Keywords: JEL Classification codes: C22, Q02, Q16 Biofuel, Oil price, Agricultural commodity, Nonlinear econometrics

Given the few studies highlighting the existence of an oil-price effect on agricultural commodity prices in the last decade, we sought to demonstrate the role of first-generation biofuel production in such a relationship. Relying on a smooth transition cointegration approach, we show that biofuel development has led to an increase in the long-term price effect of oil on agricultural commodity prices. Thus, the increasing production of biofuels contributes to the price rise of agricultural commodities. This result underlines the importance of accelerating second-generation biofuel production to replace first-generation biofuels.

Introduction

As seen in Figure 1, the sharp rise in oil prices from 2004-2006 resulted in a small increase in agricultural commodity prices1 compared with those from 2007-2008. 2 Thereafter, agricultural and oil prices displayed significant co-movements;

as illustrated by the strong correlation that exists between the price variations. In addition, this correlation has accentuated during an acceleration phase of US biofuel production development. 3 Biofuels constitute energy sources developed to replace fossil fuels. However, biofuels are produced from agricultural commodities, and this utilization of agricultural commodities competes with their food use. This competition is the main cause of the "food versus fuel" debate, according to which biofuel production accentuates the food insecurity situation in some countries, particularly developing ones. In addition, a main characteristic of the biofuels market is governmental support. Thus, if this new market has indeed contributed to the price rise of agricultural commodities, producer country governments would primarily be responsible.

Insert Figure 1 In the 2000s, the prices of several commodities increased, and many studies sought explanatory factors, particularly in agricultural raw materials. For example, the OECD (2008) attributed the agricultural commodities' price increase to five causes. First, the price increase reflected higher production costs generated by the rise in oil prices. Second, there was weak growth in production due to bad weather conditions in major producing countries, such as Australia and Canada. Third, there was a sharp rise in demand explained largely by biofuel production development. Fourth, low inventories contributed to the price increase by preventing quantity adjustment in the markets. Finally, the investment increase in agricultural derivative markets led to a short-term rise in future prices. This view was shared by Mitchell (2008), who added the role of a weak US dollar. Abbott et al. (2011) and Abbott and Borot de Battisti (2011) emphasized these factors and mentioned the economic growth increase in developing countries. These studies, and all of the literature on this subject, can help to improve our understanding of the price formation mechanisms and price volatility of agricultural commodities in the last decade.

Falling into this strand of the literature, the aim of this paper is to study the effect of oil prices on agricultural commodity prices, both in the short-and longterm. We go further than the previous literature by paying particular attention to the effect of biofuel production development on the long-term link. This effect is highly controversial because it has indeed never been proven. To this aim, we use various methods from nonlinear econometrics, including smooth transition cointegration. These methods allowed us to estimate the oil-agricultural commodity price nexus, depending on the level of biofuel production. With a nonlinear approach, we are able to determine the existence of different regimes in the oil-price effect and to verify the role of biofuel development in alternating between these regimes. Therefore, it is possible to estimate this effect both in the absence of biofuel production and when it is at a high level, and to check the widespread intuition that there has been an increase in the oil-price effect caused by biofuel development. The use of a smooth transition approach rather than a model with an instantaneous regime change is justified by the slow development of biofuel production and the time required for changes in market behavior. Our empirical analysis relies on five agricultural commodities. Three are directly linked to biofuel production as input: corn and soybean for US production and rapeseed for European production. Additionally, we use wheat and sunflower to examine whether biofuels affected previous commodities substitutes.

Our contribution to the existing literature is twofold. First, we provide new evidence about the link between oil and agricultural commodity prices using a longer period than much of the literature, including the biofuel production development phase and the last economic crisis. Second, regarding our main contribution concerning the effect of biofuel production on agricultural prices, we provide new evidence in favor of or against an inflationary effect of biofuel production on agricultural commodity prices. Thus, we contribute to the "food versus fuel" debate by showing a positive effect of biofuel production on all of the prices of studied agricultural commodities through an increase in the oil-price effect. This result confirms that biofuel production has been one of the key causes of agricultural price increase in recent years.

This paper is organized as follows. Section 2 reviews the related literature.

Section 3 presents the methodology and data used. Section 4 is devoted to our empirical results, and Section 5 draws our main conclusions.

Literature review

In the 1990s, studies on the link between commodity prices intensified. 4

The seminal contribution of Pindyck and Rotemberg (1990), although highly debated, opened a new area of research. They studied the correlation matrix of price variations unexplained by macroeconomic variables. The authors showed that macroeconomic variables do not explain the co-movement between commodities. They emphasized the existence of excess co-movement, which is unexplained by macroeconomic shocks, between different unrelated commodities.

Palaskas and Varangis (1991), Leybourne et al. (1994), with a cointegration approach, and Deb et al. (1996), through a multivariate GARCH model, then deepened this issue and showed that, although there are excess co-movements, they are of small magnitude and involve few commodities. Several reasons for this co-movement excess were given by the authors, such as the presence of a speculator's liquidity constraint on financial markets, herding behavior or the possibility that agents interpret supply shocks specific to a market as macroeconomic shocks.

Other plausible alternative causes, not mentioned in these papers include the link between energy markets -such as oil -and other commodities. This last point has generated several studies that help to highlight the effect of oil price on

4 Table 1 summarizes the main studies cited in this section.

commodity prices and the transmission mechanism between energy and other commodity markets. Hanson et al. (1993) studied the cost effect of oil on various commodities using the USDA/ERS5 Computable General Equilibrium model. Their main finding was the heterogeneity of commodity price responses to an oil shock depending upon assumptions about the exchange rate regime and the trade balance evolution. Thus, the oil-price effect on commodity markets cannot be summarized by the cost effect. However, they did not compare the ability of macroeconomic and oil shocks to explain co-movements. More recently, Gohin and Chantret (2010) studied the effect of an oil shock on agricultural commodity prices using the Computable General Equilibrium model of the GTAP6 with or without income effect. They showed that the introduction of the income effect can reverse the sign of the relationship between oil and food products for oil-importing countries. They found a decrease in world prices for beef and dairy products. For these markets, the income effect was greater than the cost effect. However, this relationship was not observed in the wheat market with an increase of the world price or in the US and European markets. According to Gohin and Chantret (2010), this absence was due to the lower income elasticity demand for wheat and other grains. The same result should be checked for all cereals. In addition, they mentioned that the production-cost effect was unlikely to exist in the short term due to the quasi-fixity of most production factors. Thus, two shock transmission mechanisms, cost and income effects, can have opposite signs and therefore compensate for one another. Ai et al. (1996) investigated the causes of comovements with the introduction of competition between two models, one macroeconomic, in which the co-movements are explained by macroeconomic variables, and the second, microeconomic, with supply and demand factors. They emphasized that the most efficient model to explain co-movements was the microeconomic model, showing that the supply factors would be the main causes of price co-movements.

Different approaches have been used to examine the presence of oil-price effects on commodity prices. Relying on the Johansen (1991) cointegration method, Kaltalioglu and Soytas (2009) Penaranda and Ruperez-Micola (2011) applied the Bai and Perron (1998, 2003) break tests on the regression between oil and agricultural commodity price Insert Table 1 3 Data and Methods

Data

We consider daily prices for oil, corn, soybean, sunflower oil, rapeseed oil and wheat. All agricultural data are from the USDA and Thomson Reuters. The oil price series comes from Thomson Reuters, whereas US biofuel production is given by the Energy Information Administration. 1986 (i.e., 7545 observations). Note that all price series are logtransformed, the corresponding estimated coefficients thus representing elasticity between prices. All series are displayed in Figure 2.

Insert Figure 2 Insert Table 2 Table 2 presents the descriptive statistics for the growth rates of the different variables used for the entire period, and for pre-and post-2006 periods;

this date marking a break. 8 Concerning the 1986-2014 period, the prices of corn, wheat and soybean increase on average by 0.01% per day, with standard deviations of 1.91, 1.61 and 2.21, respectively. Note that this growth was lower in the first period, with a zero growth rate, than in the second, in which prices rose by 0.03% for corn and 0.02% for the other two agricultural commodities previously mentioned. In addition, the standard deviations of these growth rates are higher in the second period, reflecting the greater price volatility since 2006. The sunflower oil price evolved similarly. From 2001-2014, it increases by 0.01% per day, against -0.01% and 0.02% for the pre-and post-2006 periods, respectively. The rapeseed oil price has an inverse evolution. It increases faster in the first period, with an average rate of 0.04%, than in the second, at 0.01%. Moreover, these prices have characteristics specific to financial variables with negative skewness and high kurtosis. These characteristics indicate that these prices are subject to more negative, large-scale fluctuations than the normal law would predict. The oil price increases on average by 0.01% per day over the entire period, with a higher growth rate in the pre-2006 period. Its volatility is also higher over this period.

Statistics from US biofuels production reflect its development. The production growth was 0.04% per day before 2006 and 0.06% thereafter. For the SP500 index, the crisis affected its evolution. Indeed, its average daily growth rate was 0.03% before 2006 compared to 0.02% thereafter.

Methodology

Preliminary tests

Before performing our study on the role of biofuel production to investigate a possible evolution of the oil-price effect on agricultural commodity prices, it is necessary to (i) determine the series integration order, (ii) test for nonlinearity related to biofuels, and (iii) determine its form.

As a first step, we study the series' integration degree by performing ADF (1981), Phillips-Perron (1988) (hereafter PP), and KPSS (1992) unit root tests. To account for the presence of a break, the Zivot and Andrews (1992) and Perron (1997) tests, hereafter ZA and P, respectively, are also implemented.

Prior to the estimation of our nonlinear specification, we rely on the Terasvirta (1994) procedure to test for the presence of nonlinearity and determined its functional form. To this end, we estimate the following equation using Ordinary Least Squares:9

, ≈ + . + . . + . . + . . (1)
where denotes the price of agricultural commodity i, is the oil price, stands for the biofuel production, and , for = 3,4,5, are the parameters related to the nonlinearity. The first step of this procedure consists of testing the joint significance of these parameters via a Fisher-type test of restricted model against no restricted model, i.e., linearity against nonlinearity. If nonlinearity presence is confirmed, the second step allows us to determine its shape by choosing between the exponential and logistic functions. 10 More specifically, we successively test the following hypotheses:

: = 0 against : ≠ 0 (2) : = 0 | = 0 against : ≠ 0 | = 0 (3) : = 0 | = = 0 against : ≠ 0 | = = 0 (4)
where the rejection induces the use of the logistic function, the -only rejection entails the exponential function utilization, and the rejection of leads to the choice of the logistic function.

Once the preliminary study was done, we can investigate the oil-price effect on agricultural commodities and the effect of biofuels on the oil-price effect. For this analysis, it is crucial to consider the possibility of different effects in the shortand long-term using a cointegration approach.

Long-term oil-price effect

To investigate the long-term relationship between the oil price and each agricultural commodity price i, we consider the smooth transition regression model:

, = + . + . ! " + # + # . + # . ! ". $ % , & , " + ' , (5)
To control for the economic activity effect, we include the SP500 index, noted as ! . The first part of the equation represents long-term oil and economic activity effects on agricultural commodity prices, whereas the second part also accounts for the effects related to the quantity of biofuels produced. This last effect depends upon the value taken by the transition function $, ranging between 0 and 1. This function is characterized by the transition speed, %, and the threshold, &. For the estimation, we use the maximum likelihood estimator of Fisher (1912).

To test for cointegration, we perform the Shin (1994) Next, we perform an exclusion test to check the oil inclusion in the possible cointegration relationship. Indeed, with the use of the SP500 index, we have three variables in the cointegration vector, which is problematic because the latter may include only two variables instead of three. Although the multivariate approach seems to be more adequate in this context, we prefer to use an exclusion test via the likelihood ratio test, which allows us testing the null hypothesis of the oil-price exclusion in the cointegration vector against the inclusion alternative hypothesis.

Short-term oil-price effect

Finally, if cointegration is obtained, we estimate the corresponding Error

Correction Model (ECM) by Ordinary Least Squares:12

∆ , = ) . ∆ + ) . ∆! + * . ' + , , + -, (6)
where ' + , , denotes the cointegration relationship residuals, and -, are i.i.d

Gaussian errors. In the case of no cointegration relationship, a simple equation of the short-term relationship is estimated. We perform misspecification tests, such as Jarque-Bera (1980), White (1980) and Ljung-Box (1978) tests. In addition, we apply an iterative version of the Chow (1960) test from 06/01/2007 to 12/31/2009 to account for the recent crisis effect. This period corresponds to the economic crisis period defined by the NBER, extended by 6 months before and afterward. This extension allows us to account for the subprime crisis outbreak and the beginning of the post-crisis period. We retain the break date that corresponds to the strongest rejection of the null hypothesis of stability.

Empirical Results

We now present the results corresponding to the application of the tests and methods previously presented.

Preliminary tests

The detailed unit root test results are available in Table 3. All of the agricultural commodities prices series are stationary in their first difference, regardless of whether a break date is considered, as well as the SP500 index. 13 Biofuel production and oil price series are integrated of order 1. For the latter, the various tests are in contradiction due to the presence of a break in 2003.

Insert Table 3

The Terasvirta (1994) procedure allows us to verify the relevance of our modeling choice. The first step should confirm our intuition of a biofuel production evolution impact on the oil-price effect. The results, presented in Table 4, confirm the nonlinearity presence for the five equilibrium relationships. Indeed, for each agricultural commodity, the test of linearity against nonlinearity presents a test statistic greater than the 5% critical value. This result confirms the existence of an effect of biofuel production on the link between oil and agricultural commodity prices. With the second step, we could determine the transition function form.

Considering that biofuel development could cause the emergence or increase of the link between oil and the studied agricultural commodities, the logistic function seems more appropriate. In successively rejecting the three null hypotheses of Student's t tests, this second step concludes that the logistic function is adequate to represent this nonlinearity, in line with our intuition.

13 However, this was not true for the 2001-2014 period, in which it was zero order integrated with a rupture in 2008. The ZA and P tests yielded, respectively, statistics of -5.79 and -5.76 for a 5% critical value of -5.08 and -5.59, with a break in intercept and trend for 08/29/2008 and 08/28/2008. These results are surprising. Unfortunately we must consider them to be I(1). Indeed, one known property of financial variables consists of being non-stationary for prices and stationary for returns.

Insert Table 4

Long-term oil-price effect

Knowing the shape of the function representing the nonlinearity associated with biofuel production, we are able to investigate the long-term relationship.

Before interpreting the estimation results, we focus on the test conclusions.

Afterwards, we concentrate our analysis on three points: the elasticity of agricultural commodity prices with oil prices with and without biofuel production and the dynamic of the oil-price effect. Recall that the SP500 index allows us to account for the economic environment, but its effect on commodity prices is not the subject of the present study. The value of the biofuel production threshold, &,

indicates when the transition function takes the value of 0.5. We prefer to subsequently interpret the value causing the appearance of the biofuel production effect on the price effect between oil and commodities and the moment when it is maximal.

Cointegration evidence

Existence of the cointegration relationship would allow us to highlight an equilibrium relationship in which we consider that the oil price affects the agricultural commodity prices. Indeed, various studies have already shown that oil prices cause agricultural commodity prices in the long-term relationship. 14 Table 5 displays the cointegration and exclusion test results. On the one hand, the S test unambiguously confirms the presence of a cointegration relationship between the oil price, the SP500 index and each agricultural commodity price. On the other hand, the CS test is less categorical. The null hypothesis of cointegration is rejected for the corn and wheat relationships.

However, it is also possible to interpret this result as a larger persistence of deviations from equilibrium for these commodities. Indeed, this problem is amplified by sub-sample use. Thus, we consider that all relationships are cointegration relationships. Finally, the exclusion test confirms the oil price presence in the cointegration space for each relationship and, therefore, the existence of long-term oil-price effects on agricultural prices.

Insert Table 5

On the whole, our findings show that there is a long-term oil-price effect on the agricultural commodities price for each agricultural product studied.

Oil-price effect without biofuel production

Upon verifying the cointegration relationship between agricultural commodities and oil prices, we focus on the estimated coefficients of equation ( 5)

presented in Table 5. The oil effect column represents the value of the elasticity between the prices of oil and the agricultural commodity, noted as , whereas the oil-biofuel effect column is the additional oil-price effect linked to higher biofuel production, # .

In the absence of biofuel production, the commodities studied could be classified into three categories. Soybean, rapeseed and wheat have positive price elasticities with oil price: for a 10% increase in the oil price, the prices of wheat, soybean and rapeseed increased by 0.92%, 0.24% and 4.52%, respectively.

Sunflower has price elasticity with oil that is non-significantly different from zero, meaning that its price would be independent from that of oil. Corn provides an interesting case. Its price elasticity is significantly negative, a rise in oil price of 10% would lead to a 1.13% decline in its price.

The difference in the oil-price effect between commodities can have two main causes: the oil products used in the production process and income elasticity.

The oil-price effect on commodity prices depends positively upon oil-related production cost share in total costs. Therefore, we study the production cost linked with energy and fertilizers; the latter have strong price elasticity with the oil price. 15 In parallel, the oil-price effect is inversely proportional to agricultural commodity income elasticity. However, because this value is lower for food goods, it is necessary to consider the income effect via meat. Indeed, the income elasticity for meat is higher. 16 Thus, the effect of income variation on agricultural commodity prices depends positively upon the portion of animal feed used overall. Finally, the oil-price effect could decrease, or become negative, when the share of animal feed of total demand increased.

We face one problem. To our knowledge, European farm data for sunflower and rapeseed are not available. However, according to our estimation results, it appears that the rapeseed crop requires a higher amount of oil-based product, and/or its animal feed use in the total would be of low importance. To explain this difference, we note that rapeseed requires a large amount of nitrogen during its culture and is a major consumer of fertilizer. 17

For corn, soybean and wheat, USDA data 18 allow us to interpret the results in detail. We calculate the share of oil-based production costs in total. All of these calculations are presented in Appendix 1. For example, the oil-based cost shares for these commodities in 1990 were 53.01%, 29.20% and 48.85%, respectively.

Between 1986 and 2014, the soybean crop is a low consumer of oil-dependent input in comparison to wheat. This fact explains the lower oil-price effect for soybean relative to wheat. However, this cost channel does not explain the negative-price effect for corn given the magnitude of the share of oil dependence.

We calculate the share of production allocated to animal feed. Specifically, that share is the ratio of "feed and residual use" to total domestic use. For the 1990-1991 period, soybean and wheat have a small proportion of use in animal feed, apparent in Appendix 2, with various other uses. This share is approximately 7.45% and 35.34%, respectively. 19 In contrast, corn is used at approximately 76.38% of animal feed. Thus, when the oil price increases, economic activity slows or decreases. This effect is accompanied by a decrease in income and meat consumption. In the long term, producers should reduce production to maximize 16 For example, Gallet (2010) lists 3357 income elasticities for several meats estimated in 393 studies to investigate the estimation method effect and other features of estimated value. The average income elasticities for beef, poultry, pork and lamb were approximately 1, 0.82, 0.8 and 0.74, respectively. 17 Rapeseed requires between 140 and 200 units per hectare of nitrogen against 80 for sunflower. 18 Data are available for each agricultural commodity in these two tables: "Commodity Costs and Returns" and "Supply and disappearance". 19 In the case of wheat, this share may seem high, but the average is 18.7% over the 1986-2014 period.

profits, causing a drop in corn demand and therefore its price. This process is relatively long, explaining a slower return of corn price to its equilibrium. This slower return corresponds to the CS test results yielding an absence of a cointegration relationship for corn. These data confirm the estimated coefficient and support the assertion that a negative-income effect prevails over the cost effect in the case of corn, unlike for other commodities.

Thus, we provide new evidence of the existence of a long-term positive effect of oil prices on commodity prices for three agricultural products, and a negative price effect for corn.

Oil-price effect with biofuel production

We now focus on the oil-price effect linked to biofuel production. Our results

suggest that a 10% increase in oil price would cause a rise in corn, soybean and rapeseed demand for biofuel production, with a respective effect on their prices of 9.3%, 4% and 3.65%. For wheat and sunflower, their prices increased by 3.98% and 8.85%, respectively. Note that these results are valid when biofuel production exceeds the threshold, leading to a maximum price effect through the biofuels channel, and the original oil-price effect is not considered. With the latter, the corn, soybean and rapeseed price increased by 8.17%, 4.24% and 8.17%, respectively.

For wheat and sunflower, their rise was 4.9% and 8.85%.

Let us study initially the cases of corn, soybean and rapeseed. Corn is the main feedstock used in US ethanol production. Because the latter is found more frequently in this country than biodiesel production, it is normal for the oil-biofuel effect to be high in the corn price formation mechanism. 20 The very high value of the oil-biofuel effect for corn, compared with soybean and rapeseed, can be explained by two other reasons. First, the biofuel sector used approximately 46% of the corn consumed in the US in 2011-2012 against 27% 21 for soybean. The dependence is therefore stronger between corn and biofuels. Second, the share of corn consumption for animal feed fell from 74.24% in 1995-1996 to 41.68% in 2012-2013, whereas it decreased only from 7.5% to 5.87% in soybean. The income effect, highlighted previously, has declined for corn. The additional oil-price effect was comparable between soybean and rapeseed. In 2013, they accounted for approximately 53% of biodiesel production inputs in the European Union and the United States. Unfortunately, the detailed data do not seem to be available for European rapeseed.

The cases of wheat and sunflower are even more interesting. Indeed, these two commodities are rarely used in biofuel production; their main remaining use is food. However, these results can highlight two indirect effects of biofuel production through the substitution effect. On the one hand, rising corn prices after the increase of biofuel production caused a food consumption diversion of corn to wheat and caused wheat prices to rise due to the substitution effect of demand.

This effect could be observed in the increase in the share of wheat used in food from an average of 66% between 1986 and 2000 to 79% over the 2001-2014 period. On the other hand, the rise in corn prices may have caused a diversion of wheat producers to corn crops that decreased the wheat supply and raised its price. This last channel was encouraged by the fact that crop rotation, in particular wheat to corn, is good for the soil balance. Note that this substitution effect is very important for sunflower, with an oil-price effect linked to biofuels near 0.9. This importance is due to a stronger substitutability between vegetable oils compared with cereals.

Our study shows that biofuel production has led to the increase, or the appearance, of a long-term oil-price positive effect on each commodity studied.

Biofuel production effect dynamic

Let us now study the evolution of the oil-price effect. As previously mentioned, an important advantage of the chosen methodology is the possibility to analyse the oil-price effect dynamic.

Insert Figure 3

As seen from Figure 3 above, as long as the daily biofuel production was less than approximately 200,000 barrels, i.e., before 2002, the oil-price effect did not increase for corn. This situation corresponds to a share of corn used in this sector compared with total domestic consumption of less than 10%. Once this threshold was exceeded, the oil-price effect with biofuels channel appeared. The oil-price effect reached its maximum at a daily production of nearly 1.4 million barrels, attained in 2011. On this date, corn consumption related to biofuels achieved 45%.

Note also a relatively slow evolution of the oil-price effect for corn with the lowest transition speed parameter, 5.491, compared with other commodities. This result is due to the low growth of ethanol production in 2002-2011 compared with biodiesel, with an average monthly growth rate of 1.9% against 6.5% for biodiesel.

An extremely interesting point visible on the previous figure is the proximity between the appearance of the oil-biofuel effect for wheat and the switch to a positive oil-price effect for corn and the relative parallelism between the two slopes. This confirms the substitution between these two cereals. the onset of this effect for rapeseed and sunflower suggests that there has been a substitution in oil food consumption in the European Union. However, this point should be checked with a study based on European consumption data.

Insert Figure 4

Short-term oil-price effect

Having highlighted the long-term oil-price effect on agricultural commodity prices and having described the biofuel production effect on this oil-price effect, we now focus on the short-term oil-price effect with the estimation of equation ( 6).

Table 6 presents the ECM estimation results with the associated break test. 22 The oil effect column represents the value of the short-term elasticity between the prices of oil and the agricultural commodity, noted as ) , whereas the adjustment coefficient column is the estimator for the adjustment speed, * .

Insert Table 6

As shown, a weak positive oil-price effect exists in the short term, except for sunflower. Having used daily data, we can surmise that this effect comes from the speculation channel. Indeed, as we mentioned previously, Gohin and Chantret (2010) stated that the oil-cost effect should not be visible in the short term. In a period of rising oil prices, agents expect an increase in the agricultural commodity prices and make purchases on agricultural markets. As expected, the adjustment coefficient is negative, highlighting the return to an equilibrium process. When the commodity price is above its equilibrium level, the adjustment force tends to decrease that price. However, this process is relatively low given the value of the coefficient. The half-life of the deviation from equilibrium is approximately 46 weeks for corn, 35 for soybean and wheat and 23 weeks for rapeseed. 23 The interesting. Indeed, the relationship change for rapeseed occurred nine days after an important liquidity injection by several central banks. 24 For corn, the break date was five days after a similar action. 25 It is possible that this liquidity led to a speculative rise in financial and agricultural markets. The break dates for soybean, wheat and sunflower did not correspond to any events, to our knowledge.

Conclusion

Given the rise in the prices of agricultural commodities in the last decade, this paper aims at investigating the effect of first-generation biofuel development on agricultural commodity prices via the oil-price effect. To this end, we rely on the estimation of a nonlinear, smooth transition regression model for five agricultural commodities.

Our key findings can be summarized as follows. First, the main conclusion is that there is a positive effect of biofuel production on agricultural prices. This effect confirms that biofuel production has been one of the key causes of agricultural price increases in recent years. Second, the biofuels effect is not confined to agricultural commodities used in its production; rather, it is transmitted to other agricultural markets through the substitution effect. Third, in the absence of biofuel production, there is a long-term positive oil-price effect, with a low magnitude, on some agricultural commodities prices. Fourth, corn prices are characterized by a long-term negative oil-price effect through the income channel. Finally, there is a weak positive short-term oil-price effect on agricultural prices, with a break during the last crisis.

Our results have important policy implications. They suggest that it is urgent to reduce first-generation biofuel production by accelerating the introduction of second-generation biofuels. Such acceleration would have no effect on agricultural commodities prices because the production of second-generation biofuels only uses agricultural plant residuals and non-food plants.

As a possible extension and given the presence of a break in the short-term relationship during the last recession, it would be interesting to check the presence of nonlinearity in the short-term oil-price effect with respect to various financial variables.

-Choi, I., Saikkonen, P., 2010, "Tests for Nonlinear Cointegration", Econometric Theory, Vol. 26, Is. 3, pp. 682-709 -Chow, G.C., 1960 For the Linearity test and the three hypothesis of the Terasvirta procedure, we mention the test statistics. The linearity hypothesis was rejected when the test statistic was greater than the critical value at the 5% significance level (2.61), calculated with the F-distribution for 3 and 5 degrees of freedom. For the Terasvirta procedure, the null hypothesis was rejected when the t-statistic was greater than the critical value at the 5% significance level (1.96). The rejects of and led to the logistic function use, whereas the rejection of only indicated exponential function utilization. Note: For the coefficients rows, the first line is the estimated coefficient. The second line indicates the standard error. The number of stars indicates the significance level, one for 10%, two for 5%, three for 1% and none in case of non-significance. For the Exclusion test row, the first line indicates the test statistic, and the second line mentions the critical value at the 5% significance level from the chi-2 distribution. The oil exclusion hypothesis of the cointegration vector was rejected when the test statistic exceeded the critical value. For the cointegration rows, the first line indicates the test statistics and the second line mentions the critical value at the 5% significance level. The star mentions the non-reject of the null hypothesis of cointegration. For the Choi and Saikkonen test, the number in parenthesis is the number of subsample.

  growth. They highlighted the existence of a break date with the appearance of the short-term oil-price effect in 2005 for corn and soybean and in 2003 for sugar. In addition, break dates existed for the majority of other commodities, such as 2004 and 2008 for wheat.Regarding nonlinear cointegration studies, Peri and Baldi (2010) andNatanelov et al. (2011) rely on a TVECM (Threshold Vector Error Correction Model) specification following theBalke and Fomby (1997) andHansen and Seo (2002) procedures. The authors showed that the adjustment was faster when the deviation from the equilibrium relationship between agricultural commodities and oil prices was greater than a certain threshold. Penaranda and Ruperez-Micola (2011) achieved a threshold regression on price changes, which was interesting for the short-term relationship. The oil-price effect grew when oil prices exceeded a certain threshold. Myers et al. (2014) attempted to explain the cointegration relationship that appeared in 2006, highlighted by Harry et al. (2009). They used the Gonzalo and Piterakis (2002) criterion to verify the presence of non-linearity in the relationships between oil and corn, and oil and soybean spot prices via the existence of several regimes. They assumed that this break could be created by four variables, including the amount of ethanol produced. 7 They showed no change in the relationship. After checking the lack of cointegration relationship from April 2006 through September 2008, they suggested that Harry et al. (2009)'s results may be related to the use of future prices. To our knowledge, no other paper has focused on a long-term oil-price nonlinear effect on agricultural commodity prices used in biofuel production.

Figure 4

 4 Figure 4 presents the oil-price effect dynamic for the last three commodities. The transition speed between the two extreme regimes is higher for soybean than for corn and is related to the difference in development speed between ethanol and biodiesel in the United States. This situation is difficult to interpret in the case of rapeseed and sunflower. Indeed, their parameters are both very high, indicating a fast transition, and non-significant, suggesting a lack of nonlinearity. This problem is most likely due to the difference in time between US and EU biodiesel production development. Development was very strong in 2005 and 2006 in the US, with annual respective growth rates of 225% and 176%, whereas it was slower and spread out over time in the EU. It experienced a growth rate of 40% to 50% from 2004-2007. The oil-biofuel effect for soybean appeared in 2005 when the consumption of this sector in soybean oil reached almost 10% of domestic use. The price effect was maximal for a use rate of 20%. The proximity at

Figure 1 -Figure 2 -

 12 Figure 1 -Oil and food index evolution and biofuel production

Figure

  Figure 3 -Oil-price effect for corn and wheat based on biofuel production

  tested the existence of this effect from

	1980-2008. Finding no long-term relationship between oil and various commodity link with a lag augmented-VAR model, identifying a low oil-price effect on corn and
	indices, they estimated a VAR model for the short-term relationship and cotton prices. With the same test and the Dolado and Lutkepohl (1996) causality
	highlighted weak and transitory responses to an oil shock. Zhang et al. (2010) test, Kwon and Koo (2009) found a causality relationship from crude energy goods
	obtained the same conclusion concerning the nonexistence of a long-term -including oil, natural gas or coal -to agricultural commodities from 1998-
	relationship from 1989-2008 using a similar approach with corn, soybeans, wheat, 2008.
	sugar and rice. They also failed to find a short-term relationship. Natanelov et al. Other studies using various methodologies have been conducted to
	(2011) studied several commodity prices, including corn, soybean oil and wheat, investigate the existence of a break date in the link between oil and agricultural
	and showed that only wheat had a long-term relationship with oil from 1989-2010. commodity prices. Campiche et al. (2007) used the Johansen (1991) cointegration
	However, they found a long-term oil-price effect on these three commodities approach with different sub-samples for oil, corn, sorghum, soybean oil and palm
	between 1993 and 2001. Yu et al. (2006) highlighted one vector of cointegration oil. No long-term relationship was found from 2003-2007, but a link existed between vegetable oils (soybean, sunflower, rapeseed and palm) and crude oil between oil and corn and between oil and soybean oil for the sub-sample 2006-prices from 1999-2006. However, using an exclusion test, they showed that crude 2007. With the same cointegration method but with overlapping periods, Harri et oil and sunflower oil prices did not belong to the cointegration space. In addition, al. (2009) looked for a break date on which a long-term relationship appeared
	with a causality analysis, they emphasized that oil was not part of the causal between oil and corn prices. They determined April 2006 as the break date.
	relationship. Zhang and Reed (2008) undertook the same work with corn, soybean
	meal and pork Chinese data from 2000-2007. They did not find long-or short-term
	relationships. Peri and Baldi (2010) studied the link between three vegetable oils
	and diesel prices. Unlike soybean and sunflower, a long-term relationship did exist
	with rapeseed oil from 2005-2007.
	A second approach consists of studying cointegration in a panel data
	framework. The Pedroni (1999) approach was used by Nazlioglu and Soytas
	(2012) to examine 24 agricultural commodities from 1980-2010. They showed a
	positive long-term oil-price effect, highlighting the cost effect. Using the Pedroni
	(1999) and Westerlund (2007) approaches on several commodity prices, Bremond
	et al. (2014) did not find a long-term relationship between oil and agricultural
	commodities from 2000-2011. By studying the short-term relationship, they
	showed a weak causal relationship between oil and commodities.
	Another method is to check for the existence of causality from the oil price
	to a commodity price. Nazlioglu and Soytas (2011) performed the Toda and
	Yamamoto (1995) long-term causality test in Turkey with corn, cotton, oil,
	soybean, sunflower and wheat prices from 1994-2010. They showed no long-term
	causality and therefore no oil-price effect. In addition, they studied the short-term
	5

  and Choi and Saikkonen (2010) tests, hereafter S and CS. The S test corresponds to the KPSS test applied on the residuals of the cointegration relationship, whereas the CS test is a modification of the S test to improve its empirical power in the nonlinear framework. The CS test is an implementation of the KPSS test on sub-samples and the choice of the maximum test value. 11

  break test results allow us to check for a rupture occurrence in the relationship during the last recession. If the date mentioned was one of the limits

	of the period used, either 06/01/2007 or 12/31/2009, this would indicate that a
	larger rupture most likely occurred upstream or downstream of the crisis. The
	break dates for corn and rapeseed, 9/11/2007 and 8/22/2007, respectively, are

  , "Tests of Equality Between Sets of Coefficients in Two

	-Leybourne, S. J., Lloyd, T. A., Reed, G. V., 1994, "The Excess Comovement of -Penaranda, F., Ruperez-Micola, A., 2011, "On the Drivers of Commodity Co-
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	-Dickey, D.A., Fuller, W.A., 1979, "Distribution of the Estimators for -Myers, R.J., Johnson, S.R., Helmar, M., Baumes, H., 2014, "Long-run and Macroeconomic Variables", Journal of Econometrics, Vol. 80, Is. 2, pp. 355-
	Autoregressive Time Series with a Unit Root", Journal of the American Short-run Co-movements in Energy Prices and the Prices of Agricultural 385
	Statistical Association, Vol. 74, Is. 366, pp. 427-431 Feedstocks for Biofuel", American Journal of Agricultural Economics, Vol. 96, -Phillips, P.C.B., Perron, P., 1988, "Testing for a Unit Root in Time Series
	-Dickey, D.A., Fuller, W.A., 1981, "Likelihood Ratio Statistics for Is. 4, pp. 991-1008 Regression", Biometrika, Vol. 75, Is. 2, pp. 335-346
	Autoregressive Time Series with a Unit Root", Econometrica, Vol. 49, Is. 4, -Natanelov, V., Alam, M.J., McKenzie, A.M., Van Huylenbroeck, G., 2011, "Is
	pp. 1057-1072 there Co-Movement of Agricultural Commodities Futures Prices and Crude
	-Dolado, J.J., Lutkepohl, H., 1996, "Making Wald Tests Work for Cointegrated Oil?", Energy Policy, Vol. 39, Is.9, pp. 4971-4984
	VAR Systems", Econometric Reviews, Vol. 15, Is. 4, pp. 369-386 -Nazlioglu, S., Soytas, U., 2011, "World Oil Prices and Agricultural Commodity
	-Fisher, R.A., 1912, "On an Absolute Criterion for Fitting Frequency Curves", Prices: Evidence from an Emerging Market", Energy Economics, Vol. 33, Is. 3, 91-115
	Messenger of Mathematics, Vol. 41, pp. 155-160 pp. 488-496 -Terasvirta, T., 1994, "Specification, Estimation, and Evaluation of Smooth
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	-Gilbert, C.L., 2010, "How to Understand High Food Prices", Journal of -OECD, 2008, "Rising Food Prices: Causes and Consequences", OECD Autoregressions with Possibly Integrated Processes", Journal of
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	-Pedroni, P., 1999, "Critical Values for Cointegration Tests in Heterogeneous Estimator and a Direct Test for Heteroskedasticity", Econometrica, Vol. 48, -Gonzalo, J., Pitarakis, J.Y., 2002, "Estimation and Model Selection Based Inference in Single and Multiple Threshold Models", Journal of Panels with Multiple Regressors", Oxford Bulletin of Economics and Is. 4, pp. 817-838
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-Pindyck, R.S., Rotemberg, J.J., 1990, "The Excess Co-Movement of Commodity Prices", Economic Journal, Vol. 100, Is. 403, pp. 1173-1189 -Shin, Y., 1994, "A Residual-Based Test of the Null of Cointegration against the Alternative of No Cointegration", Econometric Theory, Vol. 10, Is. 1, pp.

Table 2 -Daily growth rate statistics

 2

			Corn	Soybean	Wheat	Oil	Biofuel	SP500 Sunflower Rapeseed
	Average (percent)	1986-2014 1986-2005 2006-2014	0.01 0.00 0.03	0.01 0.00 0.02	0.01 0.00 0.02	0.01 0.02 0.00	0.04 0.04 0.06	0.03 0.03 0.02	0.01 -0.01 0.02	0.02 0.04 0.01	2001-2014 2001-2005 2006-2014
		1986-2014	1.91	1.61	1.61	2.47	1.03	1.15	2.55	1.90	2001-2014
	Standard error	1986-2005	1.64	1.56	1.56	2.54	1.12	1.07	1.57	2.08	2001-2005
		2006-2014	2.42	1.73	1.73	2.29	0.80	1.32	2.88	1.81	2006-2014
		1986-2014	-0.34	-0.85	-0.85	-0.79	0.06	-1.31	-0.19	-0.45	2001-2014
	Skewness	1986-2005	-0.42	-0.70	-0.70	-1.05	0.14	-2.09	0.50	-0.81	2001-2005
		2006-2014	-0.26	-1.09	-1.09	0.02	-0.35	-0.34	-0.24	-0.21	2006-2014
		1986-2014	19.39	27.73	27.73	18.87	59.48	31.91	133.64	19.70	2001-2014
	Kurtosis	1986-2005	8.42	37.15	37.15	21.38	57.64	48.07	13.79	30.92	2001-2005
		2006-2014	20.53	13.05	13.05	9.56	38.59	13.72	117.92	10.10	2006-2014

Table 4 -Terasvirta (1994) procedure

 4

		Linearity		Terasvirta procedure
		test	/ 01	/ 02	/ 03	Conclusion
	Corn	1579.96	-5.11	51.56	38.83	Logistic
	Soybean	2291.66	-7.55	58.46	48.01	Logistic
	Wheat	794.80	-14.99	35.04	27.34	Logistic
	Sunflower	428.87	-31.12	3.06	15.41	Logistic
	Rapeseed	184.32	-9.62	-13.58	15.83	Logistic
	Note:

Table 5 -Long-term estimation with exclusion and cointegration tests

 5

	, =	+	.	+	. ! " + # + # .	+ # . ! ". $ % , & , " + ' ,
				Corn		Soybean	Wheat	Sunflower Rapeseed
		4 5 1		1.218 *** (0.046)		2.074 *** (0.024)	1.700 *** (0.038)	4.613 *** (0.560)	6.061 *** (0.277)
		4 5 2		-0.113 *** (0.018)		0.024 *** (0.009)	0.092 *** (0.011)	0.048 (0.031)	0.452 *** (0.014)
		4 5 3		-0.011 (0.008)		-0.060 *** (0.005)	-0.126 *** (0.006)	0.239 *** (0.091)	-0.184 *** (0.043)
		6 7 1		-1.387 *** (0.166)		-2.327 *** (0.145)	-3.616 *** (0.137)	1.607 *** (0.567)	0.325 (0.290)
		6 7 2		0.930 *** (0.042)		0.400 *** (0.033)	0.398 *** (0.030)	0.885 *** (0.034)	0.365 *** (0.021)
		6 7 3		-0.248 *** (0.031)		0.179 *** (0.025)	0.338 *** (0.024)	-0.705 *** (0.093)	-0.237 *** (0.047)
		8 5		5.491 *** (0.550)		10.032 *** (1.166)	15.539 *** (3.781)	63.469 (134.935)	51.538 (61.446)
		9 +		6.349 *** (0.018)		6.245 *** (0.015)	6.245 *** (0.015)	6.363 *** (0.030)	6.216 *** (0.020)
	Exclusion test	827.33 5.99		416.48 5.99	416.48 5.99	1887.20 5.99	2022.68 5.99
		Shin test		0.267* 0.895		0.188* 0.895	0.223* 0.895	0.345* 0.895	0.204* 0.895
		Choi and		2.826 (3)	1.333* (4)	2.620 (3)	1.953* (3)	2.166* (3)
	Saikkonen test	2.421		2.627	2.421	2.421	2.421

Table 6 -ECM estimation with and without corrections and break test

 6

			∆ , = ) . ∆	+ ) . ∆! + * . ' + , , + -,
		Corn	Soybean	Wheat		Sunflower	Rapeseed
		Without corr.	With corr.	Without corr.	With corr.	Without corr.	With corr.	Without corr.	With corr.	With corr.	Without corr.	With corr.
	: 7 1	0.087*** 0.087*** 0.071*** 0.075*** 0.101*** 0.098*** (0.009) (0.011) (0.007) (0.009) (0.010) (0.013)	0.021 (0.020) (0.016) 0.007	--	0.110*** 0.118*** (0.015) (0.018)
	: 7 2	0.124*** 0.129*** 0.086*** 0.088*** 0.133*** 0.086*** (0.019) (0.024) (0.016) (0.018) (0.022) (0.029)	0.077** 0.089** (0.036) (0.037)	0.092*** 0.085*** 0.086*** (0.035) (0.027) (0.029)
	; 5	-0.003*** -0.003** -0.004*** -0.004*** -0.005*** -0.004*** -0.012*** -0.004 (0.001) (0.001) (0.001) (0.001) (0.001) (0.002) (0.003) (0.003)	-0.004 (0.003)	-0.010*** (0.003)	-0.004 (0.003)
	R-square	0.020	0.112	0.018	0.139	0.021	0.104	0.007	0.495	0.495	0.028	0.187

This food index includes selected commodities, such as cereals, vegetable oils, meats and tropical products, but it perfectly reflects the evolution of most agricultural commodity prices. In addition, the oil index is the simple average of Dated Brent, West Texas Intermediate and the Dubai Fateh.

Note that in both cases, the oil price has been multiplied by approximately 2.5, whereas the respective multiplication factors of commodity prices are 1.3 and 1.7.

We are referring to first-generation biofuels because new generation biofuels are not in the commercialization phase.

It is the Economic Research Service of the United States Department of Agriculture.

It is the Global Trade Analysis Project.

The other variables were time, oil and coarse-grain stock levels.

Gilbert (2010) used this break date to highlighting the beginning of the increase in food commodity prices.

To simplify this procedure, we remove the SP500 index from the equation. Indeed, only the nonlinearity of the oil-price effect interested us.

For further details on this functions, seeTerasvirta (1994).

For more details on the procedure including the block size choice, seeChoi and Saikkonen (2010). The critical values used were calculated byHong and Wagner (2008).

Note that, as mentioned byGohin and Chantret (2010), the oil-price effect is unlikely to appear in the short-term. Thus, in the case of a short-term effect, the oil-price effect could hardly be considered a price effect, and it might be a speculation effect for instance. Therefore, we do not include nonlinearity in the short-term relationship.

See, e.g., Nazlioglu and Soytas (2012).

According toBaffes (2007) and[START_REF] Baffes | More on the Energy/Nonenergy Price Link[END_REF] the elasticity between fertilizer and oil prices was 0.33 and 0.55 over the periods1960-2005 and 1960-2008, respectively.

Despite the rapid development of biodiesel production since 2001, ethanol production still accounts for approximately 90% of biofuel production in the United States.

To be more specific, it is the soybean oil ratio. However, 94% of US soybean consumption took the form of oil in 2012.

Regarding misspecification tests, note that despite the addition of dummies, the non-normality of residuals could not be corrected. Heteroscedasticty and autocorrelation issues are corrected using the White correction and lagged endogenous variables.

The adjustment coefficient for sunflower being not significant, we did not calculate the half-life for this commodity.

This liquidity injection was 35 billion dollars by the US Federal Reserve and 3.75 and 61 billion euros by the Japanese and European central banks, respectively. It occurred on 08/13/2007.

The US Federal Reserve and the European Central Bank injected, respectively, 31.25 billion dollars and 42 billion euros on 9/06/2007.

-Hansen, B.E., Seo, B., 2002, "Testing for Two-Regime Threshold Cointegration in Vector Error-Correction Models", Journal of Econometrics, Vol. 110, Is. 2, pp. 293-318 -Hanson, K., Robinson, S., Schluter, G., 1993, "Sectoral effects of a world oil price shock: economywide linkages to the agricultural sector", Journal of Agricultural and Resource Economics, Vol. 18, Is. 1, pp. 96-116 -Harri, A., Nalley, L., Hudson, D., 2009, "The Relationship between Oil, Exchange Rates, and Commodity Prices", Journal of Agricultural and Applied Economics, Vol. 41, Is. 2, pp. 501-510 -Hong, S.H., Wagner, M., 2008, "Nonlinear Cointegration Analysis and the Environmental Kuznets Curve", Institute for Advanced Studies Economics Series, Is. 224 -Jarque, C.M., Bera, A.K., 1980, "Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals", Economics Letters, Vol. 6, Is. 3, pp. 255-259 -Johansen, S., 1991, "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models", Econometrica, Vol. 59, Is. 6, pp. 1551-1580 -Kaltalioglu, M., Soytas, U., 2009, "Price Transmission Between World Food, Agricultural Raw Material, and Oil Prices", GBATA Eleventh International Conference Readings Book, pp. 596-603, Prague, Czech Republic, July 7-11 -Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y., 1992, "Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root", Journal of Econometrics, Vol. 54, pp. 159-178 -Kwon, D., Koo, W.W., 2009, "Price Transmission Channels of Energy and Exchange Rate on Food Sector: A Disaggregated Approach based on Stage of Process", AAEA and ACCI Joint Annual Meeting, Milwaukee, WI, July 26-29 -Yu, T.E., Bessler, D.A., Fuller, S., 2006, "Cointegration and Causality Analysis of World Vegetable Oil and Crude Oil Prices", American Agricultural Economics Association Annual Meeting, Long Beach, CA, July 23-26 -Zhang, Z., Lohr, L., Escalante, C., Wetzstein, M., 2010, "Food versus Fuel: What do Prices Tell us?", Energy Policy, Vol. 38, Is. 1, pp. 445-451 -Zhang, Q., Reed, M., 2008, " The Without corr. columns mention the estimation results without correction. The With corr. columns mention the estimation results with White correction, lagged variable and dummy. For the coefficients estimated, the first line presents the estimated coefficient and the second line is the standard error. The number of stars indicates the significance level, one for 10%, two for 5%, three for 1% and any in case of non-significance. The test rows mention the p-value, the null hypothesizes of non-normality, non-autocorrelation and homoscedasticity were rejected when the p-value is lower than 0.05. The Ljung-Box test statistics were computed with 20 lags. The last row indicates, respectively, the break date of the relationship, the test statistic and the critical value at the 5% significance level. The "Cahiers de l'Économie" Series

The "Cahiers de l'économie" Series of occasional papers was launched in 1990 with the aim to enable scholars, researchers and practitioners to share important ideas with a broad audience of stakeholders including, academics, government departments, regulators, policy organisations and energy companies.

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Veronique Smanio
email: veronique.smanio@ifp.fr

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Marion Fregonese

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Véronique Smanio

François Ropital

Marion Frégonèse

Evaluation of hydrogen embrittlement induced damages in steels using acoustic emission

Keywords: Hydrogen Induced Cracking, Acoustic Emission, hydrogen sulfide, steels

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Steels used in presence of production waters containing H 2 S can be susceptible to different types of Hydrogen Embrittlement (HE), like HIC and SSC HIC is a planar mode of cracking that develops in carbon and low alloy steels when atomic hydrogen diffuses into the metal and then combines to form molecular hydrogen at trap sites. Crack initiation and propagation result from the pressurization of trap sites by hydrogen. No external applied stress is needed for HIC development. Trap sites involved in HIC are commonly found in steels containing a high density of planar inclusions like MnS and/or regions of anomalous microstructure (e.g. pearlitic structure) produced by segregation of impurities and alloying elements. SSC leads to the early failure of the metal due to the combined effect of both hydrogen penetration and tensile stress (residual and/or applied) in wet H 2 S environment. High strength metallic materials and hard welded zones are prone to SSC.

The present work is focused on HIC, but study of SSC by AE is one of the future prospects of the study.

The selection of HIC resistant steels is usually done with standard tests (NACE TM0284 [START_REF]Evaluation of pipeline and pressure vessel steels for resistance to Hydrogen Induced Cracking[END_REF] or EFC 16 [START_REF] Eliassen | Guidelines on materials requirements for carbon and low alloy steels for H 2 S-containing environments, Oil and gas production 2 nd Edition[END_REF] recently brought together in NACE MR0175/ISO 15156 [START_REF]Petroleum and natural gas industries -Materials for use in H 2 S containing environments in oil and gas production -Part 2: Cracking-resistant carbon and low alloy steel, and the use of cast iron[END_REF] ) in sour media. In these tests, material damage is evaluated through cracking ratios measured in three equidistant cross-sections of the tested specimens (Crack Sensitivity Ratio, CSR, Crack Length Ratio, CLR and Crack Thickness Ratio, CTR). Information provided by these ratios is very local because it represents only the cracking state into 3 sections. Depending on the metallographic procedure used to observe cracks randomly distributed within the material, there is a marked deficiency in standard test sensitivity. Furthermore, metallographic examination is solely representative of the cracking that is achieved after the total duration of the test. Other important information is missing, e.g. the incubation time before initiation of cracking, or the evolution of cracking with time. If standard test methods are well suited for fit-forpurpose testing, they hardly give information on the mechanisms of cracking.

On the other hand, the AE technique is a non destructive evaluation method allowing the detection of active defects within materials in real time during the test. Each AE event gives rise to elastic waves which propagate into the material and results in detectable AE signals [START_REF] Roget | Essais non destructifs, l'émission acoustique: mise en oeuvre et applications[END_REF] . Therefore materials can spontaneously generate transient elastic waves, called AE, when they undergo solicitations. Good correlations were recently obtained between specific AE parameters and amplitude of corrosion damage in various cases: stress corrosion cracking [START_REF] Mazille | Rothéa The use of acoustic emission for the study and monitoring of localized corrosion phenomena[END_REF][START_REF] Yuyama | AE Analysis during Corrosion Cracking and Corrosion Fatigue Process[END_REF][START_REF] Pollock | Monitoring Sub-Critical Crack Growth due to Stress Corrosion or Hydrogen Embrittlement by Acoustic Emission[END_REF][START_REF] Gerberich | Acoustic Emission Monitoring of Stress Corrosion Cracking[END_REF] , abrasion or erosion corrosion [START_REF] Ferrer | On the potential of acoustic emission for the characterization and understanding of mechanical damaging during corrosion-abrasion processes[END_REF] , pitting corrosion [START_REF] Jones | Acoustic Emission during Pitting and Transgranular Crack Initiation in Type 304 Stainless Steel[END_REF][START_REF] Mazille | An Acoustic emission Technique for Monitoring Pitting Corrosion of Austenitic Stainless Steels[END_REF][START_REF] Fregonese | Monitoring Pitting Corrosion of AISI 316L austenitic stainless steel by acoustic emission technique: choice of Representative Acoustic parameters[END_REF][START_REF] Fregonese | Initiation and propagation steps in pitting corrosion of austenitic stainless steels: monitoring by acoustic emission[END_REF] , crevice corrosion [START_REF] Kim | Ability of acoustic emission technique for detection and monitoring of crevice corrosion on 304L stainless steel[END_REF][START_REF] Kim | Study of oxygen reduction on stainless steel surfaces and its contribution to acoustic emission recorded during corrosion processes[END_REF] , exfoliation corrosion [START_REF] Bellanger | Use of acoustic emission technique for the early detection of aluminum alloys exfoliation corrosion[END_REF] , and uniform "acidic" corrosion [START_REF] Jaubert | On the opportunity to use non-intrusive acoustic emission recordings for monitoring uniform corrosion of carbon steel and austenitic stainless steel in acid and neutral solutions[END_REF] . Among the variety of mechanisms of deformation and damage that can be sources of AE, corrosion processes take a particular position, as the acoustic source is often not the electrochemical corrosion driving force itself, but its consequence: bubble formation, crack propagation, modifications of corrosion products deposits [START_REF] Mazille | Rothéa The use of acoustic emission for the study and monitoring of localized corrosion phenomena[END_REF][START_REF] Yuyama | AE Analysis during Corrosion Cracking and Corrosion Fatigue Process[END_REF] , whether corrosion is localized or uniform.

The following corrosion reactions describe the behavior of steel immersed in an acidic media [START_REF] Galland | Chapitre 12: Introduction d'hydrogène dans les aciers[END_REF] :

 anodic reaction,

    e Fe Fe 2 2 (1)
 cathodic reactions,   2 ' 1 H ε  (2) ads H e H 2 2 2     abs H ε' 2 (3)
The recombination of a part of hydrogen resulting from the cathodic reaction induces hydrogen evolution (equation 2), when the other part (ε'<1) is absorbed inside the material (equation 3). Furthermore, in the presence of H 2 S, a sulfide layer is created at the steel surface. Hydrogen uptake into the material is also enhanced by H 2 S (promotion of reaction 3 or poisoning of reaction 2), leading to HE. Under these conditions, the physical phenomena potentially leading to acoustic emission are linked to: o H 2 evolution resulting from the corrosion reaction, o Presence of the sulfide layer on the metal surface, o Cracking resulting from absorption, diffusion and trapping of hydrogen.

In the past, AE technique was applied to monitor hydrogen embrittlement in several studies [START_REF] Cayard | Evaluation of various methods of reducing the duration of SSC qualification testing[END_REF][START_REF] Gingell | Observations of damage modes as a function of microstructure during NACE TM0177-96 tensile testing of API 5L grade X60 linepipe steel[END_REF][START_REF] Weng | Acoustic emission characterization of steel in H 2 S solution subjected to tensile load[END_REF][START_REF] Weng | Acoustic emission characteristics of a high-strength steel subjected to corrosive hydrogen sulfide solution[END_REF][START_REF] Tsai | Correlation between acoustic emission signals and hydrogen permeation in high-strength, low alloy steel cracking in wet H 2 S[END_REF][START_REF] Amami | Early detection and monitoring of sulfide stress cracking (SSC) of steels by an acoustic emission method[END_REF] . Cayard et al. [START_REF] Cayard | Evaluation of various methods of reducing the duration of SSC qualification testing[END_REF] and also Gingell et al. [START_REF] Gingell | Observations of damage modes as a function of microstructure during NACE TM0177-96 tensile testing of API 5L grade X60 linepipe steel[END_REF] showed that during SSC tests leading to the failure of the tested metal, a higher AE energy build-up rate was recorded compared to no-failure tests. This critical energy build-up rate associated with SSC failure remained dependant on the tested steels and the applied stress level. Weng et al. [START_REF] Weng | Acoustic emission characteristics of a high-strength steel subjected to corrosive hydrogen sulfide solution[END_REF] found a correlation between HIC damage evaluated by CLR and the AE energy level. Moreover, Gingell et al. [START_REF] Gingell | Observations of damage modes as a function of microstructure during NACE TM0177-96 tensile testing of API 5L grade X60 linepipe steel[END_REF] showed that AE could be used to discriminate initiation and propagation stages of SOHIC (Stress Oriented Hydrogen Induced Cracking) development during NACE TM0177-96 [START_REF][END_REF] method A tensile tests. In all these studies, AE data were treated in a global manner, i.e. considering simultaneously all the AE sources involved during the test.

In that context, the first step of the present work was to discriminate the different AE sources in order to identify the acoustic signature of HIC signals. Using this discrimination, a correlation between AE data and HIC damage amplitude was established and more information on cracking kinetics can be obtained.

EXPERIMENTAL PROCEDU RE

Materials

Two API X65 grade pipeline steels were used as reference materials. One was identified as X65 Sweet Service (SwS) grade because of its poor HIC resistance and the other was identified as X65 Sour Service (SS) grade.

The chemical compositions of both X65 grade steels were analyzed by optical emission spectroscopy (OES), except for carbon and sulfur, analyzed by a chemical method (Table 1).

For microstructure observation, the specimens were ground with silicon carbide (SiC) paper down to 1200 grade and polished down to 0.05µm diamond suspension and then etched with a Nital solution (2%vol. nitric acid and ethanol). The microstructure was observed in the longitudinal transverse direction (LT) using an optical microscope. The different microstructures at the mid wall location are presented in Figures 1 and2. The major phases observed for both steels are pearlite and ferrite. For the sour service grade, the microstructure is equiaxed and homogenous, which accounts for its good HIC resistance. The microstructure of the sweet service grade is highly oriented in the rolling direction, which explains its poor HIC resistance.

The tensile properties of both steels in the parallel to rolling direction are gathered in Table 2. The inclusion content was analyzed according to ASTM E45 method A 26 . The results are presented in Table 3. No significant difference is observed between both grades in terms of inclusion content, which confirms that the difference of HIC sensitivity between the selected steels is mainly linked to ferrite and pearlite distribution and grain orientation.

Experimental Set up

The experimental set up (Figure 3) was based on the SSC standard test NACE TM0177-96 [START_REF][END_REF] device. The specimen was placed in a proof ring, but without applied load. Only the gauge section of the sample was immersed in the corrosive solution. Tests were conducted in buffered solution according to EFC 16 [START_REF] Eliassen | Guidelines on materials requirements for carbon and low alloy steels for H 2 S-containing environments, Oil and gas production 2 nd Edition[END_REF] (5 wt% NaCl and 0.4% CH 3 COONa in distilled water) under 1 bar H 2 S. De-aeration conditions by nitrogen bubbling were studied, and a procedure of de-aeration was used to obtain less than 15 ppb dissolved oxygen. In this preliminary study, oxygen content during de-aeration was measured by an oxygen gas sensor placed in the cell. This deaeration procedure was applied before each test.

A circulation of the corrosive fluid (sour media) was ensured between the test cell and a vessel, in which gas bubbling was maintained. This procedure allowed to avoid any interfering AE noise detection (e.g. gas bubbling in the test cell would result in AE) and to maintain constant electrochemical conditions in the cell during the test (easier pH control). The pH was measured with a pH meter located in this second vessel and adjusted every day through HCl or NaOH additions.

The AE technique was designed for this test, two AE sensors located at the top and bottom of the specimen (Figure 3) in order to detect AE signals. AE instrumentation consisted of a transducer, a preamplifier and a data acquisition system.

Methodology

The successive experimental steps that were applied for identification of acoustic signature of individual sources are summarized in Table 4. A first series of tests was conducted without H 2 S under cathodic polarization in order to identify AE signals resulting from H 2 evolution. Then, a second series of tests was conducted under 1 bar H 2 S at pH 4.5 on both sour service and sweet service steels to discriminate sulfide layer induced AE signals from HIC induced AE signals.

After the tests, evaluation of HIC damage was performed with four equidistant cuts along the length and metallographic examination of the faces. CLR and CSR were then calculated. However, the specimen geometry was similar to SSC tensile specimens as per NACE TM0177 [START_REF][END_REF] , with a 6.35 mm diameter section. This dimension is rather small compared to standard HIC specimens which are 20 mm wide. It is therefore expected that the experimental error and scattering of crack ratios determined from the small SSC shaped specimens could be significant.

RESULTS

Identification of the different AE sources

The first test batch consisted in a cathodic polarization of the X65 SS grade immersed in the test solution under 1 bar N 2 at pH 5.5. The applied current density was -280 µA/cm², corresponding approximately to a cathodic potential of -1250 mV/(Ag/AgCl). The tests lasted 8 hours. The cathodic reaction leading to H 2 evolution was enhanced by a cathodic potential to the sample; at the same time the anodic reaction resulting in iron dissolution was limited. Under these conditions, only one AE source was active: the evolution of hydrogen gas resulting from the cathodic reaction. No HIC resulting from cathodic hydrogen charging was observed in the sour service steel. Several AE parameters were studied, AE energy and signals duration were found to be the most discriminating parameters.

Therefore, AE results are presented in a correlation chart representing the absolute energy of the signals as a function of their duration (Figure 4). On this figure, each point represents one AE event.

In order to identify the AE hits related to the sulfide layer and HIC, further tests were performed on sour service and sweet service steels at pH 4.5 under 1 bar H 2 S. Cracks were only observed in the sweet service steel grade. They are localized in the bands of pearlite at mid-wall location (Figure 5). The characteristics of the AE signals recorded during the former test (pH 4.5, 1 bar H 2 S) performed on the sour service steel are presented in Figure 6. As compared with the AE diagram obtained for H 2 evolution under cathodic polarization (Figure 4), a new AE class was detected, with higher absolute energy signals. As no cracks were detected for these experimental conditions, it can be assumed that this class B was related to the presence of the sulfide layer at the sample surface. AE results obtained during tests performed on the sweet service steel grade are presented in Figure 7. As previously, H 2 evolution and sulfide layer classes can be identified again. In addition, a third AE class exhibiting signals with higher duration was detected. Metallographic observations of the sample after the test confirmed the presence of HIC. This AE class was correlated with cracking within the material.

As a result the experimental approach was able to discriminate and identify the three main AE sources namely hydrogen evolution, iron sulfide film formation and HIC.

Correlation between HIC damage and AE data

Using the discrimination, it was then possible to separate all AE data related to HIC for one entire test. The evolution with time of HIC specific AE signals was examined for different tests.

Tests were performed on X65 SwS grade at pH 4.5 under 1 bar H 2 S with different exposure time from 5 to 65 hours in order to obtain different damage level in samples. HIC damage evaluation was performed with four equidistant cuts along the length and metallographic examination of the faces. CLR and CSR ,define according to NACE TM0284 1 ,were then calculated. AE data and cracking state of samples resulting from these interrupted tests are given in Table 5.

For tests 1 to 11, only purely internal cracks, i.e. not open to surface, were detected (Figure 8). All samples exhibit a CSR ranged between 0 and 2 % whereas the CLR varies from 0 to 82 % (Table 5). Since CSR do not vary from one sample to another, CLR was chosen as a criterion for quantifying HIC damage in the considered samples (1 to 11). Tests 12 and 13 are quite different from the other tests since open to surface cracks, and higher CSR were observed in these two samples (figure 9). Since these two samples present a different cracking behaviour, they are considered apart from the others for the correlations with AE data. At present, no explanation was found to understand the different cracking state between these two samples and the other ones.

In the past, the global AE energy recorded during the test was chosen as a criterion for detecting and quantifying the formation of cracks [START_REF] Cayard | Evaluation of various methods of reducing the duration of SSC qualification testing[END_REF][START_REF] Gingell | Observations of damage modes as a function of microstructure during NACE TM0177-96 tensile testing of API 5L grade X60 linepipe steel[END_REF][START_REF] Weng | Acoustic emission characteristics of a high-strength steel subjected to corrosive hydrogen sulfide solution[END_REF] . Weng et al. [START_REF] Weng | Acoustic emission characteristics of a high-strength steel subjected to corrosive hydrogen sulfide solution[END_REF] observed higher global AE energy level for samples exhibiting cracks but AE was treated in a global manner i.e. considering simultaneously all the AE sources involved during the test.

In this study, signals related to HIC were clearly identified so that these signals can be now isolated from all other AE signals. Thus, the evolution of either global cumulative acoustic energy or only HIC class cumulative energy for tests 1 to 11 as a function of the CLR calculated was drawn (Figure 10). No direct correlation was found between the AE global energy detected during the test (i.e. considering all the AE sources) and the damage amplitude represented by the CLR. On the other hand, a good correlation is obtained between CLR and AE cumulative energy related to HIC signals only (Figure 10). The discrimination of HIC AE events allows therefore a direct correlation with cracking extent, using CLR. The latter is the most relevant parameter because in the Sweet Service steel all cracks are located in the mid section area.

Using the same discrimination of AE sources, the evolution with time of HIC related AE was then examined (Figure 11) for tests 1 to 11. Even though some scattering is observed, probably due to the intrinsic scattering of defect areas in the small section specimens, all curves present similar trends, with three successive stages:

an initial period with hardly no AE related to HIC, a step increase of AE related to HIC, a final period with hardly no AE related to HIC.

The initiation period varies from 1 hour in specimen 7 to 25 hours in specimen 5 which could correspond to the time necessary for adsorbed hydrogen to diffuse and to be trapped within the material and generate cracking. The curves indicate that the high energy step corresponds to the crack propagation and lasts from 2 to 20 hours depending on the test. Then, it can be observed that HIC propagation stops. AE measurements give therefore real time information about the incubation time and the propagation period required for HIC.

As mentioned previously, because cracks in samples 12 and 13 are reaching the outer surface tests 12 and 13 cannot be directly compared to the others (Figure 9). At the same time, higher AE cumulative energy related to HIC were recorded for these tests. Cracks opening at the sample surface give rise to higher AE energy even if opened cracks are shorter than purely internal ones. At present, not enough samples presenting this cracking state were observed to establish a clear correlation between AE data and HIC damage. AE energy due to HIC is linked to the cracking state of the sample and improvement is expected by a better 3D quantification of HIC damage in the sample. This might be achieved by non destructive methods, such as ultrasonic inspection or X-ray tomography. Both techniques are being used and compared in order to obtain a better assessment of cracking in the specimens.

CONCLUSIONS

AE monitoring was carried out during standard tests devoted to characterize sensitivity of steels in sour environment.

The first step of the study consisted in discriminating the AE signature of HIC from other sources, such as hydrogen evolution and formation and/or cracking of the iron sulfide layer. This was achieved by a series of tests, and a good discrimination of the different sources was found from the energy and duration of each acoustic event. For a given sour service test, it was then possible to separate the AE related to HIC from all other sources. This gives a much better signal to noise ratio when evaluating HIC, as compared to previous works using all AE data with no filtering.

Using the sole AE data related to HIC, the method was used then to study both kinetic and cracking extent.

It was found that the cumulative energy of AE related to HIC correlates well with HIC damage level. A good correlation was obtained between this energy and the CLR for different samples presenting low CSR and purely internal cracks. Higher CSR and open to surface cracks seems to induce higher AE energy level. This correlation is then probably highly specific to one material and one crack mechanism. Furthermore, the identification of HIC signals also allows to study the kinetics of HIC. It was clear that HIC exhibited an incubation period, followed by the initiation and propagation of cracks, until the maximum damage was reached. Such data in mildly sour conditions would be extremely useful: indeed, the minimum test duration for HIC fit-for purpose tests in mildly sour conditions is still a topical question [START_REF] Martin | Hydrogen Induced Cracking (HIC) -Laboratory testing assessment of low allow steel linepipe[END_REF] . With that aim, some tests in the specific nodes of the pH -pH 2 S severity diagram could help for a better understanding of HIC mechanisms.

In the future, a parallel methodology will be applied to SSC. Figure 11 Evolution of cumulative energy related to HIC during the different interrupted tests (1-11)

FIGURE 1 -Figure 4 -

 14 FIGURE 1-Microstructure of X65 Sour Service grade (LT plane) FIGURE 2-Microstructure of X65 Sweet Service grade (LT plane)

Figure 5 -FIGURE 6 -

 56 Figure 5 -HIC detected in X65 SwS after a test performed in EFC 16 solution at pH 4.5 under 1bar H 2 S a) metallographic observations, b) SEM observations

FIGURE 7 -Figure 8 Figure 9 Figure 10 -

 78910 FIGURE 7 -Correlation chart representing absolute energy as a function of signals duration for a test conducted in the EFC 16 solution at pH 4.5 under 1bar H 2 S on a X65 SwS specimen.

TABLE 1 CHEMICAL

 1 COMPOSITION (WT %) OF X65 SOUR SERVICE AND SWEET SERVICE GRADES

	Steel	C	Mn	Si	P	S	Cr	Ni	Mo	Cu	Al	Ti	Nb
	X65 SS	0.046 1.36 0.322 0.008 0.001 0.041 0.036 0.008 0.047 0.036 0.019 0.045
	X65 SwS	0.09 1.56 0.28 0.014 0.001 0.05 0.03 0.01 0.02 0.030 0.000 0.040
						TABLE 2
	MECHANICAL PROPERTIES OF X65 SOUR SERVICE AND SWEET SERVICE GRADES (TESTS
			CONDUCTED ACCORDING TO EN10002-1 STANDARD)
				Steel	Rp 0,2 ±2%	Rm ± 1.5%	A% ±2.5%
			X65 SS	529 MPa	571 MPa	47
			X65 SwS	523 MPa	649 MPa	24
						TABLE 3
		COMPARISON OF INCLUSION CONTENT OF TESTED STEELS, ACCORDING TO
					ASTM E45 METHOD A
		Steel	Type A (sulfides) Thin Heavy	Type B (aluminates) Thin Heavy	Type C (silicates) Thin Heavy	Type D (oxides) Thin Heavy
	X65 SS	0	0	0		2		0	0	0.5	0.5
	X65 SwS	0	0	0		0		0	0	0.5	0.5
						TABLE 4
	ACTIVE ACOUSTIC EMISSION SOURCES AS A FUNCTION OF EXPERIMENTAL CONDITIONS AND
					TESTED MATERIALS
			Experimental conditions				Acoustic emission sources
	Gas (1 atm)	pH	Steel	Polarization	H 2 evolution	Sulfide layer	HIC
		N 2	5.5	X65 SS	cathodic			X	-		-
		H 2 S	4.5	X65 SS		no			X	X		-
		H 2 S	4.5	X65 SwS		no			X	X		X

TABLE 5

 5

			RESULTS OF INTERRUPTED TESTS
	Test	Duration (h)	Global AE energy (10 -18 J)	HIC class AE energy (10 -18 J)	CLR (%)	CSR (%)	Open to surface cracks
	1	25	3.6 x 10 5	2.0 x 10 4	12	0	no
	2	18	1.2 x 10 6	6.5 x 10 3	2	0	no
	3	18	6.5 x 10 5	1.5 x 10 4	8	0	no
	4	65	2.4 x 10 6	2.9 x 10 5	82	2	no
	5	65	1.2 x 10 6	3.4 x 10 5	75	2	no
	6	5	1.2 x 10 5	2.0 x 10 2	1	0	no
	7	5	7.0 x 10 5	1.5 x 10 5	38	0	no
	8	9	1.1 x 10 5	2.4 x 10 4	1	0	no
	9	9	1.9 x 10 5	3.2 x 10 3	10	0	no
	10	24	5.0 x 10 4	2.4 x 10 1	0	0	no
	11	46	4.7 x 10 5	1.8 x 10 4	4	0	no
	12	24	3.5 x 10 6	7.4 x 10 5	57	4	yes
	13	46	1.6 x 10 7	8.2 x 10 6	62	6	yes