Supplementary information for Manuresheds: Advancing USars.els-cdn.com/content/image/1-s2.0-S0308521X19311679-mmc1.pdfSupplementary information for Manuresheds: Advancing nutrient

Supplementary information for Manuresheds: Advancing nutrient recycling in US agriculture 1

Supplementary information for

Manuresheds: Advancing nutrient recycling in US agriculture

Sheri Spiegala⁎, Peter J.A. Kleinmanb, Dinku M. Endalec, Ray B. Bryantb, Curtis Dellb, Sarah

Gosleeb, Robert J. Meinend, K. Colton Flynne, John M. Bakerf, Dawn M. Browninga, Greg

McCartyg, Shabtai Bittmanh, Jennifer Carteri, Michel Cavigellig, Emily Duncanj, Prasanna

Gowdak, Xia Lil, Guillermo E. Ponce-Camposm, Raj Cibinn, Maria L. Silveirao, Doulas R. Smithe,

Dan K. Arthurb, Qichun Yangp a United States Department of Agriculture - Agricultural Research Service (USDA-ARS) Jornada Experimental Range, Las Cruces, NM 88003, USA bUSDA-ARS Pasture Systems and Watershed Management Research Unit, University Park, PA 16802, USA c USDA-ARS Southeast Watershed Research Laboratory, Tifton, GA 31793, USA d Department of Animal Science, Penn State University, University Park, PA 16802, USA e USDA-ARS, Grassland Soil and Water Research Laboratory, Temple, TX 76502, USA f USDA-ARS Soil & Water Management Unit, St. Paul, MN 55108, USA g USDA-ARS Beltsville Agricultural Research Center, Beltsville, MD 20705, USA h Agriculture and Agri-Food Canada, Agassiz, BC V0M 1A0, Canada i USDA-ARS, Northern Great Plains Research Laboratory, Mandan, ND 58554, USA j California Water Board, Los Angeles, CA 90013, USA k USDA-ARS, Southeast Area, Stoneville, MS 38776, USA l USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20742, USA m The University of Arizona, Tucson, AZ 85721, USA n Agricultural and Biological Engineering, Penn State University, University Park, PA 16802, USA o University of Florida, Range Cattle Research and Education Center, Ona, FL 33865, USA p School of Engineering, University of Melbourne, Melbourne, Parkville 3010, Australia

1: Data aggregation and processing

1.1 Data sources and preparation

We aggregated data from the Nutrient Use Geographic Information System (NuGIS;

IPNI, 2012) and the 2012 United States Census of Agriculture (USDA-NASS, 2014) to estimate

county-level agricultural P and N for the 3109 counties of the contiguous United States, classify

the counties for their potential to supply or use surplus manure nutrients, and delineate

manuresheds for the year 2012. Table S1 contains details about data sources and data processing

for the analyses.


Table S1. Details about data used in the county classification and manureshed analyses

Variable Source Processing

P, N in manure

produced by confined

livestock

IPNI NuGISa

for the year

2012

NuGIS variables: Tons_P2O5_Recovered, Tons_N_Recovered. IPNI used a combination of USDAc

livestock inventory and sales data for 2012, and supporting publications, to estimate the annual volume of

manure generated by livestock in confinement, by county, and then applied coefficients to estimate the

amount of nutrient from excreted manure. IPNI used a multi-step approach to in-fill undisclosed USDA

data entries. Losses from spillage, volatilization (N only), and denitrification (N only) were accounted for.

Our conversions: P2O5 to P (conversion factor 0.4364); tons to tonnes (conversion factor 1.10231).

Fertilizer P, N applied to

farmland

NuGIS variables: Farm_TonsP2O5, Farm_TonsN. IPNI used reports by the Association of American Plant

Food Control Officials (AAPFCO) and an interpolation method involving county cropland area to estimate

farm application. Our conversions: P2O5 to P; tons to tonnes.

P, N removed by crops

and forages

NuGIS variables: Tons_P2O5_Rem, Tons_N_Rem. IPNI used crop nutrient removal coefficients of land

grant universities with USDA production values of harvested cropland in 2012. Estimates represented 21

common US crops: alfalfa, apples, barley, dry beans, canola, corn for grain, corn for silage, cotton, other

hay, oranges, peanuts, potatoes, rice, sorghum, soybeans, sugar beets, sugarcane, sunflower, sweet corn,

tobacco, and wheat. As of 2012, those 21 accounted for 78% of total cropland area in the contiguous states;

92% and 96% in Iowa and Illinois, respectively. In states with major areas of specialty crops (e.g.,

California, Florida), the percentage dropped to 48% and 51%. Our conversion: P2O5 to P; tons to tonnes.

N fixation NuGIS variable: Tons_N_Fixed_Legumes. Represents N fixed by peanuts, soybeans, and/or alfalfa per

county.

Total croplandc NuGIS variable: TotalCropland. NuGIS reported county-level values from the 2012 Census of Agriculture.

Our conversion: acres to hectares (conversion factor 0.404686). Used for county classification (Table 1).

Harvested croplandc NuGIS variable: Total_Harvested_Cropland_Acres. NuGIS reported county-level values from the 2012

Census of Agriculture. Our conversion: acres to hectares. We used this variable as the denominator for the

data in Figure S1 and Table S3.

P, N in manure

produced by confined

hogs, poultry, dairy

cattle, and beef cattle

2012 US

Census of

Agriculturec

We downloaded 13 inventory and sales data Items for confined livestock from the 2012 Census of

Agriculture, then used coefficients and equations provided by Kellogg et al. (2014) to aggregate the data to

county-level, livestock-specific animal units (AU), then to tonnes of wet weight of manure produced by the

AUs (accounting for losses from collection), and ultimately to tonnes of manure P and N available for

recycling after accounting for losses from spillage, volatilization (N only), and denitrification (N only). We

did not in-fill undisclosed data entries from USDA-NASS. Details in supplement 1.2. a Nutrient Use Geographic Information System (IPNI, 2012) b US Department of Agriculture – National Agricultural Statistics Service (USDA-NASS, 2014) c Includes land used for hay and silage crops


1.2 Industry-specific manure nutrients

We used data from the 2012 United States Census of Agriculture (USDA-NASS, 2014)

and coefficients from Kellogg et al. (2014) to identify the contributions of specific industries to

the county-level estimates of total manure P and N in NuGIS (IPNI, 2012).

We began by downloading 13 inventory and sales data items from the 2012 Census of

Agriculture in April 2019 for hogs, broilers, layers, pullets, turkeys, dairy cows, and beef cattle

on feed. In total, 3052 counties in the contiguous US (out of 3109) were represented in the 13

inventory and sales data items (Table S2). All 13 data items included entries with undisclosed

values – assigned a “(D)” to denote that numeric values were “Withheld to avoid disclosing data

for individual farms.” (D)’s were reported in lieu of either a total per county or for one or more

“domain categories” per county (domain categories were used to split county inventory and sales

by operation size class). We considered different approaches to in-fill the missing data, for

example, by finding a total inventory per state, calculating the difference between the state total

and disclosed county totals, and partitioning the difference into the counties with missing data.

However, as of the 2012 Census of Agriculture, NASS notified users that it included additional

instances of (D) to prevent imputation (USDA-NASS, 2014). Accordingly, we did not attempt to

in-fill missing data (as was done for manure estimates in NuGIS; Table S1). Instead, we omitted

the undisclosed entries from further analysis. Inventory or sales in counties with all undisclosed

entries was set to zero. For counties with numeric values in domain categories, we summed those

values and assigned the sum as the value for the entire county. On average, about one-third of the

counties within each data item were “zeroed out” because of undisclosed data (Table S2).

However, we were able to calculate positive, non-zero values for at least one industry in 2990

counties – 98% of the 3052 counties represented in the initial data download.


Table S2. Data used to calculate industry-specific manure phosphorus (P) and nitrogen (N), with counts of data

records available for analysis as our calculation progressed.

Industry Livestock

type

Census of Agriculture Data Item Counties

with

entries,

numeric or

(D)

Counties

with at

least one

numeric

entry

Counties

with

AU > 0

Counties

with

AU > 12

Counties with

manure P, N >

0 b

Hogsa Hogs Hogs, inventory 2889 1966

2277 1146 1146 Hogs, Sales, Measured in Head 2827 1879

Poultry

Broilers Chickens, Broilers – Inventory 2714 2095

2195 507

964

Chickens, Broilers – Sales, Measured in Head 2367 1626

Layers Chickens, Layers – Inventory 3031 2657

2657 634 Chickens, Layers - Sales, Measured in Head 2915 2268

Pullets Chickens, Pullets, Replacement – Inventory 2628 1884

2065 270 Chickens, Pullets, Replacement – Sales, Measured in Head 2347 1111

Turkeys Turkeys – Inventory 2409 1659

1727 180 Turkeys - Sales, Measured in Head 1655 823

Dairya Dairy cows Cattle, Cows, Milk – Inventory 2499 1553 1553 1345 1345

Beefa Beef cattle

on feed

Cattle on Feed, Inventory 1728 870 1239 1190 1190

Cattle, on Feed - Sales for Slaughter, Measured in Head 2056 1152 a Contained domain categories per county b After accounting for losses from collection, spillage, volatilization (N only), and denitrification

(N only)


Proceeding with the analysis on the numeric, county-level values, first we calculated

animal units1 (AU) for each of the seven livestock types, as an average per county across 2012

(Kellogg et al., 2014 Table 1, Table 2, Equation 1, Equation 5). In some counties, there was a

numeric estimate for inventory but not sales, or vice versa. In such cases, only inventory (or

sales) were used to calculate AUs (Table S2).

Next, retaining only the 2225 counties that supported twelve or more AUs of a livestock

type in 2012, we calculated the wet weight of manure as excreted by each livestock type

(Kellogg et al., 2014 Table 5). The 12 AU threshold was suggested by Kellogg and co-authors,

with the rationale that livestock types with fewer than 12 AU in a county likely represent only

very small or specialty operations which are not practical candidates for manure export. For the

AU to wet weight calculations, we used coefficients for hog farrow to finish and turkeys for

slaughter, as the 2012 Census of Agriculture did not differentiate type of hog for slaughter (e.g.,

finish only vs. farrow to finish) or type of turkey (e.g., turkeys for slaughter vs. turkeys for

breeding) (following Kellogg et al., 2014). Next, we applied a “recoverability factor” to the wet

weight of excreted manure for each livestock type, using the average of regional and farm size-

class estimates for each livestock type (Kellogg et al., 2014 Table 9).

Finally, we calculated the manure P and N per livestock type which was available for

land spreading or other uses, by first calculating tonnes of manure P and N in the manure that is

routinely collected and removed from built areas where livestock are held (Kellogg et al., 2014

Table 5), and then subtracting the portion of P and N lost to volatilization (N only),

denitrification (N only), and spillage (Kellogg et al., 2014 Table 10). We then summed manure

nutrient estimates for all the poultry livestock types (broilers, layers, pullets, and turkeys) for an

aggregated poultry estimate per county. Results of the calculation for manure P are illustrated in

Figure 1b, and were used to characterize the four manuresheds in the main article (Table 3,

Figure 4).

1 Animal units (AU), like livestock units (LU), are used to compare abundance of animals across species or categories.

AUs are also used to estimate forage consumption by animals of different species or size classes. An AU is typically

considered to be one mature cow (1000 lb, 455kg) either dry or with a 0-6 month-old calf, with an average daily forage

consumption of 15 kg of dry matter. One LU is typically considered to be equivalent to 500kg liveweight of an animal

category (Pain and Menzi, 2011).


2: County classification

2.1 County classification presented as an areal concentration

The 3109 counties of the contiguous United States were classified progressively (Table 1)

as sources, sinks, or candidates for within-county transfers, using county-level estimates for P

and N derived from NuGIS. In the main article, we presented the county classification in terms

of tonnes of nutrient per county (Figure 3) – the same unit used for the manureshed delineation

(Figure 4). However, we recognize that presenting the county classes in terms of nutrient mass

per unit of land can aid interpretation. Accordingly, here we present results of the classification

in terms of kilograms of P or N per hectare of harvested cropland per county (Figure S1; Table 3;

data available at Spiegal et al., 2020). Hectares of harvested cropland in 2012 – which includes

haylands and other harvested grazinglands – were obtained from the NuGIS database (see Table

S1 for details about the variable). For the counties with zero hectares of harvested cropland, we

set the value of harvested cropland to 1 ha to avoid undefined values when dividing the county’s

nutrient mass by its hectares of harvested cropland. County-level values (in kg P ha-1 or kg N ha-

1) were rounded to 2 places before performing the classification.

Notably, the classification rules (Table 1) classified counties as sinks, sources, or

candidates for within-county transfers regardless of whether tonnes per county of kg ha-1 per

county were used for input variables (compare Figure 3 and Figure S1). For P, only one county

differed in its class between the two analyses: Kankakee County, Illinois was a sink for manure P

due to P fertilizer surplus when P was expressed as tonnes, but it was a candidate for within-

county transfers when the classification was conducted in terms of kg P ha-1. Kankakee County

had a very low margin of fertilizer surplus (0.36 t), and when that surplus was divided by

harvested cropland (129,648 ha), the surplus rounded to 0 – changing the county from a fertilizer

surplus county to a candidate for within-county transfers of manure nutrient (see Table 1). This

example indicates the sensitivity of the classification for values near zero.

As expected, depending on the unit used, many counties within each class shifted from

below the median to above the median (compare Figure 3 and Figure S1). For instance, when

classified in terms of kg P ha-1 harvested cropland, the manure P source county of Otero County,

New Mexico had surplus manure P above the median (Figure S1a); however, when classified on

the basis of tonnes P, Otero County’s surplus manure P was below the median (Figure 3). This

was due to low extents of harvested cropland in Otero County reported for 2012.


Figure S1. Counties classified with respect to manure nutrient source and sink potential for a)

phosphorus (P) and b) nitrogen (N), in terms of kilograms per hectare per county in 2012. Each

class’s shade of color (lighter vs. darker) represents a split at the median value of the class. See

Figure 3 for maps in terms of tonnes of P and N per county.


Table S3. Amounts of agricultural nitrogen (N) and phosphorus (P) in county classes in 2012,

with counties classified with respect to kilograms of nutrient per hectare of harvested cropland

per county. Mean and standard deviation are followed by the range in parentheses. See Table 2

for summary statistics in terms of tonnes per county.

N (kg ha-1) P (kg ha-1) N:P ratioa

Manure P source counties (390 counties)

Nutrient in manureb 538 ± 6072

(0 – 118420)

255 ± 2367

(8 – 44710)

2.0 ± 0.5

(0.07 - 4.2)

Fertilizer nutrient applied to farmland 182 ± 870

(0 – 15374)

19 ± 68

(0 - 1232)

9.8 ± 4.0

(2.1 –25.5)

Nutrient removed by cropsc,d 90 ± 37

(0 - 506)

18 ± 8

(0 - 91)

5.2 ± 1.1

(1.0 - 8.4)

Manure P sink counties due to P deficit (1642 counties)

Nutrient in manure 5 ± 10

(0 - 334)

3 ± 5

(0 - 155)

1.9 ± 3.1

(0.04 – 96.0)


(2 - 821)

9 ± 5

(0 - 30)

9.2 ± 8.8

(1.0 – 171.6)

Nutrient removed by crops 83 ± 25

(16 - 391)

20 ± 8

(8 -242)

4.3 ± 1.1

(1.0 - 8.4)

Manure P sink counties due to fertilizer P surplus (674 counties)


(0 - 48)

3 ± 4

(0 - 29)

1.6 ± 0.7

(0.4 – 9.3)


(23 - 6544)

34 ± 40

(9 - 520)

7.0 ± 3.5

(0.6 – 40.9)


(18 -229)

18± 5

(7 - 44)

4.3 ± 1.3

(1.4 - 8.5)

Candidates for within-country transfers of P (303 counties)


(0 - 80)

9 ± 6

(0 - 33)

1.7 ± 0.7

(0.5 – 7.0)


(13 - 289)

15 ± 6

(2 - 30)

7.8 ± 3.0

(2.3 - 20.8)


(28 - 186)

20 ± 6

(8 - 37)

4.3 ± 1.1

(0.9 - 8.4) a Counties with values of zero for N or P were not included. b Produced by confined livestock and available after accounting for losses from collection, spillage, volatilization (N

only), and denitrification (N only). c Includes forages. d N removed by crops = N removed by crops – N fixation.

2.2 Evaluating the 0.5 N application factor

We used 0.5 for the N manure and fertilizer terms in the county classification rules (Table

1) to account for losses of N due to nutrient use efficiencies. Average N use efficiencies for the

US are often in the range of 30-80% (e.g., Lasselletta et al., 2014; Swaney et. al., 2018). To

capture the central tendency, we selected the factor of 0.5. Here, we sought to determine how the


count and composition of manure N source counties changed when different N availability

factors were used.

When the factor was set to 0.3, N manure source counties numbered 55. When it was set

to 0.5, there were 100 N manure source counties. And, when the factor was set to 0.8, 162 N

manure source counties were found. For each of these factors, N source counties were

completely nested (i.e., all 55 source counties identified when the factor was set to 0.3 were also

source counties when the factor was set to 0.5 and 0.8; all 100 source counties identified when

the factor was set to 0.5 were also source counties when the factor was set to 0.8).

Importantly, this sensitivity analysis indicated that using P sources and sinks to drive

manureshed determination was justified at all three factor levels: 0.3, 0.5, and 0.8. This is

because when we set the N use efficiency factor to 0.5, only one county was classified as a

source of manure N, but not as a source for manure P. When we set the factor to 0.8, only three

counties were manure N sources but not manure P sources: Clark County, Nevada; Nye County,

Nevada; Bernalillo, New Mexico – three counties with cropland dominated by alfalfa hay

production (Figure 1a) that had N-fixation sufficient to render manure N in excess of cropland

assimilative capacity when N use efficiency was set to 0.8. We concluded that because the

majority of N source counties were also P source counties at all three factor levels, using the N

use efficiency factor of 0.5 would not result in a critical loss of information about source

counties for manure N when delineating manuresheds based on manure P. Considering these

results, we felt that using the factor of 0.5 was adequately justified.

2.3 Evaluating the NuGIS estimates

We encountered a few surprises in the county-level estimates of P and N in NuGIS (IPNI,

2012). For instance, when county-level crop P removal was expressed as kilograms per hectare

of harvested cropland per county class (e.g., Table S3), removal estimates were lower than

published values for many common row crops (e.g., Pierzynski and Logan, 1993). Per NuGIS

developers (pers. comm. Heidi Peterson, Sand County Foundation), this may have been due to

the complexity of generalizing P removal rates across multiple crops in a given county. In

addition to common commodity crops, there may have been be some crops with lower P

demands in 2012.


In contrast to the estimates for P, in the case of N, county-level N balances pointed to a

high number of surprising N deficits, notably in areas of intensive crop production where one

would not expect farmers to under-apply N fertilizer relative to crop demand. While our

assignment of a 50% N use efficiency may have contributed to this result, we did identify some

potential for errors in accounting that may have contributed to our high number of N deficit

counties. For example, in dryland wheat-fallow rotations of the western US (e.g., the Palouse

region of Washington State), we found that land that was in reality fallowed in 2012 was

misclassified by NASS as being in wheat production – with P removal rates in NuGIS reflecting

wheat instead of fallow (Figure 3; Figure S1). In its documentation about NuGIS, IPNI (2012)

acknowledged some uncertainty in their own deficit estimates because of such issues, but they

also concluded that their estimates reveal real, emerging trends in nutrient management that

reflect greater growth in crop demand relative to fertilizer application.

A third issue encountered in NuGIS was that several counties with low areal extents of

cropland were estimated to have received high levels of P and N farmland fertilizer in 2012 (e.g.,

San Francisco County, California; New Hanover County, North Carolina). Per NuGIS

developers (pers. comm. Heidi Peterson, Sand County Foundation), this was a known but

unresolved issue as these counties tended to represent urban areas. In light of this issue, we opted

to remove counties with < 500 ha of cropland from further classification – after classifying

manure sources (Table 1) – in part to avoid spurious classification of counties as potential sinks

due to fertilizer surplus.

3: Quantitative evaluation of counties selected for manureshed

delineation

3.1 Cluster analysis to assess industry composition of manureshed source areas

We used cluster analysis to assess the composition of livestock industries in the source

areas of the four example manuresheds, to ensure accurate description of each manureshed

(Figure 4). Counties included in the cluster analysis were the 345 counties classified as manure P

source counties for which we had industry-specific estimates of manure P production. Manure P

was expressed in terms of areal concentration of manure P produced by each livestock industry

in each county (kg P ha-1 harvested cropland, Figure S1). We initially ran the analysis with

manure expressed in tonnes to match with the manureshed delineation, but the wide variation in

county size confounded results of this cluster analysis.


We tested twelve clustering algorithms of five types (hierarchical, partitioning, model-

based, density-based, and self-organizing), with number of clusters from 2 to 20. Model selection

and choice of cluster number was based on silhouette plots as well as visual inspection of the

clusters overlaid on a PCA ordination and a map of the 345 source counties. Silhouette plots with

higher mean values indicated better clustering of the observations being clustered (for

methodological explanation see Maechler et al., 2019). In the PCA plot and maps, we sought

coherence of clusters and unexpected outliers.

The 9-class k-means model performed optimally. Average silhouette width was 0.62, and

the PCA plot illustrated a coherence of cluster membership (silhouette and PCA plots are not

included here). Overall, we found that the clusters in the map of 345 source counties overlaid

with the source areas for the four manuresheds (Figure S2). This result supported our industry-

specific characterization of source areas in the four example manuresheds (Figure 4).

Figure S2. K-means cluster membership of manure phosphorus (P) source counties. Thick black

outlines represent source areas selected for the four manuresheds delineated in Figure 4.

3.2 Spatial autocorrelation of source and sink counties

In addition to the k-means cluster analysis (supplement 3.1), we also evaluated our

selection of manureshed source area counties (Figure 4) by quantitatively assessing whether they

were surrounded by counties that could assimilate excess manure P. To do so, we first calculated

the spatial autocorrelation of surplus manure P and manure P sink strength in the ten-county


neighborhood surrounding each of the 3109 counties in the contiguous US. Spatial

autocorrelation was calculated with Global Moran’s I (Moran, 1950) and a bi-variate local

indicator of spatial autocorrelation (LISA; Anselin 1995). We then calculated the proportion of

the 106 source area counties that had significant local autocorrelation with sink strength in their

10-county neighborhoods. We deemed a proportion exceeding 66% to be adequate confirmation

that the counties we selected were indeed surrounded by counties with assimilative capacity of

manure P surplus – and thus were appropriate counties to use in our delineation of manuresheds.

Global Moran’s I and bi-variate LISA were computed in GeoDa software (Anselin,

2006), which grouped each county into one of four clusters based on its relationship with its 10

nearest neighboring counties (Anselin, 2006). We used a p-value threshold of 0.01 using 999

random iterations to denote deviation from random neighbor association.

To prepare our data for GeoDa, we first assigned the 3109 counties in the contiguous US

with values for the variables in the bi-variate LISA: 1) surplus manure P, and 2) manure P sink

strength. Manure P surplus was > 0 for the 390 source counties for manure P (browns in Figure

3a), and sink strength was > 0 for the 2317 sink counties for manure P (greens in Figure 3a). For

sink counties, surplus manure P = 0, and for source counties, manure P sink strength = 0. For

other classes of counties in our US county classification (i.e., counties excluded from further

classification due to low extents of cropland, and counties that were candidates for within-county

transfers of P; Figure 3a), values in both variables were zero.

Local Moran’s I for a county ranged from -1 to 1 and was based on relativized values for

the source and sink variables. A positive Moran’s I signified a direct relationship between the

county and its neighbors (i.e., a positive Moran’s I value would be assigned both to a county

with high surplus P surrounded by counties with high sink strength, and to a county

with low surplus surrounded by counties with low sink strength). Conversely, a negative

Moran’s I for a county indicated an inverse relationship with the county and its neighbors (i.e., a

negative Moran’s I value would be assigned to a county with low surplus P surrounded by

counties with high sink strength, and to a county with high surplus P surrounded by counties

with low sink strength). The expected value for a random association between the two variables

in a bi-variate LISA analysis is zero, and the stronger the direct or inverse relationship, the

greater the absolute value of the Moran’s I.


Of the 3109 counties, 1227 had significant direct or inverse relationships with their

neighboring ten counties – these counties were grouped into four clusters (counties colored with

reds and blues in Figure S3). One cluster contained 12 counties with high surplus manure P that

were surrounded by counties with high sink strength (dark red in Figure S3). Another cluster

contained 176 counties with high surplus manure P that were surrounded by counties with low

sink strength (light red in Figure S3). Another cluster of 647 counties had zero or low surplus

manure P and were surrounded by counties with low sink strength (dark blue in Figure

S3). Finally, 392 counties had zero surplus manure P surrounded by counties with high sink

strength (light blue in Figure S3).

Figure S3. Relationships of counties with significant local autocorrelation of surplus manure

phosphorus (P) and sink strength for P with their ten neighboring counties, per a bi-variate

Local Indicator of Spatial Autocorrelation (LISA) analysis. Thick black outlines represent source

areas selected for the four manuresheds delineated in Figure 4.

With regard to the 390 source counties for manure P, 244 had significant local

autocorrelation with sink counties in their 10-county neighborhoods; all 244 were in the first


three clusters in the legend of Figure S3 (dark red, light red, dark blue). Seventy-one of the 106

source counties selected for manureshed source areas had significant inverse relationships with

neighboring sink counties (dark and light red in counties contained in black polygons in Figure

S3). This independent, quantitative analysis indicated that 67% of the source counties selected

for manureshed delineations were indeed surrounded by counties that could potentially

assimilate surplus manure P – potentially from the source area counties. Thus, we proceeded to

use these selected source area counties for manureshed delineation (Figure 4).

References

Anselin, Luc. 1995. Local Indicators of Spatial Association — LISA. Geogr. Anal. 27, 93–115.

Anselin, L., Syabri, I., Kho, Y., 2006. GeoDa: An introduction to spatial data analysis. Geogr.

Anal. 38, 5-22. https://doi.org/10.1111/j.0016-7363.2005.00671.x.

IPNI, 2012. A Nutrient Use Information System (NuGIS) for the U.S. International Plant

Nutrition Institute, Norcross, Georgia. Available online at http://nugis.ipni.net/.

(Accessed tabular data on January 25, 2020).

Kellogg, R.L., Moffitt, D.C., Gollehon, N., 2014. Estimates of Recoverable and Non-

Recoverable Manure Nutrients Based on the Census of Agriculture. US Department of

Agriculture, Natural Resources Conservation Service, Resource Assessment Division,

Resource Economics and Analysis Division, Washington, D.C..

Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J., Garnier, J., 2014. 50 year trends in nitrogen

use efficiency of world cropping systems: The relationship between yield and nitrogen

input into cropland. Environ. Res. Lett. 9, 105011.

Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K., 2019. Cluster: Cluster

Analysis Basics and Extensions. R package version 2.1.0.

Moran, P.A.P., 1950: Notes on continuous stochastic phenomena. Biometrika 37, 12-17.

Pain, B., Menzi, H., 2011. Glossary of Terms on Livestock and Manure Management 2011.

Recycling Agricultural, Municipal and Industrial Residues in Agriculture Network

(RAMIRAN).

Pierzynski, G.M., Logan, T.J., 1993. Crop, soil, and management effects on phosphorus soil test

levels: A review. J. Prod. Agric. 6, 513–520.

https://doi.org/10.1111/j.0016-7363.2005.00671.x

http://nugis.ipni.net/


Spiegal, S., Kleinman, P.J., Endale, D.M., Bryant, R.B., Dell, C., Goslee, S., Meinen, R.J.,

Flynn, C., Baker, J., Browning, D., McCarty, G., Bittman, S., Carter, J., Cavigelli, M.,

Duncan, E., Gowda, P., Li, X., Ponce-Campos, G.E., Raj, C., Silveira, M., Smith, D.R.,

Arthur, D.K., Yang, Q., Nezat, C., Vandenberg, B. 2020. Manureshed delineation via

analysis of county-level data from IPNI-NuGIS and USDA-NASS (2012). Available

online at https://doi.org/10.15482/USDA.ADC/1518435.

Swaney, D.P., Howarth, R.W., Hong, B., 2018. Nitrogen use efficiency and crop production:

Patterns of regional variation in the United States, 1987-2012. Sci. Total Environ. 635,

498-511. https://doi.org/10.1016/j.scitotenv.2018.04.027.

USDA-NASS, 2014. 2012 United States Census of Agriculture. Census Full Report. National

Agriculture and Statistics Service Database. United States Department of Agriculture

Agricultural Statistics Board, Washington, DC.

https://doi.org/10.15482/USDA.ADC/1518435

https://doi.org/10.1016/j.scitotenv.2018.04.027

Documents

Supplementary information for Manuresheds: Advancing USars.els-cdn.com/content/image/1-s2.0-S0308521X19311679-mmc1.pdfSupplementary information for Manuresheds: Advancing nutrient