ASSESSMENT OF THE ROLE OF SOE STRUCTURE AND WATER CONTENT

IN THE INTEWRETATION OF

SPATIAL VARIATION IN YXELD AND YIELD RESPONSE TO NfLnOGEN

A Thesis

Presented to

The Faculty of Graduate Studies

of

The University of Guelph

by

JUSTIN TO

In partial fulfillment of requirements

For the degree of

Master of Science

Noveniber, 2000

O Juçtin To, 2000

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ASSESSMENT OF THE ROLE OF S01L ÇTRUCTLJRE AND WATER CONTENT IN THE INTERPRETATION OFSPATIAL VARIATION IN MELD AND YIELD RESPONSE TO

NITROGEN

Justin To University of Guelph, 2000

Advisor: Dr. B.D. Kay

The Least Lirniting Water Range (LLWR), is defined as the range of water

contents in which aeration, water and soii resistance are the least lirniting for plant

growth. It was hypothesized that the LLWR and water contents measured outside of the

LLW R would explain much of the variation found in yield and yieid response to

fertilizer N. Water contents, soi1 properties and yields were measured on 12 sites across

southem Ontario. Results showed that the LLWR parameters were poor predictors of

the variability in yields. Many observed water contents were aiso found to be Iess than

the wilting point. Kay et al. (1999) d e h e d a lower water content limit based on the

cessation of photosynthesis ( 0 4 . This study determined that the difference between

water contents and 90, (plant extractable water) correlated well with yield. Organic

carbon was significantly correlated to yield and yield response, and improved yields

under drought and saturated conditions.

ACKNOWLEDGEMENTS

1 would l&e to sincerely thank Dr. Bev Kay for his great support and

guidance during the course of this study, as weU as the members of my

conimittees, Dr. Tollenaar, Dr. Beauchamp, Dr. O'Halloran, Dr. Chesworth and

Dr. Groenevelt.

1 would also like to thank al1 the f a m s that made my research possible,

Doug Aspinaii of OMAFRA, Mr. McCracken, Podolinksi Farms, Canagra Farms,

Mr. Caiiieron, Mr. Denys, Mr. Newconibe, the Elora Research Centre and the

Corn Producers of Ontario.

Finally, 1 would like to extend special thanks to Chris McNabb, Leslie

Veale, Lisa Levesque, Chris Chroniiak, Matt Firth, Etienne Bilz, Jen Campbell,

Soo Kim, Ione Smith, Nathaniel Novosad, Andrew Wood, Co- Roberts and of

i o u rse, Ranee Pd rardjdsing hani, Dr. Fallow and Jim Ferguson.

TABLE OF CONTENTS

........................................................................ ACKNO W LEDGMENTS i

................................................................................. LIST OF TABLES iv

LIST OF FIGURES. ............................................................................... v i

LIST OF ABBREVLATIONS- .................................................................... x

............................................................... CHAPTER 1: INTRODUCTION 1

1.1 Background ............................................................................ 1 1.2 Objectives .............................................................................. 4 1.3 Format of Tliesis ...................................................................... 5

............................................................................. 1 -4 References. 6

CHAM-ER 2: USING PEDOTRANSFER FUNCTIONS TO PREDICT THE WATER RELEASE CURVE AND THE SOIL

......................................................... RESISTANCE CURVE. 7

.................................................................. Introduction 7 Materials and Methods ................................................... 10

................................................... Results a n d Discussion 14 Coriclusioi~s .................................................................. 39

.................................................................... R e ferences 41

CHAPTER 3: THE SENSTTIVITY OF CORN (Zea mays) MELD TO THE ......................... LEAST LIMITING WATER RANGE OF SOIT3 53

.................................................................. 3.1 Introduction 53 ................................................... 3.2 Materiais and h4ethods 56 ................................................... 3.3 Results and Discussion 59

.................................................................. 3.4 Conclusions 75 .................................................................... 3.5 References 77

CHAPTER 4: UNDERçTANDING YIELDS OF CORN (Zea mays) AND ITS RELATIONSHiP WZTH PLANT EXTRACTABLE WATER

............................... AND SOIL PROPERTES .- ............. 79

.................................................................. 4.1 Introduction 79 ............................... 4-2 Materids and Methods ...........-. .... 84

4.3 Resdts and Discussion .......... .... ................................. 87 ................................ 4.4 Conciusions .. .................. 104

.................................................................. 4.5 References 105

CHAM'ER 5: UNDERSTANDING THE VARL4BILICTY OF YIELD RESPONSES OF CORN TO NlTTROGEN FERTILIZER ACROSS RANGES OF WATER AND

................................................. SOIL CHARACTERISTICS 107

................................................................ 5.1 Introduction 107 .................................................. 5.2 Materials and Methods 109 .................................................. 5-3 Resul ts and Discussion 112

................................................................ 5.4 Conclusions 124 ..................... 5.5 References ..... . 126

............................................... CHAPTER 6: GENERAL CONCLUSIONS 130

LIST OF TABLES

Table 2.1. Summary of soil properties for al l plots in each site (0-30cm depth). .......................................................... -15

Table 2-2. Mode1 forriis fitted to the measured water release da ta (385 cores wi th 3412 data points overall).. ..................... 17

Table 2.3. Results of niodel fits to measured water .......................................................................... release da ta.. 22

Table 2.4. Mode1 fornu fitted to measured soil resistance to penetration data (first data set of 321 cores) .................. 29

Tn ble 2-5. Results of niodel fits to measured soil resistd nce to penetra tion data (first data set). .................................. 33

Table 2-6. Paranieter estiiiiates for the Sand water .............................................................. release curve function. 44

Table 2-7- Parameter es tinia tes for the Clav wa ter release curve function ............................................................... 45

Table 2.8. Paraiiieter estirnates for the Loam-Clay water release CU rve function. ............................................................. 46

T'ible 2.9. Paranieter estiniates for the Loani-Sand water ............................................................. release curve function. 47

Table 2.10. Parameter estiniates for the Sand soil ......................................................... resistance curve function. 48

Table 2.1 7. Parameter estimates for the Clay soil resistance curve function- ......................................................... 50

Table 2-12 Paraiiieter estimates for the Loani-Clay soil ......................................................... resistince curve function. 51

Table 2.13. Paranieter estiiiiates for the Loaiii-Sand soil resistance curve function. ......................................................... 52

Table 3.1. Sumrnary of soil properties for dl plots in each si te (0-30cni depth). ...................................................... 60

Table 3.2. Results of regression analyses between yield .............. (+N> and nieasured seasonai average water contents @seas)- 61

Table 3.3. Statistical data for LLWR, FU,,,, and final yields (+N treatments, 0-30cm depth) ........ ... ........................................ 62

Table 3.4. Results of regression analyses between y ield (tN) data and the LLWR, Fit,,,,.. .................................................. 63

Tcible 4.1. S u i i i ~ i i ~ i r \ ~ of soil properties for al1 plots on eack site (0-30cm dep th). .......................................................... 88

Table 4.2. Statistical data for average e,., PEWWs and final yield data for al1 plots.. ........~............................................- 89

Table 4.3. R e s ul ts of regression analyses between yield (+N) and ~Iverage nieasured seasonal water contents ( L s ) - . ......... .... 90

*l'cible 3.1. Resul ts ot regression cinctfvses behveen yield ....... (+NI and average uieüsured seasonal water contents (PEW,,,). -91

Table 4.5. Resul ts of regression analyses between yield (+N) and organic carbon (OC) .................................................... 97

Table 5.1. AIGOVA tables of location and fertilizer effects for each site.. ............. ,. ......................................... -115-117

l '<ible 5.2- I<rsuits u t i-egression anaivsis between yield (UN] nnd average soil water content during

............................................. the growing season for each site.. 127

Table 5.3. Results of regression analysis between yield ( O N ) and average plant extractable water

....................... during the growing season (PEWms) for each site.. 128

*l'.i blr 5.3. Rrsult t , ol' 1-egressioti ciiicilysis between y ield ................................ (ON ) .i iid u rgci nic c<irboii (OC) for each si te.. 129

L E T OF FIGURES

Figure 2.1. Site locations in southern Ontario.. ........................................... 10

Figure 2.2 Cornparison of da Silva and Kay (1997) predicted vs. measured values of volumetric water content across a range in matric potential(-0.001 to -1.5 m a ) . .................. 16

Figure 2.3. Soi1 textural classes and textural dishi bution of soi1 cores.. ............ 19

Figure 2.4. Cornparison of T3 pred icted vs. rneasured values of volumetric water content across a range in mahic potential (-0.001 to -1.5 MPa) ................................................. 22

Figure 2.5. T3, DS1 and DESORPMOD prediction of water .................................. reIease curve data of independent data set 23

Figure 2.6. Plots of DS1 and T3 predicted values vs. measured values for the critical iiiatric potentials -0.01 MPa (Field Capacitv) and -1 -5 MPa (Pem~anent Wilting Point). ............ 24

Figure 2.7. Four examples of plots of T3 predicted and measured ............................................................. wa ter release curves. 25

Figure 2.8. Cornparison of d a Silva and Kay (1997) SRC predicted vs. ineasured values for the first data set.. .................................. 27

Figure 2.9. Coniparison of 84 predicted vs. measured values of soi1 resistance for the first data set ............................................. 30

Figure 2.10. Coinparison of T7 (linear ip terni) predicted vs. measured values for the first data set.. ....................................... 31

Figure 2.11. Co~nparison of T7 (with y=) predicted vs. measured values for the first data set ....................................................... 32

Fi pu r e 2.1 2 Coni parison of 84 predicted vs. iiieasured values of ...................................... independent data set (second data set). 34

Figure 2.13. Coinparison of T7 predicted vs. rneasured values of independent data set (second data set) ....................................... 35

Figure.2.14. Comparison of TS predicted vs. measured values of independent data set. .. . . .. . .. . . . . . . . . . . ... . . . . . . ... .. . .. . .. ... ... . .. . .. . .. ... . ... 38

Figure 2.15. Comparison of predicted 0 values using the T8-iterative method vs. measured 0 values for second data set (164 data points) ............ ...........-......-. . . . 39

Figure 2.16. Coniparison of T3(Sand) predicted vs. measured values of voiumetric water content (162 data points) .................... . 44

Figure 2.17. Comparison of T3(Clay) predicted vs. measured values of volumebic water content (1306 data points).. ... ............ ... 45

Figure 2.1 8. Coin piii-ison of T3(Loaiii-Clav) predicted vs. nieasured values of voluinebic water content (1301 data points).. ... 46

Figure 2.19. Coniparison of T3(Loam-Sand) predicted vs. measured values of volumehic water content (645 data points). .. . .. 47

Figure 2.20. Corn parison of T7(Sand) predicted vs. nieasured values of soil resistance (32 data points- first data set). . . . . . . . . . . . . . . . . .. 49

Fiçu re 2.21. Coiii parison of TS(C1iiy) predicted vs. measured vdl ues of soil resis tance (196 data points- first data set). .. . . .. . . . . . . . ... 50

Figure 2.22. C o n parison of T7(Loani-Clay) predicted vs. measured values of soil resistance(74 data points- first data set). . . . . -51

Figure 2.23. Comparison of T7(Loam-Sand) predicted vs. measured values of soil resistance (142 data points- first data set). . .. 52

Figure 3.1. Map of sites in southern Ontario ............................................... 56

Figure 3.2a,b. Plot of the Elora (no tiil) 1999 yield data relationship with the LLWR and Fuw ,....... . ..... .... ...... .................. 64

Figure 3.3. Plot of Canagra North (conventional till) yield data rela tionship with the LLWR.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - . . - . . . -. . . . . . . . . . . .-. 65

Figure 3.4 a,b. Pred ictioii of wüter contents by the TS and DÇ2 SRC furictions vs. measured values for an indepeii i lei~ t data set (-1 64 data points). . . . . . . . . . . .. . .. . . . . . . . .. . .. . .. . .. ... . ..67

Figure3.5. Plot of vield (+N) vs. the frquency of water contents falling below the permanent wilting point during the growing season (Fpwp) at the Elora (conv. till) 1999 site ................... 68

Figure 3.6 a,b,c. T3, DESORPMOD predictions relative to each other using da Silva and Kay (1997) data.. ................................. ..69

Figure 3.7. Plot of difference values (minimum recorded TDR values during the growing season - core measured

............................... PWP) across al1 1998 sites.. ..y0

Figure 3.8 a, b. Coni parison of ni inim um recorded soii water vctlues with T3 and DSl PWP predictions .................................. --71

Figure 3.9. Plot of yield and the niinîmum recorded water content minus the DS1 predicted PWP, across landscape positions (CS-Canagra site). ....................................... 72

Figure 3.10. Plot of volumetric water content values illecisu i-ed bv TDR vs. voluiiietric water content convertrd trotii grcivi iiietric sani ples.. .................................-...-• -74

Figure 3 . l l . Exaitiple of n predicterl water release curve ....................................................................... froiii a clay soi1 75

Figure 4.1. Conceptual mode1 describing plant health as ............................................................ a function of soi1 water 81

............................................... Figure 4.2. Map of sites in sou thern Ontario. 84

Figure 4.3. Esciin ple of a site with little yield variation .......................................................................... (Deiivs site). 92

Figure 4.4. Examples of nonlinear behaviour between y ield and PEW,.,. .......................................................-........ ..93

Figure 4.5af b. Nega tive correlations found behveen yields ................................ and soi1 water measures (Podolinski site).. --94

Figure 4.64 b. Di tfereii t correlii tiotis between yield and ............. r r l a t ive cotii pcictioii: Elora no till 1999 (a), Cç-Canagra (b). 95

Figure 4.7. Nonlinear beliaviour of yield vs. OC (McCracken site). ................ ..96

Figure 4.8. Plots of yield with PEW,. and OC. for the Cç-Canagra site ........... 98

Figure 4.9. Exampie of the relationship between PEW,. ............................................................ and OC (Cameron site) 99

Figure 4.10. Behaviour of yield. OC and soi1 water contents ........................................................... on the Podolinski site -100

Figure 4.11. Conceptual mode1 describing yields as a function ....................................................... of plant extractable water 102

Figure 4.12 Exarnple of the temporal stability of extractable .......*....-. ......-... water across spatial patterns (McCracken site) .. -103

Figure 5.l. Map of sites in southern Ontario .............................................. 109

Figu re 5.2. Exauip les of differeiitial location effects on yields: ..................... (a) CC-Cnnagra no till 1998. @) Elora conv . till1999 112

Figure 5.3. CC-Canagra (no till) 1998 site . Evident OC effect upon yield (+N and ON) but no statistically

........................................ signifiant effect of +N treatment .....Il3

Figure 5.4. Denys site . No evident OC effect upon yields but St, itisticiiIIy signifie-ant N fertilizer effect .................................... 114

Figure 5.5. Yield +N and ON across a range in OC (McCracken site) ............... 118

........ Figure 5.6. Yield +N and ON across a range in PEW.. (McCracken site) -119

Figure 5.7. Yield +N and ON across a range of OC (Cameron site) ................. 120

........... Figure 5.8. Yield +N aiid ON dcross à range ot PEW .,.. (Caiueron site) 120

Figure 5.9. Yield +N and ON across a range of OC (Podolinski site) ................ 122

Figure 5.10. Plot of yields in the ON treatment and the frequency of seasonal water contents measured

................. above the 10% air-filled porosity limit (Podolinski site) 122

LIST OF ABB@VIXT"TONS

ANOVA = analysis of variance

AWC = Available water holding capacity

Bd = Buik density

84, B5 = Soil resistance pedo tram fer function forms derived by Boucher (1990)

COLE = Coeffiaent of Linear Extensibility

DS1= Water release curve pedotransfer function derived by da Silva and Kay (1997)

DS2 = Soi1 resistance cuve pedotransfer h c t i o n derived by da Silva and Kay (1997)

FC = FieId capacity

Fii,. = Frequencj. of seasonal w a ter contents fnlling outside the LLWR ùuring the

s rowing sectson

F P q = Frequency of seasonal water contents falling below the permanent wilting point

during the growing season

LLWR = Least Limituig Water Range

N LW II = Non-Lirniting Water Range

OC = Organic carbon

PEW,,, = average soîi water content during the growing season measured above 00,

PTF = Pedotransfer Function

PWP = Permanent wiltirig point

RMSE = Root Mean Squared Errors

R C = Relative compaction

SRC = Soii resistance curve

SSE = Sun-i of Squarecl E r ro r s

TD f? = Tinie-Do niai11 Reflec tome try

Tl-TS = Pedotransfer functions a ttenipted in this study

W R C = W a ter release curve

9 = volumetric water content

e,, = average soil water content dwing the growing season

90, = lower limit of water content at which photosynthesis ceased (Kay et al. 1999)

\y = matric potential

CHAPTER 1: INTRODUCTION

1.1 BACKGROUND

Agricultural fields Vary considerably in their soil properties, landscape features, and

management histones. As a result, this variability has been showri to contribute to variation

in yield. Colvin et al. (1996) described the yield patterns for corn and soybeans in rotation

after six consecutive years within a single field. They found that certain locations wïthin the

field had consistently high, consistentIy low, or erratic yields when compared to whole field

averages. The variation in yield and in many soil properties can be measured with current

technology, but the roo t causes of this spatial variability are yet unexplained. The broad

objective of this project was to determine the degree in which variation in yield and yield

responçe to fertilizer nitrogen (N) are explained by variation in soil structure and water

content. If the effects of soil structure and water content on yieId and yield response to

nutrients can be found to be significant, then appropriate methods can be developed to

nia9 them and develop corresponding management plans.

I t is assutned that the iniportance of soil structure to yield is related to the soil's

ability to provide oxygen, water and support the growth of roots. Carnbardella et aI. (1994)

found that aggregate size distribution contributed significantly to yield variability in seven

out of seven years, and bulk density, soil moisture, and soil texture in 4 out of 7 years. It

was tho ugh t tha t aggrega te size distributions in tegrated the effect of soil characteristics

such a s texture, minera logy, organic ma tter content, % pore space, soil matric potential, and

surface seal formation. These soil properties deal with the direct and indirect effects of

structure on soil-water relations and plant available water, which can in tuni affect yield.

To quantify soil structure we wiii attempt to use the parameter Non-Limiting Water

Ra nge (NLW R), in troduced by Letey (1985), later renanied Least Limiting Watex Range

(LLW R). The term Least Limi ting Water Range is defined as the range in soil water content

after rapid drainage has ceased within which Mtat ions to plant growth associated with

water potential, aeration and mechanical resistance to root penetration are minimal (da

Silva and Kay, 1997).

The LLWR is a ranse, defined by an upper limit and a lower limit, The upper Limit

value is chosen d s the lower vnlue of water content in which aeration to the roots becomes

liniiting, or when rapid drainage ceases. Aeration was concluded to be limituig at an air-

filled porosity of 0.1 cm3/cm~Grable and Siemer, 1968), and rapid drainage was concluded

to cease at field capacity (FC) at a water potential of -0.01 MPa (Haise et al. 1955). The lower

liriiit values were chosen as the greater value of the water content below which water

cannot be extracted by plants (perri-ii-inent wilting point or -1.5 MPa) found by Richards and

Weaver (1944), or the wa ter con tent at which mechanical impedance restrïcts root growth.

Cone penetrometer resistance is conunoniy used to simulate the impedance encountered by

plant roots. Young et al. (1997) found that mechanical impedance of root g-rowth directly

affected plant growth, and based on studies done by Taylor et al., (1966) and Greacen

( IYr i6 ) , a cone resistance of 2 MPa was used as the upper limit of penetration pressure

exerteci by the roots of iiiost field crops. Fron-i this, the other crîterion for the lower LLWR

lir~iit was based o n the water content in which the soil's penetration resistance exceeds

2MPa.

1 t is hy pothesized that the LLWR can be used as a measure of the soirs ability to

provide wa ter, air nncl support. ln essence it is hypothesized that the LLWR c m be used as

a nieasure of the soil's ability to provide water, air and a favorable environment for root

development and as such, the magnitude of the LLWR will be positively correlated with

yields. I t is further hypothesized that crop growth will. be negatively correlated to the

frequency in which seasonal water contents faU outside the LLWR (Fum)- Here, it is

reasoned that as the soil dnes during the growing season, the nurnber of seasonal water

contents measured below the lower liniits wiIl increase and yields will be negatively

affected- This reasoning is aIso applicable to seasonal water contents measured above the

upper limits. Seasonal water contents r i su ig above the upper Limits v ~ o u l d induce aeration

problems and thus also negatively affect yields.

These hypotheses are supporteci by work done by da Silva and Kay (1997) where

they used bo th the LLW R and Fii*, (in the O-20cm depth) to assess shoot growth of corn.

They found tha t shoot growth was indeed positively correlated with the magnitude of the

LLWR and negatively correlated with Fuw,- The effects of the LLWR and Fuwr upon yields

however, are unknown.

Also, within the scope of this project, soii spatial variability wiU cover a large range

of soi[ propertws- D e t e r n ~ i n u x the w a ter release curve (W RC) and the soil resistance curve

(SRC) over this range will be time consurning and expensive. Considering that both the

W R C and SRC are affected by soi1 physical properties such as texture, organic carbon (OC)

and b ulk density, i t sho ulrl be possible to determine inathematical relationships to predict

thé curves. Bounia and van Lanen (7987) introduced the terni pedotransfer functions (PTFs)

~ i s I I I ~ themd ticdl expressions thd t reldte ciiffersnt characteristics and properties with one

ai10 ther, Le., PTFs could be used in translating data that we c m easily determine (bulk

density, texture, OC) into data we require, such as the WRC and SRC. Da Silva and Kay

(1997) developed several PTFs, one of which described the WRC and another describing the

SRC, both from various soil properties withïn a single field in southem Ontario. It is hoped

that these PTFs can be used to predict the WRC and SRC for the range of soils in this

project a n d in turn predict the spatial and temporal variabiliv of critical water contents and

soit properties that atfect plant growth.

The broad objective of this study was to detennine the degree in which variation in

yield and yield response to fertilizer N are explained by variation in soil structure and

water content. Specificdy, the objectives were to: (a) assess the ability of the pedotransfer

functions deterrnined by da Silva and Kay (1997) in predicting the water release and soil

rrslstdnce properties for d rnngr of soils within southern Ontario and to develop new

pedotransfer functions for the water release and soil resistance curves if the da Siiva and

Kay (1997) functions were found to be inadequate, and (b) determine the degree in which

the LLWR and seasonal water content data in the form of Fuw, can explain the variation in

yield and y ield response of corn (Zea mays) to fertilizer. The study was focused on corn

crops from sites across southern Ontario, conducted over the two field seasons of 1998 and

1 +M. 5011 struc-t LI re ~11iCf wn ter con teil t were deternùned in the top 30cm of the prome.

1.3 FORMAT OF TH ESlS

This thesis is written in the format of 4 distinct units. Chapters 2-5 each contain

s e p rate introductions, niethods, results, references and appendices. Consequently some

overlap may exist between chapters.

1.4 REFERENCES

Bouma, J., and H.A.J. van Lanen. 1987. Transfer functions and threshold values from soil

characteris tics to land qualities. Pp. 106-1 11. In Quantified land evaluation, Proc.

Worksh. lSSS/SSSA, Washington, DC, ITC Publ., Enshede, the Netherlands-

Cambardelia, C. A., T.B. Moorman, J.M. Novak, T-B. P a r k , D.L. Karlen, R.F. Ruco, and

A.E. Konopka. 1994. Field-scale variability of soil properties in central Iowa soils. Soi1

Sci. Soc- AM- J. 58:1501-1511

Coivin, T.S., D.B. Jaynes, and D.L. Karlen, 1996. Six-year Yield varïability in central Iowa.

(in review, TSAE)

da Silva, A. P. and Kay, 6. 0. 1997- Estirnating the least Limiting water range of soils from

properties nnd nianagenient. Soil Sci, Soc. of Am. 1. 61(3):877-883.

Crable, A.R., Siemer, E.G. 1968. Effects of bulk density, aggregate size, and soil water

suction on oxygen diffusion, redox potential and elongation of corn roots. Soil Sci. Soc.

Am. Proc. 32:180-186.

Creacen, E.L. 1986. Root responsr to soil mechanical properties. Trans. 13u1 Congress intem.

Soc. Soil Sci., Hciiiibcr rg, Gerniany. 5:20-47

Haise, H.R. Haas, H.J. , Jensen, L.R. 7955. Soil moisture studies of some Great Plain soils: II.

Field capacity as related to 1/3-atniosphere percentage and "minimum point" as related

to 15 and 26- atniosphere percentages. Soil Sci. Soc. Am. Proc. 34:20-25.

Lete!:, J. 1985. Relatioiiship between soil physical properties and crop productions. Adv.

Soil Sci. 1 277-294.

Richards, L.A., Weaver, L.R. 1944. Fïfteen atmosphere percentage as related to the

permanent wilting point. Soil Sci. 56:331-339.

Taylor, 'H.M- Roberson, G.M., Parker, Jr.J.J. 1966. Soil strength-root penetration relations for

medium to coarse textured soi1 materials. Soil Scî- 10218-22.

Young, LM., Montagu, K,, Conroy, J., Bengough, A-G. 1997. Mechanical impedance of root

growth directly red uces lea F elonga tion rates of cereals. New Phytol. 135: 613-619.

CHAPTER 2: USING PEDOTRANSFER FUNCIIONS TO PREDICT THE WATER RELEASE CURVE AND THE SOIL RESISTANCE CURVE

INTRODUCTION

Agricultural fields Vary considerably in the5 soii properties, landscape features, and

management histories. As a result, this variability has been shown to infiuence yieid.

Cambardella et al. (1994) found that aggregate size distribution contri'buted sigruficantly to

yield varïability in seven out of seven years, and b u k density, soil moisture, and soil texture

in 4 out of 7 years. It was thought that aggregate size distributions integrated the effect of

so il charac teris tics such n s texture, niineralogy, organic matter content, % pore space, soi1

matric potential, and surface seal formation. The impact of soil structure on yield is related

to the soil's abiiity to support the growth of root . and to provide oxygen and water. The

broad-based purpose of this project is to determine the effect of soi1 structure and water

content on yield and yield response to fertilizer N.

To deterniine the relative effects of soil structure and seasonal water content on yield

variation, it is critical to know how matric potential and penetration resistance varies with

soil water content Water content changes with matric potential, and the water release c w e

(W RC) is a critical relationship that can d e h e things such as pIant available water, the

cessation of rapid drainage (Field Capacity, FC) and the point at which water is held too

tightly by the soi1 matrix for plants to use (Permanent Wilting Point, PWP). Soi1 penetration

resistance changes with water content, a relation called the soil resistance c u v e (SRC). Thiç

relationship becomes critical for plant growth when soil water content becomes so low that

soil strength or mechanical irnpedance restricts root growth. Cone penetrometer resistance

is commonly used to simulate the irnpedance encountered by plant roots. Young et al. (1997)

found that mechanical inlpedance of roots shows direct negative effects on leaf growth

rates, even in non-liniiting wa ter and nutrien t reginles. Also, in studies done by Taylor et al.

(1 966) and Creacen (1 9S6), a cone resis tance of 2 MPa was found as the upper lunit of

penetration pressure exerted by the roots of most field crops- Therefore, howledge of the

SRC can provide us with a tool to detennine points of critical soi1 water within a growing

season in which mechanical inipedance wiLI effect plant growth.

Soil properties are spatialiy variable. Detennining the spatial variability of the WRC

and SRC will be time consuming and expensive. Considering that both the WRC and SRC

are affected by soi1 physical properties such as texture, orgariic carbon (OC) and bulk

density, it should be possible to detennine mathematical relationships to predict the c w e s .

Bouma and van Lanen (1987) introduced the tenn pedotransfer furictions (PTFs) as

n i a thenia tical expressions tha t relate clifferent characteristics and properties with one

dnothel-, i.e., PTFs couic- be used in translating data that we can easily detennine @L&

density, texture, OC) into data we require, such as the WRC and SRC. Databases of soi1

hydrauiic properties have been developed into PTFs in the USA (Leij et al., 1996), Europe

(Wosten et al. 1995), and Australia (Minasny et al., 1999), but the utility of these h c t i o m s is

most likely restricteci to the soils from which they were developed. If we are to use PTFs

b \ : ~ t h in this pi-ojrct, the functions inust be developed to encompass the variable soil

pro perties of the local region. Da Silva and Kay (1997) developed several PTFs, one of which

described the WRC and another describing the SRC, both from various soil properties

within a single field in southern Ontario. The WRC PTF was of the fom:

8 = a q ~ "

where 8 = volumetric wa ter content. y1 = matric potential, a and b are functions of % clay,

bulk density m d OC. By linearizing this equation and using muiti-linear regression

techniques, their mode1 accounted for 94% of the variation in inû. The SRC M F was of the

form:

SR = cûdBde

where Bd is bulk density, c and dare functions of % clay, bulk density and OC and e is a

fimction of % clay and OC- Lineariurig this equation and using multi-linear regression, their

i-iio~ilel accounted for 86% of the variability in Ln(SR).

The objectives of this chapter were: (i) to assess the ability of the pedotransfer

functions determined by da Silva and Kay (1997) in predicting the water rele~se and soil

resis tance curves for a range of soils within southem Ontario and (ii) to deveiop new

pedotransfer functions for the water release and soil resistmce curves if the da Silva and

Kny (1997) functions were found to bé hadequate,

2.2 MATERIAL AND METHODS

This study was conducted upon 6 farms during the 1998 growing season and 4 farms

during the 1999 growing season. AU f a rm sites were iocated between Thamesville and

Beeton, Ontario, Canada (Figure 2.1). Ail farms were planted to corn (Zea mays) in the

season of sampling. Tillage on al1 farms was either conventional tïll or zero-till management

Figure 2.1. Site locations in southern Ontario.

Plot locations ai each site were selected on the basis of landscape position. It was

expected that the different landscape positions would encompass the range in yields, soi1

properties a n d water content on a given site. The experiniental design of this project was a

factorial experiment using randomized complete block design with several repiications,

each with several plots dividecl by landscape position and 2 treatments: 150kg/ha N

fertilization and no N fertilization. Eight of the farms were characterized by establishing 24 -

plots: 4 replicates, each with the 2 N treatments, and 3 landscape positions: uppex dope,

mid-slope and toe-dope positions. The remainirig sites were located at the Elora Research

Station where each site was characterized by 30 plots: 3 replicates with 2 N treatments and 5

landscape positions. For aii farms, plots were approximately Sm long and 6 rows wide.

At the coinpletion O t each growing season, prior to harvest, undisturbed cores (5cm

diameter x 2.5cm height) were taken at each plot Four cores were taken at 5-7.5cm depth

and another four cores were taken at 20-22.5cm depth. Ln all, 2076 cores were collected. Each

core was wrapped in cellophane and stored at 4OC unid used for experimentation.

As part of nno t t~er stuciy, 272 cores (sanie dimensions) were taken from various

id r t n s in 1997 Ln order to iiicrease the range in soi1 properties being examined. These cores

were taken at 5-7.5~111, 15-17.5cm and 25-27.5cm depths and were also used for the WRC and

SRC analyses.

The W R C was deterrnined using the methodology of Topp et al. (1993). Overd , the

272 cores ta ken in 1997 and 165 of the cores taken in 1998 (chosen to encompass much of the

vnria tion across the tii rm) were used to determine the W K. Samples were saturated and

equfibrated on pressure pIates at 9 potentials (\y = -0.001, -0.002, -0.004, -0.006, -0.01,-0.0333,

-0-1, -0.4, and -1.5 MPa).

The SRC was deterniined using similar methodology to da Silva and Kay (1997).

Rrsis t m c e to peiietrd tion was deterniined on 777 cores, 495 of the cores taken in 1998 and

222 of' the 272 cores taken in 1997. Of the 495 cores taken in 1998,165 of these cores were the

same cores that underwent the water release curve analysis. These 165 cores, when taken

out of the pressure chambers at -1.5 MPa, were weighed and then used for resistance

measurement. The remaining 552 cores were saturated and brought to different potentials

(y = 4-001, -0-003, -0.006,-0.01, 4.0333, and -0.1) in pressure chambers to achieve variable

water contents for the measureinen t of soil resistance. The soil resistance to penetration was

measured using an ELE Digital Tritest 50. Instrument control and data collection was

achieved using Sciemetric 200 interfaced with a computer. Soi1 resistance was m.eaçured in

each core at penetration of 2mm/ min. using a cone penetrometer with a 30' cone angle and

- a 4mm basal diameter. Only one penetration was performed per core, each done in the

center of the core. Through the computer interface, approximately 450 readings were taken

per penetration. The average and the maximum penetration resistance found between the

0 . 4 ~ ~ 1 and 2,Ocni depth of each core was recorded.

After both the WRC and SRC data were recorded, al l cores were oven-dried a t 100°C

and bulk density values determined- The soii from each core was then split into 2 parts; one

pdrt was sieved (2iiiiii) nnd u s e d for pdrticie-size analysis; the other was ground anci used

toi- OC malysis. Pa rtide size analysis was done using the hydrometer method and

calibrated with the pipette method (Sheldrick and Wang, 1993). Organic carbon analysis was

done using the L E C 0 SC 444.

D a ta Analysis

The WRC and SRC PTFs used by da Siiva and Kay (1997) are nonlinear functions,

iînearized to statisticaiiy fit the data and because of this, their results show prediction of Ine

or LnSR Transforiiiation froin the log form back to its original form (8 or SR) introduces

erro r, therefore eva l u a tion of the W RC and SRC PTFs were evaluated in their original

nonlinear forms to gauge their true error in prediction. Nonhear analysis cannot use

conventionai statistical tests such as the coefficient of determination (r2) so anaiysis was

based upon cornparison of the S u m of Squared Error (SSE):

SSE = C(8i - €Ipi)'

for i = I.....N

where Bi and 8pi are the ith rneasured and predicted values of 0, respectively, and N is the

nuniber of data points. The Root Mean Square Error (RMSE) is:

where p is the number of parameters in the model. The RMçE is an evaluation of the mean

of the prediction error of a model- Regressions of predicted vs. measured values were also

andlyzed. This regression gave a sense of how well the model predicts and where prediction

s tra yed from niersurrd da ta. Coefficient of deterniination (rz) for this regression was looked

a t but was no t considered an accura te assessrnent of the prediction for the model.

New PTFs were deterrnined using multiple linear regression and non-hear

regression techniques. Validation of the new models were performed on independent sets of

d a LI ond judgrd bdssd on SSE, RMSE and the same 1:l regression analysis of predicted VS.

nieasu r d values.

2.3 RESULT5 AND DECUSSION

Water Release Curve (WRC)

Water release, texture, OC and bulk density (Bd) data were generated for the 165

cores taken in 1998 and the 272 cores taken in 2997. Of the 437 cores, several cores were

rendered unusable because of pore modification due to Worms, during experimentation.

Also, some WRC data was rendered unusable due to mïssing values. Overall, 3412 data

points were generated fiom 385 cores. Textural analysis of the cores showed that our data

ranged From O -60.3% clay content and 3.2- 928% sand content Organic carbon and B d

ranged fron10.25-5.88% m d 1.05- 1.79 g/cm3, respectively. A surnmary of aU soil properties,

by site, is shown in Table 2.1.

Table 2.1. Surnmas, of soi1 propertïes for ail plots in each site (0-30an depth).

Farm: BuJk Density: Organic Carbon: % Sand % Qay EC98: Elora 1998 (conv. till)

EYti: Elom 19% (no Lili)

EC99: Elora 1999 (conv. till)

E99: Elora 1999 (no till)

Podolinski (conv. till)

CC: Canagra Sou th (no till)

Newcombe (no till)

Average: St. Dev.: Minimum: Mctxhum: A vcmcrge: SL. Dev-: Müumum: Maximum: Average: St. Dev.: Ivlinimum: Maximum: Average: St. Dev.: Minimuni: hlldxiiii u~ii: A vwiij;c*: 51. Dev.: Minimum: Maximum: Average: St. Dev.: lvlinhum: Maximum: A vcral;c: SL. Dc-v.: h4 i r i i ni LI ni : Mixmi uni: A vcrci2;e:

SL. Dev.: Minimum: Maximum: Average: St. Dev.: Minimum: Maximum: A vcv-i !;c: SL. Dcav.:

i r i i III LI 1 1 1 :

Mdxiuium: A vcrcige: St. Dev.: Minimum: Maximum:

Average: St. Dev.: Miriini uni:

The developed by da Silva a&d Kay (1997) to predict the WRC (now referred to

as DSl), was used in conjunction with data on BD, clay and OC contents to predict values of

volumetric water content (8) which were then compared with measured values for the 385

curves. Analysis of prediction for DS1 resulted in a S E = 19.65 and a RMSE = 0.076

(cmS/cm3). The regression of predîcted vs. measured values (Figure 22) resulted in a r2 =

0.72 but also an intercept of -0.06 (cm3/cm3) and a slope of 1.07, where the intercept was

significantly different from zero and the dope was sigmficantly different fiom 1. Residuals

were not randomiy distributeci about the 1:1 lîne indicating that the functional f o m of DS1

niay not be appropriate to describe our data.

0.8 4 I SSE = 19.66 -

O 0.7 Ï RMSE = 0.076 - -

O 0.2 0.4 0.6 0.8

Measured water contents (cm3lcm3)

Figure I l I t l d S U

I I

2.2 Coniparison of water contents predicted by the da Silva and Kay (1997) M'F with :red voluiiietric wa ter contents across a range in matric potential (-0.001 to -1.5 MPa).

While D S l can predict approxin-iately 70% of the variability in the WRC data, for Our

pu rposes more accura te prediction was needed and therefore attempts were made to define

a new PTF. The different models attempted in definïng the new function are shown in

Table 2.2. The new attempts included a refitting of the parameters in DS1 to Our database, as

well as attempts to fit our data to the more common WRC fom, the Van Genuchtm

equation, and finally another S-curve Like function, a logistic function.

Table 2-2- Model formç fitted to the measured water release data (385 cores with 3412 data points overall).

Mode1 Name: Equationr

Tl : refitted da Siiva and Kay (1997) 0 = a*$~

T4: Simplifieci Van Genuchten

TS: Logis tic

Where t) = Vol, wdtcr ~-ontciit (init/~m-l), = matric potcntid (MPa), and a,b,X; &Br,aand nare C-urve fitting parameters.

Equation Tl, the refitting of the DS1 parameters to our database, was of the form:

6 = a@ and was Linearized to

w h r r r 8 = Vol. w d ter content (ciii'/ctii-'), III = nia tric potential (MPa). The resulting T l M F

(data nof shown) accounted for 81.7% of the variability in inû. Prediction of 8 (the nonlinear

fom) reçulted in a SSE = 6.58 and a RMSE = 0.044 (cm3/cm3). Regression of the predicted

with measured values of 8 resulted in a r2 = 0.814, an intercept of 0.03 and a slope of 0.94,

whrre the in trrcrpt was sigiiifican tly different than zero and the slope was significantly

ditteren t than 1. 1 t ici11 be seen thàt Tl shows an irnprove~nent on the DS1 PTF.

Two tunstions were also tested to determine if the more traditional s-cuve shape of

the WRC could be sirnulated. They were: the Van Genuchten equation (T2),

8 = 8 s - 8r +8r (1 + ((a* l I)n)(1-l/n))

where 8s = €3 a i saturation, 8r = the residual8, a = the air-entry value and n = the curve

shaping parameter, and a logistic function (T5),

where a corresponds to û a t saturation, and band k are c u v e shaping parameters. Both T2

and TS were fitted to aU water retention data using nonlinear regression (Gauss-Newton)

procedures. This method attempts to rninimize the sum of squared errors (SE) using an

itera tive method when given the functional form and starting values for all coefkients

within t h e functiori-

Results for nonlinear regression of water release data across all cores for the T2 and

T5 functional forms resulted ut little or no convergence of the coefficients within the

equation. It was hypothesized that the water release data may contain ranges of soil

properties (texture, OC, B d ) that were too wide for a single equation of this complexity to

rncompass al1 the variation found within the water release curves. To decrease some of the

variability, the water release da ta were divided into 4 classes based upon texture. A study

bv Tietje and Tap kenhbrichs (1993) suggested that establishment of separate PTFs for

different textural classes can yield good results. Also, Rengasamy et al. (1984) suggested that

for soils with c 30% clay content, behaviour of soil physical properties changed with %clay

con tent but in soil with 30% clay content or greater, change in soil physical properties

drpeiided on the types of clay present and not clay content alone. Therefore, four textural

classes were detennined: Clays (>30% clay), Sands (>70% sand), Loam-Clays (<30% clay

and ~ 3 5 % sand) and Loani-Sands (<30% clay and 70% >Sand>35%). The 4 soil textural

classes are shown in Figure 2-3.

O 20 40 60 80 100 Sand Content

Figure 2.3. Soi1 textural classes and texturai distribution of soi1 cores.

Another problem encountered in the nonlinear regression process involved the use

of 0sor a, the voIumetric water content at saturation. Ln many instances at the wet end of the

W RC, measured volurnetric water content. were found to be greater than the porosity

calculated from the B d after oven drying. Measurement of Bd, experirnental error and the

in fluence of pa rticle densi ties were analyzed but it was deterrnined that the most Likely

explanation for this resuIt was shrinking and swelling. If shruiking and swelling were

occurring in our soils, the statistical determination of es within a WRC M F would be

difficdt, therefore any determination of a PTF must account for swehg . A common

d pproach to accoun t for swelling in volves the use of the Coefficient of Linear Extensibility

(COLE). Throutgh the use of C O L E a new Bd, porosity or water content accounting for

swellu-ig can be calculateci. The use of COLE however requires the standardization of the

COLE parameter for the local area with its unique clay types and swelling properties.

Rudimentary calcula tion of COLE can be niade by simply using % clay content and organic

n u tter content but i t was determined that, within our data set, s w e h g was not consistent

with clay content or organic matter. Ln general it was found that approximately 8%, 18%,

34% and 26% of the data points for the Sand, Loam-Sand, Loanx-Clay and Clay textural

classes respectively, showed evidence of swelling. W h a i degree of s w e h g (expressed as

the volunietrïc water content at 0.001 MPa suction minus the porosity of the core calculated

from the oven dry Bd) was regressed against % clay content or %organic carbon no

significant relationship was found. While there may be a relationship between X clay and

the frequency of swelling occurrence, the large amount of variation in the degree of swelling

made the regression with properties insignificant. Due to the large variation inherent in the

swelling occurrences within our data, it was concluded that the use of COLE wouid not

in~prove our ability to define a WRC MF. Therefore, to account for swelling within our

data, only data points that exhibited 8 values greater than the measured oven-dry porosity

were à d j u s t d . For the d a ta points in question it was assumed that the volume of water

rneasured at that potential was equivalent to the volume of soil pores:

Vol, = (1 - (M,/Vol~~i)/2.65)*Voltoti1

where Vol, = measured volume of water (cm3), M, = measured mass.of solids (g), and

Voll,,.,~ = calculated volume of swelleti b u k soil. W ith rearrangement this equation becomes:

Volt,t,i = Vol, + MJ2.65

Therefore for al1 data points showing the presence of swelling, variables such as volumetric

water content, b u k density and porosity were recalculated usirig Voibbi. For those data

points that did not exhibit evidence of s w e h g . calcdation of volumetric water content and

66 and porosity were baseci on the volume of the cores. Therefore, the 3 variables

vu l u nie tric wd ter content, B d a n d porosity (por) are redefïned Bd2 and porz in which aU

three variables encompass swelling and n o n - s w e h g data points for a soil.

Nonhear regression of T2 and T5 was performed for each texhval class with water

release data using 0, Bd and por, as well as their counterparts 0% Bdz and porz. Using the

testural classes dnd the swelling paranieters however, did not irnprove o u ability to reach

convergence for our WRC data- The T2 function was thought to be too complex for the

regression analysis and perhaps the simplified versions, T3 and T4, might yield better

results. ln contrast, the T5 function, the logistic function, was thought to be tao simple and

thus unable to adapt to encompass the variability of the WRC data.

Nonlinear regression of the simplified Van Genuchten equations (T3 and T4) were

performed for each textural class with water release data using 8, B d and por, as well as

their counterparts el, Bd2 and porz- Regreçsion results for all functions (Tl - T5) are shown

in Tdble 2-3. Function 7-3 resulted in the best fit (based on smallest SSE and RMSE) using:

Where:Bs = por2 Or = (a + b*% clay + c*% 0.C- + d*Bd) a = (e + f*% clay + g* % O.C. + h*Bd) n = (i + j*%clay + k*%O.C. + 1"Bd) and a . - - / a re constants.

In renioving the (1 - 1 / n) ter111 froni the Van Genuchten equation, T3 became less complex,

still retaïned its S-shape forni but lost some of its sensitivity. Mode1 T3 prediction and mode1

parameter estimates for each textural class are shown in Appendix 2.1. When all4 classes of

T3 are combined the overall prediction for the 3412 data points resulted in a SSE = 3.56 and

n RMSE = 0.032 (cnG/cni~). The regression of T3 predicted vs. measured values (Figure 24)

I - ~ S L I tted in rt 1-2 = 0.89, a n intercept of 0.04 and a dope of 0.89, where the intercept was

s isn ifican tly ciifteren t from zero a n c l the slo pe was significantiy different from 1. Despite the

significance of the slope and intercept values for the predicted vs. measured regression, the

plot indicates that there was no strong, consistent deviation f rom the 1:l Line at the wet or

LI 1-y end of the W RC.

-0. 4 SSE = 3-56 P! RMSE = 0.032

Measured water contents (crn3/cm3)

Figure 2.4. Coniparïson of T3 predicted vs. measured values of volurnetrïc water content across a range in matric potential (-0.001 to -1.5 MPa).

Table 2.3. Results of mode1 fits to measured water release data-

Faileci to converge /

T2 : Van Cenurli~ci i

T3: SimpLifiezI V a Genuchten T4: Simplifieci Van Genuchten T5: Logistic -

I 1 (4 clirsses) 1 I * not sigiuficilntly Liifferent tiian 1.0 (p = 0.05), not s i ~ c a n t l y different than zero (p = 0.05)

-

To determine if the T3 function is truly better than the DSI b c t i o n , T3 must be

evalua ted using an independen t data set. In the experimental analysis, 330 of the cores taken

in 1998 did not undergo the WRC analysis but were saturated and put at various single

pressures (-0.001, -0.003, -0.006, -0.0'1, -0.0333 and -0.1 MPa) to be used for soi1 resistance

dnalysis. Theso data represent a n independent data set of points on the WRC, and were used

Fded to converge

Predicted vs, Measured Regesion Data

Mode1 Name: DSI: da Silva and Kay (1997)

Tl : moifiiielf L f ~ l Silvrl ~ t n d Kav (1997)

N =,Ml2 (4 i l i i ~ ~ c ï ; ) N = ,3412 (4 classes) N = 3412 (4 classes) N = 3412

R2 0.74

0.814

3 -36

3 -96

Çlope 1.07

0.94

RMSE 0.076

0.044

Dafaset: N = 3412

N = ,3412

Intercept -0.06

0-03

SSE: 19.66

6-38

to test the prediction of DS1, T3 and another commonly used PTF, DESORPMOD (McBx-ide

and Mackintosh, 1984).

Overall, 101 of the original 330 cores used for this part of the analysis were lost due

to worm action and rnissing da ta points. Using the remainirig 229 data points, DS1, T3 and

DESORPMOD were used to predict the W RC. Analysis of the independent data set c m be

seen in Figure 2.5. Based upon the plots of predicted vs. measured values and RMSE values,

it can be seen that the T3 function predicts the WRC data considerably better than either

DST or DESORPMOD.

Measured water contents (an31cm3)

DE- Redi ction

Figure 2.5. T3, DS1 and DESORPMOD prediction of water release curve data of independent data set.

Thusfar, it can be seen that the T3 PTF provides the best fit for Our measured data.

For the purposes of this study however, it is critical for the &al FTF to accurately predict

certain points such as Field Capacity (FC, -0.OlMPa) and the Permanent Wilting Point

(PW P. -1.5MPa). This analysis was done upon the original data set of 3412 data points. The

independent data set did not encompass pressures of -1.5 MPa and therefore this analysis

was precluded. Results of mode1 prediction of DS1 and T3 for -0.01 and -1.5 MPa are shown

in Figure 26. Cornparison of DS1 and T3 prediction indicates that T3 prediction

considerably decreases the SSE giving the prediction a much tighter f i t The plot of T3

predicted vs. measured values of the 385 cores also shows a much better digrunent with the

1:1 lùie.

T3: 4-01 MPa Redictiai

L

Figure 2-6. Plots of DS1 and T3 predicted values vs. measured values for the critical matric potentials -0.01 MPa (Field Capacity) and -1.5 MPa (Permanent Wüting Point).

Therefore, of the existing models DSI, DESORPMOD, and of those attempted Tl -

T5, T3 is detennined the best PTF to predict the WRC of our soils, based upon its prediction

of our data as weU as its prediction of the independent data set- Four examples of how the

T3 PTFs predict the W R C can be seen in Figure 2-7.

Sand (7S%sand. 8%day. 1.72%O.C.. 1.49gkm3)

Figure 2.7. Four examples of plots of T3 predicted and measured water release curves.

Soi1 Resistance Curve (SRC)

Rrsis tance to penetra tion was determined on 717 cores, 495 cores taken in 1998 and

222 of the 272 cores taken in 1997. Of the 717 cores used, 232 of the data points were lost due

to either worm action, lost data or measurement of penetration resistance was beyond the

measuring capacity of the transducer of the Sciemetric 200 apparatus. The measuring

capacify of the transducer was approximately 12000 kPa. The remainirig 485 data points

were divided into two data sets where the 321 usable cores of the 495 cores taken in 1998

became the first data set and the 164 usable cores of the 222 cores taken Ï n 1997 became the

seccnd data set. The first data set wns used to test the da Silva and Kay (1997) ÇRC PTF

(now referred to as DS2), and the second data set was reserved for vaLidation purposes in

anticipation of generating a new PTF. Textural analysis of the first data set showed that o u

data ranged from 0.01-53.0% cIay content and 4.2- 92.8% sand content- Organic carbon and

6 c i rd nged fro III 0.25-5.88% and 0.90- 1.74 g/crn3, respectively. The second data set çhowed

thcit data 1-dngttci fi-oiii 7.0-60.3% clav content and 3.2- 80% sand content. Orgmic carbon and

B d for the second data set ranged from 0.39-3.3% and 1.18- 207 g/cm3, respectively.

To validate the da Silva and Kay (1997) SRC mode1 (DS2), predicted values of SR

were compared against measured average SR values of the first data set, Analysis of

pred iction for DS2 i-esultecl in a SSE = 9.94 x 1P and a RMSE = 1943.8 kPa. A plot of DS2

pi-ed icted vs. iiirasurd values is shown in Figure 2.8. Considering that for the purposes of

ttiis s tudy we w ish to determine the volumetric water content at which soil penetration

resistance reaches 2000 kPa, a PTF with a RMSE of approxirnately 2000 kPa is cIearly

inadequa te.

- 14000 ; m SSE = 9.94E+8 * a I

12000 ; RMSE = 1943.8 Q> I * N = 321

c.

.- 8000

M easured soif resistance (kPa)

Figure 2.8. Coinparison of da Silva and Kay (1997) SRC predicted vs. measured values for the first data set

To achieve more accurate predictions of SR, atternpts were made to define a new

SRC pedotransfer function. Busscher (1990) attempted to define a relationship to describe

how soi1 resistance to penetration varied with water content He evaluated several

functions, three of which worked particularly well, one of which was of the same form as

DS2. To define a new SRC PTF for our data we attempted to redefine the parameters of DÇ2

to fit Our data, as weU as attempting two of Busscher's better functions (B4 and B5). One

other functional form was atternpted where soil resistance was descnbed as a function of a

zoiiibination of Factors. Hillel (7980) stated that resistance encountered by a metal probe

penetra ting the soil encoun ters several processes or effects in combination: the cutting or

separaticn of the soil, shear failure, plastic flow, compression, metal to soil. friction and soil

to soil friction. While it is virtually impossible to separate each process and quantify it from

our single penetration resistance it can be theorized that the resistance to penetration arises

fi-oni the Lquici phase and the solid phase. The liquid phase contributes to penetration

resistance through etZ~ctive stress (soil particles are held together by the cohesion/adhesion

forces of pore water). The effective stress terni has been described by Bishop (1959) as:

Effective stress = ( f y )

where x is related to saturation (O/porosity). Bishop m d Blight (1960) however, found that

the x tenn was not simply a iinear function of B/porosity but showed evidence of curvatue.

The effective stress plays a role in the cohesion forces of both the separation of soil particles

(tensile strength) as well as the shear failure of soils. The contribution of the solid phase in

penetra tion resis tance can be related to O ther processes such as the cementation of mineral

particles by organic matter or clay to clay bonding, as well as the interna1 kiction forces of

the probe and soi1 particles. I t is theorized that these forces wilI be related to OC, and clay

content and thé 6J of the soil. The cohesive forces of organic matter and cIays however, will

also be dependont on the soi1 water content. Frictional forces wïil also be affected by soil

water content. Therefore it is aIso theorized that these forces wiil be related to water content

Overall, the form of the final SR M F attempted WU take the form:

SR = a*8 + b*(8/ porosity)c*yr

where a is a function of soil properties and [a*O] represents the contribution of the sohd

phase to SR, m d b and c are also functions of soi1 properties and ~*(B/porosity)c*yl] will

represent the contribution of the liquid phase to SR.

Ali equations considered in defining a new SRC PTF are shown in Table 2.4. To

furtfttlr enhance the sta tistical process, the database was again divided into soi1 textural

cldsses: Sand, C h y , Loani-Sand and Loam Clay (Figure 2.3). Also, the parameters

L-onsiciel-ed in clefining the new PTF also included those that account for swelling: 0% Bdz

and porz.

Table 24- Model forms fitted to measured soii resistance to penetration data (first data set of 321 cores).

Model Name: Equation:

Tb: refittcd da Silva and Kay (1997) SR = a*Ob*Bdc

T7- Combination function SR = a*B + b*(B/por)&y

The equa tion T6 was fi tted to the first da ta set using the same nonlinear statistical

niethoci usecf in genera ting the W RC PTFs. Nonlînear regression of T6 with the first data set

resulted in convergence with the traditional soi1 parameters of texture (%clay, %sand), Bd

and OC, but prediction was very poor. Regressions withïn each textural class consistently

resulted in RMSEs greater than 1 OûûkPa with the Sand class giving the poorest predictions.

in ni1 a tteiiip t to red uce the error in predicting SR, a trial was done incorporating matric

p t e i ~ t l d i ( \ I I ) d5 o n e of the paraiiieters included as a soil properv. introduction of the \y term

in to T6 grea tly reduced errors in prediction. The general T6 f o m that yielded the best fit

was:

SR = (a + yj h)'(e(e + f % cLiy + g'0.C- + h*Bd))*(Bd(i+ f 0.C))

tviiere n, . ..;are constants anci the presence of some paranieters are dependent on the

t e l t u rd l iidss. 1 II every telturd1 ciass except the Sand class, the y /h t e m ~ considerably reduced

the SSE and RMSE. The b constant was consistentiy and sigrufrcantly less than 1 and

different than zero. The overall prediction of the 4 classes for the 392 data points resulted in

a SSE = 1.8 x 108 and a RMSE = 685.8 kPa, a substantial irnprovement over DS2. The

regiession of T6 predicted vs. nieasured values resulted in a rz = 0.78, an intercept of 28.1

kPa (not sigi11 ticdn tl\: ditferen t than zero) and a slope of 0.98 (not sigrüFicantly different than

1 ). In an objective sense the 1-6 PTF prediction is a significant improvement over the DS2

PTF but the form of the function was based upon parameters that gave the best fit, rather

than fmctional relevance.

The other two Busscher (1990) models B4 and B5, were d s o fitted to the first data s e t

Analysis using mode1 B5 reached no convergence with any of the parameters of texture, B d

of OC- Mode1 B 4 however, which containeci a vc term, converged and resulted in a good fit.

The B 4 fit resulted in a SSE = 1.6 x IOs and a RMSE = W . 6 kPa. The regression of B4

predicted soi1 resistance values vs- the measured values for aII texturd classes combined

(Figure 29), resdted in a rz = 0.81, an intercept of 26.6 kPa (not significantly different than

zero) and a dope of 0.99 (no t sign ifican tly different than 1). The general form of the B4 PTF

wds:

SR= (a + b"% Clay + c*% 0.C. + d*B&)*(Bd,(e + r%cI.y + g'rO-C))*(@ + V c h y + k'= O-C + I 'B9 )

where parameters a.. ./are constants and the presence of some parameters are dependent on

the textural class.

SSE = 1 6E+8 RMSE = 640.6

N = 321

O 2000 4000 6000

M easured soi1 resistance (kPa)

Figure 2.9. Cornparison of 64 predicted vs. nieasured values of soi1 resistance for the first data set.

The final function, T/: was the functional form that combined te- related to the

Liquid and a solid phase. Of note is that of ail the previous MFs that have resulted in good

fits, they have contained a matric potential term as well as having the potential term appear

as a nonlinear form vb. The functiond form T7, using only y as a h e a r tenn, resulted in a

SSE = 2 3 x 1s and a RMSE = 773.9 kPa. The regression of T7 predicted vs. measured values

a re shown in Figure 2-10.

I 10000

(O 1 SSE = 2 3 6 8 4 8000 RMSE = 773.9

?? N = 321

O 2000 4000 6000 8000

Measured soi1 resistance (kPaO

Figure 2.10. Cornparison of T7 (linear yr term) predicted vs. measured values for the kst data se t

Aciciition ot LI pu~vel- tel-111 to < I I in the 1-7 fui-ictional form, as a single constant, again

decrrased the error in prediction. Changing of the power tenn to a function of soil

properties did not improve prediction. The single constant power term was consistently less

than zero and significantly different from 1 for al1 texhual classes. The power term was also

sign ifican tly Ji fieren t ironi zero for al1 textural classes eircept the Sand class. The Sand class,

I i o w r v r r wds the wedliest f i ttirig class for al1 PTF forms attempted in Table 2.4. The new

functional forni of T7 with the nonlinear ry terni resulted in a SSE = 1.6 x 108and a RMSE =

660.9 kPa. The regression of the new T7 predicted vs. measured values ( F i w e 2.11) r e d t e d

in a r' = 0.80, an in tercep t of 334.6 kPa (significantly diffzrent than zero) and a dope of 0.80

(significan tly d ifferen t fro m 1). The significan tly differen t slope and intercep t values were

heavïly uinuenced by the measurements at extremely high resistances. When comparing the

two T7 plots of predicted vs. measured values (linear \y vs. nonlirtear y km) however, it

can be seen that adding the power terrn to \CI narrows the errors in prediction, especially in

the area of lower resistances. The general form of the T7 PTF was:

S R = (a+-b"% clay+c*% 0C.+d*Bd2)"82 +

(e+f* % clay +g*% 0.C.+h"Bd2)'((8z/ porz)(i+i'%cW+k'% O-'=-+I*W)*(yz)

where a.. .x are constants and the presence of some parameters differ between texturd

classes. In the Sand and Loam-Sand functions % sand was a more significant parameter than

21 ~ - l c i ! f -

SSE = 1.6E+8

i RMSE = 660.9

N = 321 1

O 2000 4000 6000 8000

Measured soi1 resistance (kPa)

Figure 2.11. Coniparison of T7 (with tp) predicted vs. measured values for the first data set.

Results froni al1 functional f i ts a re shown in Table 2.5. I t c m be seen that the B4

functional forni, fitteci to the first data set, revealed the lowest errors in prediction and the

best fit. Based on SSE the T7 functional form showed simiiar prediction to M.

Table 2.5. Results of mode1 fits to meaçured soil resistance to penetration data (first data set),

1 1 Predicted vs- Measured - 1 1 Reeression Data

Modcl Name: D S 2 d i Si1 v* id Ksy (1 997) SRC T6 : modifieci da Silva and

1 BS: Busscher (1990) 1 N = 321 I Failed to converge.

D;ih scL: 1 SSE: N = 521 1 9-94 x 10"

Kay (1997) SRC B4: Busscher (1990)

N = 321

I 1 I I I

* no t sil:tiific.a n ~ l y diffwimt t h n 1 .[) (p = O.CE), " not signih'cantly dilferent than zero (p = 0.05)

PWîSE: 1943.8

N = 321

Tf: Combination functiun

To determine the best PTF to predict the SRC both the B4 and T7 functions were

1.8 x 108

validated on an independent data set, the second data set consisting of 164 cores taken in

R2 0.299

1.6 x 108

I

1997. The second data set had similar ranges in soil properties when compared to the first

685.8

N = 321 / 1.6 x 108

c l 4 trt set escep t tlia t the second da ta set's range in % clay and Bd were slightly higher than

Slope 0.68

640.6

t h e first data set. The secon~l data set rangecl u p to 60.3% clay (compared to 53.0%) and a B d

of 2-07g/cm3 (compared to 1.74g/cm3).

Validation of the two functions revealed that the T7 h c t i o n predicted soil resistance

in the independent data set better than the 64 function. VaLidation of 84 prediction resulted

in d SSE = 1.02 A 10s dnd d RMSE = 805.9 kPa. Regression of 64 predicted vs. measured

vd l ues (Figure 2.12) resu l ted in a r l = 0.595, an intercep t value of 373.2 kPa (significantly

differen t than zero) and a dope of 0.88 (sigpficantly different than 1).

Intercept 479.3

0.784

660.9

0.813

0.98'

0.799 1 0.80

28.1"

0.99'

334.6

26.6"

O 2000 4000 6000

M easured soi1 resistance (kPa)

Figure 2.12 Cornparison of B4 predicted vs. measured values of independent data set (second data set).

The va iida tion of T7 precliction revealed a better fit thm B4, resulting in a SSE = 6.05

- 1 IOï and d RMSE = 6 2 2 6 kPa. The regression of T7 predicted vs. measured values for the

second data set (Figure 2.13) resulted in a rz = 0.768, an intercept value of 157.0 kPa

(significan tly differen t than zero) and a slope of 0.93 (no t significantly different than 1).

Regression of the predicted vs. measured values shows that T7 variance in prediction

increases a t higher soi1 resistances, Overa II, it was judged that the T7 PTF was the best

tunction when judged on i ts perforn-iance fron-i the fitted data set and the validation data set-

The T7 mode1 parameters for each textural ciass and their significance are shown in

Appendix 22.

4 7000 i SSE = 6.05E7 Y

f 6000 RMSE = 6226 N=1o4

5 5000 -- ffl

l M easured soi1 resistance (kPa)

Figure 2.13. Cornparison of T7 predicted vs. measured values of independent data set (second data set).

I f w r are to consider the T7 PTF for the purposes of this study, the T7 function must

be used to deternline a volunietric water content at which the soil resistance to penetration

reaches a critical value. Unfortunately, the T7 PTF is a function of both 8 and q ~ :

SR = a 9 + b*(û/por)c*v

a d because of this, defiriing a critical soil resistance threshold and its correspondirig 8 value

wil l be diiticuIt. In the previous section we have defined a WRC function, T3, of the form:

w here 8 is a function of \ I I - W ith rea rrangement T3 can be made to form:

where y, can be expressed as a function of 8. Substitution of T3i into the T7 SRC h c t i o n

transforins the T7 function to predict S R as a function of only 8. Nonlinear regression of the

ti-ans foi-ined T7 function w i th soi1 parameters was again performed but no convergence of

the parameters was attained. Part of the problem of redefuwig the parameters in the

transformed T7 function was due to the nature of the T3i function. Analysis of T3 showed

that 8 cari be predicted with good accuracy by using yl. Intuitively, one would assume the

inverse function would also predict well but analysis of T3i showed that prediction of yl as a

function of 8, using the exact parameters used in T3, gave very poor results. When

considering the nature of the wa ter rslease curve, however, one can see that as one predictç

8 from y ~ , errors in 111 can cause prediction errors of 0-05 or even 0.10 cm3/cm3 in volumetric

wa ter content. In contrast, the inverse function (T3i), when predicting \y from 0, small errors

in 8 c m mean order of magnitude changes in predicting \y. Therefore modification of T7 to

enhance its utility usùig T3i or other water release functions was deemed unlikely to

An alternative to substitu ting T3i into the T7 function was to insert T3 itself to

n-iodrSl T7 into a function predicting SR from only y/:

where T7 is: SR = a% + b"(8/por)c*y1e 1 cind eacl-i 8 is substituted by 8 = 9s - 8r +Or

(1 + (a*yr )n)

This new funritional forni will be renamed T8. ln transforming T7 into Tg, the SRC h c t i o n

is now defined by only one water-related variable, y. Once a critical soil resistance to

penetra tion (Le. 2000 Wa) has been defined it should then be simple to use this new

function to calculate the \II value açsociated with that soil resistance. With that y/ value we

L - ~ I I then use T? tu c-,iIc.uld te the c-oi-1-esponding 8 value.

11-1 moci ity ing the T7 SRC PTF to the TS form, the Tt3 PTF may predict SR from only yl

but now has gaineci the errors associated with both T7 and T3. No attempt was made to

resleiïne the pdra nieters within T8 considering the new form could contain as many as 24

parameters. The existing parameters in T7 and T3 were used and prediction of SR analyzed.

From the first data set (321 data points), in our methodology, each core was

saturated and brought to a matric potential (varied from -0.001 to -1.5 MPa) where water

con ten t and soil resis tance was nieasured. Using the T3 h c t i o n , matric potential and soil

properties of each core, 9 values were then calculated. Using thïs calculated 6 value, as well

as the matric potential and soil property values once again, soil resistance was then

calculated using the T7 SRC PTE Basically, SR was calculated uçirig only the matrïc

potential and soil property values of each core, This process of predicting SR data willbe

referrecl to a s TS prediction. Analysis of Tt3 prediction resulted in a SSE = 1.8 x 10s anda

R MSE = 688.6 kPa. The regression of Tâ predic ted vs- measured values showed a rz = 0.79,

dn intercept of -41.7 kPa (not sigLificantly different than zero) and a slope of 0.98 (not

significantly different than 1). Overaii, the TS mode1 showed a slight loss in performance

when compared to the original T7.

Using the same process, analysis of T8 performance on the independent data set (164

data points) resulted in a SSE = 5-83 x 107 and a RMSE = 613.2 Wa. The regression of T8

predicted vs. nieasured values (Figure 214) showed a r' = 0.77, an intercept of 159.1 kPa

(significan tly different than zero) and a slope of 0-93 (significantly different than one). Based

on the S E and RMSE the TS PTF actualiy shows a slight improvement over the T7 f o m

The T8 PTF however, does show a slight shift by over-predictïng SR at lower resistances in

this data set-

SSE = 5.83€+7 RMSE = 613.2

N = 164

l Figure 2.14. Cornparison ofT8 predicted vs. measured values of

O 2000 4000 6000

Measured soil resistance (kPa)

the independent data set-

I t has been determined that modification of the T7 SRC function to the Tt3 f o m did

not detrimentaily alter the PTF prediction of resistance to penetration. For the purposes of

this study however, we must be able to determine a \y value at a givm SR- Unfortunately,

17ec-d use TS is so camples, i t is nlgebraically impossible to transform T8 into a function that

predicts 111 from a given SR. To circumvent this problem, determination of y at a given SR

can be done by an iterative method using either a 'solver' function in Excel6.O or by writirig

an iterative program. This method defines a dependent variable (SR) which is given to be a

specific value (for example SR = 2000 kPa) and through the iterative process changes the

iticlependen t va ria ble (11') wi thin Tt3 to achieve that specific SR value. Ln this manner a yl

value is n ttained tha t predicts the given S R value. Using this y, value and T3, the

corresponding 8 value can then be found, i.e. the volumetric water content of the soil in

which the soil resistance has the reached 2000 kPa. To evaiuate this iterative method of

deterinining q~ and then calculating 8, the second data set was again used to generate yr

v,i l ues. For the second da ta set, a simple item tive program was developed to change yr until

the TS function's predicted SR value equaied the measured SR value. Using the value

derived from the program and T3, the corresponding 8 value was cakuiated. Predicted 8

values were then compared to the rneasured 8 values within the cores. Prediction of 8

through this method resulted in a SSE = 0.183 and a RMSE = 0-036 cm3/cm3. Results of the

regression of the predicted vs. measured 8 values (Figure 215) showed a r2 = 0.84, an

in tercep t of 0.050 cm3/ cm3 (significantly different than zero) and a dope of 0.88

(significan tly differen t than 1). OveralI, using this process, for a given soi1 resistance to

penetration we can adequately predict the corresponding volumetric water content-

SSE = 0.183

N = 164 RMSE = 0.036

0.0 : 1

O 0.1 0.2 0.3 0-4 0.5 0.6

M easured water contents (crn?cm3)

Figure 2.1 5. Conipa rison of predicted 8 values using the Tg-iterative method vs. measured 8 values for second data set (164 data points).

2.4 CONCLUSION

Validation of the WRC and SRC pedotransfer functions deterrnined by da Silva and

Kay (1997) found both to be inaciequate in their prediction. The da Silva and Kay (1997)

W RC function (DS1) showed a wide spread in prediction of volumetric water content as

well a s an uneven distribution of residuals, indicating that the functional Çorm may not be

adequate to descnbe the WRC- The da Silva and Kay (1997) SRC function (DS2) showed

very inaccurate prediction with very little cohesion of the data with the functional form.

To attain better fits for our data Further attempts were made to determine more

accura te ped O tra nsfer functions. To achieve more accurate hrnctions, soils were divîded ïnto

4 textural classes (Sand, Clay, Loam-Sand, Loam-Clay) to reduce some of the variability

iriherent in Our data. Also, the varïabIes voiumetric water content (9), porosity and Bd were

altered to account for swelling. The new WRC function (T3) used a simplified Van

Genuchten equation and was found to more accurately predict 9 values than DSl or

DESORPMOD (McBride and Mackintosh 1984) using independent data sets. The new SRC

function (3) used a combination of ternis encompassing the separate contributions of the

liquid phase (effective stress) and the solid phase (the effects of organic and mineral

cementation, and friction). The Tt3 function was found to most accurately predict resistance

to penetration tor its clerivation data set as well as an independent data set. For the purposes

of this study, criticdl water contents for plant growth can also be detennined using both

functions. The T3 function was found to more accurately predict the critical points of field

capacity and pemtanen t wilting point when cornpared to DS1. The T8 function, while not

con~pctred to DS2, was founci to accurci tely predict 0 values at a given resistance to

penetra tion.

Therefore it can be seen that the two new pedotransfer function T3 and T8 can

adequately predict the WRC and SRC usïng the soil paranieters of texture, OC and Bd.

These predictions encompass a large range in soil properties but must undergo fui-ther

testing to determine if the relationships hold outside of the ranges in which they were

~ie1-1 ved.

2.5 REFERENCES

Bishop, A.W. 1959. The p ~ c i p l e of effective stress. Teknkk Ukeblad, 39,859-863.

Bishop, A.W., Blight, G E . 1960. Some aspects of effective stress in saturated and partly

saturated soils- Geotechnique, 13:177-197.

Bounia, J., and H.A.J. van Lanen. 1987. Transfer functions and threshold values from soiL

characteristics to land qualities- Pp. 106-1 11. In Quantifiecl land evaluation, Proc.

Worksti. lSSS/SSSA, Washington, DC- [TC Publ., Enshede, the Netherlands.

Busscher, W.J. 1990. Adjustrnent of flat-tipped penetrometer resistance data to a common

water content, Trans. ASAE 33: 519-524-

Cambardella, C- A., T-B. Moom~an, j-M. Novak, T.B. Parkin, D.L. Karlcn, R.F. Ruco, and

A.E. Konopkci. 1994. Field-szale variability of soi1 properties in central Iowa soils. Soil

Sci. Soc. Am. 1. 58:150 1-1 57 1

Colvin, T.S., D.B. Jaynes, and D.L. Karlen. 1996. Yield variability within a central Iowa Field.

Trans. ASAE- 40(4): 883-889.

d a Silva, A.P., Kay, B.D. 1997. Estirnating the least limiting water range of soils from

properties and management. Soi1 Sci. Soc. Am. j. 612377-883.

Crencen, E.L. 1986. Root response to soil rnechanical properties. Trans. 13tll Congress Lntem.

Soc. Soil Sci., Ham burg, Cermany. 5:20-47.

Hiiiel, D. 1980. Stress-Strain ReIa tions and Soil Strength. (rr. Fundarnentals of Soil Physics.

Academic Press. Toronto, pp. 318-352.

Leij, C.J., Alves, W.J., van Genuch ten, M.Th., W iiiïarns, 1.R. 1996. Unsaturated soil hydraulic

c i d t n bcise, U NSODA 1 .O User's Manual. US. Environmental Protection Agency, Ada,

OkIahoma, Report EPA/ 6OO/ fi-96/095,103pp.

Minasny, B-, McBratney, A-B., Bristow, K.L. 1999- Cornparison of different approaches to the

development of pedotransfer functions for water retention curves- Geoderma 93225-253.

McBrïde, R.A., Mackintosh, E.E. 1984. Soil survey interpretations from water retention data:

1, Developnient and validation of a water retention model. Soi1 Sci- Soc. Am. J. 48:1338-

1343,

Rengasamy, P., Greene, RSB., Ford, G.W. 19S4. The role of clay fraction in the partîcle

arrangement and stabïiity of soii aggregates - a review. Clay Research. Vol. 3, N o 2 53-

67.

Çheldrick, B.H., Wanp, C. 1993. Particle Size Distribution. Ln: Soi1 Sarnphg and Methods of

Analysis, M. R. Carter, Ed. PP: 499-511. Canadian Society of Soil Science. Lewis

Publishers.

Taylor, HM., Roberson, G.M., Parker, Ir., J.J.1966- Soil strength root penetration relations

for medium to coarse textured soi1 ma terials. Soil Sci. 102:lB-22.

Tietje, O. Tdpkerihinrichs, M. 1993. Evaluation of pedotransfer Functions. Soil Sci. Soc. Am. J.

57:lOSS-1095.

Topp, G.C., Galganov, Y.T., BaU, B.C., Carter, M.R. 2993. Soi1 Water Desorption Curves. In:

Soi1 Sampling and Methods of Analysis, M.R. Carter, Ed, Pp: 569-579. Canadian Society

of Soil Science. Lewis Publishers.

L.'=iii Lenuctiten, i'vl.TI-i- 1980. A closecl-form equation for predicting the hydraulic

corid uctivi ty O t unsa tura ted so ils. Soil Sci. Soc. Ani. J. 44892-898.

Wosten, J.H.M., Finke, P.A., Jansen, M.J.W. 1995. Comparison of class and continuous

pedo tram fer hinc tions to genera te soi1 hydraulic characteristics. Geoderma 66227-237-

Young, LM, Montagu, K., Conroy, J., Bengough, A.G. 1997. Mechanical irnpedance of mot

growth directly reduces leaf elongation rates of cereals. New Phytol. 135: 613-619.

APPENDIX 2.1

The general form of the T3 l'TF was of the form:

8? = Bs - B r +& I + (a* Iqt 1)")

where: 8s = por2 8r = (a + b*% clay + c7% OC + diBif) a = (e + f* X clay + g*% OC + h*Bd) n = (i t j*%day + k*%OC +- 1"Bd) and a. ,./are constants.

Ai parameters are significant to the pc0.05 level unless otherwise noted.

For the Sand T3 PTF:

N = 162 PXSE = 0.028 a n 3 / a n 3

* sigruhcant to p = 0.1, " not si&cant to p = 0.1, N / A = parameter not present in model.

ble 2-6. Parameter estimates for the Sand water release curve h c t i o n .

l 0.0 . 1 4 I 1

0.0 0.1 0.2 0.3 0.4 0.5 0.6

M easured water contents (cm3/cm3)

Pi r - ctcr:

rl

b c'

d e f

K h 1

I k

Figurc 2.lh. Cornpitrison oFT3(Sand) predicted vs. measured vdues of volumn~rii. w~iLer ~-oiiLeiit (162 ditta points).

Estimate:

0.0556 N / A O.CM3 N / A

3 77-7418 4.9023 -37.2393 -141 -2.139 2.3337 -0.0193 -0.3494

Asymptotic Std. Error:

0,0092

0.078

44.4402 1.1000 6.2776 27.8188 0.3073 0.0232 0.0799

For the Clay T3 PTF-

Table 27, Parameter esümates for the Clay water release cunre function-

Y, - w c. '= 0h6 1

0.5 7 n- I

5 5 0.4 j m -

0.3 ! m a - C

9 0.2 -0

; 0.1. 0.0 - ,

0.0 0.1 0.2 0-3 0.4 0.5 0.6 0.7

Measured water contents (crn31cm3)

I

Figure 2.17. Cornparison of T3(CIay) preclicted vs. measured values of volumelric water content (1306 data points).

Asymptotic Stcl. Errm Pax-ameter:

1.8997 1 0,2489

Estimate:

N = Y I 5 PWlSE = 0.030 u n 3 / m 3

' siy,~uCiiii i it tm p = 0-1, - IIOL ~igiufiiant to p = 0-1, N/ A = parameter not present in model.

For the Loam-Clay T3

Table 28. Parameter estimates for the Loam-Clay water release curve function- Parameter: Estimate: Asymptoüc Std. *or: '

0.0 , 7

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Measured water contents (cm31cm3)

Figure 2-18. Cornparison of T3(Lom-Clay) predicted vs. measured values of volunwtric water content (1301 data points).

For the Loam-Sand T3

Table 29. Parameter estimates for the Loam-Sand water release curve function.

L 1

" sigdi~citit tu p = O . 1, " no^ signific'ant to p = 0.1, N / A = parameter not present in model.

a b L-

d e

0.0 i 1 1

0.0 0.1 0.2 0-3 0-4 0.5 0.6

Measured water contents (cm3km3)

Asyniptotic Std- Errorr Parameter:

Fit;ure 2-19. Coiiipariçon of T3(Loani-Scind) prdicteci vs. measured values of volu metrii wa ter content (643 data points).

Es timate:

-0.6678 0,0028 o-osn U.437.l 165-4473

0.0571 0.0004 0.00~ 0.0323 21 -8756

-l

The general form of the T7 M F was of the form:

where: a = (a+b*% day+c*%OC+d*Bdz) p = (e+f*% day+g*% 0Cih*Bd2) b, = (i+jj% day+k"% 0C+l*Bd2) and a.. .,L z are constants.

Ail parame ters are significant to the pc0.05 Ievel unless O therwiç.e noted. In two instances function parameters were not statisticdy significant but were found to greatly L i e ~ ~ e a s e the errors iri prediction aiid therefore kept withii~ the model.

Table 2-10. Pilrarneter estimates for the Sand soi1 resistance curve h c t i o n .

* sipdic- an^ 10 p = 0.1, " ncit sip,iiific'dnt to p = 0.1, N/A = parameter not present in modd.

k 1 z

Asyrnp totic S td- Error: Parame ter: Estimate:

N =32 RMSE = 377.22 kPa

-0,3834 -2.1557 0.0448"

0.1281 0.642s 0.0553

I I I 1 1 i 1

O 500 1000 1500 2000 2500 3000

Measured soi1 resistance (kPa)

Fi y e 2.20. Cornparison of T7(Sand) predicted vs. measured values of soîi resistance (32 data points- h t data set),

For the Clay T7

Table 211. Parameter estimates for the Clay soil resistance curve hc t i on .

I - - - -

1

* significciii~ tu p = 0.1, " 1101 ~i~;~ûficant to p = 0.1, N/A = parameter not present in model.

2000 4000 6000 8000

Measured soi1 resistance (kPa)

Asymp totic Std, &or: Parame ter

0.0321 z

Figure 2.21. Cvnipcirisoii uf T3(CIay) predicted vs. measured values of soi1 rcsistance (196 data points- first data set).

&tirnate:

N = 166 RMSE = 785-26 kPa 0.2530

For the Loarn-Clay T7 PTF:

Table 212 Parameter estima tes for the Loam-Clay soi1 resistance curve hc t ion .

I Estimate: I Asymptotic Std. Error: I

O 1000 2000 3000 4000 5000

M easured sail resistance (kPa)

L

1 z

Fi);ri ri* 2-27. Cc,nipi riscin of T7(Lciani-Clci y) pre r l i z~d vs. masureci values of soi1 rr!sis~criiic(74 &ta FOUILS- first data set).

N =32 PWlSE = 476.63 kPa I " sil;niiiiciii~ tu p = 11.7, "* not sit;nificant to p = 0.1, N / A = parameter not present in model.

W A 0.3253 0.0251

For the Loam-Sand T7 PTF:

Table 213, Parameter estimates for the Loam-Sand soil resistance curve hction. Parame ter: Es timate: Asymptotic Std- Errorr

I

z 1 0.3146 0.0279 N = 142 RMSE = 637.59 kPa

' siyi i i f i i~i i~ tu p = O.I. - not signiticant to p = 0.1, N/ A = parameter not present in modeL

M easured soi1 resistance (kPa)

CHAPTER 3 : THE SENSITIVITY OF CORN (Zea mays) YIELD TO THE LEAST LIMITING WATER RANGE OF S O U

3-1 BACKGROUND

Agricultural fields Vary considerably in their soil properties, landscape features, and

management histones. This variability has been shown to contribute to variation in yieid.

Colvin e t al, (1996) described the yield patterns for corn and soybeans in rotation after six

consecutive years within a single field. They found that certain locations within the field had

consistently high, consistently low, or erratic yields when compared to whole field averages.

Much of the variation in yield can be measured with current technology, but the root causes

of thiç spatial variability are unexplained. The goal of this project was to assess the influence

of soil structure and water content on the spatial variability in yield.

1 t is assunieci tha t the iinportance of soii structure upon yield is related to the soil's

a bility to provide oxygen, water and support the growth of roots. The parameter Non-

Lirniting Water Range (NLWR), introduced by Letey (1985), later renamed Least Lirniting

Water Range (LLWR) has k e n used as a characteristic of soil structure (da Silva and Kay,

1997). The term Least Limiting Water Range is defined as the range in soil water content

à tter rapid drainage has ceased within which Linütations to plant growth associated with

water potentia1, aeration and nech ha ni cal resistance to root penetration are minirnal (da Silva

and Kay, 1997).

The LLWR is a range, defined by an upper limït and a lower Limit. The upper limit

value is chosen as the Iower value of water content in which aeration to the roots becomes

lin~iting, o r w hen ra pid drainage ceases. Aera tion was concluded to be lùniting at an air-

filled porosity of 0.1 cm3/cm3 (Grable and Siemer, 1968), and rapid drainage was concluded

to cease a t field capacify (FC) a t a water potential of -0.01 m a (Haise e t al. 1955)- The lower

tinut values were chosen as the greater value of the water content bdow which water cannot

be extracted by plants (permanent wilting point or -1-5 MPa) fourid by Richards and

Weaver (1944), or the water content a t which mechanical impedance restricts root growth.

Cone penetrometer resistance is comrnonly used to simulate the impedance encountered by

plant roots. Young et al. (1997) found that mechanical impedance of root growth directly

dffected plant growth, and based on studies done by Taylor et al., (1966) and Greacen (1986),

a cone resistance of 2 MPa was used as the upper iïmit of penetration pressure exerted by

the roots of most field crops. From this, the other criterion for the lower LLWR Limit was

based on the water content in which the soil's penetration resistance exceeds 2MPa.

In essence it is hypothesized that the LLWR can be used as a measure of the soil's

abiiity to provide water, air and a favorable enviromnent for root development and as such,

the magnitude of the LLWR will be positively correlated with yields- t t stands to reason that

a sod with a wider LLWR will have a greater ability to provide water, air and root

development and thus have greater yiefds. It is further hypothesized that crop growth will

be negatively correlated to the frequency in which seasonal water contents fail outside the

LLW R (Fil,,)- Here, it is reasoned that as the soi1 drïes durùig the growing season, the

n uinber of seasonal wa ter contents nieasured below the lower limits will increase and yields

will be negatively affecteci. This reasoning is also applicable to seasonal water contents

méasured above the upper liinits. Seasonal water contents rising above the upper M t s

would induce aeration problens and thus also negatively affect yields.

These hypotheses are supported by work done by da Silva and Kay (1997) where

they used both the LLWR and Fil,, (in the 0-20cm depth) to assess shoot growth of corn.

They found tha t shoot gro w th was indeed positively correlated with the magnitude of the

LWLR and negatively correlated with Fu,. The effects of the LLWR and Fu, upon yields

however, are unknown.

This project wiii attempt to determine the relationship between soi1 structure,

seasonal water contents and final yields of corn (Zea mays). Stypa et al. (1987), in a study of

corn root growth, found that over 80% of total root length was in the 0-30 c m depth. It is

assumed that if there is a relationship between soil stiucture, soi1 water and yields, the

relationship wiU be seen in the top 30cm depth. Therefore, the objectives of this study were

to: (a) d e t e m ~ e the degree in which the magnitude of the LLWR in the 0-30cm depth could

explain variation in yields of and, (b) deternune the degree in which the frequency of

seasonal water contents falling outside of the LLWR (Fu,,) in the 0-30- depth could explain

the variation in yields of corn (Zea mays). The study will be focused on corn crops from 12

sites across southern Ontario, conducted over the two growing seasons of 1998 and 1999.

3.2 METHODS AND MATERIALS

This study w a s conducted upon 6 farms during the 1998 growing season and 4 farms

during the 1999 growuig season. AU sites were located between Thamesville and Beeton,

Ontario, Canada (Figure 3.1). ALI farms were planted to corn (Zea mays) in the season of

sampling. Tiliage upon all famis was either conventional till or zero-all management pable

3-1)-

Figure 3.1. Map of sites in southern Ontario.

To characterize each site, plots were selected on the basb of landscape position to

achieve varia bility in yield, seasonal water content and soil properties. The experimental

design of this project was a factorial experiment using randomized complete block design

with several replications, each with several plots divided by landscape position and 2

treatments: 150kg/ha N fertilization and no N fertiLization. Eight of the farms were each

characterized by estabhshing 24 plots: 4 repiicates, each with the 2 N treatments, and 3

landscape positions: upper slope, niid-dope and toe-slope positions. The remainirig sites

were located at the Elora Research Station where each site was characterîzed by 30 plots: 3

replicates with 2 N treatments and 5 landscape positions (Upper slope, shoulder, mid-slope,

lower slope and toe-slope). For all farrns, plots were approximately 5m long and 6 rows

wide,

At the start of each growing season, four sets of Tirne-Domain Reflectometry P R )

probes, 30cm in dep th, were ins ta lied vertically (15cm from the corn row) in each plot.

Volumetric water content was measured weekly, starting shortly after seedling emergence

and N fertLLization. At the completion of each growing season, pnor to harvest, 2

undisturbed cores (5cm diameter x 2.5cm height) were taken next to each set of TDR probes.

Overall, 8 cores were taken frori~ each plot, four cores at 5-7.5cm depth and another four

sores were taken at 20-22.5~111 ~iepth , In all, 2076 cores were coilected. Each core was

wrappeci in cellophane and stored at 4OC until used for analysis. Lmmediately after core

collection, a 6 metre Length of corn row was hand harvested in each plot. The harvested

comcobs were kiin dried for several weeks, shelled and weighed to calculate final yields.

Crdin yields are expressed on a dry weight basis.

Soi1 t roni racti core wds split into 2 parts; one part was sieved (2mm) and used for

particle-size analysis; the other was ground and used for OC analysis. Particle size analysis

was done using the hydrometer method and calibrated with the pipette method (Sheldrick

and Wang, 1993). Organic carbon analysis was done using the LEC0 S C 444.

Pedotransfer functions (PTF) relating the water release cuve and soil resistance

curve with clay content, OC and B d were developed (Chap ter 2) and used to calcdate

available water capacity (AWC) and the LLWR for each plot. The critical water contents at

field capacity (Ofc) and the permanent wilting point (e,,) were ca lda ted using the T3 WRC

MF. The volumetric water content at 2 MPa soi1 resistance (es,) was calculated using the T8

SRC M F and the volurnetnc water content at 10% air-Fiiled porosity (&@) was calculated as:

Ba$ = (1 - Bd/Pd) - 0.1

where B d = bulk density, and Pd is a n assumed particle density of 265 g/cm3. The AWC

was calcula ted as:

Regression analysis (SAS), linear and nonlinear, were used to evaluate the

rela tionships between yield (+N) with the LLW R, Fu,, and O ther soil properties. EvaIuation

of yields, seasonal water and soil properties were restricted to N fertilized treatments only.

I t was assumed that the yields measured within the +N heatments were not nutrient limited

and the variation in yields were affected by only soil water and soil structural effects.

3.3 RESULTS AND DISCUSSION

Textural analysis of the cores withïn the +N treatments showed that our data ranged

from 0- 60.3% clay content and 3.2- 92.8% sand content, Organic carbon and B d ranged from

0.25-5.88% and 1.05- 1-79 g / c d , respectively. A summary of d soil properties is shown in

Table 3.1,

Prelin~iiwrv malysis of Our data was done to determine if seasonal water played a

role in influencing the variations in yields- Using £inal yield values in the +N treatments and

the average meaçued water content readuigs taken duririg the growing season (8-,)

regression analyses were done. Correlations of yields (+N) with 0- were found to be

significant (p<0.05) in 6 of our 12 sites. One other site (Elora no till1998) also showed a

correlation signiticant to the p<0.10 Irvel. The site determined to have the best correlation

between yield and e,.,, was the Cameron site with a r2 = 0.826. Results of this preLiminary

regression analyses can be seen in Table 3.2

Soi1 properties found in the core analyses were used to calculate the LLWR using the

W RC and SRC pedotransfer functions. The LLWR values were then averaged by depth and

wi thin r a ï h plot. Although the liiiiiting factor deternUning the upper and lower h i t s of the

LLW R varied, the water content at 10% air-filied porosity and soi1 resistance defined the

upper and Iower LLWR linut 79% and 96% of the tirne, respectively. Statistical averages of

the calculated LLWR, Fil,, and yield values in the +N treatments for ail fanns are shown in

Table 3.3. ln general, the magnitudes of the LLWR (upper limit minus the lower M t )

defineci across nmny o f the Farti-is were faund to be very narrow. Ln some instances the

Table 3.1. Summary of soi1 properties for all plots in each site (0-30cm depth). Buik Density Organic

Fann: (dm3) Carbon (99) Sand(%) Clay(%) EC98: Elora 1998

(conv. till)

E98: Hora 1998 (no M)

ECSY: Eiorü 1999 (conv. till)

E99: Elora 1999 (no tiil)

McCracken (no

Portolinski (conv. tiU)

CN: Canagra North (no tiii)

CS: Canilb~ii Nor111

(iviiv. ~ill)

CC: Caiiigra Suu th

(no kiki)

Denys (no ta)

C~\nierun (110 till)

Newcombr (no ta>

Average: St. Dev.: Minimum: Maximum: Average: St. Dev.: Minimum: Maximum: Averir ge: St. Dev.: Minimuni: Müximuni: Average: St. Dev.: Minimum: Maximum: Average: St. Dev.: Miiurii uni : Maxim uni : Average: SI. Dev.: Minimum: Maximum: Average: St, Dev.: Muiim uni : Maximum: A vert ge: SL. Dcv,: Miiiin~ uni: M ~ x i u i uui: A vcriige: St. Dev.: MUlin1un1: Ma xini uni: A vwdge: St. Dev.: Mininiurn: Ma xini uni: Average: St. Dev.: Minim uni : Maxiaiuui:

Average: St. Dev.: Mulinluni: Maximum:

Table 3.2. Results of regression analyses between yield (+N) and measured seasonal average wa ter contents (O,,).

Farm : Regession Parameters: Pre diction: EC98: Elora 1998 YieId (+N) = -9820 + 5348-1.Ot'(8,,) R2 = 0.3%

(conv. till) ÇÇE=389xlW

Yield (+N) = %25St 1 t exp(43.761$*(9,- 0.1263t)) R2 = n/a

SSE= 229 x1W

E98: Elora 1998 (no tiIl)

Yield (+N) = 1664.6 t 32507.0$*(0,) R= = 0.251

EC99 Elora 1999 (conv. t i l 1)

Y ield (+N) = -818.3 + 42384.@*(0,,)

E99: Eluri 199) (no till)

McCracken (no tiiL) Y ieId (+N) = 9437-2-f + 9936.9-1"(8,,) Rz = 0.340 SSE = 1.28 x 107

Yield (+N) = 12639.7t 1 + esp(-19.46*(0, - 0.05))

Podolinski (cunv. till)

CN: Canagrci North (no till)

Yield (+NI = 3911-8$ + 14806.0*(8,,)

CS: Canagra North (conv. till)

CC: finagra South (no t i l l )

Denys (tir) tili)

Yicl J (+N) = 3322.0t + 38673.(It'(8,,) R'- = 0.826 SSE = 8.92 x lOo

Yield (+N) = 1 6054.7t 1 + t x ~ ( - l O,83*(8, - O. 123$))

R- = n/a SSE = 8.6û x 10b

Newcondw (rio tiII) Y ieid (+N) = 6846.6t + 6578.1*(8,)

t = rcgrr~ssirm sil;~iific.Liii~ (p<0.05), $ = regressioii sigiiificant (p<0.10), d other parameter estimates Linu Ii~uiill i io~ s i l ; n i l ' i c - ~ n ~ . Rz v~ilues for lion-liiiear regressions codd not be deterrnined. Cornparison

was done using SSE.

Table 3.3. Statistical data for LLW R, Fu,, and fïnd yields (+N treatments, 0-30cm dep th). LLWR magnitude Fu, Final Yields

Farrn : (cm3/cm3): (9; 1: & g / W EC98: Elorci 1998

(conv- W)

E98: Elora 1998 (no till)

EC99: Elora 1999 (conv- till)

E99: Elorii 1 %Y (no till)

McCracken (no till)

Po Joliiiski (c-c) i i v. till)

CN: Guiagrii North

(no îdi)

CS: Canagra North

(conv. ta)

CC: C d I l d );rd sciu111

(no ta)

Denys (no till)

Cameron (no till)

Newiom hc (nu t iu)

Mes: Std- Dev.: Minimum: Maximum:

Mean: Std, Dev.: Minimum: Maximum:

Mean: Std. Dm.: Minim uni : M~ixin~uni:

Mean:

Std. Dev.: Minimum: Maximum:

Mean: Std. Dev.: Minimum: Maximum:

MC~II: SLJ. Dev.: M inioi um : M~iximuni:

M e m : Std. Dev.: Minimum: Maximum:

Mean: Std. Dev.: Mirumuni: Mdxinium:

Meciri: StJ. Dev.: Mriunl uni: Mcr xini uni :

Mean: Std. Dev.: Minin1um: Maximum:

Mean: Std. Dev.: M inim u m : M~xiriiuni:

Memx Stcl. Dev.: Muiinium:

Table 3.4. R d t s of regression analyses between yield (+N) data and the LLW R, Fh.

Fann: Regression Parameters: Prediction: EC98: Elora 1998 Yiaid (+N) = 6639.8t + 651.6'(LLWR)

(conv. till)

E98: Elora 1998 (no till)

ECm Elora 19% (conv. till)

EW: Elora 1953 (no till)

McCracken (rio till)

Podolinski (conv. till)

CN: Canagra North (no till)

CC: Ciinagri South (no till)

Denys (nu till)

Newcoilihe (nu till)

Y ieid (+N) = 10236.û-t - 43.5*(Fuw)

YieId (+N) = 7655.3-t - 20941i(LLWR)

Yield (+N) = 6479.7f + 11.9*fi,)

Y ield (+N) = 7464.6t + 5816.W(LLWR)

Yieici (+N) = 7723.2t + 3.4*(Fu,)

UieId(+N) = 53633t + 15W0t*(LLWR)

Y ield (+N) = 95337t - ~ O - I ~ * ( F U ~ ~ )

Yield (+N) = 12447.q - 41612*(LLWR)

Yield (+N) = 11482û-f + 129'(Fu,)

Y ield (+N) = 10759.û-t + 28206.w*(LLWR)

Y ieid (+N) = 17759.w - 70.8Y(FuW)

Yield (+N) = 72139t - ïl66.1*(LLWR)

Y ield (+N) = -3732-9 -t 106.0*(Fu,,)

Y irici (+N) = 8c134.4t - 19923.w*(LLWR)

YielJ (+N) = 16330 + 56.2$"(FuW,)

Yield (+N) = 4229.6t + 8861.tP(LLWR)

YieId (+N) = 163330 - 119-5*(F~wr)

Y ield (+N) = 8380.3t + 1020-F(LLWR)

Y ield (+N) = 6983.8t + 16.1i(Fu,,)

Yield (+N) = 117U.Iy- - 7613.4*(LLWR)

Yidd (+N) = 10555.ût + 7.8*(Fuw)

Yield (+N) = 8275.2-f. - 381.5*(LLWR)

Yield (+N) = 7947.5t + 3.3*(FuW)

t = regression significant (pc0.05), * = regression significant (p<0.10), aU O ther parameter estirnates are found not signhcmt.

LLWR magnitude was found to be zero and as a r e d t seasond water contents fell outside

of the LLWR 100% of the time. These values of Fu, imply that 100% of the measured soil

water content values were founci to be critically limiting to the plant. h general, occurrences

of soi1 water content falling outside of the LLWR were occurrences below the lower k t t

Statistical results for each farm c m be seen in Table 3.4. Of our 12 sites, only the Podolinski

site and the Elora (no till) 1999 site showed a significant positive relationship between yield

and the LLW R magnitude, and a signScant negative reiationship betweeri final yield and

Fii,,. Exain ples of these reid tionships c m be seen from the Elora (no till) 1999 site in Figures

3.2a and 3.2b. Conversely, the CS-Canagra North (conv. till) site reveaied a sipificant

nega tive relationship be tween y ield and LLW R magnitude (Figure 3.3). Despite this

Canagra site showing a result opposite to our first hypothesiç, this site showed the best

correlation between yield and the LLWR with a rk0.75.

L

Figure 3.2a,b. Plot of the Elora (no till) 1999 yield data relationship with the LLWR and Fu,.

Figure 3.3. Plot of Canagra North (conventional tu) yield data relationship with the LLWR.

Overall across our 12 sites, it c m be seen that the LLWR, combined with seasonal water

content data (Fiiw,), did not play significant roles in explaining the variations in yields. &O,

- we ~ n u s t consider that pIants did grow and yields were obtained across the various farms

yet in the majority of sites dnd for the mdjority of the growïng season, the water contents

were calculated to be criticaiiy linùtirig. Within the Elora (no tiL1) 1999 data (Figure 3.2b) it

can be seen that a yield of approxirnately 5000 kg/ha (57% of maximum yield for the site)

was achieved yet 100% of the soil water readings taken throughout the season were found

to be critically liriiiting- W hile these results raise many questions about the validity of using

the LLWR and Fil,, i t is in teresting to note that da Silva and Kay (1997) reported that

despite Fil,, values of 100%, shoot growth was st i l i measured to be greater than 4 cm/day.

Their data however, also showed significant positive relationships between shoot growth

and the LLWR, as well a s significant negative correlations between shoot growth and Fu,.

Clearly our d a ta show that neither the LLWR nor Fir,, measured in the top 30cm, are

ddeq ua te in ex plduiing va ria tion in yield. Therefore, either our hypo theses conceming the

LLWR and Frr,,., and their effects on yields are incorrect, or errors were made in

defining/calculating the seasonal water contents and the least lïmiting water range for corn.

Conceptually, it is difficult to imagine that as the frequency of limiting conditions for plant

gïowth increase, the pIant wiU not suffer a decrease in yieId. Therefore possible errors made

in calcdating the LLW R were assessed.

To determine the sources of the errors, attention was first direct to the calculation of

the LLWR. The most iimitirig water contents were generally found to be 10% air filled

porosity and soil resistance. The cdculation of 10% air fiUed porosity was a simple

calcdation and deemed unLikely to be a source of error. &O, the majority of measured

seasonal water contents were found to fall below the lower linut- Therefore calculation of

the soil resistance Lïn~t was exmüned. Data on measured water contents suggested that the

Iower 1uiUt of the LLWR may have been too high or that yields were less responsive to soil

resistance measured over the 0-30cm depth than were the growth rates observed by da Silva

and Kay (1997). The lower luriit n-iay have been too high because of errors in estirnating the

wà ter content d t ii soi1 resistance of 2MPa or the Limiting soi1 resistance of 2MPa was too

low.

The PTF for the SRC predicted the penetration resistance of soil as a function of soil

n~atric potential and soil properties. The T8 SRC PTF was of the fom:

SR = a@ + p(€3/poro~ity)s(vx)

where S R = soil resistance &Pa), a = (a+b"% clay+c"% O.C.+d*Bd),

p =(e+ f*%clay+g*% O.C.+h"Bd), 6 = (i+j"% clay+k*%O.C.+l"Bd), al1 8 values are replaced by

the T3 W R C MF, and a.. .l,x are constants.

When given the measured soil resistance, the T8 function was used to predict the

volumetric water content a t which the measurement was taken and was found to predict

dpproxirna tely 84% of the variation in 9, in an independent data set, with a RMSE = 0.036

~1i+/cn13 (Figure 3.4a). For cornparison the same analysis was done using DS2, the SRC lTF

fourid by da Silva and Kay (1997). Given ihe measured soil resistance values, volumetric

water content was predicted using the DS2 mode1 and was found to predict approxiniately

68 '% of the variation in 8, with a RMSE = 0.083 cm3/cm3 (Figure 3-4b).

Figure 3.4 a,b. Prediction of water contents by the (a) T8 and @) DS2 SRC hinctions vs. measured values for an independent data set (164 data points).

I t can be seen that within this independent da ta set the T8 function shows a more accurate

prediction. The DS2 function however consistently underpredicts water contents at the dry

end of the analysis (Le. the ared of high soi1 resistance). Presuming for a moment that the

measured da ta for the independen t da ta set is incorrect and the DS2 prediction is correct,

the water contents predicted by DS2 would result in wider LLWR magnitudes and

presumably reduce the frequency of seasonal water contents falling outside the LLWR. In

general however, there is no evidence to indicate that methodology and prediction of the

cri tica i wa ter content a t 2M Pa soi1 resistance using the T8 function, is at fault in describing

the LL W R. Porho ps a redefinition of the S R limit to a value greater than 2MPa might

improve on Our original analyses. In the following analyses however, data suggests that

redefining the SR limit to a higher Limit would not be a fruitfd exercise.

Considering that the majority of Our measured seasonal water contents were found

to fa11 below the lower liiiiit of the LLWR, perhaps a reanalysis of Our data using the PWP as

the lower limit instead of the SR iimit would improve our predictions of "Limiting" water

conditions. Analysis was done to test if the frequency of seasonal water contents f a h g

below the PWP (Fpwp) could better explain some of the variation found in o u yield data

(field capacity and 10% air füled porosity were not considered in this analysis because

seasonal water content rarely surpassed the upper lirnit). Overall, it was found that FPw,

showed no significant relationship with yield for ail farms (example Elora conv. till1999 -

Figure 3 -5).

Figure 3.5. Plot of yield (+N) vs. the frequency of water contents f a h g below the permanent wiiting point during the growîng season (Fpv) at the Elora (conv. till) 1999 site.

Clearly, despite altering the lower limit of the LLWR from SR to the PWP, understanding of

the variation in yielcls dicl not improve. Also, the value, Fpwpf was still found to be high. In

Figure 3.5 i t can be seen thcit although over 60% of the seasonal water content readirigs were

found below the PW P, yields of over 8000 kg/ha were still achieved.

Analysis was done to determine if the T3 PTF prediction of the PWP was at fault and

consistently over-predicted -1.5MPa conditions. To achieve this, T3 predictions of the -1.5

MPa potential were done on the original data set used by da Silva and Kay (1997). To

compare, DESORPMOD, d PTF derivéid by McBride and Mackintosh (1984), was also used

for a third reference, Resultç of thiç analysis showed that both T3 and DESORPMOD

consistently over-predicted the da Silva and Kay (1997) data (Figure 3.6a and Figure 3.6b,

respectively), but when compared to each other, T3 and DESORPMOD predictions were

similar (Figure 3 . 6 ~ ) . Therefore, it can be seen that T3 predictions of the PWP are not

consistently different than those found by another model, and therefore it is urilikely that

MT prediction of the PWP in our analyses thusfar is the soume of our errors.

0.0 0.1 02 03 0.4

Measued water caiterûs (m'lem)

l 3 and DESORPMOD Prediclion of -1.SMPa DaSiiva Data

Figure 3.6 a,b,c. T3, DESORPMOD predictions relative to each other using cla Silva and Kay (1997) data.

Further analysis of seasonal water data revealed that in many sites, the recorded

seasonal soii water values were weU below the measured PWP values. In order to remove

error associa ted w ith predicting the PW P from PTFs, the minimum recorded soil water

values were compared against actual core data. Across aii farms in 1998,2 of the 8 soil cores

taken (1 at each depth) from half of ail plots were chosen to undergo water release

laboratory analysis. Volumetrïc water content meanired at -1.5 MPa hom both cores were

averaged to gïve an average PWP value for the 0-30cm depth. Only two plots showed an

extreme change in textural characteristics within the 0-30cm depth and those were

eluninated from this analysis. The average PWP value taken from the core water release

data was then compared with the nünimum average soi1 water value as measured by the 0-

30cm TDR probes adjacent to the two cores. The difference (minimum recorded TDR value

minus the average core PWP value) was found to be on average -0.051 cm3/cm3. The

minimum recorded soi1 water values were consistently lower than the measured PWP

values except in the upper slope positions at the McCracken farm (Figure 3.7). These

pdrticular plots con tainrd high sand contents with very Little organîc carbon.

Figure 3.7. Plot of difference values (minimum recorded TDR values during the srowing sedson - core measured PW P) across ail 1998 sites.

Whilr it can be seen tliat our PTFs pradicting the PWP are unhely to be directly responsible

for our erroneous data, this evidence suggests that our errors may be due to the

methodology used in detemiining the -1.5MPa matric potential values in our cores.

However, if we are to question our methodology and the validity of our PWP data, we must

consider that our predictions of the PWP using the T3 function were similar to predictions

made by DESORPMOD in an independent data s e t Therefore, in questioning the validity of

our PWP methodology and data, we also question that of McBnde and Mackintosh (1984).

Presuming however, tha t both the T3 and DESORPMOD predictions are faulty and

the DS1 data and predictions are a more accurate and realistic depiction of soi1 water

conditions, it is conceivable that our nünimum recorded soil water values would not

surpass the PWP predicted by DSI. Given the average soil properties for each plot in d l 2

sites, the T3 and DSI functions were used to predict the water contents at PWP and were

cornparrd with the minimuni nieasured soil water values. Plots of T3 prediction can be seen

in Figure 3.Sa and DSl in Figure 3.Sb. Despite the fact that the OS1 mode1 predicted the

Iowest soi1 water values for the PWP of all three WRC models tested, it is still evident that

many of our rninïmurn recorded soil water values feu far below the lower limit-

Figure 3.8 r,b. Cornparison of i~ünimum recorded soil water values with T3 and DSI PWP predictions.

These minimum recorded water contents found below the DSl predicted PWP were at

times, also found to correspond with good yields. An example can be seen in Figure 3.9,

where in the toe-dope position, water contents were found to falï 0.055 - 0-104 crn3/cm3

below the PWP predicted by DS1, yet yields close to 9000 kg/ha were found.

Figure 3.9. Pfo t of yield and the niinimum recorded water content minus the DS1 predicted PW P, across landscape positions (CS-Canagra site).

Therefore, judging from the presented data and considering that the minimum

recorded TDR measured water contents frequently feu below alI measures of the PWP, it is

hypothesized that the errors associated with the LLWR and the seasonal water content data

are no t caused by prediction errors from the pedotransfer functions- There is also no

evidence to show tha t the methodologies were a t fault or significant experimental errors

were present. Therefore soil water contents that fell below the PW P may only be explained

by (a) loss of soi[ wa ter due to evaporation from the soil, (b) the TDR measurements were

consistentlv underestiniating actual soil water, or (c) the critical limits of PWP (-1.5 MPa)

and SR a t 2MPa are inadequate in describing the critical water extraction limits for corn.

Minimum recorded soiI water values were analyzed for tempord stability to

deternune if the values occurred a t relatively the sanie time within the season. Analysis

showed that the minimum-recorded vülues were generally around the 5 d l - 7th readings

(rnid-June to Mid August) in 1998 and 1999. By this time, for most sites, silking had or was

occurring and canopy closure was reached. Within the Canagra sites (1998) however, plant

growth was extremely poor and canopy closure was not attained within many plots for the

entire growing season. Within these plots soil cracking was evident and evaporative loss of

soil water was possible, but in general for most plots and most farms, canopy closure was

met and the possibility of evaporative loss of soil water to the atrnosphere was reduced.

Also, consider that even if evaporation had caused the extremely low measured soil water

contents, it is stiil questionable how these extremely low water contents had so Little effect

on final yields.

In a study done by McNabb and Kay (unpublished data), TDR measured soil water

contents were compared with data measured horn actual soil cores- Data was obtained at

the Elora Research Station in 1997. Five TDR probes were placed across 5 landscape

positions, TDR nieasurenients were taken and then cores were taken adjacent to the probes

to measure gravirnetric wa ter contents and buik density. Gravimetric water contenk were

converted to voIumetric water content using the measured B d and then compared to the

TDR data. A plot of the converted volumetric water content with the TDR denved

volumetric water content (Figure 3.10) resulted in a rz = 0.875, slope of 0.886 (not

significan tly ciifferen t than 2 ) and an in tercep t of 0.042 (nof significantly different than zero).

Therefore i t can be seen tha t the TDR data does not stray significantly from an altemate

form of volurnetric water content measurement Although this analysis encompasses only

five points, the data provides no evidence to explain the magnitude or the consistency of the

difference between our measured TDR water contents and our various methods of

caiculating the PW P. The possibility of a TDR operator error was also analyzed but was

deen-ied un I I kely beca u s e the opera tor for much of Our TDR data was the same for the

McNabb and Kay study. ln general, it is deemed that our TDR data was not a major source

i l 0.15 -

0.15 0-17 0.19 0.21 0.23 0.25 0.27 0.29 Converted Vol. Water Content (%vol. of soil)

Figure 3.10. Plot of volumetric water content values measured by TDR vs. volumetric water content converted h-om gravirnetric samples.

Finatly, i f ali O ther explana tions can be elirninated, the observed water contents

measured throughout the season were within the range of plant extractable water. If so, our

data indicate that the water content at 2MPa SR and -1.5 MPa PWP, defmmined under

laboratory conditions, may not adequately describe iimiting water contents under field

conditions. Certainly thrre have been studies showing that root penehation has occurred in

soi1 rcingmg h-oni 3 MPa (Ldboski et dl. 1998) to 5-7 MPa (Cerard et al., 1982) penetration

resistance. ln another study, Dexter (1987) suggested that critical root impedance was not

static (Le. 2MPa) but moved reIative to the matric potential within the soil. Perhaps these

should be considered as more likely critical lirnits for SR. More irnportmtly however, the

critical lowltr liiiiit associa ted with the PW P niust be exaniined. As with soil resistance, there

1s Cilso evidrncr showing thdt corn lias the abiiity to extract soi1 water beyond the classical s

Linut of -1.5 MPa ~natric po tential (Cabelguenne and Debaeke, 1998). The nature of a new

lower Iunit of plant extractable water however has not been discussed in great detail. If w e

are to suggest that the PWP should be shifted to a lower matric potential, we must also

consider the nature of the WRC. Implications of another 0.05 cm3/cm3 of water that corn

piants could transpire (as iç seen in our data), wodd equate to tremendous water potentials

at which plants could draw water. For example, the T3 PTF prediction of a water release

curve for an average soil from the Podolinski site with 10% sand, 45% clay, 2.3% OC and a

Bd of 1.31 g/cm-l, can be seen in Figure 3.11. At a matric potentid of -1.5MPa, the vol. water

content was calculated to be approximately 0.28 crn3/cm3. This value was consistent with

that of DESORPMOD. implications of a reduction from 0.28 cm3/cm3 to 0.25 cm3/cm3 water

content resulted in a rnatric potential of -15 MPa, an order of magnitude higher.

Clay Soil: 9.9%sand, 44.5%clay, 2,3%OC, 1 .3g/cm3

I Potential (MPa)

Figure 3.11. Example of a predicted water release cuve from a clay soil.

3.4 CONCLUSIONS

Frorn Our preiïrnhary analyses, it is clear that water (in the fonn of average water

content measured during the growing season) in the 0-30cm depth plays a role in explaining

yield variation on many of our sites. It was hoped that using these seasonal water values

and knowing the linuts in which plants experience water stress, wouid help us in explaining

more of that yield variation. From our analyses using the Least Limïting Water Range

(LLWR) as defined by da Silva and Kay (1997), it is evident that the LLWR and F u , the

frequency at which seasonal water feu outside the LLWR, were inadequate to describe the

soi1 limiting conditions in which plants grow. Ln fact, seasonal water contents were found to

fali below both lower limits of the LLWR- Seasonal water contents were found to fa11 well

below the 2MPa soil resistance lirrut, as weU as the PWP, whether those values were

predicted by the T8 or T3 h c t i o n s (Chapter 2), the da Silva and Kay (1997) functions, or

dctual corr meas ured -1 SMPa wa ter release data. Upon further analysis, it was found that

niethodology, sdlculntion dnd prediction errors were uniikely to be the cause of the poor

predictions of yield using the LLW R and Fu,,.

Finally, two possibilities remain to explain the poor results fourid. First, that plant

extractable water at greater depths than 30 c m may have played a sigruhcant role in

deterininhg final y ieids and second, that water content at 2h4Pa soi1 resistance and PWP are

inadequate in describing the critically Litniting water contents for corn growth.

Ultimately, Our abiLity to manage the variabilities inherent in agricultural systerns

wiU depend in a large part, upon our understanding of soil properties, their interactions

with seasonal water and how it ail affects plant growth. It is evident that our current

definition of critical soil water conditions are kadequate. Clearly a better definition of the

critical lower limit of water content rriust be found.

3-5 REFERENCES

Busscher, W, J. 1990- Adjus tmen t of Ba t-tipped penetrometer resis tance data to a common

water content. Trans. ASAE 33: 519-524.

Cabelguenne, M-, Debaeke, P. 1998. Experimental detennination and m o d e h g of the sorl

water extraction capacïties of crops of maize, sunflower, soya bean, sorghum and wheat

Plant and Soi(- 202:175-292.

CarnbardeLla, C. A., T-B- Moorman, J.M- Novak, T-B. Parkin, D.L. Karlen, R-Fr Ruco, and

A.E. Konopka. 1994. Field-scale variability of soil properties in central Iowa soils. Soil

Sci. Soc. Am. J- 58:1501-1511

Colvin, T.S., D.B. Jaynes, and D.L. Karlen. 1996. Yield variability within a central Iowa Field.

Trans. ASAE. 40(4): 883-889-

da Silva, A.P., Kay, 6.D. 1997. Estirnating the least limiting water range of soils from

properties and management. Soil Sci. Soc. Am. J. 61:877-883.

Dexter, A.R. 1987. Mechanics of root growth. Plant and Soil. 98: 303-312.

Grable, A.R-, Siemer, E.G. 1968. Effects of bulk density, aggregate size, and soil water

suction on oxygen diffusion, redox potential and elongation of corn roots. Soil Sci. Soc.

Ani. Proc. 32'180-'186.

Gerard, C.J., Sexton, P., Shaw, G. 1982- Physical factors influencing soil strength and xoot

growth. Agrononiy Journal. 74: 875-879.

Creacen, E.L. 1986. Root response to soi1 n~echanical properties. Trans. 13h Congress Intem.

Soc. Soi1 Sci., Hatiiburg, Gern-iany- 5:20-47.

Haise, H. R. Hdas, H.J. , Jensen, L. R. 1955. Soil nioisture studies of sonie Great Plain soils: II.

Field ca pacity as rela ted to 1 /3-atntosphere percentage and "minimum poinf' as related

to 15 and 26- atniosphere percentages. Soil Sci. Soc. Am. Proc. 3420-25.

Laboski, C.A.M., Dowdy, R.H., ~ l l m a r a & R.R, Lamb J.A. 1998. Soil strength and wafter

content influences on corn root distribution in a sandy soil. Plant and Soil. 203: 239-248.

Letey, J. 1985. Relationship between soil physical properties and crop productions. Adv. Soil

Sci. 1:277-294.

McBride, R.A., Mackintosh, E.E. 1984. Soil survey interpretations hem water retentiam data:

1. Development and validation of a water retention model. Soil Sci. Soc. Am. J. 48-:1338-

1343.

Richards, L.A., Weaver, L.R. 1944. Fifteen atmosphere percentage as related to the

permanent wiltïng point Soil Sci. 56:331-339.

Sheldrick, B.H., Wang C. 1993. Particle Size Distribution. In: Soil Sampluig and Methods of

Analysis, M.R. Cdrter. Ed. PP: 499-57 1. Canadian Society of Soi1 Science. Lewis

Publishers.

Stypa, M., Nunez-Barrios, A., Barry, D.A., Miller, M.H., Mitchell, W.A. 1987. Effects of

subsoil bulk density, nutrient availability and soil moisture on corn root growth In the

field. Can. J. Soi1 Sci. 67: 293-308-

Taylor, H.M., Ro berson, C.M., Parker, Jr., J.J. 1966. Soil strength root penetration relations

for mediuni to coarse textured soil materials. Soil Sei. 10218-22.

Young, LM., Montagu, K., Conroy, J., Bengough, A.G. 1997. Mechanicd impedance o f root

growth directly reduces leaf elongation rates of cereals. New Phytol. 135: 613-61t9.

CHAPTER 4: UNDERSTANDING YIELDS OF CORN (Zea mays) A N D ITS RELATIONSHIPS WlTH PLANT EXTRACTABLE WATER AND SOIL PROPERTIES-

4-1 BACKGROUND

Agriculhual fields vary considerably in their soil properties, landscape features,

and management histories. As a result, this variability has been shown to contribute to

yield. Much of the variation in yield can be measured with current techology, but the

roo t causes of this spatial variability are unexplained. This project attempted to assess

the infiuence of vanability in soil structure and water content on the spatial variabiliv in

y ield.

It was hypothesîzed that the importance of soil structure to yield is related to the

soil's ability to provide oxygen, water and support for the growth of roots. As such, in

Our previous work soil structure was defined by properties such as soil resistance, air-

filled porosity and matric potential. More specificdy, soil structure was defined by an

upper limit and a lower limit of plant extractable water. The upper Limit was chosen as

the lower value of wâter content-in which aeration to the roots becornes limiting, or

when rapid drainage ceases. Aeration was considered to be limiting at an air-filled

porosity of 0.1 cn$/cn13 (Grable and Sienier, 1968), and rapid drainage was considered

to cease at field capacity (FC) at a water potential of -0.01 MPa (Hake et al. 1955). The

lower limit values were chosen as the greater value of fhe water content below which

w a ter canno t be extracteci by plants (permanent wilting point or -1,5 MPa) found by

Richards and Weaver (1944), or the water content at which mechanical irnpedance

restricts root growth. Based on studies done by Taylor et ai., (1966) and Greacen (1986),

a cone resistance of 2 MPa was used as the upper M t of mechanical impedance.

Analysis described in Chapter 3 showed that water contents measured during

the growing season rarely surpassed the upper limits but often fell below both the lower

limit of 2MPa soil resistance and the PWP. When these lïxnitïng water contents were

compared with yield however, few significant relationships were found and it became

evicient that measu res of h n i ting wa ter contents, as they were currently defined, were

inadequate in explaining the vanability in yield. There have been studies showing that

root penetration has occurred in soils ranging from 3 MPa (Laboski et al. 1998) to 5-7

MPa (Gerard et al., 1982) penetration resistance. In another study, Dexter (1987)

suggested that critical root impedance was not static (i.e. 2MPa) but moved relative to

the [na tric poten tidl within the soil. There is also evidence showing that corn has the

ability to extract soil water beyond the classicd limit of -1-5 MPa matric potential

(Cabelgueme and Debaeke, 1998).

Feddes et al. (1988) and Kay et al. (1999) attempted to define three critical water

contents for transpiration and photosynthesis of corn: an upper litnit at which gas

exchangr was reduced, a threshold l imit below which rapid declines in gas exchange

occured due to drought, and lower limît in which transpiration and ph~tos~vnthesis

approached zero, due to drought. A conceptual illustration can be seen in Figure 4.1.

Soii 'dater Content

Figure 4.1. Conceptual model describing plant gas exchange as a function of soil water.

Considering Our previous work it is easy to undentand how this working model might

fit in with Our previous hypotheses. Seasonal water contents fallirig outside of the

"aeration lirnit" or the "threshold Lirnit" would impact upon plant growth and

ultirnately final yields.

Again considering our previous work, we h o w that the majority of our water

contents were found towards the dry limits. Therefore a definition of the "threshold

h u t " and a determination of how this limit varies with soil properties, would be great

step in our understanding of how soii structure and water content rnight affect plant

growth and yields. Ln their work however, Kay et al. (1999) found that defining the

threshold limit across a range of soil properties was difficult. Also, other authors

studying this concept have determined that defirüng the threshold Lùnit is dependent

not only on soil properties but atso evaporative demand (Sadras and Milroy, 1996).

Considering that it is unlikely that we wiil be able to use the threshold limit

concept in helping us to understand yield variability. perhaps we can use the "lower

l i i i i i t" concept. Contrary to the threshold limit results, Kay et al. (1999) found that the

lower limit was highiy correlated with soi1 properties. In th& analysis of the lower limit

they found that soi1 water content at which photosynthesis ceased (80,) was highly

correlated with soil properties:

80, = -0.143 + O.ûû390+(% clay) + O.Oll48*(% OC) + O.l65*(% relative compac tion)

(4-1)

where ali statistical parameters were sigruficant to p<0.05, and r2 = 0.97 was found. They

a Iso found thd t the parameter @O,,, dcrosç d range of soiis, was not equivalent to the

critical points of -1.5MPa water potential or 2MPa penetration resistance. Therefore, use

of this lower Limit parameter to determine new critical water potentials or soil resistance

M t s is udikely but c o n s i d e ~ g its excellent correlation with soi1 properties, perhaps

Equation 4.1 itself can be used as the lower limit of plant extractable water.

The concept of the lower limit, here proposed as Oop, could be considered a

quantifiable parameter in which to define the lower lirnit of plant extractable water by

using the inherent soil propertîes such as texture, B d and OC. The difference between

the seasonal average water content (QS,,,) and 80, would then provide a measure of

seasonal average plant extracta ble wa ter content (to be referred to as PEW,.,). The broad

objective of this project was to nssess the influence of soi1 properties and soi1 water on

the variability of yielcis in corn. The parameter PEW,,, is essentiaily a concept

describing plant extractable soil water independent of soi1 properties and therefore

PEW,.,should influence plant growth. Also, researd-i thus far has focused on the

influence of these soil properties on soil water and how that in turn affects yields. Yet to

be discussed is the direct rela tionship between other soi1 properties such as OC, %clay

and relative compaction, and the variability of yields. An understanding of these

rela tionships would also help in unders tanding the variabiiity of yields.

The objectives of ttus study were to: (a) assess the irifluence of PEW,, as a

measure of plant extractable water independent of soil properties, on the variability of

yields of corn and, (b) to determine the direct relationships between yields and other soil

properties such as OC, % clay and relative compaction, to better understand the scope of

the relationships between the variability in soil properties, soil water and final yields of

corn.

4-2 METHODS AND MATERIALS

This study was conducted upon 6 farms during the 1998 growing season and 4

farms during the 1999 growing season. AU sites were located between Thamesville and

Beeton, Ontario, Canada (Figure 4.2). AU farms were planted to corn (Zea mays) in the

season of sampling. Tillage upon all farms was either conventional till or zero-tiU

management.

Figure 4.2 Map of sites in southem Ontario.

To characterize each site, plots were selected on the basis of landscape position to

àchieve variabili ty in y ield, seasonal wa ter content and soi1 properties. The experimental

design of this project was a factorial experiment using a randomized complete block

design with several replications, each with several plots divided by Iandscape position

and 2 treatments: 150kg/ha N fertiiization and no N fertilization. Eight of the farms

were each characterized by establishing 24 plots: 4 replicates, each with the 2 N

treatrnents, and 3 Iandscape positions: upper slope, mid-slope and toe-siope positions.

The remaidg sites were located at the Elora Research Station where each site was

characterized by 30 plots: 3 replicates with 2 N treatrnents and 5 landscape positions

(Upper slope, shoulder, mici-slope, Lower dope and toe-slope). For all farms, plots were

approxinlately 5m long and 6 rows wide.

At the start of each growing season, four sets of 30cm Time-Domain

Reflectometry (TDR) probes were ïnstalled verticdy (15cm from the corn row) in each

plot. Volumetric wa ter content was measured weekly, starting shortly after s e e d h g

emergence and N fertilization- At the completion of each growing season, prior to

harvest, 2 undisturbed cores (5cm diameter x 25cm height) were taken next to each set

of TDR probes. Overaii, 8 cores were taken from each plot, four cores at 5-7.5cm depth

and another four cores were taken at 20-22.5cm depth. In di, 2076 cores were collected.

Each core was wrapped in cellophane and stored at 4OC untii used for analysis.

l nunedia tely after core collection, a 6 metre length of corn row was hand harvested in

each plot. The harvested corncobs were kiin dried for several weeks, sheiled and

weighed to calculate hnal yields. Yields are expressed on a dry weight basis.

Soi1 from each core was split into 2 parts; one part was sieved (2mm) and used

for particle-size analysis; the O ther was ground and used for OC analysis. Particle size

analysis was done using the hydronieter method and calibrated with the pipette method

(Sheldrick and Wang, 1993). Organic carbon analysis was done using the L E C 0 SC 444.

Relative compaction (RC) was calculated using the equation:

RC = bulk density/ Bdref (4-2)

where Bdref = the reference bulk density. Reference b~& dertsity was cdculated from

measurements of texture and OC using the pedotransfer function developed by Kay and

To (2000):

Bdref = 1.94 - 0.072*OC - 0.0066*(% day) - 0,82l*(OC/ % clay) (4-3)

The lower limit of plant extractable water (€lap) was cdculated using Equation 4-1

- The parameter PE W,,,, was calculated as the average seasonai water content (O,,,)

niinus Bu,- Regression analyses (SAS), h e a r and nonlinear, were used to evaiuate the

reiationships between yield (+N) with PEW,,,, 80, and other soil properties. Evaluation

of yields, seasonal water and soi1 properties was restricted to N fertilized treatments

only. It was assumed that the yields measured within the +N treatments were not

n utrient iirnited and the variation in yields was affected by only soil water and soil

structural effects.

4.3 RESULTS AND DJSCUSSION

Data collected from Our 12 sites varied considerably in theïr yields, water

contents and soi1 properties. A summary of OC, B d and textural properties is shown in

Table 4.1. In general, textural analysis of our plots showed that our 12 sites ranged from

0- 60.3% clay content and 3.2- 928% sand content- Organic carbon and Bd ranged from

0.20 - 5.88% and 0.89 - 2.02 g/cm3, respectively. A summary of yield data and soil water

contents is shown in Table 4.2 In total, yield(+N) data ranged from approximately 1200

kg/ha to nearly 14000 kg/ ha, e,,.,, ranged hom 0.08 crn3/crn3 to 0.37 c m 3 / c m 3 , and

PE W,,,, ranged fron-i -0.02 cm3/ cn13 to 0.28 cm3/ cm3. Unlike results hom the previous

chapter, seasonal water contents rarely fell below the lower Mt 80,. Only the three

Canagra sites showed any evidence of seasonal water contents falling below the lower

Lilliit.

Yield (+N) data was regressed against the two soi1 water parameters 8,,, and

PEW,,,; results can be seen in Table 4.3 and Table 4.4, respectively. Common

characteristics were identified among the 22 sites and sites were segregated according to

these characteristics. Three sites (Elora no till1999, Denys and Newcombe) showed very

little yield variabiiity across aii pLots. Of Our 12 sites, only these 3 showed standard

cievia tions in yieId (+N treatntents only) of less than 1000 kg/ha. It was deterrnined that

these 3 sites showed so Little variability in yield that signrficant correlation with soil and

water characteristics would be difficult to establish. As a result these sites were

eliminated from further analysis. An example of one of these sites can be seen in Figure

4.3.

Table 4.1. Summary of soi1 properties for ail plots on eadn site (0-30cm depth). Bulk Dençity Organic

Fann: (dan3) Carbon(%b Sand(%) CIay(%) EC98: Elora 1998

(conv. dl)

E98: Elora 1998 (no till)

EC99: Elora 1999 (conv. till)

E99: Elora 1999 (no till)

McCracken (no îiü)

Pudoluiski (conv. till)

CN: Canagra North

(no till)

CC: c~~ri'lgr;~' South

(no til)

Denys (no tîil)

Newconibe (no tiU)

Average: St- Dev.: Minimum: Maximum: Average: St. Dev.: Muumum: Maximuni: Average: St. Dev.: Minimum: Maximum: Average: St, Dev.: Minimum: Maximum: Average: St- Dev.: M inini uni : Miixin~uni: Averclge: St, Dev.: Minimum: Maximum: Average: St- Dev.: Minimuni: Maximum: A verilce: St. Dsv.: Miiiini U L . :

Maximuni: Average: St. Dev.: Minimum: Maximum: Average: St. Dev.: Minimum : MdxÙIl unl: A vertige:

SL. Drtv.: Miriin1 uni : Maxim uni:

Average: St. Dev.: Miltimum: Maximum:

Table 4.2. Statistical data for average 0,, PEW,,, and h a 1 yield data for ail ploîs. Avg- 0- Avg, OOp Avg, PEW- Final Yields

Farm: ( c d / a n 3 ) (cm3/cm3) (cm3/an3) &g/ha): EC98: Elora 1998

(conv. tili)

E98: Elori 1998 (no tu)

EC99: Elora 1999 (conv. till)

E99: Elora 1999 (no ta)

McCra~* ke 11

(no U)

Podolinski (conv. di)

CN: Canagri North (no till)

CC: Canagra South (no ta)

Denys (LIU lill)

Cameroii (no ta)

Newcombe (no

Mean: Std. Dev.: Muumurn: Maximum:

Mean: Std. Dev.: Miium un1:

Maxim u m : Mean:

Std. Dev.: Minimum: Maximum:

Mean: Std. Dev.: Minimum: Maxini uni:

Mean: Std. Dev-: Miiùm un-i : Maximum:

Mean: Std. Dev.: MùUmum: Maximum:

Mean: Std. Dev.: Mininiuni: M ~ i x i n ~ uni:

Mean: Std. Dev.: Minimum: Maximum:

Mean: Std. Dev.: Minimum: Maximuni:

Meiin: Ski. Dev.: M üiini un1 : Maximum:

Mem: Stci. Dev.: Minimum: Maxiniuni:

Mean: Std. Dev.: Minimum:

Table 4.3, Results of regression analyses between yield (+N) and average measured seasonal water contents ( 8 4 -

Rem Farm: on Parameters: Prediction: EC98: Elora 1998

(conv. tiii)

E98: Elora 1998 (no till)

EC99: EIora 1999 (conv. till)

E99- Elora 1999 (no till)

M c h c k e n (no till)

CN: Canagra North (no till)

CS: Canagm North (conv. till)

CC: Canagra South (no till)

Denys (nu till)

Canierux~ (nu till)

Newconihe (no till)

Yield (+N) = -9820 + 534&.w(8,,) Rz = 0.3% SSE= 289 x1W

Yield (+N) = 9625.5t R?- = n/a 1 + exp(43.761$*(8,,- O-1263t)) S E = 229 x 107

Yield (+N) = 1a.6 + 32507.0$*(8,,) R? = 0.23

Y ield (+N) = 10142O-t - 15436-Of (0,)

Y ield (+N) = 9437.2t + 9956.9t*(0,) R2 = 0.390 SSE = 1-28 x 201

YieId (+N) = 12639-7t R2 = n/a 1 + exp(-19.46*(8,, - 0.05)) SSE = 9.89 x l@

Y irld (+N) = 24407.0-f - 39127.0t*(8,,,) R'- = 03-11

Yield (+N) = 3911.8$ + 14806-CY(8,,)

Yield (+N) = 2179.1 + 99858*(8,,)

Yield (+N) = 16054.7t R= = n/a 1 + exp(-10.83*(8,, - 0.123$)) SSE = 8.60 x 1@

Yield (+N) = 6846.6t + 6378.1'(0,)

t = rcgrcssioti ~i~;nific-,.in~ (p<~.U5), $ = regressioii sigiacant (p<0.10), al l other parameter estimates Ge fou rd not sig~iificant. R' vdues for non-linear regressions could not be deternuied.

Con~parison was done using SSE.

Table 4.4. Results of regression analyses between yield (+N) and average measured seasonal water contents (PEW-),

Y

EC98: Elora 1998 Yield (+N) = -1641.2 + IW70.V(PEW,,) R2 = 0.642 (conv. tiil)

E98: Elorâ 19% (no till)

ECw Wurct 1999 (conv. dl)

E99: Bora 1999 (no tiU)

McCracken (no till)

Poddinski (cunv. tiil)

CM Canûgra North (no till)

CS: Cariagra North (conv. tif 1)

CC: Cariagrci Suuth (no lill)

Denys (i~o t i l l )

Cameron (no till)

Newconibt, (no till)

YieId (+N) = 10S17.3t R2 = n/a 1 + exp(49-9'( PEW,, - 0.069-f)) S E = 1.63 x 1W

Yield (+N) = 58û6.8t + 14113.(r( Pm,,)

Yield +N = 4826.7t - 5306.2*( PEW,,)

Y ie1d (+N) = 4793.3t + 45576.Wt( PEW,,) RI = 0-628 SSE = 1.91 x 107

Yield (+N) = 132553t 1 t exp(-29.7r(PEW,, - 0.072t)) Rz = n/a

SSE = 1.30~ 107

t = regessioit sig~iificmt (p<O.E), $ = regession sigdcant (p<0.10), alI other parameter estintates are fouiid not sigdicant. l? values for non-linear regrestions could not be detennined.

Cornparison was done using SE.

O ! t

0.0 1-0 2.0 3 .O

Organic Carbon content (%)

Figure 4.3. Example of a site with little yield variation (Denys site).

One other, the EIora no ta1998 site, was ais0 elirninated from further analysis.

On thïs site a frost event occurred early in the growing season that affected many of the

s eedhgs in the lower landscape positions and had an indeterminaie effect on yields.

Of the eight remaining sites, h o major trends were observed; one group in

which yields were found to be signrficantly correlated with PEW,,,, and another group

in which yields were not. Of the 8 sites, the 3 Canagra sites (CN, CS, and CC) were the 3

that did not show significant correlations with PEWWs. Upon further analysis al l three

sites also showed unusualiy low PEW,, values, ranging from -0.02 to +0.10 cm3/cm3.

Therefore, soi1 water contents on these sites persisted at or dropped below the @op

throughout the measurernent period. I t is possible that no correlations were found

between yields and PEW,,, on these sites because plant extractable water was so low.

The five remaining sites (Elora conv. till1998, Elora conv. till1999, McCracken,

Podolinski, and Canieron) were those in which signihcant correlations were found

between yield and PE W,,,. ln general, the relationship was found to be positive, where

yields increased with increased plant extractable water. The relationship between yield

and PEW, was not strictly h e a r however, where the relationship in the McCracken,

Cameron and the Elora conv. till1998 data showed evidence of a logistic pattern-

Nonlinear regression analysis was performed on these sites but only the Cameron farm

converged to determine nonlinear parameters significant to the p4I.05 leveL Nonlinear

regression analyses of these 3 sites can also be seen in Table 4.4. Overall, nonlinear

regression converged for all3 sites and resuited in reductions in the nim of squared

errors when compared with their h e a r equivalents, but regression parameters were not

always fomd to be significantly different than zero for the McCracken and Elora conv.

1998 sites. Plots of this nonlinear behaviour in all three sites c m be seen in Figure 4.4.

From these plots a "threshold linif' (discussed in Figure 4.1) of approxïmately 0.10-0.15

cm3/cm3 water above the lower lirnit €Iop, can be seen.

Figure 4.4. Examples of norhear behaviour between yield and PEW,,,.

A site of special note was the Podohski site, a predorninantly day site, in which

significant negative correlations were found between yield and 8, (p<0.05), and

between yield and PEW, (p<0.10) (Figure 4.5a). Contrary to the others, on this site it

appearç that the areas of high yieIds were liriked to areas with low seasonal water

contents. The critical difference in this analysis may be that the Podohski site has soils

of high clay (average 44% clay). Analysis of the Podolinski site show that seasonal water

contents fiequently surpassed the critical Lunit of 10% aeration porosity. Aeration has

been cited to be criticaiiy ümiting to plant growth at an air-med porosity of 0.10

cm3/cm3 (Grable and Siemer, 1968) and could explain the negative correlation found

between yield and PEW- for this site. Yield was found to be negatively correlated with

the frequency a t which seasonal water contents surpassed the 10% air-med porosity

lin-iit (Figure 4.5b).

Figure 4.5a, b. Nega tive correla tions found behveen yields and soü water measures (Podolinski site).

Yield data was also regressed against three other soi1 properties: OC, %day and

K. Of these soi1 pro perties, % clay and RC showed relatively poor correlations. Clay

content, when correla ted with yields (+N), was found to be signlficant (p<0.10) in only

one site (CçCanagra). The McCracken and the Cameron sites however, did show

94

evidence of n o n h e a r behaviour but the nonlinear regression parameters were found to

be not sigruficant. In general, because clay content was found to be signihcant in only a

few sites and these correlations were poor, it is unlikely that these correIations will

improve our understanding of the role of clay content upon the variations in yields.

Relative compaction was also correlated with yields (+N), and was found to be

significant (p~0.10) in 6 of our 12 sites. The relationships found however, were

contradictory. For example, the Elora no till1999 site showed a sigruficant (pC0.05)

nega tive correlation between y ields and relative compaction (Figure 4.6a). This data was

consistent with studies done by Carter (1990) that çuggested that yields of cereals

declined when relative compaction was greater ihan 0.85-0.90. In the CS-Canagra data

however, yields were found to be signihcantly (p<0.05) positively correlated with

rela rive compaction (Figure 4.6b). Ln general, it is unlikely that relative compaction of the

soi1 played a major role in deterrnining variations in yields.

Of the three so i l properties regressed with yield data, OC showed the best

I * . = - 1 . O *

a>

--1 zsoooi m 54000; 0 o!3000 t

ZOOO;

I o 0 0 - l 0 7 - - --y, 7 1

0.85 0.90 0.95 1 .O0 1.05

RelativeCarpadim

correlations. Results af these correlations can be seen in Table 4.5. Overall, 7 of our 12

, . * b)

0 1 l

0.85 0.90 0.45 1.00 1.05

Retati\RCorrpadion

sites showed sigiuhcant iinear correlations with OC Another site, the McCracken site,

Figure 4.6a,b. Different correlations between yield and relative compaction: Elora no till1999 (a), CS-Canagra (b).

did not show a significant linear relationship, but did show a clear non-linear

rela tionship . Non-linear regression of this reiationship however, did no t generate

significant (p<0.10) parameter estimates. A plot of the McCracken yield vs. OC

relationship cari be seen in Figure 4.7.

It is interesting to note however, that there were similar relationships seen

between the OC analysis and the PEW,,, analysis. The three sites that were elimiriated

from the PEW,, analysis (Elora no tiU 1999, Denys and Newcombe) because of small

standard deviations in yields were &O found to be not si&nificant in the OC analysis.

W hile a significant correla tion w as seen between yield and OC in the Elora no till1998

site, it is stiii unknown wha t affect the frost damage had on yields and therefore thiç site

was agam elinunnted fr0111 further analysis. Of the 8 sites remaining, aU 8 showed

signhcant relationship with PEW,,,,. Their relationships with OC however, varied.

0.0 1.0 2.0 3.0 4.0 5.0 6.0 Organic Carbon content (%)

Figure 4.7. Nonlinear behaviour of yield vs- OC (McCracken site).

Table 4.5. Results of regression analyses be tween yield (+N) and organic carbon (OC),

Farm: > EC98: Bora 1998 YieId (+N) = 4758.1-t + 10253*(0C)

(conv. till)

E98: Elora 1998 Y ield (+N) = 3850.2t + 1746.8r(OC) R2 = 0.357 (no till)

EC99: Elora 1999 Yielci (+N) = 46829-f + 150LL5r(OC) R2 = 0.461 (conv. till) S E = 1.35 x lW

Yield (+IV) = 8975.7t Rz = n/a 1 + exp(-2388t'(OC- 1.06t)) S E = 8.63 x 1CF

E W - Elo ra 1999 Yield (+N) = 52T5.2t + 594T(OC) (no tr'll)

McCracken (no till) Yield (+N) = 1llll.û-t + 4199(0C) R? = 0,176 S E = 1.72 x 107

Yield (+N) = 12236.6t R2 = n/a 1 + exp(-3.77$*(OC - 0.09)) SSE = 5.32 x 108

Podolinski (conv. till) Y ield (+N) = 6827.w + 1859.lt*(OC) Rz = 0.423

CN: Canagra North Yield (+N) = 6û85.q- 4 156.0*(UC) (no tiil)

rS: Cànagra North Yield (+N) = 3038.6-f + 16243y(OC) (cunv- till)

CC: Canagr- Suu th YielJ (+N) = 1109.1 + 1533.3r(OC) (no till)

Denys (no tiII) Yieid (+NI = 8636.2t - W.2*(OC)

Cmeron (no tilI) YieIci (+N) = 5539.6f- + 3366.5y(OC) Rz = 0.665 SSE = 1.72~ 107

Yield (+N) = 13ZZ8t Rz = n/a 1 + exp(-220t*(OC - 0.731t)) SSE = 1.02 x 107

Nswcoiilbe (no till) YieId (+N) = 73û6.2t + 750-8*(CC)

t = regression s i b d c m t (p<O.O3), $ = regession signihcant (p<0.10), d other parameter estinlates are f o u c i not signifiant. R- villues for non-linear regressions could not be determùied-

Con~prisoti was done using SSE.

By dividing the 8 sites into the same groupings used in the PEW,, analysk,

some interesting relationship between OC, soi1 water and yields could be seen* Of rthe

three sites in which PEW,,, was not found to have a sigdicant correlation with yield

(the three Canagra sites), two sites (CC-Canagra and CS-Canagra) did show si@cant

cordations of yield with OC. These three Canagra sites were sites in which PEWS-

values were found to be extremely low. This may have affected the yield vs. PEWea

relationship, but the fact that sigruficant relationships between yield and OC were found

on two of these sites leads to the hypothesis that on these 2 sites yields were sigüfiicantly

effected by OC in a manner unrelated to water extrackibility. Plots of yield with PEW,

and OC, for the (23-Canagra site can be seen in Figure 4.8.

--

Figure 4.8. Plots of yieid with PEWseJs and OC, for the CS-Canagra site.

Of the five sites in which significant correlations were found between yield and

PEW,,, (Elora conv. till1998, Elora conv. till 1999, McCracken, Podohski and

Cameron), three sites were also signhcantly correlated with OC. The McCracken site,

with its evident non-linear relationship could also be included. ïherefore, four of tthe

five sites (Elora conv. tiii 1999, McCracken, Podolinski and Cameron) can be said tco also

show a relationship with OC. Also much üke the relationships found with PEW,., the

relationship between yield and OC was found to be positive. Therefore, high yields were

in general linked to areas of high OC and soilç with high plant extractable water. This

can be seen in an exarnple of the Carneron site where positive relationship can be clearly

shown between PEW,,, and OC (Figure 4.9).

I

0.00 ; 1

0.0 1-0 2.0 3.0

Organic carbon content (%)

Figure 4.9. Example of the relationship between PEW,, and OC (Carneron site).

Again, the site of special note was the Podoltriski site, the predominantiy clay site

in which significant negative correlations were found between yield and PEW,,.

Coupled with this h d i n g was a s ip i f icmt positive correlation between yield and OC

(Figure 4.10a). Contrary to the relationships seen in the O ther sites, the Podolinski site

indicates that areas of high yields were linked to areas of high OC but lower plmt

extractable water contents. Again, the critical difference in this andysis may be that the

Podolinski site has soils of high clay. lt has been seen that the Podohski site

experienced several occurrences of water contents surpassing the critical Limit of 10%

aera tion porosity and that yields were found to be negatively correlated with the

Frequency of seasonal water contents surpassing this i.i..miL Therefore on the Podolinski

site, it is tikely that high OC is linked to areas of lower water contents and lower

occurences of seasonal water contents surpassing the 10% aeration porosity limit This

can be seen in Figure 4.10b, where the frequency at which seasonal water surpassed the

10% aeration porosity limit was found to be negatively correlated with OC.

Figure 4.10. Behaviour of yield, OC and soi1 water contents on the Podolinski site.

Therefore, of the original hypotheses, yieIds do show some relationship with

PEWseps on some of our sites, but the anticipated irnprovements in predictions when

compared to the independent variable of O,,, , were not seen. However, perhaps the

most significant result of our analyses may be that OC was found to be a sigruhcant

indicaior of ideal growth conditions under ds; water limiting conditions as well as wet,

aeration Limiting conditions. In essence, on many of our sites OC was ciosely linked to

those areas with the "least iinuting" water regimes during the growing season. Yet

overall, for many of our sites i t is still evident that much of the variability in yields is not

explained by wa ter parameters or soi1 properties.

Could other factors influence the variability in yields? Certainly nutnents,

insects, disease or clirnate rnay have played a role- No insect or disease damage was

evident however, and presumably clirna te (temperature, sunlight, etc.) was uni fom

across al1 plots on a site. Analyses thus far have also only included +N treatments and

these plots were assumed not to be nitrogen limited. Although other nutrients were

expected to be present at adequate levels; this expectation was not confinned. Nutrient

deficiencies by thernselves however, are unlikely to explain why so much of the

variation in yields is yet unknown. Another drawback of our analyses may be that our

water and soil analyses extended over only the 0-30cm depth- While the majoriq of

plant roofs are Çound within the 0-30cm depth it is possible that variation in water

content and OC below 30cm could improve predictions of variation in yields.

We may also lack clear and accurate iimits describing limiting conditions for

plant growth. As was seen in Figure 4.5b, yields showed a good relationship with the

frequenc y in which seasonal wa ter contents surpassed the 10% air-filled porosiv limit.

'What our knowledge lacks however, is a sirnilar lower limit which we can use

sin-iultaneously. Our current lower Linut of 80, is inadequate for this purpose because it

is theoretically the " basemen t" of Liniiting plant conditions. What is needed is a

threshold in which linxiting plant conditions begin. From Figure 4.4, our data indicated a

" threshold lirrùt" of approximately 0.10-0.15 cm3/cm3 water above the lower Mt.

However, this value was a crude estimate based on visual examination of the data.

Better data and an understanding of what determines how this threshold Limit varies

could grea tly in~prove our abilitv to predict variations in yields.

ln general, it is theorized that the relationship of yield with water is dependent

on extractable water, in a form similar to that shown in Figure 4.11. This conceptual

mode1 is similar to that proposed by Feddes et al. (1988) as seen in Figure 4.1. It is

theorized that those soil/ precipitation/ landscape position conditions that result in

PE W,,..,, falling w i thin the Type 1 range will show little or no relationship between yield

and PEW,,, because extractable wa ter is extremely limiting (i.e. the 3 Canagra sites).

Those soil/ precipitation,' landscape position conditions that fall within Type II wili

show a positive relationship between yield and PEW, (ie. the 4 sites Elora conv. tiU

1998, EIora conv- till 1999, McCracken and Cameron), and the soil/ precipitation/

landscape position conditions which have large ranges in PEW- will show logistic

relationships and span both Type II and III (the McCracken and Cameron sites). Those

soil/ precipitation/ Iandscape position conditions that experience adequate extractable

water throughout the growing season will fall solely in Type III and wiU show Little or

no relationship between yield and PEW,.. The 3 sites eliminated hom further analysis,

the Elora no till1999, Denys and Newcombe sites, which showed good yields but little

variation, could be examples of Type LI1 growth. Findy, it is hypothesized that for those

soils in which PEW,, was at times hi& enough to limit aeration, Type IV growth

would be exhibited (the Podoiinski site).

Plant Extractable Water

Figure 4.11. Conceptual model descnbing yields as a funcnction of plant extractable water.

From our data however, it is uncertain as to where or how to demark the

bo undaries within this concep tua1 model. From our introduction, determining the point

in which water stress becomes evident in photosynthesis ûr transpiration can depend on

climate conditions or plant cultivar. For example, the boundaries between Type III and

IV would be highly dependent on the specific plant adaptations to waterlogging. Ako,

yield could be dependent on the tiniing of the Lùnitations in extractable water. For

example, it is conceivable that plants experïencing low extractable water during silking

would r e d t in a greater loss of yield than at any other stage of devdopment From our

data, this temporal effect of plant extractable water is not testable. In general, our data

showed strong temporal stability of soi1 water contents withui spatial patterns. This c m

be seen in Figure 4.12, where the pattern of water content across Iandscape positions

remairis stable throughout the growing season. As Iong as this pattern remairis relatively

intact, regression of yield values with any single time perïod (i-e. water content pattern

at siiking) is essentially no dïfferent to the regression of yield values with the average

sedsonal water content during growing season. However, this again leads to the

importance of a clear deiuarkation of a "threshold" limit. With a clear threshold bit,

the temporal effects of plant extractable water on yïelds codd be tested. Water contents

found to faU below this iimït before, d u ~ g and after siLking could be compared to yield

values and used to determine the influence of temporal effects of water on the variability

in y ields.

I Julian Day

Figure 4.12. Example of the temporal stability of extractable water across spatial patterns (McCracken site).

4-4 CONCLUSION

P h t extractable water, expressed by PEW5faSI was found to significantly

correlate with yields on many of our sites but overd, much of the variability in yields

remained unexplained. Of signihcant note however, organic carbon was found to be

well correlated with yields. Organic carbon was found to be linked to increased plant

extractable water under drought conditions as well as being linked to better aeration

conditions under wet conditions. In general, organic carbon was found to be highly

iinked to areas of "Ieast limiting" water conditions and in many cases was found to be

the best predictor of yield variation compared to all other independent variables

attemp ted-

4.5 REFERENCES

Cabeigueme, M., Debaeke, P. 1998. Experïmental determiation and m o d e h g of the

soil water extraction capacities of crops of maize, sunfïower, soya bean, sorgfium

and wheat. Plant and Soil. 202175-192

Carter, M.R. 1990. Relative measures of soil bulk derisity to characterize compaction in

m a g e studies on fine sandy loarns. Can. J. Soil Sci. 70: 425-433.

Dexter, A.R. 1987. Mechanics of root growth. Plant and SoiI. 98: 303-312-

Feddes, R.A., Kabat, P., V a n Bakel, P.J.T., Bronswijk, J-J-B., HaIbertçma, J. 1988.

Modelling soi1 water dynamics in the unsaturated zone - State of the a r t Journal of

Hydrology. 100: 69-111.

Grable, A.R., Siemer, E.G. 1968. Effects of bulk density, aggregate size, and soi1 water

suction on oxygen diffusion, redox po tential and elongation of corn roots. Soil Sci.

Soc- Am. Proc. 32180-186.

Gerard, C.J., Sexton, P., Shaw, G- 1982. Physicd factors irduencing soi1 strength and root

growth. Agronomy Journal. 74: 875-879.

Greacen, E. L. 1986. Root response to soil mechanical properties. Trans. 23th Congress

In tern. Soc. Soil Sci., Harnburg, Germany. 5:20-47.

Haise, H.R. Haas, H.J., Jensen, LX. 1955. Soii moisture studies of some Great Plain soils:

II. Field capacity as related to 1/3-atmosphere percentage and "minimum point" as

related to 15 and 26- atmosphere percentages. Soil Sci. Soc. Am. Proc. 34:20-25.

Kay, B.D., Tollenaar, M., Drury, C.F., Yirig, J., Chromiak, C., Zhang, T. 1999. Increasing

ni trogen use efficiency in corn production systems: quantifying effects of quality of

soi1 structure and water regimes. Final Report to Ontario Research Enhancement

Program, Agriculture and Agri-Food Canada.

Kay, B.D., To, J. 2000- Use of data on soi1 structure and soiI water content to interpret

yield variation within fields. Final Report to Ontario Corn Producers Association.

Laboski, CA.M., Dowdy, R.H., A h a r a s , R-R., Lamb J.A. 1998. Soil strength and water

content influences on corn root distribution in a sandy soil. Plant and Soil. 203: 239-

248.

Richards, L. A., W eaver, L.R- 1944. Fifteen atmosphere percentage as related to the

permanent wiltïng point Soil Sci. 56:331-339.

Sadras, V-O. and MiLroy, S.P. 1996. Soil-water thresholds for the responses of leaf

expansion and gas exchange: A review. Field Crops Reseasrch. 47: 253-266.

Sheldrick, B.H., Wang, C. 1993- Particle Size Distribution. h: Soil Sampling and Methods

of Analysis, M.R. Carter, Ed. PP: 499-511. Canadian Society of Soil Science. Lewis

Pubiishers.

Taylor, H.M., Roberson, G.M., Parker, Jr., J.J. 1966. Soil strength root penetration

relations for medium to coarse textured soil materials. Soil Sci. 10218-22.

CHAPTER 5 : UNDERSTANDING THE VARLABILITY OF YELD RESPONSE OF CORN TO NITROGEN FERTILIZER ACROSS RANGES OF WATER AND SOIL CHARACTERISTICS

5.1 BACKGROUND

Agridtur al fields Vary considerably in their soi1 properties, landscape k a tures,

and management histories. This variability has been shown to contribute to variation in

y ield. Colvin et al. (1 996) described the yield patterns for corn and soybeans in rotation

after six consecu tive years within a single Field. They found that certain locations within

the field had consistently high, consistently low, or erratic yields when compared to

whole field averages. Understanding the root causes of these yieId variations wouid give

us important tools in leanùng how to manage our land more efficiently . Site specific

farniing is such a management stra tegy and is d e h e d as an agricultural system

~iesigned to iden tify, analyze and manage soi1 spatial and temporal variability across a

field for the purpose of increasing sustainability and profit. Soi1 structure and water

regime are obvious factors influencing yield and therefore an understanding of how

they interact with yields will bring us closer to efficient site specific farmuig. From our

previous work it was determined that the concepts of the permanent wifting point

(PWP), available water holding capacity (AWC) and other soi1 water limits associated

with the least lirniting water range (LLWR) proposed by Letey, (1985) and Da Silva and

Kay (1997), were not adequate in explaining variations in yields in Our data. From

another study, Kay et al. (1999) found that photosynthesis in plants stopped as the water

content ciecreased to a lower linzit (€Iop) and that this lower iimit was highly correlated

with organic carbon (OC), %clay and relative compaction (RC). This limit was also

found to be unrelated to those limïts proposed in the LLWR. Using the concept of BO,,, it

was theorized that the average seasonal soi1 water contents measured above the lower

limit was a measure of plant extractable water (PEW,,,) throughout the growirig season.

From our analyses, it was found that yield in the N fertïiîzed treatrnent, yield +N,

(assumed to be no t nutnent Lirnited) was sigruhcantly correlated with the average

seasonal soi1 water contents ( 8 4 and PEW,,, in many of our sites. Yield +N was also

found to be related to OC-

OveraU it was seen fhat yields +N were sigdicantly influenced by this new

description of extractable water and tha t both yields and optimal water contents for

plant growth were Luiked with OC. If PEW,, and OC were closely Iinked with yields

+N, what would their relationships be with yields under unfertilized conditions? For

instance, plants grown in soils with high OC may experience good amounts of

extractable water and experience Little N Limitations due to N mineralization from the

OC. Standard rates of N application in such locations wodd therefore be inefficient.

Considering inorganic nitrogen fertilizer is an essential component in the production of

corn and can make up > 20% of operating expenses, understanding nitrogen use

efficiency could have major implications for management and profitability of a farm.

Understanding how yield response to fertilizer varies across sod characteristics may

help us to understand what factors influence yield response and dtimately how we can

manage accordingly.

The objectives of this s tudy were: (a) to determine if the response in yields of

grain corn to an application of 150 kg N/ha varied with PEWsa, and OC, and @) to

identify the implications for idenhfying N management zones with a field.

5.2 METHODS AND MATEMALS

This study was conducted upon 6 faxms during the 1998 growing season and 4

farms during the 1999 growing season. Ail sites were located between Thamesville and

Beeton, Ontario, Canada (Figure 5.1). AU famis were planted to corn (Zea mays) in the

season of sampluig. TiUage upon all farrns was either conventional tili or zero-till

management.

Figure 5.1. Map of sites in southern Ontario.

To characterize each site, plots were located to achieve variability in yield,

seasonal water content and soi1 propertïes. To achieïe this plots were selected on the

basis of landscape position. The experimental design of thiç project was a factorial

experïment using randomized complete block design with several replications, each

with several plots divided by landscape position and 2 treatments: 150kg/ha N

fertilization and no N fertilization. Eigh t of the farms were each characterized by

establishing 24 plots: 4 replicates, each with the 2 N treatments, and 3 Iandçcape

positions: upper slope, mid-dope and toe-slope positions. The remaining sites were

located at the Elora Research Station where each site was characterized by 30 plots: 3

replicates with 2 N treatrnents and 5 iandscape positions (Upper slope, shoulder, mid-

slope, lower slope and toe-slope). For al1 farms, plots were approximately 5m long and 6

rows wide,

At the start of each growing season, four sets of 30cm Time-Domain

Reflectometry UDR) probes were installed vertically (15cm from the corn row) in each

plot. Volumetric water content was measured weekly, starting shortly after seedling

ernergence and N fertilization. At the completion of each growing season, pnor to

harvest, 2 undisturbed cores (5cm diameter x 2.5crn height) were taken next to each set

of TDR probes. Overall, S cores were taken from each plot, four cores at 5-7.5cm depth

and ano ther four cores were taken at 20-225cm dep th. In all, 2076 cores were collected.

Each core was wrapped in cellophane and stored at 4OC until used for analysis-

lnunediately after core collection, a 6 rnetre length of corn row was hand harvested in

each plot. The harvested corncobs were kiln dned for several weeks, shelled and

weighed to calculate fuial yields. Yields are expressed on a dry weight basis.

Soi1 from each core was split into 2 parts; one part was sieved (a) and used

for particle-size analysis; the other was ground and used for OC analysiç. Particle size

analysis was done using the hydron-ieter rnethod and calibrated with the pipette method 0

(Sheldrick and Wang, 1993). Organic carbon anaiysis was done uçing the LEC0 SC 444-

Relative compaction (RC), the lower liWt of extractable water (00,) and the

average plant extractable water during the growing season PEW,, was calculated using

the same methods as Chapter 4.

5-3 RESULTS AND DISCUSSION

Analysis of variance was performed on Our 12 sites to determine the effect of N

fertilizer treatment on yields (Table 5.1). Of the 12 sites, 8 sites (Elora conv. tiU1998,

Elora no till1998, Elora conv. tiii 1999, Elora no f5.ü 1999, Cç-Canagra, CN-Canagra, CC-

Canagra and Newcombe) did not show significant N treatment effectç on yields. Many

of the 12 sites did however, show si@cant location effects although these effects were

inconsistent across sites (e-g- Figure 5.2a,b)-

Figure 5.2 ExampIes of differential location effects on yields: (a) CC-Canagra no till 1998, (b) EIora conv. tiii 1999.

Regression analyses between yieids and the independent variables of e,,,,, PEW,,, and

OC were also performed on the 12 sites. Results of these analyses can be seen in

Appendix 5.1.

Of the eight sites that did not show significant N fertiiizer treatment effects,

analyses showed that six sites (Elora no tiii 1998, Elora no till1999, CN-Canagra, CS-

Canagra, CC-Canagra and Newcombe) did show significant correlations between yield

in the ON treatment and one or more of the soi1 characterstics es,, PEW,,,, and OC.

When compared with the same +N treatment analyses, simiIar relationships were seen

in aii eight sites. Supporting the ANOVA analysis, no evident yield response to N was

seen between the two treatrnents in the regression analyses and the yield response to N

was relatively uniform across the ranges of the independent variables. An example can

be seen in Figure 5.3. In general, it was evident that for many of these 8 sites variability

in yields (+N and ON) can be attributed to varying soil properties but fertiLizer N was

not a signihcant factor. Considering management costs, the applied N fertilizer on these

sites, during those particular growing seasons, showed little benefit. Because no

significant treaûnent or treatrnent/soil p r o p e q interaction effects could be found on

these sites no further analysis of yield response was undertaken.

I

I j 0 +N ~reatrnent; 7000 ; I a ON Treatment 1 * O

1000 j O ! O. 0 1 .O 2.0 3-0 4.0

Organic Carbon content (%)

Figure 5.3. CC-Canagra (no till) 1998 site. Evident OC effect upon yield (+N and O N ) but no statist icdy significant effect of +N treatment

Results of the ANOVA show that only 4 sites, the McCracken, Podolinski, Denys

and Cameron farms, showed a significant N fertilizer effect on yield. For these 4 sites,

yield in the ON trea tnient was regressed with the major soil properties 8 , PEW,, and

OC, and compared with the correlations found in the yield +N data.

Analyses of the correla tions of yield +N and ON revealed 3 different scenaxios.

The site with the simplest explanation, the Deny's site, showed no sipificant

correlations between yield (+N or ON) and the 3 regession parameters (O,, PEW, and

OC) but did show significant yield response to N fertdizer. The yield response however,

varied by less than lûûû kg/ha or I l % of the maximum. A plot of yieid +N and ON,

across a range in OC can be seen in Figure 5.4. Plots of yields with the 2 other

parame ters sho wed similar results.

0.0 1-0 2.0 3,O

Organic Carbon content (%)

Figure 5.4. Denys site. No evident OC effect upon yields but statistically significan t N fertilizer effect.

Dissirnilar to the Denys site are both the McCracken and Carneron sites in which a

signïfïcant fertiiizer effect was found but significant correlations between yield (+N and

ON) and e,,, PEW,,, and OC were also found. Also evident in the statistical correlations

are differential behaviours between yield +N and yield ON across the ranges of soi1

characteristics. The influence of OC on yields is illustrated in Figure 5.5 for the

McCracken site. The influence of PEW,.,, on yields in both N treatments (Figure 5.6) w a s

similar to that of OC. Water and organic carbon were also closely linked with slope

location on this site, where greater OC and larger water contents were found in the

depressional areas and smaller values in the upper slopes.

Table 5-1- ANOVA tables of location and fertilizer effects for each site- Elora (conv. till) 1998

ss: - MSE: F-test Faitical TO kd lM1726863

Replication 1178133.7 589066.8 0.4776 355 Treatznents 8W93562.0

Location 785606953 19640173.8 15.9238' 293

Fertilizer 10720 1.5 1072015 0.0869 4.41

Location'Fertilizer 2 L S65.2 5314163 0.4309 293

Error 22200990.6 1233388.4

Elora (no till) 1998 SS: - MSE: F-test Fuitical

To ta1 40785751 -6 Replica tion '1 537727-4 768863.7 0.5385 3.55 Treatrnents 135467338

Locr l t i~ i~ 585ü898.1 1464724.5 2.0258 393 Fwiilizc~r 2 1 ~ 1 1 bN.3 20616-49.5 1.4439 4.41 Lricir~ii)ri'Fcr~ili/.~'r 5~20175.1 14V6543.8 0-9851 393

&or 25701 301 -4 1127850.1

Elora (conv- tül) 1999 ÇÇ: - MSE: F- tes t Fmtical

To ta1 36791 069.0

Elora (no tiU) 1999 ÇS: MSE: F-tes t F m tical

To t d 243'72273-4

McGadcen (no till) 1998 ÇÇ: MSE: F-test Fcritical

To ta1 2235841473

Replication 833566 1.7 2778553.9 12îi8 3-29 Treatmen ts 181 136327.6

Location 960 17%3 480087&l.2 21.110r 3.68

Fertilizer 68351 250.2 68351250.2 30.0558' 4.54

Location'Fertifizer 16767509- 1 83a3751.5 3.6866' 3.68

Error 3-i 112~58.0 W4143.9

Podolinslcï (conv, till) 19% ÇS: MSE: F-test Fcritical

To ta1 1863~~367.1

RepLication 4 1531207.4 13813735.8 5.4938' 3.20 Treatnwnts 107193777.0

Location 1252178.2 626089.1 02485 3.68

FertiLizer 101 129632.9 1041296329 413231' 4.54

Loca tio n*FertiliLer 18 1 1965.8 9059829 03595 3.68

Error 377-7 25 19892.2

CN-Canagra (no till) 1998 SS: - MSE: F-test Fcritical

To t d 9021 1726.5

bplic-d tion I4069296.1 46897654 L .O553 3.29

Trw tme LILS w ~ 8 5 . t ) ~ o s d tion 28M-L90-1 1432245.1 03223 3.68

Fertiiizer 5830 108.8 5830108.8 13119 4-54

Losatio n*Fertilizer 7894S6.8 3942343.4 0.0889 3.68

Error 66633144.7 44.138763

CC-Canava (no till) 1998 SS: MSE: F-test Fcri tical

TV t d 4 l86-l6X 1

r\eplicdtiori 68 1 1796.7 2270598.9 33547' 3.29

Treatnients 2-1900200.0

Location 24 56760 1.2 12283800.6 18.1486* 3.68

Fer tilizr r 174 1.6 1741.6 0.0026 4-51

Loca tio n'Fertilizer 330857.2 165428.6 U.2444 3.68

Error 10 152677.4 67W5.2

MSE:

Replication 1631376.1 543792.0 0.7306 3.29

Treatments 1853429'7.9

Location 14875581.9 7437790.9 9.9925' 3.68

FertiLizer 31424823 3 1424823 4.22i8 4%

Loca tion*Fer tilizer 516233.7 258216.9 03468 3.68

Error L 1 lmû7.8 74EM05

- - - - -

Denys (no till) 1999 SS: MSE: F-test Fmtical

Total 16395619.5

Replication 44256 1.9 147520.6 0.2542 3.29

Treatments 72489121

Location 1 133999.2 578499.6 0.9969 3.68

Fertiluer 5613113.0 5613113.0 9.6732' 4.54

L O C ~ tion*fertilizer 478s00.0 239400.0 0.4 126 3.68

Error 87W145.5 580276.4

Cameron (no till) 1999 SS: MSEI F-test Fuitical

To ta1 939G-9%9

Replica tion 7795894.5 2598631.5 L T ~ ~ O 3.29 Treatmeiits 71769689.4

Location 472237533 23611876.7 24.6330' 3.68

Fertilizer 243%8653 243968653 25-45-19' 4.54

hcii tiori'Fcrtiiizer 149070.8 7.15354 0.0778 3.68

Errc)r 1 -13782 1-1 -11 9585i7.6

P

Newcombe (no tiü) 1999 SS: - MSE: F-test F m tical

T o ta1 18869 135.6

r\epLica tion 746771 4.2 2489238.1 1.0 133' 3.29

Treatmeiits 3128391.2

Location 5131 1.3 25635.8 0.0465 3.68

Fcrtiiiziv- 21 109-i8.-1 2110948.4 3.8274 4-51

Loc~~~i~~~*F~r~iiizer %hl313 483û65.6 0.6759 3-68

Err ur 8273030.2 33 15353

This is consistent with the ANOVA analysis of the McCrracken site where a significant

fertilizer and location interaction was found. On the McCracken site it was evident that

yield response to fertilizer N was greatest in those areas of low extractable water and

low OC. The data indicate that beyond 1.5-2% OC, the yield response to N was

consistent and low. This site may be a good candidate far variable rate fertilizer

application and OC could be used as an indicator for detennining management strategy.

Consideration of yield response must also account for the fact that only one N treatment

was analyzed in this study. Perhaps other N treatrnents (iess than or greater than 150 kg

N/ ha) would show different results.

+N Treatment

I Organic Carbon conternt (%)

Figure 5.5. Yield +N and ON across a range in OC (McCracken site).

,.L ON Treatme

0.00 0-05 0.10 0.15 0.20 0.25 0.30 PEW,,,, (cm31cm3)

-. - - -- - - -

Figure 5.6. Yield +N and ON across a range ~ E W , , (McCracken site).

Contrary to the McCracken data, analysis of the correlations between yields and

OC on the Carneron site showed parallel relationships (no statistically significant

interaction) between yield (+N and ON) and OC (Figure 5.7). Of note however, is the fact

that the Carneron site did not have as wide a range in OC as the McCracken site.

Perha ps if the range in OC contents on the Cameron site had been greater a sbonger

interaction would have been observed.

The Cameron site also showed significant yield response and yield response

interaction with N treatment across a range of water contents. From the plot of yield +N

and ON across a range in PEW,., (Figure 5.8) there seems to a critical water content

(a p proxuiiately PEW,,,, = 0.12 cm3/crn3) below which yield deches. Upon further

maiysis it is eviden t tha t the McCracken site also exhibits this behaviour. in both sites

however, yield +N did not seem to exhibit as large a yield reduction at the lower water

contents. It is unknown why yields in the +N treatments did not also exhibit this plant

extractable water Limitation. A hypothesis for this re-enilt may be that in these areas of

low OC and lower seasonai water, less N was mineralized and therefore the yields in the

ON treahnents were profo-mdly affected by low water, as well as low N availability. In

contrast, the +N treatments suffered due to low extractable water but did not suffer as

greatly becauçe of inorganic N inputs.

+N Treatment

Organic Carbon content (%)

Figure 5.7. Yield +N and ON across a range of OC (Cameron site).

-Isooo +N Treatment

.K.* - /. G ON Treatrnent

Figure S.S. Yield +N and ON across a range of PEW,,, (Cameron site).

The final site is the Podolinski farm. From the previous chapter, it was

determineci that high yields on this site were linked to high OC and low soil water

contents. Soils with low OC contents experienced high water contents that may have

induced aeration limitations to the plants. Correlations between yield ON and OC for

this site showed a similar relationship when compared to yield +N (Figure 5.9). A yield

response interaction with OC was seen but was not found to be significant (pi0.10). This

is consistent with the ANOVA resdts that showed no significant interaction between

treatment and location. The range in OC for this site however, was between 1.4 - 3.3%.

Perhaps if the range in OC for this site spanned OC contents Lower thm 1.4%, a greater

yield response interaction may have been seen.

From o u previous chapter, regressions of yields +N vs. L, and PEW,, for the

Podolinski site showed significant negative correlations. These relationships were

attributed to the fact that our data showed seasonal water contents faUuig above the 10%

air-filled porosity Mt. in contrast however, regession of yields ON showed no

significant correlations with either 8,, or PEW,.. However, when yields ON were

regressed agains t the frequency of seasonal water contents falling above the 10% air-

fiiied porosity Limit, a significant negative trend was seen (Figure 5.10). This again

supports some of our previous work indicating that definition of criticdy limiting plant

conditions are crucial in O u r understanding of water, soi1 and plant relationships.

+N Treatment +

Organic Carbon Content (%)

Figure 5.9. Yield +N and ON across a range of OC (PodoLinski site).

121

2000

O ! r 1 t

0.0% 2O.O0h 40.0% 60.0% 80.0%

Freq, o f Seasonal Water Contents Measured Above 10% air-filled porosity (%)

Figure 5.10. Plot of yields in the ON treatment and the frequency of seasonal water contents measured above the 10% air-filled porosity Limit (Podolinski site).

Overd, of the 12 si tes, 8 did no t show any significan t nitrogen fertilizer effect

across al! landscape positions. Another, the Denys site, did show a sigiuhcant fertüizer

effect but the gains in yield were srnail. Remaining are only 3 sites, the McCracken,

Cameron and Podolinski sites, in which significant yield gains from feridizer were

observed. On these sites evidence was also seen of differential yield responses across

ranges of landscape positions and soi1 characteristics. The sites with the most significant

evidence of differential yield response were the McCracken and Cameron sites, perhaps

because of their relatively large changes in soil properties across their landscapes. From

previous work it was found that the McCracken site varied from 0 4 6 % clay, 30-92%

sclnci and 0.2-5-976 OC- The Cniueron site varied From 0-23% clay, 31-94% sand and 0.3-

3.1 X OC. These large variations in soil properties across landscape positions created

large variations in both soi1 water and yields and resulted in clear patterns of behaviour.

Quantitative analysis of the data from these two sites iridicated that yield response to

fertilizer was linked to two boundary values, a Limit of extractable water (0.10-0.15

cn+/cin3 a bove the lower limit) below which yield response to fertilizer was high, and

the other, a boundary of approximately 1.5-2% OC, above which soik show s m d yield

responses to fertilizer.

Analyses thusfar show that yieId response to fertilizer can Vary across s d

properties and tha t significan t yield response can be associated with the lirnits of

extractable water and OC, By defining these Limits, it is now possible to define areas of

management inefficiencies- For instance, application of a a N fertilizer rate on the

McCracken site, in areas with OC greater than 2% would gain liffle in terms of increased

y ields (in Figure 5.5). Definition of management practices based on these yield responses

to soi1 properties however, should be restricted to the iimit defined by OC. Prediction of

extractable water would be difficult considering the variable nature of precipitation and

climate and therefore definition of management practices based upon extractable water

would be unlikely. More Likely would be the definition of management areas based on

the knowledge of the relationships between yield, soii water and soi1 properties. From

O u r previous work, it has been seen that OC was highly Linked to areas of "Ieast

LinUting" water conditions, where OC was found to be linked with greater plant

extractable water under drought conditions, as well as king iinked to lower water and

betîer aeration conditions under saturated conditions. Based on this data and the data

showing yield response to fertilizer across a range of OC, OC could be a usefd tool in

Liefuiing management units for the growth of corn.

5.4 CONCLUSIONS

In general, yield response to fertilizer was not seen in many of our sites-

However, in those sites in which yield response to fertilizer was fourid, evidence of

differential yield responses across ranges of soil properties was seen. Evident from the

correlations between yields (+N and ON) and the soil properties PEW-, and OC, was

that 3 sites (McCracken, Cameron and Podohski) showed a differential yield response

across a range of PEW,,,,, Also evident from the yield vs. PEW,, analysis was that

yidds in the ON trea tmen t seemed to be profoundly affected when PEW,, fell below

0.10-0.15 cm3/cm3. Differential yield response was also seen across a range in OC At the

McCracken site, relatively large yield responses were observed in soils with low OC

contents, u p to an approximate 2% OC. Soils with OC contents higher than 2% showed

relatively smali and constant yield response gains to fertilizer application.

From our previous work, it was seen that OC was linked to areas of higher plant

extractable water in drought conditions, as weil as areas of low water and better aeration

in saturated conditions, Here, OC has also been h k e d to yield response to N fertilizer

application. Clearly, OC plays a strong role in determining optimal growth conditions

for corn growth and c m play a role in influencing the efficiency of management

Considering the differential yield response to fertilizer across the range of plant

extractable water and OC, and considering that OC has been to W e d optimal or "least

liiniting" soil water conditions, perhaps OC could be used to define different

management areas. Before OC can be used to define management units however, clearer

Links between OC, plant extractable water and yields m u t be made. Thus far our data

only estabLished the existence of sud, relationships. Clearer relationships must be made

to determine where and when changes in soil water and soil properties demark

significant changes in yield response, nidi that efficiencies in management c m be

improved,

5.5 REFERENCES

Colvin, T.S., D. B. Jaynes, and D. L. Karlen- 1996. Yield variability within a central Iowa

Field. Trans. ASAE. 40(4): 883-889.

DaSilva, A.P., Kay, B.D. 1997. Estirnating the least limiting water range of soils from

properties and management. Soil Sci. Soc. Am. J.61:877-883.

Kay, B.D., Tellenaar, M., Dnuy, CF., YUig, J., Chrorniak, C, Zhang, T. 1999. hcreasing

nitrogen use efficiency in corn production systems: quantïfying effects of quality of

soi1 structure and water regixnes. Final Report to Ontario Researdi Enhancement

Program, Agriculture and Agri-Food Canada,

Le tey, J.1985. Relationship be tween çoii physical properties and crop productions. Adv.

So il Sci. 1 :277-294.

Sheldrick, EH., Wang, C. 1993. Particle Size Distribution, in: Soi1 Samplîng and Methods

of Analysis, M.R. Carter, Ed. PP: 499-511. Canadian Society of Soil Science. Lewis

Publishers.

Table 5.2. Results of regression analysis ktween Yield(0N) and average soi1 water content during the growing season for each site.

R e p e s ï Farm: on Parameters: Prediction: Yield (ON) = 3110.0 + 22513.W(8,,)

(conv. tiii)

E96: Elor- 7998 (no tiil)

Y ield (ON) = 6240.w + 3361.4*(8,,)

EC99 Elora 1999 (conv. tiii)

Yield (ON) = 7ll4.lt + 1493.3*(8,,)

E99: EIora 1999 (no till)

Yield (ON) = 1885.7 + 19653.W(%=)

McCncken (no till) Yield (ON) = 2959.2$ + 25277.0tœ(8,d Rz = 0.705 SSE = 230 x 107

Y ield (ON) = 12027.1 t 1 + exp(-28.98Y(B,, - 0-136t)) R2 = n/a

SSE = 7.M x 106

Podolinski (conv. tiil) Yield (ON) = 10648.0 - 9550.fF(€l,,)

CN: C a m p North (1iu till)

Yield (ON) = 558.1 + 29896$*(0,J

CS: Giiagm Nurth (suiiv. lil l )

Y i d J (UN) = 5-157.C3- + M1.3'(C3,,,)

CC: Ca~iagra South (no till)

Yi&l (ON) = 7295.8 - 11624W(€l,)

Denys (no till) Yield (ON) = 8736.q - 51212(9,,)

Rz = 0.614 SSE = 223 x 107

Canieron (no till)

R2 = n/a SSE = 3.52 x 10o

Newconibe (no till) Yield (ON) = 1221 7.û-f - 20876.û$*(0,,)

t = regression sisliflcant (p<0.05), $ = regression significant (p<0.10), all other parameter estimates are found not significant. R? values for non-linear regrestions could not be detennined.

Cornparison was done using SE.

Table 5.3. Results of regression analysis between yield(0N) and average plant extractable water during the gz&ing season (PEWxaJ for each site.

Farm: Regression Parame tersr Prediction: EC98: Elora 1998 ON) = 2116.9 + 48508-WCPL)

(conv. till)

E98: Elom 1998 Yield (ON) = 5954.4t + 8737.W(PEW-) (no till)

EC99.- Hom 2999 Yielci (ON) = 7619.m - 1320.8*(PEW,,) (conv. tiU)

E99: Elora 1999 Yield (ON) = 1557.8 + 33825.W(PEW,,) R2 = 0.328 (no till)

McCracken (no U) Yield (ON) = 47393 + 24559-w*(PEW,,) RZ = 0.363 SSE = 4% x 107

Yield (ON) = 10674.2-f Rz = n / a

1 + exp(-63.27$'(PEW,, S E = 3 . a x 1P - U.lU8t))

Podolinski (conv. tiii)

CN: Canagra North (no till)

CS: Canagra North (conv. till)

CC: Cünagri South (no till)

Denys (no till)

Cameron (no tilI)

YielJ ( U N ) = 1W8.0 - 9550.tY(PEW,,)

Yield (ON) = 6270.m - 30416.0 *(PEW-)

Yield (ON) = 5653.4t - 13835.ff(PEWw,)

Yielci (ON) = 35-I.3t - 20.4820*(PEW',,)

Yield (ON) = 6545.lt + 9179.5*(PEW,,)

Yield (ON) = m 3 . 9 + 23912W(PEW,,) RZ = 0.628123 SSE = 5.05 x 107

Y ieid (ON) = 95751t R= = n/a SÇE=4.& x l (P

1 + exp(-7270*(PW,,, -

0.OC)st))

Newcoinbe (no till) Yield (ON) = l0677.O-f - 19834.O'(PM,,)

.-c

t = re~ess ion sigtùficiuiL (p<WS), $ = regession significant (p-=0.10), d other parameter estirnates are found not significant. l i z values for non-linear regressions could not be determined.

Cornparison was done using SE-

Table 5.3. Results of regression anaiysis between yield(0N) and organic carbon (OC) for each site.

Fann: Regression Parame tersr Predictioxc EC98: Elora 1998 Yield (ON) = 435l.W + '1199.1*(OC)

(conv. tiü)

E98: Elora 1998 (no till)

EC99: Elora 1999 (cotiv. till)

€99: Elor- 19%) (no till)

McCracken (no till)

Pudolinski (iuiiv. till)

CN: Caiidgm Ntwtli (nu till)

CS: Canagra North (conv. till)

CC- Canagra South (no till)

Denys (no till)

Newcornbe (no till)

Yieid (ON) = 4635,7t + 1095.4r(OC) R2 = 0.266

Y ieId (ON) = 62337t + 5%.T(OC)

Y ield (ON) = 4379-1t + 1(1-17.8~(OC) Rz = 0.229

Yield (ON) = 70536t + 1138.2r(OC) Rz = 0.372 SSE = 4.89 x IW

Yield (ON) = 11208-5t Rz = n/a 1 + exp(-237t*(OC - 0.526t)) SSE= 1-92 x10a

Yield ( O N ) = -335.6 + 3279.4y(OC) R2 = 0.581

Y i d t i (ON) = 3834.7 + WlU"(0C)

Yield (ON) = 26928t + 1353.9te(OC)

Yield (ON) = 2129.3$ + 1138.w(OC)

YieId (ON) = 6733.3t + 4024'(0C)

Y ~ r l c i (UN) = 339S.v + 2910.1tr(OC) R= = 0.614 S E = 223 x 107

YieId (ON) = 1 0408.4f Rz = n/a 1 + exp(-37w(OC - O-963t)) SSE= 1.34~107

Y ield (ON) = 6461.4t + 965.0f (OC)

t = regrc-ssion sigtiificiuii (p<0.05), $ = regression significant (p<0.10), ali other parameter estimdLes dre fouiici noi signifie-crn~. R2 values for non -hea r regressions could not be determined.

Conipuison wcis donc using SSE.

CHAMTER 6: GENERAL CONCLUSIONS

This study has demonstrated that the variabdity in yield and yidd response to

fertilizer N can be infIuenced by soil structure and water content, but the l e s t limiting

water range (LLWR) and the critical limits associated with it, were inadequate in

descnbing Limiting plant conditions. The specific conclusions were:

- The W RC and SRC pedo tram fer functions derived by da Silva and Kay (1997)

were inadequate in describing the water release and soil resistance m e s for

our range in soils. New pedotransfer functions for both the WRC and SRC

were denved.

- in our sites, seasonai water contents were found to be mostly drought

Iimiting for plant growth and the critical Limits under dry soil conditions

defined in the LLW R, i.e. water content at a soil resistance of 2MPa (Taylor et

ai., 1966; Greacen, 1986), and the water content at the permanent wiiting

point (Richards and Wea ver, 1944), were inadequate in describing critically

limiting conditions for corn growth.

- Kay et al. (1999) defined the lower Lunit of water in which the photosynthesis

of corn plants reached zero (OUp)- The seasonal average water contents

rneasured above was defined as plant extractable water (PEW,) during

the growing season, and was found to be signihcantly correlated with yields

on many O f Our sites. Ho wever, analysis indica ted the critical need for a

"threshold" limit of water content in which corn plants begiri to experience

severe Iosses in health due to drying conditions.

- Yield response to fertiiizer N was also found to Vary across ranges of PEW,.

Organic carbon (OC) was also found to be highly correlated with yields,

However, organic carbon was also linked to areas of high PEW,, d h g

drought conditions, as weU as k i n g liriked to areas of low water and good

aeration during saturated conditions. In general, organic carbon was linked

to those areas with the "least Limiting" water conditions.

Yield response to fertilizer N was also found to vary across ranges of OC,

where yield response to fertilizer N was srnall and constant at OC contents

greater than 2%. Thk, combined with the data showing that OC was Iinked to

areas of least limiting water conditions, indicates that OC codd be a u5efu.I

tool in defining management areas that may irnprove on the efficiency in

which we manage the variability in our fields.

FUTURE RESEARCH

A critical need in understanding the influence of soi1 structure and soil water on

the variability in yields, is the clear definition of critical limits for plant growth. With

clear liniits, niuch more of the variation in yields could be found. The impacts of

teniporal variation of soil water, such as drought co~ditions during silking, could also

be examined. Also, much more could be inferred into the roles of soil water and soi1

properties upon the yield response of corn to fertilizer N application.

Clearly, organic carbon must also be examined more closely. What specificdy

does OC con tri bu te to soil/ w a ter interactions and as consequence to yields? Also, does

OC contribute to yields directly, even under non-nitrogen limiting conditions?

Finaliy, the influence of soil structure and soil water at depths greater than 30cm

must also be examined. l t is conceivable that soi1 properties and soil water at greater

depths could explain much of the unexplained variability in yields and yield response to

fertilizer seen thusfar-