domain: sdv sde

text:
Earthworm's influence on phytoavailability and Human gastric bioaccessibility of metals

Thibaut Leveque, Laura Lagier, Eva Schreck, Yvan Capowiez, Melanie Auffan,

Camille Dumat

To cite this version:

Thibaut

  Leveque, Laura Lagier, Eva Schreck, Yvan Capowiez, Melanie Auffan, et al.. Earthworm's influence on phytoavailability and Human gastric bioaccessibility of metals. Sustainable Urban Agricultures (UA): Vector for Ecological Transition (UA&ET-2017), Jun 2017, Toulouse, France. Institut d'Aménagement et d'Urbanisme de la Région Ile-de-France, 70 p., 2017, Les agricultures urbaines durables : vecteur de transition écologique. Actes du congrès international. hal-01655657v2


David Renaudeau

Ludovic Brossard

Bruno Duteil

Etienne Labussière

Impact of a short term dietary challenge on growth performance and feeding behavior in finishing pig

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EAAP -68 th Annual Meeting, Tallinn 2017 Session 24 Poster 16

Session 24 Poster 15

Feed restriction on growth of mice divergently selected for birth weight environmental variability N. Formoso-Rafferty Castilla 1 , I. Cervantes 1 , J.P. Sánchez 2 , J.P. Gutiérrez 1 and L. Bodin 3 1 Universidad Complutense de Madrid. Facultad de Veterinaria, Avda. Puerta de Hierro s/n, 28040, Madrid, Spain, 2 IRTA -Torre Marimon, Caldes de Montbui, 08140, Barcelona, Spain, 3 INRA -GenPhySe, CS 52627, 31326, Castanet-Tolosan, France; n.formosorafferty@ucm.es Nowadays, the selection for feed efficiency is one of the main aims in animal breeding to decrease the production costs. On the other hand, selection for less sensitivity with respect to environmental effects, as indicated by a low variation around the optimum trait value, may have benefits in terms of productivity and animal welfare. Therefore, the objective of this work was to analyze the influence of food restriction, understood as an environmental challenge, on weight at different ages in two lines divergently selected for birth weight variability in mouse lines with either a low variability (LV) or high variability (HV). A total of 40 females (four full-sib females from 10 random different litters from the 12, 13, and 14 generations of selection), were chosen within lines and fed either ad libitum or restricted from 21 to 77 days. Restriction consisted of feeding with 75%, 90%, or 85% of ad libitum feed consumption in the respective three studied generations. Weekly weights from 21 to 77 days were analyzed. The model was adjusted for the diet (restricted or ad libitum), mouse line, generation, and litter size, and included also the interaction between the line, generation, and the diet. The ASReml Release 4.1 program was used for the analysis. Animals fed ad libitum of the LV line had similar weights in all generations unlike those of the HV line, which had lower weights in successive generations. The feed restriction had a negative effect on the body weight of the animals but the interaction between line and diet was significantly different only after day 35, showing a differential response of the lines to the environmental challenge. Animals from the LV line were less sensitive to the feed restriction. This study is part of the Feed-a-Gene project and received funding from the European Union's H2020 program under grant agreement no. 633531. Pigs are frequently facing environmental perturbations with subsequent short and long-term effects on their health and performance. The objective of this study was to evaluate the consequences of an acute dietary challenge on growth performance and feeding behavior in finishing pigs. A total of 160 pigs (average initial BW of 69.1 kg, 115 d of age) were used in two successive replicates of 80 animals with 4 treatments. In the control group (CC), pigs were fed a standard diet (7.6 g digestible Lys/kg and 9.6 MJ NE/kg) during the whole experimental period (55 days). The DC, CD, and DD groups were challenged with a 'diluted' diet for a 7-d period at 130 d, at 153 d, and at 130 and 153 d of age, respectively. Digestible Lys and NE contents were reduced by 20% in the diluted diet in comparison to the control diet, using wheat bran and sunflower meal as diluters. In both diets, essential amino acids were kept constant relative to Lys. Within each replicate, all pigs were equipped with a RFID ear-tag and housed in a same room. The room consisted of a resting area and 2 feeding areas separated by a weighing-sorting station, which allowed for continuous measurements of individual BW. The 2 feeding areas were equipped with 4 feeding stations each. These electronic feeder systems automatically distributed the chosen diet to each pig and recorded the visits to the feeder, with their time and the amount of feed consumed. The average daily BW gain and the BW at slaughter were significantly reduced in the CD and DD groups when compared to the CC group (1,052 g/d and 125.5 kg on average vs 1,119 g/d and 129.5 kg, respectively; P<0.01). Intermediate results were reported for DC group (1,073 g/d and 127.1 kg). Feed conversion ratio was higher in DC and DD groups than in CC and CD groups (3.13 and 3.15 vs 2.93 and 2.95 kg/kg, respectively; P<0.01). Over the total duration of the experiment, mean feeding behavior traits were not influenced (P>0.05) by treatments. Carcass traits were similar in the 4 experimental groups.

This study demonstrates an age-of-exposure effect of a dietary challenge on growth performance in finishing pigs. This study is part of the Feed-a-Gene project and received funding from the European Union's H2020 program under grant agreement no. 633531.

Impact of a short term dietary challenge on growth performance and feeding behavior in finishing pig

  D. Renaudeau 1 , L. Brossard 1 , B. Duteil 2 and E. Labussière 1 1 INRA, PEGASE, Domaine de la Prise, 35590 Saint-Gilles, France, 2 INRA,UEPR, France; ludovic.brossard@inra.fr


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The development and improvement methods to map agricultural land cover is a timely challenge, especially for radar images. This is due to the speckle noise nature of radar, leading to a less intensive use of radar rather than optical images. Recently, the European Space Agency Sentinel-1 constellation is a satellite system providing the global coverage of Synthetic Aperture Radar (SAR) with a 6-days revisit period at a high spatial resolution about 20 m. These data are valuable aids in providing the spatial information of agricultural crops. The aim of this paper is to provide a better understanding of the capabilities of Sentinel-1 radar images for agricultural land cover mapping through the use of deep learning techniques. Analysis is carried out on a multitemporal Sentinel-1 data over an area around Camargue, France. This data set was processed in order to produce an intensity radar data stack from May 2017 to September 2017. We revealed that even with classical machine learning approaches K nearest neighbors, random forest, and support vector machine), the good performance classification could be achieved with F-measure/Accuracy greater than 94 % and Kappa coefficient better than 0.92. We found that the results of the two deep recurrent neural network (RNN)-based classifiers clearly outperformed the classical approaches. Finally, our analyses in Camargue show that the same performance of two RNN-based classifiers is on the Rice class, the most dominant practice of this region, with the F-measure metric of 99.5%. These results thus highlight that in the next future, these RNN-based techniques will play an important role in the analysis of remote sensing time series.

  , Dinh Ho Tong Minh, N. Baghdadi, Dominique Courault, Laure L. Hossard To cite this version: E. Ndikumana, Dinh Ho Tong Minh, N. Baghdadi, Dominique Courault, Laure L. Hossard. Applying deep learning for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France. Image and Signal Processing for Remote Sensing XXIV, Society of Photographic Instrumentation Engineers (SPIE). Cardiff, GBR., Sep 2018, Berlin, Germany. pp.13, 10.1117/12.2325160. hal-Ndikumana, Dinh Ho Tong Minh, Nicolas Baghdadi, Institut National de Recherche en Sciences et Technologies Pour l'Environnment et l'Agriculture (France); Dominique Courault, Laure Hossard, Institut National de la Recherche Agronomique (France)


Hammouda Elbez

Kamel Benhaoua

Member, IEEE Philippe Devienne

Progressive Compression and Weight Reinforcement for Spiking Neural Networks

Keywords: Spiking Neural Networks, neuromorphic computing, unsupervised learning, pruning, compression, dynamic threshold

Neuromorphic architectures are one of the most promising architectures to significantly reduce the energy consumption of tomorrow's computers. These architectures are inspired by the behaviour of the brain at a fairly precise level and consist of artificial Spiking Neural Networks (SNNs). To optimise the implementation of these architectures, we propose in this paper a novel progressive network compression and reinforcement technique based on two functions, progressive pruning and dynamic synaptic weight reinforcement used after each training batch. The proposed approach delivers a highly compressed network (up to 75 % of compression rate) while preserving the network performance when tested with MNIST.

II. INTRODUCTION

The human brain is considered as the most powerful biological computing machine, with the ability to do parallel processing for a long time with a relatively low energy consumption compared to the artificial computing machines and existing technologies. Composed of around 20 billion neurons and 200 trillion synapses, the human brain is able to process a huge amount of data by exchanging spikes between neurons and consuming only about 20 W of power. Recently, we have seen a rising interest in bio-inspired architectures such as neuromorphic architectures using Spiking Neural Networks (SNNs). These biologically plausible architectures imitate the brain for the purpose of exploring the advantages that the brain offers, especially low energy consumption.

Several works were presented in the past to propose a hardware implementation of such neuromorphic architectures, using different hardware components [START_REF] Merolla | A digital neurosynaptic core using embedded crossbar memory with 45pj per spike in 45nm[END_REF], [START_REF] Shahsavari | Memristor nanodevice for unconventional computing:review and applications[END_REF], but the common observed characteristic of the presented SNNs is the huge size of the networks, in order to get better performance than non-Spiking Artificial Neural Networks (ANNs). Meaning having a large amount of neurons and synapses, and a rising complexity when it comes to the hardware implementation and analysis of such architecture, in [START_REF] Diehl | Unsupervised learning of digit recognition using spike-timing-dependent plasticity[END_REF] the authors had to increase the number of neurons in the 2-layer SNNs by more than 60 times, in order to improve the performance by 12 %, in [START_REF] Lee | Training Deep Spiking Neural Networks Using Backpropagation[END_REF] the authors used 2 hidden layers of 800 neurons each in order to get an average accuracy of 98.6 % and in [START_REF] Diehl | Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing[END_REF] the authors implemented a network with two hidden layers of 1200 neurons each for an average accuracy of 98.64 %. From the presented examples, we can see that larger networks are needed for better performance which introduces the following challenges :

• Larger networks are usually harder to analyse.

• Having larger networks means longer simulations with more computational resources to use and a loss of the energy efficiency in such networks. • Larger networks are harder to implement in hardware using known technologies like the memristive crossbars [START_REF] Strukov | The missing memristor found[END_REF], [START_REF] Merolla | A digital neurosynaptic core using embedded crossbar memory with 45pj per spike in 45nm[END_REF], which are subjected to many design challenges seen when a large network is deployed [START_REF] Liu | EDA Challenges for Memristor-Crossbar based Neuromorphic Computing[END_REF].

Communication in SNNs is made using spikes, which are transmitted by the synapses that represent the channel of communication between the neurons. The number of synapses in a network scales with the number of neurons and from the hardware point of view of SNNs, more synapses means a higher cost of production, and the efficient use of such elements leads to a more energy efficient network. Pruning is one of the techniques widely used in ANNs to reduce the network size and complexity. This technique is inspired from the observation that during the development of the human brain, synaptic connections are pruned from the early stages [START_REF]Synaptic density in human frontal cortex -developmental changes and effects of aging[END_REF], such phenomena inspired researchers in the neural networks field to adapt this technique to get a smaller network while preserving the same performance or with a small loss [START_REF] Cun | Optimal brain damage[END_REF], [START_REF] Hassibi | Optimal Brain Surgeon: Extensions and performance comparisons[END_REF], using this technique in Deep Neural Networks (DNNs) with the stateof-the-art AlexNet results in a 30-times reduced number of synapses than the baseline while preserving the performance [START_REF] Han | Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding[END_REF]. The idea behind these researches is not new and based on the principle that "the simplest solution is most likely the right one" in other words "Entities should not be multiplied without necessity". The idea is attributed to English Franciscan friar William of Ockham (c. 1287-1347), a scholastic philosopher and theologian who used a preference for simplicity. As the Ockham's razor [START_REF] Blumer | Occam's razor[END_REF] says that when presented with competing hypotheses that make the same predictions, one should select in a neural network the most relevant inputs and connections, that is, the as simple as possible network. Similarly, in science, for each accepted explanation of a phenomenon, there may be an extremely large, perhaps even incomprehensible, number of possible and more complex alternatives. Since one can always burden failing explanations with ad hoc hypotheses to prevent them from being falsified, simpler theories are preferable to more complex ones because they are more testable. Furthermore, in genetics the idea is even stronger to be able to understand why the human genome is, for instance, 1.5 smaller that the genome of peas. The idea behind that there are a lot of irrelevant or redundant coding on genomes.

So according to Kolmogorov's complexity [START_REF] Li | An Introduction to Kolmogorov Complexity and Its Applications[END_REF], to compress is to understand. The final word is often attributed to Einstein, himself a master of the quotable one liner: "Everything should be made as simple as possible, but not simpler."

SNNs have the capability to handle natural signals and can be implemented physically through ultra-low power devices based, for instance, on CMOS [START_REF] Sourikopoulos | A 4-fJ/Spike Artificial Neuron in 65 nm CMOS Technology[END_REF] or memristors [START_REF] Shahsavari | Memristor nanodevice for unconventional computing:review and applications[END_REF]. CMOS artificial neuron is a very simple component (only six transistors operating in the subthreshold mode) and its energy consumption is several orders of magnitude lower than artificial neurons reported in the literature, but also two to three orders of magnitude lower than the energy consumption of living neurons.

As for recent works about pruning in SNNs, in [START_REF] Rathi | STDP-Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy-Efficient Recognition[END_REF] the author combined pruning while the network is learning with a weight quantization technique using a 2-layer SNN of 6400 neurons, in order to reduce the network size and parameters, and shows that we can achieve highly compressed networks, while preserving a good performance with a fixed pruning threshold, which is defined from the beginning, deactivating the synapses that are considered non critical, based on the non supervised local Spiking Time Dependent Plasticity (STDP) rule [START_REF] Bi | Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type[END_REF], used to update the weights of synapses with every training batch, until the end of the learning phase, where remaining non critical synapses are removed from the network. Another work [START_REF] Saunders | Locally connected spiking neural networks for unsupervised feature learning[END_REF], based on the locally connected Spiking Neural Network (LC-SNN), while using a 2-layer SNN of 900 neurons, resulted in 50 % of removed synapses and 90 % maintained accuracy, the pruning operation being made only once at the end of the learning phase. In [START_REF] Shi | A Soft-Pruning Method Applied During Training of Spiking Neural Networks for Inmemory Computing Applications[END_REF], a soft-pruning method was used during the training process, which prevents the unnecessary update of the network parameters. This was considered as different compared to the existing approaches that use pruning on an already trained network. This approach was able to maintain 90 % accuracy, on the MNIST dataset with a 75 % of synapses removed.

The novelty of our work, is the use of a dynamic threshold for pruning, instead of a fixed one like the previous works, using a progressive pruning function, that computes a new threshold, with every batch based on the last pruning operation performance. During every learning batch, the produced network is able to keep the same performance compared to the baseline with up to 75 % smaller network, also we introduce a synapse reinforcement for the critical synapses, using dynamic reinforcement function, which helps the network to preserve an average spiking frequency, close to the baseline, and push the neurons to specialize in one class. The combination of these two techniques, is able to produce trainable compressed networks, that can be trained again for a better accuracy.

The rest of this paper, is organized as follows: Section III contains background about the used network, neuron and synapse models, with a presentation of the Spiking Time Dependent Plasticity (STDP) rule. Section IV, presents the contribution in details. In Section V, the results are presented, discussed and compared to the baseline and the related works and finally Section VI contains the conclusion.

III. BACKGROUND

A. Network Topology

In our experiments, the used topology is based on Dielh and Cook work [START_REF] Diehl | Unsupervised learning of digit recognition using spike-timing-dependent plasticity[END_REF] to classify the MNIST handwritten digits [START_REF] Lecun | MNIST handwritten digit database[END_REF]. As described in Figure 1, it is a 2-layer Spiking Neural Network composed of the input layer, where each neuron represents a pixel, which is feed-forward fully connected to the next unsupervised layer, using excitatory synapses. This unsupervised layer is fully connected with itself for lateral inhibition, in order to create competition between the neurons, and to prevent one neuron from dominating all the inputs, which is also known as the Winner-Takes-All (WTA) rule. We also employ Homeostasis [START_REF] Marder | Variability, compensation and homeostasis in neuron and network function[END_REF], which is an adaptive membrane threshold technique, it is used to guarantee that every excitatory neuron, learns a unique feature and avoid neuron dominating the inputs. This technique is achieved using equation 1.

V threshold = V t + τ (1)
Where V t is a constant, represents the initial threshold defined in the network. τ is dynamically changed, if the neuron fires often, the value of τ will increase which requires more inputs to fire again, and decays if the activity of the neuron is less often. Which ensures a close average spikes activity, between all the neurons in the network. In this work, we have used the N2S3 simulator [START_REF] Boulet | N2s3, an Open-Source Scalable Spiking Neuromorphic Hardware Simulator[END_REF] for all simulations. N2S3 is an open-source scalable spiking neuromorphic hardware simulator, written in Scala and based on the Akka actor system.

Those simulations are executed on an OpenStack cluster 1 node, with the following characteristics:

• RAM : 250 GB • VCPU : 1 10-core Intel Xeon Silver 4114 CPU

B. Spiking Neuron Model

The Leaky-Integrated-and-Fire (LIF) neuron model [START_REF] Burkitt | A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input[END_REF] is used to simulate the neuron membrane potential. LIF is considered as one of the simplest models, that includes time in its operation, to update the membrane potential value of the neuron based on the input from other neurons in the network. The membrane potential keeps increasing until a specific threshold is reached, then the neuron sends a spike and enters a refractory period, before starting again to accumulate the received spikes. The membrane potential evolution is presented in Figure 2, and the LIF dynamic voltage equation is given by:

τ leak ∂v ∂t = [v(t) -v rest ] + r m z(t), v ← v rest when v ≥ v th (2)
Where v represents the membrane voltage, v rest is the resting potential after a neuron fires a spike, τ leak is the time constant of the leak with τ leak = r m c m , where r m is the membrane resistance and c m is the membrane capacitance.

C. Learning Rule

Spiking Neural Networks usually use a local unsupervised learning rule (every synapse only has the information about its post-and pre-synaptic neurons), which is biologically plausible and suitable for hardware implementation. With SNNs, it is difficult to get high recognition rates, such as those of Deep Neural Networks (DNNs) or Convolutional Neural Networks (CNNs), that are based on global supervised learning rules (e.g., backpropagation). We use in our work the Simplified Spike Timing Dependent Plasticity (STDP) rule, which is easy to implement with nanodevices [START_REF] Querlioz | Simulation of a memristorbased spiking neural network immune to device variations[END_REF]. This simplified 1 http://hpc.univ-lille.fr/cloud-openstack STDP rule is based on the t pre pre-synaptic and t post postsynaptic neuron firing times. When the pre-synaptic neuron fires before the post-synaptic one, the weight of the synapse is increased, and when the post-synaptic neuron fires before the pre-synaptic one, the weight of the synapse is decreased. This simplified STDP rule is modelled as:

∆ w =    +η w e - tpre -t post τ STDP if t pre ≤ t post -η w e - tpre -t post τ STDP if t pre > t post (3)
τ STDP represents the time constant that controls the leak, η w is the learning rate, t pre and t post are times of spikes, for pre-synaptic and post-synaptic neuron respectively.

D. MNIST Dataset

We have used the MNIST handwritten dataset [START_REF] Lecun | MNIST handwritten digit database[END_REF] for testing. This dataset is composed of 28x28 pixels images of handwritten digits with labels from 0 to 9. It contains 60,000 training images and 10,000 test images. For our experiments, training images were divided into 6 batches of 10,000 images in order to easily follow the network learning progress using the approach presented in this work, and to minimize training time. The images are presented to our network input layer and processed in the format of Poisson-distributed spike trains, the input layer neuron weights are randomly initiated based on a uniform distribution between 0 and 1. More parameters used in the network are presented in Table I.

IV. CONTRIBUTION

Compressing Spiking Neural Networks (SNNs) results in a network with reduced parameters and complexity to analyze, which will make it easier to implement in hardware using the existing technologies [START_REF] Shahsavari | Memristor nanodevice for unconventional computing:review and applications[END_REF], [START_REF] Merolla | A digital neurosynaptic core using embedded crossbar memory with 45pj per spike in 45nm[END_REF].

Pruning non-critical synapses is a widely used technique in ANNs and SNNs, and the recent works have used two different approaches to prune the network: at the end of the training, or while learning using a fixed threshold (which is defined from the beginning based on experiments). In [START_REF] Rathi | STDP-Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy-Efficient Recognition[END_REF], the author was able to achieve highly compressed networks by simply eliminating the non-critical synapses based on the STDP learning rule and a fixed threshold. We have observed from our tests using the MNIST dataset and based on the network model used in [START_REF] Diehl | Unsupervised learning of digit recognition using spike-timing-dependent plasticity[END_REF], that the neurons generally start to specialize in detecting a specific class early while learning, and the network performance starts to rise from the first learning batch as demonstrated in Figure 3. This means that we can start removing synapses earlier, and the network can maintain the majority of its performance when removing a lot of synapses, based on STDP as shown in recent works [START_REF] Rathi | STDP-Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy-Efficient Recognition[END_REF], [START_REF] Saunders | Locally connected spiking neural networks for unsupervised feature learning[END_REF]. If we run another training epoch on the new reduced network, we will get a network with better performance, and an opportunity to prune based on a higher threshold, which should result in a more compressed network with an equal or better performance. Starting from this observation and based on the recent contributions, we propose a progressive pruning function to control a dynamic threshold, which is calculated and used to prune after a fixed training batch, and a synaptic weight reinforcement mechanism, used to maintain the average spiking frequency of the network, close to the baseline and to push the neurons that did not specialize in a certain class to do so, by reinforcement of the critical maintained synapses.

Fig. 3: Learning progress in SNN

A. Progressive Pruning

Progressive Pruning (PP) is based on reducing the number of synapses, between the input layer and the unsupervised layer. This reduction is performed after each batch while learning, using a dynamic progressively increasing pruning threshold T n , n ∈ N. This threshold is calculated using equation 4.

T n+1 = T n + α * (C rn /C n ) n ∈ N (4)
Where n represents the batch number, T n+1 is the threshold to use for the next batch, α represent a constant defined by the user, the choice of α is discussed in the next section (α = 0.05 in this work), T n is the old threshold used in the last batch (T 0 = 0), C rn represents the number of remaining synapses between the two layers at batch n and finally C n represents the total number of synapses before applying pruning to this batch (which equals to the remaining synapses from the last batch, C n = C rn-1 ).

Using equation 4, the threshold is defined based on the previous pruning performance (if the pruning rate using T n from the last batch was large, T n+1 -T n will be small and vice versa), which prevents the network from a major performance loss, while reducing the network every time by eliminating the non-critical synapses.

B. Dynamic Synaptic Weight Reinforcement

After reducing the network size, the neuron will learn to react only to a specific pattern or class (in our case a digit). We observed that during the learning process, some neurons fail to learn a specific class, which affects the network performance resulting in false classifications. On the other hand, applying Progressive Pruning on the network results in a decrease of the average spiking network frequency, which will affect the energy consumption of our network positively, but may cause a frequency loss in multilayer networks, which is a known issue in Convolutional Spiking Neural Networks (CSNN) as described in [START_REF] Falez | Mastering the Output Frequency in Spiking Neural Networks[END_REF]. Based on those observations, we propose a Dynamic Synaptic Weight Reinforcement (DSWR), which concerns the synapses that are conserved and considered as critical, to improve the network performance, by pushing the neurons that did not specialize in a specific pattern or class to do so, and keep the average spiking frequency of the network near the baseline. This DSWR procedure is executed after the pruning in each iteration, and it's done based on equation 5.

W n+1 = W n + β * T n , n ∈ N. (5)
Where n represents the batch number, W n+1 is the new weight of the concerned synapse, W n represents the actual weight of that synapse, β is a defined constant, chosen based on experiments and discussed in the next section (in this work β= 0.1) and T n represents the actual used threshold for pruning from equation 4. This equation means that when the compression ratio is large, the reinforcement will be too and vice versa.

Fig. 4: Average spike frequency

Using the presented functions, we make sure to determine how much to increase the weight, and maintain the average spike frequency based on the network state. In Figure 4, using a network composed of 100 neurons, we can see the average spiking frequency of our network baseline and the proposed functions after each training batch.

A detailed flowchart concerning the proposed approach is presented in Figure 5.

C. α and β Selection

Choosing the values of parameters α and β is a multiobjective optimization problem, the two objectives being the network performance and the compression ratio. We did multiple experiments using different values, and compared them in terms of these two objectives, getting a Pareto front of non-dominated values. We finally chose the one that gave use the best performance with a compression ratio above 76 %.

In Figure 6, by trying different combinations of α and β, we can get a high compression rate but in the same time a higher loss in network accuracy, the selection of those parameters may vary from a case to another depending on the context of the application.

In Figure 7, we show the 10 possibilities from the 25, respecting the following conditions and draw the Pareto front of non-dominated solutions:

• The performance should be better than the baseline presented in [START_REF] Diehl | Unsupervised learning of digit recognition using spike-timing-dependent plasticity[END_REF] (82.90 %). • The compression rate should be better than 76 %. Based on these results, we decided to choose α = 0.05 and β = 0.1, which gave us an accuracy of 85.10 % with a compression rate of 79.42 %, using a network composed of 100 neurons.

V. EXPERIMENTAL VALIDATION & DISCUSSION

In this part, we describe and discuss our experimental validation. The main tests are conducted using a network composed of 100 neurons, in the unsupervised layer connected to the input layer by 78400 excitatory synapses. The simulation on each case is executed 10 times using one epoch (of 60000 samples), and we report the average of these 10 simulations. Besides, tests were also conducted on 400, 900 and 1600 neuron networks to evaluate the performance of the presented approach on different sizes of networks.

A. Accuracy & Compression

We compare our results to the baseline and recent works on pruning in SNN in Table II. We can see that our methodology helps to compress the network and to get a higher accuracy in different network sizes, due to the progressive and iterative pruning and reinforcement operations. For 100 neurons, the work presented by Rathi et al. [START_REF] Rathi | STDP-Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy-Efficient Recognition[END_REF] could not preserve the same accuracy compared to baseline, and one of the possible reasons are the fact that there is no training after pruning, the one time pruning approach and the weight quantization makes the network lose information. No training after pruning and one epoch leads to an accuracy of 79.50 %, which is less than the baseline with a 75 % compression rate. We can see that with a 100 neurons network, using only Progressive Pruning (PP) gives better performance, compared to the baseline with more than 78 % of the synaptic connections removed, and when using both Progressive Pruning (PP) and Dynamic Synaptic Weight Reinforcement (DSWR), we get a slight improvement in accuracy and compression rate. Having a compressed network with a better performance than the baseline, or the work presented in [START_REF] Diehl | Unsupervised learning of digit recognition using spike-timing-dependent plasticity[END_REF] is possible, using the proposed functions and training after each batch, which helps the network to learn better and faster, while eliminating many synapses that are considered as noise to the network.

In the work presented by Diehl and Cook [START_REF] Diehl | Unsupervised learning of digit recognition using spike-timing-dependent plasticity[END_REF], tests were also conducted on a network composed of 400 neurons using 3 training epochs, the accuracy was 87 %, which is also close to our 400 neurons network baseline, without the presented approach, we can see from table II that we are able to compress the network up to 79.52 %, with 87.84 % accuracy using PP & DSWR with only one epoch.

From the observed evolution of the network, using a neural network of 100 neurons with PP and DSWR, we can see in Figure 8, that the network is compressed for more than half his initial synaptic connections in the first batch, the compression rate is increasing after each batch while the accuracy does the same thing but slowly.

B. Presented Approach On Larger Networks

While the selected parameters (α and β) give good results with small and medium networks, as shown in Table II, using the presented approach with larger networks, while preserving the same parameters and epoch number (only one), may have different impact on the network. In this part, we will present the test results we had, using a network of 1600 neurons similar to Diehl and Cook work [START_REF] Diehl | Unsupervised learning of digit recognition using spike-timing-dependent plasticity[END_REF], then compare it to the earlier results.

From Figure 9, compared to the previous experiments, we notice that we have a loss in performance (85.31 %), the network needs up to three batches to get a performance above 80 %, compared to smaller networks, where one batch is enough. For the compression rate, we can see also that the network this time is less compressed, compared to smaller ones (65.02 %), or to the results of Diehl and Cook [START_REF] Diehl | Unsupervised learning of digit recognition using spike-timing-dependent plasticity[END_REF], and starts from a small compression rate (4 %) in the first batch, which is not the case compared to earlier examples with smaller networks, which compress almost the half of the network in the first batch.

To justify and explain such a result when using the presented approach with a larger network, we propose two hypotheses:

1) The used constants (especially α) initial value may be too large for such a large network, and need to be adjusted properly.

2) The network with larger number of neurons needs more time to learn before pruning, which means the number of batches per epoch needs to be reviewed. In order to get an idea on what is happening in the network, we can check in Figure 10, the heatmap of some neurons, captured after the first batch (10000 inputs) of training, as we can see the network had not enough time to start learning features, which is due to the competition between neurons to learn features, which takes more time compared to previous smaller networks. From the compression rate progress in Figure 9, we can discard the first hypothesis, since the problem is not related to pruning using high threshold, based on the low compression rate we had (4 %), which did not affect learning process of the network, but it is indeed related to the second hypothesis, about the time required for the network to start learning features, which justifies the small compression rate compared to smaller networks and the low performance. In this case, when dealing with larger networks and based on our test results, it is better to reduce the number of batches per epoch, delay the prune operation and use more than one epoch for training, when applying the approach presented in this work, in order to give the network the required time to learn for a better performance and compression rate. Due to the fact that running this simulation with a large network takes a lot of time (days for 100 to 900 neurons and almost one month for 1600 neurons) and a significant amount of energy, we chose not run our tests for more than one epoch and present the results of the proposed modifications when working with large networks.

C. Trainable Compressed Network

Reducing the number of synapses in a network is helpful, for time and energy reduction, especially when implementing in hardware. One of the features that is commonly tested with reduced networks, is the possibility to have trainable networks after compression, because one of the reasons, that huge networks are used for training is to maximise the possibility to get good performance, which is not always possible if you start with small networks. In [START_REF] Frankle | The lottery ticket hypothesis: Finding sparse, trainable neural networks[END_REF], [START_REF] Zhou | Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask[END_REF], a lottery tickets hypothesis is presented for CNNs, saying that a randomly initiated dense neural network contains a subnetwork that, if isolated and trained alone, can match the accuracy of the original network. This subnetwork can be extracted using It is hard to train a pruned network from the beginning in CNNs, which gives a worst accuracy than the original model, and is considered as not trainable. The solution was to train the pruned network with the initial synapse weights, and not randomly initiated in order for the network to learn well, and for gradient descent to be able to find a good solution. Such hypothesis was not yet tested in Spiking Neural Networks (SNNs). In Figure 11, we test using a 100 neuron network if the compressed network is trainable, using the new compressed network either without changing synapse weights, using random initial weights or using the initial weights of the original network. We can see after one batch that the network with initial synaptic weights without training reaches more than 60 % accuracy compared to the network with randomly initiated weights, that barely reaches 50 %, this observation was also presented in [START_REF] Frankle | The lottery ticket hypothesis: Finding sparse, trainable neural networks[END_REF] for CNNs. At the end of the 6 batches of training, we get an accuracy near 85 % for the three use cases. For the compression rate, we can see that the network gets more compressed in the three use cases to reach a compression rate of 80 % while preserving a better accuracy compared to baseline. This proves that many observations presented in [START_REF] Frankle | The lottery ticket hypothesis: Finding sparse, trainable neural networks[END_REF] are true in SNNs too, and the trainable network gets a higher compression rate, when trained again irrespectively of the initialisation strategy, of the synaptic weights that only influences the speed of learning, not its quality.

D. Pruning Batch

During our tests, we used a single MNIST dataset epoch (60000 input samples), divided into 6 batches (of 10000 samples each), while applying the presented approach after each batch. We study here the influence of the size and number of batches, on the accuracy and compression of the network. In Figure 12, we compare one batch of 60000 samples, 3 batches of 20000 samples and 6 batches of 10000 samples, using a neural network composed of 100 neurons.

We can see that the accuracy of the network did not decrease, and the variation of the number of batches does not Fig. 12: Pruning batch variation have a clear impact on the accuracy. However, we can see that the compression ratio increases, when the number of batches is increased, which is due to the presented approach, with the dynamic threshold used and the training of each compressed network. We conjecture that having more than 6 batches will have an effect similar to training the network again, for another epoch, but may results in a larger training time.

VI. CONCLUSION

Many researches focus on the unreasonable effectiveness of Neural Networks and our deep inability to understand causality, correlation in neural networks. To prune a neural network can be seen as a normal discriminant analysis, for dimensionality reduction before better classification and explanation.

In this work, we have presented a novel approach to reduce the size of Spiking Neural Networks, while preserving a similar or better accuracy, by the use of the Progressive Pruning, with a dynamic threshold and a Dynamic Synaptic Weight Reinforcement, compared to the existing approaches of using a fixed threshold while pruning, or pruning at the end of training without training again. Our approach is able to get a better accuracy compared to the non-compressed networks, when applied to small or medium networks, using only one epoch. But when dealing with larger networks, more than one training epoch is needed with larger batches to provide sufficient time for the network to learn before applying the presented approach. The compressed networks were proved trainable to reach even better accuracy and a better compression rate, using the initial synapses weights of the original network, based on the presented idea in [START_REF] Frankle | The lottery ticket hypothesis: Finding sparse, trainable neural networks[END_REF] for Convolutional Neural Networks. This also suggests that the Ticket Lottery Hypothesis presented in the same work may also be true for Spiking Neural Networks, to create masks for network pruning before training, which we will test and validate in future work. More datasets will be studied like colour images (CIFAR-10 [27]) or EMNIST [START_REF] Cohen | EMNIST: an extension of MNIST to handwritten letters[END_REF]. Finally, it's worth mentioning that training compressed networks will result also in a reduced training time, which is very important especially for large networks.

Fig. 1 :

 1 Fig.1: SNN topology, with feed forward excitatory synapses from the input to the unsupervised layer, which are later pruned. Inhibitory synapses between neurons of the unsupervised layer.

Fig. 2 :

 2 Fig. 2: Leaky-Integrated-and-Fire (LIF) neuron model membrane voltage activity

Fig. 5 :Fig. 6 :

 56 Fig. 5: Flowchart of the presented approach

Fig. 7 :

 7 Fig. 7: Accuracy and compression in function of α and β (line = Pareto front).

Fig. 8 :

 8 Fig. 8: Accuracy and Compression evaluation using PP and DSWR (100 neurons)

Fig. 9 :Fig. 10 :

 910 Fig. 9: Accuracy and Compression evaluation using PP and DSWR (1600 neurons)

Fig. 11 :

 11 Fig. 11: Accuracy (a) and Compression rate (b) during training batches

TABLE I :

 I Parameters used in the experiments

		Input Stream
	MinFrequency	0Hz	MaxFrequency	22Hz
	ExpositionDuration 350ms	PauseDuration	150ms
		Neuron
	VoltageThreshold	35mv	RestingVoltage	0mv
		STDP
	MaxWeight	1	MinWeight	0

TABLE II :

 II Accuracy and compression test results compared to other similar works

ACKNOWLEDGMENTS

This work was supported in part by IRCICA (Univ. Lille, CNRS, USR 3380 -IRCICA, F-59000 Lille, France) under the Bioinspired Project, PHC-CURIEN TASSILI 19MDU213 grant and PRIMA WATERMED 4.0 project.


domain: shs sdv info

text:
Zakary Diakite
email: zakary.diakite@vetagro-sup.fr

Claire Mosnier

René Baumont

Gilles Brunschwig

Grazing performance and the use of concentrate in double herds breeding systems with dairy cow and suckling cattle herds in mountain areas: a bioeconomic simulation analysis

come   L'archive ouverte pluridisciplinaire

Introduction Ruminant production is predominant in mountainous areas. In those areas, some plots of grasslands cannot be mechanized or are difficult to reach. Many mountain breeding systems combine in the same farm, a herd of dairy cows and a suckling cattle herd. We hypothesize that double herds allow a better grazed grass utilization rate than specialized herds for plots with constraints. The aim of this study was to characterise which herd distributions allow the best combination between grazed grass utilization, animal production and economical performances.

Material & methods We used the bio economic optimization model Orfee (Mosnier et al., 2017a) and data from two farm-types with dairy and suckling herds in cruising condition from the INOSYS database (Charroin et al., 2005). Farm_1 is a medium size system with a summer mountain pasture and Farm_2 is a large size system with a fragmented plot and cereal crops. These two farms are representative of the mountain double herd cattle farms. The model was adapted to take into account geographical constraints and the heterogeneity of grassland plots based on the French typology of permanent grasslands (Launay et al., 2011). Simulations are based on the two different farms and five scenarios (described as the percentage of livestock units (LU) in the dairy (D) herd): 100D for a single dairy herd, 75D for a dominant dairy herd, 50D for balanced dairy and suckling herds, 25D for a dominant suckling herd and 0D for a single suckling herd.

Milk yield is fixed, and the use of grassland, facilities and machinery are optimized in the simulations. Biotechnical and economic indicators characterizing the feeding system, the animal and the system performances have been analysed for each scenario. This leads to evaluate simultaneously; operating profit per men work unit (MWU), grazed grass utilization rate (ratio of the amount of grazed grass to the amount of available pasture grass), animal production (milk and meat quantities), forage and concentrate consumption, related with the combination of dairy and suckling herds.

Results & discussion

The annual cumulative consumption of forage is around 4 tons of dry matter (tDM) per LU in both farms for all the scenarios. Linked to the decrease of dairy LU in herds (Table 1), we observed a trend corresponding to an increase in the stocking rate and a decrease of concentrates intake per LU. Grazed grass utilization rate and operating profit per MWU reach their maximum for mixed herds systems (between 50D and 25D) as 60% of farms with double herds in the Massif Central (France). These systems that maximize grazed grass utilization also allow good economic performances.

Table 1 Levels of main indicators related to different analysed criteria for scenarios corresponding to the farm-types.

Conclusion

This study shows that systems with double herds, particularly systems with 25 to 50% dairy cattle LU, maximize the grazed grass utilization rate in a context of mountain grasslands, with constraints of mechanisation and access to dairy cows. These systems that maximize grazed grass utilization also have good animal production and economic performances. However, the structure of the farm plot is essential to specify the optimal herd's distributions, which improve grazing and economic performances.

Acknowledgements This research was supported by grants from Auvergne Region and the French Ministry of Agriculture.

  Grazing performance and the use of concentrate in double herds breeding systems with dairy cow and suckling cattle herds in mountain areas: a bioeconomic simulation analysis Zakary Diakite, Claire Mosnier, René Baumont, Gilles Brunschwig To cite this version: Zakary Diakite, Claire Mosnier, René Baumont, Gilles Brunschwig. Grazing performance and the use of concentrate in double herds breeding systems with dairy cow and suckling cattle herds in mountain areas: a bioeconomic simulation analysis. 10. International Symposium on the Nutrition of Herbivores (ISNH10), Sep 2018, Clermont-Ferrand, France. Cambridge University Press, Advances in Animal Biosciences, 9 (3), 2018, 10th. International Symposium on the Nutrition of Herbivores (ISNH10). hal-02737072

  Charroin T, Palazon R, Madeline Y, Guillaumin A and Tchakerian E 2005. Rencontres Recherches Ruminants 12, 335-338.

	Farm-types	Farm_1					Farm_2
	Scenarios	100D 75D 50D 25D 0D	100D 75D	50D	25D	0D
	Total livestock unit (LU)	65	73	80	82	80	113	125	137	132	128
	Concentrates (tDM/LU/year)	0.95	0.87 0.76 0.54 0.25	.83	0.85	0.86	0.52	0.33
	Grazed grass utilization rate (%)	58	72	85	82	85	72	84	89	100	90
	Stocking rate (LU/ha)	0.93	1.04 1.14 1.18 1.15	1.05	1.17	1.28	1.24	1.20
	Milk sold per year (1000L/year)	257	215 158 82	0	486	403	302	146	0
	Meat sold (1000kg/ year)	9	12	15	17	19	18	26	34	40	45
	Operating profit (1000€)/MWU	6.51	7.21 7.64 7.67 5.78	18.82 20.60 21.86 22.34 22.02

Launay F, Baumont R, Plantureux S, Farrié JP, Michaud A and Pottier E 2011. Éd Paris: Institut de l'Elevage. Mosnier C, Duclos A, Agabriel J and Gac

A. 2017a. Agricultural Systems 157, 202-215.


Alessandro Franzoni

Emilie Raynaud

Yannick Baumard

Estelle Audouin

Margherita Marzoni

Sonia Metayer-Coustard

Elisabeth Duval

E Cailleau-Audouin

S Métayer-Coustard

E Le Bihan-Duval

Laying performances and egg quality in two broiler breeder

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The selection of meat-type lines for increased growth and muscle development has been accompanied by significant physiological changes. While considerable gains have been made in production performances, there are limits in terms of product quality but also reproduction.

There is therefore a real challenge to better understand and exploit, the elements of the compromise between production, reproduction and meat quality.

The study focused on breeder hens at the 11th generation of selection, at which a differential of 0.5 pH unit is observed for the selection criterion (ultimate pH measured in chicken growing in the breast muscle at 6 weeks). Eighty females for the pHu+ and for the pHu-lines were housed in single cages in controlled environment from 20 to 40 weeks of age. During this period, eggs were weighted every week and eggshell mechanical properties (eggshell shape, percent, index, thickness, toughness, elasticity) have been determined to evaluate the egg quality for each line during all the laying period.

Data collected between 23 and 39 weeks of age show a decrease in the laying rate, a first egg delayed and a higher percentage of broken eggs in the high pH line (pHu+, line presenting the lower energy status) by comparison to the low pH line (pHu-). Divergent selection also changed the characteristics of the eggs, resulting in heavier eggs and a higher shape index value (more round shape eggs) in the pHu+ line.

In conclusion, these results suggest a deterioration in the reproductive performance studied and changes in the characteristics of eggs in relation to the decrease in energy storage caused by selection for an increase in pHu. These first observations pave the way for future genetic studies to evaluate the contribution of energy status in terms of improving reproductive traits whose degradation penalizes the meat-type sectors. In an original way, they also suggest the possibility of identifying new indicators or biomarkers of the energy status from measures related to reproduction and the egg.


Pierre-Éric Lauri
email: pierre-eric.lauri@inra.fr

Training and pruning the apple tree according to the SALSA System 1

Introduction

According to Robinson (2003) training systems for apple can be categorized in four tree canopy shapes, i.e., spherical, conic, flat planar and V-shaped. More recently, Musacchi and Greene (2017) considered planar, angled and vertical canopy, this latter category grouping the first two "shapes" of Robinson. More than 30 training systems have been described so far (Robinson, 2003), but considering that each grower adapts training and pruning procedures to his/her specific plant material and environment, it is likely that much more training systems co-exist all over the world. This highlights that the procedures that are given to maintain the apple tree architecture within a given "shape" do not suffice in themselves to fulfill the two main objectives of apple tree cultivation that are regular bearing and fruit quality. The intrinsic architectural and functioning characteristics of the cultivar-(interstock)rootstock entity are determinant on the success of the orchard. In the following, I will take the exampIe of applied research works developed in France to illustrate that the progress in apple orchard performance over the last 40 years was essentially based on the improvement of our knowledge in apple tree architecture and functioning.

Whole-tree versus branch and fruiting spur: what is the relevant scale?

The first initial step was the classification of apple "fruiting types" by [START_REF] Bernhard | Mise à fleur et alternance chez les arbres fruitiers[END_REF] French: "types de fructification") from Type I ("spur type" with upright branches and fruiting in lateral position) to Type IV (with down-swept branches and fruiting in terminal position). Working at the tree scale Lespinasse and colleagues proposed to sort apple trees according to two variables, position of branches along the trunk (more at the bottom or more towards the top) and branch orientation (upright versus horizontal) (Lespinasse, 1977(Lespinasse, , 1980;;Lespinasse and Delort, 1986). These classifications and others such as the ones of [START_REF] Forshey | Training and pruning apple and pear trees[END_REF] grouping apple cultivars according to their "growth" and "fruiting habits" are useful for their descriptive interest [START_REF] Laurens | Integration of architectural types in French programmes of ligneous fruit species genetic improvement[END_REF]Watkins and Smith, 1997) but they hide the variability of apple tree growth and fruiting within each apple "type" depending on the cultivar. This is the case for some 'Fuji' clones that belong to type IV according to Lespinasse typology but may be characterized by a strong irregular bearing pattern [START_REF] Lauri | Relationship between the early development of apple fruiting branches and the regularity of bearingan approach to the strategies of various cultivars[END_REF]. Furthermore, the apple tree is a composite entity combining the cultivar, grown for fruit, and the rootstock used for adaptation to the environmental conditions, possibly with an interstock. A consequence of this is that a same rootstock may significantly change the bearing pattern specific to a cultivar. For example, as shown by [START_REF] Barritt | Tree size, yield and biennial bearing relationships with 40 apple rootstocks and three scion cultivars[END_REF]

98

irregular bearing on a spur-type cultivar while increasing irregular bearing on a standard-or tip-bearing cultivar.

Researches developed in the apple from the beginning of the 1990's in France (Lauri, 2002;Lauri and Laurens, 2005;Lauri and Corelli-Grappadelli, 2014;Lauri, 2018) and further extended in New-Zealand [START_REF] Breen | Method of manipulating floral bud density affects fruit set responses and productivity in apple[END_REF][START_REF] Breen | Effects of environment and floral intensity on fruit set behaviour and annual flowering in apple[END_REF] show that precision management of fruiting in the orchard needs more knowledge on how the tree balances vegetative growth and fruiting. Two main aspects have been developed, the relationship between branching density and return-bloom, and the concept of autonomy of the fruiting shoot. 1) Working on a range of apple cultivars, we have shown that, generally speaking, cultivars with high branching density have a low bourse-over-bourse whereas cultivars with a low branching density have a high bourse-over-bourse (Figure 1a). Interestingly in these latter cultivars, low branching density is often related to longer side shoots and to the natural death of young spurs, termed "natural extinction" (Figure 1b). Our physiological hypothesis is that the tree balances the number of growing meristems and their organogenesis: the lower this number the higher the growing potential of the remaining shoots and the higher the organogenesis, and consequently the frequency of flower cluster, of the apical meristems [START_REF] Lauri | Genotypic differences in the axillary bud growth and fruiting pattern of apple fruiting branches over several yearsan approach to regulation of fruit bearing[END_REF]. 2) There is a relationship between the length of a shoot and the frequency of fruiting in apical position on that shoot. This relationship is curvilinear and may vary depending on the cultivar indicating that fruiting frequency is the highest within an optimal cultivar-dependent 'length window' (Figure 2). Interestingly this relationship has also been found on Mango (Normand et al., 2009). All together, these studies strongly support the concept of the physiological autonomy of the spur, at least for flower induction and fruit-set, provided it develops a minimal leaf area. However, regular bearing may also be observed on cultivars with high-alternating and desynchronized individual spurs [START_REF] Lauri | Genotypic differences in the axillary bud growth and fruiting pattern of apple fruiting branches over several yearsan approach to regulation of fruit bearing[END_REF]Lauri and Laurens, 2005).

From knowledge to applied aspectsthe path to the SALSA system

In the beginning of the 1990s the Solaxe system was proposed pointing to the interest of branch bending to improve flowering and avoiding the risk of inaccurate tree reaction to heavy pruning [START_REF] Lauri | The vertical axis and Solaxe systems in France[END_REF]. However, it quickly appears that such intensive bending leading to a high flowering in the 3 rd or 4 th year after planting could also increase the risk of a fast entrance into alternate bearing. It was then proposed to implement "artificial extinction" on the Solaxe trained trees as a spur thinning method to decrease spur, and therefore flowering, density (Lauri et al., 2004). This improved system was named "Centrifugal Training" because it included a "light well" or "light chimney" along the trunk where all shoots and spurs were removed in order to improve light penetration until the bottom of the tree. This chimney could enlarge as the tree gets older in a "centrifugal" way. Centrifugal training is still well adapted to cultivars with a clear hierarchy between the trunk and the side branches (e.g., spur-type, 'Braeburn'). Main rules for Centrifugal Training have been already published (Lauri, 2009) and won't be presented here again. However Centrifugal training is less adapted to cultivars with more polyarchic architectural framework where strong and competitive branches may develop along the trunk, typically at the bottom (e.g., 'Granny Smith')(Figure 3). For these cultivars, there is an interest to use as much as possible the vegetative growth potential of the

99

tree during the very first years in the orchard and to adapt the support system to the tree intrinsic development.

Basically, tree management follows two main objectives:

1) The canopy has to be porous to light and air to improve leaf functioning and fruit quality (fruit growth and internal quality, coloring) in all sites in the canopy. 2) Each fruiting spur, i.e., the morphological complex including the bourse bearing the fruit(s) and bourse-shoot(s) which develops at the axil of bourse leaves (Figure 4), has to be sufficiently autonomous to ensure both current fruit growth and floral initiation for the following year. A main condition for this is that, as shown before, the spur has to reach a certain size. The empirical knowledge that shoots of 15-20 leaves, ca. 10-20 cm ("brindles"; [START_REF] Lauri | The relationship between cultivar fruiting type and fruiting branch characteristics in Apple trees[END_REF], are those having the higher frequency of apical flowering, fruit-set and fruit size, is still valid whatever the cultivar [START_REF] Breen | Method of manipulating floral bud density affects fruit set responses and productivity in apple[END_REF].

Practical procedures to manage the SALSA system These rules have been recently defined in a paper (Lauri et al., 2016). They will be only summarized here. The architectural framework of a SALSA tree is made up with 2 to 5 "reiterative trunks", depending on the plant material (rootstock, cultivar) and on the environment, whose vigor is intermediate between the one of the initial trunk and the one of a side branch of the centrifugal trained tree. These reiterative trunks are typically branched at the bottom of the trunk. As a rule of thumb, the tree has a parallelepiped shape of about 3 m high and 0.8-1 m width. Planting distances are around of 3.5-4×1.2-1.5 m corresponding to ca. 2000-2400 trees ha -1 or ca. 6000 reiterative trunks ha -1 . A vertical support system, with horizontal wires at 0.7, 1.4, 2.1 and 2.8 m is commonly used. SALSA trained trees may be obtained in two ways. The first trees that inspired the SALSA concept were grown in an experimental site at INRA Montpellier in the beginning of the 1990s. They were obtained using M7 rootstock whose vigor entailed the concomitant growth of the initial trunk and of sylleptic (i.e., growing in the same year) "reiterative trunks". This growth pattern resulted at the end of the first or second year, in a 2-5 multiple-trunk tree.

In the past decade, SALSA trees were obtained in the same way keeping the welldeveloped reiterative trunks developing at the bottom of the tree instead of removing them as usually done on centrifugal trained trees. SALSA trained trees may also be obtained using bi-axis trees directly bought in nurseries with the "Bibaum" technique (Musacchi and Greene, 2017). Importantly, we are not looking at a fixed number of reiterative-trunks in a tree. Rather, this variable number of reiterative trunks depends on the vigor of each individual tree in the orchard and is a way to better balance vegetative growth and fruiting potential over the years. The concept of fruiting branch is abandoned, and consequently branch bending, which is a main operation on centrifugal trained trees, is consistently reduced and even cancelled especially when there is more than two reiterative trunks. As a result, the fruiting spurs are directly attached along the reiterative trunks and are distributed homogeneously within the tree canopy volume. Canopy porosity and autonomy of the fruiting spurs are improved through winter thinning cuts carried out on 2-year old and older branches in overcrowded and shaded sites in the canopy. Artificial extinction is usually made around pink stage to remove excess flowering spurs and to allow a convenient space around each fruiting spur over the years. Once the fruiting spur is positioned within the SALSA canopy, the objective is to maintain a good light

Comment citer ce document : Lauri, P.-E. ( 2018). Training and pruning the apple tree according to the SALSA System. Presented at 13. SENAFRUT -Seminario nacional sobre fruticultura de clima temperado , Sao Joaquim, BRA (2018-06-12 -2018-06-14).

100 interception at all sites within the canopy and to avoid competition with other fruiting spurs.

Discussion and conclusion

As shown here, the SALSA concept results from a history of constant knowledge improvement along past decades. It is based on the idea that a sustainable and economically sound training system has to take into account the biological specificities of the plant material in interaction with the pedoclimatic environment. All cultural operations, such as strong heading cuts, have to avoid unnecessary vegetative growth that would impair good fruiting. The training system also has to adapt to the socioeconomic context. Although Centrifugal training is still relevant for high fruit quality and regular fruiting, which is well adapted to the premium market, it also needs, more or less depending on the cultivar, high work load. Facing the socioeconomic issue, the SALSA system appears more adapted to mechanization. For example, some minimal mechanical pruning is now implemented at certain stages of tree framework management, keeping precision pruning for human-made operations.

Comment citer ce document :

FiguresFigure 1

 1 FiguresFigure1a) succession of bourses over consecutive years, b) natural extinction phenomenon (yellow circles) on 1-year-old branch indicating the death of these side shoots.

Figure 2 -Figure 3 -Figure 4 -

 234 Figure 2 -Relationship between the number of leaves of a shoot in year Y and the frequency of fruiting in apical position in year Y+1 for two cultivars, 'Pitchounette' and 'Chantecler' (from Lauri and Corelli-Grappadelli, 2014).

  , a vigorous rootstock may decrease

	Comment citer ce document :
	Lauri, P.-E. (2018). Training and pruning the apple tree according to the SALSA System.
	Presented at 13. SENAFRUT -Seminario nacional sobre fruticultura de clima temperado , Sao Joaquim,
	BRA (2018-06-12 -2018-06-14).

  From tree architecture to tree training -An overview of recent concepts developed in apple in France. Journal of the Korean Society for Horticultural Science, 43(6), p: 782-788; 2002. LAURI, P.É.; WILLAUME, M.; LARRIVE, G.; LESPINASSE, J.M. The concept of centrifugal training in apple aimed at optimizing the relationship between growth and fruiting. Acta Horticulturae, 636, p: 35-42; 2004. LAURI, P.É.; LAURENS, F. Architectural in apple (Malus x domestica Borkh.). Apple Tree Architecture and Cultivation -A Tree in a System. In: First international apple symposium. Yangling, Shaanxi, China. October 10-16, 2016. Acta Horticulturae, In press; 2018. LESPINASSE, J.M. La conduite du pommier. I -Types de fructification -Incidence sur la conduite de l'arbre. I.N.V.U.F.L.E.C. Paris, France; 1977. LESPINASSE, J.M. La conduite du pommier. II -L'axe vertical, la rénovation des vergers (2ème partie). Ctifl, Paris, France; 1980. LESPINASSE, J.M.; DELORT, F. Apple tree management in vertical axis: appraisal after ten years of experiments.

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Lauri, P.-E. (2018). Training and pruning the apple tree according to the SALSA System. Presented at 13. SENAFRUT -Seminario nacional sobre fruticultura de clima temperado , Sao Joaquim, BRA (2018-06-12 -2018-06-14). 101 LAURI, P.É. Comment citer ce document : Lauri, P.-E. (2018). Training and pruning the apple tree according to the SALSA System. Presented at 13. SENAFRUT -Seminario nacional sobre fruticultura de clima temperado , Sao Joaquim, BRA (2018-06-12 -2018-06-14).

In « SENAFRUT -Seminario nacional sobre fruticultura de clima temperado -12 a 14 Junho

». Agropecuaria Catarinense, 31(2) mai./ago., supplemento especial, 2018, 97-104.


Bénédicte Wenden

Mahendra Mariadassou

José Antonio Campoy

José Quero-Garcia

Elisabeth Dirlewanger

Statistical analysis of trends in sweet cherry flowering data across Europe

Keywords: Prunus avium L., Partial Least Square Regression, phenology

In temperate fruit trees, most key phenological stages are highly dependent on environmental conditions. In particular, correct timing for dormancy and flowering is essential to ensure good fruit production and quality. As a result, in a swiftly-changing environment, temperate fruit crop adaptation in many areas will be at risk in the coming decades. Global changes in environmental conditions include warmer winters and higher risks of frosts in the early spring, which may lead to a wide range of problems in relation to flower and fruit set, sun-scald, cross-pollination or novel host-pest interactions. With the final aim of better understanding the response of flowering to climatic conditions, we present a large-scale statistical analysis of flowering data for sweet cherry (Prunus avium) cv. 'Burlat' from three sites in Europe, characterized by contrasted climatic conditions. These phenological data were provided through the Ctifl, a national network of experimentation and the sweet cherry European COST Action that INRA-Bordeaux is leading. This approach allowed extracting the main trends in flowering behaviour under different temperature conditions, and highlighted key periods with significant temperature effect. These results lead to a finer approach for phenology modelling and represent the first step towards developing a predictive model for flowering in sweet cherry based on both genomic and phenology data.

INTRODUCTION

Climate change is now well documented (IPCC13) and its effects on plant phenology and more specifically on changes in the timing of phenophases in fruit trees have been observed for decades [START_REF] Miller-Rushing | Impact of global warming on a group of related species and their hybrids: cherry tree (Rosaceae) flowering at Mt. Takao, Japan[END_REF]. Current estimates of advancing phenology, including earlier flowering, are 1.9-5 days per °C [START_REF] Menzel | European phenological response to climate change matches the warming pattern[END_REF][START_REF] Wolkovich | Warming experiments underpredict plant phenological responses to climate change[END_REF]. In other locations, however, spring phases have been observed to occur later [START_REF] Atkinson | Declining chilling and its impact on temperate perennial crops[END_REF][START_REF] Elloumi | Chilling accumulation effects on performance of pistachio trees cv. Mateur in dry and warm area climate[END_REF], which might be the consequence of warmer autumn and winter temperatures affecting the chill requirements during dormancy.

Such alterations might threaten temperate fruit production and thus worldwide economy. As a consequence, a better understanding of temperature response and sensitivity of fruit trees phenology is essential to foresee the changes to come and to have the ability to overcome the effects of climate change on fruit tree productivity. Partial least squares (PLS) regression is a powerful statistical tool to detect distinct temperature phases as demonstrated on walnuts in California [START_REF] Luedeling | Partial Least Squares Regression for analyzing walnut phenology in California[END_REF], cherry trees in Germany [START_REF] Luedeling | chillR: statistical methods for phenology analysis in temperate fruit trees[END_REF] and chestnuts in China [START_REF] Guo | Response of chestnut phenology in China to climate variation and change[END_REF]. In this study, we used the PLS approach to analyse the temperature sensitivity of sweet cherry in different European locations and compare the flowering response between different latitudes. Results provide key information to identify trends in temperature response and to develop more adjusted phenology models.

MATERIALS AND METHODS

Data for sweet cherry cv. 'Burlat' flowering time were used in this study. They were collected from three different locations. 180 flowering dates were recorded for 'Burlat' at the experimental orchard of INRA Bordeaux research centre located in Toulenne near Bordeaux, France (44.58°N, -0.28°E) for 32 years of observation from 1981 to 2012. For the CTIFL orchard located in Bellegarde,France (43.76°N,4.46°E), 139 data points were collected from 1982 to 2012. 24 flowering dates were also collected at Klein-Altendorf, Germany, at the research station of the University of Bonn (50. 40°N, 6.99°E) between 1978 and 2008. Short gaps in daily temperature data were closed by linear interpolation, as described in [START_REF] Luedeling | Identification of chilling and heat requirements of cherry trees-a statistical approach[END_REF]. As proposed in [START_REF] Luedeling | Partial Least Squares Regression for analyzing walnut phenology in California[END_REF], an 11-day running mean of daily temperatures was constructed, which smoothes the temperature curve and eases the interpretation of PLS regression (PLSR) outputs (Figure 1).

We used the R package ChillR [START_REF] Luedeling | chillR: statistical methods for phenology analysis in temperate fruit trees[END_REF] which includes all procedures described in the different PLSR studies on fruit trees [START_REF] Luedeling | Partial Least Squares Regression for analyzing walnut phenology in California[END_REF][START_REF] Guo | Response of chestnut phenology in China to climate variation and change[END_REF][START_REF] Luedeling | Identification of chilling and heat requirements of cherry trees-a statistical approach[END_REF]. These functions were used to analyze the response of sweet cherry cv. 'Burlat' flowering dates to variation in mean daily temperatures during the 365 days of the year, from June to July.

The major outputs of the PLS analysis are the variable importance in the projection (VIP) and standardized model coefficients. Periods with VIP scores higher than the threshold set to 0.8 as previously described [START_REF] Luedeling | Partial Least Squares Regression for analyzing walnut phenology in California[END_REF], and high absolute values of model coefficients represent the relevant phases when temperatures have a significant effect on flowering dates in 'Burlat'. Positive coefficients indicate that warm temperatures during the given period delay flowering, or that cold temperatures hasten flowering, whereas negative coefficients reveal that warmer temperatures lead to earlier flowering dates.

RESULTS AND DISCUSSION

Long time series for the sweet cherry cv. 'Burlat' in three contrasted European locations confirmed that flowering periods were significantly delayed in northern sites such as Bonn (Figure2), where flowering began between the 3 rd of April and the 3 rd of May, with an average around the 17 th of April, while beginning of flowering was recorded as early as the 11 th of March in both Bordeaux and Bellegarde, and average dates of flowering on the 29 th and 26 th of March respectively.

PLSR models explaining the dependence of flowering dates to mean daily temperatures were well fitted to the data with high R² values (Figure 3; R² are 82.72%, 75.85% and 95.96% for Bordeaux, Bellegarde and Bonn, respectively), which confirmed a high correlation between flowering time and temperature conditions.

PLSR coefficients give information on the strength and the cold/warm effect of temperatures on flowering time. However, only the periods characterized by a VIP threshold value greater than 0.8, are considered to have a significant effect of the temperatures on flowering time. Although the coefficients varied greatly over the 365 days, two main distinct groups of significant coefficients were identified for the three locations (Figure 4).

Endodormancy and cold responses

The timing of flowering is directly dependent on a proper dormancy release and bud growth, which are controlled by environmental factors, mainly temperature in sweet cherry. Winter bud dormancy can be divided into two main stages: endodormancy followed by ecodormancy [START_REF] Lang | Endo-, para-, and ecodormancy: physiological terminology and classification for dormancy research[END_REF]. During endodormancy, even under optimal climatic conditions, the plant will not grow and will need sufficient accumulation of cold temperatures, namely chilling requirements, to trigger dormancy release. Subsequently, ecodormancy starts when buds are able to grow but low temperatures inhibit bud break. Flowering occurs when temperatures increase and heat requirements for bud growth are satisfied. Consequently, for endodormancy, we expected to clearly identify chilling periods defined by positive coefficients with highly significant VIP in the PLSR results. However, there was no relevant pattern in autumn and winter temperatures for the three experimental sites (Figure 4). In Bonn, in particular, only a short peak at the beginning of November indicated a response to cold temperatures. This lack of significant cold effect validate the absence of limitation in terms of cold temperature, meaning that the low winter temperatures in Bonn (Figure 1) appear to be sufficient to fulfill 'Burlat' chill requirements every year. This observation is also true for Bordeaux and Bellegarde with no long period of significant cold response except for two distinct peaks with VIP values greater than 0.8 (Figure 4). Both peaks are in autumn but the limiting cold phase is at the end of September in Bordeaux while around mid-November in Bellegarde. These contrasts might be the consequence of the different climatic zones since Bordeaux is under the influence of an oceanic climate, with mild and wet autumns while Bellegarde is characterized by a dryer environment. Global environmental conditions might differently affect physiological processes involved in the dormancy onset in autumn.

Ecodormancy and response to warm temperatures

Around February and March, negative coefficients with high absolute values and VIP values exceeding 0.8 corresponded to a critical phase when warm temperatures were needed to hasten flowering in sweet cherry cv. 'Burlat' in the three locations. This is in agreement with the heat requirement period, related to the ecodormancy. Highly significant results confirm that climatic conditions mostly affect flowering time through temperatures at the end of winter. This is even further validated by the differences observed between experimental sites. The critical heat requirement period in Bonn, that lasts from mid-January to mid-May is longer than in Bordeaux and Bellegarde (Figure 4). Lower spring temperatures in Bonn (Figure 1) could explain these differences since buds might need a longer period to fulfill the heat requirement necessary to flower. These results are consistent with the forcing phase previously identified for the sweet cherry cv. 'Schneiders späte Knorpelkirsche' [START_REF] Luedeling | Identification of chilling and heat requirements of cherry trees-a statistical approach[END_REF].

Response to summer warm temperatures

PLSR results for all three experimental sites reveal a significant peak with positive coefficients in July (Figure 4). Rather than a positive cold effect on flowering time, it is likely that during this period, warm if not hot temperatures negatively affect dormancy onset. [START_REF] Jonkers | Bud dormancy of apple and pear in relation to the temperature during the growth period[END_REF] demonstrated a tendency for dormancy to deepen when the temperatures at which the apple and pear buds had been formed were too high. Similar results were found in black alder (Alnus glutinosa [L.] Moench; [START_REF] Heide | High autumn temperature delays spring bud burst in boreal trees, counterbalancing the effect of climatic warming[END_REF] and Acer platanoides [START_REF] Westergaard | Autumn temperature affects the induction of dormancy in first-year seedlings of Acer platanoides L[END_REF].

CONCLUSION

In the context of climate change and its effects on flowering phenology, it is essential to better understand the phenological response to temperature. PLS regressions have been used on phenological data in trees for several years and have proven their value to effectively delineate key periods with significant temperature effect. We applied these analyses to sweet cherry data on the control cultivar 'Burlat' from three experimental sites with contrasted climate conditions. For the three locations, the forcing period, characterized by a response to warm temperatures, was highly correlated to variations in flowering time, thus validating the key role of spring temperatures in the control of flowering time. Further comparisons of responses for genotypes with contrasted chill and heat requirements should provide useful information on the genetic determinism for heat requirements. Results were less clear for chill requirements as no marked pattern could be observed in the three locations. Similar analyses of flowering dates collected from areas with milder winter temperatures, such as the South of Spain, might reveal more limiting cold responses. Sweet cherry is a fruit tree species with a low turnover in terms of the main varieties and thus long time series for control varieties such as 'Burlat' are available from multiple locations. In particular, data obtained in the context of the sweet cherry European COST Action that INRA-Bordeaux is leading should allow collecting and analysing more flowering data from sites with contrasted climatic conditions. Knowledge acquired on the main flowering responses to temperature provide primordial information to develop more accurate phenological and mechanistic modelling tools. Each point corresponds to an observed flowering date, compared to the predicted value using the PLS regression based on the temperature data for the year of observation. The black line is indicated as a comparison (y = x). To ease analysis between the three sites, all PLS regressions were calculated with 3 components, which define the dimensions of the regressions, and R² is an indication of the regression's accuracy.

FiguresFigure 1 .

 1 Figures

  Figure 1. 11-days averaged temperatures used for analyses. Line is overall average of running mean over 11 days of all years temperatures, and ribbons correspond to running mean over 11 days for the whole period (1980 -2012 for Bordeaux, 1981 -2012 for Bellegarde and 1978 -2008 for Bonn).

Figure 2 .

 2 Figure 2. Flowering data used for the study from Bonn (Germany), Bordeaux (France) and Bellegarde (France). Dates are of beginning of flowering for the sweet cherry cv. 'Burlat'.

Figure 3 .

 3 Figure 3. Data for beginning of flowering collected in Bordeaux, Bellegarde and Bonn compared to flowering values predicted by the PLS regression based on daily temperature data.Each point corresponds to an observed flowering date, compared to the predicted value using the PLS regression based on the temperature data for the year of observation. The black line is indicated as a comparison (y = x). To ease analysis between the three sites, all PLS regressions were calculated with 3 components, which define the dimensions of the regressions, and R² is an indication of the regression's accuracy.

Figure 4 .

 4 Figure 4. Results for the model coefficients of Partial Least Squares (PLS) regression for beginning of flowering dates in sweet cherry cv. 'Burlat', in correlation with mean daily temperature, averaged over 11 days, from July 1 st (Year N-1) to June 30 th (Year N). Grey areas indicate that the Variable Importance on Projection (VIP) is greater than 0.8, corresponding to the threshold chosen for significant effect of the temperatures on flowering time. Positive coefficients indicate that warm temperatures during the given period delay flowering, or that cold temperatures hasten flowering, whereas negative coefficients reveal that warmer

ACKNOWLEDGEMENTS

The authors would like to thank the Ctifl for Bellegarde data, and Michael Blanke for providing data from the University of Bonn.


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Acknowledgements

Alexandre Villers, Pierre de Bouët du Portal, Jean-Baptiste Perrotin, Gildas Lemonnier, Romain Bonnet, Charline Parenteau, Colette Trouvet.