Mathieu Besson
email: mathieu.besson@wur.nl

Joël Aubin

J A M Van Arendonk

H Komen

M Poelman

Edwige Quillet

Marc Vandeputte

I J M De Boer

Environmental impacts of genetic improvement in growth rate and feed conversion in fish farming under density and nitrogen limitation

Keywords: genetic improvement, life cycle assessment, fish farming, feed conversion ratio, thermal growth coefficient

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

Introduction

Fish farming can have many environmental impacts, such as emission of pollutants [START_REF] Consultants | Management of environmental impacts of marine aquaculture in Europe[END_REF], use of natural resources [START_REF] Naylor | Effect of aquaculture on world fish supplies[END_REF] and eutrophication [START_REF] Folke | The Costs of Eutrophication from Salmon Farming: Implications for Policy[END_REF]. Using life cycle assessment (LCA), feed production and farm operations were shown to be the main stages contributing to environmental impacts of fish farming [START_REF] Aubin | Characterisation of the environmental impact of a turbot (Scophthalmus maximus) re-circulating production system using Life Cycle Assessment[END_REF][START_REF] Pelletier | Not all salmon are created equal: life cycle assessment (LCA) of global salmon farming systems[END_REF]. The environmental impacts of alternative feed compositions or alternative rearing systems were, therefore, investigated. Some studies explored the potential of plant-based diets or organic feed to reduce impacts [START_REF] Boissy | Environmental impacts of plantbased salmonid diets at feed and farm scales[END_REF][START_REF] Papatryphon | Environmental impact assessment of salmonid feeds using life cycle assessment (LCA)[END_REF][START_REF] Pelletier | Feeding farmed salmon: Is organic better?[END_REF], whereas other studies explored the potential of a recirculating aquaculture system to reduce impacts [START_REF] Aubin | Assessment of the environmental impact of carrnivorous finfish production systems using life cycle assessment[END_REF]d'Orbcastel et al., 2009). These studies, however, clarified existing tradeoffs between different environmental impacts when changing feed composition or rearing conditions. It appeared difficult, therefore, to decrease all environmental impacts of fish farming. Consequently, there is a need to explore new ways of reducing environmental impacts of fish farming systems.

Genetic improvement, realised through selective breeding programs, is expected to change environmental impacts of livestock production by increasing productivity or improving production efficiency [START_REF] Wall | Developing breeding schemes to assist mitigation of greenhouse gas emissions[END_REF]. The magnitude and the direction of this change in fish farming is, however, not known. [START_REF] Wall | Developing breeding schemes to assist mitigation of greenhouse gas emissions[END_REF] suggested to model emissions at farm level in order to determine the environmental consequences (or environmental values) of a change in traits in order to evaluate the capacity of each trait to decrease environmental impacts. This approach is similar to the framework used to calculate the economic value of economic important traits [START_REF] Groen | Derivation of economic values in cattle breeding: A model at farm level[END_REF].

We explored, therefore, the capacity of a genetic improvement in thermal growth coefficient (TGC) and in feed conversion ratio (FCR) to decrease environmental impacts of 1 t of African catfish produced in a recirculating aquaculture system (RAS). We chose to investigate TGC and FCR for two reasons. First, fish breeders consider TGC as the main trait to improve in order to increase farm profitability. FCR is also an important trait because the majority of fish reared in intensive systems are carnivorous species, which require high amounts of protein and lipid in their diet, making fish diets expensive. Second, we investigated TGC and FCR because genetic improvement of these two traits is susceptible to affect environmental impacts through higher productivity and better production efficiency. A RAS was studied for three reasons: (1) it is a highly controlled system, which is easy to model, ( 2) it has a potential as a development model for fish farming because of better waste and water management and (3) the existence of technical limiting factors for production level is well known. In practice, limiting factor can influenced farm management and breeding goals have to be adapted for such limiting factor [START_REF] Gibson | Selection on the Major Components of Milk: Alternative Methods of Deriving Economic Weights[END_REF][START_REF] Groen | Economic values in cattle breeding. II. Influences of production circumstances in situations with output limitations[END_REF]. The factors limiting production are the maximum nitrogen treatment capacity of the bio-filter and the maximum fish density in rearing tanks. Changing TGC and FCR is expected to change the limiting factor and the limiting factor might influence environmental values of both traits.

The capacity of a genetic improvement in TGC and in FCR to decrease environmental impacts was investigated through environmental values calculated using a bioeconomic model combined with a LCA.

Methods

System boundaries

A cradle-to-farm-gate analysis was applied including three stages: feed production, farm operation and waste water treatment (figure 1). The farm studied was a typical commercial Dutch farm producing 500t of African catfish per year in an indoor RAS. Fish processing, hatchery operations as well as sludge agricultural utilization were not taking into account.

Life Cycle Inventory

Feed production. Crops-derived ingredients were coming from Brazil and France. Fish-derived ingredients were coming from Peruvian fish milling industry and from by-products of Norwegian fish milling industry. In case of a process yielding multiple products, such as soy bean oil and meal, the environmental impact of the process was allocated to the multiple outputs based on their relative economic value (i.e. economic allocation). The transport of feed ingredients to feed manufacture in France was assumed to be by transoceanic ship and by lorry (>32t). The resources required to produce the feed were found in the literature [START_REF] Pelletier | Not all salmon are created equal: life cycle assessment (LCA) of global salmon farming systems[END_REF]. The transport of pellets from France to the fish farm in Eindhoven were assumed to be by lorry (>32t).

Farm operations. We investigated a farm producing 500t of African catfish per year in an indoor RAS. In this indoor system, water was thermo-regulated around 27 °C through regulating the ambient air. The RAS was composed of four main parts. (1) Rearing tanks growing fish from 13g to 1300g. The maximum density in the tank was 230 kg/m 3 , which was one of the limiting factors. (2) A mechanical filter which removed solid waste. (3) A bio-filter processing nitrification with a nitrification rate of 100%. The nitrification capacity of the biofilter was limited to 40 kg of dissolved NH 3 -N per day, which was the second limiting factor. (4) A denitrification reactor using manure of the fish as C-source with a denitrification rate of 80%. The amount of equipment used (i.e. pump, tanks), the surface of building needed and the consumption of energy at farm level were considered fixed. The emission of nutrients, the consumption of feed and the production of fish were, however, dependent on growth rate and feed efficiency, which could affect the level of production. In this study, growth rate was expressed through Thermal Growth Coefficient (TGC) [START_REF] Dumas | Evidence of three growth stanzas in rainbow trout (Oncorhynchus mykiss) across life stages and adaptation of the thermal-unit growth coefficient[END_REF] and feed conversion was expressed through Feed Conversion Ratio (FCR). A bioeconomic model was developed to calculate annual fish production, annual feed consumption at farm level and annual emissions of nutrients after waste water treatment according to TGC and FCR values. The individual emission of nitrogen (N), phosphorus (P) and chemical oxygen demand (COD) were calculated using a mass-balance approach [START_REF] Cho | Nutritional energetics in fish: energy and protein utilization in rainbow trout (Salmo gairdneri)[END_REF]. The proportion of dissolved and solid fractions emitted by the fish was estimated from the digestibility of feed components. Retention capacity of the drum filter, nitrification capacity of the bio-filter and denitrification capacity of the denitrification reactor were used to calculate emission of nutrients in effluent water and in sludge.

Waste water treatment. Effluent water, highly concentrated in nutrients, coming from the fish farm was disposed in a waste water treatment plant. We considered a typical waste water treatment plant running in Europe, including three treatment stages: mechanical treatment, biological treatment, chemical treatment. It also included sludge digestion via fermentation. The treatment capacity of the waste water treatment was used in order to evaluate the amount of nitrogen, COD and phosphorus released into water. 28% of the COD, 75% of the nitrogen and 52% of the phosphorus coming from the farm were, therefore, released into water. Life cycle inventory of water treatment as well as the secondary data were extracted from ecoinvent v2.2 database (Swiss Centre for Life Cycle Inventories, 2010).

Life cycle impact assessment

The environmental impact of the analysed system was related to a functional unit of 1 t of fish. Four environmental categories were investigated: eutrophication, acidification, climate change and cumulative energy demand (CML2 method and Simapro software) and expressed per t of fish. Land use change, however, was not taking into account. The analysed system was divided in the following sub-systems: (1) feeds consumption, including processing, production of feed ingredients and transportation; (2) N, P and COD emission from biological transformation of the feed at farm level and after waste water treatment; (3) effluent water treatment at wastewater treatment plant; (4) equipment use, including its manufacturing, transport and use; (5) buildings use, including material production and transportation, construction and use; (6) energy consumption (electricity, gasoline, heat gas), including production, transportation and use. Environmental impacts of each of these subsystems were calculated separately.

Then, the results of these environmental impacts were multiplied by the quantity required at farm level calculated from the bioeconomic model. Feed consumption and nutrients emission are variable at farm level depending on TGC and FCR values. Environmental impacts at farm level of the two sub-systems feed production and nutrients emission are, therefore, variable. In contrary, use of building, use of materials and energy consumption at farm level are considered fixed. Environmental impacts at farm level of those three subsystems are, therefore, fixed.

Environmental impacts at farm level were divided by annual fish production to expressed environmental impacts per ton of fish. Environmental impacts were calculated for four TGC values corresponding to three generation of selection from the current population mean with a percentage of genetic improvement of 6.8 as calculated by [START_REF] Sae-Lim | Defining desired genetic gains for rainbow trout breeding objective using analytic hierarchy process[END_REF]. TGC values tested were: 8.33, 8.93, 9.53 and 10.11. For FCR, a wide range of values was tested from current population mean FCR = 0.81, which is a value commonly observed in African catfish farms, to FCR = 0.64.

Environmental values

Proceedings of the 9th International Conference on Life Cycle Assessment in the Agri-Food Sector

The environmental values (ENV) of the four impact categories (eutrophication = E_V, acidification = A_V, climate change = CC_V and cumulative energy demand = CED_V) of a trait t {FCR,TGC} express the environmental impact, in percentage, of a unit change in one trait while keeping the other trait constant. ENV of both traits were calculated in three steps:

1) Calculate environmental impact per ton of fish (i.e. A µt ) using current generation means for trait t (µ t ). The current generation mean (or reference scenario) is 8.33 for TGC and 0.81 for FCR. The TGC of the current population is the result of 119 days required for the fish to grow from 13 to 1300 g with a daily average temperature of 27°C. FCR was set at 0.81 in order to balance cost with revenue when TGC = 8.33.

2) The mean of trait t is increased by Δ t while keeping the mean of the other traits constant. Δ TGC = µ TGC × 6.8% and Δ FCR = µ FCR × -7.6% as calculated by [START_REF] Sae-Lim | Defining desired genetic gains for rainbow trout breeding objective using analytic hierarchy process[END_REF]. The next generation means are, for instance, TGC = 8.93; FCR = 0.81 and TGC = 8.33; FCR = 0.75. The model was run a second time to calculate environmental impacts.

3) ENV were calculated for trait t as:

A_V t = (𝐀 µ𝐭+ 𝚫𝐭 -𝐀 µ𝐭 ) 𝐀 µ𝐭
Eq. 1

Environmental values were calculated for 2 situations where the combination of TGC and FCR was: TGC = 8.93; FCR = 0.81 and TGC = 8.93; FCR = 0.69. These two situations were chosen because the limiting factor was different and also because one generation of selection in both trait did not change the limiting factor.

Results

Table 1. Percentage of contribution of the different sub-systems to the four impact categories in the reference scenario where TGC = 8.33 and FCR = 0.81. Table 1 shows the contribution of each different sub-system to the four impact categories in the reference scenario where TGC = 8.33 and FCR = 0.81. The impact of nutrient emission on acidification is 0% because effluent water are directly directed to effluent water treatment plant, which is responsible of 0.3% of the total acidification. In table 1, we can see that the main contributors to the four impact categories are different. For acidification, climate change and cumulative energy demand the main contributor is feed production (respectively 57.5%, 72.3%, 68.7%). The second contributor to those three impact categories are fixed subsystems at farm level, building use for acidification (27.3%) and energy use for climate change (21.4%) and for cumulative energy demand (23.6%). In opposite, the two main contributors of eutrophication are nutrients emission (62.2%) and feed production (33.6%).

Feed production

Nutrients emission

Effluent

Figure 1 shows the results of environmental impacts for the four impact categories investigated. First of all, the combination of TGC and FCR defines whether dissolved NH 3 -N or density is the limiting factor. Environmental response to genetic improvement in TGC and FCR are different among impact categories according to the factor limiting production. These differences are because TGC and FCR have different effects on production. Two effects are capable of decreasing environmental impacts per unit of fish produced: increasing productivity (annual fish production) and increasing production efficiency (feed consumed per ton of fish). Increasing productivity, while keeping the same production efficiency, dilutes fixed environmental impacts at farm level, such as building use, over more fish produced. Increasing production efficiency, while keeping the same productivity, decreases the amount of feed required to produce one ton of fish and decreases the amount of nutrients emitted per ton of fish which decreases environmental impacts. Depending on the limiting factor, improving TGC or FCR changes either productivity, production efficiency or both.

Dissolved NH 3 -N as limiting factor. For instance, when TGC is 8.33 and when FCR decreases from 0.81 to 0.75, the factor limiting production is dissolved NH 3 -N. In this situation, improving FCR decreases environmental impacts of the four impact categories while improving TGC does not influence it.

Improving FCR decreases environmental impacts because individual daily excretion of nutrients decreases, which allows to stock more fish per batch to reach dissolved NH 3 -N limitation. Consequently, when FCR is improved, both productivity and production efficiency increases, which decreases environmental impacts per ton of fish produced (figure 1).

Additionally, improving TGC does not influence environmental impacts because individual daily excretion of nutrients increases, which constrains to stock fewer fish per batch. This lower number of fish per batch is offset by rearing more batches per year. Consequently, when TGC is improved, productivity and production efficiency remain constant, which keep the environmental impacts at the same level (figure 1).

Density as limiting factor. For instance, when TGC is 8.33 and FCR is lower than 0.75, the factor limiting production is the density of fish in rearing tank. In this situation, the number of fish harvested per batch if fixed. Figure 1. Environmental impacts calculated per t of fish for four impact categories as a function of FCR for four values of TGC.

Improving FCR, therefore, decreases feed consumption per ton of fish and decreases nutrients emission per ton of fish. Consequently, when FCR is improved, only production efficiency increases, which decreases environmental impacts per ton of fish produced but with a lower rate than in dissolved NH 3 -N limitation situation (figure 1).

Additionally, improving TGC increases the number of batch harvested per year, which increases annual production of fish. Consequently, when TGC is improved, only productivity increases, which decreases environmental impacts (figure 1).

Different environmental responses to genetic improvement in TGC can, however, be observed when density is the limiting factor. Improving TGC decreases acidification, climate change and cumulative energy demand while the response in eutrophication is smaller. The response to genetic improvement in TGC for eutrophication is smaller because improving TGC increases productivity only, which dilute fixed environmental impacts at farm level, such as use of material, over more fish produced. The contribution of fixed environmental impacts to eutrophication is, however, smaller than the contribution of fixed environmental impacts to acidification, climate change and cumulative energy demand (table 1).

Table 2 gives ENV TGC and ENV FCR of the four impacts categories investigated in both situations, when dissolved NH 3 -N is the limiting factor and when density is the limiting factor.

When dissolved NH 3 -N is the limiting factor, all ENV TGC are null as TGC do not alter environmental impacts. These results can be explained by the fact that increasing TGC does not influence productivity nor production efficiency. The ENV FCR are, however, negative because when dissolved NH 3 -N is the limiting factor, increasing FCR increasing productivity and production efficiency, which decreases environmental impacts.

When density is the limiting factor, ENV TGC have a negative value because increasing TGC increases productivity which dilutes fixed environmental impacts at farm level over more fish produced. E_V TGC is, however, very close to zero because eutrophication is relatively insensitive to fixed environmental impacts at farm level compare to other impacts categories (table 1). The ENV FCR are also negative but CED_V FCR , E_V FCR and A_V FCR are lower when density is the limiting factor than when dissolved NH 3 -N is the limiting factor because increasing FCR when density is the limiting factor increases production efficiency only. Moreover, E_V FCR is higher than CED_V FCR , E_V FCR and A_V FCR because the two main contributors of eutrophication are nutrients emission and feed production, which are sensitive to production efficiency. Table 2. Environmental values of TGC (ENV TGC ) and FCR (ENV FCR ) of four impacts categories (eutrophication = E_V, acidification = A_V, climate change = CC_V and cumulative energy demand = CED_V) calculated when dissolved NH 3 -N is the limiting factor (TGC = 8.93, FCR = 0.81) and when density is the limiting factor (TGC = 8.93, FCR = 0.64).

Discussion

The results of our simulations show that environmental response to genetic improvement in TGC and FCR is different when density and when NH 3 -N are the limiting factors. When NH 3 -N is the limiting factor, only genetic improvement in FCR decreases environmental impacts because it increases productivity and production efficiency. On the contrary, when density is the limiting factor both genetic improvement in TGC and FCR decreases environmental impacts because improvement in FCR increases production efficiency and improvement in TGC increases productivity. When density is the limiting factor, however, environmental response to genetic improvement in TGC and FCR are different across impact categories depending on their main contributors. Eutrophication is mainly driven by feed production and nutrients emission, which are influenced by production efficiency. Conversely, other impact categories are mainly driven by feed production and building use or energy use, which are influenced by productivity.

d [START_REF] Orbcastel | Towards environmentally sustainable aquaculture: Comparison between two trout farming systems using life cycle assessment[END_REF] investigated the impact of a RAS producing rainbow trout using two different values of FCR, 1.1 and 0.8. This range corresponds to 27.3% of improvement, or to 3.6 generations of selection when percentage of improvement in FCR is 7.6%. They found, therefore, that decreasing FCR by 7.6% decreased acidification by 5.8%, eutrophication by 4.3%, climate change by 6% and cumulative energy demand by 2.4%. It is, however, difficult to use the results from d [START_REF] Orbcastel | Towards environmentally sustainable aquaculture: Comparison between two trout farming systems using life cycle assessment[END_REF] as a comparison basis for our results because they didn't take into account changes that could occur in farm management when FCR changes. d [START_REF] Orbcastel | Towards environmentally sustainable aquaculture: Comparison between two trout farming systems using life cycle assessment[END_REF] considered that improvement in FCR increased production efficiency but, using dynamic modelling of the relationship between genetic improvement and farm management, our results show that improving FCR can also increase productivity when NH 3 -N is the limiting factor. Changes in FCR can also lead to changes in limiting factor.

The results presented here confirm that FCR would be the major trait to include in the breeding goals for decreasing environmental impacts. In fish breeding, however, FCR is a difficult trait to improve as it is difficult to measure individual feed intake. FCR is expected to be correlated to TGC, however, studies diverge on this subject. In rainbow trout, [START_REF] Kause | Feed efficiency of rainbow trout can be improved through selection: Different genetic potential on alternative diets[END_REF] predicted that selection only for daily gain increases daily gain by 17.6% per generation and simultaneously increases feed efficiency (1/FCR) by 8.4%. In parallel, some other studies in salmonids did not observe any correlation between growth rate and feed efficiency and showed that genetic gain in growth is due to higher feed intake, while feed efficiency remain unchanged [START_REF] Mambrini | Selection for growth in brown trout increases feed intake capacity without affecting maintenance and growth requirements[END_REF][START_REF] Sanchez | Selection for growth of brown trout ( Salmo trutta) affects feed intake but not feed efficiency[END_REF].

Conclusion

Environmental values could be included in breeding programs in a similar way as economic values and could help fish breeders to design effective breeding programs in terms of environmental consideration by putting more emphasis on the relevant traits, in a specific limiting factor situation. Our results have, therefore, important implications for fish breeders who may need to alter their breeding objectives depending on what is limiting production on the farms of their customers. We show that environmental values of FCR and TGC are dependent on the factor limiting production. Improvement of feed efficiency always improves environmental impacts in any situation. However, selecting for increased growth rate is only relevant in situations where nitrogen emissions are not limiting production. Those results are important for the future development of selective breeding programs in fish farming taking into account environmental objectives.

Figure 1 .

 1 Figure 1. Diagram of the system studied.

Proceedings of the 9th International Conference on Life Cycle Assessment in the Agri-Food Sector

	ENV	Trait	TGC = 8.93 FCR = 0.81 Limiting factor = dissolved NH3-N	TGC = 8.93 FCR = 0.69 Limiting factor = density
	A_V	FCR TGC	-10.3% 0%	-4.3% -2.7%
	E_V	FCR TGC	-11.8% 0%	-13.4% -0.3%
	CC_V	FCR TGC	-9.3% 0%	-5.5% -1.8%
	CED_V	FCR TGC	-9.6% 0%	-5.2% -2.0%

Proceedings of the 9th International Conference on Life Cycle Assessment in the Agri-Food Sector


Marie Chave
email: marie.chave@antilles.inra.fr

Valérie Angeon
email: valerie.angeon@antilles.inra.fr

Maurice Mahieu

Nathalie Mandonnet

Agroecological engineering of crop and livestock health. Case studies in the French West Indies

Keywords: biocontrol, integrated control of health, crop, livestock, tropical environment

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.

the numerous undesirable side effects. The solutions derived from the paradigm of eradication of pathogens have shown their limits. The considerable variability and adaptability among pathogen strains results in emerging resistance and replacement of susceptible with resistant strains in many pathogen species. When extremely aggressive emergent pests or resistance breakage occur, no more food production is possible. Novel approaches and methods are needed to encourage diseases management. The development of an ecological engineering approach that combines nested multi-scale levers is necessary. Mobilizing ecological interactions, largely unexplored, is a key issue to design new health management options. Thus, it fits in with the "smart" (FAO 2011) approach required for the agroecological transition. We analyze how to enhance ecological interactions for crop and animal health. Based on a conceptual grid [START_REF] Chave | Ingénierie agroécologique et santé des cultures : conception de systèmes de culture recourant aux plantes mycorhizotrophes pour la bioprotection de la tomate contre le flétrissement bactérien[END_REF]Chave and Angeon, 2014), we show that consistent strategies for mobilizing ecological interactions rely on a complex activation of key levers at different spatial scales. We apply this analytical grid to two case studies relating to plant and animal health in the French West Indies. These areas face strong challenging diseases for which most of the conventional solutions requiring the use of chemicals barely function. By applying an analytical grid that constitutes the ABCs of ecological regulation to crop and livestock health, we point out strategic choices lead in the French West Indies to cope with diseases. We show how new multi-scale approaches are implemented to identify solutions to enhance ecological interactions for crop and livestock health.

Method

The suggested conceptual framework constitutes the ABCs of biological regulation (Chave and Angeon, 2014). It takes advantage of ecological interactions by increasing ecosystem services and reducing disservices. It originally derives from crop health management [START_REF] Chave | Bacterial wilt resistance in tomato, pepper, and eggplant: genetic resources respond to diverse strains in the Ralstonia solanacearum species complex[END_REF]), but it can also aid the comprehension of sustainable animal production (Botreau et al. 2014) as, in both animal and crop systems, the use of biodiversity is the cornerstone of agroecological principles. The grid tackles the key levers used to engineer beneficial or damaging interactions. It defines 6 main levers that appear to be key determinants of agroecological innovative practices.

Results

Two case studies are considered to discuss agroecological protection strategies. The first one deals with the biocontrol of plants diseases (i.e. the soil bacteria Ralstonia solanacearum) in the French West Indies. R. solanacearum is one of the world's most important phytopathogenic bacteria due to its lethality, persistence in soil, wide host range and broad geographic distribution. A new pathogenic variant of R. solanacearum was detected in 1999, first in Martinique [START_REF] Wicker | Epidemiological evidence for the emergence of a new pathogenic variant of Ralstonia solanacearum in Martinique (French West Indies)[END_REF]. No resistance was effective for controlling this extremely aggressive new strain [START_REF] Chave | Bacterial wilt resistance in tomato, pepper, and eggplant: genetic resources respond to diverse strains in the Ralstonia solanacearum species complex[END_REF]. As the use of methyl bromide has been banned for years, one promising approach was to encourage agroecological management. Multifunctional practices harnessing rhizosphere bioprotection were explored to overcome the limitations of conventional protection strategies. Integrating interacting species with strong ability to recruit beneficial microorganisms (i.e. arbuscular mycorrhizal fungi) or secrete toxic compounds in mixed cropping systems is a key issue. Harnessing these natural processes may also avoid the problem of pathogens quickly evolving resistance. Based on functional biodiversity management, numerous agroecological practices, such soil amendments (with compost or plant extract) and mixed cropping systems, also help to control soil diseases [START_REF] Chave | Ingénierie agroécologique et santé des cultures : conception de systèmes de culture recourant aux plantes mycorhizotrophes pour la bioprotection de la tomate contre le flétrissement bactérien[END_REF]. Breeding crop cultivars which capitalize on plantmicrobiome interactions or associating plants and biocontrol agents early in their life offers innovative ways to contribute to disease-suppressive agroecosystems design [START_REF] Chave | Bacterial wilt resistance in tomato, pepper, and eggplant: genetic resources respond to diverse strains in the Ralstonia solanacearum species complex[END_REF]. Beyond the practices themselves, it is their combination that is likely to produce synergies for an integrated bioprotection against soil-borne pests. The second case study explores management strategies for integrated parasitic control. Gastrointestinal parasitism is probably the most widespread disease in grazing small ruminants in the tropics. Half a century of suppressive drenching has resulted sooner or later in the selection of parasite strains resisting to the chemicals employed, and in increasing production losses. Some small ruminant farmers are already facing multiresistant parasites with no more chemical solution [START_REF] Mahieu | Fifteen years later, anthelmintic resistances have dramatically spread over goat farms in Guadeloupe[END_REF]. Integrated management must be thought at the whole small ruminant farming system, as a combination of strengthening the animal immune responses (through choice of parasite resistant breed, selection, suitable nutrition, vaccine policy when available), grazing management according to the parasite epidemiology, herbivore species associations, use of forage or plant with anthelmintic properties, and finally management of the residual efficacy of chemical anthelmintic using targeted selective treatments and managing the parasite population at the farm level [START_REF] Mahieu | Gestion du parasitisme gastro-intestinal des petits ruminants en zone tropicale humide[END_REF].

Conclusion

Agriculture has to shift from a simplifying industrial approach to an ecological approach, that enhances complex and non-linear interactions between living organisms and that leaves room for the farmer initiative. From these examples, we showed agroecological systems offer a wide diversity of levers of actions. Different actors implement the key levers depending on their area of action. Identifying a set of potential solutions in order to guide the stakeholders' choices to explore new combinations of levers, depending on their agrosystem and territorial contexts is a key issue to implement the agroecological


Benjamin John Hayes

Hans Daetwyler

Aurelien Capitan

Hubert Pausch

Paul Stothard

Lesley Gray

Ruedi Fries

Bernt Guldbrandtsen

Mogens S Lund

Didier Boichard

Aurelien

Birgit Gredler

Genome wide association using imputed sequence data in dairy cattle with the 1000 bull genomes project data set

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

Genome wide association (GWAS) with whole genome sequence data on phenotyped individuals would have the advantage that the causative mutations are actually in the data set, as opposed to GWAS that use SNP on standard arrays, which relies on (usually imperfect) linkage disequilibrium between these SNP and the causative mutations. However, the cost of sequencing is such that the very large numbers of animals required for GWAS to identify mutations affecting complex traits are unlikely to be sequenced. An alternative strategy is to sequence key ancestors of the population, then impute the genotypes for the sequence variants into much larger reference sets with phenotypes and SNP panel genotypes. The 1000 Bull Genomes Project is building this database of sequenced key ancestor bulls for the bovine research community. Run3.0 of the project included 429 full genome sequences of key ancestors of 15 breeds, sequenced at an average of 10.5 fold coverage. There were 31.8 million filtered sequence variants detected in the sequences, including 29.1 million SNP and 1.7 million insertion-deletions. Agreement of sequence genotypes to genotypes from a 800K SNP array in the sequenced bulls was excellent at 98.8%. To demonstrate the approach, we imputed sequence data into dairy cattle populations, genotyped with 800K SNP arrays, in regions where QTL affecting milk production and other traits had previously been identified. Association studies with this imputed sequence data identified putative causative mutations, some of which were supported by gene expression data (RNASeq). These early results are encouraging and suggest the 1000 Bull Genomes project will be a critical resource for GWAS, and genomic prediction, in dairy and beef cattle in the future.


domain: sdv sde

text:
Anne C Richer-De-Forges

Nicolas N Saby

Dominique D Arrouays

At which distance are the soils influenced by the sea spray?

Sea spray, transported as liquid droplets or evaporated particulates, can be a significant contributor to the base cation content and cations equilibrium of coastal soils. Significant quantities of aerosols derived from sea spray can be transported inland by wind. There they are directly deposited on the soil surface, or on foliage, from which they are then transported to the soil surface during subsequent precipitation. In particular, the marine salt deposition may lead to an enrichment of exchangeable sodium in soil. Large amounts of exchangeable Na in soils are known to influence some soil physical properties and may also affect the growth of some sensitive crops and impact the plant biodiversity. Recently, several large storm events happened in western France, and may have led to an increase in exchangeable Na content in soils. In this study, our aim was to map the baseline value of exchangeable Na prior to these events on a study area bordering Atlantic Ocean.

The study area covers 65,625 km² located in western France, close to the Atlantic Ocean (Fig. 1).

This study is a preliminary attempt to assess the influence on sea spray on Na/T in coastal soils. The findings demonstrate: i) an obvious direct effect of sea water on sodic soils from marine marsh, ii) an effect of forest, attributable to sea spray interception by leaves from which Na is then transported to the soil surface during subsequent rainfall events. Further research should be conducted including analyzing the effect of other potential co-variates such as soil type, texture and carbonates content. Whatever, the results demonstrates that the sea spray may influence soil Na/T at quite large distances from the coast. The variogram exhibits two nested spatial structures. We adjusted the experimental variogram using a spheric structure from 0 to 0.8 km, and then an exponential one from 0.8 to 15 km (Fig. 4).

The largest Na/T was observed in drained marine marsh followed by forest land cover (Fig. 3).

STUDY AREA

We used a recent robust geostatistical algorithm proposed by Saby et al., (2011) to map the ratio Na+/CEC (Na/T). This algorithm uses the winsorising method to separate the spatial distribution of Na/T according to: i) the effect of land cover, ii) continuous gradients within land cover classes and, iii) anomalous point processes.

The appropriateness of the fitted model was tested by leave-one-out cross-validation and the mean and the median of the squared standardizing prediction errors (SSPEs) were calculated. The geographical distribution of Na/T is highly complex, since hot-spots are superimposed upon more regular patterns linked to land-cover and to diffuse natural gradients coming from the sea spray (Fig. 5). The influence of the sea spray may reach up to 50 to 70 km far from the coast line. On the eastern part of the map a large area of high Na/T values is observed and still needs to be explained (Fig. 6).

MODELLING AND VALIDATION

  Fig. 1: location of the study area Fig. 2: land cover

DATA

We used 2512 topsoil legacy data for which measurements of exchangeable Na were available. Land cover was classified into 3 broad classes: drained marine marsh, forest and other (Fig. 2).


Mélanie Noguero

Christine Le Signor

Vanessa Vernoud

Isabelle D'erfurth

Jérôme Verdier

Myriam Sanchez

Gregoire Aubert

Judith Burstin

Karine Gallardo

Richard Thompson

Use of translational genomics to indentify genes important for pea seed development and composition

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


Mustapha Bouhrara

Michael C Maring

David A Reiter

Jean-Marie Bonny

Richard G Spencer

Enhanced Quality of Myelin Water Fraction Mapping from GRASE Imaging Data of Human Brain using a New Nonlocal Estimation of multi-Spectral Magnitudes (NESMA) Filter

). 6 To overcome this limitation, noise reduction filters may be applied during postprocessing. 7 However, conventional filtering can introduce bias and obscure small-scale structures. We have recently developed a new nonlocal multispectral filter that significantly outperforms current filters, including available nonlocal filters, in term of noise reduction and detail preservation in T 1 and T 2 weighed image sets. 8 Here, we evaluated the performance of this filter for MWF determination from multiple echo imaging data, and compared the results to those calculated from unfiltered images and from images filtered using conventional Gaussian averaging (GA) or boxcar averaging (BA) filters.

MATERIALS & METHODS:

Image acquisition: 3D GRASE images were acquired from the brains of two healthy subjects (male, 24-years-old; female, 43-years-old) using a 3T Philips Achieva MRI system (Philips, Best, The Netherlands). 32 echoes were acquired with TE n =n*ΔTE, where ΔTE=11ms, TR=1000ms, EPI factor=3, acquisition voxel size=1.5mmx1.5mmx3mm and reconstructed voxel size=1mmx1mmx3mm.

Image filtering: The filter we have recently introduced restores the amplitude of an index voxel using a maximum likelihood estimate (MLE) based on M pre-selected voxels with similar signal decays. 8 In the present implementation, to decrease processing time, the MLE is replaced here by the simple average of the amplitudes of those similar voxels. This markedly decreases the computational time while maintaining nearly equivalent filtering performance (data not shown). The number M of similar pixels is conventionally held constant in the construction of nonlocal filters. However, the optimal value may in fact vary among different image regions. We therefore implemented a spatially adaptive selection of M using relative Euclidian distance (RED), defined as the sum over all TEs of the absolute signal differences between the index voxel and a given voxel, divided by the averaged signal value over all TEs of the index voxel. Voxels with RED≤5% were considered similar to the index voxel. We denote this new filter by Nonlocal Estimation of multi-Spectral Magnitudes (NESMA). For comparison, we also implemented 5x5x3 GA and BA filters.

 shows examples of MWF maps calculated from unfiltered and filtered images using the NESMA, GA and BA filters. As is readily seen, there is substantial

random variation in derived MWF maps from unfiltered images. While the random variation was reduced using the GA or BA filters, this comes at the expense of blurring and loss of image detail. However, MWF maps calculated from images filtered with NESMA exhibited preservation of edges and small structures, as well as greatly reduced random variation compared with unfiltered images. Fig. 3 shows a comparison of derived MWF values from the brains of the two subjects. MWF maps calculated from images filtered with NESMA are displayed for three different slices. The results indicate higher MWF values in a middle-aged subject in several image regions as compared to the younger subject, in good agreement with recent literature 9 . These preliminary analyses, although only on two subjects, serve to indicate the consistency of our method with previous literature and to demonstrate the sensitivity of the measurement to MWF changes with age. CONCLUSION: Estimation of MWF in the human brain from GRASE imaging data was markedly improved through use of the NESMA filter. NESMA allows preservation of edges and small structures in derived MWF maps. The use of NESMA may contribute significantly to the goal of high quality MWF mapping in clinically feasible imaging times. REFERENCES: [1] Whittall KP et al. Magn Reson Med 1997;37:34-43. [2] Laule C et al. J Neurol 2004;251:284-293. [3] Deoni SCL et al. Brain Struct Funct 2016;221:1189-1203. [4] Bouhrara M and Spencer RG NeuroImage 2016;127:456-471. [5] Prasloski T et al. NeuroImage 2012;63:533-539. [6] Zhang J et al. Magn Reson Med 2015;73:223-232. [7] Jones CK et al. Magn Reson Med 2003;50:206-209. [8] Bouhrara M et al. IEEE TMI 2016;DOI:10.1109/TMI.2016.2601243. [9] Arshad M et al. NeuroImage 2016;143:26-39.

SYNOPSIS:

Changes in myelin water fraction (MWF) represent a biomarker for central nervous system disease. However, high quality mapping of MWF is challenging, requiring very high signal-to-noise ratio for accurate and stable results. In this work, we demonstrate the potential of a new multispectral filter to permit high quality MWF mapping using in-vivo GRASE brain imaging datasets. Indeed, unlike conventional averaging filters, our filter permits substantial reduction of the random variation in derived MWF estimates while preserving edges and small structures. Finally, our results regarding patterns of MWF as a function of age are consistent with recent literature.

Figure.1. Illustration of the instability of NNLS to noise. Left panel: signal decays as a function of TE. Right panel: T 2 distribution obtained using NNLS from a given voxel of the in-vivo data

before and after image filtering using our new multispectral filter, NESMA. Two distinct peaks, corresponding to MWF and intra/extra cellular water, 1-2,5-7,9 are visible in the T 2 -distribution obtained from the filtered signal. In contrast, only one peak was visible in the T 2 -distribution obtained from the noisy signal. This illustrates the high degree of sensitivity of the NNLS analysis to noise, and the significant improvement obtained through NESMA filtering.

Figure 2. Example of MWF maps derived using GRASE acquisition with NNLS from the original (unfiltered) images and from corresponding images obtained using the NESMA, boxcar averaging (BA), and Gaussian averaging (GA) filters. The kernel size of both the BA and GA filters was fixed to 5 x 5 x 3 voxels. The blurring effect of the BA and GA filters is evident; this blurring is absent in the NESMA-filtered image.

Figure 3 .

 3 Figure 3. MWF maps derived using GRASE acquisition with NNLS analysis and NESMA filtering. Results are shown for three corresponding slices for both subjects. For each slice, the mean and standard deviation of derived MWF values are displayed. Visual inspection of MWF maps and comparison of mean values show that the brain of the older subject exhibits greater MWF values as compared to the brain of the younger subject in several brain regions, as is consistent with recent literature. 9


Mariane Daou

Emmanuel Bertrand

François Piumi

Daniel Cullen

Eric Record

Craig B Faulds

Marianne Daou

Characterization of 2 novel H2O2 producing glyoxal oxidases from Pycnoporus cinnabarinus implicated in the breakdown of lignocelluloses

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.

Heterologous Production and Characterization of Two

Glyoxal Oxidases from Pycnoporus cinnabarinus

Introduction

Plant derived biomass is the most abundant and renewable material on earth and it is gaining great importance as a sustainable source of energy and valuable molecules. The distinctive ability of wood-decaying filamentous fungi in degrading lignocellulose helped extend the exploitation of this material in biotechnology and industry. Fungi belonging to this group modify or degrade lignocellulose through the production of a wide variety of enzymes including heme-peroxidases (manganese peroxidase, lignin peroxidase and versatile peroxidase), multicopper-containing phenol oxidase (laccase) and hydrogen peroxide-generating oxidoreductases (e.g. aryl alcohol oxidase and glyoxal oxidase).

In order to understand the role of these enzymes in the degradation of lignocellulose by the Basidiomycete Pycnoporus cinnabarinus, and to find potential applications in biotechnology, targeted enzymes were heterologous expressed and characterized. This poster will present the results of the characterisation of two secreted glyoxal oxidases (GLOX) from P. cinnabarinus heterologously produced in Aspergillus niger and will focus on certain reactions catalysed by these enzymes.

Conclusion

The genes of two glyoxal oxidases (GLOX) were cloned in A. niger and the recombinant proteins were produced, purified and characterized. A significant difference in catalytic efficiency and substrate preference was observed between GLOX1 and GLOX2 suggesting that these two enzymes are involved in different reactions in vivo. The results showing the detection of different products in the reactions of the two enzymes on the same substrate further support this hypothesis. The reactions catalysed by GLOX also reflects the importance of these enzymes as candidates for biotechnological applications.

1

  Aix Marseille Université, INRA, BBF, Biodiversité et Biotechnologie Fongiques, Marseille, France ; 2 Forest Products Laboratory, United States Department of Agriculture, University of Wisconsin, One Gifford Pinchot Drive, Madison, WI 53726, USA

		250		Catalytic Activity
	(%)	200				GLOX1 GLOX2
	activity	100 150
	Relative	50
		0
		Substrate	GLOX1		GLOX2
			Km (mM)	Catalytic efficiency (s -1 mM -1 )	Km (mM)	Catalytic efficiency (s -1 mM -1 )
		Methylglyoxal	1.3	58.46	0.2	7
		Glyoxal	13.1	6.33	2.2	0.63
		Glyoxylic acid	0.08	2136.36	0.1	17
		Glycerol	660.5	0.05	9.4	0.06
		Formaldehyde	4.9	0.58	0.6	13.33
		Furfural	-	-	3.2	1.21
		5-HMF	-	-	8.1	0.54

*Relative activity on different substrates calculated as a percentage of the activity on methylglyoxal and tested spectrophotometrically during first 30 seconds of reaction.

Acknowledgement : This

research was supported by the EU-funded project INDOX (KBBE-2013-7-613549).


Fabienne Femenia
email: fabienne.femenia@inra.fr

Laure Latruffe

Jean Paul Chavas

Jean Paul

Jean-Paul Chavas°

Responsiveness of farm investment to price changes: An empirical study of the French crop sector

Keywords: investment, expectations, prices, crop farms, France

Introduction

Farm investment in agriculture allows farmers to adapt to changes in economic conditions (e.g. price variations, policy reform, climate change) and to adjust to public regulations (e.g. environmental or animal welfare regulations). Farmers' investment decisions are influenced by both market conditions and public policies. Some policies are directly targeted to investment, e.g. subsidies for implementing specific investment projects or tax policies linked to investment. Other policies may influence farmers' investment decisions indirectly: price support, tax on inputs, etc. Evaluating the effects of policies on investment is complex as policy instruments often have heterogeneous effects across farms. The analysis of the effects of farm policy may be done using ad hoc models (e.g. [START_REF] Bojnec | Financing availability and investment decisions of Slovenian farms during the transition to a market economy[END_REF], or relying on investment models derived from producers' optimization framework (e.g. [START_REF] Sckokai | Modelling the impact of the CAP Single Farm Payment on farm investment and output[END_REF]. Similarly, the effects of market conditions can also be investigated through the responsiveness of farmers' investment to output price and investment price. Our paper presents an investigation of farm investment behavior, with an analysis of a sample of French crop farms specialized in cereals, oilseeds and protein seeds during the 2002-2014 period.

Several studies have investigated firms' investment behavior. In his literature review on the modelling of firms' investment decisions, [START_REF] Chirinko | Business fixed investment spending: Modeling strategies, empirical results, and policy implications[END_REF] concludes that firms' investment is more responsive to output quantities than to capital prices. Morisson (1986) uses data on the U.S. manufacturing sector between 1947 and 1981 to estimate the elasticities of demand for capital with respect to the prices of capital and different variable inputs under different assumptions regarding firms' expectations. Her results show stronger responses of capital to output quantities than to input prices (including capital price). On that basis, one may expect output prices to have a significant impact on investment. In the agricultural economics literature dealing with farm investment, no consensus seems to emerge on the relative impacts of investment price and output price on farmers' investment decisions. For instance, [START_REF] Thijssen | Farmers' investment behavior: An empirical assessment of two specifications of expectations[END_REF], considering Dutch farms observed from 1970 to 1982, finds significant responses of investment to both investment and output prices and concludes that investment subsidies are good policy incentives to agricultural investment. On the other hand, the results obtained by Vassavada and Chambers (1986) in the case of U.S. agriculture show no response of quasi fixed factors to their own prices and a negative response to the price of output. Oude [START_REF] Oude Lansink | Asymmetric adjustment of dynamic factors at the firm level[END_REF] obtain the same puzzling effect of output price on investment in the case of Dutch cash crop farms between 1971 and 1992. More recently, [START_REF] Serra | Investment rigidity and policy measures[END_REF], using data for Kansas farms from 1997 and 2001, compare the sensitivity of investment to output price to its sensitivity to public payments, and find investment to be more sensitive to output price in periods of favorable economic situation (i.e. increase in capital stocks) and more sensitive to government support in the case of difficult economic situation. These authors do not, however, study the sensitivity of investment to its price. Our paper contributes to this literature by using more recent data. And it examines whether farm investment behavior may have changed over time. This seems particularly relevant given the recent increase in food price volatility.

The remainder of the paper is structured as follows. The next section explains the modelling framework. The third section describes the data and estimation strategies. The fourth section presents the results and the fifth section concludes.

The modelling of farmers' investment decisions: theoretical framework

To investigate farmers' investment decisions, two types of models are generally used in the literature. The primal approach relies on a structural model obtained from the maximization of future expected profit, under technological constraints. In this case, Euler equations are derived: they show the optimal path of investment from the current period onwards based on expectations of future profit. Gardebroeck (2004) and Gardebroeck and Oude Lansink (2004) are examples of works using this approach. The second approach is the dual approach, introduced by [START_REF] Epstein | Duality theory and functional forms for dynamic factor demands[END_REF]. The producer's decision is represented by a value function, and the models are obtained from Hamilton-Jacobi-Belman equations. Examples of this approach include Oude Lansink and Stefanou (1997), [START_REF] Pietola | Investment under uncertainty and dynamic adjustment in the Finnish pork industry[END_REF] and [START_REF] Sckokai | Modelling the impact of the CAP Single Farm Payment on farm investment and output[END_REF].

Here we follow the first approach involving an explicit representation of the production technology. Our modelling framework starts with a general investment model as in [START_REF] Chirinko | Business fixed investment spending: Modeling strategies, empirical results, and policy implications[END_REF]. We consider an agricultural producer i who, at period t, takes his/her production and investment decisions so as to maximize the present value (PV) of expected payoff on his/her farm, i.e. the expected sum of all his/her future discounted profits, subject to a capital accumulation constraint: Where:

-*,+ {} is the expectation operator: it corresponds to the expectations of farmer i given the information available to him/her at time t.

: *,+ is the quantity of variable input used at period t and ; *,+ is the capital stock. These two inputs are combined, through a production technology 9':, ;* to produce a single output that is sold. At each time period, the capital stock depreciates at a rate ) and increases with new investment *,+ . These new investments are subject to adjustment costs which we assume to be an increasing function ?' * of investment.

With Various assumptions regarding the way economic agents form their expectation can be found in the literature, the three most represented cases being the rational, naïve and quasi-rational expectations. In the case of rational expectations [START_REF] Muth | Rational expectations and the theory of price movements[END_REF][START_REF] Lucas | Econometric policy evaluation: A critique[END_REF], agents are assumed to have a perfect knowledge of markets functioning, and to use all information available to him/her to forecast the future. In that case, expectations are consistent with supply-demand conditions and anticipated supply/demand shocks, and their modelling requires a representation of both supply and demand functions to determine equilibrium market conditions. Naïve expectations imply that agents forecast that all future values of market variables will be the same as the values observed when making their decision. [START_REF] Thijssen | Farmers' investment behavior: An empirical assessment of two specifications of expectations[END_REF] investigates farmers' investment behavior under the rational and naïve expectation assumptions: using a reduced form profit function and individual panel data of Dutch dairy farms, he found that elasticities are consistent with the theory only in the case of naïve expectations. These results thus call for the naïve expectation assumption over the rational expectation one. The third type of expectations frequently found in the literature, the quasi-rational expectation schemes, assume that agents use a whole set of past observations to form their expectations [START_REF] Nerlove | Analysis of economic time series: a synthesis[END_REF]: empirically, future values are predicted from time series models. The naïve and quasi-rational expectations, both based on past observations, is found by [START_REF] Chavas | On the economic rationality of market participants: the case of expectations in the US pork market[END_REF][START_REF] Chavas | On information and market dynamics: the case of the US beef market[END_REF] to be the most frequent expectation schemes in livestock farming, as a large proportion of production in the pork market is generated by producers having quasi-rational expectations [START_REF] Chavas | On the economic rationality of market participants: the case of expectations in the US pork market[END_REF] and having naïve expectations in the case of the beef market [START_REF] Chavas | On information and market dynamics: the case of the US beef market[END_REF].

Γ *,= = ∑ 0 165 123 4 5 ∝ 5"> ? *,+25 (9)
Modelling both supply and demand is complex and is out of the scope of the present study. In addition, from an empirical point of view, the estimation of quasi-rational expectations requires long time series data, which is not the case for the panel data sample used in our empirical application. For these reasons, and consistent with the literature explained above, we use the naïve expectation assumption and assume that farmers expect the last observed values of output price and marginal capital productivity to prevail in all future periods: To derive an estimable model from equation ( 7), we need to make assumptions regarding the form of the capital adjustment cost and production functions. Following the widespread assumptions in the literature, we consider a quadratic capital adjustment cost function (equation ( 13)) and a Cobb Douglas production technology (equation ( 14)):

?7 *,+ < = A + B *,+ + C D *,+ D (13)
Where B and E are scalars, with E > 0 to ensure the concavity of the function.

H *,+ = 97: *,+ , ; *,+ < = I; *,+ J : *,+ K

Where I, L and M are scalars, with I > 0, 0 < L < 1 and 0 < M < 1.

Combining equations ( 8) and ( 12), and replacing the marginal capital adjustment cost and marginal capital productivity by their expression derived from equations ( 13) and ( 14), leads to the following investment decision equation:

*,+ = - N C + J C 0 123 523 4 O 1,2P9 & 1,2P9 8 *,+61 - 1 C ( *,+ (15)
The investment of farmer in a specific period is thus determined by the discounted value of the productivity of capital in the previous period, and by the price of investment in the current period.

Data and estimation strategies

Data

We use a sample of farm-level data obtained from the French Farm Accountancy Data Network (FADN) for the period 2002-2014. The FADN database, managed by the French Ministry of Agriculture, contains yearly data for a sample of French commercial farms that are representative of the French farm population in terms of production specializations and regions. The French FADN database is a rotating panel with a rotation rate is about 10%, making the sample an unbalanced data sample. We focus on farms specialized in the production of cereals, oilseeds and protein seeds.

We expect that low levels of investment may not be subject to the same adjustment costs than higher levels. Low levels of investment may show replacement investment, while high levels of investment may indicate expansionary or innovative investment. As suggested by [START_REF] Kapelko | Investment age and dynamic productivity growth in the Spanish food processing industry[END_REF], replacement investment do not have the same adjustment as the two other types of investment. For this reason, we select the farms investing the most, i.e. the farms displaying investment levels higher than the sample average. After cleaning the data and eliminating outliers, the total number of observations in our sample during 2002-2014 is 3,886 (approximately 300 farms per year).

The investment level ( ) is an aggregate of machinery, building and land investments. As no price of investment (( ) is available in the FADN database, we use as a proxy the ratio between annual debt repayments and the annual value of fixed capital stock. The price of wheat (calculated for each farm in each year using FADN data) in Euros per kilogram of wheat is taken as a proxy of the output price (8). The productivity of capital is the ratio of total output (excluding subsidies) (y) to fixed capital (k).

The capital depreciation rate ()) is fixed at 0.051 and the average of official annual interest rate provided by the European Central Bank is taken as the actualization rate (.). All values are measured at constant 2002 prices using the corresponding national price indices.

Figure 1 reports the evolution of the yearly averages of investment level, investment price and output price over our sample period. On the figure the annual values are not reported in levels, but as percentage differences compared to the average value for the full period. The first thing to note is that we observe two distinct sub-periods in terms of levels: in the first sub-period 2002-2006 investment levels and output price levels are lower than in the second sub-period 2007-2014. The second thing to note is that, while the investment price remains relatively stable during the whole period, the output price experiences ups and downs during the second sub-period. This conforms to the general knowledge that crop prices (here wheat prices) are higher on average and much more volatile since 2007. The third point to notice on Figure 1 is that, during the second sub-period, the investment does not follow the same evolution as the output price. All this suggests that farmers' investment responses to prices may not be the same during both sub-periods. For this reason, we will run our estimations not only on the whole period, but also on two sub-periods: 2002-2006 and 2007-2014.

Table 1 provides descriptive statistics of our variables of interest for the whole 2002-2014 period and for the two sub-periods. During 2002-2014 the cereals, oilseeds and protein seeds farms in our sample operated on average 159 hectares (ha) of utilized agricultural area and used 1,517 Euros of capital per ha. The respective figures for the two sub-periods are very similar, indicating that, although it is not a balanced sample, the structural characteristics of the two sub-samples (in 2002-2006 and in 2007-2004) are similar. This suggests that the different investment levels and evolution observed on Figure 1 for both sub-periods are due to a change in investment behavior across sub-periods, and not to different sub-samples. About half of the farms are of individual (sole proprietorship) legal status, compared to partnerships or companies. About half of the farms are located in the wide Parisian basin area (administrative regions "Ille-de-France", "Picardie", "Champagne-Ardennes" and "Centre"), which is the main area for wheat production in France, while 16% of the farms are located in the other main wheat area, namely South Western France (administrative regions "Midi-Pyrénées", "Aquitaine", "Poitou-Charentes"). These two areas indeed exhibit specific characteristics, the Parisian basin being a region with particularly good cropping conditions and large farms, and the South West of France being largely composed of farms specialized in irrigated corn production.

On average during the whole period, farms produced an output of 1,028 Euros per ha per year and invested on average 415 Euros per ha each year (the minimum being 160 and the maximum 3,620 Euros). Table 1 clearly shows that output and investment are lower on average during the first subperiod than during the second sub-period. In addition, as observed on Figure 1, the investment price remains remarkably similar (0.11 Euros paid for each Euro of capital, on average), while the output price is much higher in the second than in the first sub-period (143 vs. 104 Euros per ton of wheat).

Estimation strategies

We seek to estimate the following econometric model, based on the theoretical model of farmers' investment choices defined by equation ( 15

Where ^*,+ is the random error, and the I are parameters to be estimated.

The (.[X\ *,+61 variable in equation ( 16) denotes the discounted value of the capital productivity in period t-1 in equation ( 15 Z *,+ is a matrix composed of three dummy variable vectors representing farmers' characteristics and used to partly control for the heterogeneity in investment levels among farms. These variables include the legal status of the farm, which takes the value for individual farms and 0 for partnership farms and companies, and two regional variables: the first one takes the value 1 for farms located in the large Parisian basin area (defined above) and 0 otherwise, the second one takes the value 1 for farms located in the South West of France (defined above). A trend variable is also included in order to account for the evolution of investment with time: the U.VWX + variable takes the value 1 for the first year of observation and increases by one unit each year.

We also introduce an individual random effect ] * to control for the unobserved heterogeneity in investment levels. This random effect is assumed to be independent from the error of the model, ^*,+ . Moreover, different unobserved factors may have an impact on investment levels, like farmers' personal skills or extension access, may also influence farms' productivity. To deal with this potential correlation between the random part of the model and the productivity variable, which would lead to biased estimates, we apply the [START_REF] Hausman | Panel data and unobservable individual effects[END_REF] instrumental variable approach.

Our objective is to obtain a value of the parameters for the capital adjustment cost function (α and β) and for the production technology (b) in order to compute investment elasticities with respect to output and investment prices. The value of these parameters can easily be recovered from the estimated coefficients I of our econometric model ( 16), as shown in equations ( 17)-( 19), while their significance can be computed through the delta method. These parameters allow to compute the elasticities of investment with respect to the investment and output prices. The elasticity of investment with respect to the (observed) price of investment (V ( *+ ) and the elasticity of investment with respect to the (expected) price of output (V ( *+ ) are respectively:

E _ = - 1 `ab c (
V ( *+ = 6' 12 6Y' 12 Y' 12 ' 12 = - 1 C q Y' 12 ' 12 (20)
V ( *+ = 6' 12 6r 12P9 r 12P9 ' 12 = J n C q 0 123 523 4 r 12P9 O 12P9 & 12P9 ' 12 (21)

Results

Table 2 reports the regression results of the estimation of the investment model in equation ( 16) for the whole period as well as for the two sub-periods. The three models show that many parameters are statistically significant. In the whole period and also in both sub-periods, the main variables of interest have the expected sign: positive for the value of capital productivity in the previous period and negative of the price of investment. Consistent across both sub-periods and for the whole period is the fact that individual farms invest more per ha than partnerships and companies. Some differences can be noted with respect to the regional dummies and the time trend. During the whole period only farms in South Western France invest less per ha on average than the other farms, while in the first sub-period there is no difference in investment behavior across France, and in the second period farms in both areas considered invest less per ha than the other farms in France. As for the trend variable, it is positive and significant for the whole period, but not significant during the second sub-period, confirming the saw tooth evolution for this sub-period observed on Figure 1. The trend has a significant negative effect in the first sub-period, supporting the slight decrease observed on Figure 1 for this period. The coefficients reported in Table 2, as well as the variance-covariance matrices (not reported), enable us to calculate the value (equations ( 17)-( 19)) and the significance of the parameters that underlie the investment decision, namely the parameters for the capital adjustment costs function and for the production technology. Table 3 shows the values and significance of these parameters. Some conditions on parameters are necessary for the models to be well behaved: the E parameter has to be positive for the capital adjustment cost function to be convex. Also, for the adjustment costs to be increasing in investment, i.e. for the derivative of the cost function with respect to investment, ? ' , to be positive, the following has to hold:

+ > - N C
. Finally, one expects the L parameter of the production function, which corresponds to the elasticity of production with respect to capital, to take values between 0 and 1. Table 3 shows that the conditions on E and L are fulfilled in all periods. By contrast, the negative value of B estimated here could imply decreasing adjustment costs up to a certain investment level. However, the B's are not significant at the 5% level, suggesting that there are in fact no decreasing adjustment costs and that the function is always upward sloping.

Table 4 reports the elasticities, computed with the parameters of Table 3, for each observation. Both elasticities, with respect to the price of investment and with respect to the price of output, are significant in all periods considered and have the expected sign: investment decreases with the price of investment, and increases with the price of output. The first result to note is that the elasticity with respect to the price of investment is on average the same in the whole period (-0.22) and in both sub-periods (-0.25 and -0.23). This is not the case of the elasticity with respect to the price of output: its average value is 0.14 for the whole period, while it is 0.56 for the first sub-period and 0.20 for the second sub-period. The second result is that the elasticity with respect to output price decreases between the first sub-period and the second sub-period. While in the first sub-period, it was much above the elasticity with respect to investment price, it has a similar range during the second sub-period. This reveals that in the first sub-period, farmers are more responsive to changes in the expected output price than in the investment price, while in the second sub-period they are equally responsive.

Conclusion

We investigated the investment response of French farmers during the period 2002-2014 to changes in prices (investment price and output price). Understanding whether farmers are more responsive to one or the other price can help draw policy recommendations regarding incentives for farmers' investment. We used a structural model obtained from the maximization of farmers' future expected profit under naïve expectations and a Hausman-Taylor estimation framework enabling to control for farmer's heterogeneity.

We found that the period considered was composed of two sub-periods in which farmers' behavior was different. The first sub-period between 2002 and 2006 is characterized by stable output prices, and relatively stable investment. By contrast, the second sub-period between 2007 and 2014 is characterized by higher but more volatile output prices, and similarly by higher but more volatile levels of investment. The different price evolutions between both periods indicate the date where prices became more volatile. In a context of price volatility and hence higher uncertainty of expected profitability of investment projects, the behavior of agents regarding irreversible investment is affected. In agriculture, irreversibility is strongly present, as capital is often production specific and may not be used for alternative uses. In theory, the situation of price volatility (uncertainty) combined with investment irreversibility should make agents invest less and delay investment decisions [START_REF] Dixit | Investment under Uncertainty[END_REF][START_REF] Sckokai | Modelling the impact of the CAP Single Farm Payment on farm investment and output[END_REF]. However, this is not what we observe in our data. This may be explained by the fact that in such context agents may continue to invest but do so in more malleable technologies, that is to say that are more easily reversed [START_REF] Cavallo | The role of relative price volatility in the efficiency of investment allocation[END_REF]. Another explanation is that uncertainty increases the expected return of the investment project, and hence the price that would trigger investment [START_REF] Dong | Technical efficiency, herd size, and exit intentions in U.S. dairy farms[END_REF]. Here, although the output price is more volatile after 2007, its level remains high during this period, and probably sufficiently high to trigger large investments. Finally, high investments in the presence of price volatility may be due to farmers changing the output mix in order to have crops that are less risky [START_REF] Sckokai | Modelling the impact of the CAP Single Farm Payment on farm investment and output[END_REF]. Such change in output mix may require specific investments.

In the first sub-period between 2002 and 2006, farmers' average investment levels slightly decrease with a continuous trend, and farmers' investment behavior is more responsive to the price of output than to the price of investment. By contrast, in the second sub-period between 2007 and 2014, farmers' investment behavior is more erratic and is as much responsive to the price of output as to the price of investment. Since the responsiveness with respect to the investment price is very stable across both periods, the results show that it is the output price that makes farmers' behavior change. As explained above, this may be due to the increase in output price levels during the second sub-period.

On a policy point of view, our investigations show that expected output prices strongly affect farmers' behavior, more than investment price, during periods of stable output prices. However, this is not the case anymore when prices become volatile. Provided that farmers have naïve price expectations, our results suggest that in the current context of crop prices volatility, farmers' investment behavior may not be as much influenced as it was before by output prices.

On a methodological point of view, our analysis underlines the need to carefully model the expectations scheme. The choice of the form of expectations is indeed a crucial issue. As shown here, expectations play a role in investment decisions, in the way that production and investment choices are based on expected future profit. To make his/her decisions, the producer observes input prices and investment cost in the current period, and has to forecast the output price, as well as the future evolution of input prices and investment cost in the next periods. Producers' response in terms of capital adjustments following a policy change depends on the producers' expectations of the effects of the policy change. For instance, using a Computable General Equilibrium model, [START_REF] Femenia | On the optimal implementation of agricultural policy reforms[END_REF] show that the impact of an agricultural policy reform on economic welfare partly depends the way agents form their expectation. More specifically, the way agricultural producers adjust their capital stock to adapt their production after the reform depends on their expectations of the impacts of the reform on market conditions: if they have an imperfect knowledge of the structure of the economy and do not immediately anticipate the impacts of the reform in order to consequently adjust their production, the reform can initially generate welfare losses. When data are available on a longer period and enable the use of time series data, one could estimate the investment model developed here under the assumption of quasi-rational expectations. In this scheme, farmers form their expectations on a series of past observations. In this context, it would be interesting to test whether farmers' expectations become quasi-rational in the period with high output price volatility. This appears to be a good topic for further research.

  ,5 97: *,5 , ; *,5 < -> *,5 : *,5 -?7 *,5 < -( *,5 *,5

  is thus the value of the marginal productivity of capital at period t, and -*,+ 3Γ *,+ 3? *,+25 4 corresponds the expected long run marginal value of capital.

  -*,+ 3? *,+25 4 = -*,+ 3? *,+ 4 = 8 *,+61 9 & 7: *,+61 , ; *,+61 < the long run marginal value of capital simplifies to:-*,+ 3Γ *,= 4 ,+61 9 & 7: *,+61 , ; +61 < (12)

Figure 1 .

 1 Figure 1. Evolutions of the yearly averages of investment level, investment price and output price over the 2002-2014 period, in comparison with the period average

  ): *,+ = I > + a Q ′Z *,+ + I T U.VWX + + I YZ (.[X\ *,+61 + I Y' ( *,+ + ] * + ^*,+

Table 1 . Descriptive statistics of the sample used W h o l e p e r i o d 2 0 0 2 -2 0 0 6 2 0 0 7 -2 0 1 4 Mean

 1

	S. D.	Mean	S. D.	Mean	S. D.

Table 2 . Regression results of the investment model

 2

	Whole period	2002-2006	2007-2014

Table 3 . Values and significance of the investment decision parameters

 3

	Parameters for	Whole period	2002-2006	2007-2014
	Capital adjustment cost function	E _	0.0017***	0.0016***	0.0015***
			(0.0001)	(0.0003)	(0.0002)
		B ̅	-0.74*	-0.42	-0.70
			(0.64)	(1.78)	(1.15)
	Production technology	L n	0.04***	0.22***	0.04***
			(0.01)	(0.05)	(0.01)
	Standard deviations of estimates are in parentheses
	Significance levels: * 10%, ** 5%, *** 1% Note: the parameter B ̅ is the average value of the B d *+ over i and t.
	Source: The authors based on French FADN data.

Table 4 . Average elasticities of investment computed for each observation and significance

 4

	Whole period	2002-2006	2007-2014

Different values have been tested but estimation results are unchanged.

Acknowledgements

The French FADN data were accessed through the national center for distant and secured access to data ('Centre d'accès sécurisé aux données' -CASD) maintained by the French statistical office (INSEE). In this frame, this work is supported by a public grant overseen by the French National Research Agency (ANR) as part of the 'Investissements d'avenir' program (reference: ANR-10-EQPX-17 -Centre d'accès sécurisé aux données -CASD).


Mean Turbulent Shear Stress u'w' at z = h

Context & Motivations

Wind damage is the most important disturbance to European forests.

Results

Mean Horizontal Wind Velocity U

Ratio Gap/Forest Width Single Edge ∼ 1/2 ∼ 1 ∼ 3/2 ∼ 5/2 ∼ 7/2
Christopher Poëtte -Fragmentation of the landscape:

October 28, 2015 Impact on atmospheric flow and tree stability 10/22

Results

Mean Turbulent Shear Stress u'w'

Ratio Gap/Forest Width Single Edge ∼ 1/2 ∼ 1 ∼ 3/2 ∼ 5/2 ∼ 7/2
Christopher Poëtte -Fragmentation of the landscape:

October 28, 2015 Impact on atmospheric flow and tree stability 11/22

Results

Mean Turbulent Kinetic Energy TKE

Ratio Gap/Forest Width Single Edge ∼ 1/2 ∼ 1 ∼ 3/2 ∼ 5/2 ∼ 7/2
Christopher Poëtte -Fragmentation of the landscape:

October 28, 2015 Impact on atmospheric flow and tree stability

12/22 u * eff 2 = ⟨ -u ′ w ′ ⟩ x,y ,z=h + ⟨ -u ′′ w ′′ ⟩ x,y ,z=h (1)
Normalisation for energy

u e = ( ⟨ U ⟩ x,y ,z=h * u 2 eff ) 1/3 (2)
Christopher Poëtte -Fragmentation of the landscape: October 28, 2015 Impact on atmospheric flow and tree stability The pattern of wind speed and turbulence back from the first edge is with U at tree top increasing and TKE decreasing as gap size increases No enhancement of turbulence is noticeable at a particular level of fragmentation Single Edge and ∼ 7/2 are the extrem values that can be reached Forests fall into two main groups: forests with small gaps (Ratio Gap/Forest ≤ 1) showing similar behavior to a single edged forest & forests with large gaps (Ratio Gap/Forest ≥ 1.5) acting more like isolated forest blocks A robust local normalization has been design to allows to bring measurements to equilibrium that will allows comparison with numerical simulations or field experiments

NEXT STEPS

  Example from Storm Klaus in 2009: Directly destroyed 43.1 Mm 3 timber (14% of the standing volume) Direct cost to sector > e1 billion, total economic loss ∼ e3 billion Key Questions: Does fragmentation of forest landscape induce/increase turbulence? Is there a specific forest configuration that mitigates/enhances turbulence formation? The Pye Lab. Wind Tunnel, CANBERRA, Australia Diagram of the Pye Lab Wind Tunnel (taken from Böhm (2000), modified from Wooding (1968))Dimensions of the working section:

  Compare wind tunnel measurements against Large-Eddy Simulation (LES) ModelUse LES to look at a much bigger range of forest fragmentation Predict the impact of forest fragmentation on the risk of wind damage to forests Christopher Poëtte -Fragmentation of the landscape:October 28, 2015 Impact on atmospheric flow and tree stability

Bordeaux Sciences Agro, UMR 1391 ISPA, F-33170 Gradignan, France

Centre for Australian Weather and Climate Research, CSIRO Marine and Atmospheric Research, Canberra, Australia October 28,

Acknowledgements:

INRA -Aquitaine Region -Labex Cote -CSIRO

Christopher Poëtte -Fragmentation of the landscape:

October 28, 2015 Impact on atmospheric flow and tree stability

ANNEXES

Christopher Poëtte -Fragmentation of the landscape:

October 28, 2015 Impact on atmospheric flow and tree stability 23/22


Hélène Marrou
email: helene.marrou@supagro.fr

Michel Edmond Ghanem

Hatem Belhouchette

Carina Moeller

Thomas R Sinclair

Assessing soil water trajectories and WUE: A multi-year modeling approach to design resilient cereal-legume rotations in the dry areas

Introduction

Cropping system simulation can address the complex and interactive nature of resilience and allows for biological (crop growth and development), physical (soil-water dynamics), chemical (soil carbon and N turnover), and managementrelated (e.g. crop choice, applications of nitrogen fertilizer, timing of sowing) aspects of resilience to be quantified. [START_REF] Soltani | Modeling Physiology of Crop Development, Growth and Yield[END_REF] recently presented a non-calibrated, mechanistic, simple model, called Simple Simulation Model (SSM), which uses a generic approach for simulating the growth and yield of cereal and legume crops. The robustness of the legume and cereal versions of SSM has been demonstrated for a wide range of environments and various species including wheat [START_REF] Soltani | SSM-wheat: a simulation model for wheat development, growth and yield[END_REF] and chickpea [START_REF] Vadez | Crop simulation analysis of phenological adaptation of chickpea to different latitudes of India[END_REF]. In cropping systems of the semi-arid Mediterranean region, variable and deficient rainfall and drought episodes are primary constraints to productivity. Under these conditions, maximizing water-use efficiency (WUE, defined as the ratio of yield per unit evapotranspiration) is critical. Hence the accurate prediction of soil-water dynamics and crop-water relations is required to identify management strategies that increase the use of scarce rainfall and its conversion into grain yield. We applied SSM to examine rotations including wheat and chickpea that are representative of rainfed environments of the southern and eastern Mediterranean.

Materials and Methods

Simulations were carried out for Tel Hadya, Syria. The site (36°01'N, 36°56'E; elevation 284 m) has a semi-arid Mediterranean climate. Firstly, SSM was parameterized to simulate the growth and development wheat and chickpea crops and the soil water dynamics as observed in experiments conducted in 1998/99 and 1999/2000 at Tel Hadya [START_REF] Moeller | Assessing the sustainability of wheat-based cropping systems using APSIM: model parameterization and evaluation[END_REF]. Secondly, data from a longer term two-course rotation experiment established in 1983 at Tel Hadya [START_REF] Harris | Productivity of crop rotations[END_REF] were used to assess the ability of the model to simulate long-term soil water dynamics and crop productivity as observed under field conditions. In the rotation experiment, wheat (Triticum turgidum ssp. durum, cv. Cham3) was rotated with wheat, annual fallow, and chickpea (Cicer arietinum, cv. Ghab2), and crop productivity and the soil-water dynamics were quantified under different N fertilizer regimes from 1986 to 1998 (12 crop cycles). For the purpose of simulating successive rotational cycles, a new version of the model (SSM-Rotation) was designed. In SSM, the soil water status is calculated daily as the amount of transpirable soil water (ATSW) using the water balance equation for an expanding volume of soil, corresponding to the addition of the soil layer explored by roots on that given day. Finally, SSM-Rotation was applied to simulate a continuous 30-year period and compare three long-term rotations of Wheat-Fallow (used as reference), Wheat-Chickpea and Wheat-Wheat-Chickpea. Simulations were run with no nitrogen stress effect on wheat; chickpea is supposed to be self-sufficient in Nitrogen. The performance of rotations is discussed regarding yield, in-crop WUE (WUE1), and the WUE calculated over all cycles of the rotation (WUE2). For the sake of uniformity, the wheat yield equivalent (WYE) of chickpea was calculated as: WYE =Yield of Chickpea x (Price of Chickpea/Price of Wheat) [START_REF] Chetty | A general proposal for ranking intercrop treatments[END_REF].

Results -Discussion

Overall, the model was able to simulate wheat and chickpea yields, total biomass, and the soil water dynamics as reported for contrasting conditions in [START_REF] Moeller | Assessing the sustainability of wheat-based cropping systems using APSIM: model parameterization and evaluation[END_REF] (Fig. 1 A-D). In addition, SSM-Rotation simulated the longterm soil-water dynamics in rotations with reasonable accuracy (Fig. 1E). The predictive abilities of SSM-Rotation demonstrated here showed that the model is robust enough to be applied to explore indicators of cropping systems resilience such as WUE. 1996 1987 19881989 1990 1991 1992 1993 1994 1995 1997 1998 E Fig. 1. (A-D) Simulated (curves) and measured (closed circles) of biomass chickpea and wheat grown at Tel Hadya: (A) rainfed chickpea, 1998-99, (B) irrigated chickpea, 1999-00, (C) rainfed wheat, 1998-99, with a nitrogen (N) fertilizer rate of 60 kg N.ha -1 , and (D) irrigated wheat, 1999-00, with a N fertilizer rate of 100 kg N.ha -1 . (E) Simulated (curves) and measured (closed blue circles) of actual transpirable soil water in the rootzone (ATSW-RZ) in a rainfed wheatchickpea rotation without N fertilizer applications.

Although wheat WUE was increased in wheat/fallow rotation compared to diversified rotations, the total WUE of the rotation (WUE2, Table 1) was lower in the fallow rotation due to important evaporative losses during the fallow period. The 3 years wheat/wheat/chickpea rotation was the most productive and the most efficient regarding total water use (WUE2) as it combined i) a high proportion of high economic value wheat, ii) reduced evaporative losses due to ground cover every year.

Conclusions

The overall performance of the model was sensible, providing sufficient confidence in the simulation capabilities of SSM-Rotation for subsequent scenario analyses. The analyses will be expanded to different crops (lentil, barley), sowing dates, N rates, and crop maturity types (short and long-season).

Table 1 .

 1 Simulated mean yield and WUE in three rotation schemes

	Rotation scheme	Wheat yield	Chickpea yield	Wheat	Chickpea	WUE1	WUE2
		(t.ha -1 )	(t.ha -1 )	WUE	WUE	(in-crop)	(over entire
							rotation)
	Wheat/Chickpea	1.42	0.89	0.53	0.39	0.42	0.30
	Wheat/Fallow	1.80	-	0.60	-	0.60	0.26
	Wheat/Wheat/Chickpea	1.62	0.75	0.62	0.32	0.50	0.37

Acknowledgements. This work was supported by the CGIAR Research Program on Grain Legumes.