assessing and managing soil quality for urban agriculture ... · assessing and managing soil...

land degradation & developmentLand Degrad. Develop. (2015)

Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ldr.2342

ASSESSING ANDMANAGING SOIL QUALITY FOR URBAN AGRICULTURE IN ADEGRADED VACANT LOT SOIL

Joshua W. Beniston1*,†, Rattan Lal1, Kristin L. Mercer2

1Carbon Management and Sequestration Center, School of Environment and Natural Resources, The Ohio State University, Columbus, OH 43210, USA2Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH 43210, USA

Received 10 May 2014; Revised 21 October 2014; Accepted 2 November 2014

ABSTRACT

Following the decline of industrial manufacturing, many US cities have experienced severe population reductions that have resulted in largeareas of vacant land. Urban agriculture has emerged as a desirable land use for these spaces, but degraded soils are common. Therefore, wemeasured soil and plant responses to amendments and management in urban lots where vacant houses had recently been demolished inYoungstown, OH, USA. Soil degradation was observed following demolition activities in the form of compaction (bulk density of1·5–1·8Mgm�3) and low soil microbial biomass C (21mgCkg�1 soil). Our split-plot experiment measured the effects of organic matter(OM) amendments produced from yard wastes and the use of raised beds on soil properties and vegetable crop yields. Two years after theirapplication, OM amendments resulted in significant improvement to a number of soil physical, chemical, and biological properties. Vegetablecrop yields were improved by OM amendments in 2011 and by both OM amendments and the use of raised beds in 2012. A soil quality in-dex, developed using factor analysis and the Soil Management Assessment Framework, produced values ranging from 0·60 to 0·85, which arecomparable to those reported for rural agricultural soils. All results indicate that urban agriculture can be productive in vacant urban land andthat amendments produced from urban yard wastes can improve soil quality at previously degraded sites and increase crop yields for urbanagriculture. Copyright © 2014 John Wiley & Sons, Ltd.

key words: urban agriculture; soil quality; urban soil; compost; Soil Management Assessment Framework; vacant land; shrinking cities; soil compaction

INTRODUCTION

More than 50% of the world’s people live in cities, and pop-ulations are expected to become increasingly urban duringthe coming decades (UNDP, 2011). Contrary to this globaldemographic trend, populations in many cities in the NorthCentral USA have declined severely over the past 50 yearsprimarily because of large reductions in industrialmanufacturing in the region (Dewar & Thomas, 2013). Theseshrinking populations have resulted in an abundance of va-cant land and properties. By 2010, the cities of Youngstownand Cleveland, Ohio, and Detroit, Michigan, all had lostaround 60% of their peak (mid-20th century) populationsand now contain around 2,800, 1,500, and 6,500 ha of vacantland, respectively (reviewed in Beniston & Lal, 2012).Vacant urban properties have been linked with increasedcrime and decreased safety, while “greening” of urban lotshas positively impacted surrounding property values and re-duced crime (Branas et al., 2011). Thus, many urban com-munities are attempting to repurpose vacant land asfunctional greenspace with the goal of achieving social andecological benefits (CUDC, 2008; Ozguner et al., 2012).

*Correspondence to: J. W. Beniston, Carbon Management and SequestrationCenter, School of Environment andNatural Resources, TheOhio State Univer-sity, Columbus, OH 43210, USA.E-mail: [email protected]†Current address: US Department of Agriculture, Agricultural ResearchService, Jornada Experimental Range, New Mexico State University, LasCruces, NM.

Copyright © 2014 John Wiley & Sons, Ltd.

Simultaneously, the economic recession and the promo-tion of locally produced foods have renewed interest in gar-dening in the USA (Schupp & Sharp, 2012), andparticipation in urban agriculture has increased (Blaineet al., 2010). Sparse data on crop yields in urban areas dem-onstrate that urban agriculture can produce robust yields ofvegetable and fruit crops (Beniston & Lal, 2012). While va-cant urban land often occurs in areas without regular accessto fresh food (CUDC, 2008), urban agriculture has beenlinked to increased access and consumption of fruits andvegetables (Alaimo et al., 2008; Zezza & Tasciotti, 2010).Estimates also suggest that if a large percentage of existingvacant land were utilized for urban agriculture, cities such asDetroit (Colasanti & Hamm, 2010) and Cleveland (Grewal&Grewal, 2012) could produce a large portion of the specialtycrops that their populations consume. Agricultural producersin urban areas, however, face a unique set of ecological limi-tations to crop production, and further research is needed toimprove agronomic management and productivity for urbanagriculture (Eriksen-Hamel & Danso, 2010).Urban soils, in particular, may pose a significant chal-

lenge for crop production. These soils are often highly mod-ified by disturbance and occur on a continuum from soilsthat reflect the native, regional soils to highly altered anthro-pogenic soils (Lehmann & Stahr, 2007; Pouyat et al., 2010).Severe soil degradation is common in urban areas and canmake horticultural activities difficult (De Kimpe & Morel,2000; Lehmann & Stahr, 2007). Construction activities andthe demolition of vacant structures can lead to physical soil

J. W. BENISTON ET AL.

degradation, compaction, and reduced hydrologic function(Gregory et al., 2006; USEPA, 2011). Soil contaminationby heavy metals is also common in both residential and for-mer industrial sites in urban areas, so testing sites formetals before planting crops is key to minimizing risk(Minca et al., 2013; Roy & McDonald, 2013). Meanwhile,huge quantities of organic waste products are available incities for potential processing as amendments to improvedegraded soils (Brown et al., 2012). Application of largequantities of compost has consistently improved soil phys-ical properties in urban soils under ornamental landscapesand trees, suggesting potential benefits for urban agricul-ture (Cogger, 2005).Soil quality evaluation seeks to characterize the overall

ecological function of soils by selecting soil properties as in-dicators, measuring those properties, and calculating a scoreor quantitative index for both the individual properties andthe whole soil (Doran & Parkin, 1994; Andrews et al.,2004). Multivariate statistical tests, such as factor analysis,have been used to select a subset of soil properties that ex-plain a large proportion of the variation in a larger datasetas soil quality indicators (Shukla et al., 2006). For calculat-ing an index, the Soil Management Assessment Framework(SMAF) provides researchers with scoring functions thatcalculate a score between 0·0 and 1·0 for the measuredvalues of key soil properties (Andrews et al., 2004). Thesescores can then be averaged for a soil quality index (SQI).Together, these methods provide a process for evaluatingthe ecological function of urban agriculture soils and foridentifying individual soil properties that are important tothe soil’s overall condition.The study reported here aimed to assess soil quality for

vegetable crop production in an urban lot where vacantbuildings had recently been demolished. The specific objec-tives included as follows: (i) assess soil degradation in an ur-ban soil following the demolition of vacant houses; (ii)investigate the ability of amendments produced from or-ganic waste materials to improve soil quality and supportvegetable crop production with and without raised beds ina recently disturbed vacant lot soil; and (iii) determine whichsoil properties served as indicators of crop growth underthese conditions. Urban agriculture in vacant lot soils is arapidly expanding horticultural activity, and this study pro-vided a unique opportunity to evaluate soil agronomic prop-erties, soil management for crop production, and the use of aquantitative evaluation of soil quality at an urban agriculturesite, following a demolition.

Table I. Nutrient composition of compost amendment and key propertie

Nutrient concentrations in compost:

C (g kg�1) N (g kg�1) P (mg k240 17·5 290

Key properties of biochar:a

Feedstock material Production temperature (°C) C content (Oak 400 790

aA more detailed description of the biochar is given in Hottle (2013).


MATERIALS AND METHODS

Site Description and Experimental Design

The experimental site was located on two contiguous urbanlots in the city of Youngstown, Ohio (at approximately41°04′49″N, 80°40′35″W). Two vacant houses weredemolished on the site during winter 2010/2011, and the ma-jority of the resulting material and debris were removed fromthe site. The site was then graded with heavy machinery toachieve a consistently level surface, using only soil from thesite.The field experiment was performed with a split-plot de-

sign that tested soil and plant responses to amendmentsand management practices. The treatments were establishedin the spring 2011. The main plot treatments were combina-tions of organic amendments: (i) unamended control (CNT);(ii) 15-kgm�2 compost (equivalent to 150Mgha�1; CMP);(iii) 15-kgm�2 compost and 2-kgm�2 biochar (equivalentto 20Mgha�1) (CMP + B); and (iv) 15-kgm�2 compost andmanaged under intensive cover cropping (CMP + ICC). Thecompost was produced exclusively from leaves and grassclippings, and a 10-cm layer was applied. Nutrient composi-tion of the compost and key characteristics of the biochar areprovided in Table I. A randomized split-plot treatment wasapplied to all main plots such that subplots had either as fol-lows: (i) plants grown directly in the ground or (ii) 20-cmraised beds with wooden sides that were filled with an addi-tional 10 cm of soil from the site using hand tools. Each mainplot was approximately 1·52 × 6·10m, with the long sidefollowing a north/south axis. The main plots were arrangedin a randomized complete block design with six replicationsfor a total of 24 plots.

Management Practices and Crop Measurements

All plots were rototilled in June 2011, and amendments wereincorporated to a depth of 10 cm. Supplemental irrigationwas provided uniformly to all plots via drip irrigation, anda fence was erected around the site. Weed control was byhand or with hand tools.Tomato (Solanum lycopersicum var. “Bellstar”) and

Swiss chard (Beta vulgaris subsp. cicla var. “Bright Lights”)crops were planted as transplants in the CNT, CMP, andCMP + B plots following tillage in June 2011. Tomatoeswere planted in two rows with 0·75-m spacing. Swiss chardwas planted in four rows with 0·35-m spacing. Swiss chardwas harvested in early August and tomatoes in earlySeptember 2011. All plots were broadcast seeded to an

s of biochar amendment applied in the experiment

g�1) K (mg kg�1) Ca (mg kg�1) S (mg kg�1)5,750 6,090 270

g kg�1) pH9·5

LAND DEGRADATION & DEVELOPMENT, (2015)

ASSESSING AND MANAGING SOIL QUALITY FOR URBAN AGRICULTURE

annual ryegrass (Lolium multiflorum var. “Bruiser”) inOctober 2011, at a rate equivalent to 45 kg ha�1. The rye-grass was mowed in early May 2012 and incorporated byrototilling. In 2012, tomatoes (the same variety as previouslymentioned) were planted in mid May and harvested inearly September. Sweet potatoes (Ipomoea batatas var.“Beauregard”) were planted in early June 2012 and har-vested in early October. Sweet potatoes were planted intwo rows 0·75m apart and spaced 0·35m between plants.In 2012, crops were assigned to main plots such that toma-toes were only grown in plots that did not have tomatoesduring 2011, and sweet potatoes were planted in the plotsthat did have tomatoes in 2011. During both growingseasons, each crop type had three complete blocks that wereinterspersed together.The CMP + ICC plots were planted with a succession of

cover crops during the first year of the experiment and thusdid not have tomatoes or chard. In June 2011, the CMP +ICC plots were planted to sorghum–sudangrass (Sorghumbicolor X S. bicolor var. sudanese var. “BMR”). Thesorghum–sudangrass was seeded at a rate equivalent to27 kg ha�1 and was cut with a gas-powered hedge trimmerto 5-cm height twice during the growing season. All of theclipped biomass was then placed on the plot. In late August2011, tillage radish (Raphanus sativus var. “Tillage”) wasseeded in between the rows of sorghum–sudangrass. Thesorghum–sudangrass was killed by autumn frosts, and theradishes grew to maturity in December 2011. Annual rye-grass was planted in October 2011 by broadcasting overthe radishes. Aboveground biomass samples of thesorghum–sudangrass were taken at the time of each cuttingand in October 2011 dried for 48 h at 60°C and the dry massrecorded.

Crop Yield Measurements

At harvest, vegetable crops were sorted into damaged andunripe crops or market quality and measured separately.All crop yields reported herein reflect the yield of marketquality produce.

Soil Sampling

Baseline soil samples were collected in late May 2011, afterconstruction of the raised beds but prior to the application ofthe amendments and planting. A single intact core samplewas collected from each subplot at 0–10-cm depth. Bulk soilsamples were collected with hand trowels by taking fivesamples per subplot (0–10 cm) and mixing them. Soil aggre-gate samples were separated from field moist soil by passingthe soil through an 8-mm sieve and capturing the aggregatesretained on a 4·75-mm sieve. All subplots were sampledagain in September 2012 at the conclusion of the experi-ment, including bulk soil, aggregates, and intact cores. Intactcores were collected from 0–10 and 10–20 cm at the finalsampling date. An additional set of samples was collectedfrom all plots for microbial biomass C (MBC) analysis dur-ing May 2012. The same protocols were utilized during allexperimental sampling dates.


Soil Physical Analyses

Bulk density was measured with intact core samples. Gravi-metric water content was determined by drying a subsamplefrom the cores at 105°C for 48 h. Soil aggregate stability wasmeasured with a wet sieving process (Nimmo & Perkins,2002) using a laboratory apparatus first described by Yoder(1936). The resulting data were then used to calculate thepercent of soil in water-stable macroaggregates (%WSA;>0·25mm) and aggregate mean weight diameter (MWD).Available water capacity (AWC) of the soil was estimatedby measuring water retention at matric potentials of the fieldcapacity (�33 kPa) and permanent wilting point(�1,500 kPa) using a ceramic pressure plate apparatus (SoilMoisture Equipment Corp., Santa Barbara, CA, USA; Dane& Hopmans, 2002). The volumetric water content at satura-tion was measured as an estimate of total porosity. Intactcore samples were used to measure the water retention at sat-uration and field capacity, while sieved soil (<2·0mm) wasused for the permanent wilting point measurement.

Soil Chemical Analyses

Soil pH was measured with a 1:1 soil to water solution.Cation exchange capacity (CEC) was measured using1-M-ammonium acetate extraction. The total concentrationsof lead (Pb), arsenic (As), and cadmium (Cd) were analyzedusing US Environmental Protection Agency (EPA) Method3150a, a microwave-assisted digestion of soil in a solutionof HCl and HNO3 followed by inductively coupled plasmaemission spectrometry. Plant available, or extractable (Ext.),nutrients were estimated using a Mehlich-3 process (Mehlich,1984) with inductively coupled plasma analysis.

Soil Biological Analyses

Microbial biomass C was determined in sieved (6·75mm)field moist soil (Vance et al., 1987). Soil samples (10 g)were fumigated with chloroform (50ml) for 24h then ex-tracted with 80ml of 0·5M K2(SO4) and filtered throughWhatman no. 42 filter paper. Unfumigated control sampleswere extracted using the same process. Carbon content of thesamples was then analyzed in a Shimadzu TOC-V Analyzer(Columbia, MD, USA), using the nonpurgeable organic Cmethod. Total C and N were measured by the dry combustion(900°C) method (Vario Max, Elementar Analysensysteme,Hanau, Germany). Soil C pools (0–10 cm) were calculatedusing an equivalent mass method, to account for differencesin bulk density among treatments (Ellert & Bettany, 1995).Bulk density measurementsmade on the 10–20-cm depth wereutilized in the determination of equivalent soil masses.

Data Analysis

Treatment effects were tested on all soil and crop data byanalysis of variance (ANOVA) in PROC MIXED in SASv9·2 statistical software. The model tested main plot organicmatter (OM) amendments, subplot raised beds, and the OMamendments by raised bed interaction as fixed effects, whileblock and block by OM amendments were treated as randomeffects. Because of the split-plot design, the error as a result



of the interaction between block and OM amendments wasused as the denominator in the F-test of the effect of amend-ments. Tukey’s honest significant difference was used toperform mean separations. No transformations were re-quired to improve the normality of the residuals.

Soil Quality Index

An SQI was developed from soil data from the September2012 samples using a process adapted from Andrews andCarroll (2001). The first step in reducing the dataset was toinclude only soil properties where a treatment effect was de-tected (p< 0·05), for either OM amendments or raised beds.An exploratory principal components analysis (PCA) wasthen conducted on the soil properties using the princompfunction in R version 3·0·1 software (R Core DevelopmentTeam, 2013), with a correlation matrix input. PCA was usedto determine the number of principal components needed toexplain >85% of the cumulative variation in the dataset;here, this required five principal components. A factoranalysis was then conducted on the dataset using thefactanal function in R, with a quartimax rotation from theGPARotation package (Bernaards & Jennrich, 2005). Thefactor analysis was conducted on five factors, in an attemptto approximate the cumulative variation explained in thePCA, with the goal of exploring the contribution of individ-ual soil properties to total variation in the dataset. Soil prop-erties with factor loading scores >0·70 were considered forinclusion in the SQI minimum dataset. At this point, soilproperties were included in the SQI if they met two criteria:(i) They had a loading value within 0·1 absolute value of thehighest loading under individual factors in the factor analy-sis and (ii) they had an existing scoring curve in the SMAFspreadsheet (refer to the succeeding texts).Scores were calculated for the individual soil properties in

the SQI using SMAF (Andrews et al., 2004). SMAF is botha framework for developing SQIs and a spreadsheet thatcontains scoring curves for approximately 12 soil properties.The scoring curves provide a score between 0·0 and 1·0 formeasured soil property values based on previously describedrelationships to key soil functions such as crop production,hydrologic cycling, and environmental buffering.Scores were generated for four key soil properties following

the reduction in the dataset via factor analysis. Scores for theindividual soil properties were weighted equally in the calcu-lation of the overall SQI values, which represent the averageof the individual property scores. Scoring curves were ad-justed the same for all samples according to site-specific fac-tors related to soil taxonomy, agricultural management, andclimate. Scores for both individual soil properties and overallsoil quality were calculated for each observation and then an-alyzed using the ANOVA model described previously.

RESULTS AND DISCUSSION

Baseline Soil Properties

Soil at the site had bulk density values of 1·79Mgm�3 in thein-ground plots and 1·55Mgm�3 in the raised beds


(Table II), demonstrating a high level of soil compaction.The raised beds were filled with soil from the site, and thehigh bulk density values measured in them may have beenbecause of the rapid settling of this already compacted soilwith poor structure. Compaction resulting from heavy ma-chine traffic and grading leads to reduced levels of water in-filtration and inhibits many soil-mediated ecosystemservices (Lal & Shukla, 2004; Gregory et al., 2006). Ourbaseline soil analysis suggested that compaction was a prin-cipal soil-based constraint to crop growth at the site, as bulkdensity values in this range may restrict the growth of roots(USDA NRCS, 2000).Soil at the site had a slightly alkaline pH (Table II), which

may be because of mixing of alkaline subsoil layers into thesurface from the site grading, as well as the weathering ofbuilding materials rich in calcium carbonate (Howard &Olszewska, 2011). Baseline soil lead (Pb) concentrations oc-curred at a mean of 95mgkg�1 (Table II). Soil testing for Pbis widely recommended for urban agriculture (Minca et al.,2013), as Pb represents a significant public health risk(Fillipelli & Laidlaw, 2010). The Pb concentration observedat the site is significantly lower than the US EPA’s screeninglevel of 400mgkg�1 and thus was not considered a signifi-cant risk to agriculture at the site.Mean total C concentration was 12·8 g kg�1 soil, while

MBC concentration averaged 20·8mgkg�1 soil. These areboth low levels and are largely because of the removal ormixing/burial of existing topsoil during the demolition activ-ities at the site. Low soil C and MBC levels have been doc-umented previously following construction activities inurban areas (Scharenbroch et al., 2005).

Soil Physical Properties Following 2Years of Management

The application of the main plot OM amendments drove sig-nificant changes in all physical properties except AWC inthe soil surface (0–10 cm; Table III). Many of the measuredproperties demonstrated a trend where all of the OM amend-ments performed better than the control, but there was nofurther statistical separation among the treatments. The ex-ception was aggregate MWD, where the highest values(1·43mm) were observed in the CMP + ICC plots.Sorghum–sudangrass produces a dense root system thatlikely increased macroaggregate formation through mechan-ical forcing by growing roots, through deposition of particu-late OM, and by supporting rhizosphere microbialcommunities that are important to aggregate formation(Jastrow et al., 1998). Bulk density at the 10–20-cm depthwas not affected by OM amendments (Table III), suggestingthat physical improvements from adding OM amendmentsto a highly compacted soil are limited to the soil surface,over the short timescale (2 years) reflected in this study. Im-proving soil structure and mitigating compaction below thesurface layer are major challenges for degraded sites suchas this one and are a topic that would benefit from furtherexperimentation.Raised beds did not impact bulk density or MWD, while

in-ground plots had higher %WSA, AWC, and total


Table II. Summary statistics of baseline soil properties from soil samples (n= 48) from an agriculture experiment in a vacant urban lot soilcollected in June 2011 directly after the demolition of vacant houses and regrading of the site

Soil property Mean Standard error of the mean Range Coefficient of variation

Physical properties% clay 16·8 0·17 13·4–19·6 0·07% sand 36·8 0·42 32·3–49·5 0·08Bulk density 0–10-cm depth (g cm�3)In ground (n= 24)a 1·79 0·03 1·53–1·99 0·07Raised beds (n= 24) 1·55 0·02 1·37–1·71 0·06

Available water capacity (AWC) 0–10-cm depth (cm)In ground 1·1 0·14 0·5–2·6 0·64Raised beds 1·0 0·10 0·6–1·8 0·50

Chemical propertiesTotal N (g kg�1 soil) 0·8 0·02 0·5–1·3 0·19pH 7·5 0·03 6·4–7·9 0·03CEC (cmolc kg

�1) 10·4 0·35 7·4–22·5 0·23Mehlich III nutrients (mg kg�1)P 22·6 1·30 11·9–50·8 0·40K 47·0 2·41 33·9–126·2 0·36Ca 1,810 107·24 1,120–5,070 0·41Mg 138 3·87 100–240 0·19S 158 17·75 64·7–700 0·78Fe 97·6 3·05 60·5–156 0·22Zn 18·8 1·91 8·3–88·6 0·70

Total trace elements (mg kg�1)Pb 95·0 5·05 49·2–172 0·37As 11·8 0·22 7·7–15·3 0·13Cd 0·5 0·03 0·3–1·2 2·5

Biological propertiesTotal C (g kg�1 soil) 12·8 0·05 8·2–24·3 0·03Microbial biomass C (mgCkg�1 soil) 20·8 3·45 0·9–109 1·15

aValues reported for bulk density and AWC for both in ground and raised bed subplots because soil physical properties were markedly different between thosetwo treatments.


porosity. Additionally, we observed an OM by raised bed in-teraction effect with AWC that was a result of higher AWCvalues in amended in-ground plots. The likely reason for theseobservations is that the initial bulk density in the in-groundplots (Table II) was so high that incorporation of amendments,even with heavy tillage, was difficult. So, a large proportion ofthe OM amendments remained higher in the profile, whichgave in-ground plots higher overall concentration of OMand resulted in observations of higher values across numeroussoil physical, chemical, and biological properties.

Soil Chemical Properties Following 2Years of Management

Organic matter amendments resulted in four times higher to-tal soil N and two to five times higher Ext. concentrations ofall nutrients (Table III). These data support the propositionthat applying amendments produced from urban green wasteproducts can increase plant available nutrient pools. OMamendments lowered the soil pH significantly from 7·9 inCNT plots to 7·6–7·7 in amended plots. In general, total soilN and Ext. nutrient concentrations were higher in the in-ground plots than in the raised beds, again likely driven bybetter mixing of amendments in the raised beds.

Soil Biological Properties Following 2Years of Management

Microbial biomass C was 20 times greater in May 2012 andfive to eight times greater in September 2012 in amended


versus control plots, likely because of the large C substrateavailable for decomposition, as well as increases in soil ag-gregation and water retention (Table III; Wardle, 1992).The very low levels of MBC observed in the control plotssuggest that the demolition disturbance and resulting com-paction greatly impair soil microbial activity.Organic matter amendments resulted in five times larger

concentrations of total C in the soil surface layer, raising soilC concentrations to approximately 6% by mass (Table III).Plots receiving OM amendments had soil C pools four tofive times larger than unamended CNT plots (Figure 1).Data for both C concentrations and C pools suggest thatthe CMP + B and CMP + ICC plots did not contain moreC than the CMP plots, despite receiving significant addi-tional C inputs. Despite higher concentrations of total C inthe in-ground plots (Table III), the equivalent mass methodindicated that the total C pools in the raised beds contained40% more soil C (Figure 1), suggesting that the improvedincorporation of the amendments may have facilitated in-creased preservation of C. After two growing seasons, com-parison of C pools in amended plots versus CNT plotssuggests that around 80% of that initial mass of C appliedvia compost remains in CMP plots, although it is expectedthat the compost C will continue to decompose for severalyears before reaching an equilibrium level (Brown et al.,2012). Thus, applying amendments produced from yard


Table

III.

Measuredsoilpropertiesfrom

anagricultu

reexperimentin

avacant

urbanlotsoilfrom

finalsamplingin

September2012

Organic

matteram

endm

entsa

Raisedbeds

OM

×raised

bed

Soilproperty

F-value

bCNT

CMP

CMP+B

CMP+ICC

F-value

cIn

ground

Raisedbed

F-value

d

Physicalproperties

Bulkdensity

0–10

cm(M

gm

�3)

23·6

***

1·46

af1·05

b0·98

b0·98

b0·67

ns1·10

1·14

0·73

nsBulkdensity

10–20cm

(Mgm

�3)

0·04

ns1·51

1·50

1·49

1·52

2·4ns

1·46

1·55

1·99

ns%WSA

7·0**

0·30

a0·49

b0·48

b0·49

b10·7**

0·49

a0·40

b0·18

nsMWD

(mm)

4·4*

0·60

a1·11

ab0·93

ab1·43

b0·3ns

1·05

0·99

0·37

nsAWC0–

10cm

(cm)

2·7ns

0·9

1·4

1·7

1·4

19·8**

1·6a

1·0b

3·98*

Total

porosity

(m3m

�3)

14·3***

0·37

a0·53

b0·55

b0·53

b14·0**

0·54

a0·45

b3·87*

Biologicalproperties

Total

C(gCkg

�1soil)

25·6***

12·1

a61·9

b59·7

b57·7

b16·7**

55·9

a39·8

b1·60

nsMBC(m

gCkg

�1soil)

May

2012

10·0**

7·8a

223b

229b

265b

5·8*

213a

150b

1·63

nsMBC(m

gCkg

�1soil)

September2012

11·4***

53·6

a266b

195b

284b

6·84*

233a

166b

0·56

nsChemical

prop

erties

pH19·6***

7·87

a7·63

b7·69

b7·61

b7·0*

7·72

a7·68

b0·46

nsTotal

N(gNkg

soil�

1)

28·4***

0·9a

4·7b

4·2b

4·4b

15·4**

4·1a

3·0b

1·54

nsExtractable

elem

ents(m

gkg

�1)

P85·3***

24·6

a95·6

b96·9

b100·8b

48·1***

91·2

a68·8

b1·73

nsK

46·2***

55a

211b

226b

216b

11·5**

157a

197b

1·50

nsCa

38·6***

2244

a4035

b4157

b4094

b32·0***

3983

a3282

b0·99

nsMg

33·0***

182a

558b

553b

610b

13·4**

514a

437b

3·0ns

S31

·0***

84·9

a153b

160b

156b

25·9***

151a

126b

0·45

nsAl

46·9***

455a

293b

311b

282b

13·8**

313a

357b

2·41

nsFe

13·5***

120a

147b

154b

154b

19·2**

151a

137b

0·48

nsZn

1·0ns

16·3

19·9

28·8

19·6

8·3**

28·8

a13·5

b0·75

ns

a Mainplot

organicmatteram

endm

enttreatm

entsincludeunam

endedcontrols(CNT),am

endedwith

compost(CMP),amendedwith

compost+biochar(CMP+B),andcompost+intensivecovercropping

(CMP+

ICC).Valuesaremeans

from

ANOVA.

bF-testof

organicmatteram

endm

entshaddegreesof

freedom

of3in

thenumerator

and15

inthedenominator.

c F-testof

raised

beds

haddegreesof

freedom

of1in

thenumerator

and20

inthedenominator.

dF-testof

OM

andraised

bedinteractionhaddegreesof

freedom

of3in

thenumerator

and20

inthedenominator.

e *p<0·05,*

*p<0·01,*

**p<0·0001,and

ns=notsignificant.

f Low

ercase

letters

indicate

meangroupingsaccordingto

Tukey’s

honestsignificant

difference.


Copyright © 2014 John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, (2015)

Figure 1. Soil C pools calculated on equivalent soil mass at an agriculturalexperiment in a vacant urban lot soil. Least squared means are presented formain plot treatments of unamended controls (CNT), amended with compost(CMP), amended with compost + biochar (CMP + B), and compost + inten-sive cover cropping (CMP + ICC), as well as subplots where crops weregrown either in ground (GR) or in raised beds (RB). Error bars are standarderrors, and lower case letters indicate mean groupings according to Tukey’shonest significant difference test. This figure is available in colour online at

wileyonlinelibrary.com/journal/ldr.


wastes is an effective practice for rapidly increasing soil Cpools at degraded urban sites.

Crop Yields

Organic-matter-amended plots produced greater tomatoyields than CNT plots in 2011, while surprisingly, raisedbeds did not (Figure 2). A portion of the Swiss chard plotswere damaged by herbivory from deer (Odocoileusvirginianus) during 2011, which prevented ANOVA testing

Figure 2. Measured 2011 and 2012 yields for vegetable crops grown in an agricultfor main plot treatments of unamended controls (CNT), amended with compost (Ccover cropping (CMP + ICC), as well as subplots where crops were grown eitherlower case letters indicate mean groupings according to Tukey’s honest significancause of damage by herbivory. Replication for crops was the following: 2011 toma

potatoes (n = 3). This figure is available in colour


(Figure 2). Mean values from harvested plots, however, in-dicated similar trends as the tomato crop. Sorghum–sudangrass produced the equivalent of 10·5 and 9·4Mgha�1

of dry matter in the in ground and raised bed CMP + ICCplots in summer 2011, demonstrating an excellent abilityto produce biomass on the degraded site.In 2012, OM treatments demonstrated a strong effect on

tomato yield with amended plots producing eight to tentimes more than unamended ones (Figure 2). The threeOM amendments did not differ statistically, but the trendin the data suggests that CMP + ICC plots produced slightlyhigher tomato yields. Using raised beds also increased to-mato yields (Figure 2). Experimental yields averaged only6–59% of the average on-farm yield of 6·2kgm�2 for process-ing tomatoes in Ohio (2006–2009; http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1210).Tomato yields in amended plots were very similar between 2011and 2012, but yields in the CNT plots were much lower in 2012.Both the OM and raised bed treatments demonstrated

strong positive effects on sweet potato yield, as OM-amended plots produced 10–15 times more than CNT(Figure 2). As with tomatoes, CMP + ICC plots tended toyield slightly more. Observed yields from plots amended withOM in this study were slightly higher or similar to the averageyields for commercial sweet potatoes in North Carolina(2·0kgm�2) and New Jersey (1·25kgm�2; 2007–2010; http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1492).All crop yield observations are consistent with many pre-

vious reports of increased crop yields in degraded soils as a

ural experiment in a vacant urban lot soil. Least squared means are presentedMP), amended with compost + biochar (CMP + B), and compost + intensivein ground (GR) or in raised beds (RB). Error bars are standard errors, andt difference test. Statistical tests were not run on 2011 Swiss chard crop be-toes (n = 3), 2011 Swiss chard (n = 2), 2012 tomatoes (n = 3), and 2012 sweetonline at wileyonlinelibrary.com/journal/ldr.


http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1492




result of compost application (Diacono & Montemurro,2010), biochar application (Kimetu et al., 2008), covercropping with sorghum–sudangrass (Wolfe, 1997), and covercropping with tillage radish (Williams &Weil, 2004). The ex-tremely low 2012 yields in the CNT plots were likely influ-enced by high temperatures and drought that occurred thatyear (Al-Kaisi et al., 2013).

Correlation Between Soil Properties and Crop Yields

A basic framework for determining the suitability of sites forvegetable crop production is to evaluate the nutrient levelsfrom a soil test against recommended levels for crop pro-duction. Gaus et al. (1993) reported desirable soil testlevels for tomato production that included 62–75mgkg�2

Ext. P, 162–212mgkg�2 Ext. K, 150–225mgkg�2 Ext.Mg, 1,500–2,250mgkg�2 Ext. Ca, OM >2·5%, and pH be-tween 6·4 and 6·8. Our control plots contained only24·6mgkg�2 Ext. P and 55mgkg�2 Ext. K, indicating a con-siderable deficiency of these key macronutrients (Table III).OM amendments, however, raised the levels of Ext. P, Ext.K, and all other nutrients well above the desired levels fortomato production, suggesting that vegetable nutrient re-quirements can be provided for urban agriculture with OMinputs.

Soil Quality Index

Factor analysis and the process of taking the most highlyweighted soil properties for individual factors (Table IV) re-sulted in an SQI dataset that included Ext. P, total C, AWC,and pH (Table V). The overall SQI scores calculated with

Table IV. Factor loadings of soil properties at an agriculture exper-iment in a vacant urban lot soil from a factor analysis

Factor1

Factor2

Factor3

Factor4

Factor5

Bulkdensity

�0·799 �0·191 �0·197

%WSA 0·688 0·432MWD 0·483 0·124 0·533Totalporosity

0·729 0·618

AWC 0·370 �0·143 0·806Total C 0·643 0·748 �0·104Total N 0·660 0·730 �0·107 0·106pH �0·563 �0·243 �0·685Ext. P 0·962Ext. K 0·805 0·316 �0·221 0·105Ext. Ca 0·954 �0·122 �0·177 0·130Ext. Mg 0·986 �0·120Ext. S 0·969 0·196Ext. Al �0·822 �0·110 �0·199Ext. Fe 0·814 �0·214Ext. Zn 0·278 �0·244 �0·112 0·642MBC—October

0·730 0·126 0·163

MBC—May

0·658 0·107 0·142

Using a quartimax rotation and five factors. Values in bold represent load-ings within 0·1 of the absolute value of the highest loading per factor.


SMAF indicate that all OM amendments resulted in a signif-icant increase in soil quality compared with the unamendedcontrol, while the SQI for raised beds was not significantlydifferent than that for in-ground plots (Table V). The SQIscores observed in this study (0·60–0·85) are near the rangesreported (0·75–0·95) in recent studies that used SMAF toevaluate soils in croplands in the Midwestern USA (Jokelaet al., 2011; Stott et al., 2011; Stott et al., 2013), suggestingthat OM amendments improved soil function in the surfacelayer (0–10 cm) to a level comparable with rural agriculturalsoils. CNT plots were clearly degraded compared with agri-cultural soils.Some observations in this dataset suggest that the scoring

curves from SMAF will benefit from further refinement. TheMehlich-3 P concentration in the CNT plots received a scoreof 0·99 (Table V), despite being well below the soil P levelrecommended for vegetable crop production. The overallscore for the CNT plots (0·60) appears to overestimate thecondition of the soil in these plots, as they produced almostno measurable crop yields in 2012 (Figure 2), suggestingthat their level of soil function is likely not at 60% of poten-tial. Possible reasons for these discrepancies include the fol-lowing. First, as a framework, SMAF works to incorporate anumber of ecological outcomes, such as environmental buff-ering, into scores, rather than solely focusing on crop pro-duction as an endpoint. Second, the extremely low cropyields in the CNT plots suggest that with suboptimal soilquality, soil function is greatly impaired by severe environ-mental stressors such as the drought conditions of summer2012 (Al-Kaisi et al., 2013).The method of using factor analysis to determine soil

quality indicators may not transfer well to field assessmentsof urban agriculture sites as it requires complex analysis andmay be viewed as a nontransparent process. In a previous re-port on using factor analysis for soil quality evaluation,Shukla et al. (2006) suggested that soil properties that re-ceive high loadings in the individual factors collectively rep-resent a key soil function. Viewed from this perspective, thefactor analysis in this study provides guidance toward a lesscomplex process of selecting soil quality indicators in vacanturban lots. The critical soil functions suggested by our factoranalysis include (numbers correspond to the factors inTable IV): (1) providing plant available nutrients; (2) carbonand nitrogen cycling; (3) soil structure and water holding ca-pacity; and (4) pH regulation of chemical and biological re-actions. These groupings suggest that a general approach toassessing soil quality in disturbed urban lot soils couldinclude soil nutrient and pH testing, measuring soil OM(carbon) content, and evaluating soil structure and compac-tion. Soil Pb testing should also be included in soil evalua-tion of vacant urban lots, as Pb is a public health risk atsome sites (Minca et al., 2013).

Implications for Urban Agriculture

The results presented here suggest that applying largeamounts of OM amendments, produced from yard wastes,is a viable strategy for improving soil quality and facilitating


Table V. Soil property and soil quality index (SQI) scores from SMAF

Treatmenta F-valueb CNT CMP CMP + B CMP + ICC F valuec In ground Raised bed

AWC 6·6** 0·30 ae 0·55 b 0·62 b 0·51 b 18·1** 0·60 a 0·40 bExt. P 4·2* 0·99 a 0·91 ab 0·92 ab 0·86 b 20·4*** 0·86 a 0·98 bpH 18·9*** 0·82 a 0·88 b 0·86 b 0·88 b 7·1* 0·86 a 0·87 bTotal C 183·7*** 0·32 a 1·0 b 1·0 b 1·0 b 2·0 ns 0·81 0·85Overall SQI score 41·5*** 0·60 a 0·83 b 0·85 b 0·81 b 0·36 ns 0·78 0·77

aMain plot organic matter amendment treatments include unamended controls (CNT), amended with compost (CMP), amended with compost + biochar (CMP + B),and compost + intensive cover cropping (CMP + ICC). Values are means from ANOVA.bF-test of organic matter amendments had degrees of freedom of 3 in the numerator and 15 in the denominator.cF-test of raised beds had degrees of freedom of 1 in the numerator and 20 in the denominator.d*p< 0·05, **p< 0·01, ***p< 0·0001, and ns = not significant.eLower case letters indicate mean groupings for properties according to Tukey’s honest significant difference where a significant treatment effect (p< 0·05) wasdetected.


crop production in physically degraded urban soils. Thequantity of compost applied in this study (15 kgm�2 or150Mgha�1) is quite large, and testing the effect of smaller,but still significant, compost application rates(50–100Mgha�1) on crop production in similar soils is alogical progression from these data. Compost applied atlower rates than those used in this study has improved soilproperties and crop growth in highly degraded soils in other

Figure 3. The layout of the experimental garden: (a.) the plots in July 2011with control (CNT) in the foreground, compost-amended (CMP) plot in themiddle, and compost plus intensive cover cropping (CMP + ICC) with sor-ghum/sudangrass in the background and (b.) an overview of the garden inSeptember 2011; sorghum sudangrass is still growing following the vegeta-ble harvest. This figure is available in colour online at wileyonlinelibrary.

com/journal/ldr.


ecological contexts, which suggests that it could be effec-tive in urban soils (Courtney & Harrington, 2012; Ooet al., 2013; Jaiarree et al., 2014; Vittal et al., 2014). It isworth noting that the quantity of compost necessary toamend this 0·1-ha research site was purchased for approxi-mately $225, indicating that this rate of compost applica-tion is financially feasible for many urban agriculturesites in the USA.The trends of increased crop yields, improvement to soil

physical properties, and large quantities of biomass pro-duced by the sorghum–sudangrass indicate that covercropping is an excellent practice for urban producers withsufficient space to incorporate it in their management.Sorghum–sudangrass, in particular, produced large quanti-ties of biomass and improved soil structure, but it does re-quire warm growing season temperatures. Low-cost soilremediation measures (compost and cover cropping) arelikely suitable for a much a wider application in restoringdegraded urban soils, as they have proven effective at im-proving soil properties and plant growth in systems suchas minelands that have undergone severe disturbances anddegradation (Raizada & Juyal, 2012; de Souza et al., 2013;Mukhopadhyay & Maiti, 2014; Pallavicini et al., 2014).Raised beds allow producers to create a well-aerated soil

surface layer that facilitates improved rooting depth andmixing of amendments (Figure 3). They provided significantincreases in crop yields in this study and are another man-agement strategy that may improve urban agriculture out-comes. These data also suggest that soil macronutrients,such as K, P, Ca, Mg, and S, are key to crop growth and oc-cur at variable concentrations in vacant urban lots. Thistrend suggests that conducting multiple, discreet soil testsand developing nutrient management plans may be keypractices for maximizing crop yields in similar soils.

CONCLUSIONS

This study documented soil properties and crop growth, aswell as their response to experimental treatments of OM ina physically degraded urban soil. The results indicate thatdemolition of vacant houses and regrading of the site im-paired soil function by resulting in high bulk density values



and low total C and MBC. Demolition did not, at this site,result in Pb contamination in the soil surface. The data alsoprovide evidence that the application of large quantities(15 kgm�2) of compost produced from urban yard wastecan improve numerous soil properties, result in measurabledifferences in soil quality, and facilitate favorable yields ofvegetable crops within 2 years of application. Analyses ofthe measured soil properties from the study suggested thatAWC, pH, Total C, and Ext. P were robust indicators ofoverall soil quality and illustrate that macronutrients are im-portant for crop production in these systems. Taken together,these observations provide unique experimental evidencethat vacant urban land in shrinking industrial cities holdssignificant potential for urban agriculture and that the trans-formation of biomass wastes into soil amendments can pro-vide a key input for such systems.

ACKNOWLEDGEMENTS

The authors wish to thank Ian Beniston, Liberty Merrill, andthe Youngstown Neighborhood Development Corporationfor their collaboration with the construction and mainte-nance of the experimental site. We thank Claire Sutton andRyan Hottle for assistance with lab and fieldwork, DianeStott for help with SMAF, and Christopher Holloman for ad-vice on data analysis. A portion of the funding for this studywas provided by a graduate student grant from the CERESTrust and Competitive Grant No. 2012-67011-19668 fromthe USDA National Institute of Food and Agriculture.

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