The Effect of Landscape Position on Biomass Crop Yield - Biostatistics

The Effect of Landscape Position on Biomass Crop Yield Ryan Thelemann, Gregg Johnson,* Craig Sheaffer, Sudipto Banerjee, Haowen Cai, and Donald Wyse

Integrating annual and perennial crops at the field scale creates new opportunities for increasing financial return while addressing important environmental and ecological issues. An understanding of biomass productivity on specific landscape positions is essential to realizing this goal. The objective of this research was to determine the effect of landscape position on the productivity of herbaceous and woody biomass crops. Alfalfa (Medicago sativa L.), corn (Zea mays L.), willow (Salix spp.), cottonwood (Populus deltoides W. Bartram ex Marshall), poplar (Populus maximowiczii × P. nigra), and switchgrass (Panicum virgatum) were planted on seven landscape positions. Soil attributes were measured and included soil nitrogen, P, K, and profile darkness index (PDI). Terrain attributes included specific catchment area (SpecCat) and compound terrain index (CTI). A hierarchical Bayesian approach was used to analyze spatial data. Corn grain and stover yield was lowest in depositional and flat areas that retain water for longer periods of time and highest on well drained summit positions. Corn stover yield was positively correlated to nitrogen, PDI, SpecCat, and CTI, whereas corn grain yield was not correlated to any of the soil or terrain attributes tested. Conversely, willow productivity was among the highest at the depositional position and lowest at the summit position. For SX67 willow, growth was positively correlated to SpecCat, whereas 9882 willow growth was negatively correlated to PDI and CTI. Alfalfa and poplar productivity was highest at a site characterized by a relatively steep slope with potentially erosive soils.

T

he emerging bioeconomy presents new opportunities for farmers to improve economic return and reduce risk through integration of annual and perennial crops into the farming enterprise. Annual row crops, such as corn and soybeans, are a major feedstock for the liquid fuel markets. However, the long-term sustainability of annual row crops as a biomass feedstock is in question due to environment, economic, and social challenges (Tilman et al., 2006; Jordon et al., 2007). The use of perennial crops as a feedstock for bioindustrial sectors has been suggested as an alternative to annual row crops as a way to address these economic, environmental, and social issues. In addition to providing feedstock for energy production, perennial biomass crops have the potential to provide ecosystem services such as wildlife habitat, nutrient sequestration, and erosion control (Jordon et al., 2007; Volk et al., 2004). Strategic plantings of annual and perennial monoculture crops may be a way to serve food, fiber, and fuel needs while addressing critical environmental issues (Jordan et al., 2007). This strategy may also maximize net production on all lands

G.A. Johnson, R.T. Thelemann, C. Sheaffer, and D.L. Wyse, Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 1991 Upper Buford Cir., St. Paul, MN 55198. S. Banerjee and H. Cai, Univ. of Minnesota, Division of Biostatistics, School of Public Health, Mayo Mail Code 303, Minneapolis, MN 55455. Received 10 Feb. 2009. *Corresponding author ([email protected]). Published in Agron. J. 102:513–522 (2010) Published online 13 Jan. 2010 doi:10.2134/agronj2009.0058 Copyright © 2010 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

through site-specific placement of annual and perennial crops together across a field, planting each crop only where the desired qualities are optimized in terms of economic, environmental, or social values. An understanding of biomass productivity on specific landscape positions or environments is essential to realizing the goal of supplying a reliable and consistent source of feedstock that meets quality specifications for the bioenergy market while increasing farm profit and providing ecosystem services. Plant growth at specific landscape positions is influenced by several interacting hillslope processes across a typical soil catena. Hillslope processes are defined by unique site characteristics, such as soil physical and chemical properties, water retention and flow patterns, biological processes, and topographic influences, that continuously change and interact (Malo et al., 1974; Butler et al., 1986; Jones et al., 1989). Spatial and temporal variation in hillslope processes must be understood to optimize crop placement at the field scale. Soil physical and chemical properties change depending on landscape position. In cultivated sites in the upper Midwest and Canada, surface (A) horizon thickness, nitrogen, phosphorus (P), organic matter content, clay content, and organic carbon content of soils have been shown to increase from the shoulder to the footslope, whereas the bulk density and sand content of soil decreases (Malo et al., 1974; Gregorich and Anderson, 1985; Kreznor et al., 1989; Dharmakeerthi et al., 2005). Jones et al. (1989) showed that slope length and slope gradient effected soil nitrate content. Hydrologic processes interact with terrain features to modify soil properties. Moore et al. (1993) found that slope and a wetness index accounted for about half of the variability Abbreviations: CTI, compound terrain index; PDI, profile darkness index; SpecCat, specific catchment area.

A g ro n o my J o u r n a l • Vo l u m e 10 2 , I s s u e 2 • 2 010

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Cropping Systems

ABSTRACT

in A horizon thickness, organic matter content, pH, and other soil attributes. The interaction between landscape position and soil water must also be considered when trying to determine what factors are driving differences in plant growth (Andales et al., 2007). Hydrologic studies have shown that head slopes and foot slopes received runoff and subsurface water from the shoulder, interfluve, and linear slope. Consequently, headslopes and footslopes had more water-holding capacity and more available water than shoulders, interfluves, and linear slopes (Hanna et al., 1982; Stone et al., 1985; Andales et al., 2007). The influence of landscape position and terrain attributes on annual crop yield has been well studied. Corn grain yield and total plant biomass was greater at footslopes and headslopes than at interfluves, shoulders, and linear slopes, benefiting from more available soil water at those lower landscape positions (Stone et al., 1985; Afyuni et al., 1993). Conversely, Marques de Silva and Silva (2006) found that as distance to flow accumulation increased, corn yields consistently decreased. Kaspar et al. (2003) found that in years with below-average rainfall, corn yield was negatively correlated with relative elevation, slope, and curvature, whereas in years with above-average rainfall, corn yield was positively correlated with relative elevation and slope. In recent years, the process of describing and analyzing landscape terrain features, often using Geographic Information Systems techniques, has become more accurate and precise. Consequently, farmers and landowners are now able to use this information to explore new cropping systems design strategies, such as directed placement of annual and perennial crops, at the field scale. The objective of this research was to understand differences in woody and herbaceous crop productivity as a function of landscape position.

experiment. Rainfall and temperature data were obtained from the Southern Research and Outreach Center weather station, which is approximately 2 km from the experiment site. Yearly average air temperature and growing degree units (10ºC base, 30ºC maximum) and growing season precipitation, average air temperature, and growing degree units were above the 30-yr normal in 2006 and 2007 (Table 1). Seven landscape positions were selected to represent a range of topographical features common to the region with varying soil moisture and erosion characteristics (Fig. 1): (i) a summit area with excellent water drainage but visible erosion; (ii) a depositional area that receives water from two hillslopes and is characterized by poor drainage and accumulated topsoil; (iii) a flat area that is poorly drained but has retained its topsoil; and four hillslopes: (iv) east, (v) south, (vi) southwest, and (vii) north aspect. Within each landscape position, a series of woody and herbaceous annual and perennial crops were planted. Crops included alfalfa, corn, willow, cottonwood, poplar, and switchgrass. Each plot was 10 m by 10 m with a 1.5-m tilled border in four replications. Crop and Soil Management Inoculated alfalfa (‘Garst 6420’) was planted 1 June 2005 at a rate of 17 kg ha–1 pure live seed. Alfalfa plots were sprayed with 25 mL a.i. ha–1 lambda-cyhalothrin {[1a(S*),3a(Z)]cyano(3-phenoxyphenyl)methyl-3-(2-chloro-3,3,3-trifluoro-1propenyl)-2,2 Dimethylcyclopropanecarboxylate}insecticide 14 d after the first, second, and third cuttings in both years to prevent potato leafhopper (Empoasca fabae) and other insect damage. Alfalfa at the depositional position was sprayed with 1.03 L a.i. ha–1 bentazon [(3-(1-methylethyl)-1H-2,1,3benzothiadiazin-4 (3H)-one 2,2-dioxide)] herbicide in both years to control yellow nutsedge (Cyperus esculentus). Corn (‘DK 44–92RR’, 94-d RM) was planted at 74,100 seeds ha–1 on 7 May 2006 and 29 Apr. 2007 in 12-row main plots (six 5-m rows per replication) with rows spaced 76 cm apart. Tebupirimphos [O-[2-(1,1-dimethylethyl)-5-pyrimidinyl] O-ethyl O-(1-methylethyl)] and cyfluthrin [cyano(4-fluoro-3phenoxyphenyl) methyl 3-(2,2-dichloroethenyl)-2,2-dimethylcyclopropanecarboxylate] insecticide was applied at a rate of 9.9 g a.i. ha–1 at planting in both years to control soil insects that feed on corn roots. Weeds were controlled in corn with 1140 mL a.i. ha–1 glyphosate [N-(phosphonomethyl) glycine, in the form of its potassium salt] in both years. Corn was fertilized with 157 kg ha–1 N in spring 2006 and 2007. Willow stem cuttings were obtained from the Woody Biomass Program at the State University of New York College of Environmental Science and Forestry (Syracuse, NY) and

MATERIALS AND METHODS Site Description Field studies were conducted in 2006 and 2007 on 16 ha at the University of Minnesota’s Agricultural Ecology Research Farm, which is part of the Southern Research and Outreach Center in Waseca, MN (44°03´48˝ N; 93°32´42˝ W). Soils at the site are formed in loamy, calcareous glacial till and consist of the Clarion (Fine-loamy, mixed, superactive, mesic Typic Hapludolls), Nicollet (Fine-loamy, mixed, superactive, mesic Aquic Hapludolls), and Webster (Fine-loamy, mixed, superactive, mesic Typic Endoaquolls) soil series. Soils range between 0 and 4% slope depending on location within the University of Minnesota’s Agricultural Ecology Research Farm. The experimental site has no artificial drainage and was in a long-term corn–soybean rotation before establishment of the

Table 1. Precipitation, air temperature, and growing degree units in 2006 and 2007 at Waseca, MN. Precipitation Year

Period

Total

Average air temperature

Departure†

Total

cm 2006 2007

growing season (1 May–20 Sept.) yearly total (1 Jan.–31 Dec.) growing season (1 May–20 Sept.) yearly total (1 Jan.–31 Dec.)

63.47 106.35 53.87 86.18

GDU‡

Departure

Total

Departure

+1.17 +1.83 +0.61 +0.95

1384 1384 1399 1399

+58 +46 +58 +61

ºC +11.6 +18.21 +2.0 –1.96

19.67 8.44 19.11 7.56

† Departure from 30-yr normal. ‡ GDU, growing degree units Base 10°C.

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Fig. 1. Plot layout and elevation map of the experimental site at the University of Minnesota’s Agricultural Ecology Research Farm in Waseca, MN.

planted 2 June 2005 in a high-density, twin-row configuration. Two clones of willow (9882-41 and SX67) were planted. Willow cuttings were spaced 60 cm apart within the row and 75 cm between rows with 150 cm between each set of twin rows. This configuration results in four twin rows per main plot (two 5-m twin rows per rep) and a density of 14,332 willows ha–1. Each cutting was 25 cm long and planted 20 cm into the soil, leaving one or two buds above ground. Willow plants were coppiced to a 10-cm height above ground after leaf desiccation in November 2005. On 3 June 2005, Willow plots were sprayed with 0.44 L ha–1 a.i. sulfentrazone [N-[2,4-dichloro5-[4-(difluoromethyl)-4,5-dihydro-3-methyl-5-oxo-1H-1,2,4triazol-1-yl]phenyl] methanesulfonamide] and 1.03 L a.i. ha–1 sodium salt of bentazon [(3-(1-methylethyl)-1H-2,1,3-benzothiadiazin-4 (3H)-one2,2-dioxide)] herbicide to control weeds. Manual weed removal was performed as needed in all willow plots. Cottonwood and poplar plots were sprayed with 0.44 L a.i. ha–1 sulfentrazone [N-[2,4-dichloro-5-[4-(difluoromethyl)4,5-dihydro-3-methyl-5-oxo-1H-1,2,4-triazol-1-yl]phenyl] methanesulfonamide] and 1.03 L a.i. ha–1 sodium salt of bentazon [(3-(1-methylethyl)-1H-2,1,3-benzothiadiazin-4 (3H)-one2,2-dioxide)] herbicide to control weeds. Nitrogen was applied as urea on 3 May 2006 at a rate of 112 kg ha–1 N. Cottonwood (‘D-125’) and poplar (‘NM6’) stem cuttings

were obtained from a commercial nursery in Minnesota and planted 1 June 2005 in a grid configuration with 120 cm spacing within the row and between rows, resulting in 64 trees per main plot (16 trees per replication) and a density of 6670 trees ha–1. Each cutting was 25 cm long and planted 20 cm into the soil, leaving one or two buds above ground level. Nitrogen was applied as urea at112 kg ha–1 to all poplar and cottonwood plots on 3 May 2006. Switchgrass (‘Sunburst’) was planted 2 June 2006 at a rate of 25.5 kg ha–1 pure live seed. Switchgrass at the depositional position was sprayed with 1.03 L a.i. ha–1 sodium salt of bentazon [(3-(1-methylethyl)1H-2,1,3-benzothiadiazin-4 (3H)-one 2,2-dioxide)] herbicide in both years to control yellow nutsedge (Cyperus esculentus). Nitrogen was applied in 2006 as urea at a rate of 157 kg ha–1 before switchgrass planting. Terrain Analysis A 5 m by 5 m digital elevation model was constructed before the initiation of this experiment to characterize the terrain of the entire 16-ha site. This was accomplished using differentially corrected GPS technology, including a kinematic GPS receiver and a compatible fixed base station GPS receiver that was used to supply differential data to correct the kinematic receiver’s coordinate data, providing measurement accuracy within


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Table 2. Average soil and terrain attributes for each landscape position at the University of Minnesota’s Agricultural Ecology Research Farm in Waseca, MN. Landscape position

Nitrate-N

Summit Depositional Flat W slope S slope SW slope N slope

6.6 2.6 2.5 3.7 10.4 5.8 4.6

Bray phosphorus

Potassium

17 40 36 17 20 11 18

140 177 188 150 154 161 152

0.03 m. The kinematic receiver was used to collect elevation data in a series of transects across the entire field. Transects were separated by 6 m, and data were obtained every 3 m within a transect. Kinematic receiver and base station coordinates were referenced to a Universal Transverse Mercator sprojection (Zone 15, North American Datum of 1983), and the data were processed using ArcInfo version 9.1 GIS software (ESRI, Redlands, CA). Aspect, elevation, slope, specific catchment area (SpecCat), plan curvature, and profile curvature were calculated using the TAPES-G methods (v. 6) described by Gallant and Wilson (1996) using d8 flow algorithms. Compound terrain index (CTI) was calculated by log-transforming flow accumulation (FLOW ACC) using slope as the log base (LOGslope FLOW ACC). Flow accumulation is the total upslope area contributing runoff to a given point on the landscape (Gallant and Wilson, 1996). The slope at a given point reveals whether the water accumulated from upslope areas will be retained or whether it will run off to downslope areas. Negative CTI values indicate soils at landscape positions with low slope values that receive water (via runoff and subsurface flows) from areas of higher elevations. Positive CTI values indicate sloped soils that lose water to areas of lower elevation. Values near zero indicate soils that neither receive nor lose moisture to surrounding areas. Specific catchment areas is a measure of the area of land contributing runoff water to a given point while at the same time measuring the unit width of the contour (the area perpendicular to the flow direction). Large areas with short contour widths result in water flowing through a narrow area that saturates quickly. Water flowing over saturated soil does not infiltrate, resulting in high-velocity water flow and erosion of bare soil. In this experiment, a landscape position with a high SpecCat represents an area that is likely to receive high volumes of water and topsoil quickly during flow events. Low values signify areas that receive water slowly from upslope

Compound terrain Specific index catchment area mg kg–1 0 51 –7.97 9090 –4.46 1114 1.71 256 0 87 0.79 342 1.63 301

Profile darkness index 7.13 23.33 16.17 7.33 8.43 3.07 5.63

areas, whereas a zero value signifies an area that receives no incoming flow. Soil physical and chemical properties were determined for each landscape position. Twelve soil cores were taken to a depth of 15 cm on 26 Apr. 2006 and composited. Composite soil samples were analyzed to determine soil nitrate-nitrogen (NO3–N), Bray or Olsen phosphorus (P), and potassium (K) content as well as percent organic matter and soil pH (Table 2). Samples were analyzed by the University of Minnesota’s Research Analytical Laboratory using standard soil analysis methods (http://ral.cfans.umn.edu/soil.htm). The profile darkness index (PDI) is described by Thompson and Bell (1996) and calculated by n

∑ i =1

(A horizon thickness i)/[(ViCi) + 1]

where Vi is the Munsell color value, and Ci is the Munsell color chroma for a specified A horizon. Profile darkness index values were determined by obtaining 5-cm-diameter soil cores from one main plot per site. Each core was sampled until the B horizon became visible, encompassing a range of depths from 21.5 to 70 cm. From the cores, A horizon thickness, A horizon Munsell value, and Munsell chroma were recorded and used in the PDI equation described by Thompson and Bell (1996). Profile darkness index has also been found to be strongly correlated to the duration of saturation (Reuter and Bell, 2003). Extended periods of saturation lead to anaerobic soil conditions that affect plant growth. Soil N, soil P, soil K, PDI, SpecCat, and CTI were denoted as a single value for each landscape position and tested for significance as covariates in the statistical model within each species across all seven landscape positions (Tables 2 and 3).

Table 3. Crop biomass yields between and within seven landscape positions at the University of Minnesota’s Agricultural Ecology Research Farm in Waseca, MN. Data for alfalfa, corn stover, corn grain, and switchgrass represent 2 yr of growth (2006–2007). Data for willow represent growth in the second year post-coppice. Poplar and Cottonwood data represent third-year growth. Landscape position Summit Depositional W slope Flat S slope SW slope N slope

Alfalfa 29,924 a r† 15,137 b t 26,749 b s 30,490 a s 28,431 a r 31,270 a r 30,503 a s

Corn stover 16,549 a s 13,911 b t 17,231 a s 9,600 b t 15,144 a s 14,987 a s 16,667 a s

Corn grain 22,867 a s 15,427 b t 21,022 a s 13,859 b t 21,460 a r 17,637 b s 21,688 a s

Willow SX67

Willow 9882-41

kg ha–1 18,386 b s 17,450 b s 36,701 a r 25,013 a s na 16,973 b s 29,922 a s 24,942 a s 17,286 b r 9,631 c s 27,857 a r 14,258 b s 26,334 a s 22,588 b s

Cottonwood 36,817 a r 30,594 a s 29,166 a r 33,113 a r 36,002 a r 37,589 a r 23,989 a s

Poplar

Switchgrass

33,630 a r 26,468 b s 29,868 a r 30,635 a s 28,237 a r 34,028 a r 32,256 a r

17,263 a s 9,338 b t 16,577 a s 14,929 b s 13,072 b s 11,948 b s 14,195 b s

† Means in the same column or row followed by the same letter are not significantly different based on a 90% Bayesian credible intervals. The letters a–c are used to compare landscape position for a given biomass crop (within columns). The letters r–t are used to compare biomass crops for a given landscape positions (within rows).

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Aspect, elevation, slope, plan curvature, profile curvature, and flow accumulation were not included as variables in the model because they were used to derive other terrain attributes or to characterize important general descriptive features of the landscape positions (e.g., aspect, elevation). As previously mentioned, CTI and PDI are multivariate attributes rather than direct measurements of terrain or soil fertility. Within a single CTI or PDI value are two or three baseline soil or terrain attributes. Therefore, these two attributes are unique in that they integrate multiple soil properties into a single descriptive attribute and that the same PDI or CTI value can be attained from different input attribute values. In addition, PDI, CTI, and SpecCat values complement each other in a way that generates a more complete description of a given landscape position. Profile darkness index describes topsoil thickness and soil saturation but does not explain what processes caused those results. Compound terrain index explains the topography causing water to flow into and remain at the landscape position, while SpecCat details water flow velocity into a position, where high velocity runoff is likely to also erode unprotected soil down slope. Sampling Methods Harvest intervals between woody and herbaceous crops are typically different. For example, willow is typically harvested after 3 yr of growth, whereas switchgrass is harvested annually. If comparisons were made on an annual basis, woody species productivity would be much greater in Year 2 than a single season harvest of switchgrass, even though switchgrass would be harvested over several growing seasons. Therefore, data in this study are based on a total of 2 yr of growth (2006 and 2007). Willow data represent growth in the second year post-coppice, whereas poplar and cottonwood data represent third-year growth (poplar was not coppiced). Alfalfa, switchgrass, and corn data represent combined yield from 2006 and 2007. This includes the effects of landscape variables on establishment of all perennial crops. Alfalfa herbage yield was measured by harvesting a 0.30-m2 quadrat four times throughout the growing season (23 May, 26 June, 24 July, and 30 Aug. in 2006 and 29 May, 26 June, 31 July, and 5 Sept. 2007) to a 2-cm height. Sampling quadrats were randomly placed in the interior of each subplot when alfalfa was at first flowering (stage 5; Fick and Mueller, 1989) and the remainder of the forage removed. After harvest, the wet herbage was dried for 72 h at 60ºC in a forced-air dryer and weighed to determine biomass on a dry weight basis. Alfalfa biomass was summed over all four harvests to calculate total biomass for each replicate. Corn grain was harvested in 2006 and 2007 from the two center rows of each subplot when grain moisture was below 20%. Grain mass and percent moisture were recorded for each subplot, and grain yields were standardized by correcting grain moisture to 15.5%. Corn stover was harvested in 2006 and 2007 from an inner row bordering the two center rows. Corn stover samples were obtained at the R6 growth stage (Ritchie et al., 1997) by harvesting 1 m of row bordering the two center rows. Ears and husks were removed, and stover was dried for 72 h at 60ºC in a forced-air dryer and weighed to determine sample dry mass. Cottonwood and poplar stem diameter (30 cm

above the ground) was measured on the four central trees in each replicate on 20 Sept. 2006 and on 30 Aug. 2007. A digital caliper was used to measure the diameter of each stem. Mature switchgrass was harvested from a 0.30-m2 area to a height of 15 cm in the interior of each plot after the herbage had desiccated in November 2006 and 2007. Herbage outside of the sample area was cut to a height of 15 cm and removed from the site. Harvested samples were dried for 72 h at 60ºC in a forced-air dryer and weighed to determine dry matter yield. Conversion of Stem Diameter to Biomass Yield To obtain an annual estimate of woody biomass yield, a representative plant was harvested in an area adjacent to the sample area within each of the three landscape positions (Summit, Depositional, and SW Hillslope). Stem diameter was measured for each shoot, and shoots were cut into small segments and dried at 60°C until constant weight. Each shoot was weighted again to determine biomass yield on a dry weight basis. Stem diameter and dry biomass values were compared with predicted biomass yields calculated from diameter-based allometric equations for aboveground biomass of representative willow clones (Volk, 2002; Arevalo et al., 2007). To examine the accuracy of the predicted biomass values, a Bayesian linear model with normal-inverse γ prior was fit to investigate the difference between actual biomass and predicted biomass. A Bayesian confidence interval was constructed. Results showed similar results for actual and predicted biomass values for any of the four tree species (data not presented). Based on these findings, the allometric equations described previously were used to convert stem diameter measurements in the sample area to determine dry biomass yield from each woody species. A digital caliper was used to measure willow stem diameter (30 cm above the ground) on the four central stools in each replicate on 28 Sept. 2006 and on 26 Sept. 2007 for each clone. Data Analysis Methods Our motivation was to explore plant biomass productivity across a series of unique environments within a field. Therefore, we opted for a Bayesian approach where adjustments would be made for spatial effects using a spatial stochastic process. From a Bayesian perspective, a stochastic process can be looked on as a tool to model an unknown function. Because there is likely to be a substantial amount of spatial variation, we make the function depend on the coordinates of the positions. More specifically, a “replicate specific” spatial process is incorporated that allows each replicated observation to flexibly borrow strength from other spatial information. This leads to a class of hierarchical spatial models that we discuss below (Banerjee et al., 2005). Hierarchical Bayesian methods now enjoy broad application in the analysis of spatial data, where it is natural to pool information across neighboring regions (Besag et al., 1991). Following ideas laid out in Banerjee and Johnson (2006), a general modeling framework for spatially referenced data can be written as


Yj(s) = xj(s) × β + fj(s) + εj(s) 517

where Yj(s) denotes the response for the jth replicate at location s; xj(s) is a p × 1 vector of the predictors or covariates for the jth replicate at location s; β is the p × 1 coefficient vector; and fj(s) is a function (possibly unknown) capturing the effect of space on the response for the jth replicate and an independent process, ε(s)iid N(0, τ2), also known as the nugget effect, which models measurement error or micro-scale variation for the jth replicate at time t and location s (Cressie, 1993). Micro-scale variation is often modeled as a stationary spatial disturbance at a scale lesser than the minimum intersite distance. Banerjee and Johnson (2006) consider several possible structures for modeling spatiotemporal functions; here we adapt their models to a purely spatial function, fj(s), in spatially replicated settings. In our current context, information from spatial replicates is weak, and working with fully general functional forms for ƒj(s) may cause complications regarding the identifiability (or estimability) of model parameters. Subsequently, we model fj(s) as Gaussian processes that can be regarded, in Bayesian paradigms, as a prior on unknown functions. The books by Cressie (1993), Banerjee et al. (2004), and Schabenberger and Gotway (2004) provide excellent expositions on Gaussian processes in spatial contexts. More specifically, we denote fj(s) ~ GP[0;C(∙,∙)] to be a zero-centered Gaussian Process determined by a valid covariance function C(si, sj) defined for pairs of sites si and sj. Typically the modeler specifies the covariances through a function C(si; sj) = σ2ρ(si, sj;θ), where ρ(∙;θ) is a correlation function, σ2 is the variance component attributed to spatial variation (also called the partial sill in geostatistical contexts), and θ includes parameters quantifying the rate of correlation decay and the smoothness of realizations. Likelihood-based inference could proceed by computing estimates from maximum likelihood, restricted maximum likelihood, or Generalized Estimating Equation approaches and investigating their consistency and asymptotic properties. In the Bayesian paradigm, however, we carry out exact inference based on the posterior distribution. In general, posterior distributions are analytically intractable due to the potentially complicated integral in the denominator and are evaluated by drawing samples from the posterior distribution. A suite of methods known as Markov chain Monte Carlo algorithms, such as the Gibbs sampler and Metropolis-Hastings algorithms (Gilks et al., 1996; Gelman et al., 2004; Marin and Robert, 2007; Carlin and Louis, 2008), avoids numerical integration. Markov chain Monte Carlo algorithms were calculated in WINBUGSs (www.mrc-bsu.cam.ac.uk).

All confidence intervals presented here are Bayesian credible intervals. They have a direct probabilistic interpretation that the probability of a parameter lying inside this interval is exactly 90%. In the spatial data contexts, after estimation is accomplished by evaluating the posterior distribution, prediction of the outcome Yj(s0) for species j at an arbitrary location s0 proceeds from p(Yj(s0)|y). This latter distribution can be sampled in a posterior predictive manner by treating Yj(s0) as a random variable and sampling from its posterior predictive distribution (Banerjee et al., 2004). This algorithm can then be used to estimate all the model parameters and can be used for statistically optimal spatial interpolation (for any fixed time point) over the entire domain. To determine differences in plant growth between landscape positions, Bayesian confidence intervals for all landscape positions were constructed for each species, and each landscape position’s interval was compared with the interval of the reference landscape position (the summit position) within each species. To determine differences in plant growth within a given landscape position, Bayesian confidence intervals for all species were constructed and compared with the interval of the reference species (9882-41 willow). To determine the relationship between soil/terrain attributes and growth growth, a Bayesian confidence interval for the soil and terrain attributes was constructed for each crop biomass species. RESULTS AND DISCUSSION Biomass Yield by Landscape Position Alfalfa

Total alfalfa yield was lower at the depositional and W hillslope landscape positions compared with all other positions (Table 3). However, alfalfa biomass at the W hillslope position was higher than the depositional position but lower than the other positions. Poor alfalfa yield at the depositional position can be partially attributed to water pooling and ice sheeting due to above-normal rainfall both years of the study, which reduces stand density and alfalfa biomass yield (Undersander et al., 1998). For example, alfalfa stem density on 14 May 2007 was 344 stems m–2 on one of the plots at the depositional position as compared with 820 m–2 at the W hillslope position. Soil N, PDI, and CTI were significant covariates to alfalfa biomass yield (Table 4). Soil N was positively correlated to alfalfa yield, whereas PDI and CTI were negatively correlated to alfalfa yield. The effect of CTI reinforced the PDI findings, demonstrating that soils with the potential to accumulate and

Table 4. Relationship between biomass crop yield and soil/terrain attributes at the University of Minnesota’s Agricultural Ecology Research Farm in Waseca, MN. Crop Alfalfa Corn stover Corn grain Willow (SX67) Willow (9882) Cottonwood (D125) Poplar (NM6) Switchgrass

N +† + ns – + ns ns +

P ns‡ – ns ns + ns ns –

K ns ns ns ns – ns ns +

Specific catchment area ns + ns + ns ns ns –

Compound terrain index – + ns ns – ns ns +

Profile darkness index – + ns ns – ns ns +

† Signs + and – indicate a statistically significant positive and negative relationship, respectively, to biomass crop yield based on 90% Bayesian confidence intervals. ‡ ns, no significant relationship with biomass crop yield.

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hold water yield less alfalfa biomass. For example, higher PDI values decreased alfalfa yield, illustrating the damaging effect of saturated soils on alfalfa plant health and yield. Positive CTI values, such as those at the SW and N hillslopes (landscapes that do not hold water), led to higher biomass yields, whereas negative CTI values at the depositional and flat landscapes (landscapes that receive water from upslope landscapes and retain it) were related to lower biomass yields. Mechanisms behind the observed differences between alfalfa biomass yield at the W hillslope and summit positions cannot be explained based on the soil and terrain data obtained in this study. This suggests that other mechanisms not measured in this study are responsible for the observed differences. Corn Stover

Total corn stover yield was significantly lower at the depositional and flat landscape positions compared with all other positions (Table 3). Corn stover yield differences were related to all attributes except K (Table 4). Soil NO3–N, PDI, specific catchment area, and CTI had positive effects, where higher values of those covariates resulted in higher stover biomass. In general, corn stover yield was higher where soil moisture was less compared with landscape positions with saturated soils. However, the positive effect of CTI, where higher CTI values would result in higher yields, suggests that excess soil moisture may not be as yield limiting as soil moisture deficits. Corn Grain

Corn grain yield at the depositional, flat, and SW hillslope positions were lower than yields at all other positions (Table 3). In 2006 and 2007, yearly precipitation was above normal and near normal, respectively, indicating the potential for excess soil water in depositional and flat landscape position. This finding is in agreement with previous studies showing reduced yield at lower landscape positions where excessive amounts of water tend to collect (Kravchenko and Bullock, 2000; Parent et al., 2008). Water is often an important driver of corn yields, but the lack of CTI or PDI as significant covariates suggests that other factors related to plant growth and development are driving plant productivity (Table 4). In general, poor conditions for root health, poor nutrient uptake, and increased nitrogen leaching due to excess water were likely the causes of low corn grain yields at depositional and flat landscape positions (Parent et al., 2008). Lower corn grain yield at the SW hillslope position cannot be fully explained based on our data. There were also differences in the stover to grain ratio between landscape positions. The flat landscape position had a 6.9:1 ratio of stover to grain, whereas the depositional site had a 9:1 ratio. Hillslope positions ranged from 7.1:1 to 8.5:1 in stover to grain ratio. These types of changes have significant impacts on efficient handling of stover and grain for bio-industrial applications. Willow

SX67 willow productivity was lower at summit and S hillslope positions compared with all other positions (Table 3). 9882-41 willow productivity was lower at the summit and all hillslope positions compared with the flat and depositional landscape positions. Productivity of 9882-41 willow was lowest

at the S hillslope compared with all other positions. These data show a clear genetic by environment interaction among willow clones. For example, at the SW hillslope position SX67 productivity was high, whereas 9882-41 productivity was low. SX67 willow productivity was also higher at the depositional and S hillslope position compared with 9982-41. High productivity of both willow clones at the depositional and flat positions relative to other landscape positions indicate that willow is a good cropping option in landscape positions with saturated, anaerobic soils. These areas tend to retain more soil water over longer periods of time compared with other landscape positions, as indicated by CTI, SpecCat, and PDI values. Willow plantations have been shown to use a large amount of water; therefore, water is one of the main limiting factors affecting willow yield (Lindroth and Bath, 1999). Above-normal precipitation coupled with landscape positions that tend to collect and retain water resulted in high willow productivity relative to other landscape positions. Our findings agree with Lindroth and Bath (1999) in suggesting that land selection criteria should focus on local hydrology to locate fields that use more water than is delivered by precipitation alone (e.g., stands that receive water laterally). SpecCat and NO3–N were related to SX67 willow biomass yield (Table 4). The positive correlation between SpecCat and SX67 willow biomass indicates that landscape positions that receive more surface water have higher productivity. The correlation between N and SX67 biomass yield was negative, suggesting that this willow clone is well suited for marginal lands. Soil NO3–N, P, K, CTI, and PDI were all identified as significant covariates related to 9882-41 willow biomass production (Table 4). Soil NO3–N and P were positively correlated, whereas K, PDI, and CTI were negatively correlated to 9882-41 willow biomass yield. These relationships indicate that higher soil NO3–N and P levels would lead to higher biomass yields at any landscape position but also that those effects would be countered by environments with higher available soil moisture (signified by high CTI and PDI values) that result in lower biomass yields. This finding suggests that different willow clones can be grown efficiently in saturated and well drained soils. However, careful consideration must be made with respect to choosing the right clone for the right environment. Regional breeding programs are key to maximizing production efficiently through proper selection of willow genetics (Weih and Nordh, 2002). Cottonwood and Poplar

Cottonwood biomass yields were consistent across all positions, showing no differences in yield among sites (Table 3). Poplar biomass yield was lower at the depositional position compared with all other positions. Cottonwood and poplar did not display significant correlations between biomass yields and soil/terrain attributes (Table 4). Previous studies suggest a significant genetic by environment interaction among Populus sp. (Yu and Pulkkinen, 2003; Orlovic et al., 1998). However, these studies are often conducted across large spatial scales that represent highly variable and contrasting environments. The stability of poplar and cottonwood productivity in this study suggests that poplar and cottonwood productivity are relatively stable at small scales. However, this may have been an


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artifact of higher-than-normal precipitation both years of the study. Lack of significance among measured covariates suggests that this site did not represent the extremes in soil or terrain variables that influence Populus sp. growth or that we did not measure the right variable (e.g., lower productivity of poplar at the depositional compared with summit position cannot be explained by measured covariates). Switchgrass

Total switchgrass biomass yields were lower at the depositional, S hillslope, SW hillslope, and N hillslope compared with other landscape positions (Table 3). All measured soil and terrain attributes were significant covariates for switchgrass biomass yield (Table 4). A positive correlation between biomass yield and soil NO3–N, soil K, PDI, and CTI were observed, whereas negative correlations were observed between switchgrass biomass and soil P and SpecCat. Positive correlation of PDI suggests that fertile soil with a thick A horizon at any landscape position results in higher switchgrass yields. The finding that CTI has a positive effect on yields while SpecCat has a negative effect illustrates a conflict between the effects of soil moisture on switchgrass biomass yield. Biomass Yield within a Landscape Position Yield within single landscape positions was evaluated to determine differences between species. Evaluation of each species across all landscape positions provides information that can be used to further refine management decisions by delineating one or two crops that are best suited for that particular landscape. This is also a first step toward designing systems that maximize landscape production by establishing only those crops that are likely to produce the most biomass at a given landscape position. Alfalfa, cottonwood, and poplar had similar biomass production and yielded more total biomass than all other species at the summit (Table 3). At the depositional position, SX67 willow yielded more biomass than all other species, whereas alfalfa, corn grain, corn stover, and switchgrass produced less total biomass than all other species. High SX67 biomass productivity at this position displays the ability of willow not only to tolerate saturated soils but also to thrive. Conversely, alfalfa, corn, and switchgrass yields are low at the depositional position because they are species more suited to dry soils rather than the soil conditions present at this landscape position (Tables 2 and 3). The high willow/low alfalfa-corn-switchgrass biomass yields can be explained in part by the SpecCat, CTI, and PDI values for the landscape position, which signify saturated soil conditions for most or all of the growing season. The single-stemmed woody species (cottonwood and poplar) yielded significantly more biomass at the W hillslope than all other species (Table 3). At the flat position, D125F cottonwood yielded significantly more biomass than all other species, whereas corn grain, corn stover, and switchgrass yields were significantly lower than those of all other species. The soil environment present at the flat landscape position is not optimal for the types of corn hybrids used in the Midwest. The highly positive SpecCat, highly negative CTI, and highly positive PDI values for the landscape position suggest lengthy periods of wet, highly satu520

rated soils, which are not conducive to the production of highly productive corn plants. Alfalfa, corn grain, cottonwood, poplar, and SX67 willow yielded more biomass than corn stover, switchgrass, and 9882-41 willow at the S hillslope position (Table 3). The four higher-yielding species biomass totals did not differ significantly from each other, nor did the biomass totals for the four lower-yielding species. At the SW hillslope position, alfalfa, cottonwood, poplar, and SX67 willow yielded more biomass than corn grain, corn stover, switchgrass, and 9882-41 willow. Low corn grain and corn stover yields can be explained in part by the low SpecCat and low PDI values, which indicate low levels of available soil moisture. The high SX67 willow yield is not consistent with landscape positions with low available soil water levels, so other conditions may have been present to allow the willow clone to have high biomass production (e.g., localized available soil water, positive soil fertility conditions that overcame negative soil water conditions, etc.). Poplar yielded more biomass than all other species at the N hillslope position (Table 3). The ability to integrate annual and perennial crops over space creates new opportunities for increasing financial returns and reducing risk over that of traditional crop monocultures. For example, this study and others show that corn yield is sensitive to changing landscape position (Stone et al., 1985; Kravchenko and Bullock, 2000). Therefore, field areas with low corn yield represent an opportunity to enhance overall productivity by replacing corn with a more productive crop. Results show that corn yield is lowest in depositional areas that are characterized by high PDI and CTI values. Conversely, willow and cottonwood productivity is shown to be among the highest at this same landscape position. Replacing corn with willow or cottonwood may be a viable strategy for enhancing productivity and increasing economic return. Future agricultural systems must consider other elements of decision making that ultimately strive for a multifunctional approach to agricultural land management. Environmental and ecological components must be considered, based on a sound economic foundation, to meet the goals of a multifunctional agriculture. For example, a desire to improve water quality or wildlife habit while maintaining productivity may define decision making in the context of multifunctionality. To be successful, this approach must integrate temporal and spatial components into the design of landscape management strategies (Brandt, 2003). Including perennial crops as part of the overall cropping system is one option for integrating spatial and temporal components in a way that extends soil cover, reduces erosion potential and nutrient loss, and improves soil physical and chemical properties (Borjesson, 1999; Kort et al., 1998). For example, the SW hillslope position is characterized by a relatively steep slope with potentially erosive soils. In this environment, perennial crops would be preferred over annual crops to help stabilize the soil. Results of this study show that alfalfa or poplars are good choices at the SW hillslope position and would not only help reduce erosion but may also improve productivity over corn. Depositional landscape positions also represent an area of environmental concern because of the potential for erosion and nutrient leaching due to excess water. Results suggest that SX67 willow and D125 cottonwood would Agronomy Journal • Volume 102, Issue 2 • 2010

be the preferred choice over other crops, both annual and perennial, at this site. Perennial crops can also improve wildlife habitat by providing shelter and breeding areas (Volk et al., 2004). Willow is managed as a high-density, short rotation coppice system and has a relatively short harvest cycle (3 yr) but a long production time (20+ yr). The coppice aspect of the production system maintains a dense bush-like planting with quick space capture that provides quality wildlife habitat as well as water quality and soil stabilization benefits (Volk et al., 2004). In contrast, Populous spp. are managed as a single stem (not coppiced), resulting in a longer period of growth to harvest age. This strategy also provides water quality and habitat benefits (Christian et al., 1997; Updegraff et al., 2004) but in a structurally different way compared with willow. A knowledge-based, site-specific approach to crop placement at this study site may involve planting willow or cottonwood at the depositional position, alfalfa on the SW hillslope position, and corn on the summit and gradual sloping positions. This strategy would result in greater environmental and ecological benefits through enhanced spatial and temporal diversity of cropping systems (multiple crops with different functions, form, and harvest timing) compared with a monoculture system. Furthermore, overall field productivity would be improved as well as the ability to access a range of markets resulting from a diversified cropping system. This study represents only 2 yr of data comprising a growing season and yearly total precipitation that was above normal in 2006 and near normal in 2007 compared with the 30-yr average. Growing degree units were also above normal both years of the study. Results may have been different when below-normal precipitation was experienced. Kaspar et al. (2003) found that in years with below-average rainfall, corn yield was negatively correlated with relative elevation, slope, and curvature, whereas in years with above-average rainfall, corn yield was positively correlated with relative elevation and slope. Variability in plant growth as a function of landscape position needs to be explored over longer time periods that comprise wet and dry as well as warm and cool weather conditions. Nevertheless, this study represents a novel approach to the design of cropping system strategies that lead to functional optimization of the landscape. This requires an understanding of site-specific crop growth and development in the context of economic, environmental, and social goals.

needed to characterize crop productivity across diverse climate conditions. This study provides a first step in developing cropping systems that provide a knowledge-based approach to crop selection and placement on the landscape with the goal of functional optimization.

CONCLUSIONS An understanding of biomass productivity on specific landscape positions or environments is essential to realizing the goal of supplying a reliable and consistent source of feedstock that meets quality specifications for the bioenergy market while increasing farm profit and providing ecosystem services. This research shows that biomass crop productivity is spatially variable and influenced by several hillslope processes. Alfalfa, corn, and switchgrass productivity was low in depositional areas where water tends to collect. Conversely, willow and poplar productivity was high in these areas. There were also differences in crop productivity between hillslope positions differing in slope and aspect. Data were collected over 2 yr characterized by above-normal precipitation. Therefore, more research is

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