The main objective : to promote sustainable foods systems, conciliating food security and healthy nutrition for the Mediterranean populations (South of France and Tunisia).

•

  To evaluate the nutritional potential of the current food systems in three Mediterranean areas: South-East of France (LR and PACA regions), Tunisian urban area (Greater Tunis) and Tunisian rural area (Sidi Bouzid). • To identify the food consumption changes needed to adequate patterns towards better nutrition and environmental fit • To evaluate the consequences of these changes on the whole food chains, up to the agricultural production • To identify and promote bio-cultural food diversity (local agro-biodiversity and traditional know-how) as a source of dietary diversity and quality • To build viable and acceptable scenarios promoting these changes and maximise agro-food chains' contribution to human health and nutrition in a sustainable way. harmoniza8on of data (foods, recipes, ingredients, nutri8onal & toxicological composi8on, CO 2 ) -Genera8on of new data on -environmental impact (water footprint and landuse) -indicators of biodiversity -food intakes in Sidi bouzid governorate (rural zone) Development of tables and metrics Food diversity can improve nutrition and health (FAO, 2010) -Assessment the local food agro-biodiversity by using the novel methodology of "nutritional functional diversity": number of species within a food category of the Mediterranean pyramid, number of cultivars within a species…. -Mapping the food sources in relation to 4 classes of nutrients -Classification for 23 nutrients : proteins, fibers, vitamin A and provit A-carotenes, vitamin D, vitamin E, vitamin K, thiamin, riboflavin, niacin, vitamin B6, pantothenic acid, biotin, vitamin B12, folate, vitamin C, calcium, iron, zinc, selenium, magnesium and iodine coordinator), N Achir, L Alouane, J El A+, S Bellagha, PM Bosc, M Broin, N Darmon, C Dhuique-Meyer, MC Dop, S Drogué, S Dury, A Ferchoui, Z Ghrabi, F Kamassi, Y Kameli, F Kefi, E Kesse-Guyot, D Lairon, Y Mar+n-Prevel, C Mouquet, S Njoumi, M Padilla, M Pérignon, C Sinfort, P Traissac, E Verger Financial Support ANR (ANR-12-TMED-0004 ) Bioversity and Agropolis Fundation

Quan%fica%on of Mediterranean Pyramid

Low consump+on fo nuts low affordability High consump+on of sugar High consump+on of vegetable (other than olive oil)

Confirmed by food balance sheets On the availability of ingredients/ nutrients


est destinée au dépôt

Implementation of a new rearing technique of Ooencyrtus pityocampae and Ooencyrtus kuvanae (Hymenoptera: Encyrtidae) to develop the biocontrol of lepidopterous pests in forest

Marine Venard, Hilal Tunca, Etty Colombel, Jean Claude Martin, Elisabeth Ooencyrtus masii and Ooencyrtus kuvanae (Hymenoptera: Encyrtidae), can be used to control these pests. However, it is difficult to rear these parasitoids on their natural hosts in laboratory because of pest univoltine cycle and Thaumetopoea allergen characteristic. Therefore, alternative hosts are needed to rear parasitoids. Several potential hosts were tested in our laboratory.

Philosamia ricini (Lepidoptera: Saturniidae) was selected for beneficial insects mass-rearing.

This alternative host has several advantages: it's easy to rear on widespread host plants

Implementation of a new rearing technique of Ooencyrtus pityocampae and Ooencyrtus kuvanae (Hymenoptera: Encyrtidae) to develop the biocontrol of lepidopterous pests in forest

	Marine Venard 1 , Hilal Tunca 2 , Etty-Ambre Colombel 1 , Jean Claude Martin 3 and Elisabeth
	Tabone 1
	1 INRA-UEFM, Site Villa Thuret, Laboratoire BioControle, Antibes, France;

2 Ankara University, Agriculture Faculty, Department of Plant Protection, Ankara, Turkey; 3 INRA-UEFM -UE0348, Site Agroparc, Avignon, France

In France, there are three major lepidopterous pests in the forest area: Thaumetopoea pityocampa, Thaumetopoea processionea (Thaumetopoeidae) and Lymantria dispar (Lymantriidae). These pests invade new forest sites because of global warming. Biological control is one of the main control methods that should be applied in forest. Three egg parasitoids, Ooencyrtus pityocampae,

(

  Ligustrum vulgare or Ailanthus spp.), it is not subject to diapause, it has big eggs attractive for many parasitoids species and is a multivoltine species. In this study, biological characteristics (emergence rate, development time, longevity and fecundity) of O. pityocampae and O. kuvanae were investigated on the new alternative host, P. ricini, under laboratory conditions (25±1°C, 70±5% R.H and 16L: 8D). Thus, we conclude that P. ricini has great potential for the rearing of O. pityocampae and O. kuvanae. Therefore, we can develop biological control programs on these three lepidopterous pests. Key-words: lepidopterous pests, egg parasitoid, alternative host, biological control, forest

To cite this version:

Marine Venard, Hilal Tunca, Etty Colombel, Jean Claude Martin, Elisabeth Tabone. Implementation of a new rearing technique of Ooencyrtus pityocampae and Ooencyrtus kuvanae (Hymenoptera: Encyrtidae) to develop the biocontrol of lepidopterous pests in forest. Plant Biology Europe EPSO/FESPB 2016 Congress, Jun 2016, Prague, Czech Republic. 2016. hal-02800113


Mohammed Sbihi
email: msbihi@ens2m.fr

Christophe Varnier
email: cvarnier@ens2m.fr

Single-machine scheduling with periodic and exible periodic maintenance to minimize maximum tardiness

Keywords: Scheduling, Periodic maintenance, Maximum tardiness, Non resumable job

Single-machine scheduling with periodic and flexible periodic maintenance to minimize maximum tardiness.

Introduction

The majority of the studies in machine scheduling literature assumes that machines are available all times. However, this availability may not be true in real industry settings. Unavailability periods often appear in industry due to a machine breakdown (stochastic) or preventive maintenance (deterministic) during the scheduling period. Therefore, a more realistic scheduling model should take into account associated machine maintenance activities. For a survey of scheduling problems with limited machine availability we refer the reader to the survey paper [START_REF] Schmidt | Scheduling with limited machine availability[END_REF]. Recent papers take into account these unavailability periods (see for instance [START_REF] Adiri | Scheduling on a single machine with a single breakdown to minimize stochastically the number of tardy jobs[END_REF], [START_REF] Allaoui | Integrating simulation and optimization to schedule a hybrid ow shop with maintenance constraints[END_REF], [START_REF] Allaoui | Two stage hybrid ow shop scheduling with availability constraints[END_REF], [START_REF] Espinouse | Minimizing the makespan in the two-machine no-wait ow-shop with limited machine availability[END_REF], [START_REF] Lee | Capacitated two-parallel machine scheduling to minimize sum of job completion time[END_REF], [START_REF] Lee | Parallel machines scheduling with non-simultaneous machine available time[END_REF], [START_REF] Mosheiov | Minimizing the sum of job completion times on capacitated parallel machines[END_REF], [START_REF] Sad | An improved approximation algorithm for the single machine total completion time scheduling problem with availability constraints[END_REF] etc...). Most of these papers consider that characteristics of the unavailability periods are known in advance, i.e. that the maintenance of a machine is a xed time interval known beforehand and study how to schedule jobs under the constraint of machine unavailability. However, in some cases (e.g. preventive maintenance), the maintenance of a machine is also controllable. In other words, we can decide when to maintain the machine. The jobs and the maintenances are scheduled simultaneously. In this paper, we study the problem of minimizing maximum tardiness of jobs.

In most of papers, there is only one unavailability or availability period for each machine [START_REF] Schmidt | Scheduling with limited machine availability[END_REF]. As stated earlier, however, maintenance is scheduled regularly, or periodically, in many manufacturing systems. Therefore, there is a need to develop scheduling methods to deal with periodic maintenance, which usually has more than one maintenance period. In our problem, there are several maintenance periods where each maintenance is required after a time interval.

More precisely, in this paper we consider the single machine maximum tardiness problem subject to periodic maintenance and no preemption (i.e. once a job is started it must be completed without interruption). We consider two situations. In the rst case (periodic maintenance) the maintenance periods are xed periodically. This problem was considered in [START_REF] Chen | Scheduling of jobs and maintenance in a textile company[END_REF] and [START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF]; and is NP-hard since the problem that minimizes the maximum lateness subject to one unavailability period and non resumable jobs is NP-hard [START_REF] Lee | Machine scheduling with an availability constraint[END_REF]. Chen and Liao [START_REF] Chen | Scheduling of jobs and maintenance in a textile company[END_REF] and [START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF] proposed a heuristic and branch-and-bound algorithm to solve this problem. Other works can be found in the literature regarding xed maintenance period. We can quote Allaoui and Artiba [START_REF] Allaoui | Integrating simulation and optimization to schedule a hybrid ow shop with maintenance constraints[END_REF]. They consider the problem of more complex environment such as hybrid owshop. They proposed an hybrid approach, combining simulation and heuristics to solve the problem of minimizing makespan, mean owtime and total tardiness. They showed that classical heuristics became ineective if unavailability constraints, such as maintenance, are taking into account. In [START_REF] Allaoui | Two stage hybrid ow shop scheduling with availability constraints[END_REF] same authors proposed also a branch and bound procedure for minimizing the makespan in a two stage hybrid owshop, where the rst stage has only one machine and all machines are subject to unavailability constraints. In the second case (exible periodic maintenance) that we consider, the maintenance is not xed but the maximum continuous working time allowed of the machine is xed. This problem is strongly NP-hard because it becomes a bin packing problem when all due dates are 0. It is known that bin packing is a strongly NP-hard problem [START_REF] Coman | Approximation algorithms for bin-packing: a survey[END_REF]. Note that the same problem was studied in [START_REF] Qi | Scheduling the maintenance on a single machine[END_REF] in order to minimize the total completion time of jobs, where several heuristics and a branch-and-bound algorithm are proposed. Graves and Lee [START_REF] Graves | Scheduling maintenance and semi-resumable jobs on a single machine[END_REF] study several variants of the same problem.

The rest of this paper is organized as follows. Section 2 denes the problems and introduces some notation. Sections 3 and 4 are devoted respectively to periodic maintenance and exible periodic maintenance. Various dominance rules (dierent form that of [START_REF] Chen | Scheduling of jobs and maintenance in a textile company[END_REF] and [START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF]) are derived and used in the heuristic and branch and bound algorithm proposed. Section 5 reports on computational experience with the algorithms. Finally, Section 6 provides a summary of the main results of this paper, and outlines some useful directions for future research. We consider the scheduling problem with n jobs J 1 , J 2 , ..., J n to be processed on a single machine. Each job J i has a processing time p i and a due date d i . All jobs are available at time zero and no preemption is allowed. The machine must be maintained periodically. In the periodic version the distance between two consecutive maintenance periods is ∆ (see Figure 1) whereas in the exible periodic version the distance is less than ∆ (see Figure 2). The maintenance time is t. We assume that p i ≤ ∆ for all i ∈ {1, ..., n} otherwise there is no feasible schedule. A sequence giving the processing order of the jobs denes a schedule since there is no advantage in keeping the machine idle when there are jobs to be processed. A schedule π = (J

[1] , J [2] , ...J [n 1 ] , M, J [n 1 +1] , ..., J [n 2 ]
, M, ...) contains a sequence of jobs and the maintenance inserted in job sequence. In a schedule, jobs processed continuously form a batch, denoted as B. Thus a schedule π can be denoted as π = (B 1 , t, B 2 , t, ..., t, B L ) where t is the maintenance and L is the number of batches.

Note that L is a decision variable in our problem. Let J [i] be the ith job in a schedule, C i (π) be the completion time of job J i , and T i (π) = max{C i (π)d i ; 0} be the tardiness of job J i . The objective is to nd a schedule which minimizes the maximum tardiness of jobs T max (π) = max i T i (π). It is worth mentioning that when preemption is allowed the rst problem can be solved by the preemptive EDD (PEDD) rule and any optimal solution of the rst problem is also optimal for the second one.

C i (σ): completion time of job J i ∈ J(σ) in σ. C(σ): completion time of σ.
T max (σ): maximum tardiness of σ. B m (σ): mth batch in σ. q m (σ): total processing time of jobs in batch B m (σ). m(i, σ): the index of the batch where job J i ∈ J(σ) is scheduled i.e. J i ∈ B m(i,σ) (σ). For J i / ∈ J(σ), σ • i: schedule obtained by adding job i after σ. Note that if job i requires a processing time greater than the slack time in the current (last) batch in σ, it is scheduled in the next time interval ∆.

When there is no ambiguity, C i (σ), T max (σ), B m (σ), q m (σ) and m(i, σ) are simplied into C i , T max , B m , q m and m(i), respectively.

Periodic maintenance

We propose here a rst approach considering the periodic maintenance case.

Dominance properties and lower bounds

In this section, we introduce some dominance properties and give lower bounds that will be used in our algorithms. The following property is similar to the EDD schedule in the ordinary T max problem. Property 3.1. There exists an optimal schedule in which in each batch jobs are sequenced in EDD (earliest due date) order. Property 3.1 can be proved by the pairwise interchange procedure within the same batch.

The next property states that the length of a job scheduled after batch i is longer than the gap ∆q i between scheduled jobs in batch i and the next scheduled maintenance (we remaind that q i represents the total completion time of batch B i ). Property 3.2. There exists an optimal schedule in which for each batch B i , we have ∆q i < p j , for all J j ∈ B k , k = i + 1, ..., L.

Proof. Suppose π is an optimal schedule where batch B i does not satisfy Property 3.2.

Let job J j be the rst job satisfying ∆q i ≥ p j , J j ∈ B k , k > i. Consider the sequence π obtained from π by removing J j from its original position and inserting it at the end of batch B i . Then it is clear that π is also an optimal sequence. Repeating this procedure a nite number of times, we obtain an optimal sequence verifying Property 3.2.

In order to give others dominance properties we need to introduce some notation.

From now on we restrict ourselves to schedules satisfying Property 3.1. Giving a partial schedule σ we denote by O(σ) the schedule obtained by appending to σ the optimal partial schedule of the remaining jobs while keeping Property 3.1 satised.

To compute lower bounds for T max (O(σ)), we start with the following which give a lower bound for the starting date in O(σ) for jobs not scheduled in σ. Let J i denote the last job in σ.

Lemma 3.1. Let J j / ∈ J(σ) such that p j > ∆ -q m(i) (σ) or d j < d i , then C j (O(σ)) -p j ≥ m(i)(∆ + t).
Proof. If p j > ∆ -q m(i) (σ), i.e. J j not t into batch B m(i) then it will not starts before

m(i)(∆ + t).
If d j < d i , because EDD rule within the batch, J j cannot be scheduled in B m(i) , so it will start after m(i)(∆ + t).

Lower bound 1. To each job J j not scheduled in σ we associate a ready time as follows:

r j =      m(i)(∆ + t) if p j > ∆ -q m(i) (σ) or d j < d i ; C(σ)
otherwise.

We consider then the problem P 1 (σ): Pursue the scheduling of jobs not scheduled in σ subject to preceding ready times in order to minimize the maximum tardiness while allowing preemption.

We denote by LB 1 (σ) the maximum tardiness in optimal solution of P 1 (σ). It is obtained by a dynamic version of Preemptive Earliest Due Date (D-PEDD) rule. Namely, sequencing decisions must be considered both at job completion times and at job ready times as follows:

1. At each job completion the job with minimum d j among available jobs is selected to begin processing.

2. At each ready time, r j , the due-date of the newly-available job j is compared to the due-date of the jobs being processed. If d j is lower, job j immediately preempts the job being processed, otherwise job j is simply added to the list of available jobs.

Lower bound 2. To calculate the second lower bound LB 2 (σ) we consider P 2 (σ) analogous to P 1 (σ) except in the denition of ready times:

r j =      m(i)(∆ + t) if d j < d i ; C(σ) otherwise.
It is clear that LB 2 ≤ LB 1 . In fact, the interest of the second lower bound lies in Lemma 3.2 used in the branch and bound algorithm.

Let LB(σ) denote any lower bound for T max (O(σ)) (for e.g. LB 1 or LB 2 ), then we have:

Property 3.3. For any partial schedule σ and any job j not scheduled in σ, schedule

O(σ • j) is dominated if there is a job i scheduled in σ such that m(i) < m(j), p i ≤ p j ≤ p i + (∆ -q m(i) (σ)) and C(σ • j) -(p j -p i ) -d i ≤ LB(σ),
where

m(i) = m(i, σ) and m(j) = m(j, σ • j).
Proof. Consider the sequence π obtained from O(σ • j) by putting job i at the place of job j and by putting the latter at the end of batch B m(i) which is possible thanks to the

condition p i ≤ p j ≤ p i + (∆ -q m(i) (σ)). Conditions m(i) < m(j) and p i ≤ p j ensure that only job i is completed later in π. But since C i (π) -d i = C(σ • j) -(p j -p i ) -d i , we deduce that T i (π) ≤ LB(σ) ≤ T max (O(σ))
. By re-seqencing batches B m(i) and B m(j) in the EDD order we obtain a better schedule π satisfying Property 3.1, and T max (π ) ≤ T max (O(σ)).

Property 3.4. Let σ be a partial schedule, and i the last job scheduled in σ. We suppose that there are no unscheduled jobs which t into last batch of σ. Then schedule O(σ) is dominated if there is an unscheduled job j such that

p i ≤ p j ≤ p i + (∆ -q m(i) (σ)) and d i ≥ d j -(p j -p i ).
Proof. Consider the sequence π obtained from O(σ) by putting job i at the place of job j and by putting the latter at the end of batch B m(i) which is possible thanks to the condition

p i ≤ p j ≤ p i + (∆ -q m(i) (σ)). Conditions m(i) < m(j) and p i ≤ p j ensure that only job i is completed later in π. Since T i (π) = max{C i (π)-d i ; 0} = max{C j (O(σ))-(p j -p i )-d i ; 0} ≤ max{C j (O(σ)) -d j ; 0} = T j (O(σ)), we conclude that T max (π) ≤ T max (O(σ)).
Finally, by re-sequencing each B m(i) and B m(j) in EDD order we obtain a new schedule π such that

T max (π ) ≤ T max (π) ≤ T max (O(σ)).

Heuristic algorithm

Based on the preceding dominance properties, we propose, in this section, a heuristic to provide a near-optimal schedule for the stated problem. The heuristic is denoted H1, its steps are outlined as follows:

Heuristic H1

Step 1. Sort jobs in EDD order with ties broken by nonincreasing order of p i : J 1 , J 2 ,...,J n .

Step 2. Calculate a lower bound T max for the problem by P-EDD.

Step 3. Let i = 1, j = 1, B i = ∅, q i = 0, (J, p, d) = (J j , p j , d j ) and T max = 0.

Step 4. Is there at least one batch B m such that ∆q m ≥ p j . If so, among them choose the batch with the smallest index, say k, put job J j at the end of batch B k , q k = q k + p j ,

T max = max{T max ; (k -1)(∆ + t) + q k -d j }, and moreover (J, p, d) = (J j , p j , d j ) if k = i, goto Step 8.
Step 5. If q i -p + p j > ∆ or p j < p, goto Step 6. Otherwise, goto Step 7.

Step 6. Let i = i + 1, insert a maintenance and let job J j be the rst job in the new batch

B i , q i = p j , T max = max{T max ; (i -1)(∆ + t) + q j -d j }, (J, p, d) = (J j , p j , d j ), goto Step 8.
Step 7

. If i(∆ + t) + p -d ≤ max{i(∆ + t) + p j -d j ; T max ; T max }, B i = (B i \{J}) ∪ {J j }, q i = q i -p + p j , i = i + 1
, insert a maintenance and let job J be the rst job the new batch

B i , q i = p, T max = max{T max ; (i -1)(∆ + t) + p -d; (i -2)(∆ + t) + q i-1 -d j }. Otherwise, goto Step 6.
Step 8. If j = n, stop. Otherwise, j = j + 1, return to Step 4. As an illustration of the heuristic, consider the following two examples from [START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF] and [START_REF] Chen | Scheduling of jobs and maintenance in a textile company[END_REF] respectively.

Table 1 The data for example 1 (in hours); ∆ = 8 and t = 2 Table 2 The schedule obtained by the heuristic in [START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF] J i J 1 J 5 J 2 t J 3 J 8 t J 6 J 9 t J 7 J 4

J i J 1 J 2 J 3 J 4 J 5 J 6 J 7 J 8 J 9 p i 1
T i 0 0 0 11 5 9 12
13 8

Table 3 The schedule obtained by our heuristic 10

J i J 1 J 3 J 8 t J 5 J 2 t J 6 J 9 t J 7 J 4 T i 0 2 0 2 4 9 12
13 8

Table 4 The data for example 2 (in hours); ∆ = 12 and t = 3

J i J 1 J 2 J 3 J 4 J 5 J 6 J 7 J 8 J 9 J
J i J 1 J 2 J 6 J 10 t J 4 J 5 t J 7 J 9 t J 3 J 8 J 11 T i 0 0 0 0 7 0 16 19 17 18 17
Table 6 The schedule obtained by the our heuristic

J i J 1 J 2 J 4 t J 6 J 7 J 9 t J 10 J 5 t J 3 J 8 J 11 T i 0 0 0 1 3 6 13 8
17 18 17

The algorithm will be studied by computational experiments later. It will also be compared to the heuristic proposed by Liao and Chen [START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF].

Branch and Bound algorithm

The proposed branch and bound algorithm uses a usual schema. During the computation, we keep a list of unexplored nodes arranged in increasing order according to the lower bounds of nodes, with ties broken by nonincreasing number of scheduled jobs. Each node represents a partial schedule. The algorithm always try to develop the head of the list. The branching from a node consists of creating child nodes by adding an unscheduled job to the end of the partial schedule. We will calculate a lower bound for each node that cannot be eliminated by the dominance proprieties. In particular we use the following comparison lemma.

Lemma 3.2. Let σ be a partial schedule, and i 1 and i 2 two jobs not scheduled in σ. If

d i 1 ≤ d i 2
then at least one of the following holds:

1. O(σ • i 1 ) is dominated. 2. O(σ • i 2 ) is dominated. 3. LB 2 (σ • i 1 ) ≤ LB 2 (σ • i 2 ).
Proof. Let J i be the last job in σ.

• If min{p i 1 ; p i 2 } ≤ ∆ -q m(i) (σ) < max{p i 1 ; p i 2 } = p j for j ∈ {i 1 , i 2 }, then by Property 3.3 O(σ • j) is dominated. • If max{p i 1 ; p i 2 } ≤ ∆ -q m(i) (σ). Let r 1 j (respectively r 2 j ) the ready time of job J j in P 2 (σ • i 1 ) (respectively in P 2 (σ • i 2 )). Since d i 1 ≤ d i 2 then r 1 j ≤ r 2 j for J j / ∈ J(σ) ∪{J i 1 ; J i 2 }; r 1 i 2 = C(σ • i 1 ) and r 2 i 1 ≥ C(σ • i 2 ).
Let π 2 the optimal schedule for the problem P 2 (σ • i 2 ) obtained, as said above, by D-PEDD rule. Let A be the set of jobs scheduled in π 2 in the interval [C(σ); C(σ) + p i 1 ]. Consider the schedule π 1 obtained by interchanging A with J i 1 . This interchange will not delay jobs except possibly jobs in A. But because

d i 1 ≤ d i 2 ≤ d j for J j ∈ A we have C j (π 1 ) -d j ≤ max{C i 1 (π 2 ) -d i 1 ; C j (π 2 ) -d j }
for J j ∈ A, and consequently T j (π 1 ) ≤ max{T j (π 2 ); T i 1 (π 2 )}. This shows that π 1 is better than π 2 . On the other hand, π 1 is feasible for

P 2 (σ • i 1 ) since r 1 j ≤ r 2 j . Finally, LB 2 (σ • i 1 ) ≤ T max (π 1 ) ≤ T max (π 2 ) = LB 2 (σ • i 2 ).
• We use the same arguments to tackle the case

∆ -q m(i) (σ) < min{p i 1 ; p i 2 }.
In fact if σ • i is eliminated, then we never consider σ • j for all J j such that d j ≥ d i .

The computation time of the B&B algorithm is reported in Section 5.

Flexible periodic maintenance

In this section we deal with the exible periodic version. Recall that in this case maintenance periods are not xed but the maximum continuous working time allowed of the machine is ∆.

Dominance properties and lower bounds

It is clear that Property 3.1 remains true in this case. However we have to make some modications on the others properties. Property 4.1. There exists an optimal schedule in which for each batch B i , we have ∆q i < p j for the rst job J j in the next batch B i+1 .

Proof. Suppose π is an optimal schedule which does not satisfy Property 4.1. Consider the sequence π obtained from π by removing J j from its original position and inserting it at the end of batch B i . Then it is clear that π is also an optimal sequence. Repeating this procedure a nite number of times, we obtain an optimal sequence verifying Property 4.1.

There exists an optimal schedule satisfying at the same time Properties 3.1 and 4.1.

More precisely, we can prove that every schedule not satisfying simultaneously 3.1 and 4.1 is dominated. Thus from now on we consider only partial schedules satisfying Properties 3.1 and 4.1. Given a partial schedule σ, O(σ) will denote the schedule obtained by appending to σ the optimal partial schedule of the remaining jobs while keeping Properties 3.1 and 4.1 satised.

Before stating other dominance properties let us give, as in the periodic version, a lower bound for T max (O(σ)). For that purpose we establish rst a minoration for C j (O(σ)) (J j / ∈ J(σ)). Let σ be a partial schedule, J i the last job in σ and s := m(i)-1 m=1 q m (σ)+(m(i) -1)t be the starting date of batch B m(i) . To simplify, we set q = q m(i) (σ).

Lemma 4.1. Let J j / ∈ J(σ). If p j > ∆q or d j < d i , then

C j (O(σ)) ≥ max{s + ∆ + t -p j + 1; s + q + t} + p j .
(

Proof. We are going to prove that J j starts in O(σ) after max{s +∆+t -p j +1; s + q + t}.

Keep in mind that the processing times and due dates are integers. Two cases must be examined.

Case 1: If ∆q < p j , i.e. max{s + ∆ + tp j + 1; s + q + t} = s + q + t. It is not possible to place job J j in the batch B m(i) and it is necessary to insert at least one maintenance period before processing J j . So J j starts after s + q + t.

Case 2: If ∆q ≥ p j and d j < d i , in which case max{s + ∆ + tp j + 1; s + q + t} = s + ∆ + tp j + 1. Assume for the sake of contradiction that J j starts before the latter date. Then Property 3.1 implies that m(j) > m(i), i.e. between jobs J i and J j there is at least one maintenance period. Let A be the set of jobs which are sequenced after J i and before J j . We have

s + q + J k ∈A p k + t + p j ≤ (s + ∆ + t -p j + 1) -1 + p j = s + ∆ + t.
Consequently q + J k ∈A p k + p j ≤ ∆; which contradicts the validity of Property 4.1.

Now we propose two lower bounds for T max (O(σ)).

The rst one is based on the relaxation of the "ready time" (1) and the second is based on the relaxation of the constraint on the maintenance.

Lower Bound 1: We consider the problem P 1 (σ): we x maintenance as late as possible i.e. at dates s + ∆, s + 2∆ + t, s + 3∆ + 2t,... and for each J j not scheduled in σ we associate a ready time:

r j =      s + ∆ -p j + 1 if d j < d i , C(σ) otherwise.
(

) 2
Moreover, we allow the preemption. This problem is solved optimally by D-PEDD, we denote by LB 1 (σ) the resulting optimal value. We have LB 1 (σ) ≤ T max (O(σ)). Indeed, let us construct from O(σ) a feasible schedule for P 1 (σ).

- -We advance jobs while respecting the ready time.

Clearly the obtained schedule π is better than O(σ) and is feasible for P 1 (σ). Thus,

LB 1 (σ) ≤ T max (π) ≤ T max (O(σ)).
Lower Bound 2: We consider the problem P 2 (σ), where we x maintenance at dates s + 2∆ + t, s + 3∆ + 2t,... (note that unlike the preceding case, the machine is not maintained between s and s + 2∆ + t) and we associate to every J j a ready time r j

r j =      max{s + ∆ + t -p j + 1; s + q + t} if p j > ∆ -q or d j < d i , C(σ) otherwise.
Again, we solve the resulting problem by allowing the preemption of jobs and by using the D-PEDD rule. We let LB 2 (σ) the optimal value corresponding to P 2 (σ).

Let us prove LB 2 (σ) ≤ T max (O(σ)). As above, we are going to construct from O(σ) another better schedule π as follows:

-We delete maintenance periods situated in the interval -We advance jobs while respecting the ready time.

Clearly π is better than O(σ) and is feasible for

P 2 (σ). So, LB(σ) ≤ T max (π) ≤ T max (O(σ)).
We put LB(σ) = max{LB 1 (σ); LB 2 (σ)}.

Property 4.2. For any partial schedule σ and any j not scheduled in σ, schedule O(σ

• j)
is dominated in both following cases:

1. There is a batch

B k where k < m(j, σ • j), ∆ -q k (σ) ≥ p j and C i (σ) + p j -d i ≤ LB(σ)
for all jobs J i in σ scheduled after B k .

2. There is a batch

B k where k < m(j, σ•j), ∆-q k (σ) ≥ p s and C i (σ•j)+p s -d i ≤ LB(σ•j)
for all jobs J i in σ • j scheduled after B k , where J s is the job with the smallest due date among jobs not in

J(σ • j) i.e. d s = min{d i ; J i / ∈ J(σ • j)}.
Proof. 1. Let A be the set of jobs which is sequenced after batch B k and before job J j in O(σ • j). A new feasible schedule π can be obtained from O(σ • j) by placing job J j at the end of batch B k and not increasing the completion times of jobs except jobs in A. The completion time of each job in A will increase by p j , so

C i (π) -d i = C i (O(σ)) + p j -d i for i ∈ A. Therefore, T i (π) ≤ LB(σ) ≤ T max (O(σ • j)), and T max (π) ≤ T max (O(σ • j)).
2. Let A be the set of jobs which is sequenced after batch B k and before job J s in O(σ • j).

A new feasible schedule π can be obtained by placing job J s at the end of batch B k and not increasing the completion times of jobs except jobs in A.

If J i is in A and is scheduled in σ • j, then T i (π) = max{C i (π) -d i ; 0} ≤ max{C i (O(σ)) + p s -d i ; 0} ≤ LB(σ • j) ≤ T max (O(σ • j))
.

If J i is in A but not scheduled in σ • j, then T i (π) = max{C i (π) -d i ; 0} = max{C i (O(σ • j)) + p s -d i ; 0} ≤ max{C s (O(σ • j)) -d s ; 0} ≤ T s (O(σ • j)).
Finally, we get T max (π) ≤ T max (O(σ • j)).

Property 4.3. For any partial schedule σ and any job j not scheduled in σ, schedule

O(σ • j) is dominated if there is a job i scheduled in σ such that m(i, σ) < m(j, σ • j), p i ≤ p j ≤ p i + (∆ -q m(i) (σ)), C(σ • j) -d i ≤ LB(σ) and C v (σ) + (p j -p i ) -d v ≤ LB(σ) for all jobs J v in σ scheduled after B m(i) .
Proof.

Let A be the set of jobs which is sequenced after batch B m (i) in σ. A new feasible schedule π can be obtained by replacing job J i by J j and inserting J j at the end of batch B m(i) . Only job J i and jobs in A are completed later in π. We have

C v (π) -d v = C v (O(σ • j)) + (p j -p i ) -d v for J v ∈ A. So, T v (π) ≤ LB(σ) ≤ T max (O(σ • j)). Moreover, T i (π) = {C(σ • j) -d i ; 0} ≤ LB(σ). Therefore, T max (π) ≤ T max (O(σ • j)).
Property 4.4. Let σ be a partial schedule. Let j be a job not scheduled in σ which does not t into the last batch of σ. We denote by i the last job in σ.

Then O(σ • j) is dominated if p i ≤ p s ≤ p i + (∆ -q m(i) (σ)), d i ≥ d s and C(σ • j) + (p s -p i ) -d j ≤ LB(σ • j),
where J s is the job having the smallest due date among jobs not scheduled in σ • j, i.e.

d s = min{d v ; J v / ∈ J(σ • j)}.
Proof. Let A be the set of jobs which is sequenced after job J j and before job J s in

O(σ • j).
A new feasible schedule π can be obtained by interchanging J i and J s . This will not increase the completion times of jobs except jobs J j and J i , and jobs in A. We

have C j (π) -d j = C j (σ • j) + (p s -p i ) -d j , so T j (π) ≤ LB(σ • j) ≤ T max (O(σ • j)).
On the other hand, for

J v ∈ A, we have C v (π) -d v = C v (O(σ • j)) + (p s -p i ) -d v ≤ C s (O(σ • j)) -d v ≤ C s (O(σ • j)) -d s , so T v (π) ≤ T s (O(σ • j)). Moreover, T i (π) = max{C i (π) -d i ; 0} ≤ max{C s (O(σ • j)) -d s ; 0} = T s (O(σ • j)).
Thus we have the required result that T max (π) ≤ T max (O(σ)).

Heuristic algorithm

To approximate the solution of the problem we propose the following heuristic which is an adaptation of Heuristic H1 in light of the new properties.

Heuristic H2

Step 1. Sort jobs in EDD order with ties broken by non-increasing order of p i : J 1 , J 2 ,...,J n .

Step 2. Calculate a lower bound T max for the problem by putting maintenance as late as possible i.e. at the dates ∆, 2∆ + t, 3∆ + 2t..., and scheduling jobs according PEDD rule.

Step 3. Let i = 1, j = 1, B i = ∅, q i = 0, (J, p, d) = (J j , p j , d j ), C = 0 and T max = 0.

Step 4. Is there at least one batch B m such that ∆ -q m ≥ p j and T l ≤ max{T max -

p j ; T max -p j ; 0}, ∀T l ∈ B k , k > m.
If so, among them choose the Batch with the smallest index, say k, put job J j at the end of batch B k , q k = q k + p j , C = C + p j , up date T max , and moreover (J, p, d) = (J j , p j , d j ) if k = i, goto Step 8.

Step 5. If q i -p + p j > ∆ or p j < p, goto Step 6. Otherwise, go to Step 7.

Step 6. Let i = i + 1, insert a maintenance task and let job J j be the rst job in the new batch B i ,

q i = p j , C = C + p j + t, T max = max{T max ; C -d j }, (J, p, d) = (J j , p j , d j ), go to
Step 8.

Step 7. If C + t + p j -d ≤ max{C + t + p j -d j ; T max ; T max }, B i = (B i \{J}) ∪ {J j }, q i = q ip + p j , i = i + 1, insert a maintenance task and let job J be the rst job the new batch B i ,

q i = p, C = C + t + p j , T max = max{T max ; C -d; C -p -t -d j }. Otherwise, goto Step 6.
Step 8. If j = n, stop. Otherwise, j = j + 1, return to Step 4.

Branch and Bound algorithm

The algorithm is similar to that developed in Section 3.3. Note however, for this case we do not have a Lemma analogous to that of Lemma 3.2.

Computational results

In this section, we report computational results to evaluate the eectiveness of the heuristics algorithms and the computation time of the B&B. The computational experiments, are done using Visual C++ compiler and on a duo T5600 processor with 1Go of memory. We generate the parameter values as in [START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF] : processing times were selected from a discrete uniform distribution (DU) over [START_REF] Adiri | Scheduling on a single machine with a single breakdown to minimize stochastically the number of tardy jobs[END_REF][START_REF] Lee | Machine scheduling with an availability constraint[END_REF]. The due dates were selected from another DU over

[(1 -C -Q/2) n i=1 p i , (1 -C + Q/2) n i=1 p i ],
where Q ∈ {0.2, 0.6} and C ∈ {0.2; 0.6} denote the due date range and tardiness factor, respectively.

Experiment 1. Comparison between H1 and Heuristic in [START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF] for the periodic version. For each combination of n, C, Q, ∆ and t, 50 problems are randomly generated. Each problem is solved by H1 and the heuristic proposed in [START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF] which we denote here by HCL. Table 7, corresponding to ∆ = 18 and t = 4, shows in column 'better' the number of problems (#HCL) for which Heuristic H1 is better than HCL, and the number of problems ( #H1) for which Heuristic HCL is better than H1. In column DH1 Max (respectively Mean) denotes the maximum (respectively the average) deviation of the heuristic HCL solution from the solution obtained by H1 for problems where H1 is better than HCL. Accordingly, in column DHCL Max (respectively Mean) denotes the maximum (respectively the average) deviation of the heuristic H1 solution from the solution obtained by HCL for problems where HCL is better than H1. Table 8 is similar to Table 7 with ∆ = 10 and t = 2. We can see that for all combinations of C, Q, ∆ and t our heuristic outperforms HCL especially for large n. In fact there cases where our heuristic is 100% better. Moreover, the deviation is more important for the case where H1 is better. This is because HCL is very dependent in the manner that jobs are initially ordered (no initial order of jobs is assumed in Heuristic HCL).

From the computational point of view, H1 is better than HCL: H1 spends much less computation time than HCL.

Experiment 2. The relative error of the heuristic algorithms and the average computation time of B&B (in milliseconds).

For dierent combinations of C, Q, ∆ and t 25 problems are randomly generated and solved by heuristic and branch and bound algorithms subject to both periodic and exible periodic constraint. Table 9 gives the relative error of our heuristic algorithms i.e.

T max (Hi) -T max (B&B) T max (B&B)

(i = 1 or 2) (3)
and the computation time (in milliseconds) of the branch and bound algorithms. For our choice of C and Q, namely (C, Q) = (0.2, 0.2) and (C, Q) = (0.6, 0.6), we are sure that the maximum tardiness is superior to 0.1 n j=1 p j and 0.3 n j=1 p j , respectively. To see this, consider the last job, say J i , in an optimal schedule whose completion time is

clearly C i ≥ n j=1 p j . If (C, Q) = (0.2, 0 .
2) then d i ≤ 0.9 n j=1 p j , and consequently

T max ≥ C i -d i ≥ ( n j=1 p j ) -(0.9 n j=1 p j ) = 0.1 n j=1 p j . If (C, Q) = (0.6, 0.6)
the same argument gives T max ≥ 0.3 n j=1 p j . So the error relative (3) is well dened. We can see that the B&B is not an ecient algorithm with large problems especially for the exible periodic version. Of course, we have not reported a computation time of B &B in the last case for n = 20 because we have xed the maximum computational time to be ten minutes and many problems exceed 10 minutes. As we can see in Table 9, the main interesting results is the eectiveness of both heuristics H1 and H2. The error for all treated problem does not exceed the average value of 17% for H1 and 3% for H2. Moreover, the heuristics always nd at least once the best solution for all problem sizes (this represents at least 1 problem over 20). This is true for both xed and exible maintenance cases.

Conclusion

The importance of maintenance has been gradually accepted by the decision maker.

Therefore, it has become a common practice to schedule maintenance periodically in many manufacturing systems. Unfortunately, most papers discussing maintenance assume there is only one maintenance period. In this paper, we have addressed the single machine maximum tardiness problem subject to periodic maintenance and nonresumable jobs. We have considered two versions which are both NP-hard. The rst one corresponds to a periodic maintenance. It consists of several maintenance periods where each maintenance is required after a periodic time interval. In the second situation, the maintenance is not xed but the maximum allowed continuously working time of machine is xed. We have proposed for each of them a heuristic and a branch and bound algorithm. Computational experiments have been done to evaluate the eectiveness of the algorithms. Moreover, for the rst version an extensive empirical comparison of the proposed heuristic with Liao and Chen [START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF] heuristic shows the superiority of the former. One future research direction is to extend these scheduling problems from the single machine case to the parallel machine case and ow-shop problem.

Figure 1 :

 1 Figure 1: A schedule with periodic maintenance : J [i] is the number of job in i th position and M i is the i th operation of maintenance

Figure 2 :

 2 Figure 2: A schedule with exible periodic maintenance : J [i] is the number of job in i th position and M i is the i th operation of maintenance

Remark 1 .

 1 Steps 1, 4 and 7 are motivated respectively by Properties 3.1, 3.2 and 3.3. The computational time complexity of Heuristic H1 is O(n 2 ).

5 3 5 2 2 3 4 4 d

 4 i 1 13 2 30 10 13 2012 14

Table 5

 5 The schedule obtained by the heuristic in[START_REF] Chen | Scheduling of jobs and maintenance in a textile company[END_REF]

  ≥ 1) we keep only one (in fact there is at least one) maintenance period and we insert it at the date s + m(∆ + t) + ∆ (possibly by interchanging it with jobs situated between it and s + m(∆ + t) + ∆ + t, which is possible thanks to the inequality r j ≤ s + ∆ + t.

[s; s + ∆ + t] -In each interval [s + m(∆ + t); s + (m + 1)(∆ + t)] (m

Table 7

 7 Comparison between H1 and Heuristic in[START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF] for the periodic version (∆ = 18 and t = 4)

	Better n #H1 #HCL Max Mean Max Mean H1 DH1 DHCL Comp. time (ms) HCL 0.2 0.2 10 C Q 15 15 15 5.80 13 4.53 0.32 0.60 20 22 11 25 7.55 22 5.00 0.00 1.86 30 28 10 19 7.61 5 2.30 1.26 2.18 40 28 8 22 8.04 15 5.25 1.26 3.76 50 34 12 24 9.32 19 7.67 1.26 5.30 80 34 8 44 13.82 24 8.25 1.56 11.26 110 42 4 42 14.17 17 8.25 3.14 18.42 150 47 3 67 19.60 13 6.00 5.98 32.12 170 44 3 76 21.82 20 13.67 7.74 37.56 200 40 7 54 21.23 17 8.14 9.36 46.90 0.2 0.6 10 15 6 17 7.47 7 3.50 0.00 0.94 20 33 6 35 8.33 16 7.17 0.32 2.50 30 35 8 36 12.43 17 6.13 0.32 4.38 40 41 4 45 15.15 8 4.50 1.24 6.90 50 42 2 41 16.64 7 4.00 1.24 10.00 80 47 3 55 26.34 12 6.67 2.54 27.14 110 49 1 70 40.08 15 15.00 3.12 49.06 150 50 0 103 54.74 0 0.00 3.12 99.06 170 50 0 104 59.06 0 0.00 5.36 139.32 200 50 0 129 79.44 0 0.00 8.40 190.34 0.6 0.2 10 19 12 10 3.79 14 4.33 0.00 0.64 20 28 10 22 7.18 18 5.30 0.00 2.18 30 24 12 18 7.04 22 6.58 0.32 3.14 40 38 7 31 10.05 16 7.71 1.56 3.12 50 25 12 36 8.84 17 5.50 0.94 5.96 80 36 10 37 12.31 8 3.90 1.24 12.52 110 40 6 46 12.55 22 12.17 4.40 17.78 150 41 8 40 17.15 12 5.88 6.54 33.16 170 40 9 61 22.60 22 6.78 5.34 39.02 200 45 3 54 22.24 18 11. 00 8.80 51.82 0.6 0.6 10 19 5 13 4.68 8 4.60 0.00 0.94 20 35 2 23 9.46 16 8.50 0.00 2.18 30 37 4 35 11.38 22 10.50 0.62 3.74 40 41 3 61 18.15 7 3.33 1.26 6.26 50 41 6 44 16.27 11 4.83 0.62 10.94 80 47 1 51 24.85 3 3.00 3.12 23.10 110 50 0 104 39.24 0 0.00 5.60 48.16 150 50 0 91 56.74 0 0.00 6.88 94.34 170 50 0 102 63.38 0 0.00 5.66 124.64 200 50 0 118 81.62 0 0.00 8.78 166.86

Table 8

 8 Comparison between H1 and Heuristic in[START_REF] Liao | Single-machine scheduling with periodic maintenance and nonresumable jobs[END_REF] for the periodic version (∆ = 10 and t = 2)

	Better n #H1 #HCL Max Mean Max Mean DH1 DHCL 0.2 0.2 10 C Q 14 6 11 3.00 9 5.33 0.32 Comp. time (ms) H1 HCL 0.92 20 25 6 12 6.32 12 7.50 0.00 1.86 30 23 9 16 7.43 11 6.78 0.92 2.20 40 27 9 17 8.81 12 6.11 1.56 3.76 50 36 4 24 10.50 11 7.00 1.88 4.68 80 40 6 24 11.85 12 8.67 2.18 12.20 110 41 7 37 15.56 13 9.71 6.00 17.44 150 46 2 44 20.07 6 4.50 9.38 28.44 170 48 1 49 22.10 1 1.00 11.32 36.54 200 47 2 48 25.94 10 9.00 13.86 47.08 0.2 0.6 10 18 2 12 6.78 12 12.00 0.32 0.62 20 20 5 14 6.85 12 5.60 0.00 2.18 30 33 4 32 11.12 12 5.25 0.64 2.48 40 34 8 26 13.29 12 8.75 1.24 3.44 50 30 5 42 14.97 12 9.40 0.92 6.58 80 35 0 48 17.43 0 0.00 2.50 11.26 110 39 2 49 21.23 12 10.00 3.72 19.40 150 43 3 48 21.95 12 8.00 8.52 30.54 170 42 4 45 21.76 17 11.50 11.62 33.68 200 47 3 77 27.87 12 8.00 13.80 47.80 0.6 0.2 10 21 6 12 4.81 12 5.50 0.00 0.62 20 21 5 12 5.90 8 6.60 0.60 1.58 30 25 6 18 8.56 12 9.00 0.32 3.74 40 28 4 19 8.50 12 8.75 0.96 3.44 50 28 6 24 9.96 23 10.33 1.54 5.00 80 36 7 27 13.44 20 11.57 2.20 11.56 110 38 4 36 18.11 24 10.75 4.08 18.70 150 42 3 41 20.21 24 13.67 8.94 30.10 170 43 2 48 23.47 12 8.50 11.30 37.14 200 45 3 54 26.24 12 10.00 13.66 51.34 0.6 0.6 10 15 4 18 7.07 12 8.25 0.32 0.94 20 23 3 22 10.00 6 5.33 0.96 1.54 30 29 1 22 9.69 12 12.00 0.62 2.52 40 30 2 24 11.27 6 3.50 1.28 4.04 50 37 4 35 11.30 12 6.00 2.20 5.30 80 34 5 42 18.03 12 4.80 4.68 10.62 110 43 1 39 19.21 9 9.00 5.02 18.72 150 44 1 54 22.20 12 12.00 9.72 28.10 170 48 1 50 21.67 12 12.00 8.96 38.22 200 47 1 63 30.83 2 2.00 14.12 49.32


D R Catherine

Mateo Kehler

he discovery of cheese is a narrative at least eight thousand years old, dating back to the Neolithic era. Yet, after all of these thousands of years we are still finding new ways to combine the same four basic ingredients-milk, bacteria, salt, and enzymes-into new and exciting products with vastly different shapes, sizes, and colors, and equally complex and varied tastes, textures, and, yes, aromas. In fact, after a long period of industrialized, processed, and standardized cheese, cheesemakers, cheesemongers, affineurs, and most of all consumers are rediscovering the endless variety of cheeses across cultures.

The Oxford Companion to Cheese is the first major reference work dedicated to cheese, containing 855 A-Z entries on cheese history, culture, science, and production. From cottage cheese to Camembert, from Gorgonzola to Gruyère, there are entries on all of the major cheese varieties globally, but also many cheeses that are not well known outside of their region of production. The concentrated whey cheeses popular in Norway, brunost, are covered here, as are the traditional Turkish and Iranian cheeses that are ripened in casings prepared from sheep's or goat's skin. There are entries on animal species whose milk is commonly (cow, goat, and sheep) and not so commonly (yak, camel, and reindeer) used in cheesemaking, as well as entries on a few highly important breeds within each species, such as the Nubian goat or the Holstein cow. Regional entries on places with a strong history of cheese production, biographies of influential cheesemakers, innovative and influential cheese shops, and historical and cultural entries on topics like manorial cheesemaking and cheese in children's literature round out the Companion's eclectic coverage.

C

heese is a paradox, a remarkably complex food that begins as one simple and humble ingredient: milk. When coupled with equally simple ingredients-bacteria, salt, enzymes-and manipulated under the right temperatures and conditions, a transformation occurs, resulting in a vast array of products of differing shapes, sizes, and colors, with a panoply of flavors, tastes, and textures. These are the great cheeses of the world. But there is nothing simple about cheesemaking. The hours are long and the labor backbreaking. Great care is required to lovingly tend the animals and landscapes that produce the high quality milk required for cheesemaking. Once milk is collected, many hours, days, and months are required to complete the stages of cheesemaking and aging required to produce great cheese. So much can go wrong. But when things go right, the result is over fourteen hundred named cheese varieties enjoyed throughout the world.

In approaching this book, it was necessary to explore the multitude of ways in which we interact with cheese. For many, our experience with cheese is limited to cheese as an ingredient-wonderful mozzarella topping a hot pizza, shredded cheese in tacos or on nachos or other Mexican foods, cream cheese on a bagel, Cheddar cheese in comfort foods such as macaroni and cheese, or cheese melted on top of that iconic Quebecois dish, poutine. Given consumer demand for fast food and prepared meals, the processed food industry is a major user of cheese as an ingredient throughout the world. The requirements in the processed food industry dictate that cheese function as a perfectly predictable ingredient-that it melt with consistency, brown in a predictable manner, appear as a perfect emulsion so that oil does not separate from the cheese mass. As a result cheeses used as ingredients often contain products that help them behave consistently.

There is another world of cheese, one that shares rich cultural roots and traditions with varieties that have been manufactured in Europe for centuries. These age-old cheese traditions are now being embraced by US artisan cheese producers and, as a result, over the last thirty years a bona fide cheese culture has emerged within the United States. The growth of the US artisan cheese industry and the renaissance of cheesemaking occurring throughout the world presents a tremendous opportunity to educate new populations of farmers, mongers, affineurs, students, scientists, cheesemakers, technologists, and cheese connoisseurs about the history, art, regulation, sales, marketing, and cultural impacts of cheese. We have tried our best to do justice to these many dimensions of cheese in the Companion.

As a scientist and food microbiologist, I of course am biased toward and fascinated by the microbiology and chemistry of cheese. I can think of few other places in nature where microorganisms are more magnificently displayed than when associated with cheese. The blooms, veins, sticky surfaces, gooey interiors, crystals, wrinkles, strings, and yes, for some, the strong olfactory notes, are all due to microbial action and growth. Entries in this book address the beneficial associations of microbes with cheese, from the perspective of the diversity of unique cheeses that arise due to the growth of the bacteria, yeasts, and molds that play a crucial role in cheesemaking.

Beyond the scientific, there are a multitude of other dimensions associated with cheese. The history and culture of cheese production and consumption commands a large part of the book. Archaeological evidence of cheesemaking goes back to Sumerian and Mesopotamian civilizations approximately seven thousand years ago, though it is believed that simple cheeses were made after the domestication of sheep and goats as far back as eleven thousand years ago.

Entries on these subjects and others cover the history of cheesemaking, up through modern times, including the impact of the Industrial Revolution, which led to the invention of mass-market cheeses.

Cheeses have inspired writers and artists, poets and playwrights. Cheese is featured in Homer's Odyssey (the blinded Cyclops Polyphemus made cheese from sheep's milk), praised by the Roman author and naturalist Pliny the Elder, and often given symbolic import in the Bible ("The virgin will be with child and will give birth to a son, and will call him Immanuel. He will eat curds and honey when he knows enough to reject the wrong and choose the right." Isaiah 7:14-15). It has inspired historians and filmmakers, fed armies, and been used as a sacrament to pay tribute during religious services. We have tried our best to capture these many dimensions of cheese, from ancient times to present.

While we attempted to produce the most comprehensive reference work on cheese ever assembled, we could never hope to actually be comprehensive in the true sense-so we had to be choosy. The book is not an index of (for example) cheese styles, cheesemakers, or cheese shops, but includes representative and important examples of each. For example, we present 244 cheese style entries covering the history of the style, exemplary producers, common sensory qualities when eaten, any unique production techniques, and so on. If we covered all the fourteen hundred named cheese styles we wouldn't have any space for anything else! At best, this is a starting point, a reference work dedicated to cheese that we hope will be carried forward in future editions. We welcome comments from learned readers, as these will surely inform and improve future editions of this work.

Catherine Donnelly

(excerpted from the introduction) a a Abbaye de Tamié is a washed rind cheese produced from raw cow's milk by the monks at Abbaye Notre-Dame de Tamié in the Savoie depart-ment, France. The cheese has a thin disk shape and comes in two sizes, Grand Model (4 pounds/1.6 kilograms) and Petit Model (21 ounces/600 grams). About 882 pounds (400 kilograms) of the cheese is produced daily from the milk collected from eight surrounding farms.

The abbey was founded under Cistercian order in 1133 and located in an ideal dairy farming area with very well-established mountain pastures. The cheese was produced from the first day of the abbey's history, but many people believe that the original cheese was similar to Gruyère. The original recipe was lost during the French Revolution. When the monks came back in 1861 to resurrect the abbey, they had to work with the reduced milk production from abandoned pastures by adapting a Port-Salut recipe to produce a smaller and faster-aging cheese. Over one hundred years the monks have refined the recipe to produce the current style of Tamié. The abbey has built one of the most successful monastic cheese making operations in Europe by mechaniz-ing most of the cheese making process and main-taining a strict quality control system, from milk production to whey utilization for methane gas pro-duction. More than half the monks participate in the various cheese making-related activities, gener-ating the most important economic resource for the abbey's community of seventy people.

Tamié belongs to a pressed, uncooked cheese category. To make it, raw whole milk is heated up to 93°F (34°C), and a small amount of starter culture and calf rennet are added. When the coagulation is complete, the curd is cut into pea-sized pieces and moved into perforated cylinder-shaped molds. The curds are turned several times and pressed for three to four hours before brining, which takes one to three hours, depending on the size of the cheese. The aging of the cheese is done in the vaulted cellars located at the basement of the abbey, which keeps the humidity between 85-90 percent and the temperature around 57°F (14°C). The aging process requires frequent washing and turning and takes up to forty days.

The finished cheese has 50-53 percent of solids-not-fat content. The rind is light beige to saffron in color, with a very thin layer of white mold envelop-ing a softening interior, which develops a few small pea-sized holes. The taste is nutty, milky, and delicate when young, and a typical washed rind cheese flavor, full and strong, develops with age.

See also france; haute-savoie; and monastic cheese making.

Abbaye Notre Dame de Tamié. http://www.abbayetamie.com.

Soyoung Scanlan

Abondance is a semi-cooked pressed cheese, made in the Alpine region of Haute-Savoie in eastern France. In 1990 Abondance was awarded AOC status, which dictates it can only be made in the mountains of the Haute-Savoie using the ancient skills and methods and raw milk that guarantee its quality and authenticity. See haute-savoie.

The origin of this cheese is linked to the cow with the same name, famous for it's distinctive brown eye patches like "spectacles" on their white faces. Augustine monks began manufacturing Abondance in the Abbaye d' Abondance in the twelfth century (it was previously called Toupin in Abondance Valley, Haute-Savoie, France). In 1381 Abondance was served at the papal conclave in Avignon at the election of the new pope.

Abondance is manufactured in dairy cooperatives (laitier label) or on farms ( fermier label). For fermier, milk comes from one herd composed of Abondance (45 percent minimum in the herd), Tarentaise, or Montbéliarde cows. During summer Abondance can be made in the mountains ("alpage") from milk of cows grazing between 4,265 and 6,069 feet (1,300 and 1,850 meters). See alpage. In winter cows are kept in warm barns and feast on summer hay. There are some sixty-five farmhouse (fermier) producers using their own milk and fifteen cooperatives or fruitières using milk from about two hundred milk producers. In 2014 about 2,400 tons of Abondance were produced, with 800 metric tons consisting of Abondance fermier.

A copper vat or cauldron is used to manufacture Abondance. Raw whole cow's milk derived from two milkings must be processed within fourteen hours maximum after the oldest milking (fermier) and before midday following the oldest milking (laitier). Milk may be heated only once, at the time of renneting, to a temperature of 86-95°F (30°C-35°C). Milk is inoculated with thermophilic starters (Streptococcus thermophilus, Lactobacillus delbrueckii and Lactobacillus helveticus). During Abondance fermier cheese manufacture, certain processes must be done manually such as:

• Cutting of milk gel into curd grains with a "cheese harp" (so-called because it is strung with wires like the musical instrument). See cheese knife. • For each cheese, curd is removed with a linen cloth and directly put in a mold; • Turning of cheese must be done at minimum once within thirty minutes after molding and at minimum twice during the following twelve hours. See turning.

After cutting the curd, the mixture of curd particles and whey is warmed to 113-122°F (45-50°C) and held at this temperature for forty-five minutes maximum. The ripening period varies from one hundred days minimum to eight to twelve months, at a temperature of 50-55°F (10-13°C) and relative humidity of 90 percent minimum. Matured on spruce planks in caves or cellars, the cheeses are constantly turned and rubbed with salt to create the thin reddish-brown rind.

Abondance is produced in wheels (3 inches [7-8 centimeters] high, 15-17 inches [38-43 centimeters] diameter) which weigh 13-26 pounds (6-12 kilograms) and have a typically concave edge. The golden yellow to brown rind covers an interior that is ivory to slightly yellow in color, with small, regular, and well-distributed eyes. Abondance fermier cheese has a green oval casein nameplate, while a red square is used to distinguish the laitier label.

Abondance has a supple, fine grained texture. Its yeasty aroma carries through onto the palate, which is savory yet fruity with an intense "umami" taste when melted like Raclette. See raclette. Scientific studies have shown that Abondance fermier cheeses manufactured from milk produced from mountain pastures (4,921-6,069 feet [1,500-1,850 meters) were deemed to be more "fruity," "animal," "boiled milk," and "hazelnut" and less pungent and "propionic acid" than cheeses made from milk produced from valley pastures (2,789-3,609 feet [850-1,100 meters). It was possible to partly attribute these differences in flavor to the presence of protein-based volatile compounds in the cheeses.

Abondance can be eaten raw at the end of the meal, or in a typical meal called berthoud. To make it, rub with garlic a ramekin dish; put in it thin slices of Abondance (5 ounces [150 grams] per person); add white Savoie wine and optionally Madeira, and pepper; brown it under the grill for five to ten Abondance is a semi-cooked pressed cheese made in the alpine region of Haute-Savoie in eastern France. © frédérique voisin-demery minutes; and serve hot with bread and potatoes boiled in their skins.

See also france. Eric Beuvier and Juliet Harbutt accelerated ripening of cheese has been a

subject of significant scientific an d industrial interest for many years. Ripening of cheese represents a significant investment of time and cost of storage under specific conditions, such as controlled temperature and humidity. Ways to reduce this time and burden seem highly attractive from practical and economic perspectives. How ever, in considering how to accelerate ripening, it is critical to consider exactly what is happening in ripening overall. Cheese ripening, while differing in details from cheese variety to variety, essentially always involves the combined action of a wide range of biological agents-principally bacteria (from the added starter or nonstarter lactic acid bacteria [NSLAB]), molds or other microorganisms where present, enzymes coming from these microorgan-isms, the milk from which the cheese has been made, and the added coagulant (e.g., rennet). Development of the characteristic flavor and texture of each variety depends on all of these reactions working in concert, and sometimes in delicate sequence, to break down the proteins, fats, and lipids and transform a bland and smooth fresh curd to a much more flavorsome mature cheese.

The challenge then in accelerating ripening is how to get all these reactions, which may act independ-ently or in a coordinated manner, to take place at a uniformly accelerated rate and arrive at the same destination, that is, a balanced and appropriate set of flavors, aroma and texture characteristics, in a shorter space of time.

accelerated ripening • 3
Not surprisingly, strategies for accelerating cheese ripening involve either adding higher levels or more powerful versions of the agents responsible for ripening, or manipulating the cheese environment to try and uniformly ramp up the speed of all reactions.

One of the simplest approaches involves increasing the temperature at which ripening takes place, as basic scientific principles state that most reactions (enzymatic or microbiological) take place proportionately faster as temperature increases, at least within the range of temperatures likely to be applied for cheese ripening. For example, increasing the temperature of ripening of Cheddar cheese from 54°F to 61°F (12°C to 16°C) will result in more rapid development of flavor, but may have the downside of textural problems, and so has not been widely adopted. In another processing-based approach, it was reported in the mid-1990s that holding fresh cheese curd at high pressures (50 MPa) for three days could dramatically accelerate flavor development; however, subsequent studies suggested that the reduction in ripening time was not sufficient to make this attractive. In fact later studies suggested that treating cheese at much higher pressures but for much shorter times could actually be an interesting approach for decelerating cheese ripening, in essence "freezing" the quality of cheese at an optimal stage of ripeness.

In terms of modifying the microbiological population of cheese, there have been a number of studies of attenuated starter cultures, which have been treated (e.g., by drying, freezing, pressure treatment) to reduce or eliminate their ability to produce acid while retaining key enzymes, which can contribute to ripening. The advantage of adding such bacteria is that ripening can be accelerated without resulting in an excessive rate of acid production during cheesemaking. While such cultures have been shown to accelerate ripening, their commercial adoption has not been widespread.

Finally, additional enzymes may be added to the milk or curd to try and speed up the ripening process, and such enzymes may be encapsulated or otherwise treated to ensure that they are not simply inefficiently lost in the whey; studies have also examined the possibility of adding such enzymes at the dry-salting stage. Adding lipases or proteases in this manner has been shown to accelerate ripening of several cheese varieties, but obviously incurs the additional cost of the enzyme preparation.

In conclusion, when it comes to cheese ripening, the exact journey can be the key to ensuring the right destination. While the objective of saving money and time by reducing the ripening time (by accelerating the rate of all the processes involved in that period) unquestionably remains attractive, successfully managing the complex process of cheese ripening to achieve this, in a way that the cost of the solution doesn't cancel out the benefit, remains in large part an elusive goal.

See also lipolysis; maturing; and proteolysis. Alan Kelly accreditations for cheesemongers exist in a handful of countries around the globe. These certifications demonstrate at least a minimum level of mastery of cheese knowledge and confer prestige. Most, but not all, accreditations are available only to cheese professionals. Those who earn such cre-dentials are expected to serve as promoters and ambassadors of quality cheese, and to share their expertise with customers and fellow enthusiasts. Being accredited can enhance job prospects as cre-dential holders may also serve as judges in cheese competitions. The French have probably the most comprehensive progression of certifications for cheese professionals other than cheese makers, beginning with the Certificat de Qualification Professionelle (CQP). This certification is available to young people under age twenty-six or to unemployed persons of any age wishing to follow a job retraining scheme. The requirements for obtaining CQP status involve completing a training program over a school year (September-June) that alternates between classroom work and a hands-on internship. The c andidate, school, and employer all sign a contract outlining rights, terms, and expectations that is also signed by the French agency that oversees the training programs. Candidates are guaranteed a minimum salary and worker's rights during their training. Upon completion of the training program, candidates must pass a written e xam a nd s everal p ractical e xams demonstrating their competence in such areas as product knowledge; sanitation and proper handling; sales and customer advice; and ordering, receiving, and case setting. The CQP is recognized by the French state.

Beyond the CQP there are at least three other titles that can be earned in France through winning competitions. The C oncours N ational d es Fromagers, organized by the national trade association la Fédération des Fromagers de France (Les Fromagers de France) has been held every second year (odd years) since the mid-1990s. Candidates must be resident and working in France (of any na-tionality-the first-prize winner in 2013 was Matt Feroze, an Englishman working for Mons Fromager in Lyon). There may be only sixteen candidates in any given year. The competition is held during the SIRHA trade show in Lyon, and includes live tests of creating a cheese platter, blind tasting, and accu-racy in cutting cheese. In addition Les Fromagers de France organizes the Lyre d'Or (golden harp) com-petition in even years at the Salon du Fromage in Paris. This competition is based on

The American Cheese Society's Certified Cheese Professional exam is based firmly on practical knowledge; it is only offered to individuals with 4,000+ hours of relevant experience with cheese. © american cheese society

The archaeological detection o f cheese enables an understanding of cheese making and dairying practices throughout antiquity. This is particularly useful both prior to and in the absence of iconographic and textual evidence. Cheesemaking can be detected in the archaeological record by a variety of approaches, such as by traditional methods like the identification of ruminant animal bones, but also novel biochemical approaches. Collectively these can be used to understand not only the innovative technological developments behind cheese making but also the cultural and dietary diversity of this foodstuff. In addition the human ability to digest milk sugars in adulthood, lactase persistence, represents one of the clearest examples of gene-culture coevolution. Hence the detection of dairy consumption in the archaeological record is of particular interest for understanding how behavioral practices may impact the human genome. See lactose intolerance.

The study of animal bones in the archaeological record, zooarchaeology, provides information on past patterns of herd use and management. Meat and animal products often represent a significant contribution to past human diets; subsequently, the archaeological record is littered with such evidence. Careful demographic analysis of these remains (kill-off patterns) can be used to identify dairying practices. Dairying can be inferred if an overabundance of older female animals and younger male animals are found, which suggests that female animals were kept until they were old, presumably for milk production and breeding stock, while males were killed off at a y ounger age, p resumably f or m eat. Much of the earliest evidence of dairying suggests that sheep, goats, and cows were milked. Unfor tunately many sheep and goat bone elements are often morphologically indistinguishable, making the identification of the milk's source challenging.

Often used in combination with zooarchaeological evidence, the detection of milk residues in pottery, as well as characteristic pottery forms, has informed much of our understanding about ancient cheese making. See earthenware pottery. Such detection is most often achieved through the isotopic analysis of milk lipids (fats), which can become im-pregnated in the walls of ceramic vessels during processing. Milk lipids constitute one of the main nutrient components of milk and are often retained for thousands of years within the porous walls of ceramic vessels. Analysis using gas chroma--tography mass spectrometry of lipids extracted from archae-ological artifacts, such as pottery, can reveal the original foodstuffs placed in vessels during antiq-uity. Fats from particular food sources have charac-teristic lipid residues, and the isotopic analysis of these lipids is often able to distinguish between ru-minant meat and ruminant milk products. In addi-tion the identification of ketone products in the same analysis can indicate heating. Using this approach it has been possible to identify the oldest evidence of cheese making, in vessels dating to approximately nine thousand years ago in northwest Anatolia (present-day Turkey) and eastern Europe.

In addition the shape of pottery itself may indicate evidence of dairying. Perforated "sieves," which are thought to have been used to separate curds from whey (essential for the removal of lactose), appear in European and South Asian archaeological sites in the Neolithic Era (about seven thousand years ago). Analyses of lipids extracted from these vessels often indicate the presence of dairy products, sup-porting this hypothesis. While dairying processes and cheese making may have been carried out in other vessels prior to the invention of pottery, such as vessels made of perishable organic materials, these do not usually survive in the archaeological record.

While the use of lipid analysis has been instrumental in identifying ancient patterns of dairying, this method is unable to identify which animals were utilized. How ever the analysis of preserved milk proteins has gone some way to remedy this. Bovine-specific casein has been identified in ancient pottery using immunological methods. Analysis of protein sequences extracted from dental calculus (tartar) from human teeth has also uncovered evidence of proteins from dairy products, creating a direct link between the dairy products and the individuals who consumed them. Using this technique makes it possible to recover dairy proteins that are specific to individual ruminant species, such as cows, sheep and goats.

Under the right conditions, such as very dry or waterlogged environments, whole fragments of dairy products may survive. For example, the oldest de-finitive example of a preserved ancient cheese comes from the early Bronze Age cemetery of Xiaohe (1980-1450 b.c.e.) in Xinjiang, China, where food residues were found surrounding a mummy. Analysis of proteins identified the residue as cheese, but also identified Argentina • 37 the specific bacteria involved in fermentation. Similarly in the UK a bog-preserved residue found inside a bark vessel dated to the Early Bronze Age also contained casein proteins, suggesting that the vessel contained dairy products.

A driving question in the archaeological detection of dairying is to pinpoint when humans evolved the genetic ability to consume lactose in adulthood. Analysis of DNA extracted from the remains of past individuals, as well as genetic modeling using genomic information from modern populations, can be used to explore when and where this occurred. Although models suggest that lactase persistence may have developed in eastern Europe, recent data from ancient DNA suggests that lactase persistence developed in the Eurasian Steppes and became incorporated into European populations during the Bronze Age. Looking to more recent time periods, the prevalence of lactase persistence in a medieval German population is similar to present-day levels in northern Europe (about 70 percent), which suggests that the selection pressure for this gene was extremely strong and individuals who carried this allele had a key advantage.

See also origin of cheese.

Archestratus of Gela was an Ancient Greek

poet active in the mid-fourth century b.c.e. No complete work remains, but sixty-two fragments of writing have been preserved via Athenaeus in his work Deipnosophistae. Most of the surviving fragments focus on piscine cuisine, but cheese is referenced as being particularly antithetical to the merits of high-quality fish. Cooks from Sicily, who frequently used cheese in fish dishes, come up for particular scorn. Fragment 31 suggests "plenty of cheese and oil" as a way to dress up a smaller fish, "for it takes pleasure in big spenders and is unchecked in extravagance," a sarcastic comment on the gourmet elites who would enjoy an inferior fish enhanced by cheese. From the surviving fragments, anyway, it would seem that Archestratus was no cheese fancier, dismissing the culinary significance of cheese as a cheap means of masking off flavors or inferior quality in fish.

See also ancient civilizations.

Wilkins, John, and Shaun Hill, eds. Archestratus:

Fragments from the Life of Luxury. Rev. ed. Totnes, U.K.: Prospect Books, 2011.

Vince Razionale

Ardrahan Farmhouse Cheese

See county cork.

Ardsallagh Goat Farm

See county cork.

Argentina, known worldwide for its high quality meat and dairy products, also boasts a large number of cheeses made in various parts of the country. Roberto Castañeda, in his collaboration for the book Quesos de América del Sur, documents over a hundred styles, many of them based on European varieties, particularly those from Italy and Switzerland. Cheesemaking in Argentina began as a secondary activity to meat and leather ranching. How ever, the vast plains used for cattle proved to be an ideal location for dairy cows and thus allowed for a strong cheese making industry to grow as well. The provinces of Buenos Aires, adjacent to the country's capital, and La Pampa in central part of the country are the country's most productive cheese making regions. The mountainous Tandil region is known for banquete cheese, as well as the Tandil cheese, both similar to creamy young Gouda. The city of Suipacha started designating a "cheese route," to develop agricultural tourism. Visitors can tour different cheesemaking facilities and try some of the regional cheeses.

Perhaps the most popular cheese in Argentina is queso cremoso, a semi-soft, creamy, white rind-less cheese. Along with the local version of the Italian mozzarella, a variant is "muzzarella," cremoso used to top pizzas and also can be eaten by itself. Similarly, cheeses like Criollo, goya, Mar de Plata, Tafí, and Taluhet are served as a snack, to stuff empanadas with corn or ham, to melt on top of milanesas, or, simply, to make sandwiches.

Outside of the country, three cheeses are recognized as distinctly Argentinean. Provoleta is a pulled curd cheese, sometimes smoked. The cheese is popular as a side dish during asados-the Sunday pastime of grilling on an open fire, when the cheese is melted in a ceramic container on top of the grill. This cow's milk cheese is traditionally made with goat rennet, which imparts its distinctive gamey flavor to the cheese. In Argentinean grills around the world Provoleta is served as an appetizer with a drizzle of olive oil, salt, and pepper. Two other distinct styles are Argentina's Sardo, developed using the recipe for Pecorino Sardo from the island of Sardinia in Italy, and Reggianito, developed from the recipe for Parmigiano-Reggiano. See pecorino sardo and parmigiano reggiano. What makes these distinctly Argentinean cheeses, rather than derivatives or imitations of the European versions is not only a difference of terroir, but also different aging conditions and techniques that give these cheeses a tougher and drier texture, even though they are typically aged for a shorter period of time than Italian cheeses. Sardo is made with cow's milk instead of sheep's milk as the original Pecorino from Sardinia.

After the World War II, Reggianito was popularized throughout the Americas as a cheaper alternative of the Italian original. Despite their high quality and ability to stand alone in the development of cheese making traditions of Latin America, they still tend to be commercialized and marketed as derivative, inexpensive alternatives to European products.

See also latin america. Aristaios taught human beings how to keep bees (many authors agree on this) and how to press olives for oil (as several writers confirm). One source alone, the historical compiler Diodoros of Sicily (first century b.c.e.), asserts that it was also Aristaios who taught cheese making. This fits a pattern: Aristaios's specialties, although ancient and important to humanity, are neither plant foods brought into cultivation in the Neolithic Revolution, nor the meat of animals that were Neolithic domesticates. Thus it is possible that Diodoros is reporting a real belief, one that happens to have passed unnoticed by other surviving authors on Greek mythology.

See also ancient civilizations.

Andrew Dalby

Arla is a dairy cooperative that had its beginnings in the small Scandinavian cooperatives that formed in the 1880s to maximize efficiency and produce better-quality products. Formed in 2000 from the largest cooperatives in Sweden and Denmark, Arla has grown enormously since 2005 and now owns businesses in Germany, Holland, and the United Kingdom. Today it is the third largest dairy cooperative in the world. Running a business owned by more than 13,000 dairy farmers across five countries presents logistical challenges. The business has responded to this situation by creating an equally diverse set of services. Much of its activity is focused on research and

E D I T O R I N C H I E F

Dr. Catherine Donnelly is a Professor of Nutrition and Food Science at the University of Vermont. She was the Co-Director of the Vermont Institute for Artisan Cheese, America's first and only comprehensive academic research center devoted to artisan cheese. She previously served as the Associate Director for the Northeast Center for Food Entrepreneurship, a research consortium between Cornell and the University of Vermont. Dr. Donnelly has been recognized by her colleagues for her many contributions to improving Listeria detection. Widely regarded as an international expert on this bacterial pathogen, she has published numerous articles and book chapters and delivered hundreds of presentations on the topic of Listeria. Her current scholarly interests include investigating the microbiological safety of raw milk cheeses aged for 60 days. Dr. Donnelly is the editor of the book Cheese and Microbes, and she is the Scientific Editor of the Food Microbiology and Safety Section of the Journal of Food Science.

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  Mateo Kehler, Founder, Jasper Hill Farm K E Y F E AT U R E S ❯ The most comprehensive reference work on cheese available.

  Auboiron, Bruno, and Lansard Gilles. La France des fromages A.O.C. Aix en Provence, France: Edisud, 1997.

	Bugaud, Christophe, et al. "Relationship between
	Flavour and Chemical Composition of Abondance
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	Lait 81 (2001): 757-773.
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	fromagères en France. Versailles, France: Editions
	Quae, Inra, 2006.
	Rance, Patrick. The French Cheese Book. London:
	Macmillan, 1989.
	Syndicat Interprofessionnel du Fromage Abondance
	website. http://www.fromageabondance.fr/.

  El Soda, M. "Acceleration of Cheese Ripening. " In Encyclopedia of Dairy Sciences, vol. 1, edited by Hubert Roginski, John W. Fuquay, and Patrick F. Fo x, pp. 327-329. Amsterdam: Academic Press, 2003. Fox, Patrick, Paul L. H. McSweeney, Timothy Cogan et al., eds. Cheese: Chemistry, Physics, and Microbiology,

	Vol. 1: General Aspects, 3d ed. London: Elsevier
	Academic, 2004.
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	383-398.

  the ancient Greek world. Early sources agree that he was the son of Apollo and Kyrene and that he married Autonoe, sister of Semele: this makes him the uncle of Dionysos, the Greek god of wine. He is among the figures in Greek mythology who were born mortals and became immortal. His worship in Sparta, as consort of the local goddess Ortheia, and his widespread reputation as a shepherd, seem to place him in the category of pastoral gods who die and are reborn, like Dumuzi, Adonis, and Osiris, and are married to powerful goddesses. See inanna.

	several regions of
	Battro, Pablo. Quesos Artesanales: Historia, Descripción y
	Elaboración. Buenos Aires: Editorial Albatros, 2010.
	Castañeda, R., S. Borbonet, A. Ibarra, et al. Quesos de
	América del Sur. Buenos Aires: Editorial Albatros,
	2010.
	Carlos Yescas
	Aristaios (Latin spelling Aristaeus), though not
	one of the Olympians, was honored as a god in


Géraldine Roux

Julien Haran

Alain Roques

Christelle Robinet

Christelle Robinet Pine

Pine sawyer beetle, Monochamus galloprovincialis (Coleoptera: Cerambycidae)

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.

Species in the worldwide genus Monochamus have drawn particular attention since they vector the pine wood nematode (Bursaphelencus xylophilus, PWN), responsible for pine wilt disease. Although a secondary pest in its native North America, PWN has devastated conifer forests in invaded regions of eastern Asia, and more recently of southwestern Europe, incurring huge management costs to attempt eradication which remains unsuccessful. So far, only one native European species, M. galloprovincialis, has been proven to have developed a novel association with the invasive PWN. This oligophagous species has a wide palearctic distribution clustered into two main evolutionary lineages and presents a large host-range in the genus Pinus. The insects insure transportation of the dispersal form of the nematode in their trachea and the transmission to a new host tree occurred during its two periods of flight in summer, i.e. mostly during the maturation feeding, but also during the oviposition. A spread model was developed to figure out the potential spread of PWN-infected beetles over space and time. M. galloprovincialis populations differentiated in five groups which likely resulted from distinct postglacial recolonization routes and barriers to gene flow. Although adults have strong flight ability, mountain ranges and high pine densities may act as natural barriers to spread. Conversely, low pine densities constitute corridors, suggesting that open areas can enhance dissemination of PWN throughout the Iberian Peninsula and potentially the rest of Europe. Future strategies aimed at preventing disease expansion (buffer zones, clear cut belts) must take these results into account.

0501:

  Pine sawyer beetle, Monochamus galloprovincialis (Coleoptera: Cerambycidae)

  doi: 10.1603/ICE.2016.92731

Authors