tillage Research 146 (2015) 174–183

except for N fertilization. The plant population at harvest wasabout 72,500 ha�1.

2.2. Plot layout and data collection

Five topopositions in 2009 and four topopositions in 2008 rep-resenting different topographic features (slope position andcurvature) were organized along the hillslope (Fig. 1). In 2009,experiment fields corresponded to topopositions 1, 2, 3, 4 and 5(Fig. 1). In 2008, the arrangement of experiment fields wasdifferent from that in 2009. They approximately corresponded totopopositions 1, 2.5, 3.5 and 5 (Fig. 1). Plots within eachtopoposition were six rows wide (4.6 m) and 9.1 m long (Fig. 1).Treatments of N fertilizer were assigned consecutively to plotsgoing in an uphill or downhill direction: 0, 56, 112, 168, 224, and280 kg N ha�1 in both years (Fig. 1), which corresponded to 0, 0. 33,0.67, 1.0, 1.33, and 1.67 times of recommended N fertilization rate.Nitrogen was broadcast by hand between the rows as granularNH4NO3 in 2008 and with a drop-hose applicator as liquid 30%urea–ammonium–nitrate (UAN) with Agrotain+ (Agrotain Inter-national, St. Louis, MO) in 2009.

Volumetric soil water content, u, was determined with aportable TRIME-T3 time domain reflectomery (TDR) tube probe(IMKO, Ettlingen, Germany). Measurements were collected as theT3 probe was inserted down a polyvinyl chloride (PVC) access tube.In both years, PVC access tubes (5.0-cm i.d.) were installed in sixplots for each topoposition and in the same row along the length ofthe hillslope (Fig.1). A total of 24 and 30 access tubes were installedin 2008 and 2009, respectively. To install the access tube, ahydraulic soil probe (5.4-cm diameter) was first used to remove asoil core to the 1.1-m depth (or to bedrock). After the soil core wasremoved, a 6.7-cm-diameter “clean-out” tube was used to makethe hole slightly larger. The PVC tube (6.0-cm o.d.) was insertedinto each hole with almost no observable space between the tubeand the surrounding soil. Measurements were collected at 0- to0.2-, 0.2- to 0.4-, 0.4- to 0.6-,0.6- to 0.8-, and 0.8- to 1.0-m depths(representing soil depths of 0.1, 0.3, 0.5, 0.7, and 0.9 m) weekly. In2008, data were collected between mid-June and the end of Augustbecause this coincided with a rapid increase in water demand bythe crop (approximately V5 growth stage to grain fill). Soil watercontent was collected in 2009 during July–August. Soil watercontent was not measure during the early vegetative period in2009, but when the maize was tasseling and beginning grain fill, amoisture-sensitive time for maize growth. At a given depth, themean soil water content collected at all six locations (Fig. 1) wasused to represent the wetness condition of a specific topoposition.

Soil cores were collected when PVC access tubes were installed.They were used as preplant soil samples to test soil pH, P, K, andorganic matter content at multiple soil depths (0–15, 15–30, 30–45 and 45–60 cm). Samples were air-dried and ground to pass a 2-mm sieve. Soil pH, P, K, and organic matter content weredetermined by the agricultural analytical services laboratory(AASL; http://www.aasl.psu.edu; verified 27 June 2013). Pre-plantinorganic N (NO3-N and NH4-N) content in the top 60 cm soil was19–24 kg N ha�1 among different topopositions in these two years.Inorganic N content was converted from inorganic N concentrationusing a bulk density of 1.3 g cm�3 for Hagerstown soils (Zhu et al.,2009).

In both 2008 and 2009, maize grain yield was harvested with aS550 combine (John Deere Inc., USA) modified for small plots andfitted with a moisture sensor and weigh bucket. Yield was adjustedto a water content of 155 g kg�1.

A digital elevation model (DEM) of 3-m resolution was used todetermine terrain attributes for the hillslope using ArcGIS 9.2(ESRI, Redlands, CA, USA). These terrain attributes included slope(%), plan curvature (PLC), profile curvature (PRC), and topographic

wetness index (TWI). The PRC is the curvature in the downslopedirection along a line formed by the intersection of an imaginaryvertical plane with the ground surface. The PLC is the curvature ofthe topographic contours or the curvature of a line formed by theintersection of an imaginary horizontal plane with the groundsurface. The TWI was calculated following the equation below:

TWI ¼ lna

tanb

� �(1)

where a is the upslope contributing area per unit contour length,which can be calculated following the Dinf procedure proposed byTarboton (1997); b is the local slope gradient.

An electromagnetic induction (EMI) survey measuring soilapparent electrical conductivity (ECa) was conducted in January1997 for this area. The EM31 meter (Geonics Ltd., Mississauga, ON,Canada) operated in vertical (EM31V) and horizontal (EM31H)dipole orientations was used in this survey, which had effectivemeasurement depths as 6 and 3 m, respectively. This meter has ameasurement accuracy of �5% at 20 mS m�1. This was a pedestriansurvey with a grid interval of 30-m. The EM31 meter was placed inthe center of each grid and measurements were then recorded. Soilsalt content, clay content and mineralogy, soil water content, andsoil temperature influences the pathways through which theelectrical current moves in the soil and thus directly influence soilECa. Other properties, for example organic matter content, bulkdensity and depth to bedrock, may influence soil properties thatdirectly related to electrical current movement and thus indirectlyaffect soil ECa values. Depth-weighted soil ECa measurements canbe used to map different soil and hydrologic properties as reportedin previous studies (Robinson et al., 2009; Zhu et al., 2010).

Daily rainfall and temperature were recorded at a weatherstation located within 200 m of the field and data were retrievedfrom the USEPA Clean Air Status and Trends Network (www.epa.gov/castnet/; verified 28 April, 2013). The 114-year historical dailyrainfall was obtained for the State College, PA, weather station,which is located within 5 km of the field, and data were retrievedfrom the Pennsylvania State Joel N. Myers Weather Center (http://bub2.meteo.psu.edu/WXSTN/; verified 28 April, 2013). Growingseason (from 15 June to 31 August) precipitation was 167 mm andmean temperature was 21.7 �C in 2008. The growing season of2008 was the 15th driest and 35th warmest in the 114-year record.Growing season precipitation was 199 mm and mean temperaturewas 20.3 �C in 2009. The growing season of 2009 was the 83stdriest and 96th warmest in the 114-year record.

2.3. Data analysis

Along this hillslope, soils in some topopositions may beconsistently wetter or drier than in other areas. The temporalstability analysis proposed by Vachaud et al. (1985) was used tocompare mean soil water contents of different topopositionsrelevant to each other and to the hillslope’s mean.

SMj ¼1N

XNi¼1

SMij; (2)

di ¼1M

XMi¼1

SMij � SMj

SMj

� �; (3)

where SMj is the mean soil water content of the entire hillslope ondate j at a given soil depth; SMij is the soil water content at a givendepth in topoposition i on date j; N is the number of topopositionsconsidered; di is the mean relative difference of soil water contentat a given depth in topoposition i; and M is the number of dates of

0

2

4

6

8

10

12

14

16

0 50 100 150 20 0 250 300

Corn

yiel

d (M

g ha

-1)

N rate kg ha-1

Mini mum yield

Maximum yield

Delta yield

Op�mum N rat e

Fig. 2. An example of quadratic-plateau model fitted for response of maize yield toN rate (topoposition 2 in 2009). The parameters of minimum, maximum, and deltayields and optimum N rate were illustrated.

Table 1Means of soil apparent electrical conductivity (ECa) and terrain attributes for thedifferent topopositions in 2008 and 2009. EM31 V and EM31H: EM31 meteroperated in vertical and horizontal diplole orientations, respectively; PLC: plancurvature; PRC: profile curvature; TWI: topographic wetness index. For each factorin each year, numbers followed with the different letters were significantly differentat P < 0.05.

Topoposition Soil ECa Terrain attributes

EM31V EM31H PLC PRC Slope TWImS m�1 %

20081 Toe 11.1d 8.1c �0.011a 0.002a 2.3a 5.0b2.5 # 9.4c 7.9c 0.021a 0.003a 3.3a 3.3ab3.5 8.4b 7.0b �0.005a �0.003a 3.4a 4.9b5 Top 7.3a 6.3a 0.024a �0.038a 8.4b 2.1a

20091 Toe 10.9d 8.2d �0.005a 0.004a 2.5a 5.1c2 # 9.0c 7.4cd 0.013ab �0.001a 3.1a 4.0b3 8.0b 6.5ab 0.026ab �0.003a 3.6ab 3.1ab4 8.5bc 7.0bc �0.003a 0.000a 4.2ab 4.4bc5 Top 7.4a 5.9a 0.053b �0.024a 5.3b 2.3a

Q. Zhu et al. / Soil & Tillage Research 146 (2015) 174–183 177

soil water content data used in the analysis. Positive or negative disuggest wetter or drier sites, respectively, than the mean soil watercontent of the hillslope. Standard deviation of di (Si) depicts thedegree of soil water content temporal stability at a given soil depthin topoposition i. Higher Si indicates a more temporal dynamicchange. For each topoposition, di and Si at all depths (0.1, 0.3, 0.5,0.7, and 0.9-m depth) were averaged to represent its overallwetness and temporal variation of soil water content.

A quadratic-plateau response function was used to describe theyield response to N rate for each topoposition in each year.According to our previous study, the quadratic-plateau functionwas the most appropriate for this study area (Schmidt et al., 2007).The minimum, maximum, and delta yields and optimum N rate foreach topoposition were determined from the fitted model (Fig. 2).The delta yield was used to represent the degree of maize yieldresponse to N rate.

Means of terrain attributes and soil ECa values were calculatedfor each topoposition. Pearson’s correlation coefficients weredetermined using Minitab 16 (Minitab Inc., State College, PA, USA)to evaluate the relationships between different model parameters(minimum, maximum, and delta yields and optimum N rate) andsoil–water-topography factors. The statistical significance levelwas set at P < 0.10. In addition, pairwise t-test was also conductedin Minitab 16 as significance level was set at P < 0.05.

Fig. 3. Maps of (a) EM31V, (b) EM31H, (c) plan curvature, (d) profile curvature, (e) slope2009. The EM31V and EM31H indicate EM31 electromagnetic induction meter operate

3. Results

3.1. Spatial variations of soil ECa and terrain attributes

Along the hillslope, soil ECa values from the EM31V and EM31Hsurveys generally decreased from the lower slope to upper slopepositions. For example, in 2008, mean soil ECa values from theEM31H survey gradually decreased from 8.1 mS m�1 at the toeslope position to 6.3 mS m�1 at the top of the slope (Table 1 andFig. 3b). The only exception to this trend was observed in 2009,where the topoposition 4 near the upper slope position had greatersoil ECa values at topoposition 3 on a slightly lower slope position(Table 1).

Along the hillslope, the upper slope positions were steeper thanthe back slope and toe slope positions (Table 1 and Fig. 3e). Inaddition, the upper slope positions generally had convex or planarslopes perpendicular to the contours (PRC <0 or �0), while thelower slope positions had planar or concave slopes perpendicularto the contours (PRC �0 or >0) (Table 1 and Fig. 3d). Trends in TWIand PLC variations along this hillslope were not straightforward(Fig. 3 cf). Convex slopes parallel to the contours (PLC <0) were

(%) and (f) topographic wetness index (TWI) in different topopositions in 2008 andd in vertical and horizontal dipole orientations, respectively.

178 Q. Zhu et al. / Soil & Tillage Research 146 (2015) 174–183

observed at the lowest slope positions (topoposition 1) and somemiddle slope positions (topoposition 4) (Table 1). The greatestTWIs were observed at toe slope positions with gentle and concaveslopes perpendicular to the contours, while the lowest TWIscorresponded with the steepest slope at the top slope positions(Table 1).

3.2. Variations of soil water content

Decreasing trends in soil water content were observed frommid-June to the end of August in both 2008 and 2009, especially atthe 0.1-m depth (Fig. 4). For example, in 2008 and at the 0.1-mdepth, mean soil water content at the different topopositionsdecreased from >0.25 m3m�3 in mid-June to <0.20 m3m�3 at theend of August (Fig. 4a). Comparing with the surface soil watercontent (e.g., 0.1-m depth), this decreasing trend was weaker forthe subsurface soil water content. For example, in 2009 and at the0.5-m depth, mean soil water contents of different topopositionsonly decreased by 0.02–0.03 m3m�3 from late July to late August(Fig. 4b).

Significantly (P < 0.05) higher soil water contents at 0.9-mdepth were observed at the different topopositions in both years(Fig. 4). They were 0.30–0.40 m3m�3 from mid-June to late Augustin 2008 (Fig. 4a), and were 0.25–0.40 m3m�3 from late July to lateAugust in 2009 (Fig. 4b). In comparison, soil water contents were<0.35 m3m�3 at depths of 0.1 and 0.5 m in both years (Fig. 4).

In 2008, topoposition 5 (top slope position) had the mostnegative d values (P < 0.05) at all depths (Fig. 5a), indicating thattopoposition 5 was the driest from mid-June to late August.Topoposition 3.5 had positive d values, indicating that thistopoposition was wetter than the mean of the hillslope in 2008,while topopositions 1 and 2.5 had d � 0 (Fig. 5a). In 2009,topoposition 1 at the toe slope had the most negative d value(P < 0.05) at all depths, while topoposition 5 at the upper slopeposition had the most positive d values (P < 0.05) except at 0.9-mdepth (Fig. 5b). This indicated that these two topopositions werethe driest and wettest, respectively. In addition, topoposition 3 hadthe second lowest d value and topopositions 2 and 4 hadintermediate d values in 2009 (Fig. 5b).

In 2008, topoposition 2.5 had the lowest Si at all depths(P < 0.05) and thus its soil water content was the most temporallystable (Fig. 5a). In contrast, soil water content at topoposition 5 wasthe most temporally unstable (P < 0.05) in 2008 (Fig. 5a). In 2009,topoposition 4 had the greatest Si (P < 0.05) and thus its soil watercontent was the most temporally unstable, while soil watercontent at topoposition 2 was generally the most stable (Fig. 5b).Soil water content at topopositions 1, 3 and 5 had the intermediatetemporal stability in 2009 (Fig. 5b).

3.3. Parameters for yield – N rate models

Maximum yields at all topopositions in 2009 were greater andmore variable than those in 2008 (Table 2). In 2009, maximumyields at all topopositions were �12.0 Mg ha�1, while they were all�12.0 Mg ha�1 in 2008. The difference between the greatest andthe smallest maximum yields in 2009 was 1.5 Mg ha�1, while thatin 2008 was only 0.3 Mg ha�1. Similarly, the delta yields were alsosmaller and less variable along the hillslope in 2008 than in 2009(Table 2).

The minimum yields were also less variable among the differenttopopositions in 2008 than in 2009 (Table 2). However, trends ofminimum yield along the hillslope were inconsistent in 2008 and2009. For example, while the greatest minimum yield wasobserved in topoposition 5 in 2009, the lowest minimum yieldwas also observed in this topoposition in 2008 (Table 2).

The optimum N rates were generally lower, but more variable in2008 than in 2009 (Table 2). The mean optimum N rate in 2008 and2009 was 139 and 161 kg N ha�1, respectively. The differencebetween the lowest and the greatest optimum N rates in 2008 was52 kg N ha�1, while that in 2009 was only 22 kg N ha�1.

4. Discussion

4.1. Interaction of soil–water-topography dynamics

Soil ECa, slope and PRC mainly represented variations of depthto bedrock and soil texture along the hillslope in this study area.According to our earlier study, the increase in the EM31Vrepresented the increase in the depth to bedrock (R2 = 0.59), whilethe increase in the EM31H reflected the increase in soil profile siltcontent (R2 = 0.46) in this study area (Zhu et al., 2010). In addition,upper slope areas with steep and convex slope perpendicular to thecontours are associated with the shallow and clayey Opequon soils,while toe slope areas with gentle slope or flat areas are mainlyassociated with the deep and comparatively less clayey Hagers-town soils (Zhu et al., 2010).

Temporal variation in soil water content was influenced by thecrop growth and weather. The decreasing trend in surface soilwater content from mid-June to end August can be attributed tothe great evapotranspiration due to high temperatures and greatcrop water uptake in the early summer in central Pennsylvania(Fleeger, 1999; Zhu and Lin, 2011). In addition, the decreasing trendof subsoil water content was gentler than that in the surface soil, asthe surface soil was more exposed to the temporal variable plantuptake and atmospheric forces, including precipitation, tempera-ture and solar radiation (Hupet and Vanclooster, 2002; Xue et al.,2003; De Lannoy et al., 2006; Wang et al., 2013).

Influences of topography and soil properties on soil watercontent were not consistent in different years along this hillslope.In 2008, topoposition 5 was the driest among all topopositions(P < 0.05 in pairwise t-test), which corresponded with the upperhillslope and highest elevation, shallowest soil depth (lowest ECavalue in EM31V), steepest slope and lowest TWI (Table 1).However, in 2009, the wetness conditions represented by d valueswere not correlated with either the soil ECa values nor terrainattributes. In two soil water content studies conducted in an areaadjacent to this hillslope (Zhu and Lin, 2009, 2011), factorsincluding soil horizon distribution, rock fragment, organic mattercontent, crop uptake, and subsurface flow paths stronglyinfluenced the soil water content variation. These complexcontrolling mechanisms of soil water content resulted in thevaried spatial distribution of soil water content along this hillslopein different years. Similar findings have also been reported inprevious studies (e.g., Famiglietti et al., 1998; Western et al., 2004;Williams et al., 2009).

The temporal stability of soil water content (standard deviationof di–Si) at each topoposition can be used to interpret itshydrological activeness. Lin (2006) and Guber et al. (2008)reported that low temporal stability of soil water content (highSi) is associated with the influence of surface or subsurface flowpathways. In both 2008 and 2009, topopositions located at thelower slope positions generally had lower soil water contenttemporal variation than topopositions located at the upper slopepositions. Locations at the upper slope position were alsoassociated with shallower soil depth (Table 1). Water thataccumulates above the shallow soil-bedrock interface can triggerlateral subsurface flow and thus influence the soil water contenttemporal variation (Zhu and Lin, 2009). Therefore, topopositionswith greater Si were more hydrological active and possibly hadsurface or subsurface flow pathways nearby.

Fig. 4. Time series of mean soil water content at representing soil depths (0.1, 0.5 and 0.9 m) in different topopositions and daily precipitation during the periods of soil watercontent monitoring in (a) 2008 and (b) 2009. Refer to Fig. 1 for the hillslope topoposition arrangement.

Q. Zhu et al. / Soil & Tillage Research 146 (2015) 174–183 179

a) 2008

b) 200 9

-0.2 0

-0.1 5

-0.1 0

-0.0 5

0.00

0.05

0.10

0.15

0.20

1 2.5 3.5 5

Rela

�ve

diffe

renc

e of

soi

l wat

er

cont

ent

(δ)

Topopo si�on

0.00

0.01

0.02

0.03

0.04

0.05

0.06

1 2.5 3.5 5

Stan

dard

dev

ia�o

n of

δ

Topoposi �on

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

1 2 3 4 5

Rela

�ve

diffe

renc

e of

soi

l wat

er

cont

ent (

δ)

Topopo si�on

10-cm depth30-cm depth50-cm depth70-cm depth90-cm depth

0.00

0.01

0.02

0.03

0.04

0.05

0.06

1 2 3 4 5

Stan

dard

dev

ia�o

n of

δ

Topoposi �on

Fig. 5. Relative difference of soil water content (d) and standard deviation of d (S) in different topopositions in (a) 2008 and (b) 2009. Soil water content collected betweenmid-June and the end of August in 2008 and 2009 were used to calculate these parameters. Refer to Fig. 1 for the hillslope topoposition arrangement.

180 Q. Zhu et al. / Soil & Tillage Research 146 (2015) 174–183

4.2. Minimum yield and optimal N rate as influenced by soil–water-topography dynamics

Minimum yield in this study area was influenced by the spatialvariation of soil water content and its temporal stability (Table 3).Minimum yields corresponded with the control plots. Littlewithin-field variability of preplant inorganic N availability in thisstudy hillslope was observed in 2008 and 2009 (19–24 kg N ha�1).Therefore, minimum yield was not controlled by the within-fieldvariability of soil N availability. Instead, it was more controlled bythe more spatially varied factors, for example soil water content in2008 and 2009. Schmidt et al. (2007) and Jin et al. (2010) alsoobserved that under dry condition, maize yield was more affected

Table 2Parameter estimates in models of maize yield response to N rate for the different topop

Topo-position Model Param

Model type P > F Minim

20081 Toe

Quadratic-plateau

0.0115 7.3

2.5 # 0.0113 7.5

3.5 0.0123 7.2

5 Top 0.0241 6.1

20091 Toe

Quadratic-plateau

0.0322 4.8

2 # 0.0098 7.2

3 0.0274 6.2

4 0.0227 6.7

5 Top 0.0216 7.5

by soil water content. In a dry year 2008, the degree of temporalvariability in soil water content negatively influenced theminimum yield (Table 3). Areas with temporally unstable soilwater content have been associated with subsurface flow path-ways (Lin, 2006; Guber et al., 2008), which could flush out thenutrients in the soil and result in low grain yield (Zhu et al., 2009;Tomer and Liebman, 2014). The more frequent and stronger dryingand wetting cycles in soils could also reduce the soil N availabilityand thus decrease maize yield (Zhao et al., 2010).

Minimum yield was also positively influenced by PRC andnegatively influenced by slope in a dry year 2008 (Table 3). In thisstudy area, soils distributed in the convex (negative PRC) and steepslopes were Opequon soils shallow depth to bedrock. As reported

ositions in 2008 and 2009. Refer to Fig. 2 for the meanings of different parameters.

eter estimates

um yield Delta yield Maximum yield Optimum N rateMg ha�1 kg ha�1

4.4 11.8 1414.3 11.9 1094.8 12.0 1435.6 11.7 161

8.7 13.5 1565.7 12.8 1506.7 12.9 1726.2 12.9 1644.5 12.0 163

Table 3Pearson correlation coefficients between parameters in models of maize yield response to N rate (refer to Fig. 2) and environmental factors in 2008 and 2009. For topopositioni, relative difference of soil water content (di) and its temporal variation (Si) at 0.1, 0.3, 0.5, 0.7, and 0.9-m depths were averaged to represent the overall hydrological charactersof this topoposition. ECa: apparent electrical conductivity; PLC: plan curvature; PRC: profile curvature; TWI: topographic wetness index.

Parameters Soil ECa Terrain attributes Hydrological characters

EM31V EM31H PLC PRC Slope TWI di Si

2008Minimum yield NS NS NS 0.99** �0.96** NS 0.93* �0.98**

Delta yield NS NS NS �0.98** 0.95** NS NS 0.98**

Maximum yield NS NS NS NS NS NS NS NSOptimum N rate NS NS NS NS NS NS NS 0.91*

2009Minimum yield NS NS NS NS NS NS 0.97** NSDelta yield 0.88** 0.82* NS 0.82* �0.83* NS �0.97** NSMaximum yield 0.88** 0.88** NS 0.92** �0.92** 0.90* NS NSOptimum N rate NS NS NS NS NS NS NS 0.88**

NS: not significant at P < 0.1.* Significant at 0.05 � P < 0.1.** Significant at P < 0.05.

Q. Zhu et al. / Soil & Tillage Research 146 (2015) 174–183 181

in previous studies, convex and steep slope areas were alwaysassociated with low soil water and nutrient contents (e.g.,Famiglietti et al., 1998; Western et al., 2004; Williams et al.,2009). In the dry year of 2008, these areas would also probablyrestrict root development due to their shallow soil depth. Theoptimum N rate was positively influenced by the degree oftemporal variability in soil water content in both 2008 and 2009(Table 3). Soil N in areas with temporal unstable soil water contentis more likely to be lost in subsurface runoff, leaching, anddenitrification processes (Zhu et al., 2009; Tomer and Liebman,2014). Therefore, the optimum N rate was greater in those areaswith greater temporal variability in soil water content. Schmidtet al. (2011) observed a positive (P < 0.05) relationship betweeneconomically optimum N rate and soil water content change (Dup)during a wetting and drying cycle from mid-June to late July. Ourobservation was consistent with theirs. In the dry year 2008, thegreatest Dup for the top 1.1-m depth in a wetting cycle (17–25 July)was observed at topoposition 5, which corresponded with thegreatest optimum N rate of 161 kg ha�1. The smallest Dup in thiswetting cycle was observed at topoposition 2.5, correspondingwith the smallest optimum N rate of 109 kg ha�1.

4.3. Delta and maximum yields as influenced by soil–water-topography dynamics

Soil water content negatively influenced delta yield was in2009, while degree of temporal variability in soil water contentpositively influenced delta yield in 2008 (Table 3). The negativecorrelation between delta yield and soil water content in 2009 wasinconsistent with previous studies conducted under relative drierconditions (e.g., Kyveryga et al., 2009; Zhu et al., 2009). Soil watercontents for the different topopositions were >0.20 m3m�3 in the2009 growing season (Fig. 4), which was above a threshold ofmatric potential (�450 kPa) that has been considered “intermedi-ate stress” for plants (van der Ploeg et al., 2008). In addition, thetopopositions with greater soil water content in 2009 may havebeen associated with greater N losses through denitrification and/or leaching (Davidson et al., 2000; Sogbedji et al., 2000;Eickenscheidt et al., 2014) and thus the degree of maize yieldresponse to N rate was reduced. In 2008, upper slope topopositions(i.e., 3 and 4) with greater also had lower minimum yield than theother two topopositions, while the maximum yields were similaramong all topopositions (Table 2). Therefore, in 2008, greater deltayield was also observed at the upper slope positions with greaterdegree of temporal variation in soil water content.

Depth to bedrock (represented using soil ECa in EM31V) and soilprofile silt content (represented using soil ECa in EM31H)

positively influenced delta yield and maximum yield in 2009(Table 3). Soils in this study area with higher silt content had lessclay content (e.g., Hagerstown soils) than soils with lower siltcontent (e.g., Opequon soils; Zhu et al., 2010). This indicated thatareas with deeper depth to bedrock and lower clay contentHagerstown soils had greater maximum yield and greater yieldresponse to N rate (delta yield) than in other areas. Shahandeh et al.(2011) also reported that clay content was negatively correlatedwith maize yield and greater yield response to N rate was observedat the toe slope position with deeper soils in Texas, USA. Ziadi et al.(2013) also observed that clay soils had the lowest grain yield,while clay loam and fine sandy loam soils had the intermediate andhighest grain yields, respectively, in Quebec, Canada. However,maximum yield and delta yield in 2008 were not correlated withsoil ECa values and thus depth to bedrock and silt content (Table 3).The less varied maximum yields and delta yields in 2008 (Table 2)probably resulted in these non-significant correlations.

Greater maize yield and stronger response of maize yield to Nrate occurred in vertically concave (positive PRC) and gentle slopeareas in 2009 (Table 3). Strong influences of slope and PRC(negative and positive, respectively) on maize yield have beenreported in previous studies and these influences has beenattributed to soil water redistribution and nutrient availability(e.g., Kravchenko and Bullock, 2000; Bakhsh et al., 2007; Muñozet al., 2014). Areas with concave and gentle slopes are generallyassociated with deep soils at the lower slope positions in our studyarea (Table 1). During the wetter growing season of 2009, lowerslope positions would receive lateral recharge from the upperslope area, store more water for crop uptake, and thus havestronger response of maize yield to N rate. On contrary, areas withconvex and steep slope are generally associated with shallowersoils at the upper slope positions (Table 1). Shallow subsurfacerunoff at the soil-bedrock interface can be observed in these areasunder wet conditions (Zhu and Lin, 2009). Therefore, N in theseareas can be lost by lateral subsurface flow and denitrification (Zhuet al., 2009; Tomer and Liebman, 2014). In addition, the shallowsoils with high rock fragment and clay contents could have beenthe limiting factors of maize production in 2009.

However, in a dry year of 2008, delta yield was negativelyinfluenced by PRC and positively influenced by slope (Table 3). Asdiscussed earlier, minimum yield was positively correlated withPRC and negatively correlated with slope in 2008. Maximum maizeyield had little spatial variability along the entire hillslope in 2008(Table 2), probably due to that maximum yield in dry year wasmore restricted by the low soil available water than spatialvariations of soil and topography (Kyveryga et al., 2009; Shahandehet al., 2011). Therefore, delta yield, which was the difference

182 Q. Zhu et al. / Soil & Tillage Research 146 (2015) 174–183

between maximum yield and minimum yield, was lower intopopositions with higher minimum yield and more concave andsteeper slope in 2008.

5. Conclusions

This study verified the hypothesis that the spatial variability inmaize yield and its response to N rate were influenced bysoil–water-topography dynamic, along a typical agriculturalhillslope in the Northern Appalachian Ridge and Valley Physio-graphic Province in the USA. Results showed that maize yieldresponse to N rate was influenced by silt content, soil depth, profilecurvature, slope percent, soil wetness and soil water contenttemporal stability. Regardless dry or wet, soil water contenttemporal variability, which might be attributable to subsurfaceflow, can be important in determining the optimum N rateregardless of the growing season weathers. However, in a drieryear, greater yield response to N rate was observed in upper convexand steep slope areas, while in a wetter year, it was observed inlower concave slope areas with deeper soil depth and thus greaterwater and nutrient storage. Therefore, within field variability ofsoil, topography and hydrology with the yearly variation ofweather should be properly incorporated in the site-specific Napplications.

Acknowledgements

The data collection of this study was supported by the USDA-ARS Pasture Systems and Watershed Management Research Unit.The preparation of this paper was supported by theNationalNatural Science Foundation of China (41271109), Jiangsu NaturalScience Foundation (Grant no. BK2012502) and Key “135” Projectof Nanjing Institute of Geography and Limnology, ChineseAcademy of Sciences (NIGLAS2012135005).

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