Agricultural and Food Science - Luonnonvarakeskusmeasurement point relevant for hedging against...

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ContentsPietola, K., Myyrä, S., Jauhiainen, L. and Peltonen-Sainio, P.Predicting the yield of spring wheat by weather indices in Finland: implications for designing weather index insurances.

269

Ghavi Hossein-Zadeh, N. Comparison of linear and threshold models for the estimation of genetic parameters and trends for still-birth in Holsteins cows.

287

Turner, T. D. and McNiven, M.A. In vitro N degradability and N digestibility of raw, roasted and extruded canola, linseed and soybean.

298

Lisiewska, Z, Gębczyński, P., Słupski, J. and Kur, K. Effect of processing and cooking on total and soluble oxalate content in frozen root vegetables prepared for consumption.

305

Suproniene, S., Mankeviciene, A., Kadziene, G., Feiziene, D., Feiza, V., Semaskiene, R. and Dab-kevicius, Z.The effect of different tillage-fertilization practices on the mycoflora of wheat grains.

315

Mekky, H., Mohamed, M., Lazarus, C., Brian Power, J. and Davey, M.R. Biosynthesis of very long chain polyunsaturated fatty acids in the leafy vegetable chicory.

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ISSN e lec t ronic ed i t ion 1795-1895

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A G R I C U L T U R A L A N D F O O D S C I E N C E A G R I C U L T U R A L A N D F O O D S C I E N C E

Pietola, K. et al. Predicting yield of spring wheat by weather Vol. 20(2011): 269–286.

269PB

© Agricultural and Food Science Manuscript received May 2010

Introduction

Index-based crop insurance contracts have been proposed as the sollution to the informational inef-ficiencies and problems prevailing in traditional crop insurances. In index-based insurance contracts, the

indemnity payments are functions of certain indices, such as an area yield index or weather index (e.g. Van Asseldonk and Oude Lansink 2003). A strength of these contracts is that they do not suffer from adverse selection and moral hazard problems, be-cause the insured agents cannot influence exogenous index values that trigger the indemnity payments

Predicting the yield of spring wheat by weather indices in Finland: implications for designing weather

index insurancesKyösti Pietola1, Sami Myyrä1, Lauri Jauhiainen2 and Pirjo Peltonen-Sainio2

1MTT Agrifood Research Finland, Economic Research, Economics and Social Sciences, Latokartanonkaari 9, FI-00790 Helsinki

2MTT Agrifood Research Finland, Plant Production Research, Jokioinen, FI-31600 Jokioinen

e-mail: [email protected]

This paper quantifies the spring wheat yield conditional on temperature- and rainfall-based weather indices in Finland. The estimating equations are standardized and simplified so that they provide information for designing tradable contracts. A simple basket of weather indices, consisting of growing degree days, night frost and rainfall measures, has the potential to hedge about 38% of the wheat grower yield risk, with the remaining 62% being left as uninsured basis risk. Our results have several important implications for the design of simple and tractable weather index-based insurance contracts. The data suggest that the marginal products of weather events have a large variation across time and they are the most significant within certain critical time periods. Therefore, the weather events triggering the indemnity payments should be bounded within certain critical time regimes over their distribution along the growing season.

Key words: Weather index, growing conditions, temperature, precipitation, frost, crop insurance, wheat, yields, instability, risk management


Pietola, K. et al. Predicting yield of spring wheat by weather

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(Karuaihe et al. 2008, Barnett et al. 2005, Miranda and Vedenov 2001). Index insurances also maintain the market-driven economic incentives for informal self-insurance mechanisms and investments in risk mitigation measures, such as drainage, irrigation and protection from soil erosion1. It is also fair to claim that information about the distribution of future index values is equal amongst the insured farmers and the insurers, and the problems of asym-metric information are not therefore apparent, as in traditional crop insurances. Index-based insurances are likely to have particular strengths in addressing systemic risks, but the existing literature is rather inconsistent about these claims (Xu et al. 2009).

Examples of index-based insurances designed in agriculture include the US programme Group Risk Plan, which is based on an area yield index (Barnett et al. 2005). Weather-based index insur-ances have also been designed for agricultural purposes in the context of developing countries, where the auditing of household-specific losses incurs high cost (e.g. Barnett and Mahul 2007). Index-based contracts and weather derivatives, in particular, have also received considerable at-tention in the financial market, and the trade for them has been increasing. Currently, CME Group2 (2010) offers weather futures and options in four categories: temperature (16 contract types), hur-ricanes (3 contract types), frost (2 contract types) and snowfall (2 contract types). The temperature-related products are offered for 42 cities throughout the world. Ten of these cities are in Europe, but none in Finland. The cities closest to Finland for which these products are quoted are Stockholm and Oslo. However, neither of these cities can be con-sidered to represent weather in Finland accurately enough, and cannot therefore provide a weather measurement point relevant for hedging against yield risks in Finland.

Although weather index-based crop insurances have many advantages, there is concern about how much the actual yields respond to weather condi-

1 The market refers here to the commodity market that is also incomplete, as there is no market for risk and insurance is not available. 2 The Chicago Board of Trade (CBOT) and NYMEX have merged to form CME Group.

tions during the growing season or specific weather events, such as the accumulation of temperature, rainfall, frost or hail, and how much is left as un-explained yield variation and to the basis risk (e.g. Peltonen-Sainio et al. 2009a; 2011). Thus, to de-sign attractive and efficient weather-based index insurances we need empirical information on how the yields respond to weather conditions that can be easily and precisely measured and applied in the in-surance markets. Information is currently available about linkages between weather and yields, but not in such a simple form that it could be applicable in designing efficient weather indices and insurance contracts in order to protect Finnish grain growers against risks and uncertainty caused by yield vari-ability and loss. Therefore, this paper estimates the response of spring wheat yield to certain weather indices for four different locations in Finland. The estimating equations are standardized and simpli-fied so that they can provide information, such as piece-wise linear marginal products of certain weather conditions, for the design of tradable con-tracts. Further, the accumulation of weather data is fixed to start on a specific calendar date, and high frequency daily weather data are aggregated across time in a standardized way to guarantee that the results can be used for designing tradable weather indices.

Spring wheat is used as the reference crop, because its role in Finnish cropping systems has markedly increased in recent decades: the number of hectares under wheat has doubled since 1995, with an average annual increase of 6.7%. Further-more, wheat has generally higher risks for yield losses due to being the latest maturing cereal grown in Finland, i.e. it is harvested later than spring-sown barley and oats, and winter rye (Peltonen-Sainio et al. 2009b). However, this does not necessarily indi-cate that Finnish farmers have been willing to take more risks since EU accession in 1995. There is in fact some justification for an increase in risk aver-sion behaviour (Kondouri et al. 2009). One reason for increasing the area under wheat cultivation re-lates to price risk aversion behaviour, since wheat has remained in EU price intervention schemes, whereas barley and oats have not.



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We measure both the yield and weather param-eters in the same location, and the results indicate the potential of using weather indices for predicting yields on the local scale. Our results provide infor-mation that is needed as a first step in designing weather index-based insurance contracts to protect against yield losses. The estimates also shed a base for future research in estimating the relationship between the yields and the weather when the spa-tial distance between the production site and the weather station increases.3

Data

Weather dataThe weather data are from the Finnish Meteorologi-cal Institute (FMI) over the period from 1970 to 2008. These data consist of the daily accumulation of “growing degree days” (GDD)4, the daily mini-mum temperature and precipitation measurements.

The daily data are first aggregated into weekly intervals, and we later test how these weekly data can be further aggregated for being applicable to build index based insurance contracts. For temper-ature measures (GDD and minimum temperature), the three last weeks are omitted in the analyses, be-cause the accumulation of GDD after the 17th sam-ple week (after August 27) no longer contributes to the yields. For rainfall the situation is different, since excessive rainfall at harvest can still result in severe crop damage (Peltonen-Sainio et al. 2009b).

Growing degree days (GDD)

The growing season comprises the period when the average daily temperature remains above +5 °C. The annual average GDD for our field experimental

3 The spatial correlations of weather indices, yields and indemnity payments are estimated in Myyrä et. al (2011). 4 Growing degree days (GDD) are also referred to as the effective temperature sum (Mukula and Rantanen 1987).

data decreases from the south of Finland (1,123 ºC) to the north (1,028 ºC), whereas the standardized maturation GDD’s for the wheat cropping season are between 941 and 1,056 ºC, depending on the breeding line. The growing period from sowing to maturation required for spring wheat varieties grown in Finland varies within the range of 100.9 to 109.7 days.

At FMI statistics, the accumulation of growing degree days starts from the beginning of the grow-ing season, which usually takes place in late April in Southern Finland, early May in Central Finland, and the latter half of May in Lapland. However, in this study we have defined the growing season to start each year on 1 May and to continue for 20 weeks (140 days) until 17 September.5 This approach of locking the growing season to exact calendar dates is often used in corresponding ap-plications, and is referred to as the “biofix” (Xu et al. 2009).

Thus, our weather data over 140 days each year cover the growing season of wheat, even if the true sowing time has annual variation. It also covers the conditions at harvest when risks for yield losses are typically high (Peltonen-Sainio et al. 2009b). With-in the typical wheat production areas, located from southern to central coastal regions of the country, the length of the growing season and the accumula-tion of GDD exceed, on average the requirements for growing wheat.

An alternative approach would have been to start the growing season each year at different time, such as when the accumulated GDD and other measures pass certain threshold. However, even though it is recorded in the Finnish agricul-tural statistics, the true sowing date is endogenous and could not be used as an exact predetermined base for tradable weather index insurances and de-rivatives. The tradability of contracts requires that the underlying weather measures that trigger the indemnity payments are precise and locked into exact calendar days.

The GDD index is calculated as the cumula-tive sum of those daily average air temperatures that exceed five degree Celcius. The daily aver-

5 The data are available from the authors upon request.



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age is computed using eight measurements se-quenced in three hour intervals of equal duration within each day. Formally, the GDD measure is (Kangas et al. 2009):

(1)where subscript ℓ refers to location and

refer to the air temperature measurements in degree Celcius scale at three hour intervals, start-ing at midnight ( 00

,tC ) and ending at nine o’clock in the evening ( 21

,tC ). The first day (t=1) is fixed at May 1 and the last day at September 17. Thus the total number of days (T) over which the GDD is computed is 140.

Truncating our GDD accumulation at May 1st did not result in much losses of generality in the current data since the GDD did not accumu-late to a large extent before May (Figure 1). The weekly GDD measures gradually increase during the first eleven weeks and then, for the last seven weeks, turn to a decreasing trend. The variation of the weekly GDD is larger up until midsummer (May-June) than in late summer (July-August). On

average, May accounts to only 7 – 11% of the total GDD accumulated over the 20-week period.

Daily precipitation

Daily precipitation (RAIN) is measured in mil-limetres (Figure 2). This index addresses both the drought risk and excessive rain. When the RAIN index is aggregated into weekly intervals, it has either positive or negative impacts on the yield, depending on the time period.

The distribution of precipitation across the growing season is not optimal for crop growth (Peltonen-Sainio et al. 2009b). The rainfall is too scarce in early summer and it increases gradually towards July, also remaining high at harvest. The variability in rainfall also increases from May un-til July and remains high thereafter. The highest rainfall events are typically observed in July and early August.

The aggregation of data into weekly intervals is reasonable, because rain, even when heavy on a Finnish scale, does not usually destroy the whole harvest at once unless in the form of hail. Our data did not include any two-day rains exceeding 100 mm, and the frequency for rainfall of more than

00 03 21, , ,

,1

( ... )max 5 , 0

8

Tt t t

tt

C C CGDD

=

+ += −

∑

Fig. 1. The distribution of week-ly ( week 1 starts at May 1) GDD’s during 1970–2008. The six regions (ℓ) are pooled. Tukey box: 50% of observations are within the box, with the lower and upper boundaries of the box being quartiles 1 and 3, respec-tively. The box is divided by the mode (Q2), and “+” indicates the mean. Circles indicate unu-sual observations.

0 2 4 6 8 10 12 14 16 18 200

25

50

75

100

125

Rain

week

00 03 06 21, , , ,, , ,...,t t t tC C C C



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80 mm was also too small to identify heavy rains within a shorter than weekly interval . Heavy rains cause lodging. If rains are long-lasting and abun-dant soils become wet and do not carry heavy ma-chinery. Also harvesting is hampered and cereal quality deteriorated, especially due to the prolifera-tion of microflora that are favoured by wet condi-tions (Peltonen-Sainio et al. 2009b). The weekly aggregates are also useful in the determination of drought effects as, for instance, rather short peri-ods of drought prior to heading reduce the number of set grains and cause yield penalties that largely cannot be compensated later due to the short and intensive Finnish growing season (Peltonen-Sainio et al. 2009b, 2009c, 2011).

Hail is a form of solid precipitation that consists of balls or irregular lumps of ice. Hail is seldom observed in a traditional meteorological network, because precipitation in the form of hail is a small-scale event that lasts only for a short time. The methodology to detect hail from radar measure-ment data is gradually improving. In this study we could not separate hail from the total precipitation.

Minimum air temperatures

Minimum air temperatures below zero are possible throughout the growing season in Finland, although they are most likely and frequently experienced at the beginning and at the end of the growing season (Figure 3). Below-zero air temperatures during the growing season, i.e. night frosts (FROST), have caused the worst famines over the centuries in Finland. Up until the 1950s, night frost caused frequent yield losses every fifth year on average, but it has occurred less frequently since then (Mukula and Rantanen 1987). In cereals such as wheat, night frost seldom causes total crop failure when it occurs early or late in the growing season. Wheat can survive very low temperatures, even when repeated night after night. However, early summer night frost retards growth for as much as couple of weeks, which causes yield penalties in our short growing season (Peltonen-Sainio et al. 2009b). Total crop failures are only caused by night frost when it occurs at anthesis and thereby destroys the grain primordia, which have a high water content prior to the onset of grain-filling. Hence, the timing of night frost is very important.

0 2 4 6 8 10 12 14 16 18 200

25

50

75

100

125

Rain

week

Fig. 2. The distribution of weekly rainfall (in millimetres) over the growing season in 1970-2008. The six sample regions are pooled. Tukey box: see Fig. 1 for details.



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Yield dataOver the years, spring wheat yields have varied within a large range (Figure 4). The mean yield was within the full sample 3,850 kg ha-1, and the yield varied from the lowest 1,531 to the highest 6,198 kg ha-1.

Some differences in yields occurred between sample regions (Table 1). The regional yields re-flect climate conditions, which are more favour-able in southern parts of Finland (e.g. Mietoinen, Pälkäne and Jokioinen) than in northern parts of the country (e.g. Ylistaro).

0 2 4 6 8 10 12 14 16 18 20-10

-5

0

5

10

15

20

Min

tem

p

week

Fig. 3. The weekly min-imum temperatures in 1970-2008. The six sam-ple regions are pooled. Tukey box: see Fig. 1 for details.

9

Yield data 1 2 Over the years, spring wheat yields have varied within a large range (Figure 4). The mean yield was 3 within the full sample 3850 kg ha-1, and the yield varied from the lowest 1531 to the highest 6198 4 kg ha-1. 5 6 7

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 19901000

2000

3000

4000

5000

6000

7000

kg p

er h

a

year1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

year 8 9 10 11 Figure 4. Spring wheat yields in 1970-2008. The sample regions are pooled. 12

Tukey box: see Figure 1 for details (MTT Official Variety Trials). 13 14 15 Some differences in yields occurred between sample regions (Table 1). The regional yields reflect 16 climate conditions, which are more favourable in southern parts of Finland (e.g. Mietoinen, Pälkäne 17 and Jokioinen) than in northern parts of the country (e.g. Ylistaro). 18 19 20 Table 1. Spring wheat yields (kg ha-1,) in the sample regions pooled across years (1970-2008). Data 21

from the MTT Official Variety Trials 1970–2008. The yield data are normalized to fixed 22 fertilization levels. 23

24 Region N Mean Std Dev Minimum Maximum

Jokioinen 28 3,863 944 1,581 5,734 Mietoinen 36 3,713 722 2,414 5,273

Pälkäne 36 4,201 773 2,383 6,197 Ylistaro 34 3,612 988 1,531 5,499

25 26

27

Fig. 4. Spring wheat yields in 1970-2008. The sample regions are pooled. Tukey box: see Fig. 1 for details (MTT Official Variety Trials)



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Model and Methods

Indemnity payment function for the index insurance contract

Following (Barnet et al. 2005) we define the indem-nity payment (n) for the weather index insurance contract as:ñt

w(wt)=max(

10

Model and Methods 1 2 3 Indemnity payment function for the index insurance contract 4 5 Following (Barnet et al. 2005) we define the indemnity payment (n) for the weather index insurance 6 contract as: 7 8 9

( ) max ( , 0)w ct t tn w w w= − , for shortage (2a) 10

( ) max ( , 0)w ct t tn w w w= − , for excessiveness (2b) 11

where 12 tw = a function of the stochastic weather indices at time t 13 cw = the vector of critical values that trigger the payment 14

15 The indemnity payment depends on the realization of the stochastic weather index values through 16 function ( tw , and the corresponding critical values for these indices cw . 17 18 Estimating equations for the indemnity payments 19 20 To get well defined, indemnity payments over the relevant regimes we define the underlying 21 function ( tw ) piece-wise linear in two dimensions with respect to the three above described weather 22 measurements (m): Growing Degree Days (GDD), precipitation (RAIN) and night frost (FROST). 23 The first dimension is with respect to time so that the marginal yield effect of each weather 24 measure is allowed to differ between time regimes. The location and the duration of these time 25 regimes along the cropping season are tested. In this case, we regress the yield ( ty ) on the index 26 function ( tw ) and the estimating equation is6: 27

1 1 1

( )t t t t ty m D GDD RAIN FROSTτ τ κ κ γ γ

τ κ γα β φ ϕ θ ε

Τ Κ Γ

= = =

= + + + + + (3) 28

29 where , , , andα β φ ϕ θ are parameters and tε is an error. The subscript t indices year and the index 30 function is defined at annual frequency to match the annual yield data. The superscript ℓ refers to 31 location and the dummy variable (D) is used to allow different conditional means for different 32 locations. The indices 1,... ; 1,... , 1,...andτ κ γ= Τ = Κ = Γ distinguish between different regimes in 33 the piece-wise linear specification. 34 35 The second dimension for piece-wise linearity is with respect to the values of the weather 36 measures. This way we allow for asymmetry in marginal yield effects with respect to the values of 37 the weather measures. For GDD we distinguish three different regimes WARM, NORMAL and 38 COLD. WARM and COLD regimes are identified by dummy variables DWARM and DCOLD that 39 receive value one if the summer is warm or cold and value zero otherwise. The middle regime 40 (NORMAL) is observed if DWARM =DCOLD =0. For RAIN we consider DRY, NORMAL and WET 41 regimes with the corresponding dummy variables DDRY and DWET. The underlying idea is that the 42

6 Without losses in generality, we have normalized the price of wheat to one and the marginal yield effect ( ( ) / iy m m∂ ∂ ) equals to the tick price of the contract.

-wt , 0), for shortage (2a)

ñtw(wt)=max(wt-

10






1 1 1



Τ Κ Γ

= = =

= + + + + + (3) 28



, 0), for excessiveness (2b)

where

wt = a function of the stochastic weather indices at time t

cw = the vector of critical values that trigger the payment

The indemnity payment depends on the realiza-tion of the stochastic weather index values through function wt, and the corresponding critical values for these indices cw .

Estimating equations for the indemnity payments

To get well defined, indemnity payments over the relevant regimes we define the underlying function ( tw ) piece-wise linear in two dimensions with respect

to the three above described weather measure-ments (m): Growing Degree Days (GDD), pre-cipitation (RAIN) and night frost (FROST). The first dimension is with respect to time so that the marginal yield effect of each weather measure is allowed to differ between time regimes. The loca-tion and the duration of these time regimes along the cropping season are tested. In this case, we regress the yield (yt) on the index function tw and the estimating equation is6:

6 Without losses in generality, we have normalized the price of wheat to one and the marginal yield effect

( ( ) / iy m m∂ ∂ ) equals to the tick price of the contract.

Table 1. Spring wheat yields (kg ha-1,) in the sample regions pooled across years (1970-2008). Data from the MTT Official Variety Trials 1970–2008. The yield data are normalized to fixed fertilization levels.

Region N Mean SD Minimum Maximum

Jokioinen 28 3,863 944 1,581 5,734

Mietoinen 36 3,713 722 2,414 5,273

Pälkäne 36 4,201 773 2,383 6,197

Ylistaro 34 3,612 988 1,531 5,499



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where

are parameters and εt is an error. The subscript t indices year and the index function is defined at annual frequency to match the annual yield data. The superscript ℓ refers to location and the dummy variable (D) is used to allow different conditional means for different locations. The indices τ=1,...T; κ=1,...K and γ=1,...Γ distinguish between different regimes in the piece-wise linear specification.

The second dimension for piece-wise linearity is with respect to the values of the weather meas-ures. This way we allow for asymmetry in mar-ginal yield effects with respect to the values of the weather measures. For GDD we distinguish three different regimes WARM, NORMAL and COLD. WARM and COLD regimes are identified by dum-my variables DWARM and DCOLD that receive value one if the summer is warm or cold and value zero otherwise. The middle regime (NORMAL) is observed if DWARM =DCOLD =0. For RAIN we con-sider DRY, NORMAL and WET regimes with the corresponding dummy variables DDRY and DWET. The underlying idea is that the marginal yield ef-fect for RAIN, for example, is expected to differ significantly if the growing season is DRY, NOR-MAL or WET. The regimes are optimized by a grid search method so that the mean square error between the yield and the weather measurements is minimized7. The asymmetric estimating equa-tion is:

7 The grid search is to give alternative locations and dura-tions for the regimes and then select the one with the best fit.

Our estimation approach is to start projecting the yields conditional on each weather measure-ment separately. Estimating these first-stage “par-tial” and incomplete specifications is consistent under the null-hypothesis that the weather does not significantly imply the yields. For those weather indices that turn out significant, we later estimate their effects jointly in the second stage. These second-stage estimations provide more efficient estimates. We may also expect that the weather indices have significant joint effects similar to Le-ontief technologies (e.g. Chambers 1988), because the different weather attributes are more likely to complement rather than substitute for each other8.

In the first stage, we use weekly data separately regressed on the GDD, FROST, and the RAIN. These weekly data are used to test on how best aggregate the weather indices further across time so that simple indices can be constructed and the degrees of freedom allow estimation of the yield response to all relevant weather indices jointly. To maintain sufficient over-identification restrictions in estimation, the site-specific data are pooled in most of the specifications, and the error correlation induced by unobservable time-invariant site-spe-cific effects is factored out using dummy variables (Wooldridge 2002, p. 133).

8 In Leontief technology a production factor cannot substi-tute for another production factor. Here it would imply that rainfall, for example, cannot substitute for the shortage of GDD.

10






1 1 1



Τ Κ Γ

= = =

= + + + + + (3) 28



, , , andα β φ ϕ θ

11

marginal yield effect for RAIN, for example, is expected to differ significantly if the growing 1 season is DRY, NORMAL or WET. The regimes are optimized by a grid search method so that the 2 mean square error between the yield and the weather measurements is minimized7. The 3 asymmetric estimating equation is: 4 5

1

1 1

1

1

1

1

( )COLD WARM

COLD WARM

DRY WET

DRY WET

t

COLD WARMt t t

T T

DRY WETt t t

K K

t t

y m D

D GDD GDD D GDD

D RAIN RAIN D RAIN

FROST

τ τ τ τ τ τ

τ τ τ

κ κ κ κ κ κ

κ κ κ

γ γ

γ

α β

φ φ φ

ϕ ϕ ϕ

θ ε

−

Τ − Τ − Τ

= = =

Κ − Κ Κ

= = =

Γ

=

= +

+ + +

+ + +

+ +

(4) 6

7 8 Our estimation approach is to start projecting the yields conditional on each weather measurement 9 separately. Estimating these first-stage “partial” and incomplete specifications is consistent under 10 the null-hypothesis that the weather does not significantly imply the yields. For those weather 11 indices that turn out significant, we later estimate their effects jointly in the second stage. These 12 second-stage estimations provide more efficient estimates. We may also expect that the weather 13 indices have significant joint effects similar to Leontief technologies (e.g. Chambers 1988), because 14 the different weather attributes are more likely to complement rather than substitute for each other8. 15 16 In the first stage, we use weekly data separately regressed on the GDD, FROST, and the RAIN. 17 These weekly data are used to test on how best aggregate the weather indices further across time so 18 that simple indices can be constructed and the degrees of freedom allow estimation of the yield 19 response to all relevant weather indices jointly. To maintain sufficient over-identification 20 restrictions in estimation, the site-specific data are pooled in most of the specifications, and the 21 error correlation induced by unobservable time-invariant site-specific effects is factored out using 22 dummy variables (Wooldridge 2002, p. 133). 23 24 25 Results 26 27 Growing degree days (GDD) 28 29 Our hypothesis is that, within the range of sample variation, GDD should have a positive effect on 30 the yield in the late growing season (July−August), but in early growing season (May−June) the 31 effect may also be negative or insignificant, since moderate GDD values in early summer may 32 increase the yield potential, which might be realized later in the growing season. 33 34 When the models are estimated separately for each experimental site using weekly GDD values, 35 they explain 62−74% (R2) of yield variations. Nevertheless, in these site-specific models we found 36 most of the parameter estimates to be insignificant and alternating their signs, suggesting an 37 inconsistent specification and lack of over-identification restrictions required for obtaining 38

7 The grid search is to give alternative locations and durations for the regimes and then select the one with the best fit. 8 In Leontief technology a production factor cannot substitute for another production factor. Here it would imply that rainfall, for example, cannot substitute for the shortage of GDD.



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Results

Growing degree days (GDD)

Our hypothesis is that, within the range of sample variation, GDD should have a positive effect on the yield in the late growing season (July−August), but in early growing season (May−June) the effect may also be negative or insignificant, since moder-ate GDD values in early summer may increase the yield potential, which might be realized later in the growing season.

When the models are estimated separately for each experimental site using weekly GDD values, they explain 62−74% (R2) of yield variations. Nev-ertheless, in these site-specific models we found most of the parameter estimates to be insignificant and alternating their signs, suggesting an incon-sistent specification and lack of over-identification restrictions required for obtaining consistent, well identified parameters. In general, it is possible that unfavourable conditions causing yield penalties at a relatively early stage of the growing season are partially compensated for by favourable grow-ing conditions later on. However, especially in northern growing conditions, such compensation is marginal due to the exceptionally short growing season and long days that enhance development of the wheat crop (Peltonen-Sainio et al. 2007, 2009b, 2009c). This can actually be considered to give a good premise for relating weather indices to yield losses.

When the site-specific data are pooled, the weekly GDD explain 14% of the yield devia-tions.9 Nevertheless, the site-specific dummy vari-ables were found to be insignificant, suggesting that once the GDD are controlled for, the location does not, as such, have a significant effect on the yield of wheat within the typical wheat produc-tion areas. Again, most of the parameter estimates on the weekly GDD were insignificant, and their signs alternated inconsistently. Overall, the pat-tern of the point estimates indicates that, within the sample variation, an increase in the cumula-

9 The regional dummy variables are insignificant and alone explain 0.8% of the yield variation.

tive temperatures significantly implies the yields in early summer (June) and late summer (August), but not significantly in spring (May) or immediately after midsummer (July).

Aggregating the GDD data into two-week peri-ods improves the consistency of the estimates and more clearly suggests than the results above that the GDD significantly implies yields in early sum-mer and after midsummer (Figure 5). The impact is first slightly negative in the early growth stages when yields are mostly determined, and then in-creases to become positive towards later summer. However, later on in the autumn and at harvest, the effects of GDD decline and once again become insignificant. This result is consistent with our hy-pothesis and earlier experience, so that a moderate GDD in early summer supports the formation of the yield potential that may or may not be realized, depending on the GDD and other conditions later on in the summer. In September it is likely that high temperatures associate with low precipitation, to-gether promoting the ripening processes and keep-ing plant stands unlodged, although they occur too late to enhance yields. Therefore, we drop the last observations from the estimating equations.

Fig. 5. The marginal yield effect of the cumulative de-gree days (GDD) for two-week periods. Thick line: the estimate , thin lines: the estimate plus/minus its stand-ard errors. Estimated in Equation 3, imposing φκ =θγ=0 for all κ and γ.

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When the data on GDD are further aggregat-ed into four-week cumulative values and the last (fifth) insignificant GDD variable is dropped, the projection explains 6.3% (R2) of the yield variation. In this case, the data also support the main idea that the accumulation of temperature most clearly influences the yield in early summer (June) and in late summer (August) (Figure 6). The second four-week period, which corresponds to June, has a significant negative effect on the yield. The fourth four-week period GDD, which corresponds to Au-gust, has the largest and the only significant posi-tive effect on the yield. Thus, around the sample means, GDD has not significantly influenced the yields in spring (May) or in midsummer (July). An intercept and the fourth GDD (August) vari-able alone explain about 4% of the yield deviations. Within our sample, these August temperatures more strongly influence yields than the total GDD for the whole growing season.

We might expect that under Finnish weather conditions the cold periods and shortage of GDD would delay crop growth and decrease the yield. We therefore tested for asymmetry in the GDD by defining distinct regimes for colder than average, and warmer than average temperatures respec-tively. The thresholds “mean minus 10 ºC” for the cold regime and “mean plus 25 ºC” for the warm

regime in the four-week GDD aggregates provided the best fit for the sample. Nevertheless, the data identify significant asymmetries only in the third and fourth four-week periods, suggesting that at lower than average temperatures the marginal yield effect of one GDD is higher than at the mean or at higher than average temperatures (Table 3, Figure 7). In the cold regime, the marginal value of one GDD point increases for the third four-week period from 17 to 19 kg.10

Minimum air temperatures: frosts

The night frost is defined as a day when the daily minimum air temperature falls below a thresh-old. Then the frost variable (FROST) equals that minimum temperature, and otherwise it equals zero. The threshold was first defined at 0, −1 or −2 ºC. However, the data for the latter two threshold values turned out sparse and the parameters were not so well identified. We therefore report only the results for the model using 0 ºC as the threshold.

The data suggest that frost is the most critical factor in the middle of July. The most damaging and significant frost effects are identified for the

10 Our approach of allowing and testing for asymmetry and the threshold effects is commonly adopted in the economet-ric analysis of time series for price movements (see. e.g. Serra et al. 2006; Jalonoja and Pietola 2004)

Fig. 6. The marginal yield effect of the cumulative de-gree days (GDD) for four-week periods. Thick line: the estimate; thin lines: the estimate plus/minus its stand-ard error. Estimated in Equation 3, imposing φκ =θγ=0 for all κ and γ.

Fig. 7. The marginal yield effect of from asymmetric pro-jection of yield on GDD for four-week periods. Thick line: middle regime, uppermost thin dotted line: COLD regime, lower thin dotted line: WARM regime. Estimated in Equation 5, imposing φκ =θγ=0 for all κ and γ.

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Table 3. The marginal yield effects (kg ºC-1) of asymmetric projection of yield on GDD for four-week periods. Estimated in Equation 5 with the restriction φκ =θγ=0 for all κ and γ.

Variable Weeks1) Parameter Std error

Intercept -4,470 1,500Location: Mietoinen

9.06 210Location: Palkane

129 210Location: Ylistaro

-158 219GDD 1-4 -2.81 1.75GDD 5-8 -5.09 1.63GDD 9-12 2.41 2.23GDD 13-16 16.8 4.68GDD, COLD2) 9-12 1.86 0.75GDD, COLD2) 13-16 1.75 0.934GDD, WARM2)

13-16 -2.41 0.776

1)Counting the weeks starting at 1 May.2)Thresholds: average GDD of the period -10 ºC for COLD, and average GDD of the period + 25 ºC for WARM.

two-week period in mid-July (bi-weekly aggregate ending at week number 12, Figure 8). The point estimate for the losses due to frost in mid-July is 2,400 kg, with a standard error of 1,500 kg. There-after, and towards the harvest, the adverse yield effect rapidly declines and becomes insignificant. Early in the growing season (May) or at harvest (September), frosts do not result in significant crop damages. In June, some signals of the adverse ef-fects of frost emerge, but the data are not informa-tive enough to identify these effects.

If only the statistically significant parameters are included in the estimating equation, the magni-tude of the yield loss due to mid-July frost remains at 2,100 kg. If we estimate the frost effect using a dummy variable for the minimum temperatures be-low zero, the point estimate for the mid-July frost effect is 1,200 kg, with a standard deviation of 880 kg. Nevertheless, the earlier specification performs better than this version.

Rainfall

Our hypothesis is that within the range of variation observed in our sample, rainfall has both negative and positive yield effects, depending on the timing and amount. With respect to timing, we identify

15

Minimum air temperatures: frosts 1 2 The night frost is defined as a day when the daily minimum air temperature falls below a threshold. 3 Then the frost variable (FROST) equals that minimum temperature, and otherwise it equals zero. 4 The threshold was first defined at 0, −1 or −2 ºC. However, the data for the latter two threshold 5 values turned out sparse and the parameters were not so well identified. We therefore report only 6 the results for the model using 0 ºC as the threshold. 7 8 The data suggest that frost is the most critical factor in the middle of July. The most damaging and 9 significant frost effects are identified for the two-week period in mid-July (bi-weekly aggregate 10 ending at week number 12, Figure 8). The point estimate for the losses due to frost in mid-July is 11 2400 kg, with a standard error of 1500 kg. Thereafter, and towards the harvest, the adverse yield 12 effect rapidly declines and becomes insignificant. Early in the growing season (May) or at harvest 13 (September), frosts do not result in significant crop damages. In June, some signals of the adverse 14 effects of frost emerge, but the data are not informative enough to identify these effects. 15 16 If only the statistically significant parameters are included in the estimating equation, the magnitude 17 of the yield loss due to mid-July frost remains at 2,100 kg. If we estimate the frost effect using a 18 dummy variable for the minimum temperatures below zero, the point estimate for the mid-July frost 19 effect is 1,200 kg, with a standard deviation of 880 kg. Nevertheless, the earlier specification 20 performs better than this version. 21 22

23 24 Figure 8. The marginal yield effect in regressing wheat yield deviations on the night frost (FROST) 25 in each two-week period. Thick line: the estimate, thin lines: the estimate plus/minus its standard 26 error. The middle part is not identified. Estimated in Equation 3, imposing 27

0 for all andτ κφ ϕ τ κ= = . 28 29

Fig. 8. The marginal yield effect in regressing wheat yield deviations on the night frost (FROST) in each two-week period. Thick line: the estimate, thin lines: the estimate plus/minus its standard error. The middle part is not identified. Estimated in Equation 3, imposing

three distinct regimes. The first period is defined for spring at the beginning of May, when rainfall is expected to reduce the yield potential by either delaying sowing or by causing crust formation. The second regime is in late May and June, when the deficiency of rainfall and the risk of drought are greatest and rainfall (if not excessive) would have a positive yield effect. Thereafter, towards the end of July and August (3rd time period), the positive effects of rainfall gradually decline, because rain comes too late for the crops and begins to adversely

15

Minimum air temperatures: frosts 1 2 The night frost is defined as a day when the daily minimum air temperature falls below a threshold. 3 Then the frost variable (FROST) equals that minimum temperature, and otherwise it equals zero. 4 The threshold was first defined at 0, −1 or −2 ºC. However, the data for the latter two threshold 5 values turned out sparse and the parameters were not so well identified. We therefore report only 6 the results for the model using 0 ºC as the threshold. 7 8 The data suggest that frost is the most critical factor in the middle of July. The most damaging and 9 significant frost effects are identified for the two-week period in mid-July (bi-weekly aggregate 10 ending at week number 12, Figure 8). The point estimate for the losses due to frost in mid-July is 11 2400 kg, with a standard error of 1500 kg. Thereafter, and towards the harvest, the adverse yield 12 effect rapidly declines and becomes insignificant. Early in the growing season (May) or at harvest 13 (September), frosts do not result in significant crop damages. In June, some signals of the adverse 14 effects of frost emerge, but the data are not informative enough to identify these effects. 15 16 If only the statistically significant parameters are included in the estimating equation, the magnitude 17 of the yield loss due to mid-July frost remains at 2,100 kg. If we estimate the frost effect using a 18 dummy variable for the minimum temperatures below zero, the point estimate for the mid-July frost 19 effect is 1,200 kg, with a standard deviation of 880 kg. Nevertheless, the earlier specification 20 performs better than this version. 21 22

23 24 Figure 8. The marginal yield effect in regressing wheat yield deviations on the night frost (FROST) 25 in each two-week period. Thick line: the estimate, thin lines: the estimate plus/minus its standard 26 error. The middle part is not identified. Estimated in Equation 3, imposing 27

0 for all andτ κφ ϕ τ κ= = . 28 29



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affect the harvest. Besides all these impacts within the three regimes, extremely intensive excess rainfall adversely affects yields and may result in crop dam-age throughout the growing season, from sowing to harvest.

We have two separate measures for rainfall. The first is the weekly sum of rainfall and, accord-ing to the patterns observed in the data, it is then aggregated over different durations within the three regimes described above. The second measure fo-cuses on excess rainfall. It is defined as the fre-quency of rains that exceed a minimum threshold within two days. The threshold levels are set at 40 and 60 mm.11 However, these frequencies were found to be sparse and therefore difficult to link to yield deviations, as was also shown when using daily precipitation values exceeding 10 mm or 20 mm in Peltonen-Sainio et al. (2009a). Hence, these frequency data are not informative enough to iden-tify the adverse yield effects of excessive rainfall.

In the linear and symmetric projections, the weekly rainfall explains 23.4% of the deviations in yield when all four locations are pooled and the site-specific differences in the mean yields are controlled for with dummy variables. The dummy variables were also insignificant in this specifica-tion, suggesting that once the rainfall measures are controlled for, the location does not have a significant effect on the yield of wheat within the typical wheat production areas. Similarly to GDD measures, the parameter estimates for the weekly rainfall measures alternated their signs, suggesting that a weekly aggregation interval is too short for consistently identifying rainfall effects in the data. We therefore aggregated the rainfall measures us-ing bi-weekly intervals. Then the rainfall implies more consistently and significantly the yield over the growing season.

The results suggest that before and immediate-ly after sowing in early May, rainfall reduces the yield, which may also imply problems in sowing in good time. Thereafter, rainfall in midsummer

11 We also computed the frequency of rains exceeding 80 or 100 mm within two days. The sample did not include any two days of rain exceeding 100 mm, and the number of non-zero frequencies for rainfall of more than 80 mm was too small for identification.

is predicted to increase the yield, and then later on the positive yield effect starts to diminish and becomes insignificant towards the harvest. Imme-diately before and at harvest, rainfall reduces the yield (Figure 9). This specification explains 17.0% of the total yield variation within the sample.

Aggregating the rainfall measures further across time improves the consistency of the esti-mates and highlights the three regimes with dis-tinct yield impacts (Figure 10). This specification has the power to explain 14.6% of yield variation within the sample. The marginal product of 30 mm rainfall, for example, is estimated in early May at −200 kg, and then it increases to 300 kg. At the beginning of July the marginal product starts to de-cline, being zero in August and becoming −200 kg in early September.

We could expect that the effect of rainfall on the yield is asymmetric and has thresholds, espe-cially in early and mid-summer, when drought is a concern and a deficiency of rainfall restricts crop growth. Thus, below a certain threshold, rainfall could have a larger positive impact on the yield than above the threshold. Similarly, the yield impact may become smaller or negative when rains are excessive, especially when the harvest is approaching. Once the lower (DRY) and upper

Fig. 9. The marginal yield effect in regressing the wheat yield deviations on the cumulative rainfall per two-week period. Thick line: the estimate, thin lines: the estimate plus/minus its standard error. Estimated in Equation 3, imposing

17

1

2 3 Figure 9. The marginal yield effect in regressing the wheat yield deviations on the cumulative 4 rainfall per two-week period. Thick line: the estimate, thin lines: the estimate plus/minus its 5 standard error. Estimated in Equation 3, imposing 0 for all andτ γφ θ τ γ= = . 6 7 8 Aggregating the rainfall measures further across time improves the consistency of the estimates and 9 highlights the three regimes with distinct yield impacts (Figure 10). This specification has the 10 power to explain 14.6% of yield variation within the sample. The marginal product of 30 mm 11 rainfall, for example, is estimated in early May at −200 kg, and then it increases to 300 kg. At the 12 beginning of July the marginal product starts to decline, being zero in August and becoming 13 −200 kg in early September. 14 15 16

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(WET) regimes are allowed to differ from the mid-dle regime, the middle regime may be expected to be characterized as a regime of “inaction” in which the effects of rainfall are smaller than in the lower and upper tails. We therefore further augment the estimating equation with asymmetric yield effects

18

1

2 3 Figure 10. The marginal yield effect in regressing the wheat yield deviations on the cumulative 4 rainfall. The horizontal straight lines indicate regimes where parameters are imposed equally. Thick 5 line: the estimate, thin lines: the estimate plus/minus its standard error. Estimated in Equation 3, 6 imposing 0 for all andτ γφ θ τ γ= = . 7 8 9 We could expect that the effect of rainfall on the yield is asymmetric and has thresholds, especially 10 in early and mid-summer, when drought is a concern and a deficiency of rainfall restricts crop 11 growth. Thus, below a certain threshold, rainfall could have a larger positive impact on the yield 12 than above the threshold. Similarly, the yield impact may become smaller or negative when rains 13 are excessive, especially when the harvest is approaching. Once the lower (DRY) and upper (WET) 14 regimes are allowed to differ from the middle regime, the middle regime may be expected to be 15 characterized as a regime of “inaction” in which the effects of rainfall are smaller than in the lower 16 and upper tails. We therefore further augment the estimating equation with asymmetric yield effects 17 with respect to the amount of rainfall. The yield impact of rainfall is separated into three regimes: a 18 dry regime, middle regime and wet regime. 19 20 The data were informative enough to identify the distinct effects of drought in early summer and 21 excess rainfall in late summer and towards the harvest (Table 4, Figure 11). The lower threshold of 22 30 mm per two weeks and higher threshold of 50 mm per two weeks provided the best sample fit. 23 At the beginning of the growing season and at harvest, the yield response is nevertheless symmetric 24 and negative so that all rainfall reduces the yield. Around midsummer, when drought is common 25 concern, the marginal yield effect of rainfall within the dry regime is estimated at 15.8 kg, 26 suggesting that 30 mm of rainfall within the dry regime increases the yield by almost 500 kg. 27 Within the wet regime, the marginal yield effect of rainfall decreases earlier and faster to a negative 28 value as compared to the other regimes. Within the wet regime the marginal yield effect becomes 29 negative already by the end of July. 30 31 Table 4. The parameter estimates and their standard errors in the asymmetric model for the wheat 32 yield deviations conditional on the cumulative rainfall (RAIN). Estimated in Equation 5, imposing 33

0 for all andτ γφ θ τ γ= = . 34 . 35

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with respect to the amount of rainfall. The yield impact of rainfall is separated into three regimes: a dry regime, middle regime and wet regime.

The data were informative enough to identify the distinct effects of drought in early summer and excess rainfall in late summer and towards the har-vest (Table 4, Figure 11). The lower threshold of 30 mm per two weeks and higher threshold of 50 mm per two weeks provided the best sample fit. At the beginning of the growing season and at harvest, the yield response is nevertheless symmetric and nega-tive so that all rainfall reduces the yield. Around midsummer, when drought is common concern, the marginal yield effect of rainfall within the dry regime is estimated at 15.8 kg, suggesting that 30 mm of rainfall within the dry regime increases the yield by almost 500 kg. Within the wet regime, the marginal yield effect of rainfall decreases earlier and faster to a negative value as compared to the other regimes. Within the wet regime the marginal yield effect becomes negative already by the end of July.

Table 4. The parameter estimates and their standard errors in the asymmetric model for the wheat yield deviations

conditional on the cumulative rainfall (RAIN). Estimated in Equation 5, imposing

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Variable Weeks1) Parameter Std errorIntercept -683 405Mietoinen -26.5 205Palkane -126 204Ylistaro -345 210

RAIN 1-2 -6.63 5.87

RAIN 3-10 9.73 2.47

RAIN 11-16 1.44 1.91

RAIN 17-18 -7.02 3.58

RAIN, DRY2) 3-8 6.04 2.83

RAIN, WET2) 9-10 -7.60 3.00

RAIN, WET2) 11-14 -3.79 1.68

1Counting the weeks starting from 1 May.2DRY regime: rainfall less than 30 mm per two weeks; WET regime: rainfall exceeds 50 mm per two weeks.

Fig. 10. The marginal yield effect in regressing the wheat yield deviations on the cumulative rainfall. The horizon-tal straight lines indicate regimes where parameters are imposed equally. Thick line: the estimate, thin lines: the estimate plus/minus its standard error. Estimated in Equation 3, imposing

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Joint effects

The joint effects of GDD, FROST and RAIN of the yield are estimated by regressing the yield on all vari-ables jointly, so that each of them enter the equations similarly to the above-described with asymmetric effects. This model is estimated in equation 5 and it explains 38% of the yield variation. The signs of the parameter estimates remain unchanged for all weather indices in the joint model as compared to the separate models, but the magnitudes of some of the parameters change (Table 5).

We then tested for heteroskedasticity by first regressing the squared error of the ordinary least squares (OLS) specification “All” in Table 3 on an intercept, fitted values and the squared fitted values. This testing approach combines the fea-tures of Breusch-Pagan and White tests, but re-quires only two degrees of freedom. For testing purposes, the approach is valid under the null hy-pothesis of homoskedasticity (Wooldridge 2002 p. 127). This auxiliary regression suggests that the OLS model is heteroskedastic, implying that the

19

Variable Weeks1) Parameter Std error Intercept -683 405 Mietoinen -26.5 205 Palkane -126 204 Ylistaro -345 210 RAIN 1-2 -6.63 5.87 RAIN 3-10 9.73 2.47 RAIN 11-16 1.44 1.91 RAIN 17-18 -7.02 3.58 RAIN, DRY2) 3-8 6.04 2.83 RAIN, WET2) 9-10 -7.60 3.00 RAIN, WET2) 11-14 -3.79 1.68

3) Counting the weeks starting from our BIOFIX date, 1 May. 1 4) DRY regime: rainfall less than 30 mm per two weeks; WET regime: rainfall exceeds 50 mm 2

per two weeks. 3 4 5 6 7

8 9 Figure 11. The marginal yield effect in the asymmetric model for wheat yield deviations conditional 10 on the cumulative rainfall. Thick line: middle regime, uppermost dotted thin line: DRY regime 11 (rainfall less than 30mm per two weeks), lower dotted line: WET regime (rainfall exceeds 50 mm 12 per two weeks). Estimated in Equation 5, imposing 0 for all andτ γφ θ τ γ= = . 13 14 15

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Fig. 11. The marginal yield effect in the asymmetric mod-el for wheat yield deviations conditional on the cumula-tive rainfall. Thick line: middle regime, uppermost dot-ted thin line: DRY regime (rainfall less than 30mm per two weeks), lower dotted line: WET regime (rainfall ex-ceeds 50 mm per two weeks). Estimated in Equation 5, imposing

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estimates are consistent but not efficient. The re-sults reveal thatyield volatility decreases with the yield. In other words, low yield regimes are more volatile than high yield regimes, suggesting that the growing conditions causing crop damage are likely to have heterogeneous implications and that they therefore also increase yield volatility.

We further tested for heteroskedasticity by regressing all explanatory weather events used in the specification “All” on its squared error. This auxiliary specification suggests that dry and warm early summer periods in June significantly increase yield volatility. The yield volatility is higher at Mi-etoinen and Palkane than at the other locations.

Correcting the model for homoskedasticity, we took the fit determined by the significant param-eters in this (latter) auxiliary equation by impos-ing all other parameters at zero, and computed the weight variable for the feasible generalized least squares (FGLS) by taking a square root of this fit. The parameter estimates for the re-estimated FGLS specification are similar to those in the standard OLS specification (Table 6). The results therefore suggest that the standard OLS estimates, which are known to be consistent but not efficient if the error is heteroskedastic, are robust to alternative correc-tions for heteroskedasticity.

ConclusionsOur results suggest that growing degree days (GDD), rainfall (RAIN) and night frosts (FROST) significantly influence the yield of wheat in Fin-land. When the weather measures are aggregated to obtain well-identified and consistent estimates as well as simple indices, as required by insurance contracts and tradable weather derivatives, they jointly explain about 38% of the yield variation. Thus, using simple weather event-based indices as in our model, about 38% of wheat grower yield risk could be insured at best, with the remaining 62% being left as uninsured basis risk.

Within the sample, rainfall contributes the most to the yield and alone explains 23% of the yield



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Table 5. The parameter estimates and their standard errors in the asymmetric models regressed separately on GDD and RAIN, and on all weather events jointly. Estimated in Equation 5.

Weeks start-

ing from May 1

GDD RAIN AllModel

variable Parameter Std. Error Parameter Std. Error Parameter Std. Error

Intercept -4,470 1,500 -683 405 -5,160 1,420

Mietoinen 9.060 210 -26.5 205 32.5 195

Palkane 129 210 -126 204 -59.0 194

Ylistaro -158 219 -345 210 -309 204

GDD 1-4 -2.81 1.75 -2.39 1.67

GDD 5-8 -5.09 1.63 -3.03 1.54

GDD 9-12 2.41 2.23 1.19 2.08

GDD 13-16 16.8 4.68 16.3 4.42

GDD, COLD1)

9-12 1.86 0.748 1.92 0.69

GDD, COLD1)

13-16 1.75 0.934 1.97 0.88

GDD, WARM1)

13-16 -2.41 0.776 -2.37 0.72

RAIN 1-2 -6.63 5.87 -9.47 5.69

RAIN 3-10 9.73 2.47 9.30 2.39

RAIN 11-16 1.44 1.91 1.49 1.92

RAIN 17 -7.02 3.58 -7.82 3.43

RAIN, DRY2) 3-8 6.04 2.83 5.55 2.66

RAIN, WET2) 9-10 -7.60 3.00 -7.06 2.91

RAIN, WET2) 11-14 -3.79 1.68 -2.70 1.60

FROST 11-12 3,120 1,270

R2 %

1) COLD: cold regime; WARM: warm regime.2) DRY: dry regime; WET: wet regime.



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The estimates are consistent with our expecta-tions, in terms of that each of these three meas-ures has qualitatively and quantitatively different yield impacts depending on period. The effect of GDD is initially negligible, as a mild early growing season may even increase the yield potential. The marginal yield effect of GDD increases towards August and peaks within the period from late July to mid-August (13-16 weeks from the beginning of May). In this period the marginal yield effect of a GDD point is estimated at 17 kg ºC-1. Within the cold regime, i.e. when the accumulation of GDD

Table 6. The parameter estimates and their standard errors in OLS and FGLS models. Estimated in Equation 5.

Weeks starting

from May 1

OLS (All) FGLS (All) FGLS (Restricted)

Model variable Parameter Std. Error Parameter Std. Error Parameter Std. ErrorIntercept -5,160 1,420 -4,780 1,450 -4,870 1,37Mietoinen 32.5 195 -40.6 197Palkane -59.0 194 29.2 198Ylistaro -309 204 -261. 213 -256 162GDD 1-4 -2.39 1.67 -2.21 1.65 -2.19 1.63GDD 5-8 -3.03 1.54 -3.45 1.56 -3.44 1.52GDD 9-12 1.19 2.08 -0.26 2.05GDD 13-16 16.3 4.42 16.7 4.61 16.7 4.51GDD, COLD1) 9-12 1.92 0.69 1.48 0.67 1.54 0.49GDD, COLD1) 13-16 1.97 0.88 1.78 0.89 1.78 0.87

GDD, WARM1) 13-16 -2.37 0.72 -2.40 0.71 -2.39 0.70RAIN 1-2 -9.47 5.69 -11.3 5.69 -11.4 5.54RAIN 3-10 9.30 2.39 9.69 2.42 9.88 2.32RAIN 11-16 1.49 1.92 1.52 1.91 1.51 1.85RAIN 17 -7.82 3.43 -6.08 3.45 -6.12 3.38RAIN, DRY2) 3-8 5.55 2.66 5.87 2.64 5.86 2.53RAIN, WET2) 9-10 -7.06 2.91 -6.56 2.92 -6.64 2.84

RAIN, WET2) 11-14 -2.70 1.60 -2.14 1.55 -2.13 1.52

FROST 11-12 3,120 1,270 3,100 1,310 3,090 1,300

R2 % 3) 37.9 39.4 39.3

1) COLD: cold regime; WARM: warm regime.2) DRY: dry regime; WET: wet regime.3) R2 is not fully comparable between the OLS and FGLS specifications, since FGLS is rescaled.

variation, whereas the corresponding explanatory power for GDD was estimated at 16%. Thus, when the production site and the weather measurement points are at the same location, rainfall-based measures seem to have larger potential for hedg-ing against yield risks, but it is known that rainfall has more spatial variation than temperature-based GDD. Therefore, when the distance from the weather station to the production site increases, the relative efficiency of the rainfall- and tempera-ture-based measures may become ambiguous and reversed. These spatial questions require different analysis and are addressed in Myyrä et. al (2011).



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is a particular concern, the corresponding marginal yield effect is estimated even higher (19 kg ºC-1 ).

Another temperature-related index, night frost (FROST), is estimated to be the most critical, with the most severe crop damage in the middle of July. The marginal yield effect of night frost in the mid-dle of July was estimated to be as high as 2,100-2,400 kg of wheat, depending on the specification of the frost variable. In other words, if a night frost hits in mid-July, it is expected to result in yield losses of between 2,100 and 2,400 kg per hectare. At the beginning and towards the end of the grow-ing season, the damage caused by night frost is estimated to be negligible.

Rainfall has three separate regimes with re-spect to time. First, at the beginning of May, the approximate sowing time in Southern Finland, any significant rainfall is estimated to reduce the yields, since it delays sowing and excessive rains may also result in crust formation. However, in June, when drought typically influences crop yields, the mar-ginal yield effect of rainfall peaks at a significant positive value of 10 kg mm-1. Thereafter, in July and August, the impact of rain gradually declines with the passage of time and turns negative at the end of August. Rain in late July and thereafter, comes too late to improve the growth and begins to adversely affect both standing yields and their harvest.

With respect to rainfall, the data are not inform-ative enough to identify the negative yield effects of excessive rainfall, when the rainfall is measured as the frequency of excessive rain defined as, for example, at least 40 mm within a two-day period. The data are nevertheless informative enough to identify asymmetric rainfall effects early and late in the growing season. In the early season the dry regime differs from the other regimes, and in late summer the wet regime differs from the others. In June, when a shortage of water is a particular con-cern, the marginal yield effect of one millimetre of rain is estimated within the dry regime at 16 kg. Within the wet regime, on the other hand, the marginal yield effect of rain decreases earlier and faster than in other regimes to negative values in July and August, towards the harvest. The marginal yield effect is estimated at the lowest and negative

value (-7 kg mm-1) at the end of the growing season in all regimes.

In addition to the above-described effects on the expected yields, the data indicate that weather events have heterogeneous yield implications so that, within the range of sample variation, the yield volatility increases with adverse weather events causing crop damage. In particular, dry early and midsummer periods in June increase yield volatil-ity and reduce yields.12

Our results have several important implications for the design of simple and tractable (efficient) weather index-based insurance contracts. First, the weather events triggering the indemnity payments should be focused on certain critical periods and regimes. When protecting against yield losses re-sulting from a shortage of GDD, the period from late July to mid-August is the most critical. When the shortage of GDD accumulation is of particular concern (the cold regime), the marginal yield effect of one GDD unit is estimated to be as high as 19 kg of wheat. If, for instance, the daily temperature is decreased by one degree over a month, the GDD is reduced by 31 degree points and the resulting yield loss per hectare is estimated at 590 kg.

Second, when protecting the grain grower against yield losses caused by night frosts, mid-July is the most critical time. The likelihood for a night frost in July is low, but if a frost hits it results in yield losses of more than 2,100 kg per hectare.

Third, when protecting grain grower against yield losses from a shortage of rainfall, June is the critical month, when the marginal yield effect of one millimetre of rain is estimated at 16 kg. Thus, 30 mm of rainfall has a marginal product of almost 500 kg. However, if excessive rainfall is a concern, the contract should focus on the beginning and end of the growing season.

When the distance between the location of weather measurement and the standing crop in-creases, the spatial correlation between the weather events becomes an issue (e.g. Myyrä et al. 2011). The higher the spatial correlation is the more prom-

12 The result holds only locally within the range of sample variation. The asymptotic property should be that when a weather event is so severe that the yield approaches zero, the volatility also approaches zero.



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ising the indices would be in establishing liquid market regimes to trade these contracts. But if the correlation is low (as in the case of rain and hailing) the weather index measurement has to be located at a very close to the standing crops to be relevant and efficient in hedging. In this case the contracts have to be tailored to local measurement points and can perhaps be sold at the best over the counter. These spatial questions are, nevertheless, left as a topic for another research. This paper addresses only the correlations between the weather indices and yield when the weather measurement point and the standing crops are at the same location.

Finally, complying with the WTO and CAP regulations would be a challenge for a subsidized index based contract, if these regulations require that only yield losses of more than 30% can be subsidized and receiving the indemnity payments require that the losses have to be observed on the farm. Since index-based contracts are only indi-rect measures of the true yield losses, proving yield losses of more than 30% may ultimately be chal-lenged by the heterogeneity of the weather effects, as our results suggest that not only are expected yields reduced, but the yield volatility is also in-creased when adverse weather shocks are realized.

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Koundouri, P., Laukkanen, M., Myyrä, S. & Nauges, C. 2009. The effects of EU agricultural policy changes on farmers´ risk attitudes. European Review of Agricultur-al Economics 36: 53-77.

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Peltonen-Sainio, P., Kangas, A., Salo, Y. & Jauhiainen, L. 2007. Grain number dominates grain weight in cereal yield determination: evidence basing on 30 years’ mul-ti-location trials. Field Crops Research 100: 179-188.

Peltonen-Sainio, P., Jauhiainen, L. & Hakala, K. 2009a. Are there indications of climate change induced in-creases in variability of major field crops in the north-ernmost European conditions? Agricultural and Food Science 18: 206-226.

Peltonen-Sainio, P., Rajala, A., Känkänen, H. & Haka-la, K. 2009b. Improving farming systems in northern European conditions. In: Victor O. Sadras and Daniel Calderini (eds.). Crop Physiology: Applications for Ge-netic Improvement and Agronomy. Amsterdam: Else-vier. p. 71-97.

Peltonen-Sainio, P., Jauhiainen, L., Rajala, A. and Muu-rinen, S. 2009c. Tiller traits of spring cereals in tiller-depressing long day conditions. Field Crops Research 113: 82-89.

Peltonen-Sainio, P., Jauhiainen, L. & Hakala, K. 2011. Crop responses to temperature and precipitation according to long-term multi-location trials at high-latitude conditions. Journal of Agricultural Science 149: 49-62.

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A G R I C U L T U R A L A N D F O O D S C I E N C E

Ghavi Hossein-Zadeh, N. Genetic parameters for stillbirth in dairy cows Vol. 20(2011): 287–297.

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© Agricultural and Food Science Manuscript received January 2011

IntroductionTwo general classes of phenotypes, continuous and discrete, are considered in animal breeding data. Many traits of importance, such as litter size, calving ease, disease resistance and stillbirth are measured on a discrete scale. Genetic evaluation methodology for categorical traits is different from that of continuous traits (Abdel-Azim and Berger 1999). Wright (1934) postulated that a linear variable

underlies categorical traits and thresholds define which category is observed. Based on threshold concept, non-linear methods for sire evaluation have been described for categorical traits (Gianola 1982, Harville and Mee 1984, Gilmour et al. 1985). Stillbirth in dairy cattle is one of the functional traits that receives more and more attention due to its effects on the profitability of dairy produc-tion. During the last two decades the incidence of stillbirths for Holstein cattle has increased in several countries such as Denmark (Nielsen et al.

Estimation of genetic parameters and genetic change for stillbirth in Iranian Holstein cows: a comparison

between linear and threshold models Navid Ghavi Hossein-Zadeh

Department of Animal Science, Faculty of Agriculture, University of Guilan, Rasht, Iran, P. O. Box: 41635-1314


Data on stillbirth from the Animal Breeding Center of Iran collected from January 1990 to December 2007 and comprising 668810 Holstein calving events from 2506 herds were analyzed. Linear and threshold ani-mal and sire models were used to estimate genetic parameters and genetic trends for stillbirth in the first, second, and third parities. Mean incidence of stillbirth decreased from first to third parities: 23.7%, 22.1%, and 21.8%, respectively. Phenotypic rates of stillbirth decreased from 1993 to 1998, for first, second and third calvings, and then increased from 1998 to 2007 for the first three parities. Direct heritability estimates of stillbirth for parities 1, 2 and 3 ranged from 2.2 to 8.7%, 0.6 to 5.1% and 0.1 to 3.8%, respectively, and maternal heritability estimates of stillbirth for parities 1, 2 and 3 ranged from 1.4 to 6.3%, 0.5 to 4.2% and 0.08 to 2.0%, respectively, using linear and threshold animal models. The threshold sire model estimates of heritabilities for stillbirth in this study were 0.021 to 0.071, while the linear sire model estimates of herit-abilities for stillbirth in the current study were from 0.003 to 0.021 over the parities. There was a slightly increasing genetic trend for stillbirth rate in parities 1 and 2 over time with the analysis of linear animal and linear sire models. There was a significant decreasing genetic trend for stillbirth rate in parity 1 and 3 over time with the analysis of threshold animal and threshold sire models, but the genetic trend for stillbirth rate in parity 2 with these models of analysis was significantly positive. The low estimates of heritability obtained in this study implied that much of the improvement in stillbirth could be attained by improvement of production environment rather than genetic selection.

Key words: dairy cow, genetic trend, linear model, stillbirth, threshold model


Ghavi Hossein-Zadeh, N. Genetic parameters for stillbirth in dairy cows

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2002), Sweden (Steinbock et al. 2003) and in the United States (Meyer et al. 2001a). Stillbirths in dairy cattle herds reduce the potential number of replacement heifers and reduce the revenue from bulls for fattening, but it is also an ethical problem related to animal welfare. Stillbirth has been shown to be heritable (Philipsson 1976, Weller et al. 1988, Luo et al. 1999) and this means that stillbirth is a potential trait to include in a breeding program, and, therefore, exact knowledge of genetic parameters for this trait is needed.

Trait definitions vary slightly between coun-tries, with most defining stillbirths as those calves born dead or dying within 24 h of parturition (Philipsson et al. 1979), although Germany, and the United States include deaths within 48 h of birth (Berger et al. 1998). Breed differences play a role in perinatal mortality (Philipsson 1976, Thompson et al. 1981), and Rossoni et al. (2005) reported that 10% of Italian Brown Swiss calves did not suckle by the third meal offered postpartum, contributing to increased postnatal mortality. Incidence rates and heritabilities were similar when comparing parities across countries despite differences in trait defini-tion, with the exception of Sweden (Steinbock et al. 2003). Many studies of genetic parameters of stillbirth have applied linear models (e.g., Luo et al. 1999, Meyer et al. 2001b, Jamrozik et al. 2005), or single-trait threshold models (e.g., Steinbock et al. 2003, Hansen et al. 2004 a,b). These authors found direct and maternal heritabilities between 0.04 and 0.07 for stillbirth.

In a review, Shook (1998) concluded that more genetic studies of calfhood diseases and mortality were needed. Extensive knowledge and analysis of the genetics of stillbirth are valuable for better understanding the biological background, monitor-ing cattle populations and possibly for providing a basis for selection. As focus shifts from selec-tion of production traits to functional traits in the current breeding programs for dairy cows, genetic evaluation on traits as stillbirth would be valuable and essential. Although, the subject of this study has been investigated by other researchers, but the novel item in this study included the use of data set obtained from dairy herds in Iran. Linear mod-els are currently applied for the genetic evaluation

of stillbirth in other studies. Although, a threshold model (Gianola and Foulley 1983) may be a more valid model because it takes into account the cat-egorical nature of these traits. Therefore, the first objective of this study was the estimation of herit-abilities and genetic trends for stillbirth using linear and threshold animal and sire models in the first three lactations of Holstein dairy cows in Iran. The other objective was the comparison of estimates obtained by different linear and threshold models.

Material and methods

Data setCalving records from the Animal Breeding Center of Iran, collected from January 1990 to December 2007 and comprising 668810 Holstein calving events from 2506 herds were included in the data set. First-parity cows represented 43.3%, whereas second-, and third parities accounted for 33.4 and 23.3% of the calving records, respectively. The herds used in this study are among the purebred Holsteins which are managed under conditions similar to most other developed countries and are under official performance and pedigree recording. Stillbirth was defined as a calf dead at birth. Informa-tion for individual calving events, including herd, cow identification, service sire identification, cow’s sire identification, birth date, calving date, dry date, parity, multiple births, and stillbirth were included in the data set. Stillbirth was coded as a dichotomous variable (0= alive; 1= dead). Only calving records from the first 3 parities were kept. Age at calving was between 20 and 40, 28 and 49, and 40 and 68 months in parities one, two, and three, respectively. Months of birth were grouped into four seasons: January through March (winter), April through June (spring), July through September (summer), and October through December (fall). In addition to the data file, a pedigree file was available that contained more than 497,000 cows, of which the first were born in 1963 (Table 1). The pedigree of animals was traced back for 5 generations.



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Statistical models for analysis

Model specification and the choice of fixed effects to be considered was based on the backward elimina-tion method. Also, the fit of all models was evaluated by using the Hosmer and Lemeshow goodness-of-fit test (Hosmer and Lemeshow 2000) of the LOGIS-TIC procedure of SAS (2002) by including the “lackfit” option in the model statement. Variables which were significant by the Wald statistic at p < 0.05 were included in the model. The final model included the fixed class effects of herd-year-season of calving, number of calves and the linear covariate effect of age at calving. The linear and threshold animal models were used with stillbirth defined as a dichotomous variable were as follows:

y= Xb+Zaa+Zmm+e,

(1)Where, y is a N × 1 vector of records, b denotes the fixed effects in the model with association matrix X, a and m are the vectors of direct genetic and maternal genetic effects with the incidence matrixes Za and Zm, respectively, and e denotes the vector of residual effects. In the threshold models, the observed outcome (yi) for calf i is assumed to be ordered in 2 categories (k = 2) for stillbirth. An unknown liability (Ui) with k − 1 unknown thresholds (t = t1,…,tk−1), which categorized the observed outcome. The (co)variance structure for the random effects was as follows:

V(a) = A 2σ a

V(m) =A 2σm

V(e) = In2σ e

Cov (a,m) = Aσ a,m , (2)Where, A is the additive numerator relationship ma-trix and σa

2 and σm2 are additive direct and maternal

variances, respectively. In addition, σa,m and σe2 are

direct-maternal covariance and residual variance, respectively. In is identity matrix with order equal to the number of individual records. The linear and threshold sire models were defined as follows:

y=Xb+ZSs+e, (3)Where, s is the vector of sire effect with association matrix ZS and other terms are as defined above.

Estimates of heritabilities and genetic trends

Based on previous research the most parsimonious model for the analysis of a binomial variable, such as stillbirth, is a threshold model (Gianola 1982). The linear and threshold animal model analyses were run using a restricted maximum likelihood method and average information algorithm (AIREML) of the MATVEC program (Wang et al. 2001) to ob-tain (co)variance components and heritabilities of stillbirth in first, second, and third parities. Genetic trends were obtained by regressing yearly mean es-timates of breeding values on calving year. Several linear-linear and threshold-threshold bivariate or pair-wise analyses were carried out for every pair of the traits (stillbirth in parities 1 with 2, 1 with 3, or 2 with 3) to estimate genetic correlations for stillbirth in different parities. The models applied in pair-wise analyses were the same as those fitted for stillbirth rates of each parity in the univariate

Table 1. Summary of pedigree information.Animals in total 497216Inbred animals 208201Sires 8007Dams 242176Animals with progeny 250183Animals without progeny 247033Base animals 87444Non-base animals 409772



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analyses. For sire models, the heritability estimates were calculated as:

22

2 2

4 s

s e

h σσ σ

=+

,

(4)Where 4 σS

2 is the additive genetic variance and the denominator is the total phenotypic variance.

ResultsStillbirths accounted for 23.7%, 22.1% and 21.8% of total observations in first, second and third parities, respectively. The frequency of stillbirth decreased with parity. Phenotypic rates of stillbirth decreased from 1993 to 1998, for first, second and third calv-ings, and then increased from 1998 to 2007 for the first three parities (Fig. 1). Estimates of heritabilities for stillbirth in first, sec-ond, and third parities by different animal and sire models are shown in Table 2. Estimates of genetic

trends and their standard errors for stillbirth at different parities from animal and sire models are shown in Tables 3 and 4, respectively. The mean

Fig 1. Phenotypic trend in the frequency of stillbirths by year of calving for first-, second-, and third-parity cows.

Fig 2. Direct genetic change for stillbirth in Iranian Holsteins by calving year obtained by linear animal mod-el analysis for first-, second, and third parities.

Fig 3. Direct genetic change for stillbirth in Iranian Holsteins by calving year obtained by threshold ani-mal model analysis for first-, second, and third parities.



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Fig. 4. Maternal genetic change for stillbirth in Iranian Holsteins by calving year obtained by linear animal mod-el analysis for first-, second, and third parities.

Fig. 5. Maternal genetic change for stillbirth in Iranian Holsteins by calving year obtained by threshold animal model analysis for first-, second, and third parities.

Table 3. Estimates of genetic trends and their standard errors (×1000) for stillbirth at different parities from linear and threshold animal models.

Model of analysis

Linear animal Threshold animalParity Direct trend Maternal trend Direct trend Maternal trend1 0.03 ± 0.005* 0.0003 ± 0.0001* 0.34 ± 0.03* 0.42 ± 0.03*

2 0.05 ± 0.003* 0.03 ± 0.006* -0.38 ± 0.03* -0.23 ± 0.04*

3 -0.0005 ± 0.0002 -0.004 ± 0.0002 0.25 ± 0.04* 0.37 ± 0.04*

* Significant coefficients are shown with asterisks (p<0.05)

Table 4. Estimates of genetic trends and their standard errors (×1000) for stillbirth at different parities from linear and threshold sire models.

Genetic trend*Model of analysis

Parity Linear sire Threshold sire1 -0.02 ± 0.003 0.07 ± 0.022 0.01 ± 0.001 -0.15 ± 0.013 -0.002 ± 0.001 0.04 ± 0.01

* All of the estimates were significant at p < 0.001



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estimated breeding values of cows by year of calving are in Figures 2 to 5, using different animal models. Mean estimated breeding values of bulls by year of birth obtained by linear and threshold sire models are plotted in Figures 6 and 7, respectively. Herit-ability estimates for stillbirth, from the analysis by threshold models, were greater than the correspond-ing estimates from linear models. Also, heritability estimates were greater for stillbirth in the first parity than in second and third parities, using both linear and threshold models of analysis. The heritability estimates for stillbirth obtained from linear animal and linear sire models were similar. But estimates of threshold animal models were greater than the estimates of threshold sire models. The threshold animal model estimates of direct heritabilities for stillbirth in this study were 0.038 to 0.087 while estimates for the maternal heritabilities ranged from 0.020 to 0.067. The linear model estimates of direct and maternal heritabilities for stillbirth in the current study were from 0.001 to 0.022 and from 0.0008 to 0.014 over the parities, respectively. The threshold sire model estimates of heritabilities for stillbirth in this study were 0.021 to 0.071, while the linear sire model estimates of heritabilities for

stillbirth in the current study were from 0.003 to 0.021 over the parities.

There was slightly increasing trend for stillbirth rate in parities 1 and 2 over time with the analysis by linear animal and linear sire models, but, the direct and maternal trends of stillbirth rate in parity 3 from the analysis by linear models was not sig-nificant. There was a significant decreasing trend for stillbirth rate in parity 1 and 3 over time from the analysis by threshold animal and threshold sire models, but the genetic trend for stillbirth rate in parity 2 with these models of analysis was signifi-cantly positive. Therefore, the patterns of varia-tion in genetic trends for stillbirth obtained from linear models were different from threshold models over the parities. Estimates of genetic correlation between direct and maternal effects for stillbirth in the present study were from −0.05 to −0.02 ob-tained by linear animal model and were from 0 to 0.04 obtained by threshold animal model over the parities. Direct and maternal genetic correlations for stillbirth between parities were respectively from 0.01 to 0.11 and −0.12 to 0.15 obtained by linear animal model and were from 0.03 to 0.12 and −0.35 to 0.22 obtained by threshold animal

Fig. 6. Mean estimated breeding values by year of birth obtained by linear sire model analysis for first-, second, and third parity.

Fig. 7. Mean estimated breeding values of bulls by year of birth obtained by threshold sire model analysis for first-, second, and third parity.



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Table 5. Estimates of direct and maternal genetic correlations for stillbirth between parities 1, 2, and 3 with linear and threshold animal models.

Model of analysis Trait 1 Trait 2 rg121 rm12

2

Parity 1 Parity 2 0.01 -0.06Linear animal Parity 1 Parity 3 0.11 0.15

Parity 2 Parity 3 0.11 -0.12Parity 1 Parity 2 0.03 -0.35

Threshold animal Parity 1 Parity 3 0.08 0.22Parity 2 Parity 3 0.12 -0.27Parity 1 Parity 2 -0.01 -

Linear sire Parity 1 Parity 3 0.10 -Parity 2 Parity 3 0.04 -Parity 1 Parity 2 0.03 -

Threshold sire Parity 1 Parity 3 0.08 -Parity 2 Parity 3 0.08 -

1 = Direct genetic correlation between trait 1 and trait 22 = Maternal genetic correlation between trait 1 and trait 2

model, respectively (Table 5). In addition, esti-mates of genetic correlations for stillbirth between parities were from −0.01 to 0.10 obtained by linear sire models and were from 0.03 to 0.08 obtained by threshold sire models (Table 5).

Solutions for age at calving for parity 1, 2, and 3 demonstrated that cows younger at calving are more likely to have stillbirth. Also, solutions for

Table 6. Solutions for fixed effects obtained from linear and threshold models.

ModelParity Effect

Linear animalThreshold

animal Linear sire Threshold sire1 Single 0 0 0 0

Twin 0.06 0.09 0.06 0.09Age at calving -0.0003 -0.0005 -0.0003 -0.0004

2 Single 0 0 0 0Twin 0.04 0.08 0.04 0.08Age at calving -0.00003 -0.00004 -0.00003 -0.00004

3 Single 0 0 0 0Twin 0.04 0.05 0.04 0.05Age at calving -0.0009 -0.0006 -0.0009 -0.0006



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number of calves born demonstrated that twin-calvers are more likely to have stillbirth (Table 6).

DiscussionThe rate of stillbirth is highest for first-calving cows, partly because of a disproportion between the size of the calf and the size of the pelvic area (fetopelvic-complex), which causes a difficult calv-ing (Meijering 1984). Consistent with the current results, Ghavi Hossein-Zadeh et al. (2008) and Steinbock et al. (2003) reported greater incidences of stillbirth in primiparous cows than in multiparous cows. The stillbirth frequencies for Holsteins in the current study were much greater than for other reports. These greater frequencies could be assigned to management or environmental factors in under study population. Heringstad et al. (2007) reported stillbirth rate was around 3% for first calving, and 1.5% for second and later calvings in Norwegian Red. The stillbirth rate for first-calving Holstein cows in the United States increased from 9.5% in 1985 to 13.2% in 1996 (Meyer et al. 2001a). Hansen et al. (2004b) found that the stillbirth frequency for first-calving Danish Holstein increased from 7% in 1992 to 9% in 2002. Also, the stillbirth rate for Swedish Holstein heifers has increased over the past 10 to 15 year, and about 10% of the calves are born dead or die within the first 24 h (Steinbock et al. 2003). In Swedish Red there has been a slight increase in stillbirth frequency over the last 25 year, from less than 4 to around 5% for heifers, and from 3 to 4.5% for Swedish Red cows (Philipsson et al. 2006). Cows younger at calving are more likely to have stillbirth and this might be related to the greater incidence of calving difficulty at younger ages especially for first-calvers. Consistent with the current results, Steinbock et al. (2003) reported age of the heifer at calving had a considerable impact on stillbirth and calving difficulty in dairy cows. Also, similar to the current results, Ghavi Hossein-Zadeh et al. (2008) reported the odds of stillbirth was greater after twin births, with 18.8% of the twin

calving events reporting calves as dead compared to 4.0% for singleton birthsThe greater heritability estimates for stillbirth in the first parity were probably due to greater ge-netic variation of stillbirth in the first parity than in other parities. In animal models, all relationships are considered, whereas in sire models only rela-tionships among sires are taken into account; this leads to some bias in estimates from sire models. Therefore, if selection intensity is stronger for males than for females, estimates from sire variances are expected to underestimate genetic variance. As a consequence, smaller estimates of heritability are obtained with sire than with animal models (Visscher and Thompson 1990). The threshold estimates of direct heritabilities for stillbirth in this study were generally consistent with other recent threshold model estimates ranging from 0.04 to 0.12 for heritability of stillbirth in dairy cows (Hansen et al. 2004a; Gevrekçi et al. 2006, Heringstad et al. 2007). Also, the threshold estimates of maternal heritabilities for stillbirth in the current study were generally lower than the reports of Heringstad et al. (2007), Gevrekçi et al. (2006) and Steinbock et al. (2003), but were consistent with the report of Cole et al. (2007) who reported the maternal heritability of stillbirth ranged from 0.046 to 0.065. Similar to the current results, Fuerst-Waltl and Sørensen (2009), Heringstad et al. (2007), Jamrozik et al. (2005) and Luo et al. (1999) reported linear model estimates of heritability of stillbirth in general are lower than those obtained from threshold models. Ghavi Hossein-Zadeh et al. (2006) in a simulation study reported that the linear model has always underestimated true heritabilities, but threshold models did not show consistent trends. While thresh-old model may be trusted at higher levels of true heritability, it does not behave consistently at lower heritabilities. Heritability estimates of stillbirth rate, when analyzed with a linear model, depend on the frequency of the trait (Gianola 1982). It is, therefore, not straightforward to compare estimates from different populations. From studies using linear models, Luo et al. (1999), Harbers et al. (2000), and Meyer et al. (2001b) found heritabilities similar to this study. Also, consistent with the results of this study, Steinbock et al. (2006) reported the heritability



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of stillbirth on the observable scale was 0.7−1.3% in Swedish Red dairy cattle, using linear models of analysis. Estimates of direct heritabilities were greater than maternal ones and this would be due to greater effects of animals on the genetic variation of stillbirth in Iranian Holsteins than maternal effects.

Although there was no specific reason for phe-notypic variations of stillbirth over the parities, but these would be likely related to the change in herd management, nutritional conditions or in general, changes in environmental variables affecting still-birth rate. On the other hand, selection against still-birth based on the first parity performance could be as a possible cause of decreasing phenotypic trends for stillbirth in the first and second parities. A possible explanation for the increasing genetic trend of stillbirth with some of the models may be the possible genetic association between stillbirth and some of the traits such as calving difficulty and calf size and an increase in the direct and maternal effects of these traits in the Holstein dairy cows of Iran. It seems that the observed increasing ge-netic trend (direct and maternal) for stillbirth rate with some of the models of analysis could be at-tributed to an intense use of Holstein-Friesian sires as sires of sons in Iran. Iranian Holsteins are either descendants of the cows originally imported from North America and Europe or Holstein upgrades of domestic breeds over 50 years. Therefore, these sires of sons have a large impact on the genetic trend, especially for the genetic trend of the AI bulls. Similar to the results of this study, Hansen et al. (2004b) reported an unfavorable genetic trend of stillbirth was found in Danish Holsteins. Also, in Holstein-Friesian in the US, indications of an unfavorable direct and maternal trends of stillbirth have been found (Meyer et al. 2001a), but contrary to the current results, no genetic trend was found in the Netherlands (Harbers et al. 2000). Also, Hansen et al. (2004b) found no maternal trend for stillbirth in Danish Holsteins. In addition, Heringstad et al. (2007) reported little or no genetic change in still-birth at first calving in Norwegian Red dairy cat-tle. On the other hand, inconsistent with the results of this study, Cole et al. (2007) reported neither phenotypic nor genetic trends were significant for stillbirth in US Holsteins.

A correlation close to zero implies that selec-tion on the direct effect of stillbirth would not in-fluence the maternal effect of stillbirth. The pres-ent estimates are in agreement with the results of Cole et al. (2007), Hansen et al. (2004a), Luo et al. (1999) and Steinbock et al. (2003) who re-ported the mean genetic correlations between the direct and maternal effects of stillbirth were −0.02, 0.03−0.06, 0.03 and −0.11, respectively. However, contrary to the current results, Luo et al. (1999) reported the mean genetic correlation between the direct and maternal effects of stillbirth was −0.24 obtained by a linear model.

ConclusionsThe frequency of stillbirth decreased with parity in Iranian Holstein dairy cows. Phenotypic rates of stillbirth decreased from 1993 to 1998, for first, second and third calvings, and then increased from 1998 to 2007 for the first three parities. The level of stillbirth is high in the Iranian Holsteins compared to other countries; therefore, there is a scope for the genetic improvement of this trait. Because of the economic and animal welfare issues, it is important to introduce the stillbirth in the national selection index used for selection of Holstein cows, as well as to avoid using animals that are extremely bad for this trait. Then, in populations with higher frequency of stillbirth such as Iranian Holsteins, more weight could be placed on stillbirth to avoid further deterio-ration. Heritability estimates for stillbirth, from the analysis by threshold models, were greater than the corresponding estimates from linear models. Also, heritability estimates were greater for stillbirth in the first parity than in second and third parities, using both linear and threshold models of analysis. The low estimates of heritability obtained in this study implied that much of the improvement in stillbirth could be attained by improvement of production environment rather than genetic selection.



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Turner, T. D. and McNiven, M.A. In vitro degradability of heat treated oilseeds

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© Agricultural and Food Science Manuscript received November 2010

Introduction

The protein requirements of high producing ru-minants often cannot solely be met by microbial synthesis during rapid growth or lactation. Oilseeds provide an excellent source of protein and an energy-rich supplement for ruminant diets. The extent that oilseeds are degraded in the rumen determines the supply and quality of protein, specifically the amino acid (AA) profile, available for absorption

in the small intestine. In general, rolled or ground raw oilseeds are highly degradable by microbes in the rumen, reducing their protein feed value. Heat treating oilseeds can decrease N degradability in the rumen, increasing the amount of dietary protein available for post-ruminal absorption. Effective heat treatment creates cross-linkages between peptide chains as well as exposing hydrophobic regions of peptide-carbohydrate complexes, rendering the proteins less susceptible to N degradation (Deacon et al. 1988). Adequate heat treatment is necessary

In vitro N degradability and N digestibility of raw, roasted or extruded canola, linseed and soybean

T. D. Turner*and M.A. McNivenDepartment of Health Management, Atlantic Veterinary College, University of Prince Edward Island,

550 University Avenue, Charlottetown, PE, C1A 4P3, Canada


The N degradability and N digestibility of raw, roasted or extruded oilseeds were studied using an in vitro enzyme method. The N degradability and N digestibility of canola, linseed and soybean were calculated based on the proportional difference in N remaining after incubation and the initial N content. Heat treat-ments increased the undegradable N fraction of linseed and soybean, whereas that of canola was decreased by extrusion. Heat treatments did not decrease the N digestibility of the oilseeds compared to raw samples. The high N digestibility and lower acid detergent insoluble N values of heat treated oilseeds indicated no indigestible complexes were formed. In conclusion, roasting or extrusion can be used to increase the undegradable N fraction of linseed and soybean to increase the dietary protein availability for digestion in ruminants, but was less effective for canola. The present heat treatments did not damage the protein or affect the N digestibility of the oilseeds.

Key words: extrusion, in vitro, N degradability, N digestibility, oilseed, roasting

Abbreviations: AA, amino acid; ADF, acid detergent fibre; ADIN, acid detergent insoluble nitrogen; CP, crude protein; EE, ether extract; FA, fatty acid; aNDF-NDF, neutral detergent fibre using amylase, ash inclusive; N, nitrogen


Turner, T. D. and McNiven, M.A. In vitro degradability of heat treated oilseeds Vol. 20(2011): 298–304.

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to denature the protein whereas overheating may lead to the formation of indigestible compounds via Maillard reactions (NRC 2001).

Heat treatment of oilseeds has been used to increase the proportion of rumen undegradable N with varying degrees of success. The undegradable N fraction of soybean was increased by extrusion (González et al. 2002), whereas extrusion decreased the undegradable N fraction of linseed (Mustafa et al. 2003) and canola (Ferlay et al. 1992). The oil content of oilseeds is one factor that can impede the rearrangement of protein bonds during the extru-sion process (Ferlay et al. 1992). Roasting has been shown to increase the undegradable N fraction for canola (Dakowski et al. 1996), soybean (Faldet et al. 1992) and linseed (Petit et al. 2002).

Determination of N degradability and N digest-ibility of oilseeds using cannulated animal-based models is costly and labour intensive. Calsamiglia and Stern (1995) developed an enzyme-based in vitro method for assessing N digestibility using pepsin-pancreatin solutions; however, the samples must be pre-incubated in the rumen. Enzyme-based in vitro methods are cheaper to operate and negate the need for cannulated animals. The N degradabil-ity and N digestibility values of roasted soybeans using a two-stage enzyme-based in vitro method were comparable to in sacco and mobile-bag esti-mates (McNiven et al. 2002).

The objective of this trial was to compare the effectiveness of roasting or extrusion heat treat-ments to increase the N degradability and N digest-ibility of canola, linseed and soybean compared to raw oilseeds evaluated by an enzyme-based in vitro method.

Materials and methods

FeedstuffsBatches of canola, linseed and soybean were pur-chased from local producers. Each batch was divided into three sub-batches for treatment: unprocessed, roasted or extruded. Canola and linseed were roasted

at 121 °C for 45 s, while soybeans were roasted at 143 °C for 60 s, on a Calormatic® fluidized bed roaster (Sweet Manufacturing, OH, USA). Canola and linseed were extruded at 72 °C for 30 s and for soybean it was 130 °C for 30 s, using a single screw Insta-Pro extruder (Triple F Inc. IA, USA). Selected temperatures were representative of commercial settings. Following treatment, each sub-batch was ground through a 2 mm screen using a Retch Ultra Centrifuge Mill (ZM100; Fisher Scientific Co., ON, Canada).

Chemical analysis

According to AOAC methods (2000), moisture (934.01), crude protein (CP; Kjeldahl N x 6.25, 984.13) and ether extract (EE, 920.39) were de-termined. Extraction of detergent fibre using filter bags followed the procedure of Ankom Technology (aNDF-NDF, neutral detergent fibre using amylase, ash inclusive method 6; ADF, acid detergent fibre, method 5) as referred by Ferreira and Mertens (2007). Values for ADF and aNDF-NDF were expressed as g kg-1 DM. Residual contents of bags after ADF analysis were recovered and used for acid detergent insoluble N (ADIN) determination. The ADIN content was measured as N x 6.25 after analysis with a Leco CHN 2000 analyzer (Leco Corp. MI, USA) and expressed as g kg-1 CP.

In vitro procedure

The in vitro process followed the procedure of Mc-Niven et al. (2002). Six samples of each oilseed from the three processing treatments were weighed (about 1g accurately) and sealed in Ankom bags (F57, pore size 50µm; Ankom Technology, NY, USA). Briefly, the bags were incubated at 39 °C for 4 h in protease solution (protease type xiv from S. griseus in borate-phosphate buffer, P-5147, Sigma-Aldrich Ltd., Canada). After incubation, three random bags from each treatment were removed for determina-tion of N degradability. Remaining samples to be



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used for determining N digestibility were incubated at 39 °C for 1 h in pepsin solution (P-7000 solu-tion; 2 mg/ml in 0.1 N HCl, Sigma-Aldrich Ltd., Canada), rinsed and then incubated at 39 °C for 24 h in pancreatin solution (P-7545, Sigma-Aldrich Ltd., Canada). Bags were rinsed thrice after their respective incubation end-points before N analysis (AOAC 2000). The N degradability was calculated as the difference between the N in the feed and the N remaining after the protease incubation, divided by the N in the feed. The N digestibility was calculated as the difference between the N in the feed and the N remaining after the pancreatin incubation, divided by the N in the feed. The N value from each sample was used as the experimental unit.

Statistics

The N degradability and N digestibility results were analysed using a two-way ANOVA testing for main and interaction effects between oilseed type and heat treatment using the GLM procedure of Statistical Analysis Software v9.1 (2002). The Bonferroni adjustment was used for making protected com-parisons of the means (p<0.05). Estimates of the interaction least square means are presented along with the standard error of the means (SE).

Results

Chemical analysis of oilseeds

The CP fractions of canola and linseed were not af-fected by heat treatment, whereas the CP fraction of soybean decreased by roughly 15% following heat treatment (Table 1). The EE fraction of canola was increased by the heat treatments, whereas the EE fractions of linseed and soybean were not affected. Heat treatment decreased the aNDF-NDF fraction of canola and linseed by roughly 50% and 40% respectively, whereas the aNDF-NDF fraction of soybean was decreased 16% by roasting and 30% by extrusion. The ADF fraction of canola was decreased by roughly 40% and the linseed ADF by roughly 15% by roasting or extrusion. The heat treatments affected soybean ADF differently with roasting and extrusion decreasing the ADF values roughly 31% and 46%, respectively. Roasting or extrusion decreased the ADIN value for canola and linseed by roughly 33% (Table 1). The soybean ADIN values were decreased by roughly 63% and 93% after roasting or extrusion, respectively.

Oilseed N degradability and N digest-ibility

There was an interaction effect between the oilseed type and heat treatment for the N degra-dability and (p<0.001) N digestibility (p<0.05).

Table 1. Chemical composition of raw, roasted or extruded samples of canola, linseed and soybean.

Raw Roasted Extruded Raw Roasted Extruded Raw Roasted ExtrudedDry Matter, g kg-1 910 959 936 902 952 920 864 941 914CP, g kg-1 DM 224 214 209 225 230 235 435 358 375EE, g kg-1 DM 369 442 458 332 363 330 155 180 163aNDF-NDF, g kg-1 DM 194 110 116 127 107 109 97,9 67,3 52,4ADF, g kg-1DM 276 145 122 256 157 153 135 112 91,9ADIN, g kg-1 CP 74,2 47,6 51,6 44,2 29,6 28,6 61,9 22,6 4,5abbreviations: dry matter, DM; crude protein, CP; ether extract, EE; neutral detergent fibre assayed with amylase, aNDF-NDF; acid detergent fibre, ADF; acid detergent insoluble nitrogen, ADIN.

Canola Linseed Soybean



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Compared to raw samples, the N degradabil-ity of linseed and soybean was decreased af-ter roasting or extrusion (p<0.05) (Fig. 1). The N degradability of canola was higher after extrusion (p<0.001). Raw soybean N degradability was higher than raw canola or linseed (p<0.001). The N degradability of roasted canola or soybean was significantly higher than roasted linseed (p<0.01). The N degradability of extruded canola was higher than that of extruded linseed or soybean (p<0.001).

The N digestibility of the individual oilseeds did not differ between the raw and heat treated samples (p>0.05) (Fig. 2). Soybean had the highest N digestibility within each heat treatment (p<0.05). The N digestibility of canola and linseed were similar for raw or roasted samples (p>0.05). The N digestibility of extruded canola was higher than extruded linseed (p<0.001).

Fig. 1. Least squares means for oilseed N degradability (SE= 21.4), values as measured in vit-ro. Means with different letters (a-d) indicate significant differ-ence (p<0.05)

Fig. 2. Least squares means for oilseed N digestibility (SE=9.3), values as measured in vitro. Means with different letters (a-d) indicate significant difference (p<0.05)

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Discussion

Chemical composition of oilseeds

Moisture loss during heat treatment may account for the increased canola EE content. The reduction in soybean CP content following heat treatment probably was due to hull loss during heat treat-ment. Similarly, hull loss due to physical contact during roasting or shearing effect during extrusion could account for the reduction in aNDF-NDF of all oilseeds. Reductions in the aNDF-NDF fraction after heat treatment have been previously reported (Mustafa et al. 2003, Nasri et al. 2008). Moreover, the heat treatments reduced the oilseed ADIN frac-tion. Lowering the proportion of ADIN has been shown to increase the available N and releasing some AA (Nasri et al. 2008). The ADIN fraction of canola and linseed was reduced after heat treatment to levels similar to their respective raw samples. The ADIN fraction of soybean was reduced more by extrusion than by roasting, suggesting an added benefit during the shearing processing. Most im-portantly, the ADIN fraction of the oilseeds was not increased by roasting or extrusion, which would have indicated heat damage.

Processing method effect on N degrada-bility

The interaction effect indicated that the present heat treatments affected the oilseeds differently. Lower N degradability may be considered as synonymous with higher undegradable N, which would imply a higher feeding value of the protein for ruminants. The present low undegradable N content of extruded canola was in agreement with the findings of Deacon et al. (1988) who reported undegradable N values in the range of 15 to 18%. Ferlay et al. (1992) reported that extrusion increased the undegradable N fraction for most oilseeds except canola, concluding that the high oil content of canola decreased the retention time and heat build-up in the extruder, limiting protein denaturation. Compared to raw linseed, the

undegradable N fraction of linseed was increased by extrusion in the present study. In contrast, Mustafa et al. (2003) reported that extrusion decreased the in situ undegradable N fraction of linseed from 360 mg g-1 to 220 mg g-1, concluding that the 385 g kg-1 DM oil content of the linseed hindered the thermal effects inside the extruder. Differences in extrusion conditions as well as differences between in situ and in vitro methods may account for the present contradictory findings.

In agreement with the present study, roasting has been shown to increase the undegradable N fraction of soybean (González et al. 2002, Mc-Niven et al. 2002) and linseed (Petit et al. 2002); however canola was only slightly increased. Khat-tab and Arntfield (2009) reported that roasting in-creased the disassociation of high molecular weight proteins, producing more denatured hydrophobic residues. This causes a shift in the hydrophobicity to hydrophilicity balance, reducing the susceptibil-ity to enzyme activity (Moure et al. 2006).

Oilseed type effect on N degradability

The undegradable N fraction of linseed and soybean increased about 40% after roasting, whereas canola increased about 15%. The differences in effective-ness of the roasting process could be related to the AA profile of the oilseeds and the greater proportion of hydrophobic AA found in linseed and soybean (Chung et al. 2005). Oilseeds with a greater pro-portion of methionine or cystine, as in linseed or soybean, respond more extensively to heat treatment via increased disulfide linkages. Mahadevan et al. (1980) reported an increased proportion of unde-gradable N based on the number of disulfide-bonds within the protein structure of protein supplements and Lykos and Varga (1995) reported an increase in the undegradable N level of oilseeds via the forma-tion of disulfide bonds induced by heat treatment.

Similar to Ferlay et al. (1992), extrusion in the present study was unsuitable for increasing the undegradable N fraction of canola compared to linseed or soybean. The effectiveness of extrusion on linseed compared to canola given the similar oil



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contents reiterates the influence of the AA compo-sition and their susceptibility to heat denaturation for increasing the undegradable N fraction.

Oilseed N digestibility

A high N digestibility infers a high proportion of the crude protein is available for absorption by the animal. The N digestibility differences observed between extruded oilseeds may be due to shear-ing differences incurred during extrusion. The N digestibility of linseed was slightly lower than that of canola after roasting or extrusion. As stated by Ferlay et al. (1992), variations in the protein sub-fractions, namely albumins and globulins, vary greatly between oilseed types, thus influencing the effectiveness of the extrusion process. Variations in these sub-fractions following heat treatment, namely α-helix and ß-sheet composition, can be indicative of changes to N digestibility (Marcone et al. 1998).

ConclusionsRoasting or extrusion increased the undegradable N fractions of soybean and linseed but was less effective for canola. The N digestibilities of the heat treated oilseeds were similar to those of raw oilseeds, indicating no heat damage or reduction of protein availability. In conclusion, heat treatment of these oilseeds may potentially improve the supply of dietary N available for digestion in ruminants. Acknowledgements.The authors would like to thank A. Mitchell at the Dept. Health Management, Atlantic Veterinary College, University of Prince Edward Island, PE, Canada for technical assistance with the chemical analysis.

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Lisiewska, Z. et al. Oxalates in root vegetables Vol. 20(2011): 305–314.

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© Agricultural and Food Science Manuscript received March 2011

Effect of processing and cooking on total and soluble oxalate content in frozen root vegetables

prepared for consumptionZofia Lisiewska*, Piotr Gębczyński, Jacek Słupski, Katarzyna Kur

University of Agriculture in Krakow, Department of Raw Materials and Processing of Fruit and Vegetables, Balicka 122, 30-149 Krakow, Poland

*e-mail: [email protected]

The oxalate content of beetroot, carrot, celeriac and parsnip after freezing by traditional and modified methods (the latter resulting in a convenience food product), and after the preparation of frozen products for consumption was evaluated. The highest content of total and soluble oxalates (105 and 82 mg 100 g-1 fresh matter) was found in beetroot. The lowest proportion (55%) of soluble oxalates was noted in celeriac; this proportion was higher in the remaining vegetables, being broadly similar for each of them. Blanching brought about a significant decrease in total and soluble oxalates in fresh vegetables. Cooking resulted in a higher loss of oxalates. The level of oxalates in products prepared for consumption directly after freezing approximated that before freezing. Compared with the content before freezing, vegetables prepared for consumption by cooking after frozen storage contained less oxalates, except for total oxalates in parsnip and soluble oxalates in beetroot and celeriac. The highest ratio of oxalates to calcium was found in raw beetroot; it was two times lower in raw carrot; five times lower in raw celeriac; and eight times lower in raw parsnip. These ratios were lower after technological and culinary processing. The percentage of oxalate bound calcium depended on the species; this parameter was not significantly affected by the procedures applied. The true retention of oxalates according to Judprasong et al. (2006) was lower than retention cal-culated taking its content in 100 g fresh matter into account.

Key words: root vegetables, oxalates, pretreatment before freezing, freezing; preparation for consumption


Lisiewska, Z. et al. Oxalates in root vegetables

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Introduction

Numerous studies have shown that vegetables con-tribute to cancer prevention by supplying various biologically active compounds to the diet (Noonan and Savage 1999). Hence, the consumption of these products is promoted by dieticians. However, veg-etables also contain potentially harmful compounds. Included among these are oxalates, which in the digestive tract can limit the nutritional availability of calcium and, to a lesser extent, iron and magnesium (Noonan and Savage 1999, Weaver et al. 1997).

The occurrence of oxalates in plants is com-mon; they appear in soluble form producing salts with potassium, sodium and NH4 ions, and also in insoluble form producing insoluble salts chiefly with calcium, but also with iron and magnesium (Savage et al. 2000). Numerous publications discuss the effect of genetic, agro-technical and morphological factors on the level of oxalates; however, only a few works deal with factors asso-ciated with the processing of vegetables or prepar-ing them for consumption (Catherwood et al. 2007, Kmiecik et al. 2004, Oscarsson and Savage 2007). Many vegetables, including those with a particu-larly high oxalate content, are generally consumed after culinary or technological processing. Since this is carried out with the use of water, the content of soluble oxalates in the vegetable is reduced as they are washed out into the medium; however, this decrease is negated if the water containing the leached oxalates forms part of the cooked product. The investigations usually concern vegetable spe-cies with high accumulations of these compounds. With the current tendency to reduce meat consump-tion, the increased proportion of vegetables in the diet may result in higher oxalate intake.

The aim of the work was to evaluate changes in the oxalate content of root vegetables after freez-ing by traditional and modified methods, the latter resulting in a convenience food product. A further aim was to evaluate oxalate content in vegetables prepared for consumption from frozen products directly after freezing and after 12-month storage at −20 oC.


Materials

The investigated material consisted of four root species: beetroot – Beta vulgaris L. (Czerwona Kula cv.); carrot – Daucus carota L. (Koral cv.); celeriac – Apium graveolens L. (Dukat cv.) and parsnip – Pastinaca sativa L. (Fagot cv.).

The content of components was determined in fresh vegetables (A), after blanching (B), after cooking in 2% brine to consumption consistency (C), and in frozen products after storage at −20 oC and then prepared for consumption. Frozen products from sample B were cooked in brine, this yielding sample D (after 0 months of storage) and sample F (after 12 months of storage). Frozen products from sample C were defrosted and heated in a microwave oven, yielding sample E (after 0 months of storage) and sample G (after 12 months of storage).

Production of the raw material

The vegetables were grown in the experimental field of the Department carrying out the investigation. The field was in good horticultural condition; it lies on the western outskirts of Krakow in southern Poland. The cultivation was conducted on brown soil with the mechanical composition of silt loam in the third year after manure fertilization. The pH of the soil in H2O was 7.08, with a humus content of 1.66%, nitrogen NO3 24 mg dm-3, phosphorus 53 mg dm-3, potassium 101 mg dm-3, and calcium 1020 mg dm-3.

The fertility of the soil and the nutritional re-quirements of the crops having been taken into account, and doses of mineral fertilizers for all vegetables were as follows: nitrogen 60 kg (80 kg for celeriac) as ammonium nitrate 34%N, phospho-rus P2O5 60 kg as superphosphate 46% P2O5, and potassium K2O 150 kg ha-1 as potassium chloride 60% K2O. Cultivation measures included sprinkler watering, mechanical weed control and, where nec-



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Table 1. Pre-treatment times of vegetables before freezing (blanched – B, cooked – C) and preparing frozen vegetables for consumption (from B by cooking – D and F; from C by defrosting and heating in a microwave oven – E and G).

Species Vegetables before freezing Frozen vegetables

B C D and F E and G

Beetroot 15 min 35 min –a 8 min 15 sec

Carrot 2 min 45 sec 12 min 6 min 8 min 15 sec

Celeriac 2 min 30 sec 8 min 5 min 8 min 15 sec

Parsnip 2 min 30 sec 10 min 4 min 30 sec 8 min 15 sec a The blanched frozen red beetroot was not cooked since it was regarded as a semi-finished product to be used in pre-paring beetroot soup (borsch); in this case it would be cooked in a different proportion to water while, and the vegeta-ble and the fluid fractions would be utilized.

essary, protective treatments against diseases and pests. Vegetables were harvested in September and October 2009.

Directly after harvest, mean samples repre-senting the whole batch of the material, 25–30 kg out of about 100 kg, were taken for analysis and preparation of frozen products. All the vegetables were cleaned in water. The investigation covered the following vegetables: beetroot about 6 cm in diameter; carrot 3–4 cm; celeriac 13–25 cm and parsnip 4–5 cm in diameter. The carrot, celeriac and parsnip were peeled; the carrot and celeriac were cut into cubes 10×10×10 mm and the parsnip into matchsticks 30×10×10 mm. After the process-ing described above (washing and appropriate cut-ting), the vegetables were ready for blanching or cooking. Before freezing, the raw vegetables (sam-ples A) were blanched (samples B) or boiled (sam-ples C). Beetroot was blanched and also cooked whole in the skin to protect it from loosing juice and natural color; then the beetroots were peeled and coarse grated.

Preparation of frozen products

Two methods of processing the raw materials before freezing were used. In method I, the traditional technology of blanching the raw material was ap-plied; after freezing and refrigerated storage, the frozen product was cooked to consumption consist-ency. In method II, the raw material was cooked to consumption consistency to obtain a ready-to-eat product which, after freezing and refrigerated stor-age, only required to be defrosted and heated in a microwave oven.

In method I, the fresh material was blanched in a stainless steel vessel in water, the proportion of water to the raw material being 1:5 and the blanch-ing temperature 95–98 oC. The blanching time for the different species is given in Table 1. The blanching parameters applied enabled a decrease in the activity of catalase and peroxidase to a level not exceeding 5% of the initial activity. After blanch-ing, the material was immediately cooled in cold water and left to drip on sieves for 30 min.



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In method II, the vegetables were cooked in a stainless steel vessel in brine containing 2% added salt (NaCl), the proportion by weight of the raw material to brine being 1:1. The vegetables were placed in boiling water, and the cooking time, measured from the moment when the water came to the boil again, is given in Table 1. After cooking to consumption consistency, the material was left on sieves and cooled in a stream of cold air.

The materials from blanched and cooked sam-ples were placed on trays and frozen at −40 oC in a Feutron 3626-51 blast freezer (Ilka Feutron, Germany). The time required for the inside of the product to reach −20 oC was 90 min. The frozen vegetables were then packed in 500 g polyethylene bags and stored for 12 months.

Preparation of frozen products for evalu-ation

Samples of vegetables blanched before freezing (sample B) were cooked in 2% brine, the proportion by weight of the brine to the raw material being 1:1. As was the case when cooking fresh vegetables, the frozen product was placed in boiling water. The cooking time, measured from the moment when the water came to the boil again, is given in Table 2. After cooking, the water was immediately drained; the product was cooled to 20 oC (samples D and F), and analyzed. Samples of vegetables cooked before freezing (sample C) were defrosted and heated in a Panasonic NN-F621 microwave oven (samples E and G). For microwave heating, a 500 g portion was placed in a covered heatproof vessel. The time required to defrost and heat the material to consumption temperature is given in Table 2. The samples were then cooled to 20 oC and analyzed. All cooking experiments were carried out in duplicate.

Chemical analyses

All chemical analyses as well as cooking experi-ment were carried out in duplicate. Samples of fresh

and processed vegetables were homogenized and frozen at −30 oC and freeze-dried. The freeze-dried vegetables were stored in hermetically sealed vials at −80 oC until analysis. The level of dry matter was determined by the method given in AOAC (1990) to allow the calculation of oxalate content per 100 g dry matter.

Soluble and total oxalate contents of each sam-ple of vegetable were extracted using the procedure described by Savage et al. (2000). For soluble ox-alic acid extraction, 1–2 g samples of finely ground freeze-dried plant material were weighed into 250 ml beakers and 50 ml distilled water was added. The beakers were placed in a shaking water bath at 80 oC for 15 min. The extract was allowed to cool and then transferred quantitatively to a 100 ml volumetric flask and made up to volume with distilled water. For total oxalic acid, 1−2 g samples of the freeze-dried material were weighed into a 250 ml beaker and 50 ml 2M HCl was added. The beakers were placed in a shaking water bath at 80 oC for 15 min. The extract was allowed to cool and then transferred quantitatively to a 100 ml volu-metric flask and made up to volume with 2M HCl.

An enzymatic method (Trinity Biotech 2008) proposed for the determination of oxalate content in urine was used to determine the soluble oxalate in raw and processed root vegetables. The same method was used in plant studies for example by Ilarslane et al. (1997) and Quinteros et al. (2003). This method is based on the oxidation of oxalate by oxalate oxidase action producing hydrogen per-oxide. The H2O2 then reacted with 3-methyl-2-ben-zothiazolinone hydrazone (MBTH) and 3-(dimeth-ylamino) benzoic acid (DMAB) in the presence of peroxidase to yield an indamine dye which has an absorbance maximum at 590 nm. An oxalate kit for the quantitative, enzymatic determination of oxalate in vegetables was used according to pro-cedure No. 591 (Trinity Biotech 2008). Deionized water from a Millipore-MilliQ apparatus was used in the study.

The content of calcium was determined in mineralized, diluted and filtered samples by in-ductively coupled argon plasma emission spec-trometry using JY 238 Ultrace apparatus (Jobin Yvon, France). The content of calcium was used



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to calculate the oxalate:calcium ratio and calcium bound as calcium oxalate. On the assumption that all the insoluble oxalate is in the calcium oxalate, it was calculated as follows: the content of insoluble oxalates was converted into the quantity of oxalate acid moles, this corresponding to the quantity of calcium moles bound in the form of calcium oxa-late. The quantity of grams of calcium bound in the form of oxalates was then calculated and converted into the percentage of bound calcium in relation to the calcium content in a sample. The percent true retention (%TR) was also calculated. This value was determined as the amount of oxalates remaining in the vegetables after technological and culinary treatments were applied. The calculation was carried out according to the formula given by Judprasong et al. (2006). For each sample, the raw material weight of the fresh vegetable and the weight after technological and culinary processing were determined.

Statistical analysis

All experiments were carried out in two independent experimental replications, and all chemical analyses were carried out in two parallel replications. The statistical analysis, which enabled a comparison of the oxalate content in the fresh raw material − in blanched and cooked material and in frozen material after preparation for consumption − was carried out using single-factor analysis of variance (ANOVA) on the basis of the Duncan test calculated at the probability level p < 0.05. The Statistica 6.1 (Stat-Soft Inc., Tulsa, OK, USA) programme was used.

Results and discussion

Content of total and soluble oxalatesAccording to Franceschi and Nakata (2005), the formation of oxalates is genetically controlled and hence different vegetable species contain different

amounts of these compounds. In the investigated root crops, the highest content of total and soluble oxalates was found in fresh and dry matter of beetroot (Table 2). Noonan and Savage (1999) and Santamaria et al. (1999) classified this species as an oxalate-accumulating vegetable. However, the values quoted in the literature vary considerably. Santamaria et al. (1999) report that the total content of oxalates in beetroot varied from 540–1088 mg 100 g-1 fresh matter, while according to Savage et al. (2000), the content in beetroot was only 46 mg oxalates in 100 g fresh matter. Lower amounts of oxalates in carrot and celeriac than those found in the present investigation were reported by Hönow and Hesse (2002) and higher amounts by Santamaria et al. (1999).

The accumulation of oxalates in plants is as-sociated with the activity of photosynthesis and, as the observations show, different tendencies in this process depend on the species and age of the plant as well as on the phase of growth (Franceschi and Nakata 2005, Poeydomenge and Savage 2007). Moreover, the oxalate content within a particular species depends on the conditions and year of growth (Bakr and Gawish 1997, Jaworska 2005a), as well as on the usable part of the plant (Kmiecik et al. 2004). The accumulation of oxalates is also influenced by the form of nitrogen fertilization: if the nitrogen is supplied in nitrate form, it has to undergo reduction through nitrate reductase before it is utilized by the plant. This process results in the production and accumulation of organic acids, including oxalic acid (Libert and Franceschi 1987). Investigations into different forms of fertilization revealed that a higher content of calcium in the sub-strate results in a reduced level of soluble oxalates in plants (Bakr and Gawish 1997).

The lowest proportion of soluble oxalates in total oxalates was found in celeriac (55%); this proportion was higher in the remaining vegetables, being broadly similar for each of them (68–78%). Savage et al. (2000) and Chai and Liebman (2005) reported that in many species the level of solu-ble oxalates exceeded that of insoluble oxalates. Soluble oxalates are potentially more harmful than insoluble since they reduce the nutritional avail-ability of calcium in the organism and contribute



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Table 2. Dry matter content (DM, g 100g-1), total oxalate and soluble oxalate content (mg 100 g-1 of fresh matter – FM or dry matter – DM) and retention (%) in root vegetables (raw material – A; material before freezing: blanched – B, cooked – C; product prepared for consumption after freezing: from B by cooking – D, from C by defrosting and heating in microwave oven – E; product prepared for consumption after frozen storage: from B by cooking – F, from C by de-frosting and heating in microwave oven – G).

Item DMTotal oxalate Soluble oxalate

FMa % DMa % FMa % DMa %

BeetrootA 14.2 105 ± 10d 100 739 ± 69c 100 82 ± 10b 100 577 ± 69b 100

B 14.1 90 ± 6c 86 638 ± 43b 86 60 ± 7a 73 426 ± 49a 74

C 13.5 77 ± 6ab 73 570 ± 47ab 77 52 ± 7a 63 385 ± 55a 67

D 14.0 86 ± 9bc 82 614 ± 61b 83 55 ± 6a 67 393 ± 44a 68

E 14.2 74 ± 6a 70 521 ± 40a 71 49 ± 8a 60 345 ± 54a 60

F 14.0 73 ± 3a 70 521 ± 24a 71 48 ± 6a 59 343 ± 44a 59

G 14.1 76 ± 5ab 72 539 ± 37a 73 50 ± 7a 61 355 ± 51a 61

CarrotA 13.1 74 ± 6c 100 565 ± 43e 100 50 ± 5d 100 382 ± 41c 100

B 10.8 50 ± 5b 68 463 ± 42d 82 25 ± 4c 50 231 ± 32b 61

C 12.3 45 ± 6ab 61 366 ± 49b 65 19 ± 3ab 38 154 ± 27a 40

D 10.5 48 ± 5ab 65 457 ± 43cd 81 22 ± 3bc 44 210 ± 30b 55

E 12.9 46 ± 5ab 62 357 ± 35b 63 17 ± 3ab 34 132 ± 21a 34

F 10.4 42 ± 3a 57 404 ± 32bc 71 16 ± 3a 32 154 ± 25a 40

G 13.6 41 ± 3a 55 301 ± 24a 53 18 ± 2ab 36 132 ± 16a 35

CeleriacA 16.9 40 ± 4b 100 237 ± 23c 100 22 ± 4c 100 130 ± 22c 100

B 15.4 31 ± 4a 78 201 ± 29b 85 16 ± 3b 73 104 ± 17b 80

C 18.1 28 ± 3a 70 155 ± 18a 65 11 ± 2a 50 61 ± 10a 47

D 16.7 29 ± 3a 73 174 ± 15ab 73 16 ± 3b 73 96 ± 16b 74

E 18.8 30 ± 5a 75 160 ± 26a 67 13 ± 3ab 59 69 ± 14a 53

F 16.5 25 ± 3a 63 152 ± 18a 64 11 ± 2a 50 67 ± 10a 51

G 19.1 27 ± 3a 68 141 ± 16a 60 13 ± 2ab 59 68 ± 10a 52

ParsnipA 20.7 25 ± 2c 100 121 ± 8c 100 18 ± 2c 100 87 ± 8c 100

B 17.7 20 ± 2b 80 113 ± 10bc 93 12 ± 2b 67 68 ± 10ab 78

C 19.1 19 ± 2b 76 99 ± 12ab 82 10 ± 2ab 56 52 ± 11a 60

D 15.5 18 ± 2b 72 116 ± 14bc 96 12 ± 3b 67 77 ± 17bc 89

E 20.5 20 ± 2b 80 98 ± 9a 81 12 ± 3b 67 59 ± 13a 67

F 15.5 14 ± 2a 56 90 ± 15a 75 8 ± 2a 44 52 ± 10a 59G 20.5 21 ± 3b 84 102 ± 13abc 85 11 ± 3b 61 54 ± 13a 62

a Means with different letters in the same column, within one species of vegetable, indicate significant differences (p < 0.05).



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to the formation of renal calculi (Noonan and Sav-age 1999). Soluble salts are produced when oxa-lates bind with potassium, sodium or magnesium, magnesium salts being less soluble than potassium or sodium salts. Insoluble salts are formed when oxalates bind with calcium or iron (Noonan and Savage 1999).

Thermal processing with the use of water caus-es many changes due to the leaching of soluble con-stituents to the medium; the absorption or release of water; and the shrinking of the raw material, result-ing in absolute or relative changes in the content of the constituents. In general, the above reactions are characterized by changes in the weight and content of dry matter. In the case of the investigated spe-cies, blanching in water brought about a significant decrease in the content of total and soluble oxalates both in fresh and dry matter, with the exception of total oxalate content in the dry matter of parsnip. The loss of total oxalates varied from 20–32% and that of soluble oxalates from 27–50% converted into fresh matter, and 7–18% and 20–39% re-spectively converted into dry matter. The results obtained by Bakr and Gawish (1997) showed that blanching in solutions of calcium citrate and so-dium ascorbate instead of tap water brought about a higher loss of oxalates. The above authors claim that this was due to differences in the permeability of plant tissue in these solutions.

Cooking, a process which takes longer than blanching, increased the losses of total oxalates; however, these were only significant in the fresh matter of beetroot and the dry matter of carrot and celeriac. This may have been due to fact that in the latter two species cooking caused a much greater increase in the level of dry matter than blanch-ing. Compared with blanching, cooking resulted in significant decreases in soluble oxalate content in the fresh and dry matter of carrot and celeriac. When Mosha et al. (1995) prolonged thermal pro-cessing, they observed increased losses in oxalates, as was also found in the present study; however, these were significant only in a few cases. It can be assumed that this was due to the low solubil-ity of these compounds and their resistance to the effect of temperature (Baldwin et al. 1986). How-ever, by prolonging the soaking time in water by

several dozen times, Savage and Dubois (2006) were able to show a progressive decrease in soluble oxalates from marginal quantities to 26% of their initial content. The blanching parameters applied in the present investigation were selected in order to minimize the activity of peroxidase and catalase. The cooking parameters were selected in order to obtain optimal consistency and to ensure that the frozen product after storage had all the traits of a convenience food; but also to retain the greatest possible quantities of antioxidative constituents (Gębczyński 2005, 2006a, 2006b).

Compared with the raw material before freez-ing, the level of the investigated compounds did not change significantly either in the traditional frozen product prepared for consumption by cooking in brine, or in the “convenience” frozen product after defrosting and heating to 70 oC in a microwave oven.

Frozen storage drastically curtails biochemical and chemical processes, and after 12 months of storage at −20 oC no regeneration of catalase and peroxidase was recorded. However, chemical reac-tions and changes in the permeability of cell mem-branes taking place during processing and storage may have increased the leaching of oxalates, the traditionally frozen product when prepared for con-sumption following frozen storage: after cooking it contained 12–22% less total oxalates and 13–43% less soluble oxalates compared with vegetables cooked directly after freezing. In the case of sam-ples prepared for consumption in a microwave oven the changes varied from +5 to −11% and from +6 to −9% respectively. This variation could be evoked by increase in dry matter content and from the other side by the leaching with cell sap after heating. This change was not always significant. Compared with vegetables before freezing, oxalate content in the fresh matter of frozen products obtained using the traditional method was significantly reduced, ex-cept for total oxalate content in celeriac and soluble oxalate content in beetroot and parsnip. In products obtained using the modified method, a significant decrease in the content of oxalates was noted only in the dry matter of carrot. At the same time there were observed only small changes in calcium con-tent (Table 3). The solubility of calcium could be



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limited by such components of plants like phytic acid, some proteins or polyphenols (Hurrell 2003, Vitali et al. 2008).

Products obtained using the traditional method and prepared for consumption retained 57% and 32% (carrot) and 70% and 59% (beetroot) of total and soluble oxalates respectively compared with the content in the raw material. Products of the convenience food type usually contained slightly more oxalates, significant differences being found only in the case of total and soluble oxalates in the fresh matter of parsnip. Gębczyński (2005, 2006a, 2006b) reported that the retention of antioxidative compounds and the sensory evaluation were simi-lar for the two types of product or slightly lower for frozen products of the convenience food type.

A much higher reduction in oxalates can be ob-tained if a high-calcium product, such as milk, is added during vegetable processing (Brogren and Savage 2003, Savage et al. 2009). Water with a high calcium content can play a similar role. The tap water used in the present experiment for pro-cessing and preparing vegetables for consumption contained 130–150 mg Ca dm-3, which, accord-ing to the data given by Belitz et al. (2004), can be classed as hard water containing 325–375 mg CaCO3 dm-3.

Oxalate:calcium ratio, calcium bound as calcium oxalate and oxalate true reten-

tionFresh beetroots contained the highest amount of oxalates and the lowest amount of calcium, giving a high oxalate:calcium ratio (Table 3). In fresh carrot this ratio was almost halved, while in celeriac and parsnip it was five and eight times lower respectively. Nowadays, parsnip is not a popular species in spite of its attractive sensory traits and significant levels of antioxidative compounds (Gębczyński personal report). Parsnip’s favorable oxalate:calcium ratio should recommend it for wider use.

The technological and culinary procedures ap-plied had only an insignificant effect on the content of calcium (Table 3); however, as Table 2 shows,

they contributed to a decrease in oxalate content. Therefore, the oxalate:calcium ratio in the products prepared for consumption was always lower than in the raw material, the greatest decrease being noted in carrot.

There is no clear evidence that oxalates in food preferentially bind with calcium since some solu-ble fractions can be also bound with dietary fiber (Savage et al. 2000). On the assumption, however, that insoluble oxalates in vegetables are calcium oxalates, it is possible to calculate the content of calcium bound in that molecule (Oscarsson and Savage 2007). The results given in Table 3 show that the percentage of bound calcium depended on the species, but technological and culinary process-ing did not significantly affect this parameter. Ac-cording to Brogren and Savage (2003), as much as 77% of calcium in spinach was bound in insoluble oxalates, while Oscarsson and Savage (2007) stat-ed that differences in the amounts of bound calcium in taro leaves depended on their age.

As mentioned previously, the weight of veg-etables was affected by technological and culinary treatments. Judprasong et al. (2006) suggest that changes in weight can be utilized to calculate the true retention (TR) of oxalates. The values in Table 3 calculated according to the formula given by Jud-prasong et al. (2006) show that in samples having undergone technological and culinary processing, the true retention of both total and soluble oxalates was lower than when calculated taking the content in 100 g fresh matter into account (Table 2), with the exception of blanched parsnip. Similar results were reported by Murphy et al. (1975) since in their opinion the apparent retention overestimated the TR. The results presented are among the few which reveal changes in the level of oxalates in the whole process of freezing vegetables and preparing them for consumption (Kmiecik et al. 2004, Jaworska 2005b).

Finally, hydrothermal processing, in this num-ber blanching and cooking, decreased significantly oxalate level in studied vegetables. It is especially important for the beetroot which is one of the most important root vegetable for European consumers. Products of the convenience food type usually con-tained slightly more oxalates, compared to tradi-tional products.



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Table 3. Total calcium content, oxalate:calcium ratio, calcium bound as calcium oxalate and true retention of total and soluble oxalate in root vegetables (raw material – A; material before freezing: blanched – B, cooked – C; product prepared for consumption after frozen storage: from B by cooking – F, from C by defrosting and heating in microwave oven – G).

Item

Total calcium content

(mg 100 g-1 fresh matter)

Oxalate:Calcium ratio

Calcium bound True retention of

mg 100g-1

fresh matter% of total calcium

total oxalates

soluble oxalates

BeetrootA 24.4 1.92 10.2 42 100 100B 23.4 1.71 13.4 57 84 72C 22.3 1.54 11.1 50 68 59F 22.9 1.42 11.1 49 66 56G 22.2 1.52 11.6 52 62 52CarrotA 38.8 0.85 10.7 28 100 100B 38.2 0.58 11.1 29 65 48C 36.8 0.54 11.6 31 55 35F 36.5 0.51 11.6 32 51 29G 37.6 0.49 10.2 27 45 30CeleriacA 46.1 0.39 8.0 17 100 100B 45.9 0.30 6.7 15 77 72C 41.3 0.30 7.6 18 66 47F 38.2 0.29 6.2 16 59 47G 42.3 0.28 6.2 15 58 51ParsnipA 46.8 0.24 3.1 7 100 100B 46.5 0.19 3.6 8 82 68C 45.8 0.18 4.0 9 73 53F 43.9 0.14 2.7 6 55 44G 46.8 0.20 4.4 9 74 54

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Suproniene, S. et al. Tillage and fertilization effect on grain mycoflora Vol. 20(2011): 315–326.

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© Agricultural and Food Science Manuscript received December 2010

Introduction

Wheat (Triticum aestivum L.) is one of the most important cereal crops and a staple food worldwide. In Lithuania, winter wheat prevails and occupies more farmland than any other crop. The production area of spring wheat has increased over the recent years, too.

Mouldboard ploughing is a common tillage system in Lithuania; however, direct drilling and other conservation tillage practices are becoming

increasingly popular, especially on the more fertile soils of the central part of the country. Reduced tillage has an advantage over the conventional till-age, because of reduced costs (Ribera et al. 2004, Yalcin et al. 2005, Feizienė et al. 2006) and given environmental benefits: reduction in soil erosion, nitrate leaching, fuel use, increasing soil organic matter and activity of soil organisms, improving soil structure and preserving soil moisture (Roldán et al. 2004, Subbulakshmi et al. 2009, Feiza et al. 2010, Bogužas et al. 2010). Nevertheless, no-till-age may result in increased propagule densities of

The effect of different tillage-fertilization practices on the mycoflora of wheat grains

Skaidre Suproniene*, Audrone Mankeviciene, Grazina Kadziene, Dalia Feiziene, Virginijus Feiza, Roma Semaskiene and Zenonas Dabkevicius

Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry, Instituto al. 1, Akademija, LT-58344, Kėdainiai distr., Lithuania


A two-factor field experiment was carried out at the Lithuanian Institute of Agriculture during the period 2005−2008. The influence of different tillage and fertilization practices on wheat grain fungal contamina-tion was evaluated. Grain surface contamination and internal grain infection with fungi were quantified using agar tests. Purified colonies were identified using different manuals. A total of 16 fungal genera were identified in spring and winter wheat grains. Alternaria infected 46.3% − 99.9%, Cladosporium 26.9% − 77.8%, Fusarium 0.9% − 37.1%, Penicillium 1.3% − 2.5% of grains tested. Winter wheat grain surface contamination by fungi ranged from 7.2 × 103 to 24.8 × 103 of colony forming units per g of grain (cfu g-1), spring wheat from 14.8 × 103 to 80.3 × 103 cfu g-1. No-tillage increased winter wheat grain infection by Alternaria, Aspergillus and Cladosporium species and total count of cfu g-1 on spring wheat grain surface. High fertilizer rates resulted in an increase in spring wheat grain infection by Fusarium and Penicillium species and total count of cfu g-1 on both spring and winter wheat grain surface.

Key words: contamination, fungi, fertilization, grain, tillage, wheat


Suproniene, S. et al. Tillage and fertilization effect on grain mycoflora

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most of the fungi present in the soil, including plant pathogens such as Rhizoctonia spp. and Pythium spp. (Ploetz et al. 1985). Field experiments verified that non-inversion tillage is a major factor increas-ing the severity of tan spot (Pyrenophora tritici-re-pentis) (Jørgensen and Olsen 2007), Septoria leaf blotch (Septoria tritici) (Bailey and Duczek 1995), Stagonospora leaf blotch (Stagonospora avenae) (Elen 2003) compared with conventional plough-ing. It may also result in increased Fusarium head blight (FHB) incidence and severity (Dill-Macky and Jones 2000, Fernandez et al. 2005, Lori et al. 2009).

Crop residues left on or near the soil surface in reduced tillage may be a source of inoculum not only for pathogens, but also for a wide range of fungi which may develop on grains and present a potential threat to their quality. Clear and Patrick (1993) indicated at least 59 species representing 35 fungal genera on soft white winter wheat seed from Ontario. Lõiveke et al. (2004) identified 63 fungi species on Estonian spring and winter wheat grains and grain feeds during 1992-1994. Alternaria, Cla-dosporium, Fusarium, Aspergillus, Penicillium were among the main species identified on wheat grains (Clear and Patrick 1993, Lõiveke et al. 2004, Rajput et al. 2005, Semaškienė et al. 2005, Baku-tis et al. 2006, Gohari et al. 2007). Alternaria and Cladosporium are one of the most common species on grains; they are harmless saprophytes of cereals. Some Alternaria species are (opportunistic) plant pathogens that, collectively, cause a range of dis-eases with economic impact on a large variety of important agronomic host plants including cereals, also well known as post-harvest pathogens (Thom-ma 2003). Under certain conditions they may pro-duce mycotoxins such us tenuazonic acid, alter-nariols and others (Scudamore, 2000). Fusarium species are destructive pathogens on cereal crops and produce mycotoxins before, or immediately after, harvest (D’Mello et al. 1999, Bottalico and Perrone, 2002, Thrane et al. 2004). Certain spe-cies of Aspergillus and Penicillium are also plant pathogens, but they are more commonly associated with grain storage (Pitt 2000). Penicillium chry-sogenum, Aspergillus flavus and Rhizopus chizo-podifarmis may decrease dry matter digestibility,

amino acid, vitamin and fat contents in feed dur-ing storage (Maciorowski et al. 2007). Aspergillus, Fusarium and Penicillium are the main mycotoxin producers in cereal grains. The most significant mycotoxins in naturally – contaminated foods and feeds are aflatoxins, ochratoxins, zearalenone, T-2 toxin, deoxynivalenol and fumonisins (Surai and Mezes, 2005) produced by the above mentioned fungal species. Mycotoxins, when ingested, may cause a mycotoxicosis which can result in an acute or chronic disease episode (Bryden, 2007). Alter-naria, Cladosporium, Aspergillus and Penicillium also are known as air –born allergens causing rhi-nitis and severe asthma (Breitenbach and Simon-Nobbe 2002, Gomez de Ana et al. 2006). As was shown by Kačergius et al. (2005) the same fungi (Aspergillus, Fusarium, Penicillium, Alternaria and Rhizomucor) which infected grain, vegetable and fruit were also predominant in the air of their storehouses.

Development of fungal infection in cereals has been associated primarily with environmental con-ditions. Clear and Patrick (1993) recorded yearly differences in the quantity and time of precipita-tion and the frequency of a number of fungal spe-cies (Bipolaris sorokiniana, Pyrenophora tritici-repentis, and Septoria nodorum), including a 100 fold increase in the frequency of F. graminearum between 1988 and 1989 in Ontario. In Lithuania, Semaškienė et al. (2005) indicated that host-plant, cultivar and weather conditions influenced organic grain fungal infection level. A survey of the fungal contamination of wheat and barley grains grown in organic and conventional farms was done with a focus on mycotoxin producing fungi (Bakutis et al. 2006). The findings evidenced that fungal contami-nation on wheat grain from organic farms was by 70.5% (p<0.05) higher than that on conventionally grown grain.

The effect of conventional and reduced till-age systems on FHB and wheat foliar diseases combined with the other agronomic practices is presented by researches; however the information about this effect on the total fungal contamination of cereal grains is rather scanty; the data about fer-tilization influence are also limited. There is some evidence that FHB can be affected by fertilizer



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regimes. Teich (1989) and Martin et al. (1991) ob-served that increasing the amount of nitrogen (N) applied to cereals resulted in increased incidence of FHB or Fusarium-infected grain. However Lori et al. (2009) reported that favourable weather condi-tions are more important for FHB infection than tillage practice and fertilizer treatments.

This study was designed to explore the influ-ence of different tillage and fertilization practices on winter and spring wheat grain contamination by fungi, potential cereal pathogens, or other contami-nants such as mycotoxin producers or allergens for humans and farm livestock.

Materials and methods The study site is located at the Lithuanian Institute of Agriculture (Central part of Lithuania). Studies were carried out on the basis of two long-term field trials, established in the autumn of 1999 (first trial) and 2000 (second trial) on a loamy Endocalcari – Epihypogleyic Cambisol. The experimental design was a split plot in four blocks (replications), with the tillage treatments: conventional tillage, reduced tillage and no-tillage as the main plots (Table 1). Fertilization rates of mineral NPK (none, moderate and high), designed as subplots, were calculated according to the soil properties and expected crop yield (Švedas and Tarakanovas 2000). Crop rotation was as follows: winter wheat - spring rape - spring wheat - spring barley - pea in both field experiments. For mycological study we used winter and spring wheat grain samples collected during 2005−2008 (second crop rotation). Because of poor winter wheat over winter survival in 2006, half of the field trial was re-sown with spring wheat (dividing trial plots in two equal parts across all treatments). Therefore in 2006 both winter and spring wheat grain samples were used for assessment. Plant residues of the pre-crops were collected and removed from the experimental field each year after harvest. Using of conventional crop rotation, unfavourable for wheat diseases, we expected to get results influenced only by tillage and fertilization. Each year, 3 weeks after harvesting of previous crop, non-selective herbicide

(glyphosate at a dose of 1.44 kg a.i. ha-1) was sprayed in no-tillage plots to control weeds and volunteer plants (cereals or oil seed rape).

Grain samples of 1.0 kg for laboratory analy-ses were taken from each plot at harvesting. Sub-samples were stored in plastic jars in a freezer at −20°C to prevent alterations in fungi and myco-toxin contents until the conduct of analyses. Be-fore analyzing, the grain was de-frosted up to room temperature. Grain surface contamination was de-termined using dilution plating method. Ten grams of each sample was suspended in 100 ml of sterile distilled water. A 1 ml sample of this nutrient sus-pension was used to prepare a dilution series from 1:1000. Dilution was uniformly dispensed under the surface of acidified malt agar (MA: 300 ml of maltose; 13 g of agar; 1.2 g of citric acid and 700 ml of distilled water) in Petri-dishes and incubated for 3−5 days at 25 oC in the dark. After incubation, the number of fungal colonies was calculated and expressed in colony - forming units per g of grain (cfu g-1).

Plating technique was used for internal fungal grain infection estimation. Surface-sterilized (for 3 minutes in 1 % NaOCl solution) grains were plated in Petri-dishes with potato dextrose agar (PDA: 250 g of potato, 10 g of glucose, 14 g of agar and 1 l of distilled water) and incubated for 7−8 days at 26±2 ºC (International Rules for Seed Testing 2003). The fungi were identified according to the manuals of Malone and Muskett (1997), Mathur and Kongsdal (2003), Lugauskas et al. (2002), Nel-son et al. (1983) and Leslie and Summerell (2006). The grain fungal infection level per sample was expressed in percent.

Significance of the differences between the means was determined according to the least sig-nificant difference (LSD) at 0.05 probability level. The data were processed using the software ANO-VA, SPIL-PLOT from the package SELEKCIJA (Tarakanovas and Raudonius, 2003).



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Table 1. Experimental design of different soil tillage-fertilization systems.

Tillage (factor A)treatment primary tillage pre-sowing tillageconventional tillage deep ploughing (23−25 cm) spring tine cultivation (4−5 cm)reduced tillage shallow ploughing (14−16 cm) spring tine cultivation (4−5 cm)no-tillage no tillage direct drilling combined with rotary cultivation

(2−3 cm)Fertilization (factor B) *

not fertilized no fertilizationmoderate rates mineral NPK fertilizers according to soil nutrient status and expected yield (winter

wheat 6.5 t ha-1, spring wheat 4.5 t ha-1)high rates mineral NPK fertilizers according to soil nutrient status and expected yield (winter

wheat 8.0 t ha-1, spring wheat 6.0 t ha-1)* Fertilization rates were calculated according to the soil properties and expected crop yield, using the Institute of Agriculture - developed computerised program “Tręšimas“ (Fertilization) (Švedas and Tarakanovas 2000).

Results

On winter wheat grain surface, we identified 10 fungal genera in 2005 and 6 fungal genera in 2006 (Table 2). Cladosporium and Penicillium were the most frequent species in the winter wheat grain sam-ples. Isolation frequency of Aspergillus, Fusarium and Mucor species in winter wheat grain samples was higher in 2005 than that in 2006. Acremonium, Arthrinium, Botrytis and Verticillium were identified only in 2005. Isolation frequency of Penicillium species was similar in both years, while Alternaria was more frequent in 2006.

Fungal species of 9−12 genera were identified on spring wheat grain surface. Alternaria, Fusar-ium and Cladosporium species were detected in all spring wheat grain samples in 2006, Nigros-pora and Trichoderma were present only in 2006, however Aspergillus and Pyrenophora only in 2007−2008. Acremonium, Alternaria, Arthrinium, Botrytis and Mucor were less frequent or not de-tected in 2008. Penicillium and Verticillium were more frequent in 2008 than in 2006−2007.

A total of 14 fungal genera were isolated from winter and spring wheat grain surface. Alternaria,

Cladosporium, Fusarium and Penicillium species were the most common. Cladosporium spp. was identified in all tested samples. Arthrinium, Bot-rytis, Pyrenophora, Nigrospora, Trichoderma and Ulocladium were less frequent. Acremonium and Alternaria species were more prevalent on spring wheat grain surface than on winter wheat. Pyr-enophora, Nigrospora, Trichoderma and Ulocla-dium were identified only on spring wheat grains.

The fungal contamination of winter wheat grains ranged from 7.2 × 103 to 24.8 × 103 cfu g-1 (Table 3). Grains were more contaminated in 2005 than 2006. There were no significant differences between tillage practices. However, high and mod-erate fertilizer rates in 2005 and high rates in 2006 significantly increased grain surface contamination compared to not fertilized winter wheat.

The fungal contamination of spring wheat grains ranged from 14.8 × 103 to 80.3 × 103 cfu g-1 (Table 4). Grains were more contaminated in 2006, than 2007 and 2008. No-tillage significantly increased spring wheat grain surface contamination in 2006 and 2007. High fertilizer rates increased fungal count only in 2006 and moderate rates in 2008.



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Table 2. Fungal species on the surface of wheat grain, 2005−2008.

Isolation frequency (%) in grain samplesWinter wheat Spring wheat

Fungal species 2005 2006 2006 2007 2008Acremonium 5.6 0.0 47.2 38.9 8.3Alternaria 13.9 33.3 100.0 97.2 83.3Aspergillus 94.4 2.8 0.0 47.2 25.0Arthrinium 2.8 0.0 2.8 8.3 0.0Botrytis 5.6 0.0 2.8 2.8 0.0Cladosporium 100.0 100.0 100.0 100.0 100.0Pyrenophora 0.0 0.0 0.0 8.3 5.6Fusarium 91.7 55.6 100.0 86.1 86.1Mucor 44.4 2.8 13.9 5.6 0.0Nigrospora 0.0 0.0 8.3 0.0 0.0Penicillium 94.4 97.2 69.4 72.2 83.3Trichoderma 0.0 0.0 2.8 0.0 0.0Ulocladium 0.0 0.0 2.8 0.0 2.8Verticillium 5.6 0.0 11.1 0.0 22.2

Table 3. The fungal contamination of winter wheat grain as influenced by different tillage- fertilization treatments, 2005−2006.

Fertilization (factor B) Average A (Fact n.s.)Tillage (factor A) not fertilized moderate rates high rates

Grain surface contamination by fungi, cfu g-1×103 , 2005conventional 13.8 24.6* 27.0** 21.0reduced 19.9 18.5 17.6 18.7no-tillage 16.3 22.6 21.0 19.6

Average B (Fact *) 16.6 21.5* 21.9*

Grain surface contamination by fungi, cfu g-1 × 103 , 2006conventional 7.2 9.5 15.8** 10.8reduced 11.0 12.5* 13.6** 12.4no-tillage 8.7 11.8* 11.5* 10.7

Average B (Fact **) 8.9 11.3 13.6**



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Table 4. The fungal contamination of spring wheat grain as influenced by different tillage- fertilization treatments, 2006−2008.

Fertilization (factor B)Tillage (factor A) not fertilized moderate rates high rates Average A

(Fact *)Grain surface contamination by fungi, cfu g-1 × 103, 2006

conventional 43.2 38.0 63.9* 48.4reduced 35.9 54.4 58.3 49.5

no-tillage 49.3 59.1 80.3** 62.9*Average B (Fact **) 42.8 50.5 67.5**

Grain surface contamination by fungi, cfu g-1 × 103, 2007 (Fact *)

conventional 18.6 14.8 18.3 17.2reduced 16.5 19.9 19.3 18.5no-tillage 33.9* 31.1 16.0 27.0*Average B (Fact n.s.) 23,0 21.9 17.8

Grain surface contamination by fungi, cfu g-1 × 103, 2008 (Fact n.s.)conventional 24.1 30.7* 28.7 27.8reduced 22.6 29.7 25.1 25.8no-tillage 25.1 27.3 26.9 26.4Average B (Fact *) 23.9 29.2* 26.9

Cladosporium colony accounted for nearly half of the total fungal colonies formed. As a result, the increase in the total number of colony form-ing units (cfu g-1) in some cases depended on the increase in the count of Cladosporium colonies. A multifactor analysis of variance demonstrated a significant effect of fertilization on the total count of cfu g-1 and on that of Cladosporium spp. in 2005−2006. Without Cladosporium colonies taken into account, fertilization had a significant effect on the fungal count only in spring wheat in 2006 (Table 5). Conversely, a significant tillage effect was established on the amount of the other fungal colonies than Cladosporium on spring wheat grain surface.

A total of 15 fungal genera were detected in surface sterilized wheat grains (Table 6). Internal tissues of wheat kernels were infected mainly by the same fungal species as those found on the grain surface, except for Acremoniella and Gonatobot-rys, which occurred in 0.1% − 5.3% and in 1.4%

− 14.3% of spring wheat grains, respectively. Al-ternaria spp. (mostly A. alternata) was the most frequently detected species in the internal tissues of wheat grains, occurring in 46.3% − 96.5% of winter wheat and in nearly 100% of spring wheat grains. Aspergillus spp. occurred in 3.3% (in 2005) and in 2.5− 3.9% (in 2007-2008), Cladosporium spp. in 26.9% − 63.6% and in 35.6% − 77.8% of winter and spring wheat grains, respectively. Pyre-nophora spp. infected 1.1% of winter wheat grains in 2005 and 0.3− 4.4% of spring wheat grains in 2007− 2008. Fusarium spp. were detected in 0.9% − 4.7% of winter wheat and in 32.8% − 37.1% of spring wheat grains. Penicillium spp. infected about 2% (1.3− 2.5) of winter wheat and from 1.6% to 11.2% of spring wheat grains. The other fungi (Acremonium, Arthrinium, Botrytis, Mucor, Ni-grospora, Trichoderma and Verticillium) occurred only sporadically and at negligible levels (from 0.1% to 5.3%).



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Table 5. Analysis of variance p values from grain-testing the total count of fungal colony forming units (cfu g-1), Cladosporium spp. and other fungal colony forming units (cfu g-1) from wheat grain surface, 2005−2008.

ANOVA p valuesWinter wheat grains Spring wheat grains

Source of variation 2005 2006 2006 2007 2008Total fungal colony forming units, cfu g-1

Tillage (A) 0.3120 0.3606 0.0061 0.0468 0.4794Fertilization (B) 0.0303 0.0050 0.0001 0.4117 0.0160A × B 0.0548 0.1948 0.1903 0.2044 0.7020

Cladosporium colony forming units, cfu g-1


Other fungal colony forming units (without Cladosporium), cfu g-1


Table 6. Fungal infection level of wheat grain, 2005−2008.

Percentage of grains infected with the fungal species listed ± SD.Fungal species Winter wheat Spring wheat

2005 2006 2006 2007 2008Acremoniella 0.0 0.0 5.3 ± 5.4 0.0 0.1 ± 0.3Acremonium 0.0 5.3 ± 5.4 0.7 ± 1.2 0.2 ± 0.3 0.0Alternaria 96.5 ± 2.3 46.3 ± 10.8 98.1 ± 0.9 99.9 ± 0.3 99.2 ± 0.8Aspergillus 3.3 ± 3.1 0.0 0.0 2.5 ± 1.8 3.9 ± 2.3Arthrinium 0.5 ± 0.6 0.2 ± 0.4 0.3 ± 0.5 0.0 0.1 ± 0.3Botrytis 1.5 ± 0.9 0.1 ± 0.3 0.0 0.0 0.1 ± 0.3Cladosporium 63.3 ± 9.4 26.9 ± 7.9 77.8 ± 4.5 35.6 ± 4.1 36.9 ± 17.9Pyrenophora 1.1 ± 1.9 0.0 0.0 0.3 ± 0.4 4.4 ± 3.3Fusarium 4.7 ± 3.2 0.9 ± 0.8 37.1 ± 4.7 32.8 ± 7.9 34.7 ± 5.7Gonatobotrys 0.0 0.0 1.4 ± 1.6 14.3 ± 2.8 2.2 ± 1.3Mucor 1.0 ± 1.2 0.7 ± 0.8 0.0 0.1 ± 0.3 0.0Nigrospora 0.2 ± 0.6 0.4 ± 0.6 0.0 0.0 0.0Penicillium 2.5 ± 1.1 1.3 ± 1.6 11.2 ± 4.9 1.6 ± 1.4 2.0 ± 1.6Trichoderma 0.0 0.3 ± 0.8 1.4 ± 2.8 0.0 0.1 ± 0.3Verticillium 0.0 0.7 ± 1.1 1.6 ± 1.7 0.0 0.4 ± 0.8



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Alternaria Aspergillus Cladosporium Alternaria Cladosporium

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Agricultural practices used in this experiment showed some influence on individual fungi and differed between years and seasonal type of wheat (spring or winter). With no-tillage practice, we ob-served a significant increase in winter wheat grain infection with Alternaria spp. in both years (Fact*,p = 0.0474, 2005; Fact**, p = 0.0018, 2006), Asper-gillus spp. in 2005 (Fact*, p = 0.0278) and Clad-osporium spp. in 2006 (Fact**, p = 0.0076) (Fig. 1).

High fertilizer rates significantly increased spring wheat grain infection with Penicillium spp. in 2006 (Fact**, p = 0.0044) and Fusarium spp. in 2007 (Fact**, p = 0.0047) as shown in Figure 2.

DiscussionFungal species belonging to 16 genera were iden-tified in spring and winter wheat grains during 2005−2008. Very similar fungal complexes devel-

oped on the surface and in the internal tissues of grains. Alternaria, Cladosporium, Fusarium and Penicillium were the most frequent in grain samples: Alternaria 46.3% − 99.9%, Cladosporium 26.9% − 77.8%, Fusarium 0.9% − 37.1%, Penicillium 1.3% − 2.5%. Isolation frequency of Aspergillus varied more among years (from 0 to 94.4) compared with that of the above mentioned fungi and, depending on the year, infected grain accounted for from 0 to 3.9% of the grain tested. All these fungi have been previously reported as prevalent in wheat grains (Clear and Patrick 1993, Lõiveke et al. 2004, Rajput et al. 2005, Semaškienė et al. 2005, Bakutis et al. 2006, Gohari et al. 2007).

Most of the identified fungi normally exist as saprophytes or weak plant pathogens. While Fusarium species is mainly associated with FHB, one of most important fungal diseases of wheat in all cropping areas worldwide, which reduce yield, seed quality and causes mycotoxin production in grain (Botalico et al. 1989, Tuite et al. 1990, Ar-gyris et al. 2003). The Alternaria, Fusarium, Peni-

Figure 1. The influence of no-till-age on winter wheat grain infection with Alternaria, Aspergillus and Cladosporium fungi in 2005-2006.

34

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Fusarium Penicillium Fusarium Penicillium Fusarium Penicillium

2006 2007 2008

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Not fertilized Moderate rates High rates

Figure 2. The influence of high fertilizer rates on spring wheat grain infection with Fusarium and Penicillium fungi in 2006-2007.



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cillium and Aspergillus species may decrease seed germination, induce seed discoloration, chemical and nutritional changes, and form mycotoxins, that constitute a health hazard for humans and animals, under both field and storage conditions (Sauer 1988, Pitt 2000, Bryden 2007, Maciorowski et al. 2007, Malaker et al. 2008).

Our results indicated that spring wheat grain samples had fungal counts higher (14.8 × 103 − 80.3 × 103 cfu g-1) than winter wheat grain samples (7.2 × 103 to 24.8 × 103 cfu g-1) as well as Fusarium infection level: 32.8% − 37.1% and 0.9% − 4.7%, respectively. Similar differences in the Fusarium infection level between winter and spring cereals in Lithuania were reported in previous researches (Semaškienė et al. 2005, Suproniene et al. 2010). The obtained results also agree with those obtained by Kosiak et al. (2007). This might have resulted from some environmental factors: meteorological conditions, time and length of flowering period and source of infection (Suproniene et al. 2010). Yearly differences in fungal counts and infection levels were also indicated during this study, which is in accordance with other investigations (Clear and Patrick 1993, Mankevičienė et al. 2006).

Our investigation showed that the high fertilizer rates may have influence on the increasing of fun-gal contamination on wheat grain. This agrees with the data reported by Mankevičienė et al. (2006), where the high fertilization level (N180P80K140S13), used for conventionally grown winter wheat ‘Ada’, increased grain surface contamination with fungi by 75.5% compared with the unfertilized wheat. High fertilizer rates mainly increased the amount of Cladosporium fungi on the wheat grain surface. This can be explained by the investigations done by Last (1955), who has demonstrated a significant increase in spore concentration of Cladosporium spp. and some other fungi in the air in a fertilized (N, P and K) wheat crop. The influence of high fertilizer rates on grain infection with Fusarium and Penicillium fungi was significant only in sep-arate years during our study. Previous investiga-tions also indicate that higher fertilizer rates may increase Fusarium infection in winter wheat (Teich and Nelson 1984, Martin et al. 1991, Lemmens et al. 2004, Krnjaja et al. 2009).

Conservation tillage systems involve leaving all or part of the crop residue on the soil surface af-ter harvest in an effort to reduce soil erosion caused by wind and water runoff (Dill-Macky and Jones 2000). However, a lot of fungal species are capa-ble of surviving saprophytically on plant debris, which might result in an increase in the residue-borne diseases of cereals. As was previously shown by Ploetz et al. (1985), in no-tillage plots the mean propagule densities of total fungi in soil were sig-nificantly higher than in the plots tilled to 15 cm. Perello (2010) has recently reported that increas-ing of the foliar diseases caused by necrotrophic pathogens such as Alternaria, Cladosporium, Bi-polaris, Pyrenophora and Septoria may also be due to the cultural practices such as conservation tillage, nitrogen fertilization and irrigation as well as conducive weather conditions. It was expected, that removal of the pre-crops’ straw from the fields with the purpose of using it for bioenergy could help us to decrease the fungal infection in cereals. However, the obtained results indicated that no-tillage significantly increased fungal contamination (cfu g-1) of spring wheat grains in 2006 and 2007 and winter wheat grain infection with Alternaria (2005−2006), Aspergillus (2005) and Cladospori-um (2006) species. It is likely that the residues (roots and stalks) left in the field after harvesting are still a relevant source of the fungal infection in cereals in no-tillage treatments. Since wheat pre-crops were pea and rape, we did not observe any significant increase in wheat pathogenic fungi such as Fusarium and others. However, Alternaria, As-pergillus and Cladosporium species may survive on a wide range of plants including pea and rape (Begum et al. 2004; Brazauskiene and Petraitiene 2006; Brazauskiene et al. 2006). On the other hand, the Verticillium longisporum and V. dahliae are known as Verticillium wilt causal agents of pea (Isaac and Rogers, 1974) and rape (Steventon et al. 2002) grown as pre-crops in our experiment. Small cereals are non-hosts for Verticillium pathogens. However, a greenhouse experiment showed that inoculation of wheat and barley with V. longispo-rum leads to various-degree stunting at close to the fully ripe stage (Johansson et al. 2006). During our study, Verticillium fungi were detected on the grain



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surface and in the internal tissues of wheat grains. This confirms Johansson’s et al. (2006) suggestions that the plant species outside the Brassicaceae can act as reservoirs of Verticillium inoculum.

Another point to be discussed is use of glypho-sate in the fields. Previous glyphosate use was con-sistently associated with higher FHB levels, while Cochliobolus sativus, the most important common root rot pathogen, was negatively associated, with previous glyphosate use (Fernandez et al. 2009). In our study, glyphosate was used only in no-tillage treatments; however, we did not observe any in-crease in Fusarium infection in wheat grains.

Our findings suggest that agronomic factors are very important and may increase winter wheat grain fungal contamination. Since the residues of pre-crops were collected and removed from the experimental field and pre-crops were non-hosts for wheat pathogens during the test period, further research is needed to explore the effects of these factors on grain fungal infection level.

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Mekky, H. et al. Biosynthesis of VLCPUFAs in chicory Vol. 20(2011): 327–340.

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© Agricultural and Food Science Manuscript received March 2011

Biosynthesis of very long chain polyunsaturated fatty acids in the leafy vegetable chicory

Hattem Mekky1, Maged Mohamed2, Colin Lazarus2, J. Brian Power1 and Michael R. Davey1,*

1Plant and Crop Sciences Division, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK

2School of Biological Sciences, University of Bristol, Woodland Road, Bristol

BS8 1UG, UK

*e-mail: [email protected]

The synthesis of very long chain polyunsaturated fatty acids (VLCPUFAs) was investigated in five cultivars of chicory. Genes for enzymes of the ω3/6 Δ8-desaturation biosynthetic pathways for the formation of C20 VLCPUFAs were inserted into chicory by Agrobacterium-mediated transformation of leaf explants. Plants were transformed by genes encoding Δ9-specific elongating activity from Isochrysis galbana, Δ8-desaturase from Euglena gracilis and ∆5-desaturase from Mortierella alpina, either separately or in combination; transgenic plants were selected on culture medium containing glufosinate ammonium for those transformed with the ∆9-elongase gene alone or in combination with the ∆8-desaturase gene, or kanamycin for plants transformed with the ∆5-desaturase gene. PCR showed the presence of the transgenes within the genome of selected plants, with RT-PCR confirming gene expression. Gas Chromatography of fatty acid methyl esters extracted from freeze-dried leaves of transgenic plants quantified the synthesis of omega-6 arachidonic acid and its precursors eicosadienoic and dihomo-γ-linolenic acids, and omega-3 eicosapentaenoic acid together with its precursors eicosatrienoic and eicosatetraenoic acids. This is the first report of the production of VLCPUFAs in a leafy vegetable. Since VLCPUFAs are the precursors of prostaglandins, the formation of prostaglandins was also investigated in chicory following a second transformation event using the PGHS-1 gene from Mus musculus.

Key words: Very long chain polyunsaturated fatty acids (VLCPUFAs), Agrobacterium-mediated transforma-tion, Δ8-desaturation biosynthetic pathways, transgenic plants, Gas Chromatography (GC)


Mekky, H. et al. Biosynthesis of VLCPUFAs in chicory

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Introduction

Chicory, a leafy vegetable that is consumed both in the cooked and raw states, has several important health-related attributes. Meehye and Kyung (1996) recognised its antidiabetic properties, while aqueous-methanolic extracts of the seeds show a hepatopro-tective activity due to esculetin, the latter being a major component of the plant (Gilani et al. 1998). Root extracts of chicory relieve liver ailments and haemorrhoids by reducing hepatic concentrations of lipids, triglycerides and cholesterol (Gupta et al. 1993). Extracts of chicory inhibit the growth of tumour cells through their content of the flavonoid quercetin (Hertog et al. 1992) and the sesquiterpene lactone 11β, 13-dihydrolactucopicrin (Christope et al. 1996). Balbaa et al. (1973) examined the phar-macological properties of chicory extracts on hearts isolated from toads. Extracts had a quinidine-like action, verifying the use of such extracts against dis-eases characterized by tachycardia, arrhythmias and fibrillations, as indicated in folklore. Lactucin and its derivatives lactucopicrin and 11β, 13-dihydrol-actucin, which are characteristic bitter sesquiterpene lactones of chicory, showed analgesic activities more pronounced than those of ibuprofen, the latter being used as a standard (Wesołowska et al. 2006). Ethyl acetate extracts of chicory roots had a marked anti-inflammatory activity by inhibiting prostaglandin E2 (Cavin et al. 2005), while 8-deoxylactucin had a similar effect by inhibiting DNA binding of the transcription factor NF-κB (Malarz et al. 2007). Po-lar extracts of chicory leaves also inhibited growth of the aerobic mesophilic bacteria, Leuconostoc mesentroides and Listeria monocytogenes (Pascual and Robledo 1998), water extracts had a similar ef-fect on the growth of Agrobacterium tumefaciens, Erwinia carotovora, Pseudomonas fluorescens and P. aeruginosa (Petrovic et al. 2004), while two main sesquiterpene lactones, 8-deoxylactucin and 11β, 13-dihydrolactucin, inhibited the growth of the fungus Trichophyton tonsurans var. sulfureum (Mares et al. 2005). Lactucin and lactucopicrin exhibited antimalarial properties against clone HB3 of the strain Honduras-1 of Plasmodium falciparum (Bischoff et al. 2004).

Increasing the medicinal importance of plants is a goal of pharmaceutical research (Malarz et al. 2005). VLCPUFAs that include arachidonic, ei-cosapentaenoic and docosahexaenoic acids are en-gaged in neonatal retinal and brain development, as well as cardiovascular health and disease preven-tion. Arachidonic and eicosapentaenoic acids are precursors of eicosanoids, including prostaglandins (Qi et al. 2004), in addition to maintaining cellular membranes through the regulation of cholesterol synthesis and its transport (Ani et al. 2003).

VLCPUFAs are synthesised in humans from linoleic acid (LA, C18:2 Δ9,12) and α-linolenic acid (ALA, C18:3 Δ9,12,15) obtained from the diet, but their biosynthesis is limited and is regulated by dietary and hormonal changes. Consequently, VL-CPUFAs are obtained mainly from oily fish. How-ever, the consumption of such fish has declined recently, in addition to the reduction of fish stocks and possible contamination of fish oils by pollut-ants, such as heavy metals, polychlorinated biphe-nyls, dioxins and other chlorine-based compounds. Even fish farming requires considerable amounts of fish oils to optimize growth and nutrition of the farmed animals. Aquaculture exacerbates the prob-lem (Napier 2006), rather than being a replacement for the diminishing natural reserves of marine fish. Napier et al. (2004) discussed the biosynthesis of health beneficial fatty acids in transgenic plants and the feasibility of synthesizing “Designer oils” in transgenic plants at concentrations equivalent to those found in marine organisms (Napier and Graham 2010). Venegas-Caleron et al. (2010) also reviewed progress in the metabolic engineering of oil-seed crops to synthesize fatty acids.

Improvement of seed oil quality has been achieved by transforming rice with a soybean mi-crosomal omega-3-fatty acid desaturase gene. The latter encodes the microsomal omega-3-fatty acid desaturase enzyme, essential in the production of the n-3 polyunsaturated fatty acid, α-linolenic acid. This approach resulted in a 10 fold increase in the concentration of α-linolenic acid in rice seed oil (Ani et al. 2003). Jimenez et al. (2009) compared the profiles and relative concentrations of fatty ac-ids in transgenic plants and isogenic lines of corn and soybean. More recently, Cheng et al. (2010)



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reported the biosynthesis of eicosapentaenoic ac-ids in transgenic Brassica carinata. Other authors have isolated and characterized desaturases and elongases for fatty acid biosynthesis in microalgae (Petrie et al. 2010a), while Petrie et al. (2010b) iso-lated elongases which they expressed in Nicotiana benthamiana. Similarly, Taylor et al. (2009) re-ported seed-specific expression of nervonic acid in Arabidopsis thaliana and B. carinata using a gene for 3-ketoacyl-CoA synthase from Cardamine. The model plant, A. thaliana was also used as ex-perimental material to assess the biosynthesis of omega-3-fatty acids, the plant being transformed sequentially with genes encoding a Δ9-specific elongating activity from Isochrysis galbana, a Δ8-desaturating activity from Euglena gracilis, and a Δ5-desaturating activity from Mortierella alpina. This strategy resulted in accumulation of arachi-donic and eicosapentaenoic acids in transgenic plants (Qi et al. 2004). The current investigation was instigated to evaluate the feasibility, using a similar strategy, to express VLCPUFAs in leafy vegetables, chicory being chosen as the target plant because of the ease of transforming this crop.


Plant material and bacterial strains for transformation

Five cvs. of chicory were used as target plants for transformation, namely Brussels Witloof, Pain du Sucre (E.W. King Ltd., Kelvedon, UK), Sponda da Taglio, Pan di Zucchero and Poncho (B and T World Seeds, Paguignan, France). The disarmed A. tumefaciens strain AGL1 carried a Δ9 elongase gene from Isochrysis galbana (pCB302.1; Qi et al. 2002), a Δ8 desaturase gene from Euglena gracilis (pBECKS19.6; Wallis and Browse, 1999), a Δ5 de-saturase gene from Mortierella alpina (pCAMBIA-23-EC-Δ5-desaturase), the Δ9 elongase + the Δ8 de-saturase genes on the same vector (pCB302.3; Xiang et al. 1999), and the prostaglandin endoperoxidase

gene (PGHS-1) from Mus musculus (pCAMBIA-23-EC-PGHS-1). Figure 1 shows the maps of the different constructs used in this investigation.

Preparation of leaf explants and Agro-bacterium – mediated transformation

Seeds (achenes) were surface sterilised by immersion in 20% (v/v) “Domestos” bleach solution (Unilever Ltd., Kingston-Upon-Thames, UK; 30 min), washed 3 times with sterile reverse osmosis water and blotted dry on sterile filter paper (No.1; Whatman, Maidstone, UK). Achenes (15 per 9 cm Petri dish) were placed on 25 ml aliquots of Murashige and Skoog (1962), MS-based medium supplemented with 30 g l-1 sucrose, and semi-solidified with 0.8% (w/v) agar, pH 5.8. Dishes were sealed with Nescofilm (Nippon Shoji Kaisha Ltd., Osaka, Japan) and incubated with a 16 h photoperiod (50 μmol m-2 sec-1; “Daylight” fluorescent tubes; Sylvania, Germany) at 23 ± 1 ˚C.

Leaves and cotyledons were excised from 14 d-old seedlings and scored on their abaxial surfaces. Explants were immersed (5 min) in an overnight culture of Agrobacterium (OD600 = 0.6) diluted 1:1 (v:v) with liquid MS-based medium lacking growth regulators, before blotting on sterile filter paper. Inoculated explants were cultured for 3 d on 25 ml aliquots of MS-based shoot regeneration medium containing 1.0 mg l-1 benzylaminopurine (BAP), 0.1 mg l-1 indole-3-yl acetic acid (IAA), 30 g l-1 sucrose, and semi-solidified with 0.8% (w/v) agar (Sigma-Aldrich), pH 5.8, in 9 cm diameter Petri dishes. Dishes were sealed with Nescofilm.

After 3 d of co-cultivation in the dark at 23 ± 1oC, explants were transferred to semi-solid MS-based shoot regeneration medium supplemented with 400 mg l-1 cefotaxime and 400 mg l-1 vanco-mycin to eliminate Agrobacteria, with inclusion of glufosinate ammonium (5 mg l-1) in the medium following inoculation of explants with Agrobac-terium carrying Δ9 or Δ9 + Δ8 genes, or kanamycin sulphate (50 mgl-1) with the Δ5 or Δ8 genes. Inocu-lated explants were transferred to the surface of new medium containing the same concentrations



331330

pCB302.1- Δ9-elongase

pBECKS- Δ8-desaturase

pCAMBIA-23-EC-Δ5-desaturase

pCB302.3 Δ8 -desaturaseΔ9-elongase

pCAMBIA-23-EC-PGHS-1

P35S: Cauliflower Mosaic Virus 35S promoter, Pnos: Nopaline synthase promoter, TP: transit peptidaseTnos: Nopaline synthase terminator, bar: BASTA (Glufosinate ammonium) resistance

LBKanamycinR PGHS-1Tnos RBT35S KanamycinR P35S P35S

Xho I EcoRI

SacISacI EcoRI BamHI HindIII

208 bp 450 bp 300 bp 450 bp1808 bp1025 bp

Xho I

TnosP35S ∆9- Elongase P35STnos ∆8- DesaturasePnosTnos bar LBRB Ω

450 bp1300bp300 bp600 bp600 bp300 bp1000 bp820 bp300 bp

NotI, SacIXbaI, SpeI KpnI HindIII KpnI, SacI

HindIII, KpnISalI, XHOI

450 bp1340 300 bp1025 bp 450 bp208 bp

HindIIIBamHISacIEcoRIXho IXho I

LBKanamycinR Tnos ∆5−desaturase RBT35S KanamycinRP35S P35S

T35SP35S ∆8- DesaturasePnosTnosKanamycinR RBLB600 bp1025 bp 300 bp 208 bp1300 bp300 bp

HindIII,EcoRI

820 bp

T35SP35S ∆9- Elongase PnosTnos bar LBRB600 bp600 bp300 bp1000 bp300 bp

HindIII KpnI HindIII

TP160 bp

Fig. 1. T-DNA map of pCB302.1-Δ9 elongase, pBECKS-Δ8desaturase, pCAMBIA-23-EC-Δ5desaturase, and pCAM-BIA-23-EC-PGHS-1 in Agrobacterium strain AGL1, showing the direction of transcription of the selectable marker genes (bar for resistance to glufosinate ammonium; kanamycinR [nptII] for resistance to kanamycin) and the genes of interest, Δ9 elongase, Δ8 desaturase, Δ5 desaturase and PGHS-1, located between the T-DNA left and right borders.



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of antibiotics every 14 d for 28−42 d, until regen-erated shoots each attained a height of 2−3 cm. Shoots excised from the parent tissues, were rooted on the same medium as used for selection, but with 0.1 mg l-1 indole-3-butyric acid (IBA) replacing BAP and IAA. Putatively transformed plants were established in Levington M3 compost (Scotts UK Professional, Ipswich, UK) under glasshouse con-ditions and grown to maturity for assessments of achene production. Controls involved the culture of uninoculated explants on medium lacking or con-taining selection agents. Non-transformed plants were grown in the glasshouse alongside transgen-ic plants. In experiments with the Δ5 and PGHS-1 genes, plants transformed with the Δ9 + Δ8 genes and selected using glufosinate ammonium, were subject-ed to a second transformation with the Δ5 or PGHS-1 genes using leaf explants as target tissues. Double transformants were selected on medium containing kanamycin sulphate (50 mg l-1).

Plant DNA extraction

Plant DNA was extracted from leaf tissues for PCR-analysis using a Gen EluteTM Plant Genomic DNA Miniprep Kit (Sigma-Aldrich), following the manufacturer’s protocol.

Polymerase chain reaction (PCR) analysis

Putatively transformed plants were screened by PCR for the presence of the Δ9-elongase, the Δ8-desaturase, the Δ5-desaturase and the PGHS-1 genes. The 20 bp oligonucleotide primers used to amplify coding regions were 5′-gggcgtatggatcttcatgt-3′ and 5′-gcaggggacgttgatgtagt-3′ (Δ9-elongase, 175 bp), 5′-tggagtgctgggttatttcc-3′ and 5′-ttgcagaccattgc-caaata-3′ (Δ8-desaturase, 178 bp), 5′-atcaagcccaac-caaaagtg-3′ and 5′-agtcgagatggggttgacac-3′ (Δ5-desaturase, 159 bp) and 5´-cagtgcctcaaccccatagt-3´ and 5´-gtggctatttcctgcagctc-3´ (PGHS-1). PCR was performed using approx. 100 ng of purified genomic DNA and Taq polymerase (ABgene, Epsom, UK).

The reaction conditions were 10 min denaturation at 94 °C, 35 cycles each of 1 min at 94 °C, 1 min at X °C and 1 min at 72 °C, where X = 57.3 for the Δ9-elongase gene, 55.3 for both the Δ8-desaturase and the Δ5-desaturase genes, and 59.4 for the PGHS-1 gene. DNA from non-transformed (control) plants was included in the experiments. Amplified products were separated by electrophoresis on 1.5% (w/v) agarose gels and visualized under UV illumination following staining with ethidium bromide (Sam-brook et al. 1989).

RNA extraction for reverse transcriptase (RT) PCR-analysis

Young leaf material (100 mg) was harvested from putatively transgenic and non-transformed plants. Samples, in axenic 1.5 ml microfuge tubes, were flash frozen in liquid nitrogen, ground to a fine powder and processed using an RNeasy Plant Mini Kit (Qiagen Ltd., Crawley, UK). RNA samples were treated with RNase-free DNase (Promega, Southampton, UK) according to the manufacturer’s instructions. Aliquots of 40 ng RNA template, 1 μM oligo-dT primer and 11 μl RNase-free water were placed in 0.5 ml thin walled microfuge tubes. After incubation at 70 ˚C (5 min), 2 μl 10X synthesis buffer, 2 μl dNTP mix (0.5 mM each dNTP) and 1 μl Sensiscript Reverse Transcriptase (Qiagen) were added. Tubes were incubated at 37 ˚C (60 min). A Sensiscript Reverse Transcriptase First Strand Synthesis Kit (Qiagen) was used with PCR amplification.

Gas Chromatography for identification of VLCPUFAs

Fatty acids were extracted as fatty acid methyl esters (FAMEs) from leaf tissues of glasshouse-grown plants (during the flowering stage) according to Browse et al. (1986). GC analysis was conducted by injecting 2−8μl of the hexane extract into a Hewlett Packard 5880A Series gas chromatograph



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equipped with a 0.25 mm x 30 m, 0.25 μm RSL-500 BP bonded capillary column and a flame ioniza-tion detector (Schimadzu UK Ltd, Milton Keynes, UK). Fatty acids were identified by comparison with retention times of FAMEs standards (Sigma-Aldrich). Relative percentages of the fatty acids were estimated from peak areas.

Statistics

Mean and standard deviations were calculated for all quantitative data. A one-way analysis of variance (ANOVA) was performed to test the consistency of results within a chicory cv. (ANOVA: Within Groups Design) and to test the influence of the expressed transgenes on the different cvs. (ANOVA: Between Groups Design).

Enzyme immunoassay (EIA) for quantifi-cation of prostaglandins

Aliquots (100 mg) of freeze-dried plant tissue were ground in a mortar using 5 ml 70% (v/v) ethanol and the homogenate was incubated for 15 min at 4 °C. The homogenate was centrifuged (13000 rpm, MSE Centaur 2; Fisons, Loughborough, UK), the supernatant retained and the alcohol removed from the supernatant by evaporation under reduced pres-sure. The remaining aqueous solution was adjusted to pH 4.0 using dilute HCl, passed through an activated C-18 solid phase extraction column (Cay-man Chemical Co., Ann Arbor, USA). The column was rinsed with 5 µl ultra-pure water followed by 5 µl of HPLC-grade hexane (Fisher Scientific, Loughborough, UK). The column was eluted with 5 µl HPLC-grade ethyl acetate containing 1% (v/v) HPLC-grade methanol (Fisher Scientific). Ethyl acetate was evaporated under reduced pressure, and the residue dissolved in 500 µl enzyme immunoassay (EIA) buffer for enzyme immunoassay analysis. Prostaglandin H was assayed with a prostaglandin EIA screening kit (Cayman Chemical Co.) using the manufacturer's protocol.

Results

Shoot regeneration from leaf explantsShoots regenerated from callus produced at the margins and scored areas of all leaf explants cultured on MS-based medium with 1 mg l-1 BAP, 0.1 mg l-1 IAA, 30 g l-1 sucrose and semi-solidified with 0.8% (w/v) agar, after 28 d of culture. Regener-ated shoots developed adventitious roots within 48 h after transfer to semi-solid MS-based culture medium supplemented with 0.1 mg l-1 IBA. Callus formation and shoot regeneration were inhibited on explants not inoculated with A. tumefaciens when the explants were cultured on MS-based shoot re-generation medium containing 50 mg l-1 kanamycin sulphate, or glufosinate ammonium (5 mgl-1) as selection agents. In contrast, 80 ± 5%, 78 ± 5%, 75 ± 5%, 75 ± 5% and 70 ± 5% of Agrobacterium-inoculated leaf explants formed callus on selection medium irrespective of the selection agent for the cvs. Brussels Witloof, Pain du Sucre, Pan di Zuc-chero, Poncho and Sponda da Taglio, respectively. Shoots regenerated within 2 months of the culture of explants on selection medium containing kanamycin or glufosinate ammonium (Figure 2A, B).

Adventitious roots developed on 65 ± 5% of the shoots regenerated from inoculated and unin-oculated leaf explants of all of the 5 chicory cvs. following excision of shoots from the parent callus and transfer to medium in screw-capped glass jars (Figure 2C). Ninety ± 5% of the rooted regenerated plants that were resistant to the selection agents, and control (non-transformed) plants from all cvs., survived transfer to compost. Plants regenerated from Agrobacterium-inoculated explants were morphologically identical to plants regenerated from uninoculated explants (Figure 2D).



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PCR analysis

DNA fragments of 175 bp, 178 bp, 159 bp and 209 bp (Figure 3A-D) corresponding to the coding

regions of Δ9, Δ8, Δ5 and PGHS-1 genes, respec-tively, were detected by PCR in selected, putatively transformed plants established in the glasshouse.

A B C

D

Fig. 2A, B. Callus formation (A) and subsequent shoot regeneration, arrowed, (B) from leaf explants of the cv. Brussels Witloof following inoculation with A. tumefaciens carrying the Δ9 + Δ8 genes and culture for 28 days on MS-based medium supple-mented with 1.0 mg l-1 BAP, 0.1 mg l-1 IAA and 5.0 mg l-1 glufosinate ammonium as selection agent. Fig. 2C. A regenerated shoot transferred to MS-based medium containing 0.1 mg l-1 IBA to induce root development.Fig. 2D. A plant of Brussels Witloof regenerated from a leaf explant inoculated with A. tumefaciens carrying the Δ9 + Δ8 genes (left), which is morpho-logically identical to its non-transformed counter-part (right). Bars = 1 cm - A, B, C; 6 cm - D.

A

B

C

D

1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10

Fig. 3A. Amplification of the Δ9 elongase gene in leaf DNA extracts of selected plants of cv. Brussels Witloof. Lane 1 = plasmid; lane 2 = wild type plant; lanes 3-7 = plants transformed with Δ9 + Δ8, Δ5 genes; lanes 8-12 = plants trans-formed with Δ9 + Δ8 genes.Fig. 3B. Amplification of the Δ8 desaturase gene in leaf DNA extracts of representative kanamycin-resistant plants. Lane 1 = plasmid; lanes 2, 4 and 7 = wild type plants of cvs. Sponda da Taglio, Pain du Sucre and Brussels Witloof, respec-tively; lanes 3, 5, 6, 8 and 9 = plants transformed with the Δ8 gene in the previously stated cvs.Fig. 3C. Amplification of the Δ5 gene in leaf DNA extracts of kanamycin-resistant plants. Lane 1 = plasmid; lanes 2 and 8 = wild type plants of cvs. Brussels Witloof and Sponda da Taglio, respectively; lanes 3 and 5-7 = transformed plants of the cv. Brussels Witloof; lanes 9 and 10 = transformed plants of cv. Sponda da Taglio.Fig. 3D. Amplification of PGHS-1 gene in leaf DNA extracts of glufosinate ammonium/kanamycin-resistant plants transformed with the Δ9 + Δ8 genes followed by the PGHS-1 gene. Lane = 1 plasmid; lanes 2 and 7 = wild type plants of cvs. Brussels Witloof and Sponda da Taglio, respectively. Lanes 3-6 and 8-11 = plants of Brussels Witloof and Sponda da Taglio transformed with the Δ9 + Δ8 and PGHS-1 genes.



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RT-PCR analysis using total RNA extracted from fully expanded young leaves excised from PCR-positive plants, confirmed the expression of the Δ9, Δ8, Δ5 and PGSH-1 genes in these randomly selected plants (Figure 4), although several of the plants that were PCR positive were RT-PCR negative (Table 1). Forty six plants, as confirmed by RT-PCR, expressed the Δ9 + Δ8 genes, with the majority of these being of the cv. Brussels Witloof (15), followed by Pan di Zucherro (13), Poncho (10), Sponda da Taglio (6) and Pain du Sucre (2). Thirty eight plants ex-pressed the Δ9 gene alone, with most being Poncho (16), Pan di Zucherro (13), Sponda da Taglio (6), Brussels Witloof (4) and Pain du Sucre (3). Plants

transformed with the Δ5 and Δ8 genes alone were RT-PCR negative. Seven plants of Brussels Witloof, 3 of Sponda da Taglio, 2 of Pan di Zucherro and 2 of Poncho expressed the Δ9 + Δ8 and Δ5 genes in combination following double transformation; 8 plants of Brussels Witloof and 4 of Sponda da Taglio expressed the PGHS-1 alongside the Δ9 + Δ8 genes. Double transformants were not recovered in Pain du Sucre, Pain du Zucherro and Poncho. As expected, PCR and RT-PCR analyses were negative using total RNA extracted from non-transformed plants.

Fig. 4. RT-PCR assay of total leaf mRNA of selected PCR positive plants transformed with the Δ9 gene and the Δ9 + Δ8 genes of cv. Brussels Witloof. Lanes 1 and 2 = plasmid and wild type plant, respectively; lane 3 = a plant transformed with the Δ9 gene; lanes 4-9 = plants transformed with the Δ9 + Δ8 genes, those in lanes 6, 8 and 9 are RT-PCR positive.



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GC analysisFreeze dried leaves from PCR and RT-PCR positive plants were analysed to confirm gene expression and the biosynthesis of fatty acids (Table 2). The construct pCB302.3 with the Δ9 + Δ8 genes was expressed optimally in tested plants, followed by the Δ9 gene from pCB302.1. Additionally, when plants carrying the Δ9 + Δ8 genes were transformed with the Δ5 de-saturase gene (pCAMBIA-23), this resulted in the production of arachidonic and eicosapentaenoic acids in the transgenic plants (Figure 5).

Fig. 5. GC profile of chicory leaf fatty acid methyl esters in cv. Pan di Zucchero. Fatty acids were extracted from a non-transformed plant (A), and transgenic plants express-ing the Δ9 gene (B), a transgenic plant with the Δ9 + Δ8 genes (C), and the Δ9 + Δ8, plus Δ5 genes (D). 1 = Linoleic acid (LA, C18:2 ∆9,12), 2 = α- Linolenic acid (ALA, C18:3 ∆9,12,15), 3 = Eicosadienoic acid (EDA, C20:2 ∆11,14), 4 = Eicosatrienoic acid (ETrA, C20:3 ∆11,14,17), 5 = Dihomo-γ-linolenic acid (DGLA, C20:3 ∆8,11,14), 6 = Eicosatetraenoic acid (ETA C20:4 ∆8,11,14,17), 7 = Arachidonic acid (AA C20:4 Δ5,8,11,14), 8 = Eicosapentaenoic acid (EPA C20:5 Δ5,8,11,14,17).



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Table 2: VLCPUFAs in representative examples of plants of different chicory cvs. expressing the Δ9 elongase gene, the Δ9 elongase + Δ8 desaturase genes, and the Δ9 elongase + Δ8 desaturase with the Δ5 desaturase genes.

Plant

Mol % of Fatty Acids Total 20 C Fatty Acids

6 3

6 3 Total

Linoleic acid

18:02 Substrat

e

Eicosa-dienoic

acid 20:02

9

Dihomo- -

linoleic acid

20:03 9 8

Arachidonic acid

20:04 9 8, 5

-linolenic

acid 18:03

Substrate

Eicosa-trienoic

acid 20:03

9

Eicosa- tetraenoic

acid 20:04 9 + 8

Eicosa-pentaenoic

acid 20:05

9 8, 5

B2508 6.00 1.59 7.35 1.07 13.65 1.08 5.07 7.00 10.0 13.1 23.2

B2511 5.00 0.16 4.97 0.27 15.92 0.18 1.01 4.43 5.4 5.6 11.0

S2601 1.00 0.35 1.00 1.44 4.59 0.17 4.94 1.70 2.8 6.8 9.6

S2605 10.00 6.98 1.38 0.28 6.55 0.20 0.65 3.50 8.6 4.4 13.0

B205 8.48 6.54 7.45 0.00 35.06 10.64 3.06 0.00 14.0 13.7 27.7

B233 10.05 6.50 10.44 0.00 36.21 9.80 2.85 0.00 16.9 12.7 29.6

Pa601 13.19 3.78 3.12 0.00 51.15 4.95 1.08 0.00 6.9 6 12.9

Pa602 11.53 3.85 2.54 0.00 52.74 5.52 1.18 0.00 6.4 6.7 13.1

Po1305 11.35 2.12 1.11 0.00 58.96 6.00 0.54 0.00 3.2 6.5 9.8

Po1307 9.67 2.15 1.60 0.00 52.01 3.43 1.38 0.00 3.8 4.8 8.6

Pa140 14.01 11.76 0.00 0.00 42.66 12.60 0.00 0.00 11.8 12.6 24.4

Pa153 12.94 7.32 0.00 0.00 51.41 13.52 0.00 0.00 7.3 13.5 20.8

Po745 19.84 5.15 0.00 0.00 34.34 3.09 0.00 0.00 5.2 3.1 8.2

Po749 27.72 3.02 0.00 0.00 46.02 1.72 0.00 0.00 3.0 1.7 4.7

S1701 18.19 5.86 0.00 0.00 43.42 8.66 0.00 0.00 5.9 8.7 14.5

S1705 13.84 4.49 0.00 0.00 46.25 10.27 0.00 0.00 4.5 10.3 14.8

Cn:m n= carbon chain number, m= number of double bonds.

B2508, B2511 - 9 + 8, 5 cv. Brussels Witloof.

S2601, S2605 - 9 + 8, 5 cv. Sponda da Taglio.

B205, B233, - 9 + 8 cv. Brussels Witloof.

Pa601, Pa602 - 9 + 8 cv. Pan di Zucchero.

Po1305, Po1307 - 9 + 8 cv. Poncho.

Pa140, Pa153 - 9 cv. Pan di Zucchero.

Po745, Po749 - 9 cv. Poncho.

S1701, S1705 - 9 cv. Sponda da Taglio.



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Seed production

There was considerable variation not only in the number of achenes produced by transgenic plants compared to non-transformed plants, but also be-tween the non-transformed plants themselves (n = 3, throughout). For example, the number of achenes ranged from 612 in the non-transformed cv. Poncho to 27 in Sponda da Taglio. Brussels Witloof, Pain du Sucre and Pain di Zucherro set 24, 38 and 27 achenes, respectively. Some plants transformed with the Δ9 gene produced achenes comparable in num-ber to their non-transformed counterparts, namely transformed plants of Pain du Sucre (30) and Pain di Zucherro (30). Other transgenic cvs. produced less achenes compared to non-transformed plants, as in Poncho (47) and Sponda da Taglio (1) and plants transformed with the Δ9 + Δ8 genes [Poncho (9), Pain di Zucherro (2) and Pain du Sucre (5)]. Brus-sels Witloof and Sponda da Taglio carrying the Δ9 + Δ8 genes failed to set achenes.

Enzyme immune assay (EIA)

EIA performed on PCR-positive freeze-dried leaves of 4 plants of the cv. Brussels Witloof using a pros-taglandin EIA screening kit confirmed expression of the PGHS-1 gene with significant accumulation (p<0.0004) of prostaglandin when compared with four wild-type (control) plants. Table 3 and Figure 6 show the concentrations of prostaglandins in transformed plants of the cv. Brussels Witloof.

DiscussionThe transformation protocol used in the present study to transform chicory was a modification of the method of Curtis et al. (1994), originally established for the transformation of lettuce. The main differ-ences were the use of leaves and cotyledons from 14 d-old seedlings instead of those from 7 d-old seedlings, and the use of MS liquid medium instead of Uchimiya and Murashige (UM; 1974)-based culture medium for dilution of the Agrobacterium suspensions. Co-cultivation was carried out in the dark and was performed on MS-based regeneration medium and not on UM medium. Additionally, 1 mg l-1 BAP and 0.1 mg l-1 IAA were used instead of 0.5 mg l-1 BAP and 0.04 mg l-1 NAA as growth regulators. In contrast to the results reported by Abid et al. (2001), there was no requirement for the addition of acetosyringone to achieve reliable transformation of chicory.

PCR assay for ∆9, ∆8, ∆5 and Δ9 + Δ8 genes showed that the cv. Brussels Witloof was optimal in response to transformation with all the con-structs used, with 56% of selected plants carrying the transgenes, followed by Poncho (50.5%). In the cvs. Pain du Sucre and Pain di Zucchero, 45% and 47%, respectively, of the selected plants were transformed. The cv. Sponda da Taglio showed the least response (40%). Brussels Witloof and Sponda

Fig. 6. Mean prostaglandin concentration in transgenic plants compared to wild-type control plants.

Table 3: Prostaglandin concentration in plants of the cv. Brussels Witloof expressing the PGHS-1 gene.

Plant

Prostaglandins pg 100 mg-1 freeze-dried leaf tissue

Control pg 100 mg-1 freeze-dried leaf tissue

B2701 945.28 253.78

B2704 719.99 332.04

B2707 758.22 259.85

B2708 643.93 311.21

Mean 766.85 289.22

0

100

200

300

400

500

600

700

800

900

Con

cent

atio

n of

pro

stag

land

ins

pg p

er 1

00 m

g fre

eze-

drie

d le

af p

lant

tiss

ue Wild-type plants

PG transformed plants

Plant

Mol % of Fatty Acids Total 20 C Fatty Acids

6 3

6 3 Total

Linoleic acid

18:02 Substrat

e

Eicosa-dienoic

acid 20:02

9

Dihomo- -

linoleic acid

20:03 9 8

Arachidonic acid

20:04 9 8, 5

-linolenic

acid 18:03

Substrate

Eicosa-trienoic

acid 20:03

9

Eicosa- tetraenoic

acid 20:04 9 + 8

Eicosa-pentaenoic

acid 20:05

9 8, 5

B2508 6.00 1.59 7.35 1.07 13.65 1.08 5.07 7.00 10.0 13.1 23.2

B2511 5.00 0.16 4.97 0.27 15.92 0.18 1.01 4.43 5.4 5.6 11.0

S2601 1.00 0.35 1.00 1.44 4.59 0.17 4.94 1.70 2.8 6.8 9.6

S2605 10.00 6.98 1.38 0.28 6.55 0.20 0.65 3.50 8.6 4.4 13.0

B205 8.48 6.54 7.45 0.00 35.06 10.64 3.06 0.00 14.0 13.7 27.7

B233 10.05 6.50 10.44 0.00 36.21 9.80 2.85 0.00 16.9 12.7 29.6

Pa601 13.19 3.78 3.12 0.00 51.15 4.95 1.08 0.00 6.9 6 12.9

Pa602 11.53 3.85 2.54 0.00 52.74 5.52 1.18 0.00 6.4 6.7 13.1

Po1305 11.35 2.12 1.11 0.00 58.96 6.00 0.54 0.00 3.2 6.5 9.8

Po1307 9.67 2.15 1.60 0.00 52.01 3.43 1.38 0.00 3.8 4.8 8.6

Pa140 14.01 11.76 0.00 0.00 42.66 12.60 0.00 0.00 11.8 12.6 24.4

Pa153 12.94 7.32 0.00 0.00 51.41 13.52 0.00 0.00 7.3 13.5 20.8

Po745 19.84 5.15 0.00 0.00 34.34 3.09 0.00 0.00 5.2 3.1 8.2

Po749 27.72 3.02 0.00 0.00 46.02 1.72 0.00 0.00 3.0 1.7 4.7

S1701 18.19 5.86 0.00 0.00 43.42 8.66 0.00 0.00 5.9 8.7 14.5

S1705 13.84 4.49 0.00 0.00 46.25 10.27 0.00 0.00 4.5 10.3 14.8

Cn:m n= carbon chain number, m= number of double bonds.

B2508, B2511 - 9 + 8, 5 cv. Brussels Witloof.

S2601, S2605 - 9 + 8, 5 cv. Sponda da Taglio.

B205, B233, - 9 + 8 cv. Brussels Witloof.

Pa601, Pa602 - 9 + 8 cv. Pan di Zucchero.

Po1305, Po1307 - 9 + 8 cv. Poncho.

Pa140, Pa153 - 9 cv. Pan di Zucchero.

Po745, Po749 - 9 cv. Poncho.

S1701, S1705 - 9 cv. Sponda da Taglio.



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da Taglio were the most responsive to double trans-formation i.e. transformation with the Δ9 + Δ8 genes followed by either the ∆5 or the PGHS-1 genes. Double transformed plants showed the addition of each extra transgene, without change in the copy number of the Δ9 + Δ8 genes introduced initially into their genomes. RT-PCR confirmed transgene expression in these PCR-positive plants.

GC analysis confirmed that expression of the ∆9 elongase gene alone by production of eicosadienoic and eicosatrienoic acids was optimal in Poncho, Pan di Zucchero and Sponda da Taglio. However, expression of the ∆9 elongase and ∆8 desaturase genes together, resulting in the production of ei-cosadienoic, eicosatrienoic, dihomo-γ-linolenic and eicosatetraenoic acids, was best in Brussels Witloof, Pan di Zucchero and Poncho. Those cvs. that responded to double transformation with the ∆9 elongase + ∆8 desaturase genes followed by the ∆5 desaturase gene, synthesized eicosadienoic, ei-cosatrienoic, dihomo-γ-linolenic, eicosatetraenoic, arachidonic and eicosapentaenoic acids. GC analy-sis of plants transformed with either the ∆8- or the ∆5-desaturase genes alone failed to show the syn-thesis of VLCPUFAs, probably due to the absence of the substrates eicosadienoic and eicosatrienoic acids required for their desaturating properties to produce the subsequent fatty acids. This provided a way of confirming the steps of the ∆8 pathway described by Wallis and Browse (1999) for the pro-duction of arachidonic and eicosapentaenoic acids. Moreover, GC analyses showed that if plants were transformed with the 3 genes on the same vector (∆9 + ∆8 + ∆5 in pCAMBIA-13-EC-∆5-∆8-∆9) and transformants selected using 5 mg l-1 hygromy-cin, the three genes were silent in the transformed plants. This may have been due to the use of the same promoters for the three transgenes of inter-est, although some of these promoters were in op-posite reading directions. Interestingly, this result was consistent with the work performed by Bhullar et al. (2003) and Robert et al. (2005).

The limited production of achenes, especially in transgenic plants, may have been increased if the plants had been vernalized. In future experiments to evaluate longer-term expression of transgenes in chicory, it may be advantageous to micropropagate

the original transgenic plants from stem tip cuttings or leaf explants in order to increase the number of plants for such evaluations.

The biosynthesis of considerable concentra-tions of the important VLCPUFAs, arachidonic acid (0.1 – 3.6%) and eicosapentaenoic acid (1.0 – 7.0%), has been achieved through the ω3 ∆8 and ω6 ∆8 desaturation biosynthetic pathways for VLCPU-FA production in the edible leafy vegetable, chic-ory. This was consistent with the work of Qi et al. (2004) with Arabidopsis thaliana. Importantly, the percentage accumulation of C20 VLCPUFAs fatty acids reached 29.6 Mol% in some chicory plants compared with 20 Mol% produced in A. thaliana, with arachidonic acid and eicosapentaenoic acid concentrations of 6.6 and 3%, respectively (Qi et al. 2004). These results confirm that genetic engi-neering of a biosynthetic pathway is possible, as indicated by other workers (Jimenez et al. 2009; Cheng et al. 2010; Napier and Graham 2010; Petrie et al. 2010), even if that pathway is not normally present in the target plant. In the present investiga-tion, the ∆8 desaturation biosynthetic pathway for VLCPUFAs production was inserted into chicory, a leafy vegetable that is normally consumed raw. Furthermore, the production of the physiologically important prostaglandins was confirmed by EIA in the cv. Brussels Witloof with a mean concentration of 767 pg 100 mg-1 freeze-dried leaf. This is the first report of the production of mammalian pros-taglandins in plants.

Acknowledgements.HM acknowledges support from the Egyptian Government for a Higher Degree Studentship.

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Vol. 20, 4 (2011) 269–340AG

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AGRICULTURAL AND FOOD SCIENCE

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A G R I C U L T U R A L A N D F O O D S C I E N C EVol. 20, No. 4, 2011

ContentsPietola, K., Myyrä, S., Jauhiainen, L. and Peltonen-Sainio, P.Predicting the yield of spring wheat by weather indices in Finland: implications for designing weather index insurances.

269

Ghavi Hossein-Zadeh, N. Comparison of linear and threshold models for the estimation of genetic parameters and trends for still-birth in Holsteins cows.

287

Turner, T. D. and McNiven, M.A. In vitro N degradability and N digestibility of raw, roasted and extruded canola, linseed and soybean.

298

Lisiewska, Z, Gębczyński, P., Słupski, J. and Kur, K. Effect of processing and cooking on total and soluble oxalate content in frozen root vegetables prepared for consumption.

305

Suproniene, S., Mankeviciene, A., Kadziene, G., Feiziene, D., Feiza, V., Semaskiene, R. and Dab-kevicius, Z.The effect of different tillage-fertilization practices on the mycoflora of wheat grains.

315

Mekky, H., Mohamed, M., Lazarus, C., Brian Power, J. and Davey, M.R. Biosynthesis of very long chain polyunsaturated fatty acids in the leafy vegetable chicory.

327

ISSN e lec t ronic ed i t ion 1795-1895

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