Loïc Lévi
email: loic.levi@agrocampus-ouest.fr

Laure Latruffe

Aude Ridier

Farm investment and performance in the French (Brittany) dairy sector

Keywords: farms, investment, adjustment cost model, performance, France, Brittany, dairy sector, panel data

This paper investigates the link between farm investment and farm performance considering two types of farms that may have a different investment strategy: farms that are intensive in capital, and farms that have a low capital intensity. Panel data on specialized dairy farms of one sub-region of French Brittany (Ille-et-Vilaine) for the period 2005-2014 were used. Results from adjustment cost models indicate that farms tend to smooth their investments to limit adjustment cost and that farm performance has a negative impact on investment.

Introduction

Since the end of the 20 th century and especially after 1950, the substitution of labour for capital has been one of the most striking features of the agricultural activity transformations. Technological change has been a major factor shaping agriculture in the last century [START_REF] Schultz | Transforming traditional agriculture[END_REF]. A comparison of agricultural production patterns in France between the last century (1955) and the beginning of the 21 th century (2000) shows that harvested cropland has declined, the share of the agricultural labour force in the total population has decreased substantially (from 31 to 4.8 percent), and the number of people now employed in agriculture has declined (from 6.2 million to 1.3 million); yet agricultural production in 2000 was higher than in 19551 . These statistics suggest that productivity has increased and agricultural production methods have changed significantly.

The restructuration of the agricultural sector in the mid of the 20 th century has implied the enlargement of farms and substantial technological change. In the dairy sector there has been a growth in the livestock cattle per farm (in 1963 each farm had 7 dairy cows on average, while the figures were 17 in 1983, 33 in 2000 and 45 in 2010 [START_REF] Agreste | La Statistique Agricole: Produits agroalimentaires[END_REF] and a more frequent use of selected breed and animals, so that, despite a high decrease of national dairy livestock (from 7.2 million dairy cows in 1983 to 4.4 million in 1997), the average annual deliveries per farm grew (66,000 litres per farm in 1983; 164,000 in 1997 and 320,000 in 2010), along with a higher resort to concentrate feed as well as equipment purchase such as stables and milking machines. This allowed an increase in animal productivity, labour productivity and land productivity. This restructuring was heavily influenced by the economic and political context. Indeed, since the 90's support to farmers includes the Second Pillar of the European Common Agricultural Policy (CAP), promoting farmers' investment through investment aids and settlement aids. Farms can also receive subsidies through the First Pillar of the CAP, namely coupled payments to specific crop hectares and livestock heads, and, later, decoupled payments. In the French dairy sector the share of subsidies in farm income increased throughout the years: 17.2% in 1991, 36.5% in 1995, 55.9% in 2001 and 78.4 in 2005. Another important contextual issue relates to the liberalization of trade. In the past recent decades, there have been several multilateral agreements with the objective of liberalizing trade. The CAP experienced several reforms, so as to better comply with global trade rules by lowering the guaranteed prices, export subsidies and Community preference. This has affected the dairy sector in the Western France area considered in this article: Brittany region, and particularly the Ille-et-Vilaine sub-region (whose main town is Rennes). In Brittany the dairy sector is one of the most important agricultural sectors, with 5.4 billion tons of milk produced in 2014 (21% of the regional agricultural output). The dairy sector also makes a significant contribution to sustaining Brittany's regional economy, with the dairy processing industry employing over 35,000 workers in 2013. In addition, the Ille-et-Vilaine dairy sector is the first French sub-region for the production of milk and beef calves. In 2010, the dairy farming sector in this sub-region included nearly half of the sub-region's professional farms, used half of the sub-regional utilised agricultural area (UAA) and employed 45% of the sub-regional agricultural labour. This 2003 CAP reform, called the Luxembourg reform, which replaced most of the coupled payments by decoupled ones (the Single Farm Payment -SFP), was implemented in a period of high international prices' volatility impacting farmers' decisions.

In this context, farmers need to adapt so as to remain competitive. Several strategies are possible: to decrease production costs through production optimization; to increase production level, that is to say to enlarge; to merge, specialize, or increase the added value by differentiation (e.g. via the introduction of labels or new practices) in order to maintain profitability. Most of these strategies require investments. Agricultural investment can take various forms such as land, building, planting equipment, and livestock. It can be investment for replacement as well as investment for growth or for modernisation. Investment for replacement is intended to replace worn out or obsolete goods, while investment for growth/modernisation implies the purchase of new capital or the adoption of new technology. The decreasing number of farms in France (-56% between 1988 and 2013) resulted in the decrease of the number of machines used in the agricultural sector, but farm size increase and greater specialization led to the use of more specific and powerful machines. Investment for growth/modernisation increased the productivity of labour, although this was possible up to some limits. This is why investment is now focused on the replacement of existing equipment, and on innovations aimed at increasing safety and improving farmers' working conditions (soundproof booths, air conditioning, GPS…) as well as improving the balance between farmers' private and professional life. It should also be kept in mind that farmers' investment strategies are influenced by the existence or not of shared machinery and the possibility to outsource work. In France, farmers can share machineries and pay only for their share, through farmers' cooperatives ('Coopératives d'Utilisation de Matériel Agricole', CUMA, in French), which are growing importance [START_REF] Agreste | La Statistique Agricole[END_REF]. Another increasing trend is to outsource work to private companies, most of them being run by farmers themselves ('Entreprises de Travaux Agricoles', ETA, in French), which avoids buying the machinery and may reduce costs [START_REF] Dupraz | Trends in family labour, hired labour and contract work on French field crop farms: The role of the Common Agricultural Policy[END_REF].

Investment in technology, especially in machinery, is supposed to improve farm productivity but is however a significant financial burden on farms. In French farms, the mechanization costs represented on average 19% of total farm expenses in 2014 [START_REF] Agreste | La Statistique Agricole[END_REF]. Hence, the effect of investment on farm performance is ambiguous. To assess the profitability of the purchased equipment (on the medium or long term), several factors have to be taken into account such as the purchase price, the maintenance and fuel costs, the labour productivity increase and time savings, the potential tax deductions, the future resale price, as well as the specificities of the farming sector such as sunk costs. The latter imply that investment decisions are mostly irreversible due to the fact that capital is nonconvertible (the so-called asset fixity in agriculture) [START_REF] Chavas | Production and Investment Decisions Under Sunk Cost and Temporal Uncertainty[END_REF]. Sunk costs also arise due to learning cost, and the fact that this sector is highly supported by public subsidies, as explained above.

Farms' investment behaviour is affected by systemic changes in the farm environment, forcing farms to introduce some adjustments in their organization, management and other internal characteristics, adjustments that could be different according to the farm structure, especially its capital intensity. The literature on farm investment is substantial. Most of the recent studies used an adjustment cost model, where adjustment costs are considered [START_REF] Bokusheva | Exploring Farm Investment Behaviour in Transition: The Case of Russian Agriculture[END_REF][START_REF] Gardebroek | Farm-specific Adjustment Costs in Dutch Pig Farming[END_REF][START_REF] Hüttel | Investment reluctance: irreversibility or imperfect capital markets?[END_REF]. Contrary to studies on the industrial sector [START_REF] Lizal | Investment, Credit Rationing, and the Soft Budget Constraint: Evidence from Czech Panel Data[END_REF][START_REF] Rizov | Firm investment in transition[END_REF], no empirical result obtained for the farming sector is consistent with a priori theoretical expectations [START_REF] Zinych | Financial constraints in economic transition: Empirical evidence from Ukrainian large farms[END_REF]. Consequently, much effort has been dedicated to develop efficient ways to estimate such models and to address important shortcomings that limit their applicability. One shortcoming is that all farms are supposed to use the same production technology and to have the same adjustment cost structure. As it may not be true in reality, some authors have relaxed this assumption by employing a fixed effects' specification, thus accounting for farms' heterogeneity [START_REF] Whited | Why Do Investment Euler Equations Fail[END_REF]. Another shortcoming is that dynamic models need to be adapted for negative and zero investments. Oude Lansink and Stefanou (1997) estimated separate models for negative and positive investment using only observations with non-zero observations. [START_REF] Pietola | Investment under Uncertainty and Dynamic Adjustment in the Finnish Pork Industry[END_REF] used full information maximum likelihood and a dynamic (dual) approach to model capital investment based on static price expectations. Later studies focused on the search of an empirically tractable model with the optimal regimes for positive, zero and negative investments [START_REF] Gardebroek | Farm-specific Adjustment Costs in Dutch Pig Farming[END_REF] and on the determinants of reluctant investment behaviour using a maximum entropy estimation [START_REF] Gardebroek | Farm-specific Adjustment Costs in Dutch Pig Farming[END_REF], a sequential conditional iterated Seemingly Unrelated Regression (SUR) in two stages [START_REF] Serra | Determinants of investments in non-farm assets by farm households[END_REF], or a generalized Tobit model [START_REF] Hüttel | Investment reluctance: irreversibility or imperfect capital markets?[END_REF].

In this paper we will also use the adjustment cost model as it is theoretically appealing. Contrary to the ad hoc accelerator model, the adjustment cost model provides a consistent theoretical basis for explaining agricultural investment patterns in the context of dynamically optimizing economic agents. Adjustment cost theory has been the main approach used since the early literature on investment to explain why firms partially adapt their capital stock to the optimal level [START_REF] Bond | Dynamic Investment Models and the Firm's Financial Policy[END_REF][START_REF] Hubbard | Internal Net Worth and the Investment Process: An Application to U.S. Agriculture[END_REF][START_REF] Lizal | Investment, Credit Rationing, and the Soft Budget Constraint: Evidence from Czech Panel Data[END_REF][START_REF] Rizov | Firm investment in transition[END_REF]. It is hypothesized that firms suffer a short-run output loss when they change their stocks of quasi-fixed factors. Adjustment costs include costs for frictionless flow (maintenance), gradual adjustment (refinements and improvements necessitating training) and major adjustments [START_REF] Caballero | Chapter 12 Aggregate investment[END_REF]. This short-run output loss might influence investment decisions and therefore the adjustment cost hypothesis is formalized by including investment as an argument in the profit function. The farmers' objective is to maximize the expected net present value of their profit over an infinite horizon, taking into account that, after investment, next period's profit may be reduced by adjustment costs. Marginal adjustment costs are assumed to increase with the size of investment, implying that it is always cheaper for the farmer to spread investments over time rather than to adjust instantaneously. Therefore, convex adjustment costs provide an explanation for sluggish adjustment of quasi-fixed factors. In an adjustment cost modelling framework it is assumed that firms exhibit profitmaximizing behaviour, that product and factor prices are constant across periods and firms, and that the firms' discount factor is constant over time.

As mentioned above, asset fixity is a particular feature of the agricultural sector. As early as the 30's, [START_REF] Galbraith | The Maintenance of Agricultural Production During Depression: The Explanations Reviewed[END_REF] argued that high fixed costs contribute to the lumpiness of important production factors, which makes their temporary reduction or reorganization so expensive that farms are unprofitable in the short run. Later, [START_REF] Johnson | An Opportunity Cost View of Fixed Asset Theory and the Overproduction Trap: Reply[END_REF] hypothesized that productive assets are trapped in agriculture because of a divergence between acquisition costs and salvage value: this is the sunk cost problem. Compared to agents in other economic sectors, farmers have a greater tendency to continue an activity once an investment in money, effort or time has been made. This is even more the case when considering farms' specialization process. For example, one in two farms in Ille-et-Vilaine is specialized in dairy production. Given the structure of the agricultural sector, it can be expected that adjustment costs due to asset fixity might be not negligible. In fact, the purchases of machinery and equipment can be substantial (31,600 € per farm and per year on average in the French dairy sector in 2014) and mechanization costs also constitute a large share of operating expenses (21% in the French dairy sector, 26% in the French beef sector in 2014) [START_REF] Agreste | La Statistique Agricole[END_REF]. Likewise, the resale of used equipment also increased over time, probably because of some economic and fiscal strategies. In 1980, 22% French farmers sold their equipment and tool while in 2014, the respective figure was 38%. The overall ratio between disposals and acquisitions, was 15% in 1980 and 30% in 2014, reflecting a faster equipment and machinery renewal Irreversibility is also crucial in agriculture: it may be caused by a wedge between acquisition and salvage values for farm specific assets, or because the adjustment costs for disinvestment are not the same as for investment [START_REF] Arrow | Optimal capital policy with irreversible investment[END_REF][START_REF] Caballero | On the sign of the investment-uncertainty relationship[END_REF][START_REF] Dixit | Investment under uncertainty[END_REF]. If investments can be delayed, irreversibility makes them especially sensitive to uncertainty [START_REF] Pindyck | Irreversibility, uncertainty, and investment[END_REF].

In this context, the objective of this paper is to provide a new view on investment decisions in the dairy sector. Firstly, we will account for the link between farm investment and farm performance in our modelling strategies. Secondly, we will consider two types of farms that may have a different investment strategy: farms that are intensive in capital, and farms that have a low capital intensity.

The remainder of this paper is structured as follows. Section two develops the theoretical framework. Section three describes the database while section four explains the econometric specifications. Section five presents the results and the last section concludes.

Theoretical framework

The theoretical framework model used here assumes that farmers maximize the expected net present value of their profits 𝜋 at time t, over an infinite horizon:

𝑀𝑎𝑥 𝐸 𝑡 {∑ 𝛽𝜋 𝑡 {𝐾 𝑡 , 𝐼 𝑡 } + 𝐷 𝑡 -(1 + 𝑟 𝑡-1 )𝐷 𝑡-1 ∞ 𝑡=0 } (1) on 𝐾 𝑡 , 𝐼 𝑡 , 𝐷 𝑡 subject to 𝐾 𝑡 = (1 -𝛿)𝐾 𝑡-1 + 𝐼 𝑡 (2) 𝜋 𝑡 {𝐾 𝑡 , 𝐼 𝑡 } + 𝐷 𝑡 -(1 + 𝑟 𝑡-1 )𝐷 𝑡-1 ≥ 0 (3)
where the capital 𝐾 𝑡 is a stock variable and the investment 𝐼 𝑡 is a flow variable; 𝛽 is the discount factor; 𝑟 𝑡 is the interest rate; 𝛿 is the depreciation rate; 𝐷 𝑡 denotes debts; 𝐸 𝑡 is the expectation operator conditional on information available at the start of period t.

Equation ( 2) is the expression for capital accumulation, stating that the current capital stock consists of last year's capital stock, adjusted for depreciation at rate 𝛿, plus current investment. Equation ( 3) is a non-negativity constraint that ensures that the farm profit reduced by debt payments is positive at each period.

The corresponding Lagrangian is:

L=E t {∑ βπ t {K t ,I t }+D t -(1+r t-1 )D t-1 ∞ t=0 }+⋯𝜆 𝑡 [𝐼 𝑡 -𝐾 𝑡 + (1 -𝛿)𝐾 𝑡-1 ] + 𝜆 𝑡+1 [𝐼 𝑡+1 -𝐾 𝑡+1 + (1 -𝛿)𝐾 𝑡 ] + ⋯ + 𝜇 𝑡 [𝜋 𝑡 {K t ,I t } + D t -(1+r t-1 )D t-1 ] + 𝜇 𝑡+1 [𝜋 𝑡+1 {K t+1 ,I t+1 } + D t+1 -(1+r t )D t ] (4)
where 𝜆 𝑡 and 𝜇 𝑡 are the Lagrangian multipliers associated with constraints (2) and ( 3) respectively.

The first order conditions for investment I t , capital 𝐾 𝑡 , and debts D t are respectively as follows:

𝜕𝐿 𝜕𝐼 𝑡 = 𝐸 𝑡 {(β + 𝜇 𝑡 ) 𝜕π t 𝜕𝐼 𝑡 } + 𝜆 𝑡 = 0 (5) 𝜕𝐿 𝜕𝐾 𝑡 = 𝐸 𝑡 {(β + 𝜇 𝑡 ) 𝜕π t 𝜕𝐾 𝑡 } -𝜆 𝑡 + 𝜆 𝑡+1 (1 -𝛿) = 0 (6) 𝜕𝐿 𝜕𝐷 𝑡 = (β + 𝜇 𝑡 ) -(1 + 𝑟 𝑡 )(β + 𝜇 𝑡+1 ) = 0 (7)
Combining first order conditions ( 5) and ( 6) gives:

𝐸 𝑡 { 𝜕π t 𝜕𝐼 𝑡 } + 𝐸 𝑡 { 𝜕π t 𝜕𝐾 𝑡 } -(1 -𝛿)𝐸 𝑡 { (β+𝜇 𝑡+1 ) (β+𝜇 𝑡 ) 𝜕π t+1 𝜕𝐼 𝑡+1 } = 0 (8)
Plugging equation ( 7) in equation ( 8) defines the optimal path of investment under the form of the following Euler equation:

𝐸 𝑡 { 𝜕π t 𝜕𝐼 𝑡 } = (1 -𝛿) 1 (1+𝑟 𝑡 ) 𝐸 𝑡 { 𝜕π t+1 𝜕𝐼 𝑡+1 } -𝐸 𝑡 { 𝜕π t 𝜕𝐾 𝑡 } (9)
This Euler equation equates the marginal costs of investing in current period t (left-hand side of the equation) and the marginal costs of postponing investment until the next period (right-hand side of the equation). It means that the farm is indifferent between investing today and transferring those resources to tomorrow.

The Euler equation defining the optimal investment path is generally estimated by assuming rational expectations [START_REF] Muth | Rational expectations and the theory of price movements[END_REF]. This implies that the expected value in period t-1 is equal to the value in period t corrected with an error term. Thus, we introduce an expectational error in equation ( 10). 𝐸 𝑡 { } is the expectation operator conditional on information available at the start of period t, expectations being taken over future prices and technologies [START_REF] Bond | Dynamic Investment Models and the Firm's Financial Policy[END_REF].

𝐸 𝑡 { 𝜕𝜋 𝑡 𝜕𝐼 𝑡 } - (1-𝛿) (1+𝑟 𝑡 ) 𝐸 𝑡 { 𝜕𝜋 𝑡+1 𝜕𝐼 𝑡+1 } + 𝐸 𝑡 { 𝜕𝜋 𝑡 𝜕𝐾 𝑡 } = 𝜀 𝑡+1 (10)
where 𝜀 𝑡+1 is an error term that is assumed to be uncorrelated with explanatory variables.

The profit function at time t is specified, as follows:

𝜋 𝑡 {𝐾 𝑡 , 𝐼 𝑡 , 𝑋 𝑡 } = 𝑝 𝑡 𝑌 𝑡 -𝐶 𝑡 -𝑤 𝑡 𝑋 𝑡 -𝑝 𝑡 𝐼 𝐼 𝑡 (11)
where 𝑝 𝑡 is the output price; 𝑌 𝑡 is the output supply; 𝑋 𝑡 is the level of variable inputs; 𝐶 𝑡 is the adjustment cost; 𝑤 𝑡 is the variable input price and 𝑝 𝑡 𝐼 is the investment price. This specification shows that the farm faces a loss in profit due to adjustment costs when installing the new capital. The production function is assumed to depend on the fixed and variable inputs and on a performance parameter:

𝑌 𝑡 = 𝑓 1 (𝐾 𝑡 , 𝑋 𝑡 , 𝑢 𝑡 ) (12)
where 𝑢 𝑡 is the farm performance.

𝑌 𝑡 is supposed to be quadratic and increasing with performance, with performance assumed to depend on capital stock.

𝑢 𝑡 = 𝑓 2 (𝐾 𝑡 ) (13) 𝜕𝑢 𝑡 𝜕𝐾 𝑡 = 𝑏𝑢 𝑡 (14)
It is a common assumption that output is influenced by a performance parameter which represents the managerial capacity of the farmer. This is the literature on technical efficiency.

Assuming that such performance depends on the capital stock is also realistic, as this is related to size effects. Here we make no assumption on the sign of the derivative of such performance with respect to the capital stock, that is to say with respect to size: it may be negative (positive) meaning that farmers on larger farms have a lower (higher) performance. However, as shown by equation ( 14), we assume in addition that the effect of size on performance is depends on the level of performance, and more precisely that the effect is reinforced at high levels of performance.

The first derivatives of 𝑌 𝑡 can be written as follows:

𝜕𝑌 𝑡 𝜕𝐾 𝑡 = 𝛼 0 + 𝛼 1 𝐾 𝑡 + 𝛼 2 𝑋 𝑡 + 𝛼 3 𝑢 𝑡 > 0 (15) 𝜕𝑌 𝑡 𝜕𝑢 𝑡 = 𝑎 > 0 (16)
The adjustment costs incurred by farms are assumed to be quadratic and to depend on 𝐾 𝑡 and 𝐼 𝑡 :

𝐶 𝑡 = 𝑓 3 (𝐾 𝑡 , 𝐼 𝑡 ) (17) 𝜕𝐶 𝑡 𝜕𝐼 𝑡 = 𝛽 0 + 𝛽 1 𝐼 𝑡 with 𝛽 1 > 0 (18) 𝜕𝐶 𝑡 𝜕𝐾 𝑡 = 𝛾 0 + 𝛾 1 𝐼 𝑡 2 (19)
The Euler equation in (10) can then be rewritten as follows:

𝜕𝜋 𝑡 𝜕𝐶 𝑡 𝜕𝐶 𝑡 𝜕𝐼 𝑡 -𝑝 𝑡 𝐼 - (1-𝛿) (1+𝑟 𝑡 ) ( 𝜕𝜋 𝑡+1 𝜕𝐶 𝑡+1 𝜕𝐶 𝑡+1 𝜕𝐼 𝑡+1 -𝑝 𝑡+1 𝐼 ) + 𝜕𝜋 𝑡 𝜕𝑌 𝑡 𝜕𝑌 𝑡 𝜕𝐾 𝑡 - 𝜕𝜋 𝑡 𝜕𝐶 𝑡 𝜕𝐶 𝑡 𝜕𝐾 𝑡 = 𝜀 𝑡+1 (20)
that is to say

-(𝛽 0 + 𝛽 1 𝐼 𝑡 ) -𝑝 𝑡 𝐼 - (1-𝛿) (1+𝑟 𝑡 ) (-(𝛽 0 + 𝛽 1 𝐼 𝑡+1 ) -𝑝 𝑡+1 𝐼 ) + 𝑝 𝑡 (𝛼 0 + 𝛼 1 𝐾 𝑡 + 𝛼 2 𝑋 𝑡 + 𝛼 3 𝑢 𝑡 ) - (𝛾 0 + 𝛾 1 𝐼 𝑡 2 ) = 𝜀 𝑡+1 (21)
Considering that the price of investment (𝑝 𝑡 𝐼 ) and the interest rate (𝑟 𝑡 ) are constant across farms and years, the final model is therefore:

𝐼 𝑡+1 = 𝜗 0 + 𝜗 1 𝐼 𝑡 + 𝜗 2 𝐼 𝑡 2 +𝜗 3 𝑢 𝑡 𝑝 𝑡 + 𝜗 4 𝑋 𝑡 𝑝 𝑡 + 𝜗 5 𝐾 𝑡 𝑝 𝑡 + 𝜗 6 𝑝 𝑡 + 𝜀 𝑡+1 (22)
with:

𝜗 1 = (1+𝑟) (1-𝛿) (23)
𝜗 2 = 𝛾 1 𝛽 1 (1+𝑟) (1-𝛿) (24) 𝜗 3 = - 𝛼 3 𝛽 1 (1+𝑟) (1-𝛿) (25) 𝜗 4 = - 𝛼 2 𝛽 1 (1+𝑟) (1-𝛿) (26) 𝜗 5 = - 𝛼 1 𝛽 1 (1+𝑟) (1-𝛿) (27) 𝜗 6 = - 𝛼 0 𝛽 1 (1+𝑟) (1-𝛿) (28)
Equation ( 23) shows that a positive impact of 𝐼 𝑡 on 𝐼 𝑡+1 is expected. As 𝛽 1 is assumed to be positive, and

(1-𝛿) as well, the direction of the impact of 𝐼 𝑡 2 (that is to say the sign of 𝜗 2 ) will inform on the sign of 𝛾 1 that is to say on the shape of the adjustment cost function. The sign of 𝜗 3 , related to the effect of 𝑢 𝑡 𝑝 𝑡 on 𝐼 𝑡+1 , will inform on the sign of 𝛼 3 that is to say the direction of the impact of performance 𝑢 𝑡 on the marginal productivity of 𝐾 𝑡 . The sign of 𝜗 4 , related to the effect of 𝑋 𝑡 𝑝 𝑡 on 𝐼 𝑡+1 , will inform on the sign of 𝛼 2 namely the effect of 𝑋 𝑡 on the marginal productivity of 𝐾 𝑡 . The direction of the impact of 𝐾 𝑡 𝑝 𝑡 on 𝐼 𝑡+1 (𝜗 5 ) will inform on the sign of 𝛼 1 namely of the effect of 𝐾 𝑡 on the marginal productivity of 𝐾 𝑡 .

Data

The sample

We use data on specialized dairy farms of one sub-region of Brittany (Ille-et-Vilaine) obtained from a regional private accounting agency2 for the period 2005-2014 (ten years). This accounting agency manages the accounts of the major part of farmers in Brittany. For the present study, we use a fully balanced panel of 662 annual individual observations (farms) over the ten years period3 . Hence, the pooled sample for the ten years includes 6,620 observations.

As shown in Table 1, during the period considered, farms in the sample operated 77 hectares (ha) of utilized agricultural area (UAA), used 2 full-time equivalent labour units and 52 dairy cows, and produced 7,120 litres of milk per cow, on average. In terms of representativeness of the sample, comparing to the exhaustive sample of specialized milk dairy farmers in Ille-et-Vilaine from the French 2010 Agricultural Census, farms in the sample were bigger that Agricultural Census' farms in terms of UAA and labour use, but had the same number of cows on average, and were smaller in terms of capital used [START_REF] Agreste | Recensement agricole d'Ille-et-Vilaine : premières tendances[END_REF]. They had a higher milk yield but obtained lower Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) per litre of milk and lower farm income per family labour unit.

The net capital stock (𝐾 𝑡 ) is the net value of fixed assets, including all tangible assets such as buildings, machinery4 and tools, breeding livestock and land. Net investment (𝐼 𝑡 ) is computed as the difference, between periods t and t-1, of the net capital stock plus depreciation in value5 . Values of capital and investment in period t were deflated by the price index of the means for agricultural production and more precisely investment goods with base year 2010.

The output price (𝑝 𝑡 ) is the sale price of milk in period t, and was deflated by the price index of agricultural products with base year 2010. The variable input (𝑋 𝑡 ) is proxied by operational expenses, that are the costs related to the farming operations including in particular costs for animal feed, forage, livestock litter, fuel, veterinary products, animal reproduction, as well as costs for temporary labour. This variable input proxy was deflated by the price index of the means for agricultural production and more precisely consumer goods and services, with base year 2010. The performance variable (𝑢 𝑡 ) is proxied by EBITDA, and deflated by the price index of agricultural products with base year 2010. The EBITDA measures the potential cash flow obtained from the farm operation. It enables the farmer to maintain and develop the production facilities and to remunerate the capital used (equity and debt capital). As it will be seen later, for the econometric specification all variables are divided by the net capital stock.

The ratio of EBITDA to capital, which will enter the econometric equation in place of 𝑢 𝑡 is the ratio of return on capital used by bank analysts to measure the ability of capital to generate potential cash resources. It can thus be seen as a proxy for the farmer's managerial capacity, which influences the farm output generated. Moreover, EBITDA is useful to compare farmers because its value is independent of fiscal optimization choices, although it is affected by changes due to capital intensity. The investment peak in 2007 may be due to the significant milk price increase in 2007-2008 which was followed by a significant decrease in 2010, after the beginning of the economic crisis. In 2009, the dairy sector experienced a deep crisis in the form of a sudden milk price decrease and, at the same time, input prices remained at a high level. Milk price has since then been at its lowest level in historic terms and this episode urged farmers to adapt their technology, maybe changing their perception of risk and their investment behaviour.

However, other reasons may influence investment behaviour. For instance, during the economic crisis (2008)(2009)(2010) investment in buildings increased, contrary to investment in machinery and to total investment. Policy instruments such as the Plan of Modernization of Livestock Buildings, in 2007, co-funded by both the European Union and the Brittany region in the frame of the Second Pillar of the CAP, might have influenced farmers' investment behaviour. The purpose of this plan was to give incentives to farms to upgrade their production facilities and to improve their working conditions. Farm investment may have also been influenced by the cross-compliance scheme of the CAP within the 2013 reform, especially Statutory Management Requirements (SMRs) referring to 13 legislative standards in the field of animal welfare environment, food safety, animal and plant health.

The increase in farms' and livestock's size probably encourages farmers to enhance labour productivity, explaining the development of milking robot technology, especially among young farmers looking for more flexibility in their organization. Indeed, among the participants in an international programme of performance monitoring, in France the number of farms equipped with a milking robot has doubled since 2010 and has been multiplied by ten since 2005, but with a slight downturn in 2009 due to the dairy crisis; Ille-et-Vilaine being the most equipped region with milking robot [START_REF] Idele | Robots de traite : le nombre d'élevages équipés a doublé depuis[END_REF]. Other management choices in terms of labour organization consist in merging farms and herds or in increasing the number of hired workers. Investment behaviour also changes due to the development of farm machinery cooperative (CUMA) and of work outsourcing (ETA) as explained above, which avoids buying the machinery and may reduce costs.

Classification of farms according to their capital intensity

Farms' investment behaviour is affected by systemic changes in farm environment, forcing farms to introduce some adjustments in their organization, management and other internal characteristics. We expect that these adjustments are different according to the farm structure, especially its capital intensity. For this reason, we investigate here the investment behaviour of two groups of farms that differ in terms of capital intensity.

For this, we use a Hierarchical Ascendant Classification (HAC) to separate farms into different groups based on the following specific characteristics: the herd size (i.e. number of dairy cows), the share of fodder maize in the farm forage area, the number of Livestock Units6 per hectare (LU/ha) (i.e. stocking rate), the cost of work outsourcing per LU, the cost of concentrates per dairy cow, and the capital stock per LU. In order to distinguish the different farm structures (especially their capital intensity) as well as the different changes over time, we use two types of variables: static ones (i.e. the average values over the whole period) and dynamic ones, for which the rate growth is computed between 2005 to 2014, except for the cost of work outsourcing because it was collinear with other variables.

The HAC consists in searching for homogeneous groups of individuals (the clusters) in the population, that is to say groups which have a low within variability (i.e. the groups are homogenous) and a high between variability (i.e. the groups are different between each other).

In other words, groups are chosen by ensuring a low intra-class inertia and high inter-class inertia. For that, the Duda-Hart index is computed [START_REF] Milligan | An examination of procedures for determining the number of clusters in a data set[END_REF]. The number of groups is set when the smallest pseudo-t value is associated with the highest Duda-Hart index. Then, statistical tests (t-Student test for continuous variables) are used to identify whether the observation of a mean difference between the groups is significant or not.

The HAC performed here allows identifying two clusters of farms in our sample, whose description is shown in table 3 andtable 4. Table 3 reports the variables used for the classification, while table 4 presents additional characteristics of the clusters. As shown by table 3, on average farms in cluster 1 (423 farms) exhibit significantly larger sizes (in terms of the number of dairy cows), are more intensive (with the highest share of fodder maize in forage area, stocking rate and concentrates cost per dairy cow) and have higher cost of work outsourcing per LU than farms in cluster 2 (239 farms). Likewise, farms in cluster 1 experienced a higher growth of dairy cows and stocking rate. Turning to characteristics that were not used to generate the clusters, table 4 confirms that farms in cluster 1 are on average larger (in terms of labour, land and output) and perform better in terms of milk yield. All this suggests that cluster 1 is a cluster that is more capital intensive than cluster 2. Thus, farms in cluster 1 will be called farms with high capital intensity, while farms in cluster 2 will be called farms with low capital intensity. When financial variables are compared across the clusters: although high capital intensive farms (cluster 1) have on average a higher EBITDA per farm than low capital intensive farms (cluster 2), the value of EBITDA for the former is lower when related to the milk produced and similar when related to the level of capital stock.

Similarly, income is lower on average for high capital intensive farms and indebtedness level is higher, than for low capital intensive farms.

The two clusters, that differ in terms of capital intensity as well as performance, have in addition a different global investment behaviour, especially after the economic crisis. As shown by Appendices 1 and 2, the global investment of both clusters decreased between 2009 and 2010. However, while cluster 1 manages to increase its global investments during the rest of the period (2010-2014), cluster 2 increased its investments until 2011-2012 only. For this cluster, investments increased back in 2013-2014 after a slight decline in 2012-2013. After the economic crisis, although building investment follows the same trends in both clusters, the amplitudes are greater in cluster 2. Concerning machinery, the investment increases between 2010 and 2012 and then falls until 2014 for cluster 1, while, in cluster 2, it increases continuously from 2010 to 2014.

Where subscript i refers to the i-th farm and subscript t refers to the t-th period; ϑ 0 , ϑ 1 , ϑ 2 , ϑ 3 , ϑ 4 , ϑ 5 and ϑ 6 are parameters to be estimated; 𝜀 𝑖,𝑡+1 is the remaining disturbance, where the disturbance term 𝜀 𝑖,𝑡+1 = 𝑑 𝑡 + 𝑠 𝑖 + 𝑤 𝑖,𝑡 contains farm and yearspecific effets (respectively 𝑠 𝑖 and 𝑑 𝑡 ) and impermanent shocks 𝑤 𝑖,𝑡 .

The equation is estimated several times: for total investment, for investment in machinery only and for investment in building only, and this for the whole sample, for the sub-sample of high capital intensive farms, and for the sub-sample of low intensive farms.

We use specific econometric considerations in estimating these nine investment equations. Firstly, our econometric methodology accounts for the potential endogeneity, that is to say the correlation between explanatory variables and the error terms, probably due to a simultaneity bias. To prevent this problem, we employ the generalised method of moments (GMM; , lagged over two periods. In the literature, most obvious instruments are the lagged values of model variables [START_REF] Barran | Internal finance and corporate investment: Belgian evidence with panel data[END_REF][START_REF] Bond | Dynamic Investment Models and the Firm's Financial Policy[END_REF][START_REF] Rizov | Firm investment in transition[END_REF]. Secondly, we eliminate the farm-specific effect 𝑠 𝑖 from this investment equation by estimating the model in first differences [START_REF] Bokusheva | Exploring Farm Investment Behaviour in Transition: The Case of Russian Agriculture[END_REF][START_REF] O'toole | Financing constraints and agricultural investment: effects of the Irish financial crisis[END_REF].

Results

Table 5 provides the results of the estimation when total investment in period t+1 is taken as the dependent variable, for the full sample and for both sub-samples. Results show that the coefficient 𝜗 1 for the investment to capital at period t is significant and positive, as expected. This suggests that higher investments in period t induce an increase in the investment level in t+1. This is consistent with the model specification. It shows that farmers smooth their investment over time in order to undergo the lowest adjustment costs. As the coefficient for the square of investment to capital (𝜗 2 ) is significant and negative, this indicates that the adjustment cost parameter 𝛾 1 is negative, suggesting that the marginal adjustment costs with respect to capital stock decreases for high levels of investment.

The coefficient for the performance ratio 𝑢 𝑖,𝑡 𝑝 𝑖,𝑡 𝐾 𝑖,𝑡 (𝜗 3 ) is significant and negative. This means that, although performance increases the marginal productivity of capital (through a positive coefficient 𝛼 3 ), a high performance level in period t has a negative impact on investment at t+1 period, and this effect is even higher for high levels of prices. The negative effect of performance on investment in the next period suggests that farms performing well in period t prefer to avoid the loss in performance that may occur in the short term, namely in period t+1, following investment and adjustment costs. Farmers thus refrain from investing, although this could increase their performance in future periods due to the link between 𝑢 𝑡 and 𝐾 𝑡 . Farmers thus have to make a choice between investing to be better performing in next periods, and not investing in order to avoid adjustment costs. Here farmers seem to prefer not investing to avoid adjustment costs.

The coefficient 𝜗 5 is significant and negative. Although in the econometric specification this coefficient is attached to the price of output, in the theoretical model it is related to capital stock multiplied by the price of output. The negative sign would suggest that a high capital stock in period t discourages to invest in period t+1. Reversely having a low capital stock in period t may encourage to invest in period t+1. This suggests that farms with a high capital stock feel a lower need to increase their capital size, especially when knowing that investing may result in adjustment costs. The coefficient for the variable input ratio 𝑋 𝑖,𝑡 𝑝 𝑖,𝑡 𝐾 𝑖,𝑡 (𝜗 4 ) is significant and positive. This means that farmers with a higher intensity of variable inputs with respect to capital tend to invest more. This may suggest that farmers relying highly on variable inputs invest so as to substitute variable assets with fixed assets because of the decreasing marginal productivity of inputs: indeed, one can note, from the signs of 𝜗 4 and 𝜗 5 , that the marginal productivity of capital increases with capital (𝛼 1 ) and decreases with variable inputs (𝛼 2 ), suggesting that farmers have incentives to substitute variable inputs with capital.

Price in period t has a positive impact on investment in period t+1, which is conform to intuition: higher sale opportunities give incentives to farmers to expand, and thus to invest. This effect goes in opposition to the effect of performance: depending on whether the price is very high, one or the other effect might prevail. The performance proxy incorporates the difference between output and input prices and the ratio on capital stock. It is thus much more linked to managerial performance and return on equity than the sole output prices. Thus, high prices do not always result in high performance and conversely.

Similar findings are obtained when only building investments are considered (see Appendix 3). When only machinery investments are considered, similar results to total investment are fund, except for ϑ 3 , the coefficient for the performance ratio

𝑢 𝑖,𝑡 𝑝 𝑖,𝑡 𝐾 𝑖,𝑡
, which although still negative, is now non-significant (see Table 6). This discrepancy is to be explained by the different levels of capital intensity in the sample. While the two sub-samples of low capital intensity and high capital intensity behave similarly in terms of total investment (Table 5), their behaviour is slightly different in terms of machinery investment (Table 6). The only difference regards ϑ 3 , the coefficient for the performance ratio which is significant and negative for the high capital intensity sub-sample and non-significant (and positive) for the low capital intensity sub-sample. Thus, high capital intensive farms are faced with the abovementioned trade-off of investing now to increase their size and then their performance, and to postpone investment so as to not decrease their next year performance with adjustment costs. By contrast, low capital intensive farms' machinery investment behaviour is independent of their performance. This is confirmed by evolution graphs: Appendix 4 shows a decrease in performance (EBITDA) for both farm clusters after 2011. On Appendices 1 and 2, for high capital intensive farms machinery investment became negative in 2010 and 2011 and then increased up to the highest level of the period in 2012, after which data it decreased. By contrast, for low capital intensive farms, although a similar pattern is observed up to 2012, investment then remained increasing. The fact that the latter farms, continue to invest when EBITDA drops (Appendix 4) suggests a catch-up effect, that is to say low capital intensive farms increase their capital intensity.

Conclusion

This article aimed at providing a new perspective on investment decisions in the farming dairy sector taking into account the link between farm investment and farm performance as well as the different strategies followed by farmers depending on the level of capital intensity of their farm. We used adjustment cost models in which performance is explicitly included for a sample of dairy farms in Western France (Brittany) in 2005-2014, separated into a cluster of high capital intensity farms and low capital intensity farms.

Firstly, results showned that, for both clusters of farms in the sample, smoothing their investment over time is found to be an optimal strategy in the presence of adjustment costs, as found by [START_REF] Gardebroek | Farm-specific Adjustment Costs in Dutch Pig Farming[END_REF]. Secondly, farmers have to make a choice between investing in period t so as to increase their capital size and their performance in long term, and not investing in order to undergo the least possible adjustment costs in short term. Thirdly, the effect of performance on investment behaviour is different between the two subsamples (high capital intensity vs. low capital intensity), when machinery investment is considered suggesting different investment behaviours.

Several avenues for future research can be suggested. Firstly, it would be interesting to consider present time preferences in our model by introducing the life cycle of farms and farmers in order to better understand the trade-offs faced by farmers between short term and long term. Secondly, our results show that farmer's managerial capacity, proxied by EBITDA, is an important lever of investment. One could further analyse whether findings are confirmed when other performance proxies are used, and in particular labour productivity. Indeed, some management choices are made in terms of labour organization, consisting in merging farms and herds or in increasing the number of hired workers. Investment behaviour also changes due to the development of farm machinery cooperatives (CUMA) and of work outsourcing (ETA) performed by skilled and efficient workers, which also avoids buying the machinery and may reduce costs. While it is common that farmers use such services to perform specific tasks (management, accounting, etc.), it is likely that such practices will still be developing in the coming years and influence investment behaviours. A suggestion would thus be to study more deeply this issue. Finally, even if theory suggests that it is easier to increase productivity through specialization rather than through diversification, it would be interesting to investigate the link between capital intensity and specialization in the dairy sector precisely.

Figure 1 :

 1 Figure 1: Evolution of the average level of total net investment, building investment and machinery investment in the sample used from 2006 to 2014.

Table 1 :

 1 Descriptive statistics of the sample used and of the Ille-et-Vilaine farms population

	Sample used	Ille-et-Vilaine
	(Mean 2004-2014)	(2010 Agricultural
		Census)

Source: The authors based on

CER FRANCE Ille-et-Vilaine, and Agreste (2010)

Figure

1

describes the evolution of the yearly average level of the different types of investments in our sample between 2006 (where investment in 2006 is computed with the value of capital in 2006 and the value of capital in 2005) and 2014. Total investment increases up to 2009, and then drops until 2011 where it increases again, continuously until the end of the period considered.

Table 2

 2

provides summary statistics for the key variables included in the model. The level of investment per farm and per year is € 3,243.19 on average in our sample. Most of it (72%) is made of machinery investment. As mentioned later, for the model estimation, all variables are related to capital stock (K). This enables controlling for size effects.

Table 2 :

 2 Summary statistics of variables used in the estimation

	Variables	Mean	Std. Dev	Min	Max	Number of observations
	𝐼 𝑡 𝐾 𝑡			-0.0095	0.22	-11.44	0.85	5,958
	𝐼𝑀 𝑡 𝐾 𝑡		-0.0016	0.11	-1.37	0.60	5,958
	𝐼𝐵 𝑡 𝐾 𝑡			-0.0130	0.08	-0.34	0.69	5,958
	2 𝐾 𝑡 𝐼 𝑡			7,195.71	26,390.49 0.00	1,107,676.00	5,958
	𝐼𝑀 𝑡 𝐾 𝑡	2	3,139.77	7,597.88	5,86E-06	235,212.50	5,958
	𝐼𝐵 𝑡 𝐾 𝑡	2	2,499.43	12,423.57 0.00	396,431.50	5,958
	𝑢 𝑡 𝑃 𝑡 𝐾 𝑡	10,894.17 5,866.04	-164,283.40 122,393.2	6,620
	𝑋 𝑡 𝑃 𝑡 𝐾 𝑡	14,100.15 8,974.79	1,157.76	219,101.20	6,620
	𝑃 𝑡 𝐾 𝑡			0.17	0.15	0.02	8.80	6,620
	Variables in levels
	Total investment (𝐼 𝑡 ) (€)	3,243.19	55,042.12 -333,685.30 1,467,339.00	5,958
	Machinery investment (𝐼𝑀 𝑡 ) (€)	2,347.30	32,485.54 -129,948.10 474,593.30	5,958
	Building investment (𝐼𝐵 𝑡 ) (€)	-1,341.53	33,194.94 -124,057.90 646,780.30	5,958
	Output price (𝑃 𝑡 ) (€ / 1,000 Litres) 327.40	33.45	251.94	482.85	6,620
	Variable inputs (𝑋 𝑡 ) (€)	103,213.50 84,189.79 5,352.94	1,102,166.00	6,620
	Capital stock (𝐾 𝑡 ) (€)	256,418.70 152,242.60 4,543.74	1,943,785.00	6,620
	Performance (𝑢 𝑡 ) (€)	77,830.96 46,109.86 -18,666.68 539,760.90	6,620

Notes: 𝐼𝑀 𝑡 and 𝐼𝐵 𝑡 are respectively machinery and building investment.

Source: The authors based on CER FRANCE Ille-et-Vilaine

Table 3

 3

	: Variables used for the hierarchical ascendant classification analysis: averages (2005-
	2014) for the two clusters identified
		Cluster 1	Cluster 2	t-test (equality
		(423 farms)	(239 farms)	of means)
	Mean of number of dairy cows	54.49	47.61	***
		(18.23)	(18.22)
	Mean of share of fodder maize in forage area (%)	0.09	0.04	***
		(0.13)	(0.08)
	Mean of stocking rate (LU / ha)	1.68	1.62	***
		(0.28)	(0.28)
	Mean of cost of work outsourcing per LU (€)	1.84	1.52	***
		(0.73)	(0.79)
	Mean of concentrates cost per dairy cow (€)	402.26	224.34	***
		(88.03)	(55.13)
	Mean of capital stock per LU (€)	79.08	66.78
		(36.49)	(25.63)
	Rate of growth of number of dairy cows	0.35	0.22	***
		(0.32)	(0.22)
	Rate of growth of share of fodder maize in forage area -0.10	-0.17
		(1.04)	(0.78)
	Rate of growth of stocking rate	0.06	0.01	***
		(0.21)	(0.15)
	Rate of growth of concentrates cost per dairy cow	0.64	0.75	*
		(0.55)	(1.17)
	Rate of growth of capital stock per LU	0.21	0.22
		(0.37)	(0.36)

Notes: Robust standard errors in parentheses. *, **, ***: significance at 10, 5, 1 percent level.

Source: The authors based on CER FRANCE Ille-et-Vilaine

Table 4 :

 4 Additional characteristics of the clusters: averages (in 2005 and in 2014)

	𝐼 𝑖,𝑡+1 𝐾 𝑖,𝑡	= 𝜗 0 + 𝜗 1	𝐼 𝑖,𝑡 𝐾 𝑖,𝑡	+ 𝜗 2	𝐼 𝑖,𝑡 𝐾 𝑖,𝑡 2	+𝜗 3	𝑢 𝑖,𝑡 𝑝 𝑖,𝑡 𝐾 𝑖,𝑡	+ 𝜗 4	𝑋 𝑖,𝑡 𝑝 𝑖,𝑡 𝐾 𝑖,𝑡	+ 𝜗 5 𝑝 𝑖,𝑡 + 𝜗 6	𝑝 𝑖,𝑡 𝐾 𝑖,𝑡	+ 𝜀 𝑖,𝑡+1

Source: The authors based on CER FRANCE Ille-et-Vilaine

4. Econometric specification

Our baseline empirical specification is as follows:

Table 5 :

 5 Results of the estimation with total investments per capital as the dependent variable

						High capital	Low capital
						Full sample	intensity	intensity
						farms	farms
	𝝑 𝟎 (intercept) -0.071	-0.0643964	-0.118136*
						(0.043)	(0.0576734)	(0.0648961)
	𝝑 𝟏 ( 𝑲 𝒊,𝒕 𝑰 𝒊,𝒕	)			0.230*** (0.021)	0.2410815*** (0.0274907)	0.1995209*** (0.034394)
	𝝑 𝟐 ( 𝑰𝒊, 𝒕 𝑲 𝒊,𝒕 𝟐	)			-3.64E-06*** (2.65E-07)	-3.72E-06*** (3.20E-07)	-2.97E-06*** (4.73E-07)
	𝝑 𝟑 ( 𝒖 𝒊,𝒕 𝑷 𝒊,𝒕 𝑲 𝒊,𝒕	)	-0.680*** (0.102)	-0.6309118*** (0.1251324)	-0.816617*** (0.1771849)
	𝝑 𝟒 ( 𝑿 𝒊,𝒕 𝑷 𝒊,𝒕 𝑲 𝒊,𝒕	)	0.0000115*** (1.24E-06)	0.0000105*** (1.51E-06)	0.0000122*** (2.19E-06)
	𝝑 𝟓 (𝑷 𝒊,𝒕 )				-0.0000283*** (1.42E-06)	-0.0000296*** (1.87E-06)	-0.0000252*** (2.07E-06)
	𝝑 𝟔 ( 𝑲 𝒊,𝒕 𝑷 𝒊,𝒕	)			5.36906*** (0.1564074)	6.149485*** (0.2052355)	4.427943*** (0.2470418)
	Observations 3,972	2,538	1,434

Notes: Robust standard errors in parentheses. *, **, ***: significance at 10, 5, 1 percent level.

Source: The authors based on CER FRANCE Ille-et-Vilaine

Table 6 :

 6 Results of the estimation with machinery investment per capital as the dependent variable

							Total Full sample	High capital intensity farms	Low capital intensity farm
	𝝑 𝟎 (intercept)	-0.0901555*** (0.0285742)	-0.1080946*** (0.0364888)	-0.0670906 (0.0442873)
	𝝑 𝟏 ( 𝑰𝑴 𝒊,𝒕 𝑲 𝒊,𝒕	)			0.2124933*** (0.0229108)	0.1817617*** (0.0273726)	0.3231471*** (0.0444627)
	𝝑 𝟐 ( 𝑰𝑴 𝒊,𝒕 𝑲 𝒊,𝒕	𝟐	)	-7.2e-0.6*** (4.52E-0.7)	-6.05e-06*** (4.94E-07)	-0.000013*** (1.21E-06)
	𝝑 𝟑 ( 𝒖 𝒊,𝒕 𝑷 𝒊,𝒕 𝑲 𝒊,𝒕	)	-0.1019164 (0.0670034)	-0.1415847* (0.0792673)	0.0249078 (0.1212908)
	𝝑 𝟒 ( 𝑿 𝒊,𝒕 𝑷 𝒊,𝒕 𝑲 𝒊,𝒕	)	3.41E-06*** (8.11e-07)	2.98E-06*** (9.59e-07)	2.99E-06** (1.50e-06)
	𝝑 𝟓 (𝑷 𝒊,𝒕 )					-9.71E-06*** (9.30E-07)	-0.00001*** (1.20E-06)	-7.96E-06*** (1.42E-06)
	𝝑 𝟔 ( 𝑲 𝒊,𝒕 𝑷 𝒊,𝒕	)					2.335184*** (0.103725)	2.8003127*** (0.1328556)	1.640942*** (0.1700147)
	Observations 3,972	2,538	1,434

Notes: Robust standard errors in parentheses. *, **, ***: significance at 10, 5, 1 percent level. 𝐼𝑀 𝑡 is machinery investment.

Source: The authors based on CER FRANCE Ille-et-Vilaine

Sources: French Ministry of Agriculture (agricultural censuses) and French statistical office INSEE (employment)

CER FRANCE Ille-et-Vilaine

The database provided by the accountancy office contains 28,672 observations. We removed observations that appear to suffer from data recording errors. We selected only farms having data over a complete accounting year (12 months). And we kept only farms which appeared each of the ten years to create a balanced sample. In total, around 77 per cent of the observations were dropped.

It is worth noting that an investment in a milking machine is often accompanied by an investment in new or modernized buildings to adapt to the new technology. Thus, when an investment in a milking robot is made, half of it is allocated to the machinery investment category and the other half to the building investment category.

Except for land which is not amortized in agricultural accounting.

Livestock Units are a way of aggregating the number of livestock heads from different types of animals, here dairy heifers, calves and dairy cows. Each type of animal is assigned a weight depending on its feed consumption.


enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

In situ assessment of seasonal variation of assimilated carbon transfer to biochemical compounds by tracing 13C after pulse labelling on Quercus petraea Soil-vegetation-atmosphere transfer models suffer from a lack of mechanistic integration of carbon allocation. Allocation of assimilated carbon among organs and more specially to biochemical compounds is affected by the environment and affects tree growth, the contribution of each organ to autotrophic respiration, and, in fine, carbon sequestration in the ecosystem.

The aim of this study (ANR CATS) was to assess the seasonal evolution of the contribution of assimilated carbon to leaf, stem and root biochemical compounds and the contribution of photo-assimilates to leaf exportation and autotrophic respiration. In situ 13C pulse labeling of 10-15 m tall trees was conducted in the field on six sessile oak (Quercus petraea) throughout the growing season (i.e. in May, July and September). Leaf, stem and root 13C content of starch, soluble sugars, structural matter and total organic matter was analyzed during 30 days after labeling. 13C content in leaf respired CO 2 was also determined In leaves, the proportion of 13C allocated to total organic matter decreased in average by 48% during the first 3 days after the three labeling date. The proportion of leaf respired 13C stayed stable at 4% of total recovered 13C all along the season. Leaves exported the main part of assimilated 13C to other compartments during the 3 days after labeling with an hourly export rate comprised between 0.5 and 0.8 %. Whatever the labeling date, 6 hours after labeling, in leaves, 60% of labeled organic matter corresponded to starch, soluble sugars and structural matter. In May, this proportion remained similar 30 days after labeling date. However partitioning between each biochemical fraction changed : during the 6 first days after labeling, starch corresponded to the most labeled compound, thereafter structural matter became the more labeled : it reveals the use of starch for leaf growth at this period. For July and September labeling, the proportion of labeling organic matter corresponding to starch, soluble sugars and structural matter decrease to 50 and 40% respectively, until day 30 after labeling. In this fraction, the allocation to structural matter increase to 30% and 20 % respectively for July and September labeling. For those dates, 13C also participated to construction of the leaves but in a lower extent: it progressively diminishes along the season.

In stems and roots, the proportion of 13C allocated to starch, soluble sugars and structural matter increased from 40 % to 80% whatever the season from the first day after labeling to day 30. For May and July labeling, the allocation to structural matter significantly increased: from 10 % the first day after labeling to 70 % the day 30 after labeling. 13C assimilated in May and in July contribute to the structure of stems and roots. For September labeling, allocation of 13C to starch clearly increased from 5 % to 25 % during the month after labeling and showed the priority to reserve accumulation at this period. This type of study allow determination of allocation to metabolites and exportation rates which are rarely studied

  In situ assessment of seasonal variation of assimilated carbon transfer to biochemical compounds by tracing 13C after pulse labelling on Quercus petraea Stéphane Bazot, Chantal Fresneau, Daniel Berveiller, Jérome Ngao, Claire Damesin To cite this version: Stéphane Bazot, Chantal Fresneau, Daniel Berveiller, Jérome Ngao, Claire Damesin. In situ assessment of seasonal variation of assimilated carbon transfer to biochemical compounds by tracing 13C after pulse labelling on Quercus petraea. Geophysical Research Abstracts, European Geosciences Union (EGU). DEU., 2014, NA, France. hal-01190001


Alberti, A., Theil, S Frédéric Choulet

Adriana A Alberti

Sébastien Theil

Natasha Marie Glover

Valérie Barbe

Josquin Daron

Lise Pingault

Pierre Sourdille

Arnaud Couloux

Etienne Paux

Natasha Marie

Paux, E Philippe Leroy

Guilhot, N Le Gouis

Valerie Barbe

Sophie Mangenot

Nicolas Guilhot

Jacques Le Gouis

François Balfourier

Michael Alaux

Véronique Jamilloux

Julie Poulain

Céline Durand

Arnaud Bellec

Christine Gaspin

Jan Safar

Jaroslav Dolezel

Jane Rogers

Klaas Vandepoele

Jean--Marc Aury

Klaus Mayer

Hélène Bergès

Hadi Quesneville

Patrick Wincker

Catherine Feuillet

A reference sequence of wheat chromosome 3B

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Yann Salmon

Jose Manuel

Torres Ruiz

Rafael Poyatos

Jordi Martinez-Vilalta

Patrick Meir

Hervé H Cochard

Maurizio Mencuccini

Balancing the risk of hydraulic failure and carbon starvation: a twig scale analysis in declining Scots pine

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.


domain: shs sdv info

text:
Sarah P F Bonny
email: s.bonny@murdoch.edu.au

Jean-François Hocquette

David W Pethick

Paul Allen

Isabelle Legrand

Jerzy Wierzbicki

Linda J Farmer

Rod J Polkinghorne

Graham E Gardner

DEMOGRAPHIC FACTORS HAVE ONLY MINOR EFFECTS ON A CONSUMER'S PERCEPTION OF THE EATING QUALITY OF BEEF

Keywords: Consumer testing, Europe, Sensory testing. I

The beef industry must respond to the changing market place and consumer demands. An essential part of this is quantifying consumer perception of beef quality across a broad range of demographics. Over 19,000 consumers from four European countries tasted seven beef samples and scored them for tenderness, juiciness, flavour liking and overall liking. Consumers also answered a short demographic questionnaire. The four sensory scores were analysed as dependent variables in linear mixed effects models. A fifth model was also established using a weighted combination of the four sensory scores termed the MQ4 score (0.3*tenderness, 0.1*juiciness, 0.3*flavour liking and 0.3*overall liking).

The answers to the demographic questionnaire were analysed as fixed effects in the models. Consumer, session, country, serve order and the quality of the previous sample were controlled for in the analysis. Overall, there were only small differences in a consumer's perception of beef eating quality between demographic groups. This indicates that a single quality descriptor could reliably predict eating quality for the entire market, providing a basis for a widespread, eating quality based, beef grading system in Europe.

INTRODUCTION

Quantifying consumer responses to beef across a broad range of demographics is essential for the beef industry to become more consumer focused. There is interest in using the principles of the Meat Standards Australia (MSA) model, which uses untrained consumers for the prediction of eating quality, to reduce the variability of European beef [1][2][3][4]. Consumer demographics are well established as factors that influence purchasing decisions [5]. Therefore, such an approach requires a sound understanding of the effect of demographics on consumer sensory scores in order to properly design the taste panel experiments [6] to ensure they aren't arbitrarily biased by the presence of large demographic effects. Furthermore, the use of a single quality descriptor for all consumers would be validated in the absence of large demographic effects.

Previous work on Australian and Korean consumers identified only very minor demographic effects on sensory scores of beef and lamb [6,[START_REF] Hwang | Demographic and design effects on beef sensory scores given by Korean and Australian consumers[END_REF]. The main response was that consumers who considered beef to be a more important part of their diet scored lamb more favourably [6]. Using the same experimental protocols as this study, Thompson,et al. [6] found males scored beef 2 points out of 100 lower than females. In contrast, Kubberød, et al. [START_REF] Kubberød | Gender specific preferences and attitudes towards meat[END_REF] found that males scored beef more favourably than females and Huffman, et al. [START_REF] Huffman | Effect of Beef Tenderness on Consumer Satisfaction with Steaks Consumed in the Home and Restaurant[END_REF] found no differences between the sexes. A consumer's preferred level of cooking doneness was also found to have a small effect on consumer scores [6], where consumers who preferred their beef medium-well to well-done scored beef 1-2 points higher out of 100 when testing beef cooked to a medium degree of doneness.

We hypothesise that there will be only small demographic effects, limited to a positive relationship with the importance of beef in a consumer's diet, and consumers who prefer beef cooked well-done will rate beef more favourably than consumers who prefer beef cooked rare. We also expect that males will score beef more favourably than females.

II. MATERIALS AND METHODS

The meat used for this experiment was sourced from standard commercial carcasses from all participating countries. They are described in detail by Bonny, et al.

[4] and Legrand, et al. [START_REF] Legrand | Prediction of beef eating quality in France using the Meat Standards Australia system[END_REF]. Meat was cooked by one of four cooking methods: grill, roast, slow cook and Korean BBQ (barbeque), to a rare, medium or well-done cooking doneness according to the protocols for MSA testing by personnel trained in MSA testing procedures [START_REF] Anonymous | Accessory Publication: MSA sensory testing protocols[END_REF][START_REF] Watson | Development of the Meat Standards Australia (MSA) prediction model for beef palatability[END_REF]. A total of 19 492 consumers were sourced through both commercial consumer testing organisations and local clubs and charities. Consumers scored meat from their country of origin and scored samples for tenderness (tn), juiciness (ju), flavour liking (fl) and overall liking (ov), by making a mark on a 100 mm line scale, with the low end of the scale representing a negative response and the high end of the scale representing a positive response. For a more detailed description of the testing procedures and the questionnaire, see [START_REF] Anonymous | Accessory Publication: MSA sensory testing protocols[END_REF]. In addition to scoring beef samples, consumers answered a short demographic questionnaire in their native language (Table 1). The English version of this questionnaire is detailed elsewhere [START_REF] Anonymous | Accessory Publication: MSA sensory testing protocols[END_REF].

The effect of demographic factors on the sensory scores (tn, ju, fl, ov) was investigated using linear mixed effects models with the HPMIXED procedure in SAS [START_REF]Applied statistics and the SAS programming language[END_REF]. A model was established using a weighted combination of the four sensory scores termed the MQ4 score (0.3*tn, 0.1*ju, 0.3*fl and 0.3*ov) as calculated by [START_REF] Watson | Consumer assessment of eating qualitydevelopment of protocols for Meat Standards Australia (MSA) testing[END_REF]. Consumer, session, country, serve order and the quality of the previous sample were controlled for in the analysis. All factors in the model were interacted with country and the score of the previous sample was also interacted with sample serve order. Non-significant terms (P>0.05) were then removed in a step-wise fashion to arrive at the final model. The predicted means for demographic effects were compared using the least significant differences, generated using the PDIFF function in SAS [START_REF]Applied statistics and the SAS programming language[END_REF].

III. RESULTS AND DISCUSSION

The effect of a consumer's preferred cooking doneness varied by country (Table 2).

Confirming our hypothesis, consumers from Northern Ireland, who preferred their beef cooked well-done or medium-well, scored beef samples approximately 4 points higher (P<0.05) than consumers who preferred their beef cooked blue/rare and slightly higher than those who preferred medium (Table 3). This result is similar for the Irish consumers and supported by 2 The order in which the product was served to the consumer; 3 The importance of beef in their diet; 4 The preferred degree of cooking doneness of the consumer; 5 The sensory score of the previously tasted sample Aligning with our hypothesis, the more importance consumers placed red meat in their diet, the more favourably (P<0.01) they scored beef (Table 2) in Poland, France, and Northern Ireland, but not by those tested in Ireland. The magnitude of the effect in Poland and Northern Ireland is similar to the findings of Thompson,et al. [6] who used the same technique with Australian consumers tasting lamb. However, this effect was the most pronounced for the French consumers, with a change by over 17 points out of 100 (P<0.05) (Table 3). This result should be treated with caution due to the poor spread of French consumers over the four possible responses, with only 0.13 % in the least important category. In contrast, the Polish data had between 20 to 30 % of consumers in each category. Further investigation with a more balanced distribution of consumers is required to fully quantify the effect of the importance of meat in the diet for French consumers on their perception of the eating quality of beef. Supporting our hypothesis, Males (53.1±0.60) scored beef samples higher than females (52.3±0.60) by about 1 point. This result is also supported by Gregory [START_REF] Gregory | Meat, meat eating and vegetarianism. A review of the facts[END_REF] and Kubberød, et al. [START_REF] Kubberød | Gender specific preferences and attitudes towards meat[END_REF] who also found that males scored meat more favourably than females.

IV. CONCLUSION

The way consumers score beef eating quality is remarkably consistent between different demographic groups. Where demographic groups differed, these differences were small, less than 4%. As consumers from different demographic groups have a similar appreciation of beef, this indicates that a single descriptor of eating quality will likely be applicable to different European countries. Having a reliable definition of eating quality will enable the European beef industry to develop an eating quality based grading system for beef. This would enable consumers to select beef of a desired quality when purchasing, and provide a price signal in the market, encouraging the production of quality beef. programme of the Department of Agriculture Food and the Marine, of the Irish Republic, are also formally acknowledged. Furthermore, this project would not have been possible without the practical support of the Association Institut du Charolais, the Syndicat de Défense et du promotion de la Viande de Boeuf de Charolles and the gourmet restaurants 'Jean Denaud'. The international travel required for this project has been funded by 'Egide/Fast' funds from the French and Australian governments respectively. The assistance and participation the Beef CRC and Janine Lau (MLA, Australia), Alan Gee (Cosign, Australia), Ray

4

  Bonny, S. P. F. et al. (2016). The variation in the eating quality of beef from different sexes and breed classes cannot be completely explained by carcass measurements. Animal FirstView: 1-9. 5 Berry, B. W. & Hasty, R. W. (1982). Influence of demographic factors on consumer purchasing patterns and preferences for ground beef. Journal of Consumer Studies & Home Economics 6: 351-360. 6

Table 1

 1

		Percentage of consumers in each category
		Doneness 1 Importance 2 Gender 3 Country 4
	0 5	0.68	1.33	0.71	0
	1	6.51	29.33	44.88	7.70
	2	34.84	36.09	54.41	8.93
	3	31.59	22.56	-	46.1
	4	26.37	10.69	-	37.3
	1 1=blue/rare. 2=medium. 3=medium-well. 4=well done;
	2 1=Red meat is important in my diet. 2=Red meat is a regular
	part of my diet. 3=Red meat is part of my diet. 4=I
	rarely/never eat red meat; 3 1=Male, 2=Female; 4 1=France, 2=
	Ireland, 3=Northern Ireland, 4=Poland; 5 0=unrecorded.

Table 2

 2

	The F values for the linear mixed effects model,
	predicting MQ4 1 for beef samples
	Variables	NDF Ʌ	MQ4 1
	Country	3	28.06***
	Order 2	5	163.04***
	Gender	2	9.17***
	Importance 3	3	8.05***
	Level 4	3	2.16
	Carry-over 5	1	89***
	Carry-over 5 *Carry-over 5	1	103.66***
	Order 2 *Country	15	4.34***
	Carry-over 5 *Country	3	15.2***
	Carry-over 5 *Order 2	5	108.42***
	Importance3 5 *Country	9	2.13*
	Level 4 *Country	9	9.31***
	Ʌ Numerator degrees of freedom; Denominator degrees of
	freedom is 111000; 1 MQ4= a weighted combination of
	consumer scores;

Table 3

 3 Predicted means (± standard error) of beef samples

			Preferred cooking doneness
		Rare	Medium	Med-well	Well-done
	All 51.5±0.85 a 52.6±0.73 ab 53.0±0.79 ab 53.1±0.91 ab
	Fr 1	51.8±2.65	51.9±2.29	51.9±2.59	54.4±3.16
	Ir 2	52.4±1.27 a 54.3±0.95 ab 55.7±0.99 b 55.0±0.94 b
	NI 3 47.7±0.82 a	50.6±0.54 b 51.5±0.55 c	51.9±0.52 c
	Pl 4 54.0±0.75 ab 53.8±0.53 a	53.1±0.51 b	51.1±0.61 c
			Importance of beef in the diet
		Important 5	Regular 6	Part 7	Rarely 8
	All 54.7±0.53 a	54.0±0.52 b 52.8±0.61 c	48.7±2.19 c
	Fr 1	58.7±1.23 a 57.2±1.13 ac 54.7±1.60 bc 39.4±8.33 b
	Ir 2	54.4±0.80	53.7±0.81	54.1±0.98	55.1±2.10
	NI 3 51.9±0.52 a	51.5±0.52 a	49.8±0.56 b 48.5±0.89 b
	Pl 4	53.9±0.58 a	53.5±0.57 a	52.8±0.55 b	51.9±0.57 c

  Verbeke, W. et al. (2010). European beef consumers' interest in a beef eating-quality guarantee: Insights from a qualitative study in four EU countries. Appetite 54: 289-296. 3 Bonny, S. P. et al. (2015). Ossification score is a better indicator of maturity related changes in eating quality than animal age. Animal FirstView: 1-11.

					Watson
	(Melbourne University, Australia) and John Thompson
	(UNE) are also gratefully acknowledged.
	REFERENCES
	1	Hocquette, J.-F., Legrand, I., Jurie, C.,
		Pethick, D. W. & Micol, D. (2011).
		Perception in France of the Australian
		system for the prediction of beef quality
		(Meat	Standards	Australia)	with
		perspectives for the European beef sector.
		Animal Production Science 51: 30-36.
	2

France;

Ireland;

Northern Ireland;

Poland;

Red meat is important in my diet;

Red meat is a regular part of my diet;

Red meat is part of my diet;

I rarely/never eat red meat; a,b Values within a row with different superscripts differ significantly at P<0.05.

ACKNOWLEDGEMENTS

This research was funded by Meat and Livestock Australia and Murdoch University. The authors would also like to acknowledge the financial contributions of the European research project ProSafeBeef (Contract No. FOOD-CT-2006-36241), the French 'Direction Générale de l'Alimentation' and FranceAgriMer. The highly valued contributions through the FIRM


Hélène Romé

Amandine Varenne

Frédéric Herault

Hervé Chapuis

Christophe Alleno

Patrice Dehais

Alain Vignal

Thierry Burlot

Pascale Le Roy

F Hérault

C Alleno #

Detection of QTL influencing egg quality traits in layers receiving various diets

Keywords: laying hens, GWAS, QTL

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Introduction

Since decades, traits selected in layers concern egg number and egg quality performances. Even if these traits were improved by selection, their genetic architecture, i.e. the quantitative trait loci (QTL) influencing traits, is still poorly known. As of today, 33 studies focused on QTL detection for egg production in chicken (Chicken QTLdb) and only few genes were identified as involved in egg number or egg quality determinism.

Recently, the development of a high density chip for chicken by Affymetrix, i.e. the 600K Affymetrix® Axiom® HD genotyping array [START_REF] Kranis | [END_REF]), allows envisaging the use of high density genotype data for genomic selection in laying hens. This chip could also allow narrowing the localization of QTL previously detected and to detect new QTL involved in egg production. Furthermore, this high density chip should also allow going beyond the classical hypothesis of additivity between genetical and environmental effects on traits.

Thus, the aim of the present study is first to detect QTL influencing egg quality by using this high density chip and, secondly, to observe if the QTL detected could be different according to various environments. To this end, the layers were divided into 2 groups which were fed with two different diets.

Material and Methods

Animals and traits. The population studied consists of 438 sires of a pure line, created and selected by the NOVOGEN breeding company (Le Foeil, France) and of 31,381 of their F1 crossbred female offspring. 1).

DNA extraction and genotyping. Blood samples were collected from brachial veins of sires and DNA was extracted. DNA was hybridized on the 600K Affy-metrix® Axiom® HD genotyping array [START_REF] Kranis | [END_REF]) by Ark-Genomics (Edinburg, UK). In total 438 sires were genotyped for 580,961 markers. These markers covered chromosomes from 1 to 28, two linkage groups (LGE22C19W28_E50C23 and LGE64), two sex chromosomes and a group of markers with unknown locations. Genotypes were filtered according to 5 successive steps: SNP with a call rate less than 5% were discarded; animals with a call rate less than 95% were excluded; SNP with a MAF less than 0.05 were excluded; SNP with a call rate less than 95% were discarded; SNP significantly (P<5%) deviated from Hardy-Weinberg Equilibrium were excluded. Thus, 284,643 SNP were kept for the study and no individual was excluded.

Model. In a first step, egg measurements were adjusted for environmental effects. Independently for each of the 3 hatches, the estimates of covariables and fixed effects were calculated with the SAS GLM procedure according to the following linear model:

Y = Battery + Column + Floor + Experimenter + Waiting + Age + Sire + E
where Y is the phenotypic value, i.e. 9 variables per age; Battery (4 levels), Column (2 levels) and Floor (2 levels) represent the location of the cage in the building; Experimenter (8 to 10 levels) is the person who made the measurement; Waiting and Age are covariables to take into account the waiting time between sample and measurement of egg and the real age of hens (in days); Sire is the fixed effect of the sire (438 levels) and E is the residual. In this model, the diet effect is mingled with the Battery effect. Raw data were then adjusted for all effects exceeding the significant level of P<0.2, except the Sire effect. Distributions of adjusted data were tested for each trait and extreme individual values, i.e. values that lie at more than 4 phenotypic standard deviations of the mean, were discarded. Finally, for each of the 9 variables, 2 sire performances were calculated as the 2 means of his daughters' performances for each diet separately (HE and LE).

GWAS analyses.

For each SNP i (i=1 to 284,643) and each trait j (j=1 to 18, i.e. 9 traits * 2 diets), a mixed model taking into account pedigree kinship was applied according to:

z j = 1µ ij + X i β ij + e j with V(e j ) = Aσ 2 uj +Iσ 2 ej
where z j is the vector of the sires performances for the trait j; µ ij is the general mean; X i is the incidence matrix of SNP i genotypes; β ij is the effect of the SNP i on the trait j; e j is the residual, a random variable with V(e j ) variance-covariance matrix; A is the pedigree kinship matrix; I is the identity matrix; σ 2 uj is the genetic variance of the trait j and σ 2 ej is the residual variance.

This model was fitted using the BLUPF90 program [START_REF] Misztal | Proc 7th WCGALP, CD-ROM communication[END_REF]) and the chromosome wise thresholds (P<1%) were estimated with the method described by [START_REF] Müller | [END_REF], using the Muller software [START_REF] Ricard | Version 3.0[END_REF]). Genome wise thresholds (P<5%) were then calculated according to Bonferroni correction as chromosome wise thresholds at P<0.0015 (0.05/32 chromosomes).

Results and Discussion

Table 1 shows the mean and the standard deviation for each trait and each diet. No significant differences of mean or of standard deviation between diets were detected.

Table 1. Number of observations, mean and standard deviation of traits measured at 50 and 70 weeks of age.

GWAS analyses showed 9,056 SNP with a chromosome significant (P<1%) association with at least one egg quality trait. Among these SNP, 1,768 were genome wise significant (P<5%). Large differences exist between diets and between ages of measurement. Table 2 gives an example of results for ESL. Given a trait, the correlation between estimates of SNP effects, with LE or HE diets, were generally high (up to 0.60) but significantly different of 1. One example is shown in Figure 1 for ESR70. This figure also shows some SNP which have a strong effect (P<1%) only for one diet.

Figure 1. Correlation between estimates of SNP effects on Egg Shell Redness at 70 weeks of age (ESR70) with HE diet (in abscissa) and LE diet (in ordinate).

Chromosome wide significant SNP (P<1%) with high energy diet are spotted in green and with low energy diet are spotted in red.

Egg Shell Color (ESC, ESR, ESY and ESL).

For ESC, ESR, ESY and ESL, 2,743 SNP with a significant association were found. On GGA3, 274 SNP in the QTL region detected by Wardecka et al. (2002) have significant association with ESR50 (none with ESR70). At the genome wise level, 72 SNP were found on GGA12, for ESR50, ESC50, and ESL50 for both diet and for ESR70 with low energy diet only. These significant SNP seemed to be split into two different QTL. The first was in a region of 0.623 Mb and the second in a region of 0.289 Mb. The intermediate region was of 7.48 Mb. On GGA10, 161 SNP for ESR50, ESY50, ESL50, ESC50 with high energy diet, ESY50 and ESY70 with low energy diet, were detected at the genome-wise level 5%.

Egg Shell Shape (ESSA) and Short Length of Egg (SLE).

For ESSA and SLE, 1,803 SNP with a significant association were found. On GGA26, 14 significant SNP for ESSA with high energy diet only were identified. Moreover, 14 genome-wise significant SNP for SLE50 were detected for low energy diet on GGAZ within 0.114 Mb.

Egg Weight (EW). For EW, 1,265 SNP with a significant association were found. At 50 weeks of age, 956 SNP were detected and only 417 at 70 weeks of age. For example, on GGA22, 58 SNP with significant association with EW50 were found and none with EW70. Moreover, 17 genome-wise significant SNP for EW50 were identified on GGA11 within 3.677 Mb.

Conclusion

New QTL for egg quality traits were detected, such as a QTL on GGA12 influencing ESR50, ESY50, ESL50, ESC50 with high energy diet, ESY50 and ESY70 with low energy diet. Although no differences were observed on average level of performances according to the diet, interactions between QTL and diet were observed. All these results should be useful to improve genomic evaluation procedures in layers.

  Hens were hatched in 3 batches in November 2010, May 2011 and November 2011. At 18 weeks of age, they were housed in a production farm in collective cages of 12 half-sibs of sire. They laid from 18 to 75 weeks of age.

	Half of them were fed ad libitum with a "high energy diet
	(HE)" with 2881kcal of metabolizable energy (ME) (1342
	cages) and the others with a "low energy diet (LE)" with
	2455kcal of ME (1346 cages).
	Traits were measured by the Zootests company
	(Ploufragan, France) and concerned 9 variables : Egg
	Shell Color (ESC), Egg Shell Redness (ESR), Egg Shell
	Yellowness (ESY), Egg Shell Lightness (ESL), Egg Shell
	Strength (ESSE), Egg Shell Stiffness (ESSI), Egg Shell
	Shape (ESSA), Short Length of Egg (SLE) and Egg
	Weight (EW). They were recorded at 50 and at 70 weeks
	of age, respectively on 27 747 and on 25 964 eggs (Table

Table 2 . Number of SNP with significant effect (P<1%) on Egg Shell Lighness (ESL), at 50 or 70 weeks of age and with HE or LE diets, per chromo- some.

 2

	Chromo	ESL50 LE	ESL50 HE	ESL70 LE	ESL70 HE
	1	21	2
	2	5		1
	3	3	100		3
	4			8	2
	5	19	31	25	6
	6
	7		6
	8		3	12	1

Acknowledgments

This research project was supported by the "Agence Nationale pour la Recherche" (France) within the framework of the ANR-10-GENOM_BTV-015 UtO-pIGe project. HR is a Ph.D fellow supported by the Brittany region (France) and by the Animal Genetics division of INRA.


Martin-Stpaul Nk

Badel E, Jansen S, Cochard H, Burlett R, Lenoir N, Delzon S Torres-Ruiz Jm

Plant hydraulic: new insights brought by HRCT?

Result 2: Previously published P 50 for this species with ranged between -2 to -5.5MPa with very low safety margins. HRCT demonstrates that Q. ilex has P50 of about -7.4 MPa with safety margins 2 to 5 MPa higher than previously described [2].

Result 3: Vitis vinifera exhibits no embolism in stem above -1.0MPa (Ψ 50 = -1.73MPa), whereas Vitis riparia is more drought-sensitive (Ψ 50 = -1.28MPa ). Also, petioles are more sensitive than stems (Ψ 50 = -0.98MPa).

Introduction

  Recent advances in plant hydraulics have shed light on artifacts generated by destructive hydraulic measurements.  A major issue relates to the presence of open xylem vessels in tissue used for hydraulic measurements with the centrifuge system.  These artifacts are thought to overestimate plant drought vulnerability, influencing our understanding of the whole plant water balance.  High resolution computed tomography (HRCT) allows the detection of air and sap-filled xylem conducing elements in the wood of intact plants and can provide non-biased assessment of plants adaptation to drought. Cochard H, Delzon S. Hydraulic failure and repair are not routine in trees. Ann For Sci. 2013. doi:10.1007/s13595-013-0317-5. [2] Martin-StPaul NK, Longepierre D, Huc R, et al. How reliable are methods to assess xylem vulnerability to cavitation? The issue of "open vessel" artifact in oaks. Tree Physiol. 2014;34(8):894-905. doi:10.1093/treephys/tpu059.

  .e. ring-porous, diffuse porous, and tracheids).

( 2 )

 2 We compared safety margins for stomatal conductance and minimum water potential, based on HRCT VC (3 years-old intact seedlings), with previous safety margins for the well documented Mediterranean oak Q.ilex (3) We computed HRCT-based VC in two grapevine species (Vitis vinifera cv Cabernet-Sauvignon and Vitis riparia cv 'Gloire de Montpellier'), and two organs (stems and petioles).



  HRCT allows us to work on intact plants and evaluate the xylem hydraulic functioning by direct observation HRCT is a relevant method to study plant hydraulics, especially in species that are prone to artefacts with usual hydraulic methods.

Figure 2 :

 2 Figure 2: Bench drying and centrifugre were used as standard techniques to assess VC in ring porous, diffuse porous and conifers

Result 1 :

 1 Vulnerability curves produced with common methods (cavitron, static centrifuge) yielded consistent results only in tracheid-based (Pinus) and relatively short-vesseled species (Populus). HRCT confirmed the openvessel artifact with common methods in long vessels species (Quercus).

Figure 3 :

 3 Figure 3: SOLEIL synchrtron where most of the HRCT data shows in these studies were measured.

Figure 1 :

 1 Figure 1: taken from [1]. The red VC shows a very low resistance if compared to the point of stomata closure.This curve is often obtained with classical laboratory methods in long-vesseled species. The blue curve is consistent with the idea that plant are resistant to cavitation and that stomata regulate water loss to avoid cavitation.